diff --git a/.coveragerc b/.coveragerc
index 95eea4f8f..74dccbfe1 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -1,5 +1,7 @@
 [run]
 omit =
     scripts/*
-    freqtrade/tests/*
-    freqtrade/vendor/*
\ No newline at end of file
+    freqtrade/templates/*
+    freqtrade/vendor/*
+    freqtrade/__main__.py
+    tests/*
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 000000000..a480ae1eb
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,44 @@
+{
+    "name": "freqtrade Develop",
+    "image": "ghcr.io/freqtrade/freqtrade-devcontainer:latest",
+    // Use 'forwardPorts' to make a list of ports inside the container available locally.
+    "forwardPorts": [
+        8080
+    ],
+    "workspaceMount": "source=${localWorkspaceFolder},target=/workspaces/freqtrade,type=bind,consistency=cached",
+    // Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
+    "remoteUser": "ftuser",
+    "onCreateCommand": "pip install --user -e .",
+    "postCreateCommand": "freqtrade create-userdir --userdir user_data/",
+    "workspaceFolder": "/workspaces/freqtrade",
+    "customizations": {
+        "vscode": {
+            "settings": {
+                "terminal.integrated.shell.linux": "/bin/bash",
+                "editor.insertSpaces": true,
+                "files.trimTrailingWhitespace": true,
+                "[markdown]": {
+                    "files.trimTrailingWhitespace": false
+                },
+                "python.pythonPath": "/usr/local/bin/python",
+                "[python]": {
+                    "editor.codeActionsOnSave": {
+                        "source.organizeImports": "explicit"
+                    },
+                    "editor.formatOnSave": true,
+                    "editor.defaultFormatter": "charliermarsh.ruff"
+                }
+            },
+            // Add the IDs of extensions you want installed when the container is created.
+            "extensions": [
+                "ms-python.python",
+                "ms-python.vscode-pylance",
+                "charliermarsh.ruff",
+                "davidanson.vscode-markdownlint",
+                "ms-azuretools.vscode-docker",
+                "vscode-icons-team.vscode-icons",
+                "github.vscode-github-actions",
+            ],
+        }
+    }
+}
diff --git a/.dockerignore b/.dockerignore
index 9f4726bfb..abc5b82f0 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -1,6 +1,25 @@
 .git
 .gitignore
 Dockerfile
+Dockerfile.armhf
 .dockerignore
+docker/
+.coveragerc
+.eggs
+.github
+.pylintrc
+.travis.yml
+CONTRIBUTING.md
+MANIFEST.in
+README.md
+freqtrade.service
+freqtrade.egg-info
+
 config.json*
 *.sqlite
+user_data
+*.log
+
+.vscode
+.mypy_cache
+.ipynb_checkpoints
diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 000000000..81f6f422e
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,3 @@
+*.py    eol=lf
+*.sh    eol=lf
+*.ps1   eol=crlf
diff --git a/.github/.devcontainer/Dockerfile b/.github/.devcontainer/Dockerfile
new file mode 100644
index 000000000..1deab0f54
--- /dev/null
+++ b/.github/.devcontainer/Dockerfile
@@ -0,0 +1,21 @@
+FROM freqtradeorg/freqtrade:develop_freqairl
+
+USER root
+# Install dependencies
+COPY requirements-dev.txt /freqtrade/
+
+ARG USERNAME=ftuser
+
+RUN apt-get update \
+    && apt-get -y install --no-install-recommends apt-utils dialog git ssh vim build-essential zsh \
+    && apt-get clean \
+    && mkdir -p /home/${USERNAME}/.vscode-server /home/${USERNAME}/.vscode-server-insiders /home/${USERNAME}/commandhistory \
+    && chown ${USERNAME}:${USERNAME} -R /home/${USERNAME}/.local/ \
+    && chown ${USERNAME}: -R /home/${USERNAME}/
+
+USER ftuser
+
+RUN pip install --user autopep8 -r docs/requirements-docs.txt -r requirements-dev.txt --no-cache-dir
+
+# Empty the ENTRYPOINT to allow all commands
+ENTRYPOINT []
diff --git a/.github/.devcontainer/devcontainer.json b/.github/.devcontainer/devcontainer.json
new file mode 100644
index 000000000..d87ea5fda
--- /dev/null
+++ b/.github/.devcontainer/devcontainer.json
@@ -0,0 +1,12 @@
+{
+    "name": "freqtrade Dev container image builder",
+    "build": {
+        "dockerfile": "Dockerfile",
+        "context": "../../"
+    },
+    "features": {
+        "ghcr.io/devcontainers/features/common-utils:2": {
+        },
+        "ghcr.io/stuartleeks/dev-container-features/shell-history:0.0.3": {}
+    }
+}
diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
new file mode 100644
index 000000000..3fa04cb6f
--- /dev/null
+++ b/.github/FUNDING.yml
@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: [xmatthias]
diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 2cbbc59cb..000000000
--- a/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,30 +0,0 @@
-## Step 1: Have you search for this issue before posting it?
-
-If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/gcarq/freqtrade/issues?q=is%3Aissue). 
-If it hasn't been reported, please create a new issue.
-
-## Step 2: Describe your environment
-
-  * Python Version: _____ (`python -V`)
-  * Branch: Master | Develop
-  * Last Commit ID: _____ (`git log --format="%H" -n 1`)
- 
-## Step 3: Describe the problem:
-*Explain the problem you have encountered*
-
-### Steps to reproduce:
-
-  1. _____
-  2. _____
-  3. _____
-  
-### Observed Results:
-
-  * What happened?
-  * What did you expect to happen?
-
-### Relevant code exceptions or logs:
-
-  ```
-  // paste your log here
-  ```
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 000000000..8637c0d68
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,48 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: "Triage Needed"
+assignees: ''
+
+---
+<!-- 
+Have you searched for similar issues before posting it?
+
+If you have discovered a bug in the bot, please [search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue). 
+If it hasn't been reported, please create a new issue.
+
+Please do not use bug reports to request new features.
+-->
+
+## Describe your environment
+
+  * Operating system: ____
+  * Python Version: _____ (`python -V`)
+  * CCXT version: _____ (`pip freeze | grep ccxt`)
+  * Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
+  
+Note: All issues other than enhancement requests will be closed without further comment if the above template is deleted or not filled out.
+
+## Describe the problem:
+
+*Explain the problem you have encountered*
+
+### Steps to reproduce:
+
+  1. _____
+  2. _____
+  3. _____
+  
+### Observed Results:
+
+  * What happened?
+  * What did you expect to happen?
+
+### Relevant code exceptions or logs
+
+Note: Please copy/paste text of the messages, no screenshots of logs please.
+
+  ```
+  // paste your log here
+  ```
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 000000000..5014de46a
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,6 @@
+---
+blank_issues_enabled: false
+contact_links:
+  - name: Discord Server
+    url: https://discord.gg/p7nuUNVfP7
+    about: Ask a question or get community support from our Discord server
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 000000000..db335bf09
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,26 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+<!-- 
+Note: this section will not show up in the issue.
+Have you search for this feature before requesting it? It's highly likely that a similar request was already filed.
+-->
+
+## Describe your environment
+(if applicable)
+
+  * Operating system: ____
+  * Python Version: _____ (`python -V`)
+  * CCXT version: _____ (`pip freeze | grep ccxt`)
+  * Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
+
+
+## Describe the enhancement
+
+*Explain the enhancement you would like*
diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md
new file mode 100644
index 000000000..9283f0e4f
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/question.md
@@ -0,0 +1,25 @@
+---
+name: Question
+about: Ask a question you could not find an answer in the docs
+title: ''
+labels: "Question"
+assignees: ''
+
+---
+<!-- 
+Have you searched for similar issues before posting it?
+Did you have a VERY good look at the [documentation](https://www.freqtrade.io/en/latest/) and are sure that the question is not explained there
+
+Please do not use the question template to report bugs or to request new features.
+-->
+
+## Describe your environment
+
+  * Operating system: ____
+  * Python Version: _____ (`python -V`)
+  * CCXT version: _____ (`pip freeze | grep ccxt`)
+  * Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
+  
+## Your question
+
+*Ask the question you have not been able to find an answer in the [Documentation](https://www.freqtrade.io/en/latest/)*
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index ae79dd8f0..90a10d4da 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,15 +1,17 @@
-Thank you for sending your pull request. But first, have you included
-unit tests, and is your code PEP8 conformant? [More details](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
-
+<!-- Thank you for sending your pull request. But first, have you included
+unit tests, and is your code PEP8 conformant? [More details](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
+-->
 ## Summary
-Explain in one sentence the goal of this PR
+
+<!-- Explain in one sentence the goal of this PR -->
 
 Solve the issue: #___
 
 ## Quick changelog
 
-- <change log #1>
-- <change log #2>
+- <change log 1>
+- <change log 1>
 
 ## What's new?
-*Explain in details what this PR solve or improve. You can include visuals.* 
+
+<!-- Explain in details what this PR solve or improve. You can include visuals. -->
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 000000000..d6c20a6bc
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,38 @@
+version: 2
+updates:
+- package-ecosystem: docker
+  directories:
+    - "/"
+    - "/docker"
+  schedule:
+    interval: daily
+  ignore:
+  - dependency-name: "*"
+    update-types: ["version-update:semver-major"]
+  open-pull-requests-limit: 10
+
+- package-ecosystem: pip
+  directory: "/"
+  schedule:
+    interval: weekly
+    time: "03:00"
+    timezone: "Etc/UTC"
+  open-pull-requests-limit: 15
+  target-branch: develop
+  groups:
+    types:
+      patterns:
+        - "types-*"
+    pytest:
+      patterns:
+        - "pytest*"
+    mkdocs:
+      patterns:
+        - "mkdocs*"
+
+- package-ecosystem: "github-actions"
+  directory: "/"
+  schedule:
+    interval: "weekly"
+  open-pull-requests-limit: 10
+  target-branch: develop
diff --git a/.github/workflows/binance-lev-tier-update.yml b/.github/workflows/binance-lev-tier-update.yml
new file mode 100644
index 000000000..ba1999fd6
--- /dev/null
+++ b/.github/workflows/binance-lev-tier-update.yml
@@ -0,0 +1,50 @@
+name: Binance Leverage tiers update
+
+on:
+  schedule:
+    - cron: "0 3 * * 4"
+  # on demand
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  auto-update:
+    runs-on: ubuntu-latest
+    environment:
+      name: develop
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+
+    - name: Install ccxt
+      run: pip install ccxt
+
+    - name: Run leverage tier update
+      env:
+        CI_WEB_PROXY: ${{ secrets.CI_WEB_PROXY }}
+        FREQTRADE__EXCHANGE__KEY: ${{ secrets.BINANCE_EXCHANGE_KEY }}
+        FREQTRADE__EXCHANGE__SECRET: ${{ secrets.BINANCE_EXCHANGE_SECRET }}
+      run: python build_helpers/binance_update_lev_tiers.py
+
+
+    - uses: peter-evans/create-pull-request@v7
+      with:
+        token: ${{ secrets.REPO_SCOPED_TOKEN }}
+        add-paths: freqtrade/exchange/binance_leverage_tiers.json
+        labels: |
+          Tech maintenance
+          Dependencies
+        branch: update/binance-leverage-tiers
+        title: Update Binance Leverage Tiers
+        commit-message: "chore: update pre-commit hooks"
+        committer: Freqtrade Bot <154552126+freqtrade-bot@users.noreply.github.com>
+        author: Freqtrade Bot <154552126+freqtrade-bot@users.noreply.github.com>
+        body: Update binance leverage tiers.
+        delete-branch: true
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 000000000..e45f85481
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,711 @@
+name: Freqtrade CI
+
+on:
+  push:
+    branches:
+      - stable
+      - develop
+      - ci/*
+    tags:
+  release:
+    types: [published]
+  pull_request:
+  schedule:
+    - cron:  '0 3 * * 4'
+
+concurrency:
+  group: "${{ github.workflow }}-${{ github.ref }}-${{ github.event_name }}"
+  cancel-in-progress: true
+permissions:
+  repository-projects: read
+jobs:
+  build-linux:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ "ubuntu-22.04", "ubuntu-24.04" ]
+        python-version: ["3.10", "3.11", "3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        python-version: ${{ matrix.python-version }}
+        cache-dependency-glob: "requirements**.txt"
+        cache-suffix: "${{ matrix.python-version }}"
+        prune-cache: false
+
+    - name: Cache_dependencies
+      uses: actions/cache@v4
+      id: cache
+      with:
+        path: ~/dependencies/
+        key: ${{ runner.os }}-dependencies
+
+    - name: TA binary *nix
+      if: steps.cache.outputs.cache-hit != 'true'
+      run: |
+        cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
+
+    - name: Installation - *nix
+      run: |
+        uv pip install --upgrade wheel
+        export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
+        export TA_LIBRARY_PATH=${HOME}/dependencies/lib
+        export TA_INCLUDE_PATH=${HOME}/dependencies/include
+        uv pip install -r requirements-dev.txt
+        uv pip install -e ft_client/
+        uv pip install -e .
+
+    - name: Check for version alignment
+      run: |
+        python build_helpers/freqtrade_client_version_align.py
+
+    - name: Tests
+      if: (!(runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-24.04'))
+      run: |
+        pytest --random-order
+
+    - name: Tests with Coveralls
+      if: (runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-24.04')
+      run: |
+        pytest --random-order --cov=freqtrade --cov=freqtrade_client --cov-config=.coveragerc
+
+    - name: Coveralls
+      if: (runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-24.04')
+      env:
+        # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
+        COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
+      run: |
+        # Allow failure for coveralls
+        coveralls || true
+
+    - name: Run json schema extract
+      # This should be kept before the repository check to ensure that the schema is up-to-date
+      run: |
+        python build_helpers/extract_config_json_schema.py
+
+    - name: Run command docs partials extract
+      # This should be kept before the repository check to ensure that the docs are up-to-date
+      run: |
+        python build_helpers/create_command_partials.py
+
+    - name: Check for repository changes
+      run: |
+        if [ -n "$(git status --porcelain)" ]; then
+          echo "Repository is dirty, changes detected:"
+          git status
+          git diff
+          exit 1
+        else
+          echo "Repository is clean, no changes detected."
+        fi
+
+    - name: Backtesting (multi)
+      run: |
+        cp tests/testdata/config.tests.json config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade new-strategy -s AwesomeStrategy
+        freqtrade new-strategy -s AwesomeStrategyMin --template minimal
+        freqtrade backtesting --datadir tests/testdata --strategy-list AwesomeStrategy AwesomeStrategyMin -i 5m
+
+    - name: Hyperopt
+      run: |
+        cp tests/testdata/config.tests.json config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade hyperopt --datadir tests/testdata -e 6 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
+
+    - name: Sort imports (isort)
+      run: |
+        isort --check .
+
+    - name: Run Ruff
+      run: |
+        ruff check --output-format=github
+
+    - name: Run Ruff format check
+      run: |
+        ruff format --check
+
+    - name: Mypy
+      if: matrix.os == 'ubuntu-24.04'
+      run: |
+        mypy freqtrade scripts tests
+
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
+      if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+          severity: error
+          details: Freqtrade CI failed on ${{ matrix.os }}
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
+
+  build-macos:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ "macos-13", "macos-14", "macos-15" ]
+        python-version: ["3.10", "3.11", "3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+        check-latest: true
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        python-version: ${{ matrix.python-version }}
+        cache-dependency-glob: "requirements**.txt"
+        cache-suffix: "${{ matrix.python-version }}"
+        prune-cache: false
+
+    - name: Cache_dependencies
+      uses: actions/cache@v4
+      id: cache
+      with:
+        path: ~/dependencies/
+        key: ${{ matrix.os }}-dependencies
+
+    - name: TA binary *nix
+      if: steps.cache.outputs.cache-hit != 'true'
+      run: |
+        cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
+
+    - name: Installation - macOS (Brew)
+      run: |
+        # brew update
+        # TODO: Should be the brew upgrade
+        # homebrew fails to update python due to unlinking failures
+        # https://github.com/actions/runner-images/issues/6817
+        rm /usr/local/bin/2to3 || true
+        rm /usr/local/bin/2to3-3.11 || true
+        rm /usr/local/bin/2to3-3.12 || true
+        rm /usr/local/bin/idle3 || true
+        rm /usr/local/bin/idle3.11 || true
+        rm /usr/local/bin/idle3.12 || true
+        rm /usr/local/bin/pydoc3 || true
+        rm /usr/local/bin/pydoc3.11 || true
+        rm /usr/local/bin/pydoc3.12 || true
+        rm /usr/local/bin/python3 || true
+        rm /usr/local/bin/python3.11 || true
+        rm /usr/local/bin/python3.12 || true
+        rm /usr/local/bin/python3-config || true
+        rm /usr/local/bin/python3.11-config || true
+        rm /usr/local/bin/python3.12-config || true
+
+        brew install libomp
+
+    - name: Installation (python)
+      run: |
+        uv pip install wheel
+        export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
+        export TA_LIBRARY_PATH=${HOME}/dependencies/lib
+        export TA_INCLUDE_PATH=${HOME}/dependencies/include
+        uv pip install -r requirements-dev.txt
+        uv pip install -e ft_client/
+        uv pip install -e .
+
+    - name: Tests
+      run: |
+        pytest --random-order
+
+    - name: Check for repository changes
+      run: |
+        if [ -n "$(git status --porcelain)" ]; then
+          echo "Repository is dirty, changes detected:"
+          git status
+          git diff
+          exit 1
+        else
+          echo "Repository is clean, no changes detected."
+        fi
+
+    - name: Backtesting
+      run: |
+        cp tests/testdata/config.tests.json config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade new-strategy -s AwesomeStrategyAdv --template advanced
+        freqtrade backtesting --datadir tests/testdata --strategy AwesomeStrategyAdv
+
+    - name: Hyperopt
+      run: |
+        cp tests/testdata/config.tests.json config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
+
+    - name: Sort imports (isort)
+      run: |
+        isort --check .
+
+    - name: Run Ruff
+      run: |
+        ruff check --output-format=github
+
+    - name: Run Ruff format check
+      run: |
+        ruff format --check
+
+    - name: Mypy
+      if: matrix.os == 'macos-15'
+      run: |
+        mypy freqtrade scripts
+
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
+      if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+          severity: info
+          details: Test Succeeded!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
+
+  build-windows:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ windows-latest ]
+        python-version: ["3.10", "3.11", "3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        python-version: ${{ matrix.python-version }}
+        cache-dependency-glob: "requirements**.txt"
+        cache-suffix: "${{ matrix.python-version }}"
+        prune-cache: false
+
+    - name: Installation
+      run: |
+        function uvpipFunction { uv pip $args }
+        Set-Alias -name pip -value uvpipFunction
+
+        ./build_helpers/install_windows.ps1
+
+    - name: Tests
+      run: |
+        pytest --random-order --durations 20 -n auto
+
+    - name: Check for repository changes
+      run: |
+        if (git status --porcelain) {
+          Write-Host "Repository is dirty, changes detected:"
+          git status
+          git diff
+          exit 1
+        }
+        else {
+          Write-Host "Repository is clean, no changes detected."
+        }
+
+    - name: Backtesting
+      run: |
+        cp tests/testdata/config.tests.json config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
+
+    - name: Hyperopt
+      run: |
+        cp tests/testdata/config.tests.json config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
+
+    - name: Run Ruff
+      run: |
+        ruff check --output-format=github
+
+    - name: Run Ruff format check
+      run: |
+        ruff format --check
+
+    - name: Mypy
+      run: |
+        mypy freqtrade scripts tests
+
+    - name: Run Pester tests (PowerShell)
+      run: |
+        $PSVersionTable
+        Set-PSRepository psgallery -InstallationPolicy trusted
+        Install-Module -Name Pester -RequiredVersion 5.3.1 -Confirm:$false -Force -SkipPublisherCheck
+        $Error.clear()
+        Invoke-Pester -Path "tests" -CI
+        if ($Error.Length -gt 0) {exit 1}
+
+      shell: powershell
+
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
+      if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+          severity: error
+          details: Test Failed
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
+
+  mypy-version-check:
+    runs-on: ubuntu-22.04
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+
+    - name: pre-commit dependencies
+      run: |
+        pip install pyaml
+        python build_helpers/pre_commit_update.py
+
+  pre-commit:
+    runs-on: ubuntu-22.04
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+    - uses: pre-commit/action@v3.0.1
+
+  docs-check:
+    runs-on: ubuntu-22.04
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Documentation syntax
+      run: |
+        ./tests/test_docs.sh
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+
+    - name: Documentation build
+      run: |
+        pip install -r docs/requirements-docs.txt
+        mkdocs build
+
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
+      if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+          severity: error
+          details: Freqtrade doc test failed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
+
+
+  build-linux-online:
+    # Run pytest with "live" checks
+    runs-on: ubuntu-22.04
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        python-version: "3.12"
+        cache-dependency-glob: "requirements**.txt"
+        cache-suffix: "3.12"
+        prune-cache: false
+
+    - name: Cache_dependencies
+      uses: actions/cache@v4
+      id: cache
+      with:
+        path: ~/dependencies/
+        key: ${{ runner.os }}-dependencies
+
+
+    - name: TA binary *nix
+      if: steps.cache.outputs.cache-hit != 'true'
+      run: |
+        cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
+
+    - name: Installation - *nix
+      run: |
+        uv pip install --upgrade wheel
+        export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
+        export TA_LIBRARY_PATH=${HOME}/dependencies/lib
+        export TA_INCLUDE_PATH=${HOME}/dependencies/include
+        uv pip install -r requirements-dev.txt
+        uv pip install -e ft_client/
+        uv pip install -e .
+
+    - name: Tests incl. ccxt compatibility tests
+      env:
+        CI_WEB_PROXY: http://152.67.78.211:13128
+      run: |
+        pytest --random-order --longrun --durations 20 -n auto
+
+
+  # Notify only once - when CI completes (and after deploy) in case it's successful
+  notify-complete:
+    needs: [
+      build-linux,
+      build-macos,
+      build-windows,
+      docs-check,
+      mypy-version-check,
+      pre-commit,
+      build-linux-online
+    ]
+    runs-on: ubuntu-22.04
+    # Discord notification can't handle schedule events
+    if: github.event_name != 'schedule' && github.repository == 'freqtrade/freqtrade'
+    permissions:
+      repository-projects: read
+    steps:
+
+    - name: Check user permission
+      id: check
+      uses: scherermichael-oss/action-has-permission@1.0.6
+      with:
+        required-permission: write
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
+      if: always() && steps.check.outputs.has-permission && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+          severity: info
+          details: Test Completed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
+
+  build:
+    name: "Build"
+    needs: [ build-linux, build-macos, build-windows, docs-check, mypy-version-check, pre-commit ]
+    runs-on: ubuntu-22.04
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+
+    - name: Build distribution
+      run: |
+        pip install -U build
+        python -m build --sdist --wheel
+
+    - name: Upload artifacts 📦
+      uses: actions/upload-artifact@v4
+      with:
+        name: freqtrade-build
+        path: |
+          dist
+        retention-days: 10
+
+    - name: Build Client distribution
+      run: |
+        pip install -U build
+        python -m build --sdist --wheel ft_client
+
+    - name: Upload artifacts 📦
+      uses: actions/upload-artifact@v4
+      with:
+        name: freqtrade-client-build
+        path: |
+          ft_client/dist
+        retention-days: 10
+
+  deploy-test-pypi:
+    name: "Publish Python 🐍 distribution 📦 to TestPyPI"
+    needs: [ build ]
+    runs-on: ubuntu-22.04
+    if: (github.event_name == 'release')
+    environment:
+      name: testpypi
+      url: https://test.pypi.org/p/freqtrade
+    permissions:
+      id-token: write
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Download artifact  📦
+      uses: actions/download-artifact@v4
+      with:
+        pattern: freqtrade*-build
+        path: dist
+        merge-multiple: true
+
+    - name: Publish to PyPI (Test)
+      uses: pypa/gh-action-pypi-publish@v1.12.4
+      with:
+        repository-url: https://test.pypi.org/legacy/
+
+
+  deploy-pypi:
+    name: "Publish Python 🐍 distribution 📦 to PyPI"
+    needs: [ build ]
+    runs-on: ubuntu-22.04
+    if: (github.event_name == 'release')
+    environment:
+      name: pypi
+      url: https://pypi.org/p/freqtrade
+    permissions:
+      id-token: write
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Download artifact  📦
+      uses: actions/download-artifact@v4
+      with:
+        pattern: freqtrade*-build
+        path: dist
+        merge-multiple: true
+
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@v1.12.4
+
+
+  deploy-docker:
+    needs: [ build-linux, build-macos, build-windows, docs-check, mypy-version-check, pre-commit ]
+    runs-on: ubuntu-22.04
+
+    if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+
+    - name: Extract branch name
+      id: extract-branch
+      run: |
+        echo "GITHUB_REF='${GITHUB_REF}'"
+        echo "branch=${GITHUB_REF##*/}" >> "$GITHUB_OUTPUT"
+
+    - name: Dockerhub login
+      env:
+        DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
+        DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
+      run: |
+        echo "${DOCKER_PASSWORD}" | docker login --username ${DOCKER_USERNAME} --password-stdin
+
+    # We need docker experimental to pull the ARM image.
+    - name: Switch docker to experimental
+      run: |
+          docker version -f '{{.Server.Experimental}}'
+          echo $'{\n    "experimental": true\n}' | sudo tee /etc/docker/daemon.json
+          sudo systemctl restart docker
+          docker version -f '{{.Server.Experimental}}'
+
+    - name: Set up QEMU
+      uses: docker/setup-qemu-action@v3
+
+    - name: Set up Docker Buildx
+      id: buildx
+      uses: docker/setup-buildx-action@v3
+
+    - name: Available platforms
+      run: echo ${PLATFORMS}
+      env:
+        PLATFORMS: ${{ steps.buildx.outputs.platforms }}
+
+    - name: Build and test and push docker images
+      env:
+        BRANCH_NAME: ${{ steps.extract-branch.outputs.branch }}
+      run: |
+        build_helpers/publish_docker_multi.sh
+
+  deploy-arm:
+    name: "Deploy Docker"
+    permissions:
+      packages: write
+    needs: [ deploy-docker ]
+    # Only run on 64bit machines
+    runs-on: [self-hosted, linux, ARM64]
+    if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Extract branch name
+      id: extract-branch
+      run: |
+        echo "GITHUB_REF='${GITHUB_REF}'"
+        echo "branch=${GITHUB_REF##*/}" >> "$GITHUB_OUTPUT"
+
+    - name: Dockerhub login
+      env:
+        DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
+        DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
+      run: |
+        echo "${DOCKER_PASSWORD}" | docker login --username ${DOCKER_USERNAME} --password-stdin
+
+    - name: Build and test and push docker images
+      env:
+        BRANCH_NAME: ${{ steps.extract-branch.outputs.branch }}
+        GHCR_USERNAME: ${{ github.actor }}
+        GHCR_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      run: |
+        build_helpers/publish_docker_arm64.sh
+
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false) && (github.event_name != 'schedule')
+      with:
+          severity: info
+          details: Deploy Succeeded!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
new file mode 100644
index 000000000..6577bf38b
--- /dev/null
+++ b/.github/workflows/deploy-docs.yml
@@ -0,0 +1,61 @@
+name: Build Documentation
+
+on:
+  push:
+    branches:
+      - develop
+  release:
+    types: [published]
+
+
+# disable permissions for all of the available permissions
+permissions: {}
+
+
+jobs:
+  build-docs:
+    permissions:
+      contents: write  #  for mike to push
+    name: Deploy Docs through mike
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          persist-credentials: true
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r docs/requirements-docs.txt
+
+      - name: Fetch gh-pages branch
+        run: |
+          git fetch origin gh-pages --depth=1
+
+      - name: Configure Git user
+        run: |
+          git config --local user.email "github-actions[bot]@users.noreply.github.com"
+          git config --local user.name "github-actions[bot]"
+
+      - name: Build and push Mike
+        if: ${{ github.event_name == 'push' }}
+        run: |
+          mike deploy ${REF_NAME} latest --push --update-aliases
+        env:
+          REF_NAME: ${{ github.ref_name }}
+
+      - name: Build and push Mike - Release
+        if: ${{ github.event_name == 'release' }}
+        run: |
+          mike deploy ${REF_NAME} stable --push --update-aliases
+        env:
+          REF_NAME: ${{ github.ref_name }}
+
+      - name: Show mike versions
+        run: |
+          mike list
diff --git a/.github/workflows/devcontainer-build.yml b/.github/workflows/devcontainer-build.yml
new file mode 100644
index 000000000..12b55c6ed
--- /dev/null
+++ b/.github/workflows/devcontainer-build.yml
@@ -0,0 +1,42 @@
+name: Devcontainer Pre-Build
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 3 * * 0"
+  # push:
+  #   branches:
+  #     - "master"
+  #   tags:
+  #     - "v*.*.*"
+  #   pull_requests:
+  #     branches:
+  #       - "master"
+
+concurrency:
+  group: "${{ github.workflow }}"
+  cancel-in-progress: true
+
+
+jobs:
+  build-and-push:
+    permissions:
+      packages: write
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+    - name: Login to GitHub Container Registry
+      uses: docker/login-action@v3
+      with:
+        registry: ghcr.io
+        username: ${{ github.actor }}
+        password: ${{ secrets.GITHUB_TOKEN }}
+    - name: Pre-build dev container image
+      uses: devcontainers/ci@v0.3
+      with:
+        subFolder: .github
+        imageName: ghcr.io/${{ github.repository }}-devcontainer
+        cacheFrom: ghcr.io/${{ github.repository }}-devcontainer
+        push: always
diff --git a/.github/workflows/docker-update-readme.yml b/.github/workflows/docker-update-readme.yml
new file mode 100644
index 000000000..7f00c01c0
--- /dev/null
+++ b/.github/workflows/docker-update-readme.yml
@@ -0,0 +1,23 @@
+name: Update Docker Hub Description
+on:
+  push:
+    branches:
+    - stable
+
+# disable permissions for all of the available permissions
+permissions: {}
+
+jobs:
+  dockerHubDescription:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - name: Docker Hub Description
+      uses: peter-evans/dockerhub-description@v4
+      with:
+        username: ${{ secrets.DOCKER_USERNAME }}
+        password: ${{ secrets.DOCKER_PASSWORD }}
+        repository: freqtradeorg/freqtrade
diff --git a/.github/workflows/pre-commit-update.yml b/.github/workflows/pre-commit-update.yml
new file mode 100644
index 000000000..5996dddf9
--- /dev/null
+++ b/.github/workflows/pre-commit-update.yml
@@ -0,0 +1,44 @@
+name: Pre-commit auto-update
+
+on:
+  schedule:
+    - cron: "0 3 * * 2"
+  # on demand
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  auto-update:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        persist-credentials: false
+
+    - uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+
+
+    - name: Install pre-commit
+      run: pip install pre-commit
+
+    - name: Run auto-update
+      run: pre-commit autoupdate
+
+    - uses: peter-evans/create-pull-request@v7
+      with:
+        token: ${{ secrets.REPO_SCOPED_TOKEN }}
+        add-paths: .pre-commit-config.yaml
+        labels: |
+          Tech maintenance
+          Dependencies
+        branch: update/pre-commit-hooks
+        title: Update pre-commit hooks
+        commit-message: "chore: update pre-commit hooks"
+        committer: Freqtrade Bot <154552126+freqtrade-bot@users.noreply.github.com>
+        author: Freqtrade Bot <154552126+freqtrade-bot@users.noreply.github.com>
+        body: Update versions of pre-commit hooks to latest version.
+        delete-branch: true
diff --git a/.gitignore b/.gitignore
index 8c7a6e257..d371c9dd9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,13 +1,24 @@
 # Freqtrade rules
-freqtrade/tests/testdata/*.json
-hyperopt_conf.py
-config.json
+config*.json
 *.sqlite
-.hyperopt
+*.sqlite-shm
+*.sqlite-wal
 logfile.txt
-hyperopt_trials.pickle
-user_data/
+user_data/*
+!user_data/strategy/sample_strategy.py
+!user_data/notebooks
+!user_data/models
+!user_data/freqaimodels
+user_data/freqaimodels/*
+user_data/models/*
+user_data/notebooks/*
 freqtrade-plot.html
+freqtrade-profit-plot.html
+freqtrade/rpc/api_server/ui/*
+build_helpers/ta-lib/*
+
+# Macos related
+.DS_Store
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -72,14 +83,19 @@ instance/
 # Scrapy stuff:
 .scrapy
 
+# memray
+memray-*
+
 # Sphinx documentation
 docs/_build/
+# Mkdocs documentation
+site/
 
 # PyBuilder
 target/
 
 # Jupyter Notebook
-.ipynb_checkpoints
+*.ipynb_checkpoints
 
 # pyenv
 .python-version
@@ -88,3 +104,15 @@ target/
 .venv
 .idea
 .vscode
+
+.pytest_cache/
+.mypy_cache/
+
+#exceptions
+!*.gitkeep
+!config_examples/config_binance.example.json
+!config_examples/config_full.example.json
+!config_examples/config_kraken.example.json
+!config_examples/config_freqai.example.json
+
+docker-compose-*.yml
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 000000000..a9f4f1309
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,87 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+
+  - repo: local
+    # Keep json schema in sync with the config schema
+    # This will write the files - and fail pre-commit if a file has been changed.
+    hooks:
+      - id: Extract config json schema
+        name: extract-config-json-schema
+        entry: "python build_helpers/extract_config_json_schema.py"
+        language: python
+        pass_filenames: false
+        additional_dependencies: ["python-rapidjson", "jsonschema"]
+
+  - repo: https://github.com/pycqa/flake8
+    rev: "7.2.0"
+    hooks:
+      - id: flake8
+        additional_dependencies: [Flake8-pyproject]
+        # stages: [push]
+
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: "v1.15.0"
+    hooks:
+      - id: mypy
+        exclude: build_helpers
+        additional_dependencies:
+          - types-cachetools==5.5.0.20240820
+          - types-filelock==3.2.7
+          - types-requests==2.32.0.20250328
+          - types-tabulate==0.9.0.20241207
+          - types-python-dateutil==2.9.0.20241206
+          - SQLAlchemy==2.0.40
+        # stages: [push]
+
+  - repo: https://github.com/pycqa/isort
+    rev: "6.0.1"
+    hooks:
+      - id: isort
+        name: isort (python)
+        # stages: [push]
+
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    # Ruff version.
+    rev: 'v0.11.4'
+    hooks:
+      - id: ruff
+      - id: ruff-format
+
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: end-of-file-fixer
+        exclude: |
+            (?x)^(
+                tests/.*|
+                .*\.svg|
+                .*\.yml|
+                .*\.json
+            )$
+      - id: mixed-line-ending
+      - id: debug-statements
+      - id: check-ast
+      - id: trailing-whitespace
+        exclude: |
+          (?x)^(
+                .*\.md
+            )$
+
+  - repo: https://github.com/stefmolin/exif-stripper
+    rev: 0.6.2
+    hooks:
+      - id: strip-exif
+
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.4.1
+    hooks:
+    - id: codespell
+      additional_dependencies:
+        - tomli
+
+  # Ensure github actions remain safe
+  - repo: https://github.com/woodruffw/zizmor-pre-commit
+    rev: v1.5.2
+    hooks:
+    - id: zizmor
diff --git a/.pylintrc b/.pylintrc
index dce99c067..0932ecba4 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -7,4 +7,3 @@ ignore=vendor
 
 [TYPECHECK]
 ignored-modules=numpy,talib,talib.abstract
-
diff --git a/.readthedocs.yml b/.readthedocs.yml
new file mode 100644
index 000000000..6d0d1fe64
--- /dev/null
+++ b/.readthedocs.yml
@@ -0,0 +1,14 @@
+# .readthedocs.yml
+version: 2
+
+build:
+  os: "ubuntu-22.04"
+  tools:
+    python: "3.11"
+
+python:
+  install:
+    - requirements: docs/requirements-docs.txt
+
+mkdocs:
+  configuration: mkdocs.yml
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index be330dad4..000000000
--- a/.travis.yml
+++ /dev/null
@@ -1,37 +0,0 @@
-sudo: true
-os:
-- linux
-language: python
-python:
-- 3.6
-addons:
-  apt:
-    packages:
-    - libelf-dev
-    - libdw-dev
-    - binutils-dev
-install:
-- ./install_ta-lib.sh
-- export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
-- pip install --upgrade flake8 coveralls
-- pip install -r requirements.txt
-- pip install -e .
-jobs:
-  include:
-    - script: pytest --cov=freqtrade --cov-config=.coveragerc freqtrade/tests/
-    - script:
-      - cp config.json.example config.json
-      - python freqtrade/main.py backtesting
-    - script:
-      - cp config.json.example config.json
-      - python freqtrade/main.py hyperopt -e 5
-    - script: flake8 freqtrade
-after_success:
-- coveralls
-notifications:
-  slack:
-    secure: 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
-cache:
-  directories:
-    - $HOME/.cache/pip
-    - ta-lib
\ No newline at end of file
diff --git a/.vscode/extensions.json b/.vscode/extensions.json
new file mode 100644
index 000000000..10c16da00
--- /dev/null
+++ b/.vscode/extensions.json
@@ -0,0 +1,11 @@
+{
+    "recommendations": [
+        "ms-python.python",
+        "ms-python.vscode-pylance",
+        "charliermarsh.ruff",
+        "davidanson.vscode-markdownlint",
+        "ms-azuretools.vscode-docker",
+        "vscode-icons-team.vscode-icons",
+        "github.vscode-github-actions",
+    ]
+}
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 967f57b65..66ba4f830 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,45 +1,135 @@
-# Contribute to freqtrade
+# Contributing
 
-Feel like our bot is missing a feature? We welcome your pull requests! Few pointers for contributions:
+## Contribute to freqtrade
 
-- Create your PR against the `develop` branch, not `master`.
-- New features need to contain unit tests and must be PEP8 
-conformant (max-line-length = 100).
+Feel like our bot is missing a feature? We welcome your pull requests! 
 
-If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE)
-or in a [issue](https://github.com/gcarq/freqtrade/issues) before a PR.
+Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
 
+Few pointers for contributions:
 
-**Before sending the PR:**
+- Create your PR against the `develop` branch, not `stable`.
+- New features need to contain unit tests, must conform to PEP8 (max-line-length = 100) and should be documented with the introduction PR.
+- PR's can be declared as `[WIP]` - which signify Work in Progress Pull Requests (which are not finished).
 
-## 1. Run unit tests
+If you are unsure, discuss the feature on our [discord server](https://discord.gg/p7nuUNVfP7) or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a Pull Request.
+
+## Getting started
+
+Best start by reading the [documentation](https://www.freqtrade.io/) to get a feel for what is possible with the bot, or head straight to the [Developer-documentation](https://www.freqtrade.io/en/latest/developer/) (WIP) which should help you getting started.
+
+## Before sending the PR
+
+### 1. Run unit tests
 
 All unit tests must pass. If a unit test is broken, change your code to 
 make it pass. It means you have introduced a regression.
 
-**Test the whole project**
+#### Test the whole project
+
 ```bash
-pytest freqtrade
+pytest
 ```
 
-**Test only one file**
+#### Test only one file
+
 ```bash
-pytest freqtrade/tests/test_<file_name>.py
+pytest tests/test_<file_name>.py
 ```
 
-**Test only one method from one file**
+#### Test only one method from one file
+
 ```bash
-pytest freqtrade/tests/test_<file_name>.py::test_<method_name>
+pytest tests/test_<file_name>.py::test_<method_name>
 ```
 
-## 2. Test if your code is PEP8 compliant
-**Install packages** (If not already installed)
+### 2. Test if your code is PEP8 compliant
+
+#### Run Ruff
+
 ```bash
-pip3.6 install flake8 coveralls
-``` 
-**Run Flake8**
-```bash
-flake8 freqtrade
+ruff check .
 ```
 
+We receive a lot of code that fails the `ruff` checks.
+To help with that, we encourage you to install the git pre-commit 
+hook that will warn you when you try to commit code that fails these checks.
 
+you can manually run pre-commit with `pre-commit run -a`.
+
+##### Additional styles applied
+
+* Have docstrings on all public methods
+* Use double-quotes for docstrings
+* Multiline docstrings should be indented to the level of the first quote
+* Doc-strings should follow the reST format (`:param xxx: ...`, `:return: ...`, `:raises KeyError: ... `)
+
+### 3. Test if all type-hints are correct
+
+#### Run mypy
+
+``` bash
+mypy freqtrade
+```
+
+### 4. Ensure formatting is correct
+
+#### Run ruff
+
+``` bash
+ruff format .
+```
+
+## (Core)-Committer Guide
+
+### Process: Pull Requests
+
+How to prioritize pull requests, from most to least important:
+
+1. Fixes for broken tests. Broken means broken on any supported platform or Python version.
+1. Extra tests to cover corner cases.
+1. Minor edits to docs.
+1. Bug fixes.
+1. Major edits to docs.
+1. Features.
+
+Ensure that each pull request meets all requirements in the Contributing document.
+
+### Process: Issues
+
+If an issue is a bug that needs an urgent fix, mark it for the next patch release.
+Then either fix it or mark as please-help.
+
+For other issues: encourage friendly discussion, moderate debate, offer your thoughts.
+
+### Process: Your own code changes
+
+All code changes, regardless of who does them, need to be reviewed and merged by someone else.
+This rule applies to all the core committers.
+
+Exceptions:
+
+- Minor corrections and fixes to pull requests submitted by others.
+- While making a formal release, the release manager can make necessary, appropriate changes.
+- Small documentation changes that reinforce existing subject matter. Most commonly being, but not limited to spelling and grammar corrections.
+
+### Responsibilities
+
+- Ensure cross-platform compatibility for every change that's accepted. Windows, Mac & Linux.
+- Ensure no malicious code is introduced into the core code.
+- Create issues for any major changes and enhancements that you wish to make. Discuss things transparently and get community feedback.
+- Keep feature versions as small as possible, preferably one new feature per version.
+- Be welcoming to newcomers and encourage diverse new contributors from all backgrounds. See the Python Community Code of Conduct (https://www.python.org/psf/codeofconduct/).
+
+### Becoming a Committer
+
+Contributors may be given commit privileges. Preference will be given to those with:
+
+1. Past contributions to Freqtrade and other related open-source projects. Contributions to Freqtrade include both code (both accepted and pending) and friendly participation in the issue tracker and Pull request reviews. Both quantity and quality are considered.
+1. A coding style that the other core committers find simple, minimal, and clean.
+1. Access to resources for cross-platform development and testing.
+1. Time to devote to the project regularly.
+
+Being a Committer does not grant write permission on `develop` or `stable` for security reasons (Users trust Freqtrade with their Exchange API keys).
+
+After being Committer for some time, a Committer may be named Core Committer and given full repository access.
diff --git a/Dockerfile b/Dockerfile
index 918552526..de20ef376 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,23 +1,58 @@
-FROM python:3.6.4-slim-stretch
+FROM python:3.12.10-slim-bookworm as base
 
-# Install TA-lib
-RUN apt-get update && apt-get -y install curl build-essential && apt-get clean
-RUN curl -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz | \
-  tar xzvf - && \
-  cd ta-lib && \
-  ./configure && make && make install && \
-  cd .. && rm -rf ta-lib
-ENV LD_LIBRARY_PATH /usr/local/lib
+# Setup env
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONFAULTHANDLER 1
+ENV PATH=/home/ftuser/.local/bin:$PATH
+ENV FT_APP_ENV="docker"
 
 # Prepare environment
-RUN mkdir /freqtrade
+RUN mkdir /freqtrade \
+  && apt-get update \
+  && apt-get -y install sudo libatlas3-base curl sqlite3 libgomp1 \
+  && apt-get clean \
+  && useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \
+  && chown ftuser:ftuser /freqtrade \
+  # Allow sudoers
+  && echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers
+
 WORKDIR /freqtrade
 
 # Install dependencies
-COPY requirements.txt /freqtrade/
-RUN pip install -r requirements.txt
+FROM base as python-deps
+RUN  apt-get update \
+  && apt-get -y install build-essential libssl-dev git libffi-dev libgfortran5 pkg-config cmake gcc \
+  && apt-get clean \
+  && pip install --upgrade pip wheel
 
+# Install TA-lib
+COPY build_helpers/* /tmp/
+RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+# Install dependencies
+COPY --chown=ftuser:ftuser requirements.txt requirements-hyperopt.txt /freqtrade/
+USER ftuser
+RUN  pip install --user --no-cache-dir "numpy<2.0" \
+  && pip install --user --no-cache-dir -r requirements-hyperopt.txt
+
+# Copy dependencies to runtime-image
+FROM base as runtime-image
+COPY --from=python-deps /usr/local/lib /usr/local/lib
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+COPY --from=python-deps --chown=ftuser:ftuser /home/ftuser/.local /home/ftuser/.local
+
+USER ftuser
 # Install and execute
-COPY . /freqtrade/
-RUN pip install -e .
+COPY --chown=ftuser:ftuser . /freqtrade/
+
+RUN pip install -e . --user --no-cache-dir --no-build-isolation \
+  && mkdir /freqtrade/user_data/ \
+  && freqtrade install-ui
+
 ENTRYPOINT ["freqtrade"]
+# Default to trade mode
+CMD [ "trade" ]
diff --git a/MANIFEST.in b/MANIFEST.in
index 63508c05d..0ed6da0ca 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,5 +1,9 @@
 include LICENSE
 include README.md
-include config.json.example
 recursive-include freqtrade *.py
-include freqtrade/tests/testdata/*.json
+recursive-include freqtrade/templates/ *.j2 *.ipynb
+include freqtrade/exchange/binance_leverage_tiers.json
+include freqtrade/rpc/api_server/ui/fallback_file.html
+include freqtrade/rpc/api_server/ui/favicon.ico
+
+prune tests
diff --git a/README.md b/README.md
index 51d7fc938..e0eaf55a9 100644
--- a/README.md
+++ b/README.md
@@ -1,206 +1,229 @@
-# freqtrade
+# ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)
 
-[![Build Status](https://travis-ci.org/gcarq/freqtrade.svg?branch=develop)](https://travis-ci.org/gcarq/freqtrade)
-[![Coverage Status](https://coveralls.io/repos/github/gcarq/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/gcarq/freqtrade?branch=develop)
+[![Freqtrade CI](https://github.com/freqtrade/freqtrade/actions/workflows/ci.yml/badge.svg?branch=develop)](https://github.com/freqtrade/freqtrade/actions/)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)
+[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
+[![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)
+[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
 
+Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
 
-Simple High frequency trading bot for crypto currencies designed to 
-support multi exchanges and be controlled via Telegram.
-
-![freqtrade](https://raw.githubusercontent.com/gcarq/freqtrade/develop/docs/assets/freqtrade-screenshot.png)
+![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade-screenshot.png)
 
 ## Disclaimer
-This software is for educational purposes only. Do not risk money which 
-you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS 
-AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS. 
+
+This software is for educational purposes only. Do not risk money which
+you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS
+AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
 
 Always start by running a trading bot in Dry-run and do not engage money
 before you understand how it works and what profit/loss you should
 expect.
 
-We strongly recommend you to have coding and Python knowledge. Do not 
+We strongly recommend you to have coding and Python knowledge. Do not
 hesitate to read the source code and understand the mechanism of this bot.
 
-## Table of Contents
-- [Features](#features)
-- [Quick start](#quick-start)
-- [Documentations](https://github.com/gcarq/freqtrade/blob/develop/docs/index.md)
-   - [Installation](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md)
-   - [Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)
-   - [Strategy Optimization](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md)
-   - [Backtesting](https://github.com/gcarq/freqtrade/blob/develop/docs/backtesting.md)
-   - [Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/hyperopt.md)
-- [Support](#support)
-   - [Help](#help--slack)
-   - [Bugs](#bugs--issues)
-   - [Feature Requests](#feature-requests)
-   - [Pull Requests](#pull-requests)
-- [Basic Usage](#basic-usage)
-  - [Bot commands](#bot-commands)
-  - [Telegram RPC commands](#telegram-rpc-commands)
-- [Requirements](#requirements)
-    - [Min hardware required](#min-hardware-required)
-    - [Software requirements](#software-requirements)
+## Supported Exchange marketplaces
 
-## Branches
-The project is currently setup in two main branches:
-- `develop` - This branch has often new features, but might also cause 
-breaking changes.
-- `master` - This branch contains the latest stable release. The bot 
-'should' be stable on this branch, and is generally well tested. 
+Please read the [exchange specific notes](docs/exchanges.md) to learn about eventual, special configurations needed for each exchange.
+
+- [X] [Binance](https://www.binance.com/)
+- [X] [Bitmart](https://bitmart.com/)
+- [X] [BingX](https://bingx.com/invite/0EM9RX)
+- [X] [Bybit](https://bybit.com/)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [HTX](https://www.htx.com/)
+- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)
+- [X] [Kraken](https://kraken.com/)
+- [X] [OKX](https://okx.com/)
+- [X] [MyOKX](https://okx.com/) (OKX EEA)
+- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
+
+### Supported Futures Exchanges (experimental)
+
+- [X] [Binance](https://www.binance.com/)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)
+- [X] [OKX](https://okx.com/)
+- [X] [Bybit](https://bybit.com/)
+
+Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
+
+### Community tested
+
+Exchanges confirmed working by the community:
+
+- [X] [Bitvavo](https://bitvavo.com/)
+- [X] [Kucoin](https://www.kucoin.com/)
+
+## Documentation
+
+We invite you to read the bot documentation to ensure you understand how the bot is working.
+
+Please find the complete documentation on the [freqtrade website](https://www.freqtrade.io).
 
 ## Features
-- [x] **Based on Python 3.6+**: For botting on any operating system - 
-Windows, macOS and Linux
-- [x] **Persistence**: Persistence is achieved through sqlite
-- [x] **Dry-run**: Run the bot without playing money.
-- [x] **Backtesting**: Run a simulation of your buy/sell strategy.
-- [x] **Strategy Optimization**: Optimize your buy/sell strategy 
-parameters with Hyperopts.
-- [x] **Whitelist crypto-currencies**: Select which crypto-currency you
-want to trade.
-- [x] **Blacklist crypto-currencies**: Select which crypto-currency you
-want to avoid.
-- [x] **Manageable via Telegram**: Manage the bot with Telegram
-- [x] **Display profit/loss in fiat**: Display your profit/loss in
-33 fiat.
-- [x] **Daily summary of profit/loss**: Provide a daily summary
- of your profit/loss.
-- [x] **Performance status report**: Provide a performance status of 
-your current trades.
 
-### Exchange supported
-- [x] Bittrex
-- [ ] Binance
-- [ ] Others
+- [x] **Based on Python 3.10+**: For botting on any operating system - Windows, macOS and Linux.
+- [x] **Persistence**: Persistence is achieved through sqlite.
+- [x] **Dry-run**: Run the bot without paying money.
+- [x] **Backtesting**: Run a simulation of your buy/sell strategy.
+- [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
+- [X] **Adaptive prediction modeling**: Build a smart strategy with FreqAI that self-trains to the market via adaptive machine learning methods. [Learn more](https://www.freqtrade.io/en/stable/freqai/)
+- [x] **Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/stable/edge/).
+- [x] **Whitelist crypto-currencies**: Select which crypto-currency you want to trade or use dynamic whitelists.
+- [x] **Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.
+- [x] **Builtin WebUI**: Builtin web UI to manage your bot.
+- [x] **Manageable via Telegram**: Manage the bot with Telegram.
+- [x] **Display profit/loss in fiat**: Display your profit/loss in fiat currency.
+- [x] **Performance status report**: Provide a performance status of your current trades.
 
 ## Quick start
-This quick start section is a very short explanation on how to test the 
-bot in dry-run. We invite you to read the 
-[bot documentation](https://github.com/gcarq/freqtrade/blob/develop/docs/index.md) 
-to ensure you understand how the bot is working.
 
-### Easy installation
-The script below will install all dependencies and help you to configure the bot.
-```bash
-./setup.sh --install
-```
+Please refer to the [Docker Quickstart documentation](https://www.freqtrade.io/en/stable/docker_quickstart/) on how to get started quickly.
 
-### Manual installation
-The following steps are made for Linux/MacOS environment
-
-**1. Clone the repo**
-```bash
-git clone git@github.com:gcarq/freqtrade.git
-git checkout develop
-cd freqtrade
-```
-**2. Create the config file**  
-Switch `"dry_run": true,`
-```bash
-cp config.json.example config.json
-vi config.json
-```
-**3. Build your docker image and run it**
-```bash
-docker build -t freqtrade .
-docker run --rm -v /etc/localtime:/etc/localtime:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
-```
-
-
-### Help / Slack
-For any questions not covered by the documentation or for further
-information about the bot, we encourage you to join our slack channel.
-- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE).
-
-### [Bugs / Issues](https://github.com/gcarq/freqtrade/issues?q=is%3Aissue)
-If you discover a bug in the bot, please 
-[search our issue tracker](https://github.com/gcarq/freqtrade/issues?q=is%3Aissue) 
-first. If it hasn't been reported, please 
-[create a new issue](https://github.com/gcarq/freqtrade/issues/new) and 
-ensure you follow the template guide so that our team can assist you as 
-quickly as possible.
-
-### [Feature Requests](https://github.com/gcarq/freqtrade/labels/enhancement)
-Have you a great idea to improve the bot you want to share? Please,
-first search if this feature was not [already discussed](https://github.com/gcarq/freqtrade/labels/enhancement).
-If it hasn't been requested, please 
-[create a new request](https://github.com/gcarq/freqtrade/issues/new) 
-and ensure you follow the template guide so that it does not get lost 
-in the bug reports.
-
-### [Pull Requests](https://github.com/gcarq/freqtrade/pulls)
-Feel like our bot is missing a feature? We welcome your pull requests! 
-Please read our 
-[Contributing document](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
-to understand the requirements before sending your pull-requests. 
-
-**Important:** Always create your PR against the `develop` branch, not 
-`master`.
+For further (native) installation methods, please refer to the [Installation documentation page](https://www.freqtrade.io/en/stable/installation/).
 
 ## Basic Usage
 
 ### Bot commands
 
-```bash
-usage: main.py [-h] [-v] [--version] [-c PATH] [--dry-run-db] [--datadir PATH]
-               [--dynamic-whitelist [INT]]
-               {backtesting,hyperopt} ...
+```
+usage: freqtrade [-h] [-V]
+                 {trade,create-userdir,new-config,show-config,new-strategy,download-data,convert-data,convert-trade-data,trades-to-ohlcv,list-data,backtesting,backtesting-show,backtesting-analysis,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-markets,list-pairs,list-strategies,list-hyperoptloss,list-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
+                 ...
 
-Simple High Frequency Trading Bot for crypto currencies
+Free, open source crypto trading bot
 
 positional arguments:
-  {backtesting,hyperopt}
-    backtesting         backtesting module
-    hyperopt            hyperopt module
+  {trade,create-userdir,new-config,show-config,new-strategy,download-data,convert-data,convert-trade-data,trades-to-ohlcv,list-data,backtesting,backtesting-show,backtesting-analysis,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-markets,list-pairs,list-strategies,list-hyperoptloss,list-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
+    trade               Trade module.
+    create-userdir      Create user-data directory.
+    new-config          Create new config
+    show-config         Show resolved config
+    new-strategy        Create new strategy
+    download-data       Download backtesting data.
+    convert-data        Convert candle (OHLCV) data from one format to
+                        another.
+    convert-trade-data  Convert trade data from one format to another.
+    trades-to-ohlcv     Convert trade data to OHLCV data.
+    list-data           List downloaded data.
+    backtesting         Backtesting module.
+    backtesting-show    Show past Backtest results
+    backtesting-analysis
+                        Backtest Analysis module.
+    edge                Edge module.
+    hyperopt            Hyperopt module.
+    hyperopt-list       List Hyperopt results
+    hyperopt-show       Show details of Hyperopt results
+    list-exchanges      Print available exchanges.
+    list-markets        Print markets on exchange.
+    list-pairs          Print pairs on exchange.
+    list-strategies     Print available strategies.
+    list-hyperoptloss   Print available hyperopt loss functions.
+    list-freqaimodels   Print available freqAI models.
+    list-timeframes     Print available timeframes for the exchange.
+    show-trades         Show trades.
+    test-pairlist       Test your pairlist configuration.
+    convert-db          Migrate database to different system
+    install-ui          Install FreqUI
+    plot-dataframe      Plot candles with indicators.
+    plot-profit         Generate plot showing profits.
+    webserver           Webserver module.
+    strategy-updater    updates outdated strategy files to the current version
+    lookahead-analysis  Check for potential look ahead bias.
+    recursive-analysis  Check for potential recursive formula issue.
 
-optional arguments:
+options:
   -h, --help            show this help message and exit
-  -v, --verbose         be verbose
-  --version             show program's version number and exit
-  -c PATH, --config PATH
-                        specify configuration file (default: config.json)
-  --dry-run-db          Force dry run to use a local DB
-                        "tradesv3.dry_run.sqlite" instead of memory DB. Work
-                        only if dry_run is enabled.
-  --datadir PATH        path to backtest data (default freqdata/tests/testdata
-  --dynamic-whitelist [INT]
-                        dynamically generate and update whitelist based on 24h
-                        BaseVolume (Default 20 currencies)
+  -V, --version         show program's version number and exit
 ```
-More details on:
-- [How to run the bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#bot-commands)
-- [How to use Backtesting](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#backtesting-commands)
-- [How to use Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#hyperopt-commands)
-   
-### Telegram RPC commands
-Telegram is not mandatory. However, this is a great way to control your
-bot. More details on our 
-[documentation](https://github.com/gcarq/freqtrade/blob/develop/docs/index.md)
 
-- `/start`: Starts the trader
-- `/stop`: Stops the trader
-- `/status [table]`: Lists all open trades
-- `/count`: Displays number of open trades
-- `/profit`: Lists cumulative profit from all finished trades
-- `/forcesell <trade_id>|all`: Instantly sells the given trade 
-(Ignoring `minimum_roi`).
+### Telegram RPC commands
+
+Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on the [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
+
+- `/start`: Starts the trader.
+- `/stop`: Stops the trader.
+- `/stopentry`: Stop entering new trades.
+- `/status <trade_id>|[table]`: Lists all or specific open trades.
+- `/profit [<n>]`: Lists cumulative profit from all finished trades, over the last n days.
+- `/forceexit <trade_id>|all`: Instantly exits the given trade (Ignoring `minimum_roi`).
+- `/fx <trade_id>|all`: Alias to `/forceexit`
 - `/performance`: Show performance of each finished trade grouped by pair
-- `/balance`: Show account balance per currency
-- `/daily <n>`: Shows profit or loss per day, over the last n days
-- `/help`: Show help message
-- `/version`: Show version
+- `/balance`: Show account balance per currency.
+- `/daily <n>`: Shows profit or loss per day, over the last n days.
+- `/help`: Show help message.
+- `/version`: Show version.
+
+## Development branches
+
+The project is currently setup in two main branches:
+
+- `develop` - This branch has often new features, but might also contain breaking changes. We try hard to keep this branch as stable as possible.
+- `stable` - This branch contains the latest stable release. This branch is generally well tested.
+- `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.
+
+## Support
+
+### Help / Discord
+
+For any questions not covered by the documentation or for further information about the bot, or to simply engage with like-minded individuals, we encourage you to join the Freqtrade [discord server](https://discord.gg/p7nuUNVfP7).
+
+### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
+
+If you discover a bug in the bot, please
+[search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
+first. If it hasn't been reported, please
+[create a new issue](https://github.com/freqtrade/freqtrade/issues/new/choose) and
+ensure you follow the template guide so that the team can assist you as
+quickly as possible.
+
+For every [issue](https://github.com/freqtrade/freqtrade/issues/new/choose) created, kindly follow up and mark satisfaction or reminder to close issue when equilibrium ground is reached.
+
+--Maintain github's [community policy](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct)--
+
+### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
+
+Have you a great idea to improve the bot you want to share? Please,
+first search if this feature was not [already discussed](https://github.com/freqtrade/freqtrade/labels/enhancement).
+If it hasn't been requested, please
+[create a new request](https://github.com/freqtrade/freqtrade/issues/new/choose)
+and ensure you follow the template guide so that it does not get lost
+in the bug reports.
+
+### [Pull Requests](https://github.com/freqtrade/freqtrade/pulls)
+
+Feel like the bot is missing a feature? We welcome your pull requests!
+
+Please read the
+[Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
+to understand the requirements before sending your pull-requests.
+
+Coding is not a necessity to contribute - maybe start with improving the documentation?
+Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
+
+**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/p7nuUNVfP7) (please use the #dev channel for this). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
+
+**Important:** Always create your PR against the `develop` branch, not `stable`.
 
 ## Requirements
 
-### Min hardware required
+### Up-to-date clock
+
+The clock must be accurate, synchronized to a NTP server very frequently to avoid problems with communication to the exchanges.
+
+### Minimum hardware required
+
 To run this bot we recommend you a cloud instance with a minimum of:
-* Minimal (advised) system requirements: 2GB RAM, 1GB disk space, 2vCPU
+
+- Minimal (advised) system requirements: 2GB RAM, 1GB disk space, 2vCPU
 
 ### Software requirements
-- [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/) 
+
+- [Python >= 3.10](http://docs.python-guide.org/en/latest/starting/installation/)
 - [pip](https://pip.pypa.io/en/stable/installing/)
 - [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
-- [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
-- [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
+- [TA-Lib](https://ta-lib.github.io/ta-lib-python/)
+- [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
 - [Docker](https://www.docker.com/products/docker) (Recommended)
diff --git a/bin/freqtrade b/bin/freqtrade
deleted file mode 100755
index 7476152fd..000000000
--- a/bin/freqtrade
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/usr/bin/env python3
-
-from freqtrade.main import main
-main()
\ No newline at end of file
diff --git a/build_helpers/binance_update_lev_tiers.py b/build_helpers/binance_update_lev_tiers.py
new file mode 100644
index 000000000..6d5fa5c1d
--- /dev/null
+++ b/build_helpers/binance_update_lev_tiers.py
@@ -0,0 +1,23 @@
+#!/usr/bin/env python3
+import json
+import os
+from pathlib import Path
+
+import ccxt
+
+
+key = os.environ.get("FREQTRADE__EXCHANGE__KEY")
+secret = os.environ.get("FREQTRADE__EXCHANGE__SECRET")
+
+proxy = os.environ.get("CI_WEB_PROXY")
+
+exchange = ccxt.binance(
+    {"apiKey": key, "secret": secret, "httpsProxy": proxy, "options": {"defaultType": "swap"}}
+)
+_ = exchange.load_markets()
+
+lev_tiers = exchange.fetch_leverage_tiers()
+
+# Assumes this is running in the root of the repository.
+file = Path("freqtrade/exchange/binance_leverage_tiers.json")
+json.dump(dict(sorted(lev_tiers.items())), file.open("w"), indent=2)
diff --git a/build_helpers/create_command_partials.py b/build_helpers/create_command_partials.py
new file mode 100644
index 000000000..97bec3a7d
--- /dev/null
+++ b/build_helpers/create_command_partials.py
@@ -0,0 +1,53 @@
+import subprocess  # noqa: S404, RUF100
+from pathlib import Path
+
+
+subcommands = [
+    "trade",
+    "create-userdir",
+    "new-config",
+    "show-config",
+    "new-strategy",
+    "download-data",
+    "convert-data",
+    "convert-trade-data",
+    "trades-to-ohlcv",
+    "list-data",
+    "backtesting",
+    "backtesting-show",
+    "backtesting-analysis",
+    "edge",
+    "hyperopt",
+    "hyperopt-list",
+    "hyperopt-show",
+    "list-exchanges",
+    "list-markets",
+    "list-pairs",
+    "list-strategies",
+    "list-hyperoptloss",
+    "list-freqaimodels",
+    "list-timeframes",
+    "show-trades",
+    "test-pairlist",
+    "convert-db",
+    "install-ui",
+    "plot-dataframe",
+    "plot-profit",
+    "webserver",
+    "strategy-updater",
+    "lookahead-analysis",
+    "recursive-analysis",
+]
+
+result = subprocess.run(["freqtrade", "--help"], capture_output=True, text=True)
+
+with Path("docs/commands/main.md").open("w") as f:
+    f.write(f"```\n{result.stdout}\n```\n")
+
+
+for command in subcommands:
+    print(f"Running for {command}")
+    result = subprocess.run(["freqtrade", command, "--help"], capture_output=True, text=True)
+
+    with Path(f"docs/commands/{command}.md").open("w") as f:
+        f.write(f"```\n{result.stdout}\n```\n")
diff --git a/build_helpers/extract_config_json_schema.py b/build_helpers/extract_config_json_schema.py
new file mode 100644
index 000000000..15bfd7720
--- /dev/null
+++ b/build_helpers/extract_config_json_schema.py
@@ -0,0 +1,30 @@
+"""Script to extract the configuration json schema from config_schema.py file."""
+
+from pathlib import Path
+
+import rapidjson
+
+
+def extract_config_json_schema():
+    try:
+        # Try to import from the installed package
+        from freqtrade.config_schema import CONF_SCHEMA
+    except ImportError:
+        # If freqtrade is not installed, add the parent directory to sys.path
+        # to import directly from the source
+        import sys
+
+        script_dir = Path(__file__).parent
+        freqtrade_dir = script_dir.parent
+        sys.path.insert(0, str(freqtrade_dir))
+
+        # Now try to import from the source
+        from freqtrade.config_schema import CONF_SCHEMA
+
+    schema_filename = Path(__file__).parent / "schema.json"
+    with schema_filename.open("w") as f:
+        rapidjson.dump(CONF_SCHEMA, f, indent=2)
+
+
+if __name__ == "__main__":
+    extract_config_json_schema()
diff --git a/build_helpers/freqtrade_client_version_align.py b/build_helpers/freqtrade_client_version_align.py
new file mode 100755
index 000000000..3e425100c
--- /dev/null
+++ b/build_helpers/freqtrade_client_version_align.py
@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+from freqtrade import __version__ as ft_version
+from freqtrade_client import __version__ as client_version
+
+
+def main():
+    if ft_version != client_version:
+        print(f"Versions do not match: \nft: {ft_version} \nclient: {client_version}")
+        exit(1)
+    print(f"Versions match: ft: {ft_version}, client: {client_version}")
+    exit(0)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/build_helpers/install_ta-lib.sh b/build_helpers/install_ta-lib.sh
new file mode 100755
index 000000000..0315c7025
--- /dev/null
+++ b/build_helpers/install_ta-lib.sh
@@ -0,0 +1,35 @@
+if [ -z "$1" ]; then
+  INSTALL_LOC=/usr/local
+else
+  INSTALL_LOC=${1}
+fi
+echo "Installing to ${INSTALL_LOC}"
+if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
+  tar zxvf ta-lib-0.4.0-src.tar.gz
+  cd ta-lib \
+  && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
+  && echo "Downloading gcc config.guess and config.sub" \
+  && curl -s 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.guess' -o config.guess \
+  && curl -s 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.sub' -o config.sub \
+  && ./configure --prefix=${INSTALL_LOC}/ \
+  && make
+  if [ $? -ne 0 ]; then
+    echo "Failed building ta-lib."
+    cd .. && rm -rf ./ta-lib/
+    exit 1
+  fi
+  if [ -z "$2" ]; then
+    which sudo && sudo make install || make install
+    if [ -x "$(command -v apt-get)" ]; then
+      echo "Updating library path using ldconfig"
+      sudo ldconfig
+    fi
+  else
+    # Don't install with sudo
+    make install
+  fi
+
+  cd .. && rm -rf ./ta-lib/
+else
+  echo "TA-lib already installed, skipping installation"
+fi
diff --git a/build_helpers/install_windows.ps1 b/build_helpers/install_windows.ps1
new file mode 100644
index 000000000..919d8b7da
--- /dev/null
+++ b/build_helpers/install_windows.ps1
@@ -0,0 +1,10 @@
+# vendored Wheels compiled via https://github.com/xmatthias/ta-lib-python/tree/ta_bundled_040
+
+python -m pip install --upgrade pip
+python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
+
+pip install -U wheel "numpy<2"
+pip install --only-binary ta-lib --find-links=build_helpers\ ta-lib
+
+pip install -r requirements-dev.txt
+pip install -e .
diff --git a/build_helpers/pre_commit_update.py b/build_helpers/pre_commit_update.py
new file mode 100644
index 000000000..9d313efd2
--- /dev/null
+++ b/build_helpers/pre_commit_update.py
@@ -0,0 +1,49 @@
+# File used in CI to ensure pre-commit dependencies are kept up-to-date.
+
+import sys
+from pathlib import Path
+
+import yaml
+
+
+pre_commit_file = Path(".pre-commit-config.yaml")
+require_dev = Path("requirements-dev.txt")
+require = Path("requirements.txt")
+
+with require_dev.open("r") as rfile:
+    requirements = rfile.readlines()
+
+with require.open("r") as rfile:
+    requirements.extend(rfile.readlines())
+
+# Extract types only
+type_reqs = [
+    r.strip("\n") for r in requirements if r.startswith("types-") or r.startswith("SQLAlchemy")
+]
+
+with pre_commit_file.open("r") as file:
+    f = yaml.load(file, Loader=yaml.SafeLoader)
+
+
+mypy_repo = [
+    repo for repo in f["repos"] if repo["repo"] == "https://github.com/pre-commit/mirrors-mypy"
+]
+
+hooks = mypy_repo[0]["hooks"][0]["additional_dependencies"]
+
+errors = []
+for hook in hooks:
+    if hook not in type_reqs:
+        errors.append(f"{hook} is missing in requirements-dev.txt.")
+
+for req in type_reqs:
+    if req not in hooks:
+        errors.append(f"{req} is missing in pre-config file.")
+
+
+if errors:
+    for e in errors:
+        print(e)
+    sys.exit(1)
+
+sys.exit(0)
diff --git a/build_helpers/publish_docker_arm64.sh b/build_helpers/publish_docker_arm64.sh
new file mode 100755
index 000000000..71fc01509
--- /dev/null
+++ b/build_helpers/publish_docker_arm64.sh
@@ -0,0 +1,119 @@
+#!/bin/sh
+
+# Use BuildKit, otherwise building on ARM fails
+export DOCKER_BUILDKIT=1
+
+IMAGE_NAME=freqtradeorg/freqtrade
+CACHE_IMAGE=freqtradeorg/freqtrade_cache
+GHCR_IMAGE_NAME=ghcr.io/freqtrade/freqtrade
+
+# Replace / with _ to create a valid tag
+TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
+TAG_PLOT=${TAG}_plot
+TAG_FREQAI=${TAG}_freqai
+TAG_FREQAI_RL=${TAG_FREQAI}rl
+TAG_FREQAI_TORCH=${TAG_FREQAI}torch
+TAG_PI="${TAG}_pi"
+
+TAG_ARM=${TAG}_arm
+TAG_PLOT_ARM=${TAG_PLOT}_arm
+TAG_FREQAI_ARM=${TAG_FREQAI}_arm
+TAG_FREQAI_RL_ARM=${TAG_FREQAI_RL}_arm
+
+echo "Running for ${TAG}"
+
+# Add commit and commit_message to docker container
+echo "${GITHUB_SHA}" > freqtrade_commit
+
+if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
+    echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
+    # Build regular image
+    docker build -t freqtrade:${TAG_ARM} .
+
+else
+    echo "event ${GITHUB_EVENT_NAME}: building with cache"
+    # Build regular image
+    docker pull ${IMAGE_NAME}:${TAG_ARM}
+    docker build --cache-from ${IMAGE_NAME}:${TAG_ARM} -t freqtrade:${TAG_ARM} .
+
+fi
+
+if [ $? -ne 0 ]; then
+    echo "failed building multiarch images"
+    return 1
+fi
+
+docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot .
+docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai .
+docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_FREQAI_ARM} -t freqtrade:${TAG_FREQAI_RL_ARM} -f docker/Dockerfile.freqai_rl .
+
+# Tag image for upload and next build step
+docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM
+docker tag freqtrade:$TAG_PLOT_ARM ${CACHE_IMAGE}:$TAG_PLOT_ARM
+docker tag freqtrade:$TAG_FREQAI_ARM ${CACHE_IMAGE}:$TAG_FREQAI_ARM
+docker tag freqtrade:$TAG_FREQAI_RL_ARM ${CACHE_IMAGE}:$TAG_FREQAI_RL_ARM
+
+# Run backtest
+docker run --rm -v $(pwd)/tests/testdata/config.tests.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG_ARM} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
+
+if [ $? -ne 0 ]; then
+    echo "failed running backtest"
+    return 1
+fi
+
+docker images
+
+docker push ${CACHE_IMAGE}:$TAG_PLOT_ARM
+docker push ${CACHE_IMAGE}:$TAG_FREQAI_ARM
+docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL_ARM
+docker push ${CACHE_IMAGE}:$TAG_ARM
+
+# Create multi-arch image
+# Make sure that all images contained here are pushed to github first.
+# Otherwise installation might fail.
+echo "create manifests"
+
+docker manifest create ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI}
+docker manifest push -p ${IMAGE_NAME}:${TAG}
+
+docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM}
+docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT}
+
+docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM}
+docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
+
+docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM}
+docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_RL}
+
+# Create special Torch tag - which is identical to the RL tag.
+docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_TORCH} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM}
+docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_TORCH}
+
+# copy images to ghcr.io
+
+alias crane="docker run --rm -i -v $(pwd)/.crane:/home/nonroot/.docker/ gcr.io/go-containerregistry/crane"
+mkdir .crane
+chmod a+rwx .crane
+
+echo "${GHCR_TOKEN}" | crane auth login ghcr.io -u "${GHCR_USERNAME}" --password-stdin
+
+crane copy ${IMAGE_NAME}:${TAG_FREQAI_RL} ${GHCR_IMAGE_NAME}:${TAG_FREQAI_RL}
+crane copy ${IMAGE_NAME}:${TAG_FREQAI_RL} ${GHCR_IMAGE_NAME}:${TAG_FREQAI_TORCH}
+crane copy ${IMAGE_NAME}:${TAG_FREQAI} ${GHCR_IMAGE_NAME}:${TAG_FREQAI}
+crane copy ${IMAGE_NAME}:${TAG_PLOT} ${GHCR_IMAGE_NAME}:${TAG_PLOT}
+crane copy ${IMAGE_NAME}:${TAG} ${GHCR_IMAGE_NAME}:${TAG}
+
+# Tag as latest for develop builds
+if [ "${TAG}" = "develop" ]; then
+    echo 'Tagging image as latest'
+    docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
+    docker manifest push -p ${IMAGE_NAME}:latest
+
+    crane copy ${IMAGE_NAME}:latest ${GHCR_IMAGE_NAME}:latest
+fi
+
+docker images
+rm -rf .crane
+
+# Cleanup old images from arm64 node.
+docker image prune -a --force --filter "until=24h"
diff --git a/build_helpers/publish_docker_multi.sh b/build_helpers/publish_docker_multi.sh
new file mode 100755
index 000000000..34dc1faa3
--- /dev/null
+++ b/build_helpers/publish_docker_multi.sh
@@ -0,0 +1,89 @@
+#!/bin/sh
+
+# The below assumes a correctly setup docker buildx environment
+
+IMAGE_NAME=freqtradeorg/freqtrade
+CACHE_IMAGE=freqtradeorg/freqtrade_cache
+# Replace / with _ to create a valid tag
+TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
+TAG_PLOT=${TAG}_plot
+TAG_FREQAI=${TAG}_freqai
+TAG_FREQAI_RL=${TAG_FREQAI}rl
+TAG_PI="${TAG}_pi"
+
+PI_PLATFORM="linux/arm/v7"
+echo "Running for ${TAG}"
+CACHE_TAG=${CACHE_IMAGE}:${TAG_PI}_cache
+
+# Add commit and commit_message to docker container
+echo "${GITHUB_SHA}" > freqtrade_commit
+
+if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
+    echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
+    # Build regular image
+    docker build -t freqtrade:${TAG} .
+    # Build PI image
+    docker buildx build \
+        --cache-to=type=registry,ref=${CACHE_TAG} \
+        -f docker/Dockerfile.armhf \
+        --platform ${PI_PLATFORM} \
+        -t ${IMAGE_NAME}:${TAG_PI} \
+        --push \
+        --provenance=false \
+        .
+else
+    echo "event ${GITHUB_EVENT_NAME}: building with cache"
+    # Build regular image
+    docker pull ${IMAGE_NAME}:${TAG}
+    docker build --cache-from ${IMAGE_NAME}:${TAG} -t freqtrade:${TAG} .
+
+    # Pull last build to avoid rebuilding the whole image
+    # docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
+    # disable provenance due to https://github.com/docker/buildx/issues/1509
+    docker buildx build \
+        --cache-from=type=registry,ref=${CACHE_TAG} \
+        --cache-to=type=registry,ref=${CACHE_TAG} \
+        -f docker/Dockerfile.armhf \
+        --platform ${PI_PLATFORM} \
+        -t ${IMAGE_NAME}:${TAG_PI} \
+        --push \
+        --provenance=false \
+        .
+fi
+
+if [ $? -ne 0 ]; then
+    echo "failed building multiarch images"
+    return 1
+fi
+# Tag image for upload and next build step
+docker tag freqtrade:$TAG ${CACHE_IMAGE}:$TAG
+
+docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot .
+docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG} -t freqtrade:${TAG_FREQAI} -f docker/Dockerfile.freqai .
+docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_FREQAI} -t freqtrade:${TAG_FREQAI_RL} -f docker/Dockerfile.freqai_rl .
+
+docker tag freqtrade:$TAG_PLOT ${CACHE_IMAGE}:$TAG_PLOT
+docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI
+docker tag freqtrade:$TAG_FREQAI_RL ${CACHE_IMAGE}:$TAG_FREQAI_RL
+
+# Run backtest
+docker run --rm -v $(pwd)/tests/testdata/config.tests.json:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy-path /tests/strategy/strats/ --strategy StrategyTestV3
+
+if [ $? -ne 0 ]; then
+    echo "failed running backtest"
+    return 1
+fi
+
+docker images
+
+docker push ${CACHE_IMAGE}:$TAG
+docker push ${CACHE_IMAGE}:$TAG_PLOT
+docker push ${CACHE_IMAGE}:$TAG_FREQAI
+docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL
+
+docker images
+
+if [ $? -ne 0 ]; then
+    echo "failed building image"
+    return 1
+fi
diff --git a/build_helpers/pyarrow-19.0.1-cp311-cp311-linux_armv7l.whl b/build_helpers/pyarrow-19.0.1-cp311-cp311-linux_armv7l.whl
new file mode 100644
index 000000000..0a730939a
Binary files /dev/null and b/build_helpers/pyarrow-19.0.1-cp311-cp311-linux_armv7l.whl differ
diff --git a/build_helpers/schema.json b/build_helpers/schema.json
new file mode 100644
index 000000000..0aa2ea416
--- /dev/null
+++ b/build_helpers/schema.json
@@ -0,0 +1,1676 @@
+{
+  "type": "object",
+  "properties": {
+    "max_open_trades": {
+      "description": "Maximum number of open trades. -1 for unlimited.",
+      "type": [
+        "integer",
+        "number"
+      ],
+      "minimum": -1
+    },
+    "timeframe": {
+      "description": "The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). \nUsually specified in the strategy and missing in the configuration.",
+      "type": "string"
+    },
+    "proxy_coin": {
+      "description": "Proxy coin - must be used for specific futures modes (e.g. BNFCR)",
+      "type": "string"
+    },
+    "stake_currency": {
+      "description": "Currency used for staking.",
+      "type": "string"
+    },
+    "stake_amount": {
+      "description": "Amount to stake per trade.",
+      "type": [
+        "number",
+        "string"
+      ],
+      "minimum": 0.0001,
+      "pattern": "unlimited"
+    },
+    "tradable_balance_ratio": {
+      "description": "Ratio of balance that is tradable.",
+      "type": "number",
+      "minimum": 0.0,
+      "maximum": 1,
+      "default": 0.99
+    },
+    "available_capital": {
+      "description": "Total capital available for trading.",
+      "type": "number",
+      "minimum": 0
+    },
+    "amend_last_stake_amount": {
+      "description": "Whether to amend the last stake amount.",
+      "type": "boolean",
+      "default": false
+    },
+    "last_stake_amount_min_ratio": {
+      "description": "Minimum ratio for the last stake amount.",
+      "type": "number",
+      "minimum": 0.0,
+      "maximum": 1.0,
+      "default": 0.5
+    },
+    "fiat_display_currency": {
+      "description": "Fiat currency for display purposes.",
+      "type": "string",
+      "enum": [
+        "AUD",
+        "BRL",
+        "CAD",
+        "CHF",
+        "CLP",
+        "CNY",
+        "CZK",
+        "DKK",
+        "EUR",
+        "GBP",
+        "HKD",
+        "HUF",
+        "IDR",
+        "ILS",
+        "INR",
+        "JPY",
+        "KRW",
+        "MXN",
+        "MYR",
+        "NOK",
+        "NZD",
+        "PHP",
+        "PKR",
+        "PLN",
+        "RUB",
+        "UAH",
+        "SEK",
+        "SGD",
+        "THB",
+        "TRY",
+        "TWD",
+        "ZAR",
+        "USD",
+        "BTC",
+        "ETH",
+        "XRP",
+        "LTC",
+        "BCH",
+        "BNB",
+        ""
+      ]
+    },
+    "dry_run": {
+      "description": "Enable or disable dry run mode.",
+      "type": "boolean"
+    },
+    "dry_run_wallet": {
+      "description": "Initial wallet balance for dry run mode.",
+      "type": [
+        "number",
+        "object"
+      ],
+      "default": 1000,
+      "patternProperties": {
+        "^[a-zA-Z0-9]+$": {
+          "type": "number"
+        }
+      },
+      "additionalProperties": false
+    },
+    "cancel_open_orders_on_exit": {
+      "description": "Cancel open orders when exiting.",
+      "type": "boolean",
+      "default": false
+    },
+    "process_only_new_candles": {
+      "description": "Process only new candles.",
+      "type": "boolean"
+    },
+    "minimal_roi": {
+      "description": "Minimum return on investment. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "object",
+      "patternProperties": {
+        "^[0-9.]+$": {
+          "type": "number"
+        }
+      }
+    },
+    "amount_reserve_percent": {
+      "description": "Percentage of amount to reserve.",
+      "type": "number",
+      "minimum": 0.0,
+      "maximum": 0.5
+    },
+    "stoploss": {
+      "description": "Value (as ratio) to use as Stoploss value. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "number",
+      "maximum": 0,
+      "exclusiveMaximum": true
+    },
+    "trailing_stop": {
+      "description": "Enable or disable trailing stop. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "boolean"
+    },
+    "trailing_stop_positive": {
+      "description": "Positive offset for trailing stop. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "number",
+      "minimum": 0,
+      "maximum": 1
+    },
+    "trailing_stop_positive_offset": {
+      "description": "Offset for trailing stop to activate. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "number",
+      "minimum": 0,
+      "maximum": 1
+    },
+    "trailing_only_offset_is_reached": {
+      "description": "Use trailing stop only when offset is reached. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "boolean"
+    },
+    "use_exit_signal": {
+      "description": "Use exit signal for trades. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "boolean"
+    },
+    "exit_profit_only": {
+      "description": "Exit only when in profit. Exit signals are ignored as long as profit is < exit_profit_offset. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "boolean"
+    },
+    "exit_profit_offset": {
+      "description": "Offset for profit exit. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "number"
+    },
+    "fee": {
+      "description": "Trading fee percentage. Can help to simulate slippage in backtesting",
+      "type": "number",
+      "minimum": 0,
+      "maximum": 0.1
+    },
+    "ignore_roi_if_entry_signal": {
+      "description": "Ignore ROI if entry signal is present. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "boolean"
+    },
+    "ignore_buying_expired_candle_after": {
+      "description": "Ignore buying after candle expiration time. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "number"
+    },
+    "trading_mode": {
+      "description": "Mode of trading (e.g., spot, margin).",
+      "type": "string",
+      "enum": [
+        "spot",
+        "margin",
+        "futures"
+      ]
+    },
+    "margin_mode": {
+      "description": "Margin mode for trading.",
+      "type": "string",
+      "enum": [
+        "cross",
+        "isolated",
+        ""
+      ]
+    },
+    "reduce_df_footprint": {
+      "description": "Reduce DataFrame footprint by casting columns to float32/int32.",
+      "type": "boolean",
+      "default": false
+    },
+    "minimum_trade_amount": {
+      "description": "Minimum amount for a trade - only used for lookahead-analysis",
+      "type": "number",
+      "default": 10
+    },
+    "targeted_trade_amount": {
+      "description": "Targeted trade amount for lookahead analysis.",
+      "type": "number",
+      "default": 20
+    },
+    "lookahead_analysis_exportfilename": {
+      "description": "csv Filename for lookahead analysis export.",
+      "type": "string"
+    },
+    "startup_candle": {
+      "description": "Startup candle configuration.",
+      "type": "array",
+      "uniqueItems": true,
+      "default": [
+        199,
+        399,
+        499,
+        999,
+        1999
+      ]
+    },
+    "liquidation_buffer": {
+      "description": "Buffer ratio for liquidation.",
+      "type": "number",
+      "minimum": 0.0,
+      "maximum": 0.99
+    },
+    "backtest_breakdown": {
+      "description": "Breakdown configuration for backtesting.",
+      "type": "array",
+      "items": {
+        "type": "string",
+        "enum": [
+          "day",
+          "week",
+          "month",
+          "year"
+        ]
+      }
+    },
+    "bot_name": {
+      "description": "Name of the trading bot. Passed via API to a client.",
+      "type": "string"
+    },
+    "unfilledtimeout": {
+      "description": "Timeout configuration for unfilled orders. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "object",
+      "properties": {
+        "entry": {
+          "description": "Timeout for entry orders in unit.",
+          "type": "number",
+          "minimum": 1
+        },
+        "exit": {
+          "description": "Timeout for exit orders in unit.",
+          "type": "number",
+          "minimum": 1
+        },
+        "exit_timeout_count": {
+          "description": "Number of times to retry exit orders before giving up.",
+          "type": "number",
+          "minimum": 0,
+          "default": 0
+        },
+        "unit": {
+          "description": "Unit of time for the timeout (e.g., seconds, minutes).",
+          "type": "string",
+          "enum": [
+            "minutes",
+            "seconds"
+          ],
+          "default": "minutes"
+        }
+      }
+    },
+    "entry_pricing": {
+      "description": "Configuration for entry pricing.",
+      "type": "object",
+      "properties": {
+        "price_last_balance": {
+          "description": "Balance ratio for the last price.",
+          "type": "number",
+          "minimum": 0,
+          "maximum": 1,
+          "exclusiveMaximum": false
+        },
+        "price_side": {
+          "description": "Side of the price to use (e.g., bid, ask, same).",
+          "type": "string",
+          "enum": [
+            "ask",
+            "bid",
+            "same",
+            "other"
+          ],
+          "default": "same"
+        },
+        "use_order_book": {
+          "description": "Whether to use the order book for pricing.",
+          "type": "boolean"
+        },
+        "order_book_top": {
+          "description": "Top N levels of the order book to consider.",
+          "type": "integer",
+          "minimum": 1,
+          "maximum": 50
+        },
+        "check_depth_of_market": {
+          "description": "Configuration for checking the depth of the market.",
+          "type": "object",
+          "properties": {
+            "enabled": {
+              "description": "Enable or disable depth of market check.",
+              "type": "boolean"
+            },
+            "bids_to_ask_delta": {
+              "description": "Delta between bids and asks to consider.",
+              "type": "number",
+              "minimum": 0
+            }
+          }
+        }
+      },
+      "required": [
+        "price_side"
+      ]
+    },
+    "exit_pricing": {
+      "description": "Configuration for exit pricing.",
+      "type": "object",
+      "properties": {
+        "price_side": {
+          "description": "Side of the price to use (e.g., bid, ask, same).",
+          "type": "string",
+          "enum": [
+            "ask",
+            "bid",
+            "same",
+            "other"
+          ],
+          "default": "same"
+        },
+        "price_last_balance": {
+          "description": "Balance ratio for the last price.",
+          "type": "number",
+          "minimum": 0,
+          "maximum": 1,
+          "exclusiveMaximum": false
+        },
+        "use_order_book": {
+          "description": "Whether to use the order book for pricing.",
+          "type": "boolean"
+        },
+        "order_book_top": {
+          "description": "Top N levels of the order book to consider.",
+          "type": "integer",
+          "minimum": 1,
+          "maximum": 50
+        }
+      },
+      "required": [
+        "price_side"
+      ]
+    },
+    "custom_price_max_distance_ratio": {
+      "description": "Maximum distance ratio between current and custom entry or exit price.",
+      "type": "number",
+      "minimum": 0.0,
+      "maximum": 1,
+      "default": 0.02
+    },
+    "order_types": {
+      "description": "Configuration of order types. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "object",
+      "properties": {
+        "entry": {
+          "description": "Order type for entry (e.g., limit, market).",
+          "type": "string",
+          "enum": [
+            "limit",
+            "market"
+          ]
+        },
+        "exit": {
+          "description": "Order type for exit (e.g., limit, market).",
+          "type": "string",
+          "enum": [
+            "limit",
+            "market"
+          ]
+        },
+        "force_exit": {
+          "description": "Order type for forced exit (e.g., limit, market).",
+          "type": "string",
+          "enum": [
+            "limit",
+            "market"
+          ]
+        },
+        "force_entry": {
+          "description": "Order type for forced entry (e.g., limit, market).",
+          "type": "string",
+          "enum": [
+            "limit",
+            "market"
+          ]
+        },
+        "emergency_exit": {
+          "description": "Order type for emergency exit (e.g., limit, market).",
+          "type": "string",
+          "enum": [
+            "limit",
+            "market"
+          ],
+          "default": "market"
+        },
+        "stoploss": {
+          "description": "Order type for stop loss (e.g., limit, market).",
+          "type": "string",
+          "enum": [
+            "limit",
+            "market"
+          ]
+        },
+        "stoploss_on_exchange": {
+          "description": "Whether to place stop loss on the exchange.",
+          "type": "boolean"
+        },
+        "stoploss_price_type": {
+          "description": "Price type for stop loss (e.g., last, mark, index).",
+          "type": "string",
+          "enum": [
+            "last",
+            "mark",
+            "index"
+          ]
+        },
+        "stoploss_on_exchange_interval": {
+          "description": "Interval for stop loss on exchange in seconds.",
+          "type": "number"
+        },
+        "stoploss_on_exchange_limit_ratio": {
+          "description": "Limit ratio for stop loss on exchange.",
+          "type": "number",
+          "minimum": 0.0,
+          "maximum": 1.0
+        }
+      },
+      "required": [
+        "entry",
+        "exit",
+        "stoploss",
+        "stoploss_on_exchange"
+      ]
+    },
+    "order_time_in_force": {
+      "description": "Time in force configuration for orders. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "object",
+      "properties": {
+        "entry": {
+          "description": "Time in force for entry orders.",
+          "type": "string",
+          "enum": [
+            "GTC",
+            "FOK",
+            "IOC",
+            "PO",
+            "gtc",
+            "fok",
+            "ioc",
+            "po"
+          ]
+        },
+        "exit": {
+          "description": "Time in force for exit orders.",
+          "type": "string",
+          "enum": [
+            "GTC",
+            "FOK",
+            "IOC",
+            "PO",
+            "gtc",
+            "fok",
+            "ioc",
+            "po"
+          ]
+        }
+      },
+      "required": [
+        "entry",
+        "exit"
+      ]
+    },
+    "coingecko": {
+      "description": "Configuration for CoinGecko API.",
+      "type": "object",
+      "properties": {
+        "is_demo": {
+          "description": "Whether to use CoinGecko in demo mode.",
+          "type": "boolean",
+          "default": true
+        },
+        "api_key": {
+          "description": "API key for accessing CoinGecko.",
+          "type": "string"
+        }
+      },
+      "required": [
+        "is_demo",
+        "api_key"
+      ]
+    },
+    "exchange": {
+      "description": "Exchange configuration.",
+      "$ref": "#/definitions/exchange"
+    },
+    "edge": {
+      "description": "Edge configuration.",
+      "$ref": "#/definitions/edge"
+    },
+    "log_config": {
+      "description": "Logging configuration.",
+      "$ref": "#/definitions/logging"
+    },
+    "freqai": {
+      "description": "FreqAI configuration.",
+      "$ref": "#/definitions/freqai"
+    },
+    "external_message_consumer": {
+      "description": "Configuration for external message consumer.",
+      "$ref": "#/definitions/external_message_consumer"
+    },
+    "experimental": {
+      "description": "Experimental configuration.",
+      "type": "object",
+      "properties": {
+        "block_bad_exchanges": {
+          "type": "boolean"
+        }
+      }
+    },
+    "pairlists": {
+      "description": "Configuration for pairlists.",
+      "type": "array",
+      "items": {
+        "type": "object",
+        "properties": {
+          "method": {
+            "description": "Method used for generating the pairlist.",
+            "type": "string",
+            "enum": [
+              "StaticPairList",
+              "VolumePairList",
+              "PercentChangePairList",
+              "ProducerPairList",
+              "RemotePairList",
+              "MarketCapPairList",
+              "AgeFilter",
+              "FullTradesFilter",
+              "OffsetFilter",
+              "PerformanceFilter",
+              "PrecisionFilter",
+              "PriceFilter",
+              "RangeStabilityFilter",
+              "ShuffleFilter",
+              "SpreadFilter",
+              "VolatilityFilter"
+            ]
+          }
+        },
+        "required": [
+          "method"
+        ]
+      }
+    },
+    "telegram": {
+      "description": "Telegram settings.",
+      "type": "object",
+      "properties": {
+        "enabled": {
+          "description": "Enable Telegram notifications.",
+          "type": "boolean"
+        },
+        "token": {
+          "description": "Telegram bot token.",
+          "type": "string"
+        },
+        "chat_id": {
+          "description": "Telegram chat or group ID",
+          "type": "string"
+        },
+        "topic_id": {
+          "description": "Telegram topic ID - only applicable for group chats",
+          "type": "string"
+        },
+        "authorized_users": {
+          "description": "Authorized users for the bot.",
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "uniqueItems": true
+        },
+        "allow_custom_messages": {
+          "description": "Allow sending custom messages from the Strategy.",
+          "type": "boolean",
+          "default": true
+        },
+        "balance_dust_level": {
+          "description": "Minimum balance level to consider as dust.",
+          "type": "number",
+          "minimum": 0.0
+        },
+        "notification_settings": {
+          "description": "Settings for different types of notifications.",
+          "type": "object",
+          "default": {},
+          "properties": {
+            "status": {
+              "description": "Telegram setting for status updates.",
+              "type": "string",
+              "enum": [
+                "on",
+                "off",
+                "silent"
+              ]
+            },
+            "warning": {
+              "description": "Telegram setting for warnings.",
+              "type": "string",
+              "enum": [
+                "on",
+                "off",
+                "silent"
+              ]
+            },
+            "startup": {
+              "description": "Telegram setting for startup messages.",
+              "type": "string",
+              "enum": [
+                "on",
+                "off",
+                "silent"
+              ]
+            },
+            "entry": {
+              "description": "Telegram setting for entry signals.",
+              "type": "string",
+              "enum": [
+                "on",
+                "off",
+                "silent"
+              ]
+            },
+            "entry_fill": {
+              "description": "Telegram setting for entry fill signals.",
+              "type": "string",
+              "enum": [
+                "on",
+                "off",
+                "silent"
+              ],
+              "default": "off"
+            },
+            "entry_cancel": {
+              "description": "Telegram setting for entry cancel signals.",
+              "type": "string",
+              "enum": [
+                "on",
+                "off",
+                "silent"
+              ]
+            },
+            "exit": {
+              "description": "Telegram setting for exit signals.",
+              "type": [
+                "string",
+                "object"
+              ],
+              "additionalProperties": {
+                "type": "string",
+                "enum": [
+                  "on",
+                  "off",
+                  "silent"
+                ]
+              }
+            },
+            "exit_fill": {
+              "description": "Telegram setting for exit fill signals.",
+              "type": [
+                "string",
+                "object"
+              ],
+              "additionalProperties": {
+                "type": "string",
+                "enum": [
+                  "on",
+                  "off",
+                  "silent"
+                ]
+              },
+              "default": "on"
+            },
+            "exit_cancel": {
+              "description": "Telegram setting for exit cancel signals.",
+              "type": "string",
+              "enum": [
+                "on",
+                "off",
+                "silent"
+              ]
+            },
+            "protection_trigger": {
+              "description": "Telegram setting for protection triggers.",
+              "type": "string",
+              "enum": [
+                "on",
+                "off",
+                "silent"
+              ],
+              "default": "on"
+            },
+            "protection_trigger_global": {
+              "description": "Telegram setting for global protection triggers.",
+              "type": "string",
+              "enum": [
+                "on",
+                "off",
+                "silent"
+              ],
+              "default": "on"
+            }
+          }
+        },
+        "reload": {
+          "description": "Add Reload button to certain messages.",
+          "type": "boolean"
+        }
+      },
+      "required": [
+        "enabled",
+        "token",
+        "chat_id"
+      ]
+    },
+    "webhook": {
+      "description": "Webhook settings.",
+      "type": "object",
+      "properties": {
+        "enabled": {
+          "type": "boolean"
+        },
+        "url": {
+          "type": "string"
+        },
+        "format": {
+          "type": "string",
+          "enum": [
+            "form",
+            "json",
+            "raw"
+          ],
+          "default": "form"
+        },
+        "retries": {
+          "type": "integer",
+          "minimum": 0
+        },
+        "retry_delay": {
+          "type": "number",
+          "minimum": 0
+        },
+        "status": {
+          "type": "object"
+        },
+        "warning": {
+          "type": "object"
+        },
+        "exception": {
+          "type": "object"
+        },
+        "startup": {
+          "type": "object"
+        },
+        "entry": {
+          "type": "object"
+        },
+        "entry_fill": {
+          "type": "object"
+        },
+        "entry_cancel": {
+          "type": "object"
+        },
+        "exit": {
+          "type": "object"
+        },
+        "exit_fill": {
+          "type": "object"
+        },
+        "exit_cancel": {
+          "type": "object"
+        },
+        "protection_trigger": {
+          "type": "object"
+        },
+        "protection_trigger_global": {
+          "type": "object"
+        },
+        "strategy_msg": {
+          "type": "object"
+        },
+        "whitelist": {
+          "type": "object"
+        },
+        "analyzed_df": {
+          "type": "object"
+        },
+        "new_candle": {
+          "type": "object"
+        }
+      }
+    },
+    "discord": {
+      "description": "Discord settings.",
+      "type": "object",
+      "properties": {
+        "enabled": {
+          "type": "boolean"
+        },
+        "webhook_url": {
+          "type": "string"
+        },
+        "exit_fill": {
+          "type": "array",
+          "items": {
+            "type": "object"
+          },
+          "default": [
+            {
+              "Trade ID": "{trade_id}"
+            },
+            {
+              "Exchange": "{exchange}"
+            },
+            {
+              "Pair": "{pair}"
+            },
+            {
+              "Direction": "{direction}"
+            },
+            {
+              "Open rate": "{open_rate}"
+            },
+            {
+              "Close rate": "{close_rate}"
+            },
+            {
+              "Amount": "{amount}"
+            },
+            {
+              "Open date": "{open_date:%Y-%m-%d %H:%M:%S}"
+            },
+            {
+              "Close date": "{close_date:%Y-%m-%d %H:%M:%S}"
+            },
+            {
+              "Profit": "{profit_amount} {stake_currency}"
+            },
+            {
+              "Profitability": "{profit_ratio:.2%}"
+            },
+            {
+              "Enter tag": "{enter_tag}"
+            },
+            {
+              "Exit Reason": "{exit_reason}"
+            },
+            {
+              "Strategy": "{strategy}"
+            },
+            {
+              "Timeframe": "{timeframe}"
+            }
+          ]
+        },
+        "entry_fill": {
+          "type": "array",
+          "items": {
+            "type": "object"
+          },
+          "default": [
+            {
+              "Trade ID": "{trade_id}"
+            },
+            {
+              "Exchange": "{exchange}"
+            },
+            {
+              "Pair": "{pair}"
+            },
+            {
+              "Direction": "{direction}"
+            },
+            {
+              "Open rate": "{open_rate}"
+            },
+            {
+              "Amount": "{amount}"
+            },
+            {
+              "Open date": "{open_date:%Y-%m-%d %H:%M:%S}"
+            },
+            {
+              "Enter tag": "{enter_tag}"
+            },
+            {
+              "Strategy": "{strategy} {timeframe}"
+            }
+          ]
+        }
+      }
+    },
+    "api_server": {
+      "description": "API server settings.",
+      "type": "object",
+      "properties": {
+        "enabled": {
+          "description": "Whether the API server is enabled.",
+          "type": "boolean"
+        },
+        "listen_ip_address": {
+          "description": "IP address the API server listens on.",
+          "format": "ipv4"
+        },
+        "listen_port": {
+          "description": "Port the API server listens on.",
+          "type": "integer",
+          "minimum": 1024,
+          "maximum": 65535
+        },
+        "username": {
+          "description": "Username for API server authentication.",
+          "type": "string"
+        },
+        "password": {
+          "description": "Password for API server authentication.",
+          "type": "string"
+        },
+        "ws_token": {
+          "description": "WebSocket token for API server.",
+          "type": [
+            "string",
+            "array"
+          ],
+          "items": {
+            "type": "string"
+          }
+        },
+        "jwt_secret_key": {
+          "description": "Secret key for JWT authentication.",
+          "type": "string"
+        },
+        "CORS_origins": {
+          "description": "List of allowed CORS origins.",
+          "type": "array",
+          "items": {
+            "type": "string"
+          }
+        },
+        "verbosity": {
+          "description": "Logging verbosity level.",
+          "type": "string",
+          "enum": [
+            "error",
+            "info"
+          ]
+        }
+      },
+      "required": [
+        "enabled",
+        "listen_ip_address",
+        "listen_port",
+        "username",
+        "password"
+      ]
+    },
+    "db_url": {
+      "description": "Database connection URL.",
+      "type": "string"
+    },
+    "export": {
+      "description": "Type of data to export.",
+      "type": "string",
+      "enum": [
+        "none",
+        "trades",
+        "signals"
+      ],
+      "default": "trades"
+    },
+    "disableparamexport": {
+      "description": "Disable parameter export.",
+      "type": "boolean"
+    },
+    "initial_state": {
+      "description": "Initial state of the system.",
+      "type": "string",
+      "enum": [
+        "running",
+        "paused",
+        "stopped"
+      ]
+    },
+    "force_entry_enable": {
+      "description": "Force enable entry.",
+      "type": "boolean"
+    },
+    "disable_dataframe_checks": {
+      "description": "Disable checks on dataframes.",
+      "type": "boolean"
+    },
+    "internals": {
+      "description": "Internal settings.",
+      "type": "object",
+      "default": {},
+      "properties": {
+        "process_throttle_secs": {
+          "description": "Minimum loop duration for one bot iteration in seconds.",
+          "type": "integer"
+        },
+        "interval": {
+          "description": "Interval time in seconds.",
+          "type": "integer"
+        },
+        "sd_notify": {
+          "description": "Enable systemd notify.",
+          "type": "boolean"
+        }
+      }
+    },
+    "dataformat_ohlcv": {
+      "description": "Data format for OHLCV data.",
+      "type": "string",
+      "enum": [
+        "json",
+        "jsongz",
+        "feather",
+        "parquet"
+      ],
+      "default": "feather"
+    },
+    "dataformat_trades": {
+      "description": "Data format for trade data.",
+      "type": "string",
+      "enum": [
+        "json",
+        "jsongz",
+        "feather",
+        "parquet"
+      ],
+      "default": "feather"
+    },
+    "position_adjustment_enable": {
+      "description": "Enable position adjustment. \nUsually specified in the strategy and missing in the configuration.",
+      "type": "boolean"
+    },
+    "new_pairs_days": {
+      "description": "Download data of new pairs for given number of days",
+      "type": "integer",
+      "default": 30
+    },
+    "download_trades": {
+      "description": "Download trades data by default (instead of ohlcv data).",
+      "type": "boolean"
+    },
+    "max_entry_position_adjustment": {
+      "description": "Maximum entry position adjustment allowed. \nUsually specified in the strategy and missing in the configuration.",
+      "type": [
+        "integer",
+        "number"
+      ],
+      "minimum": -1
+    },
+    "add_config_files": {
+      "description": "Additional configuration files to load.",
+      "type": "array",
+      "items": {
+        "type": "string"
+      }
+    },
+    "orderflow": {
+      "description": "Settings related to order flow.",
+      "type": "object",
+      "properties": {
+        "cache_size": {
+          "description": "Size of the cache for order flow data.",
+          "type": "number",
+          "minimum": 1,
+          "default": 1500
+        },
+        "max_candles": {
+          "description": "Maximum number of candles to consider.",
+          "type": "number",
+          "minimum": 1,
+          "default": 1500
+        },
+        "scale": {
+          "description": "Scale factor for order flow data.",
+          "type": "number",
+          "minimum": 0.0
+        },
+        "stacked_imbalance_range": {
+          "description": "Range for stacked imbalance.",
+          "type": "number",
+          "minimum": 0
+        },
+        "imbalance_volume": {
+          "description": "Volume threshold for imbalance.",
+          "type": "number",
+          "minimum": 0
+        },
+        "imbalance_ratio": {
+          "description": "Ratio threshold for imbalance.",
+          "type": "number",
+          "minimum": 0.0
+        }
+      },
+      "required": [
+        "max_candles",
+        "scale",
+        "stacked_imbalance_range",
+        "imbalance_volume",
+        "imbalance_ratio"
+      ]
+    }
+  },
+  "definitions": {
+    "exchange": {
+      "description": "Exchange configuration settings.",
+      "type": "object",
+      "properties": {
+        "name": {
+          "description": "Name of the exchange.",
+          "type": "string"
+        },
+        "enable_ws": {
+          "description": "Enable WebSocket connections to the exchange.",
+          "type": "boolean",
+          "default": true
+        },
+        "key": {
+          "description": "API key for the exchange.",
+          "type": "string",
+          "default": ""
+        },
+        "secret": {
+          "description": "API secret for the exchange.",
+          "type": "string",
+          "default": ""
+        },
+        "password": {
+          "description": "Password for the exchange, if required.",
+          "type": "string",
+          "default": ""
+        },
+        "uid": {
+          "description": "User ID for the exchange, if required.",
+          "type": "string"
+        },
+        "pair_whitelist": {
+          "description": "List of whitelisted trading pairs.",
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "uniqueItems": true
+        },
+        "pair_blacklist": {
+          "description": "List of blacklisted trading pairs.",
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "uniqueItems": true
+        },
+        "log_responses": {
+          "description": "Log responses from the exchange.Useful/required to debug issues with order processing.",
+          "type": "boolean",
+          "default": false
+        },
+        "unknown_fee_rate": {
+          "description": "Fee rate for unknown markets.",
+          "type": "number"
+        },
+        "outdated_offset": {
+          "description": "Offset for outdated data in minutes.",
+          "type": "integer",
+          "minimum": 1
+        },
+        "markets_refresh_interval": {
+          "description": "Interval for refreshing market data in minutes.",
+          "type": "integer",
+          "default": 60
+        },
+        "ccxt_config": {
+          "description": "CCXT configuration settings.",
+          "type": "object"
+        },
+        "ccxt_async_config": {
+          "description": "CCXT asynchronous configuration settings.",
+          "type": "object"
+        }
+      },
+      "required": [
+        "name"
+      ]
+    },
+    "edge": {
+      "type": "object",
+      "properties": {
+        "enabled": {
+          "type": "boolean"
+        },
+        "process_throttle_secs": {
+          "type": "integer",
+          "minimum": 600
+        },
+        "calculate_since_number_of_days": {
+          "type": "integer"
+        },
+        "allowed_risk": {
+          "type": "number"
+        },
+        "stoploss_range_min": {
+          "type": "number"
+        },
+        "stoploss_range_max": {
+          "type": "number"
+        },
+        "stoploss_range_step": {
+          "type": "number"
+        },
+        "minimum_winrate": {
+          "type": "number"
+        },
+        "minimum_expectancy": {
+          "type": "number"
+        },
+        "min_trade_number": {
+          "type": "number"
+        },
+        "max_trade_duration_minute": {
+          "type": "integer"
+        },
+        "remove_pumps": {
+          "type": "boolean"
+        }
+      },
+      "required": [
+        "process_throttle_secs",
+        "allowed_risk"
+      ]
+    },
+    "logging": {
+      "type": "object",
+      "properties": {
+        "version": {
+          "type": "number",
+          "const": 1
+        },
+        "formatters": {
+          "type": "object"
+        },
+        "handlers": {
+          "type": "object"
+        },
+        "root": {
+          "type": "object"
+        }
+      },
+      "required": [
+        "version",
+        "formatters",
+        "handlers",
+        "root"
+      ]
+    },
+    "external_message_consumer": {
+      "description": "Configuration for external message consumer.",
+      "type": "object",
+      "properties": {
+        "enabled": {
+          "description": "Whether the external message consumer is enabled.",
+          "type": "boolean",
+          "default": false
+        },
+        "producers": {
+          "description": "List of producers for the external message consumer.",
+          "type": "array",
+          "items": {
+            "type": "object",
+            "properties": {
+              "name": {
+                "description": "Name of the producer.",
+                "type": "string"
+              },
+              "host": {
+                "description": "Host of the producer.",
+                "type": "string"
+              },
+              "port": {
+                "description": "Port of the producer.",
+                "type": "integer",
+                "default": 8080,
+                "minimum": 0,
+                "maximum": 65535
+              },
+              "secure": {
+                "description": "Whether to use SSL to connect to the producer.",
+                "type": "boolean",
+                "default": false
+              },
+              "ws_token": {
+                "description": "WebSocket token for the producer.",
+                "type": "string"
+              }
+            },
+            "required": [
+              "name",
+              "host",
+              "ws_token"
+            ]
+          }
+        },
+        "wait_timeout": {
+          "description": "Wait timeout in seconds.",
+          "type": "integer",
+          "minimum": 0
+        },
+        "sleep_time": {
+          "description": "Sleep time in seconds before retrying to connect.",
+          "type": "integer",
+          "minimum": 0
+        },
+        "ping_timeout": {
+          "description": "Ping timeout in seconds.",
+          "type": "integer",
+          "minimum": 0
+        },
+        "remove_entry_exit_signals": {
+          "description": "Remove signal columns from the dataframe (set them to 0)",
+          "type": "boolean",
+          "default": false
+        },
+        "initial_candle_limit": {
+          "description": "Initial candle limit.",
+          "type": "integer",
+          "minimum": 0,
+          "maximum": 1500,
+          "default": 1500
+        },
+        "message_size_limit": {
+          "description": "Message size limit in megabytes.",
+          "type": "integer",
+          "minimum": 1,
+          "maximum": 20,
+          "default": 8
+        }
+      },
+      "required": [
+        "producers"
+      ]
+    },
+    "freqai": {
+      "type": "object",
+      "properties": {
+        "enabled": {
+          "description": "Whether freqAI is enabled.",
+          "type": "boolean",
+          "default": false
+        },
+        "identifier": {
+          "description": "A unique ID for the current model. Must be changed when modifying features.",
+          "type": "string",
+          "default": "example"
+        },
+        "write_metrics_to_disk": {
+          "description": "Write metrics to disk?",
+          "type": "boolean",
+          "default": false
+        },
+        "purge_old_models": {
+          "description": "Number of models to keep on disk.",
+          "type": [
+            "boolean",
+            "number"
+          ],
+          "default": 2
+        },
+        "conv_width": {
+          "description": "The width of a neural network input tensor.",
+          "type": "integer",
+          "default": 1
+        },
+        "train_period_days": {
+          "description": "Number of days to use for the training data (width of the sliding window)",
+          "type": "integer",
+          "default": 0
+        },
+        "backtest_period_days": {
+          "description": "Number of days to inference from the trained model before sliding the `train_period_days` window ",
+          "type": "number",
+          "default": 7
+        },
+        "live_retrain_hours": {
+          "description": "Frequency of retraining during dry/live runs.",
+          "type": "number",
+          "default": 0
+        },
+        "expiration_hours": {
+          "description": "Avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 (no expiration).",
+          "type": "number",
+          "default": 0
+        },
+        "save_backtest_models": {
+          "description": "Save models to disk when running backtesting.",
+          "type": "boolean",
+          "default": false
+        },
+        "fit_live_predictions_candles": {
+          "description": "Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset.",
+          "type": "integer"
+        },
+        "data_kitchen_thread_count": {
+          "description": "Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.).",
+          "type": "integer"
+        },
+        "activate_tensorboard": {
+          "description": "Indicate whether or not to activate tensorboard",
+          "type": "boolean",
+          "default": true
+        },
+        "wait_for_training_iteration_on_reload": {
+          "description": "Wait for the next training iteration to complete after /reload or ctrl+c.",
+          "type": "boolean",
+          "default": true
+        },
+        "continual_learning": {
+          "description": "Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning.",
+          "type": "boolean",
+          "default": false
+        },
+        "keras": {
+          "description": "Use Keras for model training.",
+          "type": "boolean",
+          "default": false
+        },
+        "feature_parameters": {
+          "description": "The parameters used to engineer the feature set",
+          "type": "object",
+          "properties": {
+            "include_corr_pairlist": {
+              "description": "List of correlated pairs to include in the features.",
+              "type": "array"
+            },
+            "include_timeframes": {
+              "description": "A list of timeframes that all indicators in `feature_engineering_expand_*()` will be created for.",
+              "type": "array"
+            },
+            "label_period_candles": {
+              "description": "Number of candles into the future to use for labeling the period.This can be used in `set_freqai_targets()`.",
+              "type": "integer"
+            },
+            "include_shifted_candles": {
+              "description": "Add features from previous candles to subsequent candles with the intent of adding historical information.",
+              "type": "integer",
+              "default": 0
+            },
+            "DI_threshold": {
+              "description": "Activates the use of the Dissimilarity Index for outlier detection when set to > 0.",
+              "type": "number",
+              "default": 0
+            },
+            "weight_factor": {
+              "description": "Weight training data points according to their recency.",
+              "type": "number",
+              "default": 0
+            },
+            "principal_component_analysis": {
+              "description": "Automatically reduce the dimensionality of the data set using Principal Component Analysis",
+              "type": "boolean",
+              "default": false
+            },
+            "indicator_periods_candles": {
+              "description": "Time periods to calculate indicators for. The indicators are added to the base indicator dataset.",
+              "type": "array",
+              "items": {
+                "type": "number",
+                "minimum": 1
+              }
+            },
+            "use_SVM_to_remove_outliers": {
+              "description": "Use SVM to remove outliers from the features.",
+              "type": "boolean",
+              "default": false
+            },
+            "plot_feature_importances": {
+              "description": "Create feature importance plots for each model.",
+              "type": "integer",
+              "default": 0
+            },
+            "svm_params": {
+              "description": "All parameters available in Sklearn's `SGDOneClassSVM()`.",
+              "type": "object",
+              "properties": {
+                "shuffle": {
+                  "description": "Whether to shuffle data before applying SVM.",
+                  "type": "boolean",
+                  "default": false
+                },
+                "nu": {
+                  "type": "number",
+                  "default": 0.1
+                }
+              }
+            },
+            "shuffle_after_split": {
+              "description": "Split the data into train and test sets, and then shuffle both sets individually.",
+              "type": "boolean",
+              "default": false
+            },
+            "buffer_train_data_candles": {
+              "description": "Cut `buffer_train_data_candles` off the beginning and end of the training data *after* the indicators were populated.",
+              "type": "integer",
+              "default": 0
+            }
+          },
+          "required": [
+            "include_timeframes",
+            "include_corr_pairlist"
+          ]
+        },
+        "data_split_parameters": {
+          "descriptions": "Additional parameters for scikit-learn's test_train_split() function.",
+          "type": "object",
+          "properties": {
+            "test_size": {
+              "type": "number"
+            },
+            "random_state": {
+              "type": "integer"
+            },
+            "shuffle": {
+              "type": "boolean",
+              "default": false
+            }
+          }
+        },
+        "model_training_parameters": {
+          "description": "Flexible dictionary that includes all parameters available by the selected model library. ",
+          "type": "object"
+        },
+        "rl_config": {
+          "type": "object",
+          "properties": {
+            "drop_ohlc_from_features": {
+              "description": "Do not include the normalized ohlc data in the feature set.",
+              "type": "boolean",
+              "default": false
+            },
+            "train_cycles": {
+              "description": "Number of training cycles to perform.",
+              "type": "integer"
+            },
+            "max_trade_duration_candles": {
+              "description": "Guides the agent training to keep trades below desired length.",
+              "type": "integer"
+            },
+            "add_state_info": {
+              "description": "Include state information in the feature set for training and inference.",
+              "type": "boolean",
+              "default": false
+            },
+            "max_training_drawdown_pct": {
+              "description": "Maximum allowed drawdown percentage during training.",
+              "type": "number",
+              "default": 0.02
+            },
+            "cpu_count": {
+              "description": "Number of threads/CPU's to use for training.",
+              "type": "integer",
+              "default": 1
+            },
+            "model_type": {
+              "description": "Model string from stable_baselines3 or SBcontrib.",
+              "type": "string",
+              "default": "PPO"
+            },
+            "policy_type": {
+              "description": "One of the available policy types from stable_baselines3.",
+              "type": "string",
+              "default": "MlpPolicy"
+            },
+            "net_arch": {
+              "description": "Architecture of the neural network.",
+              "type": "array",
+              "default": [
+                128,
+                128
+              ]
+            },
+            "randomize_starting_position": {
+              "description": "Randomize the starting point of each episode to avoid overfitting.",
+              "type": "boolean",
+              "default": false
+            },
+            "progress_bar": {
+              "description": "Display a progress bar with the current progress.",
+              "type": "boolean",
+              "default": true
+            },
+            "model_reward_parameters": {
+              "description": "Parameters for configuring the reward model.",
+              "type": "object",
+              "properties": {
+                "rr": {
+                  "type": "number",
+                  "default": 1,
+                  "description": "Reward ratio parameter."
+                },
+                "profit_aim": {
+                  "type": "number",
+                  "default": 0.025,
+                  "description": "Profit aim parameter."
+                }
+              }
+            }
+          }
+        }
+      },
+      "required": [
+        "enabled",
+        "train_period_days",
+        "backtest_period_days",
+        "identifier",
+        "feature_parameters",
+        "data_split_parameters"
+      ]
+    }
+  }
+}
\ No newline at end of file
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diff --git a/config.json.example b/config.json.example
deleted file mode 100644
index 2c464a925..000000000
--- a/config.json.example
+++ /dev/null
@@ -1,44 +0,0 @@
-{
-    "max_open_trades": 3,
-    "stake_currency": "BTC",
-    "stake_amount": 0.05,
-    "fiat_display_currency": "USD",
-    "dry_run": false,
-    "unfilledtimeout": 600,
-    "bid_strategy": {
-        "ask_last_balance": 0.0
-    },
-    "exchange": {
-        "name": "bittrex",
-        "key": "your_exchange_key",
-        "secret": "your_exchange_secret",
-        "pair_whitelist": [
-            "BTC_ETH",
-            "BTC_LTC",
-            "BTC_ETC",
-            "BTC_DASH",
-            "BTC_ZEC",
-            "BTC_XLM",
-            "BTC_NXT",
-            "BTC_POWR",
-            "BTC_ADA",
-            "BTC_XMR"
-        ],
-        "pair_blacklist": [
-            "BTC_DOGE"
-        ]
-    },
-    "experimental": {
-        "use_sell_signal": false,
-        "sell_profit_only": false
-    },
-    "telegram": {
-        "enabled": true,
-        "token": "your_telegram_token",
-        "chat_id": "your_telegram_chat_id"
-    },
-    "initial_state": "running",
-    "internals": {
-        "process_throttle_secs": 5
-    }
-}
diff --git a/config_examples/config_binance.example.json b/config_examples/config_binance.example.json
new file mode 100644
index 000000000..6c513f064
--- /dev/null
+++ b/config_examples/config_binance.example.json
@@ -0,0 +1,84 @@
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 3,
+    "stake_currency": "USDT",
+    "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "timeframe": "5m",
+    "dry_run": true,
+    "cancel_open_orders_on_exit": false,
+    "unfilledtimeout": {
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "ccxt_config": {},
+        "ccxt_async_config": {
+        },
+        "pair_whitelist": [
+            "ALGO/USDT",
+            "ATOM/USDT",
+            "BAT/USDT",
+            "BCH/USDT",
+            "BRD/USDT",
+            "EOS/USDT",
+            "ETH/USDT",
+            "IOTA/USDT",
+            "LINK/USDT",
+            "LTC/USDT",
+            "NEO/USDT",
+            "NXS/USDT",
+            "XMR/USDT",
+            "XRP/USDT",
+            "XTZ/USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id"
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}
diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json
new file mode 100644
index 000000000..6751002e4
--- /dev/null
+++ b/config_examples/config_freqai.example.json
@@ -0,0 +1,91 @@
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "max_open_trades": 5,
+    "stake_currency": "USDT",
+    "stake_amount": 200,
+    "tradable_balance_ratio": 1,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "timeframe": "3m",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "unfilledtimeout": {
+        "entry": 10,
+        "exit": 30
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "",
+        "secret": "",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "1INCH/USDT:USDT",
+            "ALGO/USDT:USDT"
+        ],
+        "pair_blacklist": []
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing": {
+        "price_side": "other",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList"
+        }
+    ],
+    "freqai": {
+        "enabled": true,
+        "purge_old_models": 2,
+        "train_period_days": 15,
+        "backtest_period_days": 7,
+        "live_retrain_hours": 0,
+        "identifier": "unique-id",
+        "feature_parameters": {
+            "include_timeframes": [
+                "3m",
+                "15m",
+                "1h"
+            ],
+            "include_corr_pairlist": [
+                "BTC/USDT:USDT",
+                "ETH/USDT:USDT"
+            ],
+            "label_period_candles": 20,
+            "include_shifted_candles": 2,
+            "DI_threshold": 0.9,
+            "weight_factor": 0.9,
+            "principal_component_analysis": false,
+            "use_SVM_to_remove_outliers": true,
+            "indicator_periods_candles": [
+                10,
+                20
+            ],
+            "plot_feature_importances": 0
+        },
+        "data_split_parameters": {
+            "test_size": 0.33,
+            "random_state": 1
+        },
+        "model_training_parameters": {}
+    },
+    "bot_name": "",
+    "force_entry_enable": true,
+    "initial_state": "running",
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}
diff --git a/config_examples/config_full.example.json b/config_examples/config_full.example.json
new file mode 100644
index 000000000..04137ed80
--- /dev/null
+++ b/config_examples/config_full.example.json
@@ -0,0 +1,212 @@
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 3,
+    "stake_currency": "BTC",
+    "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "amount_reserve_percent": 0.05,
+    "available_capital": 1000,
+    "amend_last_stake_amount": false,
+    "last_stake_amount_min_ratio": 0.5,
+    "dry_run": true,
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": false,
+    "timeframe": "5m",
+    "trailing_stop": false,
+    "trailing_stop_positive": 0.005,
+    "trailing_stop_positive_offset": 0.0051,
+    "trailing_only_offset_is_reached": false,
+    "use_exit_signal": true,
+    "exit_profit_only": false,
+    "exit_profit_offset": 0.0,
+    "ignore_roi_if_entry_signal": false,
+    "ignore_buying_expired_candle_after": 300,
+    "trading_mode": "spot",
+    "margin_mode": "",
+    "minimal_roi": {
+        "40":  0.0,
+        "30":  0.01,
+        "20":  0.02,
+        "0":  0.04
+    },
+    "stoploss": -0.10,
+    "unfilledtimeout": {
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0
+    },
+    "order_types": {
+        "entry": "limit",
+        "exit": "limit",
+        "emergency_exit": "market",
+        "force_exit": "market",
+        "force_entry": "market",
+        "stoploss": "market",
+        "stoploss_on_exchange": false,
+        "stoploss_price_type": "last",
+        "stoploss_on_exchange_interval": 60,
+        "stoploss_on_exchange_limit_ratio": 0.99
+    },
+    "order_time_in_force": {
+        "entry": "GTC",
+        "exit": "GTC"
+    },
+    "pairlists": [
+        {"method": "StaticPairList"},
+        {"method": "FullTradesFilter"},
+        {
+            "method": "VolumePairList",
+            "number_assets": 20,
+            "sort_key": "quoteVolume",
+            "refresh_period": 1800
+        },
+        {"method": "AgeFilter", "min_days_listed": 10},
+        {"method": "PrecisionFilter"},
+        {"method": "PriceFilter", "low_price_ratio": 0.01, "min_price": 0.00000010},
+        {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+        {
+            "method": "RangeStabilityFilter",
+            "lookback_days": 10,
+            "min_rate_of_change": 0.01,
+            "refresh_period": 1440
+        }
+    ],
+    "exchange": {
+        "name": "binance",
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "password": "",
+        "log_responses": false,
+        // "unknown_fee_rate": 1,
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "ALGO/BTC",
+            "ATOM/BTC",
+            "BAT/BTC",
+            "BCH/BTC",
+            "BRD/BTC",
+            "EOS/BTC",
+            "ETH/BTC",
+            "IOTA/BTC",
+            "LINK/BTC",
+            "LTC/BTC",
+            "NEO/BTC",
+            "NXS/BTC",
+            "XMR/BTC",
+            "XRP/BTC",
+            "XTZ/BTC"
+        ],
+        "pair_blacklist": [
+            "DOGE/BTC"
+        ],
+        "outdated_offset": 5,
+        "markets_refresh_interval": 60
+    },
+    "edge": {
+        "enabled": false,
+        "process_throttle_secs": 3600,
+        "calculate_since_number_of_days": 7,
+        "allowed_risk": 0.01,
+        "stoploss_range_min": -0.01,
+        "stoploss_range_max": -0.1,
+        "stoploss_range_step": -0.01,
+        "minimum_winrate": 0.60,
+        "minimum_expectancy": 0.20,
+        "min_trade_number": 10,
+        "max_trade_duration_minute": 1440,
+        "remove_pumps": false
+    },
+    "telegram": {
+        "enabled": false,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id",
+        "notification_settings": {
+            "status": "on",
+            "warning": "on",
+            "startup": "on",
+            "entry": "on",
+            "entry_fill": "on",
+            "exit": {
+                "roi": "off",
+                "emergency_exit": "off",
+                "force_exit": "off",
+                "exit_signal": "off",
+                "trailing_stop_loss": "off",
+                "stop_loss": "off",
+                "stoploss_on_exchange": "off",
+                "custom_exit": "off"
+            },
+            "exit_fill": "on",
+            "entry_cancel": "on",
+            "exit_cancel": "on",
+            "protection_trigger": "off",
+            "protection_trigger_global": "on",
+            "show_candle": "off"
+        },
+        "reload": true,
+        "balance_dust_level": 0.01
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "SuperSecurePassword",
+        "ws_token": "secret_ws_t0ken."
+    },
+    "external_message_consumer": {
+        "enabled": false,
+        "producers": [
+          {
+            "name": "default",
+            "host": "127.0.0.2",
+            "port": 8080,
+            "ws_token": "secret_ws_t0ken."
+          }
+        ],
+        "wait_timeout": 300,
+        "ping_timeout": 10,
+        "sleep_time": 10,
+        "remove_entry_exit_signals": false,
+        "message_size_limit": 8
+    },
+    "bot_name": "freqtrade",
+    "db_url": "sqlite:///tradesv3.sqlite",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5,
+        "heartbeat_interval": 60
+    },
+    "disable_dataframe_checks": false,
+    "strategy": "SampleStrategy",
+    "strategy_path": "user_data/strategies/",
+    "recursive_strategy_search": false,
+    "add_config_files": [],
+    "reduce_df_footprint": false,
+    "dataformat_ohlcv": "feather",
+    "dataformat_trades": "feather"
+}
diff --git a/config_examples/config_kraken.example.json b/config_examples/config_kraken.example.json
new file mode 100644
index 000000000..72f5e6b5f
--- /dev/null
+++ b/config_examples/config_kraken.example.json
@@ -0,0 +1,90 @@
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 5,
+    "stake_currency": "EUR",
+    "stake_amount": 10,
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "EUR",
+    "timeframe": "5m",
+    "dry_run": true,
+    "cancel_open_orders_on_exit": false,
+    "unfilledtimeout": {
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "kraken",
+        "key": "your_exchange_key",
+        "secret": "your_exchange_key",
+        "ccxt_config": {},
+        "ccxt_async_config": {
+        },
+        "pair_whitelist": [
+            "ADA/EUR",
+            "ATOM/EUR",
+            "BAT/EUR",
+            "BCH/EUR",
+            "BTC/EUR",
+            "DAI/EUR",
+            "DASH/EUR",
+            "EOS/EUR",
+            "ETC/EUR",
+            "ETH/EUR",
+            "LINK/EUR",
+            "LTC/EUR",
+            "QTUM/EUR",
+            "REP/EUR",
+            "WAVES/EUR",
+            "XLM/EUR",
+            "XMR/EUR",
+            "XRP/EUR",
+            "XTZ/EUR",
+            "ZEC/EUR"
+        ],
+        "pair_blacklist": [
+
+        ]
+    },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id"
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    },
+    "download_trades": true
+}
diff --git a/config_full.json.example b/config_full.json.example
deleted file mode 100644
index c74b59660..000000000
--- a/config_full.json.example
+++ /dev/null
@@ -1,52 +0,0 @@
-{
-    "max_open_trades": 3,
-    "stake_currency": "BTC",
-    "stake_amount": 0.05,
-    "fiat_display_currency": "USD",
-    "dry_run": false,
-    "ticker_interval": 5,
-    "minimal_roi": {
-        "40":  0.0,
-        "30":  0.01,
-        "20":  0.02,
-        "0":  0.04
-    },
-    "stoploss": -0.10,
-    "unfilledtimeout": 600,
-    "bid_strategy": {
-        "ask_last_balance": 0.0
-    },
-    "exchange": {
-        "name": "bittrex",
-        "key": "your_exchange_key",
-        "secret": "your_exchange_secret",
-        "pair_whitelist": [
-            "BTC_ETH",
-            "BTC_LTC",
-            "BTC_ETC",
-            "BTC_DASH",
-            "BTC_ZEC",
-            "BTC_XLM",
-            "BTC_NXT",
-            "BTC_POWR",
-            "BTC_ADA",
-            "BTC_XMR"
-        ],
-        "pair_blacklist": [
-            "BTC_DOGE"
-        ]
-    },
-    "experimental": {
-        "use_sell_signal": false,
-        "sell_profit_only": false
-    },
-    "telegram": {
-        "enabled": true,
-        "token": "your_telegram_token",
-        "chat_id": "your_telegram_chat_id"
-    },
-    "initial_state": "running",
-    "internals": {
-        "process_throttle_secs": 5
-    }
-}
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 000000000..6b7eefab7
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,36 @@
+---
+services:
+  freqtrade:
+    image: freqtradeorg/freqtrade:stable
+    # image: freqtradeorg/freqtrade:develop
+    # Use plotting image
+    # image: freqtradeorg/freqtrade:develop_plot
+    # # Enable GPU Image and GPU Resources (only relevant for freqAI)
+    # # Make sure to uncomment the whole deploy section
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: 1
+    #           capabilities: [gpu]
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: unless-stopped
+    container_name: freqtrade
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
+    # for more information.
+    ports:
+      - "127.0.0.1:8080:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
+      --config /freqtrade/user_data/config.json
+      --strategy SampleStrategy
diff --git a/docker/Dockerfile.armhf b/docker/Dockerfile.armhf
new file mode 100644
index 000000000..9ae810a26
--- /dev/null
+++ b/docker/Dockerfile.armhf
@@ -0,0 +1,58 @@
+FROM python:3.11.12-slim-bookworm as base
+
+# Setup env
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONFAULTHANDLER 1
+ENV PATH=/home/ftuser/.local/bin:$PATH
+ENV FT_APP_ENV="docker"
+
+# Prepare environment
+RUN mkdir /freqtrade \
+  && apt-get update \
+  && apt-get -y install sudo libatlas3-base libopenblas-dev curl sqlite3 libutf8proc-dev libsnappy-dev \
+  && apt-get clean \
+  && useradd -u 1000 -G sudo -U -m ftuser \
+  && chown ftuser:ftuser /freqtrade \
+  # Allow sudoers
+  && echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers \
+  && pip install --upgrade pip
+
+WORKDIR /freqtrade
+
+# Install dependencies
+FROM base as python-deps
+RUN  apt-get update \
+  && apt-get -y install build-essential libssl-dev libffi-dev libgfortran5 pkg-config cmake gcc \
+  && apt-get clean \
+  && echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf
+
+# Install TA-lib
+COPY build_helpers/* /tmp/
+
+# Install dependencies
+COPY --chown=ftuser:ftuser requirements.txt /freqtrade/
+USER ftuser
+RUN  pip install --user --no-cache-dir "numpy<2" \
+  && pip install --user --no-index --find-links /tmp/ pyarrow TA-Lib \
+  && pip install --user --no-cache-dir -r requirements.txt
+
+# Copy dependencies to runtime-image
+FROM base as runtime-image
+COPY --from=python-deps /usr/local/lib /usr/local/lib
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+COPY --from=python-deps --chown=ftuser:ftuser /home/ftuser/.local /home/ftuser/.local
+
+USER ftuser
+# Install and execute
+COPY --chown=ftuser:ftuser . /freqtrade/
+
+RUN pip install -e . --user --no-cache-dir --no-build-isolation\
+  && mkdir /freqtrade/user_data/ \
+  && freqtrade install-ui
+
+ENTRYPOINT ["freqtrade"]
+# Default to trade mode
+CMD [ "trade" ]
diff --git a/docker/Dockerfile.custom b/docker/Dockerfile.custom
new file mode 100644
index 000000000..6e321f14d
--- /dev/null
+++ b/docker/Dockerfile.custom
@@ -0,0 +1,11 @@
+FROM freqtradeorg/freqtrade:develop
+
+# Switch user to root if you must install something from apt
+# Don't forget to switch the user back below!
+# USER root
+
+# The below dependency - pyti - serves as an example. Please use whatever you need!
+RUN pip install --user pyti
+
+# Switch back to user (only if you required root above)
+# USER ftuser
diff --git a/docker/Dockerfile.develop b/docker/Dockerfile.develop
new file mode 100644
index 000000000..7c580f234
--- /dev/null
+++ b/docker/Dockerfile.develop
@@ -0,0 +1,10 @@
+FROM freqtradeorg/freqtrade:develop
+
+# Install dependencies
+COPY requirements-dev.txt /freqtrade/
+
+RUN pip install numpy --user --no-cache-dir \
+  && pip install -r requirements-dev.txt --user --no-cache-dir
+
+# Empty the ENTRYPOINT to allow all commands
+ENTRYPOINT []
diff --git a/docker/Dockerfile.freqai b/docker/Dockerfile.freqai
new file mode 100644
index 000000000..e9f04f3d6
--- /dev/null
+++ b/docker/Dockerfile.freqai
@@ -0,0 +1,8 @@
+ARG sourceimage=freqtradeorg/freqtrade
+ARG sourcetag=develop
+FROM ${sourceimage}:${sourcetag}
+
+# Install dependencies
+COPY requirements-freqai.txt /freqtrade/
+
+RUN pip install -r requirements-freqai.txt --user --no-cache-dir
diff --git a/docker/Dockerfile.freqai_rl b/docker/Dockerfile.freqai_rl
new file mode 100644
index 000000000..18fb9afa2
--- /dev/null
+++ b/docker/Dockerfile.freqai_rl
@@ -0,0 +1,8 @@
+ARG sourceimage=freqtradeorg/freqtrade
+ARG sourcetag=develop_freqai
+FROM ${sourceimage}:${sourcetag}
+
+# Install dependencies
+COPY requirements-freqai.txt requirements-freqai-rl.txt /freqtrade/
+
+RUN pip install -r requirements-freqai-rl.txt --user --no-cache-dir
diff --git a/docker/Dockerfile.jupyter b/docker/Dockerfile.jupyter
new file mode 100644
index 000000000..79fefb3a7
--- /dev/null
+++ b/docker/Dockerfile.jupyter
@@ -0,0 +1,8 @@
+FROM freqtradeorg/freqtrade:develop_plot
+
+
+# Pin prompt-toolkit to avoid questionary version conflict
+RUN pip install jupyterlab "prompt-toolkit<=3.0.36" jupyter-client --user --no-cache-dir
+
+# Empty the ENTRYPOINT to allow all commands
+ENTRYPOINT []
diff --git a/docker/Dockerfile.plot b/docker/Dockerfile.plot
new file mode 100644
index 000000000..e7f6bbb16
--- /dev/null
+++ b/docker/Dockerfile.plot
@@ -0,0 +1,8 @@
+ARG sourceimage=freqtradeorg/freqtrade
+ARG sourcetag=develop
+FROM ${sourceimage}:${sourcetag}
+
+# Install dependencies
+COPY requirements-plot.txt /freqtrade/
+
+RUN pip install -r requirements-plot.txt --user --no-cache-dir
diff --git a/docker/docker-compose-freqai.yml b/docker/docker-compose-freqai.yml
new file mode 100644
index 000000000..f61548734
--- /dev/null
+++ b/docker/docker-compose-freqai.yml
@@ -0,0 +1,35 @@
+---
+services:
+  freqtrade:
+    image: freqtradeorg/freqtrade:stable_freqaitorch
+    # # Enable GPU Image and GPU Resources
+    # # Make sure to uncomment the whole deploy section
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: 1
+    #           capabilities: [gpu]
+
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: unless-stopped
+    container_name: freqtrade
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
+    # for more information.
+    ports:
+      - "127.0.0.1:8080:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
+      --config /freqtrade/user_data/config.json
+      --freqaimodel XGBoostRegressor
+      --strategy FreqaiExampleStrategy
diff --git a/docker/docker-compose-jupyter.yml b/docker/docker-compose-jupyter.yml
new file mode 100644
index 000000000..e8d3629c7
--- /dev/null
+++ b/docker/docker-compose-jupyter.yml
@@ -0,0 +1,15 @@
+---
+services:
+  ft_jupyterlab:
+    build:
+      context: ..
+      dockerfile: docker/Dockerfile.jupyter
+    restart: unless-stopped
+    # container_name: freqtrade
+    ports:
+      - "127.0.0.1:8888:8888"
+    volumes:
+      - "../user_data:/freqtrade/user_data"
+    # Default command used when running `docker compose up`
+    command: >
+      jupyter lab --port=8888 --ip 0.0.0.0 --allow-root
diff --git a/docs/advanced-backtesting.md b/docs/advanced-backtesting.md
new file mode 100644
index 000000000..40584a656
--- /dev/null
+++ b/docs/advanced-backtesting.md
@@ -0,0 +1,218 @@
+# Advanced Backtesting Analysis
+
+## Analyze the buy/entry and sell/exit tags
+
+It can be helpful to understand how a strategy behaves according to the buy/entry tags used to
+mark up different buy conditions. You might want to see more complex statistics about each buy and
+sell condition above those provided by the default backtesting output. You may also want to
+determine indicator values on the signal candle that resulted in a trade opening.
+
+!!! Note
+    The following buy reason analysis is only available for backtesting, *not hyperopt*.
+
+We need to run backtesting with the `--export` option set to `signals` to enable the exporting of
+signals **and** trades:
+
+``` bash
+freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals
+```
+
+This will tell freqtrade to output a pickled dictionary of strategy, pairs and corresponding
+DataFrame of the candles that resulted in entry and exit signals.
+Depending on how many entries your strategy makes, this file may get quite large, so periodically check your `user_data/backtest_results` folder to delete old exports.
+
+Before running your next backtest, make sure you either delete your old backtest results or run
+backtesting with the `--cache none` option to make sure no cached results are used.
+
+If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl` and `backtest-result-{timestamp}_exited.pkl` files in the `user_data/backtest_results` folder.
+
+To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-analysis` command
+with `--analysis-groups` option provided with space-separated arguments:
+
+``` bash
+freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
+```
+
+This command will read from the last backtesting results. The `--analysis-groups` option is
+used to specify the various tabular outputs showing the profit of each group or trade,
+ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
+
+* 0: overall winrate and profit summary by enter_tag
+* 1: profit summaries grouped by enter_tag
+* 2: profit summaries grouped by enter_tag and exit_tag
+* 3: profit summaries grouped by pair and enter_tag
+* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
+* 5: profit summaries grouped by exit_tag
+
+More options are available by running with the `-h` option.
+
+### Using export-filename
+
+Normally, `backtesting-analysis` uses the latest backtest results, but if you wanted to go
+back to a previous backtest output, you need to supply the `--export-filename` option.
+You can supply the same parameter to `backtest-analysis` with the name of the final backtest
+output file. This allows you to keep historical versions of backtest results and re-analyse
+them at a later date:
+
+``` bash
+freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals --export-filename=/tmp/mystrat_backtest.json
+```
+
+You should see some output similar to below in the logs with the name of the timestamped
+filename that was exported:
+
+```
+2022-06-14 16:28:32,698 - freqtrade.misc - INFO - dumping json to "/tmp/mystrat_backtest-2022-06-14_16-28-32.json"
+```
+
+You can then use that filename in `backtesting-analysis`:
+
+```
+freqtrade backtesting-analysis -c <config.json> --export-filename=/tmp/mystrat_backtest-2022-06-14_16-28-32.json
+```
+
+### Tuning the buy tags and sell tags to display
+
+To show only certain buy and sell tags in the displayed output, use the following two options:
+
+```
+--enter-reason-list : Space-separated list of enter signals to analyse. Default: "all"
+--exit-reason-list : Space-separated list of exit signals to analyse. Default: "all"
+```
+
+For example:
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_tag_a stop_loss
+```
+
+### Outputting signal candle indicators
+
+The real power of `freqtrade backtesting-analysis` comes from the ability to print out the indicator
+values present on signal candles to allow fine-grained investigation and tuning of buy signal
+indicators. To print out a column for a given set of indicators, use the `--indicator-list`
+option:
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_tag_a stop_loss --indicator-list rsi rsi_1h bb_lowerband ema_9 macd macdsignal
+```
+
+The indicators have to be present in your strategy's main DataFrame (either for your main
+timeframe or for informative timeframes) otherwise they will simply be ignored in the script
+output.
+
+!!! Note "Indicator List"
+    The indicator values will be displayed for both entry and exit points. If `--indicator-list all` is specified, 
+    only the indicators at the entry point will be shown to avoid excessively large lists, which could occur depending on the strategy.
+
+There are a range of candle and trade-related fields that are included in the analysis so are 
+automatically accessible by including them on the indicator-list, and these include:
+
+- **open_date     :** trade open datetime
+- **close_date    :** trade close datetime
+- **min_rate      :** minimum price seen throughout the position
+- **max_rate      :** maximum price seen throughout the position
+- **open          :** signal candle open price
+- **close         :** signal candle close price
+- **high          :** signal candle high price
+- **low           :** signal candle low price
+- **volume        :** signal candle volume
+- **profit_ratio  :** trade profit ratio
+- **profit_abs    :** absolute profit return of the trade 
+
+#### Sample Output for Indicator Values
+
+```bash
+freqtrade backtesting-analysis -c user_data/config.json --analysis-groups 0 --indicator-list chikou_span tenkan_sen 
+```
+
+In this example,
+we aim to display the `chikou_span` and `tenkan_sen` indicator values at both the entry and exit points of trades.
+
+A sample output for indicators might look like this:
+
+| pair      | open_date                 | enter_reason | exit_reason | chikou_span (entry) | tenkan_sen (entry) | chikou_span (exit) | tenkan_sen (exit) |
+|-----------|---------------------------|--------------|-------------|---------------------|--------------------|--------------------|-------------------|
+| DOGE/USDT | 2024-07-06 00:35:00+00:00 |              | exit_signal | 0.105               | 0.106              | 0.105              | 0.107             |
+| BTC/USDT  | 2024-08-05 14:20:00+00:00 |              | roi         | 54643.440           | 51696.400          | 54386.000          | 52072.010         |
+
+As shown in the table, `chikou_span (entry)` represents the indicator value at the time of trade entry, 
+while `chikou_span (exit)` reflects its value at the time of exit. 
+This detailed view of indicator values enhances the analysis.
+
+The `(entry)` and `(exit)` suffixes are added to indicators
+to distinguish the values at the entry and exit points of the trade.
+
+!!! Note "Trade-wide Indicators"
+    Certain trade-wide indicators do not have the `(entry)` or `(exit)` suffix. These indicators include: `pair`, `stake_amount`, 
+    `max_stake_amount`, `amount`, `open_date`, `close_date`, `open_rate`, `close_rate`, `fee_open`, `fee_close`, `trade_duration`, 
+    `profit_ratio`, `profit_abs`, `exit_reason`,`initial_stop_loss_abs`, `initial_stop_loss_ratio`, `stop_loss_abs`, `stop_loss_ratio`, 
+    `min_rate`, `max_rate`, `is_open`, `enter_tag`, `leverage`, `is_short`, `open_timestamp`, `close_timestamp` and `orders`
+
+#### Filtering Indicators Based on Entry or Exit Signals
+
+The `--indicator-list` option, by default, displays indicator values for both entry and exit signals. To filter the indicator values exclusively for entry signals, you can use the `--entry-only` argument. Similarly, to display indicator values only at exit signals, use the `--exit-only` argument.
+
+Example: Display indicator values at entry signals:
+
+```bash
+freqtrade backtesting-analysis -c user_data/config.json --analysis-groups 0 --indicator-list chikou_span tenkan_sen --entry-only
+```
+
+Example: Display indicator values at exit signals:
+
+```bash
+freqtrade backtesting-analysis -c user_data/config.json --analysis-groups 0 --indicator-list chikou_span tenkan_sen --exit-only
+```
+
+!!! note 
+    When using these filters, the indicator names will not be suffixed with `(entry)` or `(exit)`.
+
+### Filtering the trade output by date
+
+To show only trades between dates within your backtested timerange, supply the usual `timerange` option in `YYYYMMDD-[YYYYMMDD]` format:
+
+```
+--timerange : Timerange to filter output trades, start date inclusive, end date exclusive. e.g. 20220101-20221231
+```
+
+For example, if your backtest timerange was `20220101-20221231` but you only want to output trades in January:
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --timerange 20220101-20220201
+```
+
+### Printing out rejected signals
+
+Use the `--rejected-signals` option to print out rejected signals.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --rejected-signals
+```
+
+### Writing tables to CSV
+
+Some of the tabular outputs can become large, so printing them out to the terminal is not preferable.
+Use the `--analysis-to-csv` option to disable printing out of tables to standard out and write them to CSV files.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-to-csv
+```
+
+By default this will write one file per output table you specified in the `backtesting-analysis` command, e.g.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --rejected-signals --analysis-groups 0 1
+```
+
+This will write to `user_data/backtest_results`:
+
+* rejected_signals.csv
+* group_0.csv
+* group_1.csv
+
+To override where the files will be written, also specify the `--analysis-csv-path` option.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --analysis-csv-path another/data/path/
+```
diff --git a/docs/advanced-hyperopt.md b/docs/advanced-hyperopt.md
new file mode 100644
index 000000000..b12d8f5b6
--- /dev/null
+++ b/docs/advanced-hyperopt.md
@@ -0,0 +1,235 @@
+# Advanced Hyperopt
+
+This page explains some advanced Hyperopt topics that may require higher
+coding skills and Python knowledge than creation of an ordinal hyperoptimization
+class.
+
+## Creating and using a custom loss function
+
+To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
+For the sample below, you then need to add the command line parameter `--hyperopt-loss SuperDuperHyperOptLoss` to your hyperopt call so this function is being used.
+
+A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in [userdata/hyperopts](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py).
+
+``` python
+from datetime import datetime
+from typing import Any, Dict
+
+from pandas import DataFrame
+
+from freqtrade.constants import Config
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+TARGET_TRADES = 600
+EXPECTED_MAX_PROFIT = 3.0
+MAX_ACCEPTED_TRADE_DURATION = 300
+
+class SuperDuperHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the default loss function for hyperopt
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        *,
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        config: Config,
+        processed: dict[str, DataFrame],
+        backtest_stats: dict[str, Any],
+        starting_balance: float,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function, returns smaller number for better results
+        This is the legacy algorithm (used until now in freqtrade).
+        Weights are distributed as follows:
+        * 0.4 to trade duration
+        * 0.25: Avoiding trade loss
+        * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
+        """
+        total_profit = results['profit_ratio'].sum()
+        trade_duration = results['trade_duration'].mean()
+
+        trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
+        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
+        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
+        result = trade_loss + profit_loss + duration_loss
+        return result
+```
+
+Currently, the arguments are:
+
+* `results`: DataFrame containing the resulting trades.
+    The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):  
+    `pair, profit_ratio, profit_abs, open_date, open_rate, fee_open, close_date, close_rate, fee_close, amount, trade_duration, is_open, exit_reason, stake_amount, min_rate, max_rate, stop_loss_ratio, stop_loss_abs`
+* `trade_count`: Amount of trades (identical to `len(results)`)
+* `min_date`: Start date of the timerange used
+* `min_date`: End date of the timerange used
+* `config`: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).
+* `processed`: Dict of Dataframes with the pair as keys containing the data used for backtesting.
+* `backtest_stats`: Backtesting statistics using the same format as the backtesting file "strategy" substructure. Available fields can be seen in `generate_strategy_stats()` in `optimize_reports.py`.
+* `starting_balance`: Starting balance used for backtesting.
+
+This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
+
+!!! Note
+    This function is called once per epoch - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
+
+!!! Note "`*args` and `**kwargs`"
+    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface in the future.
+
+## Overriding pre-defined spaces
+
+To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`, `max_open_trades_space`), define a nested class called Hyperopt and define the required spaces as follows:
+
+```python
+from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
+
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        # Define a custom stoploss space.
+        def stoploss_space():
+            return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]
+
+        # Define custom ROI space
+        def roi_space() -> List[Dimension]:
+            return [
+                Integer(10, 120, name='roi_t1'),
+                Integer(10, 60, name='roi_t2'),
+                Integer(10, 40, name='roi_t3'),
+                SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
+                SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
+                SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
+            ]
+
+        def generate_roi_table(params: Dict) -> dict[int, float]:
+
+            roi_table = {}
+            roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
+            roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
+            roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
+            roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
+
+            return roi_table
+
+        def trailing_space() -> List[Dimension]:
+            # All parameters here are mandatory, you can only modify their type or the range.
+            return [
+                # Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times.
+                Categorical([True], name='trailing_stop'),
+
+                SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
+                # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
+                # so this intermediate parameter is used as the value of the difference between
+                # them. The value of the 'trailing_stop_positive_offset' is constructed in the
+                # generate_trailing_params() method.
+                # This is similar to the hyperspace dimensions used for constructing the ROI tables.
+                SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
+
+                Categorical([True, False], name='trailing_only_offset_is_reached'),
+        ]
+
+        # Define a custom max_open_trades space
+        def max_open_trades_space(self) -> List[Dimension]:
+            return [
+                Integer(-1, 10, name='max_open_trades'),
+            ]
+```
+
+!!! Note
+    All overrides are optional and can be mixed/matched as necessary.
+
+### Dynamic parameters
+
+Parameters can also be defined dynamically, but must be available to the instance once the [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
+
+``` python
+
+class MyAwesomeStrategy(IStrategy):
+
+    def bot_start(self, **kwargs) -> None:
+        self.buy_adx = IntParameter(20, 30, default=30, optimize=True)
+
+    # ...
+```
+
+!!! Warning
+    Parameters created this way will not show up in the `list-strategies` parameter count.
+
+### Overriding Base estimator
+
+You can define your own estimator for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
+            return "RF"
+
+```
+
+Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the [scikit-optimize documentation](https://scikit-optimize.github.io/)), or "an instance of a class that inherits from `RegressorMixin` (from sklearn) and where the `predict` method has an optional `return_std` argument, which returns `std(Y | x)` along with `E[Y | x]`".
+
+Some research will be necessary to find additional Regressors.
+
+Example for `ExtraTreesRegressor` ("ET") with additional parameters:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
+            from skopt.learning import ExtraTreesRegressor
+            # Corresponds to "ET" - but allows additional parameters.
+            return ExtraTreesRegressor(n_estimators=100)
+
+```
+
+The `dimensions` parameter is the list of `skopt.space.Dimension` objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the `skopt.learning.GaussianProcessRegressor` estimator. Here's an example:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
+            from skopt.utils import cook_estimator
+            from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
+            kernel_bounds = (0.0001, 10000)
+            kernel = (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
+            )
+            kernel += (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
+            )
+
+            return cook_estimator("GP", space=dimensions, kernel=kernel, n_restarts_optimizer=2)
+```
+
+!!! Note
+    While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
+    If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.
+
+## Space options
+
+For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:
+
+* `Categorical` - Pick from a list of categories (e.g. `Categorical(['a', 'b', 'c'], name="cat")`)
+* `Integer` - Pick from a range of whole numbers (e.g. `Integer(1, 10, name='rsi')`)
+* `SKDecimal` - Pick from a range of decimal numbers with limited precision (e.g. `SKDecimal(0.1, 0.5, decimals=3, name='adx')`). *Available only with freqtrade*.
+* `Real` - Pick from a range of decimal numbers with full precision (e.g. `Real(0.1, 0.5, name='adx')`
+
+You can import all of these from `freqtrade.optimize.space`, although `Categorical`, `Integer` and `Real` are only aliases for their corresponding scikit-optimize Spaces. `SKDecimal` is provided by freqtrade for faster optimizations.
+
+``` python
+from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real  # noqa
+```
+
+!!! Hint "SKDecimal vs. Real"
+    We recommend to use `SKDecimal` instead of the `Real` space in almost all cases. While the Real space provides full accuracy (up to ~16 decimal places) - this precision is rarely needed, and leads to unnecessary long hyperopt times.
+
+    Assuming the definition of a rather small space (`SKDecimal(0.10, 0.15, decimals=2, name='xxx')`) - SKDecimal will have 5 possibilities (`[0.10, 0.11, 0.12, 0.13, 0.14, 0.15]`).
+
+    A corresponding real space `Real(0.10, 0.15 name='xxx')`  on the other hand has an almost unlimited number of possibilities (`[0.10, 0.010000000001, 0.010000000002, ... 0.014999999999, 0.01500000000]`).
diff --git a/docs/advanced-orderflow.md b/docs/advanced-orderflow.md
new file mode 100644
index 000000000..4561b35a3
--- /dev/null
+++ b/docs/advanced-orderflow.md
@@ -0,0 +1,153 @@
+# Orderflow data
+
+This guide walks you through utilizing public trade data for advanced orderflow analysis in Freqtrade.
+
+!!! Warning "Experimental Feature"
+    The orderflow feature is currently in beta and may be subject to changes in future releases. Please report any issues or feedback on the [Freqtrade GitHub repository](https://github.com/freqtrade/freqtrade/issues).
+    It's also currently not been tested with freqAI - and combining these two features is considered out of scope at this point.
+
+!!! Warning "Performance"
+    Orderflow requires raw trades data. This data is rather large, and can cause a slow initial startup, when freqtrade needs to download the trades data for the last X candles. Additionally, enabling this feature will cause increased memory usage. Please ensure to have sufficient resources available.
+
+## Getting Started
+
+### Enable Public Trades
+
+In your `config.json` file, set the `use_public_trades` option to true under the `exchange` section.
+
+```json
+"exchange": {
+   ...
+   "use_public_trades": true,
+}
+```
+
+### Configure Orderflow Processing
+
+Define your desired settings for orderflow processing within the orderflow section of config.json. Here, you can adjust factors like:
+
+- `cache_size`: How many previous orderflow candles are saved into cache instead of calculated every new candle
+- `max_candles`: Filter how many candles would you like to get trades data for.
+- `scale`: This controls the price bin size for the footprint chart.
+- `stacked_imbalance_range`: Defines the minimum consecutive imbalanced price levels required for consideration.
+- `imbalance_volume`: Filters out imbalances with volume below this threshold.
+- `imbalance_ratio`: Filters out imbalances with a ratio (difference between ask and bid volume) lower than this value.
+
+```json
+"orderflow": {
+    "cache_size": 1000, 
+    "max_candles": 1500, 
+    "scale": 0.5, 
+    "stacked_imbalance_range": 3, //  needs at least this amount of imbalance next to each other
+    "imbalance_volume": 1, //  filters out below
+    "imbalance_ratio": 3 //  filters out ratio lower than
+  },
+```
+
+## Downloading Trade Data for Backtesting
+
+To download historical trade data for backtesting, use the --dl-trades flag with the freqtrade download-data command.
+
+```bash
+freqtrade download-data -p BTC/USDT:USDT --timerange 20230101- --trading-mode futures --timeframes 5m --dl-trades
+```
+
+!!! Warning "Data availability"
+    Not all exchanges provide public trade data. For supported exchanges, freqtrade will warn you if public trade data is not available if you start downloading data with the `--dl-trades` flag.
+
+## Accessing Orderflow Data
+
+Once activated, several new columns become available in your dataframe:
+
+``` python
+
+dataframe["trades"] # Contains information about each individual trade.
+dataframe["orderflow"] # Represents a footprint chart dict (see below)
+dataframe["imbalances"] # Contains information about imbalances in the order flow.
+dataframe["bid"] # Total bid volume 
+dataframe["ask"] # Total ask volume
+dataframe["delta"] # Difference between ask and bid volume.
+dataframe["min_delta"] # Minimum delta within the candle
+dataframe["max_delta"] # Maximum delta within the candle
+dataframe["total_trades"] # Total number of trades
+dataframe["stacked_imbalances_bid"] # List of price levels of stacked bid imbalance range beginnings
+dataframe["stacked_imbalances_ask"] # List of price levels of stacked ask imbalance range beginnings
+```
+
+You can access these columns in your strategy code for further analysis. Here's an example:
+
+``` python
+def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    # Calculating cumulative delta
+    dataframe["cum_delta"] = cumulative_delta(dataframe["delta"])
+    # Accessing total trades
+    total_trades = dataframe["total_trades"]
+    ...
+
+def cumulative_delta(delta: Series):
+    cumdelta = delta.cumsum()
+    return cumdelta
+
+```
+
+### Footprint chart (`dataframe["orderflow"]`)
+
+This column provides a detailed breakdown of buy and sell orders at different price levels, offering valuable insights into order flow dynamics. The `scale` parameter in your configuration determines the price bin size for this representation
+
+The `orderflow` column contains a dict with the following structure:
+
+``` output
+{
+    "price": {
+        "bid_amount": 0.0,
+        "ask_amount": 0.0,
+        "bid": 0,
+        "ask": 0,
+        "delta": 0.0,
+        "total_volume": 0.0,
+        "total_trades": 0
+    }
+}
+```
+
+#### Orderflow column explanation
+
+- key: Price bin - binned at `scale` intervals
+- `bid_amount`: Total volume bought at each price level.
+- `ask_amount`: Total volume sold at each price level.
+- `bid`: Number of buy orders at each price level.
+- `ask`: Number of sell orders at each price level.
+- `delta`: Difference between ask and bid volume at each price level.
+- `total_volume`: Total volume (ask amount + bid amount) at each price level.
+- `total_trades`: Total number of trades (ask + bid) at each price level.
+
+By leveraging these features, you can gain valuable insights into market sentiment and potential trading opportunities based on order flow analysis.
+
+### Raw trades data (`dataframe["trades"]`)
+
+List with the individual trades that occurred during the candle. This data can be used for more granular analysis of order flow dynamics.
+
+Each individual entry contains a dict with the following keys:
+
+- `timestamp`: Timestamp of the trade.
+- `date`: Date of the trade.
+- `price`: Price of the trade.
+- `amount`: Volume of the trade.
+- `side`: Buy or sell.
+- `id`: Unique identifier for the trade.
+- `cost`: Total cost of the trade (price * amount).
+
+### Imbalances (`dataframe["imbalances"]`)
+
+This column provides a dict with information about imbalances in the order flow. An imbalance occurs when there is a significant difference between the ask and bid volume at a given price level.
+
+Each row looks as follows - with price as index, and the corresponding bid and ask imbalance values as columns
+
+``` output
+{
+    "price": {
+        "bid_imbalance": False,
+        "ask_imbalance": False
+    }
+}
+```
diff --git a/docs/advanced-setup.md b/docs/advanced-setup.md
new file mode 100644
index 000000000..ec25cb616
--- /dev/null
+++ b/docs/advanced-setup.md
@@ -0,0 +1,431 @@
+# Advanced Post-installation Tasks
+
+This page explains some advanced tasks and configuration options that can be performed after the bot installation and may be uselful in some environments.
+
+If you do not know what things mentioned here mean, you probably do not need it.
+
+## Running multiple instances of Freqtrade
+
+This section will show you how to run multiple bots at the same time, on the same machine.
+
+### Things to consider
+
+* Use different database files.
+* Use different Telegram bots (requires multiple different configuration files; applies only when Telegram is enabled).
+* Use different ports (applies only when Freqtrade REST API webserver is enabled).
+
+### Different database files
+
+In order to keep track of your trades, profits, etc., freqtrade is using a SQLite database where it stores various types of information such as the trades you performed in the past and the current position(s) you are holding at any time. This allows you to keep track of your profits, but most importantly, keep track of ongoing activity if the bot process would be restarted or would be terminated unexpectedly.
+
+Freqtrade will, by default, use separate database files for dry-run and live bots (this assumes no database-url is given in either configuration nor via command line argument).
+For live trading mode, the default database will be `tradesv3.sqlite` and for dry-run it will be `tradesv3.dryrun.sqlite`.
+
+The optional argument to the trade command used to specify the path of these files is `--db-url`, which requires a valid SQLAlchemy url.
+So when you are starting a bot with only the config and strategy arguments in dry-run mode, the following 2 commands would have the same outcome.
+
+``` bash
+freqtrade trade -c MyConfig.json -s MyStrategy
+# is equivalent to
+freqtrade trade -c MyConfig.json -s MyStrategy --db-url sqlite:///tradesv3.dryrun.sqlite
+```
+
+It means that if you are running the trade command in two different terminals, for example to test your strategy both for trades in USDT and in another instance for trades in BTC, you will have to run them with different databases.
+
+If you specify the URL of a database which does not exist, freqtrade will create one with the name you specified. So to test your custom strategy with BTC and USDT stake currencies, you could use the following commands (in 2 separate terminals):
+
+``` bash
+# Terminal 1:
+freqtrade trade -c MyConfigBTC.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesBTC.dryrun.sqlite
+# Terminal 2:
+freqtrade trade -c MyConfigUSDT.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesUSDT.dryrun.sqlite
+```
+
+Conversely, if you wish to do the same thing in production mode, you will also have to create at least one new database (in addition to the default one) and specify the path to the "live" databases, for example:
+
+``` bash
+# Terminal 1:
+freqtrade trade -c MyConfigBTC.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesBTC.live.sqlite
+# Terminal 2:
+freqtrade trade -c MyConfigUSDT.json -s MyCustomStrategy --db-url sqlite:///user_data/tradesUSDT.live.sqlite
+```
+
+For more information regarding usage of the sqlite databases, for example to manually enter or remove trades, please refer to the [SQL Cheatsheet](sql_cheatsheet.md).
+
+### Multiple instances using docker
+
+To run multiple instances of freqtrade using docker you will need to edit the docker-compose.yml file and add all the instances you want as separate services. Remember, you can separate your configuration into multiple files, so it's a good idea to think about making them modular, then if you need to edit something common to all bots, you can do that in a single config file. 
+``` yml
+---
+version: '3'
+services:
+  freqtrade1:
+    image: freqtradeorg/freqtrade:stable
+    # image: freqtradeorg/freqtrade:develop
+    # Use plotting image
+    # image: freqtradeorg/freqtrade:develop_plot
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: always
+    container_name: freqtrade1
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
+    # before enabling this.
+     ports:
+     - "127.0.0.1:8080:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade1.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3_freqtrade1.sqlite
+      --config /freqtrade/user_data/config.json
+      --config /freqtrade/user_data/config.freqtrade1.json
+      --strategy SampleStrategy
+  
+  freqtrade2:
+    image: freqtradeorg/freqtrade:stable
+    # image: freqtradeorg/freqtrade:develop
+    # Use plotting image
+    # image: freqtradeorg/freqtrade:develop_plot
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: always
+    container_name: freqtrade2
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
+    # before enabling this.
+    ports:
+      - "127.0.0.1:8081:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade2.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3_freqtrade2.sqlite
+      --config /freqtrade/user_data/config.json
+      --config /freqtrade/user_data/config.freqtrade2.json
+      --strategy SampleStrategy
+
+```
+
+You can use whatever naming convention you want, freqtrade1 and 2 are arbitrary. Note, that you will need to use different database files, port mappings and telegram configurations for each instance, as mentioned above. 
+
+## Use a different database system
+
+Freqtrade is using SQLAlchemy, which supports multiple different database systems. As such, a multitude of database systems should be supported.
+Freqtrade does not depend or install any additional database driver. Please refer to the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls) on installation instructions for the respective database systems.
+
+The following systems have been tested and are known to work with freqtrade:
+
+* sqlite (default)
+* PostgreSQL
+* MariaDB
+
+!!! Warning
+    By using one of the below database systems, you acknowledge that you know how to manage such a system. The freqtrade team will not provide any support with setup or maintenance (or backups) of the below database systems.
+
+### PostgreSQL
+
+Installation:
+`pip install psycopg2-binary`
+
+Usage:
+`... --db-url postgresql+psycopg2://<username>:<password>@localhost:5432/<database>`
+
+Freqtrade will automatically create the tables necessary upon startup.
+
+If you're running different instances of Freqtrade, you must either setup one database per Instance or use different users / schemas for your connections.
+
+### MariaDB / MySQL
+
+Freqtrade supports MariaDB by using SQLAlchemy, which supports multiple different database systems.
+
+Installation:
+`pip install pymysql`
+
+Usage:
+`... --db-url mysql+pymysql://<username>:<password>@localhost:3306/<database>`
+
+
+
+## Configure the bot running as a systemd service
+
+Copy the `freqtrade.service` file to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
+
+!!! Note
+    Certain systems (like Raspbian) don't load service unit files from the user directory. In this case, copy `freqtrade.service` into `/etc/systemd/user/` (requires superuser permissions).
+
+After that you can start the daemon with:
+
+```bash
+systemctl --user start freqtrade
+```
+
+For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
+
+```bash
+sudo loginctl enable-linger "$USER"
+```
+
+If you run the bot as a service, you can use systemd service manager as a software watchdog monitoring freqtrade bot 
+state and restarting it in the case of failures. If the `internals.sd_notify` parameter is set to true in the 
+configuration or the `--sd-notify` command line option is used, the bot will send keep-alive ping messages to systemd 
+using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running, Paused or Stopped) 
+when it changes. 
+
+The `freqtrade.service.watchdog` file contains an example of the service unit configuration file which uses systemd 
+as the watchdog.
+
+!!! Note
+    The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a Docker container.
+
+## Advanced Logging
+
+Freqtrade uses the default logging module provided by python.
+Python allows for extensive [logging configuration](https://docs.python.org/3/library/logging.config.html#logging.config.dictConfig) in this regards - way more than what can be covered here.
+
+Default logging (Colored terminal output) is setup by default if no `log_config` is provided.
+Using `--logfile logfile.log` will enable the RotatingFileHandler.
+If you're not content with the log format - or with the default settings provided for the RotatingFileHandler, you can customize logging to your liking.
+
+The default configuration looks roughly like the below - with the file handler being provided - but not enabled.
+
+``` json hl_lines="5-7 13-16 27"
+{
+  "log_config": {
+      "version": 1,
+      "formatters": {
+          "basic": {
+              "format": "%(message)s"
+          },
+          "standard": {
+              "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+          }
+      },
+      "handlers": {
+          "console": {
+              "class": "freqtrade.loggers.ft_rich_handler.FtRichHandler",
+              "formatter": "basic"
+          },
+          "file": {
+              "class": "logging.handlers.RotatingFileHandler",
+              "formatter": "standard",
+              // "filename": "someRandomLogFile.log",
+              "maxBytes": 10485760,
+              "backupCount": 10
+          }
+      },
+      "root": {
+          "handlers": [
+              "console",
+              // "file"
+          ],
+          "level": "INFO",
+      }
+  }
+}
+```
+
+!!! Note "highlighted lines"
+    Highlighted lines in the above code-block define the Rich handler and belong together.
+    The formatter "standard" and "file" will belong to the FileHandler.
+
+Each handler must use one of the defined formatters (by name) - and it's class must be available and a valid logging class.
+To actually use a handler - it must be in the "handlers" section inside the "root" segment.
+If this section is left out, freqtrade will provide no output (in the non-configured handler, anyway).
+
+!!! Tip "Explicit log configuration"
+    We recommend to extract the logging configuration from your main configuration, and provide it to your bot via [multiple configuration files](configuration.md#multiple-configuration-files) functionality. This will avoid unnecessary code duplication.
+
+---
+
+On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfile` command line option can be used for this.
+
+### Logging to syslog
+
+To send Freqtrade log messages to a local or remote `syslog` service use the `"log_config"` setup option to configure logging.
+
+``` json
+{
+  // ...
+  "log_config": {
+    "version": 1,
+    "formatters": {
+      "syslog_fmt": {
+        "format": "%(name)s - %(levelname)s - %(message)s"
+      }
+    },
+    "handlers": {
+      // Other handlers? 
+      "syslog": {
+         "class": "logging.handlers.SysLogHandler",
+          "formatter": "syslog_fmt",
+          // Use one of the other options above as address instead? 
+          "address": "/dev/log"
+      }
+    },
+    "root": {
+      "handlers": [
+        // other handlers
+        "syslog",
+        
+      ]
+    }
+
+  }
+}
+```
+
+[Additional log-handlers](#advanced-logging) may need to be configured to for example also have log output in the console.
+
+#### Syslog usage
+
+Log messages are send to `syslog` with the `user` facility. So you can see them with the following commands:
+
+* `tail -f /var/log/user`, or
+* install a comprehensive graphical viewer (for instance, 'Log File Viewer' for Ubuntu).
+
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both syslog or journald can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+
+For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
+
+```
+if $programname startswith "freqtrade" then -/var/log/freqtrade.log
+```
+
+to one of the rsyslog configuration files, for example at the end of the `/etc/rsyslog.d/50-default.conf`.
+
+For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduce the number of repeating messages. For instance, multiple bot Heartbeat messages will be reduced to a single message when nothing else happens with the bot. To achieve this, set in `/etc/rsyslog.conf`:
+
+```
+# Filter duplicated messages
+$RepeatedMsgReduction on
+```
+
+#### Syslog addressing
+
+The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
+
+
+So, the following are the examples of possible addresses:
+
+* `"address": "/dev/log"` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
+* `"address": "/var/run/syslog"` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
+* `"address": "localhost:514"` -- log to local syslog using UDP socket, if it listens on port 514.
+* `"address": "<ip>:514"` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
+
+
+??? Info "Deprecated - configure syslog via command line"
+
+  `--logfile syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
+
+  The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
+
+  So, the following are the examples of possible usages:
+
+  * `--logfile syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
+  * `--logfile syslog` -- same as above, the shortcut for `/dev/log`.
+  * `--logfile syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
+  * `--logfile syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
+  * `--logfile syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
+
+### Logging to journald
+
+This needs the `cysystemd` python package installed as dependency (`pip install cysystemd`), which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
+
+To send Freqtrade log messages to `journald` system service, add the following configuration snippet to your configuration.
+
+``` json
+{
+  // ...
+  "log_config": {
+    "version": 1,
+    "formatters": {
+      "journald_fmt": {
+        "format": "%(name)s - %(levelname)s - %(message)s"
+      }
+    },
+    "handlers": {
+      // Other handlers? 
+      "journald": {
+         "class": "cysystemd.journal.JournaldLogHandler",
+          "formatter": "journald_fmt",
+      }
+    },
+    "root": {
+      "handlers": [
+        // .. 
+        "journald",
+        
+      ]
+    }
+
+  }
+}
+```
+
+[Additional log-handlers](#advanced-logging) may need to be configured to for example also have log output in the console.
+
+Log messages are send to `journald` with the `user` facility. So you can see them with the following commands:
+
+* `journalctl -f` -- shows Freqtrade log messages sent to `journald` along with other log messages fetched by `journald`.
+* `journalctl -f -u freqtrade.service` -- this command can be used when the bot is run as a `systemd` service.
+
+There are many other options in the `journalctl` utility to filter the messages, see manual pages for this utility.
+
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+
+??? Info "Deprecated - configure journald via command line"
+    To send Freqtrade log messages to `journald` system service use the `--logfile` command line option with the value in the following format:
+
+    `--logfile journald` -- send log messages to `journald`.
+
+### Log format as JSON
+
+You can also configure the default output stream to use JSON format instead.
+The "fmt_dict" attribute defines the keys for the json output - as well as the [python logging LogRecord attributes](https://docs.python.org/3/library/logging.html#logrecord-attributes).
+
+The below configuration will change the default output to JSON. The same formatter could however also be used in combination with the `RotatingFileHandler`.
+We recommend to keep one format in human readable form.
+
+``` json
+{
+  // ...
+  "log_config": {
+    "version": 1,
+    "formatters": {
+       "json": {
+          "()": "freqtrade.loggers.json_formatter.JsonFormatter",
+          "fmt_dict": {
+              "timestamp": "asctime",
+              "level": "levelname",
+              "logger": "name",
+              "message": "message"
+          }
+      }
+    },
+    "handlers": {
+      // Other handlers? 
+      "jsonStream": {
+          "class": "logging.StreamHandler",
+          "formatter": "json"
+      }
+    },
+    "root": {
+      "handlers": [
+        // .. 
+        "jsonStream",
+        
+      ]
+    }
+
+  }
+}
+```
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+   xmlns:cc="http://creativecommons.org/ns#"
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+   xmlns:svg="http://www.w3.org/2000/svg"
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+   xmlns:xlink="http://www.w3.org/1999/xlink"
+   id="svg1557"
+   version="1.1"
+   viewBox="0 0 378.20105 327.63284"
+   height="327.63284mm"
+   width="378.20105mm">
+  <defs
+     id="defs1551">
+    <linearGradient
+       id="linearGradient1933">
+      <stop
+         style="stop-color:#800080;stop-opacity:1"
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+      <stop
+         id="stop1138"
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+         offset="1"
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+       gradientUnits="userSpaceOnUse"
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diff --git a/docs/backtesting.md b/docs/backtesting.md
index b8da09226..4d91b3ca6 100644
--- a/docs/backtesting.md
+++ b/docs/backtesting.md
@@ -1,152 +1,277 @@
 # Backtesting
-This page explains how to validate your strategy performance by using 
-Backtesting.
 
-## Table of Contents
-- [Test your strategy with Backtesting](#test-your-strategy-with-backtesting)
-- [Understand the backtesting result](#understand-the-backtesting-result)
+This page explains how to validate your strategy performance by using Backtesting.
+
+Backtesting requires historic data to be available.
+To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
+
+## Backtesting command reference
+
+--8<-- "commands/backtesting.md"
 
 ## Test your strategy with Backtesting
-Now you have good Buy and Sell strategies, you want to test it against
-real data. This is what we call 
-[backtesting](https://en.wikipedia.org/wiki/Backtesting).
+
+Now you have good Entry and exit strategies and some historic data, you want to test it against
+real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
+
+Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHLCV) data from `user_data/data/<exchange>` by default.
+If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
+For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
+
+The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
+
+All profit calculations include fees, and freqtrade will use the exchange's default fees for the calculation.
+
+!!! Warning "Using dynamic pairlists for backtesting"
+    Using dynamic pairlists is possible (not all of the handlers are allowed to be used in backtest mode), however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
+    Also, when using pairlists other than StaticPairlist, reproducibility of backtesting-results cannot be guaranteed.
+    Please read the [pairlists documentation](plugins.md#pairlists) for more information.
+
+    To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
+
+!!! Note
+    By default, Freqtrade will export backtesting results to `user_data/backtest_results`.
+    The exported trades can be used for [further analysis](#further-backtest-result-analysis) or can be used by the [plotting sub-command](plotting.md#plot-price-and-indicators) (`freqtrade plot-dataframe`) in the scripts directory.
 
 
-Backtesting will use the crypto-currencies (pair) from your config file
-and load static tickers located in 
-[/freqtrade/tests/testdata](https://github.com/gcarq/freqtrade/tree/develop/freqtrade/tests/testdata).  
-If the 5 min and 1 min ticker for the crypto-currencies to test is not 
-already in the `testdata` folder, backtesting will download them 
-automatically. Testdata files will not be updated until you specify it.
+### Starting balance
 
-The result of backtesting will confirm you if your bot as more chance to
-make a profit than a loss.
+Backtesting will require a starting balance, which can be provided as `--dry-run-wallet <balance>` or `--starting-balance <balance>` command line argument, or via `dry_run_wallet` configuration setting.
+This amount must be higher than `stake_amount`, otherwise the bot will not be able to simulate any trade.
 
+### Dynamic stake amount
 
-The backtesting is very easy with freqtrade.
+Backtesting supports [dynamic stake amount](configuration.md#dynamic-stake-amount) by configuring `stake_amount` as `"unlimited"`, which will split the starting balance into `max_open_trades` pieces.
+Profits from early trades will result in subsequent higher stake amounts, resulting in compounding of profits over the backtesting period.
+
+### Example backtesting commands
+
+With 5 min candle (OHLCV) data (per default)
 
-### Run a backtesting against the currencies listed in your config file
-**With 5 min tickers (Per default)**
 ```bash
-python3 ./freqtrade/main.py backtesting --realistic-simulation
+freqtrade backtesting --strategy AwesomeStrategy
 ```
 
-**With 1 min tickers**
+Where `--strategy AwesomeStrategy` / `-s AwesomeStrategy` refers to the class name of the strategy, which is within a python file in the `user_data/strategies` directory.
+
+---
+
+With 1 min candle (OHLCV) data
+
 ```bash
-python3 ./freqtrade/main.py backtesting --realistic-simulation --ticker-interval 1
+freqtrade backtesting --strategy AwesomeStrategy --timeframe 1m
 ```
 
-**Reload your testdata files**
+---
+
+Providing a custom starting balance of 1000 (in stake currency)
+
 ```bash
-python3 ./freqtrade/main.py backtesting --realistic-simulation --refresh-pairs-cached
+freqtrade backtesting --strategy AwesomeStrategy --dry-run-wallet 1000
 ```
 
-**With live data (do not alter your testdata files)**
+---
+
+Using a different on-disk historical candle (OHLCV) data source
+
+Assume you downloaded the history data from the Binance exchange and kept it in the `user_data/data/binance-20180101` directory. 
+You can then use this data for backtesting as follows:
+
 ```bash
-python3 ./freqtrade/main.py backtesting --realistic-simulation --live
+freqtrade backtesting --strategy AwesomeStrategy --datadir user_data/data/binance-20180101 
 ```
 
-**Using a different on-disk ticker-data source**
+---
+
+Comparing multiple Strategies
+
 ```bash
-python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
+freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --timeframe 5m
 ```
 
-**With a (custom) strategy file**
-```bash
-python3 ./freqtrade/main.py -s currentstrategy backtesting
-```
-Where `-s currentstrategy` refers to a filename `currentstrategy.py` in `freqtrade/user_data/strategies`
+Where `SampleStrategy1` and `AwesomeStrategy` refer to class names of strategies.
+
+---
+
+Prevent exporting trades to file
 
-**Exporting trades to file**
 ```bash
-python3 ./freqtrade/main.py backtesting --export trades
+freqtrade backtesting --strategy backtesting --export none --config config.json 
 ```
 
-**Running backtest with smaller testset**  
-Use the `--timerange` argument to change how much of the testset
-you want to use. The last N ticks/timeframes will be used.
+Only use this if you're sure you'll not want to plot or analyze your results further.
+
+---
+
+Exporting trades to file specifying a custom filename
 
-Example:
 ```bash
-python3 ./freqtrade/main.py backtesting --timerange=-200
+freqtrade backtesting --strategy backtesting --export trades --export-filename=backtest_samplestrategy.json
 ```
 
-***Advanced use of timerange***  
-Doing `--timerange=-200` will get the last 200 timeframes
-from your inputdata. You can also specify specific dates,
-or a range span indexed by start and stop.
+Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
+
+---
+
+Supplying custom fee value
+
+Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
+To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
+This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
+
+For example, if the commission fee per order is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
+
+```bash
+freqtrade backtesting --fee 0.001
+```
+
+!!! Note
+    Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
+
+---
+
+Running backtest with smaller test-set by using timerange
+
+Use the `--timerange` argument to change how much of the test-set you want to use.
+
+For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your input data.
+
+```bash
+freqtrade backtesting --timerange=20190501-
+```
+
+You can also specify particular date ranges.
 
 The full timerange specification:
-- Use last 123 tickframes of data: `--timerange=-123`
-- Use first 123 tickframes of data: `--timerange=123-`
-- Use tickframes from line 123 through 456: `--timerange=123-456`
 
-
-Incoming feature, not implemented yet:
-- `--timerange=-20180131`
--  `--timerange=20180101-`
-- `--timerange=20180101-20181231`
-
-
-**Update testdata directory**
-To update your testdata directory, or download into another testdata directory:
-```bash
-mkdir -p user_data/data/testdata-20180113
-cp freqtrade/tests/testdata/pairs.json user_data/data-20180113
-cd user_data/data-20180113
-```
-
-Possibly edit pairs.json file to include/exclude pairs
-
-```bash
-python3 freqtrade/tests/testdata/download_backtest_data.py -p pairs.json
-```
-
-The script will read your pairs.json file, and download ticker data
-into the current working directory.
-
-
-For help about backtesting usage, please refer to 
-[Backtesting commands](#backtesting-commands).
+- Use data until 2018/01/31: `--timerange=-20180131`
+- Use data since 2018/01/31: `--timerange=20180131-`
+- Use data since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
+- Use data between POSIX / epoch timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
 
 ## Understand the backtesting result
+
 The most important in the backtesting is to understand the result.
 
 A backtesting result will look like that:
+
 ```
-====================== BACKTESTING REPORT ================================
-pair        buy count    avg profit %    total profit BTC    avg duration
---------  -----------  --------------  ------------------  --------------
-BTC_ETH            56           -0.67         -0.00075455            62.3
-BTC_LTC            38           -0.48         -0.00036315            57.9
-BTC_ETC            42           -1.15         -0.00096469            67.0
-BTC_DASH           72           -0.62         -0.00089368            39.9
-BTC_ZEC            45           -0.46         -0.00041387            63.2
-BTC_XLM            24           -0.88         -0.00041846            47.7
-BTC_NXT            24            0.68          0.00031833            40.2
-BTC_POWR           35            0.98          0.00064887            45.3
-BTC_ADA            43           -0.39         -0.00032292            55.0
-BTC_XMR            40           -0.40         -0.00032181            47.4
-TOTAL             419           -0.41         -0.00348593            52.9
+================================================ BACKTESTING REPORT =================================================
+| Pair     | Trades |   Avg Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration |  Wins Draws Loss   Win%  |
+|----------+--------+----------------+------------------+----------------+--------------+--------------------------|
+| ADA/BTC  |     35 |          -0.11 |      -0.00019428 |          -1.94 | 4:35:00      |    14     0    21   40.0 |
+| ARK/BTC  |     11 |          -0.41 |      -0.00022647 |          -2.26 | 2:03:00      |     3     0     8   27.3 |
+| BTS/BTC  |     32 |           0.31 |       0.00048938 |           4.89 | 5:05:00      |    18     0    14   56.2 |
+| DASH/BTC |     13 |          -0.08 |      -0.00005343 |          -0.53 | 4:39:00      |     6     0     7   46.2 |
+| ENG/BTC  |     18 |           1.36 |       0.00122807 |          12.27 | 2:50:00      |     8     0    10   44.4 |
+| EOS/BTC  |     36 |           0.08 |       0.00015304 |           1.53 | 3:34:00      |    16     0    20   44.4 |
+| ETC/BTC  |     26 |           0.37 |       0.00047576 |           4.75 | 6:14:00      |    11     0    15   42.3 |
+| ETH/BTC  |     33 |           0.30 |       0.00049856 |           4.98 | 7:31:00      |    16     0    17   48.5 |
+| IOTA/BTC |     32 |           0.03 |       0.00005444 |           0.54 | 3:12:00      |    14     0    18   43.8 |
+| LSK/BTC  |     15 |           1.75 |       0.00131413 |          13.13 | 2:58:00      |     6     0     9   40.0 |
+| LTC/BTC  |     32 |          -0.04 |      -0.00006886 |          -0.69 | 4:49:00      |    11     0    21   34.4 |
+| NANO/BTC |     17 |           1.26 |       0.00107058 |          10.70 | 1:55:00      |    10     0     7   58.5 |
+| NEO/BTC  |     23 |           0.82 |       0.00094936 |           9.48 | 2:59:00      |    10     0    13   43.5 |
+| REQ/BTC  |      9 |           1.17 |       0.00052734 |           5.27 | 3:47:00      |     4     0     5   44.4 |
+| XLM/BTC  |     16 |           1.22 |       0.00097800 |           9.77 | 3:15:00      |     7     0     9   43.8 |
+| XMR/BTC  |     23 |          -0.18 |      -0.00020696 |          -2.07 | 5:30:00      |    12     0    11   52.2 |
+| XRP/BTC  |     35 |           0.66 |       0.00114897 |          11.48 | 3:49:00      |    12     0    23   34.3 |
+| ZEC/BTC  |     22 |          -0.46 |      -0.00050971 |          -5.09 | 2:22:00      |     7     0    15   31.8 |
+| TOTAL    |    429 |           0.36 |       0.00762792 |          76.20 | 4:12:00      |   186     0   243   43.4 |
+============================================= LEFT OPEN TRADES REPORT =============================================
+| Pair     |  Trades |   Avg Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Win Draw Loss Win% |
+|----------+---------+----------------+------------------+----------------+----------------+---------------------|
+| ADA/BTC  |       1 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |    1    0    0  100 |
+| LTC/BTC  |       1 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |    1    0    0  100 |
+| TOTAL    |       2 |           0.78 |       0.00007855 |           0.78 | 4:00:00        |    2    0    0  100 |
+==================== EXIT REASON STATS ====================
+| Exit Reason        |   Exits |  Wins |  Draws |  Losses |
+|--------------------+---------+-------+--------+---------|
+| trailing_stop_loss |     205 |   150 |      0 |      55 |
+| stop_loss          |     166 |     0 |      0 |     166 |
+| exit_signal        |      56 |    36 |      0 |      20 |
+| force_exit         |       2 |     0 |      0 |       2 |
+
+================== SUMMARY METRICS ==================
+| Metric                      | Value               |
+|-----------------------------+---------------------|
+| Backtesting from            | 2019-01-01 00:00:00 |
+| Backtesting to              | 2019-05-01 00:00:00 |
+| Trading Mode                | Spot                |
+| Max open trades             | 3                   |
+|                             |                     |
+| Total/Daily Avg Trades      | 429 / 3.575         |
+| Starting balance            | 0.01000000 BTC      |
+| Final balance               | 0.01762792 BTC      |
+| Absolute profit             | 0.00762792 BTC      |
+| Total profit %              | 76.2%               |
+| CAGR %                      | 460.87%             |
+| Sortino                     | 1.88                |
+| Sharpe                      | 2.97                |
+| Calmar                      | 6.29                |
+| SQN                         | 2.45                |
+| Profit factor               | 1.11                |
+| Expectancy (Ratio)          | -0.15 (-0.05)       |
+| Avg. stake amount           | 0.001      BTC      |
+| Total trade volume          | 0.429      BTC      |
+|                             |                     |
+| Long / Short                | 352 / 77            |
+| Total profit Long %         | 1250.58%            |
+| Total profit Short %        | -15.02%             |
+| Absolute profit Long        | 0.00838792 BTC      |
+| Absolute profit Short       | -0.00076 BTC        |
+|                             |                     |
+| Best Pair                   | LSK/BTC 26.26%      |
+| Worst Pair                  | ZEC/BTC -10.18%     |
+| Best Trade                  | LSK/BTC 4.25%       |
+| Worst Trade                 | ZEC/BTC -10.25%     |
+| Best day                    | 0.00076 BTC         |
+| Worst day                   | -0.00036 BTC        |
+| Days win/draw/lose          | 12 / 82 / 25        |
+| Avg. Duration Winners       | 4:23:00             |
+| Avg. Duration Loser         | 6:55:00             |
+| Max Consecutive Wins / Loss | 3 / 4               |
+| Rejected Entry signals      | 3089                |
+| Entry/Exit Timeouts         | 0 / 0               |
+| Canceled Trade Entries      | 34                  |
+| Canceled Entry Orders       | 123                 |
+| Replaced Entry Orders       | 89                  |
+|                             |                     |
+| Min balance                 | 0.00945123 BTC      |
+| Max balance                 | 0.01846651 BTC      |
+| Max % of account underwater | 25.19%              |
+| Absolute Drawdown (Account) | 13.33%              |
+| Drawdown                    | 0.0015 BTC          |
+| Drawdown high               | 0.0013 BTC          |
+| Drawdown low                | -0.0002 BTC         |
+| Drawdown Start              | 2019-02-15 14:10:00 |
+| Drawdown End                | 2019-04-11 18:15:00 |
+| Market change               | -5.88%              |
+=====================================================
 ```
 
+### Backtesting report table
+
+The 1st table contains all trades the bot made, including "left open trades".
+
 The last line will give you the overall performance of your strategy,
 here:
+
 ```
-TOTAL             419           -0.41         -0.00348593            52.9
+| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00      |   186     0   243   43.4 |
 ```
 
-We understand the bot has made `419` trades for an average duration of
-`52.9` min, with a performance of `-0.41%` (loss), that means it has 
-lost a total of `-0.00348593 BTC`.
- 
-As you will see your strategy performance will be influenced by your buy 
-strategy, your sell strategy, and also by the `minimal_roi` and 
-`stop_loss` you have set. 
+The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
+earned a total of `0.00762792 BTC` starting with a capital of 0.01 BTC.
+
+The column `Avg Profit %` shows the average profit for all trades made.
+The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
+In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the `Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
+
+Your strategy performance is influenced by your entry strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set.
+
+For example, if your `minimal_roi` is only `"0":  0.01` you cannot expect the bot to make more profit than 1% (because it will exit every time a trade reaches 1%).
 
-As for an example if your minimal_roi is only `"0":  0.01`. You cannot
-expect the bot to make more profit than 1% (because it will sell every 
-time a trade will reach 1%).
 ```json
 "minimal_roi": {
     "0":  0.01
@@ -154,11 +279,316 @@ time a trade will reach 1%).
 ```
 
 On the other hand, if you set a too high `minimal_roi` like `"0":  0.55`
-(55%), there is a lot of chance that the bot will never reach this 
-profit. Hence, keep in mind that your performance is a mix of your 
-strategies, your configuration, and the crypto-currency you have set up.
+(55%), there is almost no chance that the bot will ever reach this profit.
+Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
+
+### Exit reasons table
+
+The 2nd table contains a recap of exit reasons.
+This table can tell you which area needs some additional work (e.g. all or many of the `exit_signal` trades are losses, so you should work on improving the exit signal, or consider disabling it).
+
+### Left open trades table
+
+The 3rd table contains all trades the bot had to `force_exit` at the end of the backtesting period to present you the full picture.
+This is necessary to simulate realistic behavior, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
+These trades are also included in the first table, but are also shown separately in this table for clarity.
+
+### Summary metrics
+
+The last element of the backtest report is the summary metrics table.
+It contains some useful key metrics about performance of your strategy on backtesting data.
+
+```
+================== SUMMARY METRICS ==================
+| Metric                      | Value               |
+|-----------------------------+---------------------|
+| Backtesting from            | 2019-01-01 00:00:00 |
+| Backtesting to              | 2019-05-01 00:00:00 |
+| Trading Mode                | Spot                |
+| Max open trades             | 3                   |
+|                             |                     |
+| Total/Daily Avg Trades      | 429 / 3.575         |
+| Starting balance            | 0.01000000 BTC      |
+| Final balance               | 0.01762792 BTC      |
+| Absolute profit             | 0.00762792 BTC      |
+| Total profit %              | 76.2%               |
+| CAGR %                      | 460.87%             |
+| Sortino                     | 1.88                |
+| Sharpe                      | 2.97                |
+| Calmar                      | 6.29                |
+| SQN                         | 2.45                |
+| Profit factor               | 1.11                |
+| Expectancy (Ratio)          | -0.15 (-0.05)       |
+| Avg. stake amount           | 0.001      BTC      |
+| Total trade volume          | 0.429      BTC      |
+|                             |                     |
+| Long / Short                | 352 / 77            |
+| Total profit Long %         | 1250.58%            |
+| Total profit Short %        | -15.02%             |
+| Absolute profit Long        | 0.00838792 BTC      |
+| Absolute profit Short       | -0.00076 BTC        |
+|                             |                     |
+| Best Pair                   | LSK/BTC 26.26%      |
+| Worst Pair                  | ZEC/BTC -10.18%     |
+| Best Trade                  | LSK/BTC 4.25%       |
+| Worst Trade                 | ZEC/BTC -10.25%     |
+| Best day                    | 0.00076 BTC         |
+| Worst day                   | -0.00036 BTC        |
+| Days win/draw/lose          | 12 / 82 / 25        |
+| Avg. Duration Winners       | 4:23:00             |
+| Avg. Duration Loser         | 6:55:00             |
+| Max Consecutive Wins / Loss | 3 / 4               |
+| Rejected Entry signals      | 3089                |
+| Entry/Exit Timeouts         | 0 / 0               |
+| Canceled Trade Entries      | 34                  |
+| Canceled Entry Orders       | 123                 |
+| Replaced Entry Orders       | 89                  |
+|                             |                     |
+| Min balance                 | 0.00945123 BTC      |
+| Max balance                 | 0.01846651 BTC      |
+| Max % of account underwater | 25.19%              |
+| Absolute Drawdown (Account) | 13.33%              |
+| Drawdown                    | 0.0015 BTC          |
+| Drawdown high               | 0.0013 BTC          |
+| Drawdown low                | -0.0002 BTC         |
+| Drawdown Start              | 2019-02-15 14:10:00 |
+| Drawdown End                | 2019-04-11 18:15:00 |
+| Market change               | -5.88%              |
+=====================================================
+
+```
+
+- `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
+- `Max open trades`: Setting of `max_open_trades` (or `--max-open-trades`) - or number of pairs in the pairlist (whatever is lower).
+- `Trading Mode`: Spot or Futures trading.
+- `Total/Daily Avg Trades`: Identical to the total trades of the backtest output table / Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
+- `Starting balance`: Start balance - as given by dry-run-wallet (config or command line).
+- `Final balance`: Final balance - starting balance + absolute profit.
+- `Absolute profit`: Profit made in stake currency.
+- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
+- `CAGR %`: Compound annual growth rate.
+- `Sortino`: Annualized Sortino ratio.
+- `Sharpe`: Annualized Sharpe ratio.
+- `Calmar`: Annualized Calmar ratio.
+- `SQN`: System Quality Number (SQN) - by Van Tharp.
+- `Profit factor`: profit / loss.
+- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
+- `Total trade volume`: Volume generated on the exchange to reach the above profit.
+- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Tot Profit %`.
+- `Best Trade` / `Worst Trade`: Biggest single winning trade and biggest single losing trade.
+- `Best day` / `Worst day`: Best and worst day based on daily profit.
+- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
+- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
+- `Max Consecutive Wins / Loss`: Maximum consecutive wins/losses in a row.
+- `Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
+- `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
+- `Canceled Trade Entries`: Number of trades that have been canceled by user request via `adjust_entry_price`.
+- `Canceled Entry Orders`: Number of entry orders that have been canceled by user request via `adjust_entry_price`.
+- `Replaced Entry Orders`: Number of entry orders that have been replaced by user request via `adjust_entry_price`.
+- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
+- `Max % of account underwater`: Maximum percentage your account has decreased from the top since the simulation started.
+Calculated as the maximum of `(Max Balance - Current Balance) / (Max Balance)`.
+- `Absolute Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
+- `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point.
+- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
+- `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
+- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
+- `Long / Short`: Split long/short values (Only shown when short trades were made).
+- `Total profit Long %` / `Absolute profit Long`: Profit long trades only (Only shown when short trades were made).
+- `Total profit Short %` / `Absolute profit Short`: Profit short trades only (Only shown when short trades were made).
+
+### Daily / Weekly / Monthly breakdown
+
+You can get an overview over daily / weekly or monthly results by using the `--breakdown <>` switch.
+
+To visualize daily and weekly breakdowns, you can use the following:
+
+``` bash
+freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day week
+```
+
+``` output
+======================== DAY BREAKDOWN =========================
+|        Day |   Tot Profit USDT |   Wins |   Draws |   Losses |
+|------------+-------------------+--------+---------+----------|
+| 03/07/2021 |           200.0   |      2 |       0 |        0 |
+| 04/07/2021 |           -50.31  |      0 |       0 |        2 |
+| 05/07/2021 |           220.611 |      3 |       2 |        0 |
+| 06/07/2021 |           150.974 |      3 |       0 |        2 |
+| 07/07/2021 |           -70.193 |      1 |       0 |        2 |
+| 08/07/2021 |           212.413 |      2 |       0 |        3 |
+
+```
+
+The output will show a table containing the realized absolute Profit (in stake currency) for the given timeperiod, as well as wins, draws and losses that materialized (closed) on this day. Below that there will be a second table for the summarized values of weeks indicated by the date of the closing Sunday. The same would apply to a monthly breakdown indicated by the last day of the month.
+
+### Backtest result caching
+
+To save time, by default backtest will reuse a cached result from within the last day when the backtested strategy and config match that of a previous backtest. To force a new backtest despite existing result for an identical run specify `--cache none` parameter.
+
+!!! Warning
+    Caching is automatically disabled for open-ended timeranges (`--timerange 20210101-`), as freqtrade cannot ensure reliably that the underlying data didn't change. It can also use cached results where it shouldn't if the original backtest had missing data at the end, which was fixed by downloading more data.
+    In this instance, please use `--cache none` once to force a fresh backtest.
+
+### Further backtest-result analysis
+
+To further analyze your backtest results, freqtrade will export the trades to file by default.
+You can then load the trades to perform further analysis as shown in the [data analysis](strategy_analysis_example.md#load-backtest-results-to-pandas-dataframe) backtesting section.
+
+### Backtest output file
+
+The output file freqtrade produces is a zip file containing the following files:
+
+- The backtest report in json format
+- the market change data in feather format
+- a copy of the strategy file
+- a copy of the strategy parameters (if a parameter file was used)
+- a sanitized copy of the config file
+
+This will ensure results are reproducible - under the assumption that the same data is available.
+
+Only the strategy file and the config file are included in the zip file, eventual dependencies are not included.
+
+## Assumptions made by backtesting
+
+Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
+
+- Exchange [trading limits](#trading-limits-in-backtesting) are respected
+- Entries happen at open-price unless a custom price logic has been specified
+- All orders are filled at the requested price (no slippage) as long as the price is within the candle's high/low range
+- Exit-signal exits happen at open-price of the consecutive candle
+- Exits free their trade slot for a new trade with a different pair
+- Exit-signal is favored over Stoploss, because exit-signals are assumed to trigger on candle's open
+- ROI
+  - Exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
+  - Exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
+  - ROI entries which came into effect on the triggering candle (e.g. `120: 0.02` for 1h candles, from `60: 0.05`) will use the candle's open as exit rate
+  - Force-exits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
+- Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
+- Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
+- Low happens before high for stoploss, protecting capital first
+- Trailing stoploss
+  - Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
+  - On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point. This rule is NOT applicable to custom-stoploss scenarios, since there's no information about the stoploss logic available.
+  - High happens first - adjusting stoploss
+  - Low uses the adjusted stoploss (so exits with large high-low difference are backtested correctly)
+  - ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
+- Exit-reason does not explain if a trade was positive or negative, just what triggered the exit (this can look odd if negative ROI values are used)
+- Evaluation sequence (if multiple signals happen on the same candle)
+  - Exit-signal
+  - Stoploss
+  - ROI
+  - Trailing stoploss
+- Position reversals (futures only) happen if an entry signal in the other direction than the closing trade triggers at the candle the existing trade closes.
+
+Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
+Also, keep in mind that past results don't guarantee future success.
+
+In addition to the above assumptions, strategy authors should carefully read the [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) section, to avoid using data in backtesting which is not available in real market conditions.
+
+### Trading limits in backtesting
+
+Exchanges have certain trading limits, like minimum (and maximum) base currency, or minimum/maximum stake (quote) currency.
+These limits are usually listed in the exchange documentation as "trading rules" or similar and can be quite different between different pairs.
+
+Backtesting (as well as live and dry-run) does honor these limits, and will ensure that a stoploss can be placed below this value - so the value will be slightly higher than what the exchange specifies.
+Freqtrade has however no information about historic limits.
+
+This can lead to situations where trading-limits are inflated by using a historic price, resulting in minimum amounts > 50\$.
+
+For example:
+
+BTC minimum tradable amount is 0.001.
+BTC trades at 22.000\$ today (0.001 BTC is related to this) - but the backtesting period includes prices as high as 50.000\$.
+Today's minimum would be `0.001 * 22_000` - or 22\$.  
+However the limit could also be 50$ - based on `0.001 * 50_000` in some historic setting.
+
+#### Trading precision limits
+
+Most exchanges pose precision limits on both price and amounts, so you cannot buy 1.0020401 of a pair, or at a price of 1.24567123123.  
+Instead, these prices and amounts will be rounded or truncated (based on the exchange definition) to the defined trading precision.
+The above values may for example be rounded to an amount of 1.002, and a price of 1.24567.
+
+These precision values are based on current exchange limits (as described in the [above section](#trading-limits-in-backtesting)), as historic precision limits are not available.
+
+## Improved backtest accuracy
+
+One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or vice-versa?).
+So assuming you run backtesting with a 1h timeframe, there will be 4 prices for that candle (Open, High, Low, Close).
+
+While backtesting does take some assumptions (read above) about this - this can never be perfect, and will always be biased in one way or the other.
+To mitigate this, freqtrade can use a lower (faster) timeframe to simulate intra-candle movements.
+
+To utilize this, you can append `--timeframe-detail 5m` to your regular backtesting command.
+
+``` bash
+freqtrade backtesting --strategy AwesomeStrategy --timeframe 1h --timeframe-detail 5m
+```
+
+This will load 1h data (the main timeframe) as well as 5m data (detail timeframe) for the selected timerange.
+The strategy will be analyzed with the 1h timeframe.
+Candles where activity may take place (there's an active signal, the pair is in a trade) are  evaluated at the 5m timeframe.
+This will allow for a more accurate simulation of intra-candle movements - and can lead to different results, especially on higher timeframes.
+
+Entries will generally still happen at the main candle's open, however freed trade slots may be freed earlier (if the exit signal is triggered on the 5m candle), which can then be used for a new trade of a different pair.
+
+All callback functions (`custom_exit()`, `custom_stoploss()`, ... ) will be running for each 5m candle once the trade is opened (so 12 times in the above example of 1h timeframe, and 5m detailed timeframe).
+
+`--timeframe-detail` must be smaller than the original timeframe, otherwise backtesting will fail to start.
+
+Obviously this will require more memory (5m data is bigger than 1h data), and will also impact runtime (depending on the amount of trades and trade durations).
+Also, data must be available / downloaded already.
+
+!!! Tip
+    You can use this function as the last part of strategy development, to ensure your strategy is not exploiting one of the [backtesting assumptions](#assumptions-made-by-backtesting). Strategies that perform similarly well with this mode have a good chance to perform well in dry/live modes too (although only forward-testing (dry-mode) can really confirm a strategy).
+
+??? Sample "Extreme Difference Example"
+    Using `--timeframe-detail` on an extreme example (all below pairs have the 10:00 candle with an entry signal) may lead to the following backtesting Trade sequence with 1 max_open_trades:
+
+    | Pair | Entry Time | Exit Time | Duration |
+    |------|------------|-----------| -------- |
+    | BTC/USDT | 2024-01-01 10:00:00 | 2021-01-01 10:05:00 | 5m |
+    | ETH/USDT | 2024-01-01 10:05:00 | 2021-01-01 10:15:00 | 10m |
+    | XRP/USDT | 2024-01-01 10:15:00 | 2021-01-01 10:30:00 | 15m |
+    | SOL/USDT | 2024-01-01 10:15:00 | 2021-01-01 11:05:00 | 50m |
+    | BTC/USDT | 2024-01-01 11:05:00 | 2021-01-01 12:00:00 | 55m |
+
+    Without timeframe-detail, this would look like:
+
+    | Pair | Entry Time | Exit Time | Duration |
+    |------|------------|-----------| -------- |
+    | BTC/USDT | 2024-01-01 10:00:00 | 2021-01-01 11:00:00 | 1h |
+    | BTC/USDT | 2024-01-01 11:00:00 | 2021-01-01 12:00:00 | 1h |
+
+    The difference is significant, as without detail data, only the first `max_open_trades` signals per candle are evaluated, and the trade slots are only freed at the end of the candle, allowing for a new trade to be opened at the next candle.
+
+
+## Backtesting multiple strategies
+
+To compare multiple strategies, a list of Strategies can be provided to backtesting.
+
+This is limited to 1 timeframe value per run. However, data is only loaded once from disk so if you have multiple
+strategies you'd like to compare, this will give a nice runtime boost.
+
+All listed Strategies need to be in the same directory, unless also `--recursive-strategy-search` is specified, where sub-directories within the strategy directory are also considered.
+
+``` bash
+freqtrade backtesting --timerange 20180401-20180410 --timeframe 5m --strategy-list Strategy001 Strategy002 --export trades
+```
+
+This will save the results to `user_data/backtest_results/backtest-result-<datetime>.json`, including results for both `Strategy001` and `Strategy002`.
+There will be an additional table comparing win/losses of the different strategies (identical to the "Total" row in the first table).
+Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
+
+```
+================================================== STRATEGY SUMMARY ===================================================================
+| Strategy    |  Trades |   Avg Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws | Losses | Drawdown % |
+|-------------+---------+----------------+------------------+----------------+----------------+-------+--------+--------+------------|
+| Strategy1   |     429 |           0.36 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |    243 |       45.2 |
+| Strategy2   |    1487 |          -0.13 |      -0.00988917 |         -98.79 | 4:43:00        |   662 |      0 |    825 |     241.68 |
+```
 
 ## Next step
-Great, your strategy is profitable. What if the bot can give your the
-optimal parameters to use for your strategy?  
-Your next step is to learn [how to find optimal parameters with Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/hyperopt.md)
+
+Great, your strategy is profitable. What if the bot can give your the optimal parameters to use for your strategy?
+Your next step is to learn [how to find optimal parameters with Hyperopt](hyperopt.md)
diff --git a/docs/bot-basics.md b/docs/bot-basics.md
new file mode 100644
index 000000000..ff73aecde
--- /dev/null
+++ b/docs/bot-basics.md
@@ -0,0 +1,105 @@
+# Freqtrade basics
+
+This page provides you some basic concepts on how Freqtrade works and operates.
+
+## Freqtrade terminology
+
+* **Strategy**: Your trading strategy, telling the bot what to do.
+* **Trade**: Open position.
+* **Open Order**: Order which is currently placed on the exchange, and is not yet complete.
+* **Pair**: Tradable pair, usually in the format of Base/Quote (e.g. `XRP/USDT` for spot, `XRP/USDT:USDT` for futures).
+* **Timeframe**: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
+* **Indicators**: Technical indicators (SMA, EMA, RSI, ...).
+* **Limit order**: Limit orders which execute at the defined limit price or better.
+* **Market order**: Guaranteed to fill, may move price depending on the order size.
+* **Current Profit**: Currently pending (unrealized) profit for this trade. This is mainly used throughout the bot and UI.
+* **Realized Profit**: Already realized profit. Only relevant in combination with [partial exits](strategy-callbacks.md#adjust-trade-position) - which also explains the calculation logic for this.
+* **Total Profit**: Combined realized and unrealized profit. The relative number (%) is calculated against the total investment in this trade.
+
+## Fee handling
+
+All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt / Dry-run modes, the exchange default fee is used (lowest tier on the exchange). For live operations, fees are used as applied by the exchange (this includes BNB rebates etc.).
+
+## Pair naming
+
+Freqtrade follows the [ccxt naming convention](https://docs.ccxt.com/#/README?id=consistency-of-base-and-quote-currencies) for currencies.
+Using the wrong naming convention in the wrong market will usually result in the bot not recognizing the pair, usually resulting in errors like "this pair is not available".
+
+### Spot pair naming
+
+For spot pairs, naming will be `base/quote` (e.g. `ETH/USDT`).
+
+### Futures pair naming
+
+For futures pairs, naming will be `base/quote:settle` (e.g. `ETH/USDT:USDT`).
+
+## Bot execution logic
+
+Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
+This will also run the `bot_start()` callback.
+
+By default, the bot loop runs every few seconds (`internals.process_throttle_secs`) and performs the following actions:
+
+* Fetch open trades from persistence.
+* Calculate current list of tradable pairs.
+* Download OHLCV data for the pairlist including all [informative pairs](strategy-customization.md#get-data-for-non-tradeable-pairs)  
+  This step is only executed once per Candle to avoid unnecessary network traffic.
+* Call `bot_loop_start()` strategy callback.
+* Analyze strategy per pair.
+  * Call `populate_indicators()`
+  * Call `populate_entry_trend()`
+  * Call `populate_exit_trend()`
+* Update trades open order state from exchange.
+  * Call `order_filled()` strategy callback for filled orders.
+  * Check timeouts for open orders.
+    * Calls `check_entry_timeout()` strategy callback for open entry orders.
+    * Calls `check_exit_timeout()` strategy callback for open exit orders.
+    * Calls `adjust_order_price()` strategy callback for open orders.
+      * Calls `adjust_entry_price()` strategy callback for open entry orders. *only called when `adjust_order_price()` is not implemented*
+      * Calls `adjust_exit_price()` strategy callback for open exit orders. *only called when `adjust_order_price()` is not implemented*
+* Verifies existing positions and eventually places exit orders.
+  * Considers stoploss, ROI and exit-signal, `custom_exit()` and `custom_stoploss()`.
+  * Determine exit-price based on `exit_pricing` configuration setting or by using the `custom_exit_price()` callback.
+  * Before an exit order is placed, `confirm_trade_exit()` strategy callback is called.
+* Check position adjustments for open trades if enabled by calling `adjust_trade_position()` and place additional order if required.
+* Check if trade-slots are still available (if `max_open_trades` is reached).
+* Verifies entry signal trying to enter new positions.
+  * Determine entry-price based on `entry_pricing` configuration setting, or by using the `custom_entry_price()` callback.
+  * In Margin and Futures mode, `leverage()` strategy callback is called to determine the desired leverage.
+  * Determine stake size by calling the `custom_stake_amount()` callback.
+  * Before an entry order is placed, `confirm_trade_entry()` strategy callback is called.
+
+This loop will be repeated again and again until the bot is stopped.
+
+## Backtesting / Hyperopt execution logic
+
+[backtesting](backtesting.md) or [hyperopt](hyperopt.md) do only part of the above logic, since most of the trading operations are fully simulated.
+
+* Load historic data for configured pairlist.
+* Calls `bot_start()` once.
+* Calculate indicators (calls `populate_indicators()` once per pair).
+* Calculate entry / exit signals (calls `populate_entry_trend()` and `populate_exit_trend()` once per pair).
+* Loops per candle simulating entry and exit points.
+  * Calls `bot_loop_start()` strategy callback.
+  * Check for Order timeouts, either via the `unfilledtimeout` configuration, or via `check_entry_timeout()` / `check_exit_timeout()` strategy callbacks.
+  * Calls `adjust_order_price()` strategy callback for open orders.
+    * Calls `adjust_entry_price()` strategy callback for open entry orders. *only called when `adjust_order_price()` is not implemented!*
+    * Calls `adjust_exit_price()` strategy callback for open exit orders. *only called when `adjust_order_price()` is not implemented!*
+  * Check for trade entry signals (`enter_long` / `enter_short` columns).
+  * Confirm trade entry / exits (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
+  * Call `custom_entry_price()` (if implemented in the strategy) to determine entry price (Prices are moved to be within the opening candle).
+  * In Margin and Futures mode, `leverage()` strategy callback is called to determine the desired leverage.
+  * Determine stake size by calling the `custom_stake_amount()` callback.
+  * Check position adjustments for open trades if enabled and call `adjust_trade_position()` to determine if an additional order is requested.
+  * Call `order_filled()` strategy callback for filled entry orders.
+  * Call `custom_stoploss()` and `custom_exit()` to find custom exit points.
+  * For exits based on exit-signal, custom-exit and partial exits: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
+  * Call `order_filled()` strategy callback for filled exit orders.
+* Generate backtest report output
+
+!!! Note
+    Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.
+
+!!! Warning "Callback call frequency"
+    Backtesting will call each callback at max. once per candle (`--timeframe-detail` modifies this behavior to once per detailed candle).
+    Most callbacks will be called once per iteration in live (usually every ~5s) - which can cause backtesting mismatches.
diff --git a/docs/bot-optimization.md b/docs/bot-optimization.md
deleted file mode 100644
index 6911e9e20..000000000
--- a/docs/bot-optimization.md
+++ /dev/null
@@ -1,145 +0,0 @@
-# Bot Optimization
-This page explains where to customize your strategies, and add new 
-indicators. 
-
-## Table of Contents
-- [Install a custom strategy file](#install-a-custom-strategy-file)
-- [Customize your strategy](#change-your-strategy)
-- [Add more Indicator](#add-more-indicator)
-- [Where is the default strategy](#where-is-the-default-strategy)
-
-Since the version `0.16.0` the bot allows using custom strategy file.
-
-## Install a custom strategy file
-This is very simple. Copy paste your strategy file into the folder 
-`user_data/strategies`.
-
-Let assume you have a strategy file `awesome-strategy.py`:
-1. Move your file into `user_data/strategies` (you should have `user_data/strategies/awesome-strategy.py`
-2. Start the bot with the param `--strategy awesome-strategy` (the parameter is the name of the file without '.py') 
-
-```bash
-python3 ./freqtrade/main.py --strategy awesome_strategy
-```
-
-## Change your strategy
-The bot includes a default strategy file. However, we recommend you to 
-use your own file to not have to lose your parameters every time the default
-strategy file will be updated on Github. Put your custom strategy file
-into the folder `user_data/strategies`.
-
-A strategy file contains all the information needed to build a good strategy:
-- Buy strategy rules
-- Sell strategy rules
-- Minimal ROI recommended
-- Stoploss recommended
-- Hyperopt parameter
-
-The bot also include a sample strategy you can update: `user_data/strategies/test_strategy.py`.  
-You can test it with the parameter: `--strategy test_strategy`
- 
-```bash
-python3 ./freqtrade/main.py --strategy awesome_strategy
-```
-
-**For the following section we will use the [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
-file as reference.**
-
-### Buy strategy 
-Edit the method `populate_buy_trend()` into your strategy file to 
-update your buy strategy.
-
-Sample from `user_data/strategies/test_strategy.py`:  
-```python
-def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
-    """
-    Based on TA indicators, populates the buy signal for the given dataframe
-    :param dataframe: DataFrame
-    :return: DataFrame with buy column
-    """
-    dataframe.loc[
-        (
-            (dataframe['adx'] > 30) &
-            (dataframe['tema'] <= dataframe['blower']) &
-            (dataframe['tema'] > dataframe['tema'].shift(1))
-        ),
-        'buy'] = 1
-
-    return dataframe
-```
-
-### Sell strategy
-Edit the method `populate_sell_trend()` into your strategy file to 
-update your sell strategy.
-
-Sample from `user_data/strategies/test_strategy.py`:  
-```python
-def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
-    """
-    Based on TA indicators, populates the sell signal for the given dataframe
-    :param dataframe: DataFrame
-    :return: DataFrame with buy column
-    """
-    dataframe.loc[
-        (
-            (dataframe['adx'] > 70) &
-            (dataframe['tema'] > dataframe['blower']) &
-            (dataframe['tema'] < dataframe['tema'].shift(1))
-        ),
-        'sell'] = 1
-    return dataframe
-```
-
-## Add more Indicator
-As you have seen, buy and sell strategies need indicators. You can add 
-more indicators by extending the list contained in
-the method `populate_indicators()` from your strategy file.
-
-Sample:
-```python
-def populate_indicators(dataframe: DataFrame) -> DataFrame:
-    """
-    Adds several different TA indicators to the given DataFrame
-    """
-    dataframe['sar'] = ta.SAR(dataframe)
-    dataframe['adx'] = ta.ADX(dataframe)
-    stoch = ta.STOCHF(dataframe)
-    dataframe['fastd'] = stoch['fastd']
-    dataframe['fastk'] = stoch['fastk']
-    dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
-    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
-    dataframe['mfi'] = ta.MFI(dataframe)
-    dataframe['rsi'] = ta.RSI(dataframe)
-    dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
-    dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
-    dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
-    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
-    dataframe['ao'] = awesome_oscillator(dataframe)
-    macd = ta.MACD(dataframe)
-    dataframe['macd'] = macd['macd']
-    dataframe['macdsignal'] = macd['macdsignal']
-    dataframe['macdhist'] = macd['macdhist']
-    hilbert = ta.HT_SINE(dataframe)
-    dataframe['htsine'] = hilbert['sine']
-    dataframe['htleadsine'] = hilbert['leadsine']
-    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
-    dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
-    dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-    return dataframe
-```
-
-**Want more indicators example?**  
-Look into the [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py).  
-Then uncomment indicators you need.
-
-
-### Where is the default strategy?
-The default buy strategy is located in the file 
-[freqtrade/default_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/strategy/default_strategy.py). 
-
-
-## Next step
-Now you have a perfect strategy you probably want to backtesting it. 
-Your next step is to learn [How to use the Backtesting](https://github.com/gcarq/freqtrade/blob/develop/docs/backtesting.md).
diff --git a/docs/bot-usage.md b/docs/bot-usage.md
index 204b35c3a..593a36500 100644
--- a/docs/bot-usage.md
+++ b/docs/bot-usage.md
@@ -1,168 +1,136 @@
-# Bot usage
-This page explains the difference parameters of the bot and how to run 
-it.
+# Start the bot
 
-## Table of Contents
-- [Bot commands](#bot-commands)
-- [Backtesting commands](#backtesting-commands)
-- [Hyperopt commands](#hyperopt-commands)
+This page explains the different parameters of the bot and how to run it.
+
+!!! Note
+    If you've used `setup.sh`, don't forget to activate your virtual environment (`source .venv/bin/activate`) before running freqtrade commands.
+
+!!! Warning "Up-to-date clock"
+    The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
 
 ## Bot commands
-```
-usage: main.py [-h] [-c PATH] [-v] [--version] [--dynamic-whitelist [INT]]
-               [--dry-run-db]
-               {backtesting,hyperopt} ...
 
-Simple High Frequency Trading Bot for crypto currencies
+--8<-- "commands/main.md"
 
-positional arguments:
-  {backtesting,hyperopt}
-    backtesting         backtesting module
-    hyperopt            hyperopt module
+### Bot trading commands
 
-optional arguments:
-  -h, --help            show this help message and exit
-  -v, --verbose         be verbose
-  --version             show program's version number and exit
-  -c PATH, --config PATH
-                        specify configuration file (default: config.json)
-  -s PATH, --strategy PATH
-                        specify strategy file (default:
-                        freqtrade/strategy/default_strategy.py)
-  --dry-run-db          Force dry run to use a local DB
-                        "tradesv3.dry_run.sqlite" instead of memory DB. Work
-                        only if dry_run is enabled.
-  --datadir PATH
-                        path to backtest data (default freqdata/tests/testdata
-  --dynamic-whitelist [INT]
-                        dynamically generate and update whitelist based on 24h
-                        BaseVolume (Default 20 currencies)
-```
+--8<-- "commands/trade.md"
 
-### How to use a different config file?
-The bot allows you to select which config file you want to use. Per 
-default, the bot will load the file `./config.json`
+### How to specify which configuration file be used?
+
+The bot allows you to select which configuration file you want to use by means of
+the `-c/--config` command line option:
 
 ```bash
-python3 ./freqtrade/main.py -c path/far/far/away/config.json 
+freqtrade trade -c path/far/far/away/config.json
 ```
 
-### How to use --strategy?
-This parameter will allow you to load your custom strategy file. Per 
-default without `--strategy` or `-s` the bot will load the 
-`default_strategy` included with the bot (`freqtrade/strategy/default_strategy.py`). 
+Per default, the bot loads the `config.json` configuration file from the current
+working directory.
 
-The bot will search your strategy file into `user_data/strategies` and 
-`freqtrade/strategy`.
+### How to use multiple configuration files?
 
-To load a strategy, simply pass the file name (without .py) in this 
-parameters.
+The bot allows you to use multiple configuration files by specifying multiple
+`-c/--config` options in the command line. Configuration parameters
+defined in the latter configuration files override parameters with the same name
+defined in the previous configuration files specified in the command line earlier.
+
+For example, you can make a separate configuration file with your key and secret
+for the Exchange you use for trading, specify default configuration file with
+empty key and secret values while running in the Dry Mode (which does not actually
+require them):
 
-**Example:**  
-In `user_data/strategies` you have a file `my_awesome_strategy.py` to 
-load it:  
 ```bash
-python3 ./freqtrade/main.py --strategy my_awesome_strategy
+freqtrade trade -c ./config.json
+```
+
+and specify both configuration files when running in the normal Live Trade Mode:
+
+```bash
+freqtrade trade -c ./config.json -c path/to/secrets/keys.config.json
+```
+
+This could help you hide your private Exchange key and Exchange secret on you local machine
+by setting appropriate file permissions for the file which contains actual secrets and, additionally,
+prevent unintended disclosure of sensitive private data when you publish examples
+of your configuration in the project issues or in the Internet.
+
+See more details on this technique with examples in the documentation page on
+[configuration](configuration.md).
+
+### Where to store custom data
+
+Freqtrade allows the creation of a user-data directory using `freqtrade create-userdir --userdir someDirectory`.
+This directory will look as follows:
+
+```
+user_data/
+├── backtest_results
+├── data
+├── hyperopts
+├── hyperopt_results
+├── plot
+└── strategies
+```
+
+You can add the entry "user_data_dir" setting to your configuration, to always point your bot to this directory.
+Alternatively, pass in `--userdir` to every command.
+The bot will fail to start if the directory does not exist, but will create necessary subdirectories.
+
+This directory should contain your custom strategies, custom hyperopts and hyperopt loss functions, backtesting historical data (downloaded using either backtesting command or the download script) and plot outputs.
+
+It is recommended to use version control to keep track of changes to your strategies.
+
+### How to use **--strategy**?
+
+This parameter will allow you to load your custom strategy class.
+To test the bot installation, you can use the `SampleStrategy` installed by the `create-userdir` subcommand (usually `user_data/strategy/sample_strategy.py`).
+
+The bot will search your strategy file within `user_data/strategies`.
+To use other directories, please read the next section about `--strategy-path`.
+
+To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this parameter.
+
+**Example:**
+In `user_data/strategies` you have a file `my_awesome_strategy.py` which has
+a strategy class called `AwesomeStrategy` to load it:
+
+```bash
+freqtrade trade --strategy AwesomeStrategy
 ```
 
 If the bot does not find your strategy file, it will display in an error
- message the reason (File not found, or errors in your code).
+message the reason (File not found, or errors in your code).
 
-Learn more about strategy file in [optimize your bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md).
+Learn more about strategy file in
+[Strategy Customization](strategy-customization.md).
+
+### How to use **--strategy-path**?
+
+This parameter allows you to add an additional strategy lookup path, which gets
+checked before the default locations (The passed path must be a directory!):
+
+```bash
+freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
+```
 
 #### How to install a strategy?
-This is very simple. Copy paste your strategy file into the folder 
-`user_data/strategies`. And voila, the bot is ready to use it.
 
-### How to use --dynamic-whitelist?
-Per default `--dynamic-whitelist` will retrieve the 20 currencies based 
-on BaseVolume. This value can be changed when you run the script.
+This is very simple. Copy paste your strategy file into the directory
+`user_data/strategies` or use `--strategy-path`. And voila, the bot is ready to use it.
 
-**By Default**  
-Get the 20 currencies based on BaseVolume.  
-```bash
-python3 ./freqtrade/main.py --dynamic-whitelist
-```
+### How to use **--db-url**?
 
-**Customize the number of currencies to retrieve**  
-Get the 30 currencies based on BaseVolume.  
-```bash
-python3 ./freqtrade/main.py --dynamic-whitelist 30
-```
-
-**Exception**  
-`--dynamic-whitelist` must be greater than 0. If you enter 0 or a
-negative value (e.g -2), `--dynamic-whitelist` will use the default
-value (20).
-
-### How to use --dry-run-db?
-When you run the bot in Dry-run mode, per default no transactions are 
-stored in a database. If you want to store your bot actions in a DB 
-using `--dry-run-db`. This command will use a separate database file 
-`tradesv3.dry_run.sqlite`
+When you run the bot in Dry-run mode, per default no transactions are
+stored in a database. If you want to store your bot actions in a DB
+using `--db-url`. This can also be used to specify a custom database
+in production mode. Example command:
 
 ```bash
-python3 ./freqtrade/main.py -c config.json --dry-run-db
+freqtrade trade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
 ```
 
-
-## Backtesting commands
-
-Backtesting also uses the config specified via `-c/--config`.
-
-```
-usage: freqtrade backtesting [-h] [-l] [-i INT] [--realistic-simulation]
-                             [-r]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -l, --live            using live data
-  -i INT, --ticker-interval INT
-                        specify ticker interval in minutes (default: 5)
-  --realistic-simulation
-                        uses max_open_trades from config to simulate real
-                        world limitations
-  -r, --refresh-pairs-cached
-                        refresh the pairs files in tests/testdata with 
-                        the latest data from Bittrex. Use it if you want
-                        to run your backtesting with up-to-date data.
-```
-
-### How to use --refresh-pairs-cached parameter?
-The first time your run Backtesting, it will take the pairs you have 
-set in your config file and download data from Bittrex. 
-
-If for any reason you want to update your data set, you use 
-`--refresh-pairs-cached` to force Backtesting to update the data it has. 
-**Use it only if you want to update your data set. You will not be able
-to come back to the previous version.**
-
-To test your strategy with latest data, we recommend continuing using 
-the parameter `-l` or `--live`.
-
-
-## Hyperopt commands
-
-It is possible to use hyperopt for trading strategy optimization.
-Hyperopt uses an internal json config return by `hyperopt_optimize_conf()` 
-located in `freqtrade/optimize/hyperopt_conf.py`.
-
-```
-usage: freqtrade hyperopt [-h] [-e INT] [--use-mongodb]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -e INT, --epochs INT  specify number of epochs (default: 100)
-  --use-mongodb         parallelize evaluations with mongodb (requires mongod
-                        in PATH)
-
-```
-
-## A parameter missing in the configuration?
-All parameters for `main.py`, `backtesting`, `hyperopt` are referenced
-in [misc.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/misc.py#L84)
-
 ## Next step
-The optimal strategy of the bot will change with time depending of the
-market trends. The next step is to 
-[optimize your bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md).
+
+The optimal strategy of the bot will change with time depending of the market trends. The next step is to
+[Strategy Customization](strategy-customization.md).
diff --git a/docs/commands/backtesting-analysis.md b/docs/commands/backtesting-analysis.md
new file mode 100644
index 000000000..50707d03a
--- /dev/null
+++ b/docs/commands/backtesting-analysis.md
@@ -0,0 +1,66 @@
+```
+usage: freqtrade backtesting-analysis [-h] [-v] [--no-color] [--logfile FILE]
+                                      [-V] [-c PATH] [-d PATH]
+                                      [--userdir PATH]
+                                      [--export-filename PATH]
+                                      [--analysis-groups {0,1,2,3,4,5} [{0,1,2,3,4,5} ...]]
+                                      [--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]]
+                                      [--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
+                                      [--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
+                                      [--entry-only] [--exit-only]
+                                      [--timerange TIMERANGE]
+                                      [--rejected-signals] [--analysis-to-csv]
+                                      [--analysis-csv-path ANALYSIS_CSV_PATH]
+
+options:
+  -h, --help            show this help message and exit
+  --export-filename PATH, --backtest-filename PATH
+                        Use this filename for backtest results.Requires
+                        `--export` to be set as well. Example: `--export-filen
+                        ame=user_data/backtest_results/backtest_today.json`
+  --analysis-groups {0,1,2,3,4,5} [{0,1,2,3,4,5} ...]
+                        grouping output - 0: simple wins/losses by enter tag,
+                        1: by enter_tag, 2: by enter_tag and exit_tag, 3: by
+                        pair and enter_tag, 4: by pair, enter_ and exit_tag
+                        (this can get quite large), 5: by exit_tag
+  --enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]
+                        Space separated list of entry signals to analyse.
+                        Default: all. e.g. 'entry_tag_a entry_tag_b'
+  --exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]
+                        Space separated list of exit signals to analyse.
+                        Default: all. e.g. 'exit_tag_a roi stop_loss
+                        trailing_stop_loss'
+  --indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
+                        Space separated list of indicators to analyse. e.g.
+                        'close rsi bb_lowerband profit_abs'
+  --entry-only          Only analyze entry signals.
+  --exit-only           Only analyze exit signals.
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --rejected-signals    Analyse rejected signals
+  --analysis-to-csv     Save selected analysis tables to individual CSVs
+  --analysis-csv-path ANALYSIS_CSV_PATH
+                        Specify a path to save the analysis CSVs if
+                        --analysis-to-csv is enabled. Default:
+                        user_data/basktesting_results/
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/backtesting-show.md b/docs/commands/backtesting-show.md
new file mode 100644
index 000000000..a1b146a82
--- /dev/null
+++ b/docs/commands/backtesting-show.md
@@ -0,0 +1,37 @@
+```
+usage: freqtrade backtesting-show [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                                  [-c PATH] [-d PATH] [--userdir PATH]
+                                  [--export-filename PATH] [--show-pair-list]
+                                  [--breakdown {day,week,month,year} [{day,week,month,year} ...]]
+
+options:
+  -h, --help            show this help message and exit
+  --export-filename PATH, --backtest-filename PATH
+                        Use this filename for backtest results.Requires
+                        `--export` to be set as well. Example: `--export-filen
+                        ame=user_data/backtest_results/backtest_today.json`
+  --show-pair-list      Show backtesting pairlist sorted by profit.
+  --breakdown {day,week,month,year} [{day,week,month,year} ...]
+                        Show backtesting breakdown per [day, week, month,
+                        year].
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/backtesting.md b/docs/commands/backtesting.md
new file mode 100644
index 000000000..395ab4b00
--- /dev/null
+++ b/docs/commands/backtesting.md
@@ -0,0 +1,108 @@
+```
+usage: freqtrade backtesting [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                             [-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
+                             [--strategy-path PATH]
+                             [--recursive-strategy-search]
+                             [--freqaimodel NAME] [--freqaimodel-path PATH]
+                             [-i TIMEFRAME] [--timerange TIMERANGE]
+                             [--data-format-ohlcv {json,jsongz,feather,parquet}]
+                             [--max-open-trades INT]
+                             [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
+                             [-p PAIRS [PAIRS ...]] [--eps]
+                             [--enable-protections]
+                             [--dry-run-wallet DRY_RUN_WALLET]
+                             [--timeframe-detail TIMEFRAME_DETAIL]
+                             [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
+                             [--export {none,trades,signals}]
+                             [--export-filename PATH]
+                             [--breakdown {day,week,month,year} [{day,week,month,year} ...]]
+                             [--cache {none,day,week,month}]
+                             [--freqai-backtest-live-models]
+
+options:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --data-format-ohlcv {json,jsongz,feather,parquet}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `feather`).
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --eps, --enable-position-stacking
+                        Allow buying the same pair multiple times (position
+                        stacking).
+  --enable-protections, --enableprotections
+                        Enable protections for backtesting.Will slow
+                        backtesting down by a considerable amount, but will
+                        include configured protections
+  --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
+                        Starting balance, used for backtesting / hyperopt and
+                        dry-runs.
+  --timeframe-detail TIMEFRAME_DETAIL
+                        Specify detail timeframe for backtesting (`1m`, `5m`,
+                        `30m`, `1h`, `1d`).
+  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
+                        Provide a space-separated list of strategies to
+                        backtest. Please note that timeframe needs to be set
+                        either in config or via command line. When using this
+                        together with `--export trades`, the strategy-name is
+                        injected into the filename (so `backtest-data.json`
+                        becomes `backtest-data-SampleStrategy.json`
+  --export {none,trades,signals}
+                        Export backtest results (default: trades).
+  --export-filename PATH, --backtest-filename PATH
+                        Use this filename for backtest results.Requires
+                        `--export` to be set as well. Example: `--export-filen
+                        ame=user_data/backtest_results/backtest_today.json`
+  --breakdown {day,week,month,year} [{day,week,month,year} ...]
+                        Show backtesting breakdown per [day, week, month,
+                        year].
+  --cache {none,day,week,month}
+                        Load a cached backtest result no older than specified
+                        age (default: day).
+  --freqai-backtest-live-models
+                        Run backtest with ready models.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+
+```
diff --git a/docs/commands/convert-data.md b/docs/commands/convert-data.md
new file mode 100644
index 000000000..3bd0fa45b
--- /dev/null
+++ b/docs/commands/convert-data.md
@@ -0,0 +1,52 @@
+```
+usage: freqtrade convert-data [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                              [-c PATH] [-d PATH] [--userdir PATH]
+                              [-p PAIRS [PAIRS ...]] --format-from
+                              {json,jsongz,feather,parquet} --format-to
+                              {json,jsongz,feather,parquet} [--erase]
+                              [--exchange EXCHANGE]
+                              [-t TIMEFRAMES [TIMEFRAMES ...]]
+                              [--trading-mode {spot,margin,futures}]
+                              [--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]]
+
+options:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz,feather,parquet}
+                        Source format for data conversion.
+  --format-to {json,jsongz,feather,parquet}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+  --exchange EXCHANGE   Exchange name. Only valid if no config is provided.
+  -t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
+                        Select Trading mode
+  --candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]
+                        Select candle type to convert. Defaults to all
+                        available types.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/convert-db.md b/docs/commands/convert-db.md
new file mode 100644
index 000000000..fcaab3192
--- /dev/null
+++ b/docs/commands/convert-db.md
@@ -0,0 +1,12 @@
+```
+usage: freqtrade convert-db [-h] [--db-url PATH] [--db-url-from PATH]
+
+options:
+  -h, --help          show this help message and exit
+  --db-url PATH       Override trades database URL, this is useful in custom
+                      deployments (default: `sqlite:///tradesv3.sqlite` for
+                      Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                      Dry Run).
+  --db-url-from PATH  Source db url to use when migrating a database.
+
+```
diff --git a/docs/commands/convert-trade-data.md b/docs/commands/convert-trade-data.md
new file mode 100644
index 000000000..25843a8da
--- /dev/null
+++ b/docs/commands/convert-trade-data.md
@@ -0,0 +1,41 @@
+```
+usage: freqtrade convert-trade-data [-h] [-v] [--no-color] [--logfile FILE]
+                                    [-V] [-c PATH] [-d PATH] [--userdir PATH]
+                                    [-p PAIRS [PAIRS ...]] --format-from
+                                    {json,jsongz,feather,parquet,kraken_csv}
+                                    --format-to {json,jsongz,feather,parquet}
+                                    [--erase] [--exchange EXCHANGE]
+
+options:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --format-from {json,jsongz,feather,parquet,kraken_csv}
+                        Source format for data conversion.
+  --format-to {json,jsongz,feather,parquet}
+                        Destination format for data conversion.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+  --exchange EXCHANGE   Exchange name. Only valid if no config is provided.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/create-userdir.md b/docs/commands/create-userdir.md
new file mode 100644
index 000000000..9d52e2cf4
--- /dev/null
+++ b/docs/commands/create-userdir.md
@@ -0,0 +1,10 @@
+```
+usage: freqtrade create-userdir [-h] [--userdir PATH] [--reset]
+
+options:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  --reset               Reset sample files to their original state.
+
+```
diff --git a/docs/commands/download-data.md b/docs/commands/download-data.md
new file mode 100644
index 000000000..e70ae9a23
--- /dev/null
+++ b/docs/commands/download-data.md
@@ -0,0 +1,70 @@
+```
+usage: freqtrade download-data [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                               [-c PATH] [-d PATH] [--userdir PATH]
+                               [-p PAIRS [PAIRS ...]] [--pairs-file FILE]
+                               [--days INT] [--new-pairs-days INT]
+                               [--include-inactive-pairs]
+                               [--timerange TIMERANGE] [--dl-trades]
+                               [--convert] [--exchange EXCHANGE]
+                               [-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
+                               [--data-format-ohlcv {json,jsongz,feather,parquet}]
+                               [--data-format-trades {json,jsongz,feather,parquet}]
+                               [--trading-mode {spot,margin,futures}]
+                               [--prepend]
+
+options:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --pairs-file FILE     File containing a list of pairs. Takes precedence over
+                        --pairs or pairs configured in the configuration.
+  --days INT            Download data for given number of days.
+  --new-pairs-days INT  Download data of new pairs for given number of days.
+                        Default: `None`.
+  --include-inactive-pairs
+                        Also download data from inactive pairs.
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --dl-trades           Download trades instead of OHLCV data.
+  --convert             Convert downloaded trades to OHLCV data. Only
+                        applicable in combination with `--dl-trades`. Will be
+                        automatic for exchanges which don't have historic
+                        OHLCV (e.g. Kraken). If not provided, use `trades-to-
+                        ohlcv` to convert trades data to OHLCV data.
+  --exchange EXCHANGE   Exchange name. Only valid if no config is provided.
+  -t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+  --erase               Clean all existing data for the selected
+                        exchange/pairs/timeframes.
+  --data-format-ohlcv {json,jsongz,feather,parquet}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `feather`).
+  --data-format-trades {json,jsongz,feather,parquet}
+                        Storage format for downloaded trades data. (default:
+                        `feather`).
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
+                        Select Trading mode
+  --prepend             Allow data prepending. (Data-appending is disabled)
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/edge.md b/docs/commands/edge.md
new file mode 100644
index 000000000..06f883230
--- /dev/null
+++ b/docs/commands/edge.md
@@ -0,0 +1,69 @@
+```
+usage: freqtrade edge [-h] [-v] [--no-color] [--logfile FILE] [-V] [-c PATH]
+                      [-d PATH] [--userdir PATH] [-s NAME]
+                      [--strategy-path PATH] [--recursive-strategy-search]
+                      [--freqaimodel NAME] [--freqaimodel-path PATH]
+                      [-i TIMEFRAME] [--timerange TIMERANGE]
+                      [--data-format-ohlcv {json,jsongz,feather,parquet}]
+                      [--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
+                      [--fee FLOAT] [-p PAIRS [PAIRS ...]]
+                      [--stoplosses STOPLOSS_RANGE]
+
+options:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --data-format-ohlcv {json,jsongz,feather,parquet}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `feather`).
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --stoplosses STOPLOSS_RANGE
+                        Defines a range of stoploss values against which edge
+                        will assess the strategy. The format is "min,max,step"
+                        (without any space). Example:
+                        `--stoplosses=-0.01,-0.1,-0.001`
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+
+```
diff --git a/docs/commands/hyperopt-list.md b/docs/commands/hyperopt-list.md
new file mode 100644
index 000000000..5e8398f51
--- /dev/null
+++ b/docs/commands/hyperopt-list.md
@@ -0,0 +1,62 @@
+```
+usage: freqtrade hyperopt-list [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                               [-c PATH] [-d PATH] [--userdir PATH] [--best]
+                               [--profitable] [--min-trades INT]
+                               [--max-trades INT] [--min-avg-time FLOAT]
+                               [--max-avg-time FLOAT] [--min-avg-profit FLOAT]
+                               [--max-avg-profit FLOAT]
+                               [--min-total-profit FLOAT]
+                               [--max-total-profit FLOAT]
+                               [--min-objective FLOAT] [--max-objective FLOAT]
+                               [--print-json] [--no-details]
+                               [--hyperopt-filename FILENAME]
+                               [--export-csv FILE]
+
+options:
+  -h, --help            show this help message and exit
+  --best                Select only best epochs.
+  --profitable          Select only profitable epochs.
+  --min-trades INT      Select epochs with more than INT trades.
+  --max-trades INT      Select epochs with less than INT trades.
+  --min-avg-time FLOAT  Select epochs above average time.
+  --max-avg-time FLOAT  Select epochs below average time.
+  --min-avg-profit FLOAT
+                        Select epochs above average profit.
+  --max-avg-profit FLOAT
+                        Select epochs below average profit.
+  --min-total-profit FLOAT
+                        Select epochs above total profit.
+  --max-total-profit FLOAT
+                        Select epochs below total profit.
+  --min-objective FLOAT
+                        Select epochs above objective.
+  --max-objective FLOAT
+                        Select epochs below objective.
+  --print-json          Print output in JSON format.
+  --no-details          Do not print best epoch details.
+  --hyperopt-filename FILENAME
+                        Hyperopt result filename.Example: `--hyperopt-
+                        filename=hyperopt_results_2020-09-27_16-20-48.pickle`
+  --export-csv FILE     Export to CSV-File. This will disable table print.
+                        Example: --export-csv hyperopt.csv
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/hyperopt-show.md b/docs/commands/hyperopt-show.md
new file mode 100644
index 000000000..14d6516e9
--- /dev/null
+++ b/docs/commands/hyperopt-show.md
@@ -0,0 +1,44 @@
+```
+usage: freqtrade hyperopt-show [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                               [-c PATH] [-d PATH] [--userdir PATH] [--best]
+                               [--profitable] [-n INT] [--print-json]
+                               [--hyperopt-filename FILENAME] [--no-header]
+                               [--disable-param-export]
+                               [--breakdown {day,week,month,year} [{day,week,month,year} ...]]
+
+options:
+  -h, --help            show this help message and exit
+  --best                Select only best epochs.
+  --profitable          Select only profitable epochs.
+  -n INT, --index INT   Specify the index of the epoch to print details for.
+  --print-json          Print output in JSON format.
+  --hyperopt-filename FILENAME
+                        Hyperopt result filename.Example: `--hyperopt-
+                        filename=hyperopt_results_2020-09-27_16-20-48.pickle`
+  --no-header           Do not print epoch details header.
+  --disable-param-export
+                        Disable automatic hyperopt parameter export.
+  --breakdown {day,week,month,year} [{day,week,month,year} ...]
+                        Show backtesting breakdown per [day, week, month,
+                        year].
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/hyperopt.md b/docs/commands/hyperopt.md
new file mode 100644
index 000000000..28ad45dd2
--- /dev/null
+++ b/docs/commands/hyperopt.md
@@ -0,0 +1,122 @@
+```
+usage: freqtrade hyperopt [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                          [-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
+                          [--strategy-path PATH] [--recursive-strategy-search]
+                          [--freqaimodel NAME] [--freqaimodel-path PATH]
+                          [-i TIMEFRAME] [--timerange TIMERANGE]
+                          [--data-format-ohlcv {json,jsongz,feather,parquet}]
+                          [--max-open-trades INT]
+                          [--stake-amount STAKE_AMOUNT] [--fee FLOAT]
+                          [-p PAIRS [PAIRS ...]] [--hyperopt-path PATH]
+                          [--eps] [--enable-protections]
+                          [--dry-run-wallet DRY_RUN_WALLET]
+                          [--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
+                          [--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]]
+                          [--print-all] [--print-json] [-j JOBS]
+                          [--random-state INT] [--min-trades INT]
+                          [--hyperopt-loss NAME] [--disable-param-export]
+                          [--ignore-missing-spaces] [--analyze-per-epoch]
+
+options:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --data-format-ohlcv {json,jsongz,feather,parquet}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `feather`).
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt Loss
+                        functions.
+  --eps, --enable-position-stacking
+                        Allow buying the same pair multiple times (position
+                        stacking).
+  --enable-protections, --enableprotections
+                        Enable protections for backtesting.Will slow
+                        backtesting down by a considerable amount, but will
+                        include configured protections
+  --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
+                        Starting balance, used for backtesting / hyperopt and
+                        dry-runs.
+  --timeframe-detail TIMEFRAME_DETAIL
+                        Specify detail timeframe for backtesting (`1m`, `5m`,
+                        `30m`, `1h`, `1d`).
+  -e INT, --epochs INT  Specify number of epochs (default: 100).
+  --spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]
+                        Specify which parameters to hyperopt. Space-separated
+                        list.
+  --print-all           Print all results, not only the best ones.
+  --print-json          Print output in JSON format.
+  -j JOBS, --job-workers JOBS
+                        The number of concurrently running jobs for
+                        hyperoptimization (hyperopt worker processes). If -1
+                        (default), all CPUs are used, for -2, all CPUs but one
+                        are used, etc. If 1 is given, no parallel computing
+                        code is used at all.
+  --random-state INT    Set random state to some positive integer for
+                        reproducible hyperopt results.
+  --min-trades INT      Set minimal desired number of trades for evaluations
+                        in the hyperopt optimization path (default: 1).
+  --hyperopt-loss NAME, --hyperoptloss NAME
+                        Specify the class name of the hyperopt loss function
+                        class (IHyperOptLoss). Different functions can
+                        generate completely different results, since the
+                        target for optimization is different. Built-in
+                        Hyperopt-loss-functions are:
+                        ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
+                        SharpeHyperOptLoss, SharpeHyperOptLossDaily,
+                        SortinoHyperOptLoss, SortinoHyperOptLossDaily,
+                        CalmarHyperOptLoss, MaxDrawDownHyperOptLoss,
+                        MaxDrawDownRelativeHyperOptLoss,
+                        MaxDrawDownPerPairHyperOptLoss,
+                        ProfitDrawDownHyperOptLoss, MultiMetricHyperOptLoss
+  --disable-param-export
+                        Disable automatic hyperopt parameter export.
+  --ignore-missing-spaces, --ignore-unparameterized-spaces
+                        Suppress errors for any requested Hyperopt spaces that
+                        do not contain any parameters.
+  --analyze-per-epoch   Run populate_indicators once per epoch.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+
+```
diff --git a/docs/commands/install-ui.md b/docs/commands/install-ui.md
new file mode 100644
index 000000000..b8430e36e
--- /dev/null
+++ b/docs/commands/install-ui.md
@@ -0,0 +1,14 @@
+```
+usage: freqtrade install-ui [-h] [--erase] [--prerelease]
+                            [--ui-version UI_VERSION]
+
+options:
+  -h, --help            show this help message and exit
+  --erase               Clean UI folder, don't download new version.
+  --prerelease          Install the latest pre-release version of FreqUI. This
+                        is not recommended for production use.
+  --ui-version UI_VERSION
+                        Specify a specific version of FreqUI to install. Not
+                        specifying this installs the latest version.
+
+```
diff --git a/docs/commands/list-data.md b/docs/commands/list-data.md
new file mode 100644
index 000000000..c6e8cc94e
--- /dev/null
+++ b/docs/commands/list-data.md
@@ -0,0 +1,48 @@
+```
+usage: freqtrade list-data [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                           [-c PATH] [-d PATH] [--userdir PATH]
+                           [--exchange EXCHANGE]
+                           [--data-format-ohlcv {json,jsongz,feather,parquet}]
+                           [--data-format-trades {json,jsongz,feather,parquet}]
+                           [--trades] [-p PAIRS [PAIRS ...]]
+                           [--trading-mode {spot,margin,futures}]
+                           [--show-timerange]
+
+options:
+  -h, --help            show this help message and exit
+  --exchange EXCHANGE   Exchange name. Only valid if no config is provided.
+  --data-format-ohlcv {json,jsongz,feather,parquet}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `feather`).
+  --data-format-trades {json,jsongz,feather,parquet}
+                        Storage format for downloaded trades data. (default:
+                        `feather`).
+  --trades              Work on trades data instead of OHLCV data.
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
+                        Select Trading mode
+  --show-timerange      Show timerange available for available data. (May take
+                        a while to calculate).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/list-exchanges.md b/docs/commands/list-exchanges.md
new file mode 100644
index 000000000..c95ae3ebd
--- /dev/null
+++ b/docs/commands/list-exchanges.md
@@ -0,0 +1,29 @@
+```
+usage: freqtrade list-exchanges [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                                [-c PATH] [-d PATH] [--userdir PATH] [-1] [-a]
+
+options:
+  -h, --help            show this help message and exit
+  -1, --one-column      Print output in one column.
+  -a, --all             Print all exchanges known to the ccxt library.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/list-freqaimodels.md b/docs/commands/list-freqaimodels.md
new file mode 100644
index 000000000..3e66880ab
--- /dev/null
+++ b/docs/commands/list-freqaimodels.md
@@ -0,0 +1,31 @@
+```
+usage: freqtrade list-freqaimodels [-h] [-v] [--no-color] [--logfile FILE]
+                                   [-V] [-c PATH] [-d PATH] [--userdir PATH]
+                                   [--freqaimodel-path PATH] [-1]
+
+options:
+  -h, --help            show this help message and exit
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+  -1, --one-column      Print output in one column.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/list-hyperoptloss.md b/docs/commands/list-hyperoptloss.md
new file mode 100644
index 000000000..1a1604b0f
--- /dev/null
+++ b/docs/commands/list-hyperoptloss.md
@@ -0,0 +1,31 @@
+```
+usage: freqtrade list-hyperoptloss [-h] [-v] [--no-color] [--logfile FILE]
+                                   [-V] [-c PATH] [-d PATH] [--userdir PATH]
+                                   [--hyperopt-path PATH] [-1]
+
+options:
+  -h, --help            show this help message and exit
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt Loss
+                        functions.
+  -1, --one-column      Print output in one column.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/list-markets.md b/docs/commands/list-markets.md
new file mode 100644
index 000000000..d33b9c5d1
--- /dev/null
+++ b/docs/commands/list-markets.md
@@ -0,0 +1,46 @@
+```
+usage: freqtrade list-markets [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                              [-c PATH] [-d PATH] [--userdir PATH]
+                              [--exchange EXCHANGE] [--print-list]
+                              [--print-json] [-1] [--print-csv]
+                              [--base BASE_CURRENCY [BASE_CURRENCY ...]]
+                              [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
+                              [-a] [--trading-mode {spot,margin,futures}]
+
+options:
+  -h, --help            show this help message and exit
+  --exchange EXCHANGE   Exchange name. Only valid if no config is provided.
+  --print-list          Print list of pairs or market symbols. By default data
+                        is printed in the tabular format.
+  --print-json          Print list of pairs or market symbols in JSON format.
+  -1, --one-column      Print output in one column.
+  --print-csv           Print exchange pair or market data in the csv format.
+  --base BASE_CURRENCY [BASE_CURRENCY ...]
+                        Specify base currency(-ies). Space-separated list.
+  --quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]
+                        Specify quote currency(-ies). Space-separated list.
+  -a, --all             Print all pairs or market symbols. By default only
+                        active ones are shown.
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
+                        Select Trading mode
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/list-pairs.md b/docs/commands/list-pairs.md
new file mode 100644
index 000000000..3d8e677ee
--- /dev/null
+++ b/docs/commands/list-pairs.md
@@ -0,0 +1,46 @@
+```
+usage: freqtrade list-pairs [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                            [-c PATH] [-d PATH] [--userdir PATH]
+                            [--exchange EXCHANGE] [--print-list]
+                            [--print-json] [-1] [--print-csv]
+                            [--base BASE_CURRENCY [BASE_CURRENCY ...]]
+                            [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]] [-a]
+                            [--trading-mode {spot,margin,futures}]
+
+options:
+  -h, --help            show this help message and exit
+  --exchange EXCHANGE   Exchange name. Only valid if no config is provided.
+  --print-list          Print list of pairs or market symbols. By default data
+                        is printed in the tabular format.
+  --print-json          Print list of pairs or market symbols in JSON format.
+  -1, --one-column      Print output in one column.
+  --print-csv           Print exchange pair or market data in the csv format.
+  --base BASE_CURRENCY [BASE_CURRENCY ...]
+                        Specify base currency(-ies). Space-separated list.
+  --quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]
+                        Specify quote currency(-ies). Space-separated list.
+  -a, --all             Print all pairs or market symbols. By default only
+                        active ones are shown.
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
+                        Select Trading mode
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/list-strategies.md b/docs/commands/list-strategies.md
new file mode 100644
index 000000000..af5771860
--- /dev/null
+++ b/docs/commands/list-strategies.md
@@ -0,0 +1,34 @@
+```
+usage: freqtrade list-strategies [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                                 [-c PATH] [-d PATH] [--userdir PATH]
+                                 [--strategy-path PATH] [-1]
+                                 [--recursive-strategy-search]
+
+options:
+  -h, --help            show this help message and exit
+  --strategy-path PATH  Specify additional strategy lookup path.
+  -1, --one-column      Print output in one column.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/list-timeframes.md b/docs/commands/list-timeframes.md
new file mode 100644
index 000000000..8dc10a789
--- /dev/null
+++ b/docs/commands/list-timeframes.md
@@ -0,0 +1,30 @@
+```
+usage: freqtrade list-timeframes [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                                 [-c PATH] [-d PATH] [--userdir PATH]
+                                 [--exchange EXCHANGE] [-1]
+
+options:
+  -h, --help            show this help message and exit
+  --exchange EXCHANGE   Exchange name. Only valid if no config is provided.
+  -1, --one-column      Print output in one column.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/lookahead-analysis.md b/docs/commands/lookahead-analysis.md
new file mode 100644
index 000000000..bb7ef5d8e
--- /dev/null
+++ b/docs/commands/lookahead-analysis.md
@@ -0,0 +1,108 @@
+```
+usage: freqtrade lookahead-analysis [-h] [-v] [--no-color] [--logfile FILE]
+                                    [-V] [-c PATH] [-d PATH] [--userdir PATH]
+                                    [-s NAME] [--strategy-path PATH]
+                                    [--recursive-strategy-search]
+                                    [--freqaimodel NAME]
+                                    [--freqaimodel-path PATH] [-i TIMEFRAME]
+                                    [--timerange TIMERANGE]
+                                    [--data-format-ohlcv {json,jsongz,feather,parquet}]
+                                    [--max-open-trades INT]
+                                    [--stake-amount STAKE_AMOUNT]
+                                    [--fee FLOAT] [-p PAIRS [PAIRS ...]]
+                                    [--enable-protections]
+                                    [--dry-run-wallet DRY_RUN_WALLET]
+                                    [--timeframe-detail TIMEFRAME_DETAIL]
+                                    [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
+                                    [--export {none,trades,signals}]
+                                    [--export-filename PATH]
+                                    [--freqai-backtest-live-models]
+                                    [--minimum-trade-amount INT]
+                                    [--targeted-trade-amount INT]
+                                    [--lookahead-analysis-exportfilename LOOKAHEAD_ANALYSIS_EXPORTFILENAME]
+
+options:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --data-format-ohlcv {json,jsongz,feather,parquet}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `feather`).
+  --max-open-trades INT
+                        Override the value of the `max_open_trades`
+                        configuration setting.
+  --stake-amount STAKE_AMOUNT
+                        Override the value of the `stake_amount` configuration
+                        setting.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --enable-protections, --enableprotections
+                        Enable protections for backtesting.Will slow
+                        backtesting down by a considerable amount, but will
+                        include configured protections
+  --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
+                        Starting balance, used for backtesting / hyperopt and
+                        dry-runs.
+  --timeframe-detail TIMEFRAME_DETAIL
+                        Specify detail timeframe for backtesting (`1m`, `5m`,
+                        `30m`, `1h`, `1d`).
+  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
+                        Provide a space-separated list of strategies to
+                        backtest. Please note that timeframe needs to be set
+                        either in config or via command line. When using this
+                        together with `--export trades`, the strategy-name is
+                        injected into the filename (so `backtest-data.json`
+                        becomes `backtest-data-SampleStrategy.json`
+  --export {none,trades,signals}
+                        Export backtest results (default: trades).
+  --export-filename PATH, --backtest-filename PATH
+                        Use this filename for backtest results.Requires
+                        `--export` to be set as well. Example: `--export-filen
+                        ame=user_data/backtest_results/backtest_today.json`
+  --freqai-backtest-live-models
+                        Run backtest with ready models.
+  --minimum-trade-amount INT
+                        Minimum trade amount for lookahead-analysis
+  --targeted-trade-amount INT
+                        Targeted trade amount for lookahead analysis
+  --lookahead-analysis-exportfilename LOOKAHEAD_ANALYSIS_EXPORTFILENAME
+                        Use this csv-filename to store lookahead-analysis-
+                        results
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+
+```
diff --git a/docs/commands/main.md b/docs/commands/main.md
new file mode 100644
index 000000000..c84f4854e
--- /dev/null
+++ b/docs/commands/main.md
@@ -0,0 +1,51 @@
+```
+usage: freqtrade [-h] [-V]
+                 {trade,create-userdir,new-config,show-config,new-strategy,download-data,convert-data,convert-trade-data,trades-to-ohlcv,list-data,backtesting,backtesting-show,backtesting-analysis,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-markets,list-pairs,list-strategies,list-hyperoptloss,list-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
+                 ...
+
+Free, open source crypto trading bot
+
+positional arguments:
+  {trade,create-userdir,new-config,show-config,new-strategy,download-data,convert-data,convert-trade-data,trades-to-ohlcv,list-data,backtesting,backtesting-show,backtesting-analysis,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-markets,list-pairs,list-strategies,list-hyperoptloss,list-freqaimodels,list-timeframes,show-trades,test-pairlist,convert-db,install-ui,plot-dataframe,plot-profit,webserver,strategy-updater,lookahead-analysis,recursive-analysis}
+    trade               Trade module.
+    create-userdir      Create user-data directory.
+    new-config          Create new config
+    show-config         Show resolved config
+    new-strategy        Create new strategy
+    download-data       Download backtesting data.
+    convert-data        Convert candle (OHLCV) data from one format to
+                        another.
+    convert-trade-data  Convert trade data from one format to another.
+    trades-to-ohlcv     Convert trade data to OHLCV data.
+    list-data           List downloaded data.
+    backtesting         Backtesting module.
+    backtesting-show    Show past Backtest results
+    backtesting-analysis
+                        Backtest Analysis module.
+    edge                Edge module.
+    hyperopt            Hyperopt module.
+    hyperopt-list       List Hyperopt results
+    hyperopt-show       Show details of Hyperopt results
+    list-exchanges      Print available exchanges.
+    list-markets        Print markets on exchange.
+    list-pairs          Print pairs on exchange.
+    list-strategies     Print available strategies.
+    list-hyperoptloss   Print available hyperopt loss functions.
+    list-freqaimodels   Print available freqAI models.
+    list-timeframes     Print available timeframes for the exchange.
+    show-trades         Show trades.
+    test-pairlist       Test your pairlist configuration.
+    convert-db          Migrate database to different system
+    install-ui          Install FreqUI
+    plot-dataframe      Plot candles with indicators.
+    plot-profit         Generate plot showing profits.
+    webserver           Webserver module.
+    strategy-updater    updates outdated strategy files to the current version
+    lookahead-analysis  Check for potential look ahead bias.
+    recursive-analysis  Check for potential recursive formula issue.
+
+options:
+  -h, --help            show this help message and exit
+  -V, --version         show program's version number and exit
+
+```
diff --git a/docs/commands/new-config.md b/docs/commands/new-config.md
new file mode 100644
index 000000000..ec604321b
--- /dev/null
+++ b/docs/commands/new-config.md
@@ -0,0 +1,12 @@
+```
+usage: freqtrade new-config [-h] [-c PATH]
+
+options:
+  -h, --help            show this help message and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+
+```
diff --git a/docs/commands/new-strategy.md b/docs/commands/new-strategy.md
new file mode 100644
index 000000000..bf4bbf44f
--- /dev/null
+++ b/docs/commands/new-strategy.md
@@ -0,0 +1,19 @@
+```
+usage: freqtrade new-strategy [-h] [--userdir PATH] [-s NAME]
+                              [--strategy-path PATH]
+                              [--template {full,minimal,advanced}]
+
+options:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --template {full,minimal,advanced}
+                        Use a template which is either `minimal`, `full`
+                        (containing multiple sample indicators) or `advanced`.
+                        Default: `full`.
+
+```
diff --git a/docs/commands/plot-dataframe.md b/docs/commands/plot-dataframe.md
new file mode 100644
index 000000000..81ecc0da7
--- /dev/null
+++ b/docs/commands/plot-dataframe.md
@@ -0,0 +1,82 @@
+```
+usage: freqtrade plot-dataframe [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                                [-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
+                                [--strategy-path PATH]
+                                [--recursive-strategy-search]
+                                [--freqaimodel NAME] [--freqaimodel-path PATH]
+                                [-p PAIRS [PAIRS ...]]
+                                [--indicators1 INDICATORS1 [INDICATORS1 ...]]
+                                [--indicators2 INDICATORS2 [INDICATORS2 ...]]
+                                [--plot-limit INT] [--db-url PATH]
+                                [--trade-source {DB,file}]
+                                [--export {none,trades,signals}]
+                                [--export-filename PATH]
+                                [--timerange TIMERANGE] [-i TIMEFRAME]
+                                [--no-trades]
+
+options:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --indicators1 INDICATORS1 [INDICATORS1 ...]
+                        Set indicators from your strategy you want in the
+                        first row of the graph. Space-separated list. Example:
+                        `ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.
+  --indicators2 INDICATORS2 [INDICATORS2 ...]
+                        Set indicators from your strategy you want in the
+                        third row of the graph. Space-separated list. Example:
+                        `fastd fastk`. Default: `['macd', 'macdsignal']`.
+  --plot-limit INT      Specify tick limit for plotting. Notice: too high
+                        values cause huge files. Default: 750.
+  --db-url PATH         Override trades database URL, this is useful in custom
+                        deployments (default: `sqlite:///tradesv3.sqlite` for
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
+  --trade-source {DB,file}
+                        Specify the source for trades (Can be DB or file
+                        (backtest file)) Default: file
+  --export {none,trades,signals}
+                        Export backtest results (default: trades).
+  --export-filename PATH, --backtest-filename PATH
+                        Use this filename for backtest results.Requires
+                        `--export` to be set as well. Example: `--export-filen
+                        ame=user_data/backtest_results/backtest_today.json`
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --no-trades           Skip using trades from backtesting file and DB.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+
+```
diff --git a/docs/commands/plot-profit.md b/docs/commands/plot-profit.md
new file mode 100644
index 000000000..207697a93
--- /dev/null
+++ b/docs/commands/plot-profit.md
@@ -0,0 +1,68 @@
+```
+usage: freqtrade plot-profit [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                             [-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
+                             [--strategy-path PATH]
+                             [--recursive-strategy-search]
+                             [--freqaimodel NAME] [--freqaimodel-path PATH]
+                             [-p PAIRS [PAIRS ...]] [--timerange TIMERANGE]
+                             [--export {none,trades,signals}]
+                             [--export-filename PATH] [--db-url PATH]
+                             [--trade-source {DB,file}] [-i TIMEFRAME]
+                             [--auto-open]
+
+options:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --export {none,trades,signals}
+                        Export backtest results (default: trades).
+  --export-filename PATH, --backtest-filename PATH
+                        Use this filename for backtest results.Requires
+                        `--export` to be set as well. Example: `--export-filen
+                        ame=user_data/backtest_results/backtest_today.json`
+  --db-url PATH         Override trades database URL, this is useful in custom
+                        deployments (default: `sqlite:///tradesv3.sqlite` for
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
+  --trade-source {DB,file}
+                        Specify the source for trades (Can be DB or file
+                        (backtest file)) Default: file
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --auto-open           Automatically open generated plot.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+
+```
diff --git a/docs/commands/recursive-analysis.md b/docs/commands/recursive-analysis.md
new file mode 100644
index 000000000..b39e8fb35
--- /dev/null
+++ b/docs/commands/recursive-analysis.md
@@ -0,0 +1,60 @@
+```
+usage: freqtrade recursive-analysis [-h] [-v] [--no-color] [--logfile FILE]
+                                    [-V] [-c PATH] [-d PATH] [--userdir PATH]
+                                    [-s NAME] [--strategy-path PATH]
+                                    [--recursive-strategy-search]
+                                    [--freqaimodel NAME]
+                                    [--freqaimodel-path PATH] [-i TIMEFRAME]
+                                    [--timerange TIMERANGE]
+                                    [--data-format-ohlcv {json,jsongz,feather,parquet}]
+                                    [-p PAIRS [PAIRS ...]]
+                                    [--startup-candle STARTUP_CANDLE [STARTUP_CANDLE ...]]
+
+options:
+  -h, --help            show this help message and exit
+  -i TIMEFRAME, --timeframe TIMEFRAME
+                        Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).
+  --timerange TIMERANGE
+                        Specify what timerange of data to use.
+  --data-format-ohlcv {json,jsongz,feather,parquet}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `feather`).
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  --startup-candle STARTUP_CANDLE [STARTUP_CANDLE ...]
+                        Specify startup candles to be checked (`199`, `499`,
+                        `999`, `1999`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+
+```
diff --git a/docs/commands/show-config.md b/docs/commands/show-config.md
new file mode 100644
index 000000000..7ebf6fd84
--- /dev/null
+++ b/docs/commands/show-config.md
@@ -0,0 +1,16 @@
+```
+usage: freqtrade show-config [-h] [--userdir PATH] [-c PATH]
+                             [--show-sensitive]
+
+options:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  --show-sensitive      Show secrets in the output.
+
+```
diff --git a/docs/commands/show-trades.md b/docs/commands/show-trades.md
new file mode 100644
index 000000000..316d47448
--- /dev/null
+++ b/docs/commands/show-trades.md
@@ -0,0 +1,37 @@
+```
+usage: freqtrade show-trades [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                             [-c PATH] [-d PATH] [--userdir PATH]
+                             [--db-url PATH]
+                             [--trade-ids TRADE_IDS [TRADE_IDS ...]]
+                             [--print-json]
+
+options:
+  -h, --help            show this help message and exit
+  --db-url PATH         Override trades database URL, this is useful in custom
+                        deployments (default: `sqlite:///tradesv3.sqlite` for
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
+  --trade-ids TRADE_IDS [TRADE_IDS ...]
+                        Specify the list of trade ids.
+  --print-json          Print output in JSON format.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/strategy-updater.md b/docs/commands/strategy-updater.md
new file mode 100644
index 000000000..ba40322ec
--- /dev/null
+++ b/docs/commands/strategy-updater.md
@@ -0,0 +1,41 @@
+```
+usage: freqtrade strategy-updater [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                                  [-c PATH] [-d PATH] [--userdir PATH]
+                                  [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
+                                  [--strategy-path PATH]
+                                  [--recursive-strategy-search]
+
+options:
+  -h, --help            show this help message and exit
+  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
+                        Provide a space-separated list of strategies to
+                        backtest. Please note that timeframe needs to be set
+                        either in config or via command line. When using this
+                        together with `--export trades`, the strategy-name is
+                        injected into the filename (so `backtest-data.json`
+                        becomes `backtest-data-SampleStrategy.json`
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/test-pairlist.md b/docs/commands/test-pairlist.md
new file mode 100644
index 000000000..51a278b52
--- /dev/null
+++ b/docs/commands/test-pairlist.md
@@ -0,0 +1,22 @@
+```
+usage: freqtrade test-pairlist [-h] [--userdir PATH] [-v] [-c PATH]
+                               [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
+                               [-1] [--print-json] [--exchange EXCHANGE]
+
+options:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  --quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]
+                        Specify quote currency(-ies). Space-separated list.
+  -1, --one-column      Print output in one column.
+  --print-json          Print list of pairs or market symbols in JSON format.
+  --exchange EXCHANGE   Exchange name. Only valid if no config is provided.
+
+```
diff --git a/docs/commands/trade.md b/docs/commands/trade.md
new file mode 100644
index 000000000..b689902c2
--- /dev/null
+++ b/docs/commands/trade.md
@@ -0,0 +1,55 @@
+```
+usage: freqtrade trade [-h] [-v] [--no-color] [--logfile FILE] [-V] [-c PATH]
+                       [-d PATH] [--userdir PATH] [-s NAME]
+                       [--strategy-path PATH] [--recursive-strategy-search]
+                       [--freqaimodel NAME] [--freqaimodel-path PATH]
+                       [--db-url PATH] [--sd-notify] [--dry-run]
+                       [--dry-run-wallet DRY_RUN_WALLET] [--fee FLOAT]
+
+options:
+  -h, --help            show this help message and exit
+  --db-url PATH         Override trades database URL, this is useful in custom
+                        deployments (default: `sqlite:///tradesv3.sqlite` for
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
+  --sd-notify           Notify systemd service manager.
+  --dry-run             Enforce dry-run for trading (removes Exchange secrets
+                        and simulates trades).
+  --dry-run-wallet DRY_RUN_WALLET, --starting-balance DRY_RUN_WALLET
+                        Starting balance, used for backtesting / hyperopt and
+                        dry-runs.
+  --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
+                        entry and exit).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
+  --recursive-strategy-search
+                        Recursively search for a strategy in the strategies
+                        folder.
+  --freqaimodel NAME    Specify a custom freqaimodels.
+  --freqaimodel-path PATH
+                        Specify additional lookup path for freqaimodels.
+
+```
diff --git a/docs/commands/trades-to-ohlcv.md b/docs/commands/trades-to-ohlcv.md
new file mode 100644
index 000000000..c2d42a5cc
--- /dev/null
+++ b/docs/commands/trades-to-ohlcv.md
@@ -0,0 +1,48 @@
+```
+usage: freqtrade trades-to-ohlcv [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                                 [-c PATH] [-d PATH] [--userdir PATH]
+                                 [-p PAIRS [PAIRS ...]]
+                                 [-t TIMEFRAMES [TIMEFRAMES ...]]
+                                 [--exchange EXCHANGE]
+                                 [--data-format-ohlcv {json,jsongz,feather,parquet}]
+                                 [--data-format-trades {json,jsongz,feather,parquet}]
+                                 [--trading-mode {spot,margin,futures}]
+
+options:
+  -h, --help            show this help message and exit
+  -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
+                        Limit command to these pairs. Pairs are space-
+                        separated.
+  -t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
+                        Specify which tickers to download. Space-separated
+                        list. Default: `1m 5m`.
+  --exchange EXCHANGE   Exchange name. Only valid if no config is provided.
+  --data-format-ohlcv {json,jsongz,feather,parquet}
+                        Storage format for downloaded candle (OHLCV) data.
+                        (default: `feather`).
+  --data-format-trades {json,jsongz,feather,parquet}
+                        Storage format for downloaded trades data. (default:
+                        `feather`).
+  --trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
+                        Select Trading mode
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/commands/webserver.md b/docs/commands/webserver.md
new file mode 100644
index 000000000..a99b83e97
--- /dev/null
+++ b/docs/commands/webserver.md
@@ -0,0 +1,27 @@
+```
+usage: freqtrade webserver [-h] [-v] [--no-color] [--logfile FILE] [-V]
+                           [-c PATH] [-d PATH] [--userdir PATH]
+
+options:
+  -h, --help            show this help message and exit
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --logfile FILE, --log-file FILE
+                        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default:
+                        `userdir/config.json` or `config.json` whichever
+                        exists). Multiple --config options may be used. Can be
+                        set to `-` to read config from stdin.
+  -d PATH, --datadir PATH, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```
diff --git a/docs/configuration.md b/docs/configuration.md
index 5e3b15925..8c122b9b7 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1,138 +1,800 @@
 # Configure the bot
-This page explains how to configure your `config.json` file.
 
-## Table of Contents
-- [Bot commands](#bot-commands)
-- [Backtesting commands](#backtesting-commands)
-- [Hyperopt commands](#hyperopt-commands)
+Freqtrade has many configurable features and possibilities.
+By default, these settings are configured via the configuration file (see below).
 
-## Setup config.json
-We recommend to copy and use the `config.json.example` as a template
-for your bot configuration.
+## The Freqtrade configuration file
 
-The table below will list all configuration parameters. 
+The bot uses a set of configuration parameters during its operation that all together conform to the bot configuration. It normally reads its configuration from a file (Freqtrade configuration file).
 
-|  Command | Default | Mandatory | Description |
-|----------|---------|----------|-------------|
-| `max_open_trades` | 3 | Yes | Number of trades open your bot will have.
-| `stake_currency` | BTC | Yes | Crypto-currency used for trading.
-| `stake_amount` | 0.05 | Yes | Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged.
-| `ticker_interval` | [1, 5, 30, 60, 1440] | No | The ticker interval to use (1min, 5 min, 30 min, 1 hour or 1 day). Defaut is 5 minutes
-| `fiat_display_currency` | USD | Yes | Fiat currency used to show your profits. More information below. 
-| `dry_run` | true | Yes | Define if the bot must be in Dry-run or production mode. 
-| `minimal_roi` | See below | No | Set the threshold in percent the bot will use to sell a trade. More information below. If set, this parameter will override `minimal_roi` from your strategy file. 
-| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below. If set, this parameter will override `stoploss` from your strategy file. 
-| `unfilledtimeout` | 0 | No | How long (in minutes) the bot will wait for an unfilled order to complete, after which the order will be cancelled.
-| `bid_strategy.ask_last_balance` | 0.0 | Yes | Set the bidding price. More information below.
-| `exchange.name` | bittrex | Yes | Name of the exchange class to use.
-| `exchange.key` | key | No | API key to use for the exchange. Only required when you are in production mode.
-| `exchange.secret` | secret | No | API secret to use for the exchange. Only required when you are in production mode.
-| `exchange.pair_whitelist` | [] | No | List of currency to use by the bot. Can be overrided with `--dynamic-whitelist` param.
-| `exchange.pair_blacklist` | [] | No | List of currency the bot must avoid. Useful when using `--dynamic-whitelist` param.
-| `experimental.use_sell_signal` | false | No | Use your sell strategy in addition of the `minimal_roi`.
-| `experimental.sell_profit_only` | false | No | waits until you have made a positive profit before taking a sell decision.
-| `telegram.enabled` | true | Yes | Enable or not the usage of Telegram.
-| `telegram.token` | token | No | Your Telegram bot token. Only required if `telegram.enabled` is `true`.
-| `telegram.chat_id` | chat_id | No | Your personal Telegram account id. Only required if `telegram.enabled` is `true`.
-| `initial_state` | running | No | Defines the initial application state. More information below.
-| `internals.process_throttle_secs` | 5 | Yes | Set the process throttle. Value in second.
+Per default, the bot loads the configuration from the `config.json` file, located in the current working directory.
 
-The definition of each config parameters is in 
-[misc.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/misc.py#L205).
+You can specify a different configuration file used by the bot with the `-c/--config` command-line option.
+
+If you used the [Quick start](docker_quickstart.md#docker-quick-start) method for installing
+the bot, the installation script should have already created the default configuration file (`config.json`) for you.
+
+If the default configuration file is not created we recommend to use `freqtrade new-config --config user_data/config.json` to generate a basic configuration file.
+
+The Freqtrade configuration file is to be written in JSON format.
+
+Additionally to the standard JSON syntax, you may use one-line `// ...` and multi-line `/* ... */` comments in your configuration files and trailing commas in the lists of parameters.
+
+Do not worry if you are not familiar with JSON format -- simply open the configuration file with an editor of your choice, make some changes to the parameters you need, save your changes and, finally, restart the bot or, if it was previously stopped, run it again with the changes you made to the configuration. The bot validates the syntax of the configuration file at startup and will warn you if you made any errors editing it, pointing out problematic lines.
+
+### Environment variables
+
+Set options in the Freqtrade configuration via environment variables.
+This takes priority over the corresponding value in configuration or strategy.
+
+Environment variables must be prefixed with  `FREQTRADE__` to be loaded to the freqtrade configuration.
+
+`__` serves as level separator, so the format used should correspond to `FREQTRADE__{section}__{key}`.
+As such - an environment variable defined as  `export FREQTRADE__STAKE_AMOUNT=200` would result in `{stake_amount: 200}`.
+
+A more complex example might be `export FREQTRADE__EXCHANGE__KEY=<yourExchangeKey>` to keep your exchange key secret. This will move the value to the `exchange.key` section of the configuration.
+Using this scheme, all configuration settings will also be available as environment variables.
+
+Please note that Environment variables will overwrite corresponding settings in your configuration, but command line Arguments will always win.
+
+Common example:
+
+``` bash
+FREQTRADE__TELEGRAM__CHAT_ID=<telegramchatid>
+FREQTRADE__TELEGRAM__TOKEN=<telegramToken>
+FREQTRADE__EXCHANGE__KEY=<yourExchangeKey>
+FREQTRADE__EXCHANGE__SECRET=<yourExchangeSecret>
+```
+
+Json lists are parsed as json - so you can use the following to set a list of pairs:
+
+``` bash
+export FREQTRADE__EXCHANGE__PAIR_WHITELIST='["BTC/USDT", "ETH/USDT"]'
+```
+
+!!! Note
+    Environment variables detected are logged at startup - so if you can't find why a value is not what you think it should be based on the configuration, make sure it's not loaded from an environment variable.
+
+!!! Tip "Validate combined result"
+    You can use the [show-config subcommand](utils.md#show-config) to see the final, combined configuration.
+
+??? Warning "Loading sequence"
+    Environment variables are loaded after the initial configuration. As such, you cannot provide the path to the configuration through environment variables. Please use `--config path/to/config.json` for that.
+    This also applies to `user_dir` to some degree. while the user directory can be set through environment variables - the configuration will **not** be loaded from that location.
+
+### Multiple configuration files
+
+Multiple configuration files can be specified and used by the bot or the bot can read its configuration parameters from the process standard input stream.
+
+You can specify additional configuration files in `add_config_files`. Files specified in this parameter will be loaded and merged with the initial config file. The files are resolved relative to the initial configuration file.
+This is similar to using multiple `--config` parameters, but simpler in usage as you don't have to specify all files for all commands.
+
+!!! Tip "Validate combined result"
+    You can use the [show-config subcommand](utils.md#show-config) to see the final, combined configuration.
+
+!!! Tip "Use multiple configuration files to keep secrets secret"
+    You can use a 2nd configuration file containing your secrets. That way you can share your "primary" configuration file, while still keeping your API keys for yourself.
+    The 2nd file should only specify what you intend to override.
+    If a key is in more than one of the configurations, then the "last specified configuration" wins (in the above example, `config-private.json`).
+
+    For one-off commands, you can also use the below syntax by specifying multiple "--config" parameters.
+
+    ``` bash
+    freqtrade trade --config user_data/config1.json --config user_data/config-private.json <...>
+    ```
+
+    The below is equivalent to the example above - but having 2 configuration files in the configuration, for easier reuse.
+
+    ``` json title="user_data/config.json"
+    "add_config_files": [
+        "config1.json",
+        "config-private.json"
+    ]
+    ```
+
+    ``` bash
+    freqtrade trade --config user_data/config.json <...>
+    ```
+
+??? Note "config collision handling"
+    If the same configuration setting takes place in both `config.json` and `config-import.json`, then the parent configuration wins.
+    In the below case, `max_open_trades` would be 3 after the merging - as the reusable "import" configuration has this key overwritten.
+
+    ``` json title="user_data/config.json"
+    {
+        "max_open_trades": 3,
+        "stake_currency": "USDT",
+        "add_config_files": [
+            "config-import.json"
+        ]
+    }
+    ```
+
+    ``` json title="user_data/config-import.json"
+    {
+        "max_open_trades": 10,
+        "stake_amount": "unlimited",
+    }
+    ```
+
+    Resulting combined configuration:
+
+    ``` json title="Result"
+    {
+        "max_open_trades": 3,
+        "stake_currency": "USDT",
+        "stake_amount": "unlimited"
+    }
+    ```
+
+    If multiple files are in the `add_config_files` section, then they will be assumed to be at identical levels, having the last occurrence override the earlier config (unless a parent already defined such a key).
+
+## Editor autocomplete and validation
+
+If you are using an editor that supports JSON schema, you can use the schema provided by Freqtrade to get autocompletion and validation of your configuration file by adding the following line to the top of your configuration file:
+
+``` json
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+}
+```
+
+??? Note "Develop version"
+    The develop schema is available as `https://schema.freqtrade.io/schema_dev.json` - though we recommend to stick to the stable version for the best experience.
+
+## Configuration parameters
+
+The table below will list all configuration parameters available.
+
+Freqtrade can also load many options via command line (CLI) arguments (check out the commands `--help` output for details).
+
+### Configuration option prevalence
+
+The prevalence for all Options is as follows:
+
+* CLI arguments override any other option
+* [Environment Variables](#environment-variables)
+* Configuration files are used in sequence (the last file wins) and override Strategy configurations.
+* Strategy configurations are only used if they are not set via configuration or command-line arguments. These options are marked with [Strategy Override](#parameters-in-the-strategy) in the below table.
+
+### Parameters table
+
+Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
+
+|  Parameter | Description |
+|------------|-------------|
+| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Positive integer or -1.
+| `stake_currency` | **Required.** Crypto-currency used for trading. <br> **Datatype:** String
+| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). <br> **Datatype:** Positive float or `"unlimited"`.
+| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
+| `available_capital` | Available starting capital for the bot. Useful when running multiple bots on the same exchange account. [More information below](#configuring-amount-per-trade). <br> **Datatype:** Positive float.
+| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
+| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
+| `timeframe` | The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). Usually missing in configuration, and specified in the strategy. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
+| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
+| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in Dry Run mode. [More information below](#dry-run-wallet)<br>*Defaults to `1000`.* <br> **Datatype:** Float or Dict
+| `cancel_open_orders_on_exit` | Cancel open orders when the `/stop` RPC command is issued, `Ctrl+C` is pressed or the bot dies unexpectedly. When set to `true`, this allows you to use `/stop` to cancel unfilled and partially filled orders in the event of a market crash. It does not impact open positions. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.*  <br> **Datatype:** Boolean
+| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to exit a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `stoploss` |  **Required.** Value as ratio of the stoploss used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).  <br> **Datatype:** Float (as ratio)
+| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md#trailing-stop-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
+| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-custom-positive-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
+| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
+| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
+| `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
+| `futures_funding_rate` | User-specified funding rate to be used when historical funding rates are not available from the exchange. This does not overwrite real historical rates. It is recommended that this be set to 0 unless you are testing a specific coin and you understand how the funding rate will affect freqtrade's profit calculations. [More information here](leverage.md#unavailable-funding-rates) <br>*Defaults to `None`.*<br> **Datatype:** Float
+| `trading_mode` | Specifies if you want to trade regularly, trade with leverage, or trade contracts whose prices are derived from matching cryptocurrency prices. [leverage documentation](leverage.md). <br>*Defaults to `"spot"`.*  <br> **Datatype:** String
+| `margin_mode` | When trading with leverage, this determines if the collateral owned by the trader will be shared or isolated to each trading pair [leverage documentation](leverage.md). <br> **Datatype:** String
+| `liquidation_buffer` | A ratio specifying how large of a safety net to place between the liquidation price and the stoploss to prevent a position from reaching the liquidation price [leverage documentation](leverage.md). <br>*Defaults to `0.05`.*  <br> **Datatype:** Float
+| | **Unfilled timeout**
+| `unfilledtimeout.entry` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled entry order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `unfilledtimeout.exit` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled exit order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy). <br> *Defaults to `"minutes"`.* <br> **Datatype:** String
+| `unfilledtimeout.exit_timeout_count` | How many times can exit orders time out. Once this number of timeouts is reached, an emergency exit is triggered. 0 to disable and allow unlimited order cancels. [Strategy Override](#parameters-in-the-strategy).<br>*Defaults to `0`.* <br> **Datatype:** Integer
+| | **Pricing**
+| `entry_pricing.price_side` | Select the side of the spread the bot should look at to get the entry rate. [More information below](#entry-price).<br> *Defaults to `"same"`.* <br> **Datatype:** String (either `ask`, `bid`, `same` or `other`).
+| `entry_pricing.price_last_balance` | **Required.** Interpolate the bidding price. More information [below](#entry-price-without-orderbook-enabled).
+| `entry_pricing.use_order_book` | Enable entering using the rates in [Order Book Entry](#entry-price-with-orderbook-enabled). <br> *Defaults to `true`.*<br> **Datatype:** Boolean
+| `entry_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to enter a trade. I.e. a value of 2 will allow the bot to pick the 2nd entry in [Order Book Entry](#entry-price-with-orderbook-enabled). <br>*Defaults to `1`.*  <br> **Datatype:** Positive Integer
+| `entry_pricing. check_depth_of_market.enabled` | Do not enter if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `entry_pricing. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.*  <br> **Datatype:** Float (as ratio)
+| `exit_pricing.price_side` | Select the side of the spread the bot should look at to get the exit rate. [More information below](#exit-price-side).<br> *Defaults to `"same"`.* <br> **Datatype:** String (either `ask`, `bid`, `same` or `other`).
+| `exit_pricing.price_last_balance` | Interpolate the exiting price. More information [below](#exit-price-without-orderbook-enabled).
+| `exit_pricing.use_order_book` | Enable exiting of open trades using [Order Book Exit](#exit-price-with-orderbook-enabled). <br> *Defaults to `true`.*<br> **Datatype:** Boolean
+| `exit_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to exit. I.e. a value of 2 will allow the bot to pick the 2nd ask rate in [Order Book Exit](#exit-price-with-orderbook-enabled)<br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
+| `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price. <br>*Defaults to `0.02` 2%).*<br> **Datatype:** Positive float
+| | **Order/Signal handling**
+| `use_exit_signal` | Use exit signals produced by the strategy in addition to the `minimal_roi`. <br>Setting this to false disables the usage of `"exit_long"` and `"exit_short"` columns. Has no influence on other exit methods (Stoploss, ROI, callbacks). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `exit_profit_only` | Wait until the bot reaches `exit_profit_offset` before taking an exit decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `exit_profit_offset` | Exit-signal is only active above this value. Only active in combination with `exit_profit_only=True`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
+| `ignore_roi_if_entry_signal` | Do not exit if the entry signal is still active. This setting takes preference over `minimal_roi` and `use_exit_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `ignore_buying_expired_candle_after` | Specifies the number of seconds until a buy signal is no longer used. <br> **Datatype:** Integer
+| `order_types` | Configure order-types depending on the action (`"entry"`, `"exit"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
+| `order_time_in_force` | Configure time in force for entry and exit orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
+| `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
+| `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
+| | **Exchange**
+| `exchange.name` | **Required.** Name of the exchange class to use. <br> **Datatype:** String
+| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.uid` | API uid to use for the exchange. Only required when you are in production mode and for exchanges that use uid for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Supports regex pairs as `.*/BTC`. Not used by VolumePairList. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
+| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
+| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for additional ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://docs.ccxt.com/#/README?id=overriding-exchange-properties-upon-instantiation). Please avoid adding exchange secrets here (use the dedicated fields instead), as they may be contained in logs. <br> **Datatype:** Dict
+| `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://docs.ccxt.com/#/README?id=overriding-exchange-properties-upon-instantiation) <br> **Datatype:** Dict
+| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://docs.ccxt.com/#/README?id=overriding-exchange-properties-upon-instantiation) <br> **Datatype:** Dict
+| `exchange.enable_ws` | Enable the usage of Websockets for the exchange. <br>[More information](#consuming-exchange-websockets).<br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
+| `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`*<br> **Datatype:** Boolean
+| `exchange.unknown_fee_rate` | Fallback value to use when calculating trading fees. This can be useful for exchanges which have fees in non-tradable currencies. The value provided here will be multiplied with the "fee cost".<br>*Defaults to `None`<br> **Datatype:** float
+| `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.<br>*Defaults to `false`*<br> **Datatype:** Boolean
+| `exchange.only_from_ccxt` | Prevent data-download from data.binance.vision. Leaving this as false can greatly speed up downloads, but may be problematic if the site is not available.<br>*Defaults to `false`*<br> **Datatype:** Boolean
+| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
+| | **Plugins**
+| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation of all possible configuration options.
+| `pairlists` | Define one or more pairlists to be used. [More information](plugins.md#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
+| | **Telegram**
+| `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
+| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`. <br> **Datatype:** float
+| `telegram.reload` | Allow "reload" buttons on telegram messages. <br>*Defaults to `true`.<br> **Datatype:** boolean
+| `telegram.notification_settings.*` | Detailed notification settings. Refer to the [telegram documentation](telegram-usage.md) for details.<br> **Datatype:** dictionary
+| `telegram.allow_custom_messages` | Enable the sending of Telegram messages from strategies via the dataprovider.send_msg() function. <br> **Datatype:** Boolean
+| | **Webhook**
+| `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
+| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.entry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.entry_cancel` | Payload to send on entry order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.entry_fill` | Payload to send on entry order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.exit` | Payload to send on exit. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.exit_cancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.exit_fill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.status` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.allow_custom_messages` | Enable the sending of Webhook messages from strategies via the dataprovider.send_msg() function. <br> **Datatype:** Boolean
+| | **Rest API / FreqUI / Producer-Consumer**
+| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
+| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
+| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
+| `api_server.verbosity` | Logging verbosity. `info` will print all RPC Calls, while "error" will only display errors. <br>**Datatype:** Enum, either `info` or `error`. Defaults to `info`.
+| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
+| `api_server.ws_token` | API token for the Message WebSocket. See the [API Server documentation](rest-api.md) for more details.  <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `bot_name` | Name of the bot. Passed via API to a client - can be shown to distinguish / name bots.<br> *Defaults to `freqtrade`*<br> **Datatype:** String
+| `external_message_consumer` | Enable [Producer/Consumer mode](producer-consumer.md) for more details. <br> **Datatype:** Dict
+| | **Other**
+| `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `running`, `paused` or `stopped`
+| `force_entry_enable` | Enables the RPC Commands to force a Trade entry. More information below. <br> **Datatype:** Boolean
+| `disable_dataframe_checks` | Disable checking the OHLCV dataframe returned from the strategy methods for correctness. Only use when intentionally changing the dataframe and understand what you are doing. [Strategy Override](#parameters-in-the-strategy).<br> *Defaults to `False`*. <br> **Datatype:** Boolean
+| `internals.process_throttle_secs` | Set the process throttle, or minimum loop duration for one bot iteration loop. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
+| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
+| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](advanced-setup.md#configure-the-bot-running-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
+| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
+| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
+| `recursive_strategy_search` | Set to `true` to recursively search sub-directories inside `user_data/strategies` for a strategy. <br> **Datatype:** Boolean
+| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
+| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> **Datatype:** String, SQLAlchemy connect string
+| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
+| `add_config_files` | Additional config files. These files will be loaded and merged with the current config file. The files are resolved relative to the initial file.<br> *Defaults to `[]`*. <br> **Datatype:** List of strings
+| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `feather`*. <br> **Datatype:** String
+| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `feather`*. <br> **Datatype:** String
+| `reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage (and decreasing train/inference timing backtesting/hyperopt and in FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.
+| `log_config` | Dictionary containing the log config for python logging. [more info](advanced-setup.md#advanced-logging) <br> **Datatype:** dict. <br> Default: `FtRichHandler`
+
+### Parameters in the strategy
+
+The following parameters can be set in the configuration file or strategy.
+Values set in the configuration file always overwrite values set in the strategy.
+
+* `minimal_roi`
+* `timeframe`
+* `stoploss`
+* `max_open_trades`
+* `trailing_stop`
+* `trailing_stop_positive`
+* `trailing_stop_positive_offset`
+* `trailing_only_offset_is_reached`
+* `use_custom_stoploss`
+* `process_only_new_candles`
+* `order_types`
+* `order_time_in_force`
+* `unfilledtimeout`
+* `disable_dataframe_checks`
+* `use_exit_signal`
+* `exit_profit_only`
+* `exit_profit_offset`
+* `ignore_roi_if_entry_signal`
+* `ignore_buying_expired_candle_after`
+* `position_adjustment_enable`
+* `max_entry_position_adjustment`
+
+### Configuring amount per trade
+
+There are several methods to configure how much of the stake currency the bot will use to enter a trade. All methods respect the [available balance configuration](#tradable-balance) as explained below.
+
+#### Minimum trade stake
+
+The minimum stake amount will depend on exchange and pair and is usually listed in the exchange support pages.
+
+Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.6\$, the minimum stake amount to buy this pair is `20 * 0.6 ~= 12`.
+This exchange has also a limit on USD - where all orders must be > 10\$ - which however does not apply in this case.
+
+To guarantee safe execution, freqtrade will not allow buying with a stake-amount of 10.1\$, instead, it'll make sure that there's enough space to place a stoploss below the pair (+ an offset, defined by `amount_reserve_percent`, which defaults to 5%).
+
+With a reserve of 5%, the minimum stake amount would be ~12.6\$ (`12 * (1 + 0.05)`). If we take into account a stoploss of 10% on top of that - we'd end up with a value of ~14\$ (`12.6 / (1 - 0.1)`).
+
+To limit this calculation in case of large stoploss values, the calculated minimum stake-limit will never be more than 50% above the real limit.
+
+!!! Warning
+    Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange. Freqtrade adjusts the stake-amount to this value, unless it's > 30% more than the calculated/desired stake-amount - in which case the trade is rejected.
+
+#### Dry-run wallet
+
+When running in dry-run mode, the bot will use a simulated wallet to execute trades. The starting balance of this wallet is defined by `dry_run_wallet` (defaults to 1000).
+For more complex scenarios, you can also assign a dictionary to `dry_run_wallet` to define the starting balance for each currency.
+
+```json
+"dry_run_wallet": {
+    "BTC": 0.01,
+    "ETH": 2,
+    "USDT": 1000
+}
+```
+
+Command line options (`--dry-run-wallet`) can be used to override the configuration value, but only for the float value, not for the dictionary. If you'd like to use the dictionary, please adjust the configuration file.
+
+!!! Note
+    Balances not in stake-currency will not be used for trading, but are shown as part of the wallet balance.
+    On Cross-margin exchanges, the wallet balance may be used to calculate the available collateral for trading.
+
+#### Tradable balance
+
+By default, the bot assumes that the `complete amount - 1%` is at it's disposal, and when using [dynamic stake amount](#dynamic-stake-amount), it will split the complete balance into `max_open_trades` buckets per trade.
+Freqtrade will reserve 1% for eventual fees when entering a trade and will therefore not touch that by default.
+
+You can configure the "untouched" amount by using the `tradable_balance_ratio` setting.
+
+For example, if you have 10 ETH available in your wallet on the exchange and `tradable_balance_ratio=0.5` (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers this as an available balance. The rest of the wallet is untouched by the trades.
+
+!!! Danger
+    This setting should **not** be used when running multiple bots on the same account. Please look at [Available Capital to the bot](#assign-available-capital) instead.
+
+!!! Warning
+    The `tradable_balance_ratio` setting applies to the current balance (free balance + tied up in trades). Therefore, assuming the starting balance of 1000, a configuration with `tradable_balance_ratio=0.99` will not guarantee that 10 currency units will always remain available on the exchange. For example, the free amount may reduce to 5 units if the total balance is reduced to 500 (either by a losing streak or by withdrawing balance).
+
+#### Assign available Capital
+
+To fully utilize compounding profits when using multiple bots on the same exchange account, you'll want to limit each bot to a certain starting balance.
+This can be accomplished by setting `available_capital` to the desired starting balance.
+
+Assuming your account has 10000 USDT and you want to run 2 different strategies on this exchange.
+You'd set `available_capital=5000` - granting each bot an initial capital of 5000 USDT.
+The bot will then split this starting balance equally into `max_open_trades` buckets.
+Profitable trades will result in increased stake-sizes for this bot - without affecting the stake-sizes of the other bot.
+
+Adjusting `available_capital` requires reloading the configuration to take effect. Adjusting the `available_capital` adds the difference between the previous `available_capital` and the new `available_capital`. Decreasing the available capital when trades are open doesn't exit the trades. The difference is returned to the wallet when the trades conclude. The outcome of this differs depending on the price movement between the adjustment and exiting the trades.
+
+!!! Warning "Incompatible with `tradable_balance_ratio`"
+    Setting this option will replace any configuration of `tradable_balance_ratio`.
+
+#### Amend last stake amount
+
+Assuming we have the tradable balance of 1000 USDT, `stake_amount=400`, and `max_open_trades=3`.
+The bot would open 2 trades and will be unable to fill the last trading slot, since the requested 400 USDT are no longer available since 800 USDT are already tied in other trades.
+
+To overcome this, the option `amend_last_stake_amount` can be set to `True`, which will enable the bot to reduce stake_amount to the available balance to fill the last trade slot.
+
+In the example above this would mean:
+
+* Trade1: 400 USDT
+* Trade2: 400 USDT
+* Trade3: 200 USDT
+
+!!! Note
+    This option only applies with [Static stake amount](#static-stake-amount) - since [Dynamic stake amount](#dynamic-stake-amount) divides the balances evenly.
+
+!!! Note
+    The minimum last stake amount can be configured using `last_stake_amount_min_ratio` - which defaults to 0.5 (50%). This means that the minimum stake amount that's ever used is `stake_amount * 0.5`. This avoids very low stake amounts, that are close to the minimum tradable amount for the pair and can be refused by the exchange.
+
+#### Static stake amount
+
+The `stake_amount` configuration statically configures the amount of stake-currency your bot will use for each trade.
+
+The minimal configuration value is 0.0001, however, please check your exchange's trading minimums for the stake currency you're using to avoid problems.
+
+This setting works in combination with `max_open_trades`. The maximum capital engaged in trades is `stake_amount * max_open_trades`.
+For example, the bot will at most use (0.05 BTC x 3) = 0.15 BTC, assuming a configuration of `max_open_trades=3` and `stake_amount=0.05`.
+
+!!! Note
+    This setting respects the [available balance configuration](#tradable-balance).
+
+#### Dynamic stake amount
+
+Alternatively, you can use a dynamic stake amount, which will use the available balance on the exchange, and divide that equally by the number of allowed trades (`max_open_trades`).
+
+To configure this, set `stake_amount="unlimited"`. We also recommend to set `tradable_balance_ratio=0.99` (99%) - to keep a minimum balance for eventual fees.
+
+In this case a trade amount is calculated as:
+
+```python
+currency_balance / (max_open_trades - current_open_trades)
+```
+
+To allow the bot to trade all the available `stake_currency` in your account (minus `tradable_balance_ratio`) set
+
+```json
+"stake_amount" : "unlimited",
+"tradable_balance_ratio": 0.99,
+```
+
+!!! Tip "Compounding profits"
+    This configuration will allow increasing/decreasing stakes depending on the performance of the bot (lower stake if the bot is losing, higher stakes if the bot has a winning record since higher balances are available), and will result in profit compounding.
+
+!!! Note "When using Dry-Run Mode"
+    When using `"stake_amount" : "unlimited",` in combination with Dry-Run, Backtesting or Hyperopt, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve.
+    It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise, it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
+
+#### Dynamic stake amount with position adjustment
+
+When you want to use position adjustment with unlimited stakes, you must also implement `custom_stake_amount` to a return a value depending on your strategy.
+Typical value would be in the range of 25% - 50% of the proposed stakes, but depends highly on your strategy and how much you wish to leave into the wallet as position adjustment buffer.
+
+For example if your position adjustment assumes it can do 2 additional buys with the same stake amounts then your buffer should be 66.6667% of the initially proposed unlimited stake amount.
+
+Or another example if your position adjustment assumes it can do 1 additional buy with 3x the original stake amount then `custom_stake_amount` should return 25% of proposed stake amount and leave 75% for possible later position adjustments.
+
+--8<-- "includes/pricing.md"
+
+## Further Configuration details
 
 ### Understand minimal_roi
-`minimal_roi` is a JSON object where the key is a duration
-in minutes and the value is the minimum ROI in percent.
+
+The `minimal_roi` configuration parameter is a JSON object where the key is a duration
+in minutes and the value is the minimum ROI as a ratio.
 See the example below:
-```
+
+```json
 "minimal_roi": {
-    "40": 0.0,    # Sell after 40 minutes if the profit is not negative
-    "30": 0.01,   # Sell after 30 minutes if there is at least 1% profit
-    "20": 0.02,   # Sell after 20 minutes if there is at least 2% profit
-    "0":  0.04    # Sell immediately if there is at least 4% profit
+    "40": 0.0,    # Exit after 40 minutes if the profit is not negative
+    "30": 0.01,   # Exit after 30 minutes if there is at least 1% profit
+    "20": 0.02,   # Exit after 20 minutes if there is at least 2% profit
+    "0":  0.04    # Exit immediately if there is at least 4% profit
 },
 ```
 
-Most of the strategy files already include the optimal `minimal_roi`
-value. This parameter is optional. If you use it, it will take over the
+Most of the strategy files already include the optimal `minimal_roi` value.
+This parameter can be set in either Strategy or Configuration file. If you use it in the configuration file, it will override the
 `minimal_roi` value from the strategy file.
+If it is not set in either Strategy or Configuration, a default of 1000% `{"0": 10}` is used, and minimal ROI is disabled unless your trade generates 1000% profit.
 
-### Understand stoploss
-`stoploss` is loss in percentage that should trigger a sale.
-For example value `-0.10` will cause immediate sell if the
-profit dips below -10% for a given trade. This parameter is optional.
+!!! Note "Special case to forceexit after a specific time"
+    A special case presents using `"<N>": -1` as ROI. This forces the bot to exit a trade after N Minutes, no matter if it's positive or negative, so represents a time-limited force-exit.
 
-Most of the strategy files already include the optimal `stoploss`
-value. This parameter is optional. If you use it, it will take over the
-`stoploss` value from the strategy file.
+### Understand force_entry_enable
 
-### Understand initial_state
-`initial_state` is an optional field that defines the initial application state.
-Possible values are `running` or `stopped`. (default=`running`)
-If the value is `stopped` the bot has to be started with `/start` first.
+The `force_entry_enable` configuration parameter enables the usage of force-enter (`/forcelong`, `/forceshort`) commands via Telegram and REST API.
+For security reasons, it's disabled by default, and freqtrade will show a warning message on startup if enabled.
+For example, you can send `/forceenter ETH/BTC` to the bot, which will result in freqtrade buying the pair and holds it until a regular exit-signal (ROI, stoploss, /forceexit) appears.
 
-### Understand ask_last_balance
-`ask_last_balance` sets the bidding price. Value `0.0` will use `ask` price, `1.0` will
-use the `last` price and values between those interpolate between ask and last
-price. Using `ask` price will guarantee quick success in bid, but bot will also
-end up paying more then would probably have been necessary.
+This can be dangerous with some strategies, so use with care.
 
-### What values for fiat_display_currency?
-`fiat_display_currency` set the fiat to use for the conversion form coin to fiat in Telegram. 
-The valid value are: "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN", "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD".
+See [the telegram documentation](telegram-usage.md) for details on usage.
 
-## Switch to dry-run mode
-We recommend starting the bot in dry-run mode to see how your bot will
-behave and how is the performance of your strategy. In Dry-run mode the
+### Ignoring expired candles
+
+When working with larger timeframes (for example 1h or more) and using a low `max_open_trades` value, the last candle can be processed as soon as a trade slot becomes available. When processing the last candle, this can lead to a situation where it may not be desirable to use the buy signal on that candle. For example, when using a condition in your strategy where you use a cross-over, that point may have passed too long ago for you to start a trade on it.
+
+In these situations, you can enable the functionality to ignore candles that are beyond a specified period by setting `ignore_buying_expired_candle_after` to a positive number, indicating the number of seconds after which the buy signal becomes expired.
+
+For example, if your strategy is using a 1h timeframe, and you only want to buy within the first 5 minutes when a new candle comes in, you can add the following configuration to your strategy:
+
+``` json
+  {
+    //...
+    "ignore_buying_expired_candle_after": 300,
+    // ...
+  }
+```
+
+!!! Note
+    This setting resets with each new candle, so it will not prevent sticking-signals from executing on the 2nd or 3rd candle they're active. Best use a "trigger" selector for buy signals, which are only active for one candle.
+
+### Understand order_types
+
+The `order_types` configuration parameter maps actions (`entry`, `exit`, `stoploss`, `emergency_exit`, `force_exit`, `force_entry`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
+
+This allows to enter using limit orders, exit using limit-orders, and create stoplosses using market orders.
+It also allows to set the
+stoploss "on exchange" which means stoploss order would be placed immediately once the buy order is fulfilled.
+
+`order_types` set in the configuration file overwrites values set in the strategy as a whole, so you need to configure the whole `order_types` dictionary in one place.
+
+If this is configured, the following 4 values (`entry`, `exit`, `stoploss` and `stoploss_on_exchange`) need to be present, otherwise, the bot will fail to start.
+
+For information on (`emergency_exit`,`force_exit`, `force_entry`, `stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
+
+Syntax for Strategy:
+
+```python
+order_types = {
+    "entry": "limit",
+    "exit": "limit",
+    "emergency_exit": "market",
+    "force_entry": "market",
+    "force_exit": "market",
+    "stoploss": "market",
+    "stoploss_on_exchange": False,
+    "stoploss_on_exchange_interval": 60,
+    "stoploss_on_exchange_limit_ratio": 0.99,
+}
+```
+
+Configuration:
+
+```json
+"order_types": {
+    "entry": "limit",
+    "exit": "limit",
+    "emergency_exit": "market",
+    "force_entry": "market",
+    "force_exit": "market",
+    "stoploss": "market",
+    "stoploss_on_exchange": false,
+    "stoploss_on_exchange_interval": 60
+}
+```
+
+!!! Note "Market order support"
+    Not all exchanges support "market" orders.
+    The following message will be shown if your exchange does not support market orders:
+    `"Exchange <yourexchange> does not support market orders."` and the bot will refuse to start.
+
+!!! Warning "Using market orders"
+    Please carefully read the section [Market order pricing](#market-order-pricing) section when using market orders.
+
+!!! Note "Stoploss on exchange"
+    `order_types.stoploss_on_exchange_interval` is not mandatory. Do not change its value if you are
+    unsure of what you are doing. For more information about how stoploss works please
+    refer to [the stoploss documentation](stoploss.md).
+
+    If `order_types.stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
+
+!!! Warning "Warning: order_types.stoploss_on_exchange failures"
+    If stoploss on exchange creation fails for some reason, then an "emergency exit" is initiated. By default, this will exit the trade using a market order. The order-type for the emergency-exit can be changed by setting the `emergency_exit` value in the `order_types` dictionary - however, this is not advised.
+
+### Understand order_time_in_force
+
+The `order_time_in_force` configuration parameter defines the policy by which the order
+is executed on the exchange. Three commonly used time in force are:
+
+**GTC (Good Till Canceled):**
+
+This is most of the time the default time in force. It means the order will remain
+on exchange till it is cancelled by the user. It can be fully or partially fulfilled.
+If partially fulfilled, the remaining will stay on the exchange till cancelled.
+
+**FOK (Fill Or Kill):**
+
+It means if the order is not executed immediately AND fully then it is cancelled by the exchange.
+
+**IOC (Immediate Or Canceled):**
+
+It is the same as FOK (above) except it can be partially fulfilled. The remaining part
+is automatically cancelled by the exchange.
+
+**PO (Post only):**
+
+Post only order. The order is either placed as a maker order, or it is canceled.
+This means the order must be placed on orderbook for at least time in an unfilled state.
+
+#### time_in_force config
+
+The `order_time_in_force` parameter contains a dict with entry and exit time in force policy values.
+This can be set in the configuration file or in the strategy.
+Values set in the configuration file overwrites values set in the strategy.
+
+The possible values are: `GTC` (default), `FOK` or `IOC`.
+
+``` python
+"order_time_in_force": {
+    "entry": "GTC",
+    "exit": "GTC"
+},
+```
+
+!!! Warning
+    This is ongoing work. For now, it is supported only for binance, gate and kucoin.
+    Please don't change the default value unless you know what you are doing and have researched the impact of using different values for your particular exchange.
+
+### Fiat conversion
+
+Freqtrade uses the Coingecko API to convert the coin value to it's corresponding fiat value for the Telegram reports.
+The FIAT currency can be set in the configuration file as `fiat_display_currency`.
+
+Removing `fiat_display_currency` completely from the configuration will skip initializing coingecko, and will not show any FIAT currency conversion. This has no importance for the correct functioning of the bot.
+
+#### What values can be used for fiat_display_currency?
+
+The `fiat_display_currency` configuration parameter sets the base currency to use for the
+conversion from coin to fiat in the bot Telegram reports.
+
+The valid values are:
+
+```json
+"AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK", "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY", "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN", "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD"
+```
+
+In addition to fiat currencies, a range of crypto currencies is supported.
+
+The valid values are:
+
+```json
+"BTC", "ETH", "XRP", "LTC", "BCH", "BNB"
+```
+
+#### Coingecko Rate limit problems
+
+On some IP ranges, coingecko is heavily rate-limiting.
+In such cases, you may want to add your coingecko API key to the configuration.
+
+``` json
+{
+    "fiat_display_currency": "USD",
+    "coingecko": {
+        "api_key": "your-api",
+        "is_demo": true
+    }
+}
+```
+
+Freqtrade supports both Demo and Pro coingecko API keys.
+
+The Coingecko API key is NOT required for the bot to function correctly.
+It is only used for the conversion of coin to fiat in the Telegram reports, which usually also work without API key.
+
+## Consuming exchange Websockets
+
+Freqtrade can consume websockets through ccxt.pro.
+
+Freqtrade aims ensure data is available at all times.
+Should the websocket connection fail (or be disabled), the bot will fall back to REST API calls.
+
+Should you experience problems you suspect are caused by websockets, you can disable these via the setting `exchange.enable_ws`, which defaults to true.
+
+```jsonc
+"exchange": {
+    // ...
+    "enable_ws": false,
+    // ...
+}
+```
+
+Should you be required to use a proxy, please refer to the [proxy section](#using-proxy-with-freqtrade) for more information.
+
+!!! Info "Rollout"
+    We're implementing this out slowly, ensuring stability of your bots.
+    Currently, usage is limited to ohlcv data streams.
+    It's also limited to a few exchanges, with new exchanges being added on an ongoing basis.
+
+## Using Dry-run mode
+
+We recommend starting the bot in the Dry-run mode to see how your bot will
+behave and what is the performance of your strategy. In the Dry-run mode, the
 bot does not engage your money. It only runs a live simulation without
-creating trades.
+creating trades on the exchange.
+
+1. Edit your `config.json` configuration file.
+2. Switch `dry-run` to `true` and specify `db_url` for a persistence database.
 
-### To switch your bot in Dry-run mode:
-1. Edit your `config.json`  file
-2. Switch dry-run to true
 ```json
 "dry_run": true,
+"db_url": "sqlite:///tradesv3.dryrun.sqlite",
 ```
 
-3. Remove your Bittrex API key (change them by fake api credentials)
+3. Remove your Exchange API key and secret (change them by empty values or fake credentials):
+
 ```json
 "exchange": {
-        "name": "bittrex",
-        "key": "key",
-        "secret": "secret",
-        ...
-}   
+    "name": "binance",
+    "key": "key",
+    "secret": "secret",
+    ...
+}
 ```
 
-Once you will be happy with your bot performance, you can switch it to 
-production mode.
+Once you will be happy with your bot performance running in the Dry-run mode, you can switch it to production mode.
+
+!!! Note
+    A simulated wallet is available during dry-run mode and will assume a starting capital of `dry_run_wallet` (defaults to 1000).
+
+### Considerations for dry-run
+
+* API-keys may or may not be provided. Only Read-Only operations (i.e. operations that do not alter account state) on the exchange are performed in dry-run mode.
+* Wallets (`/balance`) are simulated based on `dry_run_wallet`.
+* Orders are simulated, and will not be posted to the exchange.
+* Market orders fill based on orderbook volume the moment the order is placed, with a maximum slippage of 5%.
+* Limit orders fill once the price reaches the defined level - or time out based on `unfilledtimeout` settings.
+* Limit orders will be converted to market orders if they cross the price by more than 1%, and will be filled immediately based regular market order rules (see point about Market orders above).
+* In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
+* Open orders (not trades, which are stored in the database) are kept open after bot restarts, with the assumption that they were not filled while being offline.
 
 ## Switch to production mode
-In production mode, the bot will engage your money. Be careful a wrong 
-strategy can lose all your money. Be aware of what you are doing when 
-you run it in production mode.
 
-### To switch your bot in production mode:
-1. Edit your `config.json`  file
+In production mode, the bot will engage your money. Be careful, since a wrong strategy can lose all your money.
+Be aware of what you are doing when you run it in production mode.
+
+When switching to Production mode, please make sure to use a different / fresh database to avoid dry-run trades messing with your exchange money and eventually tainting your statistics.
+
+### Setup your exchange account
+
+You will need to create API Keys (usually you get `key` and `secret`, some exchanges require an additional `password`) from the Exchange website and you'll need to insert this into the appropriate fields in the configuration or when asked by the `freqtrade new-config` command.
+API Keys are usually only required for live trading (trading for real money, bot running in "production mode", executing real orders on the exchange) and are not required for the bot running in dry-run (trade simulation) mode. When you set up the bot in dry-run mode, you may fill these fields with empty values.
+
+### To switch your bot in production mode
+
+**Edit your `config.json`  file.**
+
+**Switch dry-run to false and don't forget to adapt your database URL if set:**
 
-2. Switch dry-run to false
 ```json
 "dry_run": false,
 ```
 
-3. Insert your Bittrex API key (change them by fake api keys)
+**Insert your Exchange API key (change them by fake API keys):**
+
 ```json
-"exchange": {
-        "name": "bittrex",
+{
+    "exchange": {
+        "name": "binance",
         "key": "af8ddd35195e9dc500b9a6f799f6f5c93d89193b",
         "secret": "08a9dc6db3d7b53e1acebd9275677f4b0a04f1a5",
-        ...
-}       
+        //"password": "", // Optional, not needed by all exchanges)
+        // ...
+    }
+    //...
+}
 ```
-If you have not your Bittrex API key yet, 
-[see our tutorial](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md).
 
+You should also make sure to read the [Exchanges](exchanges.md) section of the documentation to be aware of potential configuration details specific to your exchange.
+
+!!! Hint "Keep your secrets secret"
+    To keep your secrets secret, we recommend using a 2nd configuration for your API keys.
+    Simply use the above snippet in a new configuration file (e.g. `config-private.json`) and keep your settings in this file.
+    You can then start the bot with `freqtrade trade --config user_data/config.json --config user_data/config-private.json <...>` to have your keys loaded.
+
+    **NEVER** share your private configuration file or your exchange keys with anyone!
+
+## Using a proxy with Freqtrade
+
+To use a proxy with freqtrade, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values.
+This will have the proxy settings applied to everything (telegram, coingecko, ...) **except** for exchange requests.
+
+``` bash
+export HTTP_PROXY="http://addr:port"
+export HTTPS_PROXY="http://addr:port"
+freqtrade
+```
+
+### Proxy exchange requests
+
+To use a proxy for exchange connections - you will have to define the proxies as part of the ccxt configuration.
+
+``` json
+{ 
+  "exchange": {
+    "ccxt_config": {
+      "httpsProxy": "http://addr:port",
+      "wsProxy": "http://addr:port",
+    }
+  }
+}
+```
+
+For more information on available proxy types, please consult the [ccxt proxy documentation](https://docs.ccxt.com/#/README?id=proxy).
 
 ## Next step
-Now you have configured your config.json, the next step is to 
-[start your bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md).
+
+Now you have configured your config.json, the next step is to [start your bot](bot-usage.md).
diff --git a/docs/data-analysis.md b/docs/data-analysis.md
new file mode 100644
index 000000000..200aa289d
--- /dev/null
+++ b/docs/data-analysis.md
@@ -0,0 +1,127 @@
+# Analyzing bot data with Jupyter notebooks
+
+You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
+
+## Quick start with docker
+
+Freqtrade provides a docker-compose file which starts up a jupyter lab server.
+You can run this server using the following command: `docker compose -f docker/docker-compose-jupyter.yml up`
+
+This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
+Please use the link that's printed in the console after startup for simplified login.
+
+For more information, Please visit the [Data analysis with Docker](docker_quickstart.md#data-analysis-using-docker-compose) section.
+
+### Pro tips
+
+* See [jupyter.org](https://jupyter.org/documentation) for usage instructions.
+* Don't forget to start a Jupyter notebook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
+* Copy the example notebook before use so your changes don't get overwritten with the next freqtrade update.
+
+### Using virtual environment with system-wide Jupyter installation
+
+Sometimes it can be desired to use a system-wide installation of Jupyter notebook, and use a jupyter kernel from the virtual environment.
+This prevents you from installing the full jupyter suite multiple times per system, and provides an easy way to switch between tasks (freqtrade / other analytics tasks).
+
+For this to work, first activate your virtual environment and run the following commands:
+
+``` bash
+# Activate virtual environment
+source .venv/bin/activate
+
+pip install ipykernel
+ipython kernel install --user --name=freqtrade
+# Restart jupyter (lab / notebook)
+# select kernel "freqtrade" in the notebook
+```
+
+!!! Note
+    This section is provided for completeness, the Freqtrade Team won't provide full support for problems with this setup and will recommend to install Jupyter in the virtual environment directly, as that is the easiest way to get jupyter notebooks up and running. For help with this setup please refer to the [Project Jupyter](https://jupyter.org/) [documentation](https://jupyter.org/documentation) or [help channels](https://jupyter.org/community).
+
+!!! Warning
+    Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
+
+## Recommended workflow
+
+| Task | Tool |
+  --- | ---
+Bot operations | CLI
+Repetitive tasks | Shell scripts
+Data analysis & visualization | Notebook
+
+1. Use the CLI to
+
+    * download historical data
+    * run a backtest
+    * run with real-time data
+    * export results
+
+1. Collect these actions in shell scripts
+
+    * save complicated commands with arguments
+    * execute multi-step operations
+    * automate testing strategies and preparing data for analysis
+
+1. Use a notebook to
+
+    * visualize data
+    * mangle and plot to generate insights
+
+## Example utility snippets
+
+### Change directory to root
+
+Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistent.
+
+```python
+import os
+from pathlib import Path
+
+# Change directory
+# Modify this cell to insure that the output shows the correct path.
+# Define all paths relative to the project root shown in the cell output
+project_root = "somedir/freqtrade"
+i=0
+try:
+    os.chdir(project_root)
+    assert Path('LICENSE').is_file()
+except:
+    while i<4 and (not Path('LICENSE').is_file()):
+        os.chdir(Path(Path.cwd(), '../'))
+        i+=1
+    project_root = Path.cwd()
+print(Path.cwd())
+```
+
+### Load multiple configuration files
+
+This option can be useful to inspect the results of passing in multiple configs.
+This will also run through the whole Configuration initialization, so the configuration is completely initialized to be passed to other methods.
+
+``` python
+import json
+from freqtrade.configuration import Configuration
+
+# Load config from multiple files
+config = Configuration.from_files(["config1.json", "config2.json"])
+
+# Show the config in memory
+print(json.dumps(config['original_config'], indent=2))
+```
+
+For Interactive environments, have an additional configuration specifying `user_data_dir` and pass this in last, so you don't have to change directories while running the bot.
+Best avoid relative paths, since this starts at the storage location of the jupyter notebook, unless the directory is changed.
+
+``` json
+{
+    "user_data_dir": "~/.freqtrade/"
+}
+```
+
+### Further Data analysis documentation
+
+* [Strategy debugging](strategy_analysis_example.md) - also available as Jupyter notebook (`user_data/notebooks/strategy_analysis_example.ipynb`)
+* [Plotting](plotting.md)
+* [Tag Analysis](advanced-backtesting.md)
+
+Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
diff --git a/docs/data-download.md b/docs/data-download.md
new file mode 100644
index 000000000..c2b104b1b
--- /dev/null
+++ b/docs/data-download.md
@@ -0,0 +1,282 @@
+# Data Downloading
+
+## Getting data for backtesting and hyperopt
+
+To download data (candles / OHLCV) needed for backtesting and hyperoptimization use the `freqtrade download-data` command.
+
+If no additional parameter is specified, freqtrade will download data for `"1m"` and `"5m"` timeframes for the last 30 days.
+Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
+Without provided configuration, `--exchange` becomes mandatory.
+
+You can use a relative timerange (`--days 20`) or an absolute starting point (`--timerange 20200101-`). For incremental downloads, the relative approach should be used.
+
+!!! Tip "Tip: Updating existing data"
+    If you already have backtesting data available in your data-directory and would like to refresh this data up to today, freqtrade will automatically calculate the missing timerange for the existing pairs and the download will occur from the latest available point until "now", neither `--days` or `--timerange` parameters are required. Freqtrade will keep the available data and only download the missing data.  
+    If you are updating existing data after inserting new pairs that you have no data for, use the `--new-pairs-days xx` parameter. Specified number of days will be downloaded for new pairs while old pairs will be updated with missing data only.  
+
+### Usage
+
+--8<-- "commands/download-data.md"
+
+!!! Tip "Downloading all data for one quote currency"
+    Often, you'll want to download data for all pairs of a specific quote-currency. In such cases, you can use the following shorthand:
+    `freqtrade download-data --exchange binance --pairs ".*/USDT" <...>`. The provided "pairs" string will be expanded to contain all active pairs on the exchange.
+    To also download data for inactive (delisted) pairs, add `--include-inactive-pairs` to the command.
+
+!!! Note "Startup period"
+    `download-data` is a strategy-independent command. The idea is to download a big chunk of data once, and then iteratively increase the amount of data stored.
+
+    For that reason, `download-data` does not care about the "startup-period" defined in a strategy. It's up to the user to download additional days if the backtest should start at a specific point in time (while respecting startup period).
+
+### Start download
+
+A very simple command (assuming an available `config.json` file) can look as follows.
+
+```bash
+freqtrade download-data --exchange binance
+```
+
+This will download historical candle (OHLCV) data for all the currency pairs defined in the configuration.
+
+Alternatively, specify the pairs directly
+
+```bash
+freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT
+```
+
+or as regex (in this case, to download all active USDT pairs)
+
+```bash
+freqtrade download-data --exchange binance --pairs ".*/USDT"
+```
+
+### Other Notes
+
+* To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
+* To change the exchange used to download the historical data from, either use `--exchange <exchange>` - or specify a different configuration file.
+* To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
+* To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
+* To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020.
+* Given starting points are ignored if data is already available, downloading only missing data up to today.
+* Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
+* To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
+
+??? Note "Permission denied errors"
+    If your configuration directory `user_data` was made by docker, you may get the following error:
+
+    ```
+    cp: cannot create regular file 'user_data/data/binance/pairs.json': Permission denied
+    ```
+
+    You can fix the permissions of your user-data directory as follows:
+
+    ```
+    sudo chown -R $UID:$GID user_data
+    ```
+
+### Download additional data before the current timerange
+
+Assuming you downloaded all data from 2022 (`--timerange 20220101-`) - but you'd now like to also backtest with earlier data.
+You can do so by using the `--prepend` flag, combined with `--timerange` - specifying an end-date.
+
+``` bash
+freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT --prepend --timerange 20210101-20220101
+```
+
+!!! Note
+    Freqtrade will ignore the end-date in this mode if data is available, updating the end-date to the existing data start point.
+
+### Data format
+
+Freqtrade currently supports the following data-formats:
+
+* `feather` - a dataformat based on Apache Arrow
+* `json` -  plain "text" json files
+* `jsongz` - a gzip-zipped version of json files
+* `parquet` - columnar datastore (OHLCV only)
+
+By default, both OHLCV data and trades data are stored in the `feather` format.
+
+This can be changed via the `--data-format-ohlcv` and `--data-format-trades` command line arguments respectively.
+To persist this change, you should also add the following snippet to your configuration, so you don't have to insert the above arguments each time:
+
+``` jsonc
+    // ...
+    "dataformat_ohlcv": "feather",
+    "dataformat_trades": "feather",
+    // ...
+```
+
+If the default data-format has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
+
+!!! Note
+    You can convert between data-formats using the [convert-data](#sub-command-convert-data) and [convert-trade-data](#sub-command-convert-trade-data) methods.
+
+#### Dataformat comparison
+
+The following comparisons have been made with the following data, and by using the linux `time` command.
+
+```
+Found 6 pair / timeframe combinations.
++----------+-------------+--------+---------------------+---------------------+
+|     Pair |   Timeframe |   Type |                From |                  To |
+|----------+-------------+--------+---------------------+---------------------|
+| BTC/USDT |          5m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:25:00 |
+| ETH/USDT |          1m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:26:00 |
+| BTC/USDT |          1m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:30:00 |
+| XRP/USDT |          5m |   spot | 2018-05-04 08:10:00 | 2022-09-13 19:15:00 |
+| XRP/USDT |          1m |   spot | 2018-05-04 08:11:00 | 2022-09-13 19:22:00 |
+| ETH/USDT |          5m |   spot | 2017-08-17 04:00:00 | 2022-09-13 19:20:00 |
++----------+-------------+--------+---------------------+---------------------+
+```
+
+Timings have been taken in a not very scientific way with the following command, which forces reading the data into memory.
+
+``` bash
+time freqtrade list-data --show-timerange --data-format-ohlcv <dataformat>
+```
+
+|  Format | Size | timing |
+|------------|-------------|-------------|
+| `feather` | 72Mb | 3.5s |
+| `json` | 149Mb | 25.6s |
+| `jsongz` | 39Mb | 27s |
+| `parquet` | 83Mb | 3.8s |
+
+Size has been taken from the BTC/USDT 1m spot combination for the timerange specified above.
+
+To have a best performance/size mix, we recommend using the default feather format, or parquet.
+
+### Pairs file
+
+In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.
+If you are using Binance for example:
+
+* create a directory `user_data/data/binance` and copy or create the `pairs.json` file in that directory.
+* update the `pairs.json` file to contain the currency pairs you are interested in.
+
+```bash
+mkdir -p user_data/data/binance
+touch user_data/data/binance/pairs.json
+```
+
+The format of the `pairs.json` file is a simple json list.
+Mixing different stake-currencies is allowed for this file, since it's only used for downloading.
+
+``` json
+[
+    "ETH/BTC",
+    "ETH/USDT",
+    "BTC/USDT",
+    "XRP/ETH"
+]
+```
+
+!!! Note
+    The `pairs.json` file is only used when no configuration is loaded (implicitly by naming, or via `--config` flag).
+    You can force the usage of this file via `--pairs-file pairs.json` - however we recommend to use the pairlist from within the configuration, either via `exchange.pair_whitelist` or `pairs` setting in the configuration.
+
+## Sub-command convert data
+
+--8<-- "commands/convert-data.md"
+
+### Example converting data
+
+The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
+It'll also remove original json data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtrade/data/binance -t 5m 15m --erase
+```
+
+## Sub-command convert trade data
+
+--8<-- "commands/convert-trade-data.md"
+
+### Example converting trades
+
+The following command will convert all available trade-data in `~/.freqtrade/data/kraken` from jsongz to json.
+It'll also remove original jsongz data files (`--erase` parameter).
+
+``` bash
+freqtrade convert-trade-data --format-from jsongz --format-to json --datadir ~/.freqtrade/data/kraken --erase
+```
+
+## Sub-command trades to ohlcv
+
+When you need to use `--dl-trades` (kraken only) to download data, conversion of trades data to ohlcv data is the last step.
+This command will allow you to repeat this last step for additional timeframes without re-downloading the data.
+
+--8<-- "commands/trades-to-ohlcv.md"
+
+### Example trade-to-ohlcv conversion
+
+``` bash
+freqtrade trades-to-ohlcv --exchange kraken -t 5m 1h 1d --pairs BTC/EUR ETH/EUR
+```
+
+## Sub-command list-data
+
+You can get a list of downloaded data using the `list-data` sub-command.
+
+--8<-- "commands/list-data.md"
+
+### Example list-data
+
+```bash
+> freqtrade list-data --userdir ~/.freqtrade/user_data/
+
+              Found 33 pair / timeframe combinations.
+┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━┓
+┃          Pair ┃                                 Timeframe ┃ Type ┃
+┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━┩
+│       ADA/BTC │     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d │ spot │
+│       ADA/ETH │     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d │ spot │
+│       ETH/BTC │     5m, 15m, 30m, 1h, 2h, 4h, 6h, 12h, 1d │ spot │
+│      ETH/USDT │                  5m, 15m, 30m, 1h, 2h, 4h │ spot │
+└───────────────┴───────────────────────────────────────────┴──────┘
+
+```
+
+Show all trades data including from/to timerange
+
+``` bash
+> freqtrade list-data --show --trades
+                     Found trades data for 1 pair.                     
+┏━━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
+┃    Pair ┃ Type ┃                From ┃                  To ┃ Trades ┃
+┡━━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
+│ XRP/ETH │ spot │ 2019-10-11 00:00:11 │ 2019-10-13 11:19:28 │  12477 │
+└─────────┴──────┴─────────────────────┴─────────────────────┴────────┘
+
+```
+
+## Trades (tick) data
+
+By default, `download-data` sub-command downloads Candles (OHLCV) data. Most exchanges also provide historic trade-data via their API.
+This data can be useful if you need many different timeframes, since it is only downloaded once, and then resampled locally to the desired timeframes.
+
+Since this data is large by default, the files use the feather file format by default. They are stored in your data-directory with the naming convention of `<pair>-trades.feather` (`ETH_BTC-trades.feather`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.
+
+To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades.
+If `--convert` is also provided, the resample step will happen automatically and overwrite eventually existing OHLCV data for the given pair/timeframe combinations.
+
+!!! Warning "Do not use"
+    You should not use this unless you're a kraken user (Kraken does not provide historic OHLCV data).  
+    Most other exchanges provide OHLCV data with sufficient history, so downloading multiple timeframes through that method will still proof to be a lot faster than downloading trades data.
+
+!!! Note "Kraken user"
+    Kraken users should read [this](exchanges.md#historic-kraken-data) before starting to download data.
+
+Example call:
+
+```bash
+freqtrade download-data --exchange kraken --pairs XRP/EUR ETH/EUR --days 20 --dl-trades
+```
+
+!!! Note
+    While this method uses async calls, it will be slow, since it requires the result of the previous call to generate the next request to the exchange.
+
+## Next step
+
+Great, you now have some data downloaded, so you can now start [backtesting](backtesting.md) your strategy.
diff --git a/docs/deprecated.md b/docs/deprecated.md
new file mode 100644
index 000000000..2e76f2413
--- /dev/null
+++ b/docs/deprecated.md
@@ -0,0 +1,95 @@
+# Deprecated features
+
+This page contains description of the command line arguments, configuration parameters
+and the bot features that were declared as DEPRECATED by the bot development team
+and are no longer supported. Please avoid their usage in your configuration.
+
+## Removed features
+
+### the `--refresh-pairs-cached` command line option
+
+`--refresh-pairs-cached` in the context of backtesting, hyperopt and edge allows to refresh candle data for backtesting.
+Since this leads to much confusion, and slows down backtesting (while not being part of backtesting) this has been singled out as a separate freqtrade sub-command `freqtrade download-data`.
+
+This command line option was deprecated in 2019.7-dev (develop branch) and removed in 2019.9.
+
+### The **--dynamic-whitelist** command line option
+
+This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch) and in freqtrade 2019.7.
+Please refer to [pairlists](plugins.md#pairlists-and-pairlist-handlers) instead.
+
+### the `--live` command line option
+
+`--live` in the context of backtesting allowed to download the latest tick data for backtesting.
+Did only download the latest 500 candles, so was ineffective in getting good backtest data.
+Removed in 2019-7-dev (develop branch) and in freqtrade 2019.8.
+
+### `ticker_interval` (now `timeframe`)
+
+Support for `ticker_interval` terminology was deprecated in 2020.6 in favor of `timeframe` - and compatibility code was removed in 2022.3.
+
+### Allow running multiple pairlists in sequence
+
+The former `"pairlist"` section in the configuration has been removed, and is replaced by `"pairlists"` - being a list to specify a sequence of pairlists.
+
+The old section of configuration parameters (`"pairlist"`) has been deprecated in 2019.11 and has been removed in 2020.4.
+
+### deprecation of bidVolume and askVolume from volume-pairlist
+
+Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4, and have been removed in 2020.9.
+
+### Using order book steps for exit price
+
+Using `order_book_min` and `order_book_max` used to allow stepping the orderbook and trying to find the next ROI slot - trying to place sell-orders early.
+As this does however increase risk and provides no benefit, it's been removed for maintainability purposes in 2021.7.
+
+### Legacy Hyperopt mode
+
+Using separate hyperopt files was deprecated in 2021.4 and was removed in 2021.9.
+Please switch to the new [Parametrized Strategies](hyperopt.md) to benefit from the new hyperopt interface.
+
+## Strategy changes between V2 and V3
+
+Isolated Futures / short trading was introduced in 2022.4. This required major changes to configuration settings, strategy interfaces, ...
+
+We have put a great effort into keeping compatibility with existing strategies, so if you just want to continue using freqtrade in spot markets, there are no changes necessary.
+While we may drop support for the current interface sometime in the future, we will announce this separately and have an appropriate transition period.
+
+Please follow the [Strategy migration](strategy_migration.md) guide to migrate your strategy to the new format to start using the new functionalities.
+
+### webhooks - changes with 2022.4
+
+#### `buy_tag` has been renamed to `enter_tag`
+
+This should apply only to your strategy and potentially to webhooks.
+We will keep a compatibility layer for 1-2 versions (so both `buy_tag` and `enter_tag` will still work), but support for this in webhooks will disappear after that.
+
+#### Naming changes
+
+Webhook terminology changed from "sell" to "exit", and from "buy" to "entry", removing "webhook" in the process.
+
+* `webhookbuy`, `webhookentry` -> `entry`
+* `webhookbuyfill`, `webhookentryfill` -> `entry_fill`
+* `webhookbuycancel`, `webhookentrycancel` -> `entry_cancel`
+* `webhooksell`, `webhookexit` -> `exit`
+* `webhooksellfill`, `webhookexitfill` -> `exit_fill`
+* `webhooksellcancel`, `webhookexitcancel` -> `exit_cancel`
+
+## Removal of `populate_any_indicators`
+
+version 2023.3 saw the removal of `populate_any_indicators` in favor of split methods for feature engineering and targets. Please read the [migration document](strategy_migration.md#freqai-strategy) for full details.
+
+## Removal of `protections` from configuration
+
+Setting protections from the configuration via `"protections": [],` has been removed in 2024.10, after having raised deprecation warnings for over 3 years.
+
+## hdf5 data storage
+
+Using hdf5 as data storage has been deprecated in 2024.12 and was removed in 2025.1. We recommend switching to the feather data format.
+
+Please use the [`convert-data` subcommand](data-download.md#sub-command-convert-data) to convert your existing data to one of the supported formats before updating.
+
+## Configuring advanced logging via config
+
+Configuring syslog and journald via `--logfile systemd` and `--logfile journald` respectively has been deprecated in 2025.3.
+Please use configuration based [log setup](advanced-setup.md#advanced-logging) instead.
diff --git a/docs/developer.md b/docs/developer.md
new file mode 100644
index 000000000..d66f615fd
--- /dev/null
+++ b/docs/developer.md
@@ -0,0 +1,503 @@
+# Development Help
+
+This page is intended for developers of Freqtrade, people who want to contribute to the Freqtrade codebase or documentation, or people who want to understand the source code of the application they're running.
+
+All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel on [discord](https://discord.gg/p7nuUNVfP7) where you can ask questions.
+
+## Documentation
+
+Documentation is available at [https://freqtrade.io](https://www.freqtrade.io/) and needs to be provided with every new feature PR.
+
+Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/reference/admonitions/).
+
+To test the documentation locally use the following commands.
+
+``` bash
+pip install -r docs/requirements-docs.txt
+mkdocs serve
+```
+
+This will spin up a local server (usually on port 8000) so you can see if everything looks as you'd like it to.
+
+## Developer setup
+
+To configure a development environment, you can either use the provided [DevContainer](#devcontainer-setup), or use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
+Alternatively (e.g. if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -r requirements-dev.txt` - followed by `pip3 install -e .[all]`.
+
+This will install all required tools for development, including `pytest`, `ruff`, `mypy`, and `coveralls`.
+
+Then install the git hook scripts by running `pre-commit install`, so your changes will be verified locally before committing.
+This avoids a lot of waiting for CI already, as some basic formatting checks are done locally on your machine.
+
+Before opening a pull request, please familiarize yourself with our [Contributing Guidelines](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md).
+
+### Devcontainer setup
+
+The fastest and easiest way to get started is to use [VSCode](https://code.visualstudio.com/) with the Remote container extension.
+This gives developers the ability to start the bot with all required dependencies *without* needing to install any freqtrade specific dependencies on your local machine.
+
+#### Devcontainer dependencies
+
+* [VSCode](https://code.visualstudio.com/)
+* [docker](https://docs.docker.com/install/)
+* [Remote container extension documentation](https://code.visualstudio.com/docs/remote)
+
+For more information about the [Remote container extension](https://code.visualstudio.com/docs/remote), best consult the documentation.
+
+### Tests
+
+New code should be covered by basic unittests. Depending on the complexity of the feature, Reviewers may request more in-depth unittests.
+If necessary, the Freqtrade team can assist and give guidance with writing good tests (however please don't expect anyone to write the tests for you).
+
+#### How to run tests
+
+Use `pytest` in root folder to run all available testcases and confirm your local environment is setup correctly
+
+!!! Note "feature branches"
+    Tests are expected to pass on the `develop` and `stable` branches. Other branches may be work in progress with tests not working yet.
+
+#### Checking log content in tests
+
+Freqtrade uses 2 main methods to check log content in tests, `log_has()` and `log_has_re()` (to check using regex, in case of dynamic log-messages).
+These are available from `conftest.py` and can be imported in any test module.
+
+A sample check looks as follows:
+
+``` python
+from tests.conftest import log_has, log_has_re
+
+def test_method_to_test(caplog):
+    method_to_test()
+
+    assert log_has("This event happened", caplog)
+    # Check regex with trailing number ...
+    assert log_has_re(r"This dynamic event happened and produced \d+", caplog)
+
+```
+
+### Debug configuration
+
+To debug freqtrade, we recommend VSCode (with the Python extension) with the following launch configuration (located in `.vscode/launch.json`).
+Details will obviously vary between setups - but this should work to get you started.
+
+``` json
+{
+    "name": "freqtrade trade",
+    "type": "debugpy",
+    "request": "launch",
+    "module": "freqtrade",
+    "console": "integratedTerminal",
+    "args": [
+        "trade",
+        // Optional:
+        // "--userdir", "user_data",
+        "--strategy", 
+        "MyAwesomeStrategy",
+    ]
+},
+```
+
+Command line arguments can be added in the `"args"` array.
+This method can also be used to debug a strategy, by setting the breakpoints within the strategy.
+
+A similar setup can also be taken for Pycharm - using `freqtrade` as module name, and setting the command line arguments as "parameters".
+
+??? Tip "Correct venv usage"
+    When using a virtual environment (which you should), make sure that your Editor is using the correct virtual environment to avoid problems or "unknown import" errors.
+
+    #### Vscode
+
+    You can select the correct environment in VSCode with the command "Python: Select Interpreter" - which will show you environments the extension detected.
+    If your environment has not been detected, you can also pick a path manually.
+
+    #### Pycharm
+
+    In pycharm, you can select the appropriate Environment in the "Run/Debug Configurations" window.
+    ![Pycharm debug configuration](assets/pycharm_debug.png)
+
+!!! Note "Startup directory"
+    This assumes that you have the repository checked out, and the editor is started at the repository root level (so pyproject.toml is at the top level of your repository).
+
+## ErrorHandling
+
+Freqtrade Exceptions all inherit from `FreqtradeException`.
+This general class of error should however not be used directly. Instead, multiple specialized sub-Exceptions exist.
+
+Below is an outline of exception inheritance hierarchy:
+
+```
++ FreqtradeException
+|
++---+ OperationalException
+|   |
+|   +---+ ConfigurationError
+|
++---+ DependencyException
+|   |
+|   +---+ PricingError
+|   |
+|   +---+ ExchangeError
+|       |
+|       +---+ TemporaryError
+|       |
+|       +---+ DDosProtection
+|       |
+|       +---+ InvalidOrderException
+|           |
+|           +---+ RetryableOrderError
+|           |
+|           +---+ InsufficientFundsError
+|
++---+ StrategyError
+```
+
+---
+
+## Plugins
+
+### Pairlists
+
+You have a great idea for a new pair selection algorithm you would like to try out? Great.
+Hopefully you also want to contribute this back upstream.
+
+Whatever your motivations are - This should get you off the ground in trying to develop a new Pairlist Handler.
+
+First of all, have a look at the [VolumePairList](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/plugins/pairlist/VolumePairList.py) Handler, and best copy this file with a name of your new Pairlist Handler.
+
+This is a simple Handler, which however serves as a good example on how to start developing.
+
+Next, modify the class-name of the Handler (ideally align this with the module filename).
+
+The base-class provides an instance of the exchange (`self._exchange`) the pairlist manager (`self._pairlistmanager`), as well as the main configuration (`self._config`), the pairlist dedicated configuration (`self._pairlistconfig`) and the absolute position within the list of pairlists.
+
+```python
+        self._exchange = exchange
+        self._pairlistmanager = pairlistmanager
+        self._config = config
+        self._pairlistconfig = pairlistconfig
+        self._pairlist_pos = pairlist_pos
+```
+
+!!! Tip
+    Don't forget to register your pairlist in `constants.py` under the variable `AVAILABLE_PAIRLISTS` - otherwise it will not be selectable.
+
+Now, let's step through the methods which require actions:
+
+#### Pairlist configuration
+
+Configuration for the chain of Pairlist Handlers is done in the bot configuration file in the element `"pairlists"`, an array of configuration parameters for each Pairlist Handlers in the chain.
+
+By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the pairlist. Please follow this to ensure a consistent user experience.
+
+Additional parameters can be configured as needed. For instance, `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successful and dynamic.
+
+#### short_desc
+
+Returns a description used for Telegram messages.
+
+This should contain the name of the Pairlist Handler, as well as a short description containing the number of assets. Please follow the format `"PairlistName - top/bottom X pairs"`.
+
+#### gen_pairlist
+
+Override this method if the Pairlist Handler can be used as the leading Pairlist Handler in the chain, defining the initial pairlist which is then handled by all Pairlist Handlers in the chain. Examples are `StaticPairList` and `VolumePairList`.
+
+This is called with each iteration of the bot (only if the Pairlist Handler is at the first location) - so consider implementing caching for compute/network heavy calculations.
+
+It must return the resulting pairlist (which may then be passed into the chain of Pairlist Handlers).
+
+Validations are optional, the parent class exposes a `verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the end-result is not shorter than expected.
+
+#### filter_pairlist
+
+This method is called for each Pairlist Handler in the chain by the pairlist manager.
+
+This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
+
+It gets passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
+
+The default implementation in the base class simply calls the `_validate_pair()` method for each pair in the pairlist, but you may override it. So you should either implement the `_validate_pair()` in your Pairlist Handler or override `filter_pairlist()` to do something else.
+
+If overridden, it must return the resulting pairlist (which may then be passed into the next Pairlist Handler in the chain).
+
+Validations are optional, the parent class exposes a `verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the end result is not shorter than expected.
+
+In `VolumePairList`, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
+
+##### sample
+
+``` python
+    def filter_pairlist(self, pairlist: list[str], tickers: dict) -> List[str]:
+        # Generate dynamic whitelist
+        pairs = self._calculate_pairlist(pairlist, tickers)
+        return pairs
+```
+
+### Protections
+
+Best read the [Protection documentation](plugins.md#protections) to understand protections.
+This Guide is directed towards Developers who want to develop a new protection.
+
+No protection should use datetime directly, but use the provided `date_now` variable for date calculations. This preserves the ability to backtest protections.
+
+!!! Tip "Writing a new Protection"
+    Best copy one of the existing Protections to have a good example.
+
+#### Implementation of a new protection
+
+All Protection implementations must have `IProtection` as parent class.
+For that reason, they must implement the following methods:
+
+* `short_desc()`
+* `global_stop()`
+* `stop_per_pair()`.
+
+`global_stop()` and `stop_per_pair()` must return a ProtectionReturn object, which consists of:
+
+* lock pair - boolean
+* lock until - datetime - until when should the pair be locked (will be rounded up to the next new candle)
+* reason - string, used for logging and storage in the database
+* lock_side - long, short or '*'.
+
+The `until` portion should be calculated using the provided `calculate_lock_end()` method.
+
+All Protections should use `"stop_duration"` / `"stop_duration_candles"` to define how long a pair (or all pairs) should be locked.
+The content of this is made available as `self._stop_duration` to the each Protection.
+
+If your protection requires a look-back period, please use `"lookback_period"` / `"lockback_period_candles"` to keep all protections aligned.
+
+#### Global vs. local stops
+
+Protections can have 2 different ways to stop trading for a limited :
+
+* Per pair (local)
+* For all Pairs (globally)
+
+##### Protections - per pair
+
+Protections that implement the per pair approach must set `has_local_stop=True`.
+The method `stop_per_pair()` will be called whenever a trade closed (exit order completed).
+
+##### Protections - global protection
+
+These Protections should do their evaluation across all pairs, and consequently will also lock all pairs from trading (called a global PairLock).
+Global protection must set `has_global_stop=True` to be evaluated for global stops.
+The method `global_stop()` will be called whenever a trade closed (exit order completed).
+
+##### Protections - calculating lock end time
+
+Protections should calculate the lock end time based on the last trade it considers.
+This avoids re-locking should the lookback-period be longer than the actual lock period.
+
+The `IProtection` parent class provides a helper method for this in `calculate_lock_end()`.
+
+---
+
+## Implement a new Exchange (WIP)
+
+!!! Note
+    This section is a Work in Progress and is not a complete guide on how to test a new exchange with Freqtrade.
+
+!!! Note
+    Make sure to use an up-to-date version of CCXT before running any of the below tests.
+    You can get the latest version of ccxt by running `pip install -U ccxt` with activated virtual environment.
+    Native docker is not supported for these tests, however the available dev-container will support all required actions and eventually necessary changes.
+
+Most exchanges supported by CCXT should work out of the box.
+
+To quickly test the public endpoints of an exchange, add a configuration for your exchange to `tests/exchange_online/conftest.py` and run these tests with `pytest --longrun tests/exchange_online/test_ccxt_compat.py`.
+Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
+
+Also try to use `freqtrade download-data` for an extended timerange (multiple months) and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
+
+These are prerequisites to have an exchange listed as either Supported or Community tested (listed on the homepage).
+The below are "extras", which will make an exchange better (feature-complete) - but are not absolutely necessary for either of the 2 categories.
+
+Additional tests / steps to complete:
+
+* Verify data provided by `fetch_ohlcv()` - and eventually adjust `ohlcv_candle_limit` for this exchange
+* Check L2 orderbook limit range (API documentation) - and eventually set as necessary
+* Check if balance shows correctly (*)
+* Create market order (*)
+* Create limit order (*)
+* Cancel order (*)
+* Complete trade (enter + exit) (*)
+  * Compare result calculation between exchange and bot
+  * Ensure fees are applied correctly (check the database against the exchange)
+
+(*) Requires API keys and Balance on the exchange.
+
+### Stoploss On Exchange
+
+Check if the new exchange supports Stoploss on Exchange orders through their API.
+
+Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need to implement the exchange-specific parameters ourselves. Best look at `binance.py` for an example implementation of this. You'll need to dig through the documentation of the Exchange's API on how exactly this can be done. [CCXT Issues](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
+
+### Incomplete candles
+
+While fetching candle (OHLCV) data, we may end up getting incomplete candles (depending on the exchange).
+To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
+We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
+
+To check how the new exchange behaves, you can use the following snippet:
+
+``` python
+import ccxt
+from datetime import datetime, timezone
+from freqtrade.data.converter import ohlcv_to_dataframe
+ct = ccxt.binance()  # Use the exchange you're testing
+timeframe = "1d"
+pair = "BTC/USDT"  # Make sure to use a pair that exists on that exchange!
+raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
+
+# convert to dataframe
+df1 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
+
+print(df1.tail(1))
+print(datetime.now(timezone.utc))
+```
+
+``` output
+                         date      open      high       low     close  volume  
+499 2019-06-08 00:00:00+00:00  0.000007  0.000007  0.000007  0.000007   26264344.0  
+2019-06-09 12:30:27.873327
+```
+
+The output will show the last entry from the Exchange as well as the current UTC date.
+If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
+Another way is to run this command multiple times in a row and observe if the volume is changing (while the date remains the same).
+
+### Update binance cached leverage tiers
+
+Updating leveraged tiers should be done regularly - and requires an authenticated account with futures enabled.
+
+``` python
+import ccxt
+import json
+from pathlib import Path
+
+exchange = ccxt.binance({
+    'apiKey': '<apikey>',
+    'secret': '<secret>',
+    'options': {'defaultType': 'swap'}
+    })
+_ = exchange.load_markets()
+
+lev_tiers = exchange.fetch_leverage_tiers()
+
+# Assumes this is running in the root of the repository.
+file = Path('freqtrade/exchange/binance_leverage_tiers.json')
+json.dump(dict(sorted(lev_tiers.items())), file.open('w'), indent=2)
+
+```
+
+This file should then be contributed upstream, so others can benefit from this, too.
+
+## Updating example notebooks
+
+To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.
+
+``` bash
+jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace freqtrade/templates/strategy_analysis_example.ipynb
+jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade/templates/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md
+```
+
+## Continuous integration
+
+This documents some decisions taken for the CI Pipeline.
+
+* CI runs on all OS variants, Linux (ubuntu), macOS and Windows.
+* Docker images are build for the branches `stable` and `develop`, and are built as multiarch builds, supporting multiple platforms via the same tag.
+* Docker images containing Plot dependencies are also available as `stable_plot` and `develop_plot`.
+* Docker images contain a file, `/freqtrade/freqtrade_commit` containing the commit this image is based of.
+* Full docker image rebuilds are run once a week via schedule.
+* Deployments run on ubuntu.
+* ta-lib binaries are contained in the build_helpers directory to avoid fails related to external unavailability.
+* All tests must pass for a PR to be merged to `stable` or `develop`.
+
+## Creating a release
+
+This part of the documentation is aimed at maintainers, and shows how to create a release.
+
+### Create release branch
+
+!!! Note
+    Make sure that the `stable` branch is up-to-date!
+
+First, pick a commit that's about one week old (to not include latest additions to releases).
+
+``` bash
+# create new branch
+git checkout -b new_release <commitid>
+```
+
+Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
+
+* Merge the release branch (stable) into this branch.
+* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
+* Commit this part.
+* Push that branch to the remote and create a PR against the **stable branch**.
+* Update develop version to next version following the pattern `2019.8-dev`.
+
+### Create changelog from git commits
+
+``` bash
+# Needs to be done before merging / pulling that branch.
+git log --oneline --no-decorate --no-merges stable..new_release
+```
+
+To keep the release-log short, best wrap the full git changelog into a collapsible details section.
+
+```markdown
+<details>
+<summary>Expand full changelog</summary>
+
+... Full git changelog
+
+</details>
+```
+
+### FreqUI release
+
+If FreqUI has been updated substantially, make sure to create a release before merging the release branch.
+Make sure that freqUI CI on the release is finished and passed before merging the release.
+
+### Create github release / tag
+
+Once the PR against stable is merged (best right after merging):
+
+* Use the button "Draft a new release" in the Github UI (subsection releases).
+* Use the version-number specified as tag.
+* Use "stable" as reference (this step comes after the above PR is merged).
+* Use the above changelog as release comment (as codeblock).
+* Use the below snippet for the new release
+
+??? Tip "Release template"
+    ````
+    --8<-- "includes/release_template.md"
+    ````
+
+## Releases
+
+### pypi
+
+!!! Warning "Manual Releases"
+    This process is automated as part of Github Actions.  
+    Manual pypi pushes should not be necessary.
+
+??? example "Manual release"
+    To manually create a pypi release, please run the following commands:
+
+    Additional requirement: `wheel`, `twine` (for uploading), account on pypi with proper permissions.
+
+    ``` bash
+    pip install -U build
+    python -m build --sdist --wheel
+
+    # For pypi test (to check if some change to the installation did work)
+    twine upload --repository-url https://test.pypi.org/legacy/ dist/*
+
+    # For production:
+    twine upload dist/*
+    ```
+
+    Please don't push non-releases to the productive / real pypi instance.
diff --git a/docs/docker_quickstart.md b/docs/docker_quickstart.md
new file mode 100644
index 000000000..0c9e8001a
--- /dev/null
+++ b/docs/docker_quickstart.md
@@ -0,0 +1,226 @@
+# Using Freqtrade with Docker
+
+This page explains how to run the bot with Docker. It is not meant to work out of the box. You'll still need to read through the documentation and understand how to properly configure it.
+
+## Install Docker
+
+Start by downloading and installing Docker / Docker Desktop for your platform:
+
+* [Mac](https://docs.docker.com/docker-for-mac/install/)
+* [Windows](https://docs.docker.com/docker-for-windows/install/)
+* [Linux](https://docs.docker.com/install/)
+
+!!! Info "Docker compose install"
+    Freqtrade documentation assumes the use of Docker desktop (or the docker compose plugin).  
+    While the docker-compose standalone installation still works, it will require changing all `docker compose` commands from `docker compose` to `docker-compose` to work (e.g. `docker compose up -d` will become `docker-compose up -d`).
+
+??? Warning "Docker on windows"
+    If you just installed docker on a windows system, make sure to reboot your system, otherwise you might encounter unexplainable Problems related to network connectivity to docker containers.
+
+## Freqtrade with docker
+
+Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker compose file](https://github.com/freqtrade/freqtrade/blob/stable/docker-compose.yml) ready for usage.
+
+!!! Note
+    - The following section assumes that `docker` is installed and available to the logged in user.
+    - All below commands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
+
+### Docker quick start
+
+Create a new directory and place the [docker-compose file](https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml) in this directory.
+
+``` bash
+mkdir ft_userdata
+cd ft_userdata/
+# Download the docker-compose file from the repository
+curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
+
+# Pull the freqtrade image
+docker compose pull
+
+# Create user directory structure
+docker compose run --rm freqtrade create-userdir --userdir user_data
+
+# Create configuration - Requires answering interactive questions
+docker compose run --rm freqtrade new-config --config user_data/config.json
+```
+
+The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
+The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections.
+
+!!! Question "How to edit the bot configuration?"
+    You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
+
+    You can also change the both Strategy and commands by editing the command section of your `docker-compose.yml` file.
+
+#### Adding a custom strategy
+
+1. The configuration is now available as `user_data/config.json`
+2. Copy a custom strategy to the directory `user_data/strategies/`
+3. Add the Strategy' class name to the `docker-compose.yml` file
+
+The `SampleStrategy` is run by default.
+
+!!! Danger "`SampleStrategy` is just a demo!"
+    The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
+    Please always backtest your strategy and use dry-run for some time before risking real money!
+    You will find more information about Strategy development in the [Strategy documentation](strategy-customization.md).
+
+Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
+
+``` bash
+docker compose up -d
+```
+
+!!! Warning "Default configuration"
+    While the configuration generated will be mostly functional, you will still need to verify that all options correspond to what you want (like Pricing, pairlist, ...) before starting the bot.
+
+#### Accessing the UI
+
+If you've selected to enable FreqUI in the `new-config` step, you will have freqUI available at port `localhost:8080`.
+
+You can now access the UI by typing localhost:8080 in your browser.
+
+??? Note "UI Access on a remote server"
+    If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
+    This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
+    Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
+    Please also read the [API configuration with docker](rest-api.md#configuration-with-docker) section to learn more about this configuration.
+
+#### Monitoring the bot
+
+You can check for running instances with `docker compose ps`.
+This should list the service `freqtrade` as `running`. If that's not the case, best check the logs (see next point).
+
+#### Docker compose logs
+
+Logs will be written to: `user_data/logs/freqtrade.log`.  
+You can also check the latest log with the command `docker compose logs -f`.
+
+#### Database
+
+The database will be located at: `user_data/tradesv3.sqlite`
+
+#### Updating freqtrade with docker
+
+Updating freqtrade when using `docker` is as simple as running the following 2 commands:
+
+``` bash
+# Download the latest image
+docker compose pull
+# Restart the image
+docker compose up -d
+```
+
+This will first pull the latest image, and will then restart the container with the just pulled version.
+
+!!! Warning "Check the Changelog"
+    You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
+
+### Editing the docker-compose file
+
+Advanced users may edit the docker-compose file further to include all possible options or arguments.
+
+All freqtrade arguments will be available by running `docker compose run --rm freqtrade <command> <optional arguments>`.
+
+!!! Warning "`docker compose` for trade commands"
+    Trade commands (`freqtrade trade <...>`) should not be ran via `docker compose run` - but should use `docker compose up -d` instead.
+    This makes sure that the container is properly started (including port forwardings) and will make sure that the container will restart after a system reboot.
+    If you intend to use freqUI, please also ensure to adjust the [configuration accordingly](rest-api.md#configuration-with-docker), otherwise the UI will not be available.
+
+!!! Note "`docker compose run --rm`"
+    Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
+
+??? Note "Using docker without docker compose"
+    "`docker compose run --rm`" will require a compose file to be provided.
+    Some freqtrade commands that don't require authentication such as `list-pairs` can be run with "`docker run --rm`" instead.  
+    For example `docker run --rm freqtradeorg/freqtrade:stable list-pairs --exchange binance --quote BTC --print-json`.  
+    This can be useful for fetching exchange information to add to your `config.json` without affecting your running containers.
+
+#### Example: Download data with docker
+
+Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
+
+``` bash
+docker compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
+```
+
+Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
+
+#### Example: Backtest with docker
+
+Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
+
+``` bash
+docker compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
+```
+
+Head over to the [Backtesting Documentation](backtesting.md) to learn more.
+
+### Additional dependencies with docker
+
+If your strategy requires dependencies not included in the default image - it will be necessary to build the image on your host.
+For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [docker/Dockerfile.custom](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.custom) for an example).
+
+You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
+
+``` yaml
+    image: freqtrade_custom
+    build:
+      context: .
+      dockerfile: "./Dockerfile.<yourextension>"
+```
+
+You can then run `docker compose build --pull` to build the docker image, and run it using the commands described above.
+
+### Plotting with docker
+
+Commands `freqtrade plot-profit` and `freqtrade plot-dataframe` ([Documentation](plotting.md)) are available by changing the image to `*_plot` in your `docker-compose.yml` file.
+You can then use these commands as follows:
+
+``` bash
+docker compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
+```
+
+The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
+
+### Data analysis using docker compose
+
+Freqtrade provides a docker-compose file which starts up a jupyter lab server.
+You can run this server using the following command:
+
+``` bash
+docker compose -f docker/docker-compose-jupyter.yml up
+```
+
+This will create a docker-container running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
+Please use the link that's printed in the console after startup for simplified login.
+
+Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) up-to-date.
+
+``` bash
+docker compose -f docker/docker-compose-jupyter.yml build --no-cache
+```
+
+## Troubleshooting
+
+### Docker on Windows
+
+* Error: `"Timestamp for this request is outside of the recvWindow."`  
+  The market api requests require a synchronized clock but the time in the docker container shifts a bit over time into the past.
+  To fix this issue temporarily you need to run `wsl --shutdown` and restart docker again (a popup on windows 10 will ask you to do so).
+  A permanent solution is either to host the docker container on a linux host or restart the wsl from time to time with the scheduler.
+
+  ``` bash
+  taskkill /IM "Docker Desktop.exe" /F
+  wsl --shutdown
+  start "" "C:\Program Files\Docker\Docker\Docker Desktop.exe"
+  ```
+
+* Cannot connect to the API (Windows)  
+  If you're on windows and just installed Docker (desktop), make sure to reboot your System. Docker can have problems with network connectivity without a restart.
+  You should obviously also make sure to have your [settings](#accessing-the-ui) accordingly.
+
+!!! Warning
+    Due to the above, we do not recommend the usage of docker on windows for production setups, but only for experimentation, datadownload and backtesting.
+    Best use a linux-VPS for running freqtrade reliably.
diff --git a/docs/edge.md b/docs/edge.md
new file mode 100644
index 000000000..e48fe592a
--- /dev/null
+++ b/docs/edge.md
@@ -0,0 +1,300 @@
+# Edge positioning
+
+The `Edge Positioning` module uses probability to calculate your win rate and risk reward ratio. It will use these statistics to control your strategy trade entry points, position size and, stoploss.
+
+!!! Danger "Deprecated functionality"
+    `Edge positioning` (or short Edge) is currently in maintenance mode only (we keep existing functionality alive) and should be considered as deprecated.
+    It will currently not receive new features until either someone stepped forward to take up ownership of that module - or we'll decide to remove edge from freqtrade.
+
+!!! Warning
+    When using `Edge positioning` with a dynamic whitelist (VolumePairList), make sure to also use `AgeFilter` and set it to at least `calculate_since_number_of_days` to avoid problems with missing data.
+
+!!! Note
+    `Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
+    `Edge Positioning` improves the performance of some trading strategies and *decreases* the performance of others.
+
+
+## Introduction
+
+Trading strategies are not perfect. They are frameworks that are susceptible to the market and its indicators. Because the market is not at all predictable, sometimes a strategy will win and sometimes the same strategy will lose.
+
+To obtain an edge in the market, a strategy has to make more money than it loses. Making money in trading is not only about *how often* the strategy makes or loses money.
+
+!!! tip "It doesn't matter how often, but how much!"
+    A bad strategy might make 1 penny in *ten* transactions but lose 1 dollar in *one* transaction. If one only checks the number of winning trades, it would be misleading to think that the strategy is actually making a profit.
+
+The Edge Positioning module seeks to improve a strategy's winning probability and the money that the strategy will make *on the long run*. 
+
+We raise the following question[^1]:
+
+!!! Question "Which trade is a better option?"
+    a) A trade with 80% of chance of losing 100\$ and 20% chance of winning 200\$<br/>
+    b) A trade with 100% of chance of losing 30\$
+
+???+ Info "Answer"
+    The expected value of *a)* is smaller than the expected value of *b)*.<br/>
+    Hence, *b*) represents a smaller loss in the long run.<br/>
+    However, the answer is: *it depends*
+
+Another way to look at it is to ask a similar question:
+
+!!! Question "Which trade is a better option?"
+    a) A trade with 80% of chance of winning 100\$ and 20% chance of losing 200\$<br/>
+    b) A trade with 100% of chance of winning 30\$
+
+Edge positioning tries to answer the hard questions about risk/reward and position size automatically, seeking to minimizes the chances of losing of a given strategy.
+
+### Trading, winning and losing
+
+Let's call $o$ the return of a single transaction $o$ where $o \in \mathbb{R}$. The collection $O = \{o_1, o_2, ..., o_N\}$ is the set of all returns of transactions made during a trading session. We say that $N$ is the cardinality of $O$, or, in lay terms, it is the number of transactions made in a trading session.
+
+!!! Example
+    In a session where a strategy made three transactions we can say that $O = \{3.5, -1, 15\}$. That means that $N = 3$ and $o_1 = 3.5$, $o_2 = -1$, $o_3 = 15$.
+
+A winning trade is a trade where a strategy *made* money. Making money means that the strategy closed the position in a value that returned a profit, after all deducted fees. Formally, a winning trade will have a return $o_i > 0$. Similarly, a losing trade will have a return $o_j \leq 0$. With that, we can discover the set of all winning trades, $T_{win}$, as follows:
+
+$$ T_{win} = \{ o \in O | o > 0 \} $$
+
+Similarly, we can discover the set of losing trades $T_{lose}$ as follows:
+
+$$ T_{lose} = \{o \in O | o \leq 0\} $$
+
+!!! Example
+    In a section where a strategy made four transactions $O = \{3.5, -1, 15, 0\}$:<br>
+    $T_{win} = \{3.5, 15\}$<br>
+    $T_{lose} = \{-1, 0\}$<br>
+
+### Win Rate and Lose Rate
+
+The win rate $W$ is the proportion of winning trades with respect to all the trades made by a strategy. We use the following function to compute the win rate:
+
+$$W = \frac{|T_{win}|}{N}$$
+
+Where $W$ is the win rate, $N$ is the number of trades and, $T_{win}$ is the set of all trades where the strategy made money.
+
+Similarly, we can compute the rate of losing trades:
+
+$$ 
+    L = \frac{|T_{lose}|}{N} 
+$$
+
+Where $L$ is the lose rate, $N$ is the amount of trades made and, $T_{lose}$ is the set of all trades where the strategy lost money. Note that the above formula is the same as calculating $L = 1 – W$ or $W = 1 – L$
+
+### Risk Reward Ratio
+
+Risk Reward Ratio ($R$) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose. Formally:
+
+$$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
+
+???+ Example "Worked example of $R$ calculation"
+    Let's say that you think that the price of *stonecoin* today is 10.0\$. You believe that, because they will start mining stonecoin, it will go up to 15.0\$ tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to 0\$ tomorrow. You are planning to invest 100\$, which will give you 10 shares (100 / 10).
+
+    Your potential profit is calculated as:
+
+    $\begin{aligned} 
+        \text{potential_profit} &= (\text{potential_price} - \text{entry_price}) * \frac{\text{investment}}{\text{entry_price}} \\
+                                &= (15 - 10) * (100 / 10) \\
+                                &= 50
+    \end{aligned}$
+
+    Since the price might go to 0\$, the 100\$ dollars invested could turn into 0.
+
+    We do however use a stoploss of 15% - so in the worst case, we'll sell 15% below entry price (or at 8.5$\).
+
+    $\begin{aligned}
+        \text{potential_loss} &= (\text{entry_price} - \text{stoploss}) * \frac{\text{investment}}{\text{entry_price}} \\
+                                &= (10 - 8.5) * (100 / 10)\\
+                                &= 15
+    \end{aligned}$
+
+    We can compute the Risk Reward Ratio as follows:
+
+    $\begin{aligned}
+        R   &= \frac{\text{potential_profit}}{\text{potential_loss}}\\
+            &= \frac{50}{15}\\
+            &= 3.33
+    \end{aligned}$<br>
+    What it effectively means is that the strategy have the potential to make 3.33\$ for each 1\$ invested.
+
+On a long horizon, that is, on many trades, we can calculate the risk reward by dividing the strategy' average profit on winning trades by the strategy' average loss on losing trades. We can calculate the average profit, $\mu_{win}$, as follows:
+
+$$ \text{average_profit} = \mu_{win} = \frac{\text{sum_of_profits}}{\text{count_winning_trades}} = \frac{\sum^{o \in T_{win}} o}{|T_{win}|} $$
+
+Similarly, we can calculate the average loss, $\mu_{lose}$, as follows:
+
+$$ \text{average_loss} = \mu_{lose} = \frac{\text{sum_of_losses}}{\text{count_losing_trades}} = \frac{\sum^{o \in T_{lose}} o}{|T_{lose}|} $$
+
+Finally, we can calculate the Risk Reward ratio, $R$, as follows:
+
+$$ R = \frac{\text{average_profit}}{\text{average_loss}} = \frac{\mu_{win}}{\mu_{lose}}\\ $$
+
+
+???+ Example "Worked example of $R$ calculation using mean profit/loss"
+    Let's say the strategy that we are using makes an average win $\mu_{win} = 2.06$ and an average loss $\mu_{loss} = 4.11$.<br>
+    We calculate the risk reward ratio as follows:<br>
+    $R = \frac{\mu_{win}}{\mu_{loss}} = \frac{2.06}{4.11} = 0.5012...$
+    
+
+### Expectancy
+
+By combining the Win Rate $W$ and the Risk Reward ratio $R$ to create an expectancy ratio $E$. A expectance ratio is the expected return of the investment made in a trade. We can compute the value of $E$ as follows:
+
+$$E = R * W - L$$
+
+!!! Example "Calculating $E$"
+    Let's say that a strategy has a win rate $W = 0.28$ and a risk reward ratio $R = 5$. What this means is that the strategy is expected to make 5 times the investment around on 28% of the trades it makes. Working out the example:<br>
+    $E = R * W - L = 5 * 0.28 - 0.72 = 0.68$
+    <br>
+
+The expectancy worked out in the example above means that, on average, this strategy' trades will return 1.68 times the size of its losses. Said another way, the strategy makes 1.68\$ for every 1\$ it loses, on average. 
+
+This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
+
+It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
+
+You can also use this value to evaluate the effectiveness of modifications to this system.
+
+!!! Note
+    It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
+
+## How does it work?
+
+Edge combines dynamic stoploss, dynamic positions, and whitelist generation into one isolated module which is then applied to the trading strategy. If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
+
+| Pair   |      Stoploss      |  Win Rate | Risk Reward Ratio | Expectancy |
+|----------|:-------------:|-------------:|------------------:|-----------:|
+| XZC/ETH  |  -0.01        |   0.50       |1.176384           | 0.088      |
+| XZC/ETH  |  -0.02        |   0.51       |1.115941           | 0.079      |
+| XZC/ETH  |  -0.03        |   0.52       |1.359670           | 0.228      |
+| XZC/ETH  |  -0.04        |   0.51       |1.234539           | 0.117      |
+
+The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at $3%$ leads to the maximum expectancy according to historical data.
+
+Edge module then forces stoploss value it evaluated to your strategy dynamically.
+
+### Position size
+
+Edge dictates the amount at stake for each trade to the bot according to the following factors:
+
+- Allowed capital at risk
+- Stoploss
+
+Allowed capital at risk is calculated as follows:
+
+```
+Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
+```
+
+Stoploss is calculated as described above with respect to historical data.
+
+The position size is calculated as follows:
+
+```
+Position size = (Allowed capital at risk) / Stoploss
+```
+
+Example:
+
+Let's say the stake currency is **ETH** and there is $10$ **ETH** on the wallet. The capital available percentage is $50%$ and the allowed risk per trade is $1\%$. Thus, the available capital for trading is $10 * 0.5 = 5$ **ETH** and the allowed capital at risk would be $5 * 0.01 = 0.05$ **ETH**.
+
+-   **Trade 1:** The strategy detects a new buy signal in the **XLM/ETH** market. `Edge Positioning` calculates a stoploss of $2\%$ and a position of $0.05 / 0.02 = 2.5$ **ETH**. The bot takes a position of $2.5$ **ETH** in the **XLM/ETH** market.
+
+-   **Trade 2:** The strategy detects a buy signal on the **BTC/ETH** market while **Trade 1** is still open. `Edge Positioning` calculates the stoploss of $4\%$ on this market. Thus, **Trade 2** position size is $0.05 / 0.04 = 1.25$ **ETH**.
+
+!!! Tip "Available Capital $\neq$ Available in wallet"
+    The available capital for trading didn't change in **Trade 2** even with **Trade 1** still open. The available capital **is not** the free amount in the wallet.
+
+-   **Trade 3:** The strategy detects a buy signal in the **ADA/ETH** market. `Edge Positioning` calculates a stoploss of $1\%$ and a position of $0.05 / 0.01 = 5$ **ETH**. Since **Trade 1** has $2.5$ **ETH** blocked and **Trade 2** has $1.25$ **ETH** blocked, there is only $5 - 1.25 - 2.5 = 1.25$ **ETH** available. Hence, the position size of **Trade 3** is $1.25$ **ETH**. 
+
+!!! Tip "Available Capital Updates"
+    The available capital does not change before a position is sold. After a trade is closed the Available Capital goes up if the trade was profitable or goes down if the trade was a loss.
+
+-   The strategy detects a sell signal in the **XLM/ETH** market. The bot exits **Trade 1** for a profit of $1$ **ETH**. The total capital in the wallet becomes $11$ **ETH** and the available capital for trading becomes $5.5$ **ETH**.
+
+-   **Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2\%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
+
+## Edge command reference
+
+--8<-- "commands/edge.md"
+
+## Configurations
+
+Edge module has following configuration options:
+
+|  Parameter | Description |
+|------------|-------------|
+| `enabled` | If true, then Edge will run periodically. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
+| `process_throttle_secs` | How often should Edge run in seconds. <br>*Defaults to `3600` (once per hour).* <br> **Datatype:** Integer
+| `calculate_since_number_of_days` | Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy. <br> **Note** that it downloads historical data so increasing this number would lead to slowing down the bot. <br>*Defaults to `7`.* <br> **Datatype:** Integer
+| `allowed_risk` | Ratio of allowed risk per trade. <br>*Defaults to `0.01` (1%)).* <br> **Datatype:** Float
+| `stoploss_range_min` | Minimum stoploss. <br>*Defaults to `-0.01`.* <br> **Datatype:** Float
+| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> **Datatype:** Float
+| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> **Datatype:** Float
+| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
+| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10\$ on a trade you expect a 12\$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
+| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
+| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
+| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
+
+## Running Edge independently
+
+You can run Edge independently in order to see in details the result. Here is an example:
+
+``` bash
+freqtrade edge
+```
+
+An example of its output:
+
+| **pair**     |   **stoploss** |   **win rate** |   **risk reward ratio** |   **required risk reward** |   **expectancy** |   **total number of trades** |   **average duration (min)** |
+|:----------|-----------:|-----------:|--------------------:|-----------------------:|-------------:|-----------------:|---------------:|
+| **AGI/BTC**   |      -0.02 |       0.64 |                5.86 |                   0.56 |         3.41 |                       14 |                       54 |
+| **NXS/BTC**   |      -0.03 |       0.64 |                2.99 |                   0.57 |         1.54 |                       11 |                       26 |
+| **LEND/BTC**  |      -0.02 |       0.82 |                2.05 |                   0.22 |         1.50 |                       11 |                       36 |
+| **VIA/BTC**   |      -0.01 |       0.55 |                3.01 |                   0.83 |         1.19 |                       11 |                       48 |
+| **MTH/BTC**   |      -0.09 |       0.56 |                2.82 |                   0.80 |         1.12 |                       18 |                       52 |
+| **ARDR/BTC**  |      -0.04 |       0.42 |                3.14 |                   1.40 |         0.73 |                       12 |                       42 |
+| **BCPT/BTC**  |      -0.01 |       0.71 |                1.34 |                   0.40 |         0.67 |                       14 |                       30 |
+| **WINGS/BTC** |      -0.02 |       0.56 |                1.97 |                   0.80 |         0.65 |                       27 |                       42 |
+| **VIBE/BTC**  |      -0.02 |       0.83 |                0.91 |                   0.20 |         0.59 |                       12 |                       35 |
+| **MCO/BTC**   |      -0.02 |       0.79 |                0.97 |                   0.27 |         0.55 |                       14 |                       31 |
+| **GNT/BTC**   |      -0.02 |       0.50 |                2.06 |                   1.00 |         0.53 |                       18 |                       24 |
+| **HOT/BTC**   |      -0.01 |       0.17 |                7.72 |                   4.81 |         0.50 |                      209 |                        7 |
+| **SNM/BTC**   |      -0.03 |       0.71 |                1.06 |                   0.42 |         0.45 |                       17 |                       38 |
+| **APPC/BTC**  |      -0.02 |       0.44 |                2.28 |                   1.27 |         0.44 |                       25 |                       43 |
+| **NEBL/BTC**  |      -0.03 |       0.63 |                1.29 |                   0.58 |         0.44 |                       19 |                       59 |
+
+Edge produced the above table by comparing `calculate_since_number_of_days` to `minimum_expectancy` to find `min_trade_number` historical information based on the config file. The timerange Edge uses for its comparisons can be further limited by using the `--timerange` switch.
+
+In live and dry-run modes, after the `process_throttle_secs` has passed, Edge will again process `calculate_since_number_of_days` against `minimum_expectancy` to find `min_trade_number`. If no `min_trade_number` is found, the bot will return "whitelist empty". Depending on the trade strategy being deployed, "whitelist empty" may be return much of the time - or *all* of the time. The use of Edge may also cause trading to occur in bursts, though this is rare.
+
+If you encounter "whitelist empty" a lot, condsider tuning `calculate_since_number_of_days`, `minimum_expectancy`  and `min_trade_number` to align to the trading frequency of your strategy.
+
+### Update cached pairs with the latest data
+
+Edge requires historic data the same way as backtesting does.
+Please refer to the [Data Downloading](data-download.md) section of the documentation for details.
+
+### Precising stoploss range
+
+```bash
+freqtrade edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step
+```
+
+### Advanced use of timerange
+
+```bash
+freqtrade edge --timerange=20181110-20181113
+```
+
+Doing `--timerange=-20190901` will get all available data until September 1st (excluding September 1st 2019).
+
+The full timerange specification:
+
+* Use tickframes till 2018/01/31: `--timerange=-20180131`
+* Use tickframes since 2018/01/31: `--timerange=20180131-`
+* Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
+* Use tickframes between POSIX timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
+
+
+[^1]: Question extracted from MIT Opencourseware S096 - Mathematics with applications in Finance: https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/
diff --git a/docs/exchanges.md b/docs/exchanges.md
new file mode 100644
index 000000000..f058ed86e
--- /dev/null
+++ b/docs/exchanges.md
@@ -0,0 +1,413 @@
+# Exchange-specific Notes
+
+This page combines common gotchas and Information which are exchange-specific and most likely don't apply to other exchanges.
+
+## Exchange configuration
+
+Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
+exchange markets and trading APIs. The complete up-to-date list can be found in the
+[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
+However, the bot was tested by the development team with only a few exchanges.
+A current list of these can be found in the "Home" section of this documentation.
+
+Feel free to test other exchanges and submit your feedback or PR to improve the bot or confirm exchanges that work flawlessly..
+
+Some exchanges require special configuration, which can be found below.
+
+### Sample exchange configuration
+
+A exchange configuration for "binance" would look as follows:
+
+```json
+"exchange": {
+    "name": "binance",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    // ... 
+```
+
+### Setting rate limits
+
+Usually, rate limits set by CCXT are reliable and work well.
+In case of problems related to rate-limits (usually DDOS Exceptions in your logs), it's easy to change rateLimit settings to other values.
+
+```json
+"exchange": {
+    "name": "kraken",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "ccxt_config": {"enableRateLimit": true},
+    "ccxt_async_config": {
+        "enableRateLimit": true,
+        "rateLimit": 3100
+    },
+```
+
+This configuration enables kraken, as well as rate-limiting to avoid bans from the exchange.
+`"rateLimit": 3100` defines a wait-event of 3.1s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
+
+!!! Note
+    Optimal settings for rate-limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
+    We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
+
+## Binance
+
+!!! Warning "Server location and geo-ip restrictions"
+    Please be aware that Binance restricts API access regarding the server country. The current and non-exhaustive countries blocked are Canada, Malaysia, Netherlands and United States. Please go to [binance terms > b. Eligibility](https://www.binance.com/en/terms) to find up to date list.
+
+Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
+
+!!! Tip "Stoploss on Exchange"
+    Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
+    On futures, Binance supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
+
+### Binance Blacklist recommendation
+
+For Binance, it is suggested to add `"BNB/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `BNB` on the account or unless you're willing to disable using `BNB` for fees.
+Binance accounts may use `BNB` for fees, and if a trade happens to be on `BNB`, further trades may consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
+
+If not enough `BNB` is available to cover transaction fees, then fees will not be covered by `BNB` and no fee reduction will occur. Freqtrade will never buy BNB to cover for fees. BNB needs to be bought and monitored manually to this end.
+
+### Binance sites
+
+Binance has been split into 2, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
+
+* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
+* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
+
+### Binance RSA keys
+
+Freqtrade supports binance RSA API keys.
+
+We recommend to use them as environment variable.
+
+``` bash
+export FREQTRADE__EXCHANGE__SECRET="$(cat ./rsa_binance.private)"
+```
+
+They can however also be configured via configuration file. Since json doesn't support multi-line strings, you'll have to replace all newlines with `\n` to have a valid json file.
+
+``` json
+// ...
+ "key": "<someapikey>",
+ "secret": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBABACAFQA<...>s8KX8=\n-----END PRIVATE KEY-----"
+// ...
+```
+
+### Binance Futures
+
+Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.
+Violating these rules will result in a trading restriction.
+
+When trading on Binance Futures market, orderbook must be used because there is no price ticker data for futures.
+
+``` jsonc
+  "entry_pricing": {
+      "use_order_book": true,
+      "order_book_top": 1,
+      "check_depth_of_market": {
+          "enabled": false,
+          "bids_to_ask_delta": 1
+      }
+  },
+  "exit_pricing": {
+      "use_order_book": true,
+      "order_book_top": 1
+  },
+```
+
+#### Binance isolated futures settings
+
+Users will also have to have the futures-setting "Position Mode" set to "One-way Mode", and "Asset Mode" set to "Single-Asset Mode".
+These settings will be checked on startup, and freqtrade will show an error if this setting is wrong.
+
+![Binance futures settings](assets/binance_futures_settings.png)
+
+Freqtrade will not attempt to change these settings.
+
+#### Binance BNFCR futures
+
+BNFCR mode are a special type of futures mode on Binance to work around regulatory issues in Europe.  
+To use BNFCR futures, you will have to have the following combination of settings:
+
+``` jsonc
+{
+    // ...
+    "trading_mode": "futures",
+    "margin_mode": "cross",
+    "proxy_coin": "BNFCR",
+    "stake_currency": "USDT" // or "USDC"
+    // ...
+}
+```
+
+The `stake_currency` setting defines the markets the bot will be operating in. This choice is really arbitrary.
+
+On the exchange, you'll have to use "Multi-asset Mode" - and "Position Mode set to "One-way Mode".  
+Freqtrade will check these settings on startup, but won't attempt to change them.
+
+## Bingx
+
+BingX supports [time_in_force](configuration.md#understand-order_time_in_force) with settings "GTC" (good till cancelled), "IOC" (immediate-or-cancel) and "PO" (Post only) settings.
+
+!!! Tip "Stoploss on Exchange"
+    Bingx supports `stoploss_on_exchange` and can use both stop-limit and stop-market orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
+
+## Kraken
+
+Kraken supports [time_in_force](configuration.md#understand-order_time_in_force) with settings "GTC" (good till cancelled), "IOC" (immediate-or-cancel) and "PO" (Post only) settings.
+
+!!! Tip "Stoploss on Exchange"
+    Kraken supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
+    You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
+
+### Historic Kraken data
+
+The Kraken API does only provide 720 historic candles, which is sufficient for Freqtrade dry-run and live trade modes, but is a problem for backtesting.
+To download data for the Kraken exchange, using `--dl-trades` is mandatory, otherwise the bot will download the same 720 candles over and over, and you'll not have enough backtest data.
+
+To speed up downloading, you can download the [trades zip files](https://support.kraken.com/hc/en-us/articles/360047543791-Downloadable-historical-market-data-time-and-sales-) kraken provides.
+These are usually updated once per quarter. Freqtrade expects these files to be placed in `user_data/data/kraken/trades_csv`.
+
+A structure as follows can make sense if using incremental files, with the "full" history in one directory, and incremental files in different directories.
+The assumption for this mode is that the data is downloaded and unzipped keeping filenames as they are.
+Duplicate content will be ignored (based on timestamp) - though the assumption is that there is no gap in the data.
+
+This means, if your "full" history ends in Q4 2022 - then both incremental updates Q1 2023 and Q2 2023 are available.
+Not having this will lead to incomplete data, and therefore invalid results while using the data.
+
+```
+└── trades_csv
+    ├── Kraken_full_history
+    │   ├── BCHEUR.csv
+    │   └── XBTEUR.csv
+    ├── Kraken_Trading_History_Q1_2023
+    │   ├── BCHEUR.csv
+    │   └── XBTEUR.csv
+    └── Kraken_Trading_History_Q2_2023
+        ├── BCHEUR.csv
+        └── XBTEUR.csv
+```
+
+You can convert these files into freqtrade files:
+
+``` bash
+freqtrade convert-trade-data --exchange kraken --format-from kraken_csv --format-to feather
+# Convert trade data to different ohlcv timeframes
+freqtrade trades-to-ohlcv -p BTC/EUR BCH/EUR --exchange kraken -t 1m 5m 15m 1h
+```
+
+The converted data also makes downloading data possible, and will start the download after the latest loaded trade.
+
+``` bash
+freqtrade download-data --exchange kraken --dl-trades -p BTC/EUR BCH/EUR 
+```
+
+!!! Warning "Downloading data from kraken"
+    Downloading kraken data will require significantly more memory (RAM) than any other exchange, as the trades-data needs to be converted into candles on your machine.
+    It will also take a long time, as freqtrade will need to download every single trade that happened on the exchange for the pair / timerange combination, therefore please be patient.
+
+!!! Warning "rateLimit tuning"
+    Please pay attention that rateLimit configuration entry holds delay in milliseconds between requests, NOT requests/sec rate.
+    So, in order to mitigate Kraken API "Rate limit exceeded" exception, this configuration should be increased, NOT decreased.
+
+## Kucoin
+
+Kucoin requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
+
+```json
+"exchange": {
+    "name": "kucoin",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "password": "your_exchange_api_key_password",
+    // ...
+}
+```
+
+Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force).
+
+!!! Tip "Stoploss on Exchange"
+    Kucoin supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
+    You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type of stoploss shall be used.
+
+### Kucoin Blacklists
+
+For Kucoin, it is suggested to add `"KCS/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `KCS` on the account or unless you're willing to disable using `KCS` for fees. 
+Kucoin accounts may use `KCS` for fees, and if a trade happens to be on `KCS`, further trades may consume this position and make the initial `KCS` trade unsellable as the expected amount is not there anymore.
+
+## HTX
+
+!!! Tip "Stoploss on Exchange"
+    HTX supports `stoploss_on_exchange` and uses `stop-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
+
+## OKX
+
+OKX requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
+
+```json
+"exchange": {
+    "name": "okx",
+    "key": "your_exchange_key",
+    "secret": "your_exchange_secret",
+    "password": "your_exchange_api_key_password",
+    // ...
+}
+```
+
+If you've registered with OKX on the host my.okx.com (OKX EAA)- you will need to use `"myokx"` as the exchange name.
+Using the wrong exchange will result in the error "OKX Error 50119: API key doesn't exist" - as the 2 are separate entities.
+
+!!! Warning
+    OKX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
+
+!!! Warning "Futures"
+    OKX Futures has the concept of "position mode" - which can be "Buy/Sell" or long/short (hedge mode).
+    Freqtrade supports both modes (we recommend to use Buy/Sell mode) - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
+    OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
+
+## Gate.io
+
+!!! Tip "Stoploss on Exchange"
+    Gate.io supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
+
+Gate.io allows the use of `POINT` to pay for fees. As this is not a tradable currency (no regular market available), automatic fee calculations will fail (and default to a fee of 0).
+The configuration parameter `exchange.unknown_fee_rate` can be used to specify the exchange rate between Point and the stake currency. Obviously, changing the stake-currency will also require changes to this value.
+
+Gate API keys require the following permissions on top of the market type you want to trade:
+
+* "Spot Trade" _or_ "Perpetual Futures" (Read and Write) (either select both, or the one matching the market you want to trade)
+* "Wallet" (read only)
+* "Account" (read only)
+
+Without these permissions, the bot will not start correctly and show errors like "permission missing".
+
+## Bybit
+
+Futures trading on bybit is currently supported for USDT markets, and will use isolated futures mode.
+
+On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors.
+
+As bybit doesn't provide funding rate history, the dry-run calculation is used for live trades as well.
+
+API Keys for live futures trading must have the following permissions:
+
+* Read-write
+* Contract - Orders
+* Contract - Positions
+
+We do strongly recommend to limit all API keys to the IP you're going to use it from.
+
+!!! Warning "Unified accounts"
+    Freqtrade assumes accounts to be dedicated to the bot.
+    We therefore recommend the usage of one subaccount per bot. This is especially important when using unified accounts.  
+    Other configurations (multiple bots on one account, manual non-bot trades on the bot account) are not supported and may lead to unexpected behavior.
+
+
+!!! Tip "Stoploss on Exchange"
+    Bybit (futures only) supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
+    On futures, Bybit supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
+
+## Bitmart
+
+Bitmart requires the API key Memo (the name you give the API key) to go along with the exchange key and secret.
+It's therefore required to pass the UID as well.
+
+```json
+"exchange": {
+    "name": "bitmart",
+    "uid": "your_bitmart_api_key_memo",
+    "secret": "your_exchange_secret",
+    "password": "your_exchange_api_key_password",
+    // ...
+}
+```
+
+!!! Warning "Necessary Verification"
+    Bitmart requires Verification Lvl2 to successfully trade on the spot market through the API - even though trading via UI works just fine with just Lvl1 verification.
+
+## Hyperliquid
+
+!!! Tip "Stoploss on Exchange"
+    Hyperliquid supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it.
+
+Hyperliquid is a Decentralized Exchange (DEX). Decentralized exchanges work a bit different compared to normal exchanges. Instead of authenticating private API calls using an API key, private API calls need to be signed with the private key of your wallet (We recommend using an api Wallet for this, generated either on Hyperliquid or in your wallet of choice).
+This needs to be configured like this:
+
+```json
+"exchange": {
+    "name": "hyperliquid",
+    "walletAddress": "your_eth_wallet_address",
+    "privateKey": "your_api_private_key",
+    // ...
+}
+```
+
+* walletAddress in hex format: `0x<40 hex characters>` - Can be easily copied from your wallet - and should be your wallet address, not your API Wallet Address.
+* privateKey in hex format: `0x<64 hex characters>` - Use the key the API Wallet shows on creation.
+
+Hyperliquid handles deposits and withdrawals on the Arbitrum One chain, a Layer 2 scaling solution built on top of Ethereum. Hyperliquid uses USDC as quote / collateral. The process of depositing USDC on Hyperliquid requires a couple of steps, see [how to start trading](https://hyperliquid.gitbook.io/hyperliquid-docs/onboarding/how-to-start-trading) for details on what steps are needed.
+
+!!! Note "Hyperliquid general usage Notes"
+    Hyperliquid does not support market orders, however ccxt will simulate market orders by placing limit orders with a maximum slippage of 5%.  
+    Unfortunately, hyperliquid only offers 5000 historic candles, so backtesting will either need to build candles historically (by waiting and downloading the data incrementally over time) - or will be limited to the last 5000 candles.
+
+!!! Info "Some general best practices (non exhaustive)"
+    * Beware of supply chain attacks, like pip package poisoning etcetera. Whenever you use your private key, make sure your environment is safe.
+    * Don't use your actual wallet private key for trading. Use the Hyperliquid [API generator](https://app.hyperliquid.xyz/API) to create a separate API wallet.
+    * Don't store your actual wallet private key on the server you use for freqtrade. Use the API wallet private key instead. This key won't allow withdrawals, only trading.
+    * Always keep your mnemonic phrase and private key private.
+    * Don't use the same mnemonic as the one you had to backup when initializing a hardware wallet, using the same mnemonic basically deletes the security of your hardware wallet.
+    * Create a different software wallet, only transfer the funds you want to trade with to that wallet, and use that wallet to trade on Hyperliquid.
+    * If you have funds you don't want to use for trading (after making a profit for example), transfer them back to your hardware wallet.
+
+### Historic Hyperliquid data
+
+The Hyperliquid API does not provide historic data beyond the single call to fetch current data, so downloading data is not possible, as the downloaded data would not constitute proper historic data.
+
+## All exchanges
+
+Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
+
+## Random notes for other exchanges
+
+* The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
+
+```shell
+pip3 install web3
+```
+
+### Getting latest price / Incomplete candles
+
+Most exchanges return current incomplete candle via their OHLCV/klines API interface.
+By default, Freqtrade assumes that incomplete candle is fetched from the exchange and removes the last candle assuming it's the incomplete candle.
+
+Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#incomplete-candles) from the Contributor documentation.
+
+Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
+
+However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.
+
+### Advanced Freqtrade Exchange configuration
+
+Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behavior.
+
+Available options are listed in the exchange-class as `_ft_has_default`.
+
+For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
+
+```json
+"exchange": {
+    "name": "kraken",
+    "_ft_has_params": {
+        "order_time_in_force": ["GTC", "FOK"],
+        "ohlcv_candle_limit": 200
+        }
+    //...
+}
+```
+
+!!! Warning
+    Please make sure to fully understand the impacts of these settings before modifying them.
diff --git a/docs/faq.md b/docs/faq.md
index b3f15613a..8d19b5db0 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -1,71 +1,305 @@
-# freqtrade FAQ
+# Freqtrade FAQ
 
-#### I have waited 5 minutes, why hasn't the bot made any trades yet?!
+## Supported Markets
 
-Depending on the buy strategy, the amount of whitelisted coins, the 
-situation of the market etc, it can take up to hours to find good entry 
+Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
+
+### Can my bot open short positions?
+
+Freqtrade can open short positions in futures markets.
+This requires the strategy to be made for this - and `"trading_mode": "futures"` in the configuration.
+Please make sure to read the [relevant documentation page](leverage.md) first.
+
+In spot markets, you can in some cases use leveraged spot tokens, which reflect an inverted pair (eg. BTCUP/USD, BTCDOWN/USD, ETHBULL/USD, ETHBEAR/USD,...) which can be traded with Freqtrade.
+
+### Can my bot trade options or futures?
+
+Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
+
+## Beginner Tips & Tricks
+
+* When you work with your strategy & hyperopt file you should use a proper code editor like VSCode or PyCharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely pointed out by Freqtrade during startup).
+
+## Freqtrade common questions
+
+### Can freqtrade open multiple positions on the same pair in parallel?
+
+No. Freqtrade will only open one position per pair at a time.
+You can however use the [`adjust_trade_position()` callback](strategy-callbacks.md#adjust-trade-position) to adjust an open position.
+
+Backtesting provides an option for this in `--eps` - however this is only there to highlight "hidden" signals, and will not work in live.
+
+### The bot does not start
+
+Running the bot with `freqtrade trade --config config.json` shows the output `freqtrade: command not found`.
+
+This could be caused by the following reasons:
+
+* The virtual environment is not active.
+  * Run `source .venv/bin/activate` to activate the virtual environment.
+* The installation did not complete successfully.
+  * Please check the [Installation documentation](installation.md).
+
+### The bot starts, but in STOPPED mode
+
+Make sure you set the `initial_state` config option to `"running"` in your config.json
+
+### I have waited 5 minutes, why hasn't the bot made any trades yet?
+
+* Depending on the entry strategy, the amount of whitelisted coins, the
+situation of the market etc, it can take up to hours or days to find a good entry
 position for a trade. Be patient!
 
-#### I have made 12 trades already, why is my total profit negative?!
+* Backtesting will tell you roughly how many trades to expect - but that won't guarantee that they'll be distributed evenly across time - so you could have 20 trades on one day, and 0 for the rest of the week.
 
-I understand your disappointment but unfortunately 12 trades is just 
-not enough to say anything. If you run backtesting, you can see that our 
-current algorithm does leave you on the plus side, but that is after 
-thousands of trades and even there, you will be left with losses on 
-specific coins that you have traded tens if not hundreds of times. We 
-of course constantly aim to improve the bot but it will _always_ be a 
-gamble, which should leave you with modest wins on monthly basis but 
+* It may be because of a configuration error. It's best to check the logs, they usually tell you if the bot is simply not getting buy signals (only heartbeat messages), or if there is something wrong (errors / exceptions in the log).
+
+### I have made 12 trades already, why is my total profit negative?
+
+I understand your disappointment but unfortunately 12 trades is just
+not enough to say anything. If you run backtesting, you can see that the
+current algorithm does leave you on the plus side, but that is after
+thousands of trades and even there, you will be left with losses on
+specific coins that you have traded tens if not hundreds of times. We
+of course constantly aim to improve the bot but it will _always_ be a
+gamble, which should leave you with modest wins on monthly basis but
 you can't say much from few trades.
 
-#### I’d like to change the stake amount. Can I just stop the bot with 
-/stop and then change the config.json and run it again?
+### I’d like to make changes to the config. Can I do that without having to kill the bot?
 
-Not quite. Trades are persisted to a database but the configuration is 
-currently only read when the bot is killed and restarted. `/stop` more 
-like pauses. You can stop your bot, adjust settings and start it again.
+Yes. You can edit your config and use the `/reload_config` command to reload the configuration. The bot will stop, reload the configuration and strategy and will restart with the new configuration and strategy.
 
-#### I want to improve the bot with a new strategy
+### Why does my bot not sell everything it bought?
 
-That's great. We have a nice backtesting and hyperoptimizing setup. See 
-the tutorial [here|Testing-new-strategies-with-Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#hyperopt-commands).
+This is called "coin dust" and can happen on all exchanges.
+It happens because many exchanges subtract fees from the "receiving currency" - so you buy 100 COIN - but you only get 99.9 COIN.
+As COIN is trading in full lot sizes (1COIN steps), you cannot sell 0.9 COIN (or 99.9 COIN) - but you need to round down to 99 COIN.
 
-#### Is there a setting to only SELL the coins being held and not 
-perform anymore BUYS?
+This is not a bot-problem, but will also happen while manual trading.
 
-You can use the `/forcesell all` command from Telegram. 
+While freqtrade can handle this (it'll sell 99 COIN), fees are often below the minimum tradable lot-size (you can only trade full COIN, not 0.9 COIN).
+Leaving the dust (0.9 COIN) on the exchange makes usually sense, as the next time freqtrade buys COIN, it'll eat into the remaining small balance, this time selling everything it bought, and therefore slowly declining the dust balance (although it most likely will never reach exactly 0).
 
-### How many epoch do I need to get a good Hyperopt result?
-Per default Hyperopts without `-e` or `--epochs` parameter will only 
-run 100 epochs, means 100 evals of your triggers, guards, .... Too few 
-to find a great result (unless if you are very lucky), so you probably 
-have to run it for 10.000 or more. But it will take an eternity to 
+Where possible (e.g. on binance), the use of the exchange's dedicated fee currency will fix this.
+On binance, it's sufficient to have BNB in your account, and have "Pay fees in BNB" enabled in your profile. Your BNB balance will slowly decline (as it's used to pay fees) - but you'll no longer encounter dust (Freqtrade will include the fees in the profit calculations).
+Other exchanges don't offer such possibilities, where it's simply something you'll have to accept or move to a different exchange.
+
+### I deposited more funds to the exchange, but my bot doesn't recognize this
+
+Freqtrade will update the exchange balance when necessary (Before placing an order).
+RPC calls (Telegram's `/balance`, API calls to `/balance`) can trigger an update at max. once per hour.
+
+If `adjust_trade_position` is enabled (and the bot has open trades eligible for position adjustments) - then the wallets will be refreshed once per hour.
+To force an immediate update, you can use `/reload_config` - which will restart the bot.
+
+### I want to use incomplete candles
+
+Freqtrade will not provide incomplete candles to strategies. Using incomplete candles will lead to repainting and consequently to strategies with "ghost" buys, which are impossible to both backtest, and verify after they happened.
+
+You can use "current" market data by using the [dataprovider](strategy-customization.md#orderbookpair-maximum)'s orderbook or ticker methods - which however cannot be used during backtesting.
+
+### Is there a setting to only Exit the trades being held and not perform any new Entries?
+
+You can use the `/stopentry` command in Telegram to prevent future trade entry, followed by `/forceexit all` (sell all open trades).
+
+### I want to run multiple bots on the same machine
+
+Please look at the [advanced setup documentation Page](advanced-setup.md#running-multiple-instances-of-freqtrade).
+
+### I'm getting "Impossible to load Strategy" when starting the bot
+
+This error message is shown when the bot cannot load the strategy.
+Usually, you can use `freqtrade list-strategies` to list all available strategies. 
+The output of this command will also include a status column, showing if the strategy can be loaded.
+
+Please check the following:
+
+* Are you using the correct strategy name? The strategy name is case-sensitive and must correspond to the Strategy class name (not the filename!).
+* Is the strategy in the `user_data/strategies` directory, and has the file-ending `.py`?
+* Does the bot show other warnings before this error? Maybe you're missing some dependencies for the strategy - which would be highlighted in the log.
+* In case of docker - is the strategy directory mounted correctly (check the volumes part of the docker-compose file)?
+
+### I'm getting "Missing data fillup" messages in the log
+
+This message is just a warning that the latest candles had missing candles in them.
+Depending on the exchange, this can indicate that the pair didn't have a trade for the timeframe you are using - and the exchange does only return candles with volume.
+On low volume pairs, this is a rather common occurrence.
+
+If this happens for all pairs in the pairlist, this might indicate a recent exchange downtime. Please check your exchange's public channels for details.
+
+Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
+
+### I'm getting "Price jump between 2 candles detected"
+
+This message is a warning that the candles had a price jump of > 30%.
+This might be a sign that the pair stopped trading, and some token exchange took place (e.g. COCOS in 2021 - where price jumped from 0.0000154 to 0.01621).
+This message is often accompanied by ["Missing data fillup"](#im-getting-missing-data-fillup-messages-in-the-log) - as trading on such pairs is often stopped for some time.
+
+### I want to reset the bot's database
+
+To reset the bot's database, you can either delete the database (by default `tradesv3.sqlite` or `tradesv3.dryrun.sqlite`), or use a different database url via `--db-url` (e.g. `sqlite:///mynewdatabase.sqlite`).
+
+### I'm getting "Outdated history for pair xxx" in the log
+
+The bot is trying to tell you that it got an outdated last candle (not the last complete candle).
+As a consequence, Freqtrade will not enter a trade for this pair - as trading on old information is usually not what is desired.
+
+This warning can point to one of the below problems:
+
+* Exchange downtime -> Check your exchange status page / blog / twitter feed for details.
+* Wrong system time -> Ensure your system-time is correct.
+* Barely traded pair -> Check the pair on the exchange webpage, look at the timeframe your strategy uses. If the pair does not have any volume in some candles (usually visualized with a "volume 0" bar, and a "_" as candle), this pair did not have any trades in this timeframe. These pairs should ideally be avoided, as they can cause problems with order-filling.
+* API problem -> API returns wrong data (this only here for completeness, and should not happen with supported exchanges).
+
+### I'm getting the "Exchange XXX does not support market orders." message and cannot run my strategy
+
+As the message says, your exchange does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Your strategy was probably written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Gate.io).
+
+To fix this, redefine order types in the strategy to use "limit" instead of "market":
+
+``` python
+    order_types = {
+        ...
+        "stoploss": "limit",
+        ...
+    }
+```
+
+The same fix should be applied in the configuration file, if order types are defined in your custom config rather than in the strategy.
+
+### I'm trying to start the bot live, but get an API permission error
+
+Errors like `Invalid API-key, IP, or permissions for action` mean exactly what they actually say.  
+Your API key is either invalid (copy/paste error? check for leading/trailing spaces in the config), expired, or the IP you're running the bot from is not enabled in the Exchange's API console.  
+Usually, the permission "Spot Trading" (or the equivalent in the exchange you use) will be necessary.  
+Futures will usually have to be enabled specifically.
+
+### How do I search the bot logs for something?
+
+By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility sub-commands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
+
+* In unix shells, this normally can be done as simple as:
+```shell
+$ freqtrade --some-options 2>&1 >/dev/null | grep 'something'
+```
+(note, `2>&1` and `>/dev/null` should be written in this order)
+
+* Bash interpreter also supports so called process substitution syntax, you can grep the log for a string with it as:
+```shell
+$ freqtrade --some-options 2> >(grep 'something') >/dev/null
+```
+or
+```shell
+$ freqtrade --some-options 2> >(grep -v 'something' 1>&2)
+```
+
+* You can also write the copy of Freqtrade log messages to a file with the `--logfile` option:
+```shell
+$ freqtrade --logfile /path/to/mylogfile.log --some-options
+```
+and then grep it as:
+```shell
+$ cat /path/to/mylogfile.log | grep 'something'
+```
+or even on the fly, as the bot works and the log file grows:
+```shell
+$ tail -f /path/to/mylogfile.log | grep 'something'
+```
+from a separate terminal window.
+
+On Windows, the `--logfile` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
+```
+> type \path\to\mylogfile.log | findstr "something"
+```
+
+## Hyperopt module
+
+### Why does freqtrade not have GPU support?
+
+First of all, most indicator libraries don't have GPU support - as such, there would be little benefit for indicator calculations.
+The GPU improvements would only apply to pandas-native calculations - or ones written by yourself.
+
+For hyperopt, freqtrade is using scikit-optimize, which is built on top of scikit-learn.
+Their statement about GPU support is [pretty clear](https://scikit-learn.org/stable/faq.html#will-you-add-gpu-support).
+
+GPU's also are only good at crunching numbers (floating point operations).
+For hyperopt, we need both number-crunching (find next parameters) and running python code (running backtesting).
+As such, GPU's are not too well suited for most parts of hyperopt.
+
+The benefit of using GPU would therefore be pretty slim - and will not justify the complexity introduced by trying to add GPU support.
+
+There is however nothing preventing you from using GPU-enabled indicators within your strategy if you think you must have this - you will however probably be disappointed by the slim gain that will give you (compared to the complexity).
+
+### How many epochs do I need to get a good Hyperopt result?
+
+Per default Hyperopt called without the `-e`/`--epochs` command line option will only
+run 100 epochs, means 100 evaluations of your triggers, guards, ... Too few
+to find a great result (unless if you are very lucky), so you probably
+have to run it for 10000 or more. But it will take an eternity to
 compute.
 
-We recommend you to run it at least 10.000 epochs:
+Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
+
+It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
+
 ```bash
-python3 ./freqtrade/main.py hyperopt -e 10000
+freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
 ```
 
-or if you want intermediate result to see
-```bash
-for i in {1..100}; do python3 ./freqtrade/main.py hyperopt -e 100; done
-```
+### Why does it take a long time to run hyperopt?
 
-#### Why it is so long to run hyperopt?
-Finding a great Hyperopt results takes time. 
+* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [discord community](https://discord.gg/p7nuUNVfP7). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
 
-If you wonder why it takes a while to find great hyperopt results
+* If you wonder why it can take from 20 minutes to days to do 1000 epochs here are some answers:
 
-This answer was written during the under the release 0.15.1, when we had
-:
-- 8 triggers
-- 9 guards: let's say we evaluate even 10 values from each
-- 1 stoploss calculation: let's say we want 10 values from that too to 
-be evaluated
+This answer was written during the release 0.15.1, when we had:
+
+* 8 triggers
+* 9 guards: let's say we evaluate even 10 values from each
+* 1 stoploss calculation: let's say we want 10 values from that too to be evaluated
 
 The following calculation is still very rough and not very precise
-but it will give the idea. With only these triggers and guards there is 
-already 8*10^9*10 evaluations. A roughly total of 80 billion evals. 
-Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th 
-of the search space.
+but it will give the idea. With only these triggers and guards there is
+already 8\*10^9\*10 evaluations. A roughly total of 80 billion evaluations.
+Did you run 100 000 evaluations? Congrats, you've done roughly 1 / 100 000 th
+of the search space, assuming that the bot never tests the same parameters more than once.
 
+* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 100000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades.
+
+Example: 4% profit 650 times vs 0,3% profit a trade 10000 times in a year. If we assume you set the --timerange to 365 days.
+
+Example:
+`freqtrade --config config.json --strategy SampleStrategy --hyperopt SampleHyperopt -e 1000 --timerange 20190601-20200601`
+
+## Edge module
+
+### Edge implements interesting approach for controlling position size, is there any theory behind it?
+
+The Edge module is mostly a result of brainstorming of [@mishaker](https://github.com/mishaker) and [@creslinux](https://github.com/creslinux) freqtrade team members.
+
+You can find further info on expectancy, win rate, risk management and position size in the following sources:
+
+- https://www.tradeciety.com/ultimate-math-guide-for-traders/
+- https://samuraitradingacademy.com/trading-expectancy/
+- https://www.learningmarkets.com/determining-expectancy-in-your-trading/
+- https://www.lonestocktrader.com/make-money-trading-positive-expectancy/
+- https://www.babypips.com/trading/trade-expectancy-matter
+
+## Official channels
+
+Freqtrade is using exclusively the following official channels:
+
+* [Freqtrade discord server](https://discord.gg/p7nuUNVfP7)
+* [Freqtrade documentation (https://freqtrade.io)](https://freqtrade.io)
+* [Freqtrade github organization](https://github.com/freqtrade)
+
+Nobody affiliated with the freqtrade project will ask you about your exchange keys or anything else exposing your funds to exploitation.
+Should you be asked to expose your exchange keys or send funds to some random wallet, then please don't follow these instructions.
+
+Failing to follow these guidelines will not be responsibility of freqtrade.
+
+## "Freqtrade token"
+
+Freqtrade does not have a Crypto token offering.
+
+Token offerings you find on the internet referring Freqtrade, FreqAI or freqUI must be considered to be a scam, trying to exploit freqtrade's popularity for their own, nefarious gains.
diff --git a/docs/freq-ui.md b/docs/freq-ui.md
new file mode 100644
index 000000000..f1eec3cb9
--- /dev/null
+++ b/docs/freq-ui.md
@@ -0,0 +1,84 @@
+# FreqUI
+
+Freqtrade provides a builtin webserver, which can serve [FreqUI](https://github.com/freqtrade/frequi), the freqtrade frontend.
+
+By default, the UI is automatically installed as part of the installation (script, docker).
+freqUI can also be manually installed by using the `freqtrade install-ui` command.
+This same command can also be used to update freqUI to new new releases.
+
+Once the bot is started in trade / dry-run mode (with `freqtrade trade`) - the UI will be available under the configured API port (by default `http://127.0.0.1:8080`).
+
+??? Note "Looking to contribute to freqUI?"
+    Developers should not use this method, but instead clone the corresponding use the method described in the [freqUI repository](https://github.com/freqtrade/frequi) to get the source-code of freqUI. A working installation of node will be required to build the frontend.
+
+!!! tip "freqUI is not required to run freqtrade"
+    freqUI is an optional component of freqtrade, and is not required to run the bot.
+    It is a frontend that can be used to monitor the bot and to interact with it - but freqtrade itself will work perfectly fine without it.
+
+## Configuration
+
+FreqUI does not have it's own configuration file - but assumes a working setup for the [rest-api](rest-api.md) is available.
+Please refer to the corresponding documentation page to get setup with freqUI
+
+## UI
+
+FreqUI is a modern, responsive web application that can be used to monitor and interact with your bot.
+
+FreqUI provides a light, as well as a dark theme.
+Themes can be easily switched via a prominent button at the top of the page.
+The theme of the screenshots on this page will adapt to the selected documentation Theme, so to see the dark (or light) version, please switch the theme of the Documentation.
+
+### Login
+
+The below screenshot shows the login screen of freqUI.
+
+![FreqUI - login](assets/frequi-login-CORS.png#only-dark)
+![FreqUI - login](assets/frequi-login-CORS-light.png#only-light)
+
+!!! Hint "CORS"
+    The Cors error shown in this screenshot is due to the fact that the UI is running on a different port than the API, and [CORS](#cors) has not been setup correctly yet.
+
+### Trade view
+
+The trade view allows you to visualize the trades that the bot is making and to interact with the bot.
+On this page, you can also interact with the bot by starting and stopping it and - if configured - force trade entries and exits.
+
+![FreqUI - trade view](assets/freqUI-trade-pane-dark.png#only-dark)
+![FreqUI - trade view](assets/freqUI-trade-pane-light.png#only-light)
+
+### Plot Configurator
+
+FreqUI Plots can be configured either via a `plot_config` configuration object in the strategy (which can be loaded via "from strategy" button) or via the UI.
+Multiple plot configurations can be created and switched at will - allowing for flexible, different views into your charts.
+
+The plot configuration can be accessed via the "Plot Configurator" (Cog icon) button in the top right corner of the trade view.
+
+![FreqUI - plot configuration](assets/freqUI-plot-configurator-dark.png#only-dark)
+![FreqUI - plot configuration](assets/freqUI-plot-configurator-light.png#only-light)
+
+### Settings
+
+Several UI related settings can be changed by accessing the settings page.
+
+Things you can change (among others):
+
+* Timezone of the UI
+* Visualization of open trades as part of the favicon (browser tab)
+* Candle colors (up/down -> red/green)
+* Enable / disable in-app notification types
+
+![FreqUI - Settings view](assets/frequi-settings-dark.png#only-dark)
+![FreqUI - Settings view](assets/frequi-settings-light.png#only-light)
+
+## Backtesting
+
+When freqtrade is started in [webserver mode](utils.md#webserver-mode) (freqtrade started with `freqtrade webserver`), the backtesting view becomes available.
+This view allows you to backtest strategies and visualize the results.
+
+You can also load and visualize previous backtest results, as well as compare the results with each other.
+
+![FreqUI - Backtesting](assets/freqUI-backtesting-dark.png#only-dark)
+![FreqUI - Backtesting](assets/freqUI-backtesting-light.png#only-light)
+
+
+--8<-- "includes/cors.md"
diff --git a/docs/freqai-configuration.md b/docs/freqai-configuration.md
new file mode 100644
index 000000000..5ab7b2602
--- /dev/null
+++ b/docs/freqai-configuration.md
@@ -0,0 +1,423 @@
+# Configuration
+
+FreqAI is configured through the typical [Freqtrade config file](configuration.md) and the standard [Freqtrade strategy](strategy-customization.md). Examples of FreqAI config and strategy files can be found in `config_examples/config_freqai.example.json` and `freqtrade/templates/FreqaiExampleStrategy.py`, respectively.
+
+## Setting up the configuration file
+
+ Although there are plenty of additional parameters to choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a FreqAI config must at minimum include the following parameters (the parameter values are only examples):
+
+```json
+    "freqai": {
+        "enabled": true,
+        "purge_old_models": 2,
+        "train_period_days": 30,
+        "backtest_period_days": 7,
+        "identifier" : "unique-id",
+        "feature_parameters" : {
+            "include_timeframes": ["5m","15m","4h"],
+            "include_corr_pairlist": [
+                "ETH/USD",
+                "LINK/USD",
+                "BNB/USD"
+            ],
+            "label_period_candles": 24,
+            "include_shifted_candles": 2,
+            "indicator_periods_candles": [10, 20]
+        },
+        "data_split_parameters" : {
+            "test_size": 0.25
+        }
+    }
+```
+
+A full example config is available in `config_examples/config_freqai.example.json`.
+
+!!! Note
+    The `identifier` is commonly overlooked by newcomers, however, this value plays an important role in your configuration. This value is a unique ID that you choose to describe one of your runs. Keeping it the same allows you to maintain crash resilience as well as faster backtesting. As soon as you want to try a new run (new features, new model, etc.), you should change this value (or delete the `user_data/models/unique-id` folder. More details available in the [parameter table](freqai-parameter-table.md#feature-parameters).
+
+## Building a FreqAI strategy
+
+The FreqAI strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md):
+
+```python
+    # user should define the maximum startup candle count (the largest number of candles
+    # passed to any single indicator)
+    startup_candle_count: int = 20
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # the model will return all labels created by user in `set_freqai_targets()`
+        # (& appended targets), an indication of whether or not the prediction should be accepted,
+        # the target mean/std values for each of the labels created by user in
+        # `set_freqai_targets()` for each training period.
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        return dataframe
+
+    def feature_engineering_expand_all(self, dataframe: DataFrame, period, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
+            )
+        return dataframe
+```
+
+Notice how the `feature_engineering_*()` is where [features](freqai-feature-engineering.md#feature-engineering) are added. Meanwhile `set_freqai_targets()` adds the labels/targets. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
+
+!!! Note
+    The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
+
+!!! Note
+    Features **must** be defined in `feature_engineering_*()`. Defining FreqAI features in `populate_indicators()`
+    will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, you should use `feature_engineering_standard()`
+    (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`).
+
+## Important dataframe key patterns
+
+Below are the values you can expect to include/use inside a typical strategy dataframe (`df[]`):
+
+|  DataFrame Key | Description |
+|------------|-------------|
+| `df['&*']` | Any dataframe column prepended with `&` in `set_freqai_targets()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
+| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
+| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
+| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
+| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%` (see details below). <br> **Datatype:** Depends on the feature created by the user.
+| `df['%%*']` | Any dataframe column prepended with `%%` in `feature_engineering_*()` is treated as a training feature, just the same as the above `%` prepend. However, in this case, the features are returned back to the strategy for FreqUI/plot-dataframe plotting and monitoring in Dry/Live/Backtesting <br> **Datatype:** Depends on the feature created by the user. Please note that features created in `feature_engineering_expand()` will have automatic FreqAI naming schemas depending on the expansions that you configured (i.e. `include_timeframes`, `include_corr_pairlist`, `indicators_periods_candles`, `include_shifted_candles`). So if you want to plot `%%-rsi` from `feature_engineering_expand_all()`, the final naming scheme for your plotting config would be: `%%-rsi-period_10_ETH/USDT:USDT_1h` for the `rsi` feature with `period=10`, `timeframe=1h`, and `pair=ETH/USDT:USDT` (the `:USDT` is added if you are using futures pairs). It is useful to simply add `print(dataframe.columns)` in your `populate_indicators()` after `self.freqai.start()` to see the full list of available features that are returned to the strategy for plotting purposes.
+
+## Setting the `startup_candle_count`
+
+The `startup_candle_count` in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., TA-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
+
+!!! Note
+    There are instances where the TA-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
+
+    ```
+    2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.
+    ```
+
+## Creating a dynamic target threshold
+
+Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and  `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
+
+```python
+dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
+dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
+```
+
+To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_predictions_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
+
+```json
+    "freqai": {
+        "fit_live_predictions_candles": 300,
+    }
+```
+
+If this value is set, FreqAI will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if you stop and restart a model with the same `identifier`.
+
+## Using different prediction models
+
+FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `CatBoost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`.
+
+Regression and classification models differ in what targets they predict - a regression model will predict a target of continuous values, for example what price BTC will be at tomorrow, whilst a classifier will predict a target of discrete values, for example if the price of BTC will go up tomorrow or not. This means that you have to specify your targets differently depending on which model type you are using (see details [below](#setting-model-targets)).
+
+All of the aforementioned model libraries implement gradient boosted decision tree algorithms. They all work on the principle of ensemble learning, where predictions from multiple simple learners are combined to get a final prediction that is more stable and generalized. The simple learners in this case are decision trees. Gradient boosting refers to the method of learning, where each simple learner is built in sequence - the subsequent learner is used to improve on the error from the previous learner. If you want to learn more about the different model libraries you can find the information in their respective docs:
+
+* CatBoost: https://catboost.ai/en/docs/
+* LightGBM: https://lightgbm.readthedocs.io/en/v3.3.2/#
+* XGBoost: https://xgboost.readthedocs.io/en/stable/#
+
+There are also numerous online articles describing and comparing the algorithms. Some relatively lightweight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.
+
+Apart from the models already available in FreqAI, it is also possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to customize various aspects of the training procedures. You can place custom FreqAI models in `user_data/freqaimodels` - and freqtrade will pick them up from there based on the provided `--freqaimodel` name - which has to correspond to the class name of your custom model.
+Make sure to use unique names to avoid overriding built-in models.
+
+### Setting model targets
+
+#### Regressors
+
+If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the `CatboostRegressor`via the flag `--freqaimodel CatboostRegressor`. An example of how you could set a regression target for predicting the price 100 candles into the future would be
+
+```python
+df['&s-close_price'] = df['close'].shift(-100)
+```
+
+If you want to predict multiple targets, you need to define multiple labels using the same syntax as shown above.
+
+#### Classifiers
+
+If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
+
+```python
+df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
+```
+
+If you want to predict multiple targets you must specify all labels in the same label column. You could, for example, add the label `same` to define where the price was unchanged by setting
+
+```python
+df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
+df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down'])
+```
+
+## PyTorch Module
+
+### Quick start
+
+The easiest way to quickly run a pytorch model is with the following command (for regression task):
+
+```bash
+freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel PyTorchMLPRegressor --strategy-path freqtrade/templates 
+```
+
+!!! Note "Installation/docker"
+    The PyTorch module requires large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl or PyTorch (~700mb additional space required) [y/N]?".
+    Users who prefer docker should ensure they use the docker image appended with `_freqaitorch`.
+    We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file.
+    This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
+
+    PyTorch dropped support for macOS x64 (intel based Apple devices) in version 2.3. Subsequently, freqtrade also dropped support for PyTorch on this platform.
+
+### Structure
+
+#### Model
+
+You can construct your own Neural Network architecture in PyTorch by simply defining your `nn.Module` class inside your custom [`IFreqaiModel` file](#using-different-prediction-models) and then using that class in your `def train()` function. Here is an example of logistic regression model implementation using PyTorch (should be used with nn.BCELoss criterion) for classification tasks.
+
+```python
+
+class LogisticRegression(nn.Module):
+    def __init__(self, input_size: int):
+        super().__init__()
+        # Define your layers
+        self.linear = nn.Linear(input_size, 1)
+        self.activation = nn.Sigmoid()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # Define the forward pass
+        out = self.linear(x)
+        out = self.activation(out)
+        return out
+
+class MyCoolPyTorchClassifier(BasePyTorchClassifier):
+    """
+    This is a custom IFreqaiModel showing how a user might setup their own 
+    custom Neural Network architecture for their training.
+    """
+
+    @property
+    def data_convertor(self) -> PyTorchDataConvertor:
+        return DefaultPyTorchDataConvertor(target_tensor_type=torch.float)
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        config = self.freqai_info.get("model_training_parameters", {})
+        self.learning_rate: float = config.get("learning_rate",  3e-4)
+        self.model_kwargs: dict[str, Any] = config.get("model_kwargs",  {})
+        self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs",  {})
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        class_names = self.get_class_names()
+        self.convert_label_column_to_int(data_dictionary, dk, class_names)
+        n_features = data_dictionary["train_features"].shape[-1]
+        model = LogisticRegression(
+            input_dim=n_features
+        )
+        model.to(self.device)
+        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
+        criterion = torch.nn.CrossEntropyLoss()
+        init_model = self.get_init_model(dk.pair)
+        trainer = PyTorchModelTrainer(
+            model=model,
+            optimizer=optimizer,
+            criterion=criterion,
+            model_meta_data={"class_names": class_names},
+            device=self.device,
+            init_model=init_model,
+            data_convertor=self.data_convertor,
+            **self.trainer_kwargs,
+        )
+        trainer.fit(data_dictionary, self.splits)
+        return trainer
+
+```
+
+#### Trainer
+
+The `PyTorchModelTrainer` performs the idiomatic PyTorch train loop:
+Define our model, loss function, and optimizer, and then move them to the appropriate device (GPU or CPU). Inside the loop, we iterate through the batches in the dataloader, move the data to the device, compute the prediction and loss, backpropagate, and update the model parameters using the optimizer. 
+
+In addition, the trainer is responsible for the following:
+ - saving and loading the model
+ - converting the data from `pandas.DataFrame` to `torch.Tensor`. 
+
+#### Integration with Freqai module 
+
+Like all freqai models, PyTorch models inherit `IFreqaiModel`. `IFreqaiModel` declares three abstract methods: `train`, `fit`, and `predict`. we implement these methods in three levels of hierarchy.
+From top to bottom:
+
+1. `BasePyTorchModel` - Implements the `train` method. all `BasePyTorch*` inherit it. responsible for general data preparation (e.g., data normalization) and calling the `fit` method. Sets `device` attribute used by children classes. Sets `model_type` attribute used by the parent class.
+2. `BasePyTorch*` -  Implements the `predict` method. Here, the `*` represents a group of algorithms, such as classifiers or regressors. responsible for data preprocessing, predicting, and postprocessing if needed.
+3. `PyTorch*Classifier` / `PyTorch*Regressor` - implements the `fit` method. responsible for the main train flaw, where we initialize the trainer and model objects.
+
+![image](assets/freqai_pytorch-diagram.png)
+
+#### Full example
+
+Building a PyTorch regressor using MLP (multilayer perceptron) model, MSELoss criterion, and AdamW optimizer.
+
+```python
+class PyTorchMLPRegressor(BasePyTorchRegressor):
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        config = self.freqai_info.get("model_training_parameters", {})
+        self.learning_rate: float = config.get("learning_rate",  3e-4)
+        self.model_kwargs: dict[str, Any] = config.get("model_kwargs",  {})
+        self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs",  {})
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        n_features = data_dictionary["train_features"].shape[-1]
+        model = PyTorchMLPModel(
+            input_dim=n_features,
+            output_dim=1,
+            **self.model_kwargs
+        )
+        model.to(self.device)
+        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
+        criterion = torch.nn.MSELoss()
+        init_model = self.get_init_model(dk.pair)
+        trainer = PyTorchModelTrainer(
+            model=model,
+            optimizer=optimizer,
+            criterion=criterion,
+            device=self.device,
+            init_model=init_model,
+            target_tensor_type=torch.float,
+            **self.trainer_kwargs,
+        )
+        trainer.fit(data_dictionary)
+        return trainer
+```
+
+Here we create a `PyTorchMLPRegressor` class that implements the `fit` method. The `fit` method specifies the training building blocks: model, optimizer, criterion, and trainer. We inherit both `BasePyTorchRegressor` and `BasePyTorchModel`, where the former implements the `predict` method that is suitable for our regression task, and the latter implements the train method.
+
+??? Note "Setting Class Names for Classifiers"
+    When using classifiers, the user must declare the class names (or targets) by overriding the `IFreqaiModel.class_names` attribute. This is achieved by setting `self.freqai.class_names` in the FreqAI strategy inside the `set_freqai_targets` method.
+    
+    For example, if you are using a binary classifier to predict price movements as up or down, you can set the class names as follows:
+    ```python
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        self.freqai.class_names = ["down", "up"]
+        dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
+                                                  dataframe["close"], 'up', 'down')
+    
+        return dataframe
+    ```
+    To see a full example, you can refer to the [classifier test strategy class](https://github.com/freqtrade/freqtrade/blob/develop/tests/strategy/strats/freqai_test_classifier.py).
+
+
+#### Improving performance with `torch.compile()`
+
+Torch provides a `torch.compile()` method that can be used to improve performance for specific GPU hardware. More details can be found [here](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html). In brief, you simply wrap your `model` in `torch.compile()`:
+
+
+```python
+        model = PyTorchMLPModel(
+            input_dim=n_features,
+            output_dim=1,
+            **self.model_kwargs
+        )
+        model.to(self.device)
+        model = torch.compile(model)
+```
+
+Then proceed to use the model as normal. Keep in mind that doing this will remove eager execution, which means errors and tracebacks will not be informative.
diff --git a/docs/freqai-developers.md b/docs/freqai-developers.md
new file mode 100644
index 000000000..37f4543b7
--- /dev/null
+++ b/docs/freqai-developers.md
@@ -0,0 +1,78 @@
+# Development
+
+## Project architecture
+
+The architecture and functions of FreqAI are generalized to encourages development of unique features, functions, models, etc.
+
+The class structure and a detailed algorithmic overview is depicted in the following diagram:
+
+![image](assets/freqai_algorithm-diagram.jpg)
+
+As shown, there are three distinct objects comprising FreqAI:
+
+* **IFreqaiModel** - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models.
+* **FreqaiDataKitchen** - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools.
+* **FreqaiDataDrawer** - A singular persistent object containing all the historical predictions, models, and save/load methods.
+
+There are a variety of built-in [prediction models](freqai-configuration.md#using-different-prediction-models) which inherit directly from `IFreqaiModel`. Each of these models have full access to all methods in `IFreqaiModel` and can therefore override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`.
+
+## Data handling
+
+FreqAI aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.
+
+### File structure
+
+The file structure is automatically generated based on the model `identifier` set in the [config](freqai-configuration.md#setting-up-the-configuration-file). The following structure shows where the data is stored for post processing:
+
+| Structure | Description |
+|-----------|-------------|
+| `config_*.json` | A copy of the model specific configuration file. |
+| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held in case of corruption on the main file. FreqAI **automatically** detects corruption and replaces the corrupted file with the backup. |
+| `pair_dictionary.json` | A file containing the training queue as well as the on disk location of the most recently trained model. |
+| `sub-train-*_TIMESTAMP` | A folder containing all the files associated with a single model, such as: <br>
+|| `*_metadata.json` - Metadata for the model, such as normalization max/min, expected training feature list, etc. <br>
+|| `*_model.*` - The model file saved to disk for reloading from a crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc. <br>
+|| `*_pca_object.pkl` - The [Principal component analysis (PCA)](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) transform (if `principal_component_analysis: True` is set in the config) which will be used to transform unseen prediction features. <br>
+|| `*_svm_model.pkl` - The [Support Vector Machine (SVM)](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm) model (if `use_SVM_to_remove_outliers: True` is set in the config) which is used to detect outliers in unseen prediction features. <br>
+|| `*_trained_df.pkl` - The dataframe containing all the training features used to train the `identifier` model. This is used for computing the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) and can also be used for post-processing. <br>
+|| `*_trained_dates.df.pkl` - The dates associated with the `trained_df.pkl`, which is useful for post-processing. |
+
+The example file structure would look like this:
+
+```
+├── models
+│   └── unique-id
+│       ├── config_freqai.example.json
+│       ├── historic_predictions.backup.pkl
+│       ├── historic_predictions.pkl
+│       ├── pair_dictionary.json
+│       ├── sub-train-1INCH_1662821319
+│       │   ├── cb_1inch_1662821319_metadata.json
+│       │   ├── cb_1inch_1662821319_model.joblib
+│       │   ├── cb_1inch_1662821319_pca_object.pkl
+│       │   ├── cb_1inch_1662821319_svm_model.joblib
+│       │   ├── cb_1inch_1662821319_trained_dates_df.pkl
+│       │   └── cb_1inch_1662821319_trained_df.pkl
+│       ├── sub-train-1INCH_1662821371
+│       │   ├── cb_1inch_1662821371_metadata.json
+│       │   ├── cb_1inch_1662821371_model.joblib
+│       │   ├── cb_1inch_1662821371_pca_object.pkl
+│       │   ├── cb_1inch_1662821371_svm_model.joblib
+│       │   ├── cb_1inch_1662821371_trained_dates_df.pkl
+│       │   └── cb_1inch_1662821371_trained_df.pkl
+│       ├── sub-train-ADA_1662821344
+│       │   ├── cb_ada_1662821344_metadata.json
+│       │   ├── cb_ada_1662821344_model.joblib
+│       │   ├── cb_ada_1662821344_pca_object.pkl
+│       │   ├── cb_ada_1662821344_svm_model.joblib
+│       │   ├── cb_ada_1662821344_trained_dates_df.pkl
+│       │   └── cb_ada_1662821344_trained_df.pkl
+│       └── sub-train-ADA_1662821399
+│           ├── cb_ada_1662821399_metadata.json
+│           ├── cb_ada_1662821399_model.joblib
+│           ├── cb_ada_1662821399_pca_object.pkl
+│           ├── cb_ada_1662821399_svm_model.joblib
+│           ├── cb_ada_1662821399_trained_dates_df.pkl
+│           └── cb_ada_1662821399_trained_df.pkl
+
+```
diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md
new file mode 100644
index 000000000..d25051291
--- /dev/null
+++ b/docs/freqai-feature-engineering.md
@@ -0,0 +1,408 @@
+# Feature engineering
+
+## Defining the features
+
+Low level feature engineering is performed in the user strategy within a set of functions called `feature_engineering_*`. These function set the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. FreqAI is equipped with a set of functions to simplify rapid large-scale feature engineering:
+
+|  Function | Description |
+|---------------|-------------|
+| `feature_engineering_expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+| `feature_engineering_expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `indicator_periods_candles`.
+| `feature_engineering_standard()` | This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns from the base asset created by the other `feature_engineering_expand` functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is also a good place for any feature that should not be auto-expanded upon (e.g., day of the week).
+| `set_freqai_targets()` | Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
+
+It is advisable to start from the template `feature_engineering_*` functions in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
+
+```python
+    def feature_engineering_expand_all(self, dataframe: DataFrame, period, metadata, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        Access metadata such as the current pair/timeframe/period with:
+
+        `metadata["pair"]` `metadata["tf"]`  `metadata["period"]`
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        :param metadata: metadata of current pair
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
+
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
+
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        Access metadata such as the current pair/timeframe with:
+
+        `metadata["pair"]` `metadata["tf"]`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, metadata, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        Access metadata such as the current pair with:
+
+        `metadata["pair"]`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        Access metadata such as the current pair with:
+
+        `metadata["pair"]`
+
+        :param df: strategy dataframe which will receive the targets
+        :param metadata: metadata of current pair
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
+            )
+        
+        return dataframe
+```
+
+In the presented example, the user does not wish to pass the `bb_lowerband` as a feature to the model,
+and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
+model for training/prediction and has therefore prepended it with `%`.
+
+After having defined the `base features`, the next step is to expand upon them using the powerful `feature_parameters` in the configuration file:
+
+```json
+    "freqai": {
+        //...
+        "feature_parameters" : {
+            "include_timeframes": ["5m","15m","4h"],
+            "include_corr_pairlist": [
+                "ETH/USD",
+                "LINK/USD",
+                "BNB/USD"
+            ],
+            "label_period_candles": 24,
+            "include_shifted_candles": 2,
+            "indicator_periods_candles": [10, 20]
+        },
+        //...
+    }
+```
+
+The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `feature_engineering_expand_*()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
+
+You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `feature_engineering_expand_*()` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
+
+`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells FreqAI to include the past 2 candles for each of the features in the feature set.
+
+In total, the number of features the user of the presented example strategy has created is: length of `include_timeframes` * no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
+ $= 3 * 3 * 3 * 2 * 2 = 108$.
+ 
+ !!! note "Learn more about creative feature engineering"
+    Check out our [medium article](https://emergentmethods.medium.com/freqai-from-price-to-prediction-6fadac18b665) geared toward helping users learn how to creatively engineer features.
+
+### Gain finer control over `feature_engineering_*` functions with `metadata`
+
+All `feature_engineering_*` and `set_freqai_targets()` functions are passed a `metadata` dictionary which contains information about the `pair`, `tf` (timeframe), and `period` that FreqAI is automating for feature building. As such, a user can use `metadata` inside `feature_engineering_*` functions as criteria for blocking/reserving features for certain timeframes, periods, pairs etc.
+
+```python
+def feature_engineering_expand_all(self, dataframe: DataFrame, period, metadata, **kwargs) -> DataFrame:
+    if metadata["tf"] == "1h":
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+```
+
+This will block `ta.ROC()` from being added to any timeframes other than `"1h"`.
+
+### Returning additional info from training
+
+Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class. 
+
+FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. You can then use the returned metrics in your strategy through `dataframe['my_new_value']`. An example of how return values can be used in FreqAI are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold).
+
+Another example, where the user wants to use live metrics from the trade database, is shown below:
+
+```json
+    "freqai": {
+        "extra_returns_per_train": {"total_profit": 4}
+    }
+```
+
+You need to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the pre-set values are what will be returned.
+
+### Weighting features for temporal importance
+
+FreqAI allows you to set a `weight_factor` to weight recent data more strongly than past data via an exponential function:
+
+$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
+
+where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points in a feature set.
+
+![weight-factor](assets/freqai_weight-factor.jpg)
+
+## Building the data pipeline
+
+By default, FreqAI builds a dynamic pipeline based on user configuration settings. The default settings are robust and designed to work with a variety of methods. These two steps are a `MinMaxScaler(-1,1)` and a `VarianceThreshold` which removes any column that has 0 variance. Users can activate other steps with more configuration parameters. For example if users add `use_SVM_to_remove_outliers: true` to the `freqai` config, then FreqAI will automatically add the [`SVMOutlierExtractor`](#identifying-outliers-using-a-support-vector-machine-svm) to the pipeline. Likewise, users can add `principal_component_analysis: true` to the `freqai` config to activate PCA. The [DissimilarityIndex](#identifying-outliers-with-the-dissimilarity-index-di) is activated with `DI_threshold: 1`. Finally, noise can also be added to the data with `noise_standard_deviation: 0.1`. Finally, users can add [DBSCAN](#identifying-outliers-with-dbscan) outlier removal with `use_DBSCAN_to_remove_outliers: true`.
+
+!!! note "More information available"
+    Please review the [parameter table](freqai-parameter-table.md) for more information on these parameters.
+
+
+### Customizing the pipeline
+
+Users are encouraged to customize the data pipeline to their needs by building their own data pipeline. This can be done by simply setting `dk.feature_pipeline` to their desired `Pipeline` object inside their `IFreqaiModel` `train()` function, or if they prefer not to touch the `train()` function, they can override `define_data_pipeline`/`define_label_pipeline` functions in their `IFreqaiModel`:
+
+!!! note "More information available"
+    FreqAI uses the [`DataSieve`](https://github.com/emergentmethods/datasieve) pipeline, which follows the SKlearn pipeline API, but adds, among other features, coherence between the X, y, and sample_weight vector point removals, feature removal, feature name following. 
+
+```python
+from datasieve.transforms import SKLearnWrapper, DissimilarityIndex
+from datasieve.pipeline import Pipeline
+from sklearn.preprocessing import QuantileTransformer, StandardScaler
+from freqai.base_models import BaseRegressionModel
+
+
+class MyFreqaiModel(BaseRegressionModel):
+    """
+    Some cool custom model
+    """
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        My custom fit function
+        """
+        model = cool_model.fit()
+        return model
+
+    def define_data_pipeline(self) -> Pipeline:
+        """
+        User defines their custom feature pipeline here (if they wish)
+        """
+        feature_pipeline = Pipeline([
+            ('qt', SKLearnWrapper(QuantileTransformer(output_distribution='normal'))),
+            ('di', ds.DissimilarityIndex(di_threshold=1))
+        ])
+
+        return feature_pipeline
+    
+    def define_label_pipeline(self) -> Pipeline:
+        """
+        User defines their custom label pipeline here (if they wish)
+        """
+        label_pipeline = Pipeline([
+            ('qt', SKLearnWrapper(StandardScaler())),
+        ])
+
+        return label_pipeline
+```
+
+Here, you are defining the exact pipeline that will be used for your feature set during training and prediction. You can use *most* SKLearn transformation steps by wrapping them in the `SKLearnWrapper` class as shown above. In addition, you can use any of the transformations available in the [`DataSieve` library](https://github.com/emergentmethods/datasieve). 
+
+You can easily add your own transformation by creating a class that inherits from the datasieve `BaseTransform` and implementing your `fit()`, `transform()` and `inverse_transform()` methods:
+
+```python
+from datasieve.transforms.base_transform import BaseTransform
+# import whatever else you need
+
+class MyCoolTransform(BaseTransform):
+    def __init__(self, **kwargs):
+        self.param1 = kwargs.get('param1', 1)
+
+    def fit(self, X, y=None, sample_weight=None, feature_list=None, **kwargs):
+        # do something with X, y, sample_weight, or/and feature_list
+        return X, y, sample_weight, feature_list
+
+    def transform(self, X, y=None, sample_weight=None,
+                  feature_list=None, outlier_check=False, **kwargs):
+        # do something with X, y, sample_weight, or/and feature_list
+        return X, y, sample_weight, feature_list
+
+    def inverse_transform(self, X, y=None, sample_weight=None, feature_list=None, **kwargs):
+        # do/dont do something with X, y, sample_weight, or/and feature_list
+        return X, y, sample_weight, feature_list
+```
+
+!!! note "Hint"
+    You can define this custom class in the same file as your `IFreqaiModel`.
+
+### Migrating a custom `IFreqaiModel` to the new Pipeline
+
+If you have created your own custom `IFreqaiModel` with a custom `train()`/`predict()` function, *and* you still rely on `data_cleaning_train/predict()`, then you will need to migrate to the new pipeline. If your model does *not* rely on `data_cleaning_train/predict()`, then you do not need to worry about this migration.
+
+More details about the migration can be found [here](strategy_migration.md#freqai---new-data-pipeline).
+
+## Outlier detection
+
+Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. FreqAI implements a variety of methods to identify such outliers and hence mitigate risk.
+
+### Identifying outliers with the Dissimilarity Index (DI)
+
+The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. 
+
+You can tell FreqAI to remove outlier data points from the training/test data sets using the DI by including the following statement in the config:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "DI_threshold": 1
+        }
+    }
+```
+
+Which will add `DissimilarityIndex` step to your `feature_pipeline` and set the threshold to 1. The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points:
+
+$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
+
+where $d_{ab}$ is the distance between the normalized points $a$ and $b$, and $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$, for a set of training data points is simply the mean of the average distances:
+
+$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$
+
+$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data:
+
+$$ d_k = \arg \min d_{k,i} $$
+
+This enables the estimation of the Dissimilarity Index as:
+
+$$ DI_k = d_k/\overline{d} $$
+
+You can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model. A higher `DI_threshold` means that the DI is more lenient and allows predictions further away from the training data to be used whilst a lower `DI_threshold` has the opposite effect and hence discards more predictions.
+
+Below is a figure that describes the DI for a 3D data set.
+
+![DI](assets/freqai_DI.jpg)
+
+### Identifying outliers using a Support Vector Machine (SVM)
+
+You can tell FreqAI to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "use_SVM_to_remove_outliers": true
+        }
+    }
+```
+
+Which will add `SVMOutlierExtractor` step to your `feature_pipeline`. The SVM will be trained on the training data and any data point that the SVM deems to be beyond the feature space will be removed.
+
+You can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu` via the `feature_parameters.svm_params` dictionary in the config.
+
+The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time.
+
+The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers and should be between 0 and 1.
+
+### Identifying outliers with DBSCAN
+
+You can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "use_DBSCAN_to_remove_outliers": true
+        }
+    }
+```
+
+Which will add the `DataSieveDBSCAN` step to your `feature_pipeline`. This is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be.
+
+Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$.
+
+![dbscan](assets/freqai_dbscan.jpg)
+
+FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points (candles) in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.
+
+
+### Data dimensionality reduction with Principal Component Analysis
+
+You can reduce the dimensionality of your features by activating the principal_component_analysis in the config:
+
+```json
+    "freqai": {
+        "feature_parameters" : {
+            "principal_component_analysis": true
+        }
+    }
+```
+
+This will perform PCA on the features and reduce their dimensionality so that the explained variance of the data set is >= 0.999. Reducing data dimensionality makes training the model faster and hence allows for more up-to-date models.
diff --git a/docs/freqai-parameter-table.md b/docs/freqai-parameter-table.md
new file mode 100644
index 000000000..3bb289313
--- /dev/null
+++ b/docs/freqai-parameter-table.md
@@ -0,0 +1,118 @@
+# Parameter table
+
+The table below will list all configuration parameters available for FreqAI. Some of the parameters are exemplified in `config_examples/config_freqai.example.json`.
+
+Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways.
+
+### General configuration parameters
+
+|  Parameter | Description |
+|------------|-------------|
+|  |  **General configuration parameters within the `config.freqai` tree**
+| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling FreqAI. <br> **Datatype:** Dictionary.
+| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
+| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
+| `identifier` | **Required.** <br> A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data. <br> **Datatype:** String.
+| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: `0` (models retrain as often as possible).
+| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: `0` (models never expire).
+| `purge_old_models` | Number of models to keep on disk (not relevant to backtesting). Default is 2, which means that dry/live runs will keep the latest 2 models on disk. Setting to 0 keeps all models. This parameter also accepts a boolean to maintain backwards compatibility. <br> **Datatype:** Integer. <br> Default: `2`.
+| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
+| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
+| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). Beware that this is currently a naive approach to incremental learning, and it has a high probability of overfitting/getting stuck in local minima while the market moves away from your model. We have the connections here primarily for experimental purposes and so that it is ready for more mature approaches to continual learning in chaotic systems like the crypto market. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
+| `data_kitchen_thread_count` | <br> Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.). This has no impact on the number of threads used for training. If user does not set it (default), FreqAI will use max number of threads - 2 (leaving 1 physical core available for Freqtrade bot and FreqUI) <br> **Datatype:** Positive integer.
+| `activate_tensorboard` | <br> Indicate whether or not to activate tensorboard for the tensorboard enabled modules (currently Reinforcment Learning, XGBoost, Catboost, and PyTorch). Tensorboard needs Torch installed, which means you will need the torch/RL docker image or you need to answer "yes" to the install question about whether or not you wish to install Torch. <br> **Datatype:** Boolean. <br> Default: `True`.
+| `wait_for_training_iteration_on_reload` | <br> When using /reload or ctrl-c, wait for the current training iteration to finish before completing graceful shutdown. If set to `False`, FreqAI will break the current training iteration, allowing you to shutdown gracefully more quickly, but you will lose your current training iteration. <br> **Datatype:** Boolean. <br> Default: `True`.
+
+### Feature parameters
+
+|  Parameter | Description |
+|------------|-------------|
+|  |  **Feature parameters within the `freqai.feature_parameters` sub dictionary**
+| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
+| `include_timeframes` | A list of timeframes that all indicators in `feature_engineering_expand_*()` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
+| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `feature_engineering_expand_*()` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
+| `label_period_candles` | Number of candles into the future that the labels are created for. This can be used in `set_freqai_targets()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). This parameter is not necessarily required, you can create custom labels and choose whether to make use of this parameter or not. Please see `templates/FreqaiExampleStrategy.py` to see the example usage. <br> **Datatype:** Positive integer.
+| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
+| `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
+| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `feature_engineering_*()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
+| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
+| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
+| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. Plot is stored in `user_data/models/<identifier>/sub-train-<COIN>_<timestamp>.html`. <br> **Datatype:** Integer. <br> Default: `0`.
+| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Positive float (typically < 1).
+| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
+| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
+| `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan). <br> **Datatype:** Boolean. 
+| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
+| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
+| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
+| `shuffle_after_split` | Split the data into train and test sets, and then shuffle both sets individually. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `buffer_train_data_candles` | Cut `buffer_train_data_candles` off the beginning and end of the training data *after* the indicators were populated. The main example use is when predicting maxima and minima, the argrelextrema function  cannot know the maxima/minima at the edges of the timerange. To improve model accuracy, it is best to compute argrelextrema on the full timerange and then use this function to cut off the edges (buffer) by the kernel. In another case, if the targets are set to a shifted price movement, this buffer is unnecessary because the shifted candles at the end of the timerange will be NaN and FreqAI will automatically cut those off of the training dataset.<br> **Datatype:** Integer. <br> Default: `0`.
+
+### Data split parameters
+
+|  Parameter | Description |
+|------------|-------------|
+|  |  **Data split parameters within the `freqai.data_split_parameters` sub dictionary**
+| `data_split_parameters` | Include any additional parameters available from scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
+| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
+| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Default: `False`.
+
+### Model training parameters
+
+|  Parameter | Description |
+|------------|-------------|
+|  |  **Model training parameters within the `freqai.model_training_parameters` sub dictionary**
+| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. A list of the currently available models can be found [here](freqai-configuration.md#using-different-prediction-models).  <br> **Datatype:** Dictionary.
+| `n_estimators` | The number of boosted trees to fit in the training of the model. <br> **Datatype:** Integer.
+| `learning_rate` | Boosting learning rate during training of the model. <br> **Datatype:** Float.
+| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
+
+### Reinforcement Learning parameters
+
+|  Parameter | Description |
+|------------|-------------|
+|  |  **Reinforcement Learning Parameters within the `freqai.rl_config` sub dictionary**
+| `rl_config` | A dictionary containing the control parameters for a Reinforcement Learning model. <br> **Datatype:** Dictionary.
+| `train_cycles` | Training time steps will be set based on the `train_cycles * number of training data points. <br> **Datatype:** Integer.
+| `max_trade_duration_candles`| Guides the agent training to keep trades below desired length. Example usage shown in `prediction_models/ReinforcementLearner.py` within the customizable `calculate_reward()` function. <br> **Datatype:** int.
+| `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentation. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
+| `policy_type` | One of the available policy types from stable_baselines3 <br> **Datatype:** string.
+| `max_training_drawdown_pct` | The maximum drawdown that the agent is allowed to experience during training. <br> **Datatype:** float. <br> Default: 0.8
+| `cpu_count` | Number of threads/cpus to dedicate to the Reinforcement Learning training process (depending on if `ReinforcementLearning_multiproc` is selected or not). Recommended to leave this untouched, by default, this value is set to the total number of physical cores minus 1. <br> **Datatype:** int. 
+| `model_reward_parameters` | Parameters used inside the customizable `calculate_reward()` function in `ReinforcementLearner.py` <br> **Datatype:** int.
+| `add_state_info` | Tell FreqAI to include state information in the feature set for training and inferencing. The current state variables include trade duration, current profit, trade position. This is only available in dry/live runs, and is automatically switched to false for backtesting. <br> **Datatype:** bool. <br> Default: `False`.
+| `net_arch` | Network architecture which is well described in [`stable_baselines3` doc](https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#examples). In summary: `[<shared layers>, dict(vf=[<non-shared value network layers>], pi=[<non-shared policy network layers>])]`. By default this is set to `[128, 128]`, which defines 2 shared hidden layers with 128 units each.
+| `randomize_starting_position` | Randomize the starting point of each episode to avoid overfitting. <br> **Datatype:** bool. <br> Default: `False`.
+| `drop_ohlc_from_features` | Do not include the normalized ohlc data in the feature set passed to the agent during training (ohlc will still be used for driving the environment in all cases) <br> **Datatype:** Boolean. <br> **Default:** `False`
+| `progress_bar` | Display a progress bar with the current progress, elapsed time and estimated remaining time. <br> **Datatype:** Boolean. <br> Default: `False`.
+
+### PyTorch parameters
+
+#### general
+
+|  Parameter | Description |
+|------------|-------------|
+|  |  **Model training parameters within the `freqai.model_training_parameters` sub dictionary**
+| `learning_rate` | Learning rate to be passed to the optimizer. <br> **Datatype:** float. <br> Default: `3e-4`.
+| `model_kwargs` | Parameters to be passed to the model class. <br> **Datatype:** dict. <br> Default: `{}`.
+| `trainer_kwargs` | Parameters to be passed to the trainer class. <br> **Datatype:** dict. <br> Default: `{}`.
+
+#### trainer_kwargs
+
+| Parameter    | Description |
+|--------------|-------------|
+|              |  **Model training parameters within the `freqai.model_training_parameters.model_kwargs` sub dictionary**
+| `n_epochs`   | The `n_epochs` parameter is a crucial setting in the PyTorch training loop that determines the number of times the entire training dataset will be used to update the model's parameters. An epoch represents one full pass through the entire training dataset. Overrides `n_steps`. Either `n_epochs` or `n_steps` must be set. <br><br> **Datatype:** int. optional. <br> Default: `10`.
+| `n_steps`    | An alternative way of setting `n_epochs` -  the number of training iterations to run. Iteration here refer to the number of times we call `optimizer.step()`. Ignored if `n_epochs` is set. A simplified version of the function: <br><br> n_epochs = n_steps / (n_obs / batch_size) <br><br> The motivation here is that `n_steps` is easier to optimize and keep stable across different n_obs - the number of data points.  <br> <br> **Datatype:** int. optional. <br> Default: `None`.
+| `batch_size` | The size of the batches to use during training. <br><br> **Datatype:** int. <br> Default: `64`.
+
+
+### Additional parameters
+
+|  Parameter | Description |
+|------------|-------------|
+|  |  **Extraneous parameters**
+| `freqai.keras` | If the selected model makes use of Keras (typical for TensorFlow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `freqai.conv_width` | The width of a neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
+| `freqai.reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.
diff --git a/docs/freqai-reinforcement-learning.md b/docs/freqai-reinforcement-learning.md
new file mode 100644
index 000000000..3b75e6b71
--- /dev/null
+++ b/docs/freqai-reinforcement-learning.md
@@ -0,0 +1,280 @@
+# Reinforcement Learning
+
+!!! Note "Installation size"
+    Reinforcement learning dependencies include large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl (~700mb additional space required) [y/N]?".
+    Users who prefer docker should ensure they use the docker image appended with `_freqairl`.
+
+## Background and terminology
+
+### What is RL and why does FreqAI need it?
+
+Reinforcement learning involves two important components, the *agent* and the training *environment*. During agent training, the agent moves through historical data candle by candle, always making 1 of a set of actions: Long entry, long exit, short entry, short exit, neutral). During this training process, the environment tracks the performance of these actions and rewards the agent according to a custom user made `calculate_reward()` (here we offer a default reward for users to build on if they wish [details here](#creating-a-custom-reward-function)). The reward is used to train weights in a neural network.
+
+A second important component of the FreqAI RL implementation is the use of *state* information. State information is fed into the network at each step, including current profit, current position, and current trade duration. These are used to train the agent in the training environment, and to reinforce the agent in dry/live (this functionality is not available in backtesting). *FreqAI + Freqtrade is a perfect match for this reinforcing mechanism since this information is readily available in live deployments.*
+
+Reinforcement learning is a natural progression for FreqAI, since it adds a new layer of adaptivity and market reactivity that Classifiers and Regressors cannot match. However, Classifiers and Regressors have strengths that RL does not have such as robust predictions. Improperly trained RL agents may find "cheats" and "tricks" to maximize reward without actually winning any trades. For this reason, RL is more complex and demands a higher level of understanding than typical Classifiers and Regressors.
+
+### The RL interface
+
+With the current framework, we aim to expose the training environment via the common "prediction model" file, which is a user inherited `BaseReinforcementLearner` object (e.g. `freqai/prediction_models/ReinforcementLearner`). Inside this user class, the RL environment is available and customized via `MyRLEnv` as [shown below](#creating-a-custom-reward-function).
+
+We envision the majority of users focusing their effort on creative design of the `calculate_reward()` function [details here](#creating-a-custom-reward-function), while leaving the rest of the environment untouched. Other users may not touch the environment at all, and they will only play with the configuration settings and the powerful feature engineering that already exists in FreqAI. Meanwhile, we enable advanced users to create their own model classes entirely.
+
+The framework is built on stable_baselines3 (torch) and OpenAI gym for the base environment class. But generally speaking, the model class is well isolated. Thus, the addition of competing libraries can be easily integrated into the existing framework. For the environment, it is inheriting from `gym.Env` which means that it is necessary to write an entirely new environment in order to switch to a different library.
+
+### Important considerations
+
+As explained above, the agent is "trained" in an artificial trading "environment". In our case, that environment may seem quite similar to a real Freqtrade backtesting environment, but it is *NOT*. In fact, the RL training environment is much more simplified. It does not incorporate any of the complicated strategy logic, such as callbacks like `custom_exit`, `custom_stoploss`, leverage controls, etc. The RL environment is instead a very "raw" representation of the true market, where the agent has free will to learn the policy (read: stoploss, take profit, etc.) which is enforced by the `calculate_reward()`. Thus, it is important to consider that the agent training environment is not identical to the real world.
+
+## Running Reinforcement Learning
+
+Setting up and running a Reinforcement Learning model is the same as running a Regressor or Classifier. The same two flags, `--freqaimodel` and `--strategy`, must be defined on the command line:
+
+```bash
+freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
+```
+
+where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `feature_engineering_*` as a typical Regressor. The difference lies in the creation of the targets, Reinforcement Learning doesn't require them. However, FreqAI requires a default (neutral) value to be set in the action column:
+
+```python
+    def set_freqai_targets(self, dataframe, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        # For RL, there are no direct targets to set. This is filler (neutral)
+        # until the agent sends an action.
+        dataframe["&-action"] = 0
+        return dataframe
+```
+
+Most of the function remains the same as for typical Regressors, however, the function below shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:
+
+```python
+    def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame:
+        # The following features are necessary for RL models
+        dataframe[f"%-raw_close"] = dataframe["close"]
+        dataframe[f"%-raw_open"] = dataframe["open"]
+        dataframe[f"%-raw_high"] = dataframe["high"]
+        dataframe[f"%-raw_low"] = dataframe["low"]
+    return dataframe
+```
+
+Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
+
+After users realize there are no labels to set, they will soon understand that the agent is making its "own" entry and exit decisions. This makes strategy construction rather simple. The entry and exit signals come from the agent in the form of an integer - which are used directly to decide entries and exits in the strategy:
+
+```python
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
+
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
+            ] = (1, "long")
+
+        enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]
+
+        if enter_short_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
+            ] = (1, "short")
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
+        if exit_long_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
+
+        exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
+        if exit_short_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
+
+        return df
+```
+
+It is important to consider that `&-action` depends on which environment they choose to use. The example above shows 5 actions, where 0 is neutral, 1 is enter long, 2 is exit long, 3 is enter short and 4 is exit short.
+
+## Configuring the Reinforcement Learner
+
+In order to configure the `Reinforcement Learner` the following dictionary must exist in the `freqai` config:
+
+```json
+        "rl_config": {
+            "train_cycles": 25,
+            "add_state_info": true,
+            "max_trade_duration_candles": 300,
+            "max_training_drawdown_pct": 0.02,
+            "cpu_count": 8,
+            "model_type": "PPO",
+            "policy_type": "MlpPolicy",
+            "model_reward_parameters": {
+                "rr": 1,
+                "profit_aim": 0.025
+            }
+        }
+```
+
+Parameter details can be found [here](freqai-parameter-table.md), but in general the `train_cycles` decides how many times the agent should cycle through the candle data in its artificial environment to train weights in the model. `model_type` is a string which selects one of the available models in [stable_baselines](https://stable-baselines3.readthedocs.io/en/master/)(external link).
+
+!!! Note
+    If you would like to experiment with `continual_learning`, then you should set that value to `true` in the main `freqai` configuration dictionary. This will tell the Reinforcement Learning library to continue training new models from the final state of previous models, instead of retraining new models from scratch each time a retrain is initiated.
+
+!!! Note
+    Remember that the general `model_training_parameters` dictionary should contain all the model hyperparameter customizations for the particular `model_type`. For example, `PPO` parameters can be found [here](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html).
+
+## Creating a custom reward function
+
+!!! danger "Not for production"
+    Warning!
+    The reward function provided with the Freqtrade source code is a showcase of functionality designed to show/test as many possible environment control features as possible. It is also designed to run quickly on small computers. This is a benchmark, it is *not* for live production. Please beware that you will need to create your own custom_reward() function or use a template built by other users outside of the Freqtrade source code.
+
+As you begin to modify the strategy and the prediction model, you will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, you set the `calculate_reward()` function inside the `MyRLEnv` class (see below). A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to demonstrate the necessary building blocks for creating rewards, but this is *not* designed for production. Users *must* create their own custom reinforcement learning model class or use a pre-built one from outside the Freqtrade source code and save it to `user_data/freqaimodels`. It is inside the `calculate_reward()` where creative theories about the market can be expressed. For example, you can reward your agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, you wish to reward the agent for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
+
+!!! note "Hint"
+    The best reward functions are ones that are continuously differentiable, and well scaled. In other words, adding a single large negative penalty to a rare event is not a good idea, and the neural net will not be able to learn that function. Instead, it is better to add a small negative penalty to a common event. This will help the agent learn faster. Not only this, but you can help improve the continuity of your rewards/penalties by having them scale with severity according to some linear/exponential functions. In other words, you'd slowly scale the penalty as the duration of the trade increases. This is better than a single large penalty occurring at a single point in time.
+
+```python
+from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+
+
+class MyCoolRLModel(ReinforcementLearner):
+    """
+    User created RL prediction model.
+
+    Save this file to `freqtrade/user_data/freqaimodels`
+
+    then use it with:
+
+    freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
+
+    Here the users can override any of the functions
+    available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
+    is where the user overrides `MyRLEnv` (see below), to define custom
+    `calculate_reward()` function, or to override any other parts of the environment.
+
+    This class also allows users to override any other part of the IFreqaiModel tree.
+    For example, the user can override `def fit()` or `def train()` or `def predict()`
+    to take fine-tuned control over these processes.
+
+    Another common override may be `def data_cleaning_predict()` where the user can
+    take fine-tuned control over the data handling pipeline.
+    """
+    class MyRLEnv(Base5ActionRLEnv):
+        """
+        User made custom environment. This class inherits from BaseEnvironment and gym.Env.
+        Users can override any functions from those parent classes. Here is an example
+        of a user customized `calculate_reward()` function.
+
+        Warning!
+        This is function is a showcase of functionality designed to show as many possible
+        environment control features as possible. It is also designed to run quickly
+        on small computers. This is a benchmark, it is *not* for live production.
+        """
+        def calculate_reward(self, action: int) -> float:
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+            pnl = self.get_unrealized_profit()
+
+            factor = 100
+
+            pair = self.pair.replace(':', '')
+
+            # you can use feature values from dataframe
+            # Assumes the shifted RSI indicator has been generated in the strategy.
+            rsi_now = self.raw_features[f"%-rsi-period_10_shift-1_{pair}_"
+                            f"{self.config['timeframe']}"].iloc[self._current_tick]
+
+            # reward agent for entering trades
+            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                    and self._position == Positions.Neutral):
+                if rsi_now < 40:
+                    factor = 40 / rsi_now
+                else:
+                    factor = 1
+                return 25 * factor
+
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
+            trade_duration = self._current_tick - self._last_trade_tick
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+            # discourage sitting in position
+            if self._position in (Positions.Short, Positions.Long) and \
+            action == Actions.Neutral.value:
+                return -1 * trade_duration / max_trade_duration
+            # close long
+            if action == Actions.Long_exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+            # close short
+            if action == Actions.Short_exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+            return 0.
+```
+
+## Using Tensorboard
+
+Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
+
+```bash
+tensorboard --logdir user_data/models/unique-id
+```
+
+where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell to view the output in the browser at 127.0.0.1:6006 (6006 is the default port used by Tensorboard).
+
+![tensorboard](assets/tensorboard.jpg)
+
+## Custom logging
+
+FreqAI also provides a built in episodic summary logger called `self.tensorboard_log` for adding custom information to the Tensorboard log. By default, this function is already called once per step inside the environment to record the agent actions. All values accumulated for all steps in a single episode are reported at the conclusion of each episode, followed by a full reset of all metrics to 0 in preparation for the subsequent episode.
+
+`self.tensorboard_log` can also be used anywhere inside the environment, for example, it can be added to the `calculate_reward` function to collect more detailed information about how often various parts of the reward were called:
+
+```python
+    class MyRLEnv(Base5ActionRLEnv):
+        """
+        User made custom environment. This class inherits from BaseEnvironment and gym.Env.
+        Users can override any functions from those parent classes. Here is an example
+        of a user customized `calculate_reward()` function.
+        """
+        def calculate_reward(self, action: int) -> float:
+            if not self._is_valid(action):
+                self.tensorboard_log("invalid")
+                return -2
+
+```
+
+!!! Note
+    The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)`. In this case the metric values are not incremented.
+
+## Choosing a base environment
+
+FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:
+
+* the actions available in the `calculate_reward`
+* the actions consumed by the user strategy
+
+All of the FreqAI provided environments inherit from an action/position agnostic environment object called the `BaseEnvironment`, which contains all shared logic. The architecture is designed to be easily customized. The simplest customization is the `calculate_reward()` (see details [here](#creating-a-custom-reward-function)). However, the customizations can be further extended into any of the functions inside the environment. You can do this by simply overriding those functions inside your `MyRLEnv` in the prediction model file. Or for more advanced customizations, it is encouraged to create an entirely new environment inherited from `BaseEnvironment`.
+
+!!! Note
+    Only the `Base3ActionRLEnv` can do long-only training/trading (set the user strategy attribute `can_short = False`).
diff --git a/docs/freqai-running.md b/docs/freqai-running.md
new file mode 100644
index 000000000..d506ac3ce
--- /dev/null
+++ b/docs/freqai-running.md
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+# Running FreqAI
+
+There are two ways to train and deploy an adaptive machine learning model - live deployment and historical backtesting. In both cases, FreqAI runs/simulates periodic retraining of models as shown in the following figure:
+
+![freqai-window](assets/freqai_moving-window.jpg)
+
+## Live deployments
+
+FreqAI can be run dry/live using the following command:
+
+```bash
+freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
+```
+
+When launched, FreqAI will start training a new model, with a new `identifier`, based on the config settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If you do not want FreqAI to retrain new models as often as possible, you can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, you can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours.
+
+Trained models are by default saved to disk to allow for reuse during backtesting or after a crash. You can opt to [purge old models](#purging-old-model-data) to save disk space by setting `"purge_old_models": true` in the config.
+
+To start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), you only need to specify the `identifier` of the specific model:
+
+```json
+    "freqai": {
+        "identifier": "example",
+        "live_retrain_hours": 0.5
+    }
+```
+
+In this case, although FreqAI will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained. If a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will start training a new model.
+
+### Automatic data download
+
+FreqAI automatically downloads the proper amount of data needed to ensure training of a model through the defined `train_period_days` and `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters). 
+
+### Saving prediction data
+
+All predictions made during the lifetime of a specific `identifier` model are stored in `historic_predictions.pkl` to allow for reloading after a crash or changes made to the config.
+
+### Purging old model data
+
+FreqAI stores new model files after each successful training. These files become obsolete as new models are generated to adapt to new market conditions. If you are planning to leave FreqAI running for extended periods of time with high frequency retraining, you should enable `purge_old_models` in the config:
+
+```json
+    "freqai": {
+        "purge_old_models": 4,
+    }
+```
+
+This will automatically purge all models older than the four most recently trained ones to save disk space. Inputing "0" will never purge any models.
+
+## Backtesting
+
+The FreqAI backtesting module can be executed with the following command:
+
+```bash
+freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
+```
+
+If this command has never been executed with the existing config file, FreqAI will train a new model
+for each pair, for each backtesting window within the expanded `--timerange`.
+
+Backtesting mode requires [downloading the necessary data](#downloading-data-to-cover-the-full-backtest-period) before deployment (unlike in dry/live mode where FreqAI handles the data downloading automatically). You should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-size-of-the-sliding-training-window-and-backtesting-duration).
+
+!!! Note "Model reuse"
+    Once the training is completed, you can execute the backtesting again with the same config file and
+    FreqAI will find the trained models and load them instead of spending time training. This is useful
+    if you want to tweak (or even hyperopt) buy and sell criteria inside the strategy. If you
+    *want* to retrain a new model with the same config file, you should simply change the `identifier`.
+    This way, you can return to using any model you wish by simply specifying the `identifier`.
+
+!!! Note
+    Backtesting calls `set_freqai_targets()` one time for each backtest window (where the number of windows is the full backtest timerange divided by the `backtest_period_days` parameter). Doing this means that the targets simulate dry/live behavior without look ahead bias. However, the definition of the features in `feature_engineering_*()` is performed once on the entire training timerange. This means that you should be sure that features do not look-ahead into the future.
+    More details about look-ahead bias can be found in [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies).
+
+---
+
+### Saving backtesting prediction data
+
+To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria.
+
+An additional directory called `backtesting_predictions`, which contains all the predictions stored in `feather` format, will be created in the `unique-id` folder.
+
+To change your **features**, you **must** set a new `identifier` in the config to signal to FreqAI to train new models.
+
+To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
+
+!!! Note
+    To ensure that the model can be reused, freqAI will call your strategy with a dataframe of length 1. 
+    If your strategy requires more data than this to generate the same features, you can't reuse backtest predictions for live deployment and need to update your `identifier` for each new backtest.
+
+### Backtest live collected predictions
+
+FreqAI allow you to reuse live historic predictions through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse predictions generated in dry/run for comparison or other study.
+
+The `--timerange` parameter must not be informed, as it will be automatically calculated through the data in the historic predictions file.
+
+### Downloading data to cover the full backtest period
+
+For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting time range. The amount of additional data can be roughly estimated by moving the start date of the time range backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting time range. 
+
+As an example, to backtest the `--timerange 20210501-20210701` using the [example config](freqai-configuration.md#setting-up-the-configuration-file) which sets `train_period_days` to 30, together with `startup_candle_count: 40` on a maximum `include_timeframes` of 1h, the start date for the downloaded data needs to be `20210501` - 30 days - 40 * 1h / 24 hours = 20210330 (31.7 days earlier than the start of the desired training time range).
+
+### Deciding the size of the sliding training window and backtesting duration
+
+The backtesting time range is defined with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
+a float to indicate sub-daily retraining in live/dry mode). In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file) (found in `config_examples/config_freqai.example.json`), the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`.  This means that if you set `--timerange 20210501-20210701`, FreqAI will have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks).
+
+!!! Note
+    Although fractional `backtest_period_days` is allowed, you should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, by setting a `--timerange` of 10 days, and a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it train constantly. In this case, backtesting would take the exact same amount of time as a dry run.
+
+## Defining model expirations
+
+During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If you are training on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. This may be undesirable if the characteristic time scale (the trade duration target) for a strategy is less than 4 hours. You can decide to only make trade entries if the model is less than a certain number of hours old by setting the `expiration_hours` in the config file:
+
+```json
+    "freqai": {
+        "expiration_hours": 0.5,
+    }
+```
+
+In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old.
+
+## Controlling the model learning process
+
+Model training parameters are unique to the selected machine learning library. FreqAI allows you to set any parameter for any library using the `model_training_parameters` dictionary in the config. The example config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but you can add any parameters available in those libraries or any other machine learning library you choose to implement.
+
+Data split parameters are defined in `data_split_parameters` which can be any parameters associated with scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
+
+The FreqAI specific parameter `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file), the user is asking for `labels` that are 24 candles in the future.
+
+## Continual learning
+
+You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `False` which means that all new models are trained from scratch, without input from previous models.
+
+???+ danger "Continual learning enforces a constant parameter space"
+    Since `continual_learning` means that the model parameter space *cannot* change between trainings, `principal_component_analysis` is automatically disabled when `continual_learning` is enabled. Hint: PCA changes the parameter space and the number of features, learn more about PCA [here](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis).
+
+???+ danger "Experimental functionality"
+    Beware that this is currently a naive approach to incremental learning, and it has a high probability of overfitting/getting stuck in local minima while the market moves away from your model. We have the mechanics available in FreqAI primarily for experimental purposes and so that it is ready for more mature approaches to continual learning in chaotic systems like the crypto market.
+
+## Hyperopt
+
+You can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md):
+
+```bash
+freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507
+```
+
+`hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies:
+
+- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
+- It's not possible to hyperopt indicators in the `feature_engineering_*()` and `set_freqai_targets()` functions. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
+- The backtesting instructions also apply to hyperopt.
+
+The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
+
+A good example of a hyperoptable parameter in FreqAI is a threshold for the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) `DI_values` beyond which we consider data points as outliers:
+
+```python
+di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True)
+dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0)
+```
+
+This specific hyperopt would help you understand the appropriate `DI_values` for your particular parameter space.
+
+## Using Tensorboard
+
+!!! note "Availability"
+    FreqAI includes tensorboard for a variety of models, including XGBoost, all PyTorch models, Reinforcement Learning, and Catboost. If you would like to see Tensorboard integrated into another model type, please open an issue on the [Freqtrade GitHub](https://github.com/freqtrade/freqtrade/issues)
+
+!!! danger "Requirements"
+    Tensorboard logging requires the FreqAI torch installation/docker image.
+
+
+The easiest way to use tensorboard is to ensure `freqai.activate_tensorboard` is set to `True` (default setting) in your configuration file, run FreqAI, then open a separate shell and run:
+
+```bash
+cd freqtrade
+tensorboard --logdir user_data/models/unique-id
+```
+
+where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell if you wish to view the output in your browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
+
+![tensorboard](assets/tensorboard.jpg)
+
+
+!!! note "Deactivate for improved performance"
+    Tensorboard logging can slow down training and should be deactivated for production use.
diff --git a/docs/freqai.md b/docs/freqai.md
new file mode 100644
index 000000000..cda8efe13
--- /dev/null
+++ b/docs/freqai.md
@@ -0,0 +1,141 @@
+![freqai-logo](assets/freqai_doc_logo.svg)
+
+# FreqAI
+
+## Introduction
+
+FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input signals. In general, FreqAI aims to be a sandbox for easily deploying robust machine learning libraries on real-time data ([details](#freqai-position-in-open-source-machine-learning-landscape)).
+
+!!! Note
+    FreqAI is, and always will be, a not-for-profit, open-source project. FreqAI does *not* have a crypto token, FreqAI does *not* sell signals, and FreqAI does not have a domain besides the present [freqtrade documentation](https://www.freqtrade.io/en/latest/freqai/).
+
+Features include:
+
+* **Self-adaptive retraining** - Retrain models during [live deployments](freqai-running.md#live-deployments) to self-adapt to the market in a supervised manner
+* **Rapid feature engineering** - Create large rich [feature sets](freqai-feature-engineering.md#feature-engineering) (10k+ features) based on simple user-created strategies
+* **High performance** - Threading allows for adaptive model retraining on a separate thread (or on GPU if available) from model inferencing (prediction) and bot trade operations. Newest models and data are kept in RAM for rapid inferencing
+* **Realistic backtesting** - Emulate self-adaptive training on historic data with a [backtesting module](freqai-running.md#backtesting) that automates retraining
+* **Extensibility** - The generalized and robust architecture allows for incorporating any [machine learning library/method](freqai-configuration.md#using-different-prediction-models) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network
+* **Smart outlier removal** - Remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-feature-engineering.md#outlier-detection)
+* **Crash resilience** - Store trained models to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-running.md#purging-old-model-data) for sustained dry/live runs
+* **Automatic data normalization** - [Normalize the data](freqai-feature-engineering.md#building-the-data-pipeline) in a smart and statistically safe way
+* **Automatic data download** - Compute timeranges for data downloads and update historic data (in live deployments)
+* **Cleaning of incoming data** - Handle NaNs safely before training and model inferencing
+* **Dimensionality reduction** - Reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis)
+* **Deploying bot fleets** - Set one bot to train models while a fleet of [consumers](producer-consumer.md) use signals.
+
+## Quick start
+
+The easiest way to quickly test FreqAI is to run it in dry mode with the following command:
+
+```bash
+freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
+```
+
+You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading. 
+
+!!! danger "Not for production"
+    The example strategy provided with the Freqtrade source code is designed for showcasing/testing a wide variety of FreqAI features. It is also designed to run on small computers so that it can be used as a benchmark between developers and users. It is *not* designed to be run in production.
+
+An example strategy, prediction model, and config to use as a starting points can be found in
+`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and
+`config_examples/config_freqai.example.json`, respectively.
+
+## General approach
+
+You provide FreqAI with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, FreqAI trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread to keep models as up to date as possible.
+
+An overview of the algorithm, explaining the data processing pipeline and model usage, is shown below.
+
+![freqai-algo](assets/freqai_algo.jpg)
+
+### Important machine learning vocabulary
+
+**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle are stored as a vector. In FreqAI, you build a feature data set from anything you can construct in the strategy.
+
+**Labels** - the target values that the model is trained toward. Each feature vector is associated with a single label that is defined by you within the strategy. These labels intentionally look into the future and are what you are training the model to be able to predict.
+
+**Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways which means that one might be better than another for a specific application. More information about the different models that are already implemented in FreqAI can be found [here](freqai-configuration.md#using-different-prediction-models).
+
+**Train data** - a subset of the feature data set that is fed to the model during training to "teach" the model how to predict the targets. This data directly influences weight connections in the model.
+
+**Test data** - a subset of the feature data set that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.
+
+**Inferencing** - the process of feeding a trained model new unseen data on which it will make a prediction. 
+
+## Install prerequisites
+
+The normal Freqtrade install process will ask if you wish to install FreqAI dependencies. You should reply "yes" to this question if you wish to use FreqAI. If you did not reply yes, you can manually install these dependencies after the install with:
+
+``` bash
+pip install -r requirements-freqai.txt
+```
+
+!!! Note
+    Catboost will not be installed on low-powered arm devices (raspberry), since it does not provide wheels for this platform.
+
+### Usage with docker
+
+If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker compose file with `image: freqtradeorg/freqtrade:stable_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. If you would like to use PyTorch or Reinforcement learning, you should use the torch or RL tags, `image: freqtradeorg/freqtrade:stable_freqaitorch`, `image: freqtradeorg/freqtrade:stable_freqairl`.
+
+!!! note "docker-compose-freqai.yml"
+   We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file. This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
+
+### FreqAI position in open-source machine learning landscape
+
+Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citizen scientists" to use their basic Python skills for data exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. 
+
+### Citing FreqAI
+
+FreqAI is [published in the Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.04864). If you find FreqAI useful in your research, please use the following citation:
+
+```bibtex
+@article{Caulk2022, 
+    doi = {10.21105/joss.04864},
+    url = {https://doi.org/10.21105/joss.04864},
+    year = {2022}, publisher = {The Open Journal},
+    volume = {7}, number = {80}, pages = {4864},
+    author = {Robert A. Caulk and Elin Törnquist and Matthias Voppichler and Andrew R. Lawless and Ryan McMullan and Wagner Costa Santos and Timothy C. Pogue and Johan van der Vlugt and Stefan P. Gehring and Pascal Schmidt},
+    title = {FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts},
+    journal = {Journal of Open Source Software} } 
+```
+
+## Common pitfalls
+
+FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
+This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,
+it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends
+new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume).
+
+## Additional learning materials
+
+Here we compile some external materials that provide deeper looks into various components of FreqAI:
+
+- [Real-time head-to-head: Adaptive modeling of financial market data using XGBoost and CatBoost](https://emergentmethods.medium.com/real-time-head-to-head-adaptive-modeling-of-financial-market-data-using-xgboost-and-catboost-995a115a7495)
+- [FreqAI - from price to prediction](https://emergentmethods.medium.com/freqai-from-price-to-prediction-6fadac18b665)
+
+
+## Support
+
+You can find support for FreqAI in a variety of places, including the [Freqtrade discord](https://discord.gg/Jd8JYeWHc4), the dedicated [FreqAI discord](https://discord.gg/7AMWACmbjT), and in [github issues](https://github.com/freqtrade/freqtrade/issues).
+
+## Credits
+
+FreqAI is developed by a group of individuals who all contribute specific skillsets to the project.
+
+Conception and software development:
+Robert Caulk @robcaulk
+
+Theoretical brainstorming and data analysis:
+Elin Törnquist @th0rntwig
+
+Code review and software architecture brainstorming:
+@xmatthias
+
+Software development:
+Wagner Costa @wagnercosta
+Emre Suzen @aemr3
+Timothy Pogue @wizrds
+
+Beta testing and bug reporting:
+Stefan Gehring @bloodhunter4rc, @longyu, Andrew Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau, Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza
diff --git a/docs/hyperopt.md b/docs/hyperopt.md
index 0bfa9ae2d..ecd32552e 100644
--- a/docs/hyperopt.md
+++ b/docs/hyperopt.md
@@ -1,315 +1,837 @@
 # Hyperopt
+
 This page explains how to tune your strategy by finding the optimal
-parameters with Hyperopt.
+parameters, a process called hyperparameter optimization. The bot uses algorithms included in the `scikit-optimize` package to accomplish this.
+The search will burn all your CPU cores, make your laptop sound like a fighter jet and still take a long time.
 
-## Table of Contents
-- [Prepare your Hyperopt](#prepare-hyperopt)
-    - [1. Configure your Guards and Triggers](#1-configure-your-guards-and-triggers)
-    - [2. Update the hyperopt config file](#2-update-the-hyperopt-config-file)
-- [Advanced Hyperopt notions](#advanced-notions)
-    - [Understand the Guards and Triggers](#understand-the-guards-and-triggers)
-- [Execute Hyperopt](#execute-hyperopt)
-    - [Hyperopt with MongoDB](#hyperopt-with-mongoDB)
-- [Understand the hyperopts result](#understand-the-backtesting-result)
+In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
 
-## Prepare Hyperopt
-Before we start digging in Hyperopt, we recommend you to take a look at 
-your strategy file located into [user_data/strategies/](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
- 
-### 1. Configure your Guards and Triggers
-There are two places you need to change in your strategy file to add a 
-new buy strategy for testing:
-- Inside [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L278-L294).
-- Inside [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297) known as `SPACE`.
+Hyperopt requires historic data to be available, just as backtesting does (hyperopt runs backtesting many times with different parameters).
+To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
 
-There you have two different type of indicators: 1. `guards` and 2. 
-`triggers`.
-1. Guards are conditions like "never buy if ADX < 10", or never buy if 
-current price is over EMA10.
-2. Triggers are ones that actually trigger buy in specific moment, like 
-"buy when EMA5 crosses over EMA10" or buy when close price touches lower 
-bollinger band.
+!!! Bug
+    Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
 
-HyperOpt will, for each eval round, pick just ONE trigger, and possibly 
-multiple guards. So that the constructed strategy will be something like 
-"*buy exactly when close price touches lower bollinger band, BUT only if 
-ADX > 10*".
+!!! Note
+    Since 2021.4 release you no longer have to write a separate hyperopt class, but can configure the parameters directly in the strategy.
+    The legacy method was supported up to 2021.8 and has been removed in 2021.9.
 
+## Install hyperopt dependencies
 
-If you have updated the buy strategy, means change the content of
-`populate_buy_trend()` method you have to update the `guards` and 
-`triggers` hyperopts must used.
+Since Hyperopt dependencies are not needed to run the bot itself, are heavy, can not be easily built on some platforms (like Raspberry PI), they are not installed by default. Before you run Hyperopt, you need to install the corresponding dependencies, as described in this section below.
 
-As for an example if your `populate_buy_trend()` method is:
-```python
-def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
-    dataframe.loc[
-        (dataframe['rsi'] < 35) &
-        (dataframe['adx'] > 65),
-        'buy'] = 1
+!!! Note
+    Since Hyperopt is a resource intensive process, running it on a Raspberry Pi is not recommended nor supported.
 
-    return dataframe
+### Docker
+
+The docker-image includes hyperopt dependencies, no further action needed.
+
+### Easy installation script (setup.sh) / Manual installation
+
+```bash
+source .venv/bin/activate
+pip install -r requirements-hyperopt.txt
 ```
 
-Your hyperopt file must contain `guards` to find the right value for 
-`(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That 
-means you will need to enable/disable triggers.
- 
-In our case the `SPACE` and `populate_buy_trend` in your strategy file 
-will look like:
+## Hyperopt command reference
+
+--8<-- "commands/hyperopt.md"
+
+### Hyperopt checklist
+
+Checklist on all tasks / possibilities in hyperopt
+
+Depending on the space you want to optimize, only some of the below are required:
+
+* define parameters with `space='buy'` - for entry signal optimization
+* define parameters with `space='sell'` - for exit signal optimization
+
+!!! Note
+    `populate_indicators` needs to create all indicators any of the spaces may use, otherwise hyperopt will not work.
+
+Rarely you may also need to create a [nested class](advanced-hyperopt.md#overriding-pre-defined-spaces) named `HyperOpt` and implement
+
+* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
+* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
+* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
+* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
+* `max_open_trades_space` - for custom max_open_trades optimization (if you need the ranges for the max_open_trades parameter in the optimization hyperspace that differ from default)
+
+!!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
+    You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything in your strategy.
+
+    ``` bash
+    # Have a working strategy at hand.
+    freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
+    ```
+
+### Hyperopt execution logic
+
+Hyperopt will first load your data into memory and will then run `populate_indicators()` once per Pair to generate all indicators, unless `--analyze-per-epoch` is specified.
+
+Hyperopt will then spawn into different processes (number of processors, or `-j <n>`), and run backtesting over and over again, changing the parameters that are part of the `--spaces` defined.
+
+For every new set of parameters, freqtrade will run first `populate_entry_trend()` followed by `populate_exit_trend()`, and then run the regular backtesting process to simulate trades.
+
+After backtesting, the results are passed into the [loss function](#loss-functions), which will evaluate if this result was better or worse than previous results.  
+Based on the loss function result, hyperopt will determine the next set of parameters to try in the next round of backtesting.
+
+### Configure your Guards and Triggers
+
+There are two places you need to change in your strategy file to add a new buy hyperopt for testing:
+
+* Define the parameters at the class level hyperopt shall be optimizing.
+* Within `populate_entry_trend()` - use defined parameter values instead of raw constants.
+
+There you have two different types of indicators: 1. `guards` and 2. `triggers`.
+
+1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
+2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower Bollinger band".
+
+!!! Hint "Guards and Triggers"
+    Technically, there is no difference between Guards and Triggers.  
+    However, this guide will make this distinction to make it clear that signals should not be "sticking".
+    Sticking signals are signals that are active for multiple candles. This can lead into entering a signal late (right before the signal disappears - which means that the chance of success is a lot lower than right at the beginning).
+
+Hyper-optimization will, for each epoch round, pick one trigger and possibly multiple guards.
+
+#### Exit signal optimization
+
+Similar to the entry-signal above, exit-signals can also be optimized.
+Place the corresponding settings into the following methods
+
+* Define the parameters at the class level hyperopt shall be optimizing, either naming them `sell_*`, or by explicitly defining `space='sell'`.
+* Within `populate_exit_trend()` - use defined parameter values instead of raw constants.
+
+The configuration and rules are the same than for buy signals.
+
+## Solving a Mystery
+
+Let's say you are curious: should you use MACD crossings or lower Bollinger Bands to trigger your long entries.
+And you also wonder should you use RSI or ADX to help with those decisions.
+If you decide to use RSI or ADX, which values should I use for them?
+
+So let's use hyperparameter optimization to solve this mystery.
+
+### Defining indicators to be used
+
+We start by calculating the indicators our strategy is going to use.
+
+``` python
+class MyAwesomeStrategy(IStrategy):
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Generate all indicators used by the strategy
+        """
+        dataframe['adx'] = ta.ADX(dataframe)
+        dataframe['rsi'] = ta.RSI(dataframe)
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        bollinger = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
+        dataframe['bb_lowerband'] = bollinger['lowerband']
+        dataframe['bb_middleband'] = bollinger['middleband']
+        dataframe['bb_upperband'] = bollinger['upperband']
+        return dataframe
+```
+
+### Hyperoptable parameters
+
+We continue to define hyperoptable parameters:
+
 ```python
-space = {
-    'rsi': hp.choice('rsi', [
-        {'enabled': False},
-        {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
-    ]),
-    'adx': hp.choice('adx', [
-        {'enabled': False},
-        {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
-    ]),
-    'trigger': hp.choice('trigger', [
-        {'type': 'lower_bb'},
-        {'type': 'faststoch10'},
-        {'type': 'ao_cross_zero'},
-        {'type': 'ema5_cross_ema10'},
-        {'type': 'macd_cross_signal'},
-        {'type': 'sar_reversal'},
-        {'type': 'stochf_cross'},
-        {'type': 'ht_sine'},
-    ]),
-}
+class MyAwesomeStrategy(IStrategy):
+    buy_adx = DecimalParameter(20, 40, decimals=1, default=30.1, space="buy")
+    buy_rsi = IntParameter(20, 40, default=30, space="buy")
+    buy_adx_enabled = BooleanParameter(default=True, space="buy")
+    buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
+    buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
+```
 
-... 
+The above definition says: I have five parameters I want to randomly combine to find the best combination.  
+`buy_rsi` is an integer parameter, which will be tested between 20 and 40. This space has a size of 20.  
+`buy_adx` is a decimal parameter, which will be evaluated between 20 and 40 with 1 decimal place (so values are 20.1, 20.2, ...). This space has a size of 200.  
+Then we have three category variables. First two are either `True` or `False`.
+We use these to either enable or disable the ADX and RSI guards.
+The last one we call `trigger` and use it to decide which buy trigger we want to use.
 
-def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+!!! Note "Parameter space assignment"
+    Parameters must either be assigned to a variable named `buy_*` or `sell_*` - or contain `space='buy'` | `space='sell'` to be assigned to a space correctly.
+    If no parameter is available for a space, you'll receive the error that no space was found when running hyperopt.  
+    Parameters with unclear space (e.g. `adx_period = IntParameter(4, 24, default=14)` - no explicit nor implicit space) will not be detected and will therefore be ignored.
+
+So let's write the buy strategy using these values:
+
+```python
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         conditions = []
         # GUARDS AND TRENDS
-        if params['adx']['enabled']:
-            conditions.append(dataframe['adx'] > params['adx']['value'])
-        if params['rsi']['enabled']:
-            conditions.append(dataframe['rsi'] < params['rsi']['value'])
+        if self.buy_adx_enabled.value:
+            conditions.append(dataframe['adx'] > self.buy_adx.value)
+        if self.buy_rsi_enabled.value:
+            conditions.append(dataframe['rsi'] < self.buy_rsi.value)
 
         # TRIGGERS
-        triggers = {
-            'lower_bb': dataframe['tema'] <= dataframe['blower'],
-            'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
-            'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
-            'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
-            'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
-            'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
-            'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
-            'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
+        if self.buy_trigger.value == 'bb_lower':
+            conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
+        if self.buy_trigger.value == 'macd_cross_signal':
+            conditions.append(qtpylib.crossed_above(
+                dataframe['macd'], dataframe['macdsignal']
+            ))
+
+        # Check that volume is not 0
+        conditions.append(dataframe['volume'] > 0)
+
+        if conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'enter_long'] = 1
+
+        return dataframe
+```
+
+Hyperopt will now call `populate_entry_trend()` many times (`epochs`) with different value combinations.  
+It will use the given historical data and simulate buys based on the buy signals generated with the above function.  
+Based on the results, hyperopt will tell you which parameter combination produced the best results (based on the configured [loss function](#loss-functions)).
+
+!!! Note
+    The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
+    When you want to test an indicator that isn't used by the bot currently, remember to
+    add it to the `populate_indicators()` method in your strategy or hyperopt file.
+
+## Parameter types
+
+There are four parameter types each suited for different purposes.
+
+* `IntParameter` - defines an integral parameter with upper and lower boundaries of search space.
+* `DecimalParameter` - defines a floating point parameter with a limited number of decimals (default 3). Should be preferred instead of `RealParameter` in most cases.
+* `RealParameter` - defines a floating point parameter with upper and lower boundaries and no precision limit. Rarely used as it creates a space with a near infinite number of possibilities.
+* `CategoricalParameter` - defines a parameter with a predetermined number of choices.
+* `BooleanParameter` - Shorthand for `CategoricalParameter([True, False])` - great for "enable" parameters.
+
+### Parameter options
+
+There are two parameter options that can help you to quickly test various ideas:
+
+* `optimize` - when set to `False`, the parameter will not be included in optimization process. (Default: True)
+* `load` - when set to `False`, results of a previous hyperopt run (in `buy_params` and `sell_params` either in your strategy or the JSON output file) will not be used as the starting value for subsequent hyperopts. The default value specified in the parameter will be used instead. (Default: True)
+
+!!! Tip "Effects of `load=False` on backtesting"
+    Be aware that setting the `load` option to `False` will mean backtesting will also use the default value specified in the parameter and *not* the value found through hyperoptimisation.
+
+!!! Warning
+    Hyperoptable parameters cannot be used in `populate_indicators` - as hyperopt does not recalculate indicators for each epoch, so the starting value would be used in this case.
+
+## Optimizing an indicator parameter
+
+Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy.
+By default, we assume a stoploss of 5% - and a take-profit (`minimal_roi`) of 10% - which means freqtrade will sell the trade once 10% profit has been reached.
+
+``` python
+from pandas import DataFrame
+from functools import reduce
+
+import talib.abstract as ta
+
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, 
+                                IStrategy, IntParameter)
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+class MyAwesomeStrategy(IStrategy):
+    stoploss = -0.05
+    timeframe = '15m'
+    minimal_roi = {
+        "0":  0.10
+    }
+    # Define the parameter spaces
+    buy_ema_short = IntParameter(3, 50, default=5)
+    buy_ema_long = IntParameter(15, 200, default=50)
+
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """Generate all indicators used by the strategy"""
+        
+        # Calculate all ema_short values
+        for val in self.buy_ema_short.range:
+            dataframe[f'ema_short_{val}'] = ta.EMA(dataframe, timeperiod=val)
+        
+        # Calculate all ema_long values
+        for val in self.buy_ema_long.range:
+            dataframe[f'ema_long_{val}'] = ta.EMA(dataframe, timeperiod=val)
+        
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = []
+        conditions.append(qtpylib.crossed_above(
+                dataframe[f'ema_short_{self.buy_ema_short.value}'], dataframe[f'ema_long_{self.buy_ema_long.value}']
+            ))
+
+        # Check that volume is not 0
+        conditions.append(dataframe['volume'] > 0)
+
+        if conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'enter_long'] = 1
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        conditions = []
+        conditions.append(qtpylib.crossed_above(
+                dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
+            ))
+
+        # Check that volume is not 0
+        conditions.append(dataframe['volume'] > 0)
+
+        if conditions:
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'exit_long'] = 1
+        return dataframe
+```
+
+Breaking it down:
+
+Using `self.buy_ema_short.range` will return a range object containing all entries between the Parameters low and high value.
+In this case (`IntParameter(3, 50, default=5)`), the loop would run for all numbers between 3 and 50 (`[3, 4, 5, ... 49, 50]`).
+By using this in a loop, hyperopt will generate 48 new columns (`['buy_ema_3', 'buy_ema_4', ... , 'buy_ema_50']`).
+
+Hyperopt itself will then use the selected value to create the buy and sell signals.
+
+While this strategy is most likely too simple to provide consistent profit, it should serve as an example how optimize indicator parameters.
+
+!!! Note
+    `self.buy_ema_short.range` will act differently between hyperopt and other modes. For hyperopt, the above example may generate 48 new columns, however for all other modes (backtesting, dry/live), it will only generate the column for the selected value. You should therefore avoid using the resulting column with explicit values (values other than `self.buy_ema_short.value`).
+
+!!! Note
+    `range` property may also be used with `DecimalParameter` and `CategoricalParameter`. `RealParameter` does not provide this property due to infinite search space.
+
+??? Hint "Performance tip"
+    During normal hyperopting, indicators are calculated once and supplied to each epoch, linearly increasing RAM usage as a factor of increasing cores. As this also has performance implications, there are two alternatives to reduce RAM usage
+
+    * Move `ema_short` and `ema_long` calculations from `populate_indicators()` to `populate_entry_trend()`. Since `populate_entry_trend()` will be calculated every epoch, you don't need to use `.range` functionality.
+    * hyperopt provides `--analyze-per-epoch` which will move the execution of `populate_indicators()` to the epoch process, calculating a single value per parameter per epoch instead of using the `.range` functionality. In this case, `.range` functionality will only return the actually used value.
+
+    These alternatives will reduce RAM usage, but increase CPU usage. However, your hyperopting run will be less likely to fail due to Out Of Memory (OOM) issues.
+
+    Whether you are using `.range` functionality or the alternatives above, you should try to use space ranges as small as possible since this will improve CPU/RAM usage.
+
+## Optimizing protections
+
+Freqtrade can also optimize protections. How you optimize protections is up to you, and the following should be considered as example only.
+
+The strategy will simply need to define the "protections" entry as property returning a list of protection configurations.
+
+``` python
+from pandas import DataFrame
+from functools import reduce
+
+import talib.abstract as ta
+
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, 
+                                IStrategy, IntParameter)
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+class MyAwesomeStrategy(IStrategy):
+    stoploss = -0.05
+    timeframe = '15m'
+    # Define the parameter spaces
+    cooldown_lookback = IntParameter(2, 48, default=5, space="protection", optimize=True)
+    stop_duration = IntParameter(12, 200, default=5, space="protection", optimize=True)
+    use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True)
+
+
+    @property
+    def protections(self):
+        prot = []
+
+        prot.append({
+            "method": "CooldownPeriod",
+            "stop_duration_candles": self.cooldown_lookback.value
+        })
+        if self.use_stop_protection.value:
+            prot.append({
+                "method": "StoplossGuard",
+                "lookback_period_candles": 24 * 3,
+                "trade_limit": 4,
+                "stop_duration_candles": self.stop_duration.value,
+                "only_per_pair": False
+            })
+
+        return prot
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ...
+        
+```
+
+You can then run hyperopt as follows:
+`freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy --spaces protection`
+
+!!! Note
+    The protection space is not part of the default space, and is only available with the Parameters Hyperopt interface, not with the legacy hyperopt interface (which required separate hyperopt files).
+    Freqtrade will also automatically change the "--enable-protections" flag if the protection space is selected.
+
+!!! Warning
+    If protections are defined as property, entries from the configuration will be ignored.
+    It is therefore recommended to not define protections in the configuration.
+
+### Migrating from previous property setups
+
+A migration from a previous setup is pretty simple, and can be accomplished by converting the protections entry to a property.
+In simple terms, the following configuration will be converted to the below.
+
+``` python
+class MyAwesomeStrategy(IStrategy):
+    protections = [
+        {
+            "method": "CooldownPeriod",
+            "stop_duration_candles": 4
         }
-        ...
+    ]
 ```
 
+Result
 
-### 2. Update the hyperopt config file
-Hyperopt is using a dedicated config file. Currently hyperopt 
-cannot use your config file. It is also made on purpose to allow you
-testing your strategy with different configurations.
-
-The Hyperopt configuration is located in 
-[user_data/hyperopt_conf.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopt_conf.py).
-
-
-## Advanced notions
-### Understand the Guards and Triggers
-When you need to add the new guards and triggers to be hyperopt 
-parameters, you do this by adding them into the [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297).
-
-If it's a trigger, you add one line to the 'trigger' choice group and that's it.
-
-If it's a guard, you will add a line like this:
-```
-'rsi': hp.choice('rsi', [
-    {'enabled': False},
-    {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
-]),
-```
-This says, "*one of the guards is RSI, it can have two values, enabled or 
-disabled. If it is enabled, try different values for it between 20 and 40*".
-
-So, the part of the strategy builder using the above setting looks like 
-this:
-
-```
-if params['rsi']['enabled']:
-    conditions.append(dataframe['rsi'] < params['rsi']['value'])
+``` python
+class MyAwesomeStrategy(IStrategy):
+    
+    @property
+    def protections(self):
+        return [
+            {
+                "method": "CooldownPeriod",
+                "stop_duration_candles": 4
+            }
+        ]
 ```
 
-It checks if Hyperopt wants the RSI guard to be enabled for this 
-round `params['rsi']['enabled']` and if it is, then it will add a 
-condition that says RSI must be smaller than the value hyperopt picked 
-for this evaluation, which is given in the `params['rsi']['value']`.
+You will then obviously also change potential interesting entries to parameters to allow hyper-optimization.
 
-That's it. Now you can add new parts of strategies to Hyperopt and it 
-will try all the combinations with all different values in the search 
-for best working algo.
+### Optimizing `max_entry_position_adjustment`
 
+While `max_entry_position_adjustment` is not a separate space, it can still be used in hyperopt by using the property approach shown above.
 
-### Add a new Indicators
-If you want to test an indicator that isn't used by the bot currently, 
-you need to add it to the `populate_indicators()` method in `hyperopt.py`.
+``` python
+from pandas import DataFrame
+from functools import reduce
+
+import talib.abstract as ta
+
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, 
+                                IStrategy, IntParameter)
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+class MyAwesomeStrategy(IStrategy):
+    stoploss = -0.05
+    timeframe = '15m'
+
+    # Define the parameter spaces
+    max_epa = CategoricalParameter([-1, 0, 1, 3, 5, 10], default=1, space="buy", optimize=True)
+
+    @property
+    def max_entry_position_adjustment(self):
+        return self.max_epa.value
+        
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ...
+```
+
+??? Tip "Using `IntParameter`"
+    You can also use the `IntParameter` for this optimization, but you must explicitly return an integer:
+    ``` python
+    max_epa = IntParameter(-1, 10, default=1, space="buy", optimize=True)
+
+    @property
+    def max_entry_position_adjustment(self):
+        return int(self.max_epa.value)
+    ```
+
+## Loss-functions
+
+Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
+
+A loss function must be specified via the `--hyperopt-loss <Class-name>` argument (or optionally via the configuration under the `"hyperopt_loss"` key).
+This class should be in its own file within the `user_data/hyperopts/` directory.
+
+Currently, the following loss functions are builtin:
+
+* `ShortTradeDurHyperOptLoss` - (default legacy Freqtrade hyperoptimization loss function) - Mostly for short trade duration and avoiding losses.
+* `OnlyProfitHyperOptLoss` - takes only amount of profit into consideration.
+* `SharpeHyperOptLoss` - Optimizes Sharpe Ratio calculated on trade returns relative to standard deviation.
+* `SharpeHyperOptLossDaily` - Optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation.
+* `SortinoHyperOptLoss` - Optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation.
+* `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation.
+* `MaxDrawDownHyperOptLoss` - Optimizes Maximum absolute drawdown.
+* `MaxDrawDownRelativeHyperOptLoss` -  Optimizes both maximum absolute drawdown while also adjusting for maximum relative drawdown.
+* `MaxDrawDownPerPairHyperOptLoss` - Calculates the profit/drawdown ratio per pair and returns the worst result as objective, forcing hyperopt to optimize the parameters for all pairs in the pairlist. This way, we prevent one or more pairs with good results from inflating the metrics, while the pairs with poor results are not represented and therefore not optimized.
+* `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown.
+* `ProfitDrawDownHyperOptLoss` - Optimizes by max Profit & min Drawdown objective. `DRAWDOWN_MULT` variable within the hyperoptloss file can be adjusted to be stricter or more flexible on drawdown purposes.
+* `MultiMetricHyperOptLoss` -  Optimizes by several key metrics to achieve balanced performance. The primary focus is on maximizing Profit and minimizing Drawdown, while also considering additional metrics such as Profit Factor, Expectancy Ratio and Winrate. Moreover, it applies a penalty for epochs with a low number of trades, encouraging strategies with adequate trade frequency.
+
+Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
 
 ## Execute Hyperopt
-Once you have updated your hyperopt configuration you can run it. 
-Because hyperopt tries a lot of combination to find the best parameters
-it will take time you will have the result (more than 30 mins).
 
-We strongly recommend to use `screen` to prevent any connection loss.
-```bash
-python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
-```
+Once you have updated your hyperopt configuration you can run it.
+Because hyperopt tries a lot of combinations to find the best parameters it will take time to get a good result.
 
-The `-e` flag will set how many evaluations hyperopt will do. We recommend
-running at least several thousand evaluations.
-
-### Execute hyperopt with different ticker-data source
-If you would like to hyperopt parameters using an alternate ticker data that
-you have on-disk, use the `--datadir PATH` option. Default hyperopt will
-use data from directory `user_data/data`.
-
-### Running hyperopt with smaller testset
-Use the `--timeperiod` argument to change how much of the testset
-you want to use. The last N ticks/timeframes will be used.
-Example:
+We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
 
 ```bash
-python3 ./freqtrade/main.py hyperopt --timeperiod -200
+freqtrade hyperopt --config config.json --hyperopt-loss <hyperoptlossname> --strategy <strategyname> -e 500 --spaces all
 ```
 
-### Running hyperopt with smaller search space
-Use the `--spaces` argument to limit the search space used by hyperopt.
-Letting Hyperopt optimize everything is a huuuuge search space. Often it 
-might make more sense to start by just searching for initial buy algorithm. 
-Or maybe you just want to optimize your stoploss or roi table for that awesome 
-new buy strategy you have.
+The `-e` option will set how many evaluations hyperopt will do. Since hyperopt uses Bayesian search, running too many epochs at once may not produce greater results. Experience has shown that best results are usually not improving much after 500-1000 epochs.  
+Doing multiple runs (executions) with a few 1000 epochs and different random state will most likely produce different results.
+
+The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
+
+!!! Note
+    Hyperopt will store hyperopt results with the timestamp of the hyperopt start time.
+    Reading commands (`hyperopt-list`, `hyperopt-show`) can use `--hyperopt-filename <filename>` to read and display older hyperopt results.
+    You can find a list of filenames with `ls -l user_data/hyperopt_results/`.
+
+### Execute Hyperopt with different historical data source
+
+If you would like to hyperopt parameters using an alternate historical data set that
+you have on-disk, use the `--datadir PATH` option. By default, hyperopt uses data from directory `user_data/data`.
+
+### Running Hyperopt with a smaller test-set
+
+Use the `--timerange` argument to change how much of the test-set you want to use.
+For example, to use one month of data, pass `--timerange 20210101-20210201` (from january 2021 - february 2021) to the hyperopt call.
+
+Full command:
+
+```bash
+freqtrade hyperopt --strategy <strategyname> --timerange 20210101-20210201
+```
+
+### Running Hyperopt with Smaller Search Space
+
+Use the `--spaces` option to limit the search space used by hyperopt.
+Letting Hyperopt optimize everything is a huuuuge search space.
+Often it might make more sense to start by just searching for initial buy algorithm.
+Or maybe you just want to optimize your stoploss or roi table for that awesome new buy strategy you have.
 
 Legal values are:
 
-- `all`: optimize everything
-- `buy`: just search for a new buy strategy
-- `roi`: just optimize the minimal profit table for your strategy
-- `stoploss`: search for the best stoploss value
-- space-separated list of any of the above values for example `--spaces roi stoploss`
+* `all`: optimize everything
+* `buy`: just search for a new buy strategy
+* `sell`: just search for a new sell strategy
+* `roi`: just optimize the minimal profit table for your strategy
+* `stoploss`: search for the best stoploss value
+* `trailing`: search for the best trailing stop values
+* `trades`: search for the best max open trades values
+* `protection`: search for the best protection parameters (read the [protections section](#optimizing-protections) on how to properly define these)
+* `default`: `all` except `trailing` and `protection`
+* space-separated list of any of the above values for example `--spaces roi stoploss`
 
-### Hyperopt with MongoDB
-Hyperopt with MongoDB, is like Hyperopt under steroids. As you saw by
-executing the previous command is the execution takes a long time. 
-To accelerate it you can use hyperopt with MongoDB.
+The default Hyperopt Search Space, used when no `--space` command line option is specified, does not include the `trailing` hyperspace. We recommend you to run optimization for the `trailing` hyperspace separately, when the best parameters for other hyperspaces were found, validated and pasted into your custom strategy.
 
-To run hyperopt with MongoDb you will need 3 terminals.
+## Understand the Hyperopt Result
+
+Once Hyperopt is completed you can use the result to update your strategy.
+Given the following result from hyperopt:
 
-**Terminal 1: Start MongoDB**
-```bash
-cd <freqtrade> 
-source .env/bin/activate
-python3 scripts/start-mongodb.py
 ```
+Best result:
 
-**Terminal 2: Start Hyperopt worker**
-```bash
-cd <freqtrade> 
-source .env/bin/activate
-python3 scripts/start-hyperopt-worker.py
-```
+    44/100:    135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722%). Avg duration 180.4 mins. Objective: 1.94367
 
-**Terminal 3: Start Hyperopt with MongoDB**
-```bash
-cd <freqtrade> 
-source .env/bin/activate
-python3 ./freqtrade/main.py -c config.json hyperopt --use-mongodb
-```
-
-**Re-run an Hyperopt**
-To re-run Hyperopt you have to delete the existing MongoDB table.
-```bash
-cd <freqtrade> 
-rm -rf .hyperopt/mongodb/
-```
-
-## Understand the hyperopts result 
-Once Hyperopt is completed you can use the result to adding new buy 
-signal. Given following result from hyperopt:
-```
-Best parameters:
-{
-    "adx": {
-        "enabled": true,
-        "value": 15.0
-    },
-    "fastd": {
-        "enabled": true,
-        "value": 40.0
-    },
-    "green_candle": {
-        "enabled": true
-    },
-    "mfi": {
-        "enabled": false
-    },
-    "over_sar": {
-        "enabled": false
-    },
-    "rsi": {
-        "enabled": true,
-        "value": 37.0
-    },
-    "trigger": {
-        "type": "lower_bb"
-    },
-    "uptrend_long_ema": {
-        "enabled": true
-    },
-    "uptrend_short_ema": {
-        "enabled": false
-    },
-    "uptrend_sma": {
-        "enabled": false
+    # Buy hyperspace params:
+    buy_params = {
+        'buy_adx': 44,
+        'buy_rsi': 29,
+        'buy_adx_enabled': False,
+        'buy_rsi_enabled': True,
+        'buy_trigger': 'bb_lower'
     }
-}
-
-Best Result:
-  2197 trades. Avg profit  1.84%. Total profit  0.79367541 BTC. Avg duration 241.0 mins.
 ```
 
 You should understand this result like:
-- You should **consider** the guard "adx" (`"adx"` is `"enabled": true`) 
-and the best value is `15.0` (`"value": 15.0,`)
-- You should **consider** the guard "fastd" (`"fastd"` is `"enabled": 
-true`) and the best value is `40.0` (`"value": 40.0,`)
-- You should **consider** to enable the guard "green_candle" 
-(`"green_candle"` is `"enabled": true`) but this guards as no 
-customizable value.
-- You should **ignore** the guard "mfi" (`"mfi"` is `"enabled": false`)
-- and so on...
 
-You have to look inside your strategy file into `buy_strategy_generator()` 
-method, what those values match to.   
-  
-So for example you had `adx:` with the `value: 15.0` so we would look 
-at `adx`-block, that translates to the following code block:
-```
-(dataframe['adx'] > 15.0)
-```
-  
-Translating your whole hyperopt result to as the new buy-signal 
-would be the following:
-```
-def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
-    dataframe.loc[
-        (
-            (dataframe['adx'] > 15.0) & # adx-value
-            (dataframe['fastd'] < 40.0) & # fastd-value
-            (dataframe['close'] > dataframe['open']) & # green_candle
-            (dataframe['rsi'] < 37.0) & # rsi-value
-            (dataframe['ema50'] > dataframe['ema100']) # uptrend_long_ema
-        ),
-        'buy'] = 1
-    return dataframe
+* The buy trigger that worked best was `bb_lower`.
+* You should not use ADX because `'buy_adx_enabled': False`.
+* You should **consider** using the RSI indicator (`'buy_rsi_enabled': True`) and the best value is `29.0` (`'buy_rsi': 29.0`)
+
+### Automatic parameter application to the strategy
+
+When using Hyperoptable parameters, the result of your hyperopt-run will be written to a json file next to your strategy (so for `MyAwesomeStrategy.py`, the file would be `MyAwesomeStrategy.json`).  
+This file is also updated when using the `hyperopt-show` sub-command, unless `--disable-param-export` is provided to either of the 2 commands.
+
+
+Your strategy class can also contain these results explicitly. Simply copy hyperopt results block and paste them at class level, replacing old parameters (if any). New parameters will automatically be loaded next time strategy is executed.
+
+Transferring your whole hyperopt result to your strategy would then look like:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    # Buy hyperspace params:
+    buy_params = {
+        'buy_adx': 44,
+        'buy_rsi': 29,
+        'buy_adx_enabled': False,
+        'buy_rsi_enabled': True,
+        'buy_trigger': 'bb_lower'
+    }
 ```
 
-## Next step
-Now you have a perfect bot and want to control it from Telegram. Your
-next step is to learn the [Telegram usage](https://github.com/gcarq/freqtrade/blob/develop/docs/telegram-usage.md).
+!!! Note
+    Values in the configuration file will overwrite Parameter-file level parameters - and both will overwrite parameters within the strategy.
+    The prevalence is therefore: config > parameter file > strategy `*_params` > parameter default
+
+### Understand Hyperopt ROI results
+
+If you are optimizing ROI (i.e. if optimization search-space contains 'all', 'default' or 'roi'), your result will look as follows and include a ROI table:
+
+```
+Best result:
+
+    44/100:    135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722%). Avg duration 180.4 mins. Objective: 1.94367
+
+    # ROI table:
+    minimal_roi = {
+        0: 0.10674,
+        21: 0.09158,
+        78: 0.03634,
+        118: 0
+    }
+```
+
+In order to use this best ROI table found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `minimal_roi` attribute of your custom strategy:
+
+```
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        0: 0.10674,
+        21: 0.09158,
+        78: 0.03634,
+        118: 0
+    }
+```
+
+As stated in the comment, you can also use it as the value of the `minimal_roi` setting in the configuration file.
+
+#### Default ROI Search Space
+
+If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the timeframe used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 3 digits after the decimal point):
+
+| # step | 1m     |               | 5m       |             | 1h         |               | 1d           |               |
+| ------ | ------ | ------------- | -------- | ----------- | ---------- | ------------- | ------------ | ------------- |
+| 1      | 0      | 0.011...0.119 | 0        | 0.03...0.31 | 0          | 0.068...0.711 | 0            | 0.121...1.258 |
+| 2      | 2...8  | 0.007...0.042 | 10...40  | 0.02...0.11 | 120...480  | 0.045...0.252 | 2880...11520 | 0.081...0.446 |
+| 3      | 4...20 | 0.003...0.015 | 20...100 | 0.01...0.04 | 240...1200 | 0.022...0.091 | 5760...28800 | 0.040...0.162 |
+| 4      | 6...44 | 0.0           | 30...220 | 0.0         | 360...2640 | 0.0           | 8640...63360 | 0.0           |
+
+These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
+
+If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
+
+Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps).
+
+A sample for these methods can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
+
+!!! Note "Reduced search space"
+    To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#overriding-pre-defined-spaces) to change this to your needs.
+
+### Understand Hyperopt Stoploss results
+
+If you are optimizing stoploss values (i.e. if optimization search-space contains 'all', 'default' or 'stoploss'), your result will look as follows and include stoploss:
+
+```
+Best result:
+
+    44/100:    135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722%). Avg duration 180.4 mins. Objective: 1.94367
+
+    # Buy hyperspace params:
+    buy_params = {
+        'buy_adx': 44,
+        'buy_rsi': 29,
+        'buy_adx_enabled': False,
+        'buy_rsi_enabled': True,
+        'buy_trigger': 'bb_lower'
+    }
+
+    stoploss: -0.27996
+```
+
+In order to use this best stoploss value found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `stoploss` attribute of your custom strategy:
+
+``` python
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.27996
+```
+
+As stated in the comment, you can also use it as the value of the `stoploss` setting in the configuration file.
+
+#### Default Stoploss Search Space
+
+If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace vary in the range -0.35...-0.02, which is sufficient in most cases.
+
+If you have the `stoploss_space()` method in your custom hyperopt file, remove it in order to utilize Stoploss hyperoptimization space generated by Freqtrade by default.
+
+Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
+
+!!! Note "Reduced search space"
+    To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#overriding-pre-defined-spaces) to change this to your needs.
+
+### Understand Hyperopt Trailing Stop results
+
+If you are optimizing trailing stop values (i.e. if optimization search-space contains 'all' or 'trailing'), your result will look as follows and include trailing stop parameters:
+
+```
+Best result:
+
+    45/100:    606 trades. Avg profit  1.04%. Total profit  0.31555614 BTC ( 630.48%). Avg duration 150.3 mins. Objective: -1.10161
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.02001
+    trailing_stop_positive_offset = 0.06038
+    trailing_only_offset_is_reached = True
+```
+
+In order to use these best trailing stop parameters found by Hyperopt in backtesting and for live trades/dry-run, copy-paste them as the values of the corresponding attributes of your custom strategy:
+
+``` python
+    # Trailing stop
+    # These attributes will be overridden if the config file contains corresponding values.
+    trailing_stop = True
+    trailing_stop_positive = 0.02001
+    trailing_stop_positive_offset = 0.06038
+    trailing_only_offset_is_reached = True
+```
+
+As stated in the comment, you can also use it as the values of the corresponding settings in the configuration file.
+
+#### Default Trailing Stop Search Space
+
+If you are optimizing trailing stop values, Freqtrade creates the 'trailing' optimization hyperspace for you. By default, the `trailing_stop` parameter is always set to True in that hyperspace, the value of the `trailing_only_offset_is_reached` vary between True and False, the values of the `trailing_stop_positive` and `trailing_stop_positive_offset` parameters vary in the ranges 0.02...0.35 and 0.01...0.1 correspondingly, which is sufficient in most cases.
+
+Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
+
+!!! Note "Reduced search space"
+    To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#overriding-pre-defined-spaces) to change this to your needs.
+
+### Reproducible results
+
+The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with an asterisk character (`*`) in the first column in the Hyperopt output.
+
+The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
+
+If you have not set this value explicitly in the command line options, Hyperopt seeds the random state with some random value for you. The random state value for each Hyperopt run is shown in the log, so you can copy and paste it into the `--random-state` command line option to repeat the set of the initial random epochs used.
+
+If you have not changed anything in the command line options, configuration, timerange, Strategy and Hyperopt classes, historical data and the Loss Function -- you should obtain same hyper-optimization results with same random state value used.
+
+## Output formatting
+
+By default, hyperopt prints colorized results -- epochs with positive profit are printed in the green color. This highlighting helps you find epochs that can be interesting for later analysis. Epochs with zero total profit or with negative profits (losses) are printed in the normal color. If you do not need colorization of results (for instance, when you are redirecting hyperopt output to a file) you can switch colorization off by specifying the `--no-color` option in the command line.
+
+You can use the `--print-all` command line option if you would like to see all results in the hyperopt output, not only the best ones. When `--print-all` is used, current best results are also colorized by default -- they are printed in bold (bright) style. This can also be switched off with the `--no-color` command line option.
+
+!!! Note "Windows and color output"
+    Windows does not support color-output natively, therefore it is automatically disabled. To have color-output for hyperopt running under windows, please consider using WSL.
+
+## Position stacking and disabling max market positions
+
+In some situations, you may need to run Hyperopt (and Backtesting) with the `--eps`/`--enable-position-staking` argument, or you may need to set `max_open_trades` to a very high number to disable the limit on the number of open trades.
+
+By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
+open trade per pair is allowed. The total number of trades open for all pairs
+is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
+potential trades being hidden (or masked) by already open trades.
+
+The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times.
+Using `--max-open-trades` with a very high number will disable the limit on the number of open trades.
+
+!!! Note
+    Dry/live runs will **NOT** use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.
+
+You can also enable position stacking in the configuration file by explicitly setting
+`"position_stacking"=true`.
+
+## Out of Memory errors
+
+As hyperopt consumes a lot of memory (the complete data needs to be in memory once per parallel backtesting process), it's likely that you run into "out of memory" errors.
+To combat these, you have multiple options:
+
+* Reduce the amount of pairs.
+* Reduce the timerange used (`--timerange <timerange>`).
+* Avoid using `--timeframe-detail` (this loads a lot of additional data into memory).
+* Reduce the number of parallel processes (`-j <n>`).
+* Increase the memory of your machine.
+* Use `--analyze-per-epoch` if you're using a lot of parameters with `.range` functionality.
+
+
+## The objective has been evaluated at this point before.
+
+If you see `The objective has been evaluated at this point before.` - then this is a sign that your space has been exhausted, or is close to that.
+Basically all points in your space have been hit (or a local minima has been hit) - and hyperopt does no longer find points in the multi-dimensional space it did not try yet.
+Freqtrade tries to counter the "local minima" problem by using new, randomized points in this case.
+
+Example:
+
+``` python
+buy_ema_short = IntParameter(5, 20, default=10, space="buy", optimize=True)
+# This is the only parameter in the buy space
+```
+
+The `buy_ema_short` space has 15 possible values (`5, 6, ... 19, 20`). If you now run hyperopt for the buy space, hyperopt will only have 15 values to try before running out of options.
+Your epochs should therefore be aligned to the possible values - or you should be ready to interrupt a run if you norice a lot of `The objective has been evaluated at this point before.` warnings.
+
+## Show details of Hyperopt results
+
+After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` sub-commands. The usage of these sub-commands is described in the [Utils](utils.md#list-hyperopt-results) chapter.
+
+## Output debug messages from your strategy
+
+If you want to output debug messages from your strategy, you can use the `logging` module. By default, Freqtrade will output all messages with a level of `INFO` or higher. 
+
+
+``` python
+import logging
+
+
+logger = logging.getLogger(__name__)
+
+
+class MyAwesomeStrategy(IStrategy):
+    ...
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        logger.info("This is a debug message")
+        ...
+
+```
+
+!!! Note "using print"
+    Messages printed via `print()` will not be shown in the hyperopt output unless parallelism is disabled (`-j 1`). 
+    It is recommended to use the `logging` module instead.
+
+## Validate backtesting results
+
+Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
+
+To achieve same the results (number of trades, their durations, profit, etc.) as during Hyperopt, please use the same configuration and parameters (timerange, timeframe, ...) used for hyperopt for Backtesting.
+
+### Why do my backtest results not match my hyperopt results?
+
+Should results not match, check the following factors:
+
+* You may have added parameters to hyperopt in `populate_indicators()` where they will be calculated only once **for all epochs**. If you are, for example, trying to optimise multiple SMA timeperiod values, the hyperoptable timeperiod parameter should be placed in `populate_entry_trend()` which is calculated every epoch. See [Optimizing an indicator parameter](https://www.freqtrade.io/en/stable/hyperopt/#optimizing-an-indicator-parameter).
+* If you have disabled the auto-export of hyperopt parameters into the JSON parameters file, double-check to make sure you transferred all hyperopted values into your strategy correctly.
+* Check the logs to verify what parameters are being set and what values are being used.
+* Pay special care to the stoploss, max_open_trades and trailing stoploss parameters, as these are often set in configuration files, which override changes to the strategy. Check the logs of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss`, `max_open_trades` or `trailing_stop`).
+* Verify that you do not have an unexpected parameters JSON file overriding the parameters or the default hyperopt settings in your strategy.
+* Verify that any protections that are enabled in backtesting are also enabled when hyperopting, and vice versa. When using `--space protection`, protections are auto-enabled for hyperopting.
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+## CORS
+
+This whole section is only necessary in cross-origin cases (where you multiple bot API's running on `localhost:8081`, `localhost:8082`, ...), and want to combine them into one FreqUI instance.
+
+??? info "Technical explanation"
+    All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
+    Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
+    Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
+
+Users can allow access from different origin URL's to the bot API via the `CORS_origins` configuration setting.
+It consists of a list of allowed URL's that are allowed to consume resources from the bot's API.
+
+Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - this would mean that the following configuration becomes necessary:
+
+```jsonc
+{
+    //...
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": ["https://frequi.freqtrade.io"],
+    //...
+}
+```
+
+In the following (pretty common) case, FreqUI is accessible on `http://localhost:8080/trade` (this is what you see in your navbar when navigating to freqUI).
+![freqUI url](assets/frequi_url.png)
+
+The correct configuration for this case is `http://localhost:8080` - the main part of the URL including the port.
+
+```jsonc
+{
+    //...
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": ["http://localhost:8080"],
+    //...
+}
+```
+
+!!! Tip "trailing Slash"
+    The trailing slash is not allowed in the `CORS_origins` configuration (e.g. `"http://localhots:8080/"`).
+    Such a configuration will not take effect, and the cors errors will remain.
+
+!!! Note
+    We strongly recommend to also set `jwt_secret_key` to something random and known only to yourself to avoid unauthorized access to your bot.
diff --git a/docs/includes/pairlists.md b/docs/includes/pairlists.md
new file mode 100644
index 000000000..32b97605c
--- /dev/null
+++ b/docs/includes/pairlists.md
@@ -0,0 +1,634 @@
+## Pairlists and Pairlist Handlers
+
+Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
+
+In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) and [`PercentChangePairList`](#percent-change-pair-list) Pairlist Handlers).
+
+Additionally, [`AgeFilter`](#agefilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter), [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
+
+If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You can define either `StaticPairList`, `VolumePairList`, `ProducerPairList`, `RemotePairList`, `MarketCapPairList` or `PercentChangePairList` as the starting Pairlist Handler.
+
+Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
+
+### Pair blacklist
+
+The pair blacklist (configured via `exchange.pair_blacklist` in the configuration) disallows certain pairs from trading.
+This can be as simple as excluding `DOGE/BTC` - which will remove exactly this pair.
+
+The pair-blacklist does also support wildcards (in regex-style) - so `BNB/.*` will exclude ALL pairs that start with BNB.
+You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged tokens (check Pair naming conventions for your exchange!)
+
+### Available Pairlist Handlers
+
+* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
+* [`VolumePairList`](#volume-pair-list)
+* [`PercentChangePairList`](#percent-change-pair-list)
+* [`ProducerPairList`](#producerpairlist)
+* [`RemotePairList`](#remotepairlist)
+* [`MarketCapPairList`](#marketcappairlist)
+* [`AgeFilter`](#agefilter)
+* [`FullTradesFilter`](#fulltradesfilter)
+* [`OffsetFilter`](#offsetfilter)
+* [`PerformanceFilter`](#performancefilter)
+* [`PrecisionFilter`](#precisionfilter)
+* [`PriceFilter`](#pricefilter)
+* [`ShuffleFilter`](#shufflefilter)
+* [`SpreadFilter`](#spreadfilter)
+* [`RangeStabilityFilter`](#rangestabilityfilter)
+* [`VolatilityFilter`](#volatilityfilter)
+
+!!! Tip "Testing pairlists"
+    Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility sub-command to test your configuration quickly.
+
+#### Static Pair List
+
+By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration. The pairlist also supports wildcards (in regex-style) - so `.*/BTC` will include all pairs with BTC as a stake.
+
+It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`, which in the below example, will trade BTC/USDT and ETH/USDT - and will prevent BNB/USDT trading.
+
+Both `pair_*list` parameters support regex - so values like  `.*/USDT` would enable trading all pairs that are not in the blacklist.
+
+```json
+"exchange": {
+    "name": "...",
+    // ... 
+    "pair_whitelist": [
+        "BTC/USDT",
+        "ETH/USDT",
+        // ...
+    ],
+    "pair_blacklist": [
+        "BNB/USDT",
+        // ...
+    ]
+},
+"pairlists": [
+    {"method": "StaticPairList"}
+],
+```
+
+By default, only currently enabled pairs are allowed.
+To skip pair validation against active markets, set `"allow_inactive": true` within the `StaticPairList` configuration.
+This can be useful for backtesting expired pairs (like quarterly spot-markets).
+
+When used in a "follow-up" position (e.g. after VolumePairlist), all pairs in `'pair_whitelist'` will be added to the end of the pairlist.
+
+#### Volume Pair List
+
+`VolumePairList` employs sorting/filtering of pairs by their trading volume. It selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
+
+When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
+
+When used in the leading position of the chain of Pairlist Handlers, the `pair_whitelist` configuration setting is ignored. Instead, `VolumePairList` selects the top assets from all available markets with matching stake-currency on the exchange.
+
+The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
+The pairlist cache (`refresh_period`) on `VolumePairList` is only applicable to generating pairlists.
+Filtering instances (not the first position in the list) will not apply any cache (beyond caching candles for the duration of the candle in advanced mode) and will always use up-to-date data.
+
+`VolumePairList` is per default based on the ticker data from exchange, as reported by the ccxt library:
+
+* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
+
+```json
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+        "min_value": 0,
+        "max_value": 8000000,
+        "refresh_period": 1800
+    }
+],
+```
+
+You can define a minimum volume with `min_value` - which will filter out pairs with a volume lower than the specified value in the specified timerange.
+In addition to that, you can also define a maximum volume with `max_value` - which will filter out pairs with a volume higher than the specified value in the specified timerange.
+
+##### VolumePairList Advanced mode
+
+`VolumePairList` can also operate in an advanced mode to build volume over a given timerange of specified candle size. It utilizes exchange historical candle data, builds a typical price (calculated by (open+high+low)/3) and multiplies the typical price with every candle's volume. The sum is the `quoteVolume` over the given range. This allows different scenarios, for a  more smoothened volume, when using longer ranges with larger candle sizes, or the opposite when using a short range with small candles.
+
+For convenience `lookback_days` can be specified, which will imply that 1d candles will be used for the lookback. In the example below the pairlist would be created based on the last 7 days:
+
+```json
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+        "min_value": 0,
+        "refresh_period": 86400,
+        "lookback_days": 7
+    }
+],
+```
+
+!!! Warning "Range look back and refresh period"
+    When used in conjunction with `lookback_days` and `lookback_timeframe` the `refresh_period` can not be smaller than the candle size in seconds. As this will result in unnecessary requests to the exchanges API.
+
+!!! Warning "Performance implications when using lookback range"
+    If used in first position in combination with lookback, the computation of the range based volume can be time and resource consuming, as it downloads candles for all tradable pairs. Hence it's highly advised to use the standard approach with `VolumeFilter` to narrow the pairlist down for further range volume calculation.
+
+??? Tip "Unsupported exchanges"
+    On some exchanges (like Gemini), regular VolumePairList does not work as the api does not natively provide 24h volume. This can be worked around by using candle data to build the volume.
+    To roughly simulate 24h volume, you can use the following configuration.
+    Please note that These pairlists will only refresh once per day.
+
+    ```json
+    "pairlists": [
+        {
+            "method": "VolumePairList",
+            "number_assets": 20,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 86400,
+            "lookback_days": 1
+        }
+    ],
+    ```
+
+More sophisticated approach can be used, by using `lookback_timeframe` for candle size and `lookback_period` which specifies the amount of candles. This example will build the volume pairs based on a rolling period of 3 days of 1h candles:
+
+```json
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+        "min_value": 0,
+        "refresh_period": 3600,
+        "lookback_timeframe": "1h",
+        "lookback_period": 72
+    }
+],
+```
+
+!!! Note
+    `VolumePairList` does not support backtesting mode.
+
+#### Percent Change Pair List
+
+`PercentChangePairList` filters and sorts pairs based on the percentage change in their price over the last 24 hours or any defined timeframe as part of advanced options. This allows traders to focus on assets that have experienced significant price movements, either positive or negative.
+
+**Configuration Options**
+
+* `number_assets`: Specifies the number of top pairs to select based on the 24-hour percentage change.
+* `min_value`: Sets a minimum percentage change threshold. Pairs with a percentage change below this value will be filtered out.
+* `max_value`: Sets a maximum percentage change threshold. Pairs with a percentage change above this value will be filtered out.
+* `sort_direction`: Specifies the order in which pairs are sorted based on their percentage change. Accepts two values: `asc` for ascending order and `desc` for descending order.
+* `refresh_period`: Defines the interval (in seconds) at which the pairlist will be refreshed. The default is 1800 seconds (30 minutes).
+* `lookback_days`: Number of days to look back. When `lookback_days` is selected, the `lookback_timeframe` is defaulted to 1 day.
+* `lookback_timeframe`: Timeframe to use for the lookback period.
+* `lookback_period`: Number of periods to look back at. 
+
+When PercentChangePairList is used after other Pairlist Handlers, it will operate on the outputs of those handlers. If it is the leading Pairlist Handler, it will select pairs from all available markets with the specified stake currency.
+
+`PercentChangePairList` uses ticker data from the exchange, provided via the ccxt library:
+The percentage change is calculated as the change in price over the last 24 hours.
+
+??? Note "Unsupported exchanges"
+    On some exchanges (like HTX), regular PercentChangePairList does not work as the api does not natively provide 24h percent change in price. This can be worked around by using candle data to calculate the percentage change. To roughly simulate 24h percent change, you can use the following configuration. Please note that these pairlists will only refresh once per day.
+    ```json
+    "pairlists": [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 20,
+            "min_value": 0,
+            "refresh_period": 86400,
+            "lookback_days": 1
+        }
+    ],
+    ```
+
+**Example Configuration to Read from Ticker**
+
+```json
+"pairlists": [
+    {
+        "method": "PercentChangePairList",
+        "number_assets": 15,
+        "min_value": -10,
+        "max_value": 50
+    }
+],
+```
+
+In this configuration:
+
+1. The top 15 pairs are selected based on the highest percentage change in price over the last 24 hours.
+2. Only pairs with a percentage change between -10% and 50% are considered.
+
+**Example Configuration to Read from Candles**
+
+```json
+"pairlists": [
+    {
+        "method": "PercentChangePairList",
+        "number_assets": 15,
+        "sort_key": "percentage",
+        "min_value": 0,
+        "refresh_period": 3600,
+        "lookback_timeframe": "1h",
+        "lookback_period": 72
+    }
+],
+```
+
+This example builds the percent change pairs based on a rolling period of 3 days of 1-hour candles by using `lookback_timeframe` for candle size and `lookback_period` which specifies the number of candles.
+
+The percent change in price is calculated using the following formula, which expresses the percentage difference between the current candle's close price and the previous candle's close price, as defined by the specified timeframe and lookback period:
+
+$$ Percent Change = (\frac{Current Close - Previous Close}{Previous Close}) * 100 $$
+
+!!! Warning "Range look back and refresh period"
+    When used in conjunction with `lookback_days` and `lookback_timeframe` the `refresh_period` can not be smaller than the candle size in seconds. As this will result in unnecessary requests to the exchanges API.
+
+!!! Warning "Performance implications when using lookback range"
+    If used in first position in combination with lookback, the computation of the range-based percent change can be time and resource consuming, as it downloads candles for all tradable pairs. Hence it's highly advised to use the standard approach with `PercentChangePairList` to narrow the pairlist down for further percent-change calculation.
+
+!!! Note "Backtesting"
+    `PercentChangePairList` does not support backtesting mode.
+
+#### ProducerPairList
+
+With `ProducerPairList`, you can reuse the pairlist from a [Producer](producer-consumer.md) without explicitly defining the pairlist on each consumer.
+
+[Consumer mode](producer-consumer.md) is required for this pairlist to work.
+
+The pairlist will perform a check on active pairs against the current exchange configuration to avoid attempting to trade on invalid markets.
+
+You can limit the length of the pairlist with the optional parameter `number_assets`. Using `"number_assets"=0` or omitting this key will result in the reuse of all producer pairs valid for the current setup.
+
+```json
+"pairlists": [
+    {
+        "method": "ProducerPairList",
+        "number_assets": 5,
+        "producer_name": "default",
+    }
+],
+```
+
+
+!!! Tip "Combining pairlists"
+    This pairlist can be combined with all other pairlists and filters for further pairlist reduction, and can also act as an "additional" pairlist, on top of already defined pairs.
+    `ProducerPairList` can also be used multiple times in sequence, combining the pairs from multiple producers.
+    Obviously in complex such configurations, the Producer may not provide data for all pairs, so the strategy must be fit for this.
+
+#### RemotePairList
+
+It allows the user to fetch a pairlist from a remote server or a locally stored json file within the freqtrade directory, enabling dynamic updates and customization of the trading pairlist.
+
+The RemotePairList is defined in the pairlists section of the configuration settings. It uses the following configuration options:
+
+```json
+"pairlists": [
+    {
+        "method": "RemotePairList",
+        "mode": "whitelist",
+        "processing_mode": "filter",
+        "pairlist_url": "https://example.com/pairlist",
+        "number_assets": 10,
+        "refresh_period": 1800,
+        "keep_pairlist_on_failure": true,
+        "read_timeout": 60,
+        "bearer_token": "my-bearer-token",
+        "save_to_file": "user_data/filename.json" 
+    }
+]
+```
+
+The optional `mode` option specifies if the pairlist should be used as a `blacklist` or as a `whitelist`. The default value is "whitelist".
+
+The optional `processing_mode` option in the RemotePairList configuration determines how the retrieved pairlist is processed. It can have two values: "filter" or "append". The default value is "filter".
+
+In "filter" mode, the retrieved pairlist is used as a filter. Only the pairs present in both the original pairlist and the retrieved pairlist are included in the final pairlist. Other pairs are filtered out.
+
+In "append" mode, the retrieved pairlist is added to the original pairlist. All pairs from both lists are included in the final pairlist without any filtering.
+
+The `pairlist_url` option specifies the URL of the remote server where the pairlist is located, or the path to a local file (if file:/// is prepended). This allows the user to use either a remote server or a local file as the source for the pairlist.
+
+The `save_to_file` option, when provided with a valid filename, saves the processed pairlist to that file in JSON format. This option is optional, and by default, the pairlist is not saved to a file.
+
+??? Example "Multi bot with shared pairlist example"
+    
+    `save_to_file` can be used to save the pairlist to a file with Bot1:
+
+    ```json
+    "pairlists": [
+        {
+            "method": "RemotePairList",
+            "mode": "whitelist",
+            "pairlist_url": "https://example.com/pairlist",
+            "number_assets": 10,
+            "refresh_period": 1800,
+            "keep_pairlist_on_failure": true,
+            "read_timeout": 60,
+            "save_to_file": "user_data/filename.json" 
+        }
+    ]
+    ```
+
+    This saved pairlist file can be loaded by Bot2, or any additional bot with this configuration:
+
+    ```json
+    "pairlists": [
+        {
+            "method": "RemotePairList",
+            "mode": "whitelist",
+            "pairlist_url": "file:///user_data/filename.json",
+            "number_assets": 10,
+            "refresh_period": 10,
+            "keep_pairlist_on_failure": true,
+        }
+    ]
+    ```    
+
+The user is responsible for providing a server or local file that returns a JSON object with the following structure:
+
+```json
+{
+    "pairs": ["XRP/USDT", "ETH/USDT", "LTC/USDT"],
+    "refresh_period": 1800
+}
+```
+
+The `pairs` property should contain a list of strings with the trading pairs to be used by the bot. The `refresh_period` property is optional and specifies the number of seconds that the pairlist should be cached before being refreshed.
+
+The optional `keep_pairlist_on_failure` specifies whether the previous received pairlist should be used if the remote server is not reachable or returns an error. The default value is true.
+
+The optional `read_timeout` specifies the maximum amount of time (in seconds) to wait for a response from the remote source, The default value is 60.
+
+The optional `bearer_token` will be included in the requests Authorization Header.
+
+!!! Note
+    In case of a server error the last received pairlist will be kept if `keep_pairlist_on_failure` is set to true, when set to false a empty pairlist is returned.
+
+#### MarketCapPairList
+
+`MarketCapPairList` employs sorting/filtering of pairs by their marketcap rank based of CoinGecko. The returned pairlist will be sorted based of their marketcap ranks.
+
+```json
+"pairlists": [
+    {
+        "method": "MarketCapPairList",
+        "number_assets": 20,
+        "max_rank": 50,
+        "refresh_period": 86400,
+        "categories": ["layer-1"]
+    }
+]
+```
+
+`number_assets` defines the maximum number of pairs returned by the pairlist. `max_rank` will determine the maximum rank used in creating/filtering the pairlist. It's expected that some coins within the top `max_rank` marketcap will not be included in the resulting pairlist since not all pairs will have active trading pairs in your preferred market/stake/exchange combination.  
+While using a `max_rank` bigger than 250 is supported, it's not recommended, as it'll cause multiple API calls to CoinGecko, which can lead to rate limit issues.
+
+The `refresh_period` setting defines the interval (in seconds) at which the marketcap rank data will be refreshed. The default is 86,400 seconds (1 day). The pairlist cache (`refresh_period`) applies to both generating pairlists (when in the first position in the list) and filtering instances (when not in the first position in the list).
+
+The `categories` setting specifies the [coingecko categories](https://www.coingecko.com/en/categories) from which to select coins from. The default is an empty list `[]`, meaning no category filtering is applied.
+If an incorrect category string is chosen, the plugin will print the available categories from CoinGecko and fail. The category should be the ID of the category, for example, for `https://www.coingecko.com/en/categories/layer-1`, the category ID would be `layer-1`. You can pass multiple categories such as `["layer-1", "meme-token"]` to select from several categories.
+
+!!! Warning "Many categories"
+    Each added category corresponds to one API call to CoinGecko. The more categories you add, the longer the pairlist generation will take, potentially causing rate limit issues.
+
+!!! Danger "Duplicate symbols in coingecko"
+    Coingecko often has duplicate symbols, where the same symbol is used for different coins. Freqtrade will use the symbol as is and try to search for it on the exchange. If the symbol exists - it will be used. Freqtrade will however not check if the _intended_ symbol is the one coingecko meant. This can sometimes lead to unexpected results, especially on low volume coins or with meme coin categories.
+
+#### AgeFilter
+
+Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`) or more than `max_days_listed` days (defaults `None` mean infinity).
+
+When pairs are first listed on an exchange they can suffer huge price drops and volatility
+in the first few days while the pair goes through its price-discovery period. Bots can often
+be caught out buying before the pair has finished dropping in price.
+
+This filter allows freqtrade to ignore pairs until they have been listed for at least `min_days_listed` days and listed before `max_days_listed`.
+
+#### FullTradesFilter
+
+Shrink whitelist to consist only in-trade pairs when the trade slots are full (when `max_open_trades` isn't being set to `-1` in the config).
+
+When the trade slots are full, there is no need to calculate indicators of the rest of the pairs (except informative pairs) since no new trade can be opened. By shrinking the whitelist to just the in-trade pairs, you can improve calculation speeds and reduce CPU usage. When a trade slot is free (either a trade is closed or `max_open_trades` value in config is increased), then the whitelist will return to normal state.
+
+When multiple pairlist filters are being used, it's recommended to put this filter at second position directly below the primary pairlist, so when the trade slots are full, the bot doesn't have to download data for the rest of the filters.
+
+!!! Warning "Backtesting"
+    `FullTradesFilter` does not support backtesting mode.
+
+#### OffsetFilter
+
+Offsets an incoming pairlist by a given `offset` value.
+
+As an example it can be used in conjunction with `VolumeFilter` to remove the top X volume pairs. Or to split a larger pairlist on two bot instances.
+
+Example to remove the first 10 pairs from the pairlist, and takes the next 20 (taking items 10-30 of the initial list):
+
+```json
+"pairlists": [
+    // ...
+    {
+        "method": "OffsetFilter",
+        "offset": 10,
+        "number_assets": 20
+    }
+],
+```
+
+!!! Warning
+    When `OffsetFilter` is used to split a larger pairlist among multiple bots in combination with `VolumeFilter` 
+    it can not be guaranteed that pairs won't overlap due to slightly different refresh intervals for the
+    `VolumeFilter`.
+
+!!! Note
+    An offset larger than the total length of the incoming pairlist will result in an empty pairlist.
+
+#### PerformanceFilter
+
+Sorts pairs by past trade performance, as follows:
+
+1. Positive performance.
+2. No closed trades yet.
+3. Negative performance.
+
+Trade count is used as a tie breaker.
+
+You can use the `minutes` parameter to only consider performance of the past X minutes (rolling window).
+Not defining this parameter (or setting it to 0) will use all-time performance.
+
+The optional `min_profit` (as ratio -> a setting of `0.01` corresponds to 1%) parameter defines the minimum profit a pair must have to be considered.
+Pairs below this level will be filtered out.
+Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without a way to recover.
+
+```json
+"pairlists": [
+    // ...
+    {
+        "method": "PerformanceFilter",
+        "minutes": 1440,  // rolling 24h
+        "min_profit": 0.01  // minimal profit 1%
+    }
+],
+```
+
+As this Filter uses past performance of the bot, it'll have some startup-period - and should only be used after the bot has a few 100 trades in the database.
+
+!!! Warning "Backtesting"
+    `PerformanceFilter` does not support backtesting mode.
+
+#### PrecisionFilter
+
+Filters low-value coins which would not allow setting stoplosses.
+
+Namely, pairs are blacklisted if a variance of one percent or more in the stop price would be caused by precision rounding on the exchange, i.e. `rounded(stop_price) <= rounded(stop_price * 0.99)`. The idea is to avoid coins with a value VERY close to their lower trading boundary, not allowing setting of proper stoploss.
+
+!!! Tip "PrecisionFilter is pointless for futures trading"
+    The above does not apply to shorts. And for longs, in theory the trade will be liquidated first.
+
+!!! Warning "Backtesting"
+    `PrecisionFilter` does not support backtesting mode using multiple strategies.
+
+#### PriceFilter
+
+The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
+
+* `min_price`
+* `max_price`
+* `max_value`
+* `low_price_ratio`
+
+The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs.
+This option is disabled by default, and will only apply if set to > 0.
+
+The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs.
+This option is disabled by default, and will only apply if set to > 0.
+
+The `max_value` setting removes pairs where the minimum value change is above a specified value.
+This is useful when an exchange has unbalanced limits. For example, if step-size = 1 (so you can only buy 1, or 2, or 3, but not 1.1 Coins) - and the price is pretty high (like 20\$) as the coin has risen sharply since the last limit adaption.
+As a result of the above, you can only buy for 20\$, or 40\$ - but not for 25\$.
+On exchanges that deduct fees from the receiving currency (e.g. binance) - this can result in high value coins / amounts that are unsellable as the amount is slightly below the limit.
+
+The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
+This option is disabled by default, and will only apply if set to > 0.
+
+For `PriceFilter` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
+
+Calculation example:
+
+Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 - one price step above would be 0.00000012, which is ~9% higher than the previous price value. You may filter out this pair by using PriceFilter with `low_price_ratio` set to 0.09 (9%) or with `min_price` set to 0.00000011, correspondingly.
+
+!!! Warning "Low priced pairs"
+    Low priced pairs with high "1 pip movements" are dangerous since they are often illiquid and it may also be impossible to place the desired stoploss, which can often result in high losses since price needs to be rounded to the next tradable price - so instead of having a stoploss of -5%, you could end up with a stoploss of -9% simply due to price rounding.
+
+#### ShuffleFilter
+
+Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
+
+By default, ShuffleFilter will shuffle pairs once per candle.
+To shuffle on every iteration, set `"shuffle_frequency"` to `"iteration"` instead of  the default of `"candle"`.
+
+``` json
+    {
+        "method": "ShuffleFilter", 
+        "shuffle_frequency": "candle",
+        "seed": 42
+    }
+
+```
+
+!!! Tip
+    You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order. ShuffleFilter will automatically detect runmodes and apply the `seed` only for backtesting modes - if a `seed` value is set.
+
+#### SpreadFilter
+
+Removes pairs that have a difference between asks and bids above the specified ratio, `max_spread_ratio` (defaults to `0.005`).
+
+Example:
+
+If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027, the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` and this pair will be filtered out.
+
+#### RangeStabilityFilter
+
+Removes pairs where the difference between lowest low and highest high over `lookback_days` days is below `min_rate_of_change` or above `max_rate_of_change`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
+
+In the below example:
+If the trading range over the last 10 days is <1% or >99%, remove the pair from the whitelist.
+
+```json
+"pairlists": [
+    {
+        "method": "RangeStabilityFilter",
+        "lookback_days": 10,
+        "min_rate_of_change": 0.01,
+        "max_rate_of_change": 0.99,
+        "refresh_period": 86400
+    }
+]
+```
+
+Adding `"sort_direction": "asc"` or `"sort_direction": "desc"` enables sorting for this pairlist.
+
+!!! Tip
+    This Filter can be used to automatically remove stable coin pairs, which have a very low trading range, and are therefore extremely difficult to trade with profit.
+    Additionally, it can also be used to automatically remove pairs with extreme high/low variance over a given amount of time.
+
+#### VolatilityFilter
+
+Volatility is the degree of historical variation of a pairs over time, it is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
+
+This filter removes pairs if the average volatility over a `lookback_days` days is below `min_volatility` or above `max_volatility`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
+
+This filter can be used to narrow down your pairs to a certain volatility or avoid very volatile pairs.
+
+In the below example:
+If the volatility over the last 10 days is not in the range of 0.05-0.50, remove the pair from the whitelist. The filter is applied every 24h.
+
+```json
+"pairlists": [
+    {
+        "method": "VolatilityFilter",
+        "lookback_days": 10,
+        "min_volatility": 0.05,
+        "max_volatility": 0.50,
+        "refresh_period": 86400
+    }
+]
+```
+
+Adding `"sort_direction": "asc"` or `"sort_direction": "desc"` enables sorting mode for this pairlist.
+
+### Full example of Pairlist Handlers
+
+The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#pricefilter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value.
+
+```json
+"exchange": {
+    "pair_whitelist": [],
+    "pair_blacklist": ["BNB/BTC"]
+},
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume"
+    },
+    {"method": "AgeFilter", "min_days_listed": 10},
+    {"method": "PrecisionFilter"},
+    {"method": "PriceFilter", "low_price_ratio": 0.01},
+    {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+    {
+        "method": "RangeStabilityFilter",
+        "lookback_days": 10,
+        "min_rate_of_change": 0.01,
+        "refresh_period": 86400
+    },
+    {
+        "method": "VolatilityFilter",
+        "lookback_days": 10,
+        "min_volatility": 0.05,
+        "max_volatility": 0.50,
+        "refresh_period": 86400
+    },
+    {"method": "ShuffleFilter", "seed": 42}
+],
+```
diff --git a/docs/includes/pricing.md b/docs/includes/pricing.md
new file mode 100644
index 000000000..ee6ec0cce
--- /dev/null
+++ b/docs/includes/pricing.md
@@ -0,0 +1,146 @@
+## Prices used for orders
+
+Prices for regular orders can be controlled via the parameter structures `entry_pricing` for trade entries and `exit_pricing` for trade exits.
+Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
+
+!!! Note
+    Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
+
+!!! Warning "Using market orders"
+    Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
+
+### Entry price
+
+#### Enter price side
+
+The configuration setting `entry_pricing.price_side` defines the side of the orderbook the bot looks for when buying.
+
+The following displays an orderbook.
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `entry_pricing.price_side` is set to `"bid"`, then the bot will use 99 as entry price.  
+In line with that, if `entry_pricing.price_side` is set to `"ask"`, then the bot will use 101 as entry price.
+
+Depending on the order direction (_long_/_short_), this will lead to different results. Therefore we recommend to use `"same"` or `"other"` for this configuration instead.
+This would result in the following pricing matrix:
+
+| direction | Order | setting | price | crosses spread |
+|------ |--------|-----|-----|-----|
+| long  | buy  | ask   | 101 | yes |
+| long  | buy  | bid   | 99  | no  |
+| long  | buy  | same  | 99  | no  |
+| long  | buy  | other | 101 | yes |
+| short | sell | ask   | 101 | no  |
+| short | sell | bid   | 99  | yes |
+| short | sell | same  | 101 | no  |
+| short | sell | other | 99  | yes |
+
+Using the other side of the orderbook often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
+Taker fees instead of maker fees will most likely apply even when using limit buy orders.
+Also, prices at the "other" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
+
+#### Entry price with Orderbook enabled
+
+When entering a trade with the orderbook enabled (`entry_pricing.use_order_book=True`), Freqtrade fetches the `entry_pricing.order_book_top` entries from the orderbook and uses the entry specified as `entry_pricing.order_book_top` on the configured side (`entry_pricing.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+
+#### Entry price without Orderbook enabled
+
+The following section uses `side` as the configured `entry_pricing.price_side` (defaults to `"same"`).
+
+When not using orderbook (`entry_pricing.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price based on `entry_pricing.price_last_balance`.
+
+The `entry_pricing.price_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
+
+#### Check depth of market
+
+When check depth of market is enabled (`entry_pricing.check_depth_of_market.enabled=True`), the entry signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
+
+Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `entry_pricing.check_depth_of_market.bids_to_ask_delta` parameter. The entry order is only executed if the orderbook delta is greater than or equal to the configured delta value.
+
+!!! Note
+    A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
+
+### Exit price
+
+#### Exit price side
+
+The configuration setting `exit_pricing.price_side` defines the side of the spread the bot looks for when exiting a trade.
+
+The following displays an orderbook:
+
+``` explanation
+...
+103
+102
+101  # ask
+-------------Current spread
+99   # bid
+98
+97
+...
+```
+
+If `exit_pricing.price_side` is set to `"ask"`, then the bot will use 101 as exiting price.  
+In line with that, if `exit_pricing.price_side` is set to `"bid"`, then the bot will use 99 as exiting price.
+
+Depending on the order direction (_long_/_short_), this will lead to different results. Therefore we recommend to use `"same"` or `"other"` for this configuration instead.
+This would result in the following pricing matrix:
+
+| Direction | Order | setting | price | crosses spread |
+|------ |--------|-----|-----|-----|
+| long  | sell | ask   | 101 | no  |
+| long  | sell | bid   | 99  | yes |
+| long  | sell | same  | 101 | no  |
+| long  | sell | other | 99  | yes |
+| short | buy  | ask   | 101 | yes |
+| short | buy  | bid   | 99  | no  |
+| short | buy  | same  | 99  | no  |
+| short | buy  | other | 101 | yes |
+
+#### Exit price with Orderbook enabled
+
+When exiting with the orderbook enabled (`exit_pricing.use_order_book=True`), Freqtrade fetches the `exit_pricing.order_book_top` entries in the orderbook and uses the entry specified as `exit_pricing.order_book_top` from the configured side (`exit_pricing.price_side`) as trade exit price.
+
+1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
+
+#### Exit price without Orderbook enabled
+
+The following section uses `side` as the configured `exit_pricing.price_side` (defaults to `"ask"`).
+
+When not using orderbook (`exit_pricing.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's above the `last` traded price from the ticker. Otherwise (when the `side` price is below the `last` price), it calculates a rate between `side` and `last` price based on `exit_pricing.price_last_balance`.
+
+The `exit_pricing.price_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price.
+
+### Market order pricing
+
+When using market orders, prices should be configured to use the "correct" side of the orderbook to allow realistic pricing detection.
+Assuming both entry and exits are using market orders, a configuration similar to the following must be used
+
+``` jsonc
+  "order_types": {
+    "entry": "market",
+    "exit": "market"
+    // ...
+  },
+  "entry_pricing": {
+    "price_side": "other",
+    // ...
+  },
+  "exit_pricing":{
+    "price_side": "other",
+    // ...
+  },
+```
+
+Obviously, if only one side is using limit orders, different pricing combinations can be used.
diff --git a/docs/includes/protections.md b/docs/includes/protections.md
new file mode 100644
index 000000000..c32846165
--- /dev/null
+++ b/docs/includes/protections.md
@@ -0,0 +1,191 @@
+## Protections
+
+Protections will protect your strategy from unexpected events and market conditions by temporarily stop trading for either one pair, or for all pairs.
+All protection end times are rounded up to the next candle to avoid sudden, unexpected intra-candle buys.
+
+!!! Tip "Usage tips"
+    Not all Protections will work for all strategies, and parameters will need to be tuned for your strategy to improve performance.  
+
+    Each Protection can be configured multiple times with different parameters, to allow different levels of protection (short-term / long-term).
+
+!!! Note "Backtesting"
+    Protections are supported by backtesting and hyperopt, but must be explicitly enabled by using the `--enable-protections` flag.
+
+### Available Protections
+
+* [`StoplossGuard`](#stoploss-guard) Stop trading if a certain amount of stoploss occurred within a certain time window.
+* [`MaxDrawdown`](#maxdrawdown) Stop trading if max-drawdown is reached.
+* [`LowProfitPairs`](#low-profit-pairs) Lock pairs with low profits
+* [`CooldownPeriod`](#cooldown-period) Don't enter a trade right after selling a trade.
+
+### Common settings to all Protections
+
+|  Parameter| Description |
+|------------|-------------|
+| `method` | Protection name to use. <br> **Datatype:** String, selected from [available Protections](#available-protections)
+| `stop_duration_candles` | For how many candles should the lock be set? <br> **Datatype:** Positive integer (in candles)
+| `stop_duration` | how many minutes should protections be locked. <br>Cannot be used together with `stop_duration_candles`. <br> **Datatype:** Float (in minutes)
+| `lookback_period_candles` | Only trades that completed within the last `lookback_period_candles` candles will be considered. This setting may be ignored by some Protections. <br> **Datatype:** Positive integer (in candles).
+| `lookback_period` | Only trades that completed after `current_time - lookback_period` will be considered. <br>Cannot be used together with `lookback_period_candles`. <br>This setting may be ignored by some Protections. <br> **Datatype:**  Float (in minutes)
+| `trade_limit` | Number of trades required at minimum (not used by all Protections). <br> **Datatype:** Positive integer
+| `unlock_at` | Time when trading will be unlocked regularly (not used by all Protections). <br> **Datatype:** string <br>**Input Format:** "HH:MM" (24-hours)
+
+!!! Note "Durations"
+    Durations (`stop_duration*` and `lookback_period*` can be defined in either minutes or candles).
+    For more flexibility when testing different timeframes, all below examples will use the "candle" definition.
+
+#### Stoploss Guard
+
+`StoplossGuard` selects all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`).
+If `trade_limit` or more trades resulted in stoploss, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`, or until the set time when using `unlock_at`).
+
+This applies across all pairs, unless `only_per_pair` is set to true, which will then only look at one pair at a time.
+
+Similarly, this protection will by default look at all trades (long and short). For futures bots, setting `only_per_side` will make the bot only consider one side, and will then only lock this one side, allowing for example shorts to continue after a series of long stoplosses.
+
+`required_profit` will determine the required relative profit (or loss) for stoplosses to consider. This should normally not be set and defaults to 0.0 - which means all losing stoplosses will be triggering a block.
+
+The below example stops trading for all pairs for 4 candles after the last trade if the bot hit stoploss 4 times within the last 24 candles.
+
+``` python
+@property
+def protections(self):
+    return [
+        {
+            "method": "StoplossGuard",
+            "lookback_period_candles": 24,
+            "trade_limit": 4,
+            "stop_duration_candles": 4,
+            "required_profit": 0.0,
+            "only_per_pair": False,
+            "only_per_side": False
+        }
+    ]
+```
+
+!!! Note
+    `StoplossGuard` considers all trades with the results `"stop_loss"`, `"stoploss_on_exchange"` and `"trailing_stop_loss"` if the resulting profit was negative.
+    `trade_limit` and `lookback_period` will need to be tuned for your strategy.
+
+#### MaxDrawdown
+
+`MaxDrawdown` uses all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the maximum drawdown. If the drawdown is below `max_allowed_drawdown`, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after the last trade - assuming that the bot needs some time to let markets recover.
+
+The below sample stops trading for 12 candles if max-drawdown is > 20% considering all pairs - with a minimum of `trade_limit` trades - within the last 48 candles. If desired, `lookback_period` and/or `stop_duration` can be used.
+
+``` python
+@property
+def protections(self):
+    return  [
+        {
+            "method": "MaxDrawdown",
+            "lookback_period_candles": 48,
+            "trade_limit": 20,
+            "stop_duration_candles": 12,
+            "max_allowed_drawdown": 0.2
+        },
+    ]
+```
+
+#### Low Profit Pairs
+
+`LowProfitPairs` uses all trades for a pair within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the overall profit ratio.
+If that ratio is below `required_profit`, that pair will be locked for `stop_duration` in minutes (or in candles when using `stop_duration_candles`, or until the set time when using `unlock_at`).
+
+For futures bots, setting `only_per_side` will make the bot only consider one side, and will then only lock this one side, allowing for example shorts to continue after a series of long losses.
+
+The below example will stop trading a pair for 60 minutes if the pair does not have a required profit of 2% (and a minimum of 2 trades) within the last 6 candles.
+
+``` python
+@property
+def protections(self):
+    return [
+        {
+            "method": "LowProfitPairs",
+            "lookback_period_candles": 6,
+            "trade_limit": 2,
+            "stop_duration": 60,
+            "required_profit": 0.02,
+            "only_per_pair": False,
+        }
+    ]
+```
+
+#### Cooldown Period
+
+`CooldownPeriod` locks a pair for `stop_duration` in minutes (or in candles when using `stop_duration_candles`, or until the set time when using `unlock_at`) after exiting, avoiding a re-entry for this pair for `stop_duration` minutes.
+
+The below example will stop trading a pair for 2 candles after closing a trade, allowing this pair to "cool down".
+
+``` python
+@property
+def protections(self):
+    return  [
+        {
+            "method": "CooldownPeriod",
+            "stop_duration_candles": 2
+        }
+    ]
+```
+
+!!! Note
+    This Protection applies only at pair-level, and will never lock all pairs globally.
+    This Protection does not consider `lookback_period` as it only looks at the latest trade.
+
+### Full example of Protections
+
+All protections can be combined at will, also with different parameters, creating a increasing wall for under-performing pairs.
+All protections are evaluated in the sequence they are defined.
+
+The below example assumes a timeframe of 1 hour:
+
+* Locks each pair after selling for an additional 5 candles (`CooldownPeriod`), giving other pairs a chance to get filled.
+* Stops trading for 4 hours (`4 * 1h candles`) if the last 2 days (`48 * 1h candles`) had 20 trades, which caused a max-drawdown of more than 20%. (`MaxDrawdown`).
+* Stops trading if more than 4 stoploss occur for all pairs within a 1 day (`24 * 1h candles`) limit (`StoplossGuard`).
+* Locks all pairs that had 2 Trades within the last 6 hours (`6 * 1h candles`) with a combined profit ratio of below 0.02 (<2%) (`LowProfitPairs`).
+* Locks all pairs for 2 candles that had a profit of below 0.01 (<1%) within the last 24h (`24 * 1h candles`), a minimum of 4 trades.
+
+``` python
+from freqtrade.strategy import IStrategy
+
+class AwesomeStrategy(IStrategy)
+    timeframe = '1h'
+    
+    @property
+    def protections(self):
+        return [
+            {
+                "method": "CooldownPeriod",
+                "stop_duration_candles": 5
+            },
+            {
+                "method": "MaxDrawdown",
+                "lookback_period_candles": 48,
+                "trade_limit": 20,
+                "stop_duration_candles": 4,
+                "max_allowed_drawdown": 0.2
+            },
+            {
+                "method": "StoplossGuard",
+                "lookback_period_candles": 24,
+                "trade_limit": 4,
+                "stop_duration_candles": 2,
+                "only_per_pair": False
+            },
+            {
+                "method": "LowProfitPairs",
+                "lookback_period_candles": 6,
+                "trade_limit": 2,
+                "stop_duration_candles": 60,
+                "required_profit": 0.02
+            },
+            {
+                "method": "LowProfitPairs",
+                "lookback_period_candles": 24,
+                "trade_limit": 4,
+                "stop_duration_candles": 2,
+                "required_profit": 0.01
+            }
+        ]
+    # ...
+```
diff --git a/docs/includes/release_template.md b/docs/includes/release_template.md
new file mode 100644
index 000000000..87a3564da
--- /dev/null
+++ b/docs/includes/release_template.md
@@ -0,0 +1,37 @@
+## Highlighted changes
+
+- ...
+
+### How to update
+
+As always, you can update your bot using one of the following commands:
+
+#### docker-compose
+
+```bash
+docker-compose pull
+docker-compose up -d
+```
+
+#### Installation via setup script
+
+```
+# Deactivate venv and run 
+./setup.sh --update
+```
+
+#### Plain native installation
+
+```
+git pull
+pip install -U -r requirements.txt
+```
+
+<details>
+<summary>Expand full changelog</summary>
+
+```
+<Paste your changelog here>
+```
+
+</details>
diff --git a/docs/includes/showcase.md b/docs/includes/showcase.md
new file mode 100644
index 000000000..766703b9f
--- /dev/null
+++ b/docs/includes/showcase.md
@@ -0,0 +1,11 @@
+This section will highlight a few projects from members of the community.
+!!! Note
+    The projects below are for the most part not maintained by the freqtrade , therefore use your own caution before using them.
+
+- [Example freqtrade strategies](https://github.com/freqtrade/freqtrade-strategies/)
+- [FrequentHippo - Grafana dashboard with dry/live runs and backtests](http://frequenthippo.ddns.net:3000/) (by hippocritical).
+- [Online pairlist generator](https://remotepairlist.com/) (by Blood4rc).
+- [Freqtrade Backtesting Project](https://strat.ninja/) (by Blood4rc).
+- [Freqtrade analysis notebook](https://github.com/froggleston/freqtrade_analysis_notebook) (by Froggleston).
+- [TUI for freqtrade](https://github.com/froggleston/freqtrade-frogtrade9000) (by Froggleston).
+- [Bot Academy](https://botacademy.ddns.net/) (by stash86) - Blog about crypto bot projects.
diff --git a/docs/includes/strategy-imports.md b/docs/includes/strategy-imports.md
new file mode 100644
index 000000000..63cda329c
--- /dev/null
+++ b/docs/includes/strategy-imports.md
@@ -0,0 +1,45 @@
+## Imports necessary for a strategy
+
+When creating a strategy, you will need to import the necessary modules and classes. The following imports are required for a strategy:
+
+By default, we recommend the following imports as a base line for your strategy:
+This will cover all imports necessary for freqtrade functions to work.
+Obviously you can add more imports as needed for your strategy.
+
+``` python
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these imports ---
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta, timezone
+from pandas import DataFrame
+from typing import Dict, Optional, Union, Tuple
+
+from freqtrade.strategy import (
+    IStrategy,
+    Trade, 
+    Order,
+    PairLocks,
+    informative,  # @informative decorator
+    # Hyperopt Parameters
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+    # timeframe helpers
+    timeframe_to_minutes,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    # Strategy helper functions
+    merge_informative_pair,
+    stoploss_from_absolute,
+    stoploss_from_open,
+)
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+from technical import qtpylib
+```
diff --git a/docs/index.md b/docs/index.md
index ed940d132..d05fcc2c1 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,32 +1,105 @@
-# freqtrade documentation
-Welcome to freqtrade documentation. Please feel free to contribute to
-this documentation if you see it became outdated by sending us a 
-Pull-request. Do not hesitate to reach us on 
-[Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE)
- if you do not find the answer to your questions.
+![freqtrade](assets/freqtrade_poweredby.svg)
 
-## Table of Contents
-- [Pre-requisite](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md)
-	- [Setup your Bittrex account](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md#setup-your-bittrex-account)
-	- [Setup your Telegram bot](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md#setup-your-telegram-bot)
-- [Bot Installation](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md)
-    - [Install with Docker (all platforms)](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md#docker)
-    - [Install on Linux Ubuntu](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md#21-linux---ubuntu-1604)
-    - [Install on MacOS](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md#23-macos-installation)
-    - [Install on Windows](https://github.com/gcarq/freqtrade/blob/develop/docs/installation.md#windows)
-- [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)
-- [Bot usage (Start your bot)](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md)
-    - [Bot commands](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#bot-commands)
-    - [Backtesting commands](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#backtesting-commands)
-    - [Hyperopt commands](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#hyperopt-commands)
-- [Bot Optimization](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md)
-	- [Change your strategy](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md#change-your-strategy)
-    - [Add more Indicator](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md#add-more-indicator)
-    - [Test your strategy with Backtesting](https://github.com/gcarq/freqtrade/blob/develop/docs/backtesting.md)
-    - [Find optimal parameters with Hyperopt](https://github.com/gcarq/freqtrade/blob/develop/docs/hyperopt.md)
-- [Control the bot with telegram](https://github.com/gcarq/freqtrade/blob/develop/docs/telegram-usage.md)
-- [Contribute to the project](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
-	- [How to contribute](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
-	- [Run tests & Check PEP8 compliance](https://github.com/gcarq/freqtrade/blob/develop/CONTRIBUTING.md)
-- [FAQ](https://github.com/gcarq/freqtrade/blob/develop/docs/faq.md)
-    - [SQL cheatsheet](https://github.com/gcarq/freqtrade/blob/develop/docs/sql_cheatsheet.md)
\ No newline at end of file
+[![Freqtrade CI](https://github.com/freqtrade/freqtrade/actions/workflows/ci.yml/badge.svg?branch=develop)](https://github.com/freqtrade/freqtrade/actions/)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)
+[![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
+[![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
+
+<!-- Place this tag where you want the button to render. -->
+<a class="github-button" href="https://github.com/freqtrade/freqtrade" data-icon="octicon-star" data-size="large" aria-label="Star freqtrade/freqtrade on GitHub">Star</a>
+<a class="github-button" href="https://github.com/freqtrade/freqtrade/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork freqtrade/freqtrade on GitHub">Fork</a>
+<a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/stable.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
+
+## Introduction
+
+Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
+
+!!! Danger "DISCLAIMER"
+    This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
+
+    Always start by running a trading bot in Dry-run and do not engage money before you understand how it works and what profit/loss you should expect.
+
+    We strongly recommend you to have basic coding skills and Python knowledge. Do not hesitate to read the source code and understand the mechanisms of this bot, algorithms and techniques implemented in it.
+
+![freqtrade screenshot](assets/freqtrade-screenshot.png)
+
+## Features
+
+- Develop your Strategy: Write your strategy in python, using [pandas](https://pandas.pydata.org/). Example strategies to inspire you are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
+- Download market data: Download historical data of the exchange and the markets your may want to trade with.
+- Backtest: Test your strategy on downloaded historical data.
+- Optimize: Find the best parameters for your strategy using hyperoptimization which employs machine learning methods. You can optimize buy, sell, take profit (ROI), stop-loss and trailing stop-loss parameters for your strategy.
+- Select markets: Create your static list or use an automatic one based on top traded volumes and/or prices (not available during backtesting). You can also explicitly blacklist markets you don't want to trade.
+- Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
+- Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
+- Control/Monitor: Use Telegram or a WebUI (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
+- Analyze: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
+
+## Supported exchange marketplaces
+
+Please read the [exchange specific notes](exchanges.md) to learn about eventual, special configurations needed for each exchange.
+
+- [X] [Binance](https://www.binance.com/)
+- [X] [BingX](https://bingx.com/invite/0EM9RX)
+- [X] [Bitmart](https://bitmart.com/)
+- [X] [Bybit](https://bybit.com/)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [HTX](https://www.htx.com/)
+- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)
+- [X] [Kraken](https://kraken.com/)
+- [X] [OKX](https://okx.com/)
+- [X] [MyOKX](https://okx.com/) (OKX EEA)
+- [ ] [potentially many others through <img alt="ccxt" width="30px" src="assets/ccxt-logo.svg" />](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
+
+### Supported Futures Exchanges (experimental)
+
+- [X] [Binance](https://www.binance.com/)
+- [X] [Bybit](https://bybit.com/)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)
+- [X] [OKX](https://okx.com/)
+
+Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.
+
+### Community tested
+
+Exchanges confirmed working by the community:
+
+- [X] [Bitvavo](https://bitvavo.com/)
+- [X] [Kucoin](https://www.kucoin.com/)
+
+## Community showcase
+
+--8<-- "includes/showcase.md"
+
+## Requirements
+
+### Hardware requirements
+
+To run this bot we recommend you a linux cloud instance with a minimum of:
+
+- 2GB RAM
+- 1GB disk space
+- 2vCPU
+
+### Software requirements
+
+- Docker (Recommended)
+
+Alternatively
+
+- Python 3.10+
+- pip (pip3)
+- git
+- TA-Lib
+- virtualenv (Recommended)
+
+## Support
+
+### Help / Discord
+
+For any questions not covered by the documentation or for further information about the bot, or to simply engage with like-minded individuals, we encourage you to join the Freqtrade [discord server](https://discord.gg/p7nuUNVfP7).
+
+## Ready to try?
+
+Begin by reading the installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).
diff --git a/docs/installation.md b/docs/installation.md
index a7e61bbe5..30deb07ba 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -2,349 +2,435 @@
 
 This page explains how to prepare your environment for running the bot.
 
-To understand how to set up the bot please read the [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md) page.
+The freqtrade documentation describes various ways to install freqtrade
 
-## Table of Contents
+* [Docker images](docker_quickstart.md) (separate page)
+* [Script Installation](#script-installation)
+* [Manual Installation](#manual-installation)
+* [Installation with Conda](#installation-with-conda)
 
-* [Table of Contents](#table-of-contents)
-* [Easy Installation - Linux Script](#easy-installation---linux-script)
-* [Automatic Installation - Docker](#automatic-installation---docker)
-* [Custom Linux MacOS Installation](#custom-installation)
-	- [Requirements](#requirements)
-	- [Linux - Ubuntu 16.04](#linux---ubuntu-1604)
-	- [MacOS](#macos)
-	- [Setup Config and virtual env](#setup-config-and-virtual-env)
-* [Windows](#windows)
-	
-
-<!-- /TOC -->
+Please consider using the prebuilt [docker images](docker_quickstart.md) to get started quickly while evaluating how freqtrade works.
 
 ------
 
-## Easy Installation - Linux Script
+## Information
 
-If you are on Debian, Ubuntu or MacOS a freqtrade provides a script to Install, Update, Configure, and Reset your bot.
+For Windows installation, please use the [windows installation guide](windows_installation.md).
 
-```bash
-$ ./setup.sh
-usage:
-	-i,--install    Install freqtrade from scratch
-	-u,--update     Command git pull to update.
-	-r,--reset      Hard reset your develop/master branch.
-	-c,--config     Easy config generator (Will override your existing file).
-```
+The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the `./setup.sh` script, if it's available for your platform.
 
-### --install
-This script will install everything you need to run the bot:
-* Mandatory software as: `Python3`, `ta-lib`, `wget`
-* Setup your virtualenv
-* Configure your `config.json` file
+!!! Note "Version considerations"
+    When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests).
+    The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
 
-This script is a combination of `install script` `--reset`, `--config`
+!!! Note
+    Python3.10 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.  
+    Also, python headers (`python<yourversion>-dev` / `python<yourversion>-devel`) must be available for the installation to complete successfully.
 
-### --update
-Update parameter will pull the last version of your current branch and update your virtualenv.
-
-### --reset
-Reset parameter will hard reset your branch (only if you are on `master` or `develop`) and recreate your virtualenv.
-
-### --config
-Config parameter is a `config.json` configurator. This script will ask you questions to setup your bot and create your `config.json`.
+!!! Warning "Up-to-date clock"
+    The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
 
 ------
 
-## Automatic Installation - Docker
+## Requirements
 
-Start by downloading Docker for your platform:
+These requirements apply to both [Script Installation](#script-installation) and [Manual Installation](#manual-installation).
 
-* [Mac](https://www.docker.com/products/docker#/mac)
-* [Windows](https://www.docker.com/products/docker#/windows)
-* [Linux](https://www.docker.com/products/docker#/linux)
+!!! Note "ARM64 systems"
+    If you are running an ARM64 system (like a MacOS M1 or an Oracle VM), please use [docker](docker_quickstart.md) to run freqtrade.
+    While native installation is possible with some manual effort, this is not supported at the moment.
 
-Once you have Docker installed, simply create the config file (e.g. `config.json`) and then create a Docker image for `freqtrade` using the Dockerfile in this repo.
+### Install guide
+
+* [Python >= 3.10](http://docs.python-guide.org/en/latest/starting/installation/)
+* [pip](https://pip.pypa.io/en/stable/installing/)
+* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+* [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
+* [TA-Lib](https://ta-lib.github.io/ta-lib-python/) (install instructions [below](#install-ta-lib))
+
+### Install code
+
+We've included/collected install instructions for Ubuntu, MacOS, and Windows. These are guidelines and your success may vary with other distros.
+OS Specific steps are listed first, the common section below is necessary for all systems.
+
+!!! Note
+    Python3.10 or higher and the corresponding pip are assumed to be available.
+
+=== "Debian/Ubuntu"
+    #### Install necessary dependencies
+
+    ```bash
+    # update repository
+    sudo apt-get update
+
+    # install packages
+    sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git curl
+    ```
+
+=== "MacOS"
+    #### Install necessary dependencies
+
+    Install [Homebrew](https://brew.sh/) if you don't have it already.
+
+    ```bash
+    # install packages
+    brew install gettext libomp
+    ```
+    !!! Note
+        The `setup.sh` script will install these dependencies for you - assuming brew is installed on your system.
+
+=== "RaspberryPi/Raspbian"
+    The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/).
+    This image comes with python3.11 preinstalled, making it easy to get freqtrade up and running.
+
+    Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
 
 
-### 1. Prepare the Bot
+    ```bash
+    sudo apt-get install python3-venv libatlas-base-dev cmake curl
+    # Use piwheels.org to speed up installation
+    sudo echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > tee /etc/pip.conf
 
-#### 1.1. Clone the git repository
+    git clone https://github.com/freqtrade/freqtrade.git
+    cd freqtrade
+
+    bash setup.sh -i
+    ```
+
+    !!! Note "Installation duration"
+        Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
+        Due to this, we recommend to use the pre-build docker-image for Raspberry, by following the [Docker quickstart documentation](docker_quickstart.md)
+
+    !!! Note
+        The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
+        We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
+
+------
+
+## Freqtrade repository
+
+Freqtrade is an open source crypto-currency trading bot, whose code is hosted on `github.com`
 
 ```bash
-git clone https://github.com/gcarq/freqtrade.git
-```
+# Download `develop` branch of freqtrade repository
+git clone https://github.com/freqtrade/freqtrade.git
 
-#### 1.2. (Optional) Checkout the develop branch
+# Enter downloaded directory
+cd freqtrade
 
-```bash
+# your choice (1): novice user
+git checkout stable
+
+# your choice (2): advanced user
 git checkout develop
 ```
 
-#### 1.3. Go into the new directory
+(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed, if you wish to stay on the (2) `develop` branch.
 
-```bash
-cd freqtrade
-```
+You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
 
-#### 1.4. Copy `config.json.example` to `config.json`
+??? Note "Install from pypi"
+    An alternative way to install Freqtrade is from [pypi](https://pypi.org/project/freqtrade/). The downside is that this method requires ta-lib to be correctly installed beforehand, and is therefore currently not the recommended way to install Freqtrade.
 
-```bash
-cp -n config.json.example config.json
-```
-
-> To edit the config please refer to the [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md) page.
-
-#### 1.5. Create your database file *(optional - the bot will create it if it is missing)*
-
-Production
-```bash
-touch tradesv3.sqlite
-````
-
-Dry-Run
-```bash
-touch tradesv3.dryrun.sqlite
-```
-
-
-### 2. Build the Docker image
-
-```bash
-cd freqtrade
-docker build -t freqtrade .
-```
-
-For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see the "5. Run a restartable docker image" section) to keep it between  updates.
-
-
-### 3. Verify the Docker image
-
-After the build process you can verify that the image was created with:
-
-```bash
-docker images
-```
-
-
-### 4. Run the Docker image
-
-You can run a one-off container that is immediately deleted upon exiting with the following command (`config.json` must be in the current working directory):
-
-```bash
-docker run --rm -v /etc/localtime:/etc/localtime:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
-```
-
-In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.
-
-
-### 5. Run a restartable docker image
-
-To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
-
-#### 5.1. Move your config file and database
-
-```bash
-mkdir ~/.freqtrade
-mv config.json ~/.freqtrade
-mv tradesv3.sqlite ~/.freqtrade
-```
-
-#### 5.2. Run the docker image
-
-```bash
-docker run -d \
-  --name freqtrade \
-  -v /etc/localtime:/etc/localtime:ro \
-  -v ~/.freqtrade/config.json:/freqtrade/config.json \
-  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  freqtrade
-```
-
-If you are using `dry_run=True` it's not necessary to mount `tradesv3.sqlite`, but you can mount `tradesv3.dryrun.sqlite` if you plan to use the dry run mode with the param `--dry-run-db`.
-
-### 6. Monitor your Docker instance
-
-You can then use the following commands to monitor and manage your container:
-
-```bash
-docker logs freqtrade
-docker logs -f freqtrade
-docker restart freqtrade
-docker stop freqtrade
-docker start freqtrade
-```
-
-You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
+    ``` bash
+    pip install freqtrade
+    ```
 
 ------
 
-## Custom Installation
+## Script Installation
 
-We've included/collected install instructions for Ubuntu 16.04, MacOS, and Windows. These are guidelines and your success may vary with other distros.
+First of the ways to install Freqtrade, is to use provided the Linux/MacOS `./setup.sh` script, which install all dependencies and help you configure the bot.
 
-### Requirements
+Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
 
-Click each one for install guide:
-* [Python 3.6.x](http://docs.python-guide.org/en/latest/starting/installation/), note the bot was not tested on Python >= 3.7.x
-* [pip](https://pip.pypa.io/en/stable/installing/)
-* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
-* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
-* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
+### Use /setup.sh -install (Linux/MacOS)
 
-
-### Linux - Ubuntu 16.04
-
-#### 1. Install Python 3.6, Git, and wget
+If you are on Debian, Ubuntu or MacOS, freqtrade provides the script to install freqtrade.
 
 ```bash
-sudo add-apt-repository ppa:jonathonf/python-3.6
-sudo apt-get update
-sudo apt-get install python3.6 python3.6-venv python3.6-dev build-essential autoconf libtool pkg-config make wget git
+# --install, Install freqtrade from scratch
+./setup.sh -i
 ```
 
-#### 2. Install TA-Lib
+### Activate your virtual environment
 
-Official webpage: https://mrjbq7.github.io/ta-lib/install.html
+Each time you open a new terminal, you must run `source .venv/bin/activate` to activate your virtual environment.
+
+```bash
+# activate virtual environment
+source ./.venv/bin/activate
+```
+
+[You are now ready](#you-are-ready) to run the bot.
+
+### Other options of /setup.sh script
+
+You can as well update, configure and reset the codebase of your bot with `./script.sh`
+
+```bash
+# --update, Command git pull to update.
+./setup.sh -u
+# --reset, Hard reset your develop/stable branch.
+./setup.sh -r
+```
+
+```
+** --install **
+
+With this option, the script will install the bot and most dependencies:
+You will need to have git and python3.10+ installed beforehand for this to work.
+
+* Mandatory software as: `ta-lib`
+* Setup your virtualenv under `.venv/`
+
+This option is a combination of installation tasks and `--reset`
+
+** --update **
+
+This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
+
+** --reset **
+
+This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
+```
+
+-----
+
+## Manual Installation
+
+Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
+
+### Install TA-Lib
+
+#### TA-Lib script installation
+
+```bash
+sudo ./build_helpers/install_ta-lib.sh
+```
+
+!!! Note
+    This will use the ta-lib tar.gz included in this repository.
+
+##### TA-Lib manual installation
+
+[Official installation guide](https://ta-lib.github.io/ta-lib-python/install.html)
 
 ```bash
 wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
 tar xvzf ta-lib-0.4.0-src.tar.gz
 cd ta-lib
-./configure --prefix=/usr
+sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h
+./configure --prefix=/usr/local
 make
-make install
+sudo make install
+# On debian based systems (debian, ubuntu, ...) - updating ldconfig might be necessary.
+sudo ldconfig  
 cd ..
 rm -rf ./ta-lib*
 ```
 
-#### 3. [Optional] Install MongoDB
+### Setup Python virtual environment (virtualenv)
 
-Install MongoDB if you plan to optimize your strategy with Hyperopt.
+You will run freqtrade in separated `virtual environment`
 
 ```bash
-sudo apt-get install mongodb-org
+# create virtualenv in directory /freqtrade/.venv
+python3 -m venv .venv
+
+# run virtualenv
+source .venv/bin/activate
 ```
 
-> Complete tutorial from Digital Ocean: [How to Install MongoDB on Ubuntu 16.04](https://www.digitalocean.com/community/tutorials/how-to-install-mongodb-on-ubuntu-16-04).
-
-#### 4. Install FreqTrade
-
-Clone the git repository:
+### Install python dependencies
 
 ```bash
-git clone https://github.com/gcarq/freqtrade.git
+python3 -m pip install --upgrade pip
+python3 -m pip install -r requirements.txt
+# install freqtrade
+python3 -m pip install -e .
 ```
 
-Optionally checkout the develop branch:
+[You are now ready](#you-are-ready) to run the bot.
 
-```bash
-git checkout develop
-```
+### (Optional) Post-installation Tasks
 
-#### 5. Configure `freqtrade` as a `systemd` service
+!!! Note 
+    If you run the bot on a server, you should consider using [Docker](docker_quickstart.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
 
-From the freqtrade repo... copy `freqtrade.service` to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
-
-After that you can start the daemon with:
-```bash
-systemctl --user start freqtrade
-```
-
-For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
-
-```bash
-sudo loginctl enable-linger "$USER"
-```
-
-
-### MacOS
-
-#### 1. Install Python 3.6, git, wget and ta-lib
-
-```bash
-brew install python3 git wget ta-lib
-```
-
-#### 2. [Optional] Install MongoDB
-
-Install MongoDB if you plan to optimize your strategy with Hyperopt.
-
-```bash
-curl -O https://fastdl.mongodb.org/osx/mongodb-osx-ssl-x86_64-3.4.10.tgz
-tar -zxvf mongodb-osx-ssl-x86_64-3.4.10.tgz
-mkdir -p <path_freqtrade>/env/mongodb
-cp -R -n mongodb-osx-x86_64-3.4.10/ <path_freqtrade>/env/mongodb
-export PATH=<path_freqtrade>/env/mongodb/bin:$PATH
-```
-
-#### 3. Install FreqTrade
-
-Clone the git repository:
-
-```bash
-git clone https://github.com/gcarq/freqtrade.git
-```
-
-Optionally checkout the develop branch:
-
-```bash
-git checkout develop
-```
-
-
-### Setup Config and virtual env
-
-#### 1. Initialize the configuration
-
-```bash
-cd freqtrade
-cp config.json.example config.json
-```
-
-> *To edit the config please refer to [Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md).*
-
-
-#### 2. Setup your Python virtual environment (virtualenv)
-
-```bash
-python3.6 -m venv .env
-source .env/bin/activate
-pip3.6 install --upgrade pip
-pip3.6 install -r requirements.txt
-pip3.6 install -e .
-```
-
-#### 3. Run the Bot
-
-If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
-
-```bash
-python3.6 ./freqtrade/main.py -c config.json
-```
+On Linux with software suite `systemd`, as an optional post-installation task, you may wish to setup the bot to run as a `systemd service` or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
 
 ------
 
-## Windows
+## Installation with Conda
 
-We recommend that Windows users use [Docker](#docker) as this will work
-much easier and smoother (also more secure).
+Freqtrade can also be installed with Miniconda or Anaconda. We recommend using Miniconda as it's installation footprint is smaller. Conda will automatically prepare and manage the extensive library-dependencies of the Freqtrade program.
 
-### Install freqtrade
+### What is Conda?
 
-copy paste `config.json` to ``\path\freqtrade-develop\freqtrade`
+Conda is a package, dependency and environment manager for multiple programming languages: [conda docs](https://docs.conda.io/projects/conda/en/latest/index.html)
 
-```cmd
->cd \path\freqtrade-develop
->python -m venv .env
->cd .env\Scripts
->activate.bat
->cd \path\freqtrade-develop
->pip install -r requirements.txt
->pip install -e .
->cd freqtrade
->python main.py
+### Installation with conda
+
+#### Install Conda
+
+[Installing on linux](https://conda.io/projects/conda/en/latest/user-guide/install/linux.html#install-linux-silent)
+
+[Installing on windows](https://conda.io/projects/conda/en/latest/user-guide/install/windows.html)
+
+Answer all questions. After installation, it is mandatory to turn your terminal OFF and ON again.
+
+#### Freqtrade download
+
+Download and install freqtrade.
+
+```bash
+# download freqtrade
+git clone https://github.com/freqtrade/freqtrade.git
+
+# enter downloaded directory 'freqtrade'
+cd freqtrade      
 ```
 
-> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/gcarq/freqtrade/issues/222)
+#### Freqtrade install: Conda Environment
 
+```bash
+conda create --name freqtrade python=3.12
+```
 
-Now you have an environment ready, the next step is
-[Bot Configuration](https://github.com/gcarq/freqtrade/blob/develop/docs/configuration.md)...
+!!! Note "Creating Conda Environment"
+    The conda command `create -n` automatically installs all nested dependencies for the selected libraries, general structure of installation command is:
+
+    ```bash
+    # choose your own packages
+    conda env create -n [name of the environment] [python version] [packages]
+    ```
+
+#### Enter/exit freqtrade environment
+
+To check available environments, type
+
+```bash
+conda env list
+```
+
+Enter installed environment
+
+```bash
+# enter conda environment
+conda activate freqtrade
+
+# exit conda environment - don't do it now
+conda deactivate
+```
+
+Install last python dependencies with pip
+
+```bash
+python3 -m pip install --upgrade pip
+python3 -m pip install -r requirements.txt
+python3 -m pip install -e .
+```
+
+Patch conda libta-lib (Linux only)
+
+```bash
+# Ensure that the environment is active!
+conda activate freqtrade
+
+cd build_helpers
+bash install_ta-lib.sh ${CONDA_PREFIX} nosudo
+```
+
+[You are now ready](#you-are-ready) to run the bot.
+
+### Important shortcuts
+
+```bash
+# list installed conda environments
+conda env list
+
+# activate base environment
+conda activate
+
+# activate freqtrade environment
+conda activate freqtrade
+
+#deactivate any conda environments
+conda deactivate                              
+```
+
+### Further info on anaconda
+
+!!! Info "New heavy packages"
+    It may happen that creating a new Conda environment, populated with selected packages at the moment of creation takes less time than installing a large, heavy library or application, into previously set environment.
+
+!!! Warning "pip install within conda"
+    The documentation of conda says that pip should NOT be used within conda, because internal problems can occur.
+    However, they are rare. [Anaconda Blogpost](https://www.anaconda.com/blog/using-pip-in-a-conda-environment)
+
+    Nevertheless, that is why, the `conda-forge` channel is preferred:
+
+    * more libraries are available (less need for `pip`)
+    * `conda-forge` works better with `pip`
+    * the libraries are newer
+
+Happy trading!
+
+-----
+
+## You are ready
+
+You've made it this far, so you have successfully installed freqtrade.
+
+### Initialize the configuration
+
+```bash
+# Step 1 - Initialize user folder
+freqtrade create-userdir --userdir user_data
+
+# Step 2 - Create a new configuration file
+freqtrade new-config --config user_data/config.json
+```
+
+You are ready to run, read [Bot Configuration](configuration.md), remember to start with `dry_run: True` and verify that everything is working.
+
+To learn how to setup your configuration, please refer to the [Bot Configuration](configuration.md) documentation page.
+
+### Start the Bot
+
+```bash
+freqtrade trade --config user_data/config.json --strategy SampleStrategy
+```
+
+!!! Warning
+    You should read through the rest of the documentation, backtest the strategy you're going to use, and use dry-run before enabling trading with real money.
+
+-----
+
+## Troubleshooting
+
+### Common problem: "command not found"
+
+If you used (1)`Script` or (2)`Manual` installation, you need to run the bot in virtual environment. If you get error as below, make sure venv is active.
+
+```bash
+# if:
+bash: freqtrade: command not found
+
+# then activate your virtual environment
+source ./.venv/bin/activate
+```
+
+### MacOS installation error
+
+Newer versions of MacOS may have installation failed with errors like `error: command 'g++' failed with exit status 1`.
+
+This error will require explicit installation of the SDK Headers, which are not installed by default in this version of MacOS.
+For MacOS 10.14, this can be accomplished with the below command.
+
+```bash
+open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
+```
+
+If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
diff --git a/docs/javascripts/config.js b/docs/javascripts/config.js
new file mode 100644
index 000000000..80e81ba59
--- /dev/null
+++ b/docs/javascripts/config.js
@@ -0,0 +1,12 @@
+window.MathJax = {
+  tex: {
+    inlineMath: [["\\(", "\\)"]],
+    displayMath: [["\\[", "\\]"]],
+    processEscapes: true,
+    processEnvironments: true
+  },
+  options: {
+    ignoreHtmlClass: ".*|",
+    processHtmlClass: "arithmatex"
+  }
+};
diff --git a/docs/leverage.md b/docs/leverage.md
new file mode 100644
index 000000000..d1517e2d3
--- /dev/null
+++ b/docs/leverage.md
@@ -0,0 +1,143 @@
+# Trading with Leverage
+
+!!! Warning "Beta feature"
+    This feature is still in it's testing phase. Should you notice something you think is wrong please let us know via Discord or via Github Issue.
+
+!!! Note "Multiple bots on one account"
+    You can't run 2 bots on the same account with leverage. For leveraged / margin trading, freqtrade assumes it's the only user of the account, and all liquidation levels are calculated based on this assumption.
+
+!!! Danger "Trading with leverage is very risky"
+    Do not trade with a leverage > 1 using a strategy that hasn't shown positive results in a live run using the spot market. Check the stoploss of your strategy. With a leverage of 2, a stoploss of 0.5 (50%) would be too low, and these trades would be liquidated before reaching that stoploss.
+    We do not assume any responsibility for eventual losses that occur from using this software or this mode.
+
+    Please only use advanced trading modes when you know how freqtrade (and your strategy) works.
+    Also, never risk more than what you can afford to lose.
+
+If you already have an existing strategy, please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to strategy of version 3 which can short and trade futures.
+
+## Shorting
+
+Shorting is not possible when trading with [`trading_mode`](#leverage-trading-modes) set to `spot`. To short trade, `trading_mode` must be set to `margin`(currently unavailable) or [`futures`](#futures), with [`margin_mode`](#margin-mode) set to `cross`(currently unavailable) or [`isolated`](#isolated-margin-mode)
+
+For a strategy to short, the strategy class must set the class variable `can_short = True`
+
+Please read [strategy customization](strategy-customization.md#entry-signal-rules) for instructions on how to set signals to enter and exit short trades.
+
+## Understand `trading_mode`
+
+The possible values are: `spot` (default), `margin`(*Currently unavailable*) or `futures`.
+
+### Spot
+
+Regular trading mode (low risk)
+
+- Long trades only (No short trades).
+- No leverage.
+- No Liquidation.
+- Profits gained/lost are equal to the change in value of the assets (minus trading fees).
+
+### Leverage trading modes
+
+With leverage, a trader borrows capital from the exchange. The capital must be re-payed fully to the exchange (potentially with interest), and the trader keeps any profits, or pays any losses, from any trades made using the borrowed capital.
+
+Because the capital must always be re-payed, exchanges will **liquidate** (forcefully sell the traders assets) a trade made using borrowed capital when the total value of assets in the leverage account drops to a certain point (a point where the total value of losses is less than the value of the collateral that the trader actually owns in the leverage account), in order to ensure that the trader has enough capital to pay the borrowed assets back to the exchange. The exchange will also charge a **liquidation fee**, adding to the traders losses.
+
+For this reason, **DO NOT TRADE WITH LEVERAGE IF YOU DON'T KNOW EXACTLY WHAT YOUR DOING. LEVERAGE TRADING IS HIGH RISK, AND CAN RESULT IN THE VALUE OF YOUR ASSETS DROPPING TO 0 VERY QUICKLY, WITH NO CHANCE OF INCREASING IN VALUE AGAIN.**
+
+#### Margin (currently unavailable)
+
+Trading occurs on the spot market, but the exchange lends currency to you in an amount equal to the chosen leverage. You pay the amount lent to you back to the exchange with interest, and your profits/losses are multiplied by the leverage specified.
+
+#### Futures
+
+Perpetual swaps (also known as Perpetual Futures) are contracts traded at a price that is closely tied to the underlying asset they are based off of (ex.). You are not trading the actual asset but instead are trading a derivative contract. Perpetual swap contracts can last indefinitely, in contrast to futures or option contracts.
+
+In addition to the gains/losses from the change in price of the futures contract, traders also exchange _funding fees_, which are gains/losses worth an amount that is derived from the difference in price between the futures contract and the underlying asset. The difference in price between a futures contract and the underlying asset varies between exchanges.
+
+To trade in futures markets, you'll have to set `trading_mode` to "futures".
+You will also have to pick a "margin mode" (explanation below) - with freqtrade currently only supporting isolated margin.
+
+``` json
+"trading_mode": "futures",
+"margin_mode": "isolated"
+```
+
+##### Pair namings
+
+Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/#/README?id=perpetual-swap-perpetual-future).
+A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
+
+### Margin mode
+
+On top of `trading_mode` - you will also have to configure your `margin_mode`.
+While freqtrade currently only supports one margin mode, this will change, and by configuring it now you're all set for future updates.
+
+The possible values are: `isolated`, or `cross`(*currently unavailable*).
+
+#### Isolated margin mode
+
+Each market(trading pair), keeps collateral in a separate account
+
+``` json
+"margin_mode": "isolated"
+```
+
+#### Cross margin mode
+
+One account is used to share collateral between markets (trading pairs). Margin is taken from total account balance to avoid liquidation when needed.
+
+``` json
+"margin_mode": "cross"
+```
+
+Please read the [exchange specific notes](exchanges.md) for exchanges that support this mode and how they differ.
+
+## Set leverage to use
+
+Different strategies and risk profiles will require different levels of leverage.
+While you could configure one static leverage value - freqtrade offers you the flexibility to adjust this via [strategy leverage callback](strategy-callbacks.md#leverage-callback) - which allows you to use different leverages by pair, or based on some other factor benefitting your strategy result.
+
+If not implemented, leverage defaults to 1x (no leverage).
+
+!!! Warning
+    Higher leverage also equals higher risk - be sure you fully understand the implications of using leverage!
+
+## Understand `liquidation_buffer`
+
+*Defaults to `0.05`*
+
+A ratio specifying how large of a safety net to place between the liquidation price and the stoploss to prevent a position from reaching the liquidation price.
+This artificial liquidation price is calculated as:
+
+`freqtrade_liquidation_price = liquidation_price ± (abs(open_rate - liquidation_price) * liquidation_buffer)`
+
+- `±` = `+` for long trades
+- `±` = `-` for short trades
+
+Possible values are any floats between 0.0 and 0.99
+
+**ex:** If a trade is entered at a price of 10 coin/USDT, and the liquidation price of this trade is 8 coin/USDT, then with `liquidation_buffer` set to `0.05` the minimum stoploss for this trade would be $8 + ((10 - 8) * 0.05) = 8 + 0.1 = 8.1$
+
+!!! Danger "A `liquidation_buffer` of 0.0, or a low `liquidation_buffer` is likely to result in liquidations, and liquidation fees"
+    Currently Freqtrade is able to calculate liquidation prices, but does not calculate liquidation fees. Setting your `liquidation_buffer` to 0.0, or using a low `liquidation_buffer` could result in your positions being liquidated. Freqtrade does not track liquidation fees, so liquidations will result in inaccurate profit/loss results for your bot. If you use a low `liquidation_buffer`, it is recommended to use `stoploss_on_exchange` if your exchange supports this.
+
+## Unavailable funding rates
+
+For futures data, exchanges commonly provide the futures candles, the marks, and the funding rates. However, it is common that whilst candles and marks might be available, the funding rates are not. This can affect backtesting timeranges, i.e. you may only be able to test recent timeranges and not earlier, experiencing the `No data found. Terminating.` error. To get around this, add the `futures_funding_rate` config option as listed in [configuration.md](configuration.md), and it is recommended that you set this to `0`, unless you know a given specific funding rate for your pair, exchange and timerange. Setting this to anything other than `0` can have drastic effects on your profit calculations within strategy, e.g. within the `custom_exit`, `custom_stoploss`, etc functions.
+
+!!! Warning "This will mean your backtests are inaccurate."
+    This will not overwrite funding rates that are available from the exchange, but bear in mind that setting a false funding rate will mean backtesting results will be inaccurate for historical timeranges where funding rates are not available.
+
+### Developer
+
+#### Margin mode
+
+For shorts, the currency which pays the interest fee for the `borrowed` currency is purchased at the same time of the closing trade (This means that the amount purchased in short closing trades is greater than the amount sold in short opening trades).
+
+For longs, the currency which pays the interest fee for the `borrowed` will already be owned by the user and does not need to be purchased. The interest is subtracted from the `close_value` of the trade.
+
+All Fees are included in `current_profit` calculations during the trade.
+
+#### Futures mode
+
+Funding fees are either added or subtracted from the total amount of a trade
diff --git a/docs/lookahead-analysis.md b/docs/lookahead-analysis.md
new file mode 100644
index 000000000..a64479fe6
--- /dev/null
+++ b/docs/lookahead-analysis.md
@@ -0,0 +1,72 @@
+# Lookahead analysis
+
+This page explains how to validate your strategy in terms of look ahead bias.
+
+Checking look ahead bias is the bane of any strategy since it is sometimes very easy to introduce backtest bias -
+but very hard to detect.
+
+Backtesting initializes all timestamps at once and calculates all indicators in the beginning.
+This means that if your indicators or entry/exit signals could look into future candles and falsify your backtest.
+
+Lookahead-analysis requires historic data to be available.
+To learn how to get data for the pairs and exchange you're interested in,
+head over to the [Data Downloading](data-download.md) section of the documentation.
+
+This command is built upon backtesting since it internally chains backtests and pokes at the strategy to provoke it to show look ahead bias.
+This is done by not looking at the strategy itself - but at the results it returned.
+The results are things like changed indicator-values and moved entries/exits compared to the full backtest.
+
+You can use commands of [Backtesting](backtesting.md).
+It also supports the lookahead-analysis of freqai strategies.
+
+- `--cache` is forced to "none".
+- `--max-open-trades` is forced to be at least equal to the number of pairs.
+- `--dry-run-wallet` is forced to be basically infinite (1 billion).
+- `--stake-amount` is forced to be a static 10000 (10k).
+- `--enable-protections` is forced to be off.
+
+Those are set to avoid users accidentally generating false positives.
+
+## Lookahead-analysis command reference
+
+--8<-- "commands/lookahead-analysis.md"
+
+!!! Note ""
+    The above Output was reduced to options `lookahead-analysis` adds on top of regular backtesting commands.
+
+### Summary
+
+Checks a given strategy for look ahead bias via lookahead-analysis
+Look ahead bias means that the backtest uses data from future candles thereby not making it viable beyond backtesting
+and producing false hopes for the one backtesting.
+
+### Introduction
+
+Many strategies - without the programmer knowing - have fallen prey to look ahead bias.
+
+Any backtest will populate the full dataframe including all time stamps at the beginning.
+If the programmer is not careful or oblivious how things work internally
+(which sometimes can be really hard to find out) then it will just look into the future making the strategy amazing
+but not realistic.
+
+This command is made to try to verify the validity in the form of the aforementioned look ahead bias.
+
+### How does the command work?
+
+It will start with a backtest of all pairs to generate a baseline for indicators and entries/exits.
+After the backtest ran, it will look if the `minimum-trade-amount` is met
+and if not cancel the lookahead-analysis for this strategy.
+
+After setting the baseline it will then do additional runs for every entry and exit separately.
+When a verification-backtest is done, it will compare the indicators as the signal (either entry or exit) and report the bias.
+After all signals have been verified or falsified a result-table will be generated for the user to see.
+
+### Caveats
+
+- `lookahead-analysis` can only verify / falsify the trades it calculated and verified.
+If the strategy has many different signals / signal types, it's up to you to select appropriate parameters to ensure that all signals have triggered at least once. Not triggered signals will not have been verified.
+This could lead to a false-negative (the strategy will then be reported as non-biased).
+- `lookahead-analysis` has access to everything that backtesting has too.
+Please don't provoke any configs like enabling position stacking.
+If you decide to do so, then make doubly sure that you won't ever run out of `max_open_trades` amount and neither leftover money in your wallet.
+- In the results table, the `biased_indicators` column will falsely flag FreqAI target indicators defined in `set_freqai_targets()` as biased. These are not biased and can safely be ignored.
diff --git a/docs/overrides/main.html b/docs/overrides/main.html
new file mode 100644
index 000000000..cba627ead
--- /dev/null
+++ b/docs/overrides/main.html
@@ -0,0 +1,44 @@
+{% extends "base.html" %}
+
+
+<!-- Navigation -->
+{% block site_nav %}
+
+  <!-- Main navigation -->
+  {% if nav %}
+  {% if page and page.meta and page.meta.hide %}
+  {% set hidden = "hidden" if "navigation" in page.meta.hide %}
+  {% endif %}
+  <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" {{ hidden }}>
+    <div class="md-sidebar__scrollwrap">
+      <div class="md-sidebar__inner">
+        {% include "partials/nav.html" %}
+      </div>
+    </div>
+  </div>
+  {% endif %}
+
+  <!-- Table of contents -->
+  {% if page.toc and not "toc.integrate" in features %}
+  {% if page and page.meta and page.meta.hide %}
+  {% set hidden = "hidden" if "toc" in page.meta.hide %}
+  {% endif %}
+  <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" {{ hidden }}>
+    <div class="md-sidebar__scrollwrap">
+      <div class="md-sidebar__inner">
+        {% include "partials/toc.html" %}
+      </div>
+    </div>
+  </div>
+  {% endif %}
+{% endblock %}
+
+{% block footer %}
+    {{ super() }}
+
+    <!-- Place this tag in your head or just before your close body tag. -->
+    <script async defer src="https://buttons.github.io/buttons.js"></script>
+    <script src="https://code.jquery.com/jquery-3.4.1.min.js"
+      integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
+
+{% endblock %}
diff --git a/docs/plotting.md b/docs/plotting.md
index 80ab6866e..ae480e78f 100644
--- a/docs/plotting.md
+++ b/docs/plotting.md
@@ -1,77 +1,260 @@
 # Plotting
-This page explains how to plot prices, indicator, profits.
 
-## Table of Contents
-- [Plot price and indicators](#plot-price-and-indicators)
-- [Plot profit](#plot-profit)
+This page explains how to plot prices, indicators and profits.
 
-## Installation
+!!! Warning "Deprecated"
+    The commands described in this page (`plot-dataframe`, `plot-profit`) should be considered deprecated and are in maintenance mode.
+    This is mostly for the performance problems even medium sized plots can cause, but also because "store a file and open it in a browser" isn't very intuitive from a UI perspective.
 
-Plotting scripts use Plotly library. Install/upgrade it with:
+    While there are no immediate plans to remove them, they are not actively maintained - and may be removed short-term should major changes be required to keep them working.
+    
+    Please use [FreqUI](freq-ui.md) for plotting needs, which doesn't struggle with the same performance problems.
 
+## Installation / Setup
+
+Plotting modules use the Plotly library. You can install / upgrade this by running the following command:
+
+``` bash
+pip install -U -r requirements-plot.txt
 ```
-pip install --upgrade plotly
-```
-
-At least version 2.3.0 is required.
 
 ## Plot price and indicators
-Usage for the price plotter:
 
-```
-script/plot_dataframe.py [-h] [-p pair] [--live]
+The `freqtrade plot-dataframe` subcommand shows an interactive graph with three subplots:
+
+* Main plot with candlesticks and indicators following price (sma/ema)
+* Volume bars
+* Additional indicators as specified by `--indicators2`
+
+![plot-dataframe](assets/plot-dataframe.png)
+
+Possible arguments:
+
+--8<-- "commands/plot-dataframe.md"
+
+Example:
+
+``` bash
+freqtrade plot-dataframe -p BTC/ETH --strategy AwesomeStrategy
 ```
 
-Example
-```
-python scripts/plot_dataframe.py -p BTC_ETH
+The `-p/--pairs` argument can be used to specify pairs you would like to plot.
+
+!!! Note
+    The `freqtrade plot-dataframe` subcommand generates one plot-file per pair.
+
+Specify custom indicators.
+Use `--indicators1` for the main plot and `--indicators2` for the subplot below (if values are in a different range than prices).
+
+``` bash
+freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --indicators1 sma ema --indicators2 macd
 ```
 
-The `-p` pair argument, can be used to specify what
-pair you would like to plot.
+### Further usage examples
 
-**Advanced use**
+To plot multiple pairs, separate them with a space:
 
-To plot the current live price use the `--live` flag:
-```
-python scripts/plot_dataframe.py -p BTC_ETH --live
+``` bash
+freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH XRP/ETH
 ```
 
-To plot a timerange (to zoom in):
-```
-python scripts/plot_dataframe.py -p BTC_ETH --timerange=100-200
-```
-Timerange doesn't work with live data.
+To plot a timerange (to zoom in)
 
+``` bash
+freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
+```
+
+To plot trades stored in a database use `--db-url` in combination with `--trade-source DB`:
+
+``` bash
+freqtrade plot-dataframe --strategy AwesomeStrategy --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
+```
+
+To plot trades from a backtesting result, use `--export-filename <filename>`
+
+``` bash
+freqtrade plot-dataframe --strategy AwesomeStrategy --export-filename user_data/backtest_results/backtest-result.json -p BTC/ETH
+```
+
+### Plot dataframe basics
+
+![plot-dataframe2](assets/plot-dataframe2.png)
+
+The `plot-dataframe` subcommand requires backtesting data, a strategy and either a backtesting-results file or a database, containing trades corresponding to the strategy.
+
+The resulting plot will have the following elements:
+
+* Green triangles: Buy signals from the strategy. (Note: not every buy signal generates a trade, compare to cyan circles.)
+* Red triangles: Sell signals from the strategy. (Also, not every sell signal terminates a trade, compare to red and green squares.)
+* Cyan circles: Trade entry points.
+* Red squares: Trade exit points for trades with loss or 0% profit.
+* Green squares: Trade exit points for profitable trades.
+* Indicators with values corresponding to the candle scale (e.g. SMA/EMA), as specified with `--indicators1`.
+* Volume (bar chart at the bottom of the main chart).
+* Indicators with values in different scales (e.g. MACD, RSI) below the volume bars, as specified with `--indicators2`.
+
+!!! Note "Bollinger Bands"
+    Bollinger bands are automatically added to the plot if the columns `bb_lowerband` and `bb_upperband` exist, and are painted as a light blue area spanning from the lower band to the upper band.
+
+#### Advanced plot configuration
+
+An advanced plot configuration can be specified in the strategy in the `plot_config` parameter.
+
+Additional features when using `plot_config` include:
+
+* Specify colors per indicator
+* Specify additional subplots
+* Specify indicator pairs to fill area in between
+
+The sample plot configuration below specifies fixed colors for the indicators. Otherwise, consecutive plots may produce different color schemes each time, making comparisons difficult.
+It also allows multiple subplots to display both MACD and RSI at the same time.
+
+Plot type can be configured using `type` key. Possible types are:
+
+* `scatter` corresponding to `plotly.graph_objects.Scatter` class (default).
+* `bar` corresponding to `plotly.graph_objects.Bar` class.
+
+Extra parameters to `plotly.graph_objects.*` constructor can be specified in `plotly` dict.
+
+Sample configuration with inline comments explaining the process:
+
+``` python
+@property
+def plot_config(self):
+    """
+        There are a lot of solutions how to build the return dictionary.
+        The only important point is the return value.
+        Example:
+            plot_config = {'main_plot': {}, 'subplots': {}}
+
+    """
+    plot_config = {}
+    plot_config['main_plot'] = {
+        # Configuration for main plot indicators.
+        # Assumes 2 parameters, emashort and emalong to be specified.
+        f'ema_{self.emashort.value}': {'color': 'red'},
+        f'ema_{self.emalong.value}': {'color': '#CCCCCC'},
+        # By omitting color, a random color is selected.
+        'sar': {},
+        # fill area between senkou_a and senkou_b
+        'senkou_a': {
+            'color': 'green', #optional
+            'fill_to': 'senkou_b',
+            'fill_label': 'Ichimoku Cloud', #optional
+            'fill_color': 'rgba(255,76,46,0.2)', #optional
+        },
+        # plot senkou_b, too. Not only the area to it.
+        'senkou_b': {}
+    }
+    plot_config['subplots'] = {
+         # Create subplot MACD
+        "MACD": {
+            'macd': {'color': 'blue', 'fill_to': 'macdhist'},
+            'macdsignal': {'color': 'orange'},
+            'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}}
+        },
+        # Additional subplot RSI
+        "RSI": {
+            'rsi': {'color': 'red'}
+        }
+    }
+
+    return plot_config
+```
+
+??? Note "As attribute (former method)"
+    Assigning plot_config is also possible as Attribute (this used to be the default way).
+    This has the disadvantage that strategy parameters are not available, preventing certain configurations from working.
+
+    ``` python
+        plot_config = {
+            'main_plot': {
+                # Configuration for main plot indicators.
+                # Specifies `ema10` to be red, and `ema50` to be a shade of gray
+                'ema10': {'color': 'red'},
+                'ema50': {'color': '#CCCCCC'},
+                # By omitting color, a random color is selected.
+                'sar': {},
+            # fill area between senkou_a and senkou_b
+            'senkou_a': {
+                'color': 'green', #optional
+                'fill_to': 'senkou_b',
+                'fill_label': 'Ichimoku Cloud', #optional
+                'fill_color': 'rgba(255,76,46,0.2)', #optional
+            },
+            # plot senkou_b, too. Not only the area to it.
+            'senkou_b': {}
+            },
+            'subplots': {
+                # Create subplot MACD
+                "MACD": {
+                    'macd': {'color': 'blue', 'fill_to': 'macdhist'},
+                    'macdsignal': {'color': 'orange'},
+                    'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}}
+                },
+                # Additional subplot RSI
+                "RSI": {
+                    'rsi': {'color': 'red'}
+                }
+            }
+        }
+
+    ```
+
+
+!!! Note
+    The above configuration assumes that `ema10`, `ema50`, `senkou_a`, `senkou_b`,
+    `macd`, `macdsignal`, `macdhist` and `rsi` are columns in the DataFrame created by the strategy.
+
+!!! Warning
+    `plotly` arguments are only supported with plotly library and will not work with freq-ui.
+
+!!! Note "Trade position adjustments"
+    If `position_adjustment_enable` / `adjust_trade_position()` is used, the trade initial buy price is averaged over multiple orders and the trade start price will most likely appear outside the candle range.
 
 ## Plot profit
 
-The profit plotter show a picture with three plots:
-1) Average closing price for all pairs
-2) The summarized profit made by backtesting.
-   Note that this is not the real-world profit, but
-   more of an estimate.
-3) Each pair individually profit
+![plot-profit](assets/plot-profit.png)
 
-The first graph is good to get a grip of how the overall market
-progresses.
+The `plot-profit` subcommand shows an interactive graph with three plots:
 
-The second graph will show how you algorithm works or doesnt.
-Perhaps you want an algorithm that steadily makes small profits,
-or one that acts less seldom, but makes big swings.
+* Average closing price for all pairs.
+* The summarized profit made by backtesting.
+Note that this is not the real-world profit, but more of an estimate.
+* Profit for each individual pair.
+* Parallelism of trades.
+* Underwater (Periods of drawdown).
 
-The third graph can be useful to spot outliers, events in pairs
-that makes profit spikes.
+The first graph is good to get a grip of how the overall market progresses.
 
-Usage for the profit plotter:
+The second graph will show if your algorithm works or doesn't.
+Perhaps you want an algorithm that steadily makes small profits, or one that acts less often, but makes big swings.
+This graph will also highlight the start (and end) of the Max drawdown period.
 
-```
-script/plot_profit.py [-h] [-p pair] [--datadir directory] [--ticker_interval num]
+The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
+
+The forth graph can help you analyze trade parallelism, showing how often max_open_trades have been maxed out.
+
+Possible options for the `freqtrade plot-profit` subcommand:
+
+--8<-- "commands/plot-profit.md"
+
+The `-p/--pairs`  argument, can be used to limit the pairs that are considered for this calculation.
+
+Examples:
+
+Use custom backtest-export file
+
+``` bash
+freqtrade plot-profit  -p LTC/BTC --export-filename user_data/backtest_results/backtest-result.json
 ```
 
-The `-p` pair argument, can be used to plot a single pair
+Use custom database
 
-Example
+``` bash
+freqtrade plot-profit  -p LTC/BTC --db-url sqlite:///tradesv3.sqlite --trade-source DB
 ```
-python3 scripts/plot_profit.py --datadir ../freqtrade/freqtrade/tests/testdata-20171221/ -p BTC_LTC
+
+``` bash
+freqtrade --datadir user_data/data/binance_save/ plot-profit -p LTC/BTC
 ```
diff --git a/docs/plugins.md b/docs/plugins.md
new file mode 100644
index 000000000..1f785bbaa
--- /dev/null
+++ b/docs/plugins.md
@@ -0,0 +1,3 @@
+# Plugins
+--8<-- "includes/pairlists.md"
+--8<-- "includes/protections.md"
diff --git a/docs/pre-requisite.md b/docs/pre-requisite.md
deleted file mode 100644
index 79232a89d..000000000
--- a/docs/pre-requisite.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# Pre-requisite
-Before running your bot in production you will need to setup few
-external API. In production mode, the bot required valid Bittrex API
-credentials and a Telegram bot (optional but recommended).
-
-## Table of Contents
-- [Setup your Bittrex account](#setup-your-bittrex-account)
-- [Backtesting commands](#setup-your-telegram-bot)
-
-## Setup your Bittrex account
-*To be completed, please feel free to complete this section.*
-
-## Setup your Telegram bot
-The only things you need is a working Telegram bot and its API token.
-Below we explain how to create your Telegram Bot, and how to get your
-Telegram user id.
-
-### 1. Create your Telegram bot
-**1.1. Start a chat with https://telegram.me/BotFather**  
-**1.2. Send the message** `/newbot`  
-*BotFather response:*
-```
-Alright, a new bot. How are we going to call it? Please choose a name for your bot.
-```
-**1.3. Choose the public name of your bot (e.g "`Freqtrade bot`")**  
-*BotFather response:*
-```
-Good. Now let's choose a username for your bot. It must end in `bot`. Like this, for example: TetrisBot or tetris_bot.
-```
-**1.4. Choose the name id of your bot (e.g "`My_own_freqtrade_bot`")**  
-**1.5. Father bot will return you the token (API key)**  
-Copy it and keep it you will use it for the config parameter `token`.  
-*BotFather response:*
-```
-Done! Congratulations on your new bot. You will find it at t.me/My_own_freqtrade_bot. You can now add a description, about section and profile picture for your bot, see /help for a list of commands. By the way, when you've finished creating your cool bot, ping our Bot Support if you want a better username for it. Just make sure the bot is fully operational before you do this.
-
-Use this token to access the HTTP API:
-521095879:AAEcEZEL7ADJ56FtG_qD0bQJSKETbXCBCi0
-
-For a description of the Bot API, see this page: https://core.telegram.org/bots/api
-```
-**1.6. Don't forget to start the conversation with your bot, by clicking /START button**  
-
-### 2. Get your user id
-**2.1. Talk to https://telegram.me/userinfobot**  
-**2.2. Get your "Id", you will use it for the config parameter 
-`chat_id`.**
-
diff --git a/docs/producer-consumer.md b/docs/producer-consumer.md
new file mode 100644
index 000000000..0bd52ac93
--- /dev/null
+++ b/docs/producer-consumer.md
@@ -0,0 +1,165 @@
+# Producer / Consumer mode
+
+freqtrade provides a mechanism whereby an instance (also called `consumer`) may listen to messages from an upstream freqtrade instance (also called `producer`) using the message websocket. Mainly, `analyzed_df` and `whitelist` messages. This allows the reuse of computed indicators (and signals) for pairs in multiple bots without needing to compute them multiple times.
+
+See [Message Websocket](rest-api.md#message-websocket) in the Rest API docs for setting up the `api_server` configuration for your message websocket (this will be your producer).
+
+!!! Note
+    We strongly recommend to set `ws_token` to something random and known only to yourself to avoid unauthorized access to your bot.
+
+## Configuration
+
+Enable subscribing to an instance by adding the `external_message_consumer` section to the consumer's config file.
+
+```json
+{
+    //...
+   "external_message_consumer": {
+        "enabled": true,
+        "producers": [
+            {
+                "name": "default", // This can be any name you'd like, default is "default"
+                "host": "127.0.0.1", // The host from your producer's api_server config
+                "port": 8080, // The port from your producer's api_server config
+                "secure": false, // Use a secure websockets connection, default false
+                "ws_token": "sercet_Ws_t0ken" // The ws_token from your producer's api_server config
+            }
+        ],
+        // The following configurations are optional, and usually not required
+        // "wait_timeout": 300,
+        // "ping_timeout": 10,
+        // "sleep_time": 10,
+        // "remove_entry_exit_signals": false,
+        // "message_size_limit": 8
+    }
+    //...
+}
+```
+
+|  Parameter | Description |
+|------------|-------------|
+| `enabled` | **Required.** Enable consumer mode. If set to false, all other settings in this section are ignored.<br>*Defaults to `false`.*<br> **Datatype:** boolean .
+| `producers` | **Required.** List of producers <br> **Datatype:** Array.
+| `producers.name` | **Required.** Name of this producer. This name must be used in calls to `get_producer_pairs()` and `get_producer_df()` if more than one producer is used.<br> **Datatype:** string
+| `producers.host` | **Required.** The hostname or IP address from your producer.<br> **Datatype:** string
+| `producers.port` | **Required.** The port matching the above host.<br>*Defaults to `8080`.*<br> **Datatype:** Integer
+| `producers.secure` | **Optional.**  Use ssl in websockets connection. Default False.<br> **Datatype:** string
+| `producers.ws_token` | **Required.**  `ws_token` as configured on the producer.<br> **Datatype:** string
+| | **Optional settings**
+| `wait_timeout` | Timeout until we ping again if no message is received. <br>*Defaults to `300`.*<br> **Datatype:** Integer - in seconds.
+| `ping_timeout` | Ping timeout <br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
+| `sleep_time` | Sleep time before retrying to connect.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
+| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.<br>*Defaults to `false`.*<br> **Datatype:** Boolean.
+| `message_size_limit` | Size limit per message<br>*Defaults to `8`.*<br> **Datatype:** Integer - Megabytes.
+
+Instead of (or as well as) calculating indicators in `populate_indicators()` the follower instance listens on the connection to a producer instance's messages (or multiple producer instances in advanced configurations) and requests the producer's most recently analyzed dataframes for each pair in the active whitelist.
+
+A consumer instance will then have a full copy of the analyzed dataframes without the need to calculate them itself.
+
+## Examples
+
+### Example - Producer Strategy
+
+A simple strategy with multiple indicators. No special considerations are required in the strategy itself.
+
+```py
+class ProducerStrategy(IStrategy):
+    #...
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Calculate indicators in the standard freqtrade way which can then be broadcast to other instances
+        """
+        dataframe['rsi'] = ta.RSI(dataframe)
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populates the entry signal for the given dataframe
+        """
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
+                (dataframe['tema'] <= dataframe['bb_middleband']) &
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &
+                (dataframe['volume'] > 0)
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+```
+
+!!! Tip "FreqAI"
+    You can use this to setup [FreqAI](freqai.md) on a powerful machine, while you run consumers on simple machines like raspberries, which can interpret the signals generated from the producer in different ways.
+
+
+### Example - Consumer Strategy
+
+A logically equivalent strategy which calculates no indicators itself, but will have the same analyzed dataframes available to make trading decisions based on the indicators calculated in the producer. In this example the consumer has the same entry criteria, however this is not necessary. The consumer may use different logic to enter/exit trades, and only use the indicators as specified.
+
+```py
+class ConsumerStrategy(IStrategy):
+    #...
+    process_only_new_candles = False # required for consumers
+
+    _columns_to_expect = ['rsi_default', 'tema_default', 'bb_middleband_default']
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Use the websocket api to get pre-populated indicators from another freqtrade instance.
+        Use `self.dp.get_producer_df(pair)` to get the dataframe
+        """
+        pair = metadata['pair']
+        timeframe = self.timeframe
+
+        producer_pairs = self.dp.get_producer_pairs()
+        # You can specify which producer to get pairs from via:
+        # self.dp.get_producer_pairs("my_other_producer")
+
+        # This func returns the analyzed dataframe, and when it was analyzed
+        producer_dataframe, _ = self.dp.get_producer_df(pair)
+        # You can get other data if the producer makes it available:
+        # self.dp.get_producer_df(
+        #   pair,
+        #   timeframe="1h",
+        #   candle_type=CandleType.SPOT,
+        #   producer_name="my_other_producer"
+        # )
+
+        if not producer_dataframe.empty:
+            # If you plan on passing the producer's entry/exit signal directly,
+            # specify ffill=False or it will have unintended results
+            merged_dataframe = merge_informative_pair(dataframe, producer_dataframe,
+                                                      timeframe, timeframe,
+                                                      append_timeframe=False,
+                                                      suffix="default")
+            return merged_dataframe
+        else:
+            dataframe[self._columns_to_expect] = 0
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populates the entry signal for the given dataframe
+        """
+        # Use the dataframe columns as if we calculated them ourselves
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi_default'], self.buy_rsi.value)) &
+                (dataframe['tema_default'] <= dataframe['bb_middleband_default']) &
+                (dataframe['tema_default'] > dataframe['tema_default'].shift(1)) &
+                (dataframe['volume'] > 0)
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+```
+
+!!! Tip "Using upstream signals"
+    By setting `remove_entry_exit_signals=false`, you can also use the producer's signals directly. They should be available as `enter_long_default` (assuming `suffix="default"` was used) - and can be used as either signal directly, or as additional indicator.
diff --git a/docs/recursive-analysis.md b/docs/recursive-analysis.md
new file mode 100644
index 000000000..033b6605c
--- /dev/null
+++ b/docs/recursive-analysis.md
@@ -0,0 +1,70 @@
+# Recursive analysis
+
+This page explains how to validate your strategy for inaccuracies due to recursive issues with certain indicators.
+
+A recursive formula defines any term of a sequence relative to its preceding term(s). An example of a recursive formula is a<sub>n</sub> = a<sub>n-1</sub> + b.
+
+Why does this matter for Freqtrade? In backtesting, the bot will get full data of the pairs according to the timerange specified. But in a dry/live run, the bot will be limited by the amount of data each exchanges gives.
+
+For example, to calculate a very basic indicator called `steps`, the first row's value is always 0, while the following rows' values are equal to the value of the previous row plus 1. If I were to calculate it using the latest 1000 candles, then the `steps` value of the first row is 0, and the `steps` value at the last closed candle is 999.
+
+What happens if the calculation is using only the latest 500 candles? Then instead of 999, the `steps` value at last closed candle is 499. The difference of the value means your backtest result can differ from your dry/live run result.
+
+The `recursive-analysis` command requires historic data to be available. To learn how to get data for the pairs and exchange you're interested in,
+head over to the [Data Downloading](data-download.md) section of the documentation.
+
+This command is built upon preparing different lengths of data and calculates indicators based on them.
+This does not backtest the strategy itself, but rather only calculates the indicators. After calculating the indicators of different startup candle values (`startup_candle_count`) are done, the values of last rows across all specified `startup_candle_count` are compared to see how much variance they show compared to the base calculation.
+
+Command settings:
+
+- Use the `-p` option to set your desired pair to analyze. Since we are only looking at indicator values, using more than one pair is redundant. Preferably use a pair with a relatively high price and at least moderate volatility, such as BTC or ETH, to avoid rounding issues that can make the results inaccurate. If no pair is set on the command, the pair used for this analysis is the first pair in the whitelist.
+- It is recommended to set a long timerange (at least 5000 candles) so that the initial indicators' calculation that is going to be used as a benchmark has very small or no recursive issues itself. For example, for a 5m timeframe, a timerange of 5000 candles would be equal to 18 days.
+- `--cache` is forced to "none" to avoid loading previous indicators calculation automatically.
+
+In addition to the recursive formula check, this command also carries out a simple lookahead bias check on the indicator values only. For a full lookahead check, use [Lookahead-analysis](lookahead-analysis.md).
+
+## Recursive-analysis command reference
+
+--8<-- "commands/recursive-analysis.md"
+
+### Why are odd-numbered default startup candles used?
+
+The default value for startup candles are odd numbers. When the bot fetches candle data from the exchange's API, the last candle is the one being checked by the bot and the rest of the data are the "startup candles".
+
+For example, Binance allows 1000 candles per API call. When the bot receives 1000 candles, the last candle is the "current candle", and the preceding 999 candles are the "startup candles". By setting the startup candle count as 1000 instead of 999, the bot will try to fetch 1001 candles instead. The exchange API will then send candle data in a paginated form, i.e. in case of the Binance API, this will be two groups- one of length 1000 and another of length 1. This results in the bot thinking the strategy needs 1001 candles of data, and so it will download 2000 candles worth of data instead, which means there will be 1 "current candle" and 1999 "startup candles".
+
+Furthermore, exchanges limit the number of consecutive bulk API calls, e.g. Binance allows 5 calls. In this case, only 5000 candles can be downloaded from Binance API without hitting the API rate limit, which means the max `startup_candle_count` you can have is 4999.
+
+Please note that this candle limit may be changed in the future by the exchanges without any prior notice.
+
+### How does the command work?
+
+- Firstly an initial indicator calculation is carried out using the supplied timerange to generate a benchmark for indicator values.
+- After setting the benchmark it will then carry out additional runs for each of the different startup candle count values.
+- The command will then compare the indicator values at the last candle rows and report the differences in a table.
+
+## Understanding the recursive-analysis output
+
+This is an example of an output results table where at least one indicator has a recursive formula issue:
+
+```
+| indicators   | 20      | 40      | 80     | 100    | 150     | 300     | 999    |
+|--------------+---------+---------+--------+--------+---------+---------+--------|
+| rsi_30       | nan%    | -6.025% | 0.612% | 0.828% | -0.140% | 0.000%  | 0.000% |
+| rsi_14       | 24.141% | -0.876% | 0.070% | 0.007% | -0.000% | -0.000% | -      |
+```
+
+The column headers indicate the different `startup_candle_count` used in the analysis. The values in the table indicate the variance of the calculated indicators compared to the benchmark value.
+
+`nan%` means the value of that indicator cannot be calculated due to lack of data. In this example, you cannot calculate RSI with length 30 with just 21 candles (1 current candle + 20 startup candles).
+
+Users should assess the table per indicator to decide if the specified `startup_candle_count` results in a sufficiently small variance so that the indicator does not have any effect on entries and/or exits.
+
+As such, aiming for absolute zero variance (shown by `-` value) might not be the best option, because some indicators might require you to use such a long `startup_candle_count` to have zero variance.
+
+## Caveats
+
+- `recursive-analysis` will only calculate and compare the indicator values at the last row. The output table reports the percentage differences between the different startup candle count calculations and the original benchmark calculation. Whether it has any actual impact on your entries and exits is not included.
+- The ideal scenario is that indicators will have no variance (or at least very close to 0%) despite the startup candle being varied. In reality, indicators such as EMA are using a recursive formula to calculate indicator values, so the goal is not necessarily to have zero percentage variance, but to have the variance low enough (and therefore `startup_candle_count` high enough) that the recursion inherent in the indicator will not have any real impact on trading decisions.
+- `recursive-analysis` will only run calculations on `populate_indicators` and `@informative` decorator(s). If you put any indicator calculation on `populate_entry_trend` or `populate_exit_trend`, it won't be calculated.
diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt
new file mode 100644
index 000000000..b7d8fd974
--- /dev/null
+++ b/docs/requirements-docs.txt
@@ -0,0 +1,7 @@
+markdown==3.7
+mkdocs==1.6.1
+mkdocs-material==9.6.11
+mdx_truly_sane_lists==1.3
+pymdown-extensions==10.14.3
+jinja2==3.1.6
+mike==2.1.3
diff --git a/docs/rest-api.md b/docs/rest-api.md
new file mode 100644
index 000000000..cea4d2ee9
--- /dev/null
+++ b/docs/rest-api.md
@@ -0,0 +1,522 @@
+# REST API
+
+## FreqUI
+
+FreqUI now has it's own dedicated [documentation section](freq-ui.md) - please refer to that section for all information regarding the FreqUI.
+
+## Configuration
+
+Enable the rest API by adding the api_server section to your configuration and setting `api_server.enabled` to `true`.
+
+Sample configuration:
+
+``` json
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "Freqtrader",
+        "password": "SuperSecret1!",
+        "ws_token": "sercet_Ws_t0ken"
+    },
+```
+
+!!! Danger "Security warning"
+    By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
+
+??? Note "API/UI Access on a remote servers"
+    If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
+    This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
+    Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
+
+You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
+This should return the response:
+
+``` output
+{"status":"pong"}
+```
+
+All other endpoints return sensitive info and require authentication and are therefore not available through a web browser.
+
+### Security
+
+To generate a secure password, best use a password manager, or use the below code.
+
+``` python
+import secrets
+secrets.token_hex()
+```
+
+!!! Hint "JWT token"
+    Use the same method to also generate a JWT secret key (`jwt_secret_key`).
+
+!!! Danger "Password selection"
+    Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
+    Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!).
+
+### Configuration with docker
+
+If you run your bot using docker, you'll need to have the bot listen to incoming connections. The security is then handled by docker.
+
+``` json
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8080,
+        "username": "Freqtrader",
+        "password": "SuperSecret1!",
+        //...
+    },
+```
+
+Make sure that the following 2 lines are available in your docker-compose file:
+
+```yml
+    ports:
+      - "127.0.0.1:8080:8080"
+```
+
+!!! Danger "Security warning"
+    By using `"8080:8080"` (or `"0.0.0.0:8080:8080"`) in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
+    This **may** be safe if you're running the bot in a secure environment (like your home network), but it's not recommended to expose the API to the internet.
+
+## Rest API
+
+### Consuming the API
+
+We advise consuming the API by using the supported `freqtrade-client` package (also available as `scripts/rest_client.py`).
+
+This command can be installed independent of any running freqtrade bot by using `pip install freqtrade-client`.
+
+This module is designed to be lightweight, and only depends on the `requests` and `python-rapidjson` modules, skipping all heavy dependencies freqtrade otherwise needs.
+
+``` bash
+freqtrade-client <command> [optional parameters]
+```
+
+By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be used, however you can specify a configuration file to override this behaviour.
+
+#### Minimalistic client config
+
+``` json
+{
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8080,
+        "username": "Freqtrader",
+        "password": "SuperSecret1!",
+        //...
+    }
+}
+```
+
+``` bash
+freqtrade-client --config rest_config.json <command> [optional parameters]
+```
+
+Commands with many arguments may require keyword arguments (for clarity) - which can be provided as follows:
+
+``` bash
+freqtrade-client --config rest_config.json forceenter BTC/USDT long enter_tag=GutFeeling
+```
+
+This method will work for all arguments - check the "show" command for a list of available parameters.
+
+??? Note "Programmatic use"
+    The `freqtrade-client` package (installable independent of freqtrade) can be used in your own scripts to interact with the freqtrade API.
+    to do so, please use the following:
+
+    ``` python
+    from freqtrade_client import FtRestClient
+    
+
+    client = FtRestClient(server_url, username, password)
+
+    # Get the status of the bot
+    ping = client.ping()
+    print(ping)
+    # ... 
+    ```
+
+    For a full list of available commands, please refer to the list below.
+
+Possible commands can be listed from the rest-client script using the `help` command.
+
+``` bash
+freqtrade-client help
+```
+
+``` output
+Possible commands:
+
+available_pairs
+	Return available pair (backtest data) based on timeframe / stake_currency selection
+
+        :param timeframe: Only pairs with this timeframe available.
+        :param stake_currency: Only pairs that include this timeframe
+
+balance
+	Get the account balance.
+
+blacklist
+	Show the current blacklist.
+
+        :param add: List of coins to add (example: "BNB/BTC")
+
+cancel_open_order
+	Cancel open order for trade.
+
+        :param trade_id: Cancels open orders for this trade.
+
+count
+	Return the amount of open trades.
+
+daily
+	Return the profits for each day, and amount of trades.
+
+delete_lock
+	Delete (disable) lock from the database.
+
+        :param lock_id: ID for the lock to delete
+
+delete_trade
+	Delete trade from the database.
+        Tries to close open orders. Requires manual handling of this asset on the exchange.
+
+        :param trade_id: Deletes the trade with this ID from the database.
+
+edge
+	Return information about edge.
+
+forcebuy
+	Buy an asset.
+
+        :param pair: Pair to buy (ETH/BTC)
+        :param price: Optional - price to buy
+
+forceenter
+	Force entering a trade
+
+        :param pair: Pair to buy (ETH/BTC)
+        :param side: 'long' or 'short'
+        :param price: Optional - price to buy
+
+forceexit
+	Force-exit a trade.
+
+        :param tradeid: Id of the trade (can be received via status command)
+        :param ordertype: Order type to use (must be market or limit)
+        :param amount: Amount to sell. Full sell if not given
+
+health
+	Provides a quick health check of the running bot.
+
+lock_add
+    Manually lock a specific pair
+
+        :param pair: Pair to lock
+        :param until: Lock until this date (format "2024-03-30 16:00:00Z")
+        :param side: Side to lock (long, short, *)
+        :param reason: Reason for the lock        
+
+locks
+	Return current locks
+
+logs
+	Show latest logs.
+
+        :param limit: Limits log messages to the last <limit> logs. No limit to get the entire log.
+
+pair_candles
+	Return live dataframe for <pair><timeframe>.
+
+        :param pair: Pair to get data for
+        :param timeframe: Only pairs with this timeframe available.
+        :param limit: Limit result to the last n candles.
+
+pair_history
+	Return historic, analyzed dataframe
+
+        :param pair: Pair to get data for
+        :param timeframe: Only pairs with this timeframe available.
+        :param strategy: Strategy to analyze and get values for
+        :param timerange: Timerange to get data for (same format than --timerange endpoints)
+
+performance
+	Return the performance of the different coins.
+
+ping
+	simple ping
+
+plot_config
+	Return plot configuration if the strategy defines one.
+
+profit
+	Return the profit summary.
+
+reload_config
+	Reload configuration.
+
+show_config
+        Returns part of the configuration, relevant for trading operations.
+
+start
+	Start the bot if it's in the stopped state.
+
+pause
+	Pause the bot if it's in the running state. If triggered on stopped state will handle open positions.
+
+stats
+	Return the stats report (durations, sell-reasons).
+
+status
+	Get the status of open trades.
+
+stop
+	Stop the bot. Use `start` to restart.
+
+stopbuy
+	Stop buying (but handle sells gracefully). Use `reload_config` to reset.
+
+strategies
+	Lists available strategies
+
+strategy
+	Get strategy details
+
+        :param strategy: Strategy class name
+
+sysinfo
+	Provides system information (CPU, RAM usage)
+
+trade
+	Return specific trade
+
+        :param trade_id: Specify which trade to get.
+
+trades
+	Return trades history, sorted by id
+
+        :param limit: Limits trades to the X last trades. Max 500 trades.
+        :param offset: Offset by this amount of trades.
+
+list_open_trades_custom_data
+    Return a dict containing open trades custom-datas
+
+        :param key: str, optional - Key of the custom-data
+        :param limit: Limits trades to X trades.
+        :param offset: Offset by this amount of trades.
+
+list_custom_data
+    Return a dict containing custom-datas of a specified trade
+
+        :param trade_id: int - ID of the trade
+        :param key: str, optional - Key of the custom-data
+
+version
+	Return the version of the bot.
+
+whitelist
+	Show the current whitelist.
+
+
+```
+
+### Available endpoints
+
+If you wish to call the REST API manually via another route, e.g. directly via `curl`, the table below shows the relevant URL endpoints and parameters.
+All endpoints in the below table need to be prefixed with the base URL of the API, e.g. `http://127.0.0.1:8080/api/v1/` - so the command becomes `http://127.0.0.1:8080/api/v1/<command>`.
+
+|  Endpoint | Method | Description / Parameters |
+|-----------|--------|--------------------------|
+| `/ping` | GET | Simple command testing the API Readiness - requires no authentication.
+| `/start` | POST | Starts the trader.
+| `/pause` | POST | Pause the trader. Gracefully handle open trades according to their rules. Do not enter new positions.
+| `/stop` | POST | Stops the trader.
+| `/stopbuy` | POST | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
+| `/reload_config` | POST | Reloads the configuration file.
+| `/trades` | GET | List last trades. Limited to 500 trades per call.
+| `/trade/<tradeid>` | GET | Get specific trade.<br/>*Params:*<br/>- `tradeid` (`int`)
+| `/trades/<tradeid>` | DELETE | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.<br/>*Params:*<br/>- `tradeid` (`int`) 
+| `/trades/<tradeid>/open-order` | DELETE | Cancel open order for this trade.<br/>*Params:*<br/>- `tradeid` (`int`) 
+| `/trades/<tradeid>/reload` | POST | Reload a trade from the Exchange. Only works in live, and can potentially help recover a trade that was manually sold on the exchange.<br/>*Params:*<br/>- `tradeid` (`int`) 
+| `/show_config` | GET | Shows part of the current configuration with relevant settings to operation.
+| `/logs` | GET | Shows last log messages.
+| `/status` | GET | Lists all open trades.
+| `/count` | GET | Displays number of trades used and available.
+| `/entries` | GET | Shows profit statistics for each enter tags for given pair (or all pairs if pair isn't given). Pair is optional.<br/>*Params:*<br/>- `pair` (`str`) 
+| `/exits` | GET | Shows profit statistics for each exit reasons for given pair (or all pairs if pair isn't given). Pair is optional.<br/>*Params:*<br/>- `pair` (`str`) 
+| `/mix_tags` | GET | Shows profit statistics for each combinations of enter tag + exit reasons for given pair (or all pairs if pair isn't given). Pair is optional.<br/>*Params:*<br/>- `pair` (`str`) 
+| `/locks` | GET | Displays currently locked pairs.
+| `/locks` | POST | Locks a pair until "until". (Until will be rounded up to the nearest timeframe). Side is optional and is either `long` or `short` (default is `long`). Reason is optional.<br/>*Params:*<br/>- `<pair>` (`str`)<br/>- `<until>` (`datetime`)<br/>- `[side]` (`str`)<br/>- `[reason]` (`str`) 
+| `/locks/<lockid>` | DELETE | Deletes (disables) the lock by id.<br/>*Params:*<br/>- `lockid` (`int`) 
+| `/profit` | GET | Display a summary of your profit/loss from close trades and some stats about your performance.
+| `/forceexit` | POST | Instantly exits the given trade (ignoring `minimum_roi`), using the given order type ("market" or "limit", uses your config setting if not specified), and the chosen amount (full sell if not specified). If `all` is supplied as the `tradeid`, then all currently open trades will be forced to exit.<br/>*Params:*<br/>- `<tradeid>` (`int` or `str`)<br/>- `<ordertype>` (`str`)<br/>- `[amount]` (`float`)
+| `/forceenter` | POST | Instantly enters the given pair. Side is optional and is either `long` or `short` (default is `long`). Rate is optional. (`force_entry_enable` must be set to True)<br/>*Params:*<br/>- `<pair>` (`str`)<br/>- `<side>` (`str`)<br/>- `[rate]` (`float`)
+| `/performance` | GET | Show performance of each finished trade grouped by pair.
+| `/balance` | GET | Show account balance per currency.
+| `/daily` | GET | Shows profit or loss per day, over the last n days (n defaults to 7).<br/>*Params:*<br/>- `<n>` (`int`)
+| `/weekly` | GET | Shows profit or loss per week, over the last n days (n defaults to 4).<br/>*Params:*<br/>- `<n>` (`int`)
+| `/monthly` | GET | Shows profit or loss per month, over the last n days (n defaults to 3).<br/>*Params:*<br/>- `<n>` (`int`)
+| `/stats` | GET | Display a summary of profit / loss reasons as well as average holding times.
+| `/whitelist` | GET | Show the current whitelist.
+| `/blacklist` | GET | Show the current blacklist.
+| `/blacklist` | POST | Adds the specified pair to the blacklist.<br/>*Params:*<br/>- `pair` (`str`)
+| `/blacklist` | DELETE | Deletes the specified list of pairs from the blacklist.<br/>*Params:*<br/>- `[pair,pair]` (`list[str]`) 
+| `/edge` | GET | Show validated pairs by Edge if it is enabled.
+| `/pair_candles` | GET | Returns dataframe for a pair / timeframe combination while the bot is running. **Alpha**
+| `/pair_candles` | POST | Returns dataframe for a pair / timeframe combination while the bot is running, filtered by a provided list of columns to return. **Alpha**<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
+| `/pair_history` | GET | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy. **Alpha**
+| `/pair_history` | POST | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy, filtered by a provided list of columns to return. **Alpha**<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
+| `/plot_config` | GET | Get plot config from the strategy (or nothing if not configured). **Alpha**
+| `/strategies` | GET | List strategies in strategy directory. **Alpha**
+| `/strategy/<strategy>` | GET | Get specific Strategy content by strategy class name. **Alpha**<br/>*Params:*<br/>- `<strategy>` (`str`)
+| `/available_pairs` | GET | List available backtest data. **Alpha**
+| `/version` | GET | Show version.
+| `/sysinfo` | GET | Show information about the system load.
+| `/health` | GET | Show bot health (last bot loop).
+
+!!! Warning "Alpha status"
+    Endpoints labeled with *Alpha status* above may change at any time without notice.
+
+### Message WebSocket
+
+The API Server includes a websocket endpoint for subscribing to RPC messages from the freqtrade Bot.
+This can be used to consume real-time data from your bot, such as entry/exit fill messages, whitelist changes, populated indicators for pairs, and more.
+
+This is also used to setup [Producer/Consumer mode](producer-consumer.md) in Freqtrade.
+
+Assuming your rest API is set to `127.0.0.1` on port `8080`, the endpoint is available at `http://localhost:8080/api/v1/message/ws`.
+
+To access the websocket endpoint, the `ws_token` is required as a query parameter in the endpoint URL.
+
+To generate a safe `ws_token` you can run the following code:
+
+``` python
+>>> import secrets
+>>> secrets.token_urlsafe(25)
+'hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q'
+```
+
+You would then add that token under `ws_token` in your `api_server` config. Like so:
+
+``` json
+"api_server": {
+    "enabled": true,
+    "listen_ip_address": "127.0.0.1",
+    "listen_port": 8080,
+    "verbosity": "error",
+    "enable_openapi": false,
+    "jwt_secret_key": "somethingrandom",
+    "CORS_origins": [],
+    "username": "Freqtrader",
+    "password": "SuperSecret1!",
+    "ws_token": "hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q" // <-----
+},
+```
+
+You can now connect to the endpoint at `http://localhost:8080/api/v1/message/ws?token=hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q`.
+
+!!! Danger "Reuse of example tokens"
+    Please do not use the above example token. To make sure you are secure, generate a completely new token.
+
+#### Using the WebSocket
+
+Once connected to the WebSocket, the bot will broadcast RPC messages to anyone who is subscribed to them. To subscribe to a list of messages, you must send a JSON request through the WebSocket like the one below. The `data` key must be a list of message type strings.
+
+``` json
+{
+  "type": "subscribe",
+  "data": ["whitelist", "analyzed_df"] // A list of string message types
+}
+```
+
+For a list of message types, please refer to the RPCMessageType enum in `freqtrade/enums/rpcmessagetype.py`
+
+Now anytime those types of RPC messages are sent in the bot, you will receive them through the WebSocket as long as the connection is active. They typically take the same form as the request:
+
+``` json
+{
+  "type": "analyzed_df",
+  "data": {
+      "key": ["NEO/BTC", "5m", "spot"],
+      "df": {}, // The dataframe
+      "la": "2022-09-08 22:14:41.457786+00:00"
+  }
+}
+```
+
+#### Reverse Proxy setup
+
+When using [Nginx](https://nginx.org/en/docs/), the following configuration is required for WebSockets to work (Note this configuration is incomplete, it's missing some information and can not be used as is):
+
+Please make sure to replace `<freqtrade_listen_ip>` (and the subsequent port) with the IP and Port matching your configuration/setup.
+
+```
+http {
+    map $http_upgrade $connection_upgrade {
+        default upgrade;
+        '' close;
+    }
+
+    #...
+
+    server {
+        #...
+
+        location / {
+            proxy_http_version 1.1;
+            proxy_pass http://<freqtrade_listen_ip>:8080;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection $connection_upgrade;
+            proxy_set_header Host $host;
+        }
+    }
+}
+```
+
+To properly configure your reverse proxy (securely), please consult it's documentation for proxying websockets.
+
+- **Traefik**: Traefik supports websockets out of the box, see the [documentation](https://doc.traefik.io/traefik/)
+- **Caddy**: Caddy v2 supports websockets out of the box, see the [documentation](https://caddyserver.com/docs/v2-upgrade#proxy)
+
+!!! Tip "SSL certificates"
+    You can use tools like certbot to setup ssl certificates to access your bot's UI through encrypted connection by using any of the above reverse proxies.
+    While this will protect your data in transit, we do not recommend to run the freqtrade API outside of your private network (VPN, SSH tunnel).
+
+### OpenAPI interface
+
+To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.
+This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs - but it'll depend on your settings.
+
+### Advanced API usage using JWT tokens
+
+!!! Note
+    The below should be done in an application (a Freqtrade REST API client, which fetches info via API), and is not intended to be used on a regular basis.
+
+Freqtrade's REST API also offers JWT (JSON Web Tokens).
+You can login using the following command, and subsequently use the resulting access_token.
+
+``` bash
+> curl -X POST --user Freqtrader http://localhost:8080/api/v1/token/login
+{"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiMmEwYmY0NWUtMjhmOS00YTUzLTlmNzItMmM5ZWVlYThkNzc2IiwiZXhwIjoxNTg5MTIwNTgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.qt6MAXYIa-l556OM7arBvYJ0SDI9J8bIk3_glDujF5g","refresh_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiZWQ1ZWI3YjAtYjMwMy00YzAyLTg2N2MtNWViMjIxNWQ2YTMxIiwiZXhwIjoxNTkxNzExNjgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJ0eXBlIjoicmVmcmVzaCJ9.d1AT_jYICyTAjD0fiQAr52rkRqtxCjUGEMwlNuuzgNQ"}
+
+> access_token="eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiMmEwYmY0NWUtMjhmOS00YTUzLTlmNzItMmM5ZWVlYThkNzc2IiwiZXhwIjoxNTg5MTIwNTgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.qt6MAXYIa-l556OM7arBvYJ0SDI9J8bIk3_glDujF5g"
+# Use access_token for authentication
+> curl -X GET --header "Authorization: Bearer ${access_token}" http://localhost:8080/api/v1/count
+
+```
+
+Since the access token has a short timeout (15 min) - the `token/refresh` request should be used periodically to get a fresh access token:
+
+``` bash
+> curl -X POST --header "Authorization: Bearer ${refresh_token}"http://localhost:8080/api/v1/token/refresh
+{"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk5NzQsIm5iZiI6MTU4OTExOTk3NCwianRpIjoiMDBjNTlhMWUtMjBmYS00ZTk0LTliZjAtNWQwNTg2MTdiZDIyIiwiZXhwIjoxNTg5MTIwODc0LCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.1seHlII3WprjjclY6DpRhen0rqdF4j6jbvxIhUFaSbs"}
+```
+
+--8<-- "includes/cors.md"
diff --git a/docs/sql_cheatsheet.md b/docs/sql_cheatsheet.md
index 065f264f1..21e03d695 100644
--- a/docs/sql_cheatsheet.md
+++ b/docs/sql_cheatsheet.md
@@ -1,13 +1,36 @@
 # SQL Helper
-This page constains some help if you want to edit your sqlite db.
+
+This page contains some help if you want to query your sqlite db.
+
+!!! Tip "Other Database systems"
+    To use other Database Systems like PostgreSQL or MariaDB, you can use the same queries, but you need to use the respective client for the database system. [Click here](advanced-setup.md#use-a-different-database-system) to learn how to setup a different database system with freqtrade.
+
+!!! Warning
+    If you are not familiar with SQL, you should be very careful when running queries on your database.  
+    Always make sure to have a backup of your database before running any queries.
 
 ## Install sqlite3
-**Ubuntu/Debian installation**
+
+Sqlite3 is a terminal based sqlite application.
+Feel free to use a visual Database editor like SqliteBrowser if you feel more comfortable with that.
+
+### Ubuntu/Debian installation
+
 ```bash
 sudo apt-get install sqlite3
 ```
 
+### Using sqlite3 via docker
+
+The freqtrade docker image does contain sqlite3, so you can edit the database without having to install anything on the host system.
+
+``` bash
+docker compose exec freqtrade /bin/bash
+sqlite3 <database-file>.sqlite
+```
+
 ## Open the DB
+
 ```bash
 sqlite3
 .open <filepath>
@@ -16,75 +39,83 @@ sqlite3
 ## Table structure
 
 ### List tables
+
 ```bash
 .tables
 ```
 
 ### Display table structure
+
 ```bash
 .schema <table_name>
 ```
 
-### Trade table structure
-```sql
-CREATE TABLE trades (
-	id INTEGER NOT NULL,
-	exchange VARCHAR NOT NULL,
-	pair VARCHAR NOT NULL,
-	is_open BOOLEAN NOT NULL,
-	fee FLOAT NOT NULL,
-	open_rate FLOAT,
-	close_rate FLOAT,
-	close_profit FLOAT,
-	stake_amount FLOAT NOT NULL,
-	amount FLOAT,
-	open_date DATETIME NOT NULL,
-	close_date DATETIME,
-	open_order_id VARCHAR,
-	PRIMARY KEY (id),
-	CHECK (is_open IN (0, 1))
-);
-```
-
-## Get all trades in the table
+### Get all trades in the table
 
 ```sql
 SELECT * FROM trades;
 ```
 
-## Fix trade still open after a /forcesell
+## Destructive queries
+
+Queries that write to the database.
+These queries should usually not be necessary as freqtrade tries to handle all database operations itself - or exposes them via API or telegram commands.
+
+!!! Warning
+    Please make sure you have a backup of your database before running any of the below queries.
+
+!!! Danger
+    You should also **never** run any writing query (`update`, `insert`, `delete`) while a bot is connected to the database.
+    This can and will lead to data corruption - most likely, without the possibility of recovery.
+
+### Fix trade still open after a manual exit on the exchange
+
+!!! Warning
+    Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, /forceexit <tradeid> should be used to accomplish the same thing.  
+    It is strongly advised to backup your database file before making any manual changes.
+
+!!! Note
+    This should not be necessary after /forceexit, as force_exit orders are closed automatically by the bot on the next iteration.
 
 ```sql
 UPDATE trades
-SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate  
+SET is_open=0,
+  close_date=<close_date>,
+  close_rate=<close_rate>,
+  close_profit = close_rate / open_rate - 1,
+  close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
+  exit_reason=<exit_reason>
 WHERE id=<trade_ID_to_update>;
 ```
 
-**Example:**
+#### Example
+
 ```sql
 UPDATE trades
-SET is_open=0, close_date='2017-12-20 03:08:45.103418', close_rate=0.19638016, close_profit=0.0496  
+SET is_open=0,
+  close_date='2020-06-20 03:08:45.103418',
+  close_rate=0.19638016,
+  close_profit=0.0496,
+  close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
+  exit_reason='force_exit'  
 WHERE id=31;
 ```
 
-## Insert manually a new trade
+### Remove trade from the database
+
+!!! Tip "Use RPC Methods to delete trades"
+    Consider using `/delete <tradeid>` via telegram or rest API. That's the recommended way to deleting trades.
+
+If you'd still like to remove a trade from the database directly, you can use the below query.
+
+!!! Danger
+    Some systems (Ubuntu) disable foreign keys in their sqlite3 packaging. When using sqlite - please ensure that foreign keys are on by running `PRAGMA foreign_keys = ON` before the above query.
 
 ```sql
-INSERT 
-INTO trades (exchange, pair, is_open, fee, open_rate, stake_amount, amount, open_date) 
-VALUES ('BITTREX', 'BTC_<COIN>', 1, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')
+DELETE FROM trades WHERE id = <tradeid>;
+
+DELETE FROM trades WHERE id = 31;
 ```
 
-**Example:**
-```sql
-INSERT INTO trades (exchange, pair, is_open, fee, open_rate, stake_amount, amount, open_date) VALUES ('BITTREX', 'BTC_ETC', 1, 0.0025, 0.00258580, 0.002, 0.7715262081, '2017-11-28 12:44:24.000000')
-```
-
-## Fix wrong fees in the table
-If your DB was created before 
-[PR#200](https://github.com/gcarq/freqtrade/pull/200) was merged
-(before 12/23/17).
-
-```sql
-UPDATE trades SET fee=0.0025 WHERE fee=0.005;
-```
\ No newline at end of file
+!!! Warning
+    This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.
diff --git a/docs/stoploss.md b/docs/stoploss.md
new file mode 100644
index 000000000..7fbe6dabc
--- /dev/null
+++ b/docs/stoploss.md
@@ -0,0 +1,259 @@
+# Stop Loss
+
+The `stoploss` configuration parameter is loss as ratio that should trigger a sale.
+For example, value `-0.10` will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
+Stoploss calculations do include fees, so a stoploss of -10% is placed exactly 10% below the entry point.
+
+Most of the strategy files already include the optimal `stoploss` value.
+
+!!! Info
+    All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration.  
+    <ins>Configuration values will override the strategy values.</ins>
+
+## Stop Loss On-Exchange/Freqtrade
+
+Those stoploss modes can be *on exchange* or *off exchange*.
+
+These modes can be configured with these values:
+
+``` python
+    'emergency_exit': 'market',
+    'stoploss_on_exchange': False
+    'stoploss_on_exchange_interval': 60,
+    'stoploss_on_exchange_limit_ratio': 0.99
+```
+
+Stoploss on exchange is only supported for the following exchanges, and not all exchanges support both stop-limit and stop-market.
+The Order-type will be ignored if only one mode is available.
+
+| Exchange | stop-loss type |
+|----------|-------------|
+| Binance  | limit |
+| Binance Futures  | market, limit |
+| Bingx    | market, limit |
+| HTX      | limit |
+| kraken   | market, limit |
+| Gate     | limit |
+| Okx      | limit |
+| Kucoin   | stop-limit, stop-market|
+| Hyperliquid (futures only)   | limit |
+
+!!! Note "Tight stoploss"
+    <ins>Do not set too low/tight stoploss value when using stop loss on exchange!</ins>  
+    If set to low/tight you will have greater risk of missing fill on the order and stoploss will not work.
+
+### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
+
+Enable or Disable stop loss on exchange.
+If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order fills. This will protect you against sudden crashes in market, as the order execution happens purely within the exchange, and has no potential network overhead.
+
+If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.  
+`stoploss` defines the stop-price where the limit order is placed - and limit should be slightly below this.  
+If an exchange supports both limit and market stoploss orders, then the value of `stoploss` will be used to determine the stoploss type.  
+
+Calculation example: we bought the asset at 100\$.  
+Stop-price is 95\$, then limit would be `95 * 0.99 = 94.05$` - so the limit order fill can happen between 95$ and 94.05$.  
+
+For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
+
+!!! Note
+    If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
+
+### stoploss_on_exchange_interval
+
+In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.  
+The bot cannot do these every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
+So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
+This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
+
+### stoploss_price_type
+
+!!! Warning "Only applies to futures"
+    `stoploss_price_type` only applies to futures markets (on exchanges where it's available).
+    Freqtrade will perform a validation of this setting on startup, failing to start if an invalid setting for your exchange has been selected.
+    Supported price types are gonna differs between each exchanges. Please check with your exchange on which price types it supports.
+
+Stoploss on exchange on futures markets can trigger on different price types.
+The naming for these prices in exchange terminology often varies, but is usually something around "last" (or "contract price" ), "mark" and "index".
+
+Acceptable values for this setting are `"last"`, `"mark"` and `"index"` - which freqtrade will transfer automatically to the corresponding API type, and place the [stoploss on exchange](#stoploss_on_exchange-and-stoploss_on_exchange_limit_ratio) order correspondingly.
+
+### force_exit
+
+`force_exit` is an optional value, which defaults to the same value as `exit` and is used when sending a `/forceexit` command from Telegram or from the Rest API.
+
+### force_entry
+
+`force_entry` is an optional value, which defaults to the same value as `entry` and is used when sending a `/forceentry` command from Telegram or from the Rest API.
+
+### emergency_exit
+
+`emergency_exit` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
+The below is the default which is used if not changed in strategy or configuration file.
+
+Example from strategy file:
+
+``` python
+order_types = {
+    "entry": "limit",
+    "exit": "limit",
+    "emergency_exit": "market",
+    "stoploss": "market",
+    "stoploss_on_exchange": True,
+    "stoploss_on_exchange_interval": 60,
+    "stoploss_on_exchange_limit_ratio": 0.99
+}
+```
+
+## Stop Loss Types
+
+At this stage the bot contains the following stoploss support modes:
+
+1. Static stop loss.
+2. Trailing stop loss.
+3. Trailing stop loss, custom positive loss.
+4. Trailing stop loss only once the trade has reached a certain offset.
+5. [Custom stoploss function](strategy-callbacks.md#custom-stoploss)
+
+### Static Stop Loss
+
+This is very simple, you define a stop loss of x (as a ratio of price, i.e. x * 100% of price). This will try to sell the asset once the loss exceeds the defined loss.
+
+Example of stop loss:
+
+``` python
+    stoploss = -0.10
+```
+
+For example, simplified math:
+
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+
+### Trailing Stop Loss
+
+The initial value for this is `stoploss`, just as you would define your static Stop loss.
+To enable trailing stoploss:
+
+``` python
+    stoploss = -0.10
+    trailing_stop = True
+```
+
+This will now activate an algorithm, which automatically moves the stop loss up every time the price of your asset increases.
+
+For example, simplified math:
+
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+* assuming the asset now increases to 102$
+* the stop loss will now be -10% of 102$ = 91.8$
+* now the asset drops in value to 101\$, the stop loss will still be 91.8$ and would trigger at 91.8$.
+
+In summary: The stoploss will be adjusted to be always be -10% of the highest observed price.
+
+### Trailing stop loss, different positive loss
+
+You could also have a default stop loss when you are in the red with your buy (buy - fee), but once you hit a positive result (or an offset you define) the system will utilize a new stop loss, with a different value.
+For example, your default stop loss is -10%, but once you have reached profitability (example 0.1%) a different trailing stoploss will be used.
+
+!!! Note
+    If you want the stoploss to only be changed when you break even of making a profit (what most users want) please refer to next section with [offset enabled](#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset).
+
+Both values require `trailing_stop` to be set to true and `trailing_stop_positive` with a value.
+
+``` python
+    stoploss = -0.10
+    trailing_stop = True
+    trailing_stop_positive = 0.02
+    trailing_stop_positive_offset = 0.0
+    trailing_only_offset_is_reached = False  # Default - not necessary for this example
+```
+
+For example, simplified math:
+
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+* assuming the asset now increases to 102$
+* the stop loss will now be -2% of 102$ = 99.96$ (99.96$ stop loss will be locked in and will follow asset price increments with -2%)
+* now the asset drops in value to 101\$, the stop loss will still be 99.96$ and would trigger at 99.96$
+
+The 0.02 would translate to a -2% stop loss.
+Before this, `stoploss` is used for the trailing stoploss.
+
+!!! Tip "Use an offset to change your stoploss"
+    Use `trailing_stop_positive_offset` to ensure that your new trailing stoploss will be in profit by setting `trailing_stop_positive_offset` higher than `trailing_stop_positive`. Your first new stoploss value will then already have locked in profits.
+
+    Example with simplified math:
+
+    ``` python
+        stoploss = -0.10
+        trailing_stop = True
+        trailing_stop_positive = 0.02
+        trailing_stop_positive_offset = 0.03
+    ```
+
+    * the bot buys an asset at a price of 100$
+    * the stop loss is defined at -10%, so the stop loss would get triggered once the asset drops below 90$
+    * assuming the asset now increases to 102$
+    * the stoploss will now be at 91.8$ - 10% below the highest observed rate
+    * assuming the asset now increases to 103.5$ (above the offset configured)
+    * the stop loss will now be -2% of 103.5$ = 101.43$
+    * now the asset drops in value to 102\$, the stop loss will still be 101.43$ and would trigger once price breaks below 101.43$
+
+### Trailing stop loss only once the trade has reached a certain offset
+
+You can also keep a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
+
+If `trailing_only_offset_is_reached = True` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss` and is not trailing.
+Leaving this value as `trailing_only_offset_is_reached=False` will allow the trailing stoploss to start trailing as soon as the asset price increases above the initial entry price.
+
+This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset.
+
+Configuration (offset is buy-price + 3%):
+
+``` python
+    stoploss = -0.10
+    trailing_stop = True
+    trailing_stop_positive = 0.02
+    trailing_stop_positive_offset = 0.03
+    trailing_only_offset_is_reached = True
+```
+
+For example, simplified math:
+
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at -10%
+* the stop loss would get triggered once the asset drops below 90$
+* stoploss will remain at 90$ unless asset increases to or above the configured offset
+* assuming the asset now increases to 103$ (where we have the offset configured)
+* the stop loss will now be -2% of 103$ = 100.94$
+* now the asset drops in value to 101\$, the stop loss will still be 100.94$ and would trigger at 100.94$
+
+!!! Tip
+    Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
+
+## Stoploss and Leverage
+
+Stoploss should be thought of as "risk on this trade" - so a stoploss of 10% on a 100$ trade means you are willing to lose 10$ (10%) on this trade - which would trigger if the price moves 10% to the downside.
+
+When using leverage, the same principle is applied - with stoploss defining the risk on the trade (the amount you are willing to lose).
+
+Therefore, a stoploss of 10% on a 10x trade would trigger on a 1% price move.
+If your stake amount (own capital) was 100$ - this trade would be 1000$ at 10x (after leverage).
+If price moves 1% - you've lost 10$ of your own capital - therefore stoploss will trigger in this case.
+
+Make sure to be aware of this, and avoid using too tight stoploss (at 10x leverage, 10% risk may be too little to allow the trade to "breath" a little).
+
+## Changing stoploss on open trades
+
+A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the `/reload_config` command (alternatively, completely stopping and restarting the bot also works).
+
+The new stoploss value will be applied to open trades (and corresponding log-messages will be generated).
+
+### Limitations
+
+Stoploss values cannot be changed if `trailing_stop` is enabled and the stoploss has already been adjusted, or if [Edge](edge.md) is enabled (since Edge would recalculate stoploss based on the current market situation).
diff --git a/docs/strategy-101.md b/docs/strategy-101.md
new file mode 100644
index 000000000..9226537e1
--- /dev/null
+++ b/docs/strategy-101.md
@@ -0,0 +1,198 @@
+# Freqtrade Strategies 101: A Quick Start for Strategy Development
+
+For the purposes of this quick start, we are assuming you are familiar with the basics of trading, and have read the 
+[Freqtrade basics](bot-basics.md) page.
+
+## Required Knowledge
+
+A strategy in Freqtrade is a Python class that defines the logic for buying and selling cryptocurrency `assets`.
+
+Assets are defined as `pairs`, which represent the `coin` and the `stake`. The coin is the asset you are trading using another currency as the stake.
+
+Data is supplied by the exchange in the form of `candles`, which are made up of a six values: `date`, `open`, `high`, `low`, `close` and `volume`.
+
+`Technical analysis` functions analyse the candle data using various computational and statistical formulae, and produce secondary values called `indicators`.
+
+Indicators are analysed on the asset pair candles to generate `signals`.
+
+Signals are turned into `orders` on a cryptocurrency `exchange`, i.e. `trades`.
+
+We use the terms `entry` and `exit` instead of `buying` and `selling` because Freqtrade supports both `long` and `short` trades.
+
+- **long**: You buy the coin based on a stake, e.g. buying the coin BTC using USDT as your stake, and you make a profit by selling the coin at a higher rate than you paid for. In long trades, profits are made by the coin value going up versus the stake.
+- **short**: You borrow capital from the exchange in the form of the coin, and you pay back the stake value of the coin later. In short trades profits are made by the coin value going down versus the stake (you pay the loan off at a lower rate).
+
+Whilst Freqtrade supports spot and futures markets for certain exchanges, for simplicity we will focus on spot (long) trades only.
+
+## Structure of a Basic Strategy
+
+### Main dataframe
+
+Freqtrade strategies use a tabular data structure with rows and columns known as a `dataframe` to generate signals to enter and exit trades.
+
+Each pair in your configured pairlist has its own dataframe. Dataframes are indexed by the `date` column, e.g. `2024-06-31 12:00`.
+
+The next 5 columns represent the `open`, `high`, `low`, `close` and `volume` (OHLCV) data.
+
+### Populate indicator values
+
+The `populate_indicators` function adds columns to the dataframe that represent the technical analysis indicator values.
+
+Examples of common indicators include Relative Strength Index, Bollinger Bands, Money Flow Index, Moving Average, and Average True Range.
+
+Columns are added to the dataframe by calling technical analysis functions, e.g. ta-lib's RSI function `ta.RSI()`, and assigning them to a column name, e.g. `rsi`
+
+```python
+dataframe['rsi'] = ta.RSI(dataframe)
+```
+
+??? Hint "Technical Analysis libraries"
+    Different libraries work in different ways to generate indicator values. Please check the documentation of each library to understand
+    how to integrate it into your strategy. You can also check the [Freqtrade example strategies](https://github.com/freqtrade/freqtrade-strategies) to give you ideas.
+
+### Populate entry signals
+
+The `populate_entry_trend` function defines conditions for an entry signal.
+
+The dataframe column `enter_long` is added to the dataframe, and when a value of `1` is in this column, Freqtrade sees an entry signal.
+
+??? Hint "Shorting"
+    To enter short trades, use the `enter_short` column.
+
+### Populate exit signals
+
+The `populate_exit_trend` function defines conditions for an exit signal.
+
+The dataframe column `exit_long` is added to the dataframe, and when a value of `1` is in this column, Freqtrade sees an exit signal.
+
+??? Hint "Shorting"
+    To exit short trades, use the `exit_short` column.
+
+## A simple strategy
+
+Here is a minimal example of a Freqtrade strategy:
+
+```python
+from freqtrade.strategy import IStrategy
+from pandas import DataFrame
+import talib.abstract as ta
+
+class MyStrategy(IStrategy):
+
+    timeframe = '15m'
+
+    # set the initial stoploss to -10%
+    stoploss = -0.10
+
+    # exit profitable positions at any time when the profit is greater than 1%
+    minimal_roi = {"0": 0.01}
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # generate values for technical analysis indicators
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # generate entry signals based on indicator values
+        dataframe.loc[
+            (dataframe['rsi'] < 30),
+            'enter_long'] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # generate exit signals based on indicator values
+        dataframe.loc[
+            (dataframe['rsi'] > 70),
+            'exit_long'] = 1
+
+        return dataframe
+```
+
+## Making trades
+
+When a signal is found (a `1` in an entry or exit column), Freqtrade will attempt to make an order, i.e. a `trade` or `position`.
+
+Each new trade position takes up a `slot`. Slots represent the maximum number of concurrent new trades that can be opened.
+
+The number of slots is defined by the `max_open_trades` [configuration](configuration.md) option.
+
+However, there can be a range of scenarios where generating a signal does not always create a trade order. These include:
+
+- not enough remaining stake to buy an asset, or funds in your wallet to sell an asset (including any fees)
+- not enough remaining free slots for a new trade to be opened (the number of positions you have open equals the `max_open_trades` option)
+- there is already an open trade for a pair (Freqtrade cannot stack positions - however it can [adjust existing positions](strategy-callbacks.md#adjust-trade-position))
+- if an entry and exit signal is present on the same candle, they are considered as [colliding](strategy-customization.md#colliding-signals), and no order will be raised
+- the strategy actively rejects the trade order due to logic you specify by using one of the relevant [entry](strategy-callbacks.md#trade-entry-buy-order-confirmation) or [exit](strategy-callbacks.md#trade-exit-sell-order-confirmation) callbacks
+
+Read through the [strategy customization](strategy-customization.md) documentation for more details.
+
+## Backtesting and forward testing
+
+Strategy development can be a long and frustrating process, as turning our human "gut instincts" into a working computer-controlled
+("algo") strategy is not always straightforward.
+
+Therefore a strategy should be tested to verify that it is going to work as intended.
+
+Freqtrade has two testing modes:
+
+- **backtesting**: using historical data that you [download from an exchange](data-download.md), backtesting is a quick way to assess performance of a strategy. However, it can be very easy to distort results so a strategy will look a lot more profitable than it really is. Check the [backtesting documentation](backtesting.md) for more information.
+- **dry run**: often referred to as _forward testing_, dry runs use real time data from the exchange. However, any signals that would result in trades are tracked as normal by Freqtrade, but do not have any trades opened on the exchange itself. Forward testing runs in real time, so whilst it takes longer to get results it is a much more reliable indicator of **potential** performance than backtesting.
+
+Dry runs are enabled by setting `dry_run` to true in your [configuration](configuration.md#using-dry-run-mode).
+
+!!! Warning "Backtests can be very inaccurate"
+    There are many reasons why backtest results may not match reality. Please check the [backtesting assumptions](backtesting.md#assumptions-made-by-backtesting) and [common strategy mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) documentation.
+    Some websites that list and rank Freqtrade strategies show impressive backtest results. Do not assume these results are achieveable or realistic.
+
+??? Hint "Useful commands"
+    Freqtrade includes two useful commands to check for basic flaws in strategies: [lookahead-analysis](lookahead-analysis.md) and [recursive-analysis](recursive-analysis.md).
+
+### Assessing backtesting and dry run results
+
+Always dry run your strategy after backtesting it to see if backtesting and dry run results are sufficiently similar.
+
+If there is any significant difference, verify that your entry and exit signals are consistent and appear on the same candles between the two modes. However, there will always be differences between dry runs and backtests:
+
+- Backtesting assumes all orders fill. In dry runs this might not be the case if using limit orders or there is no volume on the exchange.
+- Following an entry signal on candle close, backtesting assumes trades enter at the next candle's open price (unless you have custom pricing callbacks in your strategy). In dry runs, there is often a delay between signals and trades opening.
+  This is because when new candles come in on your main timeframe, e.g. every 5 minutes, it takes time for Freqtrade to analyse all pair dataframes. Therefore, Freqtrade will attempt to open trades a few seconds (ideally a small a delay as possible)
+  after candle open.
+- As entry rates in dry runs might not match backtesting, this means profit calculations will also differ. Therefore, it is normal if ROI, stoploss, trailing stoploss and callback exits are not identical.
+- The more computational "lag" you have between new candles coming in and your signals being raised and trades being opened will result in greater price unpredictability. Make sure your computer is powerful enough to process the data for the number 
+  of pairs you have in your pairlist within a reasonable time. Freqtrade will warn you in the logs if there are significant data processing delays.
+
+## Controlling or monitoring a running bot
+
+Once your bot is running in dry or live mode, Freqtrade has five mechanisms to control or monitor a running bot:
+
+- **[FreqUI](freq-ui.md)**: The easiest to get started with, FreqUI is a web interface to see and control current activity of your bot.
+- **[Telegram](telegram-usage.md)**: On mobile devices, Telegram integration is available to get alerts about your bot activity and to control certain aspects.
+- **[FTUI](https://github.com/freqtrade/ftui)**: FTUI is a terminal (command line) interface to Freqtrade, and allows monitoring of a running bot only.
+- **[REST API](rest-api.md)**: The REST API allows programmers to develop their own tools to interact with a Freqtrade bot.
+- **[Webhooks](webhook-config.md)**: Freqtrade can send information to other services, e.g. discord, by webhooks.
+
+### Logs
+
+Freqtrade generates extensive debugging logs to help you understand what's happening. Please familiarise yourself with the information and error messages you might see in your bot logs.
+
+## Final Thoughts
+
+Algo trading is difficult, and most public strategies are not good performers due to the time and effort to make a strategy work profitably in multiple scenarios.
+
+Therefore, taking public strategies and using backtests as a way to assess performance is often problematic. However, Freqtrade provides useful ways to help you make decisions and do your due diligence.
+
+There are many different ways to achieve profitability, and there is no one single tip, trick or config option that will fix a poorly performing strategy.
+
+Freqtrade is an open source platform with a large and helpful community - make sure to visit our [discord channel](https://discord.gg/p7nuUNVfP7) to discuss your strategy with others!
+
+As always, only invest what you are willing to lose.
+
+## Conclusion
+
+Developing a strategy in Freqtrade involves defining entry and exit signals based on technical indicators. By following the structure and methods outlined above, you can create and test your own trading strategies.
+
+Common questions and answers are available on our [FAQ](faq.md).
+
+To continue, refer to the more in-depth [Freqtrade strategy customization documentation](strategy-customization.md).
diff --git a/docs/strategy-advanced.md b/docs/strategy-advanced.md
new file mode 100644
index 000000000..8bb23c5da
--- /dev/null
+++ b/docs/strategy-advanced.md
@@ -0,0 +1,329 @@
+# Advanced Strategies
+
+This page explains some advanced concepts available for strategies.
+If you're just getting started, please familiarize yourself with the [Freqtrade basics](bot-basics.md) and methods described in [Strategy Customization](strategy-customization.md) first.
+
+The call sequence of the methods described here is covered under [bot execution logic](bot-basics.md#bot-execution-logic). Those docs are also helpful in deciding which method is most suitable for your customisation needs.
+
+!!! Note
+    Callback methods should *only* be implemented if a strategy uses them.
+
+!!! Tip
+    Start off with a strategy template containing all available callback methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
+
+## Storing information (Persistent)
+
+Freqtrade allows storing/retrieving user custom information associated with a specific trade in the database.
+
+Using a trade object, information can be stored using `trade.set_custom_data(key='my_key', value=my_value)` and retrieved using `trade.get_custom_data(key='my_key')`. Each data entry is associated with a trade and a user supplied key (of type `string`). This means that this can only be used in callbacks that also provide a trade object.
+
+For the data to be able to be stored within the database, freqtrade must serialized the data. This is done by converting the data to a JSON formatted string.
+Freqtrade will attempt to reverse this action on retrieval, so from a strategy perspective, this should not be relevant.
+
+```python
+from freqtrade.persistence import Trade
+from datetime import timedelta
+
+class AwesomeStrategy(IStrategy):
+
+    def bot_loop_start(self, **kwargs) -> None:
+        for trade in Trade.get_open_order_trades():
+            fills = trade.select_filled_orders(trade.entry_side)
+            if trade.pair == 'ETH/USDT':
+                trade_entry_type = trade.get_custom_data(key='entry_type')
+                if trade_entry_type is None:
+                    trade_entry_type = 'breakout' if 'entry_1' in trade.enter_tag else 'dip'
+                elif fills > 1:
+                    trade_entry_type = 'buy_up'
+                trade.set_custom_data(key='entry_type', value=trade_entry_type)
+        return super().bot_loop_start(**kwargs)
+
+    def adjust_entry_price(self, trade: Trade, order: Order | None, pair: str,
+                           current_time: datetime, proposed_rate: float, current_order_rate: float,
+                           entry_tag: str | None, side: str, **kwargs) -> float:
+        # Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
+        if (
+            pair == 'BTC/USDT' 
+            and entry_tag == 'long_sma200' 
+            and side == 'long' 
+            and (current_time - timedelta(minutes=10)) > trade.open_date_utc 
+            and order.filled == 0.0
+        ):
+            dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+            current_candle = dataframe.iloc[-1].squeeze()
+            # store information about entry adjustment
+            existing_count = trade.get_custom_data('num_entry_adjustments', default=0)
+            if not existing_count:
+                existing_count = 1
+            else:
+                existing_count += 1
+            trade.set_custom_data(key='num_entry_adjustments', value=existing_count)
+
+            # adjust order price
+            return current_candle['sma_200']
+
+        # default: maintain existing order
+        return current_order_rate
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs):
+
+        entry_adjustment_count = trade.get_custom_data(key='num_entry_adjustments')
+        trade_entry_type = trade.get_custom_data(key='entry_type')
+        if entry_adjustment_count is None:
+            if current_profit > 0.01 and (current_time - timedelta(minutes=100) > trade.open_date_utc):
+                return True, 'exit_1'
+        else
+            if entry_adjustment_count > 0 and if current_profit > 0.05:
+                return True, 'exit_2'
+            if trade_entry_type == 'breakout' and current_profit > 0.1:
+                return True, 'exit_3
+
+        return False, None
+```
+
+The above is a simple example - there are simpler ways to retrieve trade data like entry-adjustments.
+
+!!! Note
+    It is recommended that simple data types are used `[bool, int, float, str]` to ensure no issues when serializing the data that needs to be stored.
+    Storing big junks of data may lead to unintended side-effects, like a database becoming big (and as a consequence, also slow).
+
+!!! Warning "Non-serializable data"
+    If supplied data cannot be serialized a warning is logged and the entry for the specified `key` will contain `None` as data.
+
+??? Note "All attributes"
+    custom-data has the following accessors through the Trade object (assumed as `trade` below):
+
+    * `trade.get_custom_data(key='something', default=0)` - Returns the actual value given in the type provided.
+    * `trade.get_custom_data_entry(key='something')` - Returns the entry - including metadata. The value is accessible via `.value` property.
+    * `trade.set_custom_data(key='something', value={'some': 'value'})` - set or update the corresponding key for this trade. Value must be serializable - and we recommend to keep the stored data relatively small.
+
+    "value" can be any type (both in setting and receiving) - but must be json serializable.
+
+## Storing information (Non-Persistent)
+
+!!! Warning "Deprecated"
+    This method of storing information is deprecated and we do advise against using non-persistent storage.  
+    Please use [Persistent Storage](#storing-information-persistent) instead.
+
+    It's content has therefore been collapsed.
+
+??? Abstract "Storing information"
+    Storing information can be accomplished by creating a new dictionary within the strategy class.
+
+    The name of the variable can be chosen at will, but should be prefixed with `custom_` to avoid naming collisions with predefined strategy variables.
+
+    ```python
+    class AwesomeStrategy(IStrategy):
+        # Create custom dictionary
+        custom_info = {}
+
+        def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            # Check if the entry already exists
+            if not metadata["pair"] in self.custom_info:
+                # Create empty entry for this pair
+                self.custom_info[metadata["pair"]] = {}
+
+            if "crosstime" in self.custom_info[metadata["pair"]]:
+                self.custom_info[metadata["pair"]]["crosstime"] += 1
+            else:
+                self.custom_info[metadata["pair"]]["crosstime"] = 1
+    ```
+
+    !!! Warning
+        The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
+
+    !!! Note
+        If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
+
+## Dataframe access
+
+You may access dataframe in various strategy functions by querying it from dataprovider.
+
+``` python
+from freqtrade.exchange import timeframe_to_prev_date
+
+class AwesomeStrategy(IStrategy):
+    def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
+                           rate: float, time_in_force: str, exit_reason: str,
+                           current_time: 'datetime', **kwargs) -> bool:
+        # Obtain pair dataframe.
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+
+        # Obtain last available candle. Do not use current_time to look up latest candle, because 
+        # current_time points to current incomplete candle whose data is not available.
+        last_candle = dataframe.iloc[-1].squeeze()
+        # <...>
+
+        # In dry/live runs trade open date will not match candle open date therefore it must be 
+        # rounded.
+        trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
+        # Look up trade candle.
+        trade_candle = dataframe.loc[dataframe['date'] == trade_date]
+        # trade_candle may be empty for trades that just opened as it is still incomplete.
+        if not trade_candle.empty:
+            trade_candle = trade_candle.squeeze()
+            # <...>
+```
+
+!!! Warning "Using .iloc[-1]"
+    You can use `.iloc[-1]` here because `get_analyzed_dataframe()` only returns candles that backtesting is allowed to see.
+    This will not work in `populate_*` methods, so make sure to not use `.iloc[]` in that area.
+    Also, this will only work starting with version 2021.5.
+
+***
+
+## Enter Tag
+
+When your strategy has multiple buy signals, you can name the signal that triggered.
+Then you can access your buy signal on `custom_exit`
+
+```python
+def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (dataframe['rsi'] < 35) &
+            (dataframe['volume'] > 0)
+        ),
+        ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
+
+    return dataframe
+
+def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
+                current_profit: float, **kwargs):
+    dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+    last_candle = dataframe.iloc[-1].squeeze()
+    if trade.enter_tag == 'buy_signal_rsi' and last_candle['rsi'] > 80:
+        return 'sell_signal_rsi'
+    return None
+
+```
+
+!!! Note
+    `enter_tag` is limited to 100 characters, remaining data will be truncated.
+
+!!! Warning
+    There is only one `enter_tag` column, which is used for both long and short trades.
+    As a consequence, this column must be treated as "last write wins" (it's just a dataframe column after all).
+    In fancy situations, where multiple signals collide (or if signals are deactivated again based on different conditions), this can lead to odd results with the wrong tag applied to an entry signal.
+    These results are a consequence of the strategy overwriting prior tags - where the last tag will "stick" and will be the one freqtrade will use.
+
+## Exit tag
+
+Similar to [Entry Tagging](#enter-tag), you can also specify an exit tag.
+
+``` python
+def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (dataframe['rsi'] > 70) &
+            (dataframe['volume'] > 0)
+        ),
+        ['exit_long', 'exit_tag']] = (1, 'exit_rsi')
+
+    return dataframe
+```
+
+The provided exit-tag is then used as sell-reason - and shown as such in backtest results.
+
+!!! Note
+    `exit_reason` is limited to 100 characters, remaining data will be truncated.
+
+## Strategy version
+
+You can implement custom strategy versioning by using the "version" method, and returning the version you would like this strategy to have.
+
+``` python
+def version(self) -> str:
+    """
+    Returns version of the strategy.
+    """
+    return "1.1"
+```
+
+!!! Note
+    You should make sure to implement proper version control (like a git repository) alongside this, as freqtrade will not keep historic versions of your strategy, so it's up to the user to be able to eventually roll back to a prior version of the strategy.
+
+## Derived strategies
+
+The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
+
+``` python title="user_data/strategies/myawesomestrategy.py"
+class MyAwesomeStrategy(IStrategy):
+    ...
+    stoploss = 0.13
+    trailing_stop = False
+    # All other attributes and methods are here as they
+    # should be in any custom strategy...
+    ...
+
+```
+
+``` python title="user_data/strategies/MyAwesomeStrategy2.py"
+from myawesomestrategy import MyAwesomeStrategy
+class MyAwesomeStrategy2(MyAwesomeStrategy):
+    # Override something
+    stoploss = 0.08
+    trailing_stop = True
+```
+
+Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.
+
+While keeping the subclass in the same file is technically possible, it can lead to some problems with hyperopt parameter files, we therefore recommend to use separate strategy files, and import the parent strategy as shown above.
+
+## Embedding Strategies
+
+Freqtrade provides you with an easy way to embed the strategy into your configuration file.
+This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
+in your chosen config file.
+
+### Encoding a string as BASE64
+
+This is a quick example, how to generate the BASE64 string in python
+
+```python
+from base64 import urlsafe_b64encode
+
+with open(file, 'r') as f:
+    content = f.read()
+content = urlsafe_b64encode(content.encode('utf-8'))
+```
+
+The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
+
+```json
+"strategy": "NameOfStrategy:BASE64String"
+```
+
+Please ensure that 'NameOfStrategy' is identical to the strategy name!
+
+## Performance warning
+
+When executing a strategy, one can sometimes be greeted by the following in the logs
+
+> PerformanceWarning: DataFrame is highly fragmented.
+
+This is a warning from [`pandas`](https://github.com/pandas-dev/pandas) and as the warning continues to say:
+use `pd.concat(axis=1)`.
+This can have slight performance implications, which are usually only visible during hyperopt (when optimizing an indicator).
+
+For example:
+
+```python
+for val in self.buy_ema_short.range:
+    dataframe[f'ema_short_{val}'] = ta.EMA(dataframe, timeperiod=val)
+```
+
+should be rewritten to
+
+```python
+frames = [dataframe]
+for val in self.buy_ema_short.range:
+    frames.append(DataFrame({
+        f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
+    }))
+
+# Combine all dataframes, and reassign the original dataframe column
+dataframe = pd.concat(frames, axis=1)
+```
+
+Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existent.
diff --git a/docs/strategy-callbacks.md b/docs/strategy-callbacks.md
new file mode 100644
index 000000000..4564a0f03
--- /dev/null
+++ b/docs/strategy-callbacks.md
@@ -0,0 +1,1109 @@
+# Strategy Callbacks
+
+While the main strategy functions (`populate_indicators()`, `populate_entry_trend()`, `populate_exit_trend()`) should be used in a vectorized way, and are only called [once during backtesting](bot-basics.md#backtesting-hyperopt-execution-logic), callbacks are called "whenever needed".
+
+As such, you should avoid doing heavy calculations in callbacks to avoid delays during operations.
+Depending on the callback used, they may be called when entering / exiting a trade, or throughout the duration of a trade.
+
+Currently available callbacks:
+
+* [`bot_start()`](#bot-start)
+* [`bot_loop_start()`](#bot-loop-start)
+* [`custom_stake_amount()`](#stake-size-management)
+* [`custom_exit()`](#custom-exit-signal)
+* [`custom_stoploss()`](#custom-stoploss)
+* [`custom_entry_price()` and `custom_exit_price()`](#custom-order-price-rules)
+* [`check_entry_timeout()` and `check_exit_timeout()`](#custom-order-timeout-rules)
+* [`confirm_trade_entry()`](#trade-entry-buy-order-confirmation)
+* [`confirm_trade_exit()`](#trade-exit-sell-order-confirmation)
+* [`adjust_trade_position()`](#adjust-trade-position)
+* [`adjust_entry_price()`](#adjust-entry-price)
+* [`leverage()`](#leverage-callback)
+* [`order_filled()`](#order-filled-callback)
+
+!!! Tip "Callback calling sequence"
+    You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
+
+--8<-- "includes/strategy-imports.md"
+
+## Bot start
+
+A simple callback which is called once when the strategy is loaded.
+This can be used to perform actions that must only be performed once and runs after dataprovider and wallet are set
+
+``` python
+import requests
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def bot_start(self, **kwargs) -> None:
+        """
+        Called only once after bot instantiation.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        if self.config["runmode"].value in ("live", "dry_run"):
+            # Assign this to the class by using self.*
+            # can then be used by populate_* methods
+            self.custom_remote_data = requests.get("https://some_remote_source.example.com")
+
+```
+
+During hyperopt, this runs only once at startup.
+
+## Bot loop start
+
+A simple callback which is called once at the start of every bot throttling iteration in dry/live mode (roughly every 5
+seconds, unless configured differently) or once per candle in backtest/hyperopt mode.
+This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
+
+``` python
+# Default imports
+import requests
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def bot_loop_start(self, current_time: datetime, **kwargs) -> None:
+        """
+        Called at the start of the bot iteration (one loop).
+        Might be used to perform pair-independent tasks
+        (e.g. gather some remote resource for comparison)
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        if self.config["runmode"].value in ("live", "dry_run"):
+            # Assign this to the class by using self.*
+            # can then be used by populate_* methods
+            self.remote_data = requests.get("https://some_remote_source.example.com")
+
+```
+
+## Stake size management
+
+Called before entering a trade, makes it possible to manage your position size when placing a new trade.
+
+```python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float | None, max_stake: float,
+                            leverage: float, entry_tag: str | None, side: str,
+                            **kwargs) -> float:
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+        current_candle = dataframe.iloc[-1].squeeze()
+
+        if current_candle["fastk_rsi_1h"] > current_candle["fastd_rsi_1h"]:
+            if self.config["stake_amount"] == "unlimited":
+                # Use entire available wallet during favorable conditions when in compounding mode.
+                return max_stake
+            else:
+                # Compound profits during favorable conditions instead of using a static stake.
+                return self.wallets.get_total_stake_amount() / self.config["max_open_trades"]
+
+        # Use default stake amount.
+        return proposed_stake
+```
+
+Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
+
+!!! Tip
+    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this action will be logged.
+
+!!! Tip
+    Returning `0` or `None` will prevent trades from being placed.
+
+## Custom exit signal
+
+Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed.
+
+Allows to define custom exit signals, indicating that specified position should be sold. This is very useful when we need to customize exit conditions for each individual trade, or if you need trade data to make an exit decision.
+
+For example you could implement a 1:2 risk-reward ROI with `custom_exit()`.
+
+Using `custom_exit()` signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
+
+!!! Note
+    Returning a (none-empty) `string` or `True` from this method is equal to setting exit signal on a candle at specified time. This method is not called when exit signal is set already, or if exit signals are disabled (`use_exit_signal=False`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
+    `custom_exit()` will ignore `exit_profit_only`, and will always be called unless `use_exit_signal=False`, even if there is a new enter signal.
+
+An example of how we can use different indicators depending on the current profit and also exit trades that were open longer than one day:
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
+                    current_profit: float, **kwargs):
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        # Above 20% profit, sell when rsi < 80
+        if current_profit > 0.2:
+            if last_candle["rsi"] < 80:
+                return "rsi_below_80"
+
+        # Between 2% and 10%, sell if EMA-long above EMA-short
+        if 0.02 < current_profit < 0.1:
+            if last_candle["emalong"] > last_candle["emashort"]:
+                return "ema_long_below_80"
+
+        # Sell any positions at a loss if they are held for more than one day.
+        if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
+            return "unclog"
+```
+
+See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
+
+## Custom stoploss
+
+Called for open trade every iteration (roughly every 5 seconds) until a trade is closed.
+
+The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
+
+The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss (before this method is called for the first time for a trade), and is still mandatory.  
+As custom stoploss acts as regular, changing stoploss, it will behave similar to `trailing_stop` - and trades exiting due to this will have the exit_reason of `"trailing_stop_loss"`.
+
+The method must return a stoploss value (float / number) as a percentage of the current price.
+E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
+During backtesting, `current_rate` (and `current_profit`) are provided against the candle's high (or low for short trades) - while the resulting stoploss is evaluated against the candle's low (or high for short trades).
+
+The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
+Returning `None` will be interpreted as "no desire to change", and is the only safe way to return when you'd like to not modify the stoploss.
+`NaN` and `inf` values are considered invalid and will be ignored (identical to `None`).
+
+Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchangefreqtrade)).
+
+!!! Note "Use of dates"
+    All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
+
+!!! Tip "Trailing stoploss"
+    It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
+
+### Adjust stoploss after position adjustments
+
+Depending on your strategy, you may encounter the need to adjust the stoploss in both directions after a [position adjustment](#adjust-trade-position).
+For this, freqtrade will make an additional call with `after_fill=True` after an order fills, which will allow the strategy to move the stoploss in any direction (also widening the gap between stoploss and current price, which is otherwise forbidden).
+
+!!! Note "backwards compatibility"
+    This call will only be made if the `after_fill` parameter is part of the function definition of your `custom_stoploss` function.
+    As such, this will not impact (and with that, surprise) existing, running strategies.
+
+### Custom stoploss examples
+
+The next section will show some examples on what's possible with the custom stoploss function.
+Of course, many more things are possible, and all examples can be combined at will.
+
+#### Trailing stop via custom stoploss
+
+To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                        current_rate: float, current_profit: float, after_fill: bool, 
+                        **kwargs) -> float | None:
+        """
+        Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
+        e.g. returning -0.05 would create a stoploss 5% below current_rate.
+        The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns the initial stoploss value.
+        Only called when use_custom_stoploss is set to True.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param after_fill: True if the stoploss is called after the order was filled.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: New stoploss value, relative to the current_rate
+        """
+        return -0.04
+```
+
+#### Time based trailing stop
+
+Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                        current_rate: float, current_profit: float, after_fill: bool, 
+                        **kwargs) -> float | None:
+
+        # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
+        if current_time - timedelta(minutes=120) > trade.open_date_utc:
+            return -0.05
+        elif current_time - timedelta(minutes=60) > trade.open_date_utc:
+            return -0.10
+        return None
+```
+
+#### Time based trailing stop with after-fill adjustments
+
+Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
+If an additional order fills, set stoploss to -10% below the new `open_rate` ([Averaged across all entries](#position-adjust-calculations)).
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                        current_rate: float, current_profit: float, after_fill: bool, 
+                        **kwargs) -> float | None:
+
+        if after_fill: 
+            # After an additional order, start with a stoploss of 10% below the new open rate
+            return stoploss_from_open(0.10, current_profit, is_short=trade.is_short, leverage=trade.leverage)
+        # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
+        if current_time - timedelta(minutes=120) > trade.open_date_utc:
+            return -0.05
+        elif current_time - timedelta(minutes=60) > trade.open_date_utc:
+            return -0.10
+        return None
+```
+
+#### Different stoploss per pair
+
+Use a different stoploss depending on the pair.
+In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                        current_rate: float, current_profit: float, after_fill: bool,
+                        **kwargs) -> float | None:
+
+        if pair in ("ETH/BTC", "XRP/BTC"):
+            return -0.10
+        elif pair in ("LTC/BTC"):
+            return -0.05
+        return -0.15
+```
+
+#### Trailing stoploss with positive offset
+
+Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
+
+Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                        current_rate: float, current_profit: float, after_fill: bool,
+                        **kwargs) -> float | None:
+
+        if current_profit < 0.04:
+            return None # return None to keep using the initial stoploss
+
+        # After reaching the desired offset, allow the stoploss to trail by half the profit
+        desired_stoploss = current_profit / 2
+
+        # Use a minimum of 2.5% and a maximum of 5%
+        return max(min(desired_stoploss, 0.05), 0.025)
+```
+
+#### Stepped stoploss
+
+Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
+
+* Use the regular stoploss until 20% profit is reached
+* Once profit is > 20% - set stoploss to 7% above open price.
+* Once profit is > 25% - set stoploss to 15% above open price.
+* Once profit is > 40% - set stoploss to 25% above open price.
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                        current_rate: float, current_profit: float, after_fill: bool,
+                        **kwargs) -> float | None:
+
+        # evaluate highest to lowest, so that highest possible stop is used
+        if current_profit > 0.40:
+            return stoploss_from_open(0.25, current_profit, is_short=trade.is_short, leverage=trade.leverage)
+        elif current_profit > 0.25:
+            return stoploss_from_open(0.15, current_profit, is_short=trade.is_short, leverage=trade.leverage)
+        elif current_profit > 0.20:
+            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short, leverage=trade.leverage)
+
+        # return maximum stoploss value, keeping current stoploss price unchanged
+        return None
+```
+
+#### Custom stoploss using an indicator from dataframe example
+
+Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # <...>
+        dataframe["sar"] = ta.SAR(dataframe)
+
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                        current_rate: float, current_profit: float, after_fill: bool,
+                        **kwargs) -> float | None:
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        # Use parabolic sar as absolute stoploss price
+        stoploss_price = last_candle["sar"]
+
+        # Convert absolute price to percentage relative to current_rate
+        if stoploss_price < current_rate:
+            return stoploss_from_absolute(stoploss_price, current_rate, is_short=trade.is_short)
+
+        # return maximum stoploss value, keeping current stoploss price unchanged
+        return None
+```
+
+See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
+
+### Common helpers for stoploss calculations
+
+#### Stoploss relative to open price
+
+Stoploss values returned from `custom_stoploss()` must specify a percentage relative to `current_rate`, but sometimes you may want to specify a stoploss relative to the _entry_ price instead.
+`stoploss_from_open()` is a helper function to calculate a stoploss value that can be returned from `custom_stoploss` which will be equivalent to the desired trade profit above the entry point.
+
+??? Example "Returning a stoploss relative to the open price from the custom stoploss function"
+
+    Say the open price was $100, and `current_price` is $121 (`current_profit` will be `0.21`).  
+
+    If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit, False)` which will return `0.1157024793`.  11.57% below $121 is $107, which is the same as 7% above $100.
+
+    This function will consider leverage - so at 10x leverage, the actual stoploss would be 0.7% above $100 (0.7% * 10x = 7%).
+
+
+    ``` python
+    # Default imports
+
+    class AwesomeStrategy(IStrategy):
+
+        # ... populate_* methods
+
+        use_custom_stoploss = True
+
+        def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                            current_rate: float, current_profit: float, after_fill: bool,
+                            **kwargs) -> float | None:
+
+            # once the profit has risen above 10%, keep the stoploss at 7% above the open price
+            if current_profit > 0.10:
+                return stoploss_from_open(0.07, current_profit, is_short=trade.is_short, leverage=trade.leverage)
+
+            return 1
+
+    ```
+
+    Full examples can be found in the [Custom stoploss](strategy-callbacks.md#custom-stoploss) section of the Documentation.
+
+!!! Note
+    Providing invalid input to `stoploss_from_open()` may produce "CustomStoploss function did not return valid stoploss" warnings.
+    This may happen if `current_profit` parameter is below specified `open_relative_stop`. Such situations may arise when closing trade
+    is blocked by `confirm_trade_exit()` method. Warnings can be solved by never blocking stop loss sells by checking `exit_reason` in
+    `confirm_trade_exit()`, or by using `return stoploss_from_open(...) or 1` idiom, which will request to not change stop loss when
+    `current_profit < open_relative_stop`.
+
+#### Stoploss percentage from absolute price
+
+Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
+
+The helper function `stoploss_from_absolute()` can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`.
+
+??? Example "Returning a stoploss using absolute price from the custom stoploss function"
+
+    If we want to trail a stop price at 2xATR below current price we can call `stoploss_from_absolute(current_rate + (side * candle["atr"] * 2), current_rate=current_rate, is_short=trade.is_short, leverage=trade.leverage)`.
+    For futures, we need to adjust the direction (up or down), as well as adjust for leverage, since the [`custom_stoploss`](strategy-callbacks.md#custom-stoploss) callback  returns the ["risk for this trade"](stoploss.md#stoploss-and-leverage) - not the relative price movement.
+
+    ``` python
+    # Default imports
+
+    class AwesomeStrategy(IStrategy):
+
+        use_custom_stoploss = True
+
+        def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
+            return dataframe
+
+        def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                            current_rate: float, current_profit: float, after_fill: bool,
+                            **kwargs) -> float | None:
+            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+            trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
+            candle = dataframe.iloc[-1].squeeze()
+            side = 1 if trade.is_short else -1
+            return stoploss_from_absolute(current_rate + (side * candle["atr"] * 2), 
+                                          current_rate=current_rate, 
+                                          is_short=trade.is_short,
+                                          leverage=trade.leverage)
+
+    ```
+
+---
+
+## Custom order price rules
+
+By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
+
+You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
+
+Each of these methods are called right before placing an order on the exchange.
+
+!!! Note
+    If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
+
+!!! Note
+    Using custom_entry_price, the Trade object will be available as soon as the first entry order associated with the trade is created, for the first entry, `trade` parameter value will be `None`.
+
+### Custom order entry and exit price example
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
+                           entry_tag: str | None, side: str, **kwargs) -> float:
+
+        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
+                                                                timeframe=self.timeframe)
+        new_entryprice = dataframe["bollinger_10_lowerband"].iat[-1]
+
+        return new_entryprice
+
+    def custom_exit_price(self, pair: str, trade: Trade,
+                          current_time: datetime, proposed_rate: float,
+                          current_profit: float, exit_tag: str | None, **kwargs) -> float:
+
+        dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
+                                                                timeframe=self.timeframe)
+        new_exitprice = dataframe["bollinger_10_upperband"].iat[-1]
+
+        return new_exitprice
+
+```
+
+!!! Warning
+    Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.
+    **Example**:
+    If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98, which is 2% below the current (proposed) rate.
+
+!!! Warning "Backtesting"
+    Custom prices are supported in backtesting (starting with 2021.12), and orders will fill if the price falls within the candle's low/high range.
+    Orders that don't fill immediately are subject to regular timeout handling, which happens once per (detail) candle.
+    `custom_exit_price()` is only called for sells of type exit_signal, Custom exit and partial exits. All other exit-types will use regular backtesting prices.
+
+## Custom order timeout rules
+
+Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
+
+However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if an order did time out or not.
+
+!!! Note
+    Backtesting fills orders if their price falls within the candle's low/high range.
+    The below callbacks will be called once per (detail) candle for orders that don't fill immediately (which use custom pricing).
+
+### Custom order timeout example
+
+Called for every open order until that order is either filled or cancelled.
+`check_entry_timeout()` is called for trade entries, while `check_exit_timeout()` is called for trade exit orders.
+
+A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
+It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
+
+The function must return either `True` (cancel order) or `False` (keep order alive).
+
+``` python
+    # Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        "entry": 60 * 25,
+        "exit": 60 * 25
+    }
+
+    def check_entry_timeout(self, pair: str, trade: Trade, order: Order,
+                            current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
+           return True
+        return False
+
+
+    def check_exit_timeout(self, pair: str, trade: Trade, order: Order,
+                           current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
+           return True
+        return False
+```
+
+!!! Note
+    For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
+
+### Custom order timeout example (using additional data)
+
+``` python
+    # Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        "entry": 60 * 25,
+        "exit": 60 * 25
+    }
+
+    def check_entry_timeout(self, pair: str, trade: Trade, order: Order,
+                            current_time: datetime, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob["bids"][0][0]
+        # Cancel buy order if price is more than 2% above the order.
+        if current_price > order.price * 1.02:
+            return True
+        return False
+
+
+    def check_exit_timeout(self, pair: str, trade: Trade, order: Order,
+                           current_time: datetime, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob["asks"][0][0]
+        # Cancel sell order if price is more than 2% below the order.
+        if current_price < order.price * 0.98:
+            return True
+        return False
+```
+
+---
+
+## Bot order confirmation
+
+Confirm trade entry / exits.
+This are the last methods that will be called before an order is placed.
+
+### Trade entry (buy order) confirmation
+
+`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: str | None,
+                            side: str, **kwargs) -> bool:
+        """
+        Called right before placing a entry order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair that's about to be bought/shorted.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in target (base) currency that's going to be traded.
+        :param rate: Rate that's going to be used when using limit orders 
+                     or current rate for market orders.
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param current_time: datetime object, containing the current datetime
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: "long" or "short" - indicating the direction of the proposed trade
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the buy-order is placed on the exchange.
+            False aborts the process
+        """
+        return True
+
+```
+
+### Trade exit (sell order) confirmation
+
+`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
+
+`confirm_trade_exit()` may be called multiple times within one iteration for the same trade if different exit-reasons apply.
+The exit-reasons (if applicable) will be in the following sequence:
+
+* `exit_signal` / `custom_exit`
+* `stop_loss`
+* `roi`
+* `trailing_stop_loss`
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
+                           rate: float, time_in_force: str, exit_reason: str,
+                           current_time: datetime, **kwargs) -> bool:
+        """
+        Called right before placing a regular exit order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair for trade that's about to be exited.
+        :param trade: trade object.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in base currency.
+        :param rate: Rate that's going to be used when using limit orders
+                     or current rate for market orders.
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param exit_reason: Exit reason.
+            Can be any of ["roi", "stop_loss", "stoploss_on_exchange", "trailing_stop_loss",
+                           "exit_signal", "force_exit", "emergency_exit"]
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True, then the exit-order is placed on the exchange.
+            False aborts the process
+        """
+        if exit_reason == "force_exit" and trade.calc_profit_ratio(rate) < 0:
+            # Reject force-sells with negative profit
+            # This is just a sample, please adjust to your needs
+            # (this does not necessarily make sense, assuming you know when you're force-selling)
+            return False
+        return True
+
+```
+
+!!! Warning
+    `confirm_trade_exit()` can prevent stoploss exits, causing significant losses as this would ignore stoploss exits.
+    `confirm_trade_exit()` will not be called for Liquidations - as liquidations are forced by the exchange, and therefore cannot be rejected.
+
+## Adjust trade position
+
+The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
+For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
+`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging) or to increase or decrease positions.
+
+Additional orders also result in additional fees and those orders don't count towards `max_open_trades`.
+
+This callback is also called when there is an open order (either buy or sell) waiting for execution - and will cancel the existing open order to place a new order if the amount, price or direction is different. Also partially filled orders will be canceled, and will be replaced with the new amount as returned by the callback.
+
+`adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
+
+Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade.
+Adjustment orders can be assigned with a tag by returning a 2 element Tuple, with the first element being the adjustment amount, and the 2nd element the tag (e.g. `return 250, "increase_favorable_conditions"`).
+
+Modifications to leverage are not possible, and the stake-amount returned is assumed to be before applying leverage.
+
+The combined stake currently allocated to the position is held in `trade.stake_amount`. Therefore `trade.stake_amount` will always be updated on every additional entry and partial exit made through `adjust_trade_position()`.
+
+!!! Danger "Loose Logic"
+    On dry and live run, this function will be called every `throttle_process_secs` (default to 5s). If you have a loose logic, (e.g. increase position if RSI of the last candle is below 30), your bot will do extra re-entry every 5 secs until you either it run out of money, hit the `max_position_adjustment` limit, or a new candle with RSI more than 30 arrived.
+
+    Same thing also can happen with partial exit.  
+    So be sure to have a strict logic and/or check for the last filled order and if an order is already open.
+
+!!! Warning "Performance with many position adjustments"
+    Position adjustments can be a good approach to increase a strategy's output - but it can also have drawbacks if using this feature extensively.  
+    Each of the orders will be attached to the trade object for the duration of the trade - hence increasing memory usage.
+    Trades with long duration and 10s or even 100ds of position adjustments are therefore not recommended, and should be closed at regular intervals to not affect performance.
+
+!!! Warning "Backtesting"
+    During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
+    This can also cause deviating results between live and backtesting, since backtesting can adjust the trade only once per candle, whereas live could adjust the trade multiple times per candle.
+
+### Increase position
+
+The strategy is expected to return a positive **stake_amount** (in stake currency) between `min_stake` and `max_stake` if and when an additional entry order should be made (position is increased -> buy order for long trades, sell order for short trades).
+
+If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
+`max_entry_position_adjustment` property is used to limit the number of additional entries per trade (on top of the first entry order) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment entries.
+
+Additional entries are ignored once you have reached the maximum amount of extra entries that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits.
+
+!!! Note "About stake size"
+    Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
+    If you wish to buy additional orders with DCA, then make sure to leave enough funds in the wallet for that.
+    Using `"unlimited"` stake amount with DCA orders requires you to also implement the `custom_stake_amount()` callback to avoid allocating all funds to the initial order.
+
+### Decrease position
+
+The strategy is expected to return a negative stake_amount (in stake currency) for a partial exit.
+Returning the full owned stake at that point (`-trade.stake_amount`) results in a full exit.  
+Returning a value more than the above (so remaining stake_amount would become negative) will result in the bot ignoring the signal.
+
+For a partial exit, it's important to know that the formula used to calculate the amount of the coin for the partial exit order is `amount to be exited partially = negative_stake_amount * trade.amount / trade.stake_amount`, where `negative_stake_amount` is the value returned from the `adjust_trade_position` function. As seen in the formula, the formula doesn't care about current profit/loss of the position. It only cares about `trade.amount` and `trade.stake_amount` which aren't affected by the price movement at all.
+
+For example, let's say you buy 2 SHITCOIN/USDT at open rate of 50, which means the trade's stake amount is 100 USDT. Now the price raises to 200 and you want to sell half of it. In that case, you have to return -50% of `trade.stake_amount` (0.5 * 100 USDT) which equals to -50. The bot will calculate the amount it needed to sell, which is `50 * 2 / 100` which equals 1 SHITCOIN/USDT. If you return -200 (50% of 2 * 200), the bot will ignore it since `trade.stake_amount` is only 100 USDT but you asked to sell 200 USDT which means you are asking to sell 4 SHITCOIN/USDT.
+
+Back to the example above, since current rate is 200, the current USDT value of your trade is now 400 USDT. Let's say you want to partially sell 100 USDT to take out the initial investment and leave the profit in the trade hoping that the price keeps rising. In that case, you have to do a different approach. First, you need to calculate the exact amount you needed to sell. In this case, since you want to sell 100 USDT worth based of current rate, the exact amount you need to partially sell is `100 * 2 / 400` which equals 0.5 SHITCOIN/USDT. Since we know now the exact amount we want to sell (0.5), the value you need to return in the `adjust_trade_position` function is `-amount to be exited partially * trade.stake_amount / trade.amount`, which equals -25. The bot will sell 0.5 SHITCOIN/USDT, keeping 1.5 in trade. You will receive 100 USDT from the partial exit.
+
+!!! Warning "Stoploss calculation"
+    Stoploss is still calculated from the initial opening price, not averaged price.
+    Regular stoploss rules still apply (cannot move down).
+
+    While `/stopentry` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
+
+``` python
+# Default imports
+
+class DigDeeperStrategy(IStrategy):
+
+    position_adjustment_enable = True
+
+    # Attempts to handle large drops with DCA. High stoploss is required.
+    stoploss = -0.30
+
+    # ... populate_* methods
+
+    # Example specific variables
+    max_entry_position_adjustment = 3
+    # This number is explained a bit further down
+    max_dca_multiplier = 5.5
+
+    # This is called when placing the initial order (opening trade)
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float | None, max_stake: float,
+                            leverage: float, entry_tag: str | None, side: str,
+                            **kwargs) -> float:
+
+        # We need to leave most of the funds for possible further DCA orders
+        # This also applies to fixed stakes
+        return proposed_stake / self.max_dca_multiplier
+
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float,
+                              min_stake: float | None, max_stake: float,
+                              current_entry_rate: float, current_exit_rate: float,
+                              current_entry_profit: float, current_exit_profit: float,
+                              **kwargs
+                              ) -> float | None | tuple[float | None, str | None]:
+        """
+        Custom trade adjustment logic, returning the stake amount that a trade should be
+        increased or decreased.
+        This means extra entry or exit orders with additional fees.
+        Only called when `position_adjustment_enable` is set to True.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns None
+
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Current entry rate (same as current_entry_profit)
+        :param current_profit: Current profit (as ratio), calculated based on current_rate 
+                               (same as current_entry_profit).
+        :param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
+        :param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
+        :param current_entry_rate: Current rate using entry pricing.
+        :param current_exit_rate: Current rate using exit pricing.
+        :param current_entry_profit: Current profit using entry pricing.
+        :param current_exit_profit: Current profit using exit pricing.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: Stake amount to adjust your trade,
+                       Positive values to increase position, Negative values to decrease position.
+                       Return None for no action.
+                       Optionally, return a tuple with a 2nd element with an order reason
+        """
+        if trade.has_open_orders:
+            # Only act if no orders are open
+            return
+
+        if current_profit > 0.05 and trade.nr_of_successful_exits == 0:
+            # Take half of the profit at +5%
+            return -(trade.stake_amount / 2), "half_profit_5%"
+
+        if current_profit > -0.05:
+            return None
+
+        # Obtain pair dataframe (just to show how to access it)
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        # Only buy when not actively falling price.
+        last_candle = dataframe.iloc[-1].squeeze()
+        previous_candle = dataframe.iloc[-2].squeeze()
+        if last_candle["close"] < previous_candle["close"]:
+            return None
+
+        filled_entries = trade.select_filled_orders(trade.entry_side)
+        count_of_entries = trade.nr_of_successful_entries
+        # Allow up to 3 additional increasingly larger buys (4 in total)
+        # Initial buy is 1x
+        # If that falls to -5% profit, we buy 1.25x more, average profit should increase to roughly -2.2%
+        # If that falls down to -5% again, we buy 1.5x more
+        # If that falls once again down to -5%, we buy 1.75x more
+        # Total stake for this trade would be 1 + 1.25 + 1.5 + 1.75 = 5.5x of the initial allowed stake.
+        # That is why max_dca_multiplier is 5.5
+        # Hope you have a deep wallet!
+        try:
+            # This returns first order stake size
+            stake_amount = filled_entries[0].stake_amount_filled
+            # This then calculates current safety order size
+            stake_amount = stake_amount * (1 + (count_of_entries * 0.25))
+            return stake_amount, "1/3rd_increase"
+        except Exception as exception:
+            return None
+
+        return None
+
+```
+
+### Position adjust calculations
+
+* Entry rates are calculated using weighted averages.
+* Exits will not influence the average entry rate.
+* Partial exit relative profit is relative to the average entry price at this point.
+* Final exit relative profit is calculated based on the total invested capital. (See example below)
+
+??? example "Calculation example"
+    *This example assumes 0 fees for simplicity, and a long position on an imaginary coin.*  
+    
+    * Buy 100@8\$ 
+    * Buy 100@9\$ -> Avg price: 8.5\$
+    * Sell 100@10\$ -> Avg price: 8.5\$, realized profit 150\$, 17.65%
+    * Buy 150@11\$ -> Avg price: 10\$, realized profit 150\$, 17.65%
+    * Sell 100@12\$ -> Avg price: 10\$, total realized profit 350\$, 20%
+    * Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40%  <- *This will be the last "Exit" message*
+
+    The total profit for this trade was 950$ on a 3350$ investment (`100@8$ + 100@9$ + 150@11$`). As such - the final relative profit is 28.35% (`950 / 3350`).
+
+## Adjust order Price
+
+The `adjust_order_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles.  
+This callback is called once every iteration unless the order has been (re)placed within the current candle - limiting the maximum (re)placement of each order to once per candle.
+This also means that the first call will be at the start of the next candle after the initial order was placed.
+
+Be aware that `custom_entry_price()`/`custom_exit_price()` is still the one dictating initial limit order price target at the time of the signal.
+
+Orders can be cancelled out of this callback by returning `None`.
+
+Returning `current_order_rate` will keep the order on the exchange "as is".
+Returning any other price will cancel the existing order, and replace it with a new order.
+
+If the cancellation of the original order fails, then the order will not be replaced - though the order will most likely have been canceled on exchange. Having this happen on initial entries will result in the deletion of the order, while on position adjustment orders, it'll result in the trade size remaining as is.  
+If the order has been partially filled, the order will not be replaced. You can however use [`adjust_trade_position()`](#adjust-trade-position) to adjust the trade size to the expected position size, should this be necessary / desired.
+
+!!! Warning "Regular timeout"
+    Entry `unfilledtimeout` mechanism (as well as `check_entry_timeout()`/`check_exit_timeout()`) takes precedence over this callback.
+    Orders that are cancelled via the above methods will not have this callback called. Be sure to update timeout values to match your expectations.
+
+```python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def adjust_order_price(
+        self,
+        trade: Trade,
+        order: Order | None,
+        pair: str,
+        current_time: datetime,
+        proposed_rate: float,
+        current_order_rate: float,
+        entry_tag: str | None,
+        side: str,
+        is_entry: bool,
+        **kwargs,
+    ) -> float | None:
+        """
+        Exit and entry order price re-adjustment logic, returning the user desired limit price.
+        This only executes when a order was already placed, still open (unfilled fully or partially)
+        and not timed out on subsequent candles after entry trigger.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-callbacks/
+
+        When not implemented by a strategy, returns current_order_rate as default.
+        If current_order_rate is returned then the existing order is maintained.
+        If None is returned then order gets canceled but not replaced by a new one.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: Trade object.
+        :param order: Order object
+        :param current_time: datetime object, containing the current datetime
+        :param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
+        :param current_order_rate: Rate of the existing order in place.
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param is_entry: True if the order is an entry order, False if it's an exit order.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float or None: New entry price value if provided
+        """
+
+        # Limit entry orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
+        if (
+            is_entry
+            and pair == "BTC/USDT" 
+            and entry_tag == "long_sma200" 
+            and side == "long" 
+            and (current_time - timedelta(minutes=10)) <= trade.open_date_utc
+        ):
+            # just cancel the order if it has been filled more than half of the amount
+            if order.filled > order.remaining:
+                return None
+            else:
+                dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+                current_candle = dataframe.iloc[-1].squeeze()
+                # desired price
+                return current_candle["sma_200"]
+        # default: maintain existing order
+        return current_order_rate
+```
+
+!!! danger "Incompatibility with `adjust_*_price()`"
+    If you have both `adjust_order_price()` and `adjust_entry_price()`/`adjust_exit_price()` implemented, only `adjust_order_price()` will be used.
+    If you need to adjust entry/exit prices, you can either implement the logic in `adjust_order_price()`, or use the split `adjust_entry_price()` / `adjust_exit_price()` callbacks, but not both.
+    Mixing these is not supported and will raise an error during bot startup.
+
+### Adjust Entry Price
+
+The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace entry limit orders upon arrival.
+It's a sub-set of `adjust_order_price()` and is called only for entry orders.
+All remaining behavior is identical to `adjust_order_price()`.
+
+The trade open-date (`trade.open_date_utc`) will remain at the time of the very first order placed.
+Please make sure to be aware of this - and eventually adjust your logic in other callbacks to account for this, and use the date of the first filled order instead.
+
+### Adjust Exit Price
+
+The `adjust_exit_price()` callback may be used by strategy developer to refresh/replace exit limit orders upon arrival.
+It's a sub-set of `adjust_order_price()` and is called only for exit orders.
+All remaining behavior is identical to `adjust_order_price()`.
+
+## Leverage Callback
+
+When trading in markets that allow leverage, this method must return the desired Leverage (Defaults to 1 -> No leverage).
+
+Assuming a capital of 500USDT, a trade with leverage=3 would result in a position with 500 x 3 = 1500 USDT.
+
+Values that are above `max_leverage` will be adjusted to `max_leverage`.
+For markets / exchanges that don't support leverage, this method is ignored.
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                 proposed_leverage: float, max_leverage: float, entry_tag: str | None, side: str,
+                 **kwargs) -> float:
+        """
+        Customize leverage for each new trade. This method is only called in futures mode.
+
+        :param pair: Pair that's currently analyzed
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param proposed_leverage: A leverage proposed by the bot.
+        :param max_leverage: Max leverage allowed on this pair
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: "long" or "short" - indicating the direction of the proposed trade
+        :return: A leverage amount, which is between 1.0 and max_leverage.
+        """
+        return 1.0
+```
+
+All profit calculations include leverage. Stoploss / ROI also include leverage in their calculation.
+Defining a stoploss of 10% at 10x leverage would trigger the stoploss with a 1% move to the downside.
+
+## Order filled Callback
+
+The `order_filled()` callback may be used to perform specific actions based on the current trade state after an order is filled.
+It will be called independent of the order type (entry, exit, stoploss or position adjustment).
+
+Assuming that your strategy needs to store the high value of the candle at trade entry, this is possible with this callback as the following example show.
+
+``` python
+# Default imports
+
+class AwesomeStrategy(IStrategy):
+    def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
+        """
+        Called right after an order fills. 
+        Will be called for all order types (entry, exit, stoploss, position adjustment).
+        :param pair: Pair for trade
+        :param trade: trade object.
+        :param order: Order object.
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        # Obtain pair dataframe (just to show how to access it)
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+        
+        if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
+            trade.set_custom_data(key="entry_candle_high", value=last_candle["high"])
+
+        return None
+
+```
diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md
new file mode 100644
index 000000000..1220db492
--- /dev/null
+++ b/docs/strategy-customization.md
@@ -0,0 +1,1149 @@
+# Strategy Customization
+
+This page explains how to customize your strategies, add new indicators and set up trading rules.
+
+If you haven't already, please familiarize yourself with:
+
+- the [Freqtrade strategy 101](strategy-101.md), which provides a quick start to strategy development
+- the [Freqtrade bot basics](bot-basics.md), which provides overall info on how the bot operates
+
+## Develop your own strategy
+
+The bot includes a default strategy file.
+
+Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
+
+You will however most likely have your own idea for a strategy.
+
+This document intends to help you convert your ideas into a working strategy.
+
+### Generating a strategy template
+
+To get started, you can use the command:
+
+```bash
+freqtrade new-strategy --strategy AwesomeStrategy
+```
+
+This will create a new strategy called `AwesomeStrategy` from a template, which will be located using the filename `user_data/strategies/AwesomeStrategy.py`.
+
+!!! Note
+    There is a difference between the *name* of the strategy and the filename. In most commands, Freqtrade uses the *name* of the strategy, *not the filename*.
+
+!!! Note
+    The `new-strategy` command generates starting examples which will not be profitable out of the box.
+
+??? Hint "Different template levels"
+    `freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with more complicated features defined.
+
+### Anatomy of a strategy
+
+A strategy file contains all the information needed to build the strategy logic:
+
+- Candle data in OHLCV format
+- Indicators
+- Entry logic
+  - Signals
+- Exit logic
+  - Signals
+  - Minimal ROI
+  - Callbacks ("custom functions")
+- Stoploss
+  - Fixed/absolute
+  - Trailing
+  - Callbacks ("custom functions")
+- Pricing [optional]
+- Position adjustment [optional]
+
+The bot includes a sample strategy called `SampleStrategy` that you can use as a basis: `user_data/strategies/sample_strategy.py`.
+You can test it with the parameter: `--strategy SampleStrategy`. Remember that you use the strategy class name, not the filename.
+
+Additionally, there is an attribute called `INTERFACE_VERSION`, which defines the version of the strategy interface the bot should use.
+The current version is 3 - which is also the default when it's not set explicitly in the strategy.
+
+You may see older strategies set to interface version 2, and these will need to be updated to v3 terminology as future versions will require this to be set.
+
+Starting the bot in dry or live mode is accomplished using the `trade` command:
+
+```bash
+freqtrade trade --strategy AwesomeStrategy
+```
+
+### Bot modes
+
+Freqtrade strategies can be processed by the Freqtrade bot in 5 main modes:
+
+- backtesting
+- hyperopting
+- dry ("forward testing")
+- live
+- FreqAI (not covered here)
+
+Check the [configuration documentation](configuration.md) about how to set the bot to dry or live mode.
+
+**Always use dry mode when testing as this gives you an idea of how your strategy will work in reality without risking capital.**
+
+## Diving in deeper
+**For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py)
+file as reference.**
+
+!!! Note "Strategies and Backtesting"
+    To avoid problems and unexpected differences between backtesting and dry/live modes, please be aware
+    that during backtesting the full time range is passed to the `populate_*()` methods at once.
+    It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
+    avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
+
+!!! Warning "Warning: Using future data"
+    Since backtesting passes the full time range to the `populate_*()` methods, the strategy author
+    needs to take care to avoid having the strategy utilize data from the future.
+    Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
+
+??? Hint "Lookahead and recursive analysis"
+    Freqtrade includes two helpful commands to help assess common lookahead (using future data) and 
+    recursive bias (variance in indicator values) issues. Before running a strategy in dry or live more, 
+    you should always use these commands first. Please check the relevant documentation for 
+    [lookahead](lookahead-analysis.md) and [recursive](recursive-analysis.md) analysis.
+
+### Dataframe
+
+Freqtrade uses [pandas](https://pandas.pydata.org/) to store/provide the candlestick (OHLCV) data.
+Pandas is a great library developed for processing large amounts of data in tabular format.
+
+Each row in a dataframe corresponds to one candle on a chart, with the latest complete candle always being the last in the dataframe (sorted by date).
+
+If we were to look at the first few rows of the main dataframe using the pandas `head()` function, we would see:
+
+```output
+> dataframe.head()
+                       date      open      high       low     close     volume
+0 2021-11-09 23:25:00+00:00  67279.67  67321.84  67255.01  67300.97   44.62253
+1 2021-11-09 23:30:00+00:00  67300.97  67301.34  67183.03  67187.01   61.38076
+2 2021-11-09 23:35:00+00:00  67187.02  67187.02  67031.93  67123.81  113.42728
+3 2021-11-09 23:40:00+00:00  67123.80  67222.40  67080.33  67160.48   78.96008
+4 2021-11-09 23:45:00+00:00  67160.48  67160.48  66901.26  66943.37  111.39292
+```
+
+A dataframe is a table where columns are not single values, but a series of data values. As such, simple python comparisons like the following will not work:
+
+``` python
+    if dataframe['rsi'] > 30:
+        dataframe['enter_long'] = 1
+```
+
+The above section will fail with `The truth value of a Series is ambiguous [...]`.
+
+This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe, i.e. `vectorisation`.
+
+``` python
+    dataframe.loc[
+        (dataframe['rsi'] > 30)
+    , 'enter_long'] = 1
+```
+
+With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
+
+Freqtrade uses this new column as an entry signal, where it is assumed that a trade will subsequently open on the next open candle.
+
+Pandas provides fast ways to calculate metrics, i.e. "vectorisation". To benefit from this speed, it is advised to not use loops, but use vectorized methods instead.
+
+Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
+
+??? Hint "Signals vs Trades"
+    - Signals are generated from indicators at candle close, and are intentions to enter a trade.
+    - Trades are orders that are executed (on the exchange in live mode) where a trade will then open as close to next candle open as possible.
+
+!!! Warning "Trade order assumptions"
+    In backtesting, signals are generated on candle close. Trades are then initiated immeditely on next candle open.
+    
+    In dry and live, this may be delayed due to all pair dataframes needing to be analysed first, then trade processing 
+    for each of those pairs happens. This means that in dry/live you need to be mindful of having as low a computation 
+    delay as possible, usually by running a low number of pairs and having a CPU with a good clock speed.
+
+#### Why can't I see "real time" candle data?
+
+Freqtrade does not store incomplete/unfinished candles in the dataframe.
+
+The use of incomplete data for making strategy decisions is called "repainting" and you might see other platforms allow this.
+
+Freqtrade does not. Only complete/finished candle data is available in the dataframe.
+
+### Customize Indicators
+
+Entry and exit signals need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
+
+You should only add the indicators used in either `populate_entry_trend()`, `populate_exit_trend()`, or to populate another indicator, otherwise performance may suffer.
+
+It's important to always return the dataframe from these three functions without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
+
+Sample:
+
+```python
+def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    """
+    Adds several different TA indicators to the given DataFrame
+
+    Performance Note: For the best performance be frugal on the number of indicators
+    you are using. Let uncomment only the indicator you are using in your strategies
+    or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+    :param dataframe: Dataframe with data from the exchange
+    :param metadata: Additional information, like the currently traded pair
+    :return: a Dataframe with all mandatory indicators for the strategies
+    """
+    dataframe['sar'] = ta.SAR(dataframe)
+    dataframe['adx'] = ta.ADX(dataframe)
+    stoch = ta.STOCHF(dataframe)
+    dataframe['fastd'] = stoch['fastd']
+    dataframe['fastk'] = stoch['fastk']
+    dataframe['bb_lower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
+    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+    dataframe['mfi'] = ta.MFI(dataframe)
+    dataframe['rsi'] = ta.RSI(dataframe)
+    dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+    dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+    dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+    dataframe['ao'] = awesome_oscillator(dataframe)
+    macd = ta.MACD(dataframe)
+    dataframe['macd'] = macd['macd']
+    dataframe['macdsignal'] = macd['macdsignal']
+    dataframe['macdhist'] = macd['macdhist']
+    hilbert = ta.HT_SINE(dataframe)
+    dataframe['htsine'] = hilbert['sine']
+    dataframe['htleadsine'] = hilbert['leadsine']
+    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+    dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+    dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+    # remember to always return the dataframe
+    return dataframe
+```
+
+!!! Note "Want more indicator examples?"
+    Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py).
+    Then uncomment indicators you need.
+
+#### Indicator libraries
+
+Out of the box, freqtrade installs the following technical libraries:
+
+- [ta-lib](https://ta-lib.github.io/ta-lib-python/)
+- [pandas-ta](https://twopirllc.github.io/pandas-ta/)
+- [technical](https://technical.freqtrade.io)
+
+Additional technical libraries can be installed as necessary, or custom indicators may be written / invented by the strategy author.
+
+### Strategy startup period
+
+Some indicators have an unstable startup period in which there isn't enough candle data to calculate any values (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this unstable period is and uses whatever indicator values are in the dataframe.
+
+To account for this, the strategy can be assigned the `startup_candle_count` attribute.
+
+This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the `startup_candle_count` does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators.
+
+You can use [recursive-analysis](recursive-analysis.md) to check and find the correct `startup_candle_count` to be used. When recursive analysis shows a variance of 0%, then you can be sure that you have enough startup candle data.
+
+In this example strategy, this should be set to 400 (`startup_candle_count = 400`), since the minimum needed history for ema100 calculation to make sure the value is correct is 400 candles.
+
+``` python
+    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+```
+
+By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
+
+!!! Warning "Using x calls to get OHLCV"
+    If you receive a warning like `WARNING - Using 3 calls to get OHLCV. This can result in slower operations for the bot. Please check if you really need 1500 candles for your strategy` - you should consider if you really need this much historic data for your signals.
+    Having this will cause Freqtrade to make multiple calls for the same pair, which will obviously be slower than one network request.
+    As a consequence, Freqtrade will take longer to refresh candles - and should therefore be avoided if possible.
+    This is capped to 5 total calls to avoid overloading the exchange, or make freqtrade too slow.
+
+!!! Warning
+    `startup_candle_count` should be below `ohlcv_candle_limit * 5` (which is 500 * 5 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
+
+#### Example
+
+Let's try to backtest 1 month (January 2019) of 5m candles using an example strategy with EMA100, as above.
+
+``` bash
+freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m
+```
+
+Assuming `startup_candle_count` is set to 400, backtesting knows it needs 400 candles to generate valid entry signals. It will load data from `20190101 - (400 * 5m)` - which is ~2018-12-30 11:40:00.
+
+If this data is available, indicators will be calculated with this extended timerange. The unstable startup period (up to 2019-01-01 00:00:00) will then be removed before backtesting is carried out.
+
+!!! Note "Unavailable startup candle data"
+    If data for the startup period is not available, then the timerange will be adjusted to account for this startup period. In our example, backtesting would then start from 2019-01-02 09:20:00.
+
+### Entry signal rules
+
+Edit the method `populate_entry_trend()` in your strategy file to update your entry strategy.
+
+It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. The strategy may then produce invalid values, or cease to work entirely.
+
+This method will also define a new column, `"enter_long"` (`"enter_short"` for shorts), which needs to contain `1` for entries, and `0` for "no action". `enter_long` is a mandatory column that must be set even if the strategy is shorting only.
+
+You can name your entry signals by using the `"enter_tag"` column, which can help debug and assess your strategy later. 
+
+Sample from `user_data/strategies/sample_strategy.py`:
+
+```python
+def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    """
+    Based on TA indicators, populates the buy signal for the given dataframe
+    :param dataframe: DataFrame populated with indicators
+    :param metadata: Additional information, like the currently traded pair
+    :return: DataFrame with buy column
+    """
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
+
+    return dataframe
+```
+
+??? Note "Enter short trades"
+    Short entries can be created by setting `enter_short` (corresponds to `enter_long` for long trades).
+    The `enter_tag` column remains identical.
+    Shorting needs to be supported by your exchange and market configuration!
+    Also, make sure you set [`can_short`](#can-short) appropriately on your strategy if you intend to short.
+
+    ```python
+    # allow both long and short trades
+    can_short = True
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+                (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
+
+        dataframe.loc[
+            (
+                (qtpylib.crossed_below(dataframe['rsi'], 70)) &  # Signal: RSI crosses below 70
+                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
+                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            ['enter_short', 'enter_tag']] = (1, 'rsi_cross')
+
+        return dataframe
+    ```
+
+!!! Note
+    Buying requires sellers to buy from. Therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
+
+### Exit signal rules
+
+Edit the method `populate_exit_trend()` into your strategy file to update your exit strategy.
+
+The exit-signal can be suppressed by setting `use_exit_signal` to false in the configuration or strategy.
+
+`use_exit_signal` will not influence [signal collision rules](#colliding-signals) - which will still apply and can prevent entries.
+
+It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected. The strategy may then produce invalid values, or cease to work entirely.
+
+This method will also define a new column, `"exit_long"` (`"exit_short"` for shorts), which needs to contain `1` for exits, and `0` for "no action".
+
+You can name your exit signals by using the `"exit_tag"` column, which can help debug and assess your strategy later.
+
+Sample from `user_data/strategies/sample_strategy.py`:
+
+```python
+def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    """
+    Based on TA indicators, populates the exit signal for the given dataframe
+    :param dataframe: DataFrame populated with indicators
+    :param metadata: Additional information, like the currently traded pair
+    :return: DataFrame with buy column
+    """
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
+    return dataframe
+```
+
+??? Note "Exit short trades"
+    Short exits can be created by setting `exit_short` (corresponds to `exit_long`).
+    The `exit_tag` column remains identical.
+    Shorting needs to be supported by your exchange and market configuration!
+    Also, make sure you set [`can_short`](#can-short) appropriately on your strategy if you intend to short.
+
+    ```python
+    # allow both long and short trades
+    can_short = True
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
+                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            ['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
+        dataframe.loc[
+            (
+                (qtpylib.crossed_below(dataframe['rsi'], 30)) &  # Signal: RSI crosses below 30
+                (dataframe['tema'] < dataframe['bb_middleband']) &  # Guard
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            ['exit_short', 'exit_tag']] = (1, 'rsi_too_low')
+        return dataframe
+    ```
+
+### Minimal ROI
+
+The `minimal_roi` strategy variable defines the minimal Return On Investment (ROI) a trade should reach before exiting, independent from the exit signal.
+
+It is of the following format, i.e. a python `dict`, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
+
+```python
+minimal_roi = {
+    "40": 0.0,
+    "30": 0.01,
+    "20": 0.02,
+    "0": 0.04
+}
+```
+
+The above configuration would therefore mean:
+
+- Exit whenever 4% profit was reached
+- Exit when 2% profit was reached (in effect after 20 minutes)
+- Exit when 1% profit was reached (in effect after 30 minutes)
+- Exit when trade is non-loosing (in effect after 40 minutes)
+
+The calculation does include fees.
+
+#### Disabling minimal ROI
+
+To disable ROI completely, set it to an empty dictionary:
+
+```python
+minimal_roi = {}
+```
+
+#### Using calculations in minimal ROI
+
+To use times based on candle duration (timeframe), the following snippet can be handy.
+
+This will allow you to change the timeframe for the strategy, but the minimal ROI times will still be set as candles, e.g. after 3 candles.
+
+``` python
+from freqtrade.exchange import timeframe_to_minutes
+
+class AwesomeStrategy(IStrategy):
+
+    timeframe = "1d"
+    timeframe_mins = timeframe_to_minutes(timeframe)
+    minimal_roi = {
+        "0": 0.05,                      # 5% for the first 3 candles
+        str(timeframe_mins * 3): 0.02,  # 2% after 3 candles
+        str(timeframe_mins * 6): 0.01,  # 1% After 6 candles
+    }
+```
+
+??? info "Orders that don't fill immediately"
+    `minimal_roi` will take the `trade.open_date` as reference, which is the time the trade was initialized, i.e. when the first order for this trade was placed.
+    This will also hold true for limit orders that don't fill immediately (usually in combination with "off-spot" prices through `custom_entry_price()`), as well as for cases where the initial order price is replaced through `adjust_entry_price()`.
+    The time used will still be from the initial `trade.open_date` (when the initial order was first placed), not from the newly placed or adjusted order date.
+
+### Stoploss
+
+Setting a stoploss is highly recommended to protect your capital from strong moves against you.
+
+Sample of setting a 10% stoploss:
+
+``` python
+stoploss = -0.10
+```
+
+For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md).
+
+### Timeframe
+
+This is the periodicity of candles the bot should use in the strategy.
+
+Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
+
+Please note that the same entry/exit signals may work well with one timeframe, but not with others.
+
+This setting is accessible within the strategy methods as the `self.timeframe` attribute.
+
+### Can short
+
+To use short signals in futures markets, you will have to set `can_short = True`.
+
+Strategies which enable this will fail to load on spot markets.
+
+If you have `1` values in the `enter_short` column to raise short signals, setting `can_short = False` (which is the default) will mean that these short signals are ignored, even if you have specified futures markets in your configuration.
+
+### Metadata dict
+
+The `metadata` dict (available for `populate_entry_trend`, `populate_exit_trend`, `populate_indicators`) contains additional information.
+Currently this is `pair`, which can be accessed using `metadata['pair']`, and will return a pair in the format `XRP/BTC` (or `XRP/BTC:BTC` for futures markets).
+
+The metadata dict should not be modified and does not persist information across multiple functions in your strategy.
+
+Instead, please check the [Storing information](strategy-advanced.md#storing-information-persistent) section.
+
+--8<-- "includes/strategy-imports.md"
+
+## Strategy file loading
+
+By default, freqtrade will attempt to load strategies from all `.py` files within the `userdir` (default `user_data/strategies`).
+
+Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade in dry (or live, depending on your configuration) mode with:
+
+```bash
+freqtrade trade --strategy AwesomeStrategy
+```
+
+Note that we're using the class name, not the file name.
+
+You can use `freqtrade list-strategies` to see a list of all strategies Freqtrade is able to load (all strategies in the correct folder).
+It will also include a "status" field, highlighting potential problems.
+
+??? Hint "Customize strategy directory"
+    You can use a different directory by using `--strategy-path user_data/otherPath`. This parameter is available to all commands that require a strategy.
+
+## Informative Pairs
+
+### Get data for non-tradeable pairs
+
+Data for additional, informative pairs (reference pairs) can be beneficial for some strategies to see data on a wider timeframe.
+
+OHLCV data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
+
+These pairs will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting, e.g. `VolumePairlist`.
+
+The pairs need to be specified as tuples in the format `("pair", "timeframe")`, with pair as the first and timeframe as the second argument.
+
+Sample:
+
+``` python
+def informative_pairs(self):
+    return [("ETH/USDT", "5m"),
+            ("BTC/TUSD", "15m"),
+            ]
+```
+
+A full sample can be found [in the DataProvider section](#complete-dataprovider-sample).
+
+!!! Warning
+    As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
+    All timeframes and all pairs can be specified as long as they are available (and active) on the used exchange.
+    It is however better to use resampling to longer timeframes whenever possible
+    to avoid hammering the exchange with too many requests and risk being blocked.
+
+??? Note "Alternative candle types"
+    Informative_pairs can also provide a 3rd tuple element defining the candle type explicitly.
+    Availability of alternative candle-types will depend on the trading-mode and the exchange. 
+    In general, spot pairs cannot be used in futures markets, and futures candles can't be used as informative pairs for spot bots.
+    Details about this may vary, if they do, this can be found in the exchange documentation.
+
+    ``` python
+    def informative_pairs(self):
+        return [
+            ("ETH/USDT", "5m", ""),   # Uses default candletype, depends on trading_mode (recommended)
+            ("ETH/USDT", "5m", "spot"),   # Forces usage of spot candles (only valid for bots running on spot markets).
+            ("BTC/TUSD", "15m", "futures"),  # Uses futures candles (only bots with `trading_mode=futures`)
+            ("BTC/TUSD", "15m", "mark"),  # Uses mark candles (only bots with `trading_mode=futures`)
+        ]
+    ```
+***
+
+### Informative pairs decorator (`@informative()`)
+
+To easily define informative pairs, use the `@informative` decorator. All decorated `populate_indicators_*` methods run in isolation,
+and do not have access to data from other informative pairs. However, all informative dataframes for each pair are merged and passed to main `populate_indicators()` method.
+
+!!! Note
+    Do not use the `@informative` decorator if you need to use data from one informative pair when generating another informative pair. Instead, define informative pairs manually as described [in the DataProvider section](#complete-dataprovider-sample).
+
+When hyperopting, use of the hyperoptable parameter `.value` attribute is not supported. Please use the `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter) for more information.
+
+??? info "Full documentation"
+    ``` python
+    def informative(
+        timeframe: str,
+        asset: str = "",
+        fmt: str | Callable[[Any], str] | None = None,
+        *,
+        candle_type: CandleType | str | None = None,
+        ffill: bool = True,
+    ) -> Callable[[PopulateIndicators], PopulateIndicators]:
+        """
+        A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
+        define informative indicators.
+
+        Example usage:
+
+            @informative('1h')
+            def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+                dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+                return dataframe
+
+        :param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe.
+        :param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use
+                    current pair. Also supports limited pair format strings (see below)
+        :param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not
+        specified, defaults to:
+        * {base}_{quote}_{column}_{timeframe} if asset is specified.
+        * {column}_{timeframe} if asset is not specified.
+        Pair format supports these format variables:
+        * {base} - base currency in lower case, for example 'eth'.
+        * {BASE} - same as {base}, except in upper case.
+        * {quote} - quote currency in lower case, for example 'usdt'.
+        * {QUOTE} - same as {quote}, except in upper case.
+        Format string additionally supports this variables.
+        * {asset} - full name of the asset, for example 'BTC/USDT'.
+        * {column} - name of dataframe column.
+        * {timeframe} - timeframe of informative dataframe.
+        :param ffill: ffill dataframe after merging informative pair.
+        :param candle_type: '', mark, index, premiumIndex, or funding_rate
+        """
+    ```
+
+??? Example "Fast and easy way to define informative pairs"
+
+    Most of the time we do not need power and flexibility offered by `merge_informative_pair()`, therefore we can use a decorator to quickly define informative pairs.
+
+    ``` python
+
+    from datetime import datetime
+    from freqtrade.persistence import Trade
+    from freqtrade.strategy import IStrategy, informative
+
+    class AwesomeStrategy(IStrategy):
+        
+        # This method is not required. 
+        # def informative_pairs(self): ...
+
+        # Define informative upper timeframe for each pair. Decorators can be stacked on same 
+        # method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'.
+        @informative('30m')
+        @informative('1h')
+        def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+            return dataframe
+
+        # Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
+        # 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable 
+        # instead of hard-coding actual stake currency. Available in populate_indicators and other 
+        # methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
+        @informative('1h', 'BTC/{stake}')
+        def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+            return dataframe
+
+        # Define BTC/ETH informative pair. You must specify quote currency if it is different from
+        # stake currency. Available in populate_indicators and other methods as 'eth_btc_rsi_1h'.
+        @informative('1h', 'ETH/BTC')
+        def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+            return dataframe
+    
+        # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
+        # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
+        # formatting. Available in populate_indicators and other methods as 'rsi_upper_1h'.
+        @informative('1h', 'BTC/{stake}', '{column}_{timeframe}')
+        def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['rsi_upper'] = ta.RSI(dataframe, timeperiod=14)
+            return dataframe
+    
+        def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            # Strategy timeframe indicators for current pair.
+            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+            # Informative pairs are available in this method.
+            dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
+            return dataframe
+
+    ```
+
+!!! Note
+    Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code.
+
+    ``` python
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        stake = self.config['stake_currency']
+        dataframe.loc[
+            (
+                (dataframe[f'btc_{stake}_rsi_1h'] < 35)
+                &
+                (dataframe['volume'] > 0)
+            ),
+            ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
+    
+        return dataframe
+    ```
+
+    Alternatively column renaming may be used to remove stake currency from column names: `@informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}')`.
+
+!!! Warning "Duplicate method names"
+    Methods tagged with the `@informative()` decorator must always have unique names! Reusing the same name (for example when copy-pasting already defined informative methods) will overwrite previously defined methods and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators created in methods higher up in the strategy file are not available in the dataframe. Carefully review method names and make sure they are unique!
+
+### *merge_informative_pair()*
+
+This method helps you merge an informative pair to the regular main dataframe safely and consistently, without lookahead bias.
+
+Options:
+
+- Rename the columns to create unique columns
+- Merge the dataframe without lookahead bias
+- Forward-fill (optional)
+
+For a full sample, please refer to the [complete data provider example](#complete-dataprovider-sample) below.
+
+All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:
+
+!!! Example "Column renaming"
+    Assuming `inf_tf = '1d'` the resulting columns will be:
+
+    ``` python
+    'date', 'open', 'high', 'low', 'close', 'rsi'                     # from the original dataframe
+    'date_1d', 'open_1d', 'high_1d', 'low_1d', 'close_1d', 'rsi_1d'   # from the informative dataframe
+    ```
+
+??? Example "Column renaming - 1h"
+    Assuming `inf_tf = '1h'` the resulting columns will be:
+
+    ``` python
+    'date', 'open', 'high', 'low', 'close', 'rsi'                     # from the original dataframe
+    'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h'   # from the informative dataframe
+    ```
+
+??? Example "Custom implementation"
+    A custom implementation for this is possible, and can be done as follows:
+
+    ``` python
+
+    # Shift date by 1 candle
+    # This is necessary since the data is always the "open date"
+    # and a 15m candle starting at 12:15 should not know the close of the 1h candle from 12:00 to 13:00
+    minutes = timeframe_to_minutes(inf_tf)
+    # Only do this if the timeframes are different:
+    informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
+
+    # Rename columns to be unique
+    informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
+    # Assuming inf_tf = '1d' - then the columns will now be:
+    # date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
+
+    # Combine the 2 dataframes
+    # all indicators on the informative sample MUST be calculated before this point
+    dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{inf_tf}', how='left')
+    # FFill to have the 1d value available in every row throughout the day.
+    # Without this, comparisons would only work once per day.
+    dataframe = dataframe.ffill()
+
+    ```
+
+!!! Warning "Informative timeframe < timeframe"
+    Using informative timeframes smaller than the main dataframe timeframe is not recommended with this method, as it will not use any of the additional information this would provide.
+    To use the more detailed information properly, more advanced methods should be applied (which are out of scope for this documentation).
+
+## Additional data (DataProvider)
+
+The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
+
+All methods return `None` in case of failure, i.e. failures do not raise an exception.
+
+Please always check the mode of operation to select the correct method to get data (see below for examples).
+
+!!! Warning "Hyperopt Limitations"
+    The DataProvider is available during hyperopt, however it can only be used in `populate_indicators()` **within a strategy**, not within a hyperopt class file.
+    It is also not available in `populate_entry_trend()` and `populate_exit_trend()` methods.
+
+### Possible options for DataProvider
+
+- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their timeframe (pair, timeframe).
+- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (i.e. VolumePairlist)
+- [`get_pair_dataframe(pair, timeframe)`](#get_pair_dataframepair-timeframe) - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
+- [`get_analyzed_dataframe(pair, timeframe)`](#get_analyzed_dataframepair-timeframe) - Returns the analyzed dataframe (after calling `populate_indicators()`, `populate_buy()`, `populate_sell()`) and the time of the latest analysis.
+- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
+- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on the Market data structure.
+- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
+- [`orderbook(pair, maximum)`](#orderbookpair-maximum) - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
+- [`ticker(pair)`](#tickerpair) - Returns current ticker data for the pair. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#price-tickers) for more details on the Ticker data structure.
+- `runmode` - Property containing the current runmode.
+
+### Example Usages
+
+### *available_pairs*
+
+``` python
+for pair, timeframe in self.dp.available_pairs:
+    print(f"available {pair}, {timeframe}")
+```
+
+### *current_whitelist()*
+
+Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 exchange pairs by volume.
+
+The strategy logic might look something like this:
+
+*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to enter and exit.*
+
+Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit users to just 500-1000 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
+
+Since we can't resample the data we will have to use an informative pair, and since the whitelist will be dynamic we don't know which pair(s) to use! We have a problem!
+
+This is where calling `self.dp.current_whitelist()` comes in handy to retrieve only those pairs in the whitelist.
+
+```python
+    def informative_pairs(self):
+
+        # get access to all pairs available in whitelist.
+        pairs = self.dp.current_whitelist()
+        # Assign timeframe to each pair so they can be downloaded and cached for strategy.
+        informative_pairs = [(pair, '1d') for pair in pairs]
+        return informative_pairs
+```
+
+??? Note "Plotting with current_whitelist"
+    Current whitelist is not supported for `plot-dataframe`, as this command is usually used by providing an explicit pairlist and would therefore make the return values of this method misleading.
+    It's also not supported for FreqUI visualization in [webserver mode](utils.md#webserver-mode), as the configuration for webserver mode doesn't require a pairlist to be set.
+
+### *get_pair_dataframe(pair, timeframe)*
+
+``` python
+# fetch live / historical candle (OHLCV) data for the first informative pair
+inf_pair, inf_timeframe = self.informative_pairs()[0]
+informative = self.dp.get_pair_dataframe(pair=inf_pair,
+                                         timeframe=inf_timeframe)
+```
+
+!!! Warning "Warning about backtesting"
+    In backtesting, `dp.get_pair_dataframe()` behavior differs depending on where it's called.
+    Within `populate_*()` methods, `dp.get_pair_dataframe()` returns the full timerange. Please make sure to not "look into the future" to avoid surprises when running in dry/live mode.
+    Within [callbacks](strategy-callbacks.md), you'll get the full timerange up to the current (simulated) candle.
+
+### *get_analyzed_dataframe(pair, timeframe)*
+
+This method is used by freqtrade internally to determine the last signal.
+It can also be used in specific callbacks to get the signal that caused the action (see [Advanced Strategy Documentation](strategy-advanced.md) for more details on available callbacks).
+
+``` python
+# fetch current dataframe
+dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
+                                                         timeframe=self.timeframe)
+```
+
+!!! Note "No data available"
+    Returns an empty dataframe if the requested pair was not cached.
+    You can check for this with `if dataframe.empty:` and handle this case accordingly.
+    This should not happen when using whitelisted pairs.
+
+### *orderbook(pair, maximum)*
+
+``` python
+if self.dp.runmode.value in ('live', 'dry_run'):
+    ob = self.dp.orderbook(metadata['pair'], 1)
+    dataframe['best_bid'] = ob['bids'][0][0]
+    dataframe['best_ask'] = ob['asks'][0][0]
+```
+
+The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will be formatted as follows:
+
+``` js
+{
+    'bids': [
+        [ price, amount ], // [ float, float ]
+        [ price, amount ],
+        ...
+    ],
+    'asks': [
+        [ price, amount ],
+        [ price, amount ],
+        //...
+    ],
+    //...
+}
+```
+
+Therefore, using `ob['bids'][0][0]` as demonstrated above will use the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
+
+!!! Warning "Warning about backtesting"
+    The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return up-to-date values.
+
+### *ticker(pair)*
+
+``` python
+if self.dp.runmode.value in ('live', 'dry_run'):
+    ticker = self.dp.ticker(metadata['pair'])
+    dataframe['last_price'] = ticker['last']
+    dataframe['volume24h'] = ticker['quoteVolume']
+    dataframe['vwap'] = ticker['vwap']
+```
+
+!!! Warning
+    Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can
+    vary for different exchanges. For instance, many exchanges do not return `vwap` values, and some exchanges
+    do not always fill in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
+    data returned from the exchange and add appropriate error handling / defaults.
+
+!!! Warning "Warning about backtesting"
+    This method will always return up-to-date / real-time values. As such, usage during backtesting / hyperopt without runmode checks will lead to wrong results, e.g. your whole dataframe will contain the same single value in all rows.
+
+### Send Notification
+
+The dataprovider `.send_msg()` function allows you to send custom notifications from your strategy.
+Identical notifications will only be sent once per candle, unless the 2nd argument (`always_send`) is set to True.
+
+``` python
+    self.dp.send_msg(f"{metadata['pair']} just got hot!")
+
+    # Force send this notification, avoid caching (Please read warning below!)
+    self.dp.send_msg(f"{metadata['pair']} just got hot!", always_send=True)
+```
+
+Notifications will only be sent in trading modes (Live/Dry-run) - so this method can be called without conditions for backtesting.
+
+!!! Warning "Spamming"
+    You can spam yourself pretty good by setting `always_send=True` in this method. Use this with great care and only in conditions you know will not happen throughout a candle to avoid a message every 5 seconds.
+
+### Complete DataProvider sample
+
+```python
+from freqtrade.strategy import IStrategy, merge_informative_pair
+from pandas import DataFrame
+
+class SampleStrategy(IStrategy):
+    # strategy init stuff...
+
+    timeframe = '5m'
+
+    # more strategy init stuff..
+
+    def informative_pairs(self):
+
+        # get access to all pairs available in whitelist.
+        pairs = self.dp.current_whitelist()
+        # Assign tf to each pair so they can be downloaded and cached for strategy.
+        informative_pairs = [(pair, '1d') for pair in pairs]
+        # Optionally Add additional "static" pairs
+        informative_pairs += [("ETH/USDT", "5m"),
+                              ("BTC/TUSD", "15m"),
+                            ]
+        return informative_pairs
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        if not self.dp:
+            # Don't do anything if DataProvider is not available.
+            return dataframe
+
+        inf_tf = '1d'
+        # Get the informative pair
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        # Get the 14 day rsi
+        informative['rsi'] = ta.RSI(informative, timeperiod=14)
+
+        # Use the helper function merge_informative_pair to safely merge the pair
+        # Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
+        # use ffill to have the 1d value available in every row throughout the day.
+        # Without this, comparisons between columns of the original and the informative pair would only work once per day.
+        # Full documentation of this method, see below
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
+
+        # Calculate rsi of the original dataframe (5m timeframe)
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+
+        # Do other stuff
+        # ...
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+                (dataframe['rsi_1d'] < 30) &                     # Ensure daily RSI is < 30
+                (dataframe['volume'] > 0)                        # Ensure this candle had volume (important for backtesting)
+            ),
+            ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
+
+```
+
+***
+
+## Additional data (Wallets)
+
+The strategy provides access to the `wallets` object. This contains the current balances of your wallets/accounts on the exchange.
+
+!!! Note "Backtesting / Hyperopt"
+    Wallets behaves differently depending on the function from which it is called.
+    Within `populate_*()` methods, it'll return the full wallet as configured.
+    Within [callbacks](strategy-callbacks.md), you'll get the wallet state corresponding to the actual simulated wallet at that point in the simulation process.
+
+Always check if `wallets` is available to avoid failures during backtesting.
+
+``` python
+if self.wallets:
+    free_eth = self.wallets.get_free('ETH')
+    used_eth = self.wallets.get_used('ETH')
+    total_eth = self.wallets.get_total('ETH')
+```
+
+### Possible options for Wallets
+
+- `get_free(asset)` - currently available balance to trade
+- `get_used(asset)` - currently tied up balance (open orders)
+- `get_total(asset)` - total available balance - sum of the 2 above
+
+***
+
+## Additional data (Trades)
+
+A history of trades can be retrieved in the strategy by querying the database.
+
+At the top of the file, import the required object:
+
+```python
+from freqtrade.persistence import Trade
+```
+
+The following example queries trades from today for the current pair (`metadata['pair']`). Other filters can easily be added.
+
+``` python
+trades = Trade.get_trades_proxy(pair=metadata['pair'],
+                                open_date=datetime.now(timezone.utc) - timedelta(days=1),
+                                is_open=False,
+            ]).order_by(Trade.close_date).all()
+# Summarize profit for this pair.
+curdayprofit = sum(trade.close_profit for trade in trades)
+```
+
+For a full list of available methods, please consult the [Trade object](trade-object.md) documentation.
+
+!!! Warning
+    Trade history is not available in `populate_*` methods during backtesting or hyperopt, and will result in empty results.
+
+## Prevent trades from happening for a specific pair
+
+Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair exits, preventing an immediate re-entry of that pair.
+
+This is to prevent "waterfalls" of many and frequent trades within a single candle.
+
+Locked pairs will show the message `Pair <pair> is currently locked.`.
+
+### Locking pairs from within the strategy
+
+Sometimes it may be desired to lock a pair after certain events happen (e.g. multiple losing trades in a row).
+
+Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until, [reason])`.
+`until` must be a datetime object in the future, after which trading will be re-enabled for that pair, while `reason` is an optional string detailing why the pair was locked.
+
+Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)`, providing the reason the pair was unlocked.
+`self.unlock_reason(<reason>)` will unlock all pairs currently locked with the provided reason.
+
+To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
+
+!!! Note
+    Locked pairs will always be rounded up to the next candle. So assuming a `5m` timeframe, a lock with `until` set to 10:18 will lock the pair until the candle from 10:15-10:20 will be finished.
+
+!!! Warning
+    Manually locking pairs is not available during backtesting. Only locks via Protections are allowed.
+
+#### Pair locking example
+
+``` python
+from freqtrade.persistence import Trade
+from datetime import timedelta, datetime, timezone
+# Put the above lines a the top of the strategy file, next to all the other imports
+# --------
+
+# Within populate indicators (or populate_entry_trend):
+if self.config['runmode'].value in ('live', 'dry_run'):
+    # fetch closed trades for the last 2 days
+    trades = Trade.get_trades_proxy(
+        pair=metadata['pair'], is_open=False, 
+        open_date=datetime.now(timezone.utc) - timedelta(days=2))
+    # Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
+    sumprofit = sum(trade.close_profit for trade in trades)
+    if sumprofit < 0:
+        # Lock pair for 12 hours
+        self.lock_pair(metadata['pair'], until=datetime.now(timezone.utc) + timedelta(hours=12))
+```
+
+## Print the main dataframe
+
+To inspect the current main dataframe, you can issue a print-statement in either `populate_entry_trend()` or `populate_exit_trend()`.
+You may also want to print the pair so it's clear what data is currently shown.
+
+``` python
+def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            #>> whatever condition<<<
+        ),
+        ['enter_long', 'enter_tag']] = (1, 'somestring')
+
+    # Print the Analyzed pair
+    print(f"result for {metadata['pair']}")
+
+    # Inspect the last 5 rows
+    print(dataframe.tail())
+
+    return dataframe
+```
+
+Printing more than a few rows is also possible by using `print(dataframe)` instead of `print(dataframe.tail())`. However this is not recommended, as can results in a lot of output (~500 lines per pair every 5 seconds).
+
+## Common mistakes when developing strategies
+
+### Looking into the future while backtesting
+
+Backtesting analyzes the whole dataframe timerange at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not lookahead into the future, i.e. using data that would not be available in dry or live mode.
+
+This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods. Strategies that look into the future will perform well during backtesting, often with incredible profits or winrates, but will fail or perform badly in real conditions.
+
+The following list contains some common patterns which should be avoided to prevent frustration:
+
+- don't use `shift(-1)` or other negative values. This uses data from the future in backtesting, which is not available in dry or live modes.
+- don't use `.iloc[-1]` or any other absolute position in the dataframe within `populate_` functions, as this will be different between dry-run and backtesting. Absolute `iloc` indexing is safe to use in callbacks however - see [Strategy Callbacks](strategy-callbacks.md).
+- don't use functions that use all dataframe or column values, e.g. `dataframe['mean_volume'] = dataframe['volume'].mean()`. As backtesting uses the full dataframe, at any point in the dataframe, the `'mean_volume'` series would include data from the future. Use rolling() calculations instead, e.g. `dataframe['volume'].rolling(<window>).mean()`.
+- don't use `.resample('1h')`. This uses the left border of the period interval, so moves data from an hour boundary to the start of the hour. Use `.resample('1h', label='right')` instead.
+- don't use `.merge()` to combine longer timeframes onto shorter ones. Instead, use the [informative pair](#informative-pairs) helpers. (A plain merge can implicitly cause a lookahead bias as date refers to open date, not close date).
+
+!!! Tip "Identifying problems"
+    You should always use the two helper commands [lookahead-analysis](lookahead-analysis.md) and [recursive-analysis](recursive-analysis.md), which can each help you figure out problems with your strategy in different ways.
+    Please treat them as what they are - helpers to identify most common problems. A negative result of each does not guarantee that there are none of the above errors included.
+
+### Colliding signals
+
+When conflicting signals collide (e.g. both `'enter_long'` and `'exit_long'` are set to `1`), freqtrade will do nothing and ignore the entry signal. This will avoid trades that enter, and exit immediately. Obviously, this can potentially lead to missed entries.
+
+The following rules apply, and entry signals will be ignored if more than one of the 3 signals is set:
+
+- `enter_long` -> `exit_long`, `enter_short`
+- `enter_short` -> `exit_short`, `enter_long`
+
+## Further strategy ideas
+
+To get additional ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as examples, but results will depend on the current market situation, pairs used, etc. Therefore, these strategies should be considered only for learning purposes, not real world trading. Please backtest the strategy for your exchange/desired pairs first, then dry run to evaluate carefully, and use at your own risk.
+
+Feel free to use any of them as inspiration for your own strategies. We're happy to accept Pull Requests containing new strategies to the repository.
+
+## Next steps
+
+Now you have a perfect strategy you probably want to backtest it.
+Your next step is to learn [how to use backtesting](backtesting.md).
diff --git a/docs/strategy_analysis_example.md b/docs/strategy_analysis_example.md
new file mode 100644
index 000000000..814531c2d
--- /dev/null
+++ b/docs/strategy_analysis_example.md
@@ -0,0 +1,281 @@
+# Strategy analysis example
+
+Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.
+The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.
+Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details.
+
+## Setup
+
+### Change Working directory to repository root
+
+
+```python
+import os
+from pathlib import Path
+
+
+# Change directory
+# Modify this cell to insure that the output shows the correct path.
+# Define all paths relative to the project root shown in the cell output
+project_root = "somedir/freqtrade"
+i = 0
+try:
+    os.chdir(project_root)
+    if not Path("LICENSE").is_file():
+        i = 0
+        while i < 4 and (not Path("LICENSE").is_file()):
+            os.chdir(Path(Path.cwd(), "../"))
+            i += 1
+        project_root = Path.cwd()
+except FileNotFoundError:
+    print("Please define the project root relative to the current directory")
+print(Path.cwd())
+```
+
+### Configure Freqtrade environment
+
+
+```python
+from freqtrade.configuration import Configuration
+
+
+# Customize these according to your needs.
+
+# Initialize empty configuration object
+config = Configuration.from_files([])
+# Optionally (recommended), use existing configuration file
+# config = Configuration.from_files(["user_data/config.json"])
+
+# Define some constants
+config["timeframe"] = "5m"
+# Name of the strategy class
+config["strategy"] = "SampleStrategy"
+# Location of the data
+data_location = config["datadir"]
+# Pair to analyze - Only use one pair here
+pair = "BTC/USDT"
+```
+
+
+```python
+# Load data using values set above
+from freqtrade.data.history import load_pair_history
+from freqtrade.enums import CandleType
+
+
+candles = load_pair_history(
+    datadir=data_location,
+    timeframe=config["timeframe"],
+    pair=pair,
+    data_format="json",  # Make sure to update this to your data
+    candle_type=CandleType.SPOT,
+)
+
+# Confirm success
+print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
+candles.head()
+```
+
+## Load and run strategy
+* Rerun each time the strategy file is changed
+
+
+```python
+# Load strategy using values set above
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.resolvers import StrategyResolver
+
+
+strategy = StrategyResolver.load_strategy(config)
+strategy.dp = DataProvider(config, None, None)
+strategy.ft_bot_start()
+
+# Generate buy/sell signals using strategy
+df = strategy.analyze_ticker(candles, {"pair": pair})
+df.tail()
+```
+
+### Display the trade details
+
+* Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
+* Some possible problems
+    * Columns with NaN values at the end of the dataframe
+    * Columns used in `crossed*()` functions with completely different units
+* Comparison with full backtest
+    * having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
+    * Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.  
+
+
+
+```python
+# Report results
+print(f"Generated {df['enter_long'].sum()} entry signals")
+data = df.set_index("date", drop=False)
+data.tail()
+```
+
+## Load existing objects into a Jupyter notebook
+
+The following cells assume that you have already generated data using the cli.  
+They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.
+
+### Load backtest results to pandas dataframe
+
+Analyze a trades dataframe (also used below for plotting)
+
+
+```python
+from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
+
+
+# if backtest_dir points to a directory, it'll automatically load the last backtest file.
+backtest_dir = config["user_data_dir"] / "backtest_results"
+# backtest_dir can also point to a specific file
+# backtest_dir = (
+#   config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
+# )
+```
+
+
+```python
+# You can get the full backtest statistics by using the following command.
+# This contains all information used to generate the backtest result.
+stats = load_backtest_stats(backtest_dir)
+
+strategy = "SampleStrategy"
+# All statistics are available per strategy, so if `--strategy-list` was used during backtest,
+# this will be reflected here as well.
+# Example usages:
+print(stats["strategy"][strategy]["results_per_pair"])
+# Get pairlist used for this backtest
+print(stats["strategy"][strategy]["pairlist"])
+# Get market change (average change of all pairs from start to end of the backtest period)
+print(stats["strategy"][strategy]["market_change"])
+# Maximum drawdown ()
+print(stats["strategy"][strategy]["max_drawdown_abs"])
+# Maximum drawdown start and end
+print(stats["strategy"][strategy]["drawdown_start"])
+print(stats["strategy"][strategy]["drawdown_end"])
+
+
+# Get strategy comparison (only relevant if multiple strategies were compared)
+print(stats["strategy_comparison"])
+```
+
+
+```python
+# Load backtested trades as dataframe
+trades = load_backtest_data(backtest_dir)
+
+# Show value-counts per pair
+trades.groupby("pair")["exit_reason"].value_counts()
+```
+
+## Plotting daily profit / equity line
+
+
+```python
+# Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)
+
+import pandas as pd
+import plotly.express as px
+
+from freqtrade.configuration import Configuration
+from freqtrade.data.btanalysis import load_backtest_stats
+
+
+# strategy = 'SampleStrategy'
+# config = Configuration.from_files(["user_data/config.json"])
+# backtest_dir = config["user_data_dir"] / "backtest_results"
+
+stats = load_backtest_stats(backtest_dir)
+strategy_stats = stats["strategy"][strategy]
+
+df = pd.DataFrame(columns=["dates", "equity"], data=strategy_stats["daily_profit"])
+df["equity_daily"] = df["equity"].cumsum()
+
+fig = px.line(df, x="dates", y="equity_daily")
+fig.show()
+```
+
+### Load live trading results into a pandas dataframe
+
+In case you did already some trading and want to analyze your performance
+
+
+```python
+from freqtrade.data.btanalysis import load_trades_from_db
+
+
+# Fetch trades from database
+trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
+
+# Display results
+trades.groupby("pair")["exit_reason"].value_counts()
+```
+
+## Analyze the loaded trades for trade parallelism
+This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with a very high `max_open_trades` setting.
+
+`analyze_trade_parallelism()` returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.
+
+
+```python
+from freqtrade.data.btanalysis import analyze_trade_parallelism
+
+
+# Analyze the above
+parallel_trades = analyze_trade_parallelism(trades, "5m")
+
+parallel_trades.plot()
+```
+
+## Plot results
+
+Freqtrade offers interactive plotting capabilities based on plotly.
+
+
+```python
+from freqtrade.plot.plotting import generate_candlestick_graph
+
+
+# Limit graph period to keep plotly quick and reactive
+
+# Filter trades to one pair
+trades_red = trades.loc[trades["pair"] == pair]
+
+data_red = data["2019-06-01":"2019-06-10"]
+# Generate candlestick graph
+graph = generate_candlestick_graph(
+    pair=pair,
+    data=data_red,
+    trades=trades_red,
+    indicators1=["sma20", "ema50", "ema55"],
+    indicators2=["rsi", "macd", "macdsignal", "macdhist"],
+)
+```
+
+
+```python
+# Show graph inline
+# graph.show()
+
+# Render graph in a separate window
+graph.show(renderer="browser")
+```
+
+## Plot average profit per trade as distribution graph
+
+
+```python
+import plotly.figure_factory as ff
+
+
+hist_data = [trades.profit_ratio]
+group_labels = ["profit_ratio"]  # name of the dataset
+
+fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)
+fig.show()
+```
+
+Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
diff --git a/docs/strategy_migration.md b/docs/strategy_migration.md
new file mode 100644
index 000000000..07744af58
--- /dev/null
+++ b/docs/strategy_migration.md
@@ -0,0 +1,814 @@
+# Strategy Migration between V2 and V3
+
+To support new markets and trade-types (namely short trades / trades with leverage), some things had to change in the interface.
+If you intend on using markets other than spot markets, please migrate your strategy to the new format.
+
+We have put a great effort into keeping compatibility with existing strategies, so if you just want to continue using freqtrade in __spot markets__, there should be no changes necessary for now.
+
+You can use the quick summary as checklist. Please refer to the detailed sections below for full migration details.
+
+## Quick summary / migration checklist
+
+Note : `forcesell`, `forcebuy`, `emergencysell` are changed to `force_exit`, `force_enter`, `emergency_exit` respectively.
+
+* Strategy methods:
+  * [`populate_buy_trend()` -> `populate_entry_trend()`](#populate_buy_trend)
+  * [`populate_sell_trend()` -> `populate_exit_trend()`](#populate_sell_trend)
+  * [`custom_sell()` -> `custom_exit()`](#custom_sell)
+  * [`check_buy_timeout()` -> `check_entry_timeout()`](#custom_entry_timeout)
+  * [`check_sell_timeout()` -> `check_exit_timeout()`](#custom_entry_timeout)
+  * New `side` argument to callbacks without trade object
+    * [`custom_stake_amount`](#custom_stake_amount)
+    * [`confirm_trade_entry`](#confirm_trade_entry)
+    * [`custom_entry_price`](#custom_entry_price)
+  * [Changed argument name in `confirm_trade_exit`](#confirm_trade_exit)
+* Dataframe columns:
+  * [`buy` -> `enter_long`](#populate_buy_trend)
+  * [`sell` -> `exit_long`](#populate_sell_trend)
+  * [`buy_tag` -> `enter_tag` (used for both long and short trades)](#populate_buy_trend)
+  * [New column `enter_short` and corresponding new column `exit_short`](#populate_sell_trend)
+* trade-object now has the following new properties:
+  * `is_short`
+  * `entry_side`
+  * `exit_side`
+  * `trade_direction`
+  * renamed: `sell_reason` -> `exit_reason`
+* [Renamed `trade.nr_of_successful_buys` to `trade.nr_of_successful_entries` (mostly relevant for `adjust_trade_position()`)](#adjust-trade-position-changes)
+* Introduced new [`leverage` callback](strategy-callbacks.md#leverage-callback).
+* Informative pairs can now pass a 3rd element in the Tuple, defining the candle type.
+* `@informative` decorator now takes an optional `candle_type` argument.
+* [helper methods](#helper-methods) `stoploss_from_open` and `stoploss_from_absolute` now take `is_short` as additional argument.
+* `INTERFACE_VERSION` should be set to 3.
+* [Strategy/Configuration settings](#strategyconfiguration-settings).
+  * `order_time_in_force` buy -> entry, sell -> exit.
+  * `order_types` buy -> entry, sell -> exit.
+  * `unfilledtimeout` buy -> entry, sell -> exit.
+  * `ignore_buying_expired_candle_after` -> moved to root level instead of "ask_strategy/exit_pricing"
+* Terminology changes
+  * Sell reasons changed to reflect the new naming of "exit" instead of sells. Be careful in your strategy if you're using `exit_reason` checks and eventually update your strategy.
+    * `sell_signal` -> `exit_signal`
+    * `custom_sell` -> `custom_exit`
+    * `force_sell` -> `force_exit`
+    * `emergency_sell` -> `emergency_exit`
+  * Order pricing
+    * `bid_strategy` -> `entry_pricing`
+    * `ask_strategy` -> `exit_pricing`
+    * `ask_last_balance` -> `price_last_balance`
+    * `bid_last_balance` -> `price_last_balance`
+  * Webhook terminology changed from "sell" to "exit", and from "buy" to entry
+    * `webhookbuy` -> `entry`
+    * `webhookbuyfill` -> `entry_fill`
+    * `webhookbuycancel` -> `entry_cancel`
+    * `webhooksell` -> `exit`
+    * `webhooksellfill` -> `exit_fill`
+    * `webhooksellcancel` -> `exit_cancel`
+  * Telegram notification settings
+    * `buy` -> `entry`
+    * `buy_fill` -> `entry_fill`
+    * `buy_cancel` -> `entry_cancel`
+    * `sell` -> `exit`
+    * `sell_fill` -> `exit_fill`
+    * `sell_cancel` -> `exit_cancel`
+  * Strategy/config settings:
+    * `use_sell_signal` -> `use_exit_signal`
+    * `sell_profit_only` -> `exit_profit_only`
+    * `sell_profit_offset` -> `exit_profit_offset`
+    * `ignore_roi_if_buy_signal` -> `ignore_roi_if_entry_signal`
+    * `forcebuy_enable` -> `force_entry_enable`
+
+## Extensive explanation
+
+### `populate_buy_trend`
+
+In `populate_buy_trend()` - you will want to change the columns you assign from `'buy`' to `'enter_long'`, as well as the method name from `populate_buy_trend` to `populate_entry_trend`.
+
+```python hl_lines="1 9"
+def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['buy', 'buy_tag']] = (1, 'rsi_cross')
+
+    return dataframe
+```
+
+After:
+
+```python hl_lines="1 9"
+def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['enter_long', 'enter_tag']] = (1, 'rsi_cross')
+
+    return dataframe
+```
+
+Please refer to the [Strategy documentation](strategy-customization.md#entry-signal-rules) on how to enter and exit short trades.
+
+### `populate_sell_trend`
+
+Similar to `populate_buy_trend`, `populate_sell_trend()` will be renamed to `populate_exit_trend()`.
+We'll also change the column from `'sell'` to `'exit_long'`.
+
+``` python hl_lines="1 9"
+def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['sell', 'exit_tag']] = (1, 'some_exit_tag')
+    return dataframe
+```
+
+After
+
+``` python hl_lines="1 9"
+def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    dataframe.loc[
+        (
+            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
+            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
+            (dataframe['volume'] > 0)  # Make sure Volume is not 0
+        ),
+        ['exit_long', 'exit_tag']] = (1, 'some_exit_tag')
+    return dataframe
+```
+
+Please refer to the [Strategy documentation](strategy-customization.md#exit-signal-rules) on how to enter and exit short trades.
+
+### `custom_sell`
+
+`custom_sell` has been renamed to `custom_exit`.
+It's now also being called for every iteration, independent of current profit and `exit_profit_only` settings.
+
+``` python hl_lines="2"
+class AwesomeStrategy(IStrategy):
+    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+                    current_profit: float, **kwargs):
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+        # ...
+```
+
+``` python hl_lines="2"
+class AwesomeStrategy(IStrategy):
+    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+                    current_profit: float, **kwargs):
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+        # ...
+```
+
+### `custom_entry_timeout`
+
+`check_buy_timeout()` has been renamed to `check_entry_timeout()`, and `check_sell_timeout()` has been renamed to `check_exit_timeout()`.
+
+``` python hl_lines="2 6"
+class AwesomeStrategy(IStrategy):
+    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, 
+                            current_time: datetime, **kwargs) -> bool:
+        return False
+
+    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, 
+                            current_time: datetime, **kwargs) -> bool:
+        return False 
+```
+
+``` python hl_lines="2 6"
+class AwesomeStrategy(IStrategy):
+    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order', 
+                            current_time: datetime, **kwargs) -> bool:
+        return False
+
+    def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order', 
+                            current_time: datetime, **kwargs) -> bool:
+        return False 
+```
+
+### `custom_stake_amount`
+
+New string argument `side` - which can be either `"long"` or `"short"`.
+
+``` python hl_lines="4"
+class AwesomeStrategy(IStrategy):
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
+                            entry_tag: Optional[str], **kwargs) -> float:
+        # ... 
+        return proposed_stake
+```
+
+``` python hl_lines="4"
+class AwesomeStrategy(IStrategy):
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float | None, max_stake: float,
+                            entry_tag: str | None, side: str, **kwargs) -> float:
+        # ... 
+        return proposed_stake
+```
+
+### `confirm_trade_entry`
+
+New string argument `side` - which can be either `"long"` or `"short"`.
+
+``` python hl_lines="4"
+class AwesomeStrategy(IStrategy):
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str], 
+                            **kwargs) -> bool:
+      return True
+```
+
+After: 
+
+``` python hl_lines="4"
+class AwesomeStrategy(IStrategy):
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: str | None, 
+                            side: str, **kwargs) -> bool:
+      return True
+```
+
+### `confirm_trade_exit`
+
+Changed argument `sell_reason` to `exit_reason`.
+For compatibility, `sell_reason` will still be provided for a limited time.
+
+``` python hl_lines="3"
+class AwesomeStrategy(IStrategy):
+    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
+                           rate: float, time_in_force: str, sell_reason: str,
+                           current_time: datetime, **kwargs) -> bool:
+    return True
+```
+
+After:
+
+``` python hl_lines="3"
+class AwesomeStrategy(IStrategy):
+    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
+                           rate: float, time_in_force: str, exit_reason: str,
+                           current_time: datetime, **kwargs) -> bool:
+    return True
+```
+
+### `custom_entry_price`
+
+New string argument `side` - which can be either `"long"` or `"short"`.
+
+``` python hl_lines="3"
+class AwesomeStrategy(IStrategy):
+    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
+                           entry_tag: Optional[str], **kwargs) -> float:
+      return proposed_rate
+```
+
+After:
+
+``` python hl_lines="3"
+class AwesomeStrategy(IStrategy):
+    def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
+                           entry_tag: str | None, side: str, **kwargs) -> float:
+      return proposed_rate
+```
+
+### Adjust trade position changes
+
+While adjust-trade-position itself did not change, you should no longer use `trade.nr_of_successful_buys` - and instead use `trade.nr_of_successful_entries`, which will also include short entries.
+
+### Helper methods
+
+Added argument "is_short" to `stoploss_from_open` and `stoploss_from_absolute`.
+This should be given the value of `trade.is_short`.
+
+``` python hl_lines="5 7"
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
+        if current_profit > 0.10:
+            return stoploss_from_open(0.07, current_profit)
+
+        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)
+
+        return 1
+
+```
+
+After:
+
+``` python hl_lines="5 7"
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, after_fill: bool, 
+                        **kwargs) -> float | None:
+        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
+        if current_profit > 0.10:
+            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
+
+        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short, leverage=trade.leverage)
+
+
+```
+
+### Strategy/Configuration settings
+
+#### `order_time_in_force`
+
+`order_time_in_force` attributes changed from `"buy"` to `"entry"` and `"sell"` to `"exit"`.
+
+``` python
+    order_time_in_force: dict = {
+        "buy": "gtc",
+        "sell": "gtc",
+    }
+```
+
+After:
+
+``` python hl_lines="2 3"
+    order_time_in_force: dict = {
+        "entry": "GTC",
+        "exit": "GTC",
+    }
+```
+
+#### `order_types`
+
+`order_types` have changed all wordings from `buy` to `entry` - and `sell` to `exit`.
+And two words are joined with `_`. 
+
+``` python hl_lines="2-6"
+    order_types = {
+        "buy": "limit",
+        "sell": "limit",
+        "emergencysell": "market",
+        "forcesell": "market",
+        "forcebuy": "market",
+        "stoploss": "market",
+        "stoploss_on_exchange": false,
+        "stoploss_on_exchange_interval": 60
+    }
+```
+
+After:
+
+``` python hl_lines="2-6"
+    order_types = {
+        "entry": "limit",
+        "exit": "limit",
+        "emergency_exit": "market",
+        "force_exit": "market",
+        "force_entry": "market",
+        "stoploss": "market",
+        "stoploss_on_exchange": false,
+        "stoploss_on_exchange_interval": 60
+    }
+```
+
+#### Strategy level settings
+
+* `use_sell_signal` -> `use_exit_signal`
+* `sell_profit_only` -> `exit_profit_only`
+* `sell_profit_offset` -> `exit_profit_offset`
+* `ignore_roi_if_buy_signal` -> `ignore_roi_if_entry_signal`
+
+``` python hl_lines="2-5"
+    # These values can be overridden in the config.
+    use_sell_signal = True
+    sell_profit_only = True
+    sell_profit_offset: 0.01
+    ignore_roi_if_buy_signal = False
+```
+
+After:
+
+``` python hl_lines="2-5"
+    # These values can be overridden in the config.
+    use_exit_signal = True
+    exit_profit_only = True
+    exit_profit_offset: 0.01
+    ignore_roi_if_entry_signal = False
+```
+
+#### `unfilledtimeout`
+
+`unfilledtimeout` have changed all wordings from `buy` to `entry` - and `sell` to `exit`.
+
+``` python hl_lines="2-3"
+unfilledtimeout = {
+        "buy": 10,
+        "sell": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    }
+```
+
+After:
+
+``` python hl_lines="2-3"
+unfilledtimeout = {
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    }
+```
+
+#### `order pricing`
+
+Order pricing changed in 2 ways. `bid_strategy` was renamed to `entry_pricing` and `ask_strategy` was renamed to `exit_pricing`.
+The attributes `ask_last_balance` -> `price_last_balance` and `bid_last_balance` -> `price_last_balance` were renamed as well.
+Also, price-side can now be defined as `ask`, `bid`, `same` or `other`.
+Please refer to the [pricing documentation](configuration.md#prices-used-for-orders) for more information.
+
+``` json hl_lines="2-3 6 12-13 16"
+{
+    "bid_strategy": {
+        "price_side": "bid",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "ask_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "ask_strategy":{
+        "price_side": "ask",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "bid_last_balance": 0.0
+        "ignore_buying_expired_candle_after": 120
+    }
+}
+```
+
+after:
+
+``` json  hl_lines="2-3 6 12-13 16"
+{
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0
+    },
+    "ignore_buying_expired_candle_after": 120
+}
+```
+
+## FreqAI strategy
+
+The `populate_any_indicators()` method has been split into `feature_engineering_expand_all()`, `feature_engineering_expand_basic()`, `feature_engineering_standard()` and`set_freqai_targets()`.
+
+For each new function, the pair (and timeframe where necessary) will be automatically added to the column.
+As such, the definition of features becomes much simpler with the new logic.
+
+For a full explanation of each method, please go to the corresponding [freqAI documentation page](freqai-feature-engineering.md#defining-the-features)
+
+``` python linenums="1" hl_lines="12-37 39-42 63-65 67-75"
+
+def populate_any_indicators(
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
+    ):
+
+        if informative is None:
+            informative = self.dp.get_pair_dataframe(pair, tf)
+
+        # first loop is automatically duplicating indicators for time periods
+        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+
+            t = int(t)
+            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
+            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
+            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
+            informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
+            informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
+
+            bollinger = qtpylib.bollinger_bands(
+                qtpylib.typical_price(informative), window=t, stds=2.2
+            )
+            informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
+            informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
+            informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
+
+            informative[f"%-{pair}bb_width-period_{t}"] = (
+                informative[f"{pair}bb_upperband-period_{t}"]
+                - informative[f"{pair}bb_lowerband-period_{t}"]
+            ) / informative[f"{pair}bb_middleband-period_{t}"]
+            informative[f"%-{pair}close-bb_lower-period_{t}"] = (
+                informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
+            )
+
+            informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
+
+            informative[f"%-{pair}relative_volume-period_{t}"] = (
+                informative["volume"] / informative["volume"].rolling(t).mean()
+            ) # (1)
+
+        informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
+        informative[f"%-{pair}raw_volume"] = informative["volume"]
+        informative[f"%-{pair}raw_price"] = informative["close"]
+        # (2)
+
+        indicators = [col for col in informative if col.startswith("%")]
+        # This loop duplicates and shifts all indicators to add a sense of recency to data
+        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
+            if n == 0:
+                continue
+            informative_shift = informative[indicators].shift(n)
+            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
+            informative = pd.concat((informative, informative_shift), axis=1)
+
+        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
+        skip_columns = [
+            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        df = df.drop(columns=skip_columns)
+
+        # Add generalized indicators here (because in live, it will call this
+        # function to populate indicators during training). Notice how we ensure not to
+        # add them multiple times
+        if set_generalized_indicators:
+            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
+            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
+            # (3)
+
+            # user adds targets here by prepending them with &- (see convention below)
+            df["&-s_close"] = (
+                df["close"]
+                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+                .mean()
+                / df["close"]
+                - 1
+            )  # (4)
+
+        return df
+```
+
+1. Features - Move to `feature_engineering_expand_all`
+2. Basic features, not expanded across `indicator_periods_candles` - move to`feature_engineering_expand_basic()`.
+3. Standard features which should not be expanded - move to `feature_engineering_standard()`.
+4. Targets - Move this part to `set_freqai_targets()`.
+
+### freqai - feature engineering expand all
+
+Features will now expand automatically. As such, the expansion loops, as well as the `{pair}` / `{timeframe}` parts will need to be removed.
+
+``` python linenums="1"
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs) -> DataFrame::
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
+
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
+
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+```
+
+### Freqai - feature engineering basic
+
+Basic features. Make sure to remove the `{pair}` part from your features.
+
+``` python linenums="1"
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs) -> DataFrame::
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+```
+
+### FreqAI - feature engineering standard
+
+``` python linenums="1"
+    def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        return dataframe
+```
+
+### FreqAI - set Targets
+
+Targets now get their own, dedicated method.
+
+``` python linenums="1"
+    def set_freqai_targets(self, dataframe: DataFrame, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
+            )
+
+        return dataframe
+```
+
+
+### FreqAI - New data Pipeline
+
+If you have created your own custom `IFreqaiModel` with a custom `train()`/`predict()` function, *and* you still rely on `data_cleaning_train/predict()`, then you will need to migrate to the new pipeline. If your model does *not* rely on `data_cleaning_train/predict()`, then you do not need to worry about this migration. That means that this migration guide is relevant for a very small percentage of power-users. If you stumbled upon this guide by mistake, feel free to inquire in depth about your problem in the Freqtrade discord server.
+
+The conversion involves first removing `data_cleaning_train/predict()` and replacing them with a `define_data_pipeline()` and `define_label_pipeline()` function to your `IFreqaiModel` class:
+
+```python  linenums="1" hl_lines="11-14 47-49 55-57"
+class MyCoolFreqaiModel(BaseRegressionModel):
+    """
+    Some cool custom IFreqaiModel you made before Freqtrade version 2023.6
+    """
+    def train(
+        self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
+    ) -> Any:
+
+        # ... your custom stuff
+
+        # Remove these lines
+        # data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        # self.data_cleaning_train(dk)
+        # data_dictionary = dk.normalize_data(data_dictionary)
+        # (1)
+
+        # Add these lines. Now we control the pipeline fit/transform ourselves
+        dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
+        dk.label_pipeline = self.define_label_pipeline(threads=dk.thread_count)
+
+        (dd["train_features"],
+         dd["train_labels"],
+         dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"],
+                                                                  dd["train_labels"],
+                                                                  dd["train_weights"])
+
+        (dd["test_features"],
+         dd["test_labels"],
+         dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],
+                                                             dd["test_labels"],
+                                                             dd["test_weights"])
+
+        dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
+        dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
+
+        # ... your custom code
+
+        return model
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
+
+        # ... your custom stuff
+
+        # Remove these lines:
+        # self.data_cleaning_predict(dk)
+        # (2)
+
+        # Add these lines:
+        dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
+            dk.data_dictionary["prediction_features"], outlier_check=True)
+
+        # Remove this line
+        # pred_df = dk.denormalize_labels_from_metadata(pred_df)
+        # (3)
+
+        # Replace with these lines
+        pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)
+        if self.freqai_info.get("DI_threshold", 0) > 0:
+            dk.DI_values = dk.feature_pipeline["di"].di_values
+        else:
+            dk.DI_values = np.zeros(outliers.shape[0])
+        dk.do_predict = outliers
+
+        # ... your custom code
+        return (pred_df, dk.do_predict)
+```
+
+
+1. Data normalization and cleaning is now homogenized with the new pipeline definition. This is created in the new `define_data_pipeline()` and `define_label_pipeline()` functions. The `data_cleaning_train()` and `data_cleaning_predict()` functions are no longer used. You can override `define_data_pipeline()` to create your own custom pipeline if you wish.
+2. Data normalization and cleaning is now homogenized with the new pipeline definition. This is created in the new `define_data_pipeline()` and `define_label_pipeline()` functions. The `data_cleaning_train()` and `data_cleaning_predict()` functions are no longer used. You can override `define_data_pipeline()` to create your own custom pipeline if you wish.
+3. Data denormalization is done with the new pipeline. Replace this with the lines below.
diff --git a/docs/stylesheets/ft.extra.css b/docs/stylesheets/ft.extra.css
new file mode 100644
index 000000000..930463ca8
--- /dev/null
+++ b/docs/stylesheets/ft.extra.css
@@ -0,0 +1,21 @@
+.rst-versions  {
+    font-size: .7rem;
+    color: white;
+}
+
+.rst-versions.rst-badge .rst-current-version {
+    font-size: .7rem;
+    color: white;
+}
+
+.rst-versions .rst-other-versions {
+    color: white;
+}
+
+.md-version__list {
+    font-weight: 500 !important;
+}
+
+#available-endpoints ~ .md-typeset__scrollwrap .md-typeset__table th:first-of-type {
+    width: 35% !important;
+}
diff --git a/docs/telegram-usage.md b/docs/telegram-usage.md
index 7de9809ec..a7a27f150 100644
--- a/docs/telegram-usage.md
+++ b/docs/telegram-usage.md
@@ -1,129 +1,495 @@
 # Telegram usage
 
-This page explains how to command your bot with Telegram.
+## Setup your Telegram bot
 
-## Pre-requisite
-To control your bot with Telegram, you need first to 
-[set up a Telegram bot](https://github.com/gcarq/freqtrade/blob/develop/docs/pre-requisite.md) 
-and add your Telegram API keys into your config file.
+Below we explain how to create your Telegram Bot, and how to get your
+Telegram user id.
+
+### 1. Create your Telegram bot
+
+Start a chat with the [Telegram BotFather](https://telegram.me/BotFather)
+
+Send the message `/newbot`.
+
+*BotFather response:*
+
+> Alright, a new bot. How are we going to call it? Please choose a name for your bot.
+
+Choose the public name of your bot (e.x. `Freqtrade bot`)
+
+*BotFather response:*
+
+> Good. Now let's choose a username for your bot. It must end in `bot`. Like this, for example: TetrisBot or tetris_bot.
+
+Choose the name id of your bot and send it to the BotFather (e.g. "`My_own_freqtrade_bot`")
+
+*BotFather response:*
+
+> Done! Congratulations on your new bot. You will find it at `t.me/yourbots_name_bot`. You can now add a description, about section and profile picture for your bot, see /help for a list of commands. By the way, when you've finished creating your cool bot, ping our Bot Support if you want a better username for it. Just make sure the bot is fully operational before you do this.
+
+> Use this token to access the HTTP API: `22222222:APITOKEN`
+
+> For a description of the Bot API, see this page: https://core.telegram.org/bots/api Father bot will return you the token (API key)
+
+Copy the API Token (`22222222:APITOKEN` in the above example) and keep use it for the config parameter `token`.
+
+Don't forget to start the conversation with your bot, by clicking `/START` button
+
+### 2. Telegram user_id
+
+#### Get your user id
+
+Talk to the [userinfobot](https://telegram.me/userinfobot)
+
+Get your "Id", you will use it for the config parameter `chat_id`.
+
+#### Use Group id
+
+To get the group ID, you can add the bot to the group, start freqtrade, and issue a `/tg_info` command.
+This will return the group id to you, without having to use some random bot.
+While "chat_id" is still required, it doesn't need to be set to this particular group id for this command.
+
+The response will also contain the "topic_id" if necessary - both in a format ready to copy/paste into your configuration.
+
+``` json
+ {
+    "enabled": true,
+    "token": "********",
+    "chat_id": "-1001332619709",
+    "topic_id": "122"
+}
+```
+
+For the Freqtrade configuration, you can then use the full value (including `-` ) as string:
+
+```json
+   "chat_id": "-1001332619709"
+```
+
+!!! Warning "Using telegram groups"
+    When using telegram groups, you're giving every member of the telegram group access to your freqtrade bot and to all commands possible via telegram. Please make sure that you can trust everyone in the telegram group to avoid unpleasant surprises.
+
+##### Group Topic ID
+
+To use a specific topic in a group, you can use the `topic_id` parameter in the configuration. This will allow you to use the bot in a specific topic in a group.  
+Without this, the bot will always respond to the general channel in the group if topics are enabled for a group chat.
+
+```json
+   "chat_id": "-1001332619709",
+   "topic_id": "3"
+```
+
+Similar to the group-id - you can use `/tg_info` from the topic/thread to get the correct topic-id.
+
+#### Authorized users
+
+For groups, it can be useful to limit who can send commands to the bot.
+
+If `"authorized_users": []` is present and empty, no user will be allowed to control the bot.
+In the below example, only the user with the id "1234567" is allowed to control the bot - all other users will only be able to receive messages.
+
+```json
+   "chat_id": "-1001332619709",
+   "topic_id": "3",
+   "authorized_users": ["1234567"]
+```
+
+## Control telegram noise
+
+Freqtrade provides means to control the verbosity of your telegram bot.
+Each setting has the following possible values:
+
+* `on` - Messages will be sent, and user will be notified.
+* `silent` - Message will be sent, Notification will be without sound / vibration.
+* `off` - Skip sending a message-type all together.
+
+Example configuration showing the different settings:
+
+``` json
+"telegram": {
+    "enabled": true,
+    "token": "your_telegram_token",
+    "chat_id": "your_telegram_chat_id",
+    "allow_custom_messages": true,
+    "notification_settings": {
+        "status": "silent",
+        "warning": "on",
+        "startup": "off",
+        "entry": "silent",
+        "entry_fill": "on",
+        "entry_cancel": "silent",
+        "exit": {
+            "roi": "silent",
+            "emergency_exit": "on",
+            "force_exit": "on",
+            "exit_signal": "silent",
+            "trailing_stop_loss": "on",
+            "stop_loss": "on",
+            "stoploss_on_exchange": "on",
+            "custom_exit": "silent",  // custom_exit without specifying an exit reason
+            "partial_exit": "on",
+            // "custom_exit_message": "silent",  // Disable individual custom exit reasons
+            "*": "off"  // Disable all other exit reasons
+        },
+        // "exit": "off",  // Simplistic configuration to disable all exit messages
+        "exit_cancel": "on",
+        "exit_fill": "off",
+        "protection_trigger": "off",
+        "protection_trigger_global": "on",
+        "strategy_msg": "off",
+        "show_candle": "off"
+    },
+    "reload": true,
+    "balance_dust_level": 0.01
+},
+```
+
+* `entry` notifications are sent when the order is placed, while `entry_fill` notifications are sent when the order is filled on the exchange.  
+* `exit` notifications are sent when the order is placed, while `exit_fill` notifications are sent when the order is filled on the exchange.  
+    Exit messages (`exit` and `exit_fill`) can be further controlled at individual exit reasons level, with the specific exit reason as the key. the default for all exit reasons is `on` - but can be configured via special `*` key - which will act as a wildcard for all exit reasons that are not explicitly defined.
+* `*_fill` notifications are off by default and must be explicitly enabled.  
+* `protection_trigger` notifications are sent when a protection triggers and `protection_trigger_global` notifications trigger when global protections are triggered.  
+* `strategy_msg` - Receive notifications from the strategy, sent via `self.dp.send_msg()` from the strategy [more details](strategy-customization.md#send-notification).  
+* `show_candle` - show candle values as part of entry/exit messages. Only possible values are `"ohlc"` or `"off"`.  
+* `balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.  
+* `allow_custom_messages` completely disable strategy messages.  
+* `reload` allows you to disable reload-buttons on selected messages.  
+
+## Create a custom keyboard (command shortcut buttons)
+
+Telegram allows us to create a custom keyboard with buttons for commands.
+The default custom keyboard looks like this.
+
+```python
+[
+    ["/daily", "/profit", "/balance"], # row 1, 3 commands
+    ["/status", "/status table", "/performance"], # row 2, 3 commands
+    ["/count", "/start", "/stop", "/help"] # row 3, 4 commands
+]
+```
+
+### Usage
+
+You can create your own keyboard in `config.json`:
+
+``` json
+"telegram": {
+      "enabled": true,
+      "token": "your_telegram_token",
+      "chat_id": "your_telegram_chat_id",
+      "keyboard": [
+          ["/daily", "/stats", "/balance", "/profit"],
+          ["/status table", "/performance"],
+          ["/reload_config", "/count", "/logs"]
+      ]
+   },
+```
+
+!!! Note "Supported Commands"
+    Only the following commands are allowed. Command arguments are not supported!
+
+    `/start`, `/pause`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopentry`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version`, `/marketdir`
 
 ## Telegram commands
-Per default, the Telegram bot shows predefined commands. Some commands 
-are only available by sending them to the bot. The table below list the 
+
+Per default, the Telegram bot shows predefined commands. Some commands
+are only available by sending them to the bot. The table below list the
 official commands. You can ask at any moment for help with `/help`.
 
-|  Command | Default | Description |
-|----------|---------|-------------|
-| `/start` | | Starts the trader
-| `/stop` | | Stops the trader
-| `/status` | | Lists all open trades
-| `/status table` | | List all open trades in a table format
-| `/count` | | Displays number of trades used and available
-| `/profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
-| `/forcesell <trade_id>` | | Instantly sells the given trade  (Ignoring `minimum_roi`).
-| `/forcesell all` | | Instantly sells all open trades (Ignoring `minimum_roi`).
-| `/performance` | | Show performance of each finished trade grouped by pair
-| `/balance` | | Show account balance per currency
-| `/daily <n>` | 7 | Shows profit or loss per day, over the last n days
-| `/help` | | Show help message
-| `/version` | | Show version
+|  Command | Description |
+|----------|-------------|
+| **System commands**
+| `/start` | Starts the trader
+| `/pause | /stopentry | /stopbuy` | Pause the trader. Gracefully handle open trades according to their rules. Do not enter new positions.
+| `/stop` | Stops the trader
+| `/reload_config` | Reloads the configuration file
+| `/show_config` | Shows part of the current configuration with relevant settings to operation
+| `/logs [limit]` | Show last log messages.
+| `/help` | Show help message
+| `/version` | Show version
+| **Status** |
+| `/status` | Lists all open trades
+| `/status <trade_id>` | Lists one or more specific trade. Separate multiple <trade_id> with a blank space.
+| `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
+| `/order <trade_id>` | Lists orders of one or more specific trade. Separate multiple <trade_id> with a blank space.
+| `/trades [limit]` | List all recently closed trades in a table format.
+| `/count` | Displays number of trades used and available
+| `/locks` | Show currently locked pairs.
+| `/unlock <pair or lock_id>` | Remove the lock for this pair (or for this lock id).
+| `/marketdir [long | short | even | none]` | Updates the user managed variable that represents the current market direction. If no direction is provided, the currently set direction will be displayed.
+| `/list_custom_data <trade_id> [key]` | List custom_data for Trade ID & Key combination. If no Key is supplied it will list all key-value pairs found for that Trade ID.
+| **Modify Trade states** |
+| `/forceexit <trade_id> | /fx <tradeid>` | Instantly exits the given trade  (Ignoring `minimum_roi`).
+| `/forceexit all | /fx all` | Instantly exits all open trades (Ignoring `minimum_roi`).
+| `/fx` | alias for `/forceexit`
+| `/forcelong <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`force_entry_enable` must be set to True)
+| `/forceshort <pair> [rate]` | Instantly shorts the given pair. Rate is optional and only applies to limit orders. This will only work on non-spot markets. (`force_entry_enable` must be set to True)
+| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
+| `/reload_trade <trade_id>` | Reload a trade from the Exchange. Only works in live, and can potentially help recover a trade that was manually sold on the exchange.
+| `/cancel_open_order <trade_id> | /coo <trade_id>` | Cancel an open order for a trade.
+| **Metrics** |
+| `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
+| `/performance` | Show performance of each finished trade grouped by pair
+| `/balance` | Show bot managed balance per currency
+| `/balance full` | Show account balance per currency
+| `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
+| `/weekly <n>` | Shows profit or loss per week, over the last n weeks (n defaults to 8)
+| `/monthly <n>` | Shows profit or loss per month, over the last n months (n defaults to 6)
+| `/stats` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells
+| `/exits` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells
+| `/entries` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells
+| `/whitelist [sorted] [baseonly]` | Show the current whitelist. Optionally display in alphabetical order and/or with just the base currency of each pairing.
+| `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
+| `/edge` | Show validated pairs by Edge if it is enabled.
 
 ## Telegram commands in action
+
 Below, example of Telegram message you will receive for each command.
 
 ### /start
+
 > **Status:** `running`
 
+### /pause | /stopentry | /stopbuy
+
+> **Status:** `paused, no more entries will occur from now. Run /start to enable entries.`
+
+Prevents the bot from opening new trades by changing the state to `paused`.
+Open trades will continue to be managed according to their regular rules (ROI/exit signals, stop-loss, etc.).
+Note that position adjustment remains active, but only on the exit side — meaning that when the bot is `paused`, it can only reduce the position size of open trades.
+
+After this, give the bot time to close off open trades (can be checked via `/status table`).
+Once all positions are closed, run `/stop` to completely stop the bot.
+
+Use `/start` to resume the bot to the `running` state, allowing it to open new positions.
+
+!!! Warning
+    The pause/stopentry signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
+
 ### /stop
-> `Stopping trader ...`  
+
+> `Stopping trader ...`
 > **Status:** `stopped`
 
-## /status
+### /status
+
 For each open trade, the bot will send you the following message.
+Enter Tag is configurable via Strategy.
 
-> **Trade ID:** `123`  
-> **Current Pair:** BTC_CVC  
-> **Open Since:** `1 days ago`  
+> **Trade ID:** `123` `(since 1 days ago)`  
+> **Current Pair:** CVC/BTC  
+> **Direction:** Long  
+> **Leverage:** 1.0  
 > **Amount:** `26.64180098`  
+> **Enter Tag:** Awesome Long Signal  
 > **Open Rate:** `0.00007489`  
-> **Close Rate:** `None`  
 > **Current Rate:** `0.00007489`  
-> **Close Profit:** `None`  
-> **Current Profit:** `12.95%`  
-> **Open Order:** `None`
+> **Unrealized Profit:** `12.95%`  
+> **Stoploss:** `0.00007389 (-0.02%)`  
+
+### /status table
 
-## /status table
 Return the status of all open trades in a table format.
+
 ```
-   ID  Pair      Since    Profit  
-----  --------  -------  --------  
-  67  BTC_SC    1 d      13.33%  
- 123  BTC_CVC   1 h      12.95%  
+ID L/S    Pair     Since   Profit
+----    --------  -------  --------
+  67 L   SC/BTC    1 d      13.33%
+ 123 S   CVC/BTC   1 h      12.95%
 ```
 
-## /count
+### /count
+
 Return the number of trades used and available.
+
 ```
 current    max
----------  -----  
-     2     10  
+---------  -----
+     2     10
 ```
 
-## /profit
+### /profit
+
 Return a summary of your profit/loss and performance.
 
 > **ROI:** Close trades  
->   ∙ `0.00485701 BTC (258.45%)`  
+>   ∙ `0.00485701 BTC (2.2%) (15.2 Σ%)`  
 >   ∙ `62.968 USD`  
 > **ROI:** All trades  
->   ∙ `0.00255280 BTC (143.43%)`  
+>   ∙ `0.00255280 BTC (1.5%) (6.43 Σ%)`  
 >   ∙ `33.095 EUR`  
->   
+>  
 > **Total Trade Count:** `138`  
+> **Bot started:** `2022-07-11 18:40:44`  
 > **First Trade opened:** `3 days ago`  
 > **Latest Trade opened:** `2 minutes ago`  
 > **Avg. Duration:** `2:33:45`  
-> **Best Performing:** `BTC_PAY: 50.23%`
+> **Best Performing:** `PAY/BTC: 50.23%`  
+> **Trading volume:** `0.5 BTC`  
+> **Profit factor:** `1.04`  
+> **Win / Loss:** `102 / 36`  
+> **Winrate:** `73.91%`  
+> **Expectancy (Ratio):** `4.87 (1.66)`  
+> **Max Drawdown:** `9.23% (0.01255 BTC)`  
 
-## /forcesell <trade_id>
+The relative profit of `1.2%` is the average profit per trade.  
+The relative profit of `15.2 Σ%` is be based on the starting capital - so in this case, the starting capital was `0.00485701 * 1.152 = 0.00738 BTC`.  
+**Starting capital(**) is either taken from the `available_capital` setting, or calculated by using current wallet size - profits.  
+**Profit Factor** is calculated as gross profits / gross losses - and should serve as an overall metric for the strategy.  
+**Expectancy** corresponds to the average return per currency unit at risk, i.e. the winrate and the risk-reward ratio (the average gain of winning trades compared to the average loss of losing trades).  
+**Expectancy Ratio** is expected profit or loss of a subsequent trade based on the performance of all past trades.  
+**Max drawdown** corresponds to the backtesting metric `Absolute Drawdown (Account)` - calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.  
+**Bot started date** will refer to the date the bot was first started. For older bots, this will default to the first trade's open date.  
 
-> **BITTREX:** Selling BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
+### /forceexit <trade_id>
+
+> **BINANCE:** Exiting BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
+
+!!! Tip
+    You can get a list of all open trades by calling `/forceexit` without parameter, which will show a list of buttons to simply exit a trade.
+    This command has an alias in `/fx` - which has the same capabilities, but is faster to type in "emergency" situations.
+
+### /forcelong <pair> [rate] | /forceshort <pair> [rate]
+
+`/forcebuy <pair> [rate]` is also supported for longs but should be considered deprecated.
+
+> **BINANCE:** Long ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`)
+
+Omitting the pair will open a query asking for the pair to trade (based on the current whitelist).
+Trades created through `/forcelong` will have the buy-tag of `force_entry`.
+
+![Telegram force-buy screenshot](assets/telegram_forcebuy.png)
+
+Note that for this to work, `force_entry_enable` needs to be set to true.
+
+[More details](configuration.md#understand-force_entry_enable)
+
+### /performance
 
-## /performance
 Return the performance of each crypto-currency the bot has sold.
 > Performance:  
-> 1. `BTC_RCN 57.77%`  
-> 2. `BTC_PAY 56.91%`  
-> 3. `BTC_VIB 47.07%`  
-> 4. `BTC_SALT 30.24%`  
-> 5. `BTC_STORJ 27.24%`  
-> ...
+> 1. `RCN/BTC 0.003 BTC (57.77%) (1)`  
+> 2. `PAY/BTC 0.0012 BTC (56.91%) (1)`  
+> 3. `VIB/BTC 0.0011 BTC (47.07%) (1)`  
+> 4. `SALT/BTC 0.0010 BTC (30.24%) (1)`  
+> 5. `STORJ/BTC 0.0009 BTC (27.24%) (1)`  
+> ...  
+
+The relative performance is calculated against the total investment in the currency, aggregating all filled entries for the currency.
+
+### /balance
 
-## /balance
 Return the balance of all crypto-currency your have on the exchange.
 
 > **Currency:** BTC  
 > **Available:** 3.05890234  
 > **Balance:** 3.05890234  
 > **Pending:** 0.0  
-
+>
 > **Currency:** CVC  
 > **Available:** 86.64180098  
 > **Balance:** 86.64180098  
-> **Pending:** 0.0
+> **Pending:** 0.0  
 
-## /daily <n>
-Per default `/daily` will return the 7 last days. 
-The example below if for `/daily 3`:
+### /daily <n>
+
+Per default `/daily` will return the 7 last days. The example below if for `/daily 3`:
 
 > **Daily Profit over the last 3 days:**
+
 ```
-Day         Profit BTC      Profit USD  
-----------  --------------  ------------  
-2018-01-03  0.00224175 BTC  29,142 USD  
-2018-01-02  0.00033131 BTC   4,307 USD  
-2018-01-01  0.00269130 BTC  34.986 USD
+Day (count)     USDT          USD         Profit %
+--------------  ------------  ----------  ----------
+2022-06-11 (1)  -0.746 USDT   -0.75 USD   -0.08%
+2022-06-10 (0)  0 USDT        0.00 USD    0.00%
+2022-06-09 (5)  20 USDT       20.10 USD   5.00%
 ```
 
-## /version
-> **Version:** `0.14.3` 
+### /weekly <n>
+
+Per default `/weekly` will return the 8 last weeks, including the current week. Each week starts
+from Monday. The example below if for `/weekly 3`:
+
+> **Weekly Profit over the last 3 weeks (starting from Monday):**
+
+```
+Monday (count)  Profit BTC      Profit USD   Profit %
+-------------  --------------  ------------    ----------
+2018-01-03 (5)  0.00224175 BTC  29,142 USD   4.98%
+2017-12-27 (1)  0.00033131 BTC   4,307 USD   0.00%
+2017-12-20 (4)  0.00269130 BTC  34.986 USD   5.12%
+```
+
+### /monthly <n>
+
+Per default `/monthly` will return the 6 last months, including the current month. The example below
+if for `/monthly 3`:
+
+> **Monthly Profit over the last 3 months:**
+```
+Month (count)  Profit BTC      Profit USD    Profit %
+-------------  --------------  ------------    ----------
+2018-01 (20)    0.00224175 BTC  29,142 USD  4.98%
+2017-12 (5)    0.00033131 BTC   4,307 USD   0.00%
+2017-11 (10)    0.00269130 BTC  34.986 USD  5.10%
+```
+
+### /whitelist
+
+Shows the current whitelist
+
+> Using whitelist `StaticPairList` with 22 pairs  
+> `IOTA/BTC, NEO/BTC, TRX/BTC, VET/BTC, ADA/BTC, ETC/BTC, NCASH/BTC, DASH/BTC, XRP/BTC, XVG/BTC, EOS/BTC, LTC/BTC, OMG/BTC, BTG/BTC, LSK/BTC, ZEC/BTC, HOT/BTC, IOTX/BTC, XMR/BTC, AST/BTC, XLM/BTC, NANO/BTC`
+
+### /blacklist [pair]
+
+Shows the current blacklist.
+If Pair is set, then this pair will be added to the pairlist.
+Also supports multiple pairs, separated by a space.  
+Use `/reload_config` to reset the blacklist.
+
+> Using blacklist `StaticPairList` with 2 pairs  
+>`DODGE/BTC`, `HOT/BTC`.  
+
+### /edge
+
+Shows pairs validated by Edge along with their corresponding win-rate, expectancy and stoploss values.
+
+> **Edge only validated following pairs:**
+```
+Pair        Winrate    Expectancy    Stoploss
+--------  ---------  ------------  ----------
+DOCK/ETH   0.522727      0.881821       -0.03
+PHX/ETH    0.677419      0.560488       -0.03
+HOT/ETH    0.733333      0.490492       -0.03
+HC/ETH     0.588235      0.280988       -0.02
+ARDR/ETH   0.366667      0.143059       -0.01
+```
+
+### /version
+
+> **Version:** `0.14.3`
+
+### /marketdir
+
+If a market direction is provided the command updates the user managed variable that represents the current market direction.
+This variable is not set to any valid market direction on bot startup and must be set by the user. The example below is for `/marketdir long`:
+
+```
+Successfully updated marketdirection from none to long.
+```
+
+If no market direction is provided the command outputs the currently set market directions. The example below is for `/marketdir`:
+
+```
+Currently set marketdirection: even
+```
+
+You can use the market direction in your strategy via `self.market_direction`.
+
+!!! Warning "Bot restarts"
+    Please note that the market direction is not persisted, and will be reset after a bot restart/reload.
+
+!!! Danger "Backtesting"
+    As this value/variable is intended to be changed manually in dry/live trading.
+    Strategies using `market_direction` will probably not produce reliable, reproducible results (changes to this variable will not be reflected for backtesting). Use at your own risk.
diff --git a/docs/trade-object.md b/docs/trade-object.md
new file mode 100644
index 000000000..26f5e9131
--- /dev/null
+++ b/docs/trade-object.md
@@ -0,0 +1,151 @@
+# Trade Object
+
+## Trade
+
+A position freqtrade enters is stored in a `Trade` object - which is persisted to the database.
+It's a core concept of freqtrade - and something you'll come across in many sections of the documentation, which will most likely point you to this location.
+
+It will be passed to the strategy in many [strategy callbacks](strategy-callbacks.md). The object passed to the strategy cannot be modified directly. Indirect modifications may occur based on callback results.
+
+## Trade - Available attributes
+
+The following attributes / properties are available for each individual trade - and can be used with `trade.<property>` (e.g. `trade.pair`).
+
+|  Attribute | DataType | Description |
+|------------|-------------|-------------|
+| `pair` | string | Pair of this trade. |
+| `is_open` | boolean | Is the trade currently open, or has it been concluded. |
+| `open_rate` | float | Rate this trade was entered at (Avg. entry rate in case of trade-adjustments). |
+| `close_rate` | float | Close rate - only set when is_open = False. |
+| `stake_amount` | float | Amount in Stake (or Quote) currency. |
+| `amount` | float | Amount in Asset / Base currency that is currently owned. Will be 0.0 until the initial order fills. |
+| `open_date` | datetime | Timestamp when trade was opened **use `open_date_utc` instead** |
+| `open_date_utc` | datetime | Timestamp when trade was opened - in UTC. |
+| `close_date` | datetime | Timestamp when trade was closed **use `close_date_utc` instead** |
+| `close_date_utc` | datetime | Timestamp when trade was closed - in UTC. |
+| `close_profit` | float | Relative profit at the time of trade closure. `0.01` == 1% |
+| `close_profit_abs` | float | Absolute profit (in stake currency) at the time of trade closure. |
+| `leverage` | float | Leverage used for this trade - defaults to 1.0 in spot markets. |
+| `enter_tag` | string | Tag provided on entry via the `enter_tag` column in the dataframe. |
+| `is_short` | boolean | True for short trades, False otherwise. |
+| `orders` | Order[] | List of order objects attached to this trade (includes both filled and cancelled orders). |
+| `date_last_filled_utc` | datetime | Time of the last filled order. |
+| `entry_side` | "buy" / "sell" | Order Side the trade was entered. |
+| `exit_side` | "buy" / "sell" | Order Side that will result in a trade exit / position reduction. |
+| `trade_direction` | "long" / "short" | Trade direction in text - long or short. |
+| `nr_of_successful_entries` | int | Number of successful (filled) entry orders. |
+| `nr_of_successful_exits` | int | Number of successful (filled) exit orders. |
+| `has_open_orders` | boolean | Has the trade open orders (excluding stoploss orders). |
+
+## Class methods
+
+The following are class methods - which return generic information, and usually result in an explicit query against the database.
+They can be used as `Trade.<method>` - e.g. `open_trades = Trade.get_open_trade_count()`
+
+!!! Warning "Backtesting/hyperopt"
+    Most methods will work in both backtesting / hyperopt and live/dry modes.
+    During backtesting, it's limited to usage in [strategy callbacks](strategy-callbacks.md). Usage in `populate_*()` methods is not supported and will result in wrong results.
+
+### get_trades_proxy
+
+When your strategy needs some information on existing (open or close) trades - it's best to use `Trade.get_trades_proxy()`.
+
+Usage:
+
+``` python
+from freqtrade.persistence import Trade
+from datetime import timedelta
+
+# ...
+trade_hist = Trade.get_trades_proxy(pair='ETH/USDT', is_open=False, open_date=current_date - timedelta(days=2))
+
+```
+
+`get_trades_proxy()` supports the following keyword arguments. All arguments are optional - calling `get_trades_proxy()` without arguments will return a list of all trades in the database.
+
+* `pair` e.g. `pair='ETH/USDT'`
+* `is_open` e.g. `is_open=False`
+* `open_date` e.g. `open_date=current_date - timedelta(days=2)`
+* `close_date` e.g. `close_date=current_date - timedelta(days=5)`
+
+### get_open_trade_count
+
+Get the number of currently open trades
+
+``` python
+from freqtrade.persistence import Trade
+# ...
+open_trades = Trade.get_open_trade_count()
+```
+
+### get_total_closed_profit
+
+Retrieve the total profit the bot has generated so far.
+Aggregates `close_profit_abs` for all closed trades.
+
+``` python
+from freqtrade.persistence import Trade
+
+# ...
+profit = Trade.get_total_closed_profit()
+```
+
+### total_open_trades_stakes
+
+Retrieve the total stake_amount that's currently in trades.
+
+``` python
+from freqtrade.persistence import Trade
+
+# ...
+profit = Trade.total_open_trades_stakes()
+```
+
+### get_overall_performance
+
+Retrieve the overall performance - similar to the `/performance` telegram command.
+
+``` python
+from freqtrade.persistence import Trade
+
+# ...
+if self.config['runmode'].value in ('live', 'dry_run'):
+    performance = Trade.get_overall_performance()
+```
+
+Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
+
+``` json
+{"pair": "ETH/BTC", "profit": 0.015, "count": 5}
+```
+
+## Order Object
+
+An `Order` object represents an order on the exchange (or a simulated order in dry-run mode).
+An `Order` object will always be tied to it's corresponding [`Trade`](#trade-object), and only really makes sense in the context of a trade.
+
+### Order - Available attributes
+
+an Order object is typically attached to a trade.
+Most properties here can be None as they are dependent on the exchange response.
+
+|  Attribute | DataType | Description |
+|------------|-------------|-------------|
+| `trade` | Trade | Trade object this order is attached to |
+| `ft_pair` | string | Pair this order is for |
+| `ft_is_open` | boolean | is the order filled? |
+| `order_type` | string | Order type as defined on the exchange - usually market, limit or stoploss |
+| `status` | string | Status as defined by ccxt. Usually open, closed, expired or canceled |
+| `side` | string | Buy or Sell |
+| `price` | float | Price the order was placed at |
+| `average` | float | Average price the order filled at |
+| `amount` | float | Amount in base currency |
+| `filled` | float | Filled amount (in base currency) |
+| `remaining` | float | Remaining amount |
+| `cost` | float | Cost of the order - usually average * filled (*Exchange dependent on futures, may contain the cost with or without leverage and may be in contracts.*) |
+| `stake_amount` | float | Stake amount used for this order. *Added in 2023.7.* |
+| `stake_amount_filled` | float | Filled Stake amount used for this order. *Added in 2024.11.* |
+| `order_date` | datetime | Order creation date **use `order_date_utc` instead** |
+| `order_date_utc` | datetime | Order creation date (in UTC) |
+| `order_fill_date` | datetime |  Order fill date **use `order_fill_utc` instead** |
+| `order_fill_date_utc` | datetime | Order fill date |
diff --git a/docs/updating.md b/docs/updating.md
new file mode 100644
index 000000000..5841d205a
--- /dev/null
+++ b/docs/updating.md
@@ -0,0 +1,48 @@
+# How to update
+
+To update your freqtrade installation, please use one of the below methods, corresponding to your installation method.
+
+!!! Note "Tracking changes"
+    Breaking changes / changed behavior will be documented in the changelog that is posted alongside every release.
+    For the develop branch, please follow PR's to avoid being surprised by changes.
+
+## Docker
+
+!!! Note "Legacy installations using the `master` image"
+    We're switching from master to stable for the release Images - please adjust your docker-file and replace `freqtradeorg/freqtrade:master` with `freqtradeorg/freqtrade:stable`
+
+``` bash
+docker compose pull
+docker compose up -d
+```
+
+## Installation via setup script
+
+``` bash
+./setup.sh --update
+```
+
+!!! Note
+    Make sure to run this command with your virtual environment disabled!
+
+## Plain native installation
+
+Please ensure that you're also updating dependencies - otherwise things might break without you noticing.
+
+``` bash
+git pull
+pip install -U -r requirements.txt
+pip install -e .
+
+# Ensure freqUI is at the latest version
+freqtrade install-ui 
+```
+
+### Problems updating
+
+Update-problems usually come missing dependencies (you didn't follow the above instructions) - or from updated dependencies, which fail to install (for example TA-lib).
+Please refer to the corresponding installation sections (common problems linked below)
+
+Common problems and their solutions:
+
+* [ta-lib update on windows](windows_installation.md#2-install-ta-lib)
diff --git a/docs/utils.md b/docs/utils.md
new file mode 100644
index 000000000..335785539
--- /dev/null
+++ b/docs/utils.md
@@ -0,0 +1,421 @@
+# Utility Subcommands
+
+Besides the Live-Trade and Dry-Run run modes, the `backtesting`, `edge` and `hyperopt` optimization subcommands, and the `download-data` subcommand which prepares historical data, the bot contains a number of utility subcommands. They are described in this section.
+
+## Create userdir
+
+Creates the directory structure to hold your files for freqtrade.
+Will also create strategy and hyperopt examples for you to get started.
+Can be used multiple times - using `--reset` will reset the sample strategy and hyperopt files to their default state.
+
+--8<-- "commands/create-userdir.md"
+
+!!! Warning
+    Using `--reset` may result in loss of data, since this will overwrite all sample files without asking again.
+
+```
+├── backtest_results
+├── data
+├── hyperopt_results
+├── hyperopts
+│   ├── sample_hyperopt_loss.py
+├── notebooks
+│   └── strategy_analysis_example.ipynb
+├── plot
+└── strategies
+    └── sample_strategy.py
+```
+
+## Create new config
+
+Creates a new configuration file, asking some questions which are important selections for a configuration.
+
+--8<-- "commands/new-config.md"
+
+!!! Warning
+    Only vital questions are asked. Freqtrade offers a lot more configuration possibilities, which are listed in the [Configuration documentation](configuration.md#configuration-parameters)
+
+### Create config examples
+
+```
+$ freqtrade new-config --config user_data/config_binance.json
+
+? Do you want to enable Dry-run (simulated trades)?  Yes
+? Please insert your stake currency: BTC
+? Please insert your stake amount: 0.05
+? Please insert max_open_trades (Integer or -1 for unlimited open trades): 3
+? Please insert your desired timeframe (e.g. 5m): 5m
+? Please insert your display Currency (for reporting): USD
+? Select exchange  binance
+? Do you want to enable Telegram?  No
+```
+
+## Show config
+
+Show configuration file (with sensitive values redacted by default).
+Especially useful with [split configuration files](configuration.md#multiple-configuration-files) or [environment variables](configuration.md#environment-variables), where this command will show the merged configuration.
+
+![Show config output](assets/show-config-output.png)
+
+--8<-- "commands/show-config.md"
+
+``` output
+Your combined configuration is:
+{
+  "exit_pricing": {
+    "price_side": "other",
+    "use_order_book": true,
+    "order_book_top": 1
+  },
+  "stake_currency": "USDT",
+  "exchange": {
+    "name": "binance",
+    "key": "REDACTED",
+    "secret": "REDACTED",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+  }
+  // ...
+}
+```
+
+!!! Warning "Sharing information provided by this command"
+    We try to remove all known sensitive information from the default output (without `--show-sensitive`). 
+    Yet, please do double-check for sensitive values in your output to make sure you're not accidentally exposing some private info.
+
+## Create new strategy
+
+Creates a new strategy from a template similar to SampleStrategy.
+The file will be named inline with your class name, and will not overwrite existing files.
+
+Results will be located in `user_data/strategies/<strategyclassname>.py`.
+
+--8<-- "commands/new-strategy.md"
+
+### Sample usage of new-strategy
+
+```bash
+freqtrade new-strategy --strategy AwesomeStrategy
+```
+
+With custom user directory
+
+```bash
+freqtrade new-strategy --userdir ~/.freqtrade/ --strategy AwesomeStrategy
+```
+
+Using the advanced template (populates all optional functions and methods)
+
+```bash
+freqtrade new-strategy --strategy AwesomeStrategy --template advanced
+```
+
+## List Strategies
+
+Use the `list-strategies` subcommand to see all strategies in one particular directory.
+
+This subcommand is useful for finding problems in your environment with loading strategies: modules with strategies that contain errors and failed to load are printed in red (LOAD FAILED), while strategies with duplicate names are printed in yellow (DUPLICATE NAME).
+
+--8<-- "commands/list-strategies.md"
+
+!!! Warning
+    Using these commands will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
+
+Example: Search default strategies directories (within the default userdir).
+
+``` bash
+freqtrade list-strategies
+```
+
+Example: Search strategies  directory within the userdir.
+
+``` bash
+freqtrade list-strategies --userdir ~/.freqtrade/
+```
+
+Example: Search dedicated strategy path.
+
+``` bash
+freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
+```
+
+## List Hyperopt-Loss functions
+
+Use the `list-hyperoptloss` subcommand to see all hyperopt loss functions available.
+
+It provides a quick list of all available loss functions in your environment.
+
+This subcommand can be useful for finding problems in your environment with loading loss functions: modules with Hyperopt-Loss functions that contain errors and failed to load are printed in red (LOAD FAILED), while hyperopt-Loss functions with duplicate names are printed in yellow (DUPLICATE NAME).
+
+--8<-- "commands/list-hyperoptloss.md"
+
+## List freqAI models
+
+Use the `list-freqaimodels` subcommand to see all freqAI models available.
+
+This subcommand is useful for finding problems in your environment with loading freqAI models: modules with models that contain errors and failed to load are printed in red (LOAD FAILED), while models with duplicate names are printed in yellow (DUPLICATE NAME).
+
+--8<-- "commands/list-freqaimodels.md"
+
+## List Exchanges
+
+Use the `list-exchanges` subcommand to see the exchanges available for the bot.
+
+--8<-- "commands/list-exchanges.md"
+
+Example: see exchanges available for the bot:
+
+```
+$ freqtrade list-exchanges
+Exchanges available for Freqtrade:
+Exchange name       Supported    Markets                 Reason
+------------------  -----------  ----------------------  ------------------------------------------------------------------------
+binance             Official     spot, isolated futures
+bitmart             Official     spot
+bybit                            spot, isolated futures
+gate                Official     spot, isolated futures
+htx                 Official     spot
+huobi                            spot
+kraken              Official     spot
+okx                 Official     spot, isolated futures
+```
+
+!!! info ""
+    Output reduced for clarity - supported and available exchanges may change over time.
+
+!!! Note "missing opt exchanges"
+    Values with "missing opt:" might need special configuration (e.g. using orderbook if `fetchTickers` is missing) - but should in theory work (although we cannot guarantee they will).
+
+Example: see all exchanges supported by the ccxt library (including 'bad' ones, i.e. those that are known to not work with Freqtrade)
+
+```
+$ freqtrade list-exchanges -a
+All exchanges supported by the ccxt library:
+Exchange name       Valid    Supported    Markets                 Reason
+------------------  -------  -----------  ----------------------  ---------------------------------------------------------------------------------
+binance             True     Official     spot, isolated futures
+bitflyer            False                 spot                    missing: fetchOrder. missing opt: fetchTickers.
+bitmart             True     Official     spot
+bybit               True                  spot, isolated futures
+gate                True     Official     spot, isolated futures
+htx                 True     Official     spot
+kraken              True     Official     spot
+okx                 True     Official     spot, isolated futures
+```
+
+!!! info ""
+    Reduced output - supported and available exchanges may change over time.
+
+## List Timeframes
+
+Use the `list-timeframes` subcommand to see the list of timeframes available for the exchange.
+
+--8<-- "commands/list-timeframes.md"
+
+* Example: see the timeframes for the 'binance' exchange, set in the configuration file:
+
+```
+$ freqtrade list-timeframes -c config_binance.json
+...
+Timeframes available for the exchange `binance`: 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1M
+```
+
+* Example: enumerate exchanges available for Freqtrade and print timeframes supported by each of them:
+```
+$ for i in `freqtrade list-exchanges -1`; do freqtrade list-timeframes --exchange $i; done
+```
+
+## List pairs/list markets
+
+The `list-pairs` and `list-markets` subcommands allow to see the pairs/markets available on exchange.
+
+Pairs are markets with the '/' character between the base currency part and the quote currency part in the market symbol.
+For example, in the 'ETH/BTC' pair 'ETH' is the base currency, while 'BTC' is the quote currency.
+
+For pairs traded by Freqtrade the pair quote currency is defined by the value of the `stake_currency` configuration setting.
+
+You can print info about any pair/market with these subcommands - and you can filter output by quote-currency using `--quote BTC`, or by base-currency using `--base ETH` options correspondingly.
+
+These subcommands have same usage and same set of available options:
+
+--8<-- "commands/list-pairs.md"
+
+By default, only active pairs/markets are shown. Active pairs/markets are those that can currently be traded on the exchange.
+You can use the `-a`/`-all` option to see the list of all pairs/markets, including the inactive ones.
+Pairs may be listed as untradeable if the smallest tradeable price for the market is very small, i.e. less than `1e-11` (`0.00000000001`)
+
+Pairs/markets are sorted by its symbol string in the printed output.
+
+### Examples
+
+* Print the list of active pairs with quote currency USD on exchange, specified in the default
+configuration file (i.e. pairs on the "Binance" exchange) in JSON format:
+
+```
+$ freqtrade list-pairs --quote USD --print-json
+```
+
+* Print the list of all pairs on the exchange, specified in the `config_binance.json` configuration file
+(i.e. on the "Binance" exchange) with base currencies BTC or ETH and quote currencies USDT or USD, as the
+human-readable list with summary:
+
+```
+$ freqtrade list-pairs -c config_binance.json --all --base BTC ETH --quote USDT USD --print-list
+```
+
+* Print all markets on exchange "Kraken", in the tabular format:
+
+```
+$ freqtrade list-markets --exchange kraken --all
+```
+
+## Test pairlist
+
+Use the `test-pairlist` subcommand to test the configuration of [dynamic pairlists](plugins.md#pairlists).
+
+Requires a configuration with specified `pairlists` attribute.
+Can be used to generate static pairlists to be used during backtesting / hyperopt.
+
+--8<-- "commands/test-pairlist.md"
+
+### Examples
+
+Show whitelist when using a [dynamic pairlist](plugins.md#pairlists).
+
+```
+freqtrade test-pairlist --config config.json --quote USDT BTC
+```
+
+## Convert database
+
+`freqtrade convert-db` can be used to convert your database from one system to another (sqlite -> postgres, postgres -> other postgres), migrating all trades, orders and Pairlocks.
+
+Please refer to the [corresponding documentation](advanced-setup.md#use-a-different-database-system) to learn about requirements for different database systems.
+
+--8<-- "commands/convert-db.md"
+
+!!! Warning
+    Please ensure to only use this on an empty target database. Freqtrade will perform a regular migration, but may fail if entries already existed.
+
+## Webserver mode
+
+!!! Warning "Experimental"
+    Webserver mode is an experimental mode to increase backesting and strategy development productivity.
+    There may still be bugs - so if you happen to stumble across these, please report them as github issues, thanks.
+
+Run freqtrade in webserver mode.
+Freqtrade will start the webserver and allow FreqUI to start and control backtesting processes.
+This has the advantage that data will not be reloaded between backtesting runs (as long as timeframe and timerange remain identical).
+FreqUI will also show the backtesting results.
+
+--8<-- "commands/webserver.md"
+
+### Webserver mode - docker
+
+You can also use webserver mode via docker.
+Starting a one-off container requires the configuration of the port explicitly, as ports are not exposed by default.
+You can use `docker compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port.
+
+Alternatively, you can reconfigure the docker-compose file to have the command updated:
+
+``` yml
+    command: >
+      webserver
+      --config /freqtrade/user_data/config.json
+```
+
+You can now use `docker compose up` to start the webserver.
+This assumes that the configuration has a webserver enabled and configured for docker (listening port = `0.0.0.0`).
+
+!!! Tip
+    Don't forget to reset the command back to the trade command if you want to start a live or dry-run bot. 
+
+## Show previous Backtest results
+
+Allows you to show previous backtest results.
+Adding `--show-pair-list` outputs a sorted pair list you can easily copy/paste into your configuration (omitting bad pairs).
+
+??? Warning "Strategy overfitting"
+    Only using winning pairs can lead to an overfitted strategy, which will not work well on future data. Make sure to extensively test your strategy in dry-run before risking real money.
+
+--8<-- "commands/backtesting-show.md"
+
+## Detailed backtest analysis
+
+Advanced backtest result analysis.
+
+More details in the [Backtesting analysis](advanced-backtesting.md#analyze-the-buyentry-and-sellexit-tags) Section.
+
+--8<-- "commands/backtesting-analysis.md"
+
+## List Hyperopt results
+
+You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` sub-command.
+
+--8<-- "commands/hyperopt-list.md"
+
+!!! Note
+    `hyperopt-list` will automatically use the latest available hyperopt results file.
+    You can override this using the `--hyperopt-filename` argument, and specify another, available filename (without path!).
+
+### Examples
+
+List all results, print details of the best result at the end:
+```
+freqtrade hyperopt-list
+```
+
+List only epochs with positive profit. Do not print the details of the best epoch, so that the list can be iterated in a script:
+```
+freqtrade hyperopt-list --profitable --no-details
+```
+
+## Show details of Hyperopt results
+
+You can show the details of any hyperoptimization epoch previously evaluated by the Hyperopt module with the `hyperopt-show` subcommand.
+
+--8<-- "commands/hyperopt-show.md"
+
+!!! Note
+    `hyperopt-show` will automatically use the latest available hyperopt results file.
+    You can override this using the `--hyperopt-filename` argument, and specify another, available filename (without path!).
+
+### Examples
+
+Print details for the epoch 168 (the number of the epoch is shown by the `hyperopt-list` subcommand or by Hyperopt itself during hyperoptimization run):
+
+```
+freqtrade hyperopt-show -n 168
+```
+
+Prints JSON data with details for the last best epoch (i.e., the best of all epochs):
+
+```
+freqtrade hyperopt-show --best -n -1 --print-json --no-header
+```
+
+## Show trades
+
+Print selected (or all) trades from database to screen.
+
+--8<-- "commands/show-trades.md"
+
+### Examples
+
+Print trades with id 2 and 3 as json
+
+``` bash
+freqtrade show-trades --db-url sqlite:///tradesv3.sqlite --trade-ids 2 3 --print-json
+```
+
+## Strategy-Updater
+
+Updates listed strategies or all strategies within the strategies folder to be v3 compliant.
+If the command runs without --strategy-list then all strategies inside the strategies folder will be converted.
+Your original strategy will remain available in the `user_data/strategies_orig_updater/` directory.
+
+!!! Warning "Conversion results"
+    Strategy updater will work on a "best effort" approach. Please do your due diligence and verify the results of the conversion.
+    We also recommend to run a python formatter (e.g. `black`) to format results in a sane manner.
+
+--8<-- "commands/strategy-updater.md"
diff --git a/docs/webhook-config.md b/docs/webhook-config.md
new file mode 100644
index 000000000..9125ff361
--- /dev/null
+++ b/docs/webhook-config.md
@@ -0,0 +1,327 @@
+# Webhook usage
+
+## Configuration
+
+Enable webhooks by adding a webhook-section to your configuration file, and setting `webhook.enabled` to `true`.
+
+Sample configuration (tested using IFTTT).
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://maker.ifttt.com/trigger/<YOUREVENT>/with/key/<YOURKEY>/",
+        "entry": {
+            "value1": "Buying {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "{stake_amount:8f} {stake_currency}"
+        },
+        "entry_cancel": {
+            "value1": "Cancelling Open Buy Order for {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "{stake_amount:8f} {stake_currency}"
+        },
+         "entry_fill": {
+            "value1": "Buy Order for {pair} filled",
+            "value2": "at {open_rate:8f}",
+            "value3": ""
+        },
+        "exit": {
+            "value1": "Exiting {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
+        },
+        "exit_cancel": {
+            "value1": "Cancelling Open Exit Order for {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
+        },
+        "exit_fill": {
+            "value1": "Exit Order for {pair} filled",
+            "value2": "at {close_rate:8f}.",
+            "value3": ""
+        },
+        "status": {
+            "value1": "Status: {status}",
+            "value2": "",
+            "value3": ""
+        }
+    },
+```
+
+The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert your event and key to the url.
+
+You can set the POST body format to Form-Encoded (default), JSON-Encoded, or raw data. Use `"format": "form"`, `"format": "json"`, or `"format": "raw"` respectively. Example configuration for Mattermost Cloud integration:
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://<YOURSUBDOMAIN>.cloud.mattermost.com/hooks/<YOURHOOK>",
+        "format": "json",
+        "status": {
+            "text": "Status: {status}"
+        }
+    },
+```
+
+The result would be a POST request with e.g. `{"text":"Status: running"}` body and `Content-Type: application/json` header which results `Status: running` message in the Mattermost channel.
+
+When using the Form-Encoded or JSON-Encoded configuration you can configure any number of payload values, and both the key and value will be output in the POST request. However, when using the raw data format you can only configure one value and it **must** be named `"data"`. In this instance the data key will not be output in the POST request, only the value. For example:
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://<YOURHOOKURL>",
+        "format": "raw",
+        "webhookstatus": {
+            "data": "Status: {status}"
+        }
+    },
+```
+
+The result would be a POST request with e.g. `Status: running` body and `Content-Type: text/plain` header.
+
+## Additional configurations
+
+The `webhook.retries` parameter can be set for the maximum number of retries the webhook request should attempt if it is unsuccessful (i.e. HTTP response status is not 200). By default this is set to `0` which is disabled. An additional `webhook.retry_delay` parameter can be set to specify the time in seconds between retry attempts. By default this is set to `0.1` (i.e. 100ms). Note that increasing the number of retries or retry delay may slow down the trader if there are connectivity issues with the webhook.
+You can also specify `webhook.timeout` - which defines how long the bot will wait until it assumes the other host as unresponsive (defaults to 10s).
+
+Example configuration for retries:
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://<YOURHOOKURL>",
+        "timeout": 10,
+        "retries": 3,
+        "retry_delay": 0.2,
+        "status": {
+            "status": "Status: {status}"
+        }
+    },
+```
+
+Custom messages can be sent to Webhook endpoints via the `self.dp.send_msg()` function from within the strategy. To enable this, set the `allow_custom_messages` option to `true`:
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://<YOURHOOKURL>",
+        "allow_custom_messages": true,
+        "strategy_msg": {
+            "status": "StrategyMessage: {msg}"
+        }
+    },
+```
+
+Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
+
+## Webhook Message types
+
+### Entry
+
+The fields in `webhook.entry` are filled when the bot executes a long/short. Parameters are filled using string.format.
+Possible parameters are:
+
+* `trade_id`
+* `exchange`
+* `pair`
+* `direction`
+* `leverage`
+* ~~`limit` # Deprecated - should no longer be used.~~
+* `open_rate`
+* `amount`
+* `open_date`
+* `stake_amount`
+* `stake_currency`
+* `base_currency`
+* `quote_currency`
+* `fiat_currency`
+* `order_type`
+* `current_rate`
+* `enter_tag`
+
+### Entry cancel
+
+The fields in `webhook.entry_cancel` are filled when the bot cancels a long/short order. Parameters are filled using string.format.
+Possible parameters are:
+
+* `trade_id`
+* `exchange`
+* `pair`
+* `direction`
+* `leverage`
+* `limit`
+* `amount`
+* `open_date`
+* `stake_amount`
+* `stake_currency`
+* `base_currency`
+* `quote_currency`
+* `fiat_currency`
+* `order_type`
+* `current_rate`
+* `enter_tag`
+
+### Entry fill
+
+The fields in `webhook.entry_fill` are filled when the bot filled a long/short order. Parameters are filled using string.format.
+Possible parameters are:
+
+* `trade_id`
+* `exchange`
+* `pair`
+* `direction`
+* `leverage`
+* `open_rate`
+* `amount`
+* `open_date`
+* `stake_amount`
+* `stake_currency`
+* `base_currency`
+* `quote_currency`
+* `fiat_currency`
+* `order_type`
+* `current_rate`
+* `enter_tag`
+
+### Exit
+
+The fields in `webhook.exit` are filled when the bot exits a trade. Parameters are filled using string.format.
+Possible parameters are:
+
+* `trade_id`
+* `exchange`
+* `pair`
+* `direction`
+* `leverage`
+* `gain`
+* `limit`
+* `amount`
+* `open_rate`
+* `profit_amount`
+* `profit_ratio`
+* `stake_currency`
+* `base_currency`
+* `quote_currency`
+* `fiat_currency`
+* `exit_reason`
+* `order_type`
+* `open_date`
+* `close_date`
+
+### Exit fill
+
+The fields in `webhook.exit_fill` are filled when the bot fills a exit order (closes a Trade). Parameters are filled using string.format.
+Possible parameters are:
+
+* `trade_id`
+* `exchange`
+* `pair`
+* `direction`
+* `leverage`
+* `gain`
+* `close_rate`
+* `amount`
+* `open_rate`
+* `current_rate`
+* `profit_amount`
+* `profit_ratio`
+* `stake_currency`
+* `base_currency`
+* `quote_currency`
+* `fiat_currency`
+* `exit_reason`
+* `order_type`
+* `open_date`
+* `close_date`
+
+### Exit cancel
+
+The fields in `webhook.exit_cancel` are filled when the bot cancels a exit order. Parameters are filled using string.format.
+Possible parameters are:
+
+* `trade_id`
+* `exchange`
+* `pair`
+* `direction`
+* `leverage`
+* `gain`
+* `limit`
+* `amount`
+* `open_rate`
+* `current_rate`
+* `profit_amount`
+* `profit_ratio`
+* `stake_currency`
+* `base_currency`
+* `quote_currency`
+* `fiat_currency`
+* `exit_reason`
+* `order_type`
+* `open_date`
+* `close_date`
+
+### Status
+
+The fields in `webhook.status` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
+
+The only possible value here is `{status}`.
+
+## Discord
+
+A special form of webhooks is available for discord.
+You can configure this as follows:
+
+```json
+"discord": {
+    "enabled": true,
+    "webhook_url": "https://discord.com/api/webhooks/<Your webhook URL ...>",
+    "exit_fill": [
+        {"Trade ID": "{trade_id}"},
+        {"Exchange": "{exchange}"},
+        {"Pair": "{pair}"},
+        {"Direction": "{direction}"},
+        {"Open rate": "{open_rate}"},
+        {"Close rate": "{close_rate}"},
+        {"Amount": "{amount}"},
+        {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
+        {"Close date": "{close_date:%Y-%m-%d %H:%M:%S}"},
+        {"Profit": "{profit_amount} {stake_currency}"},
+        {"Profitability": "{profit_ratio:.2%}"},
+        {"Enter tag": "{enter_tag}"},
+        {"Exit Reason": "{exit_reason}"},
+        {"Strategy": "{strategy}"},
+        {"Timeframe": "{timeframe}"},
+    ],
+    "entry_fill": [
+        {"Trade ID": "{trade_id}"},
+        {"Exchange": "{exchange}"},
+        {"Pair": "{pair}"},
+        {"Direction": "{direction}"},
+        {"Open rate": "{open_rate}"},
+        {"Amount": "{amount}"},
+        {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
+        {"Enter tag": "{enter_tag}"},
+        {"Strategy": "{strategy} {timeframe}"},
+    ]
+}
+```
+
+The above represents the default (`exit_fill` and `entry_fill` are optional and will default to the above configuration) - modifications are obviously possible.
+To disable either of the two default values (`entry_fill` / `exit_fill`), you can assign them an empty array (`exit_fill: []`).
+
+Available fields correspond to the fields for webhooks and are documented in the corresponding webhook sections.
+
+The notifications will look as follows by default.
+
+![discord-notification](assets/discord_notification.png)
+
+Custom messages can be sent from a strategy to Discord endpoints via the dataprovider.send_msg() function. To enable this, set the `allow_custom_messages` option to `true`:
+
+```json
+  "discord": {
+        "enabled": true,
+        "webhook_url": "https://discord.com/api/webhooks/<Your webhook URL ...>",
+        "allow_custom_messages": true,
+    },
+```
diff --git a/docs/windows_installation.md b/docs/windows_installation.md
new file mode 100644
index 000000000..c824ee5de
--- /dev/null
+++ b/docs/windows_installation.md
@@ -0,0 +1,78 @@
+# Windows installation
+
+We **strongly** recommend that Windows users use [Docker](docker_quickstart.md) as this will work much easier and smoother (also more secure).
+
+If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
+Otherwise, please follow the instructions below.
+
+All instructions assume that python 3.10+ is installed and available.
+
+## Clone the git repository
+
+First of all clone the repository by running:
+
+``` powershell
+git clone https://github.com/freqtrade/freqtrade.git
+```
+
+Now, choose your installation method, either automatically via script (recommended) or manually following the corresponding instructions.
+
+## Install freqtrade automatically
+
+### Run the installation script
+
+The script will ask you a few questions to determine which parts should be installed.
+
+```powershell
+Set-ExecutionPolicy -ExecutionPolicy Bypass
+cd freqtrade
+. .\setup.ps1
+```
+
+## Install freqtrade manually
+
+!!! Note "64bit Python version"
+    Please make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
+    32bit python versions are no longer supported under Windows.
+
+!!! Hint
+    Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Anaconda installation section](installation.md#installation-with-conda) in the documentation for more information.
+
+### Install ta-lib
+
+Install ta-lib according to the [ta-lib documentation](https://github.com/TA-Lib/ta-lib-python#windows).
+
+As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), Freqtrade provides these dependencies (in the binary wheel format) for the latest 3 Python versions (3.10, 3.11 and 3.12) and for 64bit Windows.
+These Wheels are also used by CI running on windows, and are therefore tested together with freqtrade.
+
+Other versions must be downloaded from the above link.
+
+``` powershell
+cd \path\freqtrade
+python -m venv .venv
+.venv\Scripts\activate.ps1
+# optionally install ta-lib from wheel
+# Eventually adjust the below filename to match the downloaded wheel
+pip install --find-links build_helpers\ TA-Lib -U
+pip install -r requirements.txt
+pip install -e .
+freqtrade
+```
+
+!!! Note "Use Powershell"
+    The above installation script assumes you're using powershell on a 64bit windows.
+    Commands for the legacy CMD windows console may differ.
+
+### Error during installation on Windows
+
+``` bash
+error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
+```
+
+Unfortunately, many packages requiring compilation don't provide a pre-built wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
+
+You can download the Visual C++ build tools from [here](https://visualstudio.microsoft.com/visual-cpp-build-tools/) and install "Desktop development with C++" in it's default configuration. Unfortunately, this is a heavy download / dependency so you might want to consider WSL2 or [docker compose](docker_quickstart.md) first.
+
+![Windows installation](assets/windows_install.png)
+
+---
diff --git a/freqtrade.service b/freqtrade.service
index 76f9b6016..6f0c73ee4 100644
--- a/freqtrade.service
+++ b/freqtrade.service
@@ -6,9 +6,8 @@ After=network.target
 # Set WorkingDirectory and ExecStart to your file paths accordingly
 # NOTE: %h will be resolved to /home/<username>
 WorkingDirectory=%h/freqtrade
-ExecStart=/usr/bin/freqtrade --dynamic-whitelist 40
+ExecStart=/usr/bin/freqtrade trade
 Restart=on-failure
 
 [Install]
 WantedBy=default.target
-
diff --git a/freqtrade.service.watchdog b/freqtrade.service.watchdog
new file mode 100644
index 000000000..dcd32ae18
--- /dev/null
+++ b/freqtrade.service.watchdog
@@ -0,0 +1,29 @@
+[Unit]
+Description=Freqtrade Daemon
+After=network.target
+
+[Service]
+# Set WorkingDirectory and ExecStart to your file paths accordingly
+# NOTE: %h will be resolved to /home/<username>
+WorkingDirectory=%h/freqtrade
+ExecStart=/usr/bin/freqtrade trade --sd-notify
+
+Restart=always
+#Restart=on-failure
+
+# Note that we use Type=notify here
+Type=notify
+
+# Currently required if Type=notify
+NotifyAccess=all
+
+StartLimitInterval=1min
+StartLimitBurst=5
+
+TimeoutStartSec=1min
+
+# Use here (process_throttle_secs * 2) or longer time interval
+WatchdogSec=20
+
+[Install]
+WantedBy=default.target
diff --git a/freqtrade/__init__.py b/freqtrade/__init__.py
index cd4515a3b..48852fd44 100644
--- a/freqtrade/__init__.py
+++ b/freqtrade/__init__.py
@@ -1,16 +1,34 @@
-""" FreqTrade bot """
-__version__ = '0.16.0'
+"""Freqtrade bot"""
 
+__version__ = "2025.4-dev"
 
-class DependencyException(BaseException):
-    """
-    Indicates that a assumed dependency is not met.
-    This could happen when there is currently not enough money on the account.
-    """
+if "dev" in __version__:
+    from pathlib import Path
 
+    try:
+        import subprocess  # noqa: S404, RUF100
 
-class OperationalException(BaseException):
-    """
-    Requires manual intervention.
-    This happens when an exchange returns an unexpected error during runtime.
-    """
+        freqtrade_basedir = Path(__file__).parent
+
+        __version__ = (
+            __version__
+            + "-"
+            + subprocess.check_output(
+                ["git", "log", '--format="%h"', "-n 1"],
+                stderr=subprocess.DEVNULL,
+                cwd=freqtrade_basedir,
+            )
+            .decode("utf-8")
+            .rstrip()
+            .strip('"')
+        )
+
+    except Exception:  # pragma: no cover
+        # git not available, ignore
+        try:
+            # Try Fallback to freqtrade_commit file (created by CI while building docker image)
+            versionfile = Path("./freqtrade_commit")
+            if versionfile.is_file():
+                __version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"
+        except Exception:  # noqa: S110
+            pass
diff --git a/freqtrade/__main__.py b/freqtrade/__main__.py
new file mode 100755
index 000000000..caa26ce0a
--- /dev/null
+++ b/freqtrade/__main__.py
@@ -0,0 +1,13 @@
+#!/usr/bin/env python3
+"""
+__main__.py for Freqtrade
+To launch Freqtrade as a module
+
+> python -m freqtrade (with Python >= 3.10)
+"""
+
+from freqtrade import main
+
+
+if __name__ == "__main__":
+    main.main()
diff --git a/freqtrade/analyze.py b/freqtrade/analyze.py
deleted file mode 100644
index b056cdf80..000000000
--- a/freqtrade/analyze.py
+++ /dev/null
@@ -1,122 +0,0 @@
-"""
-Functions to analyze ticker data with indicators and produce buy and sell signals
-"""
-import logging
-from datetime import timedelta
-from enum import Enum
-from typing import Dict, List
-
-import arrow
-from pandas import DataFrame, to_datetime
-
-from freqtrade.exchange import get_ticker_history
-from freqtrade.strategy.strategy import Strategy
-
-logger = logging.getLogger(__name__)
-
-
-class SignalType(Enum):
-    """ Enum to distinguish between buy and sell signals """
-    BUY = "buy"
-    SELL = "sell"
-
-
-def parse_ticker_dataframe(ticker: list) -> DataFrame:
-    """
-    Analyses the trend for the given ticker history
-    :param ticker: See exchange.get_ticker_history
-    :return: DataFrame
-    """
-    columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
-    frame = DataFrame(ticker) \
-        .rename(columns=columns)
-    if 'BV' in frame:
-        frame.drop('BV', 1, inplace=True)
-    frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
-    frame.sort_values('date', inplace=True)
-    return frame
-
-
-def populate_indicators(dataframe: DataFrame) -> DataFrame:
-    """
-    Adds several different TA indicators to the given DataFrame
-
-    Performance Note: For the best performance be frugal on the number of indicators
-    you are using. Let uncomment only the indicator you are using in your strategies
-    or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
-    """
-    strategy = Strategy()
-    return strategy.populate_indicators(dataframe=dataframe)
-
-
-def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
-    """
-    Based on TA indicators, populates the buy signal for the given dataframe
-    :param dataframe: DataFrame
-    :return: DataFrame with buy column
-    """
-    strategy = Strategy()
-    return strategy.populate_buy_trend(dataframe=dataframe)
-
-
-def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
-    """
-    Based on TA indicators, populates the sell signal for the given dataframe
-    :param dataframe: DataFrame
-    :return: DataFrame with buy column
-    """
-    strategy = Strategy()
-    return strategy.populate_sell_trend(dataframe=dataframe)
-
-
-def analyze_ticker(ticker_history: List[Dict]) -> DataFrame:
-    """
-    Parses the given ticker history and returns a populated DataFrame
-    add several TA indicators and buy signal to it
-    :return DataFrame with ticker data and indicator data
-    """
-    dataframe = parse_ticker_dataframe(ticker_history)
-    dataframe = populate_indicators(dataframe)
-    dataframe = populate_buy_trend(dataframe)
-    dataframe = populate_sell_trend(dataframe)
-    return dataframe
-
-
-# FIX: Maybe return False, if an error has occured,
-#      Otherwise we might mask an error as an non-signal-scenario
-def get_signal(pair: str, interval: int) -> (bool, bool):
-    """
-    Calculates current signal based several technical analysis indicators
-    :param pair: pair in format BTC_ANT or BTC-ANT
-    :return: (Buy, Sell) A bool-tuple indicating buy/sell signal
-    """
-    ticker_hist = get_ticker_history(pair, interval)
-    if not ticker_hist:
-        logger.warning('Empty ticker history for pair %s', pair)
-        return (False, False)  # return False ?
-
-    try:
-        dataframe = analyze_ticker(ticker_hist)
-    except ValueError as ex:
-        logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
-        return (False, False)  # return False ?
-    except Exception as ex:
-        logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
-        return (False, False)  # return False ?
-
-    if dataframe.empty:
-        logger.warning('Empty dataframe for pair %s', pair)
-        return (False, False)  # return False ?
-
-    latest = dataframe.iloc[-1]
-
-    # Check if dataframe is out of date
-    signal_date = arrow.get(latest['date'])
-    if signal_date < arrow.utcnow() - timedelta(minutes=(interval + 5)):
-        logger.warning('Outdated history for pair %s. Last tick is %s minutes old',
-                       pair, (arrow.utcnow() - signal_date).seconds // 60)
-        return (False, False)  # return False ?
-
-    (buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
-    logger.debug('trigger: %s (pair=%s) buy=%s sell=%s', latest['date'], pair, str(buy), str(sell))
-    return (buy, sell)
diff --git a/freqtrade/commands/__init__.py b/freqtrade/commands/__init__.py
new file mode 100644
index 000000000..969b8df09
--- /dev/null
+++ b/freqtrade/commands/__init__.py
@@ -0,0 +1,48 @@
+# flake8: noqa: F401
+"""
+Commands module.
+Contains all start-commands, subcommands and CLI Interface creation.
+
+Note: Be careful with file-scoped imports in these subfiles.
+    as they are parsed on startup, nothing containing optional modules should be loaded.
+"""
+
+from freqtrade.commands.analyze_commands import start_analysis_entries_exits
+from freqtrade.commands.arguments import Arguments
+from freqtrade.commands.build_config_commands import start_new_config, start_show_config
+from freqtrade.commands.data_commands import (
+    start_convert_data,
+    start_convert_trades,
+    start_download_data,
+    start_list_data,
+    start_list_trades_data,
+)
+from freqtrade.commands.db_commands import start_convert_db
+from freqtrade.commands.deploy_commands import (
+    start_create_userdir,
+    start_install_ui,
+    start_new_strategy,
+)
+from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show
+from freqtrade.commands.list_commands import (
+    start_list_exchanges,
+    start_list_freqAI_models,
+    start_list_hyperopt_loss_functions,
+    start_list_markets,
+    start_list_strategies,
+    start_list_timeframes,
+    start_show_trades,
+)
+from freqtrade.commands.optimize_commands import (
+    start_backtesting,
+    start_backtesting_show,
+    start_edge,
+    start_hyperopt,
+    start_lookahead_analysis,
+    start_recursive_analysis,
+)
+from freqtrade.commands.pairlist_commands import start_test_pairlist
+from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit
+from freqtrade.commands.strategy_utils_commands import start_strategy_update
+from freqtrade.commands.trade_commands import start_trading
+from freqtrade.commands.webserver_commands import start_webserver
diff --git a/freqtrade/commands/analyze_commands.py b/freqtrade/commands/analyze_commands.py
new file mode 100644
index 000000000..d76abd1ef
--- /dev/null
+++ b/freqtrade/commands/analyze_commands.py
@@ -0,0 +1,24 @@
+import logging
+from typing import Any
+
+from freqtrade.enums import RunMode
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_analysis_entries_exits(args: dict[str, Any]) -> None:
+    """
+    Start analysis script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.data.entryexitanalysis import process_entry_exit_reasons
+
+    # Initialize configuration
+    config = setup_utils_configuration(args, RunMode.BACKTEST)
+
+    logger.info("Starting freqtrade in analysis mode")
+
+    process_entry_exit_reasons(config)
diff --git a/freqtrade/commands/arguments.py b/freqtrade/commands/arguments.py
new file mode 100755
index 000000000..c42e46711
--- /dev/null
+++ b/freqtrade/commands/arguments.py
@@ -0,0 +1,687 @@
+"""
+This module contains the argument manager class
+"""
+
+from argparse import ArgumentParser, Namespace, _ArgumentGroup
+from functools import partial
+from pathlib import Path
+from typing import Any
+
+from freqtrade.commands.cli_options import AVAILABLE_CLI_OPTIONS
+from freqtrade.constants import DEFAULT_CONFIG
+
+
+ARGS_COMMON = [
+    "verbosity",
+    "print_colorized",
+    "logfile",
+    "version",
+    "config",
+    "datadir",
+    "user_data_dir",
+]
+
+ARGS_MAIN = ["version_main"]
+
+ARGS_STRATEGY = [
+    "strategy",
+    "strategy_path",
+    "recursive_strategy_search",
+    "freqaimodel",
+    "freqaimodel_path",
+]
+
+ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"]
+
+ARGS_WEBSERVER: list[str] = []
+
+ARGS_COMMON_OPTIMIZE = [
+    "timeframe",
+    "timerange",
+    "dataformat_ohlcv",
+    "max_open_trades",
+    "stake_amount",
+    "fee",
+    "pairs",
+]
+
+ARGS_BACKTEST = [
+    *ARGS_COMMON_OPTIMIZE,
+    "position_stacking",
+    "enable_protections",
+    "dry_run_wallet",
+    "timeframe_detail",
+    "strategy_list",
+    "export",
+    "exportfilename",
+    "backtest_breakdown",
+    "backtest_cache",
+    "freqai_backtest_live_models",
+]
+
+ARGS_HYPEROPT = [
+    *ARGS_COMMON_OPTIMIZE,
+    "hyperopt",
+    "hyperopt_path",
+    "position_stacking",
+    "enable_protections",
+    "dry_run_wallet",
+    "timeframe_detail",
+    "epochs",
+    "spaces",
+    "print_all",
+    "print_json",
+    "hyperopt_jobs",
+    "hyperopt_random_state",
+    "hyperopt_min_trades",
+    "hyperopt_loss",
+    "disableparamexport",
+    "hyperopt_ignore_missing_space",
+    "analyze_per_epoch",
+]
+
+ARGS_EDGE = [*ARGS_COMMON_OPTIMIZE, "stoploss_range"]
+
+ARGS_LIST_STRATEGIES = [
+    "strategy_path",
+    "print_one_column",
+    "recursive_strategy_search",
+]
+
+ARGS_LIST_FREQAIMODELS = ["freqaimodel_path", "print_one_column"]
+
+ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column"]
+
+ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list", "backtest_breakdown"]
+
+ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
+
+ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
+
+ARGS_LIST_PAIRS = [
+    "exchange",
+    "print_list",
+    "list_pairs_print_json",
+    "print_one_column",
+    "print_csv",
+    "base_currencies",
+    "quote_currencies",
+    "list_pairs_all",
+    "trading_mode",
+]
+
+ARGS_TEST_PAIRLIST = [
+    "user_data_dir",
+    "verbosity",
+    "config",
+    "quote_currencies",
+    "print_one_column",
+    "list_pairs_print_json",
+    "exchange",
+]
+
+ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
+
+ARGS_BUILD_CONFIG = ["config"]
+ARGS_SHOW_CONFIG = ["user_data_dir", "config", "show_sensitive"]
+
+ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "strategy_path", "template"]
+
+ARGS_CONVERT_DATA_TRADES = ["pairs", "format_from_trades", "format_to", "erase", "exchange"]
+ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase", "exchange"]
+ARGS_CONVERT_DATA_OHLCV = [*ARGS_CONVERT_DATA, "timeframes", "trading_mode", "candle_types"]
+
+ARGS_CONVERT_TRADES = [
+    "pairs",
+    "timeframes",
+    "exchange",
+    "dataformat_ohlcv",
+    "dataformat_trades",
+    "trading_mode",
+]
+
+ARGS_LIST_DATA = [
+    "exchange",
+    "dataformat_ohlcv",
+    "dataformat_trades",
+    "trades",
+    "pairs",
+    "trading_mode",
+    "show_timerange",
+]
+
+ARGS_DOWNLOAD_DATA = [
+    "pairs",
+    "pairs_file",
+    "days",
+    "new_pairs_days",
+    "include_inactive",
+    "timerange",
+    "download_trades",
+    "convert_trades",
+    "exchange",
+    "timeframes",
+    "erase",
+    "dataformat_ohlcv",
+    "dataformat_trades",
+    "trading_mode",
+    "prepend_data",
+]
+
+ARGS_PLOT_DATAFRAME = [
+    "pairs",
+    "indicators1",
+    "indicators2",
+    "plot_limit",
+    "db_url",
+    "trade_source",
+    "export",
+    "exportfilename",
+    "timerange",
+    "timeframe",
+    "no_trades",
+]
+
+ARGS_PLOT_PROFIT = [
+    "pairs",
+    "timerange",
+    "export",
+    "exportfilename",
+    "db_url",
+    "trade_source",
+    "timeframe",
+    "plot_auto_open",
+]
+
+ARGS_CONVERT_DB = ["db_url", "db_url_from"]
+
+ARGS_INSTALL_UI = ["erase_ui_only", "ui_prerelease", "ui_version"]
+
+ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
+
+ARGS_HYPEROPT_LIST = [
+    "hyperopt_list_best",
+    "hyperopt_list_profitable",
+    "hyperopt_list_min_trades",
+    "hyperopt_list_max_trades",
+    "hyperopt_list_min_avg_time",
+    "hyperopt_list_max_avg_time",
+    "hyperopt_list_min_avg_profit",
+    "hyperopt_list_max_avg_profit",
+    "hyperopt_list_min_total_profit",
+    "hyperopt_list_max_total_profit",
+    "hyperopt_list_min_objective",
+    "hyperopt_list_max_objective",
+    "print_json",
+    "hyperopt_list_no_details",
+    "hyperoptexportfilename",
+    "export_csv",
+]
+
+ARGS_HYPEROPT_SHOW = [
+    "hyperopt_list_best",
+    "hyperopt_list_profitable",
+    "hyperopt_show_index",
+    "print_json",
+    "hyperoptexportfilename",
+    "hyperopt_show_no_header",
+    "disableparamexport",
+    "backtest_breakdown",
+]
+
+ARGS_ANALYZE_ENTRIES_EXITS = [
+    "exportfilename",
+    "analysis_groups",
+    "enter_reason_list",
+    "exit_reason_list",
+    "indicator_list",
+    "entry_only",
+    "exit_only",
+    "timerange",
+    "analysis_rejected",
+    "analysis_to_csv",
+    "analysis_csv_path",
+]
+
+
+ARGS_STRATEGY_UPDATER = ["strategy_list", "strategy_path", "recursive_strategy_search"]
+
+ARGS_LOOKAHEAD_ANALYSIS = [
+    a
+    for a in ARGS_BACKTEST
+    if a not in ("position_stacking", "backtest_cache", "backtest_breakdown")
+] + ["minimum_trade_amount", "targeted_trade_amount", "lookahead_analysis_exportfilename"]
+
+ARGS_RECURSIVE_ANALYSIS = ["timeframe", "timerange", "dataformat_ohlcv", "pairs", "startup_candle"]
+
+# Command level configs - keep at the bottom of the above definitions
+NO_CONF_REQURIED = [
+    "convert-data",
+    "convert-trade-data",
+    "download-data",
+    "list-timeframes",
+    "list-markets",
+    "list-pairs",
+    "list-strategies",
+    "list-freqaimodels",
+    "list-hyperoptloss",
+    "list-data",
+    "hyperopt-list",
+    "hyperopt-show",
+    "backtest-filter",
+    "plot-dataframe",
+    "plot-profit",
+    "show-trades",
+    "trades-to-ohlcv",
+    "strategy-updater",
+]
+
+NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
+
+
+class Arguments:
+    """
+    Arguments Class. Manage the arguments received by the cli
+    """
+
+    def __init__(self, args: list[str] | None) -> None:
+        self.args = args
+        self._parsed_arg: Namespace | None = None
+
+    def get_parsed_arg(self) -> dict[str, Any]:
+        """
+        Return the list of arguments
+        :return: List[str] List of arguments
+        """
+        if self._parsed_arg is None:
+            self._build_subcommands()
+            self._parsed_arg = self._parse_args()
+
+        return vars(self._parsed_arg)
+
+    def _parse_args(self) -> Namespace:
+        """
+        Parses given arguments and returns an argparse Namespace instance.
+        """
+        parsed_arg = self.parser.parse_args(self.args)
+
+        # Workaround issue in argparse with action='append' and default value
+        # (see https://bugs.python.org/issue16399)
+        # Allow no-config for certain commands (like downloading / plotting)
+        if "config" in parsed_arg and parsed_arg.config is None:
+            conf_required = "command" in parsed_arg and parsed_arg.command in NO_CONF_REQURIED
+
+            if "user_data_dir" in parsed_arg and parsed_arg.user_data_dir is not None:
+                user_dir = parsed_arg.user_data_dir
+            else:
+                # Default case
+                user_dir = "user_data"
+                # Try loading from "user_data/config.json"
+            cfgfile = Path(user_dir) / DEFAULT_CONFIG
+            if cfgfile.is_file():
+                parsed_arg.config = [str(cfgfile)]
+            else:
+                # Else use "config.json".
+                cfgfile = Path.cwd() / DEFAULT_CONFIG
+                if cfgfile.is_file() or not conf_required:
+                    parsed_arg.config = [DEFAULT_CONFIG]
+
+        return parsed_arg
+
+    def _build_args(self, optionlist: list[str], parser: ArgumentParser | _ArgumentGroup) -> None:
+        for val in optionlist:
+            opt = AVAILABLE_CLI_OPTIONS[val]
+            parser.add_argument(*opt.cli, dest=val, **opt.kwargs)
+
+    def _build_subcommands(self) -> None:
+        """
+        Builds and attaches all subcommands.
+        :return: None
+        """
+        # Build shared arguments (as group Common Options)
+        _common_parser = ArgumentParser(add_help=False)
+        group = _common_parser.add_argument_group("Common arguments")
+        self._build_args(optionlist=ARGS_COMMON, parser=group)
+
+        _strategy_parser = ArgumentParser(add_help=False)
+        strategy_group = _strategy_parser.add_argument_group("Strategy arguments")
+        self._build_args(optionlist=ARGS_STRATEGY, parser=strategy_group)
+
+        # Build main command
+        self.parser = ArgumentParser(
+            prog="freqtrade", description="Free, open source crypto trading bot"
+        )
+        self._build_args(optionlist=ARGS_MAIN, parser=self.parser)
+
+        from freqtrade.commands import (
+            start_analysis_entries_exits,
+            start_backtesting,
+            start_backtesting_show,
+            start_convert_data,
+            start_convert_db,
+            start_convert_trades,
+            start_create_userdir,
+            start_download_data,
+            start_edge,
+            start_hyperopt,
+            start_hyperopt_list,
+            start_hyperopt_show,
+            start_install_ui,
+            start_list_data,
+            start_list_exchanges,
+            start_list_freqAI_models,
+            start_list_hyperopt_loss_functions,
+            start_list_markets,
+            start_list_strategies,
+            start_list_timeframes,
+            start_lookahead_analysis,
+            start_new_config,
+            start_new_strategy,
+            start_plot_dataframe,
+            start_plot_profit,
+            start_recursive_analysis,
+            start_show_config,
+            start_show_trades,
+            start_strategy_update,
+            start_test_pairlist,
+            start_trading,
+            start_webserver,
+        )
+
+        subparsers = self.parser.add_subparsers(
+            dest="command",
+            # Use custom message when no subhandler is added
+            # shown from `main.py`
+            # required=True
+        )
+
+        # Add trade subcommand
+        trade_cmd = subparsers.add_parser(
+            "trade", help="Trade module.", parents=[_common_parser, _strategy_parser]
+        )
+        trade_cmd.set_defaults(func=start_trading)
+        self._build_args(optionlist=ARGS_TRADE, parser=trade_cmd)
+
+        # add create-userdir subcommand
+        create_userdir_cmd = subparsers.add_parser(
+            "create-userdir",
+            help="Create user-data directory.",
+        )
+        create_userdir_cmd.set_defaults(func=start_create_userdir)
+        self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
+
+        # add new-config subcommand
+        build_config_cmd = subparsers.add_parser(
+            "new-config",
+            help="Create new config",
+        )
+        build_config_cmd.set_defaults(func=start_new_config)
+        self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
+
+        # add show-config subcommand
+        show_config_cmd = subparsers.add_parser(
+            "show-config",
+            help="Show resolved config",
+        )
+        show_config_cmd.set_defaults(func=start_show_config)
+        self._build_args(optionlist=ARGS_SHOW_CONFIG, parser=show_config_cmd)
+
+        # add new-strategy subcommand
+        build_strategy_cmd = subparsers.add_parser(
+            "new-strategy",
+            help="Create new strategy",
+        )
+        build_strategy_cmd.set_defaults(func=start_new_strategy)
+        self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
+
+        # Add download-data subcommand
+        download_data_cmd = subparsers.add_parser(
+            "download-data",
+            help="Download backtesting data.",
+            parents=[_common_parser],
+        )
+        download_data_cmd.set_defaults(func=start_download_data)
+        self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
+
+        # Add convert-data subcommand
+        convert_data_cmd = subparsers.add_parser(
+            "convert-data",
+            help="Convert candle (OHLCV) data from one format to another.",
+            parents=[_common_parser],
+        )
+        convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
+        self._build_args(optionlist=ARGS_CONVERT_DATA_OHLCV, parser=convert_data_cmd)
+
+        # Add convert-trade-data subcommand
+        convert_trade_data_cmd = subparsers.add_parser(
+            "convert-trade-data",
+            help="Convert trade data from one format to another.",
+            parents=[_common_parser],
+        )
+        convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
+        self._build_args(optionlist=ARGS_CONVERT_DATA_TRADES, parser=convert_trade_data_cmd)
+
+        # Add trades-to-ohlcv subcommand
+        convert_trade_data_cmd = subparsers.add_parser(
+            "trades-to-ohlcv",
+            help="Convert trade data to OHLCV data.",
+            parents=[_common_parser],
+        )
+        convert_trade_data_cmd.set_defaults(func=start_convert_trades)
+        self._build_args(optionlist=ARGS_CONVERT_TRADES, parser=convert_trade_data_cmd)
+
+        # Add list-data subcommand
+        list_data_cmd = subparsers.add_parser(
+            "list-data",
+            help="List downloaded data.",
+            parents=[_common_parser],
+        )
+        list_data_cmd.set_defaults(func=start_list_data)
+        self._build_args(optionlist=ARGS_LIST_DATA, parser=list_data_cmd)
+
+        # Add backtesting subcommand
+        backtesting_cmd = subparsers.add_parser(
+            "backtesting", help="Backtesting module.", parents=[_common_parser, _strategy_parser]
+        )
+        backtesting_cmd.set_defaults(func=start_backtesting)
+        self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
+
+        # Add backtesting-show subcommand
+        backtesting_show_cmd = subparsers.add_parser(
+            "backtesting-show",
+            help="Show past Backtest results",
+            parents=[_common_parser],
+        )
+        backtesting_show_cmd.set_defaults(func=start_backtesting_show)
+        self._build_args(optionlist=ARGS_BACKTEST_SHOW, parser=backtesting_show_cmd)
+
+        # Add backtesting analysis subcommand
+        analysis_cmd = subparsers.add_parser(
+            "backtesting-analysis", help="Backtest Analysis module.", parents=[_common_parser]
+        )
+        analysis_cmd.set_defaults(func=start_analysis_entries_exits)
+        self._build_args(optionlist=ARGS_ANALYZE_ENTRIES_EXITS, parser=analysis_cmd)
+
+        # Add edge subcommand
+        edge_cmd = subparsers.add_parser(
+            "edge", help="Edge module.", parents=[_common_parser, _strategy_parser]
+        )
+        edge_cmd.set_defaults(func=start_edge)
+        self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
+
+        # Add hyperopt subcommand
+        hyperopt_cmd = subparsers.add_parser(
+            "hyperopt",
+            help="Hyperopt module.",
+            parents=[_common_parser, _strategy_parser],
+        )
+        hyperopt_cmd.set_defaults(func=start_hyperopt)
+        self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
+
+        # Add hyperopt-list subcommand
+        hyperopt_list_cmd = subparsers.add_parser(
+            "hyperopt-list",
+            help="List Hyperopt results",
+            parents=[_common_parser],
+        )
+        hyperopt_list_cmd.set_defaults(func=start_hyperopt_list)
+        self._build_args(optionlist=ARGS_HYPEROPT_LIST, parser=hyperopt_list_cmd)
+
+        # Add hyperopt-show subcommand
+        hyperopt_show_cmd = subparsers.add_parser(
+            "hyperopt-show",
+            help="Show details of Hyperopt results",
+            parents=[_common_parser],
+        )
+        hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
+        self._build_args(optionlist=ARGS_HYPEROPT_SHOW, parser=hyperopt_show_cmd)
+
+        # Add list-exchanges subcommand
+        list_exchanges_cmd = subparsers.add_parser(
+            "list-exchanges",
+            help="Print available exchanges.",
+            parents=[_common_parser],
+        )
+        list_exchanges_cmd.set_defaults(func=start_list_exchanges)
+        self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
+
+        # Add list-markets subcommand
+        list_markets_cmd = subparsers.add_parser(
+            "list-markets",
+            help="Print markets on exchange.",
+            parents=[_common_parser],
+        )
+        list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
+        self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
+
+        # Add list-pairs subcommand
+        list_pairs_cmd = subparsers.add_parser(
+            "list-pairs",
+            help="Print pairs on exchange.",
+            parents=[_common_parser],
+        )
+        list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
+        self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_pairs_cmd)
+
+        # Add list-strategies subcommand
+        list_strategies_cmd = subparsers.add_parser(
+            "list-strategies",
+            help="Print available strategies.",
+            parents=[_common_parser],
+        )
+        list_strategies_cmd.set_defaults(func=start_list_strategies)
+        self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
+
+        # Add list-Hyperopt loss subcommand
+        list_hyperopt_loss_cmd = subparsers.add_parser(
+            "list-hyperoptloss",
+            help="Print available hyperopt loss functions.",
+            parents=[_common_parser],
+        )
+        list_hyperopt_loss_cmd.set_defaults(func=start_list_hyperopt_loss_functions)
+        self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopt_loss_cmd)
+
+        # Add list-freqAI Models subcommand
+        list_freqaimodels_cmd = subparsers.add_parser(
+            "list-freqaimodels",
+            help="Print available freqAI models.",
+            parents=[_common_parser],
+        )
+        list_freqaimodels_cmd.set_defaults(func=start_list_freqAI_models)
+        self._build_args(optionlist=ARGS_LIST_FREQAIMODELS, parser=list_freqaimodels_cmd)
+
+        # Add list-timeframes subcommand
+        list_timeframes_cmd = subparsers.add_parser(
+            "list-timeframes",
+            help="Print available timeframes for the exchange.",
+            parents=[_common_parser],
+        )
+        list_timeframes_cmd.set_defaults(func=start_list_timeframes)
+        self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
+
+        # Add show-trades subcommand
+        show_trades = subparsers.add_parser(
+            "show-trades",
+            help="Show trades.",
+            parents=[_common_parser],
+        )
+        show_trades.set_defaults(func=start_show_trades)
+        self._build_args(optionlist=ARGS_SHOW_TRADES, parser=show_trades)
+
+        # Add test-pairlist subcommand
+        test_pairlist_cmd = subparsers.add_parser(
+            "test-pairlist",
+            help="Test your pairlist configuration.",
+        )
+        test_pairlist_cmd.set_defaults(func=start_test_pairlist)
+        self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
+
+        # Add db-convert subcommand
+        convert_db = subparsers.add_parser(
+            "convert-db",
+            help="Migrate database to different system",
+        )
+        convert_db.set_defaults(func=start_convert_db)
+        self._build_args(optionlist=ARGS_CONVERT_DB, parser=convert_db)
+
+        # Add install-ui subcommand
+        install_ui_cmd = subparsers.add_parser(
+            "install-ui",
+            help="Install FreqUI",
+        )
+        install_ui_cmd.set_defaults(func=start_install_ui)
+        self._build_args(optionlist=ARGS_INSTALL_UI, parser=install_ui_cmd)
+
+        # Add Plotting subcommand
+        plot_dataframe_cmd = subparsers.add_parser(
+            "plot-dataframe",
+            help="Plot candles with indicators.",
+            parents=[_common_parser, _strategy_parser],
+        )
+        plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
+        self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
+
+        # Plot profit
+        plot_profit_cmd = subparsers.add_parser(
+            "plot-profit",
+            help="Generate plot showing profits.",
+            parents=[_common_parser, _strategy_parser],
+        )
+        plot_profit_cmd.set_defaults(func=start_plot_profit)
+        self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)
+
+        # Add webserver subcommand
+        webserver_cmd = subparsers.add_parser(
+            "webserver", help="Webserver module.", parents=[_common_parser]
+        )
+        webserver_cmd.set_defaults(func=start_webserver)
+        self._build_args(optionlist=ARGS_WEBSERVER, parser=webserver_cmd)
+
+        # Add strategy_updater subcommand
+        strategy_updater_cmd = subparsers.add_parser(
+            "strategy-updater",
+            help="updates outdated strategy files to the current version",
+            parents=[_common_parser],
+        )
+        strategy_updater_cmd.set_defaults(func=start_strategy_update)
+        self._build_args(optionlist=ARGS_STRATEGY_UPDATER, parser=strategy_updater_cmd)
+
+        # Add lookahead_analysis subcommand
+        lookahead_analayis_cmd = subparsers.add_parser(
+            "lookahead-analysis",
+            help="Check for potential look ahead bias.",
+            parents=[_common_parser, _strategy_parser],
+        )
+        lookahead_analayis_cmd.set_defaults(func=start_lookahead_analysis)
+
+        self._build_args(optionlist=ARGS_LOOKAHEAD_ANALYSIS, parser=lookahead_analayis_cmd)
+
+        # Add recursive_analysis subcommand
+        recursive_analayis_cmd = subparsers.add_parser(
+            "recursive-analysis",
+            help="Check for potential recursive formula issue.",
+            parents=[_common_parser, _strategy_parser],
+        )
+        recursive_analayis_cmd.set_defaults(func=start_recursive_analysis)
+
+        self._build_args(optionlist=ARGS_RECURSIVE_ANALYSIS, parser=recursive_analayis_cmd)
diff --git a/freqtrade/commands/build_config_commands.py b/freqtrade/commands/build_config_commands.py
new file mode 100644
index 000000000..63d313b70
--- /dev/null
+++ b/freqtrade/commands/build_config_commands.py
@@ -0,0 +1,53 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_new_config(args: dict[str, Any]) -> None:
+    """
+    Create a new strategy from a template
+    Asking the user questions to fill out the template accordingly.
+    """
+
+    from freqtrade.configuration.deploy_config import (
+        ask_user_config,
+        ask_user_overwrite,
+        deploy_new_config,
+    )
+    from freqtrade.configuration.directory_operations import chown_user_directory
+
+    config_path = Path(args["config"][0])
+    chown_user_directory(config_path.parent)
+    if config_path.exists():
+        overwrite = ask_user_overwrite(config_path)
+        if overwrite:
+            config_path.unlink()
+        else:
+            raise OperationalException(
+                f"Configuration file `{config_path}` already exists. "
+                "Please delete it or use a different configuration file name."
+            )
+    selections = ask_user_config()
+    deploy_new_config(config_path, selections)
+
+
+def start_show_config(args: dict[str, Any]) -> None:
+    from freqtrade.configuration import sanitize_config
+    from freqtrade.configuration.config_setup import setup_utils_configuration
+
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE, set_dry=False)
+
+    print("Your combined configuration is:")
+    config_sanitized = sanitize_config(
+        config["original_config"], show_sensitive=args.get("show_sensitive", False)
+    )
+
+    from rich import print_json
+
+    print_json(data=config_sanitized)
diff --git a/freqtrade/commands/cli_options.py b/freqtrade/commands/cli_options.py
new file mode 100755
index 000000000..9620be7ab
--- /dev/null
+++ b/freqtrade/commands/cli_options.py
@@ -0,0 +1,792 @@
+"""
+Definition of cli arguments used in arguments.py
+"""
+
+from argparse import SUPPRESS, ArgumentTypeError
+
+from freqtrade import constants
+from freqtrade.constants import HYPEROPT_LOSS_BUILTIN
+from freqtrade.enums import CandleType
+
+
+def check_int_positive(value: str) -> int:
+    try:
+        uint = int(value)
+        if uint <= 0:
+            raise ValueError
+    except ValueError:
+        raise ArgumentTypeError(
+            f"{value} is invalid for this parameter, should be a positive integer value"
+        )
+    return uint
+
+
+def check_int_nonzero(value: str) -> int:
+    try:
+        uint = int(value)
+        if uint == 0:
+            raise ValueError
+    except ValueError:
+        raise ArgumentTypeError(
+            f"{value} is invalid for this parameter, should be a non-zero integer value"
+        )
+    return uint
+
+
+class Arg:
+    # Optional CLI arguments
+    def __init__(self, *args, **kwargs):
+        self.cli = args
+        self.kwargs = kwargs
+
+
+# List of available command line options
+AVAILABLE_CLI_OPTIONS = {
+    # Common options
+    "verbosity": Arg(
+        "-v",
+        "--verbose",
+        help="Verbose mode (-vv for more, -vvv to get all messages).",
+        action="count",
+    ),
+    "logfile": Arg(
+        "--logfile",
+        "--log-file",
+        help="Log to the file specified. Special values are: 'syslog', 'journald'. "
+        "See the documentation for more details.",
+        metavar="FILE",
+    ),
+    "version": Arg(
+        "-V",
+        "--version",
+        help="show program's version number and exit",
+        action="store_true",
+    ),
+    "version_main": Arg(
+        # Copy of version - used to have -V available with and without subcommand.
+        "-V",
+        "--version",
+        help="show program's version number and exit",
+        action="store_true",
+    ),
+    "config": Arg(
+        "-c",
+        "--config",
+        help=f"Specify configuration file (default: `userdir/{constants.DEFAULT_CONFIG}` "
+        f"or `config.json` whichever exists). "
+        f"Multiple --config options may be used. "
+        f"Can be set to `-` to read config from stdin.",
+        action="append",
+        metavar="PATH",
+    ),
+    "datadir": Arg(
+        "-d",
+        "--datadir",
+        "--data-dir",
+        help="Path to directory with historical backtesting data.",
+        metavar="PATH",
+    ),
+    "user_data_dir": Arg(
+        "--userdir",
+        "--user-data-dir",
+        help="Path to userdata directory.",
+        metavar="PATH",
+    ),
+    "reset": Arg(
+        "--reset",
+        help="Reset sample files to their original state.",
+        action="store_true",
+    ),
+    "recursive_strategy_search": Arg(
+        "--recursive-strategy-search",
+        help="Recursively search for a strategy in the strategies folder.",
+        action="store_true",
+    ),
+    # Main options
+    "strategy": Arg(
+        "-s",
+        "--strategy",
+        help="Specify strategy class name which will be used by the bot.",
+        metavar="NAME",
+    ),
+    "strategy_path": Arg(
+        "--strategy-path",
+        help="Specify additional strategy lookup path.",
+        metavar="PATH",
+    ),
+    "db_url": Arg(
+        "--db-url",
+        help=f"Override trades database URL, this is useful in custom deployments "
+        f"(default: `{constants.DEFAULT_DB_PROD_URL}` for Live Run mode, "
+        f"`{constants.DEFAULT_DB_DRYRUN_URL}` for Dry Run).",
+        metavar="PATH",
+    ),
+    "db_url_from": Arg(
+        "--db-url-from",
+        help="Source db url to use when migrating a database.",
+        metavar="PATH",
+    ),
+    "sd_notify": Arg(
+        "--sd-notify",
+        help="Notify systemd service manager.",
+        action="store_true",
+    ),
+    "dry_run": Arg(
+        "--dry-run",
+        help="Enforce dry-run for trading (removes Exchange secrets and simulates trades).",
+        action="store_true",
+    ),
+    "dry_run_wallet": Arg(
+        "--dry-run-wallet",
+        "--starting-balance",
+        help="Starting balance, used for backtesting / hyperopt and dry-runs.",
+        type=float,
+    ),
+    # Optimize common
+    "timeframe": Arg(
+        "-i",
+        "--timeframe",
+        help="Specify timeframe (`1m`, `5m`, `30m`, `1h`, `1d`).",
+    ),
+    "timerange": Arg(
+        "--timerange",
+        help="Specify what timerange of data to use.",
+    ),
+    "max_open_trades": Arg(
+        "--max-open-trades",
+        help="Override the value of the `max_open_trades` configuration setting.",
+        type=int,
+        metavar="INT",
+    ),
+    "stake_amount": Arg(
+        "--stake-amount",
+        help="Override the value of the `stake_amount` configuration setting.",
+    ),
+    # Backtesting
+    "timeframe_detail": Arg(
+        "--timeframe-detail",
+        help="Specify detail timeframe for backtesting (`1m`, `5m`, `30m`, `1h`, `1d`).",
+    ),
+    "position_stacking": Arg(
+        "--eps",
+        "--enable-position-stacking",
+        help="Allow buying the same pair multiple times (position stacking).",
+        action="store_true",
+        default=False,
+    ),
+    "backtest_show_pair_list": Arg(
+        "--show-pair-list",
+        help="Show backtesting pairlist sorted by profit.",
+        action="store_true",
+        default=False,
+    ),
+    "enable_protections": Arg(
+        "--enable-protections",
+        "--enableprotections",
+        help="Enable protections for backtesting."
+        "Will slow backtesting down by a considerable amount, but will include "
+        "configured protections",
+        action="store_true",
+        default=False,
+    ),
+    "strategy_list": Arg(
+        "--strategy-list",
+        help="Provide a space-separated list of strategies to backtest. "
+        "Please note that timeframe needs to be set either in config "
+        "or via command line. When using this together with `--export trades`, "
+        "the strategy-name is injected into the filename "
+        "(so `backtest-data.json` becomes `backtest-data-SampleStrategy.json`",
+        nargs="+",
+    ),
+    "export": Arg(
+        "--export",
+        help="Export backtest results (default: trades).",
+        choices=constants.EXPORT_OPTIONS,
+    ),
+    "exportfilename": Arg(
+        "--export-filename",
+        "--backtest-filename",
+        help="Use this filename for backtest results."
+        "Requires `--export` to be set as well. "
+        "Example: `--export-filename=user_data/backtest_results/backtest_today.json`",
+        metavar="PATH",
+    ),
+    "disableparamexport": Arg(
+        "--disable-param-export",
+        help="Disable automatic hyperopt parameter export.",
+        action="store_true",
+    ),
+    "fee": Arg(
+        "--fee",
+        help="Specify fee ratio. Will be applied twice (on trade entry and exit).",
+        type=float,
+        metavar="FLOAT",
+    ),
+    "backtest_breakdown": Arg(
+        "--breakdown",
+        help="Show backtesting breakdown per [day, week, month, year].",
+        nargs="+",
+        choices=constants.BACKTEST_BREAKDOWNS,
+    ),
+    "backtest_cache": Arg(
+        "--cache",
+        help="Load a cached backtest result no older than specified age (default: %(default)s).",
+        default=constants.BACKTEST_CACHE_DEFAULT,
+        choices=constants.BACKTEST_CACHE_AGE,
+    ),
+    # Edge
+    "stoploss_range": Arg(
+        "--stoplosses",
+        help="Defines a range of stoploss values against which edge will assess the strategy. "
+        'The format is "min,max,step" (without any space). '
+        "Example: `--stoplosses=-0.01,-0.1,-0.001`",
+    ),
+    # Hyperopt
+    "hyperopt": Arg(
+        "--hyperopt",
+        help=SUPPRESS,
+        metavar="NAME",
+        required=False,
+    ),
+    "hyperopt_path": Arg(
+        "--hyperopt-path",
+        help="Specify additional lookup path for Hyperopt Loss functions.",
+        metavar="PATH",
+    ),
+    "epochs": Arg(
+        "-e",
+        "--epochs",
+        help="Specify number of epochs (default: %(default)d).",
+        type=check_int_positive,
+        metavar="INT",
+        default=constants.HYPEROPT_EPOCH,
+    ),
+    "spaces": Arg(
+        "--spaces",
+        help="Specify which parameters to hyperopt. Space-separated list.",
+        choices=[
+            "all",
+            "buy",
+            "sell",
+            "roi",
+            "stoploss",
+            "trailing",
+            "protection",
+            "trades",
+            "default",
+        ],
+        nargs="+",
+        default="default",
+    ),
+    "analyze_per_epoch": Arg(
+        "--analyze-per-epoch",
+        help="Run populate_indicators once per epoch.",
+        action="store_true",
+        default=False,
+    ),
+    "print_all": Arg(
+        "--print-all",
+        help="Print all results, not only the best ones.",
+        action="store_true",
+        default=False,
+    ),
+    "print_colorized": Arg(
+        "--no-color",
+        help="Disable colorization of hyperopt results. May be useful if you are "
+        "redirecting output to a file.",
+        action="store_false",
+        default=True,
+    ),
+    "print_json": Arg(
+        "--print-json",
+        help="Print output in JSON format.",
+        action="store_true",
+        default=False,
+    ),
+    "export_csv": Arg(
+        "--export-csv",
+        help="Export to CSV-File."
+        " This will disable table print."
+        " Example: --export-csv hyperopt.csv",
+        metavar="FILE",
+    ),
+    "hyperopt_jobs": Arg(
+        "-j",
+        "--job-workers",
+        help="The number of concurrently running jobs for hyperoptimization "
+        "(hyperopt worker processes). "
+        "If -1 (default), all CPUs are used, for -2, all CPUs but one are used, etc. "
+        "If 1 is given, no parallel computing code is used at all.",
+        type=int,
+        metavar="JOBS",
+        default=-1,
+    ),
+    "hyperopt_random_state": Arg(
+        "--random-state",
+        help="Set random state to some positive integer for reproducible hyperopt results.",
+        type=check_int_positive,
+        metavar="INT",
+    ),
+    "hyperopt_min_trades": Arg(
+        "--min-trades",
+        help="Set minimal desired number of trades for evaluations in the hyperopt "
+        "optimization path (default: 1).",
+        type=check_int_positive,
+        metavar="INT",
+        default=1,
+    ),
+    "hyperopt_loss": Arg(
+        "--hyperopt-loss",
+        "--hyperoptloss",
+        help="Specify the class name of the hyperopt loss function class (IHyperOptLoss). "
+        "Different functions can generate completely different results, "
+        "since the target for optimization is different. Built-in Hyperopt-loss-functions are: "
+        f"{', '.join(HYPEROPT_LOSS_BUILTIN)}",
+        metavar="NAME",
+    ),
+    "hyperoptexportfilename": Arg(
+        "--hyperopt-filename",
+        help="Hyperopt result filename."
+        "Example: `--hyperopt-filename=hyperopt_results_2020-09-27_16-20-48.pickle`",
+        metavar="FILENAME",
+    ),
+    # List exchanges
+    "print_one_column": Arg(
+        "-1",
+        "--one-column",
+        help="Print output in one column.",
+        action="store_true",
+    ),
+    "list_exchanges_all": Arg(
+        "-a",
+        "--all",
+        help="Print all exchanges known to the ccxt library.",
+        action="store_true",
+    ),
+    # List pairs / markets
+    "list_pairs_all": Arg(
+        "-a",
+        "--all",
+        help="Print all pairs or market symbols. By default only active ones are shown.",
+        action="store_true",
+    ),
+    "print_list": Arg(
+        "--print-list",
+        help="Print list of pairs or market symbols. By default data is "
+        "printed in the tabular format.",
+        action="store_true",
+    ),
+    "list_pairs_print_json": Arg(
+        "--print-json",
+        help="Print list of pairs or market symbols in JSON format.",
+        action="store_true",
+        default=False,
+    ),
+    "print_csv": Arg(
+        "--print-csv",
+        help="Print exchange pair or market data in the csv format.",
+        action="store_true",
+    ),
+    "quote_currencies": Arg(
+        "--quote",
+        help="Specify quote currency(-ies). Space-separated list.",
+        nargs="+",
+        metavar="QUOTE_CURRENCY",
+    ),
+    "base_currencies": Arg(
+        "--base",
+        help="Specify base currency(-ies). Space-separated list.",
+        nargs="+",
+        metavar="BASE_CURRENCY",
+    ),
+    "trading_mode": Arg(
+        "--trading-mode",
+        "--tradingmode",
+        help="Select Trading mode",
+        choices=constants.TRADING_MODES,
+    ),
+    "candle_types": Arg(
+        "--candle-types",
+        help="Select candle type to convert. Defaults to all available types.",
+        choices=[c.value for c in CandleType],
+        nargs="+",
+    ),
+    # Script options
+    "pairs": Arg(
+        "-p",
+        "--pairs",
+        help="Limit command to these pairs. Pairs are space-separated.",
+        nargs="+",
+    ),
+    # Download data
+    "pairs_file": Arg(
+        "--pairs-file",
+        help="File containing a list of pairs. "
+        "Takes precedence over --pairs or pairs configured in the configuration.",
+        metavar="FILE",
+    ),
+    "days": Arg(
+        "--days",
+        help="Download data for given number of days.",
+        type=check_int_positive,
+        metavar="INT",
+    ),
+    "include_inactive": Arg(
+        "--include-inactive-pairs",
+        help="Also download data from inactive pairs.",
+        action="store_true",
+    ),
+    "new_pairs_days": Arg(
+        "--new-pairs-days",
+        help="Download data of new pairs for given number of days. Default: `%(default)s`.",
+        type=check_int_positive,
+        metavar="INT",
+    ),
+    "download_trades": Arg(
+        "--dl-trades",
+        help="Download trades instead of OHLCV data.",
+        action="store_true",
+    ),
+    "trades": Arg(
+        "--trades",
+        help="Work on trades data instead of OHLCV data.",
+        action="store_true",
+    ),
+    "convert_trades": Arg(
+        "--convert",
+        help="Convert downloaded trades to OHLCV data. Only applicable in combination with "
+        "`--dl-trades`. "
+        "Will be automatic for exchanges which don't have historic OHLCV (e.g. Kraken). "
+        "If not provided, use `trades-to-ohlcv` to convert trades data to OHLCV data.",
+        action="store_true",
+    ),
+    "format_from_trades": Arg(
+        "--format-from",
+        help="Source format for data conversion.",
+        choices=[*constants.AVAILABLE_DATAHANDLERS, "kraken_csv"],
+        required=True,
+    ),
+    "format_from": Arg(
+        "--format-from",
+        help="Source format for data conversion.",
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        required=True,
+    ),
+    "format_to": Arg(
+        "--format-to",
+        help="Destination format for data conversion.",
+        choices=constants.AVAILABLE_DATAHANDLERS,
+        required=True,
+    ),
+    "dataformat_ohlcv": Arg(
+        "--data-format-ohlcv",
+        help="Storage format for downloaded candle (OHLCV) data. (default: `feather`).",
+        choices=constants.AVAILABLE_DATAHANDLERS,
+    ),
+    "dataformat_trades": Arg(
+        "--data-format-trades",
+        help="Storage format for downloaded trades data. (default: `feather`).",
+        choices=constants.AVAILABLE_DATAHANDLERS,
+    ),
+    "show_timerange": Arg(
+        "--show-timerange",
+        help="Show timerange available for available data. (May take a while to calculate).",
+        action="store_true",
+    ),
+    "exchange": Arg(
+        "--exchange",
+        help="Exchange name. Only valid if no config is provided.",
+    ),
+    "timeframes": Arg(
+        "-t",
+        "--timeframes",
+        help="Specify which tickers to download. Space-separated list. Default: `1m 5m`.",
+        nargs="+",
+    ),
+    "prepend_data": Arg(
+        "--prepend",
+        help="Allow data prepending. (Data-appending is disabled)",
+        action="store_true",
+    ),
+    "erase": Arg(
+        "--erase",
+        help="Clean all existing data for the selected exchange/pairs/timeframes.",
+        action="store_true",
+    ),
+    "erase_ui_only": Arg(
+        "--erase",
+        help="Clean UI folder, don't download new version.",
+        action="store_true",
+        default=False,
+    ),
+    "ui_version": Arg(
+        "--ui-version",
+        help=(
+            "Specify a specific version of FreqUI to install. "
+            "Not specifying this installs the latest version."
+        ),
+        type=str,
+    ),
+    "ui_prerelease": Arg(
+        "--prerelease",
+        help=(
+            "Install the latest pre-release version of FreqUI. "
+            "This is not recommended for production use."
+        ),
+        action="store_true",
+        default=False,
+    ),
+    # Templating options
+    "template": Arg(
+        "--template",
+        help="Use a template which is either `minimal`, "
+        "`full` (containing multiple sample indicators) or `advanced`. Default: `%(default)s`.",
+        choices=["full", "minimal", "advanced"],
+        default="full",
+    ),
+    # Plot dataframe
+    "indicators1": Arg(
+        "--indicators1",
+        help="Set indicators from your strategy you want in the first row of the graph. "
+        "Space-separated list. Example: `ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.",
+        nargs="+",
+    ),
+    "indicators2": Arg(
+        "--indicators2",
+        help="Set indicators from your strategy you want in the third row of the graph. "
+        "Space-separated list. Example: `fastd fastk`. Default: `['macd', 'macdsignal']`.",
+        nargs="+",
+    ),
+    "plot_limit": Arg(
+        "--plot-limit",
+        help="Specify tick limit for plotting. Notice: too high values cause huge files. "
+        "Default: %(default)s.",
+        type=check_int_positive,
+        metavar="INT",
+        default=750,
+    ),
+    "plot_auto_open": Arg(
+        "--auto-open",
+        help="Automatically open generated plot.",
+        action="store_true",
+    ),
+    "no_trades": Arg(
+        "--no-trades",
+        help="Skip using trades from backtesting file and DB.",
+        action="store_true",
+    ),
+    "trade_source": Arg(
+        "--trade-source",
+        help="Specify the source for trades (Can be DB or file (backtest file)) "
+        "Default: %(default)s",
+        choices=["DB", "file"],
+        default="file",
+    ),
+    "trade_ids": Arg(
+        "--trade-ids",
+        help="Specify the list of trade ids.",
+        nargs="+",
+    ),
+    # hyperopt-list, hyperopt-show
+    "hyperopt_list_profitable": Arg(
+        "--profitable",
+        help="Select only profitable epochs.",
+        action="store_true",
+    ),
+    "hyperopt_list_best": Arg(
+        "--best",
+        help="Select only best epochs.",
+        action="store_true",
+    ),
+    "hyperopt_list_min_trades": Arg(
+        "--min-trades",
+        help="Select epochs with more than INT trades.",
+        type=check_int_positive,
+        metavar="INT",
+    ),
+    "hyperopt_list_max_trades": Arg(
+        "--max-trades",
+        help="Select epochs with less than INT trades.",
+        type=check_int_positive,
+        metavar="INT",
+    ),
+    "hyperopt_list_min_avg_time": Arg(
+        "--min-avg-time",
+        help="Select epochs above average time.",
+        type=float,
+        metavar="FLOAT",
+    ),
+    "hyperopt_list_max_avg_time": Arg(
+        "--max-avg-time",
+        help="Select epochs below average time.",
+        type=float,
+        metavar="FLOAT",
+    ),
+    "hyperopt_list_min_avg_profit": Arg(
+        "--min-avg-profit",
+        help="Select epochs above average profit.",
+        type=float,
+        metavar="FLOAT",
+    ),
+    "hyperopt_list_max_avg_profit": Arg(
+        "--max-avg-profit",
+        help="Select epochs below average profit.",
+        type=float,
+        metavar="FLOAT",
+    ),
+    "hyperopt_list_min_total_profit": Arg(
+        "--min-total-profit",
+        help="Select epochs above total profit.",
+        type=float,
+        metavar="FLOAT",
+    ),
+    "hyperopt_list_max_total_profit": Arg(
+        "--max-total-profit",
+        help="Select epochs below total profit.",
+        type=float,
+        metavar="FLOAT",
+    ),
+    "hyperopt_list_min_objective": Arg(
+        "--min-objective",
+        help="Select epochs above objective.",
+        type=float,
+        metavar="FLOAT",
+    ),
+    "hyperopt_list_max_objective": Arg(
+        "--max-objective",
+        help="Select epochs below objective.",
+        type=float,
+        metavar="FLOAT",
+    ),
+    "hyperopt_list_no_details": Arg(
+        "--no-details",
+        help="Do not print best epoch details.",
+        action="store_true",
+    ),
+    "hyperopt_show_index": Arg(
+        "-n",
+        "--index",
+        help="Specify the index of the epoch to print details for.",
+        type=check_int_nonzero,
+        metavar="INT",
+    ),
+    "hyperopt_show_no_header": Arg(
+        "--no-header",
+        help="Do not print epoch details header.",
+        action="store_true",
+    ),
+    "hyperopt_ignore_missing_space": Arg(
+        "--ignore-missing-spaces",
+        "--ignore-unparameterized-spaces",
+        help=(
+            "Suppress errors for any requested Hyperopt spaces that do not contain any parameters."
+        ),
+        action="store_true",
+    ),
+    "analysis_groups": Arg(
+        "--analysis-groups",
+        help=(
+            "grouping output - "
+            "0: simple wins/losses by enter tag, "
+            "1: by enter_tag, "
+            "2: by enter_tag and exit_tag, "
+            "3: by pair and enter_tag, "
+            "4: by pair, enter_ and exit_tag (this can get quite large), "
+            "5: by exit_tag"
+        ),
+        nargs="+",
+        default=[],
+        choices=["0", "1", "2", "3", "4", "5"],
+    ),
+    "enter_reason_list": Arg(
+        "--enter-reason-list",
+        help=(
+            "Space separated list of entry signals to analyse. Default: all. "
+            "e.g. 'entry_tag_a entry_tag_b'"
+        ),
+        nargs="+",
+        default=["all"],
+    ),
+    "exit_reason_list": Arg(
+        "--exit-reason-list",
+        help=(
+            "Space separated list of exit signals to analyse. Default: all. "
+            "e.g. 'exit_tag_a roi stop_loss trailing_stop_loss'"
+        ),
+        nargs="+",
+        default=["all"],
+    ),
+    "indicator_list": Arg(
+        "--indicator-list",
+        help=(
+            "Space separated list of indicators to analyse. "
+            "e.g. 'close rsi bb_lowerband profit_abs'"
+        ),
+        nargs="+",
+        default=[],
+    ),
+    "entry_only": Arg(
+        "--entry-only", help=("Only analyze entry signals."), action="store_true", default=False
+    ),
+    "exit_only": Arg(
+        "--exit-only", help=("Only analyze exit signals."), action="store_true", default=False
+    ),
+    "analysis_rejected": Arg(
+        "--rejected-signals",
+        help="Analyse rejected signals",
+        action="store_true",
+    ),
+    "analysis_to_csv": Arg(
+        "--analysis-to-csv",
+        help="Save selected analysis tables to individual CSVs",
+        action="store_true",
+    ),
+    "analysis_csv_path": Arg(
+        "--analysis-csv-path",
+        help=(
+            "Specify a path to save the analysis CSVs "
+            "if --analysis-to-csv is enabled. Default: user_data/basktesting_results/"
+        ),
+    ),
+    "freqaimodel": Arg(
+        "--freqaimodel",
+        help="Specify a custom freqaimodels.",
+        metavar="NAME",
+    ),
+    "freqaimodel_path": Arg(
+        "--freqaimodel-path",
+        help="Specify additional lookup path for freqaimodels.",
+        metavar="PATH",
+    ),
+    "freqai_backtest_live_models": Arg(
+        "--freqai-backtest-live-models", help="Run backtest with ready models.", action="store_true"
+    ),
+    "minimum_trade_amount": Arg(
+        "--minimum-trade-amount",
+        help="Minimum trade amount for lookahead-analysis",
+        type=check_int_positive,
+        metavar="INT",
+    ),
+    "targeted_trade_amount": Arg(
+        "--targeted-trade-amount",
+        help="Targeted trade amount for lookahead analysis",
+        type=check_int_positive,
+        metavar="INT",
+    ),
+    "lookahead_analysis_exportfilename": Arg(
+        "--lookahead-analysis-exportfilename",
+        help="Use this csv-filename to store lookahead-analysis-results",
+        type=str,
+    ),
+    "startup_candle": Arg(
+        "--startup-candle",
+        help="Specify startup candles to be checked (`199`, `499`, `999`, `1999`).",
+        nargs="+",
+    ),
+    "show_sensitive": Arg(
+        "--show-sensitive",
+        help="Show secrets in the output.",
+        action="store_true",
+        default=False,
+    ),
+}
diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py
new file mode 100644
index 000000000..756c5a287
--- /dev/null
+++ b/freqtrade/commands/data_commands.py
@@ -0,0 +1,229 @@
+import logging
+import sys
+from collections import defaultdict
+from typing import Any
+
+from freqtrade.constants import DATETIME_PRINT_FORMAT, DL_DATA_TIMEFRAMES, Config
+from freqtrade.enums import CandleType, RunMode, TradingMode
+from freqtrade.exceptions import ConfigurationError
+from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
+
+
+logger = logging.getLogger(__name__)
+
+
+def _check_data_config_download_sanity(config: Config) -> None:
+    if "days" in config and "timerange" in config:
+        raise ConfigurationError(
+            "--days and --timerange are mutually exclusive. You can only specify one or the other."
+        )
+
+    if "pairs" not in config:
+        raise ConfigurationError(
+            "Downloading data requires a list of pairs. "
+            "Please check the documentation on how to configure this."
+        )
+
+
+def start_download_data(args: dict[str, Any]) -> None:
+    """
+    Download data (former download_backtest_data.py script)
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.data.history import download_data_main
+
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    _check_data_config_download_sanity(config)
+
+    try:
+        download_data_main(config)
+
+    except KeyboardInterrupt:
+        sys.exit("SIGINT received, aborting ...")
+
+
+def start_convert_trades(args: dict[str, Any]) -> None:
+    from freqtrade.configuration import TimeRange, setup_utils_configuration
+    from freqtrade.data.converter import convert_trades_to_ohlcv
+    from freqtrade.resolvers import ExchangeResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    timerange = TimeRange()
+
+    # Remove stake-currency to skip checks which are not relevant for datadownload
+    config["stake_currency"] = ""
+
+    if "timeframes" not in config:
+        config["timeframes"] = DL_DATA_TIMEFRAMES
+
+    # Init exchange
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
+    # Manual validations of relevant settings
+
+    for timeframe in config["timeframes"]:
+        exchange.validate_timeframes(timeframe)
+    available_pairs = [
+        p
+        for p in exchange.get_markets(
+            tradable_only=True, active_only=not config.get("include_inactive")
+        ).keys()
+    ]
+
+    expanded_pairs = dynamic_expand_pairlist(config, available_pairs)
+
+    # Convert downloaded trade data to different timeframes
+    convert_trades_to_ohlcv(
+        pairs=expanded_pairs,
+        timeframes=config["timeframes"],
+        datadir=config["datadir"],
+        timerange=timerange,
+        erase=bool(config.get("erase")),
+        data_format_ohlcv=config["dataformat_ohlcv"],
+        data_format_trades=config["dataformat_trades"],
+        candle_type=config.get("candle_type_def", CandleType.SPOT),
+    )
+
+
+def start_convert_data(args: dict[str, Any], ohlcv: bool = True) -> None:
+    """
+    Convert data from one format to another
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
+    from freqtrade.util.migrations import migrate_data
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+    if ohlcv:
+        migrate_data(config)
+        convert_ohlcv_format(
+            config,
+            convert_from=args["format_from"],
+            convert_to=args["format_to"],
+            erase=args["erase"],
+        )
+    else:
+        convert_trades_format(
+            config,
+            convert_from=args["format_from_trades"],
+            convert_to=args["format_to"],
+            erase=args["erase"],
+        )
+
+
+def start_list_data(args: dict[str, Any]) -> None:
+    """
+    List available OHLCV data
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.exchange import timeframe_to_minutes
+    from freqtrade.util import print_rich_table
+
+    if args["trades"]:
+        start_list_trades_data(args)
+        return
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    from freqtrade.data.history import get_datahandler
+
+    dhc = get_datahandler(config["datadir"], config["dataformat_ohlcv"])
+
+    paircombs = dhc.ohlcv_get_available_data(
+        config["datadir"], config.get("trading_mode", TradingMode.SPOT)
+    )
+    if args["pairs"]:
+        paircombs = [comb for comb in paircombs if comb[0] in args["pairs"]]
+    title = f"Found {len(paircombs)} pair / timeframe combinations."
+    if not config.get("show_timerange"):
+        groupedpair = defaultdict(list)
+        for pair, timeframe, candle_type in sorted(
+            paircombs, key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
+        ):
+            groupedpair[(pair, candle_type)].append(timeframe)
+
+        if groupedpair:
+            print_rich_table(
+                [
+                    (pair, ", ".join(timeframes), candle_type)
+                    for (pair, candle_type), timeframes in groupedpair.items()
+                ],
+                ("Pair", "Timeframe", "Type"),
+                title,
+                table_kwargs={"min_width": 50},
+            )
+    else:
+        paircombs1 = [
+            (pair, timeframe, candle_type, *dhc.ohlcv_data_min_max(pair, timeframe, candle_type))
+            for pair, timeframe, candle_type in paircombs
+        ]
+        print_rich_table(
+            [
+                (
+                    pair,
+                    timeframe,
+                    candle_type,
+                    start.strftime(DATETIME_PRINT_FORMAT),
+                    end.strftime(DATETIME_PRINT_FORMAT),
+                    str(length),
+                )
+                for pair, timeframe, candle_type, start, end, length in sorted(
+                    paircombs1, key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
+                )
+            ],
+            ("Pair", "Timeframe", "Type", "From", "To", "Candles"),
+            summary=title,
+            table_kwargs={"min_width": 50},
+        )
+
+
+def start_list_trades_data(args: dict[str, Any]) -> None:
+    """
+    List available Trades data
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.misc import plural
+    from freqtrade.util import print_rich_table
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    from freqtrade.data.history import get_datahandler
+
+    dhc = get_datahandler(config["datadir"], config["dataformat_trades"])
+
+    paircombs = dhc.trades_get_available_data(
+        config["datadir"], config.get("trading_mode", TradingMode.SPOT)
+    )
+
+    if args["pairs"]:
+        paircombs = [comb for comb in paircombs if comb in args["pairs"]]
+
+    title = f"Found trades data for {len(paircombs)} {plural(len(paircombs), 'pair')}."
+    if not config.get("show_timerange"):
+        print_rich_table(
+            [(pair, config.get("candle_type_def", CandleType.SPOT)) for pair in sorted(paircombs)],
+            ("Pair", "Type"),
+            title,
+            table_kwargs={"min_width": 50},
+        )
+    else:
+        paircombs1 = [
+            (pair, *dhc.trades_data_min_max(pair, config.get("trading_mode", TradingMode.SPOT)))
+            for pair in paircombs
+        ]
+        print_rich_table(
+            [
+                (
+                    pair,
+                    config.get("candle_type_def", CandleType.SPOT),
+                    start.strftime(DATETIME_PRINT_FORMAT),
+                    end.strftime(DATETIME_PRINT_FORMAT),
+                    str(length),
+                )
+                for pair, start, end, length in sorted(paircombs1, key=lambda x: (x[0]))
+            ],
+            ("Pair", "Type", "From", "To", "Trades"),
+            summary=title,
+            table_kwargs={"min_width": 50},
+        )
diff --git a/freqtrade/commands/db_commands.py b/freqtrade/commands/db_commands.py
new file mode 100644
index 000000000..0d856e8a4
--- /dev/null
+++ b/freqtrade/commands/db_commands.py
@@ -0,0 +1,56 @@
+import logging
+from typing import Any
+
+from freqtrade.enums import RunMode
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_convert_db(args: dict[str, Any]) -> None:
+    from sqlalchemy import func, select
+    from sqlalchemy.orm import make_transient
+
+    from freqtrade.configuration.config_setup import setup_utils_configuration
+    from freqtrade.persistence import Order, Trade, init_db
+    from freqtrade.persistence.migrations import set_sequence_ids
+    from freqtrade.persistence.pairlock import PairLock
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    init_db(config["db_url"])
+    session_target = Trade.session
+    init_db(config["db_url_from"])
+    logger.info("Starting db migration.")
+
+    trade_count = 0
+    pairlock_count = 0
+    for trade in Trade.get_trades():
+        trade_count += 1
+        make_transient(trade)
+        for o in trade.orders:
+            make_transient(o)
+
+        session_target.add(trade)
+
+    session_target.commit()
+
+    for pairlock in PairLock.get_all_locks():
+        pairlock_count += 1
+        make_transient(pairlock)
+        session_target.add(pairlock)
+    session_target.commit()
+
+    # Update sequences
+    max_trade_id = session_target.scalar(select(func.max(Trade.id)))
+    max_order_id = session_target.scalar(select(func.max(Order.id)))
+    max_pairlock_id = session_target.scalar(select(func.max(PairLock.id)))
+
+    set_sequence_ids(
+        session_target.get_bind(),
+        trade_id=max_trade_id,
+        order_id=max_order_id,
+        pairlock_id=max_pairlock_id,
+    )
+
+    logger.info(f"Migrated {trade_count} Trades, and {pairlock_count} Pairlocks.")
diff --git a/freqtrade/commands/deploy_commands.py b/freqtrade/commands/deploy_commands.py
new file mode 100644
index 000000000..c2a715971
--- /dev/null
+++ b/freqtrade/commands/deploy_commands.py
@@ -0,0 +1,133 @@
+import logging
+import sys
+from pathlib import Path
+from typing import Any
+
+from freqtrade.constants import USERPATH_STRATEGIES
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import ConfigurationError, OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+# Timeout for requests
+req_timeout = 30
+
+
+def start_create_userdir(args: dict[str, Any]) -> None:
+    """
+    Create "user_data" directory to contain user data strategies, hyperopt, ...)
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    from freqtrade.configuration.directory_operations import copy_sample_files, create_userdata_dir
+
+    if user_data_dir := args.get("user_data_dir"):
+        userdir = create_userdata_dir(user_data_dir, create_dir=True)
+        copy_sample_files(userdir, overwrite=args["reset"])
+    else:
+        logger.warning("`create-userdir` requires --userdir to be set.")
+        sys.exit(1)
+
+
+def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: str) -> None:
+    """
+    Deploy new strategy from template to strategy_path
+    """
+    from freqtrade.util import render_template, render_template_with_fallback
+
+    fallback = "full"
+    attributes = render_template_with_fallback(
+        templatefile=f"strategy_subtemplates/strategy_attributes_{subtemplate}.j2",
+        templatefallbackfile=f"strategy_subtemplates/strategy_attributes_{fallback}.j2",
+    )
+    indicators = render_template_with_fallback(
+        templatefile=f"strategy_subtemplates/indicators_{subtemplate}.j2",
+        templatefallbackfile=f"strategy_subtemplates/indicators_{fallback}.j2",
+    )
+    buy_trend = render_template_with_fallback(
+        templatefile=f"strategy_subtemplates/buy_trend_{subtemplate}.j2",
+        templatefallbackfile=f"strategy_subtemplates/buy_trend_{fallback}.j2",
+    )
+    sell_trend = render_template_with_fallback(
+        templatefile=f"strategy_subtemplates/sell_trend_{subtemplate}.j2",
+        templatefallbackfile=f"strategy_subtemplates/sell_trend_{fallback}.j2",
+    )
+    plot_config = render_template_with_fallback(
+        templatefile=f"strategy_subtemplates/plot_config_{subtemplate}.j2",
+        templatefallbackfile=f"strategy_subtemplates/plot_config_{fallback}.j2",
+    )
+    additional_methods = render_template_with_fallback(
+        templatefile=f"strategy_subtemplates/strategy_methods_{subtemplate}.j2",
+        templatefallbackfile="strategy_subtemplates/strategy_methods_empty.j2",
+    )
+
+    strategy_text = render_template(
+        templatefile="base_strategy.py.j2",
+        arguments={
+            "strategy": strategy_name,
+            "attributes": attributes,
+            "indicators": indicators,
+            "buy_trend": buy_trend,
+            "sell_trend": sell_trend,
+            "plot_config": plot_config,
+            "additional_methods": additional_methods,
+        },
+    )
+
+    logger.info(f"Writing strategy to `{strategy_path}`.")
+    strategy_path.write_text(strategy_text)
+
+
+def start_new_strategy(args: dict[str, Any]) -> None:
+    from freqtrade.configuration import setup_utils_configuration
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if strategy := args.get("strategy"):
+        if strategy_path := args.get("strategy_path"):
+            strategy_dir = Path(strategy_path)
+        else:
+            strategy_dir = config["user_data_dir"] / USERPATH_STRATEGIES
+        if not strategy_dir.is_dir():
+            logger.info(f"Creating strategy directory {strategy_dir}")
+            strategy_dir.mkdir(parents=True)
+        new_path = strategy_dir / (strategy + ".py")
+
+        if new_path.exists():
+            raise OperationalException(
+                f"`{new_path}` already exists. Please choose another Strategy Name."
+            )
+
+        deploy_new_strategy(strategy, new_path, args["template"])
+
+    else:
+        raise ConfigurationError("`new-strategy` requires --strategy to be set.")
+
+
+def start_install_ui(args: dict[str, Any]) -> None:
+    from freqtrade.commands.deploy_ui import (
+        clean_ui_subdir,
+        download_and_install_ui,
+        get_ui_download_url,
+        read_ui_version,
+    )
+
+    dest_folder = Path(__file__).parents[1] / "rpc/api_server/ui/installed/"
+    # First make sure the assets are removed.
+    dl_url, latest_version = get_ui_download_url(
+        args.get("ui_version"), args.get("ui_prerelease", False)
+    )
+
+    curr_version = read_ui_version(dest_folder)
+    if curr_version == latest_version and not args.get("erase_ui_only"):
+        logger.info(f"UI already up-to-date, FreqUI Version {curr_version}.")
+        return
+
+    clean_ui_subdir(dest_folder)
+    if args.get("erase_ui_only"):
+        logger.info("Erased UI directory content. Not downloading new version.")
+    else:
+        # Download a new version
+        download_and_install_ui(dest_folder, dl_url, latest_version)
diff --git a/freqtrade/commands/deploy_ui.py b/freqtrade/commands/deploy_ui.py
new file mode 100644
index 000000000..be96d6d11
--- /dev/null
+++ b/freqtrade/commands/deploy_ui.py
@@ -0,0 +1,87 @@
+import logging
+from pathlib import Path
+
+import requests
+
+
+logger = logging.getLogger(__name__)
+
+# Timeout for requests
+req_timeout = 30
+
+
+def clean_ui_subdir(directory: Path):
+    if directory.is_dir():
+        logger.info("Removing UI directory content.")
+
+        for p in reversed(list(directory.glob("**/*"))):  # iterate contents from leaves to root
+            if p.name in (".gitkeep", "fallback_file.html"):
+                continue
+            if p.is_file():
+                p.unlink()
+            elif p.is_dir():
+                p.rmdir()
+
+
+def read_ui_version(dest_folder: Path) -> str | None:
+    file = dest_folder / ".uiversion"
+    if not file.is_file():
+        return None
+
+    with file.open("r") as f:
+        return f.read()
+
+
+def download_and_install_ui(dest_folder: Path, dl_url: str, version: str):
+    from io import BytesIO
+    from zipfile import ZipFile
+
+    logger.info(f"Downloading {dl_url}")
+    resp = requests.get(dl_url, timeout=req_timeout).content
+    dest_folder.mkdir(parents=True, exist_ok=True)
+    with ZipFile(BytesIO(resp)) as zf:
+        for fn in zf.filelist:
+            with zf.open(fn) as x:
+                destfile = dest_folder / fn.filename
+                if fn.is_dir():
+                    destfile.mkdir(exist_ok=True)
+                else:
+                    destfile.write_bytes(x.read())
+    with (dest_folder / ".uiversion").open("w") as f:
+        f.write(version)
+
+
+def get_ui_download_url(version: str | None, prerelease: bool) -> tuple[str, str]:
+    base_url = "https://api.github.com/repos/freqtrade/frequi/"
+    # Get base UI Repo path
+
+    resp = requests.get(f"{base_url}releases", timeout=req_timeout)
+    resp.raise_for_status()
+    r = resp.json()
+
+    if version:
+        tmp = [x for x in r if x["name"] == version]
+    else:
+        tmp = [x for x in r if prerelease or not x.get("prerelease")]
+
+    if tmp:
+        # Ensure we have the latest version
+        if version is None:
+            tmp.sort(key=lambda x: x["created_at"], reverse=True)
+        latest_version = tmp[0]["name"]
+        assets = tmp[0].get("assets", [])
+    else:
+        raise ValueError("UI-Version not found.")
+
+    dl_url = ""
+    if assets and len(assets) > 0:
+        dl_url = assets[0]["browser_download_url"]
+
+    # URL not found - try assets url
+    if not dl_url:
+        assets = r[0]["assets_url"]
+        resp = requests.get(assets, timeout=req_timeout)
+        r = resp.json()
+        dl_url = r[0]["browser_download_url"]
+
+    return dl_url, latest_version
diff --git a/freqtrade/commands/hyperopt_commands.py b/freqtrade/commands/hyperopt_commands.py
new file mode 100644
index 000000000..9dfab02c0
--- /dev/null
+++ b/freqtrade/commands/hyperopt_commands.py
@@ -0,0 +1,113 @@
+import logging
+from operator import itemgetter
+from typing import Any
+
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_hyperopt_list(args: dict[str, Any]) -> None:
+    """
+    List hyperopt epochs previously evaluated
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.data.btanalysis import get_latest_hyperopt_file
+    from freqtrade.optimize.hyperopt.hyperopt_output import HyperoptOutput
+    from freqtrade.optimize.hyperopt_tools import HyperoptTools
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    print_colorized = config.get("print_colorized", False)
+    print_json = config.get("print_json", False)
+    export_csv = config.get("export_csv")
+    no_details = config.get("hyperopt_list_no_details", False)
+    no_header = False
+
+    results_file = get_latest_hyperopt_file(
+        config["user_data_dir"] / "hyperopt_results", config.get("hyperoptexportfilename")
+    )
+
+    # Previous evaluations
+    epochs, total_epochs = HyperoptTools.load_filtered_results(results_file, config)
+
+    if not export_csv:
+        try:
+            h_out = HyperoptOutput()
+            h_out.add_data(
+                config,
+                epochs,
+                total_epochs,
+                not config.get("hyperopt_list_best", False),
+            )
+            h_out.print(print_colorized=print_colorized)
+
+        except KeyboardInterrupt:
+            print("User interrupted..")
+
+    if epochs and not no_details:
+        sorted_epochs = sorted(epochs, key=itemgetter("loss"))
+        results = sorted_epochs[0]
+        HyperoptTools.show_epoch_details(results, total_epochs, print_json, no_header)
+
+    if epochs and export_csv:
+        HyperoptTools.export_csv_file(config, epochs, export_csv)
+
+
+def start_hyperopt_show(args: dict[str, Any]) -> None:
+    """
+    Show details of a hyperopt epoch previously evaluated
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.data.btanalysis import get_latest_hyperopt_file
+    from freqtrade.optimize.hyperopt_tools import HyperoptTools
+    from freqtrade.optimize.optimize_reports import show_backtest_result
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    print_json = config.get("print_json", False)
+    no_header = config.get("hyperopt_show_no_header", False)
+    results_file = get_latest_hyperopt_file(
+        config["user_data_dir"] / "hyperopt_results", config.get("hyperoptexportfilename")
+    )
+
+    n = config.get("hyperopt_show_index", -1)
+
+    # Previous evaluations
+    epochs, total_epochs = HyperoptTools.load_filtered_results(results_file, config)
+
+    filtered_epochs = len(epochs)
+
+    if n > filtered_epochs:
+        raise OperationalException(
+            f"The index of the epoch to show should be less than {filtered_epochs + 1}."
+        )
+    if n < -filtered_epochs:
+        raise OperationalException(
+            f"The index of the epoch to show should be greater than {-filtered_epochs - 1}."
+        )
+
+    # Translate epoch index from human-readable format to pythonic
+    if n > 0:
+        n -= 1
+
+    if epochs:
+        val = epochs[n]
+
+        metrics = val["results_metrics"]
+        if "strategy_name" in metrics:
+            strategy_name = metrics["strategy_name"]
+            show_backtest_result(
+                strategy_name,
+                metrics,
+                metrics["stake_currency"],
+                config.get("backtest_breakdown", []),
+            )
+
+            HyperoptTools.try_export_params(config, strategy_name, val)
+
+        HyperoptTools.show_epoch_details(
+            val, total_epochs, print_json, no_header, header_str="Epoch details"
+        )
diff --git a/freqtrade/commands/list_commands.py b/freqtrade/commands/list_commands.py
new file mode 100644
index 000000000..6a1ca819d
--- /dev/null
+++ b/freqtrade/commands/list_commands.py
@@ -0,0 +1,370 @@
+import csv
+import logging
+import sys
+from typing import Any
+
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import ConfigurationError, OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_list_exchanges(args: dict[str, Any]) -> None:
+    """
+    Print available exchanges
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    from rich.table import Table
+    from rich.text import Text
+
+    from freqtrade.exchange import list_available_exchanges
+    from freqtrade.ft_types import ValidExchangesType
+    from freqtrade.loggers.rich_console import get_rich_console
+
+    available_exchanges: list[ValidExchangesType] = list_available_exchanges(
+        args["list_exchanges_all"]
+    )
+
+    if args["print_one_column"]:
+        print("\n".join([e["classname"] for e in available_exchanges]))
+    else:
+        if args["list_exchanges_all"]:
+            title = (
+                f"All exchanges supported by the ccxt library "
+                f"({len(available_exchanges)} exchanges):"
+            )
+        else:
+            available_exchanges = [e for e in available_exchanges if e["valid"] is not False]
+            title = f"Exchanges available for Freqtrade ({len(available_exchanges)} exchanges):"
+
+        table = Table(title=title)
+
+        table.add_column("Exchange Name")
+        table.add_column("Class Name")
+        table.add_column("Markets")
+        table.add_column("Reason")
+
+        for exchange in available_exchanges:
+            name = Text(exchange["name"])
+            if exchange["supported"]:
+                name.append(" (Supported)", style="italic")
+                name.stylize("green bold")
+            classname = Text(exchange["classname"])
+            if exchange["is_alias"]:
+                name.stylize("strike")
+                classname.stylize("strike")
+                classname.append(f" (use {exchange['alias_for']})", style="italic")
+
+            trade_modes = Text(
+                ", ".join(
+                    (f"{a.get('margin_mode', '')} {a['trading_mode']}").lstrip()
+                    for a in exchange["trade_modes"]
+                ),
+                style="",
+            )
+            if exchange["dex"]:
+                trade_modes = Text("DEX: ") + trade_modes
+                trade_modes.stylize("bold", 0, 3)
+
+            table.add_row(
+                name,
+                classname,
+                trade_modes,
+                exchange["comment"],
+                style=None if exchange["valid"] else "red",
+            )
+            # table.add_row(*[exchange[header] for header in headers])
+
+        console = get_rich_console()
+        console.print(table)
+
+
+def _print_objs_tabular(objs: list, print_colorized: bool) -> None:
+    from rich.table import Table
+    from rich.text import Text
+
+    from freqtrade.loggers.rich_console import get_rich_console
+
+    names = [s["name"] for s in objs]
+    objs_to_print: list[dict[str, Text | str]] = [
+        {
+            "name": Text(s["name"] if s["name"] else "--"),
+            "location": s["location_rel"],
+            "status": (
+                Text("LOAD FAILED", style="bold red")
+                if s["class"] is None
+                else Text("OK", style="bold green")
+                if names.count(s["name"]) == 1
+                else Text("DUPLICATE NAME", style="bold yellow")
+            ),
+        }
+        for s in objs
+    ]
+    for idx, s in enumerate(objs):
+        if "hyperoptable" in s:
+            objs_to_print[idx].update(
+                {
+                    "hyperoptable": "Yes" if s["hyperoptable"]["count"] > 0 else "No",
+                    "buy-Params": str(len(s["hyperoptable"].get("buy", []))),
+                    "sell-Params": str(len(s["hyperoptable"].get("sell", []))),
+                }
+            )
+    table = Table()
+
+    for header in objs_to_print[0].keys():
+        table.add_column(header.capitalize(), justify="right")
+
+    for row in objs_to_print:
+        table.add_row(*[row[header] for header in objs_to_print[0].keys()])
+
+    console = get_rich_console(color_system="auto" if print_colorized else None)
+    console.print(table)
+
+
+def start_list_strategies(args: dict[str, Any]) -> None:
+    """
+    Print files with Strategy custom classes available in the directory
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.resolvers import StrategyResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    strategy_objs = StrategyResolver.search_all_objects(
+        config, not args["print_one_column"], config.get("recursive_strategy_search", False)
+    )
+    # Sort alphabetically
+    strategy_objs = sorted(strategy_objs, key=lambda x: x["name"])
+    for obj in strategy_objs:
+        if obj["class"]:
+            obj["hyperoptable"] = obj["class"].detect_all_parameters()
+        else:
+            obj["hyperoptable"] = {"count": 0}
+
+    if args["print_one_column"]:
+        print("\n".join([s["name"] for s in strategy_objs]))
+    else:
+        _print_objs_tabular(strategy_objs, config.get("print_colorized", False))
+
+
+def start_list_freqAI_models(args: dict[str, Any]) -> None:
+    """
+    Print files with FreqAI models custom classes available in the directory
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    model_objs = FreqaiModelResolver.search_all_objects(config, not args["print_one_column"])
+    # Sort alphabetically
+    model_objs = sorted(model_objs, key=lambda x: x["name"])
+    if args["print_one_column"]:
+        print("\n".join([s["name"] for s in model_objs]))
+    else:
+        _print_objs_tabular(model_objs, config.get("print_colorized", False))
+
+
+def start_list_hyperopt_loss_functions(args: dict[str, Any]) -> None:
+    """
+    Print files with FreqAI models custom classes available in the directory
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    model_objs = HyperOptLossResolver.search_all_objects(config, not args["print_one_column"])
+    # Sort alphabetically
+    model_objs = sorted(model_objs, key=lambda x: x["name"])
+    if args["print_one_column"]:
+        print("\n".join([s["name"] for s in model_objs]))
+    else:
+        _print_objs_tabular(model_objs, config.get("print_colorized", False))
+
+
+def start_list_timeframes(args: dict[str, Any]) -> None:
+    """
+    Print timeframes available on Exchange
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.resolvers import ExchangeResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+    # Do not use timeframe set in the config
+    config["timeframe"] = None
+
+    # Init exchange
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
+
+    if args["print_one_column"]:
+        print("\n".join(exchange.timeframes))
+    else:
+        print(
+            f"Timeframes available for the exchange `{exchange.name}`: "
+            f"{', '.join(exchange.timeframes)}"
+        )
+
+
+def start_list_markets(args: dict[str, Any], pairs_only: bool = False) -> None:
+    """
+    Print pairs/markets on the exchange
+    :param args: Cli args from Arguments()
+    :param pairs_only: if True print only pairs, otherwise print all instruments (markets)
+    :return: None
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.exchange import market_is_active
+    from freqtrade.misc import plural, safe_value_fallback
+    from freqtrade.resolvers import ExchangeResolver
+    from freqtrade.util import print_rich_table
+
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    # Init exchange
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
+
+    # By default only active pairs/markets are to be shown
+    active_only = not args.get("list_pairs_all", False)
+
+    base_currencies = args.get("base_currencies", [])
+    quote_currencies = args.get("quote_currencies", [])
+
+    try:
+        pairs = exchange.get_markets(
+            base_currencies=base_currencies,
+            quote_currencies=quote_currencies,
+            tradable_only=pairs_only,
+            active_only=active_only,
+        )
+        # Sort the pairs/markets by symbol
+        pairs = dict(sorted(pairs.items()))
+    except Exception as e:
+        raise OperationalException(f"Cannot get markets. Reason: {e}") from e
+
+    tickers = exchange.get_tickers()
+
+    summary_str = (
+        (f"Exchange {exchange.name} has {len(pairs)} ")
+        + ("active " if active_only else "")
+        + (plural(len(pairs), "pair" if pairs_only else "market"))
+        + (
+            f" with {', '.join(base_currencies)} as base "
+            f"{plural(len(base_currencies), 'currency', 'currencies')}"
+            if base_currencies
+            else ""
+        )
+        + (" and" if base_currencies and quote_currencies else "")
+        + (
+            f" with {', '.join(quote_currencies)} as quote "
+            f"{plural(len(quote_currencies), 'currency', 'currencies')}"
+            if quote_currencies
+            else ""
+        )
+    )
+
+    headers = [
+        "Id",
+        "Symbol",
+        "Base",
+        "Quote",
+        "Active",
+        "Spot",
+        "Margin",
+        "Future",
+        "Leverage",
+        "Min Stake",
+    ]
+
+    tabular_data = [
+        {
+            "Id": v["id"],
+            "Symbol": v["symbol"],
+            "Base": v["base"],
+            "Quote": v["quote"],
+            "Active": market_is_active(v),
+            "Spot": "Spot" if exchange.market_is_spot(v) else "",
+            "Margin": "Margin" if exchange.market_is_margin(v) else "",
+            "Future": "Future" if exchange.market_is_future(v) else "",
+            "Leverage": exchange.get_max_leverage(v["symbol"], 20),
+            "Min Stake": round(
+                exchange.get_min_pair_stake_amount(
+                    v["symbol"],
+                    safe_value_fallback(tickers.get(v["symbol"], {}), "last", "ask", 0.0),
+                    0.0,
+                )
+                or 0.0,
+                8,
+            ),
+        }
+        for _, v in pairs.items()
+    ]
+
+    if (
+        args.get("print_one_column", False)
+        or args.get("list_pairs_print_json", False)
+        or args.get("print_csv", False)
+    ):
+        # Print summary string in the log in case of machine-readable
+        # regular formats.
+        logger.info(f"{summary_str}.")
+    else:
+        # Print empty string separating leading logs and output in case of
+        # human-readable formats.
+        print()
+
+    if pairs:
+        if args.get("print_list", False):
+            # print data as a list, with human-readable summary
+            print(f"{summary_str}: {', '.join(pairs.keys())}.")
+        elif args.get("print_one_column", False):
+            print("\n".join(pairs.keys()))
+        elif args.get("list_pairs_print_json", False):
+            import rapidjson
+
+            print(rapidjson.dumps(list(pairs.keys()), default=str))
+        elif args.get("print_csv", False):
+            writer = csv.DictWriter(sys.stdout, fieldnames=headers)
+            writer.writeheader()
+            writer.writerows(tabular_data)
+        else:
+            print_rich_table(tabular_data, headers, summary_str)
+    elif not (
+        args.get("print_one_column", False)
+        or args.get("list_pairs_print_json", False)
+        or args.get("print_csv", False)
+    ):
+        print(f"{summary_str}.")
+
+
+def start_show_trades(args: dict[str, Any]) -> None:
+    """
+    Show trades
+    """
+    import json
+
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.misc import parse_db_uri_for_logging
+    from freqtrade.persistence import Trade, init_db
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if "db_url" not in config:
+        raise ConfigurationError("--db-url is required for this command.")
+
+    logger.info(f'Using DB: "{parse_db_uri_for_logging(config["db_url"])}"')
+    init_db(config["db_url"])
+    tfilter = []
+
+    if config.get("trade_ids"):
+        tfilter.append(Trade.id.in_(config["trade_ids"]))
+
+    trades = Trade.get_trades(tfilter).all()
+    logger.info(f"Printing {len(trades)} Trades: ")
+    if config.get("print_json", False):
+        print(json.dumps([trade.to_json() for trade in trades], indent=4))
+    else:
+        for trade in trades:
+            print(trade)
diff --git a/freqtrade/commands/optimize_commands.py b/freqtrade/commands/optimize_commands.py
new file mode 100644
index 000000000..9695a313b
--- /dev/null
+++ b/freqtrade/commands/optimize_commands.py
@@ -0,0 +1,166 @@
+import logging
+from typing import Any
+
+from freqtrade import constants
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import ConfigurationError, OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def setup_optimize_configuration(args: dict[str, Any], method: RunMode) -> dict[str, Any]:
+    """
+    Prepare the configuration for the Hyperopt module
+    :param args: Cli args from Arguments()
+    :param method: Bot running mode
+    :return: Configuration
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.util import fmt_coin, get_dry_run_wallet
+
+    config = setup_utils_configuration(args, method)
+
+    no_unlimited_runmodes = {
+        RunMode.BACKTEST: "backtesting",
+        RunMode.HYPEROPT: "hyperoptimization",
+    }
+    if method in no_unlimited_runmodes.keys():
+        wallet_size = get_dry_run_wallet(config) * config["tradable_balance_ratio"]
+        # tradable_balance_ratio
+        if (
+            config["stake_amount"] != constants.UNLIMITED_STAKE_AMOUNT
+            and config["stake_amount"] > wallet_size
+        ):
+            wallet = fmt_coin(wallet_size, config["stake_currency"])
+            stake = fmt_coin(config["stake_amount"], config["stake_currency"])
+            raise ConfigurationError(
+                f"Starting balance ({wallet}) is smaller than stake_amount {stake}. "
+                f"Wallet is calculated as `dry_run_wallet * tradable_balance_ratio`."
+            )
+
+    return config
+
+
+def start_backtesting(args: dict[str, Any]) -> None:
+    """
+    Start Backtesting script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    # Import here to avoid loading backtesting module when it's not used
+    from freqtrade.optimize.backtesting import Backtesting
+
+    # Initialize configuration
+    config = setup_optimize_configuration(args, RunMode.BACKTEST)
+
+    logger.info("Starting freqtrade in Backtesting mode")
+
+    # Initialize backtesting object
+    backtesting = Backtesting(config)
+    backtesting.start()
+
+
+def start_backtesting_show(args: dict[str, Any]) -> None:
+    """
+    Show previous backtest result
+    """
+    from freqtrade.configuration import setup_utils_configuration
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    from freqtrade.data.btanalysis import load_backtest_stats
+    from freqtrade.optimize.optimize_reports import show_backtest_results, show_sorted_pairlist
+
+    results = load_backtest_stats(config["exportfilename"])
+
+    show_backtest_results(config, results)
+    show_sorted_pairlist(config, results)
+
+
+def start_hyperopt(args: dict[str, Any]) -> None:
+    """
+    Start hyperopt script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    # Import here to avoid loading hyperopt module when it's not used
+    try:
+        from filelock import FileLock, Timeout
+
+        from freqtrade.optimize.hyperopt import Hyperopt
+    except ImportError as e:
+        raise OperationalException(
+            f"{e}. Please ensure that the hyperopt dependencies are installed."
+        ) from e
+    # Initialize configuration
+    config = setup_optimize_configuration(args, RunMode.HYPEROPT)
+
+    logger.info("Starting freqtrade in Hyperopt mode")
+
+    lock = FileLock(Hyperopt.get_lock_filename(config))
+
+    try:
+        with lock.acquire(timeout=1):
+            # Remove noisy log messages
+            logging.getLogger("hyperopt.tpe").setLevel(logging.WARNING)
+            logging.getLogger("filelock").setLevel(logging.WARNING)
+
+            # Initialize backtesting object
+            hyperopt = Hyperopt(config)
+            hyperopt.start()
+
+    except Timeout:
+        logger.info("Another running instance of freqtrade Hyperopt detected.")
+        logger.info(
+            "Simultaneous execution of multiple Hyperopt commands is not supported. "
+            "Hyperopt module is resource hungry. Please run your Hyperopt sequentially "
+            "or on separate machines."
+        )
+        logger.info("Quitting now.")
+        # TODO: return False here in order to help freqtrade to exit
+        # with non-zero exit code...
+        # Same in Edge and Backtesting start() functions.
+
+
+def start_edge(args: dict[str, Any]) -> None:
+    """
+    Start Edge script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    from freqtrade.optimize.edge_cli import EdgeCli
+
+    # Initialize configuration
+    config = setup_optimize_configuration(args, RunMode.EDGE)
+    logger.info("Starting freqtrade in Edge mode")
+
+    # Initialize Edge object
+    edge_cli = EdgeCli(config)
+    edge_cli.start()
+
+
+def start_lookahead_analysis(args: dict[str, Any]) -> None:
+    """
+    Start the backtest bias tester script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.optimize.analysis.lookahead_helpers import LookaheadAnalysisSubFunctions
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+    LookaheadAnalysisSubFunctions.start(config)
+
+
+def start_recursive_analysis(args: dict[str, Any]) -> None:
+    """
+    Start the backtest recursive tester script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.optimize.analysis.recursive_helpers import RecursiveAnalysisSubFunctions
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+    RecursiveAnalysisSubFunctions.start(config)
diff --git a/freqtrade/commands/pairlist_commands.py b/freqtrade/commands/pairlist_commands.py
new file mode 100644
index 000000000..4330f501c
--- /dev/null
+++ b/freqtrade/commands/pairlist_commands.py
@@ -0,0 +1,45 @@
+import logging
+from typing import Any
+
+import rapidjson
+
+from freqtrade.enums import RunMode
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_test_pairlist(args: dict[str, Any]) -> None:
+    """
+    Test Pairlist configuration
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.persistence import FtNoDBContext
+    from freqtrade.plugins.pairlistmanager import PairListManager
+    from freqtrade.resolvers import ExchangeResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
+
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
+
+    quote_currencies = args.get("quote_currencies")
+    if not quote_currencies:
+        quote_currencies = [config.get("stake_currency")]
+    results = {}
+    with FtNoDBContext():
+        for curr in quote_currencies:
+            config["stake_currency"] = curr
+            pairlists = PairListManager(exchange, config)
+            pairlists.refresh_pairlist()
+            results[curr] = pairlists.whitelist
+
+    for curr, pairlist in results.items():
+        if not args.get("print_one_column", False) and not args.get("list_pairs_print_json", False):
+            print(f"Pairs for {curr}: ")
+
+        if args.get("print_one_column", False):
+            print("\n".join(pairlist))
+        elif args.get("list_pairs_print_json", False):
+            print(rapidjson.dumps(list(pairlist), default=str))
+        else:
+            print(pairlist)
diff --git a/freqtrade/commands/plot_commands.py b/freqtrade/commands/plot_commands.py
new file mode 100644
index 000000000..065559ab9
--- /dev/null
+++ b/freqtrade/commands/plot_commands.py
@@ -0,0 +1,40 @@
+from typing import Any
+
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import ConfigurationError
+
+
+def validate_plot_args(args: dict[str, Any]) -> None:
+    if not args.get("datadir") and not args.get("config"):
+        raise ConfigurationError(
+            "You need to specify either `--datadir` or `--config` "
+            "for plot-profit and plot-dataframe."
+        )
+
+
+def start_plot_dataframe(args: dict[str, Any]) -> None:
+    """
+    Entrypoint for dataframe plotting
+    """
+    # Import here to avoid errors if plot-dependencies are not installed.
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.plot.plotting import load_and_plot_trades
+
+    validate_plot_args(args)
+    config = setup_utils_configuration(args, RunMode.PLOT)
+
+    load_and_plot_trades(config)
+
+
+def start_plot_profit(args: dict[str, Any]) -> None:
+    """
+    Entrypoint for plot_profit
+    """
+    # Import here to avoid errors if plot-dependencies are not installed.
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.plot.plotting import plot_profit
+
+    validate_plot_args(args)
+    config = setup_utils_configuration(args, RunMode.PLOT)
+
+    plot_profit(config)
diff --git a/freqtrade/commands/strategy_utils_commands.py b/freqtrade/commands/strategy_utils_commands.py
new file mode 100644
index 000000000..97d606f0c
--- /dev/null
+++ b/freqtrade/commands/strategy_utils_commands.py
@@ -0,0 +1,54 @@
+import logging
+import time
+from pathlib import Path
+from typing import Any
+
+from freqtrade.enums import RunMode
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_strategy_update(args: dict[str, Any]) -> None:
+    """
+    Start the strategy updating script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.resolvers import StrategyResolver
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    strategy_objs = StrategyResolver.search_all_objects(
+        config, enum_failed=False, recursive=config.get("recursive_strategy_search", False)
+    )
+
+    filtered_strategy_objs = []
+    if args["strategy_list"]:
+        filtered_strategy_objs = [
+            strategy_obj
+            for strategy_obj in strategy_objs
+            if strategy_obj["name"] in args["strategy_list"]
+        ]
+
+    else:
+        # Use all available entries.
+        filtered_strategy_objs = strategy_objs
+
+    processed_locations = set()
+    for strategy_obj in filtered_strategy_objs:
+        if strategy_obj["location"] not in processed_locations:
+            processed_locations.add(strategy_obj["location"])
+            start_conversion(strategy_obj, config)
+
+
+def start_conversion(strategy_obj, config):
+    from freqtrade.strategy.strategyupdater import StrategyUpdater
+
+    print(f"Conversion of {Path(strategy_obj['location']).name} started.")
+    instance_strategy_updater = StrategyUpdater()
+    start = time.perf_counter()
+    instance_strategy_updater.start(config, strategy_obj)
+    elapsed = time.perf_counter() - start
+    print(f"Conversion of {Path(strategy_obj['location']).name} took {elapsed:.1f} seconds.")
diff --git a/freqtrade/commands/trade_commands.py b/freqtrade/commands/trade_commands.py
new file mode 100644
index 000000000..59e38c98b
--- /dev/null
+++ b/freqtrade/commands/trade_commands.py
@@ -0,0 +1,30 @@
+import logging
+import signal
+from typing import Any
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_trading(args: dict[str, Any]) -> int:
+    """
+    Main entry point for trading mode
+    """
+    # Import here to avoid loading worker module when it's not used
+    from freqtrade.worker import Worker
+
+    def term_handler(signum, frame):
+        # Raise KeyboardInterrupt - so we can handle it in the same way as Ctrl-C
+        raise KeyboardInterrupt()
+
+    # Create and run worker
+    worker = None
+    try:
+        signal.signal(signal.SIGTERM, term_handler)
+        worker = Worker(args)
+        worker.run()
+    finally:
+        if worker:
+            logger.info("worker found ... calling exit")
+            worker.exit()
+    return 0
diff --git a/freqtrade/commands/webserver_commands.py b/freqtrade/commands/webserver_commands.py
new file mode 100644
index 000000000..b60f01773
--- /dev/null
+++ b/freqtrade/commands/webserver_commands.py
@@ -0,0 +1,16 @@
+from typing import Any
+
+from freqtrade.enums import RunMode
+
+
+def start_webserver(args: dict[str, Any]) -> None:
+    """
+    Main entry point for webserver mode
+    """
+    from freqtrade.configuration import setup_utils_configuration
+    from freqtrade.rpc.api_server import ApiServer
+
+    # Initialize configuration
+
+    config = setup_utils_configuration(args, RunMode.WEBSERVER)
+    ApiServer(config, standalone=True)
diff --git a/freqtrade/config_schema/__init__.py b/freqtrade/config_schema/__init__.py
new file mode 100644
index 000000000..0a5c19c1f
--- /dev/null
+++ b/freqtrade/config_schema/__init__.py
@@ -0,0 +1,4 @@
+from freqtrade.config_schema.config_schema import CONF_SCHEMA
+
+
+__all__ = ["CONF_SCHEMA"]
diff --git a/freqtrade/config_schema/config_schema.py b/freqtrade/config_schema/config_schema.py
new file mode 100644
index 000000000..8b88e655a
--- /dev/null
+++ b/freqtrade/config_schema/config_schema.py
@@ -0,0 +1,1362 @@
+# Required json-schema for user specified config
+
+from freqtrade.constants import (
+    AVAILABLE_DATAHANDLERS,
+    AVAILABLE_PAIRLISTS,
+    BACKTEST_BREAKDOWNS,
+    DRY_RUN_WALLET,
+    EXPORT_OPTIONS,
+    MARGIN_MODES,
+    ORDERTIF_POSSIBILITIES,
+    ORDERTYPE_POSSIBILITIES,
+    PRICING_SIDES,
+    REQUIRED_ORDERTIF,
+    STOPLOSS_PRICE_TYPES,
+    SUPPORTED_FIAT,
+    TELEGRAM_SETTING_OPTIONS,
+    TIMEOUT_UNITS,
+    TRADING_MODES,
+    UNLIMITED_STAKE_AMOUNT,
+    WEBHOOK_FORMAT_OPTIONS,
+)
+from freqtrade.enums import RPCMessageType
+
+
+__MESSAGE_TYPE_DICT: dict[str, dict[str, str]] = {x: {"type": "object"} for x in RPCMessageType}
+
+__IN_STRATEGY = "\nUsually specified in the strategy and missing in the configuration."
+
+CONF_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "max_open_trades": {
+            "description": "Maximum number of open trades. -1 for unlimited.",
+            "type": ["integer", "number"],
+            "minimum": -1,
+        },
+        "timeframe": {
+            "description": (
+                f"The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). {__IN_STRATEGY}"
+            ),
+            "type": "string",
+        },
+        "proxy_coin": {
+            "description": "Proxy coin - must be used for specific futures modes (e.g. BNFCR)",
+            "type": "string",
+        },
+        "stake_currency": {
+            "description": "Currency used for staking.",
+            "type": "string",
+        },
+        "stake_amount": {
+            "description": "Amount to stake per trade.",
+            "type": ["number", "string"],
+            "minimum": 0.0001,
+            "pattern": UNLIMITED_STAKE_AMOUNT,
+        },
+        "tradable_balance_ratio": {
+            "description": "Ratio of balance that is tradable.",
+            "type": "number",
+            "minimum": 0.0,
+            "maximum": 1,
+            "default": 0.99,
+        },
+        "available_capital": {
+            "description": "Total capital available for trading.",
+            "type": "number",
+            "minimum": 0,
+        },
+        "amend_last_stake_amount": {
+            "description": "Whether to amend the last stake amount.",
+            "type": "boolean",
+            "default": False,
+        },
+        "last_stake_amount_min_ratio": {
+            "description": "Minimum ratio for the last stake amount.",
+            "type": "number",
+            "minimum": 0.0,
+            "maximum": 1.0,
+            "default": 0.5,
+        },
+        "fiat_display_currency": {
+            "description": "Fiat currency for display purposes.",
+            "type": "string",
+            "enum": SUPPORTED_FIAT,
+        },
+        "dry_run": {
+            "description": "Enable or disable dry run mode.",
+            "type": "boolean",
+        },
+        "dry_run_wallet": {
+            "description": "Initial wallet balance for dry run mode.",
+            "type": ["number", "object"],
+            "default": DRY_RUN_WALLET,
+            "patternProperties": {r"^[a-zA-Z0-9]+$": {"type": "number"}},
+            "additionalProperties": False,
+        },
+        "cancel_open_orders_on_exit": {
+            "description": "Cancel open orders when exiting.",
+            "type": "boolean",
+            "default": False,
+        },
+        "process_only_new_candles": {
+            "description": "Process only new candles.",
+            "type": "boolean",
+        },
+        "minimal_roi": {
+            "description": f"Minimum return on investment. {__IN_STRATEGY}",
+            "type": "object",
+            "patternProperties": {"^[0-9.]+$": {"type": "number"}},
+        },
+        "amount_reserve_percent": {
+            "description": "Percentage of amount to reserve.",
+            "type": "number",
+            "minimum": 0.0,
+            "maximum": 0.5,
+        },
+        "stoploss": {
+            "description": f"Value (as ratio) to use as Stoploss value. {__IN_STRATEGY}",
+            "type": "number",
+            "maximum": 0,
+            "exclusiveMaximum": True,
+        },
+        "trailing_stop": {
+            "description": f"Enable or disable trailing stop. {__IN_STRATEGY}",
+            "type": "boolean",
+        },
+        "trailing_stop_positive": {
+            "description": f"Positive offset for trailing stop. {__IN_STRATEGY}",
+            "type": "number",
+            "minimum": 0,
+            "maximum": 1,
+        },
+        "trailing_stop_positive_offset": {
+            "description": f"Offset for trailing stop to activate. {__IN_STRATEGY}",
+            "type": "number",
+            "minimum": 0,
+            "maximum": 1,
+        },
+        "trailing_only_offset_is_reached": {
+            "description": f"Use trailing stop only when offset is reached. {__IN_STRATEGY}",
+            "type": "boolean",
+        },
+        "use_exit_signal": {
+            "description": f"Use exit signal for trades. {__IN_STRATEGY}",
+            "type": "boolean",
+        },
+        "exit_profit_only": {
+            "description": (
+                "Exit only when in profit. Exit signals are ignored as "
+                f"long as profit is < exit_profit_offset. {__IN_STRATEGY}"
+            ),
+            "type": "boolean",
+        },
+        "exit_profit_offset": {
+            "description": f"Offset for profit exit. {__IN_STRATEGY}",
+            "type": "number",
+        },
+        "fee": {
+            "description": "Trading fee percentage. Can help to simulate slippage in backtesting",
+            "type": "number",
+            "minimum": 0,
+            "maximum": 0.1,
+        },
+        "ignore_roi_if_entry_signal": {
+            "description": f"Ignore ROI if entry signal is present. {__IN_STRATEGY}",
+            "type": "boolean",
+        },
+        "ignore_buying_expired_candle_after": {
+            "description": f"Ignore buying after candle expiration time. {__IN_STRATEGY}",
+            "type": "number",
+        },
+        "trading_mode": {
+            "description": "Mode of trading (e.g., spot, margin).",
+            "type": "string",
+            "enum": TRADING_MODES,
+        },
+        "margin_mode": {
+            "description": "Margin mode for trading.",
+            "type": "string",
+            "enum": MARGIN_MODES,
+        },
+        "reduce_df_footprint": {
+            "description": "Reduce DataFrame footprint by casting columns to float32/int32.",
+            "type": "boolean",
+            "default": False,
+        },
+        # Lookahead analysis section
+        "minimum_trade_amount": {
+            "description": "Minimum amount for a trade - only used for lookahead-analysis",
+            "type": "number",
+            "default": 10,
+        },
+        "targeted_trade_amount": {
+            "description": "Targeted trade amount for lookahead analysis.",
+            "type": "number",
+            "default": 20,
+        },
+        "lookahead_analysis_exportfilename": {
+            "description": "csv Filename for lookahead analysis export.",
+            "type": "string",
+        },
+        "startup_candle": {
+            "description": "Startup candle configuration.",
+            "type": "array",
+            "uniqueItems": True,
+            "default": [199, 399, 499, 999, 1999],
+        },
+        "liquidation_buffer": {
+            "description": "Buffer ratio for liquidation.",
+            "type": "number",
+            "minimum": 0.0,
+            "maximum": 0.99,
+        },
+        "backtest_breakdown": {
+            "description": "Breakdown configuration for backtesting.",
+            "type": "array",
+            "items": {"type": "string", "enum": BACKTEST_BREAKDOWNS},
+        },
+        "bot_name": {
+            "description": "Name of the trading bot. Passed via API to a client.",
+            "type": "string",
+        },
+        "unfilledtimeout": {
+            "description": f"Timeout configuration for unfilled orders. {__IN_STRATEGY}",
+            "type": "object",
+            "properties": {
+                "entry": {
+                    "description": "Timeout for entry orders in unit.",
+                    "type": "number",
+                    "minimum": 1,
+                },
+                "exit": {
+                    "description": "Timeout for exit orders in unit.",
+                    "type": "number",
+                    "minimum": 1,
+                },
+                "exit_timeout_count": {
+                    "description": "Number of times to retry exit orders before giving up.",
+                    "type": "number",
+                    "minimum": 0,
+                    "default": 0,
+                },
+                "unit": {
+                    "description": "Unit of time for the timeout (e.g., seconds, minutes).",
+                    "type": "string",
+                    "enum": TIMEOUT_UNITS,
+                    "default": "minutes",
+                },
+            },
+        },
+        "entry_pricing": {
+            "description": "Configuration for entry pricing.",
+            "type": "object",
+            "properties": {
+                "price_last_balance": {
+                    "description": "Balance ratio for the last price.",
+                    "type": "number",
+                    "minimum": 0,
+                    "maximum": 1,
+                    "exclusiveMaximum": False,
+                },
+                "price_side": {
+                    "description": "Side of the price to use (e.g., bid, ask, same).",
+                    "type": "string",
+                    "enum": PRICING_SIDES,
+                    "default": "same",
+                },
+                "use_order_book": {
+                    "description": "Whether to use the order book for pricing.",
+                    "type": "boolean",
+                },
+                "order_book_top": {
+                    "description": "Top N levels of the order book to consider.",
+                    "type": "integer",
+                    "minimum": 1,
+                    "maximum": 50,
+                },
+                "check_depth_of_market": {
+                    "description": "Configuration for checking the depth of the market.",
+                    "type": "object",
+                    "properties": {
+                        "enabled": {
+                            "description": "Enable or disable depth of market check.",
+                            "type": "boolean",
+                        },
+                        "bids_to_ask_delta": {
+                            "description": "Delta between bids and asks to consider.",
+                            "type": "number",
+                            "minimum": 0,
+                        },
+                    },
+                },
+            },
+            "required": ["price_side"],
+        },
+        "exit_pricing": {
+            "description": "Configuration for exit pricing.",
+            "type": "object",
+            "properties": {
+                "price_side": {
+                    "description": "Side of the price to use (e.g., bid, ask, same).",
+                    "type": "string",
+                    "enum": PRICING_SIDES,
+                    "default": "same",
+                },
+                "price_last_balance": {
+                    "description": "Balance ratio for the last price.",
+                    "type": "number",
+                    "minimum": 0,
+                    "maximum": 1,
+                    "exclusiveMaximum": False,
+                },
+                "use_order_book": {
+                    "description": "Whether to use the order book for pricing.",
+                    "type": "boolean",
+                },
+                "order_book_top": {
+                    "description": "Top N levels of the order book to consider.",
+                    "type": "integer",
+                    "minimum": 1,
+                    "maximum": 50,
+                },
+            },
+            "required": ["price_side"],
+        },
+        "custom_price_max_distance_ratio": {
+            "description": "Maximum distance ratio between current and custom entry or exit price.",
+            "type": "number",
+            "minimum": 0.0,
+            "maximum": 1,
+            "default": 0.02,
+        },
+        "order_types": {
+            "description": f"Configuration of order types. {__IN_STRATEGY}",
+            "type": "object",
+            "properties": {
+                "entry": {
+                    "description": "Order type for entry (e.g., limit, market).",
+                    "type": "string",
+                    "enum": ORDERTYPE_POSSIBILITIES,
+                },
+                "exit": {
+                    "description": "Order type for exit (e.g., limit, market).",
+                    "type": "string",
+                    "enum": ORDERTYPE_POSSIBILITIES,
+                },
+                "force_exit": {
+                    "description": "Order type for forced exit (e.g., limit, market).",
+                    "type": "string",
+                    "enum": ORDERTYPE_POSSIBILITIES,
+                },
+                "force_entry": {
+                    "description": "Order type for forced entry (e.g., limit, market).",
+                    "type": "string",
+                    "enum": ORDERTYPE_POSSIBILITIES,
+                },
+                "emergency_exit": {
+                    "description": "Order type for emergency exit (e.g., limit, market).",
+                    "type": "string",
+                    "enum": ORDERTYPE_POSSIBILITIES,
+                    "default": "market",
+                },
+                "stoploss": {
+                    "description": "Order type for stop loss (e.g., limit, market).",
+                    "type": "string",
+                    "enum": ORDERTYPE_POSSIBILITIES,
+                },
+                "stoploss_on_exchange": {
+                    "description": "Whether to place stop loss on the exchange.",
+                    "type": "boolean",
+                },
+                "stoploss_price_type": {
+                    "description": "Price type for stop loss (e.g., last, mark, index).",
+                    "type": "string",
+                    "enum": STOPLOSS_PRICE_TYPES,
+                },
+                "stoploss_on_exchange_interval": {
+                    "description": "Interval for stop loss on exchange in seconds.",
+                    "type": "number",
+                },
+                "stoploss_on_exchange_limit_ratio": {
+                    "description": "Limit ratio for stop loss on exchange.",
+                    "type": "number",
+                    "minimum": 0.0,
+                    "maximum": 1.0,
+                },
+            },
+            "required": ["entry", "exit", "stoploss", "stoploss_on_exchange"],
+        },
+        "order_time_in_force": {
+            "description": f"Time in force configuration for orders. {__IN_STRATEGY}",
+            "type": "object",
+            "properties": {
+                "entry": {
+                    "description": "Time in force for entry orders.",
+                    "type": "string",
+                    "enum": ORDERTIF_POSSIBILITIES,
+                },
+                "exit": {
+                    "description": "Time in force for exit orders.",
+                    "type": "string",
+                    "enum": ORDERTIF_POSSIBILITIES,
+                },
+            },
+            "required": REQUIRED_ORDERTIF,
+        },
+        "coingecko": {
+            "description": "Configuration for CoinGecko API.",
+            "type": "object",
+            "properties": {
+                "is_demo": {
+                    "description": "Whether to use CoinGecko in demo mode.",
+                    "type": "boolean",
+                    "default": True,
+                },
+                "api_key": {"description": "API key for accessing CoinGecko.", "type": "string"},
+            },
+            "required": ["is_demo", "api_key"],
+        },
+        "exchange": {
+            "description": "Exchange configuration.",
+            "$ref": "#/definitions/exchange",
+        },
+        "edge": {
+            "description": "Edge configuration.",
+            "$ref": "#/definitions/edge",
+        },
+        "log_config": {
+            "description": "Logging configuration.",
+            "$ref": "#/definitions/logging",
+        },
+        "freqai": {
+            "description": "FreqAI configuration.",
+            "$ref": "#/definitions/freqai",
+        },
+        "external_message_consumer": {
+            "description": "Configuration for external message consumer.",
+            "$ref": "#/definitions/external_message_consumer",
+        },
+        "experimental": {
+            "description": "Experimental configuration.",
+            "type": "object",
+            "properties": {"block_bad_exchanges": {"type": "boolean"}},
+        },
+        "pairlists": {
+            "description": "Configuration for pairlists.",
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "method": {
+                        "description": "Method used for generating the pairlist.",
+                        "type": "string",
+                        "enum": AVAILABLE_PAIRLISTS,
+                    },
+                },
+                "required": ["method"],
+            },
+        },
+        # RPC section
+        "telegram": {
+            "description": "Telegram settings.",
+            "type": "object",
+            "properties": {
+                "enabled": {
+                    "description": "Enable Telegram notifications.",
+                    "type": "boolean",
+                },
+                "token": {"description": "Telegram bot token.", "type": "string"},
+                "chat_id": {
+                    "description": "Telegram chat or group ID",
+                    "type": "string",
+                },
+                "topic_id": {
+                    "description": "Telegram topic ID - only applicable for group chats",
+                    "type": "string",
+                },
+                "authorized_users": {
+                    "description": "Authorized users for the bot.",
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "uniqueItems": True,
+                },
+                "allow_custom_messages": {
+                    "description": "Allow sending custom messages from the Strategy.",
+                    "type": "boolean",
+                    "default": True,
+                },
+                "balance_dust_level": {
+                    "description": "Minimum balance level to consider as dust.",
+                    "type": "number",
+                    "minimum": 0.0,
+                },
+                "notification_settings": {
+                    "description": "Settings for different types of notifications.",
+                    "type": "object",
+                    "default": {},
+                    "properties": {
+                        "status": {
+                            "description": "Telegram setting for status updates.",
+                            "type": "string",
+                            "enum": TELEGRAM_SETTING_OPTIONS,
+                        },
+                        "warning": {
+                            "description": "Telegram setting for warnings.",
+                            "type": "string",
+                            "enum": TELEGRAM_SETTING_OPTIONS,
+                        },
+                        "startup": {
+                            "description": "Telegram setting for startup messages.",
+                            "type": "string",
+                            "enum": TELEGRAM_SETTING_OPTIONS,
+                        },
+                        "entry": {
+                            "description": "Telegram setting for entry signals.",
+                            "type": "string",
+                            "enum": TELEGRAM_SETTING_OPTIONS,
+                        },
+                        "entry_fill": {
+                            "description": "Telegram setting for entry fill signals.",
+                            "type": "string",
+                            "enum": TELEGRAM_SETTING_OPTIONS,
+                            "default": "off",
+                        },
+                        "entry_cancel": {
+                            "description": "Telegram setting for entry cancel signals.",
+                            "type": "string",
+                            "enum": TELEGRAM_SETTING_OPTIONS,
+                        },
+                        "exit": {
+                            "description": "Telegram setting for exit signals.",
+                            "type": ["string", "object"],
+                            "additionalProperties": {
+                                "type": "string",
+                                "enum": TELEGRAM_SETTING_OPTIONS,
+                            },
+                        },
+                        "exit_fill": {
+                            "description": "Telegram setting for exit fill signals.",
+                            "type": ["string", "object"],
+                            "additionalProperties": {
+                                "type": "string",
+                                "enum": TELEGRAM_SETTING_OPTIONS,
+                            },
+                            "default": "on",
+                        },
+                        "exit_cancel": {
+                            "description": "Telegram setting for exit cancel signals.",
+                            "type": "string",
+                            "enum": TELEGRAM_SETTING_OPTIONS,
+                        },
+                        "protection_trigger": {
+                            "description": "Telegram setting for protection triggers.",
+                            "type": "string",
+                            "enum": TELEGRAM_SETTING_OPTIONS,
+                            "default": "on",
+                        },
+                        "protection_trigger_global": {
+                            "description": "Telegram setting for global protection triggers.",
+                            "type": "string",
+                            "enum": TELEGRAM_SETTING_OPTIONS,
+                            "default": "on",
+                        },
+                    },
+                },
+                "reload": {
+                    "description": "Add Reload button to certain messages.",
+                    "type": "boolean",
+                },
+            },
+            "required": ["enabled", "token", "chat_id"],
+        },
+        "webhook": {
+            "description": "Webhook settings.",
+            "type": "object",
+            "properties": {
+                "enabled": {"type": "boolean"},
+                "url": {"type": "string"},
+                "format": {"type": "string", "enum": WEBHOOK_FORMAT_OPTIONS, "default": "form"},
+                "retries": {"type": "integer", "minimum": 0},
+                "retry_delay": {"type": "number", "minimum": 0},
+                **__MESSAGE_TYPE_DICT,
+            },
+        },
+        "discord": {
+            "description": "Discord settings.",
+            "type": "object",
+            "properties": {
+                "enabled": {"type": "boolean"},
+                "webhook_url": {"type": "string"},
+                "exit_fill": {
+                    "type": "array",
+                    "items": {"type": "object"},
+                    "default": [
+                        {"Trade ID": "{trade_id}"},
+                        {"Exchange": "{exchange}"},
+                        {"Pair": "{pair}"},
+                        {"Direction": "{direction}"},
+                        {"Open rate": "{open_rate}"},
+                        {"Close rate": "{close_rate}"},
+                        {"Amount": "{amount}"},
+                        {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
+                        {"Close date": "{close_date:%Y-%m-%d %H:%M:%S}"},
+                        {"Profit": "{profit_amount} {stake_currency}"},
+                        {"Profitability": "{profit_ratio:.2%}"},
+                        {"Enter tag": "{enter_tag}"},
+                        {"Exit Reason": "{exit_reason}"},
+                        {"Strategy": "{strategy}"},
+                        {"Timeframe": "{timeframe}"},
+                    ],
+                },
+                "entry_fill": {
+                    "type": "array",
+                    "items": {"type": "object"},
+                    "default": [
+                        {"Trade ID": "{trade_id}"},
+                        {"Exchange": "{exchange}"},
+                        {"Pair": "{pair}"},
+                        {"Direction": "{direction}"},
+                        {"Open rate": "{open_rate}"},
+                        {"Amount": "{amount}"},
+                        {"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
+                        {"Enter tag": "{enter_tag}"},
+                        {"Strategy": "{strategy} {timeframe}"},
+                    ],
+                },
+            },
+        },
+        "api_server": {
+            "description": "API server settings.",
+            "type": "object",
+            "properties": {
+                "enabled": {"description": "Whether the API server is enabled.", "type": "boolean"},
+                "listen_ip_address": {
+                    "description": "IP address the API server listens on.",
+                    "format": "ipv4",
+                },
+                "listen_port": {
+                    "description": "Port the API server listens on.",
+                    "type": "integer",
+                    "minimum": 1024,
+                    "maximum": 65535,
+                },
+                "username": {
+                    "description": "Username for API server authentication.",
+                    "type": "string",
+                },
+                "password": {
+                    "description": "Password for API server authentication.",
+                    "type": "string",
+                },
+                "ws_token": {
+                    "description": "WebSocket token for API server.",
+                    "type": ["string", "array"],
+                    "items": {"type": "string"},
+                },
+                "jwt_secret_key": {
+                    "description": "Secret key for JWT authentication.",
+                    "type": "string",
+                },
+                "CORS_origins": {
+                    "description": "List of allowed CORS origins.",
+                    "type": "array",
+                    "items": {"type": "string"},
+                },
+                "verbosity": {
+                    "description": "Logging verbosity level.",
+                    "type": "string",
+                    "enum": ["error", "info"],
+                },
+            },
+            "required": ["enabled", "listen_ip_address", "listen_port", "username", "password"],
+        },
+        # end of RPC section
+        "db_url": {
+            "description": "Database connection URL.",
+            "type": "string",
+        },
+        "export": {
+            "description": "Type of data to export.",
+            "type": "string",
+            "enum": EXPORT_OPTIONS,
+            "default": "trades",
+        },
+        "disableparamexport": {
+            "description": "Disable parameter export.",
+            "type": "boolean",
+        },
+        "initial_state": {
+            "description": "Initial state of the system.",
+            "type": "string",
+            "enum": ["running", "paused", "stopped"],
+        },
+        "force_entry_enable": {
+            "description": "Force enable entry.",
+            "type": "boolean",
+        },
+        "disable_dataframe_checks": {
+            "description": "Disable checks on dataframes.",
+            "type": "boolean",
+        },
+        "internals": {
+            "description": "Internal settings.",
+            "type": "object",
+            "default": {},
+            "properties": {
+                "process_throttle_secs": {
+                    "description": "Minimum loop duration for one bot iteration in seconds.",
+                    "type": "integer",
+                },
+                "interval": {
+                    "description": "Interval time in seconds.",
+                    "type": "integer",
+                },
+                "sd_notify": {
+                    "description": "Enable systemd notify.",
+                    "type": "boolean",
+                },
+            },
+        },
+        "dataformat_ohlcv": {
+            "description": "Data format for OHLCV data.",
+            "type": "string",
+            "enum": AVAILABLE_DATAHANDLERS,
+            "default": "feather",
+        },
+        "dataformat_trades": {
+            "description": "Data format for trade data.",
+            "type": "string",
+            "enum": AVAILABLE_DATAHANDLERS,
+            "default": "feather",
+        },
+        "position_adjustment_enable": {
+            "description": f"Enable position adjustment. {__IN_STRATEGY}",
+            "type": "boolean",
+        },
+        # Download data section
+        "new_pairs_days": {
+            "description": "Download data of new pairs for given number of days",
+            "type": "integer",
+            "default": 30,
+        },
+        "download_trades": {
+            "description": "Download trades data by default (instead of ohlcv data).",
+            "type": "boolean",
+        },
+        "max_entry_position_adjustment": {
+            "description": f"Maximum entry position adjustment allowed. {__IN_STRATEGY}",
+            "type": ["integer", "number"],
+            "minimum": -1,
+        },
+        "add_config_files": {
+            "description": "Additional configuration files to load.",
+            "type": "array",
+            "items": {"type": "string"},
+        },
+        "orderflow": {
+            "description": "Settings related to order flow.",
+            "type": "object",
+            "properties": {
+                "cache_size": {
+                    "description": "Size of the cache for order flow data.",
+                    "type": "number",
+                    "minimum": 1,
+                    "default": 1500,
+                },
+                "max_candles": {
+                    "description": "Maximum number of candles to consider.",
+                    "type": "number",
+                    "minimum": 1,
+                    "default": 1500,
+                },
+                "scale": {
+                    "description": "Scale factor for order flow data.",
+                    "type": "number",
+                    "minimum": 0.0,
+                },
+                "stacked_imbalance_range": {
+                    "description": "Range for stacked imbalance.",
+                    "type": "number",
+                    "minimum": 0,
+                },
+                "imbalance_volume": {
+                    "description": "Volume threshold for imbalance.",
+                    "type": "number",
+                    "minimum": 0,
+                },
+                "imbalance_ratio": {
+                    "description": "Ratio threshold for imbalance.",
+                    "type": "number",
+                    "minimum": 0.0,
+                },
+            },
+            "required": [
+                "max_candles",
+                "scale",
+                "stacked_imbalance_range",
+                "imbalance_volume",
+                "imbalance_ratio",
+            ],
+        },
+    },
+    "definitions": {
+        "exchange": {
+            "description": "Exchange configuration settings.",
+            "type": "object",
+            "properties": {
+                "name": {"description": "Name of the exchange.", "type": "string"},
+                "enable_ws": {
+                    "description": "Enable WebSocket connections to the exchange.",
+                    "type": "boolean",
+                    "default": True,
+                },
+                "key": {
+                    "description": "API key for the exchange.",
+                    "type": "string",
+                    "default": "",
+                },
+                "secret": {
+                    "description": "API secret for the exchange.",
+                    "type": "string",
+                    "default": "",
+                },
+                "password": {
+                    "description": "Password for the exchange, if required.",
+                    "type": "string",
+                    "default": "",
+                },
+                "uid": {"description": "User ID for the exchange, if required.", "type": "string"},
+                "pair_whitelist": {
+                    "description": "List of whitelisted trading pairs.",
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "uniqueItems": True,
+                },
+                "pair_blacklist": {
+                    "description": "List of blacklisted trading pairs.",
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "uniqueItems": True,
+                },
+                "log_responses": {
+                    "description": (
+                        "Log responses from the exchange."
+                        "Useful/required to debug issues with order processing."
+                    ),
+                    "type": "boolean",
+                    "default": False,
+                },
+                "unknown_fee_rate": {
+                    "description": "Fee rate for unknown markets.",
+                    "type": "number",
+                },
+                "outdated_offset": {
+                    "description": "Offset for outdated data in minutes.",
+                    "type": "integer",
+                    "minimum": 1,
+                },
+                "markets_refresh_interval": {
+                    "description": "Interval for refreshing market data in minutes.",
+                    "type": "integer",
+                    "default": 60,
+                },
+                "ccxt_config": {"description": "CCXT configuration settings.", "type": "object"},
+                "ccxt_async_config": {
+                    "description": "CCXT asynchronous configuration settings.",
+                    "type": "object",
+                },
+            },
+            "required": ["name"],
+        },
+        "edge": {
+            "type": "object",
+            "properties": {
+                "enabled": {"type": "boolean"},
+                "process_throttle_secs": {"type": "integer", "minimum": 600},
+                "calculate_since_number_of_days": {"type": "integer"},
+                "allowed_risk": {"type": "number"},
+                "stoploss_range_min": {"type": "number"},
+                "stoploss_range_max": {"type": "number"},
+                "stoploss_range_step": {"type": "number"},
+                "minimum_winrate": {"type": "number"},
+                "minimum_expectancy": {"type": "number"},
+                "min_trade_number": {"type": "number"},
+                "max_trade_duration_minute": {"type": "integer"},
+                "remove_pumps": {"type": "boolean"},
+            },
+            "required": ["process_throttle_secs", "allowed_risk"],
+        },
+        "logging": {
+            "type": "object",
+            "properties": {
+                "version": {"type": "number", "const": 1},
+                "formatters": {
+                    "type": "object",
+                    # In theory the below, but can be more flexible
+                    # based on logging.config documentation
+                    # "additionalProperties": {
+                    #     "type": "object",
+                    #     "properties": {
+                    #         "format": {"type": "string"},
+                    #         "datefmt": {"type": "string"},
+                    #     },
+                    #     "required": ["format"],
+                    # },
+                },
+                "handlers": {"type": "object"},
+                "root": {"type": "object"},
+            },
+            "required": ["version", "formatters", "handlers", "root"],
+        },
+        "external_message_consumer": {
+            "description": "Configuration for external message consumer.",
+            "type": "object",
+            "properties": {
+                "enabled": {
+                    "description": "Whether the external message consumer is enabled.",
+                    "type": "boolean",
+                    "default": False,
+                },
+                "producers": {
+                    "description": "List of producers for the external message consumer.",
+                    "type": "array",
+                    "items": {
+                        "type": "object",
+                        "properties": {
+                            "name": {
+                                "description": "Name of the producer.",
+                                "type": "string",
+                            },
+                            "host": {
+                                "description": "Host of the producer.",
+                                "type": "string",
+                            },
+                            "port": {
+                                "description": "Port of the producer.",
+                                "type": "integer",
+                                "default": 8080,
+                                "minimum": 0,
+                                "maximum": 65535,
+                            },
+                            "secure": {
+                                "description": "Whether to use SSL to connect to the producer.",
+                                "type": "boolean",
+                                "default": False,
+                            },
+                            "ws_token": {
+                                "description": "WebSocket token for the producer.",
+                                "type": "string",
+                            },
+                        },
+                        "required": ["name", "host", "ws_token"],
+                    },
+                },
+                "wait_timeout": {
+                    "description": "Wait timeout in seconds.",
+                    "type": "integer",
+                    "minimum": 0,
+                },
+                "sleep_time": {
+                    "description": "Sleep time in seconds before retrying to connect.",
+                    "type": "integer",
+                    "minimum": 0,
+                },
+                "ping_timeout": {
+                    "description": "Ping timeout in seconds.",
+                    "type": "integer",
+                    "minimum": 0,
+                },
+                "remove_entry_exit_signals": {
+                    "description": "Remove signal columns from the dataframe (set them to 0)",
+                    "type": "boolean",
+                    "default": False,
+                },
+                "initial_candle_limit": {
+                    "description": "Initial candle limit.",
+                    "type": "integer",
+                    "minimum": 0,
+                    "maximum": 1500,
+                    "default": 1500,
+                },
+                "message_size_limit": {
+                    "description": "Message size limit in megabytes.",
+                    "type": "integer",
+                    "minimum": 1,
+                    "maximum": 20,
+                    "default": 8,
+                },
+            },
+            "required": ["producers"],
+        },
+        "freqai": {
+            "type": "object",
+            "properties": {
+                "enabled": {
+                    "description": "Whether freqAI is enabled.",
+                    "type": "boolean",
+                    "default": False,
+                },
+                "identifier": {
+                    "description": (
+                        "A unique ID for the current model. "
+                        "Must be changed when modifying features."
+                    ),
+                    "type": "string",
+                    "default": "example",
+                },
+                "write_metrics_to_disk": {
+                    "description": "Write metrics to disk?",
+                    "type": "boolean",
+                    "default": False,
+                },
+                "purge_old_models": {
+                    "description": "Number of models to keep on disk.",
+                    "type": ["boolean", "number"],
+                    "default": 2,
+                },
+                "conv_width": {
+                    "description": "The width of a neural network input tensor.",
+                    "type": "integer",
+                    "default": 1,
+                },
+                "train_period_days": {
+                    "description": (
+                        "Number of days to use for the training data (width of the sliding window)"
+                    ),
+                    "type": "integer",
+                    "default": 0,
+                },
+                "backtest_period_days": {
+                    "description": (
+                        "Number of days to inference from the trained model before sliding the "
+                        "`train_period_days` window "
+                    ),
+                    "type": "number",
+                    "default": 7,
+                },
+                "live_retrain_hours": {
+                    "description": "Frequency of retraining during dry/live runs.",
+                    "type": "number",
+                    "default": 0,
+                },
+                "expiration_hours": {
+                    "description": (
+                        "Avoid making predictions if a model is more than `expiration_hours` "
+                        "old. Defaults to 0 (no expiration)."
+                    ),
+                    "type": "number",
+                    "default": 0,
+                },
+                "save_backtest_models": {
+                    "description": "Save models to disk when running backtesting.",
+                    "type": "boolean",
+                    "default": False,
+                },
+                "fit_live_predictions_candles": {
+                    "description": (
+                        "Number of historical candles to use for computing target (label) "
+                        "statistics from prediction data, instead of from the training dataset."
+                    ),
+                    "type": "integer",
+                },
+                "data_kitchen_thread_count": {
+                    "description": (
+                        "Designate the number of threads you want to use for data processing "
+                        "(outlier methods, normalization, etc.)."
+                    ),
+                    "type": "integer",
+                },
+                "activate_tensorboard": {
+                    "description": "Indicate whether or not to activate tensorboard",
+                    "type": "boolean",
+                    "default": True,
+                },
+                "wait_for_training_iteration_on_reload": {
+                    "description": (
+                        "Wait for the next training iteration to complete after /reload or ctrl+c."
+                    ),
+                    "type": "boolean",
+                    "default": True,
+                },
+                "continual_learning": {
+                    "description": (
+                        "Use the final state of the most recently trained model "
+                        "as starting point for the new model, allowing for "
+                        "incremental learning."
+                    ),
+                    "type": "boolean",
+                    "default": False,
+                },
+                "keras": {
+                    "description": "Use Keras for model training.",
+                    "type": "boolean",
+                    "default": False,
+                },
+                "feature_parameters": {
+                    "description": "The parameters used to engineer the feature set",
+                    "type": "object",
+                    "properties": {
+                        "include_corr_pairlist": {
+                            "description": "List of correlated pairs to include in the features.",
+                            "type": "array",
+                        },
+                        "include_timeframes": {
+                            "description": (
+                                "A list of timeframes that all indicators in "
+                                "`feature_engineering_expand_*()` will be created for."
+                            ),
+                            "type": "array",
+                        },
+                        "label_period_candles": {
+                            "description": (
+                                "Number of candles into the future to use for labeling the period."
+                                "This can be used in `set_freqai_targets()`."
+                            ),
+                            "type": "integer",
+                        },
+                        "include_shifted_candles": {
+                            "description": (
+                                "Add features from previous candles to subsequent candles with "
+                                "the intent of adding historical information."
+                            ),
+                            "type": "integer",
+                            "default": 0,
+                        },
+                        "DI_threshold": {
+                            "description": (
+                                "Activates the use of the Dissimilarity Index for "
+                                "outlier detection when set to > 0."
+                            ),
+                            "type": "number",
+                            "default": 0,
+                        },
+                        "weight_factor": {
+                            "description": (
+                                "Weight training data points according to their recency."
+                            ),
+                            "type": "number",
+                            "default": 0,
+                        },
+                        "principal_component_analysis": {
+                            "description": (
+                                "Automatically reduce the dimensionality of the data set using "
+                                "Principal Component Analysis"
+                            ),
+                            "type": "boolean",
+                            "default": False,
+                        },
+                        "indicator_periods_candles": {
+                            "description": (
+                                "Time periods to calculate indicators for. "
+                                "The indicators are added to the base indicator dataset."
+                            ),
+                            "type": "array",
+                            "items": {"type": "number", "minimum": 1},
+                        },
+                        "use_SVM_to_remove_outliers": {
+                            "description": "Use SVM to remove outliers from the features.",
+                            "type": "boolean",
+                            "default": False,
+                        },
+                        "plot_feature_importances": {
+                            "description": "Create feature importance plots for each model.",
+                            "type": "integer",
+                            "default": 0,
+                        },
+                        "svm_params": {
+                            "description": (
+                                "All parameters available in Sklearn's `SGDOneClassSVM()`."
+                            ),
+                            "type": "object",
+                            "properties": {
+                                "shuffle": {
+                                    "description": "Whether to shuffle data before applying SVM.",
+                                    "type": "boolean",
+                                    "default": False,
+                                },
+                                "nu": {
+                                    "type": "number",
+                                    "default": 0.1,
+                                },
+                            },
+                        },
+                        "shuffle_after_split": {
+                            "description": (
+                                "Split the data into train and test sets, and then shuffle "
+                                "both sets individually."
+                            ),
+                            "type": "boolean",
+                            "default": False,
+                        },
+                        "buffer_train_data_candles": {
+                            "description": (
+                                "Cut `buffer_train_data_candles` off the beginning and end of the "
+                                "training data *after* the indicators were populated."
+                            ),
+                            "type": "integer",
+                            "default": 0,
+                        },
+                    },
+                    "required": [
+                        "include_timeframes",
+                        "include_corr_pairlist",
+                    ],
+                },
+                "data_split_parameters": {
+                    "descriptions": (
+                        "Additional parameters for scikit-learn's test_train_split() function."
+                    ),
+                    "type": "object",
+                    "properties": {
+                        "test_size": {"type": "number"},
+                        "random_state": {"type": "integer"},
+                        "shuffle": {"type": "boolean", "default": False},
+                    },
+                },
+                "model_training_parameters": {
+                    "description": (
+                        "Flexible dictionary that includes all parameters available by "
+                        "the selected model library. "
+                    ),
+                    "type": "object",
+                },
+                "rl_config": {
+                    "type": "object",
+                    "properties": {
+                        "drop_ohlc_from_features": {
+                            "description": (
+                                "Do not include the normalized ohlc data in the feature set."
+                            ),
+                            "type": "boolean",
+                            "default": False,
+                        },
+                        "train_cycles": {
+                            "description": "Number of training cycles to perform.",
+                            "type": "integer",
+                        },
+                        "max_trade_duration_candles": {
+                            "description": (
+                                "Guides the agent training to keep trades below desired length."
+                            ),
+                            "type": "integer",
+                        },
+                        "add_state_info": {
+                            "description": (
+                                "Include state information in the feature set for "
+                                "training and inference."
+                            ),
+                            "type": "boolean",
+                            "default": False,
+                        },
+                        "max_training_drawdown_pct": {
+                            "description": "Maximum allowed drawdown percentage during training.",
+                            "type": "number",
+                            "default": 0.02,
+                        },
+                        "cpu_count": {
+                            "description": "Number of threads/CPU's to use for training.",
+                            "type": "integer",
+                            "default": 1,
+                        },
+                        "model_type": {
+                            "description": "Model string from stable_baselines3 or SBcontrib.",
+                            "type": "string",
+                            "default": "PPO",
+                        },
+                        "policy_type": {
+                            "description": (
+                                "One of the available policy types from stable_baselines3."
+                            ),
+                            "type": "string",
+                            "default": "MlpPolicy",
+                        },
+                        "net_arch": {
+                            "description": "Architecture of the neural network.",
+                            "type": "array",
+                            "default": [128, 128],
+                        },
+                        "randomize_starting_position": {
+                            "description": (
+                                "Randomize the starting point of each episode to avoid overfitting."
+                            ),
+                            "type": "boolean",
+                            "default": False,
+                        },
+                        "progress_bar": {
+                            "description": "Display a progress bar with the current progress.",
+                            "type": "boolean",
+                            "default": True,
+                        },
+                        "model_reward_parameters": {
+                            "description": "Parameters for configuring the reward model.",
+                            "type": "object",
+                            "properties": {
+                                "rr": {
+                                    "type": "number",
+                                    "default": 1,
+                                    "description": "Reward ratio parameter.",
+                                },
+                                "profit_aim": {
+                                    "type": "number",
+                                    "default": 0.025,
+                                    "description": "Profit aim parameter.",
+                                },
+                            },
+                        },
+                    },
+                },
+            },
+            "required": [
+                "enabled",
+                "train_period_days",
+                "backtest_period_days",
+                "identifier",
+                "feature_parameters",
+                "data_split_parameters",
+            ],
+        },
+    },
+}
+
+SCHEMA_TRADE_REQUIRED = [
+    "exchange",
+    "timeframe",
+    "max_open_trades",
+    "stake_currency",
+    "stake_amount",
+    "tradable_balance_ratio",
+    "last_stake_amount_min_ratio",
+    "dry_run",
+    "dry_run_wallet",
+    "exit_pricing",
+    "entry_pricing",
+    "stoploss",
+    "minimal_roi",
+    "internals",
+    "dataformat_ohlcv",
+    "dataformat_trades",
+]
+
+SCHEMA_BACKTEST_REQUIRED = [
+    "exchange",
+    "stake_currency",
+    "stake_amount",
+    "dry_run_wallet",
+    "dataformat_ohlcv",
+    "dataformat_trades",
+]
+SCHEMA_BACKTEST_REQUIRED_FINAL = [
+    *SCHEMA_BACKTEST_REQUIRED,
+    "stoploss",
+    "minimal_roi",
+    "max_open_trades",
+]
+
+SCHEMA_MINIMAL_REQUIRED = [
+    "exchange",
+    "dry_run",
+    "dataformat_ohlcv",
+    "dataformat_trades",
+]
+SCHEMA_MINIMAL_WEBSERVER = [*SCHEMA_MINIMAL_REQUIRED, "api_server"]
diff --git a/freqtrade/configuration/__init__.py b/freqtrade/configuration/__init__.py
new file mode 100644
index 000000000..aa06a70c9
--- /dev/null
+++ b/freqtrade/configuration/__init__.py
@@ -0,0 +1,8 @@
+# flake8: noqa: F401
+
+from freqtrade.configuration.config_secrets import sanitize_config
+from freqtrade.configuration.config_setup import setup_utils_configuration
+from freqtrade.configuration.config_validation import validate_config_consistency
+from freqtrade.configuration.configuration import Configuration
+from freqtrade.configuration.detect_environment import running_in_docker
+from freqtrade.configuration.timerange import TimeRange
diff --git a/freqtrade/configuration/config_secrets.py b/freqtrade/configuration/config_secrets.py
new file mode 100644
index 000000000..2bb0d97f8
--- /dev/null
+++ b/freqtrade/configuration/config_secrets.py
@@ -0,0 +1,46 @@
+from copy import deepcopy
+
+from freqtrade.constants import Config
+
+
+def sanitize_config(config: Config, *, show_sensitive: bool = False) -> Config:
+    """
+    Remove sensitive information from the config.
+    :param config: Configuration
+    :param show_sensitive: Show sensitive information
+    :return: Configuration
+    """
+    if show_sensitive:
+        return config
+    keys_to_remove = [
+        "exchange.key",
+        "exchange.api_key",
+        "exchange.apiKey",
+        "exchange.secret",
+        "exchange.password",
+        "exchange.uid",
+        "exchange.account_id",
+        "exchange.accountId",
+        "exchange.wallet_address",
+        "exchange.walletAddress",
+        "exchange.private_key",
+        "exchange.privateKey",
+        "telegram.token",
+        "telegram.chat_id",
+        "discord.webhook_url",
+        "api_server.password",
+    ]
+    config = deepcopy(config)
+    for key in keys_to_remove:
+        if "." in key:
+            nested_keys = key.split(".")
+            nested_config = config
+            for nested_key in nested_keys[:-1]:
+                nested_config = nested_config.get(nested_key, {})
+            if nested_keys[-1] in nested_config:
+                nested_config[nested_keys[-1]] = "REDACTED"
+        else:
+            if key in config:
+                config[key] = "REDACTED"
+
+    return config
diff --git a/freqtrade/configuration/config_setup.py b/freqtrade/configuration/config_setup.py
new file mode 100644
index 000000000..20b6b2557
--- /dev/null
+++ b/freqtrade/configuration/config_setup.py
@@ -0,0 +1,30 @@
+import logging
+from typing import Any
+
+from freqtrade.enums import RunMode
+
+from .config_validation import validate_config_consistency
+from .configuration import Configuration
+
+
+logger = logging.getLogger(__name__)
+
+
+def setup_utils_configuration(
+    args: dict[str, Any], method: RunMode, *, set_dry: bool = True
+) -> dict[str, Any]:
+    """
+    Prepare the configuration for utils subcommands
+    :param args: Cli args from Arguments()
+    :param method: Bot running mode
+    :return: Configuration
+    """
+    configuration = Configuration(args, method)
+    config = configuration.get_config()
+
+    # Ensure these modes are using Dry-run
+    if set_dry:
+        config["dry_run"] = True
+    validate_config_consistency(config, preliminary=True)
+
+    return config
diff --git a/freqtrade/configuration/config_validation.py b/freqtrade/configuration/config_validation.py
new file mode 100644
index 000000000..07a109bd7
--- /dev/null
+++ b/freqtrade/configuration/config_validation.py
@@ -0,0 +1,427 @@
+import logging
+from collections import Counter
+from copy import deepcopy
+from typing import Any
+
+from jsonschema import Draft4Validator, validators
+from jsonschema.exceptions import ValidationError, best_match
+
+from freqtrade.config_schema.config_schema import (
+    CONF_SCHEMA,
+    SCHEMA_BACKTEST_REQUIRED,
+    SCHEMA_BACKTEST_REQUIRED_FINAL,
+    SCHEMA_MINIMAL_REQUIRED,
+    SCHEMA_MINIMAL_WEBSERVER,
+    SCHEMA_TRADE_REQUIRED,
+)
+from freqtrade.configuration.deprecated_settings import process_deprecated_setting
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
+from freqtrade.enums import RunMode, TradingMode
+from freqtrade.exceptions import ConfigurationError
+
+
+logger = logging.getLogger(__name__)
+
+
+def _extend_validator(validator_class):
+    """
+    Extended validator for the Freqtrade configuration JSON Schema.
+    Currently it only handles defaults for subschemas.
+    """
+    validate_properties = validator_class.VALIDATORS["properties"]
+
+    def set_defaults(validator, properties, instance, schema):
+        for prop, subschema in properties.items():
+            if "default" in subschema:
+                instance.setdefault(prop, subschema["default"])
+
+        yield from validate_properties(validator, properties, instance, schema)
+
+    return validators.extend(validator_class, {"properties": set_defaults})
+
+
+FreqtradeValidator = _extend_validator(Draft4Validator)
+
+
+def validate_config_schema(conf: dict[str, Any], preliminary: bool = False) -> dict[str, Any]:
+    """
+    Validate the configuration follow the Config Schema
+    :param conf: Config in JSON format
+    :return: Returns the config if valid, otherwise throw an exception
+    """
+    conf_schema = deepcopy(CONF_SCHEMA)
+    if conf.get("runmode", RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
+        conf_schema["required"] = SCHEMA_TRADE_REQUIRED
+    elif conf.get("runmode", RunMode.OTHER) in (RunMode.BACKTEST, RunMode.HYPEROPT):
+        if preliminary:
+            conf_schema["required"] = SCHEMA_BACKTEST_REQUIRED
+        else:
+            conf_schema["required"] = SCHEMA_BACKTEST_REQUIRED_FINAL
+    elif conf.get("runmode", RunMode.OTHER) == RunMode.WEBSERVER:
+        conf_schema["required"] = SCHEMA_MINIMAL_WEBSERVER
+    else:
+        conf_schema["required"] = SCHEMA_MINIMAL_REQUIRED
+    try:
+        FreqtradeValidator(conf_schema).validate(conf)
+        return conf
+    except ValidationError as e:
+        logger.critical(f"Invalid configuration. Reason: {e}")
+        raise ValidationError(best_match(Draft4Validator(conf_schema).iter_errors(conf)).message)
+
+
+def validate_config_consistency(conf: dict[str, Any], *, preliminary: bool = False) -> None:
+    """
+    Validate the configuration consistency.
+    Should be ran after loading both configuration and strategy,
+    since strategies can set certain configuration settings too.
+    :param conf: Config in JSON format
+    :return: Returns None if everything is ok, otherwise throw an ConfigurationError
+    """
+
+    # validating trailing stoploss
+    _validate_trailing_stoploss(conf)
+    _validate_price_config(conf)
+    _validate_edge(conf)
+    _validate_whitelist(conf)
+    _validate_unlimited_amount(conf)
+    _validate_ask_orderbook(conf)
+    _validate_freqai_hyperopt(conf)
+    _validate_freqai_backtest(conf)
+    _validate_freqai_include_timeframes(conf, preliminary=preliminary)
+    _validate_consumers(conf)
+    validate_migrated_strategy_settings(conf)
+    _validate_orderflow(conf)
+
+    # validate configuration before returning
+    logger.info("Validating configuration ...")
+    validate_config_schema(conf, preliminary=preliminary)
+
+
+def _validate_unlimited_amount(conf: dict[str, Any]) -> None:
+    """
+    If edge is disabled, either max_open_trades or stake_amount need to be set.
+    :raise: ConfigurationError if config validation failed
+    """
+    if (
+        not conf.get("edge", {}).get("enabled")
+        and conf.get("max_open_trades") == float("inf")
+        and conf.get("stake_amount") == UNLIMITED_STAKE_AMOUNT
+    ):
+        raise ConfigurationError("`max_open_trades` and `stake_amount` cannot both be unlimited.")
+
+
+def _validate_price_config(conf: dict[str, Any]) -> None:
+    """
+    When using market orders, price sides must be using the "other" side of the price
+    """
+    # TODO: The below could be an enforced setting when using market orders
+    if conf.get("order_types", {}).get("entry") == "market" and conf.get("entry_pricing", {}).get(
+        "price_side"
+    ) not in ("ask", "other"):
+        raise ConfigurationError('Market entry orders require entry_pricing.price_side = "other".')
+
+    if conf.get("order_types", {}).get("exit") == "market" and conf.get("exit_pricing", {}).get(
+        "price_side"
+    ) not in ("bid", "other"):
+        raise ConfigurationError('Market exit orders require exit_pricing.price_side = "other".')
+
+
+def _validate_trailing_stoploss(conf: dict[str, Any]) -> None:
+    if conf.get("stoploss") == 0.0:
+        raise ConfigurationError(
+            "The config stoploss needs to be different from 0 to avoid problems with sell orders."
+        )
+    # Skip if trailing stoploss is not activated
+    if not conf.get("trailing_stop", False):
+        return
+
+    tsl_positive = float(conf.get("trailing_stop_positive", 0))
+    tsl_offset = float(conf.get("trailing_stop_positive_offset", 0))
+    tsl_only_offset = conf.get("trailing_only_offset_is_reached", False)
+
+    if tsl_only_offset:
+        if tsl_positive == 0.0:
+            raise ConfigurationError(
+                "The config trailing_only_offset_is_reached needs "
+                "trailing_stop_positive_offset to be more than 0 in your config."
+            )
+    if tsl_positive > 0 and 0 < tsl_offset <= tsl_positive:
+        raise ConfigurationError(
+            "The config trailing_stop_positive_offset needs "
+            "to be greater than trailing_stop_positive in your config."
+        )
+
+    # Fetch again without default
+    if "trailing_stop_positive" in conf and float(conf["trailing_stop_positive"]) == 0.0:
+        raise ConfigurationError(
+            "The config trailing_stop_positive needs to be different from 0 "
+            "to avoid problems with sell orders."
+        )
+
+
+def _validate_edge(conf: dict[str, Any]) -> None:
+    """
+    Edge and Dynamic whitelist should not both be enabled, since edge overrides dynamic whitelists.
+    """
+
+    if not conf.get("edge", {}).get("enabled"):
+        return
+
+    if not conf.get("use_exit_signal", True):
+        raise ConfigurationError(
+            "Edge requires `use_exit_signal` to be True, otherwise no sells will happen."
+        )
+
+
+def _validate_whitelist(conf: dict[str, Any]) -> None:
+    """
+    Dynamic whitelist does not require pair_whitelist to be set - however StaticWhitelist does.
+    """
+    if conf.get("runmode", RunMode.OTHER) in [
+        RunMode.OTHER,
+        RunMode.PLOT,
+        RunMode.UTIL_NO_EXCHANGE,
+        RunMode.UTIL_EXCHANGE,
+    ]:
+        return
+
+    for pl in conf.get("pairlists", [{"method": "StaticPairList"}]):
+        if (
+            isinstance(pl, dict)
+            and pl.get("method") == "StaticPairList"
+            and not conf.get("exchange", {}).get("pair_whitelist")
+        ):
+            raise ConfigurationError("StaticPairList requires pair_whitelist to be set.")
+
+
+def _validate_ask_orderbook(conf: dict[str, Any]) -> None:
+    ask_strategy = conf.get("exit_pricing", {})
+    ob_min = ask_strategy.get("order_book_min")
+    ob_max = ask_strategy.get("order_book_max")
+    if ob_min is not None and ob_max is not None and ask_strategy.get("use_order_book"):
+        if ob_min != ob_max:
+            raise ConfigurationError(
+                "Using order_book_max != order_book_min in exit_pricing is no longer supported."
+                "Please pick one value and use `order_book_top` in the future."
+            )
+        else:
+            # Move value to order_book_top
+            ask_strategy["order_book_top"] = ob_min
+            logger.warning(
+                "DEPRECATED: "
+                "Please use `order_book_top` instead of `order_book_min` and `order_book_max` "
+                "for your `exit_pricing` configuration."
+            )
+
+
+def validate_migrated_strategy_settings(conf: dict[str, Any]) -> None:
+    _validate_time_in_force(conf)
+    _validate_order_types(conf)
+    _validate_unfilledtimeout(conf)
+    _validate_pricing_rules(conf)
+    _strategy_settings(conf)
+
+
+def _validate_time_in_force(conf: dict[str, Any]) -> None:
+    time_in_force = conf.get("order_time_in_force", {})
+    if "buy" in time_in_force or "sell" in time_in_force:
+        if conf.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT:
+            raise ConfigurationError(
+                "Please migrate your time_in_force settings to use 'entry' and 'exit'."
+            )
+        else:
+            logger.warning(
+                "DEPRECATED: Using 'buy' and 'sell' for time_in_force is deprecated."
+                "Please migrate your time_in_force settings to use 'entry' and 'exit'."
+            )
+            process_deprecated_setting(
+                conf, "order_time_in_force", "buy", "order_time_in_force", "entry"
+            )
+
+            process_deprecated_setting(
+                conf, "order_time_in_force", "sell", "order_time_in_force", "exit"
+            )
+
+
+def _validate_order_types(conf: dict[str, Any]) -> None:
+    order_types = conf.get("order_types", {})
+    old_order_types = [
+        "buy",
+        "sell",
+        "emergencysell",
+        "forcebuy",
+        "forcesell",
+        "emergencyexit",
+        "forceexit",
+        "forceentry",
+    ]
+    if any(x in order_types for x in old_order_types):
+        if conf.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT:
+            raise ConfigurationError(
+                "Please migrate your order_types settings to use the new wording."
+            )
+        else:
+            logger.warning(
+                "DEPRECATED: Using 'buy' and 'sell' for order_types is deprecated."
+                "Please migrate your order_types settings to use 'entry' and 'exit' wording."
+            )
+            for o, n in [
+                ("buy", "entry"),
+                ("sell", "exit"),
+                ("emergencysell", "emergency_exit"),
+                ("forcesell", "force_exit"),
+                ("forcebuy", "force_entry"),
+                ("emergencyexit", "emergency_exit"),
+                ("forceexit", "force_exit"),
+                ("forceentry", "force_entry"),
+            ]:
+                process_deprecated_setting(conf, "order_types", o, "order_types", n)
+
+
+def _validate_unfilledtimeout(conf: dict[str, Any]) -> None:
+    unfilledtimeout = conf.get("unfilledtimeout", {})
+    if any(x in unfilledtimeout for x in ["buy", "sell"]):
+        if conf.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT:
+            raise ConfigurationError(
+                "Please migrate your unfilledtimeout settings to use the new wording."
+            )
+        else:
+            logger.warning(
+                "DEPRECATED: Using 'buy' and 'sell' for unfilledtimeout is deprecated."
+                "Please migrate your unfilledtimeout settings to use 'entry' and 'exit' wording."
+            )
+            for o, n in [
+                ("buy", "entry"),
+                ("sell", "exit"),
+            ]:
+                process_deprecated_setting(conf, "unfilledtimeout", o, "unfilledtimeout", n)
+
+
+def _validate_pricing_rules(conf: dict[str, Any]) -> None:
+    if conf.get("ask_strategy") or conf.get("bid_strategy"):
+        if conf.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT:
+            raise ConfigurationError("Please migrate your pricing settings to use the new wording.")
+        else:
+            logger.warning(
+                "DEPRECATED: Using 'ask_strategy' and 'bid_strategy' is deprecated."
+                "Please migrate your settings to use 'entry_pricing' and 'exit_pricing'."
+            )
+            conf["entry_pricing"] = {}
+            for obj in list(conf.get("bid_strategy", {}).keys()):
+                if obj == "ask_last_balance":
+                    process_deprecated_setting(
+                        conf, "bid_strategy", obj, "entry_pricing", "price_last_balance"
+                    )
+                else:
+                    process_deprecated_setting(conf, "bid_strategy", obj, "entry_pricing", obj)
+            del conf["bid_strategy"]
+
+            conf["exit_pricing"] = {}
+            for obj in list(conf.get("ask_strategy", {}).keys()):
+                if obj == "bid_last_balance":
+                    process_deprecated_setting(
+                        conf, "ask_strategy", obj, "exit_pricing", "price_last_balance"
+                    )
+                else:
+                    process_deprecated_setting(conf, "ask_strategy", obj, "exit_pricing", obj)
+            del conf["ask_strategy"]
+
+
+def _validate_freqai_hyperopt(conf: dict[str, Any]) -> None:
+    freqai_enabled = conf.get("freqai", {}).get("enabled", False)
+    analyze_per_epoch = conf.get("analyze_per_epoch", False)
+    if analyze_per_epoch and freqai_enabled:
+        raise ConfigurationError(
+            "Using analyze-per-epoch parameter is not supported with a FreqAI strategy."
+        )
+
+
+def _validate_freqai_include_timeframes(conf: dict[str, Any], preliminary: bool) -> None:
+    freqai_enabled = conf.get("freqai", {}).get("enabled", False)
+    if freqai_enabled:
+        main_tf = conf.get("timeframe", "5m")
+        freqai_include_timeframes = (
+            conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes", [])
+        )
+
+        from freqtrade.exchange import timeframe_to_seconds
+
+        main_tf_s = timeframe_to_seconds(main_tf)
+        offending_lines = []
+        for tf in freqai_include_timeframes:
+            tf_s = timeframe_to_seconds(tf)
+            if tf_s < main_tf_s:
+                offending_lines.append(tf)
+        if offending_lines:
+            raise ConfigurationError(
+                f"Main timeframe of {main_tf} must be smaller or equal to FreqAI "
+                f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}"
+            )
+
+        # Ensure that the base timeframe is included in the include_timeframes list
+        if not preliminary and main_tf not in freqai_include_timeframes:
+            feature_parameters = conf.get("freqai", {}).get("feature_parameters", {})
+            include_timeframes = [main_tf, *freqai_include_timeframes]
+            conf.get("freqai", {}).get("feature_parameters", {}).update(
+                {**feature_parameters, "include_timeframes": include_timeframes}
+            )
+
+
+def _validate_freqai_backtest(conf: dict[str, Any]) -> None:
+    if conf.get("runmode", RunMode.OTHER) == RunMode.BACKTEST:
+        freqai_enabled = conf.get("freqai", {}).get("enabled", False)
+        timerange = conf.get("timerange")
+        freqai_backtest_live_models = conf.get("freqai_backtest_live_models", False)
+        if freqai_backtest_live_models and freqai_enabled and timerange:
+            raise ConfigurationError(
+                "Using timerange parameter is not supported with "
+                "--freqai-backtest-live-models parameter."
+            )
+
+        if freqai_backtest_live_models and not freqai_enabled:
+            raise ConfigurationError(
+                "Using --freqai-backtest-live-models parameter is only "
+                "supported with a FreqAI strategy."
+            )
+
+        if freqai_enabled and not freqai_backtest_live_models and not timerange:
+            raise ConfigurationError(
+                "Please pass --timerange if you intend to use FreqAI for backtesting."
+            )
+
+
+def _validate_consumers(conf: dict[str, Any]) -> None:
+    emc_conf = conf.get("external_message_consumer", {})
+    if emc_conf.get("enabled", False):
+        if len(emc_conf.get("producers", [])) < 1:
+            raise ConfigurationError("You must specify at least 1 Producer to connect to.")
+
+        producer_names = [p["name"] for p in emc_conf.get("producers", [])]
+        duplicates = [item for item, count in Counter(producer_names).items() if count > 1]
+        if duplicates:
+            raise ConfigurationError(
+                f"Producer names must be unique. Duplicate: {', '.join(duplicates)}"
+            )
+        if conf.get("process_only_new_candles", True):
+            # Warning here or require it?
+            logger.warning(
+                "To receive best performance with external data, "
+                "please set `process_only_new_candles` to False"
+            )
+
+
+def _validate_orderflow(conf: dict[str, Any]) -> None:
+    if conf.get("exchange", {}).get("use_public_trades"):
+        if "orderflow" not in conf:
+            raise ConfigurationError(
+                "Orderflow is a required configuration key when using public trades."
+            )
+
+
+def _strategy_settings(conf: dict[str, Any]) -> None:
+    process_deprecated_setting(conf, None, "use_sell_signal", None, "use_exit_signal")
+    process_deprecated_setting(conf, None, "sell_profit_only", None, "exit_profit_only")
+    process_deprecated_setting(conf, None, "sell_profit_offset", None, "exit_profit_offset")
+    process_deprecated_setting(
+        conf, None, "ignore_roi_if_buy_signal", None, "ignore_roi_if_entry_signal"
+    )
diff --git a/freqtrade/configuration/configuration.py b/freqtrade/configuration/configuration.py
new file mode 100644
index 000000000..11aff879f
--- /dev/null
+++ b/freqtrade/configuration/configuration.py
@@ -0,0 +1,518 @@
+"""
+This module contains the configuration class
+"""
+
+import ast
+import logging
+import warnings
+from collections.abc import Callable
+from copy import deepcopy
+from pathlib import Path
+from typing import Any
+
+from freqtrade import constants
+from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
+from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
+from freqtrade.configuration.environment_vars import enironment_vars_to_dict
+from freqtrade.configuration.load_config import load_file, load_from_files
+from freqtrade.constants import Config
+from freqtrade.enums import (
+    NON_UTIL_MODES,
+    TRADE_MODES,
+    CandleType,
+    MarginMode,
+    RunMode,
+    TradingMode,
+)
+from freqtrade.exceptions import OperationalException
+from freqtrade.loggers import setup_logging
+from freqtrade.misc import deep_merge_dicts, parse_db_uri_for_logging, safe_value_fallback
+
+
+logger = logging.getLogger(__name__)
+
+
+class Configuration:
+    """
+    Class to read and init the bot configuration
+    Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
+    """
+
+    def __init__(self, args: dict[str, Any], runmode: RunMode | None = None) -> None:
+        self.args = args
+        self.config: Config | None = None
+        self.runmode = runmode
+
+    def get_config(self) -> Config:
+        """
+        Return the config. Use this method to get the bot config
+        :return: Dict: Bot config
+        """
+        if self.config is None:
+            self.config = self.load_config()
+
+        return self.config
+
+    @staticmethod
+    def from_files(files: list[str]) -> dict[str, Any]:
+        """
+        Iterate through the config files passed in, loading all of them
+        and merging their contents.
+        Files are loaded in sequence, parameters in later configuration files
+        override the same parameter from an earlier file (last definition wins).
+        Runs through the whole Configuration initialization, so all expected config entries
+        are available to interactive environments.
+        :param files: List of file paths
+        :return: configuration dictionary
+        """
+        # Keep this method as staticmethod, so it can be used from interactive environments
+        c = Configuration({"config": files}, RunMode.OTHER)
+        return c.get_config()
+
+    def load_config(self) -> dict[str, Any]:
+        """
+        Extract information for sys.argv and load the bot configuration
+        :return: Configuration dictionary
+        """
+        # Load all configs
+        config: Config = load_from_files(self.args.get("config", []))
+
+        # Load environment variables
+        from freqtrade.commands.arguments import NO_CONF_ALLOWED
+
+        if self.args.get("command") not in NO_CONF_ALLOWED:
+            env_data = enironment_vars_to_dict()
+            config = deep_merge_dicts(env_data, config)
+
+        # Normalize config
+        if "internals" not in config:
+            config["internals"] = {}
+
+        if "pairlists" not in config:
+            config["pairlists"] = []
+
+        # Keep a copy of the original configuration file
+        config["original_config"] = deepcopy(config)
+
+        self._process_logging_options(config)
+
+        self._process_runmode(config)
+
+        self._process_common_options(config)
+
+        self._process_trading_options(config)
+
+        self._process_optimize_options(config)
+
+        self._process_plot_options(config)
+
+        self._process_data_options(config)
+
+        self._process_analyze_options(config)
+
+        self._process_freqai_options(config)
+
+        # Import check_exchange here to avoid import cycle problems
+        from freqtrade.exchange.check_exchange import check_exchange
+
+        # Check if the exchange set by the user is supported
+        check_exchange(config, config.get("experimental", {}).get("block_bad_exchanges", True))
+
+        self._resolve_pairs_list(config)
+
+        process_temporary_deprecated_settings(config)
+
+        return config
+
+    def _process_logging_options(self, config: Config) -> None:
+        """
+        Extract information for sys.argv and load logging configuration:
+        the -v/--verbose, --logfile options
+        """
+        # Log level
+        if "verbosity" not in config or self.args.get("verbosity") is not None:
+            config.update(
+                {"verbosity": safe_value_fallback(self.args, "verbosity", default_value=0)}
+            )
+
+        if self.args.get("logfile"):
+            config.update({"logfile": self.args["logfile"]})
+
+        if "print_colorized" in self.args and not self.args["print_colorized"]:
+            logger.info("Parameter --no-color detected ...")
+            config.update({"print_colorized": False})
+        else:
+            config.update({"print_colorized": True})
+
+        setup_logging(config)
+
+    def _process_trading_options(self, config: Config) -> None:
+        if config["runmode"] not in TRADE_MODES:
+            return
+
+        if config.get("dry_run", False):
+            logger.info("Dry run is enabled")
+            if config.get("db_url") in [None, constants.DEFAULT_DB_PROD_URL]:
+                # Default to in-memory db for dry_run if not specified
+                config["db_url"] = constants.DEFAULT_DB_DRYRUN_URL
+        else:
+            if not config.get("db_url"):
+                config["db_url"] = constants.DEFAULT_DB_PROD_URL
+            logger.info("Dry run is disabled")
+
+        logger.info(f'Using DB: "{parse_db_uri_for_logging(config["db_url"])}"')
+
+    def _process_common_options(self, config: Config) -> None:
+        # Set strategy if not specified in config and or if it's non default
+        if self.args.get("strategy") or not config.get("strategy"):
+            config.update({"strategy": self.args.get("strategy")})
+
+        self._args_to_config(
+            config, argname="strategy_path", logstring="Using additional Strategy lookup path: {}"
+        )
+
+        if (
+            "db_url" in self.args
+            and self.args["db_url"]
+            and self.args["db_url"] != constants.DEFAULT_DB_PROD_URL
+        ):
+            config.update({"db_url": self.args["db_url"]})
+            logger.info("Parameter --db-url detected ...")
+
+        self._args_to_config(
+            config, argname="db_url_from", logstring="Parameter --db-url-from detected ..."
+        )
+
+        if config.get("force_entry_enable", False):
+            logger.warning("`force_entry_enable` RPC message enabled.")
+
+        # Support for sd_notify
+        if self.args.get("sd_notify"):
+            config["internals"].update({"sd_notify": True})
+
+    def _process_datadir_options(self, config: Config) -> None:
+        """
+        Extract information for sys.argv and load directory configurations
+        --user-data, --datadir
+        """
+        # Check exchange parameter here - otherwise `datadir` might be wrong.
+        if self.args.get("exchange"):
+            config["exchange"]["name"] = self.args["exchange"]
+            logger.info(f"Using exchange {config['exchange']['name']}")
+
+        if "pair_whitelist" not in config["exchange"]:
+            config["exchange"]["pair_whitelist"] = []
+
+        if self.args.get("user_data_dir"):
+            config.update({"user_data_dir": self.args["user_data_dir"]})
+        elif "user_data_dir" not in config:
+            # Default to cwd/user_data (legacy option ...)
+            config.update({"user_data_dir": str(Path.cwd() / "user_data")})
+
+        # reset to user_data_dir so this contains the absolute path.
+        config["user_data_dir"] = create_userdata_dir(config["user_data_dir"], create_dir=False)
+        logger.info("Using user-data directory: %s ...", config["user_data_dir"])
+
+        config.update({"datadir": create_datadir(config, self.args.get("datadir"))})
+        logger.info("Using data directory: %s ...", config.get("datadir"))
+
+        if self.args.get("exportfilename"):
+            self._args_to_config(
+                config, argname="exportfilename", logstring="Storing backtest results to {} ..."
+            )
+            config["exportfilename"] = Path(config["exportfilename"])
+        else:
+            config["exportfilename"] = config["user_data_dir"] / "backtest_results"
+
+        if self.args.get("show_sensitive"):
+            logger.warning(
+                "Sensitive information will be shown in the upcoming output. "
+                "Please make sure to never share this output without redacting "
+                "the information yourself."
+            )
+
+    def _process_optimize_options(self, config: Config) -> None:
+        # This will override the strategy configuration
+        self._args_to_config(
+            config,
+            argname="timeframe",
+            logstring="Parameter -i/--timeframe detected ... Using timeframe: {} ...",
+        )
+
+        self._args_to_config(
+            config,
+            argname="position_stacking",
+            logstring="Parameter --enable-position-stacking detected ...",
+        )
+
+        self._args_to_config(
+            config,
+            argname="enable_protections",
+            logstring="Parameter --enable-protections detected, enabling Protections. ...",
+        )
+
+        if self.args.get("max_open_trades"):
+            config.update({"max_open_trades": self.args["max_open_trades"]})
+            logger.info(
+                "Parameter --max-open-trades detected, overriding max_open_trades to: %s ...",
+                config.get("max_open_trades"),
+            )
+        elif config["runmode"] in NON_UTIL_MODES:
+            logger.info("Using max_open_trades: %s ...", config.get("max_open_trades"))
+        # Setting max_open_trades to infinite if -1
+        if config.get("max_open_trades") == -1:
+            config["max_open_trades"] = float("inf")
+
+        if self.args.get("stake_amount"):
+            # Convert explicitly to float to support CLI argument for both unlimited and value
+            try:
+                self.args["stake_amount"] = float(self.args["stake_amount"])
+            except ValueError:
+                pass
+
+        configurations = [
+            (
+                "timeframe_detail",
+                "Parameter --timeframe-detail detected, using {} for intra-candle backtesting ...",
+            ),
+            ("backtest_show_pair_list", "Parameter --show-pair-list detected."),
+            (
+                "stake_amount",
+                "Parameter --stake-amount detected, overriding stake_amount to: {} ...",
+            ),
+            (
+                "dry_run_wallet",
+                "Parameter --dry-run-wallet detected, overriding dry_run_wallet to: {} ...",
+            ),
+            ("fee", "Parameter --fee detected, setting fee to: {} ..."),
+            ("timerange", "Parameter --timerange detected: {} ..."),
+        ]
+
+        self._args_to_config_loop(config, configurations)
+
+        self._process_datadir_options(config)
+
+        self._args_to_config(
+            config,
+            argname="strategy_list",
+            logstring="Using strategy list of {} strategies",
+            logfun=len,
+        )
+
+        configurations = [
+            (
+                "recursive_strategy_search",
+                "Recursively searching for a strategy in the strategies folder.",
+            ),
+            ("timeframe", "Overriding timeframe with Command line argument"),
+            ("export", "Parameter --export detected: {} ..."),
+            ("backtest_breakdown", "Parameter --breakdown detected ..."),
+            ("backtest_cache", "Parameter --cache={} detected ..."),
+            ("disableparamexport", "Parameter --disableparamexport detected: {} ..."),
+            ("freqai_backtest_live_models", "Parameter --freqai-backtest-live-models detected ..."),
+        ]
+        self._args_to_config_loop(config, configurations)
+
+        # Edge section:
+        if self.args.get("stoploss_range"):
+            txt_range = ast.literal_eval(self.args["stoploss_range"])
+            config["edge"].update({"stoploss_range_min": txt_range[0]})
+            config["edge"].update({"stoploss_range_max": txt_range[1]})
+            config["edge"].update({"stoploss_range_step": txt_range[2]})
+            logger.info("Parameter --stoplosses detected: %s ...", self.args["stoploss_range"])
+
+        # Hyperopt section
+
+        configurations = [
+            ("hyperopt", "Using Hyperopt class name: {}"),
+            ("hyperopt_path", "Using additional Hyperopt lookup path: {}"),
+            ("hyperoptexportfilename", "Using hyperopt file: {}"),
+            ("lookahead_analysis_exportfilename", "Saving lookahead analysis results into {} ..."),
+            ("epochs", "Parameter --epochs detected ... Will run Hyperopt with for {} epochs ..."),
+            ("spaces", "Parameter -s/--spaces detected: {}"),
+            ("analyze_per_epoch", "Parameter --analyze-per-epoch detected."),
+            ("print_all", "Parameter --print-all detected ..."),
+        ]
+        self._args_to_config_loop(config, configurations)
+
+        configurations = [
+            ("print_json", "Parameter --print-json detected ..."),
+            ("export_csv", "Parameter --export-csv detected: {}"),
+            ("hyperopt_jobs", "Parameter -j/--job-workers detected: {}"),
+            ("hyperopt_random_state", "Parameter --random-state detected: {}"),
+            ("hyperopt_min_trades", "Parameter --min-trades detected: {}"),
+            ("hyperopt_loss", "Using Hyperopt loss class name: {}"),
+            ("hyperopt_show_index", "Parameter -n/--index detected: {}"),
+            ("hyperopt_list_best", "Parameter --best detected: {}"),
+            ("hyperopt_list_profitable", "Parameter --profitable detected: {}"),
+            ("hyperopt_list_min_trades", "Parameter --min-trades detected: {}"),
+            ("hyperopt_list_max_trades", "Parameter --max-trades detected: {}"),
+            ("hyperopt_list_min_avg_time", "Parameter --min-avg-time detected: {}"),
+            ("hyperopt_list_max_avg_time", "Parameter --max-avg-time detected: {}"),
+            ("hyperopt_list_min_avg_profit", "Parameter --min-avg-profit detected: {}"),
+            ("hyperopt_list_max_avg_profit", "Parameter --max-avg-profit detected: {}"),
+            ("hyperopt_list_min_total_profit", "Parameter --min-total-profit detected: {}"),
+            ("hyperopt_list_max_total_profit", "Parameter --max-total-profit detected: {}"),
+            ("hyperopt_list_min_objective", "Parameter --min-objective detected: {}"),
+            ("hyperopt_list_max_objective", "Parameter --max-objective detected: {}"),
+            ("hyperopt_list_no_details", "Parameter --no-details detected: {}"),
+            ("hyperopt_show_no_header", "Parameter --no-header detected: {}"),
+            ("hyperopt_ignore_missing_space", "Parameter --ignore-missing-space detected: {}"),
+        ]
+
+        self._args_to_config_loop(config, configurations)
+
+    def _process_plot_options(self, config: Config) -> None:
+        configurations = [
+            ("pairs", "Using pairs {}"),
+            ("indicators1", "Using indicators1: {}"),
+            ("indicators2", "Using indicators2: {}"),
+            ("trade_ids", "Filtering on trade_ids: {}"),
+            ("plot_limit", "Limiting plot to: {}"),
+            ("plot_auto_open", "Parameter --auto-open detected."),
+            ("trade_source", "Using trades from: {}"),
+            ("prepend_data", "Prepend detected. Allowing data prepending."),
+            ("erase", "Erase detected. Deleting existing data."),
+            ("no_trades", "Parameter --no-trades detected."),
+            ("timeframes", "timeframes --timeframes: {}"),
+            ("days", "Detected --days: {}"),
+            ("include_inactive", "Detected --include-inactive-pairs: {}"),
+            ("download_trades", "Detected --dl-trades: {}"),
+            ("convert_trades", "Detected --convert: {} - Converting Trade data to OHCV {}"),
+            ("dataformat_ohlcv", 'Using "{}" to store OHLCV data.'),
+            ("dataformat_trades", 'Using "{}" to store trades data.'),
+            ("show_timerange", "Detected --show-timerange"),
+        ]
+        self._args_to_config_loop(config, configurations)
+
+    def _process_data_options(self, config: Config) -> None:
+        self._args_to_config(
+            config, argname="new_pairs_days", logstring="Detected --new-pairs-days: {}"
+        )
+        self._args_to_config(
+            config, argname="trading_mode", logstring="Detected --trading-mode: {}"
+        )
+        config["candle_type_def"] = CandleType.get_default(
+            config.get("trading_mode", "spot") or "spot"
+        )
+        config["trading_mode"] = TradingMode(config.get("trading_mode", "spot") or "spot")
+        config["margin_mode"] = MarginMode(config.get("margin_mode", "") or "")
+        self._args_to_config(
+            config, argname="candle_types", logstring="Detected --candle-types: {}"
+        )
+
+    def _process_analyze_options(self, config: Config) -> None:
+        configurations = [
+            ("analysis_groups", "Analysis reason groups: {}"),
+            ("enter_reason_list", "Analysis enter tag list: {}"),
+            ("exit_reason_list", "Analysis exit tag list: {}"),
+            ("indicator_list", "Analysis indicator list: {}"),
+            ("entry_only", "Only analyze entry signals: {}"),
+            ("exit_only", "Only analyze exit signals: {}"),
+            ("timerange", "Filter trades by timerange: {}"),
+            ("analysis_rejected", "Analyse rejected signals: {}"),
+            ("analysis_to_csv", "Store analysis tables to CSV: {}"),
+            ("analysis_csv_path", "Path to store analysis CSVs: {}"),
+            # Lookahead analysis results
+            ("targeted_trade_amount", "Targeted Trade amount: {}"),
+            ("minimum_trade_amount", "Minimum Trade amount: {}"),
+            ("lookahead_analysis_exportfilename", "Path to store lookahead-analysis-results: {}"),
+            ("startup_candle", "Startup candle to be used on recursive analysis: {}"),
+        ]
+        self._args_to_config_loop(config, configurations)
+
+    def _args_to_config_loop(self, config, configurations: list[tuple[str, str]]) -> None:
+        for argname, logstring in configurations:
+            self._args_to_config(config, argname=argname, logstring=logstring)
+
+    def _process_runmode(self, config: Config) -> None:
+        self._args_to_config(
+            config,
+            argname="dry_run",
+            logstring="Parameter --dry-run detected, overriding dry_run to: {} ...",
+        )
+
+        if not self.runmode:
+            # Handle real mode, infer dry/live from config
+            self.runmode = RunMode.DRY_RUN if config.get("dry_run", True) else RunMode.LIVE
+            logger.info(f"Runmode set to {self.runmode.value}.")
+
+        config.update({"runmode": self.runmode})
+
+    def _process_freqai_options(self, config: Config) -> None:
+        self._args_to_config(
+            config, argname="freqaimodel", logstring="Using freqaimodel class name: {}"
+        )
+
+        self._args_to_config(
+            config, argname="freqaimodel_path", logstring="Using freqaimodel path: {}"
+        )
+
+        return
+
+    def _args_to_config(
+        self,
+        config: Config,
+        argname: str,
+        logstring: str,
+        logfun: Callable | None = None,
+        deprecated_msg: str | None = None,
+    ) -> None:
+        """
+        :param config: Configuration dictionary
+        :param argname: Argumentname in self.args - will be copied to config dict.
+        :param logstring: Logging String
+        :param logfun: logfun is applied to the configuration entry before passing
+                        that entry to the log string using .format().
+                        sample: logfun=len (prints the length of the found
+                        configuration instead of the content)
+        """
+        if (
+            argname in self.args
+            and self.args[argname] is not None
+            and self.args[argname] is not False
+        ):
+            config.update({argname: self.args[argname]})
+            if logfun:
+                logger.info(logstring.format(logfun(config[argname])))
+            else:
+                logger.info(logstring.format(config[argname]))
+            if deprecated_msg:
+                warnings.warn(f"DEPRECATED: {deprecated_msg}", DeprecationWarning, stacklevel=1)
+
+    def _resolve_pairs_list(self, config: Config) -> None:
+        """
+        Helper for download script.
+        Takes first found:
+        * -p (pairs argument)
+        * --pairs-file
+        * whitelist from config
+        """
+
+        if "pairs" in config:
+            config["exchange"]["pair_whitelist"] = config["pairs"]
+            return
+
+        if self.args.get("pairs_file"):
+            pairs_file = Path(self.args["pairs_file"])
+            logger.info(f'Reading pairs file "{pairs_file}".')
+            # Download pairs from the pairs file if no config is specified
+            # or if pairs file is specified explicitly
+            if not pairs_file.exists():
+                raise OperationalException(f'No pairs file found with path "{pairs_file}".')
+            config["pairs"] = load_file(pairs_file)
+            if isinstance(config["pairs"], list):
+                config["pairs"].sort()
+            return
+
+        if self.args.get("config"):
+            logger.info("Using pairlist from configuration.")
+            config["pairs"] = config.get("exchange", {}).get("pair_whitelist")
+        else:
+            # Fall back to /dl_path/pairs.json
+            pairs_file = config["datadir"] / "pairs.json"
+            if pairs_file.exists():
+                logger.info(f'Reading pairs file "{pairs_file}".')
+                config["pairs"] = load_file(pairs_file)
+                if "pairs" in config and isinstance(config["pairs"], list):
+                    config["pairs"].sort()
diff --git a/freqtrade/configuration/deploy_config.py b/freqtrade/configuration/deploy_config.py
new file mode 100644
index 000000000..071ee0b11
--- /dev/null
+++ b/freqtrade/configuration/deploy_config.py
@@ -0,0 +1,250 @@
+import logging
+import secrets
+from pathlib import Path
+from typing import Any
+
+from questionary import Separator, prompt
+
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def validate_is_int(val):
+    try:
+        _ = int(val)
+        return True
+    except Exception:
+        return False
+
+
+def validate_is_float(val):
+    try:
+        _ = float(val)
+        return True
+    except Exception:
+        return False
+
+
+def ask_user_overwrite(config_path: Path) -> bool:
+    questions = [
+        {
+            "type": "confirm",
+            "name": "overwrite",
+            "message": f"File {config_path} already exists. Overwrite?",
+            "default": False,
+        },
+    ]
+    answers = prompt(questions)
+    return answers["overwrite"]
+
+
+def ask_user_config() -> dict[str, Any]:
+    """
+    Ask user a few questions to build the configuration.
+    Interactive questions built using https://github.com/tmbo/questionary
+    :returns: Dict with keys to put into template
+    """
+
+    from freqtrade.configuration.detect_environment import running_in_docker
+    from freqtrade.exchange import available_exchanges
+
+    questions: list[dict[str, Any]] = [
+        {
+            "type": "confirm",
+            "name": "dry_run",
+            "message": "Do you want to enable Dry-run (simulated trades)?",
+            "default": True,
+        },
+        {
+            "type": "text",
+            "name": "stake_currency",
+            "message": "Please insert your stake currency:",
+            "default": "USDT",
+        },
+        {
+            "type": "text",
+            "name": "stake_amount",
+            "message": f"Please insert your stake amount (Number or '{UNLIMITED_STAKE_AMOUNT}'):",
+            "default": "unlimited",
+            "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_float(val),
+            "filter": lambda val: (
+                '"' + UNLIMITED_STAKE_AMOUNT + '"' if val == UNLIMITED_STAKE_AMOUNT else val
+            ),
+        },
+        {
+            "type": "text",
+            "name": "max_open_trades",
+            "message": "Please insert max_open_trades (Integer or -1 for unlimited open trades):",
+            "default": "3",
+            "validate": lambda val: validate_is_int(val),
+        },
+        {
+            "type": "select",
+            "name": "timeframe_in_config",
+            "message": "Time",
+            "choices": ["Have the strategy define timeframe.", "Override in configuration."],
+        },
+        {
+            "type": "text",
+            "name": "timeframe",
+            "message": "Please insert your desired timeframe (e.g. 5m):",
+            "default": "5m",
+            "when": lambda x: x["timeframe_in_config"] == "Override in configuration.",
+        },
+        {
+            "type": "text",
+            "name": "fiat_display_currency",
+            "message": (
+                "Please insert your display Currency for reporting "
+                "(leave empty to disable FIAT conversion):"
+            ),
+            "default": "USD",
+        },
+        {
+            "type": "select",
+            "name": "exchange_name",
+            "message": "Select exchange",
+            "choices": [
+                "binance",
+                "binanceus",
+                "bingx",
+                "gate",
+                "htx",
+                "kraken",
+                "kucoin",
+                "okx",
+                Separator("------------------"),
+                "other",
+            ],
+        },
+        {
+            "type": "confirm",
+            "name": "trading_mode",
+            "message": "Do you want to trade Perpetual Swaps (perpetual futures)?",
+            "default": False,
+            "filter": lambda val: "futures" if val else "spot",
+            "when": lambda x: x["exchange_name"] in ["binance", "gate", "okx", "bybit"],
+        },
+        {
+            "type": "autocomplete",
+            "name": "exchange_name",
+            "message": "Type your exchange name (Must be supported by ccxt)",
+            "choices": available_exchanges(),
+            "when": lambda x: x["exchange_name"] == "other",
+        },
+        {
+            "type": "password",
+            "name": "exchange_key",
+            "message": "Insert Exchange Key",
+            "when": lambda x: not x["dry_run"],
+        },
+        {
+            "type": "password",
+            "name": "exchange_secret",
+            "message": "Insert Exchange Secret",
+            "when": lambda x: not x["dry_run"],
+        },
+        {
+            "type": "password",
+            "name": "exchange_key_password",
+            "message": "Insert Exchange API Key password",
+            "when": lambda x: not x["dry_run"] and x["exchange_name"] in ("kucoin", "okx"),
+        },
+        {
+            "type": "confirm",
+            "name": "telegram",
+            "message": "Do you want to enable Telegram?",
+            "default": False,
+        },
+        {
+            "type": "password",
+            "name": "telegram_token",
+            "message": "Insert Telegram token",
+            "when": lambda x: x["telegram"],
+        },
+        {
+            "type": "password",
+            "name": "telegram_chat_id",
+            "message": "Insert Telegram chat id",
+            "when": lambda x: x["telegram"],
+        },
+        {
+            "type": "confirm",
+            "name": "api_server",
+            "message": "Do you want to enable the Rest API (includes FreqUI)?",
+            "default": False,
+        },
+        {
+            "type": "text",
+            "name": "api_server_listen_addr",
+            "message": (
+                "Insert Api server Listen Address (0.0.0.0 for docker, "
+                "otherwise best left untouched)"
+            ),
+            "default": "127.0.0.1" if not running_in_docker() else "0.0.0.0",  # noqa: S104
+            "when": lambda x: x["api_server"],
+        },
+        {
+            "type": "text",
+            "name": "api_server_username",
+            "message": "Insert api-server username",
+            "default": "freqtrader",
+            "when": lambda x: x["api_server"],
+        },
+        {
+            "type": "password",
+            "name": "api_server_password",
+            "message": "Insert api-server password",
+            "when": lambda x: x["api_server"],
+        },
+    ]
+    answers = prompt(questions)
+
+    if not answers:
+        # Interrupted questionary sessions return an empty dict.
+        raise OperationalException("User interrupted interactive questions.")
+    # Ensure default is set for non-futures exchanges
+    answers["trading_mode"] = answers.get("trading_mode", "spot")
+    answers["margin_mode"] = "isolated" if answers.get("trading_mode") == "futures" else ""
+    # Force JWT token to be a random string
+    answers["api_server_jwt_key"] = secrets.token_hex()
+    answers["api_server_ws_token"] = secrets.token_urlsafe(25)
+
+    return answers
+
+
+def deploy_new_config(config_path: Path, selections: dict[str, Any]) -> None:
+    """
+    Applies selections to the template and writes the result to config_path
+    :param config_path: Path object for new config file. Should not exist yet
+    :param selections: Dict containing selections taken by the user.
+    """
+    from jinja2.exceptions import TemplateNotFound
+
+    from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS
+    from freqtrade.util import render_template
+
+    try:
+        exchange_template = MAP_EXCHANGE_CHILDCLASS.get(
+            selections["exchange_name"], selections["exchange_name"]
+        )
+
+        selections["exchange"] = render_template(
+            templatefile=f"subtemplates/exchange_{exchange_template}.j2", arguments=selections
+        )
+    except TemplateNotFound:
+        selections["exchange"] = render_template(
+            templatefile="subtemplates/exchange_generic.j2", arguments=selections
+        )
+
+    config_text = render_template(templatefile="base_config.json.j2", arguments=selections)
+
+    logger.info(f"Writing config to `{config_path}`.")
+    logger.info(
+        "Please make sure to check the configuration contents and adjust settings to your needs."
+    )
+
+    config_path.write_text(config_text)
diff --git a/freqtrade/configuration/deprecated_settings.py b/freqtrade/configuration/deprecated_settings.py
new file mode 100644
index 000000000..202c317de
--- /dev/null
+++ b/freqtrade/configuration/deprecated_settings.py
@@ -0,0 +1,181 @@
+"""
+Functions to handle deprecated settings
+"""
+
+import logging
+
+from freqtrade.constants import Config
+from freqtrade.exceptions import ConfigurationError, OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def check_conflicting_settings(
+    config: Config,
+    section_old: str | None,
+    name_old: str,
+    section_new: str | None,
+    name_new: str,
+) -> None:
+    section_new_config = config.get(section_new, {}) if section_new else config
+    section_old_config = config.get(section_old, {}) if section_old else config
+    if name_new in section_new_config and name_old in section_old_config:
+        new_name = f"{section_new}.{name_new}" if section_new else f"{name_new}"
+        old_name = f"{section_old}.{name_old}" if section_old else f"{name_old}"
+        raise OperationalException(
+            f"Conflicting settings `{new_name}` and `{old_name}` "
+            "(DEPRECATED) detected in the configuration file. "
+            "This deprecated setting will be removed in the next versions of Freqtrade. "
+            f"Please delete it from your configuration and use the `{new_name}` "
+            "setting instead."
+        )
+
+
+def process_removed_setting(
+    config: Config, section1: str, name1: str, section2: str | None, name2: str
+) -> None:
+    """
+    :param section1: Removed section
+    :param name1: Removed setting name
+    :param section2: new section for this key
+    :param name2: new setting name
+    """
+    section1_config = config.get(section1, {})
+    if name1 in section1_config:
+        section_2 = f"{section2}.{name2}" if section2 else f"{name2}"
+        raise ConfigurationError(
+            f"Setting `{section1}.{name1}` has been moved to `{section_2}. "
+            f"Please delete it from your configuration and use the `{section_2}` "
+            "setting instead."
+        )
+
+
+def process_deprecated_setting(
+    config: Config,
+    section_old: str | None,
+    name_old: str,
+    section_new: str | None,
+    name_new: str,
+) -> None:
+    check_conflicting_settings(config, section_old, name_old, section_new, name_new)
+    section_old_config = config.get(section_old, {}) if section_old else config
+
+    if name_old in section_old_config:
+        section_1 = f"{section_old}.{name_old}" if section_old else f"{name_old}"
+        section_2 = f"{section_new}.{name_new}" if section_new else f"{name_new}"
+        logger.warning(
+            "DEPRECATED: "
+            f"The `{section_1}` setting is deprecated and "
+            "will be removed in the next versions of Freqtrade. "
+            f"Please use the `{section_2}` setting in your configuration instead."
+        )
+
+        section_new_config = config.get(section_new, {}) if section_new else config
+        section_new_config[name_new] = section_old_config[name_old]
+        del section_old_config[name_old]
+
+
+def process_temporary_deprecated_settings(config: Config) -> None:
+    # Kept for future deprecated / moved settings
+    # check_conflicting_settings(config, 'ask_strategy', 'use_sell_signal',
+    #                            'experimental', 'use_sell_signal')
+
+    process_deprecated_setting(
+        config,
+        "ask_strategy",
+        "ignore_buying_expired_candle_after",
+        None,
+        "ignore_buying_expired_candle_after",
+    )
+
+    process_deprecated_setting(config, None, "forcebuy_enable", None, "force_entry_enable")
+
+    # New settings
+    if config.get("telegram"):
+        process_deprecated_setting(
+            config["telegram"], "notification_settings", "sell", "notification_settings", "exit"
+        )
+        process_deprecated_setting(
+            config["telegram"],
+            "notification_settings",
+            "sell_fill",
+            "notification_settings",
+            "exit_fill",
+        )
+        process_deprecated_setting(
+            config["telegram"],
+            "notification_settings",
+            "sell_cancel",
+            "notification_settings",
+            "exit_cancel",
+        )
+        process_deprecated_setting(
+            config["telegram"], "notification_settings", "buy", "notification_settings", "entry"
+        )
+        process_deprecated_setting(
+            config["telegram"],
+            "notification_settings",
+            "buy_fill",
+            "notification_settings",
+            "entry_fill",
+        )
+        process_deprecated_setting(
+            config["telegram"],
+            "notification_settings",
+            "buy_cancel",
+            "notification_settings",
+            "entry_cancel",
+        )
+    if config.get("webhook"):
+        process_deprecated_setting(config, "webhook", "webhookbuy", "webhook", "webhookentry")
+        process_deprecated_setting(
+            config, "webhook", "webhookbuycancel", "webhook", "webhookentrycancel"
+        )
+        process_deprecated_setting(
+            config, "webhook", "webhookbuyfill", "webhook", "webhookentryfill"
+        )
+        process_deprecated_setting(config, "webhook", "webhooksell", "webhook", "webhookexit")
+        process_deprecated_setting(
+            config, "webhook", "webhooksellcancel", "webhook", "webhookexitcancel"
+        )
+        process_deprecated_setting(
+            config, "webhook", "webhooksellfill", "webhook", "webhookexitfill"
+        )
+
+    # Legacy way - having them in experimental ...
+
+    process_removed_setting(config, "experimental", "use_sell_signal", None, "use_exit_signal")
+    process_removed_setting(config, "experimental", "sell_profit_only", None, "exit_profit_only")
+    process_removed_setting(
+        config, "experimental", "ignore_roi_if_buy_signal", None, "ignore_roi_if_entry_signal"
+    )
+
+    process_removed_setting(config, "ask_strategy", "use_sell_signal", None, "use_exit_signal")
+    process_removed_setting(config, "ask_strategy", "sell_profit_only", None, "exit_profit_only")
+    process_removed_setting(
+        config, "ask_strategy", "sell_profit_offset", None, "exit_profit_offset"
+    )
+    process_removed_setting(
+        config, "ask_strategy", "ignore_roi_if_buy_signal", None, "ignore_roi_if_entry_signal"
+    )
+    if config.get("edge", {}).get(
+        "enabled", False
+    ) and "capital_available_percentage" in config.get("edge", {}):
+        raise ConfigurationError(
+            "DEPRECATED: "
+            "Using 'edge.capital_available_percentage' has been deprecated in favor of "
+            "'tradable_balance_ratio'. Please migrate your configuration to "
+            "'tradable_balance_ratio' and remove 'capital_available_percentage' "
+            "from the edge configuration."
+        )
+    if "ticker_interval" in config:
+        raise ConfigurationError(
+            "DEPRECATED: 'ticker_interval' detected. "
+            "Please use 'timeframe' instead of 'ticker_interval."
+        )
+
+    if "protections" in config:
+        raise ConfigurationError(
+            "DEPRECATED: Setting 'protections' in the configuration is deprecated."
+        )
diff --git a/freqtrade/configuration/detect_environment.py b/freqtrade/configuration/detect_environment.py
new file mode 100644
index 000000000..1f9185548
--- /dev/null
+++ b/freqtrade/configuration/detect_environment.py
@@ -0,0 +1,8 @@
+import os
+
+
+def running_in_docker() -> bool:
+    """
+    Check if we are running in a docker container
+    """
+    return os.environ.get("FT_APP_ENV") == "docker"
diff --git a/freqtrade/configuration/directory_operations.py b/freqtrade/configuration/directory_operations.py
new file mode 100644
index 000000000..1fa5c7947
--- /dev/null
+++ b/freqtrade/configuration/directory_operations.py
@@ -0,0 +1,112 @@
+import logging
+import shutil
+from pathlib import Path
+
+from freqtrade.configuration.detect_environment import running_in_docker
+from freqtrade.constants import (
+    USER_DATA_FILES,
+    USERPATH_FREQAIMODELS,
+    USERPATH_HYPEROPTS,
+    USERPATH_NOTEBOOKS,
+    USERPATH_STRATEGIES,
+    Config,
+)
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def create_datadir(config: Config, datadir: str | None = None) -> Path:
+    folder = Path(datadir) if datadir else Path(f"{config['user_data_dir']}/data")
+    if not datadir:
+        # set datadir
+        exchange_name = config.get("exchange", {}).get("name", "").lower()
+        folder = folder.joinpath(exchange_name)
+
+    if not folder.is_dir():
+        folder.mkdir(parents=True)
+        logger.info(f"Created data directory: {datadir}")
+    return folder
+
+
+def chown_user_directory(directory: Path) -> None:
+    """
+    Use Sudo to change permissions of the home-directory if necessary
+    Only applies when running in docker!
+    """
+    if running_in_docker():
+        try:
+            import subprocess  # noqa: S404, RUF100
+
+            subprocess.check_output(["sudo", "chown", "-R", "ftuser:", str(directory.resolve())])
+        except Exception:
+            logger.warning(f"Could not chown {directory}")
+
+
+def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
+    """
+    Create userdata directory structure.
+    if create_dir is True, then the parent-directory will be created if it does not exist.
+    Sub-directories will always be created if the parent directory exists.
+    Raises OperationalException if given a non-existing directory.
+    :param directory: Directory to check
+    :param create_dir: Create directory if it does not exist.
+    :return: Path object containing the directory
+    """
+    sub_dirs = [
+        "backtest_results",
+        "data",
+        USERPATH_HYPEROPTS,
+        "hyperopt_results",
+        "logs",
+        USERPATH_NOTEBOOKS,
+        "plot",
+        USERPATH_STRATEGIES,
+        USERPATH_FREQAIMODELS,
+    ]
+    folder = Path(directory)
+    chown_user_directory(folder)
+    if not folder.is_dir():
+        if create_dir:
+            folder.mkdir(parents=True)
+            logger.info(f"Created user-data directory: {folder}")
+        else:
+            raise OperationalException(
+                f"Directory `{folder}` does not exist. "
+                "Please use `freqtrade create-userdir` to create a user directory"
+            )
+
+    # Create required subdirectories
+    for f in sub_dirs:
+        subfolder = folder / f
+        if not subfolder.is_dir():
+            if subfolder.exists() or subfolder.is_symlink():
+                raise OperationalException(
+                    f"File `{subfolder}` exists already and is not a directory. "
+                    "Freqtrade requires this to be a directory."
+                )
+            subfolder.mkdir(parents=False)
+    return folder
+
+
+def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
+    """
+    Copy files from templates to User data directory.
+    :param directory: Directory to copy data to
+    :param overwrite: Overwrite existing sample files
+    """
+    if not directory.is_dir():
+        raise OperationalException(f"Directory `{directory}` does not exist.")
+    sourcedir = Path(__file__).parents[1] / "templates"
+    for source, target in USER_DATA_FILES.items():
+        targetdir = directory / target
+        if not targetdir.is_dir():
+            raise OperationalException(f"Directory `{targetdir}` does not exist.")
+        targetfile = targetdir / source
+        if targetfile.exists():
+            if not overwrite:
+                logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
+                continue
+            logger.warning(f"File `{targetfile}` exists already, overwriting.")
+        shutil.copy(str(sourcedir / source), str(targetfile))
diff --git a/freqtrade/configuration/environment_vars.py b/freqtrade/configuration/environment_vars.py
new file mode 100644
index 000000000..8a825f59b
--- /dev/null
+++ b/freqtrade/configuration/environment_vars.py
@@ -0,0 +1,70 @@
+import logging
+import os
+from typing import Any
+
+import rapidjson
+
+from freqtrade.constants import ENV_VAR_PREFIX
+from freqtrade.misc import deep_merge_dicts
+
+
+logger = logging.getLogger(__name__)
+
+
+def _get_var_typed(val):
+    try:
+        return int(val)
+    except ValueError:
+        try:
+            return float(val)
+        except ValueError:
+            if val.lower() in ("t", "true"):
+                return True
+            elif val.lower() in ("f", "false"):
+                return False
+            # try to convert from json
+            try:
+                value = rapidjson.loads(val)
+                # Limited to lists for now
+                if isinstance(value, list):
+                    return value
+            except rapidjson.JSONDecodeError:
+                pass
+    # keep as string
+    return val
+
+
+def _flat_vars_to_nested_dict(env_dict: dict[str, Any], prefix: str) -> dict[str, Any]:
+    """
+    Environment variables must be prefixed with FREQTRADE.
+    FREQTRADE__{section}__{key}
+    :param env_dict: Dictionary to validate - usually os.environ
+    :param prefix: Prefix to consider (usually FREQTRADE__)
+    :return: Nested dict based on available and relevant variables.
+    """
+    no_convert = ["CHAT_ID", "PASSWORD"]
+    relevant_vars: dict[str, Any] = {}
+
+    for env_var, val in sorted(env_dict.items()):
+        if env_var.startswith(prefix):
+            logger.info(f"Loading variable '{env_var}'")
+            key = env_var.replace(prefix, "")
+            for k in reversed(key.split("__")):
+                val = {
+                    k.lower(): (
+                        _get_var_typed(val)
+                        if not isinstance(val, dict) and k not in no_convert
+                        else val
+                    )
+                }
+            relevant_vars = deep_merge_dicts(val, relevant_vars)
+    return relevant_vars
+
+
+def enironment_vars_to_dict() -> dict[str, Any]:
+    """
+    Read environment variables and return a nested dict for relevant variables
+    Relevant variables must follow the FREQTRADE__{section}__{key} pattern
+    :return: Nested dict based on available and relevant variables.
+    """
+    return _flat_vars_to_nested_dict(os.environ.copy(), ENV_VAR_PREFIX)
diff --git a/freqtrade/configuration/load_config.py b/freqtrade/configuration/load_config.py
new file mode 100644
index 000000000..e18707a99
--- /dev/null
+++ b/freqtrade/configuration/load_config.py
@@ -0,0 +1,120 @@
+"""
+This module contain functions to load the configuration file
+"""
+
+import logging
+import re
+import sys
+from copy import deepcopy
+from pathlib import Path
+from typing import Any
+
+import rapidjson
+
+from freqtrade.constants import MINIMAL_CONFIG, Config
+from freqtrade.exceptions import ConfigurationError, OperationalException
+from freqtrade.misc import deep_merge_dicts
+
+
+logger = logging.getLogger(__name__)
+
+
+CONFIG_PARSE_MODE = rapidjson.PM_COMMENTS | rapidjson.PM_TRAILING_COMMAS
+
+
+def log_config_error_range(path: str, errmsg: str) -> str:
+    """
+    Parses configuration file and prints range around error
+    """
+    if path != "-":
+        offsetlist = re.findall(r"(?<=Parse\serror\sat\soffset\s)\d+", errmsg)
+        if offsetlist:
+            offset = int(offsetlist[0])
+            text = Path(path).read_text()
+            # Fetch an offset of 80 characters around the error line
+            subtext = text[offset - min(80, offset) : offset + 80]
+            segments = subtext.split("\n")
+            if len(segments) > 3:
+                # Remove first and last lines, to avoid odd truncations
+                return "\n".join(segments[1:-1])
+            else:
+                return subtext
+    return ""
+
+
+def load_file(path: Path) -> dict[str, Any]:
+    try:
+        with path.open("r") as file:
+            config = rapidjson.load(file, parse_mode=CONFIG_PARSE_MODE)
+    except FileNotFoundError:
+        raise OperationalException(f'File "{path}" not found!') from None
+    return config
+
+
+def load_config_file(path: str) -> dict[str, Any]:
+    """
+    Loads a config file from the given path
+    :param path: path as str
+    :return: configuration as dictionary
+    """
+    try:
+        # Read config from stdin if requested in the options
+        with Path(path).open() if path != "-" else sys.stdin as file:
+            config = rapidjson.load(file, parse_mode=CONFIG_PARSE_MODE)
+    except FileNotFoundError:
+        raise OperationalException(
+            f'Config file "{path}" not found!'
+            " Please create a config file or check whether it exists."
+        ) from None
+    except rapidjson.JSONDecodeError as e:
+        err_range = log_config_error_range(path, str(e))
+        raise ConfigurationError(
+            f"{e}\nPlease verify the following segment of your configuration:\n{err_range}"
+            if err_range
+            else "Please verify your configuration file for syntax errors."
+        )
+
+    return config
+
+
+def load_from_files(
+    files: list[str], base_path: Path | None = None, level: int = 0
+) -> dict[str, Any]:
+    """
+    Recursively load configuration files if specified.
+    Sub-files are assumed to be relative to the initial config.
+    """
+    config: Config = {}
+    if level > 5:
+        raise ConfigurationError("Config loop detected.")
+
+    if not files:
+        return deepcopy(MINIMAL_CONFIG)
+    files_loaded = []
+    # We expect here a list of config filenames
+    for filename in files:
+        logger.info(f"Using config: {filename} ...")
+        if filename == "-":
+            # Immediately load stdin and return
+            return load_config_file(filename)
+        file = Path(filename)
+        if base_path:
+            # Prepend basepath to allow for relative assignments
+            file = base_path / file
+
+        config_tmp = load_config_file(str(file))
+        if "add_config_files" in config_tmp:
+            config_sub = load_from_files(
+                config_tmp["add_config_files"], file.resolve().parent, level + 1
+            )
+            files_loaded.extend(config_sub.get("config_files", []))
+            config_tmp = deep_merge_dicts(config_tmp, config_sub)
+
+        files_loaded.insert(0, str(file))
+
+        # Merge config options, overwriting prior values
+        config = deep_merge_dicts(config_tmp, config)
+
+    config["config_files"] = files_loaded
+
+    return config
diff --git a/freqtrade/configuration/timerange.py b/freqtrade/configuration/timerange.py
new file mode 100644
index 000000000..30860cd63
--- /dev/null
+++ b/freqtrade/configuration/timerange.py
@@ -0,0 +1,180 @@
+"""
+This module contains the argument manager class
+"""
+
+import logging
+import re
+from datetime import datetime, timezone
+
+from typing_extensions import Self
+
+from freqtrade.constants import DATETIME_PRINT_FORMAT
+from freqtrade.exceptions import ConfigurationError
+from freqtrade.util import dt_from_ts
+
+
+logger = logging.getLogger(__name__)
+
+
+class TimeRange:
+    """
+    object defining timerange inputs.
+    [start/stop]type defines if [start/stop]ts shall be used.
+    if *type is None, don't use corresponding startvalue.
+    """
+
+    def __init__(
+        self,
+        starttype: str | None = None,
+        stoptype: str | None = None,
+        startts: int = 0,
+        stopts: int = 0,
+    ):
+        self.starttype: str | None = starttype
+        self.stoptype: str | None = stoptype
+        self.startts: int = startts
+        self.stopts: int = stopts
+
+    @property
+    def startdt(self) -> datetime | None:
+        if self.startts:
+            return dt_from_ts(self.startts)
+        return None
+
+    @property
+    def stopdt(self) -> datetime | None:
+        if self.stopts:
+            return dt_from_ts(self.stopts)
+        return None
+
+    @property
+    def timerange_str(self) -> str:
+        """
+        Returns a string representation of the timerange as used by parse_timerange.
+        Follows the format yyyymmdd-yyyymmdd - leaving out the parts that are not set.
+        """
+        start = ""
+        stop = ""
+        if startdt := self.startdt:
+            start = startdt.strftime("%Y%m%d")
+        if stopdt := self.stopdt:
+            stop = stopdt.strftime("%Y%m%d")
+        return f"{start}-{stop}"
+
+    @property
+    def start_fmt(self) -> str:
+        """
+        Returns a string representation of the start date
+        """
+        val = "unbounded"
+        if (startdt := self.startdt) is not None:
+            val = startdt.strftime(DATETIME_PRINT_FORMAT)
+        return val
+
+    @property
+    def stop_fmt(self) -> str:
+        """
+        Returns a string representation of the stop date
+        """
+        val = "unbounded"
+        if (stopdt := self.stopdt) is not None:
+            val = stopdt.strftime(DATETIME_PRINT_FORMAT)
+        return val
+
+    def __eq__(self, other):
+        """Override the default Equals behavior"""
+        return (
+            self.starttype == other.starttype
+            and self.stoptype == other.stoptype
+            and self.startts == other.startts
+            and self.stopts == other.stopts
+        )
+
+    def subtract_start(self, seconds: int) -> None:
+        """
+        Subtracts <seconds> from startts if startts is set.
+        :param seconds: Seconds to subtract from starttime
+        :return: None (Modifies the object in place)
+        """
+        if self.startts:
+            self.startts = self.startts - seconds
+
+    def adjust_start_if_necessary(
+        self, timeframe_secs: int, startup_candles: int, min_date: datetime
+    ) -> None:
+        """
+        Adjust startts by <startup_candles> candles.
+        Applies only if no startup-candles have been available.
+        :param timeframe_secs: Timeframe in seconds e.g. `timeframe_to_seconds('5m')`
+        :param startup_candles: Number of candles to move start-date forward
+        :param min_date: Minimum data date loaded. Key kriterium to decide if start-time
+                         has to be moved
+        :return: None (Modifies the object in place)
+        """
+        if not self.starttype or (startup_candles and min_date.timestamp() >= self.startts):
+            # If no startts was defined, or backtest-data starts at the defined backtest-date
+            logger.warning(
+                "Moving start-date by %s candles to account for startup time.", startup_candles
+            )
+            self.startts = int(min_date.timestamp() + timeframe_secs * startup_candles)
+            self.starttype = "date"
+
+    @classmethod
+    def parse_timerange(cls, text: str | None) -> Self:
+        """
+        Parse the value of the argument --timerange to determine what is the range desired
+        :param text: value from --timerange
+        :return: Start and End range period
+        """
+        if not text:
+            return cls(None, None, 0, 0)
+        syntax = [
+            (r"^-(\d{8})$", (None, "date")),
+            (r"^(\d{8})-$", ("date", None)),
+            (r"^(\d{8})-(\d{8})$", ("date", "date")),
+            (r"^-(\d{10})$", (None, "date")),
+            (r"^(\d{10})-$", ("date", None)),
+            (r"^(\d{10})-(\d{10})$", ("date", "date")),
+            (r"^-(\d{13})$", (None, "date")),
+            (r"^(\d{13})-$", ("date", None)),
+            (r"^(\d{13})-(\d{13})$", ("date", "date")),
+        ]
+        for rex, stype in syntax:
+            # Apply the regular expression to text
+            match = re.match(rex, text)
+            if match:  # Regex has matched
+                rvals = match.groups()
+                index = 0
+                start: int = 0
+                stop: int = 0
+                if stype[0]:
+                    starts = rvals[index]
+                    if stype[0] == "date" and len(starts) == 8:
+                        start = int(
+                            datetime.strptime(starts, "%Y%m%d")
+                            .replace(tzinfo=timezone.utc)
+                            .timestamp()
+                        )
+                    elif len(starts) == 13:
+                        start = int(starts) // 1000
+                    else:
+                        start = int(starts)
+                    index += 1
+                if stype[1]:
+                    stops = rvals[index]
+                    if stype[1] == "date" and len(stops) == 8:
+                        stop = int(
+                            datetime.strptime(stops, "%Y%m%d")
+                            .replace(tzinfo=timezone.utc)
+                            .timestamp()
+                        )
+                    elif len(stops) == 13:
+                        stop = int(stops) // 1000
+                    else:
+                        stop = int(stops)
+                if start > stop > 0:
+                    raise ConfigurationError(
+                        f'Start date is after stop date for timerange "{text}"'
+                    )
+                return cls(stype[0], stype[1], start, stop)
+        raise ConfigurationError(f'Incorrect syntax for timerange "{text}"')
diff --git a/freqtrade/constants.py b/freqtrade/constants.py
new file mode 100644
index 000000000..a5c07ae1f
--- /dev/null
+++ b/freqtrade/constants.py
@@ -0,0 +1,214 @@
+# pragma pylint: disable=too-few-public-methods
+
+"""
+bot constants
+"""
+
+from typing import Any, Literal
+
+from freqtrade.enums import CandleType, PriceType
+
+
+DOCS_LINK = "https://www.freqtrade.io/en/stable"
+DEFAULT_CONFIG = "config.json"
+PROCESS_THROTTLE_SECS = 5  # sec
+HYPEROPT_EPOCH = 100  # epochs
+RETRY_TIMEOUT = 30  # sec
+TIMEOUT_UNITS = ["minutes", "seconds"]
+EXPORT_OPTIONS = ["none", "trades", "signals"]
+DEFAULT_DB_PROD_URL = "sqlite:///tradesv3.sqlite"
+DEFAULT_DB_DRYRUN_URL = "sqlite:///tradesv3.dryrun.sqlite"
+UNLIMITED_STAKE_AMOUNT = "unlimited"
+DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
+REQUIRED_ORDERTIF = ["entry", "exit"]
+REQUIRED_ORDERTYPES = ["entry", "exit", "stoploss", "stoploss_on_exchange"]
+PRICING_SIDES = ["ask", "bid", "same", "other"]
+ORDERTYPE_POSSIBILITIES = ["limit", "market"]
+_ORDERTIF_POSSIBILITIES = ["GTC", "FOK", "IOC", "PO"]
+ORDERTIF_POSSIBILITIES = _ORDERTIF_POSSIBILITIES + [t.lower() for t in _ORDERTIF_POSSIBILITIES]
+STOPLOSS_PRICE_TYPES = [p for p in PriceType]
+HYPEROPT_LOSS_BUILTIN = [
+    "ShortTradeDurHyperOptLoss",
+    "OnlyProfitHyperOptLoss",
+    "SharpeHyperOptLoss",
+    "SharpeHyperOptLossDaily",
+    "SortinoHyperOptLoss",
+    "SortinoHyperOptLossDaily",
+    "CalmarHyperOptLoss",
+    "MaxDrawDownHyperOptLoss",
+    "MaxDrawDownRelativeHyperOptLoss",
+    "MaxDrawDownPerPairHyperOptLoss",
+    "ProfitDrawDownHyperOptLoss",
+    "MultiMetricHyperOptLoss",
+]
+AVAILABLE_PAIRLISTS = [
+    "StaticPairList",
+    "VolumePairList",
+    "PercentChangePairList",
+    "ProducerPairList",
+    "RemotePairList",
+    "MarketCapPairList",
+    "AgeFilter",
+    "FullTradesFilter",
+    "OffsetFilter",
+    "PerformanceFilter",
+    "PrecisionFilter",
+    "PriceFilter",
+    "RangeStabilityFilter",
+    "ShuffleFilter",
+    "SpreadFilter",
+    "VolatilityFilter",
+]
+AVAILABLE_DATAHANDLERS = ["json", "jsongz", "feather", "parquet"]
+BACKTEST_BREAKDOWNS = ["day", "week", "month", "year"]
+BACKTEST_CACHE_AGE = ["none", "day", "week", "month"]
+BACKTEST_CACHE_DEFAULT = "day"
+DRY_RUN_WALLET = 1000
+DATETIME_PRINT_FORMAT = "%Y-%m-%d %H:%M:%S"
+MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
+DEFAULT_DATAFRAME_COLUMNS = ["date", "open", "high", "low", "close", "volume"]
+# Don't modify sequence of DEFAULT_TRADES_COLUMNS
+# it has wide consequences for stored trades files
+DEFAULT_TRADES_COLUMNS = ["timestamp", "id", "type", "side", "price", "amount", "cost"]
+DEFAULT_ORDERFLOW_COLUMNS = ["level", "bid", "ask", "delta"]
+TRADES_DTYPES = {
+    "timestamp": "int64",
+    "id": "str",
+    "type": "str",
+    "side": "str",
+    "price": "float64",
+    "amount": "float64",
+    "cost": "float64",
+}
+TRADING_MODES = ["spot", "margin", "futures"]
+MARGIN_MODES = ["cross", "isolated", ""]
+
+LAST_BT_RESULT_FN = ".last_result.json"
+FTHYPT_FILEVERSION = "fthypt_fileversion"
+
+USERPATH_HYPEROPTS = "hyperopts"
+USERPATH_STRATEGIES = "strategies"
+USERPATH_NOTEBOOKS = "notebooks"
+USERPATH_FREQAIMODELS = "freqaimodels"
+
+TELEGRAM_SETTING_OPTIONS = ["on", "off", "silent"]
+WEBHOOK_FORMAT_OPTIONS = ["form", "json", "raw"]
+FULL_DATAFRAME_THRESHOLD = 100
+CUSTOM_TAG_MAX_LENGTH = 255
+DL_DATA_TIMEFRAMES = ["1m", "5m"]
+
+ENV_VAR_PREFIX = "FREQTRADE__"
+
+CANCELED_EXCHANGE_STATES = ("cancelled", "canceled", "expired", "rejected")
+NON_OPEN_EXCHANGE_STATES = (*CANCELED_EXCHANGE_STATES, "closed")
+
+# Define decimals per coin for outputs
+# Only used for outputs.
+DECIMAL_PER_COIN_FALLBACK = 3  # Should be low to avoid listing all possible FIAT's
+DECIMALS_PER_COIN = {
+    "BTC": 8,
+    "ETH": 5,
+}
+
+DUST_PER_COIN = {"BTC": 0.0001, "ETH": 0.01}
+
+# Source files with destination directories within user-directory
+USER_DATA_FILES = {
+    "sample_strategy.py": USERPATH_STRATEGIES,
+    "sample_hyperopt_loss.py": USERPATH_HYPEROPTS,
+    "strategy_analysis_example.ipynb": USERPATH_NOTEBOOKS,
+}
+
+SUPPORTED_FIAT = [
+    "AUD",
+    "BRL",
+    "CAD",
+    "CHF",
+    "CLP",
+    "CNY",
+    "CZK",
+    "DKK",
+    "EUR",
+    "GBP",
+    "HKD",
+    "HUF",
+    "IDR",
+    "ILS",
+    "INR",
+    "JPY",
+    "KRW",
+    "MXN",
+    "MYR",
+    "NOK",
+    "NZD",
+    "PHP",
+    "PKR",
+    "PLN",
+    "RUB",
+    "UAH",
+    "SEK",
+    "SGD",
+    "THB",
+    "TRY",
+    "TWD",
+    "ZAR",
+    "USD",
+    "BTC",
+    "ETH",
+    "XRP",
+    "LTC",
+    "BCH",
+    "BNB",
+    "",  # Allow empty field in config.
+]
+
+MINIMAL_CONFIG = {
+    "stake_currency": "",
+    "dry_run": True,
+    "exchange": {
+        "name": "",
+        "key": "",
+        "secret": "",
+        "pair_whitelist": [],
+        "ccxt_async_config": {},
+    },
+}
+
+
+CANCEL_REASON = {
+    "TIMEOUT": "cancelled due to timeout",
+    "PARTIALLY_FILLED_KEEP_OPEN": "partially filled - keeping order open",
+    "PARTIALLY_FILLED": "partially filled",
+    "FULLY_CANCELLED": "fully cancelled",
+    "ALL_CANCELLED": "cancelled (all unfilled and partially filled open orders cancelled)",
+    "CANCELLED_ON_EXCHANGE": "cancelled on exchange",
+    "FORCE_EXIT": "forcesold",
+    "REPLACE": "cancelled to be replaced by new limit order",
+    "REPLACE_FAILED": "failed to replace order, deleting Trade",
+    "USER_CANCEL": "user requested order cancel",
+}
+
+# List of pairs with their timeframes
+PairWithTimeframe = tuple[str, str, CandleType]
+ListPairsWithTimeframes = list[PairWithTimeframe]
+
+# Type for trades list
+TradeList = list[list]
+# ticks, pair, timeframe, CandleType
+TickWithTimeframe = tuple[str, str, CandleType, int | None, int | None]
+ListTicksWithTimeframes = list[TickWithTimeframe]
+
+LongShort = Literal["long", "short"]
+EntryExit = Literal["entry", "exit"]
+BuySell = Literal["buy", "sell"]
+MakerTaker = Literal["maker", "taker"]
+BidAsk = Literal["bid", "ask"]
+OBLiteral = Literal["asks", "bids"]
+
+Config = dict[str, Any]
+# Exchange part of the configuration.
+ExchangeConfig = dict[str, Any]
+IntOrInf = float
+
+
+EntryExecuteMode = Literal["initial", "pos_adjust", "replace"]
diff --git a/freqtrade/data/__init__.py b/freqtrade/data/__init__.py
new file mode 100644
index 000000000..492155031
--- /dev/null
+++ b/freqtrade/data/__init__.py
@@ -0,0 +1,9 @@
+"""
+Module to handle data operations for freqtrade
+"""
+
+from freqtrade.data import converter
+
+
+# limit what's imported when using `from freqtrade.data import *`
+__all__ = ["converter"]
diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py
new file mode 100644
index 000000000..1d580a7dd
--- /dev/null
+++ b/freqtrade/data/btanalysis.py
@@ -0,0 +1,618 @@
+"""
+Helpers when analyzing backtest data
+"""
+
+import logging
+import zipfile
+from copy import copy
+from datetime import datetime, timezone
+from io import BytesIO, StringIO
+from pathlib import Path
+from typing import Any, Literal
+
+import numpy as np
+import pandas as pd
+
+from freqtrade.constants import LAST_BT_RESULT_FN, IntOrInf
+from freqtrade.exceptions import ConfigurationError, OperationalException
+from freqtrade.ft_types import BacktestHistoryEntryType, BacktestResultType
+from freqtrade.misc import file_dump_json, json_load
+from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
+from freqtrade.persistence import LocalTrade, Trade, init_db
+
+
+logger = logging.getLogger(__name__)
+
+# Newest format
+BT_DATA_COLUMNS = [
+    "pair",
+    "stake_amount",
+    "max_stake_amount",
+    "amount",
+    "open_date",
+    "close_date",
+    "open_rate",
+    "close_rate",
+    "fee_open",
+    "fee_close",
+    "trade_duration",
+    "profit_ratio",
+    "profit_abs",
+    "exit_reason",
+    "initial_stop_loss_abs",
+    "initial_stop_loss_ratio",
+    "stop_loss_abs",
+    "stop_loss_ratio",
+    "min_rate",
+    "max_rate",
+    "is_open",
+    "enter_tag",
+    "leverage",
+    "is_short",
+    "open_timestamp",
+    "close_timestamp",
+    "orders",
+]
+
+
+def get_latest_optimize_filename(directory: Path | str, variant: str) -> str:
+    """
+    Get latest backtest export based on '.last_result.json'.
+    :param directory: Directory to search for last result
+    :param variant: 'backtest' or 'hyperopt' - the method to return
+    :return: string containing the filename of the latest backtest result
+    :raises: ValueError in the following cases:
+        * Directory does not exist
+        * `directory/.last_result.json` does not exist
+        * `directory/.last_result.json` has the wrong content
+    """
+    if isinstance(directory, str):
+        directory = Path(directory)
+    if not directory.is_dir():
+        raise ValueError(f"Directory '{directory}' does not exist.")
+    filename = directory / LAST_BT_RESULT_FN
+
+    if not filename.is_file():
+        raise ValueError(
+            f"Directory '{directory}' does not seem to contain backtest statistics yet."
+        )
+
+    with filename.open() as file:
+        data = json_load(file)
+
+    if f"latest_{variant}" not in data:
+        raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
+
+    return data[f"latest_{variant}"]
+
+
+def get_latest_backtest_filename(directory: Path | str) -> str:
+    """
+    Get latest backtest export based on '.last_result.json'.
+    :param directory: Directory to search for last result
+    :return: string containing the filename of the latest backtest result
+    :raises: ValueError in the following cases:
+        * Directory does not exist
+        * `directory/.last_result.json` does not exist
+        * `directory/.last_result.json` has the wrong content
+    """
+    return get_latest_optimize_filename(directory, "backtest")
+
+
+def get_latest_hyperopt_filename(directory: Path | str) -> str:
+    """
+    Get latest hyperopt export based on '.last_result.json'.
+    :param directory: Directory to search for last result
+    :return: string containing the filename of the latest hyperopt result
+    :raises: ValueError in the following cases:
+        * Directory does not exist
+        * `directory/.last_result.json` does not exist
+        * `directory/.last_result.json` has the wrong content
+    """
+    try:
+        return get_latest_optimize_filename(directory, "hyperopt")
+    except ValueError:
+        # Return default (legacy) pickle filename
+        return "hyperopt_results.pickle"
+
+
+def get_latest_hyperopt_file(directory: Path | str, predef_filename: str | None = None) -> Path:
+    """
+    Get latest hyperopt export based on '.last_result.json'.
+    :param directory: Directory to search for last result
+    :return: string containing the filename of the latest hyperopt result
+    :raises: ValueError in the following cases:
+        * Directory does not exist
+        * `directory/.last_result.json` does not exist
+        * `directory/.last_result.json` has the wrong content
+    """
+    if isinstance(directory, str):
+        directory = Path(directory)
+    if predef_filename:
+        if Path(predef_filename).is_absolute():
+            raise ConfigurationError(
+                "--hyperopt-filename expects only the filename, not an absolute path."
+            )
+        return directory / predef_filename
+    return directory / get_latest_hyperopt_filename(directory)
+
+
+def load_backtest_metadata(filename: Path | str) -> dict[str, Any]:
+    """
+    Read metadata dictionary from backtest results file without reading and deserializing entire
+    file.
+    :param filename: path to backtest results file.
+    :return: metadata dict or None if metadata is not present.
+    """
+    filename = get_backtest_metadata_filename(filename)
+    try:
+        with filename.open() as fp:
+            return json_load(fp)
+    except FileNotFoundError:
+        return {}
+    except Exception as e:
+        raise OperationalException("Unexpected error while loading backtest metadata.") from e
+
+
+def load_backtest_stats(filename: Path | str) -> BacktestResultType:
+    """
+    Load backtest statistics file.
+    :param filename: pathlib.Path object, or string pointing to the file.
+    :return: a dictionary containing the resulting file.
+    """
+    if isinstance(filename, str):
+        filename = Path(filename)
+    if filename.is_dir():
+        filename = filename / get_latest_backtest_filename(filename)
+    if not filename.is_file():
+        raise ValueError(f"File {filename} does not exist.")
+    logger.info(f"Loading backtest result from {filename}")
+
+    if filename.suffix == ".zip":
+        data = json_load(
+            StringIO(
+                load_file_from_zip(filename, filename.with_suffix(".json").name).decode("utf-8")
+            )
+        )
+    else:
+        with filename.open() as file:
+            data = json_load(file)
+
+    # Legacy list format does not contain metadata.
+    if isinstance(data, dict):
+        data["metadata"] = load_backtest_metadata(filename)
+    return data
+
+
+def load_and_merge_backtest_result(strategy_name: str, filename: Path, results: dict[str, Any]):
+    """
+    Load one strategy from multi-strategy result and merge it with results
+    :param strategy_name: Name of the strategy contained in the result
+    :param filename: Backtest-result-filename to load
+    :param results: dict to merge the result to.
+    """
+    bt_data = load_backtest_stats(filename)
+    k: Literal["metadata", "strategy"]
+    for k in ("metadata", "strategy"):
+        results[k][strategy_name] = bt_data[k][strategy_name]
+    results["metadata"][strategy_name]["filename"] = filename.stem
+    comparison = bt_data["strategy_comparison"]
+    for i in range(len(comparison)):
+        if comparison[i]["key"] == strategy_name:
+            results["strategy_comparison"].append(comparison[i])
+            break
+
+
+def _get_backtest_files(dirname: Path) -> list[Path]:
+    # Get both json and zip files separately and combine the results
+    json_files = dirname.glob("backtest-result-*-[0-9][0-9]*.json")
+    zip_files = dirname.glob("backtest-result-*-[0-9][0-9]*.zip")
+    return list(reversed(sorted(list(json_files) + list(zip_files))))
+
+
+def _extract_backtest_result(filename: Path) -> list[BacktestHistoryEntryType]:
+    metadata = load_backtest_metadata(filename)
+    return [
+        {
+            "filename": filename.stem,
+            "strategy": s,
+            "run_id": v["run_id"],
+            "notes": v.get("notes", ""),
+            # Backtest "run" time
+            "backtest_start_time": v["backtest_start_time"],
+            # Backtest timerange
+            "backtest_start_ts": v.get("backtest_start_ts", None),
+            "backtest_end_ts": v.get("backtest_end_ts", None),
+            "timeframe": v.get("timeframe", None),
+            "timeframe_detail": v.get("timeframe_detail", None),
+        }
+        for s, v in metadata.items()
+    ]
+
+
+def get_backtest_result(filename: Path) -> list[BacktestHistoryEntryType]:
+    """
+    Get backtest result read from metadata file
+    """
+    return _extract_backtest_result(filename)
+
+
+def get_backtest_resultlist(dirname: Path) -> list[BacktestHistoryEntryType]:
+    """
+    Get list of backtest results read from metadata files
+    """
+    return [
+        result
+        for filename in _get_backtest_files(dirname)
+        for result in _extract_backtest_result(filename)
+    ]
+
+
+def delete_backtest_result(file_abs: Path):
+    """
+    Delete backtest result file and corresponding metadata file.
+    """
+    # *.meta.json
+    logger.info(f"Deleting backtest result file: {file_abs.name}")
+
+    for file in file_abs.parent.glob(f"{file_abs.stem}*"):
+        logger.info(f"Deleting file: {file}")
+        file.unlink()
+
+
+def update_backtest_metadata(filename: Path, strategy: str, content: dict[str, Any]):
+    """
+    Updates backtest metadata file with new content.
+    :raises: ValueError if metadata file does not exist, or strategy is not in this file.
+    """
+    metadata = load_backtest_metadata(filename)
+    if not metadata:
+        raise ValueError("File does not exist.")
+    if strategy not in metadata:
+        raise ValueError("Strategy not in metadata.")
+    metadata[strategy].update(content)
+    # Write data again.
+    file_dump_json(get_backtest_metadata_filename(filename), metadata)
+
+
+def get_backtest_market_change(filename: Path, include_ts: bool = True) -> pd.DataFrame:
+    """
+    Read backtest market change file.
+    """
+    if filename.suffix == ".zip":
+        data = load_file_from_zip(filename, f"{filename.stem}_market_change.feather")
+        df = pd.read_feather(BytesIO(data))
+    else:
+        df = pd.read_feather(filename)
+    if include_ts:
+        df.loc[:, "__date_ts"] = df.loc[:, "date"].astype(np.int64) // 1000 // 1000
+    return df
+
+
+def find_existing_backtest_stats(
+    dirname: Path | str, run_ids: dict[str, str], min_backtest_date: datetime | None = None
+) -> dict[str, Any]:
+    """
+    Find existing backtest stats that match specified run IDs and load them.
+    :param dirname: pathlib.Path object, or string pointing to the file.
+    :param run_ids: {strategy_name: id_string} dictionary.
+    :param min_backtest_date: do not load a backtest older than specified date.
+    :return: results dict.
+    """
+    # Copy so we can modify this dict without affecting parent scope.
+    run_ids = copy(run_ids)
+    dirname = Path(dirname)
+    results: dict[str, Any] = {
+        "metadata": {},
+        "strategy": {},
+        "strategy_comparison": [],
+    }
+
+    for filename in _get_backtest_files(dirname):
+        metadata = load_backtest_metadata(filename)
+        if not metadata:
+            # Files are sorted from newest to oldest. When file without metadata is encountered it
+            # is safe to assume older files will also not have any metadata.
+            break
+
+        for strategy_name, run_id in list(run_ids.items()):
+            strategy_metadata = metadata.get(strategy_name, None)
+            if not strategy_metadata:
+                # This strategy is not present in analyzed backtest.
+                continue
+
+            if min_backtest_date is not None:
+                backtest_date = strategy_metadata["backtest_start_time"]
+                backtest_date = datetime.fromtimestamp(backtest_date, tz=timezone.utc)
+                if backtest_date < min_backtest_date:
+                    # Do not use a cached result for this strategy as first result is too old.
+                    del run_ids[strategy_name]
+                    continue
+
+            if strategy_metadata["run_id"] == run_id:
+                del run_ids[strategy_name]
+                load_and_merge_backtest_result(strategy_name, filename, results)
+
+        if len(run_ids) == 0:
+            break
+    return results
+
+
+def _load_backtest_data_df_compatibility(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Compatibility support for older backtest data.
+    """
+    df["open_date"] = pd.to_datetime(df["open_date"], utc=True)
+    df["close_date"] = pd.to_datetime(df["close_date"], utc=True)
+    # Compatibility support for pre short Columns
+    if "is_short" not in df.columns:
+        df["is_short"] = False
+    if "leverage" not in df.columns:
+        df["leverage"] = 1.0
+    if "enter_tag" not in df.columns:
+        df["enter_tag"] = df["buy_tag"]
+        df = df.drop(["buy_tag"], axis=1)
+    if "max_stake_amount" not in df.columns:
+        df["max_stake_amount"] = df["stake_amount"]
+    if "orders" not in df.columns:
+        df["orders"] = None
+    return df
+
+
+def load_backtest_data(filename: Path | str, strategy: str | None = None) -> pd.DataFrame:
+    """
+    Load backtest data file.
+    :param filename: pathlib.Path object, or string pointing to a file or directory
+    :param strategy: Strategy to load - mainly relevant for multi-strategy backtests
+                     Can also serve as protection to load the correct result.
+    :return: a dataframe with the analysis results
+    :raise: ValueError if loading goes wrong.
+    """
+    data = load_backtest_stats(filename)
+    if not isinstance(data, list):
+        # new, nested format
+        if "strategy" not in data:
+            raise ValueError("Unknown dataformat.")
+
+        if not strategy:
+            if len(data["strategy"]) == 1:
+                strategy = next(iter(data["strategy"].keys()))
+            else:
+                raise ValueError(
+                    "Detected backtest result with more than one strategy. "
+                    "Please specify a strategy."
+                )
+
+        if strategy not in data["strategy"]:
+            raise ValueError(
+                f"Strategy {strategy} not available in the backtest result. "
+                f"Available strategies are '{','.join(data['strategy'].keys())}'"
+            )
+
+        data = data["strategy"][strategy]["trades"]
+        df = pd.DataFrame(data)
+        if not df.empty:
+            df = _load_backtest_data_df_compatibility(df)
+
+    else:
+        # old format - only with lists.
+        raise OperationalException(
+            "Backtest-results with only trades data are no longer supported."
+        )
+    if not df.empty:
+        df = df.sort_values("open_date").reset_index(drop=True)
+    return df
+
+
+def load_file_from_zip(zip_path: Path, filename: str) -> bytes:
+    """
+    Load a file from a zip file
+    :param zip_path: Path to the zip file
+    :param filename: Name of the file to load
+    :return: Bytes of the file
+    :raises: ValueError if loading goes wrong.
+    """
+    try:
+        with zipfile.ZipFile(zip_path) as zipf:
+            try:
+                with zipf.open(filename) as file:
+                    return file.read()
+            except KeyError:
+                logger.error(f"File {filename} not found in zip: {zip_path}")
+                raise ValueError(f"File {filename} not found in zip: {zip_path}") from None
+    except FileNotFoundError:
+        raise ValueError(f"Zip file {zip_path} not found.")
+    except zipfile.BadZipFile:
+        logger.error(f"Bad zip file: {zip_path}.")
+        raise ValueError(f"Bad zip file: {zip_path}.") from None
+
+
+def load_backtest_analysis_data(backtest_dir: Path, name: str):
+    """
+    Load backtest analysis data either from a pickle file or from within a zip file
+    :param backtest_dir: Directory containing backtest results
+    :param name: Name of the analysis data to load (signals, rejected, exited)
+    :return: Analysis data
+    """
+    import joblib
+
+    if backtest_dir.is_dir():
+        lbf = Path(get_latest_backtest_filename(backtest_dir))
+        zip_path = backtest_dir / lbf
+    else:
+        zip_path = backtest_dir
+
+    if zip_path.suffix == ".zip":
+        # Load from zip file
+        analysis_name = f"{zip_path.stem}_{name}.pkl"
+        data = load_file_from_zip(zip_path, analysis_name)
+        if not data:
+            return None
+        loaded_data = joblib.load(BytesIO(data))
+
+        logger.info(f"Loaded {name} candles from zip: {str(zip_path)}:{analysis_name}")
+        return loaded_data
+
+    else:
+        # Load from separate pickle file
+        if backtest_dir.is_dir():
+            scpf = Path(backtest_dir, f"{zip_path.stem}_{name}.pkl")
+        else:
+            scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")
+
+        try:
+            with scpf.open("rb") as scp:
+                loaded_data = joblib.load(scp)
+                logger.info(f"Loaded {name} candles: {str(scpf)}")
+                return loaded_data
+        except Exception:
+            logger.exception(f"Cannot load {name} data from pickled results.")
+            return None
+
+
+def load_rejected_signals(backtest_dir: Path):
+    """
+    Load rejected signals from backtest directory
+    """
+    return load_backtest_analysis_data(backtest_dir, "rejected")
+
+
+def load_signal_candles(backtest_dir: Path):
+    """
+    Load signal candles from backtest directory
+    """
+    return load_backtest_analysis_data(backtest_dir, "signals")
+
+
+def load_exit_signal_candles(backtest_dir: Path) -> dict[str, dict[str, pd.DataFrame]]:
+    """
+    Load exit signal candles from backtest directory
+    """
+    return load_backtest_analysis_data(backtest_dir, "exited")
+
+
+def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
+    """
+    Find overlapping trades by expanding each trade once per period it was open
+    and then counting overlaps.
+    :param results: Results Dataframe - can be loaded
+    :param timeframe: Timeframe used for backtest
+    :return: dataframe with open-counts per time-period in timeframe
+    """
+    from freqtrade.exchange import timeframe_to_resample_freq
+
+    timeframe_freq = timeframe_to_resample_freq(timeframe)
+    dates = [
+        pd.Series(
+            pd.date_range(
+                row[1]["open_date"],
+                row[1]["close_date"],
+                freq=timeframe_freq,
+                # Exclude right boundary - the date is the candle open date.
+                inclusive="left",
+            )
+        )
+        for row in results[["open_date", "close_date"]].iterrows()
+    ]
+    deltas = [len(x) for x in dates]
+    dates = pd.Series(pd.concat(dates).values, name="date")
+    df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
+
+    df2 = pd.concat([dates, df2], axis=1)
+    df2 = df2.set_index("date")
+    df_final = df2.resample(timeframe_freq)[["pair"]].count()
+    df_final = df_final.rename({"pair": "open_trades"}, axis=1)
+    return df_final
+
+
+def evaluate_result_multi(
+    results: pd.DataFrame, timeframe: str, max_open_trades: IntOrInf
+) -> pd.DataFrame:
+    """
+    Find overlapping trades by expanding each trade once per period it was open
+    and then counting overlaps
+    :param results: Results Dataframe - can be loaded
+    :param timeframe: Frequency used for the backtest
+    :param max_open_trades: parameter max_open_trades used during backtest run
+    :return: dataframe with open-counts per time-period in freq
+    """
+    df_final = analyze_trade_parallelism(results, timeframe)
+    return df_final[df_final["open_trades"] > max_open_trades]
+
+
+def trade_list_to_dataframe(trades: list[Trade] | list[LocalTrade]) -> pd.DataFrame:
+    """
+    Convert list of Trade objects to pandas Dataframe
+    :param trades: List of trade objects
+    :return: Dataframe with BT_DATA_COLUMNS
+    """
+    df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS)
+    if len(df) > 0:
+        df["close_date"] = pd.to_datetime(df["close_date"], utc=True)
+        df["open_date"] = pd.to_datetime(df["open_date"], utc=True)
+        df["close_rate"] = df["close_rate"].astype("float64")
+    return df
+
+
+def load_trades_from_db(db_url: str, strategy: str | None = None) -> pd.DataFrame:
+    """
+    Load trades from a DB (using dburl)
+    :param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
+    :param strategy: Strategy to load - mainly relevant for multi-strategy backtests
+                     Can also serve as protection to load the correct result.
+    :return: Dataframe containing Trades
+    """
+    init_db(db_url)
+
+    filters = []
+    if strategy:
+        filters.append(Trade.strategy == strategy)
+    trades = trade_list_to_dataframe(list(Trade.get_trades(filters).all()))
+
+    return trades
+
+
+def load_trades(
+    source: str,
+    db_url: str,
+    exportfilename: Path,
+    no_trades: bool = False,
+    strategy: str | None = None,
+) -> pd.DataFrame:
+    """
+    Based on configuration option 'trade_source':
+    * loads data from DB (using `db_url`)
+    * loads data from backtestfile (using `exportfilename`)
+    :param source: "DB" or "file" - specify source to load from
+    :param db_url: sqlalchemy formatted url to a database
+    :param exportfilename: Json file generated by backtesting
+    :param no_trades: Skip using trades, only return backtesting data columns
+    :return: DataFrame containing trades
+    """
+    if no_trades:
+        df = pd.DataFrame(columns=BT_DATA_COLUMNS)
+        return df
+
+    if source == "DB":
+        return load_trades_from_db(db_url)
+    elif source == "file":
+        return load_backtest_data(exportfilename, strategy)
+
+
+def extract_trades_of_period(
+    dataframe: pd.DataFrame, trades: pd.DataFrame, date_index=False
+) -> pd.DataFrame:
+    """
+    Compare trades and backtested pair DataFrames to get trades performed on backtested period
+    :return: the DataFrame of a trades of period
+    """
+    if date_index:
+        trades_start = dataframe.index[0]
+        trades_stop = dataframe.index[-1]
+    else:
+        trades_start = dataframe.iloc[0]["date"]
+        trades_stop = dataframe.iloc[-1]["date"]
+    trades = trades.loc[
+        (trades["open_date"] >= trades_start) & (trades["close_date"] <= trades_stop)
+    ]
+    return trades
diff --git a/freqtrade/data/converter/__init__.py b/freqtrade/data/converter/__init__.py
new file mode 100644
index 000000000..5966d23b4
--- /dev/null
+++ b/freqtrade/data/converter/__init__.py
@@ -0,0 +1,40 @@
+from freqtrade.data.converter.converter import (
+    clean_ohlcv_dataframe,
+    convert_ohlcv_format,
+    ohlcv_fill_up_missing_data,
+    ohlcv_to_dataframe,
+    order_book_to_dataframe,
+    reduce_dataframe_footprint,
+    trim_dataframe,
+    trim_dataframes,
+)
+from freqtrade.data.converter.orderflow import populate_dataframe_with_trades
+from freqtrade.data.converter.trade_converter import (
+    convert_trades_format,
+    convert_trades_to_ohlcv,
+    trades_convert_types,
+    trades_df_remove_duplicates,
+    trades_dict_to_list,
+    trades_list_to_df,
+    trades_to_ohlcv,
+)
+
+
+__all__ = [
+    "clean_ohlcv_dataframe",
+    "convert_ohlcv_format",
+    "ohlcv_fill_up_missing_data",
+    "ohlcv_to_dataframe",
+    "order_book_to_dataframe",
+    "reduce_dataframe_footprint",
+    "trim_dataframe",
+    "trim_dataframes",
+    "convert_trades_format",
+    "convert_trades_to_ohlcv",
+    "populate_dataframe_with_trades",
+    "trades_convert_types",
+    "trades_df_remove_duplicates",
+    "trades_dict_to_list",
+    "trades_list_to_df",
+    "trades_to_ohlcv",
+]
diff --git a/freqtrade/data/converter/converter.py b/freqtrade/data/converter/converter.py
new file mode 100644
index 000000000..48a07082e
--- /dev/null
+++ b/freqtrade/data/converter/converter.py
@@ -0,0 +1,301 @@
+"""
+Functions to convert data from one format to another
+"""
+
+import logging
+
+import numpy as np
+import pandas as pd
+from pandas import DataFrame, to_datetime
+
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, Config
+from freqtrade.enums import CandleType, TradingMode
+
+
+logger = logging.getLogger(__name__)
+
+
+def ohlcv_to_dataframe(
+    ohlcv: list,
+    timeframe: str,
+    pair: str,
+    *,
+    fill_missing: bool = True,
+    drop_incomplete: bool = True,
+) -> DataFrame:
+    """
+    Converts a list with candle (OHLCV) data (in format returned by ccxt.fetch_ohlcv)
+    to a Dataframe
+    :param ohlcv: list with candle (OHLCV) data, as returned by exchange.async_get_candle_history
+    :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
+    :param pair: Pair this data is for (used to warn if fillup was necessary)
+    :param fill_missing: fill up missing candles with 0 candles
+                         (see ohlcv_fill_up_missing_data for details)
+    :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
+    :return: DataFrame
+    """
+    logger.debug(f"Converting candle (OHLCV) data to dataframe for pair {pair}.")
+    cols = DEFAULT_DATAFRAME_COLUMNS
+    df = DataFrame(ohlcv, columns=cols)
+
+    df["date"] = to_datetime(df["date"], unit="ms", utc=True)
+
+    # Some exchanges return int values for Volume and even for OHLC.
+    # Convert them since TA-LIB indicators used in the strategy assume floats
+    # and fail with exception...
+    df = df.astype(
+        dtype={
+            "open": "float",
+            "high": "float",
+            "low": "float",
+            "close": "float",
+            "volume": "float",
+        }
+    )
+    return clean_ohlcv_dataframe(
+        df, timeframe, pair, fill_missing=fill_missing, drop_incomplete=drop_incomplete
+    )
+
+
+def clean_ohlcv_dataframe(
+    data: DataFrame, timeframe: str, pair: str, *, fill_missing: bool, drop_incomplete: bool
+) -> DataFrame:
+    """
+    Cleanse a OHLCV dataframe by
+      * Grouping it by date (removes duplicate tics)
+      * dropping last candles if requested
+      * Filling up missing data (if requested)
+    :param data: DataFrame containing candle (OHLCV) data.
+    :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
+    :param pair: Pair this data is for (used to warn if fillup was necessary)
+    :param fill_missing: fill up missing candles with 0 candles
+                         (see ohlcv_fill_up_missing_data for details)
+    :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
+    :return: DataFrame
+    """
+    # group by index and aggregate results to eliminate duplicate ticks
+    data = data.groupby(by="date", as_index=False, sort=True).agg(
+        {
+            "open": "first",
+            "high": "max",
+            "low": "min",
+            "close": "last",
+            "volume": "max",
+        }
+    )
+    # eliminate partial candle
+    if drop_incomplete:
+        data.drop(data.tail(1).index, inplace=True)
+        logger.debug("Dropping last candle")
+
+    if fill_missing:
+        return ohlcv_fill_up_missing_data(data, timeframe, pair)
+    else:
+        return data
+
+
+def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
+    """
+    Fills up missing data with 0 volume rows,
+    using the previous close as price for "open", "high", "low" and "close", volume is set to 0
+
+    """
+    from freqtrade.exchange import timeframe_to_resample_freq
+
+    ohlcv_dict = {"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"}
+    resample_interval = timeframe_to_resample_freq(timeframe)
+    # Resample to create "NAN" values
+    df = dataframe.resample(resample_interval, on="date").agg(ohlcv_dict)
+
+    # Forwardfill close for missing columns
+    df["close"] = df["close"].ffill()
+    # Use close for "open, high, low"
+    df.loc[:, ["open", "high", "low"]] = df[["open", "high", "low"]].fillna(
+        value={
+            "open": df["close"],
+            "high": df["close"],
+            "low": df["close"],
+        }
+    )
+    df.reset_index(inplace=True)
+    len_before = len(dataframe)
+    len_after = len(df)
+    pct_missing = (len_after - len_before) / len_before if len_before > 0 else 0
+    if len_before != len_after:
+        message = (
+            f"Missing data fillup for {pair}, {timeframe}: "
+            f"before: {len_before} - after: {len_after} - {pct_missing:.2%}"
+        )
+        if pct_missing > 0.01:
+            logger.info(message)
+        else:
+            # Don't be verbose if only a small amount is missing
+            logger.debug(message)
+    return df
+
+
+def trim_dataframe(
+    df: DataFrame, timerange, *, df_date_col: str = "date", startup_candles: int = 0
+) -> DataFrame:
+    """
+    Trim dataframe based on given timerange
+    :param df: Dataframe to trim
+    :param timerange: timerange (use start and end date if available)
+    :param df_date_col: Column in the dataframe to use as Date column
+    :param startup_candles: When not 0, is used instead the timerange start date
+    :return: trimmed dataframe
+    """
+    if startup_candles:
+        # Trim candles instead of timeframe in case of given startup_candle count
+        df = df.iloc[startup_candles:, :]
+    else:
+        if timerange.starttype == "date":
+            df = df.loc[df[df_date_col] >= timerange.startdt, :]
+    if timerange.stoptype == "date":
+        df = df.loc[df[df_date_col] <= timerange.stopdt, :]
+    return df
+
+
+def trim_dataframes(
+    preprocessed: dict[str, DataFrame], timerange, startup_candles: int
+) -> dict[str, DataFrame]:
+    """
+    Trim startup period from analyzed dataframes
+    :param preprocessed: Dict of pair: dataframe
+    :param timerange: timerange (use start and end date if available)
+    :param startup_candles: Startup-candles that should be removed
+    :return: Dict of trimmed dataframes
+    """
+    processed: dict[str, DataFrame] = {}
+
+    for pair, df in preprocessed.items():
+        trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles)
+        if not trimed_df.empty:
+            processed[pair] = trimed_df
+        else:
+            logger.warning(
+                f"{pair} has no data left after adjusting for startup candles, skipping."
+            )
+    return processed
+
+
+def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
+    """
+    TODO: This should get a dedicated test
+    Gets order book list, returns dataframe with below format per suggested by creslin
+    -------------------------------------------------------------------
+     b_sum       b_size       bids       asks       a_size       a_sum
+    -------------------------------------------------------------------
+    """
+    cols = ["bids", "b_size"]
+
+    bids_frame = DataFrame(bids, columns=cols)
+    # add cumulative sum column
+    bids_frame["b_sum"] = bids_frame["b_size"].cumsum()
+    cols2 = ["asks", "a_size"]
+    asks_frame = DataFrame(asks, columns=cols2)
+    # add cumulative sum column
+    asks_frame["a_sum"] = asks_frame["a_size"].cumsum()
+
+    frame = pd.concat(
+        [
+            bids_frame["b_sum"],
+            bids_frame["b_size"],
+            bids_frame["bids"],
+            asks_frame["asks"],
+            asks_frame["a_size"],
+            asks_frame["a_sum"],
+        ],
+        axis=1,
+        keys=["b_sum", "b_size", "bids", "asks", "a_size", "a_sum"],
+    )
+    # logger.info('order book %s', frame )
+    return frame
+
+
+def convert_ohlcv_format(
+    config: Config,
+    convert_from: str,
+    convert_to: str,
+    erase: bool,
+):
+    """
+    Convert OHLCV from one format to another
+    :param config: Config dictionary
+    :param convert_from: Source format
+    :param convert_to: Target format
+    :param erase: Erase source data (does not apply if source and target format are identical)
+    """
+    from freqtrade.data.history import get_datahandler
+
+    src = get_datahandler(config["datadir"], convert_from)
+    trg = get_datahandler(config["datadir"], convert_to)
+    timeframes = config.get("timeframes", [config.get("timeframe")])
+    logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
+
+    candle_types = [
+        CandleType.from_string(ct)
+        for ct in config.get("candle_types", [c.value for c in CandleType])
+    ]
+    logger.info(candle_types)
+    paircombs = src.ohlcv_get_available_data(config["datadir"], TradingMode.SPOT)
+    paircombs.extend(src.ohlcv_get_available_data(config["datadir"], TradingMode.FUTURES))
+
+    if "pairs" in config:
+        # Filter pairs
+        paircombs = [comb for comb in paircombs if comb[0] in config["pairs"]]
+
+    if "timeframes" in config:
+        paircombs = [comb for comb in paircombs if comb[1] in config["timeframes"]]
+    paircombs = [comb for comb in paircombs if comb[2] in candle_types]
+
+    paircombs = sorted(paircombs, key=lambda x: (x[0], x[1], x[2].value))
+
+    formatted_paircombs = "\n".join(
+        [f"{pair}, {timeframe}, {candle_type}" for pair, timeframe, candle_type in paircombs]
+    )
+
+    logger.info(
+        f"Converting candle (OHLCV) data for the following pair combinations:\n"
+        f"{formatted_paircombs}"
+    )
+    for pair, timeframe, candle_type in paircombs:
+        data = src.ohlcv_load(
+            pair=pair,
+            timeframe=timeframe,
+            timerange=None,
+            fill_missing=False,
+            drop_incomplete=False,
+            startup_candles=0,
+            candle_type=candle_type,
+        )
+        logger.info(f"Converting {len(data)} {timeframe} {candle_type} candles for {pair}")
+        if len(data) > 0:
+            trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data, candle_type=candle_type)
+            if erase and convert_from != convert_to:
+                logger.info(f"Deleting source data for {pair} / {timeframe}")
+                src.ohlcv_purge(pair=pair, timeframe=timeframe, candle_type=candle_type)
+
+
+def reduce_dataframe_footprint(df: DataFrame) -> DataFrame:
+    """
+    Ensure all values are float32 in the incoming dataframe.
+    :param df: Dataframe to be converted to float/int 32s
+    :return: Dataframe converted to float/int 32s
+    """
+
+    logger.debug(f"Memory usage of dataframe is {df.memory_usage().sum() / 1024**2:.2f} MB")
+
+    df_dtypes = df.dtypes
+    for column, dtype in df_dtypes.items():
+        if column in ["open", "high", "low", "close", "volume"]:
+            continue
+        if dtype == np.float64:
+            df_dtypes[column] = np.float32
+        elif dtype == np.int64:
+            df_dtypes[column] = np.int32
+    df = df.astype(df_dtypes)
+
+    logger.debug(f"Memory usage after optimization is: {df.memory_usage().sum() / 1024**2:.2f} MB")
+
+    return df
diff --git a/freqtrade/data/converter/orderflow.py b/freqtrade/data/converter/orderflow.py
new file mode 100644
index 000000000..4b4923cfb
--- /dev/null
+++ b/freqtrade/data/converter/orderflow.py
@@ -0,0 +1,279 @@
+"""
+Functions to convert orderflow data from public_trades
+"""
+
+import logging
+import time
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+
+from freqtrade.constants import DEFAULT_ORDERFLOW_COLUMNS, Config
+from freqtrade.exceptions import DependencyException
+
+
+logger = logging.getLogger(__name__)
+
+ORDERFLOW_ADDED_COLUMNS = [
+    "trades",
+    "orderflow",
+    "imbalances",
+    "stacked_imbalances_bid",
+    "stacked_imbalances_ask",
+    "max_delta",
+    "min_delta",
+    "bid",
+    "ask",
+    "delta",
+    "total_trades",
+]
+
+
+def _init_dataframe_with_trades_columns(dataframe: pd.DataFrame):
+    """
+    Populates a dataframe with trades columns
+    :param dataframe: Dataframe to populate
+    """
+    # Initialize columns with appropriate dtypes
+    for column in ORDERFLOW_ADDED_COLUMNS:
+        dataframe[column] = np.nan
+
+    # Set columns to object type
+    for column in (
+        "trades",
+        "orderflow",
+        "imbalances",
+        "stacked_imbalances_bid",
+        "stacked_imbalances_ask",
+    ):
+        dataframe[column] = dataframe[column].astype(object)
+
+
+def timeframe_to_DateOffset(timeframe: str) -> pd.DateOffset:
+    """
+    Translates the timeframe interval value written in the human readable
+    form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
+    of seconds for one timeframe interval.
+    """
+    from freqtrade.exchange import timeframe_to_seconds
+
+    timeframe_seconds = timeframe_to_seconds(timeframe)
+    timeframe_minutes = timeframe_seconds // 60
+    if timeframe_minutes < 1:
+        return pd.DateOffset(seconds=timeframe_seconds)
+    elif 59 < timeframe_minutes < 1440:
+        return pd.DateOffset(hours=timeframe_minutes // 60)
+    elif 1440 <= timeframe_minutes < 10080:
+        return pd.DateOffset(days=timeframe_minutes // 1440)
+    elif 10000 < timeframe_minutes < 43200:
+        return pd.DateOffset(weeks=1)
+    elif timeframe_minutes >= 43200 and timeframe_minutes < 525600:
+        return pd.DateOffset(months=1)
+    elif timeframe == "1y":
+        return pd.DateOffset(years=1)
+    else:
+        return pd.DateOffset(minutes=timeframe_minutes)
+
+
+def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str):
+    from freqtrade.exchange import timeframe_to_resample_freq
+
+    if df is not None and not df.empty:
+        timeframe_frequency = timeframe_to_resample_freq(timeframe)
+        dofs = timeframe_to_DateOffset(timeframe)
+        # calculate ohlcv candle start and end
+        df["datetime"] = pd.to_datetime(df["date"], unit="ms")
+        df["candle_start"] = df["datetime"].dt.floor(timeframe_frequency)
+        # used in _now_is_time_to_refresh_trades
+        df["candle_end"] = df["candle_start"] + dofs
+        df.drop(columns=["datetime"], inplace=True)
+
+
+def populate_dataframe_with_trades(
+    cached_grouped_trades: pd.DataFrame | None,
+    config: Config,
+    dataframe: pd.DataFrame,
+    trades: pd.DataFrame,
+) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Populates a dataframe with trades
+    :param dataframe: Dataframe to populate
+    :param trades: Trades to populate with
+    :return: Dataframe with trades populated
+    """
+
+    timeframe = config["timeframe"]
+    config_orderflow = config["orderflow"]
+
+    # create columns for trades
+    _init_dataframe_with_trades_columns(dataframe)
+    if trades is None or trades.empty:
+        return dataframe, cached_grouped_trades
+
+    try:
+        start_time = time.time()
+        # calculate ohlcv candle start and end
+        _calculate_ohlcv_candle_start_and_end(trades, timeframe)
+
+        # get date of earliest max_candles candle
+        max_candles = config_orderflow["max_candles"]
+        start_date = dataframe.tail(max_candles).date.iat[0]
+        # slice of trades that are before current ohlcv candles to make groupby faster
+        trades = trades.loc[trades["candle_start"] >= start_date]
+        trades.reset_index(inplace=True, drop=True)
+
+        # group trades by candle start
+        trades_grouped_by_candle_start = trades.groupby("candle_start", group_keys=False)
+
+        candle_start: datetime
+        for candle_start, trades_grouped_df in trades_grouped_by_candle_start:
+            is_between = candle_start == dataframe["date"]
+            if is_between.any():
+                # there can only be one row with the same date
+                index = dataframe.index[is_between][0]
+
+                if (
+                    cached_grouped_trades is not None
+                    and (candle_start == cached_grouped_trades["date"]).any()
+                ):
+                    # Check if the trades are already in the cache
+                    cache_idx = cached_grouped_trades.index[
+                        cached_grouped_trades["date"] == candle_start
+                    ][0]
+                    for col in ORDERFLOW_ADDED_COLUMNS:
+                        dataframe.at[index, col] = cached_grouped_trades.at[cache_idx, col]
+                    continue
+
+                dataframe.at[index, "trades"] = trades_grouped_df.drop(
+                    columns=["candle_start", "candle_end"]
+                ).to_dict(orient="records")
+
+                # Calculate orderflow for each candle
+                orderflow = trades_to_volumeprofile_with_total_delta_bid_ask(
+                    trades_grouped_df, scale=config_orderflow["scale"]
+                )
+                dataframe.at[index, "orderflow"] = orderflow.to_dict(orient="index")
+                # orderflow_series.loc[[index]] = [orderflow.to_dict(orient="index")]
+                # Calculate imbalances for each candle's orderflow
+                imbalances = trades_orderflow_to_imbalances(
+                    orderflow,
+                    imbalance_ratio=config_orderflow["imbalance_ratio"],
+                    imbalance_volume=config_orderflow["imbalance_volume"],
+                )
+                dataframe.at[index, "imbalances"] = imbalances.to_dict(orient="index")
+
+                stacked_imbalance_range = config_orderflow["stacked_imbalance_range"]
+                dataframe.at[index, "stacked_imbalances_bid"] = stacked_imbalance(
+                    imbalances, label="bid", stacked_imbalance_range=stacked_imbalance_range
+                )
+
+                dataframe.at[index, "stacked_imbalances_ask"] = stacked_imbalance(
+                    imbalances, label="ask", stacked_imbalance_range=stacked_imbalance_range
+                )
+
+                bid = np.where(
+                    trades_grouped_df["side"].str.contains("sell"), trades_grouped_df["amount"], 0
+                )
+
+                ask = np.where(
+                    trades_grouped_df["side"].str.contains("buy"), trades_grouped_df["amount"], 0
+                )
+                deltas_per_trade = ask - bid
+                dataframe.at[index, "max_delta"] = deltas_per_trade.cumsum().max()
+                dataframe.at[index, "min_delta"] = deltas_per_trade.cumsum().min()
+
+                dataframe.at[index, "bid"] = bid.sum()
+                dataframe.at[index, "ask"] = ask.sum()
+                dataframe.at[index, "delta"] = (
+                    dataframe.at[index, "ask"] - dataframe.at[index, "bid"]
+                )
+                dataframe.at[index, "total_trades"] = len(trades_grouped_df)
+
+        logger.debug(f"trades.groups_keys in {time.time() - start_time} seconds")
+
+        # Cache the entire dataframe
+        cached_grouped_trades = dataframe.tail(config_orderflow["cache_size"]).copy()
+
+    except Exception as e:
+        logger.exception("Error populating dataframe with trades")
+        raise DependencyException(e)
+
+    return dataframe, cached_grouped_trades
+
+
+def trades_to_volumeprofile_with_total_delta_bid_ask(
+    trades: pd.DataFrame, scale: float
+) -> pd.DataFrame:
+    """
+    :param trades: dataframe
+    :param scale: scale aka bin size e.g. 0.5
+    :return: trades binned to levels according to scale aka orderflow
+    """
+    df = pd.DataFrame([], columns=DEFAULT_ORDERFLOW_COLUMNS)
+    # create bid, ask where side is sell or buy
+    df["bid_amount"] = np.where(trades["side"].str.contains("sell"), trades["amount"], 0)
+    df["ask_amount"] = np.where(trades["side"].str.contains("buy"), trades["amount"], 0)
+    df["bid"] = np.where(trades["side"].str.contains("sell"), 1, 0)
+    df["ask"] = np.where(trades["side"].str.contains("buy"), 1, 0)
+    # round the prices to the nearest multiple of the scale
+    df["price"] = ((trades["price"] / scale).round() * scale).astype("float64").values
+    if df.empty:
+        df["total"] = np.nan
+        df["delta"] = np.nan
+        return df
+
+    df["delta"] = df["ask_amount"] - df["bid_amount"]
+    df["total_volume"] = df["ask_amount"] + df["bid_amount"]
+    df["total_trades"] = df["ask"] + df["bid"]
+
+    # group to bins aka apply scale
+    df = df.groupby("price").sum(numeric_only=True)
+    return df
+
+
+def trades_orderflow_to_imbalances(df: pd.DataFrame, imbalance_ratio: int, imbalance_volume: int):
+    """
+    :param df: dataframes with bid and ask
+    :param imbalance_ratio: imbalance_ratio e.g. 3
+    :param imbalance_volume: imbalance volume e.g. 10
+    :return: dataframe with bid and ask imbalance
+    """
+    bid = df.bid
+    # compares bid and ask diagonally
+    ask = df.ask.shift(-1)
+    bid_imbalance = (bid / ask) > (imbalance_ratio)
+    # overwrite bid_imbalance with False if volume is not big enough
+    bid_imbalance_filtered = np.where(df.total_volume < imbalance_volume, False, bid_imbalance)
+    ask_imbalance = (ask / bid) > (imbalance_ratio)
+    # overwrite ask_imbalance with False if volume is not big enough
+    ask_imbalance_filtered = np.where(df.total_volume < imbalance_volume, False, ask_imbalance)
+    dataframe = pd.DataFrame(
+        {"bid_imbalance": bid_imbalance_filtered, "ask_imbalance": ask_imbalance_filtered},
+        index=df.index,
+    )
+
+    return dataframe
+
+
+def stacked_imbalance(df: pd.DataFrame, label: str, stacked_imbalance_range: int):
+    """
+    y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)
+    https://stackoverflow.com/questions/27626542/counting-consecutive-positive-values-in-python-pandas-array
+    """
+    imbalance = df[f"{label}_imbalance"]
+    int_series = pd.Series(np.where(imbalance, 1, 0))
+    # Group consecutive True values and get their counts
+    groups = (int_series != int_series.shift()).cumsum()
+    counts = int_series.groupby(groups).cumsum()
+
+    # Find indices where count meets or exceeds the range requirement
+    valid_indices = counts[counts >= stacked_imbalance_range].index
+
+    stacked_imbalance_prices = []
+    if not valid_indices.empty:
+        # Get all prices from valid indices from beginning of the range
+        stacked_imbalance_prices = [
+            imbalance.index.values[idx - (stacked_imbalance_range - 1)] for idx in valid_indices
+        ]
+    return stacked_imbalance_prices
diff --git a/freqtrade/data/converter/trade_converter.py b/freqtrade/data/converter/trade_converter.py
new file mode 100644
index 000000000..4d34fa83f
--- /dev/null
+++ b/freqtrade/data/converter/trade_converter.py
@@ -0,0 +1,164 @@
+"""
+Functions to convert data from one format to another
+"""
+
+import logging
+from pathlib import Path
+
+import pandas as pd
+from pandas import DataFrame, to_datetime
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import (
+    DEFAULT_DATAFRAME_COLUMNS,
+    DEFAULT_TRADES_COLUMNS,
+    TRADES_DTYPES,
+    Config,
+    TradeList,
+)
+from freqtrade.enums import CandleType, TradingMode
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def trades_df_remove_duplicates(trades: pd.DataFrame) -> pd.DataFrame:
+    """
+    Removes duplicates from the trades DataFrame.
+    Uses pandas.DataFrame.drop_duplicates to remove duplicates based on the 'timestamp' column.
+    :param trades: DataFrame with the columns constants.DEFAULT_TRADES_COLUMNS
+    :return: DataFrame with duplicates removed based on the 'timestamp' column
+    """
+    return trades.drop_duplicates(subset=["timestamp", "id"])
+
+
+def trades_dict_to_list(trades: list[dict]) -> TradeList:
+    """
+    Convert fetch_trades result into a List (to be more memory efficient).
+    :param trades: List of trades, as returned by ccxt.fetch_trades.
+    :return: List of Lists, with constants.DEFAULT_TRADES_COLUMNS as columns
+    """
+    return [[t[col] for col in DEFAULT_TRADES_COLUMNS] for t in trades]
+
+
+def trades_convert_types(trades: DataFrame) -> DataFrame:
+    """
+    Convert Trades dtypes and add 'date' column
+    """
+    trades = trades.astype(TRADES_DTYPES)
+    trades["date"] = to_datetime(trades["timestamp"], unit="ms", utc=True)
+    return trades
+
+
+def trades_list_to_df(trades: TradeList, convert: bool = True):
+    """
+    convert trades list to dataframe
+    :param trades: List of Lists with constants.DEFAULT_TRADES_COLUMNS as columns
+    """
+    if not trades:
+        df = DataFrame(columns=DEFAULT_TRADES_COLUMNS)
+    else:
+        df = DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
+
+    if convert:
+        df = trades_convert_types(df)
+
+    return df
+
+
+def trades_to_ohlcv(trades: DataFrame, timeframe: str) -> DataFrame:
+    """
+    Converts trades list to OHLCV list
+    :param trades: List of trades, as returned by ccxt.fetch_trades.
+    :param timeframe: Timeframe to resample data to
+    :return: OHLCV Dataframe.
+    :raises: ValueError if no trades are provided
+    """
+    from freqtrade.exchange import timeframe_to_resample_freq
+
+    if trades.empty:
+        raise ValueError("Trade-list empty.")
+    df = trades.set_index("date", drop=True)
+    resample_interval = timeframe_to_resample_freq(timeframe)
+    df_new = df["price"].resample(resample_interval).ohlc()
+    df_new["volume"] = df["amount"].resample(resample_interval).sum()
+    df_new["date"] = df_new.index
+    # Drop 0 volume rows
+    df_new = df_new.dropna()
+    return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
+
+
+def convert_trades_to_ohlcv(
+    pairs: list[str],
+    timeframes: list[str],
+    datadir: Path,
+    timerange: TimeRange,
+    erase: bool,
+    data_format_ohlcv: str,
+    data_format_trades: str,
+    candle_type: CandleType,
+) -> None:
+    """
+    Convert stored trades data to ohlcv data
+    """
+    from freqtrade.data.history import get_datahandler
+
+    data_handler_trades = get_datahandler(datadir, data_format=data_format_trades)
+    data_handler_ohlcv = get_datahandler(datadir, data_format=data_format_ohlcv)
+
+    logger.info(
+        f"About to convert pairs: '{', '.join(pairs)}', "
+        f"intervals: '{', '.join(timeframes)}' to {datadir}"
+    )
+    trading_mode = TradingMode.FUTURES if candle_type != CandleType.SPOT else TradingMode.SPOT
+    for pair in pairs:
+        trades = data_handler_trades.trades_load(pair, trading_mode)
+        for timeframe in timeframes:
+            if erase:
+                if data_handler_ohlcv.ohlcv_purge(pair, timeframe, candle_type=candle_type):
+                    logger.info(f"Deleting existing data for pair {pair}, interval {timeframe}.")
+            try:
+                ohlcv = trades_to_ohlcv(trades, timeframe)
+                # Store ohlcv
+                data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv, candle_type=candle_type)
+            except ValueError:
+                logger.warning(f"Could not convert {pair} to OHLCV.")
+
+
+def convert_trades_format(config: Config, convert_from: str, convert_to: str, erase: bool):
+    """
+    Convert trades from one format to another format.
+    :param config: Config dictionary
+    :param convert_from: Source format
+    :param convert_to: Target format
+    :param erase: Erase source data (does not apply if source and target format are identical)
+    """
+    if convert_from == "kraken_csv":
+        if config["exchange"]["name"] != "kraken":
+            raise OperationalException(
+                "Converting from csv is only supported for kraken."
+                "Please refer to the documentation for details about this special mode."
+            )
+        from freqtrade.data.converter.trade_converter_kraken import import_kraken_trades_from_csv
+
+        import_kraken_trades_from_csv(config, convert_to)
+        return
+
+    from freqtrade.data.history import get_datahandler
+
+    src = get_datahandler(config["datadir"], convert_from)
+    trg = get_datahandler(config["datadir"], convert_to)
+
+    if "pairs" not in config:
+        config["pairs"] = src.trades_get_pairs(config["datadir"])
+    logger.info(f"Converting trades for {config['pairs']}")
+    trading_mode: TradingMode = config.get("trading_mode", TradingMode.SPOT)
+    for pair in config["pairs"]:
+        data = src.trades_load(pair, trading_mode)
+        logger.info(f"Converting {len(data)} trades for {pair}")
+        trg.trades_store(pair, data, trading_mode)
+
+        if erase and convert_from != convert_to:
+            logger.info(f"Deleting source Trade data for {pair}.")
+            src.trades_purge(pair, trading_mode)
diff --git a/freqtrade/data/converter/trade_converter_kraken.py b/freqtrade/data/converter/trade_converter_kraken.py
new file mode 100644
index 000000000..911fcd17b
--- /dev/null
+++ b/freqtrade/data/converter/trade_converter_kraken.py
@@ -0,0 +1,88 @@
+import logging
+from pathlib import Path
+
+import pandas as pd
+
+from freqtrade.constants import DATETIME_PRINT_FORMAT, DEFAULT_TRADES_COLUMNS, Config
+from freqtrade.data.converter.trade_converter import (
+    trades_convert_types,
+    trades_df_remove_duplicates,
+)
+from freqtrade.data.history import get_datahandler
+from freqtrade.enums import TradingMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
+from freqtrade.resolvers import ExchangeResolver
+
+
+logger = logging.getLogger(__name__)
+
+KRAKEN_CSV_TRADE_COLUMNS = ["timestamp", "price", "amount"]
+
+
+def import_kraken_trades_from_csv(config: Config, convert_to: str):
+    """
+    Import kraken trades from csv
+    """
+    if config["exchange"]["name"] != "kraken":
+        raise OperationalException("This function is only for the kraken exchange.")
+
+    datadir: Path = config["datadir"]
+    data_handler = get_datahandler(datadir, data_format=convert_to)
+
+    tradesdir: Path = config["datadir"] / "trades_csv"
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
+    # iterate through directories in this directory
+    data_symbols = {p.stem for p in tradesdir.rglob("*.csv")}
+
+    # create pair/filename mapping
+    markets = {
+        (m["symbol"], m["altname"])
+        for m in exchange.markets.values()
+        if m.get("altname") in data_symbols
+    }
+    logger.info(f"Found csv files for {', '.join(data_symbols)}.")
+
+    if pairs_raw := config.get("pairs"):
+        pairs = expand_pairlist(pairs_raw, [m[0] for m in markets])
+        markets = {m for m in markets if m[0] in pairs}
+        if not markets:
+            logger.info(f"No data found for pairs {', '.join(pairs_raw)}.")
+            return
+    logger.info(f"Converting pairs: {', '.join(m[0] for m in markets)}.")
+
+    for pair, name in markets:
+        logger.debug(f"Converting pair {pair}, files */{name}.csv")
+        dfs = []
+        # Load and combine all csv files for this pair
+        for f in tradesdir.rglob(f"{name}.csv"):
+            df = pd.read_csv(f, names=KRAKEN_CSV_TRADE_COLUMNS)
+            if not df.empty:
+                dfs.append(df)
+
+        # Load existing trades data
+        if not dfs:
+            # edgecase, can only happen if the file was deleted between the above glob and here
+            logger.info(f"No data found for pair {pair}")
+            continue
+
+        trades = pd.concat(dfs, ignore_index=True)
+        del dfs
+
+        trades.loc[:, "timestamp"] = trades["timestamp"] * 1e3
+        trades.loc[:, "cost"] = trades["price"] * trades["amount"]
+        for col in DEFAULT_TRADES_COLUMNS:
+            if col not in trades.columns:
+                trades.loc[:, col] = ""
+        trades = trades[DEFAULT_TRADES_COLUMNS]
+        trades = trades_convert_types(trades)
+
+        trades_df = trades_df_remove_duplicates(trades)
+        del trades
+        logger.info(
+            f"{pair}: {len(trades_df)} trades, from "
+            f"{trades_df['date'].min():{DATETIME_PRINT_FORMAT}} to "
+            f"{trades_df['date'].max():{DATETIME_PRINT_FORMAT}}"
+        )
+
+        data_handler.trades_store(pair, trades_df, TradingMode.SPOT)
diff --git a/freqtrade/data/dataprovider.py b/freqtrade/data/dataprovider.py
new file mode 100644
index 000000000..1f2bcd82d
--- /dev/null
+++ b/freqtrade/data/dataprovider.py
@@ -0,0 +1,583 @@
+"""
+Dataprovider
+Responsible to provide data to the bot
+including ticker and orderbook data, live and historical candle (OHLCV) data
+Common Interface for bot and strategy to access data.
+"""
+
+import logging
+from collections import deque
+from datetime import datetime, timezone
+from typing import Any
+
+from pandas import DataFrame, Timedelta, Timestamp, to_timedelta
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import (
+    FULL_DATAFRAME_THRESHOLD,
+    Config,
+    ListPairsWithTimeframes,
+    PairWithTimeframe,
+)
+from freqtrade.data.history import get_datahandler, load_pair_history
+from freqtrade.enums import CandleType, RPCMessageType, RunMode, TradingMode
+from freqtrade.exceptions import ExchangeError, OperationalException
+from freqtrade.exchange import Exchange, timeframe_to_prev_date, timeframe_to_seconds
+from freqtrade.exchange.exchange_types import OrderBook
+from freqtrade.misc import append_candles_to_dataframe
+from freqtrade.rpc import RPCManager
+from freqtrade.rpc.rpc_types import RPCAnalyzedDFMsg
+from freqtrade.util import PeriodicCache
+
+
+logger = logging.getLogger(__name__)
+
+NO_EXCHANGE_EXCEPTION = "Exchange is not available to DataProvider."
+MAX_DATAFRAME_CANDLES = 1000
+
+
+class DataProvider:
+    def __init__(
+        self,
+        config: Config,
+        exchange: Exchange | None,
+        pairlists=None,
+        rpc: RPCManager | None = None,
+    ) -> None:
+        self._config = config
+        self._exchange = exchange
+        self._pairlists = pairlists
+        self.__rpc = rpc
+        self.__cached_pairs: dict[PairWithTimeframe, tuple[DataFrame, datetime]] = {}
+        self.__slice_index: dict[str, int] = {}
+        self.__slice_date: datetime | None = None
+
+        self.__cached_pairs_backtesting: dict[PairWithTimeframe, DataFrame] = {}
+        self.__producer_pairs_df: dict[
+            str, dict[PairWithTimeframe, tuple[DataFrame, datetime]]
+        ] = {}
+        self.__producer_pairs: dict[str, list[str]] = {}
+        self._msg_queue: deque = deque()
+
+        self._default_candle_type = self._config.get("candle_type_def", CandleType.SPOT)
+        self._default_timeframe = self._config.get("timeframe", "1h")
+
+        self.__msg_cache = PeriodicCache(
+            maxsize=1000, ttl=timeframe_to_seconds(self._default_timeframe)
+        )
+
+        self.producers = self._config.get("external_message_consumer", {}).get("producers", [])
+        self.external_data_enabled = len(self.producers) > 0
+
+    def _set_dataframe_max_index(self, pair: str, limit_index: int):
+        """
+        Limit analyzed dataframe to max specified index.
+        Only relevant in backtesting.
+        :param limit_index: dataframe index.
+        """
+        self.__slice_index[pair] = limit_index
+
+    def _set_dataframe_max_date(self, limit_date: datetime):
+        """
+        Limit informative dataframe to max specified index.
+        Only relevant in backtesting.
+        :param limit_date: "current date"
+        """
+        self.__slice_date = limit_date
+
+    def _set_cached_df(
+        self, pair: str, timeframe: str, dataframe: DataFrame, candle_type: CandleType
+    ) -> None:
+        """
+        Store cached Dataframe.
+        Using private method as this should never be used by a user
+        (but the class is exposed via `self.dp` to the strategy)
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param dataframe: analyzed dataframe
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        pair_key = (pair, timeframe, candle_type)
+        self.__cached_pairs[pair_key] = (dataframe, datetime.now(timezone.utc))
+
+    # For multiple producers we will want to merge the pairlists instead of overwriting
+    def _set_producer_pairs(self, pairlist: list[str], producer_name: str = "default"):
+        """
+        Set the pairs received to later be used.
+
+        :param pairlist: List of pairs
+        """
+        self.__producer_pairs[producer_name] = pairlist
+
+    def get_producer_pairs(self, producer_name: str = "default") -> list[str]:
+        """
+        Get the pairs cached from the producer
+
+        :returns: List of pairs
+        """
+        return self.__producer_pairs.get(producer_name, []).copy()
+
+    def _emit_df(self, pair_key: PairWithTimeframe, dataframe: DataFrame, new_candle: bool) -> None:
+        """
+        Send this dataframe as an ANALYZED_DF message to RPC
+
+        :param pair_key: PairWithTimeframe tuple
+        :param dataframe: Dataframe to emit
+        :param new_candle: This is a new candle
+        """
+        if self.__rpc:
+            msg: RPCAnalyzedDFMsg = {
+                "type": RPCMessageType.ANALYZED_DF,
+                "data": {
+                    "key": pair_key,
+                    "df": dataframe.tail(1),
+                    "la": datetime.now(timezone.utc),
+                },
+            }
+            self.__rpc.send_msg(msg)
+            if new_candle:
+                self.__rpc.send_msg(
+                    {
+                        "type": RPCMessageType.NEW_CANDLE,
+                        "data": pair_key,
+                    }
+                )
+
+    def _replace_external_df(
+        self,
+        pair: str,
+        dataframe: DataFrame,
+        last_analyzed: datetime,
+        timeframe: str,
+        candle_type: CandleType,
+        producer_name: str = "default",
+    ) -> None:
+        """
+        Add the pair data to this class from an external source.
+
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        pair_key = (pair, timeframe, candle_type)
+
+        if producer_name not in self.__producer_pairs_df:
+            self.__producer_pairs_df[producer_name] = {}
+
+        _last_analyzed = datetime.now(timezone.utc) if not last_analyzed else last_analyzed
+
+        self.__producer_pairs_df[producer_name][pair_key] = (dataframe, _last_analyzed)
+        logger.debug(f"External DataFrame for {pair_key} from {producer_name} added.")
+
+    def _add_external_df(
+        self,
+        pair: str,
+        dataframe: DataFrame,
+        last_analyzed: datetime,
+        timeframe: str,
+        candle_type: CandleType,
+        producer_name: str = "default",
+    ) -> tuple[bool, int]:
+        """
+        Append a candle to the existing external dataframe. The incoming dataframe
+        must have at least 1 candle.
+
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :returns: False if the candle could not be appended, or the int number of missing candles.
+        """
+        pair_key = (pair, timeframe, candle_type)
+
+        if dataframe.empty:
+            # The incoming dataframe must have at least 1 candle
+            return (False, 0)
+
+        if len(dataframe) >= FULL_DATAFRAME_THRESHOLD:
+            # This is likely a full dataframe
+            # Add the dataframe to the dataprovider
+            self._replace_external_df(
+                pair,
+                dataframe,
+                last_analyzed=last_analyzed,
+                timeframe=timeframe,
+                candle_type=candle_type,
+                producer_name=producer_name,
+            )
+            return (True, 0)
+
+        if (
+            producer_name not in self.__producer_pairs_df
+            or pair_key not in self.__producer_pairs_df[producer_name]
+        ):
+            # We don't have data from this producer yet,
+            # or we don't have data for this pair_key
+            # return False and 1000 for the full df
+            return (False, 1000)
+
+        existing_df, _ = self.__producer_pairs_df[producer_name][pair_key]
+
+        # CHECK FOR MISSING CANDLES
+        # Convert the timeframe to a timedelta for pandas
+        timeframe_delta: Timedelta = to_timedelta(timeframe)
+        local_last: Timestamp = existing_df.iloc[-1]["date"]  # We want the last date from our copy
+        # We want the first date from the incoming
+        incoming_first: Timestamp = dataframe.iloc[0]["date"]
+
+        # Remove existing candles that are newer than the incoming first candle
+        existing_df1 = existing_df[existing_df["date"] < incoming_first]
+
+        candle_difference = (incoming_first - local_last) / timeframe_delta
+
+        # If the difference divided by the timeframe is 1, then this
+        # is the candle we want and the incoming data isn't missing any.
+        # If the candle_difference is more than 1, that means
+        # we missed some candles between our data and the incoming
+        # so return False and candle_difference.
+        if candle_difference > 1:
+            return (False, int(candle_difference))
+        if existing_df1.empty:
+            appended_df = dataframe
+        else:
+            appended_df = append_candles_to_dataframe(existing_df1, dataframe)
+
+        # Everything is good, we appended
+        self._replace_external_df(
+            pair,
+            appended_df,
+            last_analyzed=last_analyzed,
+            timeframe=timeframe,
+            candle_type=candle_type,
+            producer_name=producer_name,
+        )
+        return (True, 0)
+
+    def get_producer_df(
+        self,
+        pair: str,
+        timeframe: str | None = None,
+        candle_type: CandleType | None = None,
+        producer_name: str = "default",
+    ) -> tuple[DataFrame, datetime]:
+        """
+        Get the pair data from producers.
+
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :returns: Tuple of the DataFrame and last analyzed timestamp
+        """
+        _timeframe = self._default_timeframe if not timeframe else timeframe
+        _candle_type = self._default_candle_type if not candle_type else candle_type
+
+        pair_key = (pair, _timeframe, _candle_type)
+
+        # If we have no data from this Producer yet
+        if producer_name not in self.__producer_pairs_df:
+            # We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
+            return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+
+        # If we do have data from that Producer, but no data on this pair_key
+        if pair_key not in self.__producer_pairs_df[producer_name]:
+            # We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
+            return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+
+        # We have it, return this data
+        df, la = self.__producer_pairs_df[producer_name][pair_key]
+        return (df.copy(), la)
+
+    def add_pairlisthandler(self, pairlists) -> None:
+        """
+        Allow adding pairlisthandler after initialization
+        """
+        self._pairlists = pairlists
+
+    def historic_ohlcv(self, pair: str, timeframe: str, candle_type: str = "") -> DataFrame:
+        """
+        Get stored historical candle (OHLCV) data
+        :param pair: pair to get the data for
+        :param timeframe: timeframe to get data for
+        :param candle_type: '', mark, index, premiumIndex, or funding_rate
+        """
+        _candle_type = (
+            CandleType.from_string(candle_type)
+            if candle_type != ""
+            else self._config["candle_type_def"]
+        )
+        saved_pair: PairWithTimeframe = (pair, str(timeframe), _candle_type)
+        if saved_pair not in self.__cached_pairs_backtesting:
+            timerange = TimeRange.parse_timerange(
+                None
+                if self._config.get("timerange") is None
+                else str(self._config.get("timerange"))
+            )
+
+            startup_candles = self.get_required_startup(str(timeframe))
+            tf_seconds = timeframe_to_seconds(str(timeframe))
+            timerange.subtract_start(tf_seconds * startup_candles)
+
+            logger.info(
+                f"Loading data for {pair} {timeframe} "
+                f"from {timerange.start_fmt} to {timerange.stop_fmt}"
+            )
+
+            self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
+                pair=pair,
+                timeframe=timeframe,
+                datadir=self._config["datadir"],
+                timerange=timerange,
+                data_format=self._config["dataformat_ohlcv"],
+                candle_type=_candle_type,
+            )
+        return self.__cached_pairs_backtesting[saved_pair].copy()
+
+    def get_required_startup(self, timeframe: str) -> int:
+        freqai_config = self._config.get("freqai", {})
+        if not freqai_config.get("enabled", False):
+            return self._config.get("startup_candle_count", 0)
+        else:
+            startup_candles = self._config.get("startup_candle_count", 0)
+            indicator_periods = freqai_config["feature_parameters"]["indicator_periods_candles"]
+            # make sure the startupcandles is at least the set maximum indicator periods
+            self._config["startup_candle_count"] = max(startup_candles, max(indicator_periods))
+            tf_seconds = timeframe_to_seconds(timeframe)
+            train_candles = freqai_config["train_period_days"] * 86400 / tf_seconds
+            total_candles = int(self._config["startup_candle_count"] + train_candles)
+            logger.info(
+                f"Increasing startup_candle_count for freqai on {timeframe} to {total_candles}"
+            )
+        return total_candles
+
+    def get_pair_dataframe(
+        self, pair: str, timeframe: str | None = None, candle_type: str = ""
+    ) -> DataFrame:
+        """
+        Return pair candle (OHLCV) data, either live or cached historical -- depending
+        on the runmode.
+        Only combinations in the pairlist or which have been specified as informative pairs
+        will be available.
+        :param pair: pair to get the data for
+        :param timeframe: timeframe to get data for
+        :return: Dataframe for this pair
+        :param candle_type: '', mark, index, premiumIndex, or funding_rate
+        """
+        if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+            # Get live OHLCV data.
+            data = self.ohlcv(pair=pair, timeframe=timeframe, candle_type=candle_type)
+        else:
+            # Get historical OHLCV data (cached on disk).
+            timeframe = timeframe or self._config["timeframe"]
+            data = self.historic_ohlcv(pair=pair, timeframe=timeframe, candle_type=candle_type)
+            # Cut date to timeframe-specific date.
+            # This is necessary to prevent lookahead bias in callbacks through informative pairs.
+            if self.__slice_date:
+                cutoff_date = timeframe_to_prev_date(timeframe, self.__slice_date)
+                data = data.loc[data["date"] < cutoff_date]
+        if len(data) == 0:
+            logger.warning(f"No data found for ({pair}, {timeframe}, {candle_type}).")
+        return data
+
+    def get_analyzed_dataframe(self, pair: str, timeframe: str) -> tuple[DataFrame, datetime]:
+        """
+        Retrieve the analyzed dataframe. Returns the full dataframe in trade mode (live / dry),
+        and the last 1000 candles (up to the time evaluated at this moment) in all other modes.
+        :param pair: pair to get the data for
+        :param timeframe: timeframe to get data for
+        :return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe
+            combination.
+            Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
+        """
+        pair_key = (pair, timeframe, self._config.get("candle_type_def", CandleType.SPOT))
+        if pair_key in self.__cached_pairs:
+            if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+                df, date = self.__cached_pairs[pair_key]
+            else:
+                df, date = self.__cached_pairs[pair_key]
+                if (max_index := self.__slice_index.get(pair)) is not None:
+                    df = df.iloc[max(0, max_index - MAX_DATAFRAME_CANDLES) : max_index]
+                else:
+                    return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+            return df, date
+        else:
+            return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+
+    @property
+    def runmode(self) -> RunMode:
+        """
+        Get runmode of the bot
+        can be "live", "dry-run", "backtest", "edgecli", "hyperopt" or "other".
+        """
+        return RunMode(self._config.get("runmode", RunMode.OTHER))
+
+    def current_whitelist(self) -> list[str]:
+        """
+        fetch latest available whitelist.
+
+        Useful when you have a large whitelist and need to call each pair as an informative pair.
+        As available pairs does not show whitelist until after informative pairs have been cached.
+        :return: list of pairs in whitelist
+        """
+
+        if self._pairlists:
+            return self._pairlists.whitelist.copy()
+        else:
+            raise OperationalException("Dataprovider was not initialized with a pairlist provider.")
+
+    def clear_cache(self):
+        """
+        Clear pair dataframe cache.
+        """
+        self.__cached_pairs = {}
+        # Don't reset backtesting pairs -
+        # otherwise they're reloaded each time during hyperopt due to with analyze_per_epoch
+        # self.__cached_pairs_backtesting = {}
+        self.__slice_index = {}
+
+    # Exchange functions
+
+    def refresh(
+        self,
+        pairlist: ListPairsWithTimeframes,
+        helping_pairs: ListPairsWithTimeframes | None = None,
+    ) -> None:
+        """
+        Refresh data, called with each cycle
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        final_pairs = (pairlist + helping_pairs) if helping_pairs else pairlist
+        # refresh latest ohlcv data
+        self._exchange.refresh_latest_ohlcv(final_pairs)
+        # refresh latest trades data
+        self.refresh_latest_trades(pairlist)
+
+    def refresh_latest_trades(self, pairlist: ListPairsWithTimeframes) -> None:
+        """
+        Refresh latest trades data (if enabled in config)
+        """
+
+        use_public_trades = self._config.get("exchange", {}).get("use_public_trades", False)
+        if use_public_trades:
+            if self._exchange:
+                self._exchange.refresh_latest_trades(pairlist)
+
+    @property
+    def available_pairs(self) -> ListPairsWithTimeframes:
+        """
+        Return a list of tuples containing (pair, timeframe) for which data is currently cached.
+        Should be whitelist + open trades.
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        return list(self._exchange._klines.keys())
+
+    def ohlcv(
+        self, pair: str, timeframe: str | None = None, copy: bool = True, candle_type: str = ""
+    ) -> DataFrame:
+        """
+        Get candle (OHLCV) data for the given pair as DataFrame
+        Please use the `available_pairs` method to verify which pairs are currently cached.
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param candle_type: '', mark, index, premiumIndex, or funding_rate
+        :param copy: copy dataframe before returning if True.
+                     Use False only for read-only operations (where the dataframe is not modified)
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+            _candle_type = (
+                CandleType.from_string(candle_type)
+                if candle_type != ""
+                else self._config["candle_type_def"]
+            )
+            return self._exchange.klines(
+                (pair, timeframe or self._config["timeframe"], _candle_type), copy=copy
+            )
+        else:
+            return DataFrame()
+
+    def trades(
+        self, pair: str, timeframe: str | None = None, copy: bool = True, candle_type: str = ""
+    ) -> DataFrame:
+        """
+        Get candle (TRADES) data for the given pair as DataFrame
+        Please use the `available_pairs` method to verify which pairs are currently cached.
+        This is not meant to be used in callbacks because of lookahead bias.
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param candle_type: '', mark, index, premiumIndex, or funding_rate
+        :param copy: copy dataframe before returning if True.
+                     Use False only for read-only operations (where the dataframe is not modified)
+        """
+        if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+            if self._exchange is None:
+                raise OperationalException(NO_EXCHANGE_EXCEPTION)
+            _candle_type = (
+                CandleType.from_string(candle_type)
+                if candle_type != ""
+                else self._config["candle_type_def"]
+            )
+            return self._exchange.trades(
+                (pair, timeframe or self._config["timeframe"], _candle_type), copy=copy
+            )
+        else:
+            data_handler = get_datahandler(
+                self._config["datadir"], data_format=self._config["dataformat_trades"]
+            )
+            trades_df = data_handler.trades_load(
+                pair, self._config.get("trading_mode", TradingMode.SPOT)
+            )
+            return trades_df
+
+    def market(self, pair: str) -> dict[str, Any] | None:
+        """
+        Return market data for the pair
+        :param pair: Pair to get the data for
+        :return: Market data dict from ccxt or None if market info is not available for the pair
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        return self._exchange.markets.get(pair)
+
+    def ticker(self, pair: str):
+        """
+        Return last ticker data from exchange
+        :param pair: Pair to get the data for
+        :return: Ticker dict from exchange or empty dict if ticker is not available for the pair
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        try:
+            return self._exchange.fetch_ticker(pair)
+        except ExchangeError:
+            return {}
+
+    def orderbook(self, pair: str, maximum: int) -> OrderBook:
+        """
+        Fetch latest l2 orderbook data
+        Warning: Does a network request - so use with common sense.
+        :param pair: pair to get the data for
+        :param maximum: Maximum number of orderbook entries to query
+        :return: dict including bids/asks with a total of `maximum` entries.
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        return self._exchange.fetch_l2_order_book(pair, maximum)
+
+    def send_msg(self, message: str, *, always_send: bool = False) -> None:
+        """
+        Send custom RPC Notifications from your bot.
+        Will not send any bot in modes other than Dry-run or Live.
+        :param message: Message to be sent. Must be below 4096.
+        :param always_send: If False, will send the message only once per candle, and suppress
+                            identical messages.
+                            Careful as this can end up spaming your chat.
+                            Defaults to False
+        """
+        if self.runmode not in (RunMode.DRY_RUN, RunMode.LIVE):
+            return
+
+        if always_send or message not in self.__msg_cache:
+            self._msg_queue.append(message)
+        self.__msg_cache[message] = True
diff --git a/freqtrade/data/entryexitanalysis.py b/freqtrade/data/entryexitanalysis.py
new file mode 100644
index 000000000..2bea8a5db
--- /dev/null
+++ b/freqtrade/data/entryexitanalysis.py
@@ -0,0 +1,431 @@
+import logging
+from pathlib import Path
+
+import pandas as pd
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
+from freqtrade.data.btanalysis import (
+    BT_DATA_COLUMNS,
+    load_backtest_data,
+    load_backtest_stats,
+    load_exit_signal_candles,
+    load_rejected_signals,
+    load_signal_candles,
+)
+from freqtrade.exceptions import ConfigurationError, OperationalException
+from freqtrade.util import print_df_rich_table
+
+
+logger = logging.getLogger(__name__)
+
+
+def _process_candles_and_indicators(
+    pairlist, strategy_name, trades, signal_candles, date_col: str = "open_date"
+):
+    analysed_trades_dict: dict[str, dict] = {strategy_name: {}}
+
+    try:
+        logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs")
+
+        for pair in pairlist:
+            if pair in signal_candles[strategy_name]:
+                analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
+                    pair, trades, signal_candles[strategy_name][pair], date_col
+                )
+    except Exception as e:
+        print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
+
+    return analysed_trades_dict
+
+
+def _analyze_candles_and_indicators(
+    pair: str, trades: pd.DataFrame, signal_candles: pd.DataFrame, date_col: str = "open_date"
+) -> pd.DataFrame:
+    buyf = signal_candles
+
+    if len(buyf) > 0:
+        buyf = buyf.set_index("date", drop=False)
+        trades_red = trades.loc[trades["pair"] == pair].copy()
+
+        trades_inds = pd.DataFrame()
+
+        if trades_red.shape[0] > 0 and buyf.shape[0] > 0:
+            for t, v in trades_red.iterrows():
+                allinds = buyf.loc[(buyf["date"] < v[date_col])]
+                if allinds.shape[0] > 0:
+                    tmp_inds = allinds.iloc[[-1]]
+
+                    trades_red.loc[t, "signal_date"] = tmp_inds["date"].values[0]
+                    trades_red.loc[t, "enter_reason"] = trades_red.loc[t, "enter_tag"]
+                    tmp_inds.index.rename("signal_date", inplace=True)
+                    trades_inds = pd.concat([trades_inds, tmp_inds])
+
+            if "signal_date" in trades_red:
+                trades_red["signal_date"] = pd.to_datetime(trades_red["signal_date"], utc=True)
+                trades_red.set_index("signal_date", inplace=True)
+
+                try:
+                    trades_red = pd.merge(trades_red, trades_inds, on="signal_date", how="outer")
+                except Exception as e:
+                    raise e
+        return trades_red
+    else:
+        return pd.DataFrame()
+
+
+def _do_group_table_output(
+    bigdf,
+    glist,
+    csv_path: Path,
+    to_csv=False,
+):
+    for g in glist:
+        # 0: summary wins/losses grouped by enter tag
+        if g == "0":
+            group_mask = ["enter_reason"]
+            wins = (
+                bigdf.loc[bigdf["profit_abs"] >= 0].groupby(group_mask).agg({"profit_abs": ["sum"]})
+            )
+
+            wins.columns = ["profit_abs_wins"]
+            loss = (
+                bigdf.loc[bigdf["profit_abs"] < 0].groupby(group_mask).agg({"profit_abs": ["sum"]})
+            )
+            loss.columns = ["profit_abs_loss"]
+
+            new = bigdf.groupby(group_mask).agg(
+                {"profit_abs": ["count", lambda x: sum(x > 0), lambda x: sum(x <= 0)]}
+            )
+            new = pd.concat([new, wins, loss], axis=1).fillna(0)
+
+            new["profit_tot"] = new["profit_abs_wins"] - abs(new["profit_abs_loss"])
+            new["wl_ratio_pct"] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
+            new["avg_win"] = (new["profit_abs_wins"] / new.iloc[:, 1]).fillna(0)
+            new["avg_loss"] = (new["profit_abs_loss"] / new.iloc[:, 2]).fillna(0)
+
+            new["exp_ratio"] = (
+                ((1 + (new["avg_win"] / abs(new["avg_loss"]))) * (new["wl_ratio_pct"] / 100)) - 1
+            ).fillna(0)
+
+            new.columns = [
+                "total_num_buys",
+                "wins",
+                "losses",
+                "profit_abs_wins",
+                "profit_abs_loss",
+                "profit_tot",
+                "wl_ratio_pct",
+                "avg_win",
+                "avg_loss",
+                "exp_ratio",
+            ]
+
+            sortcols = ["total_num_buys"]
+
+            _print_table(
+                new, sortcols, show_index=True, name="Group 0:", to_csv=to_csv, csv_path=csv_path
+            )
+
+        else:
+            agg_mask = {
+                "profit_abs": ["count", "sum", "median", "mean"],
+                "profit_ratio": ["median", "mean", "sum"],
+            }
+            agg_cols = [
+                "num_buys",
+                "profit_abs_sum",
+                "profit_abs_median",
+                "profit_abs_mean",
+                "median_profit_pct",
+                "mean_profit_pct",
+                "total_profit_pct",
+            ]
+            sortcols = ["profit_abs_sum", "enter_reason"]
+
+            # 1: profit summaries grouped by enter_tag
+            if g == "1":
+                group_mask = ["enter_reason"]
+
+            # 2: profit summaries grouped by enter_tag and exit_tag
+            if g == "2":
+                group_mask = ["enter_reason", "exit_reason"]
+
+            # 3: profit summaries grouped by pair and enter_tag
+            if g == "3":
+                group_mask = ["pair", "enter_reason"]
+
+            # 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
+            if g == "4":
+                group_mask = ["pair", "enter_reason", "exit_reason"]
+
+            # 5: profit summaries grouped by exit_tag
+            if g == "5":
+                group_mask = ["exit_reason"]
+                sortcols = ["exit_reason"]
+
+            if group_mask:
+                new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
+                new.columns = group_mask + agg_cols
+                new["median_profit_pct"] = new["median_profit_pct"] * 100
+                new["mean_profit_pct"] = new["mean_profit_pct"] * 100
+                new["total_profit_pct"] = new["total_profit_pct"] * 100
+
+                _print_table(new, sortcols, name=f"Group {g}:", to_csv=to_csv, csv_path=csv_path)
+            else:
+                logger.warning("Invalid group mask specified.")
+
+
+def _do_rejected_signals_output(
+    rejected_signals_df: pd.DataFrame, to_csv: bool = False, csv_path=None
+) -> None:
+    cols = ["pair", "date", "enter_tag"]
+    sortcols = ["date", "pair", "enter_tag"]
+    _print_table(
+        rejected_signals_df[cols],
+        sortcols,
+        show_index=False,
+        name="Rejected Signals:",
+        to_csv=to_csv,
+        csv_path=csv_path,
+    )
+
+
+def _select_rows_within_dates(df, timerange=None, df_date_col: str = "date"):
+    if timerange:
+        if timerange.starttype == "date":
+            df = df.loc[(df[df_date_col] >= timerange.startdt)]
+        if timerange.stoptype == "date":
+            df = df.loc[(df[df_date_col] < timerange.stopdt)]
+    return df
+
+
+def _select_rows_by_tags(df, enter_reason_list, exit_reason_list):
+    if enter_reason_list and "all" not in enter_reason_list:
+        df = df.loc[(df["enter_reason"].isin(enter_reason_list))]
+
+    if exit_reason_list and "all" not in exit_reason_list:
+        df = df.loc[(df["exit_reason"].isin(exit_reason_list))]
+    return df
+
+
+def prepare_results(
+    analysed_trades, stratname, enter_reason_list, exit_reason_list, timerange=None
+) -> pd.DataFrame:
+    res_df = pd.DataFrame()
+    for pair, trades in analysed_trades[stratname].items():
+        if trades.shape[0] > 0:
+            trades.dropna(subset=["close_date"], inplace=True)
+            res_df = pd.concat([res_df, trades], ignore_index=True)
+
+    res_df = _select_rows_within_dates(res_df, timerange)
+
+    if res_df is not None and res_df.shape[0] > 0 and ("enter_reason" in res_df.columns):
+        res_df = _select_rows_by_tags(res_df, enter_reason_list, exit_reason_list)
+
+    return res_df
+
+
+def print_results(
+    res_df: pd.DataFrame,
+    exit_df: pd.DataFrame,
+    analysis_groups: list[str],
+    indicator_list: list[str],
+    entry_only: bool,
+    exit_only: bool,
+    csv_path: Path,
+    rejected_signals=None,
+    to_csv=False,
+):
+    if res_df.shape[0] > 0:
+        if analysis_groups:
+            _do_group_table_output(res_df, analysis_groups, to_csv=to_csv, csv_path=csv_path)
+
+        if rejected_signals is not None:
+            if rejected_signals.empty:
+                print("There were no rejected signals.")
+            else:
+                _do_rejected_signals_output(rejected_signals, to_csv=to_csv, csv_path=csv_path)
+
+        # NB this can be large for big dataframes!
+        if "all" in indicator_list:
+            _print_table(
+                res_df, show_index=False, name="Indicators:", to_csv=to_csv, csv_path=csv_path
+            )
+        elif indicator_list is not None and indicator_list:
+            available_inds = []
+            for ind in indicator_list:
+                if ind in res_df:
+                    available_inds.append(ind)
+
+            merged_df = _merge_dfs(res_df, exit_df, available_inds, entry_only, exit_only)
+
+            _print_table(
+                merged_df,
+                sortcols=["exit_reason"],
+                show_index=False,
+                name="Indicators:",
+                to_csv=to_csv,
+                csv_path=csv_path,
+            )
+    else:
+        print("\\No trades to show")
+
+
+def _merge_dfs(
+    entry_df: pd.DataFrame,
+    exit_df: pd.DataFrame,
+    available_inds: list[str],
+    entry_only: bool,
+    exit_only: bool,
+):
+    merge_on = ["pair", "open_date"]
+    signal_wide_indicators = list(set(available_inds) - set(BT_DATA_COLUMNS))
+    columns_to_keep = [*merge_on, "enter_reason", "exit_reason"]
+
+    if exit_df is None or exit_df.empty or entry_only is True:
+        return entry_df[columns_to_keep + available_inds]
+
+    if exit_only is True:
+        return pd.merge(
+            entry_df[columns_to_keep],
+            exit_df[merge_on + signal_wide_indicators],
+            on=merge_on,
+            suffixes=(" (entry)", " (exit)"),
+        )
+
+    return pd.merge(
+        entry_df[columns_to_keep + available_inds],
+        exit_df[merge_on + signal_wide_indicators],
+        on=merge_on,
+        suffixes=(" (entry)", " (exit)"),
+    )
+
+
+def _print_table(
+    df: pd.DataFrame, sortcols=None, *, show_index=False, name=None, to_csv=False, csv_path: Path
+):
+    if sortcols is not None:
+        data = df.sort_values(sortcols)
+    else:
+        data = df
+
+    if to_csv:
+        safe_name = Path(csv_path, name.lower().replace(" ", "_").replace(":", "") + ".csv")
+        data.to_csv(safe_name)
+        print(f"Saved {name} to {safe_name}")
+    else:
+        if name is not None:
+            print(name)
+
+        print_df_rich_table(data, data.keys(), show_index=show_index)
+
+
+def process_entry_exit_reasons(config: Config):
+    try:
+        analysis_groups = config.get("analysis_groups", [])
+        enter_reason_list = config.get("enter_reason_list", ["all"])
+        exit_reason_list = config.get("exit_reason_list", ["all"])
+        indicator_list = config.get("indicator_list", [])
+        entry_only = config.get("entry_only", False)
+        exit_only = config.get("exit_only", False)
+        do_rejected = config.get("analysis_rejected", False)
+        to_csv = config.get("analysis_to_csv", False)
+        csv_path = Path(config.get("analysis_csv_path", config["exportfilename"]))
+
+        if entry_only is True and exit_only is True:
+            raise OperationalException(
+                "Cannot use --entry-only and --exit-only at the same time. Please choose one."
+            )
+        if to_csv and not csv_path.is_dir():
+            raise OperationalException(f"Specified directory {csv_path} does not exist.")
+
+        timerange = TimeRange.parse_timerange(
+            None if config.get("timerange") is None else str(config.get("timerange"))
+        )
+        try:
+            backtest_stats = load_backtest_stats(config["exportfilename"])
+        except ValueError as e:
+            raise ConfigurationError(e) from e
+
+        for strategy_name, results in backtest_stats["strategy"].items():
+            trades = load_backtest_data(config["exportfilename"], strategy_name)
+
+            if trades is not None and not trades.empty:
+                signal_candles = load_signal_candles(config["exportfilename"])
+                exit_signals = load_exit_signal_candles(config["exportfilename"])
+
+                rej_df = None
+                if do_rejected:
+                    rejected_signals_dict = load_rejected_signals(config["exportfilename"])
+                    rej_df = prepare_results(
+                        rejected_signals_dict,
+                        strategy_name,
+                        enter_reason_list,
+                        exit_reason_list,
+                        timerange=timerange,
+                    )
+
+                entry_df = _generate_dfs(
+                    config["exchange"]["pair_whitelist"],
+                    enter_reason_list,
+                    exit_reason_list,
+                    signal_candles,
+                    strategy_name,
+                    timerange,
+                    trades,
+                    "open_date",
+                )
+
+                exit_df = _generate_dfs(
+                    config["exchange"]["pair_whitelist"],
+                    enter_reason_list,
+                    exit_reason_list,
+                    exit_signals,
+                    strategy_name,
+                    timerange,
+                    trades,
+                    "close_date",
+                )
+
+                print_results(
+                    entry_df,
+                    exit_df,
+                    analysis_groups,
+                    indicator_list,
+                    entry_only,
+                    exit_only,
+                    rejected_signals=rej_df,
+                    to_csv=to_csv,
+                    csv_path=csv_path,
+                )
+
+    except ValueError as e:
+        raise OperationalException(e) from e
+
+
+def _generate_dfs(
+    pairlist: list,
+    enter_reason_list: list,
+    exit_reason_list: list,
+    signal_candles: dict,
+    strategy_name: str,
+    timerange: TimeRange,
+    trades: pd.DataFrame,
+    date_col: str,
+) -> pd.DataFrame:
+    analysed_trades_dict = _process_candles_and_indicators(
+        pairlist,
+        strategy_name,
+        trades,
+        signal_candles,
+        date_col,
+    )
+    res_df = prepare_results(
+        analysed_trades_dict,
+        strategy_name,
+        enter_reason_list,
+        exit_reason_list,
+        timerange=timerange,
+    )
+    return res_df
diff --git a/freqtrade/data/history/__init__.py b/freqtrade/data/history/__init__.py
new file mode 100644
index 000000000..bce4bc284
--- /dev/null
+++ b/freqtrade/data/history/__init__.py
@@ -0,0 +1,21 @@
+"""
+Handle historic data (ohlcv).
+
+Includes:
+* load data for a pair (or a list of pairs) from disk
+* download data from exchange and store to disk
+"""
+
+# flake8: noqa: F401
+from .datahandlers import get_datahandler
+from .history_utils import (
+    convert_trades_to_ohlcv,
+    download_data_main,
+    get_timerange,
+    load_data,
+    load_pair_history,
+    refresh_backtest_ohlcv_data,
+    refresh_backtest_trades_data,
+    refresh_data,
+    validate_backtest_data,
+)
diff --git a/freqtrade/data/history/datahandlers/__init__.py b/freqtrade/data/history/datahandlers/__init__.py
new file mode 100644
index 000000000..d76c6aada
--- /dev/null
+++ b/freqtrade/data/history/datahandlers/__init__.py
@@ -0,0 +1,2 @@
+# flake8: noqa: F401
+from .idatahandler import IDataHandler, get_datahandler
diff --git a/freqtrade/data/history/datahandlers/featherdatahandler.py b/freqtrade/data/history/datahandlers/featherdatahandler.py
new file mode 100644
index 000000000..46fd7e3ae
--- /dev/null
+++ b/freqtrade/data/history/datahandlers/featherdatahandler.py
@@ -0,0 +1,135 @@
+import logging
+
+from pandas import DataFrame, read_feather, to_datetime
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
+from freqtrade.enums import CandleType, TradingMode
+
+from .idatahandler import IDataHandler
+
+
+logger = logging.getLogger(__name__)
+
+
+class FeatherDataHandler(IDataHandler):
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    def ohlcv_store(
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :param timeframe: Timeframe - used to generate filename
+        :param data: Dataframe containing OHLCV data
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        self.create_dir_if_needed(filename)
+
+        data.reset_index(drop=True).loc[:, self._columns].to_feather(
+            filename, compression_level=9, compression="lz4"
+        )
+
+    def _ohlcv_load(
+        self, pair: str, timeframe: str, timerange: TimeRange | None, candle_type: CandleType
+    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type)
+        if not filename.exists():
+            # Fallback mode for 1M files
+            filename = self._pair_data_filename(
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True
+            )
+            if not filename.exists():
+                return DataFrame(columns=self._columns)
+        try:
+            pairdata = read_feather(filename)
+            pairdata.columns = self._columns
+            pairdata = pairdata.astype(
+                dtype={
+                    "open": "float",
+                    "high": "float",
+                    "low": "float",
+                    "close": "float",
+                    "volume": "float",
+                }
+            )
+            pairdata["date"] = to_datetime(pairdata["date"], unit="ms", utc=True)
+            return pairdata
+        except Exception as e:
+            logger.exception(
+                f"Error loading data from {filename}. Exception: {e}. Returning empty dataframe."
+            )
+            return DataFrame(columns=self._columns)
+
+    def ohlcv_append(
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        raise NotImplementedError()
+
+    def _trades_store(self, pair: str, data: DataFrame, trading_mode: TradingMode) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        """
+        filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
+        self.create_dir_if_needed(filename)
+        data.reset_index(drop=True).to_feather(filename, compression_level=9, compression="lz4")
+
+    def trades_append(self, pair: str, data: DataFrame):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        raise NotImplementedError()
+
+    def _trades_load(
+        self, pair: str, trading_mode: TradingMode, timerange: TimeRange | None = None
+    ) -> DataFrame:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: Dataframe containing trades
+        """
+        filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
+        if not filename.exists():
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)
+
+        tradesdata = read_feather(filename)
+
+        return tradesdata
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "feather"
diff --git a/freqtrade/data/history/datahandlers/idatahandler.py b/freqtrade/data/history/datahandlers/idatahandler.py
new file mode 100644
index 000000000..e368c1d41
--- /dev/null
+++ b/freqtrade/data/history/datahandlers/idatahandler.py
@@ -0,0 +1,584 @@
+"""
+Abstract datahandler interface.
+It's subclasses handle and storing data from disk.
+
+"""
+
+import logging
+import re
+from abc import ABC, abstractmethod
+from copy import deepcopy
+from datetime import datetime, timezone
+from pathlib import Path
+
+from pandas import DataFrame, to_datetime
+
+from freqtrade import misc
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_TRADES_COLUMNS, ListPairsWithTimeframes
+from freqtrade.data.converter import (
+    clean_ohlcv_dataframe,
+    trades_convert_types,
+    trades_df_remove_duplicates,
+    trim_dataframe,
+)
+from freqtrade.enums import CandleType, TradingMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_seconds
+
+
+logger = logging.getLogger(__name__)
+
+
+class IDataHandler(ABC):
+    _OHLCV_REGEX = r"^([a-zA-Z_\d-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)"
+    _TRADES_REGEX = r"^([a-zA-Z_\d-]+)\-(trades)?(?=\.)"
+
+    def __init__(self, datadir: Path) -> None:
+        self._datadir = datadir
+
+    @classmethod
+    def _get_file_extension(cls) -> str:
+        """
+        Get file extension for this particular datahandler
+        """
+        raise NotImplementedError()
+
+    @classmethod
+    def ohlcv_get_available_data(
+        cls, datadir: Path, trading_mode: TradingMode
+    ) -> ListPairsWithTimeframes:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        :param datadir: Directory to search for ohlcv files
+        :param trading_mode: trading-mode to be used
+        :return: List of Tuples of (pair, timeframe, CandleType)
+        """
+        if trading_mode == TradingMode.FUTURES:
+            datadir = datadir.joinpath("futures")
+        _tmp = [
+            re.search(cls._OHLCV_REGEX, p.name)
+            for p in datadir.glob(f"*.{cls._get_file_extension()}")
+        ]
+        return [
+            (
+                cls.rebuild_pair_from_filename(match[1]),
+                cls.rebuild_timeframe_from_filename(match[2]),
+                CandleType.from_string(match[3]),
+            )
+            for match in _tmp
+            if match and len(match.groups()) > 1
+        ]
+
+    @classmethod
+    def ohlcv_get_pairs(cls, datadir: Path, timeframe: str, candle_type: CandleType) -> list[str]:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        for the specified timeframe
+        :param datadir: Directory to search for ohlcv files
+        :param timeframe: Timeframe to search pairs for
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: List of Pairs
+        """
+        candle = ""
+        if candle_type != CandleType.SPOT:
+            datadir = datadir.joinpath("futures")
+            candle = f"-{candle_type}"
+        ext = cls._get_file_extension()
+        _tmp = [
+            re.search(r"^(\S+)(?=\-" + timeframe + candle + f".{ext})", p.name)
+            for p in datadir.glob(f"*{timeframe}{candle}.{ext}")
+        ]
+        # Check if regex found something and only return these results
+        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
+
+    @abstractmethod
+    def ohlcv_store(
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
+        """
+        Store ohlcv data.
+        :param pair: Pair - used to generate filename
+        :param timeframe: Timeframe - used to generate filename
+        :param data: Dataframe containing OHLCV data
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: None
+        """
+
+    def ohlcv_data_min_max(
+        self, pair: str, timeframe: str, candle_type: CandleType
+    ) -> tuple[datetime, datetime, int]:
+        """
+        Returns the min and max timestamp for the given pair and timeframe.
+        :param pair: Pair to get min/max for
+        :param timeframe: Timeframe to get min/max for
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: (min, max, len)
+        """
+        df = self._ohlcv_load(pair, timeframe, None, candle_type)
+        if df.empty:
+            return (
+                datetime.fromtimestamp(0, tz=timezone.utc),
+                datetime.fromtimestamp(0, tz=timezone.utc),
+                0,
+            )
+        return df.iloc[0]["date"].to_pydatetime(), df.iloc[-1]["date"].to_pydatetime(), len(df)
+
+    @abstractmethod
+    def _ohlcv_load(
+        self, pair: str, timeframe: str, timerange: TimeRange | None, candle_type: CandleType
+    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+
+    def ohlcv_purge(self, pair: str, timeframe: str, candle_type: CandleType) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :param timeframe: Timeframe (e.g. "5m")
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: True when deleted, false if file did not exist.
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
+
+    @abstractmethod
+    def ohlcv_append(
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+
+    @classmethod
+    def trades_get_available_data(cls, datadir: Path, trading_mode: TradingMode) -> list[str]:
+        """
+        Returns a list of all pairs with ohlcv data available in this datadir
+        :param datadir: Directory to search for ohlcv files
+        :param trading_mode: trading-mode to be used
+        :return: List of Tuples of (pair, timeframe, CandleType)
+        """
+        if trading_mode == TradingMode.FUTURES:
+            datadir = datadir.joinpath("futures")
+        _tmp = [
+            re.search(cls._TRADES_REGEX, p.name)
+            for p in datadir.glob(f"*.{cls._get_file_extension()}")
+        ]
+        return [
+            cls.rebuild_pair_from_filename(match[1])
+            for match in _tmp
+            if match and len(match.groups()) > 1
+        ]
+
+    def trades_data_min_max(
+        self,
+        pair: str,
+        trading_mode: TradingMode,
+    ) -> tuple[datetime, datetime, int]:
+        """
+        Returns the min and max timestamp for the given pair's trades data.
+        :param pair: Pair to get min/max for
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        :return: (min, max, len)
+        """
+        df = self._trades_load(pair, trading_mode)
+        if df.empty:
+            return (
+                datetime.fromtimestamp(0, tz=timezone.utc),
+                datetime.fromtimestamp(0, tz=timezone.utc),
+                0,
+            )
+        return (
+            to_datetime(df.iloc[0]["timestamp"], unit="ms", utc=True).to_pydatetime(),
+            to_datetime(df.iloc[-1]["timestamp"], unit="ms", utc=True).to_pydatetime(),
+            len(df),
+        )
+
+    @classmethod
+    def trades_get_pairs(cls, datadir: Path) -> list[str]:
+        """
+        Returns a list of all pairs for which trade data is available in this
+        :param datadir: Directory to search for ohlcv files
+        :return: List of Pairs
+        """
+        _ext = cls._get_file_extension()
+        _tmp = [
+            re.search(r"^(\S+)(?=\-trades." + _ext + ")", p.name)
+            for p in datadir.glob(f"*trades.{_ext}")
+        ]
+        # Check if regex found something and only return these results to avoid exceptions.
+        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]
+
+    @abstractmethod
+    def _trades_store(self, pair: str, data: DataFrame, trading_mode: TradingMode) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        """
+
+    @abstractmethod
+    def trades_append(self, pair: str, data: DataFrame):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+
+    @abstractmethod
+    def _trades_load(
+        self, pair: str, trading_mode: TradingMode, timerange: TimeRange | None = None
+    ) -> DataFrame:
+        """
+        Load a pair from file, either .json.gz or .json
+        :param pair: Load trades for this pair
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: Dataframe containing trades
+        """
+
+    def trades_store(self, pair: str, data: DataFrame, trading_mode: TradingMode) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        """
+        # Filter on expected columns (will remove the actual date column).
+        self._trades_store(pair, data[DEFAULT_TRADES_COLUMNS], trading_mode)
+
+    def trades_purge(self, pair: str, trading_mode: TradingMode) -> bool:
+        """
+        Remove data for this pair
+        :param pair: Delete data for this pair.
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        :return: True when deleted, false if file did not exist.
+        """
+        filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
+
+    def trades_load(
+        self, pair: str, trading_mode: TradingMode, timerange: TimeRange | None = None
+    ) -> DataFrame:
+        """
+        Load a pair from file, either .json.gz or .json
+        Removes duplicates in the process.
+        :param pair: Load trades for this pair
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        try:
+            trades = self._trades_load(pair, trading_mode, timerange=timerange)
+        except Exception:
+            logger.exception(f"Error loading trades for {pair}")
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)
+
+        trades = trades_df_remove_duplicates(trades)
+
+        trades = trades_convert_types(trades)
+        return trades
+
+    @classmethod
+    def create_dir_if_needed(cls, datadir: Path):
+        """
+        Creates datadir if necessary
+        should only create directories for "futures" mode at the moment.
+        """
+        if not datadir.parent.is_dir():
+            datadir.parent.mkdir()
+
+    @classmethod
+    def _pair_data_filename(
+        cls,
+        datadir: Path,
+        pair: str,
+        timeframe: str,
+        candle_type: CandleType,
+        no_timeframe_modify: bool = False,
+    ) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        candle = ""
+        if not no_timeframe_modify:
+            timeframe = cls.timeframe_to_file(timeframe)
+
+        if candle_type != CandleType.SPOT:
+            datadir = datadir.joinpath("futures")
+            candle = f"-{candle_type}"
+        filename = datadir.joinpath(f"{pair_s}-{timeframe}{candle}.{cls._get_file_extension()}")
+        return filename
+
+    @classmethod
+    def _pair_trades_filename(cls, datadir: Path, pair: str, trading_mode: TradingMode) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        if trading_mode == TradingMode.FUTURES:
+            # Futures pair ...
+            datadir = datadir.joinpath("futures")
+
+        filename = datadir.joinpath(f"{pair_s}-trades.{cls._get_file_extension()}")
+        return filename
+
+    @staticmethod
+    def timeframe_to_file(timeframe: str):
+        return timeframe.replace("M", "Mo")
+
+    @staticmethod
+    def rebuild_timeframe_from_filename(timeframe: str) -> str:
+        """
+        converts timeframe from disk to file
+        Replaces mo with M (to avoid problems on case-insensitive filesystems)
+        """
+        return re.sub("1mo", "1M", timeframe, flags=re.IGNORECASE)
+
+    @staticmethod
+    def rebuild_pair_from_filename(pair: str) -> str:
+        """
+        Rebuild pair name from filename
+        Assumes a asset name of max. 7 length to also support BTC-PERP and BTC-PERP:USD names.
+        """
+        res = re.sub(r"^(([A-Za-z\d]{1,10})|^([A-Za-z\-]{1,6}))(_)", r"\g<1>/", pair, count=1)
+        res = re.sub("_", ":", res, count=1)
+        return res
+
+    def ohlcv_load(
+        self,
+        pair,
+        timeframe: str,
+        candle_type: CandleType,
+        *,
+        timerange: TimeRange | None = None,
+        fill_missing: bool = True,
+        drop_incomplete: bool = False,
+        startup_candles: int = 0,
+        warn_no_data: bool = True,
+    ) -> DataFrame:
+        """
+        Load cached candle (OHLCV) data for the given pair.
+
+        :param pair: Pair to load data for
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange
+        :param fill_missing: Fill missing values with "No action"-candles
+        :param drop_incomplete: Drop last candle assuming it may be incomplete.
+        :param startup_candles: Additional candles to load at the start of the period
+        :param warn_no_data: Log a warning message when no data is found
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        # Fix startup period
+        timerange_startup = deepcopy(timerange)
+        if startup_candles > 0 and timerange_startup:
+            timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
+
+        pairdf = self._ohlcv_load(
+            pair, timeframe, timerange=timerange_startup, candle_type=candle_type
+        )
+        if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
+            return pairdf
+        else:
+            enddate = pairdf.iloc[-1]["date"]
+
+            if timerange_startup:
+                self._validate_pairdata(pair, pairdf, timeframe, candle_type, timerange_startup)
+                pairdf = trim_dataframe(pairdf, timerange_startup)
+                if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
+                    return pairdf
+
+            # incomplete candles should only be dropped if we didn't trim the end beforehand.
+            pairdf = clean_ohlcv_dataframe(
+                pairdf,
+                timeframe,
+                pair=pair,
+                fill_missing=fill_missing,
+                drop_incomplete=(drop_incomplete and enddate == pairdf.iloc[-1]["date"]),
+            )
+            self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data)
+            return pairdf
+
+    def _check_empty_df(
+        self,
+        pairdf: DataFrame,
+        pair: str,
+        timeframe: str,
+        candle_type: CandleType,
+        warn_no_data: bool,
+        warn_price: bool = False,
+    ) -> bool:
+        """
+        Warn on empty dataframe
+        """
+        if pairdf.empty:
+            if warn_no_data:
+                logger.warning(
+                    f"No history for {pair}, {candle_type}, {timeframe} found. "
+                    "Use `freqtrade download-data` to download the data"
+                )
+            return True
+        elif warn_price:
+            candle_price_gap = 0
+            if (
+                candle_type in (CandleType.SPOT, CandleType.FUTURES)
+                and not pairdf.empty
+                and "close" in pairdf.columns
+                and "open" in pairdf.columns
+            ):
+                # Detect gaps between prior close and open
+                gaps = (pairdf["open"] - pairdf["close"].shift(1)) / pairdf["close"].shift(1)
+                gaps = gaps.dropna()
+                if len(gaps):
+                    candle_price_gap = max(abs(gaps))
+            if candle_price_gap > 0.1:
+                logger.info(
+                    f"Price jump in {pair}, {timeframe}, {candle_type} between two candles "
+                    f"of {candle_price_gap:.2%} detected."
+                )
+
+        return False
+
+    def _validate_pairdata(
+        self,
+        pair,
+        pairdata: DataFrame,
+        timeframe: str,
+        candle_type: CandleType,
+        timerange: TimeRange,
+    ):
+        """
+        Validates pairdata for missing data at start end end and logs warnings.
+        :param pairdata: Dataframe to validate
+        :param timerange: Timerange specified for start and end dates
+        """
+
+        if timerange.starttype == "date":
+            if pairdata.iloc[0]["date"] > timerange.startdt:
+                logger.warning(
+                    f"{pair}, {candle_type}, {timeframe}, "
+                    f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}"
+                )
+        if timerange.stoptype == "date":
+            if pairdata.iloc[-1]["date"] < timerange.stopdt:
+                logger.warning(
+                    f"{pair}, {candle_type}, {timeframe}, "
+                    f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}"
+                )
+
+    def rename_futures_data(
+        self, pair: str, new_pair: str, timeframe: str, candle_type: CandleType
+    ):
+        """
+        Temporary method to migrate data from old naming to new naming (BTC/USDT -> BTC/USDT:USDT)
+        Only used for binance to support the binance futures naming unification.
+        """
+
+        file_old = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        file_new = self._pair_data_filename(self._datadir, new_pair, timeframe, candle_type)
+        # print(file_old, file_new)
+        if file_new.exists():
+            logger.warning(f"{file_new} exists already, can't migrate {pair}.")
+            return
+        file_old.rename(file_new)
+
+    def fix_funding_fee_timeframe(self, ff_timeframe: str):
+        """
+        Temporary method to migrate data from old funding fee timeframe to the correct timeframe
+        Applies to bybit and okx, where funding-fee and mark candles have different timeframes.
+        """
+        paircombs = self.ohlcv_get_available_data(self._datadir, TradingMode.FUTURES)
+        funding_rate_combs = [
+            f for f in paircombs if f[2] == CandleType.FUNDING_RATE and f[1] != ff_timeframe
+        ]
+
+        if funding_rate_combs:
+            logger.warning(
+                f"Migrating {len(funding_rate_combs)} funding fees to correct timeframe."
+            )
+
+        for pair, timeframe, candletype in funding_rate_combs:
+            old_name = self._pair_data_filename(self._datadir, pair, timeframe, candletype)
+            new_name = self._pair_data_filename(self._datadir, pair, ff_timeframe, candletype)
+
+            if not Path(old_name).exists():
+                logger.warning(f"{old_name} does not exist, skipping.")
+                continue
+
+            if Path(new_name).exists():
+                logger.warning(f"{new_name} already exists, Removing.")
+                Path(new_name).unlink()
+
+            Path(old_name).rename(new_name)
+
+
+def get_datahandlerclass(datatype: str) -> type[IDataHandler]:
+    """
+    Get datahandler class.
+    Could be done using Resolvers, but since this may be called often and resolvers
+    are rather expensive, doing this directly should improve performance.
+    :param datatype: datatype to use.
+    :return: Datahandler class
+    """
+
+    if datatype == "json":
+        from .jsondatahandler import JsonDataHandler
+
+        return JsonDataHandler
+    elif datatype == "jsongz":
+        from .jsondatahandler import JsonGzDataHandler
+
+        return JsonGzDataHandler
+    elif datatype == "hdf5":
+        raise OperationalException(
+            "DEPRECATED: The hdf5 dataformat is deprecated and has been removed in 2025.1. "
+            "Please downgrade to 2024.12 and use the convert-data command to convert your data "
+            "to a supported format."
+            "We recommend using the feather format, as it is faster and is more space-efficient."
+        )
+
+    elif datatype == "feather":
+        from .featherdatahandler import FeatherDataHandler
+
+        return FeatherDataHandler
+    elif datatype == "parquet":
+        from .parquetdatahandler import ParquetDataHandler
+
+        return ParquetDataHandler
+    else:
+        raise ValueError(f"No datahandler for datatype {datatype} available.")
+
+
+def get_datahandler(
+    datadir: Path, data_format: str | None = None, data_handler: IDataHandler | None = None
+) -> IDataHandler:
+    """
+    :param datadir: Folder to save data
+    :param data_format: dataformat to use
+    :param data_handler: returns this datahandler if it exists or initializes a new one
+    """
+
+    if not data_handler:
+        HandlerClass = get_datahandlerclass(data_format or "feather")
+        data_handler = HandlerClass(datadir)
+    return data_handler
diff --git a/freqtrade/data/history/datahandlers/jsondatahandler.py b/freqtrade/data/history/datahandlers/jsondatahandler.py
new file mode 100644
index 000000000..1a33b3e2f
--- /dev/null
+++ b/freqtrade/data/history/datahandlers/jsondatahandler.py
@@ -0,0 +1,150 @@
+import logging
+
+import numpy as np
+from pandas import DataFrame, read_json, to_datetime
+
+from freqtrade import misc
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
+from freqtrade.data.converter import trades_dict_to_list, trades_list_to_df
+from freqtrade.enums import CandleType, TradingMode
+
+from .idatahandler import IDataHandler
+
+
+logger = logging.getLogger(__name__)
+
+
+class JsonDataHandler(IDataHandler):
+    _use_zip = False
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    def ohlcv_store(
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :param timeframe: Timeframe - used to generate filename
+        :param data: Dataframe containing OHLCV data
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        self.create_dir_if_needed(filename)
+        _data = data.copy()
+        # Convert date to int
+        _data["date"] = _data["date"].astype(np.int64) // 1000 // 1000
+
+        # Reset index, select only appropriate columns and save as json
+        _data.reset_index(drop=True).loc[:, self._columns].to_json(
+            filename, orient="values", compression="gzip" if self._use_zip else None
+        )
+
+    def _ohlcv_load(
+        self, pair: str, timeframe: str, timerange: TimeRange | None, candle_type: CandleType
+    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type)
+        if not filename.exists():
+            # Fallback mode for 1M files
+            filename = self._pair_data_filename(
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True
+            )
+            if not filename.exists():
+                return DataFrame(columns=self._columns)
+        try:
+            pairdata = read_json(filename, orient="values")
+            pairdata.columns = self._columns
+        except ValueError:
+            logger.error(f"Could not load data for {pair}.")
+            return DataFrame(columns=self._columns)
+        pairdata = pairdata.astype(
+            dtype={
+                "open": "float",
+                "high": "float",
+                "low": "float",
+                "close": "float",
+                "volume": "float",
+            }
+        )
+        pairdata["date"] = to_datetime(pairdata["date"], unit="ms", utc=True)
+        return pairdata
+
+    def ohlcv_append(
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        raise NotImplementedError()
+
+    def _trades_store(self, pair: str, data: DataFrame, trading_mode: TradingMode) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        """
+        filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
+        trades = data.values.tolist()
+        misc.file_dump_json(filename, trades, is_zip=self._use_zip)
+
+    def trades_append(self, pair: str, data: DataFrame):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        raise NotImplementedError()
+
+    def _trades_load(
+        self, pair: str, trading_mode: TradingMode, timerange: TimeRange | None = None
+    ) -> DataFrame:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: Dataframe containing trades
+        """
+        filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
+        tradesdata = misc.file_load_json(filename)
+
+        if not tradesdata:
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)
+
+        if isinstance(tradesdata[0], dict):
+            # Convert trades dict to list
+            logger.info("Old trades format detected - converting")
+            tradesdata = trades_dict_to_list(tradesdata)
+            pass
+        return trades_list_to_df(tradesdata, convert=False)
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "json.gz" if cls._use_zip else "json"
+
+
+class JsonGzDataHandler(JsonDataHandler):
+    _use_zip = True
diff --git a/freqtrade/data/history/datahandlers/parquetdatahandler.py b/freqtrade/data/history/datahandlers/parquetdatahandler.py
new file mode 100644
index 000000000..1813f9991
--- /dev/null
+++ b/freqtrade/data/history/datahandlers/parquetdatahandler.py
@@ -0,0 +1,133 @@
+import logging
+
+from pandas import DataFrame, read_parquet, to_datetime
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
+from freqtrade.enums import CandleType, TradingMode
+
+from .idatahandler import IDataHandler
+
+
+logger = logging.getLogger(__name__)
+
+
+class ParquetDataHandler(IDataHandler):
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    def ohlcv_store(
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :param timeframe: Timeframe - used to generate filename
+        :param data: Dataframe containing OHLCV data
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        self.create_dir_if_needed(filename)
+
+        data.reset_index(drop=True).loc[:, self._columns].to_parquet(filename)
+
+    def _ohlcv_load(
+        self, pair: str, timeframe: str, timerange: TimeRange | None, candle_type: CandleType
+    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type)
+        if not filename.exists():
+            # Fallback mode for 1M files
+            filename = self._pair_data_filename(
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True
+            )
+            if not filename.exists():
+                return DataFrame(columns=self._columns)
+        try:
+            pairdata = read_parquet(filename)
+            pairdata.columns = self._columns
+            pairdata = pairdata.astype(
+                dtype={
+                    "open": "float",
+                    "high": "float",
+                    "low": "float",
+                    "close": "float",
+                    "volume": "float",
+                }
+            )
+            pairdata["date"] = to_datetime(pairdata["date"], unit="ms", utc=True)
+            return pairdata
+        except Exception as e:
+            logger.exception(
+                f"Error loading data from {filename}. Exception: {e}. Returning empty dataframe."
+            )
+            return DataFrame(columns=self._columns)
+
+    def ohlcv_append(
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        raise NotImplementedError()
+
+    def _trades_store(self, pair: str, data: DataFrame, trading_mode: TradingMode) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        """
+        filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
+        self.create_dir_if_needed(filename)
+        data.reset_index(drop=True).to_parquet(filename)
+
+    def trades_append(self, pair: str, data: DataFrame):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        raise NotImplementedError()
+
+    def _trades_load(
+        self, pair: str, trading_mode: TradingMode, timerange: TimeRange | None = None
+    ) -> DataFrame:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param trading_mode: Trading mode to use (used to determine the filename)
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        filename = self._pair_trades_filename(self._datadir, pair, trading_mode)
+        if not filename.exists():
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)
+
+        tradesdata = read_parquet(filename)
+
+        return tradesdata
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "parquet"
diff --git a/freqtrade/data/history/history_utils.py b/freqtrade/data/history/history_utils.py
new file mode 100644
index 000000000..9fd254057
--- /dev/null
+++ b/freqtrade/data/history/history_utils.py
@@ -0,0 +1,712 @@
+import logging
+import operator
+from datetime import datetime, timedelta
+from pathlib import Path
+
+from pandas import DataFrame, concat
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DATETIME_PRINT_FORMAT, DL_DATA_TIMEFRAMES, DOCS_LINK, Config
+from freqtrade.data.converter import (
+    clean_ohlcv_dataframe,
+    convert_trades_to_ohlcv,
+    trades_df_remove_duplicates,
+    trades_list_to_df,
+)
+from freqtrade.data.history.datahandlers import IDataHandler, get_datahandler
+from freqtrade.enums import CandleType, TradingMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import Exchange
+from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
+from freqtrade.util import dt_now, dt_ts, format_ms_time, format_ms_time_det
+from freqtrade.util.migrations import migrate_data
+from freqtrade.util.progress_tracker import CustomProgress, retrieve_progress_tracker
+
+
+logger = logging.getLogger(__name__)
+
+
+def load_pair_history(
+    pair: str,
+    timeframe: str,
+    datadir: Path,
+    *,
+    timerange: TimeRange | None = None,
+    fill_up_missing: bool = True,
+    drop_incomplete: bool = False,
+    startup_candles: int = 0,
+    data_format: str | None = None,
+    data_handler: IDataHandler | None = None,
+    candle_type: CandleType = CandleType.SPOT,
+) -> DataFrame:
+    """
+    Load cached ohlcv history for the given pair.
+
+    :param pair: Pair to load data for
+    :param timeframe: Timeframe (e.g. "5m")
+    :param datadir: Path to the data storage location.
+    :param data_format: Format of the data. Ignored if data_handler is set.
+    :param timerange: Limit data to be loaded to this timerange
+    :param fill_up_missing: Fill missing values with "No action"-candles
+    :param drop_incomplete: Drop last candle assuming it may be incomplete.
+    :param startup_candles: Additional candles to load at the start of the period
+    :param data_handler: Initialized data-handler to use.
+                         Will be initialized from data_format if not set
+    :param candle_type: Any of the enum CandleType (must match trading mode!)
+    :return: DataFrame with ohlcv data, or empty DataFrame
+    """
+    data_handler = get_datahandler(datadir, data_format, data_handler)
+
+    return data_handler.ohlcv_load(
+        pair=pair,
+        timeframe=timeframe,
+        timerange=timerange,
+        fill_missing=fill_up_missing,
+        drop_incomplete=drop_incomplete,
+        startup_candles=startup_candles,
+        candle_type=candle_type,
+    )
+
+
+def load_data(
+    datadir: Path,
+    timeframe: str,
+    pairs: list[str],
+    *,
+    timerange: TimeRange | None = None,
+    fill_up_missing: bool = True,
+    startup_candles: int = 0,
+    fail_without_data: bool = False,
+    data_format: str = "feather",
+    candle_type: CandleType = CandleType.SPOT,
+    user_futures_funding_rate: int | None = None,
+) -> dict[str, DataFrame]:
+    """
+    Load ohlcv history data for a list of pairs.
+
+    :param datadir: Path to the data storage location.
+    :param timeframe: Timeframe (e.g. "5m")
+    :param pairs: List of pairs to load
+    :param timerange: Limit data to be loaded to this timerange
+    :param fill_up_missing: Fill missing values with "No action"-candles
+    :param startup_candles: Additional candles to load at the start of the period
+    :param fail_without_data: Raise OperationalException if no data is found.
+    :param data_format: Data format which should be used. Defaults to json
+    :param candle_type: Any of the enum CandleType (must match trading mode!)
+    :return: dict(<pair>:<Dataframe>)
+    """
+    result: dict[str, DataFrame] = {}
+    if startup_candles > 0 and timerange:
+        logger.info(f"Using indicator startup period: {startup_candles} ...")
+
+    data_handler = get_datahandler(datadir, data_format)
+
+    for pair in pairs:
+        hist = load_pair_history(
+            pair=pair,
+            timeframe=timeframe,
+            datadir=datadir,
+            timerange=timerange,
+            fill_up_missing=fill_up_missing,
+            startup_candles=startup_candles,
+            data_handler=data_handler,
+            candle_type=candle_type,
+        )
+        if not hist.empty:
+            result[pair] = hist
+        else:
+            if candle_type is CandleType.FUNDING_RATE and user_futures_funding_rate is not None:
+                logger.warning(f"{pair} using user specified [{user_futures_funding_rate}]")
+            elif candle_type not in (CandleType.SPOT, CandleType.FUTURES):
+                result[pair] = DataFrame(columns=["date", "open", "close", "high", "low", "volume"])
+
+    if fail_without_data and not result:
+        raise OperationalException("No data found. Terminating.")
+    return result
+
+
+def refresh_data(
+    *,
+    datadir: Path,
+    timeframe: str,
+    pairs: list[str],
+    exchange: Exchange,
+    data_format: str | None = None,
+    timerange: TimeRange | None = None,
+    candle_type: CandleType,
+) -> None:
+    """
+    Refresh ohlcv history data for a list of pairs.
+
+    :param datadir: Path to the data storage location.
+    :param timeframe: Timeframe (e.g. "5m")
+    :param pairs: List of pairs to load
+    :param exchange: Exchange object
+    :param data_format: dataformat to use
+    :param timerange: Limit data to be loaded to this timerange
+    :param candle_type: Any of the enum CandleType (must match trading mode!)
+    """
+    data_handler = get_datahandler(datadir, data_format)
+    for pair in pairs:
+        _download_pair_history(
+            pair=pair,
+            timeframe=timeframe,
+            datadir=datadir,
+            timerange=timerange,
+            exchange=exchange,
+            data_handler=data_handler,
+            candle_type=candle_type,
+        )
+
+
+def _load_cached_data_for_updating(
+    pair: str,
+    timeframe: str,
+    timerange: TimeRange | None,
+    data_handler: IDataHandler,
+    candle_type: CandleType,
+    prepend: bool = False,
+) -> tuple[DataFrame, int | None, int | None]:
+    """
+    Load cached data to download more data.
+    If timerange is passed in, checks whether data from an before the stored data will be
+    downloaded.
+    If that's the case then what's available should be completely overwritten.
+    Otherwise downloads always start at the end of the available data to avoid data gaps.
+    Note: Only used by download_pair_history().
+    """
+    start = None
+    end = None
+    if timerange:
+        if timerange.starttype == "date":
+            start = timerange.startdt
+        if timerange.stoptype == "date":
+            end = timerange.stopdt
+
+    # Intentionally don't pass timerange in - since we need to load the full dataset.
+    data = data_handler.ohlcv_load(
+        pair,
+        timeframe=timeframe,
+        timerange=None,
+        fill_missing=False,
+        drop_incomplete=True,
+        warn_no_data=False,
+        candle_type=candle_type,
+    )
+    if not data.empty:
+        if prepend:
+            end = data.iloc[0]["date"]
+        else:
+            if start and start < data.iloc[0]["date"]:
+                # Earlier data than existing data requested, Update start date
+                logger.info(
+                    f"{pair}, {timeframe}, {candle_type}: "
+                    f"Requested start date {start:{DATETIME_PRINT_FORMAT}} earlier than local "
+                    f"data start date {data.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}. "
+                    f"Use `--prepend` to download data prior "
+                    f"to {data.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}, or "
+                    "`--erase` to redownload all data."
+                )
+            start = data.iloc[-1]["date"]
+
+    start_ms = int(start.timestamp() * 1000) if start else None
+    end_ms = int(end.timestamp() * 1000) if end else None
+    return data, start_ms, end_ms
+
+
+def _download_pair_history(
+    pair: str,
+    *,
+    datadir: Path,
+    exchange: Exchange,
+    timeframe: str = "5m",
+    new_pairs_days: int = 30,
+    data_handler: IDataHandler | None = None,
+    timerange: TimeRange | None = None,
+    candle_type: CandleType,
+    erase: bool = False,
+    prepend: bool = False,
+) -> bool:
+    """
+    Download latest candles from the exchange for the pair and timeframe passed in parameters
+    The data is downloaded starting from the last correct data that
+    exists in a cache. If timerange starts earlier than the data in the cache,
+    the full data will be redownloaded
+
+    :param pair: pair to download
+    :param timeframe: Timeframe (e.g "5m")
+    :param timerange: range of time to download
+    :param candle_type: Any of the enum CandleType (must match trading mode!)
+    :param erase: Erase existing data
+    :return: bool with success state
+    """
+    data_handler = get_datahandler(datadir, data_handler=data_handler)
+
+    try:
+        if erase:
+            if data_handler.ohlcv_purge(pair, timeframe, candle_type=candle_type):
+                logger.info(f"Deleting existing data for pair {pair}, {timeframe}, {candle_type}.")
+
+        data, since_ms, until_ms = _load_cached_data_for_updating(
+            pair,
+            timeframe,
+            timerange,
+            data_handler=data_handler,
+            candle_type=candle_type,
+            prepend=prepend,
+        )
+
+        logger.info(
+            f'Download history data for "{pair}", {timeframe}, '
+            f"{candle_type} and store in {datadir}. "
+            f"From {format_ms_time(since_ms) if since_ms else 'start'} to "
+            f"{format_ms_time(until_ms) if until_ms else 'now'}"
+        )
+
+        logger.debug(
+            "Current Start: %s",
+            f"{data.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}" if not data.empty else "None",
+        )
+        logger.debug(
+            "Current End: %s",
+            f"{data.iloc[-1]['date']:{DATETIME_PRINT_FORMAT}}" if not data.empty else "None",
+        )
+
+        # Default since_ms to 30 days if nothing is given
+        new_dataframe = exchange.get_historic_ohlcv(
+            pair=pair,
+            timeframe=timeframe,
+            since_ms=(
+                since_ms
+                if since_ms
+                else int((datetime.now() - timedelta(days=new_pairs_days)).timestamp()) * 1000
+            ),
+            is_new_pair=data.empty,
+            candle_type=candle_type,
+            until_ms=until_ms if until_ms else None,
+        )
+        logger.info(f"Downloaded data for {pair} with length {len(new_dataframe)}.")
+        if data.empty:
+            data = new_dataframe
+        else:
+            # Run cleaning again to ensure there were no duplicate candles
+            # Especially between existing and new data.
+            data = clean_ohlcv_dataframe(
+                concat([data, new_dataframe], axis=0),
+                timeframe,
+                pair,
+                fill_missing=False,
+                drop_incomplete=False,
+            )
+
+        logger.debug(
+            "New Start: %s",
+            f"{data.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}" if not data.empty else "None",
+        )
+        logger.debug(
+            "New End: %s",
+            f"{data.iloc[-1]['date']:{DATETIME_PRINT_FORMAT}}" if not data.empty else "None",
+        )
+
+        data_handler.ohlcv_store(pair, timeframe, data=data, candle_type=candle_type)
+        return True
+
+    except Exception:
+        logger.exception(
+            f'Failed to download history data for pair: "{pair}", timeframe: {timeframe}.'
+        )
+        return False
+
+
+def refresh_backtest_ohlcv_data(
+    exchange: Exchange,
+    pairs: list[str],
+    timeframes: list[str],
+    datadir: Path,
+    trading_mode: str,
+    timerange: TimeRange | None = None,
+    new_pairs_days: int = 30,
+    erase: bool = False,
+    data_format: str | None = None,
+    prepend: bool = False,
+    progress_tracker: CustomProgress | None = None,
+) -> list[str]:
+    """
+    Refresh stored ohlcv data for backtesting and hyperopt operations.
+    Used by freqtrade download-data subcommand.
+    :return: List of pairs that are not available.
+    """
+    progress_tracker = retrieve_progress_tracker(progress_tracker)
+
+    pairs_not_available = []
+    data_handler = get_datahandler(datadir, data_format)
+    candle_type = CandleType.get_default(trading_mode)
+    with progress_tracker as progress:
+        tf_length = len(timeframes) if trading_mode != "futures" else len(timeframes) + 2
+        timeframe_task = progress.add_task("Timeframe", total=tf_length)
+        pair_task = progress.add_task("Downloading data...", total=len(pairs))
+
+        for pair in pairs:
+            progress.update(pair_task, description=f"Downloading {pair}")
+            progress.update(timeframe_task, completed=0)
+
+            if pair not in exchange.markets:
+                pairs_not_available.append(f"{pair}: Pair not available on exchange.")
+                logger.info(f"Skipping pair {pair}...")
+                continue
+            for timeframe in timeframes:
+                progress.update(timeframe_task, description=f"Timeframe {timeframe}")
+                logger.debug(f"Downloading pair {pair}, {candle_type}, interval {timeframe}.")
+                _download_pair_history(
+                    pair=pair,
+                    datadir=datadir,
+                    exchange=exchange,
+                    timerange=timerange,
+                    data_handler=data_handler,
+                    timeframe=str(timeframe),
+                    new_pairs_days=new_pairs_days,
+                    candle_type=candle_type,
+                    erase=erase,
+                    prepend=prepend,
+                )
+                progress.update(timeframe_task, advance=1)
+            if trading_mode == "futures":
+                # Predefined candletype (and timeframe) depending on exchange
+                # Downloads what is necessary to backtest based on futures data.
+                tf_mark = exchange.get_option("mark_ohlcv_timeframe")
+                tf_funding_rate = exchange.get_option("funding_fee_timeframe")
+
+                fr_candle_type = CandleType.from_string(exchange.get_option("mark_ohlcv_price"))
+                # All exchanges need FundingRate for futures trading.
+                # The timeframe is aligned to the mark-price timeframe.
+                combs = ((CandleType.FUNDING_RATE, tf_funding_rate), (fr_candle_type, tf_mark))
+                for candle_type_f, tf in combs:
+                    logger.debug(f"Downloading pair {pair}, {candle_type_f}, interval {tf}.")
+                    _download_pair_history(
+                        pair=pair,
+                        datadir=datadir,
+                        exchange=exchange,
+                        timerange=timerange,
+                        data_handler=data_handler,
+                        timeframe=str(tf),
+                        new_pairs_days=new_pairs_days,
+                        candle_type=candle_type_f,
+                        erase=erase,
+                        prepend=prepend,
+                    )
+                    progress.update(
+                        timeframe_task, advance=1, description=f"Timeframe {candle_type_f}, {tf}"
+                    )
+
+            progress.update(pair_task, advance=1)
+            progress.update(timeframe_task, description="Timeframe")
+
+    return pairs_not_available
+
+
+def _download_trades_history(
+    exchange: Exchange,
+    pair: str,
+    *,
+    new_pairs_days: int = 30,
+    timerange: TimeRange | None = None,
+    data_handler: IDataHandler,
+    trading_mode: TradingMode,
+) -> bool:
+    """
+    Download trade history from the exchange.
+    Appends to previously downloaded trades data.
+    """
+    until = None
+    since = 0
+    if timerange:
+        if timerange.starttype == "date":
+            since = timerange.startts * 1000
+        if timerange.stoptype == "date":
+            until = timerange.stopts * 1000
+
+    trades = data_handler.trades_load(pair, trading_mode)
+
+    # TradesList columns are defined in constants.DEFAULT_TRADES_COLUMNS
+    # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
+    # DEFAULT_TRADES_COLUMNS: 1 -> id
+
+    if not trades.empty and since > 0 and (since + 1000) < trades.iloc[0]["timestamp"]:
+        # since is before the first trade
+        raise ValueError(
+            f"Start {format_ms_time_det(since)} earlier than "
+            f"available data ({format_ms_time_det(trades.iloc[0]['timestamp'])}). "
+            f"Please use `--erase` if you'd like to redownload {pair}."
+        )
+
+    from_id = trades.iloc[-1]["id"] if not trades.empty else None
+    if not trades.empty and since < trades.iloc[-1]["timestamp"]:
+        # Reset since to the last available point
+        # - 5 seconds (to ensure we're getting all trades)
+        since = int(trades.iloc[-1]["timestamp"] - (5 * 1000))
+        logger.info(
+            f"Using last trade date -5s - Downloading trades for {pair} "
+            f"since: {format_ms_time(since)}."
+        )
+
+    if not since:
+        since = dt_ts(dt_now() - timedelta(days=new_pairs_days))
+
+    logger.debug(
+        "Current Start: %s",
+        "None" if trades.empty else f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}",
+    )
+    logger.debug(
+        "Current End: %s",
+        "None" if trades.empty else f"{trades.iloc[-1]['date']:{DATETIME_PRINT_FORMAT}}",
+    )
+    logger.info(f"Current Amount of trades: {len(trades)}")
+
+    new_trades = exchange.get_historic_trades(
+        pair=pair,
+        since=since,
+        until=until,
+        from_id=from_id,
+    )
+    new_trades_df = trades_list_to_df(new_trades[1])
+    trades = concat([trades, new_trades_df], axis=0)
+    # Remove duplicates to make sure we're not storing data we don't need
+    trades = trades_df_remove_duplicates(trades)
+    data_handler.trades_store(pair, trades, trading_mode)
+
+    logger.debug(
+        "New Start: %s",
+        "None" if trades.empty else f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}",
+    )
+    logger.debug(
+        "New End: %s",
+        "None" if trades.empty else f"{trades.iloc[-1]['date']:{DATETIME_PRINT_FORMAT}}",
+    )
+    logger.info(f"New Amount of trades: {len(trades)}")
+    return True
+
+
+def refresh_backtest_trades_data(
+    exchange: Exchange,
+    pairs: list[str],
+    datadir: Path,
+    timerange: TimeRange,
+    trading_mode: TradingMode,
+    new_pairs_days: int = 30,
+    erase: bool = False,
+    data_format: str = "feather",
+    progress_tracker: CustomProgress | None = None,
+) -> list[str]:
+    """
+    Refresh stored trades data for backtesting and hyperopt operations.
+    Used by freqtrade download-data subcommand.
+    :return: List of pairs that are not available.
+    """
+    progress_tracker = retrieve_progress_tracker(progress_tracker)
+    pairs_not_available = []
+    data_handler = get_datahandler(datadir, data_format=data_format)
+    with progress_tracker as progress:
+        pair_task = progress.add_task("Downloading data...", total=len(pairs))
+        for pair in pairs:
+            progress.update(pair_task, description=f"Downloading trades [{pair}]")
+            if pair not in exchange.markets:
+                pairs_not_available.append(f"{pair}: Pair not available on exchange.")
+                logger.info(f"Skipping pair {pair}...")
+                continue
+
+            if erase:
+                if data_handler.trades_purge(pair, trading_mode):
+                    logger.info(f"Deleting existing data for pair {pair}.")
+
+            logger.info(f"Downloading trades for pair {pair}.")
+            try:
+                _download_trades_history(
+                    exchange=exchange,
+                    pair=pair,
+                    new_pairs_days=new_pairs_days,
+                    timerange=timerange,
+                    data_handler=data_handler,
+                    trading_mode=trading_mode,
+                )
+            except ValueError as e:
+                pairs_not_available.append(f"{pair}: {str(e)}")
+            except Exception:
+                logger.exception(
+                    f'Failed to download and store historic trades for pair: "{pair}". '
+                )
+
+            progress.update(pair_task, advance=1)
+
+    return pairs_not_available
+
+
+def get_timerange(data: dict[str, DataFrame]) -> tuple[datetime, datetime]:
+    """
+    Get the maximum common timerange for the given backtest data.
+
+    :param data: dictionary with preprocessed backtesting data
+    :return: tuple containing min_date, max_date
+    """
+    timeranges = [
+        (frame["date"].min().to_pydatetime(), frame["date"].max().to_pydatetime())
+        for frame in data.values()
+    ]
+    return (
+        min(timeranges, key=operator.itemgetter(0))[0],
+        max(timeranges, key=operator.itemgetter(1))[1],
+    )
+
+
+def validate_backtest_data(
+    data: DataFrame, pair: str, min_date: datetime, max_date: datetime, timeframe_min: int
+) -> bool:
+    """
+    Validates preprocessed backtesting data for missing values and shows warnings about it that.
+
+    :param data: preprocessed backtesting data (as DataFrame)
+    :param pair: pair used for log output.
+    :param min_date: start-date of the data
+    :param max_date: end-date of the data
+    :param timeframe_min: Timeframe in minutes
+    """
+    # total difference in minutes / timeframe-minutes
+    expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)
+    found_missing = False
+    dflen = len(data)
+    if dflen < expected_frames:
+        found_missing = True
+        logger.warning(
+            "%s has missing frames: expected %s, got %s, that's %s missing values",
+            pair,
+            expected_frames,
+            dflen,
+            expected_frames - dflen,
+        )
+    return found_missing
+
+
+def download_data_main(config: Config) -> None:
+    from freqtrade.resolvers.exchange_resolver import ExchangeResolver
+
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
+
+    download_data(config, exchange)
+
+
+def download_data(
+    config: Config,
+    exchange: Exchange,
+    *,
+    progress_tracker: CustomProgress | None = None,
+) -> None:
+    """
+    Download data function. Used from both cli and API.
+    """
+    timerange = TimeRange()
+    if "days" in config and config["days"] is not None:
+        time_since = (datetime.now() - timedelta(days=config["days"])).strftime("%Y%m%d")
+        timerange = TimeRange.parse_timerange(f"{time_since}-")
+
+    if "timerange" in config:
+        timerange = TimeRange.parse_timerange(config["timerange"])
+
+    # Remove stake-currency to skip checks which are not relevant for datadownload
+    config["stake_currency"] = ""
+
+    pairs_not_available: list[str] = []
+
+    available_pairs = [
+        p
+        for p in exchange.get_markets(
+            tradable_only=True, active_only=not config.get("include_inactive")
+        ).keys()
+    ]
+
+    expanded_pairs = dynamic_expand_pairlist(config, available_pairs)
+    if "timeframes" not in config:
+        config["timeframes"] = DL_DATA_TIMEFRAMES
+
+    if len(expanded_pairs) == 0:
+        logger.warning(
+            "No pairs available for download. "
+            "Please make sure you're using the correct Pair naming for your selected trade mode. \n"
+            f"More info: {DOCS_LINK}/bot-basics/#pair-naming"
+        )
+        return
+
+    logger.info(
+        f"About to download pairs: {expanded_pairs}, "
+        f"intervals: {config['timeframes']} to {config['datadir']}"
+    )
+
+    for timeframe in config["timeframes"]:
+        exchange.validate_timeframes(timeframe)
+
+    # Start downloading
+    try:
+        if config.get("download_trades"):
+            if not exchange.get_option("trades_has_history", True):
+                raise OperationalException(
+                    f"Trade history not available for {exchange.name}. "
+                    "You cannot use --dl-trades for this exchange."
+                )
+            pairs_not_available = refresh_backtest_trades_data(
+                exchange,
+                pairs=expanded_pairs,
+                datadir=config["datadir"],
+                timerange=timerange,
+                new_pairs_days=config["new_pairs_days"],
+                erase=bool(config.get("erase")),
+                data_format=config["dataformat_trades"],
+                trading_mode=config.get("trading_mode", TradingMode.SPOT),
+                progress_tracker=progress_tracker,
+            )
+
+            if config.get("convert_trades") or not exchange.get_option("ohlcv_has_history", True):
+                # Convert downloaded trade data to different timeframes
+                # Only auto-convert for exchanges without historic klines
+
+                convert_trades_to_ohlcv(
+                    pairs=expanded_pairs,
+                    timeframes=config["timeframes"],
+                    datadir=config["datadir"],
+                    timerange=timerange,
+                    erase=bool(config.get("erase")),
+                    data_format_ohlcv=config["dataformat_ohlcv"],
+                    data_format_trades=config["dataformat_trades"],
+                    candle_type=config.get("candle_type_def", CandleType.SPOT),
+                )
+        else:
+            if not exchange.get_option("ohlcv_has_history", True):
+                if not exchange.get_option("trades_has_history", True):
+                    raise OperationalException(
+                        f"Historic data not available for {exchange.name}. "
+                        f"{exchange.name} does not support downloading trades or ohlcv data."
+                    )
+                else:
+                    raise OperationalException(
+                        f"Historic klines not available for {exchange.name}. "
+                        "Please use `--dl-trades` instead for this exchange "
+                        "(will unfortunately take a long time)."
+                    )
+            migrate_data(config, exchange)
+            pairs_not_available = refresh_backtest_ohlcv_data(
+                exchange,
+                pairs=expanded_pairs,
+                timeframes=config["timeframes"],
+                datadir=config["datadir"],
+                timerange=timerange,
+                new_pairs_days=config["new_pairs_days"],
+                erase=bool(config.get("erase")),
+                data_format=config["dataformat_ohlcv"],
+                trading_mode=config.get("trading_mode", "spot"),
+                prepend=config.get("prepend_data", False),
+                progress_tracker=progress_tracker,
+            )
+    finally:
+        if pairs_not_available:
+            errors = "\n" + ("\n".join(pairs_not_available))
+            logger.warning(
+                f"Encountered a problem downloading the following pairs from {exchange.name}: "
+                f"{errors}"
+            )
diff --git a/freqtrade/data/metrics.py b/freqtrade/data/metrics.py
new file mode 100644
index 000000000..8e234df85
--- /dev/null
+++ b/freqtrade/data/metrics.py
@@ -0,0 +1,404 @@
+import logging
+import math
+from dataclasses import dataclass
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+
+
+logger = logging.getLogger(__name__)
+
+
+def calculate_market_change(data: dict[str, pd.DataFrame], column: str = "close") -> float:
+    """
+    Calculate market change based on "column".
+    Calculation is done by taking the first non-null and the last non-null element of each column
+    and calculating the pctchange as "(last - first) / first".
+    Then the results per pair are combined as mean.
+
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return:
+    """
+    tmp_means = []
+    for pair, df in data.items():
+        start = df[column].dropna().iloc[0]
+        end = df[column].dropna().iloc[-1]
+        tmp_means.append((end - start) / start)
+
+    return float(np.mean(tmp_means))
+
+
+def combine_dataframes_by_column(
+    data: dict[str, pd.DataFrame], column: str = "close"
+) -> pd.DataFrame:
+    """
+    Combine multiple dataframes "column"
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key.
+    :raise: ValueError if no data is provided.
+    """
+    if not data:
+        raise ValueError("No data provided.")
+    df_comb = pd.concat(
+        [data[pair].set_index("date").rename({column: pair}, axis=1)[pair] for pair in data], axis=1
+    )
+    return df_comb
+
+
+def combined_dataframes_with_rel_mean(
+    data: dict[str, pd.DataFrame], fromdt: datetime, todt: datetime, column: str = "close"
+) -> pd.DataFrame:
+    """
+    Combine multiple dataframes "column"
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key, and a column
+        named mean, containing the mean of all pairs.
+    :raise: ValueError if no data is provided.
+    """
+    df_comb = combine_dataframes_by_column(data, column)
+    # Trim dataframes to the given timeframe
+    df_comb = df_comb.iloc[(df_comb.index >= fromdt) & (df_comb.index < todt)]
+    df_comb["count"] = df_comb.count(axis=1)
+    df_comb["mean"] = df_comb.mean(axis=1)
+    df_comb["rel_mean"] = df_comb["mean"].pct_change().fillna(0).cumsum()
+    return df_comb[["mean", "rel_mean", "count"]]
+
+
+def combine_dataframes_with_mean(
+    data: dict[str, pd.DataFrame], column: str = "close"
+) -> pd.DataFrame:
+    """
+    Combine multiple dataframes "column"
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key, and a column
+        named mean, containing the mean of all pairs.
+    :raise: ValueError if no data is provided.
+    """
+    df_comb = combine_dataframes_by_column(data, column)
+
+    df_comb["mean"] = df_comb.mean(axis=1)
+
+    return df_comb
+
+
+def create_cum_profit(
+    df: pd.DataFrame, trades: pd.DataFrame, col_name: str, timeframe: str
+) -> pd.DataFrame:
+    """
+    Adds a column `col_name` with the cumulative profit for the given trades array.
+    :param df: DataFrame with date index
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :param col_name: Column name that will be assigned the results
+    :param timeframe: Timeframe used during the operations
+    :return: Returns df with one additional column, col_name, containing the cumulative profit.
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    from freqtrade.exchange import timeframe_to_resample_freq
+
+    timeframe_freq = timeframe_to_resample_freq(timeframe)
+    # Resample to timeframe to make sure trades match candles
+    _trades_sum = trades.resample(timeframe_freq, on="close_date")[["profit_abs"]].sum()
+    df.loc[:, col_name] = _trades_sum["profit_abs"].cumsum()
+    # Set first value to 0
+    df.loc[df.iloc[0].name, col_name] = 0
+    # FFill to get continuous
+    df[col_name] = df[col_name].ffill()
+    return df
+
+
+def _calc_drawdown_series(
+    profit_results: pd.DataFrame, *, date_col: str, value_col: str, starting_balance: float
+) -> pd.DataFrame:
+    max_drawdown_df = pd.DataFrame()
+    max_drawdown_df["cumulative"] = profit_results[value_col].cumsum()
+    max_drawdown_df["high_value"] = np.maximum(0, max_drawdown_df["cumulative"].cummax())
+    max_drawdown_df["drawdown"] = max_drawdown_df["cumulative"] - max_drawdown_df["high_value"]
+    max_drawdown_df["date"] = profit_results.loc[:, date_col]
+    if starting_balance:
+        cumulative_balance = starting_balance + max_drawdown_df["cumulative"]
+        max_balance = starting_balance + max_drawdown_df["high_value"]
+        max_drawdown_df["drawdown_relative"] = (max_balance - cumulative_balance) / max_balance
+    else:
+        # NOTE: This is not completely accurate,
+        # but might good enough if starting_balance is not available
+        max_drawdown_df["drawdown_relative"] = (
+            max_drawdown_df["high_value"] - max_drawdown_df["cumulative"]
+        ) / max_drawdown_df["high_value"]
+    return max_drawdown_df
+
+
+def calculate_underwater(
+    trades: pd.DataFrame,
+    *,
+    date_col: str = "close_date",
+    value_col: str = "profit_ratio",
+    starting_balance: float = 0.0,
+):
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
+    :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
+    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
+             high and low time and high and low value.
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    profit_results = trades.sort_values(date_col).reset_index(drop=True)
+    max_drawdown_df = _calc_drawdown_series(
+        profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance
+    )
+
+    return max_drawdown_df
+
+
+@dataclass()
+class DrawDownResult:
+    drawdown_abs: float = 0.0
+    high_date: pd.Timestamp = None
+    low_date: pd.Timestamp = None
+    high_value: float = 0.0
+    low_value: float = 0.0
+    relative_account_drawdown: float = 0.0
+
+
+def calculate_max_drawdown(
+    trades: pd.DataFrame,
+    *,
+    date_col: str = "close_date",
+    value_col: str = "profit_abs",
+    starting_balance: float = 0,
+    relative: bool = False,
+) -> DrawDownResult:
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
+    :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
+    :param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
+    :return: DrawDownResult object
+             with absolute max drawdown, high and low time and high and low value,
+             and the relative account drawdown
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    profit_results = trades.sort_values(date_col).reset_index(drop=True)
+    max_drawdown_df = _calc_drawdown_series(
+        profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance
+    )
+
+    idxmin = (
+        max_drawdown_df["drawdown_relative"].idxmax()
+        if relative
+        else max_drawdown_df["drawdown"].idxmin()
+    )
+
+    high_idx = max_drawdown_df.iloc[: idxmin + 1]["high_value"].idxmax()
+    high_date = profit_results.loc[high_idx, date_col]
+    low_date = profit_results.loc[idxmin, date_col]
+    high_val = max_drawdown_df.loc[high_idx, "cumulative"]
+    low_val = max_drawdown_df.loc[idxmin, "cumulative"]
+    max_drawdown_rel = max_drawdown_df.loc[idxmin, "drawdown_relative"]
+
+    return DrawDownResult(
+        drawdown_abs=abs(max_drawdown_df.loc[idxmin, "drawdown"]),
+        high_date=high_date,
+        low_date=low_date,
+        high_value=high_val,
+        low_value=low_val,
+        relative_account_drawdown=max_drawdown_rel,
+    )
+
+
+def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> tuple[float, float]:
+    """
+    Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
+    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
+    :param starting_balance: Add starting balance to results, to show the wallets high / low points
+    :return: Tuple (float, float) with cumsum of profit_abs
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+
+    csum_df = pd.DataFrame()
+    csum_df["sum"] = trades["profit_abs"].cumsum()
+    csum_min = csum_df["sum"].min() + starting_balance
+    csum_max = csum_df["sum"].max() + starting_balance
+
+    return csum_min, csum_max
+
+
+def calculate_cagr(days_passed: int, starting_balance: float, final_balance: float) -> float:
+    """
+    Calculate CAGR
+    :param days_passed: Days passed between start and ending balance
+    :param starting_balance: Starting balance
+    :param final_balance: Final balance to calculate CAGR against
+    :return: CAGR
+    """
+    if final_balance < 0:
+        # With leveraged trades, final_balance can become negative.
+        return 0
+    return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
+
+
+def calculate_expectancy(trades: pd.DataFrame) -> tuple[float, float]:
+    """
+    Calculate expectancy
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :return: expectancy, expectancy_ratio
+    """
+
+    expectancy = 0.0
+    expectancy_ratio = 100.0
+
+    if len(trades) > 0:
+        winning_trades = trades.loc[trades["profit_abs"] > 0]
+        losing_trades = trades.loc[trades["profit_abs"] < 0]
+        profit_sum = winning_trades["profit_abs"].sum()
+        loss_sum = abs(losing_trades["profit_abs"].sum())
+        nb_win_trades = len(winning_trades)
+        nb_loss_trades = len(losing_trades)
+
+        average_win = (profit_sum / nb_win_trades) if nb_win_trades > 0 else 0
+        average_loss = (loss_sum / nb_loss_trades) if nb_loss_trades > 0 else 0
+        winrate = nb_win_trades / len(trades)
+        loserate = nb_loss_trades / len(trades)
+
+        expectancy = (winrate * average_win) - (loserate * average_loss)
+        if average_loss > 0:
+            risk_reward_ratio = average_win / average_loss
+            expectancy_ratio = ((1 + risk_reward_ratio) * winrate) - 1
+
+    return expectancy, expectancy_ratio
+
+
+def calculate_sortino(
+    trades: pd.DataFrame, min_date: datetime, max_date: datetime, starting_balance: float
+) -> float:
+    """
+    Calculate sortino
+    :param trades: DataFrame containing trades (requires columns profit_abs)
+    :return: sortino
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades["profit_abs"] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+
+    down_stdev = np.std(trades.loc[trades["profit_abs"] < 0, "profit_abs"] / starting_balance)
+
+    if down_stdev != 0 and not np.isnan(down_stdev):
+        sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+        sortino_ratio = -100
+
+    # print(expected_returns_mean, down_stdev, sortino_ratio)
+    return sortino_ratio
+
+
+def calculate_sharpe(
+    trades: pd.DataFrame, min_date: datetime, max_date: datetime, starting_balance: float
+) -> float:
+    """
+    Calculate sharpe
+    :param trades: DataFrame containing trades (requires column profit_abs)
+    :return: sharpe
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades["profit_abs"] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+    up_stdev = np.std(total_profit)
+
+    if up_stdev != 0:
+        sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+        sharp_ratio = -100
+
+    # print(expected_returns_mean, up_stdev, sharp_ratio)
+    return sharp_ratio
+
+
+def calculate_calmar(
+    trades: pd.DataFrame, min_date: datetime, max_date: datetime, starting_balance: float
+) -> float:
+    """
+    Calculate calmar
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :return: calmar
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades["profit_abs"].sum() / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    # adding slippage of 0.1% per trade
+    # total_profit = total_profit - 0.0005
+    expected_returns_mean = total_profit / days_period * 100
+
+    # calculate max drawdown
+    try:
+        drawdown = calculate_max_drawdown(
+            trades, value_col="profit_abs", starting_balance=starting_balance
+        )
+        max_drawdown = drawdown.relative_account_drawdown
+    except ValueError:
+        max_drawdown = 0
+
+    if max_drawdown != 0:
+        calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
+    else:
+        # Define high (negative) calmar ratio to be clear that this is NOT optimal.
+        calmar_ratio = -100
+
+    # print(expected_returns_mean, max_drawdown, calmar_ratio)
+    return calmar_ratio
+
+
+def calculate_sqn(trades: pd.DataFrame, starting_balance: float) -> float:
+    """
+    Calculate System Quality Number (SQN) - Van K. Tharp.
+    SQN measures systematic trading quality and takes into account both
+    the number of trades and their standard deviation.
+
+    :param trades: DataFrame containing trades (requires column profit_abs)
+    :param starting_balance: Starting balance of the trading system
+    :return: SQN value
+    """
+    if len(trades) == 0:
+        return 0.0
+
+    total_profit = trades["profit_abs"] / starting_balance
+    number_of_trades = len(trades)
+
+    # Calculate average trade and standard deviation
+    average_profits = total_profit.mean()
+    profits_std = total_profit.std()
+
+    if profits_std != 0 and not np.isnan(profits_std):
+        sqn = math.sqrt(number_of_trades) * (average_profits / profits_std)
+    else:
+        # Define negative SQN to indicate this is NOT optimal
+        sqn = -100.0
+
+    return round(sqn, 4)
diff --git a/freqtrade/edge/__init__.py b/freqtrade/edge/__init__.py
new file mode 100644
index 000000000..d275a80e3
--- /dev/null
+++ b/freqtrade/edge/__init__.py
@@ -0,0 +1 @@
+from .edge_positioning import Edge, PairInfo  # noqa: F401
diff --git a/freqtrade/edge/edge_positioning.py b/freqtrade/edge/edge_positioning.py
new file mode 100644
index 000000000..546126513
--- /dev/null
+++ b/freqtrade/edge/edge_positioning.py
@@ -0,0 +1,524 @@
+# pragma pylint: disable=W0603
+"""Edge positioning package"""
+
+import logging
+from collections import defaultdict
+from copy import deepcopy
+from datetime import timedelta
+from typing import Any, NamedTuple
+
+import numpy as np
+import utils_find_1st as utf1st
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT, Config
+from freqtrade.data.history import get_timerange, load_data, refresh_data
+from freqtrade.enums import CandleType, ExitType, RunMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
+from freqtrade.strategy.interface import IStrategy
+from freqtrade.util import dt_now
+
+
+logger = logging.getLogger(__name__)
+
+
+class PairInfo(NamedTuple):
+    stoploss: float
+    winrate: float
+    risk_reward_ratio: float
+    required_risk_reward: float
+    expectancy: float
+    nb_trades: int
+    avg_trade_duration: float
+
+
+class Edge:
+    """
+    Calculates Win Rate, Risk Reward Ratio, Expectancy
+    against historical data for a give set of markets and a strategy
+    it then adjusts stoploss and position size accordingly
+    and force it into the strategy
+    Author: https://github.com/mishaker
+    """
+
+    _cached_pairs: dict[str, Any] = {}  # Keeps a list of pairs
+
+    def __init__(self, config: Config, exchange, strategy) -> None:
+        self.config = config
+        self.exchange = exchange
+        self.strategy: IStrategy = strategy
+
+        self.edge_config = self.config.get("edge", {})
+        self._cached_pairs: dict[str, Any] = {}  # Keeps a list of pairs
+        self._final_pairs: list = []
+
+        # checking max_open_trades. it should be -1 as with Edge
+        # the number of trades is determined by position size
+        if self.config["max_open_trades"] != float("inf"):
+            logger.critical("max_open_trades should be -1 in config !")
+
+        if self.config["stake_amount"] != UNLIMITED_STAKE_AMOUNT:
+            raise OperationalException("Edge works only with unlimited stake amount")
+
+        self._capital_ratio: float = self.config["tradable_balance_ratio"]
+        self._allowed_risk: float = self.edge_config.get("allowed_risk")
+        self._since_number_of_days: int = self.edge_config.get("calculate_since_number_of_days", 14)
+        self._last_updated: int = 0  # Timestamp of pairs last updated time
+        self._refresh_pairs = True
+
+        self._stoploss_range_min = float(self.edge_config.get("stoploss_range_min", -0.01))
+        self._stoploss_range_max = float(self.edge_config.get("stoploss_range_max", -0.05))
+        self._stoploss_range_step = float(self.edge_config.get("stoploss_range_step", -0.001))
+
+        # calculating stoploss range
+        self._stoploss_range = np.arange(
+            self._stoploss_range_min, self._stoploss_range_max, self._stoploss_range_step
+        )
+
+        self._timerange: TimeRange = TimeRange.parse_timerange(
+            f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-"
+        )
+        if config.get("fee"):
+            self.fee = config["fee"]
+        else:
+            try:
+                self.fee = self.exchange.get_fee(
+                    symbol=expand_pairlist(
+                        self.config["exchange"]["pair_whitelist"], list(self.exchange.markets)
+                    )[0]
+                )
+            except IndexError:
+                self.fee = None
+
+    def calculate(self, pairs: list[str]) -> bool:
+        if self.fee is None and pairs:
+            self.fee = self.exchange.get_fee(pairs[0])
+
+        heartbeat = self.edge_config.get("process_throttle_secs")
+
+        if (self._last_updated > 0) and (
+            self._last_updated + heartbeat > int(dt_now().timestamp())
+        ):
+            return False
+
+        data: dict[str, Any] = {}
+        logger.info("Using stake_currency: %s ...", self.config["stake_currency"])
+        logger.info("Using local backtesting data (using whitelist in given config) ...")
+
+        if self._refresh_pairs:
+            timerange_startup = deepcopy(self._timerange)
+            timerange_startup.subtract_start(
+                timeframe_to_seconds(self.strategy.timeframe) * self.strategy.startup_candle_count
+            )
+            refresh_data(
+                datadir=self.config["datadir"],
+                pairs=pairs,
+                exchange=self.exchange,
+                timeframe=self.strategy.timeframe,
+                timerange=timerange_startup,
+                data_format=self.config["dataformat_ohlcv"],
+                candle_type=self.config.get("candle_type_def", CandleType.SPOT),
+            )
+            # Download informative pairs too
+            res = defaultdict(list)
+            for pair, timeframe, _ in self.strategy.gather_informative_pairs():
+                res[timeframe].append(pair)
+            for timeframe, inf_pairs in res.items():
+                timerange_startup = deepcopy(self._timerange)
+                timerange_startup.subtract_start(
+                    timeframe_to_seconds(timeframe) * self.strategy.startup_candle_count
+                )
+                refresh_data(
+                    datadir=self.config["datadir"],
+                    pairs=inf_pairs,
+                    exchange=self.exchange,
+                    timeframe=timeframe,
+                    timerange=timerange_startup,
+                    data_format=self.config["dataformat_ohlcv"],
+                    candle_type=self.config.get("candle_type_def", CandleType.SPOT),
+                )
+
+        data = load_data(
+            datadir=self.config["datadir"],
+            pairs=pairs,
+            timeframe=self.strategy.timeframe,
+            timerange=self._timerange,
+            startup_candles=self.strategy.startup_candle_count,
+            data_format=self.config["dataformat_ohlcv"],
+            candle_type=self.config.get("candle_type_def", CandleType.SPOT),
+        )
+
+        if not data:
+            # Reinitializing cached pairs
+            self._cached_pairs = {}
+            logger.critical("No data found. Edge is stopped ...")
+            return False
+        # Fake run-mode to Edge
+        prior_rm = self.config["runmode"]
+        self.config["runmode"] = RunMode.EDGE
+        preprocessed = self.strategy.advise_all_indicators(data)
+        self.config["runmode"] = prior_rm
+
+        # Print timeframe
+        min_date, max_date = get_timerange(preprocessed)
+        logger.info(
+            f"Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} "
+            f"up to {max_date.strftime(DATETIME_PRINT_FORMAT)} "
+            f"({(max_date - min_date).days} days).."
+        )
+        # TODO: Should edge support shorts? needs to be investigated further
+        # * (add enter_short exit_short)
+        headers = ["date", "open", "high", "low", "close", "enter_long", "exit_long"]
+
+        trades: list = []
+        for pair, pair_data in preprocessed.items():
+            # Sorting dataframe by date and reset index
+            pair_data = pair_data.sort_values(by=["date"])
+            pair_data = pair_data.reset_index(drop=True)
+
+            df_analyzed = self.strategy.ft_advise_signals(pair_data, {"pair": pair})[headers].copy()
+
+            trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
+
+        # If no trade found then exit
+        if len(trades) == 0:
+            logger.info("No trades found.")
+            return False
+
+        # Fill missing, calculable columns, profit, duration , abs etc.
+        trades_df = self._fill_calculable_fields(DataFrame(trades))
+        self._cached_pairs = self._process_expectancy(trades_df)
+        self._last_updated = int(dt_now().timestamp())
+
+        return True
+
+    def stake_amount(
+        self, pair: str, free_capital: float, total_capital: float, capital_in_trade: float
+    ) -> float:
+        stoploss = self.get_stoploss(pair)
+        available_capital = (total_capital + capital_in_trade) * self._capital_ratio
+        allowed_capital_at_risk = available_capital * self._allowed_risk
+        max_position_size = abs(allowed_capital_at_risk / stoploss)
+        # Position size must be below available capital.
+        position_size = min(min(max_position_size, free_capital), available_capital)
+        if pair in self._cached_pairs:
+            logger.info(
+                "winrate: %s, expectancy: %s, position size: %s, pair: %s,"
+                " capital in trade: %s, free capital: %s, total capital: %s,"
+                " stoploss: %s, available capital: %s.",
+                self._cached_pairs[pair].winrate,
+                self._cached_pairs[pair].expectancy,
+                position_size,
+                pair,
+                capital_in_trade,
+                free_capital,
+                total_capital,
+                stoploss,
+                available_capital,
+            )
+        return round(position_size, 15)
+
+    def get_stoploss(self, pair: str) -> float:
+        if pair in self._cached_pairs:
+            return self._cached_pairs[pair].stoploss
+        else:
+            logger.warning(
+                f"Tried to access stoploss of non-existing pair {pair}, "
+                "strategy stoploss is returned instead."
+            )
+            return self.strategy.stoploss
+
+    def adjust(self, pairs: list[str]) -> list:
+        """
+        Filters out and sorts "pairs" according to Edge calculated pairs
+        """
+        final = []
+        for pair, info in self._cached_pairs.items():
+            if (
+                info.expectancy > float(self.edge_config.get("minimum_expectancy", 0.2))
+                and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60))
+                and pair in pairs
+            ):
+                final.append(pair)
+
+        if self._final_pairs != final:
+            self._final_pairs = final
+            if self._final_pairs:
+                logger.info(
+                    "Minimum expectancy and minimum winrate are met only for %s,"
+                    " so other pairs are filtered out.",
+                    self._final_pairs,
+                )
+            else:
+                logger.info(
+                    "Edge removed all pairs as no pair with minimum expectancy "
+                    "and minimum winrate was found !"
+                )
+
+        return self._final_pairs
+
+    def accepted_pairs(self) -> list[dict[str, Any]]:
+        """
+        return a list of accepted pairs along with their winrate, expectancy and stoploss
+        """
+        final = []
+        for pair, info in self._cached_pairs.items():
+            if info.expectancy > float(
+                self.edge_config.get("minimum_expectancy", 0.2)
+            ) and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60)):
+                final.append(
+                    {
+                        "Pair": pair,
+                        "Winrate": info.winrate,
+                        "Expectancy": info.expectancy,
+                        "Stoploss": info.stoploss,
+                    }
+                )
+        return final
+
+    def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
+        """
+        The result frame contains a number of columns that are calculable
+        from other columns. These are left blank till all rows are added,
+        to be populated in single vector calls.
+
+        Columns to be populated are:
+        - Profit
+        - trade duration
+        - profit abs
+        :param result Dataframe
+        :return: result Dataframe
+        """
+        # We set stake amount to an arbitrary amount, as it doesn't change the calculation.
+        # All returned values are relative, they are defined as ratios.
+        stake = 0.015
+
+        result["trade_duration"] = result["close_date"] - result["open_date"]
+
+        result["trade_duration"] = result["trade_duration"].map(
+            lambda x: int(x.total_seconds() / 60)
+        )
+
+        # Spends, Takes, Profit, Absolute Profit
+
+        # Buy Price
+        result["buy_vol"] = stake / result["open_rate"]  # How many target are we buying
+        result["buy_fee"] = stake * self.fee
+        result["buy_spend"] = stake + result["buy_fee"]  # How much we're spending
+
+        # Sell price
+        result["sell_sum"] = result["buy_vol"] * result["close_rate"]
+        result["sell_fee"] = result["sell_sum"] * self.fee
+        result["sell_take"] = result["sell_sum"] - result["sell_fee"]
+
+        # profit_ratio
+        result["profit_ratio"] = (result["sell_take"] - result["buy_spend"]) / result["buy_spend"]
+
+        # Absolute profit
+        result["profit_abs"] = result["sell_take"] - result["buy_spend"]
+
+        return result
+
+    def _process_expectancy(self, results: DataFrame) -> dict[str, Any]:
+        """
+        This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
+        The calculation will be done per pair and per strategy.
+        """
+        # Removing pairs having less than min_trades_number
+        min_trades_number = self.edge_config.get("min_trade_number", 10)
+        results = results.groupby(["pair", "stoploss"]).filter(lambda x: len(x) > min_trades_number)
+        ###################################
+
+        # Removing outliers (Only Pumps) from the dataset
+        # The method to detect outliers is to calculate standard deviation
+        # Then every value more than (standard deviation + 2*average) is out (pump)
+        #
+        # Removing Pumps
+        if self.edge_config.get("remove_pumps", False):
+            results = results[
+                results["profit_abs"]
+                < 2 * results["profit_abs"].std() + results["profit_abs"].mean()
+            ]
+        ##########################################################################
+
+        # Removing trades having a duration more than X minutes (set in config)
+        max_trade_duration = self.edge_config.get("max_trade_duration_minute", 1440)
+        results = results[results.trade_duration < max_trade_duration]
+        #######################################################################
+
+        if results.empty:
+            return {}
+
+        groupby_aggregator = {
+            "profit_abs": [
+                ("nb_trades", "count"),  # number of all trades
+                ("profit_sum", lambda x: x[x > 0].sum()),  # cumulative profit of all winning trades
+                ("loss_sum", lambda x: abs(x[x < 0].sum())),  # cumulative loss of all losing trades
+                ("nb_win_trades", lambda x: x[x > 0].count()),  # number of winning trades
+            ],
+            "trade_duration": [("avg_trade_duration", "mean")],
+        }
+
+        # Group by (pair and stoploss) by applying above aggregator
+        df = (
+            results.groupby(["pair", "stoploss"])[["profit_abs", "trade_duration"]]
+            .agg(groupby_aggregator)
+            .reset_index(col_level=1)
+        )
+
+        # Dropping level 0 as we don't need it
+        df.columns = df.columns.droplevel(0)
+
+        # Calculating number of losing trades, average win and average loss
+        df["nb_loss_trades"] = df["nb_trades"] - df["nb_win_trades"]
+        df["average_win"] = np.where(
+            df["nb_win_trades"] == 0, 0.0, df["profit_sum"] / df["nb_win_trades"]
+        )
+        df["average_loss"] = np.where(
+            df["nb_loss_trades"] == 0, 0.0, df["loss_sum"] / df["nb_loss_trades"]
+        )
+
+        # Win rate = number of profitable trades / number of trades
+        df["winrate"] = df["nb_win_trades"] / df["nb_trades"]
+
+        # risk_reward_ratio = average win / average loss
+        df["risk_reward_ratio"] = df["average_win"] / df["average_loss"]
+
+        # required_risk_reward = (1 / winrate) - 1
+        df["required_risk_reward"] = (1 / df["winrate"]) - 1
+
+        # expectancy = (risk_reward_ratio * winrate) - (lossrate)
+        df["expectancy"] = (df["risk_reward_ratio"] * df["winrate"]) - (1 - df["winrate"])
+
+        # sort by expectancy and stoploss
+        df = (
+            df.sort_values(by=["expectancy", "stoploss"], ascending=False)
+            .groupby("pair")
+            .first()
+            .sort_values(by=["expectancy"], ascending=False)
+            .reset_index()
+        )
+
+        final = {}
+        for x in df.itertuples():
+            final[x.pair] = PairInfo(
+                x.stoploss,
+                x.winrate,
+                x.risk_reward_ratio,
+                x.required_risk_reward,
+                x.expectancy,
+                x.nb_trades,
+                x.avg_trade_duration,
+            )
+
+        # Returning a list of pairs in order of "expectancy"
+        return final
+
+    def _find_trades_for_stoploss_range(self, df, pair: str, stoploss_range) -> list:
+        buy_column = df["enter_long"].values
+        sell_column = df["exit_long"].values
+        date_column = df["date"].values
+        ohlc_columns = df[["open", "high", "low", "close"]].values
+
+        result: list = []
+        for stoploss in stoploss_range:
+            result += self._detect_next_stop_or_sell_point(
+                buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
+            )
+
+        return result
+
+    def _detect_next_stop_or_sell_point(
+        self, buy_column, sell_column, date_column, ohlc_columns, stoploss, pair: str
+    ):
+        """
+        Iterate through ohlc_columns in order to find the next trade
+        Next trade opens from the first buy signal noticed to
+        The sell or stoploss signal after it.
+        It then cuts OHLC, buy_column, sell_column and date_column.
+        Cut from (the exit trade index) + 1.
+
+        Author: https://github.com/mishaker
+        """
+
+        result: list = []
+        start_point = 0
+
+        while True:
+            open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
+
+            # Return empty if we don't find trade entry (i.e. buy==1) or
+            # we find a buy but at the end of array
+            if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
+                break
+            else:
+                # When a buy signal is seen,
+                # trade opens in reality on the next candle
+                open_trade_index += 1
+
+            open_price = ohlc_columns[open_trade_index, 0]
+            stop_price = open_price * (stoploss + 1)
+
+            # Searching for the index where stoploss is hit
+            stop_index = utf1st.find_1st(
+                ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller
+            )
+
+            # If we don't find it then we assume stop_index will be far in future (infinite number)
+            if stop_index == -1:
+                stop_index = float("inf")
+
+            # Searching for the index where sell is hit
+            sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
+
+            # If we don't find it then we assume sell_index will be far in future (infinite number)
+            if sell_index == -1:
+                sell_index = float("inf")
+
+            # Check if we don't find any stop or sell point (in that case trade remains open)
+            # It is not interesting for Edge to consider it so we simply ignore the trade
+            # And stop iterating there is no more entry
+            if stop_index == sell_index == float("inf"):
+                break
+
+            if stop_index <= sell_index:
+                exit_index = open_trade_index + stop_index
+                exit_type = ExitType.STOP_LOSS
+                exit_price = stop_price
+            elif stop_index > sell_index:
+                # If exit is SELL then we exit at the next candle
+                exit_index = open_trade_index + sell_index + 1
+
+                # Check if we have the next candle
+                if len(ohlc_columns) - 1 < exit_index:
+                    break
+
+                exit_type = ExitType.EXIT_SIGNAL
+                exit_price = ohlc_columns[exit_index, 0]
+
+            trade = {
+                "pair": pair,
+                "stoploss": stoploss,
+                "profit_ratio": "",
+                "profit_abs": "",
+                "open_date": date_column[open_trade_index],
+                "close_date": date_column[exit_index],
+                "trade_duration": "",
+                "open_rate": round(open_price, 15),
+                "close_rate": round(exit_price, 15),
+                "exit_type": exit_type,
+            }
+
+            result.append(trade)
+
+            # Giving a view of exit_index till the end of array
+            buy_column = buy_column[exit_index:]
+            sell_column = sell_column[exit_index:]
+            date_column = date_column[exit_index:]
+            ohlc_columns = ohlc_columns[exit_index:]
+            start_point += exit_index
+
+        return result
diff --git a/freqtrade/enums/__init__.py b/freqtrade/enums/__init__.py
new file mode 100644
index 000000000..55ab130ae
--- /dev/null
+++ b/freqtrade/enums/__init__.py
@@ -0,0 +1,15 @@
+# flake8: noqa: F401
+from freqtrade.enums.backteststate import BacktestState
+from freqtrade.enums.candletype import CandleType
+from freqtrade.enums.exitchecktuple import ExitCheckTuple
+from freqtrade.enums.exittype import ExitType
+from freqtrade.enums.hyperoptstate import HyperoptState
+from freqtrade.enums.marginmode import MarginMode
+from freqtrade.enums.marketstatetype import MarketDirection
+from freqtrade.enums.ordertypevalue import OrderTypeValues
+from freqtrade.enums.pricetype import PriceType
+from freqtrade.enums.rpcmessagetype import NO_ECHO_MESSAGES, RPCMessageType, RPCRequestType
+from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADE_MODES, RunMode
+from freqtrade.enums.signaltype import SignalDirection, SignalTagType, SignalType
+from freqtrade.enums.state import State
+from freqtrade.enums.tradingmode import TradingMode
diff --git a/freqtrade/enums/backteststate.py b/freqtrade/enums/backteststate.py
new file mode 100644
index 000000000..f9c8d0ab9
--- /dev/null
+++ b/freqtrade/enums/backteststate.py
@@ -0,0 +1,16 @@
+from enum import Enum
+
+
+class BacktestState(Enum):
+    """
+    Bot application states
+    """
+
+    STARTUP = 1
+    DATALOAD = 2
+    ANALYZE = 3
+    CONVERT = 4
+    BACKTEST = 5
+
+    def __str__(self):
+        return f"{self.name.lower()}"
diff --git a/freqtrade/enums/candletype.py b/freqtrade/enums/candletype.py
new file mode 100644
index 000000000..eb7ae50b8
--- /dev/null
+++ b/freqtrade/enums/candletype.py
@@ -0,0 +1,31 @@
+from enum import Enum
+
+
+class CandleType(str, Enum):
+    """Enum to distinguish candle types"""
+
+    SPOT = "spot"
+    FUTURES = "futures"
+    MARK = "mark"
+    INDEX = "index"
+    PREMIUMINDEX = "premiumIndex"
+
+    # TODO: Could take up less memory if these weren't a CandleType
+    FUNDING_RATE = "funding_rate"
+    # BORROW_RATE = "borrow_rate"  # * unimplemented
+
+    def __str__(self):
+        return f"{self.name.lower()}"
+
+    @staticmethod
+    def from_string(value: str) -> "CandleType":
+        if not value:
+            # Default to spot
+            return CandleType.SPOT
+        return CandleType(value)
+
+    @staticmethod
+    def get_default(trading_mode: str) -> "CandleType":
+        if trading_mode == "futures":
+            return CandleType.FUTURES
+        return CandleType.SPOT
diff --git a/freqtrade/enums/exitchecktuple.py b/freqtrade/enums/exitchecktuple.py
new file mode 100644
index 000000000..686b1742c
--- /dev/null
+++ b/freqtrade/enums/exitchecktuple.py
@@ -0,0 +1,24 @@
+from freqtrade.enums.exittype import ExitType
+
+
+class ExitCheckTuple:
+    """
+    NamedTuple for Exit type + reason
+    """
+
+    exit_type: ExitType
+    exit_reason: str = ""
+
+    def __init__(self, exit_type: ExitType, exit_reason: str = ""):
+        self.exit_type = exit_type
+        self.exit_reason = exit_reason or exit_type.value
+
+    @property
+    def exit_flag(self):
+        return self.exit_type != ExitType.NONE
+
+    def __eq__(self, other):
+        return self.exit_type == other.exit_type and self.exit_reason == other.exit_reason
+
+    def __repr__(self):
+        return f"ExitCheckTuple({self.exit_type}, {self.exit_reason})"
diff --git a/freqtrade/enums/exittype.py b/freqtrade/enums/exittype.py
new file mode 100644
index 000000000..630cb3dd1
--- /dev/null
+++ b/freqtrade/enums/exittype.py
@@ -0,0 +1,24 @@
+from enum import Enum
+
+
+class ExitType(Enum):
+    """
+    Enum to distinguish between exit reasons
+    """
+
+    ROI = "roi"
+    STOP_LOSS = "stop_loss"
+    STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
+    TRAILING_STOP_LOSS = "trailing_stop_loss"
+    LIQUIDATION = "liquidation"
+    EXIT_SIGNAL = "exit_signal"
+    FORCE_EXIT = "force_exit"
+    EMERGENCY_EXIT = "emergency_exit"
+    CUSTOM_EXIT = "custom_exit"
+    PARTIAL_EXIT = "partial_exit"
+    SOLD_ON_EXCHANGE = "sold_on_exchange"
+    NONE = ""
+
+    def __str__(self):
+        # explicitly convert to String to help with exporting data.
+        return self.value
diff --git a/freqtrade/enums/hyperoptstate.py b/freqtrade/enums/hyperoptstate.py
new file mode 100644
index 000000000..68326505e
--- /dev/null
+++ b/freqtrade/enums/hyperoptstate.py
@@ -0,0 +1,13 @@
+from enum import Enum
+
+
+class HyperoptState(Enum):
+    """Hyperopt states"""
+
+    STARTUP = 1
+    DATALOAD = 2
+    INDICATORS = 3
+    OPTIMIZE = 4
+
+    def __str__(self):
+        return f"{self.name.lower()}"
diff --git a/freqtrade/enums/marginmode.py b/freqtrade/enums/marginmode.py
new file mode 100644
index 000000000..9aa814c39
--- /dev/null
+++ b/freqtrade/enums/marginmode.py
@@ -0,0 +1,16 @@
+from enum import Enum
+
+
+class MarginMode(str, Enum):
+    """
+    Enum to distinguish between
+    cross margin/futures margin_mode and
+    isolated margin/futures margin_mode
+    """
+
+    CROSS = "cross"
+    ISOLATED = "isolated"
+    NONE = ""
+
+    def __str__(self):
+        return f"{self.name.lower()}"
diff --git a/freqtrade/enums/marketstatetype.py b/freqtrade/enums/marketstatetype.py
new file mode 100644
index 000000000..92eb75377
--- /dev/null
+++ b/freqtrade/enums/marketstatetype.py
@@ -0,0 +1,16 @@
+from enum import Enum
+
+
+class MarketDirection(Enum):
+    """
+    Enum for various market directions.
+    """
+
+    LONG = "long"
+    SHORT = "short"
+    EVEN = "even"
+    NONE = "none"
+
+    def __str__(self):
+        # convert to string
+        return self.value
diff --git a/freqtrade/enums/ordertypevalue.py b/freqtrade/enums/ordertypevalue.py
new file mode 100644
index 000000000..3a253166d
--- /dev/null
+++ b/freqtrade/enums/ordertypevalue.py
@@ -0,0 +1,6 @@
+from enum import Enum
+
+
+class OrderTypeValues(str, Enum):
+    limit = "limit"
+    market = "market"
diff --git a/freqtrade/enums/pricetype.py b/freqtrade/enums/pricetype.py
new file mode 100644
index 000000000..f2e3bef23
--- /dev/null
+++ b/freqtrade/enums/pricetype.py
@@ -0,0 +1,9 @@
+from enum import Enum
+
+
+class PriceType(str, Enum):
+    """Enum to distinguish possible trigger prices for stoplosses"""
+
+    LAST = "last"
+    MARK = "mark"
+    INDEX = "index"
diff --git a/freqtrade/enums/rpcmessagetype.py b/freqtrade/enums/rpcmessagetype.py
new file mode 100644
index 000000000..6fdd788e8
--- /dev/null
+++ b/freqtrade/enums/rpcmessagetype.py
@@ -0,0 +1,45 @@
+from enum import Enum
+
+
+class RPCMessageType(str, Enum):
+    STATUS = "status"
+    WARNING = "warning"
+    EXCEPTION = "exception"
+    STARTUP = "startup"
+
+    ENTRY = "entry"
+    ENTRY_FILL = "entry_fill"
+    ENTRY_CANCEL = "entry_cancel"
+
+    EXIT = "exit"
+    EXIT_FILL = "exit_fill"
+    EXIT_CANCEL = "exit_cancel"
+
+    PROTECTION_TRIGGER = "protection_trigger"
+    PROTECTION_TRIGGER_GLOBAL = "protection_trigger_global"
+
+    STRATEGY_MSG = "strategy_msg"
+
+    WHITELIST = "whitelist"
+    ANALYZED_DF = "analyzed_df"
+    NEW_CANDLE = "new_candle"
+
+    def __repr__(self):
+        return self.value
+
+    def __str__(self):
+        return self.value
+
+
+# Enum for parsing requests from ws consumers
+class RPCRequestType(str, Enum):
+    SUBSCRIBE = "subscribe"
+
+    WHITELIST = "whitelist"
+    ANALYZED_DF = "analyzed_df"
+
+    def __str__(self):
+        return self.value
+
+
+NO_ECHO_MESSAGES = (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST, RPCMessageType.NEW_CANDLE)
diff --git a/freqtrade/enums/runmode.py b/freqtrade/enums/runmode.py
new file mode 100644
index 000000000..a24dd6e2c
--- /dev/null
+++ b/freqtrade/enums/runmode.py
@@ -0,0 +1,24 @@
+from enum import Enum
+
+
+class RunMode(str, Enum):
+    """
+    Bot running mode (backtest, hyperopt, ...)
+    can be "live", "dry-run", "backtest", "edge", "hyperopt".
+    """
+
+    LIVE = "live"
+    DRY_RUN = "dry_run"
+    BACKTEST = "backtest"
+    EDGE = "edge"
+    HYPEROPT = "hyperopt"
+    UTIL_EXCHANGE = "util_exchange"
+    UTIL_NO_EXCHANGE = "util_no_exchange"
+    PLOT = "plot"
+    WEBSERVER = "webserver"
+    OTHER = "other"
+
+
+TRADE_MODES = [RunMode.LIVE, RunMode.DRY_RUN]
+OPTIMIZE_MODES = [RunMode.BACKTEST, RunMode.EDGE, RunMode.HYPEROPT]
+NON_UTIL_MODES = TRADE_MODES + OPTIMIZE_MODES
diff --git a/freqtrade/enums/signaltype.py b/freqtrade/enums/signaltype.py
new file mode 100644
index 000000000..267a37ab3
--- /dev/null
+++ b/freqtrade/enums/signaltype.py
@@ -0,0 +1,35 @@
+from enum import Enum
+
+
+class SignalType(Enum):
+    """
+    Enum to distinguish between enter and exit signals
+    """
+
+    ENTER_LONG = "enter_long"
+    EXIT_LONG = "exit_long"
+    ENTER_SHORT = "enter_short"
+    EXIT_SHORT = "exit_short"
+
+    def __str__(self):
+        return f"{self.name.lower()}"
+
+
+class SignalTagType(Enum):
+    """
+    Enum for signal columns
+    """
+
+    ENTER_TAG = "enter_tag"
+    EXIT_TAG = "exit_tag"
+
+    def __str__(self):
+        return f"{self.name.lower()}"
+
+
+class SignalDirection(str, Enum):
+    LONG = "long"
+    SHORT = "short"
+
+    def __str__(self):
+        return f"{self.name.lower()}"
diff --git a/freqtrade/enums/state.py b/freqtrade/enums/state.py
new file mode 100644
index 000000000..20b3766b9
--- /dev/null
+++ b/freqtrade/enums/state.py
@@ -0,0 +1,15 @@
+from enum import Enum
+
+
+class State(Enum):
+    """
+    Bot application states
+    """
+
+    RUNNING = 1
+    PAUSED = 2
+    STOPPED = 3
+    RELOAD_CONFIG = 4
+
+    def __str__(self):
+        return f"{self.name.lower()}"
diff --git a/freqtrade/enums/tradingmode.py b/freqtrade/enums/tradingmode.py
new file mode 100644
index 000000000..a681d60f9
--- /dev/null
+++ b/freqtrade/enums/tradingmode.py
@@ -0,0 +1,15 @@
+from enum import Enum
+
+
+class TradingMode(str, Enum):
+    """
+    Enum to distinguish between
+    spot, margin, futures or any other trading method
+    """
+
+    SPOT = "spot"
+    MARGIN = "margin"
+    FUTURES = "futures"
+
+    def __str__(self):
+        return f"{self.name.lower()}"
diff --git a/freqtrade/exceptions.py b/freqtrade/exceptions.py
new file mode 100644
index 000000000..8b2c9c993
--- /dev/null
+++ b/freqtrade/exceptions.py
@@ -0,0 +1,84 @@
+class FreqtradeException(Exception):
+    """
+    Freqtrade base exception. Handled at the outermost level.
+    All other exception types are subclasses of this exception type.
+    """
+
+
+class OperationalException(FreqtradeException):
+    """
+    Requires manual intervention and will stop the bot.
+    Most of the time, this is caused by an invalid Configuration.
+    """
+
+
+class ConfigurationError(OperationalException):
+    """
+    Configuration error. Usually caused by invalid configuration.
+    """
+
+
+class DependencyException(FreqtradeException):
+    """
+    Indicates that an assumed dependency is not met.
+    This could happen when there is currently not enough money on the account.
+    """
+
+
+class PricingError(DependencyException):
+    """
+    Subclass of DependencyException.
+    Indicates that the price could not be determined.
+    Implicitly a buy / sell operation.
+    """
+
+
+class ExchangeError(DependencyException):
+    """
+    Error raised out of the exchange.
+    Has multiple Errors to determine the appropriate error.
+    """
+
+
+class InvalidOrderException(ExchangeError):
+    """
+    This is returned when the order is not valid. Example:
+    If stoploss on exchange order is hit, then trying to cancel the order
+    should return this exception.
+    """
+
+
+class RetryableOrderError(InvalidOrderException):
+    """
+    This is returned when the order is not found.
+    This Error will be repeated with increasing backoff (in line with DDosError).
+    """
+
+
+class InsufficientFundsError(InvalidOrderException):
+    """
+    This error is used when there are not enough funds available on the exchange
+    to create an order.
+    """
+
+
+class TemporaryError(ExchangeError):
+    """
+    Temporary network or exchange related error.
+    This could happen when an exchange is congested, unavailable, or the user
+    has networking problems. Usually resolves itself after a time.
+    """
+
+
+class DDosProtection(TemporaryError):
+    """
+    Temporary error caused by DDOS protection.
+    Bot will wait for a second and then retry.
+    """
+
+
+class StrategyError(FreqtradeException):
+    """
+    Errors with custom user-code detected.
+    Usually caused by errors in the strategy.
+    """
diff --git a/freqtrade/exchange/__init__.py b/freqtrade/exchange/__init__.py
index dc85bfedb..01f9d93ed 100644
--- a/freqtrade/exchange/__init__.py
+++ b/freqtrade/exchange/__init__.py
@@ -1,185 +1,46 @@
-# pragma pylint: disable=W0603
-""" Cryptocurrency Exchanges support """
-import enum
-import logging
-from random import randint
-from typing import List, Dict, Any, Optional
+# flake8: noqa: F401
+# isort: off
+from freqtrade.exchange.common import remove_exchange_credentials, MAP_EXCHANGE_CHILDCLASS
+from freqtrade.exchange.exchange import Exchange
 
-import arrow
-import requests
-from cachetools import cached, TTLCache
-
-from freqtrade import OperationalException
-from freqtrade.exchange.bittrex import Bittrex
-from freqtrade.exchange.interface import Exchange
-
-logger = logging.getLogger(__name__)
-
-# Current selected exchange
-_API: Exchange = None
-_CONF: dict = {}
-
-# Holds all open sell orders for dry_run
-_DRY_RUN_OPEN_ORDERS: Dict[str, Any] = {}
-
-
-class Exchanges(enum.Enum):
-    """
-    Maps supported exchange names to correspondent classes.
-    """
-    BITTREX = Bittrex
-
-
-def init(config: dict) -> None:
-    """
-    Initializes this module with the given config,
-    it does basic validation whether the specified
-    exchange and pairs are valid.
-    :param config: config to use
-    :return: None
-    """
-    global _CONF, _API
-
-    _CONF.update(config)
-
-    if config['dry_run']:
-        logger.info('Instance is running with dry_run enabled')
-
-    exchange_config = config['exchange']
-
-    # Find matching class for the given exchange name
-    name = exchange_config['name']
-    try:
-        exchange_class = Exchanges[name.upper()].value
-    except KeyError:
-        raise OperationalException('Exchange {} is not supported'.format(name))
-
-    _API = exchange_class(exchange_config)
-
-    # Check if all pairs are available
-    validate_pairs(config['exchange']['pair_whitelist'])
-
-
-def validate_pairs(pairs: List[str]) -> None:
-    """
-    Checks if all given pairs are tradable on the current exchange.
-    Raises OperationalException if one pair is not available.
-    :param pairs: list of pairs
-    :return: None
-    """
-    try:
-        markets = _API.get_markets()
-    except requests.exceptions.RequestException as e:
-        logger.warning('Unable to validate pairs (assuming they are correct). Reason: %s', e)
-        return
-
-    stake_cur = _CONF['stake_currency']
-    for pair in pairs:
-        if not pair.startswith(stake_cur):
-            raise OperationalException(
-                'Pair {} not compatible with stake_currency: {}'.format(pair, stake_cur)
-            )
-        if pair not in markets:
-            raise OperationalException(
-                'Pair {} is not available at {}'.format(pair, _API.name.lower()))
-
-
-def buy(pair: str, rate: float, amount: float) -> str:
-    if _CONF['dry_run']:
-        global _DRY_RUN_OPEN_ORDERS
-        order_id = 'dry_run_buy_{}'.format(randint(0, 10**6))
-        _DRY_RUN_OPEN_ORDERS[order_id] = {
-            'pair': pair,
-            'rate': rate,
-            'amount': amount,
-            'type': 'LIMIT_BUY',
-            'remaining': 0.0,
-            'opened': arrow.utcnow().datetime,
-            'closed': arrow.utcnow().datetime,
-        }
-        return order_id
-
-    return _API.buy(pair, rate, amount)
-
-
-def sell(pair: str, rate: float, amount: float) -> str:
-    if _CONF['dry_run']:
-        global _DRY_RUN_OPEN_ORDERS
-        order_id = 'dry_run_sell_{}'.format(randint(0, 10**6))
-        _DRY_RUN_OPEN_ORDERS[order_id] = {
-            'pair': pair,
-            'rate': rate,
-            'amount': amount,
-            'type': 'LIMIT_SELL',
-            'remaining': 0.0,
-            'opened': arrow.utcnow().datetime,
-            'closed': arrow.utcnow().datetime,
-        }
-        return order_id
-
-    return _API.sell(pair, rate, amount)
-
-
-def get_balance(currency: str) -> float:
-    if _CONF['dry_run']:
-        return 999.9
-
-    return _API.get_balance(currency)
-
-
-def get_balances():
-    if _CONF['dry_run']:
-        return []
-
-    return _API.get_balances()
-
-
-def get_ticker(pair: str, refresh: Optional[bool] = True) -> dict:
-    return _API.get_ticker(pair, refresh)
-
-
-@cached(TTLCache(maxsize=100, ttl=30))
-def get_ticker_history(pair: str, tick_interval) -> List[Dict]:
-    return _API.get_ticker_history(pair, tick_interval)
-
-
-def cancel_order(order_id: str) -> None:
-    if _CONF['dry_run']:
-        return
-
-    return _API.cancel_order(order_id)
-
-
-def get_order(order_id: str) -> Dict:
-    if _CONF['dry_run']:
-        order = _DRY_RUN_OPEN_ORDERS[order_id]
-        order.update({
-            'id': order_id
-        })
-        return order
-
-    return _API.get_order(order_id)
-
-
-def get_pair_detail_url(pair: str) -> str:
-    return _API.get_pair_detail_url(pair)
-
-
-def get_markets() -> List[str]:
-    return _API.get_markets()
-
-
-def get_market_summaries() -> List[Dict]:
-    return _API.get_market_summaries()
-
-
-def get_name() -> str:
-    return _API.name
-
-
-def get_fee() -> float:
-    return _API.fee
-
-
-def get_wallet_health() -> List[Dict]:
-    return _API.get_wallet_health()
+# isort: on
+from freqtrade.exchange.binance import Binance
+from freqtrade.exchange.bingx import Bingx
+from freqtrade.exchange.bitmart import Bitmart
+from freqtrade.exchange.bitpanda import Bitpanda
+from freqtrade.exchange.bitvavo import Bitvavo
+from freqtrade.exchange.bybit import Bybit
+from freqtrade.exchange.cryptocom import Cryptocom
+from freqtrade.exchange.exchange_utils import (
+    ROUND_DOWN,
+    ROUND_UP,
+    amount_to_contract_precision,
+    amount_to_contracts,
+    amount_to_precision,
+    available_exchanges,
+    ccxt_exchanges,
+    contracts_to_amount,
+    date_minus_candles,
+    is_exchange_known_ccxt,
+    list_available_exchanges,
+    market_is_active,
+    price_to_precision,
+    validate_exchange,
+)
+from freqtrade.exchange.exchange_utils_timeframe import (
+    timeframe_to_minutes,
+    timeframe_to_msecs,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    timeframe_to_resample_freq,
+    timeframe_to_seconds,
+)
+from freqtrade.exchange.gate import Gate
+from freqtrade.exchange.hitbtc import Hitbtc
+from freqtrade.exchange.htx import Htx
+from freqtrade.exchange.hyperliquid import Hyperliquid
+from freqtrade.exchange.idex import Idex
+from freqtrade.exchange.kraken import Kraken
+from freqtrade.exchange.kucoin import Kucoin
+from freqtrade.exchange.lbank import Lbank
+from freqtrade.exchange.okx import Okx
diff --git a/freqtrade/exchange/binance.py b/freqtrade/exchange/binance.py
new file mode 100644
index 000000000..c67263096
--- /dev/null
+++ b/freqtrade/exchange/binance.py
@@ -0,0 +1,428 @@
+"""Binance exchange subclass"""
+
+import logging
+from datetime import datetime, timezone
+from pathlib import Path
+
+import ccxt
+from pandas import DataFrame
+
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
+from freqtrade.enums import CandleType, MarginMode, PriceType, TradingMode
+from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.binance_public_data import (
+    concat_safe,
+    download_archive_ohlcv,
+    download_archive_trades,
+)
+from freqtrade.exchange.common import retrier
+from freqtrade.exchange.exchange_types import FtHas, Tickers
+from freqtrade.exchange.exchange_utils_timeframe import timeframe_to_msecs
+from freqtrade.misc import deep_merge_dicts, json_load
+from freqtrade.util.datetime_helpers import dt_from_ts, dt_ts
+
+
+logger = logging.getLogger(__name__)
+
+
+class Binance(Exchange):
+    _ft_has: FtHas = {
+        "stoploss_on_exchange": True,
+        "stop_price_param": "stopPrice",
+        "stop_price_prop": "stopPrice",
+        "stoploss_order_types": {"limit": "stop_loss_limit"},
+        "order_time_in_force": ["GTC", "FOK", "IOC", "PO"],
+        "trades_pagination": "id",
+        "trades_pagination_arg": "fromId",
+        "trades_has_history": True,
+        "l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],
+        "ws_enabled": True,
+    }
+    _ft_has_futures: FtHas = {
+        "funding_fee_candle_limit": 1000,
+        "stoploss_order_types": {"limit": "stop", "market": "stop_market"},
+        "order_time_in_force": ["GTC", "FOK", "IOC"],
+        "tickers_have_price": False,
+        "floor_leverage": True,
+        "stop_price_type_field": "workingType",
+        "order_props_in_contracts": ["amount", "cost", "filled", "remaining"],
+        "stop_price_type_value_mapping": {
+            PriceType.LAST: "CONTRACT_PRICE",
+            PriceType.MARK: "MARK_PRICE",
+        },
+        "ws_enabled": False,
+        "proxy_coin_mapping": {
+            "BNFCR": "USDC",
+            "BFUSD": "USDT",
+        },
+    }
+
+    _supported_trading_mode_margin_pairs: list[tuple[TradingMode, MarginMode]] = [
+        # TradingMode.SPOT always supported and not required in this list
+        # (TradingMode.MARGIN, MarginMode.CROSS),
+        (TradingMode.FUTURES, MarginMode.CROSS),
+        (TradingMode.FUTURES, MarginMode.ISOLATED),
+    ]
+
+    def get_proxy_coin(self) -> str:
+        """
+        Get the proxy coin for the given coin
+        Falls back to the stake currency if no proxy coin is found
+        :return: Proxy coin or stake currency
+        """
+        if self.margin_mode == MarginMode.CROSS:
+            return self._config.get("proxy_coin", self._config["stake_currency"])
+        return self._config["stake_currency"]
+
+    def get_tickers(
+        self,
+        symbols: list[str] | None = None,
+        *,
+        cached: bool = False,
+        market_type: TradingMode | None = None,
+    ) -> Tickers:
+        tickers = super().get_tickers(symbols=symbols, cached=cached, market_type=market_type)
+        if self.trading_mode == TradingMode.FUTURES:
+            # Binance's future result has no bid/ask values.
+            # Therefore we must fetch that from fetch_bids_asks and combine the two results.
+            bidsasks = self.fetch_bids_asks(symbols, cached=cached)
+            tickers = deep_merge_dicts(bidsasks, tickers, allow_null_overrides=False)
+        return tickers
+
+    @retrier
+    def additional_exchange_init(self) -> None:
+        """
+        Additional exchange initialization logic.
+        .api will be available at this point.
+        Must be overridden in child methods if required.
+        """
+        try:
+            if self.trading_mode == TradingMode.FUTURES and not self._config["dry_run"]:
+                position_side = self._api.fapiPrivateGetPositionSideDual()
+                self._log_exchange_response("position_side_setting", position_side)
+                assets_margin = self._api.fapiPrivateGetMultiAssetsMargin()
+                self._log_exchange_response("multi_asset_margin", assets_margin)
+                msg = ""
+                if position_side.get("dualSidePosition") is True:
+                    msg += (
+                        "\nHedge Mode is not supported by freqtrade. "
+                        "Please change 'Position Mode' on your binance futures account."
+                    )
+                if (
+                    assets_margin.get("multiAssetsMargin") is True
+                    and self.margin_mode != MarginMode.CROSS
+                ):
+                    msg += (
+                        "\nMulti-Asset Mode is not supported by freqtrade. "
+                        "Please change 'Asset Mode' on your binance futures account."
+                    )
+                if msg:
+                    raise OperationalException(msg)
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def get_historic_ohlcv(
+        self,
+        pair: str,
+        timeframe: str,
+        since_ms: int,
+        candle_type: CandleType,
+        is_new_pair: bool = False,
+        until_ms: int | None = None,
+    ) -> DataFrame:
+        """
+        Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
+        Does not work for other exchanges, which don't return the earliest data when called with "0"
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        if is_new_pair:
+            with self._loop_lock:
+                x = self.loop.run_until_complete(
+                    self._async_get_candle_history(pair, timeframe, candle_type, 0)
+                )
+            if x and x[3] and x[3][0] and x[3][0][0] > since_ms:
+                # Set starting date to first available candle.
+                since_ms = x[3][0][0]
+                logger.info(
+                    f"Candle-data for {pair} available starting with "
+                    f"{datetime.fromtimestamp(since_ms // 1000, tz=timezone.utc).isoformat()}."
+                )
+                if until_ms and since_ms >= until_ms:
+                    logger.warning(
+                        f"No available candle-data for {pair} before "
+                        f"{dt_from_ts(until_ms).isoformat()}"
+                    )
+                    return DataFrame(columns=DEFAULT_DATAFRAME_COLUMNS)
+
+        if (
+            self._config["exchange"].get("only_from_ccxt", False)
+            or
+            # only download timeframes with significant improvements,
+            # otherwise fall back to rest API
+            not (
+                (candle_type == CandleType.SPOT and timeframe in ["1s", "1m", "3m", "5m"])
+                or (
+                    candle_type == CandleType.FUTURES
+                    and timeframe in ["1m", "3m", "5m", "15m", "30m"]
+                )
+            )
+        ):
+            return super().get_historic_ohlcv(
+                pair=pair,
+                timeframe=timeframe,
+                since_ms=since_ms,
+                candle_type=candle_type,
+                is_new_pair=is_new_pair,
+                until_ms=until_ms,
+            )
+        else:
+            # Download from data.binance.vision
+            return self.get_historic_ohlcv_fast(
+                pair=pair,
+                timeframe=timeframe,
+                since_ms=since_ms,
+                candle_type=candle_type,
+                is_new_pair=is_new_pair,
+                until_ms=until_ms,
+            )
+
+    def get_historic_ohlcv_fast(
+        self,
+        pair: str,
+        timeframe: str,
+        since_ms: int,
+        candle_type: CandleType,
+        is_new_pair: bool = False,
+        until_ms: int | None = None,
+    ) -> DataFrame:
+        """
+        Fastly fetch OHLCV data by leveraging https://data.binance.vision.
+        """
+        with self._loop_lock:
+            df = self.loop.run_until_complete(
+                download_archive_ohlcv(
+                    candle_type=candle_type,
+                    pair=pair,
+                    timeframe=timeframe,
+                    since_ms=since_ms,
+                    until_ms=until_ms,
+                    markets=self.markets,
+                )
+            )
+
+        # download the remaining data from rest API
+        if df.empty:
+            rest_since_ms = since_ms
+        else:
+            rest_since_ms = dt_ts(df.iloc[-1].date) + timeframe_to_msecs(timeframe)
+
+        # make sure since <= until
+        if until_ms and rest_since_ms > until_ms:
+            rest_df = DataFrame()
+        else:
+            rest_df = super().get_historic_ohlcv(
+                pair=pair,
+                timeframe=timeframe,
+                since_ms=rest_since_ms,
+                candle_type=candle_type,
+                is_new_pair=is_new_pair,
+                until_ms=until_ms,
+            )
+        all_df = concat_safe([df, rest_df])
+        return all_df
+
+    def funding_fee_cutoff(self, open_date: datetime):
+        """
+        Funding fees are only charged at full hours (usually every 4-8h).
+        Therefore a trade opening at 10:00:01 will not be charged a funding fee until the next hour.
+        On binance, this cutoff is 15s.
+        https://github.com/freqtrade/freqtrade/pull/5779#discussion_r740175931
+        :param open_date: The open date for a trade
+        :return: True if the date falls on a full hour, False otherwise
+        """
+        return open_date.minute == 0 and open_date.second < 15
+
+    def fetch_funding_rates(self, symbols: list[str] | None = None) -> dict[str, dict[str, float]]:
+        """
+        Fetch funding rates for the given symbols.
+        :param symbols: List of symbols to fetch funding rates for
+        :return: Dict of funding rates for the given symbols
+        """
+        try:
+            if self.trading_mode == TradingMode.FUTURES:
+                rates = self._api.fetch_funding_rates(symbols)
+                return rates
+            return {}
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def dry_run_liquidation_price(
+        self,
+        pair: str,
+        open_rate: float,
+        is_short: bool,
+        amount: float,
+        stake_amount: float,
+        leverage: float,
+        wallet_balance: float,
+        open_trades: list,
+    ) -> float | None:
+        """
+        Important: Must be fetching data from cached values as this is used by backtesting!
+        MARGIN: https://www.binance.com/en/support/faq/f6b010588e55413aa58b7d63ee0125ed
+        PERPETUAL: https://www.binance.com/en/support/faq/b3c689c1f50a44cabb3a84e663b81d93
+
+        :param pair: Pair to calculate liquidation price for
+        :param open_rate: Entry price of position
+        :param is_short: True if the trade is a short, false otherwise
+        :param amount: Absolute value of position size incl. leverage (in base currency)
+        :param stake_amount: Stake amount - Collateral in settle currency.
+        :param leverage: Leverage used for this position.
+        :param wallet_balance: Amount of margin_mode in the wallet being used to trade
+            Cross-Margin Mode: crossWalletBalance
+            Isolated-Margin Mode: isolatedWalletBalance
+        :param open_trades: List of open trades in the same wallet
+
+        # * Only required for Cross
+        :param mm_ex_1: (TMM)
+            Cross-Margin Mode: Maintenance Margin of all other contracts, excluding Contract 1
+            Isolated-Margin Mode: 0
+        :param upnl_ex_1: (UPNL)
+            Cross-Margin Mode: Unrealized PNL of all other contracts, excluding Contract 1.
+            Isolated-Margin Mode: 0
+        :param other
+        """
+        cross_vars: float = 0.0
+
+        # mm_ratio: Binance's formula specifies maintenance margin rate which is mm_ratio * 100%
+        # maintenance_amt: (CUM) Maintenance Amount of position
+        mm_ratio, maintenance_amt = self.get_maintenance_ratio_and_amt(pair, stake_amount)
+
+        if self.margin_mode == MarginMode.CROSS:
+            mm_ex_1: float = 0.0
+            upnl_ex_1: float = 0.0
+            pairs = [trade.pair for trade in open_trades]
+            if self._config["runmode"] in ("live", "dry_run"):
+                funding_rates = self.fetch_funding_rates(pairs)
+            for trade in open_trades:
+                if trade.pair == pair:
+                    # Only "other" trades are considered
+                    continue
+                if self._config["runmode"] in ("live", "dry_run"):
+                    mark_price = funding_rates[trade.pair]["markPrice"]
+                else:
+                    # Fall back to open rate for backtesting
+                    mark_price = trade.open_rate
+                mm_ratio1, maint_amnt1 = self.get_maintenance_ratio_and_amt(
+                    trade.pair, trade.stake_amount
+                )
+                maint_margin = trade.amount * mark_price * mm_ratio1 - maint_amnt1
+                mm_ex_1 += maint_margin
+
+                upnl_ex_1 += trade.amount * mark_price - trade.amount * trade.open_rate
+
+            cross_vars = upnl_ex_1 - mm_ex_1
+
+        side_1 = -1 if is_short else 1
+
+        if maintenance_amt is None:
+            raise OperationalException(
+                "Parameter maintenance_amt is required by Binance.liquidation_price"
+                f"for {self.trading_mode}"
+            )
+
+        if self.trading_mode == TradingMode.FUTURES:
+            return (
+                (wallet_balance + cross_vars + maintenance_amt) - (side_1 * amount * open_rate)
+            ) / ((amount * mm_ratio) - (side_1 * amount))
+        else:
+            raise OperationalException(
+                "Freqtrade only supports isolated futures for leverage trading"
+            )
+
+    def load_leverage_tiers(self) -> dict[str, list[dict]]:
+        if self.trading_mode == TradingMode.FUTURES:
+            if self._config["dry_run"]:
+                leverage_tiers_path = Path(__file__).parent / "binance_leverage_tiers.json"
+                with leverage_tiers_path.open() as json_file:
+                    return json_load(json_file)
+            else:
+                return self.get_leverage_tiers()
+        else:
+            return {}
+
+    async def _async_get_trade_history_id_startup(
+        self, pair: str, since: int
+    ) -> tuple[list[list], str]:
+        """
+        override for initial call
+
+        Binance only provides a limited set of historic trades data.
+        Using from_id=0, we can get the earliest available trades.
+        So if we don't get any data with the provided "since", we can assume to
+        download all available data.
+        """
+        t, from_id = await self._async_fetch_trades(pair, since=since)
+        if not t:
+            return [], "0"
+        return t, from_id
+
+    async def _async_get_trade_history_id(
+        self, pair: str, until: int, since: int, from_id: str | None = None
+    ) -> tuple[str, list[list]]:
+        logger.info(f"Fetching trades from Binance, {from_id=}, {since=}, {until=}")
+
+        if not self._config["exchange"].get("only_from_ccxt", False):
+            if from_id is None or not since:
+                trades = await self._api_async.fetch_trades(
+                    pair,
+                    params={
+                        self._trades_pagination_arg: "0",
+                    },
+                    limit=5,
+                )
+                listing_date: int = trades[0]["timestamp"]
+                since = max(since, listing_date)
+
+            _, res = await download_archive_trades(
+                CandleType.SPOT,
+                pair,
+                since_ms=since,
+                until_ms=until,
+                markets=self.markets,
+            )
+
+            if not res:
+                end_time = since
+                end_id = from_id
+            else:
+                end_time = res[-1][0]
+                end_id = res[-1][1]
+
+            if end_time and end_time >= until:
+                return pair, res
+            else:
+                _, res2 = await super()._async_get_trade_history_id(
+                    pair, until=until, since=end_time, from_id=end_id
+                )
+                res.extend(res2)
+                return pair, res
+
+        return await super()._async_get_trade_history_id(
+            pair, until=until, since=since, from_id=from_id
+        )
diff --git a/freqtrade/exchange/binance_leverage_tiers.json b/freqtrade/exchange/binance_leverage_tiers.json
new file mode 100644
index 000000000..b45689af5
--- /dev/null
+++ b/freqtrade/exchange/binance_leverage_tiers.json
@@ -0,0 +1,66155 @@
+{
+  "1000000MOG/USDT:USDT": [
+    {
+      "tier": 1.0,
+      "symbol": "1000000MOG/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 25.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "25",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "symbol": "1000000MOG/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.015,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "20",
+        "notionalCap": "10000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.015",
+        "cum": "25.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "symbol": "1000000MOG/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 10000.0,
+      "maxNotional": 30000.0,
+      "maintenanceMarginRate": 0.02,
+      "maxLeverage": 15.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "15",
+        "notionalCap": "30000",
+        "notionalFloor": "10000",
+        "maintMarginRatio": "0.02",
+        "cum": "75.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "1000000MOG/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 30000.0,
+      "maxNotional": 60000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "10",
+        "notionalCap": "60000",
+        "notionalFloor": "30000",
+        "maintMarginRatio": "0.025",
+        "cum": "225.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "symbol": "1000000MOG/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 60000.0,
+      "maxNotional": 300000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 8.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "8",
+        "notionalCap": "300000",
+        "notionalFloor": "60000",
+        "maintMarginRatio": "0.05",
+        "cum": "1725.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "symbol": "1000000MOG/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 300000.0,
+      "maxNotional": 600000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "5",
+        "notionalCap": "600000",
+        "notionalFloor": "300000",
+        "maintMarginRatio": "0.1",
+        "cum": "16725.0"
+      }
+    },
+    {
+      "tier": 7.0,
+      "symbol": "1000000MOG/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 600000.0,
+      "maxNotional": 750000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 4.0,
+      "info": {
+        "bracket": "7",
+        "initialLeverage": "4",
+        "notionalCap": "750000",
+        "notionalFloor": "600000",
+        "maintMarginRatio": "0.125",
+        "cum": "31725.0"
+      }
+    },
+    {
+      "tier": 8.0,
+      "symbol": "1000000MOG/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 750000.0,
+      "maxNotional": 1500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "8",
+        "initialLeverage": "2",
+        "notionalCap": "1500000",
+        "notionalFloor": "750000",
+        "maintMarginRatio": "0.25",
+        "cum": "125475.0"
+      }
+    },
+    {
+      "tier": 9.0,
+      "symbol": "1000000MOG/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 1500000.0,
+      "maxNotional": 2000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "9",
+        "initialLeverage": "1",
+        "notionalCap": "2000000",
+        "notionalFloor": "1500000",
+        "maintMarginRatio": "0.5",
+        "cum": "500475.0"
+      }
+    }
+  ],
+  "1000BONK/USDC:USDC": [
+    {
+      "tier": 1.0,
+      "symbol": "1000BONK/USDC:USDC",
+      "currency": "USDC",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 50.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "50",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "symbol": "1000BONK/USDC:USDC",
+      "currency": "USDC",
+      "minNotional": 5000.0,
+      "maxNotional": 50000.0,
+      "maintenanceMarginRate": 0.02,
+      "maxLeverage": 25.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "25",
+        "notionalCap": "50000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.02",
+        "cum": "50.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "symbol": "1000BONK/USDC:USDC",
+      "currency": "USDC",
+      "minNotional": 50000.0,
+      "maxNotional": 600000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "20",
+        "notionalCap": "600000",
+        "notionalFloor": "50000",
+        "maintMarginRatio": "0.025",
+        "cum": "300.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "1000BONK/USDC:USDC",
+      "currency": "USDC",
+      "minNotional": 600000.0,
+      "maxNotional": 1200000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "10",
+        "notionalCap": "1200000",
+        "notionalFloor": "600000",
+        "maintMarginRatio": "0.05",
+        "cum": "15300.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "symbol": "1000BONK/USDC:USDC",
+      "currency": "USDC",
+      "minNotional": 1200000.0,
+      "maxNotional": 3000000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "5",
+        "notionalCap": "3000000",
+        "notionalFloor": "1200000",
+        "maintMarginRatio": "0.1",
+        "cum": "75300.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "symbol": "1000BONK/USDC:USDC",
+      "currency": "USDC",
+      "minNotional": 3000000.0,
+      "maxNotional": 4000000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 4.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "4",
+        "notionalCap": "4000000",
+        "notionalFloor": "3000000",
+        "maintMarginRatio": "0.125",
+        "cum": "150300.0"
+      }
+    },
+    {
+      "tier": 7.0,
+      "symbol": "1000BONK/USDC:USDC",
+      "currency": "USDC",
+      "minNotional": 4000000.0,
+      "maxNotional": 6000000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "7",
+        "initialLeverage": "2",
+        "notionalCap": "6000000",
+        "notionalFloor": "4000000",
+        "maintMarginRatio": "0.25",
+        "cum": "650300.0"
+      }
+    },
+    {
+      "tier": 8.0,
+      "symbol": "1000BONK/USDC:USDC",
+      "currency": "USDC",
+      "minNotional": 6000000.0,
+      "maxNotional": 10000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "8",
+        "initialLeverage": "1",
+        "notionalCap": "10000000",
+        "notionalFloor": "6000000",
+        "maintMarginRatio": "0.5",
+        "cum": "2150300.0"
+      }
+    }
+  ],
+  "1000BONK/USDT:USDT": [
+    {
+      "tier": 1.0,
+      "symbol": "1000BONK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "20",
+        "notionalCap": "10000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "symbol": "1000BONK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 10000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.015,
+      "maxLeverage": 8.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "8",
+        "notionalCap": "100000",
+        "notionalFloor": "10000",
+        "maintMarginRatio": "0.015",
+        "cum": "50.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "symbol": "1000BONK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 500000.0,
+      "maintenanceMarginRate": 0.02,
+      "maxLeverage": 7.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "7",
+        "notionalCap": "500000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.02",
+        "cum": "550.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "1000BONK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 500000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 6.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "6",
+        "notionalCap": "1000000",
+        "notionalFloor": "500000",
+        "maintMarginRatio": "0.025",
+        "cum": "3050.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "symbol": "1000BONK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 1000000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "5",
+        "notionalCap": "5000000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.05",
+        "cum": "28050.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "symbol": "1000BONK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 5000000.0,
+      "maxNotional": 6000000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 4.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "4",
+        "notionalCap": "6000000",
+        "notionalFloor": "5000000",
+        "maintMarginRatio": "0.1",
+        "cum": "278050.0"
+      }
+    },
+    {
+      "tier": 7.0,
+      "symbol": "1000BONK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 6000000.0,
+      "maxNotional": 7000000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 3.0,
+      "info": {
+        "bracket": "7",
+        "initialLeverage": "3",
+        "notionalCap": "7000000",
+        "notionalFloor": "6000000",
+        "maintMarginRatio": "0.125",
+        "cum": "428050.0"
+      }
+    },
+    {
+      "tier": 8.0,
+      "symbol": "1000BONK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 7000000.0,
+      "maxNotional": 8000000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "8",
+        "initialLeverage": "2",
+        "notionalCap": "8000000",
+        "notionalFloor": "7000000",
+        "maintMarginRatio": "0.25",
+        "cum": "1303050.0"
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+      "minNotional": 0.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "20",
+        "notionalCap": "10000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "symbol": "ZK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 10000.0,
+      "maxNotional": 20000.0,
+      "maintenanceMarginRate": 0.015,
+      "maxLeverage": 15.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "15",
+        "notionalCap": "20000",
+        "notionalFloor": "10000",
+        "maintMarginRatio": "0.015",
+        "cum": "50.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "symbol": "ZK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 20000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.02,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "10",
+        "notionalCap": "100000",
+        "notionalFloor": "20000",
+        "maintMarginRatio": "0.02",
+        "cum": "150.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "ZK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 200000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 6.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "6",
+        "notionalCap": "200000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.025",
+        "cum": "650.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "symbol": "ZK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 200000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "5",
+        "notionalCap": "1000000",
+        "notionalFloor": "200000",
+        "maintMarginRatio": "0.05",
+        "cum": "5650.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "symbol": "ZK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 1000000.0,
+      "maxNotional": 2000000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 4.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "4",
+        "notionalCap": "2000000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.1",
+        "cum": "55650.0"
+      }
+    },
+    {
+      "tier": 7.0,
+      "symbol": "ZK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 2000000.0,
+      "maxNotional": 2500000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 3.0,
+      "info": {
+        "bracket": "7",
+        "initialLeverage": "3",
+        "notionalCap": "2500000",
+        "notionalFloor": "2000000",
+        "maintMarginRatio": "0.125",
+        "cum": "105650.0"
+      }
+    },
+    {
+      "tier": 8.0,
+      "symbol": "ZK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 2500000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "8",
+        "initialLeverage": "2",
+        "notionalCap": "5000000",
+        "notionalFloor": "2500000",
+        "maintMarginRatio": "0.25",
+        "cum": "418150.0"
+      }
+    },
+    {
+      "tier": 9.0,
+      "symbol": "ZK/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 5000000.0,
+      "maxNotional": 10000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "9",
+        "initialLeverage": "1",
+        "notionalCap": "10000000",
+        "notionalFloor": "5000000",
+        "maintMarginRatio": "0.5",
+        "cum": "1668150.0"
+      }
+    }
+  ],
+  "ZRO/USDT:USDT": [
+    {
+      "tier": 1.0,
+      "symbol": "ZRO/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "20",
+        "notionalCap": "10000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "symbol": "ZRO/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 10000.0,
+      "maxNotional": 30000.0,
+      "maintenanceMarginRate": 0.015,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "10",
+        "notionalCap": "30000",
+        "notionalFloor": "10000",
+        "maintMarginRatio": "0.015",
+        "cum": "50.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "symbol": "ZRO/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 30000.0,
+      "maxNotional": 150000.0,
+      "maintenanceMarginRate": 0.02,
+      "maxLeverage": 7.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "7",
+        "notionalCap": "150000",
+        "notionalFloor": "30000",
+        "maintMarginRatio": "0.02",
+        "cum": "200.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "ZRO/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 150000.0,
+      "maxNotional": 300000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 6.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "6",
+        "notionalCap": "300000",
+        "notionalFloor": "150000",
+        "maintMarginRatio": "0.025",
+        "cum": "950.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "symbol": "ZRO/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 300000.0,
+      "maxNotional": 1500000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "5",
+        "notionalCap": "1500000",
+        "notionalFloor": "300000",
+        "maintMarginRatio": "0.05",
+        "cum": "8450.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "symbol": "ZRO/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 1500000.0,
+      "maxNotional": 3000000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 4.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "4",
+        "notionalCap": "3000000",
+        "notionalFloor": "1500000",
+        "maintMarginRatio": "0.1",
+        "cum": "83450.0"
+      }
+    },
+    {
+      "tier": 7.0,
+      "symbol": "ZRO/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 3000000.0,
+      "maxNotional": 3750000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 3.0,
+      "info": {
+        "bracket": "7",
+        "initialLeverage": "3",
+        "notionalCap": "3750000",
+        "notionalFloor": "3000000",
+        "maintMarginRatio": "0.125",
+        "cum": "158450.0"
+      }
+    },
+    {
+      "tier": 8.0,
+      "symbol": "ZRO/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 3750000.0,
+      "maxNotional": 7500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "8",
+        "initialLeverage": "2",
+        "notionalCap": "7500000",
+        "notionalFloor": "3750000",
+        "maintMarginRatio": "0.25",
+        "cum": "627200.0"
+      }
+    },
+    {
+      "tier": 9.0,
+      "symbol": "ZRO/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 7500000.0,
+      "maxNotional": 15000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "9",
+        "initialLeverage": "1",
+        "notionalCap": "15000000",
+        "notionalFloor": "7500000",
+        "maintMarginRatio": "0.5",
+        "cum": "2502200.0"
+      }
+    }
+  ],
+  "ZRX/USDT:USDT": [
+    {
+      "tier": 1.0,
+      "symbol": "ZRX/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.01,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "20",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.01",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "symbol": "ZRX/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.015,
+      "maxLeverage": 15.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "15",
+        "notionalCap": "10000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.015",
+        "cum": "25.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "symbol": "ZRX/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 10000.0,
+      "maxNotional": 50000.0,
+      "maintenanceMarginRate": 0.02,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "10",
+        "notionalCap": "50000",
+        "notionalFloor": "10000",
+        "maintMarginRatio": "0.02",
+        "cum": "75.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "ZRX/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 50000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 6.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "6",
+        "notionalCap": "100000",
+        "notionalFloor": "50000",
+        "maintMarginRatio": "0.025",
+        "cum": "325.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "symbol": "ZRX/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 500000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "5",
+        "notionalCap": "500000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.05",
+        "cum": "2825.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "symbol": "ZRX/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 500000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 4.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "4",
+        "notionalCap": "1000000",
+        "notionalFloor": "500000",
+        "maintMarginRatio": "0.1",
+        "cum": "27825.0"
+      }
+    },
+    {
+      "tier": 7.0,
+      "symbol": "ZRX/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 1000000.0,
+      "maxNotional": 1250000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 3.0,
+      "info": {
+        "bracket": "7",
+        "initialLeverage": "3",
+        "notionalCap": "1250000",
+        "notionalFloor": "1000000",
+        "maintMarginRatio": "0.125",
+        "cum": "52825.0"
+      }
+    },
+    {
+      "tier": 8.0,
+      "symbol": "ZRX/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 1250000.0,
+      "maxNotional": 2500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "8",
+        "initialLeverage": "2",
+        "notionalCap": "2500000",
+        "notionalFloor": "1250000",
+        "maintMarginRatio": "0.25",
+        "cum": "209075.0"
+      }
+    },
+    {
+      "tier": 9.0,
+      "symbol": "ZRX/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 2500000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "9",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "2500000",
+        "maintMarginRatio": "0.5",
+        "cum": "834075.0"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/freqtrade/exchange/binance_public_data.py b/freqtrade/exchange/binance_public_data.py
new file mode 100644
index 000000000..f61eb2a53
--- /dev/null
+++ b/freqtrade/exchange/binance_public_data.py
@@ -0,0 +1,503 @@
+"""
+Fetch daily-archived OHLCV data from https://data.binance.vision/
+Documentation can be found in https://github.com/binance/binance-public-data
+"""
+
+import asyncio
+import logging
+import zipfile
+from datetime import date, timedelta
+from io import BytesIO
+from typing import Any
+
+import aiohttp
+import numpy as np
+import pandas as pd
+from pandas import DataFrame
+
+from freqtrade.constants import DEFAULT_TRADES_COLUMNS
+from freqtrade.enums import CandleType
+from freqtrade.misc import chunks
+from freqtrade.util.datetime_helpers import dt_from_ts, dt_now
+
+
+logger = logging.getLogger(__name__)
+
+
+class Http404(Exception):
+    def __init__(self, msg, date, url):
+        super().__init__(msg)
+        self.date = date
+        self.url = url
+
+
+class BadHttpStatus(Exception):
+    """Not 200/404"""
+
+    pass
+
+
+async def download_archive_ohlcv(
+    candle_type: CandleType,
+    pair: str,
+    timeframe: str,
+    *,
+    since_ms: int,
+    until_ms: int | None,
+    markets: dict[str, Any],
+    stop_on_404: bool = True,
+) -> DataFrame:
+    """
+    Fetch OHLCV data from https://data.binance.vision
+    The function makes its best effort to download data within the time range
+    [`since_ms`, `until_ms`] -- including `since_ms`, but excluding `until_ms`.
+    If `stop_one_404` is True, this returned DataFrame is guaranteed to start from `since_ms`
+    with no gaps in the data.
+
+    :candle_type: Currently only spot and futures are supported
+    :pair: symbol name in CCXT convention
+    :since_ms: the start timestamp of data, including itself
+    :until_ms: the end timestamp of data, excluding itself
+    :param until_ms: `None` indicates the timestamp of the latest available data
+    :markets: the CCXT markets dict, when it's None, the function will load the markets data
+        from a new `ccxt.binance` instance
+    :param stop_on_404: Stop to download the following data when a 404 returned
+    :return: the date range is between [since_ms, until_ms), return an empty DataFrame if no data
+        available in the time range
+    """
+    try:
+        symbol = markets[pair]["id"]
+
+        start = dt_from_ts(since_ms)
+        end = dt_from_ts(until_ms) if until_ms else dt_now()
+
+        # We use two days ago as the last available day because the daily archives are daily
+        # uploaded and have several hours delay
+        last_available_date = dt_now() - timedelta(days=2)
+        end = min(end, last_available_date)
+        if start >= end:
+            return DataFrame()
+        df = await _download_archive_ohlcv(
+            symbol, pair, timeframe, candle_type, start, end, stop_on_404
+        )
+        logger.debug(
+            f"Downloaded data for {pair} from https://data.binance.vision with length {len(df)}."
+        )
+    except Exception as e:
+        logger.warning(
+            "An exception occurred during fast download from Binance, falling back to "
+            "the slower REST API, this can take more time.",
+            exc_info=e,
+        )
+        df = DataFrame()
+
+    if not df.empty:
+        # only return the data within the requested time range
+        return df.loc[(df["date"] >= start) & (df["date"] < end)]
+    else:
+        return df
+
+
+def concat_safe(dfs) -> DataFrame:
+    if all(df is None for df in dfs):
+        return DataFrame()
+    else:
+        return pd.concat(dfs)
+
+
+async def _download_archive_ohlcv(
+    symbol: str,
+    pair: str,
+    timeframe: str,
+    candle_type: CandleType,
+    start: date,
+    end: date,
+    stop_on_404: bool,
+) -> DataFrame:
+    # daily dataframes, `None` indicates missing data in that day (when `stop_on_404` is False)
+    dfs: list[DataFrame | None] = []
+    # the current day being processing, starting at 1.
+    current_day = 0
+
+    connector = aiohttp.TCPConnector(limit=100)
+    async with aiohttp.ClientSession(connector=connector, trust_env=True) as session:
+        # the HTTP connections has been throttled by TCPConnector
+        for dates in chunks(list(date_range(start, end)), 1000):
+            tasks = [
+                asyncio.create_task(get_daily_ohlcv(symbol, timeframe, candle_type, date, session))
+                for date in dates
+            ]
+            for task in tasks:
+                current_day += 1
+                try:
+                    df = await task
+                except Http404 as e:
+                    if stop_on_404:
+                        logger.debug(f"Failed to download {e.url} due to 404.")
+
+                        # A 404 error on the first day indicates missing data
+                        # on https://data.binance.vision, we provide the warning and the advice.
+                        # https://github.com/freqtrade/freqtrade/blob/acc53065e5fa7ab5197073276306dc9dc3adbfa3/tests/exchange_online/test_binance_compare_ohlcv.py#L7
+                        if current_day == 1:
+                            logger.warning(
+                                f"Fast download is unavailable due to missing data: "
+                                f"{e.url}. Falling back to the slower REST API, "
+                                "which may take more time."
+                            )
+                            if pair in ["BTC/USDT:USDT", "ETH/USDT:USDT", "BCH/USDT:USDT"]:
+                                logger.warning(
+                                    f"To avoid the delay, you can first download {pair} using "
+                                    "`--timerange <start date>-20200101`, and then download the "
+                                    "remaining data with `--timerange 20200101-<end date>`."
+                                )
+                        else:
+                            logger.warning(
+                                f"Binance fast download for {pair} stopped at {e.date} due to "
+                                f"missing data: {e.url}, falling back to rest API for the "
+                                "remaining data, this can take more time."
+                            )
+                        await cancel_and_await_tasks(tasks[tasks.index(task) + 1 :])
+                        return concat_safe(dfs)
+                    else:
+                        dfs.append(None)
+                except Exception as e:
+                    logger.warning(f"An exception raised: {e}")
+                    # Directly return the existing data, do not allow the gap within the data
+                    await cancel_and_await_tasks(tasks[tasks.index(task) + 1 :])
+                    return concat_safe(dfs)
+                else:
+                    dfs.append(df)
+    return concat_safe(dfs)
+
+
+async def cancel_and_await_tasks(unawaited_tasks):
+    """Cancel and await the tasks"""
+    logger.debug("Try to cancel uncompleted download tasks.")
+    for task in unawaited_tasks:
+        task.cancel()
+    await asyncio.gather(*unawaited_tasks, return_exceptions=True)
+    logger.debug("All download tasks were awaited.")
+
+
+def date_range(start: date, end: date):
+    date = start
+    while date <= end:
+        yield date
+        date += timedelta(days=1)
+
+
+def binance_vision_zip_name(symbol: str, timeframe: str, date: date) -> str:
+    return f"{symbol}-{timeframe}-{date.strftime('%Y-%m-%d')}.zip"
+
+
+def candle_type_to_url_segment(candle_type: CandleType) -> str:
+    if candle_type == CandleType.SPOT:
+        return "spot"
+    elif candle_type == CandleType.FUTURES:
+        return "futures/um"
+    else:
+        raise ValueError(f"Unsupported CandleType: {candle_type}")
+
+
+def binance_vision_ohlcv_zip_url(
+    symbol: str, timeframe: str, candle_type: CandleType, date: date
+) -> str:
+    """
+    example urls:
+    https://data.binance.vision/data/spot/daily/klines/BTCUSDT/1s/BTCUSDT-1s-2023-10-27.zip
+    https://data.binance.vision/data/futures/um/daily/klines/BTCUSDT/1h/BTCUSDT-1h-2023-10-27.zip
+    """
+    asset_type_url_segment = candle_type_to_url_segment(candle_type)
+    url = (
+        f"https://data.binance.vision/data/{asset_type_url_segment}/daily/klines/{symbol}"
+        f"/{timeframe}/{binance_vision_zip_name(symbol, timeframe, date)}"
+    )
+    return url
+
+
+def binance_vision_trades_zip_url(symbol: str, candle_type: CandleType, date: date) -> str:
+    """
+    example urls:
+    https://data.binance.vision/data/spot/daily/aggTrades/BTCUSDT/BTCUSDT-aggTrades-2023-10-27.zip
+    https://data.binance.vision/data/futures/um/daily/aggTrades/BTCUSDT/BTCUSDT-aggTrades-2023-10-27.zip
+    """
+    asset_type_url_segment = candle_type_to_url_segment(candle_type)
+    url = (
+        f"https://data.binance.vision/data/{asset_type_url_segment}/daily/aggTrades/{symbol}"
+        f"/{symbol}-aggTrades-{date.strftime('%Y-%m-%d')}.zip"
+    )
+    return url
+
+
+async def get_daily_ohlcv(
+    symbol: str,
+    timeframe: str,
+    candle_type: CandleType,
+    date: date,
+    session: aiohttp.ClientSession,
+    retry_count: int = 3,
+    retry_delay: float = 0.0,
+) -> DataFrame:
+    """
+    Get daily OHLCV from https://data.binance.vision
+    See https://github.com/binance/binance-public-data
+
+    :symbol: binance symbol name, e.g. BTCUSDT
+    :timeframe: e.g. 1m, 1h
+    :candle_type: SPOT or FUTURES
+    :date: the returned DataFrame will cover the entire day of `date` in UTC
+    :session: an aiohttp.ClientSession instance
+    :retry_count: times to retry before returning the exceptions
+    :retry_delay: the time to wait before every retry
+    :return: A dataframe containing columns date,open,high,low,close,volume
+    """
+
+    url = binance_vision_ohlcv_zip_url(symbol, timeframe, candle_type, date)
+
+    logger.debug(f"download data from binance: {url}")
+
+    retry = 0
+    while True:
+        if retry > 0:
+            sleep_secs = retry * retry_delay
+            logger.debug(
+                f"[{retry}/{retry_count}] retry to download {url} after {sleep_secs} seconds"
+            )
+            await asyncio.sleep(sleep_secs)
+        try:
+            async with session.get(url) as resp:
+                if resp.status == 200:
+                    content = await resp.read()
+                    logger.debug(f"Successfully downloaded {url}")
+                    with zipfile.ZipFile(BytesIO(content)) as zipf:
+                        with zipf.open(zipf.namelist()[0]) as csvf:
+                            # https://github.com/binance/binance-public-data/issues/283
+                            first_byte = csvf.read(1)[0]
+                            if chr(first_byte).isdigit():
+                                header = None
+                            else:
+                                header = 0
+                            csvf.seek(0)
+
+                            df = pd.read_csv(
+                                csvf,
+                                usecols=[0, 1, 2, 3, 4, 5],
+                                names=["date", "open", "high", "low", "close", "volume"],
+                                header=header,
+                            )
+                            df["date"] = pd.to_datetime(
+                                np.where(df["date"] > 1e13, df["date"] // 1000, df["date"]),
+                                unit="ms",
+                                utc=True,
+                            )
+                            return df
+                elif resp.status == 404:
+                    logger.debug(f"Failed to download {url}")
+                    raise Http404(f"404: {url}", date, url)
+                else:
+                    raise BadHttpStatus(f"{resp.status} - {resp.reason}")
+        except Exception as e:
+            retry += 1
+            if isinstance(e, Http404) or retry > retry_count:
+                logger.debug(f"Failed to get data from {url}: {e}")
+                raise
+
+
+async def download_archive_trades(
+    candle_type: CandleType,
+    pair: str,
+    *,
+    since_ms: int,
+    until_ms: int | None,
+    markets: dict[str, Any],
+    stop_on_404: bool = True,
+) -> tuple[str, list[list]]:
+    try:
+        symbol = markets[pair]["id"]
+
+        last_available_date = dt_now() - timedelta(days=2)
+
+        start = dt_from_ts(since_ms)
+        end = dt_from_ts(until_ms) if until_ms else dt_now()
+        end = min(end, last_available_date)
+        if start >= end:
+            return pair, []
+        result_list = await _download_archive_trades(
+            symbol, pair, candle_type, start, end, stop_on_404
+        )
+        return pair, result_list
+
+    except Exception as e:
+        logger.warning(
+            "An exception occurred during fast trades download from Binance, falling back to "
+            "the slower REST API, this can take a lot more time.",
+            exc_info=e,
+        )
+        return pair, []
+
+
+def parse_trades_from_zip(csvf):
+    # https://github.com/binance/binance-public-data/issues/283
+    first_byte = csvf.read(1)[0]
+    if chr(first_byte).isdigit():
+        # spot
+        header = None
+        names = [
+            "id",
+            "price",
+            "amount",
+            "first_trade_id",
+            "last_trade_id",
+            "timestamp",
+            "is_buyer_maker",
+            "is_best_match",
+        ]
+    else:
+        # futures
+        header = 0
+        names = [
+            "id",
+            "price",
+            "amount",
+            "first_trade_id",
+            "last_trade_id",
+            "timestamp",
+            "is_buyer_maker",
+        ]
+    csvf.seek(0)
+
+    df = pd.read_csv(
+        csvf,
+        names=names,
+        header=header,
+    )
+    df.loc[:, "cost"] = df["price"] * df["amount"]
+    # Side is reversed intentionally
+    # based on ccxt parseTrade logic.
+    df.loc[:, "side"] = np.where(df["is_buyer_maker"], "sell", "buy")
+    df.loc[:, "type"] = None
+    # Convert timestamp to ms
+    df.loc[:, "timestamp"] = np.where(
+        df["timestamp"] > 1e13,
+        df["timestamp"] // 1000,
+        df["timestamp"],
+    )
+    return df.loc[:, DEFAULT_TRADES_COLUMNS].to_records(index=False).tolist()
+
+
+async def get_daily_trades(
+    symbol: str,
+    candle_type: CandleType,
+    date: date,
+    session: aiohttp.ClientSession,
+    retry_count: int = 3,
+    retry_delay: float = 0.0,
+) -> list[list]:
+    """
+    Get daily OHLCV from https://data.binance.vision
+    See https://github.com/binance/binance-public-data
+
+    :symbol: binance symbol name, e.g. BTCUSDT
+    :candle_type: SPOT or FUTURES
+    :date: the returned DataFrame will cover the entire day of `date` in UTC
+    :session: an aiohttp.ClientSession instance
+    :retry_count: times to retry before returning the exceptions
+    :retry_delay: the time to wait before every retry
+    :return: a list containing trades in DEFAULT_TRADES_COLUMNS format
+    """
+
+    url = binance_vision_trades_zip_url(symbol, candle_type, date)
+
+    logger.debug(f"download trades data from binance: {url}")
+
+    retry = 0
+    while True:
+        if retry > 0:
+            sleep_secs = retry * retry_delay
+            logger.debug(
+                f"[{retry}/{retry_count}] retry to download {url} after {sleep_secs} seconds"
+            )
+            await asyncio.sleep(sleep_secs)
+        try:
+            async with session.get(url) as resp:
+                if resp.status == 200:
+                    content = await resp.read()
+                    logger.debug(f"Successfully downloaded {url}")
+                    with zipfile.ZipFile(BytesIO(content)) as zipf:
+                        with zipf.open(zipf.namelist()[0]) as csvf:
+                            return parse_trades_from_zip(csvf)
+                elif resp.status == 404:
+                    logger.debug(f"Failed to download {url}")
+                    raise Http404(f"404: {url}", date, url)
+                else:
+                    raise BadHttpStatus(f"{resp.status} - {resp.reason}")
+        except Exception as e:
+            logger.info("download Daily_trades raised: %s", e)
+            retry += 1
+            if isinstance(e, Http404) or retry > retry_count:
+                logger.debug(f"Failed to get data from {url}: {e}")
+                raise
+
+
+async def _download_archive_trades(
+    symbol: str,
+    pair: str,
+    candle_type: CandleType,
+    start: date,
+    end: date,
+    stop_on_404: bool,
+) -> list[list]:
+    # daily dataframes, `None` indicates missing data in that day (when `stop_on_404` is False)
+    results: list[list] = []
+    # the current day being processing, starting at 1.
+    current_day = 0
+
+    connector = aiohttp.TCPConnector(limit=100)
+    async with aiohttp.ClientSession(connector=connector, trust_env=True) as session:
+        # the HTTP connections has been throttled by TCPConnector
+        for dates in chunks(list(date_range(start, end)), 30):
+            tasks = [
+                asyncio.create_task(get_daily_trades(symbol, candle_type, date, session))
+                for date in dates
+            ]
+            for task in tasks:
+                current_day += 1
+                try:
+                    result = await task
+                except Http404 as e:
+                    if stop_on_404:
+                        logger.debug(f"Failed to download {e.url} due to 404.")
+
+                        # A 404 error on the first day indicates missing data
+                        # on https://data.binance.vision, we provide the warning and the advice.
+                        # https://github.com/freqtrade/freqtrade/blob/acc53065e5fa7ab5197073276306dc9dc3adbfa3/tests/exchange_online/test_binance_compare_ohlcv.py#L7
+                        if current_day == 1:
+                            logger.warning(
+                                f"Fast download is unavailable due to missing data: "
+                                f"{e.url}. Falling back to the slower REST API, "
+                                "which may take more time."
+                            )
+                            if pair in ["BTC/USDT:USDT", "ETH/USDT:USDT", "BCH/USDT:USDT"]:
+                                logger.warning(
+                                    f"To avoid the delay, you can first download {pair} using "
+                                    "`--timerange <start date>-20200101`, and then download the "
+                                    "remaining data with `--timerange 20200101-<end date>`."
+                                )
+                        else:
+                            logger.warning(
+                                f"Binance fast download for {pair} stopped at {e.date} due to "
+                                f"missing data: {e.url}, falling back to rest API for the "
+                                "remaining data, this can take more time."
+                            )
+                        await cancel_and_await_tasks(tasks[tasks.index(task) + 1 :])
+                        return results
+                except Exception as e:
+                    logger.warning(f"An exception raised: {e}")
+                    # Directly return the existing data, do not allow the gap within the data
+                    await cancel_and_await_tasks(tasks[tasks.index(task) + 1 :])
+                    return results
+                else:
+                    # Happy case
+                    results.extend(result)
+
+    return results
diff --git a/freqtrade/exchange/bingx.py b/freqtrade/exchange/bingx.py
new file mode 100644
index 000000000..1caf7a737
--- /dev/null
+++ b/freqtrade/exchange/bingx.py
@@ -0,0 +1,24 @@
+"""Bingx exchange subclass"""
+
+import logging
+
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import FtHas
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bingx(Exchange):
+    """
+    Bingx exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    _ft_has: FtHas = {
+        "ohlcv_candle_limit": 1000,
+        "stoploss_on_exchange": True,
+        "stoploss_order_types": {"limit": "limit", "market": "market"},
+        "order_time_in_force": ["GTC", "IOC", "PO"],
+        "trades_has_history": False,  # Endpoint doesn't seem to support pagination
+    }
diff --git a/freqtrade/exchange/bitmart.py b/freqtrade/exchange/bitmart.py
new file mode 100644
index 000000000..8fed36bec
--- /dev/null
+++ b/freqtrade/exchange/bitmart.py
@@ -0,0 +1,22 @@
+"""Bitmart exchange subclass"""
+
+import logging
+
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import FtHas
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bitmart(Exchange):
+    """
+    Bitmart exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    _ft_has: FtHas = {
+        "stoploss_on_exchange": False,  # Bitmart API does not support stoploss orders
+        "ohlcv_candle_limit": 200,
+        "trades_has_history": False,  # Endpoint doesn't seem to support pagination
+    }
diff --git a/freqtrade/exchange/bitpanda.py b/freqtrade/exchange/bitpanda.py
new file mode 100644
index 000000000..05c5af6ac
--- /dev/null
+++ b/freqtrade/exchange/bitpanda.py
@@ -0,0 +1,38 @@
+"""Bitpanda exchange subclass"""
+
+import logging
+from datetime import datetime, timezone
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bitpanda(Exchange):
+    """
+    Bitpanda exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    def get_trades_for_order(
+        self, order_id: str, pair: str, since: datetime, params: dict | None = None
+    ) -> list:
+        """
+        Fetch Orders using the "fetch_my_trades" endpoint and filter them by order-id.
+        The "since" argument passed in is coming from the database and is in UTC,
+        as timezone-native datetime object.
+        From the python documentation:
+            > Naive datetime instances are assumed to represent local time
+        Therefore, calling "since.timestamp()" will get the UTC timestamp, after applying the
+        transformation from local timezone to UTC.
+        This works for timezones UTC+ since then the result will contain trades from a few hours
+        instead of from the last 5 seconds, however fails for UTC- timezones,
+        since we're then asking for trades with a "since" argument in the future.
+
+        :param order_id order_id: Order-id as given when creating the order
+        :param pair: Pair the order is for
+        :param since: datetime object of the order creation time. Assumes object is in UTC.
+        """
+        params = {"to": int(datetime.now(timezone.utc).timestamp() * 1000)}
+        return super().get_trades_for_order(order_id, pair, since, params)
diff --git a/freqtrade/exchange/bittrex.py b/freqtrade/exchange/bittrex.py
deleted file mode 100644
index 5aa07e460..000000000
--- a/freqtrade/exchange/bittrex.py
+++ /dev/null
@@ -1,211 +0,0 @@
-import logging
-from typing import Dict, List, Optional
-
-from bittrex.bittrex import Bittrex as _Bittrex
-from bittrex.bittrex import API_V1_1, API_V2_0
-from requests.exceptions import ContentDecodingError
-
-from freqtrade import OperationalException
-from freqtrade.exchange.interface import Exchange
-
-logger = logging.getLogger(__name__)
-
-_API: _Bittrex = None
-_API_V2: _Bittrex = None
-_EXCHANGE_CONF: dict = {}
-
-
-class Bittrex(Exchange):
-    """
-    Bittrex API wrapper.
-    """
-    # Base URL and API endpoints
-    BASE_URL: str = 'https://www.bittrex.com'
-    PAIR_DETAIL_METHOD: str = BASE_URL + '/Market/Index'
-
-    def __init__(self, config: dict) -> None:
-        global _API, _API_V2, _EXCHANGE_CONF
-
-        _EXCHANGE_CONF.update(config)
-        _API = _Bittrex(
-            api_key=_EXCHANGE_CONF['key'],
-            api_secret=_EXCHANGE_CONF['secret'],
-            calls_per_second=1,
-            api_version=API_V1_1,
-        )
-        _API_V2 = _Bittrex(
-            api_key=_EXCHANGE_CONF['key'],
-            api_secret=_EXCHANGE_CONF['secret'],
-            calls_per_second=1,
-            api_version=API_V2_0,
-        )
-        self.cached_ticker = {}
-
-    @staticmethod
-    def _validate_response(response) -> None:
-        """
-        Validates the given bittrex response
-        and raises a ContentDecodingError if a non-fatal issue happened.
-        """
-        temp_error_messages = [
-            'NO_API_RESPONSE',
-            'MIN_TRADE_REQUIREMENT_NOT_MET',
-        ]
-        if response['message'] in temp_error_messages:
-            raise ContentDecodingError(response['message'])
-
-    @property
-    def fee(self) -> float:
-        # 0.25 %: See https://bittrex.com/fees
-        return 0.0025
-
-    def buy(self, pair: str, rate: float, amount: float) -> str:
-        data = _API.buy_limit(pair.replace('_', '-'), amount, rate)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({pair}, {rate}, {amount})'.format(
-                message=data['message'],
-                pair=pair,
-                rate=rate,
-                amount=amount))
-        return data['result']['uuid']
-
-    def sell(self, pair: str, rate: float, amount: float) -> str:
-        data = _API.sell_limit(pair.replace('_', '-'), amount, rate)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({pair}, {rate}, {amount})'.format(
-                message=data['message'],
-                pair=pair,
-                rate=rate,
-                amount=amount))
-        return data['result']['uuid']
-
-    def get_balance(self, currency: str) -> float:
-        data = _API.get_balance(currency)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({currency})'.format(
-                message=data['message'],
-                currency=currency))
-        return float(data['result']['Balance'] or 0.0)
-
-    def get_balances(self):
-        data = _API.get_balances()
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message}'.format(message=data['message']))
-        return data['result']
-
-    def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
-        if refresh or pair not in self.cached_ticker.keys():
-            data = _API.get_ticker(pair.replace('_', '-'))
-            if not data['success']:
-                Bittrex._validate_response(data)
-                raise OperationalException('{message} params=({pair})'.format(
-                    message=data['message'],
-                    pair=pair))
-            keys = ['Bid', 'Ask', 'Last']
-            if not data.get('result') or\
-                    not all(key in data.get('result', {}) for key in keys) or\
-                    not all(data.get('result', {})[key] is not None for key in keys):
-                raise ContentDecodingError('Invalid response from Bittrex params=({pair})'.format(
-                    pair=pair))
-            # Update the pair
-            self.cached_ticker[pair] = {
-                'bid': float(data['result']['Bid']),
-                'ask': float(data['result']['Ask']),
-                'last': float(data['result']['Last']),
-            }
-        return self.cached_ticker[pair]
-
-    def get_ticker_history(self, pair: str, tick_interval: int) -> List[Dict]:
-        if tick_interval == 1:
-            interval = 'oneMin'
-        elif tick_interval == 5:
-            interval = 'fiveMin'
-        elif tick_interval == 30:
-            interval = 'thirtyMin'
-        elif tick_interval == 60:
-            interval = 'hour'
-        elif tick_interval == 1440:
-            interval = 'Day'
-        else:
-            raise ValueError('Unknown tick_interval: {}'.format(tick_interval))
-
-        data = _API_V2.get_candles(pair.replace('_', '-'), interval)
-
-        # These sanity check are necessary because bittrex cannot keep their API stable.
-        if not data.get('result'):
-            raise ContentDecodingError('Invalid response from Bittrex params=({pair})'.format(
-                pair=pair))
-
-        for prop in ['C', 'V', 'O', 'H', 'L', 'T']:
-            for tick in data['result']:
-                if prop not in tick.keys():
-                    raise ContentDecodingError('Required property {} not present '
-                                               'in response params=({})'.format(prop, pair))
-
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({pair})'.format(
-                message=data['message'],
-                pair=pair))
-
-        return data['result']
-
-    def get_order(self, order_id: str) -> Dict:
-        data = _API.get_order(order_id)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({order_id})'.format(
-                message=data['message'],
-                order_id=order_id))
-        data = data['result']
-        return {
-            'id': data['OrderUuid'],
-            'type': data['Type'],
-            'pair': data['Exchange'].replace('-', '_'),
-            'opened': data['Opened'],
-            'rate': data['PricePerUnit'],
-            'amount': data['Quantity'],
-            'remaining': data['QuantityRemaining'],
-            'closed': data['Closed'],
-        }
-
-    def cancel_order(self, order_id: str) -> None:
-        data = _API.cancel(order_id)
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException('{message} params=({order_id})'.format(
-                message=data['message'],
-                order_id=order_id))
-
-    def get_pair_detail_url(self, pair: str) -> str:
-        return self.PAIR_DETAIL_METHOD + '?MarketName={}'.format(pair.replace('_', '-'))
-
-    def get_markets(self) -> List[str]:
-        data = _API.get_markets()
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException(data['message'])
-        return [m['MarketName'].replace('-', '_') for m in data['result']]
-
-    def get_market_summaries(self) -> List[Dict]:
-        data = _API.get_market_summaries()
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException(data['message'])
-        return data['result']
-
-    def get_wallet_health(self) -> List[Dict]:
-        data = _API_V2.get_wallet_health()
-        if not data['success']:
-            Bittrex._validate_response(data)
-            raise OperationalException(data['message'])
-        return [{
-            'Currency': entry['Health']['Currency'],
-            'IsActive': entry['Health']['IsActive'],
-            'LastChecked': entry['Health']['LastChecked'],
-            'Notice': entry['Currency'].get('Notice'),
-        } for entry in data['result']]
diff --git a/freqtrade/exchange/bitvavo.py b/freqtrade/exchange/bitvavo.py
new file mode 100644
index 000000000..fdbb8a7d4
--- /dev/null
+++ b/freqtrade/exchange/bitvavo.py
@@ -0,0 +1,34 @@
+"""Bitvavo exchange subclass."""
+
+import logging
+
+from ccxt import DECIMAL_PLACES
+
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import FtHas
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bitvavo(Exchange):
+    """Bitvavo exchange class.
+
+    Contains adjustments needed for Freqtrade to work with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    _ft_has: FtHas = {
+        "ohlcv_candle_limit": 1440,
+    }
+
+    @property
+    def precisionMode(self) -> int:
+        """
+        Exchange ccxt precisionMode
+        Override due to https://github.com/ccxt/ccxt/issues/20408
+        """
+        return DECIMAL_PLACES
diff --git a/freqtrade/exchange/bybit.py b/freqtrade/exchange/bybit.py
new file mode 100644
index 000000000..1f5c19b2a
--- /dev/null
+++ b/freqtrade/exchange/bybit.py
@@ -0,0 +1,291 @@
+"""Bybit exchange subclass"""
+
+import logging
+from datetime import datetime, timedelta
+from typing import Any
+
+import ccxt
+
+from freqtrade.constants import BuySell
+from freqtrade.enums import MarginMode, PriceType, TradingMode
+from freqtrade.exceptions import DDosProtection, ExchangeError, OperationalException, TemporaryError
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.common import retrier
+from freqtrade.exchange.exchange_types import CcxtOrder, FtHas
+from freqtrade.util.datetime_helpers import dt_now, dt_ts
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bybit(Exchange):
+    """
+    Bybit exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    unified_account = False
+
+    _ft_has: FtHas = {
+        "ohlcv_has_history": True,
+        "order_time_in_force": ["GTC", "FOK", "IOC", "PO"],
+        "ws_enabled": True,
+        "trades_has_history": False,  # Endpoint doesn't support pagination
+        "exchange_has_overrides": {
+            # Bybit spot does not support fetch_order
+            # Unless the account is unified.
+            # TODO: Can be removed once bybit fully forces all accounts to unified mode.
+            "fetchOrder": False,
+        },
+    }
+    _ft_has_futures: FtHas = {
+        "ohlcv_has_history": True,
+        "mark_ohlcv_timeframe": "4h",
+        "funding_fee_timeframe": "8h",
+        "funding_fee_candle_limit": 200,
+        "stoploss_on_exchange": True,
+        "stoploss_order_types": {"limit": "limit", "market": "market"},
+        # bybit response parsing fails to populate stopLossPrice
+        "stop_price_prop": "stopPrice",
+        "stop_price_type_field": "triggerBy",
+        "stop_price_type_value_mapping": {
+            PriceType.LAST: "LastPrice",
+            PriceType.MARK: "MarkPrice",
+            PriceType.INDEX: "IndexPrice",
+        },
+        "exchange_has_overrides": {
+            "fetchOrder": True,
+        },
+    }
+
+    _supported_trading_mode_margin_pairs: list[tuple[TradingMode, MarginMode]] = [
+        # TradingMode.SPOT always supported and not required in this list
+        # (TradingMode.FUTURES, MarginMode.CROSS),
+        (TradingMode.FUTURES, MarginMode.ISOLATED)
+    ]
+
+    @property
+    def _ccxt_config(self) -> dict:
+        # Parameters to add directly to ccxt sync/async initialization.
+        # ccxt defaults to swap mode.
+        config = {}
+        if self.trading_mode == TradingMode.SPOT:
+            config.update({"options": {"defaultType": "spot"}})
+        config.update(super()._ccxt_config)
+        return config
+
+    def market_is_future(self, market: dict[str, Any]) -> bool:
+        main = super().market_is_future(market)
+        # For ByBit, we'll only support USDT markets for now.
+        return main and market["settle"] == "USDT"
+
+    @retrier
+    def additional_exchange_init(self) -> None:
+        """
+        Additional exchange initialization logic.
+        .api will be available at this point.
+        Must be overridden in child methods if required.
+        """
+        try:
+            if not self._config["dry_run"]:
+                if self.trading_mode == TradingMode.FUTURES:
+                    position_mode = self._api.set_position_mode(False)
+                    self._log_exchange_response("set_position_mode", position_mode)
+                is_unified = self._api.is_unified_enabled()
+                # Returns a tuple of bools, first for margin, second for Account
+                if is_unified and len(is_unified) > 1 and is_unified[1]:
+                    self.unified_account = True
+                    logger.info(
+                        "Bybit: Unified account. Assuming dedicated subaccount for this bot."
+                    )
+                else:
+                    self.unified_account = False
+                    logger.info("Bybit: Standard account.")
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
+        if self.trading_mode != TradingMode.SPOT:
+            params = {"leverage": leverage}
+            self.set_margin_mode(pair, self.margin_mode, accept_fail=True, params=params)
+            self._set_leverage(leverage, pair, accept_fail=True)
+
+    def _get_params(
+        self,
+        side: BuySell,
+        ordertype: str,
+        leverage: float,
+        reduceOnly: bool,
+        time_in_force: str = "GTC",
+    ) -> dict:
+        params = super()._get_params(
+            side=side,
+            ordertype=ordertype,
+            leverage=leverage,
+            reduceOnly=reduceOnly,
+            time_in_force=time_in_force,
+        )
+        if self.trading_mode == TradingMode.FUTURES and self.margin_mode:
+            params["position_idx"] = 0
+        return params
+
+    def _order_needs_price(self, side: BuySell, ordertype: str) -> bool:
+        # Bybit requires price for market orders - but only for classic accounts,
+        # and only in spot mode
+        return (
+            ordertype != "market"
+            or (
+                side == "buy" and not self.unified_account and self.trading_mode == TradingMode.SPOT
+            )
+            or self._ft_has.get("marketOrderRequiresPrice", False)
+        )
+
+    def dry_run_liquidation_price(
+        self,
+        pair: str,
+        open_rate: float,  # Entry price of position
+        is_short: bool,
+        amount: float,
+        stake_amount: float,
+        leverage: float,
+        wallet_balance: float,  # Or margin balance
+        open_trades: list,
+    ) -> float | None:
+        """
+        Important: Must be fetching data from cached values as this is used by backtesting!
+        PERPETUAL:
+         bybit:
+          https://www.bybithelp.com/HelpCenterKnowledge/bybitHC_Article?language=en_US&id=000001067
+          https://www.bybit.com/en/help-center/article/Liquidation-Price-Calculation-under-Isolated-Mode-Unified-Trading-Account#b
+
+        Long:
+        Liquidation Price = (
+            Entry Price - [(Initial Margin - Maintenance Margin)/Contract Quantity]
+            - (Extra Margin Added/Contract Quantity))
+        Short:
+        Liquidation Price = (
+            Entry Price + [(Initial Margin - Maintenance Margin)/Contract Quantity]
+            + (Extra Margin Added/Contract Quantity))
+
+        Implementation Note: Extra margin is currently not used.
+
+        :param pair: Pair to calculate liquidation price for
+        :param open_rate: Entry price of position
+        :param is_short: True if the trade is a short, false otherwise
+        :param amount: Absolute value of position size incl. leverage (in base currency)
+        :param stake_amount: Stake amount - Collateral in settle currency.
+        :param leverage: Leverage used for this position.
+        :param wallet_balance: Amount of margin_mode in the wallet being used to trade
+            Cross-Margin Mode: crossWalletBalance
+            Isolated-Margin Mode: isolatedWalletBalance
+        :param open_trades: List of other open trades in the same wallet
+        """
+
+        market = self.markets[pair]
+        mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, stake_amount)
+
+        if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.ISOLATED:
+            if market["inverse"]:
+                raise OperationalException("Freqtrade does not yet support inverse contracts")
+            position_value = amount * open_rate
+            initial_margin = position_value / leverage
+            maintenance_margin = position_value * mm_ratio
+            margin_diff_per_contract = (initial_margin - maintenance_margin) / amount
+
+            # See docstring - ignores extra margin!
+            if is_short:
+                return open_rate + margin_diff_per_contract
+            else:
+                return open_rate - margin_diff_per_contract
+
+        else:
+            raise OperationalException(
+                "Freqtrade only supports isolated futures for leverage trading"
+            )
+
+    def get_funding_fees(
+        self, pair: str, amount: float, is_short: bool, open_date: datetime
+    ) -> float:
+        """
+        Fetch funding fees, either from the exchange (live) or calculates them
+        based on funding rate/mark price history
+        :param pair: The quote/base pair of the trade
+        :param is_short: trade direction
+        :param amount: Trade amount
+        :param open_date: Open date of the trade
+        :return: funding fee since open_date
+        :raises: ExchangeError if something goes wrong.
+        """
+        # Bybit does not provide "applied" funding fees per position.
+        if self.trading_mode == TradingMode.FUTURES:
+            try:
+                return self._fetch_and_calculate_funding_fees(pair, amount, is_short, open_date)
+            except ExchangeError:
+                logger.warning(f"Could not update funding fees for {pair}.")
+        return 0.0
+
+    def fetch_orders(
+        self, pair: str, since: datetime, params: dict | None = None
+    ) -> list[CcxtOrder]:
+        """
+        Fetch all orders for a pair "since"
+        :param pair: Pair for the query
+        :param since: Starting time for the query
+        """
+        # On bybit, the distance between since and "until" can't exceed 7 days.
+        # we therefore need to split the query into multiple queries.
+        orders = []
+
+        while since < dt_now():
+            until = since + timedelta(days=7, minutes=-1)
+            orders += super().fetch_orders(pair, since, params={"until": dt_ts(until)})
+            since = until
+
+        return orders
+
+    def fetch_order(self, order_id: str, pair: str, params: dict | None = None) -> CcxtOrder:
+        if self.exchange_has("fetchOrder"):
+            # Set acknowledged to True to avoid ccxt exception
+            params = {"acknowledged": True}
+
+        order = super().fetch_order(order_id, pair, params)
+        if not order:
+            order = self.fetch_order_emulated(order_id, pair, {})
+        if (
+            order.get("status") == "canceled"
+            and order.get("filled") == 0.0
+            and order.get("remaining") == 0.0
+        ):
+            # Canceled orders will have "remaining=0" on bybit.
+            order["remaining"] = None
+        return order
+
+    @retrier
+    def get_leverage_tiers(self) -> dict[str, list[dict]]:
+        """
+        Cache leverage tiers for 1 day, since they are not expected to change often, and
+        bybit requires pagination to fetch all tiers.
+        """
+
+        # Load cached tiers
+        tiers_cached = self.load_cached_leverage_tiers(
+            self._config["stake_currency"], timedelta(days=1)
+        )
+        if tiers_cached:
+            return tiers_cached
+
+        # Fetch tiers from exchange
+        tiers = super().get_leverage_tiers()
+
+        self.cache_leverage_tiers(tiers, self._config["stake_currency"])
+        return tiers
diff --git a/freqtrade/exchange/check_exchange.py b/freqtrade/exchange/check_exchange.py
new file mode 100644
index 000000000..1402e40cc
--- /dev/null
+++ b/freqtrade/exchange/check_exchange.py
@@ -0,0 +1,72 @@
+import logging
+
+from freqtrade.constants import Config
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import available_exchanges, is_exchange_known_ccxt, validate_exchange
+from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS, SUPPORTED_EXCHANGES
+
+
+logger = logging.getLogger(__name__)
+
+
+def check_exchange(config: Config, check_for_bad: bool = True) -> bool:
+    """
+    Check if the exchange name in the config file is supported by Freqtrade
+    :param check_for_bad: if True, check the exchange against the list of known 'bad'
+                          exchanges
+    :return: False if exchange is 'bad', i.e. is known to work with the bot with
+             critical issues or does not work at all, crashes, etc. True otherwise.
+             raises an exception if the exchange if not supported by ccxt
+             and thus is not known for the Freqtrade at all.
+    """
+
+    if config["runmode"] in [
+        RunMode.PLOT,
+        RunMode.UTIL_NO_EXCHANGE,
+        RunMode.OTHER,
+    ] and not config.get("exchange", {}).get("name"):
+        # Skip checking exchange in plot mode, since it requires no exchange
+        return True
+    logger.info("Checking exchange...")
+
+    exchange = config.get("exchange", {}).get("name", "").lower()
+    if not exchange:
+        raise OperationalException(
+            f"This command requires a configured exchange. You should either use "
+            f"`--exchange <exchange_name>` or specify a configuration file via `--config`.\n"
+            f"The following exchanges are available for Freqtrade: "
+            f"{', '.join(available_exchanges())}"
+        )
+
+    if not is_exchange_known_ccxt(exchange):
+        raise OperationalException(
+            f'Exchange "{exchange}" is not known to the ccxt library '
+            f"and therefore not available for the bot.\n"
+            f"The following exchanges are available for Freqtrade: "
+            f"{', '.join(available_exchanges())}"
+        )
+
+    valid, reason, _ = validate_exchange(exchange)
+    if not valid:
+        if check_for_bad:
+            raise OperationalException(
+                f'Exchange "{exchange}"  will not work with Freqtrade. Reason: {reason}'
+            )
+        else:
+            logger.warning(f'Exchange "{exchange}"  will not work with Freqtrade. Reason: {reason}')
+
+    if MAP_EXCHANGE_CHILDCLASS.get(exchange, exchange) in SUPPORTED_EXCHANGES:
+        logger.info(
+            f'Exchange "{exchange}" is officially supported by the Freqtrade development team.'
+        )
+    else:
+        logger.warning(
+            f'Exchange "{exchange}" is known to the ccxt library, '
+            f"available for the bot, but not officially supported "
+            f"by the Freqtrade development team. "
+            f"It may work flawlessly (please report back) or have serious issues. "
+            f"Use it at your own discretion."
+        )
+
+    return True
diff --git a/freqtrade/exchange/common.py b/freqtrade/exchange/common.py
new file mode 100644
index 000000000..617fe0d01
--- /dev/null
+++ b/freqtrade/exchange/common.py
@@ -0,0 +1,210 @@
+import asyncio
+import logging
+import time
+from collections.abc import Callable
+from functools import wraps
+from typing import Any, TypeVar, cast, overload
+
+from freqtrade.constants import ExchangeConfig
+from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
+from freqtrade.mixins import LoggingMixin
+
+
+logger = logging.getLogger(__name__)
+__logging_mixin = None
+
+
+def _reset_logging_mixin():
+    """
+    Reset global logging mixin - used in tests only.
+    """
+    global __logging_mixin
+    __logging_mixin = LoggingMixin(logger)
+
+
+def _get_logging_mixin():
+    # Logging-mixin to cache kucoin responses
+    # Only to be used in retrier
+    global __logging_mixin
+    if not __logging_mixin:
+        __logging_mixin = LoggingMixin(logger)
+    return __logging_mixin
+
+
+# Maximum default retry count.
+# Functions are always called RETRY_COUNT + 1 times (for the original call)
+API_RETRY_COUNT = 4
+API_FETCH_ORDER_RETRY_COUNT = 5
+
+BAD_EXCHANGES = {
+    "bitmex": "Various reasons.",
+    "probit": "Requires additional, regular calls to `signIn()`.",
+    "poloniex": "Does not provide fetch_order endpoint to fetch both open and closed orders.",
+    "kucoinfutures": "Unsupported futures exchange.",
+    "poloniexfutures": "Unsupported futures exchange.",
+    "binancecoinm": "Unsupported futures exchange.",
+}
+
+MAP_EXCHANGE_CHILDCLASS = {
+    "binanceus": "binance",
+    "binanceje": "binance",
+    "binanceusdm": "binance",
+    "okex": "okx",
+    "myokx": "okx",
+    "gateio": "gate",
+    "huboi": "htx",
+}
+
+SUPPORTED_EXCHANGES = [
+    "binance",
+    "bingx",
+    "bitmart",
+    "bybit",
+    "gate",
+    "htx",
+    "hyperliquid",
+    "kraken",
+    "okx",
+]
+
+# either the main, or replacement methods (array) is required
+EXCHANGE_HAS_REQUIRED: dict[str, list[str]] = {
+    # Required / private
+    "fetchOrder": ["fetchOpenOrder", "fetchClosedOrder"],
+    "fetchL2OrderBook": ["fetchTicker"],
+    "cancelOrder": [],
+    "createOrder": [],
+    "fetchBalance": [],
+    # Public endpoints
+    "fetchOHLCV": [],
+}
+
+EXCHANGE_HAS_OPTIONAL = [
+    # Private
+    "fetchMyTrades",  # Trades for order - fee detection
+    "createLimitOrder",
+    "createMarketOrder",  # Either OR for orders
+    # 'setLeverage',  # Margin/Futures trading
+    # 'setMarginMode',  # Margin/Futures trading
+    # 'fetchFundingHistory', # Futures trading
+    # Public
+    "fetchOrderBook",
+    "fetchL2OrderBook",
+    "fetchTicker",  # OR for pricing
+    "fetchTickers",  # For volumepairlist?
+    "fetchTrades",  # Downloading trades data
+    # 'fetchFundingRateHistory',  # Futures trading
+    # 'fetchPositions',  # Futures trading
+    # 'fetchLeverageTiers',  # Futures initialization
+    # 'fetchMarketLeverageTiers',  # Futures initialization
+    # 'fetchOpenOrder', 'fetchClosedOrder',  # replacement for fetchOrder
+    # 'fetchOpenOrders', 'fetchClosedOrders',  # 'fetchOrders',  # Refinding balance...
+    # ccxt.pro
+    "watchOHLCV",
+]
+
+
+def remove_exchange_credentials(exchange_config: ExchangeConfig, dry_run: bool) -> None:
+    """
+    Removes exchange keys from the configuration and specifies dry-run
+    Used for backtesting / hyperopt / edge and utils.
+    Modifies the input dict!
+    """
+    if dry_run:
+        exchange_config["key"] = ""
+        exchange_config["apiKey"] = ""
+        exchange_config["secret"] = ""
+        exchange_config["password"] = ""
+        exchange_config["uid"] = ""
+
+
+def calculate_backoff(retrycount, max_retries):
+    """
+    Calculate backoff
+    """
+    return (max_retries - retrycount) ** 2 + 1
+
+
+def retrier_async(f):
+    async def wrapper(*args, **kwargs):
+        count = kwargs.pop("count", API_RETRY_COUNT)
+        kucoin = args[0].name == "KuCoin"  # Check if the exchange is KuCoin.
+        try:
+            return await f(*args, **kwargs)
+        except TemporaryError as ex:
+            msg = f'{f.__name__}() returned exception: "{ex}". '
+            if count > 0:
+                msg += f"Retrying still for {count} times."
+                count -= 1
+                kwargs["count"] = count
+                if isinstance(ex, DDosProtection):
+                    if kucoin and "429000" in str(ex):
+                        # Temporary fix for 429000 error on kucoin
+                        # see https://github.com/freqtrade/freqtrade/issues/5700 for details.
+                        _get_logging_mixin().log_once(
+                            f"Kucoin 429 error, avoid triggering DDosProtection backoff delay. "
+                            f"{count} tries left before giving up",
+                            logmethod=logger.warning,
+                        )
+                        # Reset msg to avoid logging too many times.
+                        msg = ""
+                    else:
+                        backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
+                        logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
+                        await asyncio.sleep(backoff_delay)
+                if msg:
+                    logger.warning(msg)
+                return await wrapper(*args, **kwargs)
+            else:
+                logger.warning(msg + "Giving up.")
+                raise ex
+
+    return wrapper
+
+
+F = TypeVar("F", bound=Callable[..., Any])
+
+
+# Type shenanigans
+@overload
+def retrier(_func: F) -> F: ...
+
+
+@overload
+def retrier(_func: F, *, retries=API_RETRY_COUNT) -> F: ...
+
+
+@overload
+def retrier(*, retries=API_RETRY_COUNT) -> Callable[[F], F]: ...
+
+
+def retrier(_func: F | None = None, *, retries=API_RETRY_COUNT):
+    def decorator(f: F) -> F:
+        @wraps(f)
+        def wrapper(*args, **kwargs):
+            count = kwargs.pop("count", retries)
+            try:
+                return f(*args, **kwargs)
+            except (TemporaryError, RetryableOrderError) as ex:
+                msg = f'{f.__name__}() returned exception: "{ex}". '
+                if count > 0:
+                    logger.warning(msg + f"Retrying still for {count} times.")
+                    count -= 1
+                    kwargs.update({"count": count})
+                    if isinstance(ex, DDosProtection | RetryableOrderError):
+                        # increasing backoff
+                        backoff_delay = calculate_backoff(count + 1, retries)
+                        logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
+                        time.sleep(backoff_delay)
+                    return wrapper(*args, **kwargs)
+                else:
+                    logger.warning(msg + "Giving up.")
+                    raise ex
+
+        return cast(F, wrapper)
+
+    # Support both @retrier and @retrier(retries=2) syntax
+    if _func is None:
+        return decorator
+    else:
+        return decorator(_func)
diff --git a/freqtrade/exchange/cryptocom.py b/freqtrade/exchange/cryptocom.py
new file mode 100644
index 000000000..4782c5a49
--- /dev/null
+++ b/freqtrade/exchange/cryptocom.py
@@ -0,0 +1,19 @@
+"""Crypto.com exchange subclass"""
+
+import logging
+
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import FtHas
+
+
+logger = logging.getLogger(__name__)
+
+
+class Cryptocom(Exchange):
+    """Crypto.com exchange class.
+    Contains adjustments needed for Freqtrade to work with this exchange.
+    """
+
+    _ft_has: FtHas = {
+        "ohlcv_candle_limit": 300,
+    }
diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py
new file mode 100644
index 000000000..b78d98b99
--- /dev/null
+++ b/freqtrade/exchange/exchange.py
@@ -0,0 +1,3783 @@
+# pragma pylint: disable=W0603
+"""
+Cryptocurrency Exchanges support
+"""
+
+import asyncio
+import inspect
+import logging
+import signal
+from collections.abc import Coroutine, Generator
+from copy import deepcopy
+from datetime import datetime, timedelta, timezone
+from math import floor, isnan
+from threading import Lock
+from typing import Any, Literal, TypeGuard, TypeVar
+
+import ccxt
+import ccxt.pro as ccxt_pro
+from cachetools import TTLCache
+from ccxt import TICK_SIZE
+from dateutil import parser
+from pandas import DataFrame, concat
+
+from freqtrade.constants import (
+    DEFAULT_AMOUNT_RESERVE_PERCENT,
+    DEFAULT_TRADES_COLUMNS,
+    NON_OPEN_EXCHANGE_STATES,
+    BidAsk,
+    BuySell,
+    Config,
+    EntryExit,
+    ExchangeConfig,
+    ListPairsWithTimeframes,
+    MakerTaker,
+    OBLiteral,
+    PairWithTimeframe,
+)
+from freqtrade.data.converter import (
+    clean_ohlcv_dataframe,
+    ohlcv_to_dataframe,
+    trades_df_remove_duplicates,
+    trades_dict_to_list,
+    trades_list_to_df,
+)
+from freqtrade.enums import (
+    OPTIMIZE_MODES,
+    TRADE_MODES,
+    CandleType,
+    MarginMode,
+    PriceType,
+    RunMode,
+    TradingMode,
+)
+from freqtrade.exceptions import (
+    ConfigurationError,
+    DDosProtection,
+    ExchangeError,
+    InsufficientFundsError,
+    InvalidOrderException,
+    OperationalException,
+    PricingError,
+    RetryableOrderError,
+    TemporaryError,
+)
+from freqtrade.exchange.common import (
+    API_FETCH_ORDER_RETRY_COUNT,
+    remove_exchange_credentials,
+    retrier,
+    retrier_async,
+)
+from freqtrade.exchange.exchange_types import (
+    CcxtBalances,
+    CcxtOrder,
+    CcxtPosition,
+    FtHas,
+    OHLCVResponse,
+    OrderBook,
+    Ticker,
+    Tickers,
+)
+from freqtrade.exchange.exchange_utils import (
+    ROUND,
+    ROUND_DOWN,
+    ROUND_UP,
+    amount_to_contract_precision,
+    amount_to_contracts,
+    amount_to_precision,
+    contracts_to_amount,
+    date_minus_candles,
+    is_exchange_known_ccxt,
+    market_is_active,
+    price_to_precision,
+)
+from freqtrade.exchange.exchange_utils_timeframe import (
+    timeframe_to_minutes,
+    timeframe_to_msecs,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    timeframe_to_seconds,
+)
+from freqtrade.exchange.exchange_ws import ExchangeWS
+from freqtrade.misc import (
+    chunks,
+    deep_merge_dicts,
+    file_dump_json,
+    file_load_json,
+    safe_value_fallback2,
+)
+from freqtrade.util import dt_from_ts, dt_now
+from freqtrade.util.datetime_helpers import dt_humanize_delta, dt_ts, format_ms_time
+from freqtrade.util.periodic_cache import PeriodicCache
+
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar("T")
+
+
+class Exchange:
+    # Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
+    _params: dict = {}
+
+    # Additional parameters - added to the ccxt object
+    _ccxt_params: dict = {}
+
+    # Dict to specify which options each exchange implements
+    # This defines defaults, which can be selectively overridden by subclasses using _ft_has
+    # or by specifying them in the configuration.
+    _ft_has_default: FtHas = {
+        "stoploss_on_exchange": False,
+        "stop_price_param": "stopLossPrice",  # Used for stoploss_on_exchange request
+        "stop_price_prop": "stopLossPrice",  # Used for stoploss_on_exchange response parsing
+        "stoploss_order_types": {},
+        "order_time_in_force": ["GTC"],
+        "ohlcv_params": {},
+        "ohlcv_has_history": True,  # Some exchanges (Kraken) don't provide history via ohlcv
+        "ohlcv_partial_candle": True,
+        "ohlcv_require_since": False,
+        # Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
+        "ohlcv_volume_currency": "base",  # "base" or "quote"
+        "tickers_have_quoteVolume": True,
+        "tickers_have_percentage": True,
+        "tickers_have_bid_ask": True,  # bid / ask empty for fetch_tickers
+        "tickers_have_price": True,
+        "trades_limit": 1000,  # Limit for 1 call to fetch_trades
+        "trades_pagination": "time",  # Possible are "time" or "id"
+        "trades_pagination_arg": "since",
+        "trades_has_history": False,
+        "l2_limit_range": None,
+        "l2_limit_range_required": True,  # Allow Empty L2 limit (kucoin)
+        "l2_limit_upper": None,  # Upper limit for L2 limit
+        "mark_ohlcv_price": "mark",
+        "mark_ohlcv_timeframe": "8h",
+        "funding_fee_timeframe": "8h",
+        "ccxt_futures_name": "swap",
+        "needs_trading_fees": False,  # use fetch_trading_fees to cache fees
+        "order_props_in_contracts": ["amount", "filled", "remaining"],
+        # Override createMarketBuyOrderRequiresPrice where ccxt has it wrong
+        "marketOrderRequiresPrice": False,
+        "exchange_has_overrides": {},  # Dictionary overriding ccxt's "has".
+        "proxy_coin_mapping": {},  # Mapping for proxy coins
+        # Expected to be in the format {"fetchOHLCV": True} or {"fetchOHLCV": False}
+        "ws_enabled": False,  # Set to true for exchanges with tested websocket support
+    }
+    _ft_has: FtHas = {}
+    _ft_has_futures: FtHas = {}
+
+    _supported_trading_mode_margin_pairs: list[tuple[TradingMode, MarginMode]] = [
+        # TradingMode.SPOT always supported and not required in this list
+    ]
+
+    def __init__(
+        self,
+        config: Config,
+        *,
+        exchange_config: ExchangeConfig | None = None,
+        validate: bool = True,
+        load_leverage_tiers: bool = False,
+    ) -> None:
+        """
+        Initializes this module with the given config,
+        it does basic validation whether the specified exchange and pairs are valid.
+        :return: None
+        """
+        self._api: ccxt.Exchange
+        self._api_async: ccxt_pro.Exchange
+        self._ws_async: ccxt_pro.Exchange = None
+        self._exchange_ws: ExchangeWS | None = None
+        self._markets: dict = {}
+        self._trading_fees: dict[str, Any] = {}
+        self._leverage_tiers: dict[str, list[dict]] = {}
+        # Lock event loop. This is necessary to avoid race-conditions when using force* commands
+        # Due to funding fee fetching.
+        self._loop_lock = Lock()
+        self.loop = self._init_async_loop()
+        self._config: Config = {}
+
+        self._config.update(config)
+
+        # Holds last candle refreshed time of each pair
+        self._pairs_last_refresh_time: dict[PairWithTimeframe, int] = {}
+        # Timestamp of last markets refresh
+        self._last_markets_refresh: int = 0
+
+        self._cache_lock = Lock()
+        # Cache for 10 minutes ...
+        self._fetch_tickers_cache: TTLCache = TTLCache(maxsize=4, ttl=60 * 10)
+        # Cache values for 300 to avoid frequent polling of the exchange for prices
+        # Caching only applies to RPC methods, so prices for open trades are still
+        # refreshed once every iteration.
+        # Shouldn't be too high either, as it'll freeze UI updates in case of open orders.
+        self._exit_rate_cache: TTLCache = TTLCache(maxsize=100, ttl=300)
+        self._entry_rate_cache: TTLCache = TTLCache(maxsize=100, ttl=300)
+
+        # Holds candles
+        self._klines: dict[PairWithTimeframe, DataFrame] = {}
+        self._expiring_candle_cache: dict[tuple[str, int], PeriodicCache] = {}
+
+        # Holds public_trades
+        self._trades: dict[PairWithTimeframe, DataFrame] = {}
+
+        # Holds all open sell orders for dry_run
+        self._dry_run_open_orders: dict[str, Any] = {}
+
+        if config["dry_run"]:
+            logger.info("Instance is running with dry_run enabled")
+        logger.info(f"Using CCXT {ccxt.__version__}")
+        exchange_conf: dict[str, Any] = exchange_config if exchange_config else config["exchange"]
+        remove_exchange_credentials(exchange_conf, config.get("dry_run", False))
+        self.log_responses = exchange_conf.get("log_responses", False)
+
+        # Leverage properties
+        self.trading_mode: TradingMode = config.get("trading_mode", TradingMode.SPOT)
+        self.margin_mode: MarginMode = (
+            MarginMode(config.get("margin_mode")) if config.get("margin_mode") else MarginMode.NONE
+        )
+        self.liquidation_buffer = config.get("liquidation_buffer", 0.05)
+
+        # Deep merge ft_has with default ft_has options
+        self._ft_has = deep_merge_dicts(self._ft_has, deepcopy(self._ft_has_default))
+        if self.trading_mode == TradingMode.FUTURES:
+            self._ft_has = deep_merge_dicts(self._ft_has_futures, self._ft_has)
+        if exchange_conf.get("_ft_has_params"):
+            self._ft_has = deep_merge_dicts(exchange_conf.get("_ft_has_params"), self._ft_has)
+            logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
+
+        # Assign this directly for easy access
+        self._ohlcv_partial_candle = self._ft_has["ohlcv_partial_candle"]
+
+        self._max_trades_limit = self._ft_has["trades_limit"]
+
+        self._trades_pagination = self._ft_has["trades_pagination"]
+        self._trades_pagination_arg = self._ft_has["trades_pagination_arg"]
+
+        # Initialize ccxt objects
+        ccxt_config = self._ccxt_config
+        ccxt_config = deep_merge_dicts(exchange_conf.get("ccxt_config", {}), ccxt_config)
+        ccxt_config = deep_merge_dicts(exchange_conf.get("ccxt_sync_config", {}), ccxt_config)
+
+        self._api = self._init_ccxt(exchange_conf, True, ccxt_config)
+
+        ccxt_async_config = self._ccxt_config
+        ccxt_async_config = deep_merge_dicts(
+            exchange_conf.get("ccxt_config", {}), ccxt_async_config
+        )
+        ccxt_async_config = deep_merge_dicts(
+            exchange_conf.get("ccxt_async_config", {}), ccxt_async_config
+        )
+        self._api_async = self._init_ccxt(exchange_conf, False, ccxt_async_config)
+        self._has_watch_ohlcv = self.exchange_has("watchOHLCV") and self._ft_has["ws_enabled"]
+        if (
+            self._config["runmode"] in TRADE_MODES
+            and exchange_conf.get("enable_ws", True)
+            and self._has_watch_ohlcv
+        ):
+            self._ws_async = self._init_ccxt(exchange_conf, False, ccxt_async_config)
+            self._exchange_ws = ExchangeWS(self._config, self._ws_async)
+
+        logger.info(f'Using Exchange "{self.name}"')
+        self.required_candle_call_count = 1
+        # Converts the interval provided in minutes in config to seconds
+        self.markets_refresh_interval: int = (
+            exchange_conf.get("markets_refresh_interval", 60) * 60 * 1000
+        )
+
+        if validate:
+            # Initial markets load
+            self.reload_markets(True, load_leverage_tiers=False)
+            self.validate_config(config)
+            self._startup_candle_count: int = config.get("startup_candle_count", 0)
+            self.required_candle_call_count = self.validate_required_startup_candles(
+                self._startup_candle_count, config.get("timeframe", "")
+            )
+
+        if self.trading_mode != TradingMode.SPOT and load_leverage_tiers:
+            self.fill_leverage_tiers()
+        self.additional_exchange_init()
+
+    def __del__(self):
+        """
+        Destructor - clean up async stuff
+        """
+        self.close()
+
+    def close(self):
+        if self._exchange_ws:
+            self._exchange_ws.cleanup()
+        logger.debug("Exchange object destroyed, closing async loop")
+        if (
+            getattr(self, "_api_async", None)
+            and inspect.iscoroutinefunction(self._api_async.close)
+            and self._api_async.session
+        ):
+            logger.debug("Closing async ccxt session.")
+            self.loop.run_until_complete(self._api_async.close())
+        if (
+            self._ws_async
+            and inspect.iscoroutinefunction(self._ws_async.close)
+            and self._ws_async.session
+        ):
+            logger.debug("Closing ws ccxt session.")
+            self.loop.run_until_complete(self._ws_async.close())
+
+        if self.loop and not self.loop.is_closed():
+            self.loop.close()
+
+    def _init_async_loop(self) -> asyncio.AbstractEventLoop:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        return loop
+
+    def validate_config(self, config: Config) -> None:
+        # Check if timeframe is available
+        self.validate_timeframes(config.get("timeframe"))
+
+        # Check if all pairs are available
+        self.validate_stakecurrency(config["stake_currency"])
+        self.validate_ordertypes(config.get("order_types", {}))
+        self.validate_order_time_in_force(config.get("order_time_in_force", {}))
+        self.validate_trading_mode_and_margin_mode(self.trading_mode, self.margin_mode)
+        self.validate_pricing(config["exit_pricing"])
+        self.validate_pricing(config["entry_pricing"])
+        self.validate_orderflow(config["exchange"])
+        self.validate_freqai(config)
+
+    def _init_ccxt(
+        self, exchange_config: dict[str, Any], sync: bool, ccxt_kwargs: dict[str, Any]
+    ) -> ccxt.Exchange:
+        """
+        Initialize ccxt with given config and return valid ccxt instance.
+        """
+        # Find matching class for the given exchange name
+        name = exchange_config["name"]
+        if sync:
+            ccxt_module = ccxt
+        else:
+            ccxt_module = ccxt_pro
+            if not is_exchange_known_ccxt(name, ccxt_module):
+                # Fall back to async if pro doesn't support this exchange
+                import ccxt.async_support as ccxt_async
+
+                ccxt_module = ccxt_async
+
+        if not is_exchange_known_ccxt(name, ccxt_module):
+            raise OperationalException(f"Exchange {name} is not supported by ccxt")
+
+        ex_config = {
+            "apiKey": exchange_config.get(
+                "api_key", exchange_config.get("apiKey", exchange_config.get("key"))
+            ),
+            "secret": exchange_config.get("secret"),
+            "password": exchange_config.get("password"),
+            "uid": exchange_config.get("uid", ""),
+            "accountId": exchange_config.get("account_id", exchange_config.get("accountId", "")),
+            # DEX attributes:
+            "walletAddress": exchange_config.get(
+                "wallet_address", exchange_config.get("walletAddress")
+            ),
+            "privateKey": exchange_config.get("private_key", exchange_config.get("privateKey")),
+        }
+        if ccxt_kwargs:
+            logger.info("Applying additional ccxt config: %s", ccxt_kwargs)
+        if self._ccxt_params:
+            # Inject static options after the above output to not confuse users.
+            ccxt_kwargs = deep_merge_dicts(self._ccxt_params, deepcopy(ccxt_kwargs))
+        if ccxt_kwargs:
+            ex_config.update(ccxt_kwargs)
+        try:
+            api = getattr(ccxt_module, name.lower())(ex_config)
+        except (KeyError, AttributeError) as e:
+            raise OperationalException(f"Exchange {name} is not supported") from e
+        except ccxt.BaseError as e:
+            raise OperationalException(f"Initialization of ccxt failed. Reason: {e}") from e
+
+        return api
+
+    @property
+    def _ccxt_config(self) -> dict:
+        # Parameters to add directly to ccxt sync/async initialization.
+        if self.trading_mode == TradingMode.MARGIN:
+            return {"options": {"defaultType": "margin"}}
+        elif self.trading_mode == TradingMode.FUTURES:
+            return {"options": {"defaultType": self._ft_has["ccxt_futures_name"]}}
+        else:
+            return {}
+
+    @property
+    def name(self) -> str:
+        """exchange Name (from ccxt)"""
+        return self._api.name
+
+    @property
+    def id(self) -> str:
+        """exchange ccxt id"""
+        return self._api.id
+
+    @property
+    def timeframes(self) -> list[str]:
+        return list((self._api.timeframes or {}).keys())
+
+    @property
+    def markets(self) -> dict[str, Any]:
+        """exchange ccxt markets"""
+        if not self._markets:
+            logger.info("Markets were not loaded. Loading them now..")
+            self.reload_markets(True)
+        return self._markets
+
+    @property
+    def precisionMode(self) -> int:
+        """Exchange ccxt precisionMode"""
+        return self._api.precisionMode
+
+    @property
+    def precision_mode_price(self) -> int:
+        """
+        Exchange ccxt precisionMode used for price
+        Workaround for ccxt limitation to not have precisionMode for price
+        if it differs for an exchange
+        Might need to be updated if https://github.com/ccxt/ccxt/issues/20408 is fixed.
+        """
+        return self._api.precisionMode
+
+    def additional_exchange_init(self) -> None:
+        """
+        Additional exchange initialization logic.
+        .api will be available at this point.
+        Must be overridden in child methods if required.
+        """
+        pass
+
+    def _log_exchange_response(self, endpoint: str, response, *, add_info=None) -> None:
+        """Log exchange responses"""
+        if self.log_responses:
+            add_info_str = "" if add_info is None else f" {add_info}: "
+            logger.info(f"API {endpoint}: {add_info_str}{response}")
+
+    def ohlcv_candle_limit(
+        self, timeframe: str, candle_type: CandleType, since_ms: int | None = None
+    ) -> int:
+        """
+        Exchange ohlcv candle limit
+        Uses ohlcv_candle_limit_per_timeframe if the exchange has different limits
+        per timeframe (e.g. bittrex), otherwise falls back to ohlcv_candle_limit
+        :param timeframe: Timeframe to check
+        :param candle_type: Candle-type
+        :param since_ms: Starting timestamp
+        :return: Candle limit as integer
+        """
+
+        ccxt_val = self.features(
+            "spot" if candle_type == CandleType.SPOT else "futures", "fetchOHLCV", "limit", 500
+        )
+        if not isinstance(ccxt_val, float | int):
+            ccxt_val = 500
+        fallback_val = self._ft_has.get("ohlcv_candle_limit", ccxt_val)
+        if candle_type == CandleType.FUNDING_RATE:
+            fallback_val = self._ft_has.get("funding_fee_candle_limit", fallback_val)
+        return int(
+            self._ft_has.get("ohlcv_candle_limit_per_timeframe", {}).get(
+                timeframe, str(fallback_val)
+            )
+        )
+
+    def get_markets(
+        self,
+        base_currencies: list[str] | None = None,
+        quote_currencies: list[str] | None = None,
+        spot_only: bool = False,
+        margin_only: bool = False,
+        futures_only: bool = False,
+        tradable_only: bool = True,
+        active_only: bool = False,
+    ) -> dict[str, Any]:
+        """
+        Return exchange ccxt markets, filtered out by base currency and quote currency
+        if this was requested in parameters.
+        """
+        markets = self.markets
+        if not markets:
+            raise OperationalException("Markets were not loaded.")
+
+        if base_currencies:
+            markets = {k: v for k, v in markets.items() if v["base"] in base_currencies}
+        if quote_currencies:
+            markets = {k: v for k, v in markets.items() if v["quote"] in quote_currencies}
+        if tradable_only:
+            markets = {k: v for k, v in markets.items() if self.market_is_tradable(v)}
+        if spot_only:
+            markets = {k: v for k, v in markets.items() if self.market_is_spot(v)}
+        if margin_only:
+            markets = {k: v for k, v in markets.items() if self.market_is_margin(v)}
+        if futures_only:
+            markets = {k: v for k, v in markets.items() if self.market_is_future(v)}
+        if active_only:
+            markets = {k: v for k, v in markets.items() if market_is_active(v)}
+        return markets
+
+    def get_quote_currencies(self) -> list[str]:
+        """
+        Return a list of supported quote currencies
+        """
+        markets = self.markets
+        return sorted(set([x["quote"] for _, x in markets.items()]))
+
+    def get_pair_quote_currency(self, pair: str) -> str:
+        """Return a pair's quote currency (base/quote:settlement)"""
+        return self.markets.get(pair, {}).get("quote", "")
+
+    def get_pair_base_currency(self, pair: str) -> str:
+        """Return a pair's base currency (base/quote:settlement)"""
+        return self.markets.get(pair, {}).get("base", "")
+
+    def market_is_future(self, market: dict[str, Any]) -> bool:
+        return (
+            market.get(self._ft_has["ccxt_futures_name"], False) is True
+            and market.get("type", False) == "swap"
+            and market.get("linear", False) is True
+        )
+
+    def market_is_spot(self, market: dict[str, Any]) -> bool:
+        return market.get("spot", False) is True
+
+    def market_is_margin(self, market: dict[str, Any]) -> bool:
+        return market.get("margin", False) is True
+
+    def market_is_tradable(self, market: dict[str, Any]) -> bool:
+        """
+        Check if the market symbol is tradable by Freqtrade.
+        Ensures that Configured mode aligns to
+        """
+        return (
+            market.get("quote", None) is not None
+            and market.get("base", None) is not None
+            and (
+                self.precisionMode != TICK_SIZE
+                # Too low precision will falsify calculations
+                or market.get("precision", {}).get("price") is None
+                or market.get("precision", {}).get("price") > 1e-11
+            )
+            and (
+                (self.trading_mode == TradingMode.SPOT and self.market_is_spot(market))
+                or (self.trading_mode == TradingMode.MARGIN and self.market_is_margin(market))
+                or (self.trading_mode == TradingMode.FUTURES and self.market_is_future(market))
+            )
+        )
+
+    def klines(self, pair_interval: PairWithTimeframe, copy: bool = True) -> DataFrame:
+        if pair_interval in self._klines:
+            return self._klines[pair_interval].copy() if copy else self._klines[pair_interval]
+        else:
+            return DataFrame()
+
+    def trades(self, pair_interval: PairWithTimeframe, copy: bool = True) -> DataFrame:
+        if pair_interval in self._trades:
+            if copy:
+                return self._trades[pair_interval].copy()
+            else:
+                return self._trades[pair_interval]
+        else:
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)
+
+    def get_contract_size(self, pair: str) -> float | None:
+        if self.trading_mode == TradingMode.FUTURES:
+            market = self.markets.get(pair, {})
+            contract_size: float = 1.0
+            if not market:
+                return None
+            if market.get("contractSize") is not None:
+                # ccxt has contractSize in markets as string
+                contract_size = float(market["contractSize"])
+            return contract_size
+        else:
+            return 1
+
+    def _trades_contracts_to_amount(self, trades: list) -> list:
+        if len(trades) > 0 and "symbol" in trades[0]:
+            contract_size = self.get_contract_size(trades[0]["symbol"])
+            if contract_size != 1:
+                for trade in trades:
+                    trade["amount"] = trade["amount"] * contract_size
+        return trades
+
+    def _order_contracts_to_amount(self, order: CcxtOrder) -> CcxtOrder:
+        if "symbol" in order and order["symbol"] is not None:
+            contract_size = self.get_contract_size(order["symbol"])
+            if contract_size != 1:
+                for prop in self._ft_has.get("order_props_in_contracts", []):
+                    if prop in order and order[prop] is not None:
+                        order[prop] = order[prop] * contract_size
+        return order
+
+    def _amount_to_contracts(self, pair: str, amount: float) -> float:
+        contract_size = self.get_contract_size(pair)
+        return amount_to_contracts(amount, contract_size)
+
+    def _contracts_to_amount(self, pair: str, num_contracts: float) -> float:
+        contract_size = self.get_contract_size(pair)
+        return contracts_to_amount(num_contracts, contract_size)
+
+    def amount_to_contract_precision(self, pair: str, amount: float) -> float:
+        """
+        Helper wrapper around amount_to_contract_precision
+        """
+        contract_size = self.get_contract_size(pair)
+
+        return amount_to_contract_precision(
+            amount, self.get_precision_amount(pair), self.precisionMode, contract_size
+        )
+
+    def ws_connection_reset(self):
+        """
+        called at regular intervals to reset the websocket connection
+        """
+        if self._exchange_ws:
+            self._exchange_ws.reset_connections()
+
+    async def _api_reload_markets(self, reload: bool = False) -> dict[str, Any]:
+        try:
+            return await self._api_async.load_markets(reload=reload, params={})
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Error in reload_markets due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise TemporaryError(e) from e
+
+    def _load_async_markets(self, reload: bool = False) -> dict[str, Any]:
+        try:
+            with self._loop_lock:
+                markets = self.loop.run_until_complete(self._api_reload_markets(reload=reload))
+
+            if isinstance(markets, Exception):
+                raise markets
+            return markets
+        except asyncio.TimeoutError as e:
+            logger.warning("Could not load markets. Reason: %s", e)
+            raise TemporaryError from e
+
+    def reload_markets(self, force: bool = False, *, load_leverage_tiers: bool = True) -> None:
+        """
+        Reload / Initialize markets both sync and async if refresh interval has passed
+
+        """
+        # Check whether markets have to be reloaded
+        is_initial = self._last_markets_refresh == 0
+        if (
+            not force
+            and self._last_markets_refresh > 0
+            and (self._last_markets_refresh + self.markets_refresh_interval > dt_ts())
+        ):
+            return None
+        logger.debug("Performing scheduled market reload..")
+        try:
+            # on initial load, we retry 3 times to ensure we get the markets
+            retries: int = 3 if force else 0
+            # Reload async markets, then assign them to sync api
+            self._markets = retrier(self._load_async_markets, retries=retries)(reload=True)
+            self._api.set_markets(self._api_async.markets, self._api_async.currencies)
+            # Assign options array, as it contains some temporary information from the exchange.
+            self._api.options = self._api_async.options
+            if self._exchange_ws:
+                # Set markets to avoid reloading on websocket api
+                self._ws_async.set_markets(self._api.markets, self._api.currencies)
+                self._ws_async.options = self._api.options
+            self._last_markets_refresh = dt_ts()
+
+            if is_initial and self._ft_has["needs_trading_fees"]:
+                self._trading_fees = self.fetch_trading_fees()
+
+            if load_leverage_tiers and self.trading_mode == TradingMode.FUTURES:
+                self.fill_leverage_tiers()
+        except (ccxt.BaseError, TemporaryError):
+            logger.exception("Could not load markets.")
+
+    def validate_stakecurrency(self, stake_currency: str) -> None:
+        """
+        Checks stake-currency against available currencies on the exchange.
+        Only runs on startup. If markets have not been loaded, there's been a problem with
+        the connection to the exchange.
+        :param stake_currency: Stake-currency to validate
+        :raise: OperationalException if stake-currency is not available.
+        """
+        if not self._markets:
+            raise OperationalException(
+                "Could not load markets, therefore cannot start. "
+                "Please investigate the above error for more details."
+            )
+        quote_currencies = self.get_quote_currencies()
+        if stake_currency not in quote_currencies:
+            raise ConfigurationError(
+                f"{stake_currency} is not available as stake on {self.name}. "
+                f"Available currencies are: {', '.join(quote_currencies)}"
+            )
+
+    def get_valid_pair_combination(self, curr_1: str, curr_2: str) -> Generator[str, None, None]:
+        """
+        Get valid pair combination of curr_1 and curr_2 by trying both combinations.
+        """
+        yielded = False
+        for pair in (
+            f"{curr_1}/{curr_2}",
+            f"{curr_2}/{curr_1}",
+            f"{curr_1}/{curr_2}:{curr_2}",
+            f"{curr_2}/{curr_1}:{curr_1}",
+        ):
+            if pair in self.markets and self.markets[pair].get("active"):
+                yielded = True
+                yield pair
+        if not yielded:
+            raise ValueError(f"Could not combine {curr_1} and {curr_2} to get a valid pair.")
+
+    def validate_timeframes(self, timeframe: str | None) -> None:
+        """
+        Check if timeframe from config is a supported timeframe on the exchange
+        """
+        if not hasattr(self._api, "timeframes") or self._api.timeframes is None:
+            # If timeframes attribute is missing (or is None), the exchange probably
+            # has no fetchOHLCV method.
+            # Therefore we also show that.
+            raise OperationalException(
+                f"The ccxt library does not provide the list of timeframes "
+                f"for the exchange {self.name} and this exchange "
+                f"is therefore not supported. ccxt fetchOHLCV: {self.exchange_has('fetchOHLCV')}"
+            )
+
+        if timeframe and (timeframe not in self.timeframes):
+            raise ConfigurationError(
+                f"Invalid timeframe '{timeframe}'. This exchange supports: {self.timeframes}"
+            )
+
+        if (
+            timeframe
+            and self._config["runmode"] != RunMode.UTIL_EXCHANGE
+            and timeframe_to_minutes(timeframe) < 1
+        ):
+            raise ConfigurationError("Timeframes < 1m are currently not supported by Freqtrade.")
+
+    def validate_ordertypes(self, order_types: dict) -> None:
+        """
+        Checks if order-types configured in strategy/config are supported
+        """
+        if any(v == "market" for k, v in order_types.items()):
+            if not self.exchange_has("createMarketOrder"):
+                raise ConfigurationError(f"Exchange {self.name} does not support market orders.")
+        self.validate_stop_ordertypes(order_types)
+
+    def validate_stop_ordertypes(self, order_types: dict) -> None:
+        """
+        Validate stoploss order types
+        """
+        if order_types.get("stoploss_on_exchange") and not self._ft_has.get(
+            "stoploss_on_exchange", False
+        ):
+            raise ConfigurationError(f"On exchange stoploss is not supported for {self.name}.")
+        if self.trading_mode == TradingMode.FUTURES:
+            price_mapping = self._ft_has.get("stop_price_type_value_mapping", {}).keys()
+            if (
+                order_types.get("stoploss_on_exchange", False) is True
+                and "stoploss_price_type" in order_types
+                and order_types["stoploss_price_type"] not in price_mapping
+            ):
+                raise ConfigurationError(
+                    f"On exchange stoploss price type is not supported for {self.name}."
+                )
+
+    def validate_pricing(self, pricing: dict) -> None:
+        if pricing.get("use_order_book", False) and not self.exchange_has("fetchL2OrderBook"):
+            raise ConfigurationError(f"Orderbook not available for {self.name}.")
+        if not pricing.get("use_order_book", False) and (
+            not self.exchange_has("fetchTicker") or not self._ft_has["tickers_have_price"]
+        ):
+            raise ConfigurationError(f"Ticker pricing not available for {self.name}.")
+
+    def validate_order_time_in_force(self, order_time_in_force: dict) -> None:
+        """
+        Checks if order time in force configured in strategy/config are supported
+        """
+        if any(
+            v.upper() not in self._ft_has["order_time_in_force"]
+            for k, v in order_time_in_force.items()
+        ):
+            raise ConfigurationError(
+                f"Time in force policies are not supported for {self.name} yet."
+            )
+
+    def validate_orderflow(self, exchange: dict) -> None:
+        if exchange.get("use_public_trades", False) and (
+            not self.exchange_has("fetchTrades") or not self._ft_has["trades_has_history"]
+        ):
+            raise ConfigurationError(
+                f"Trade data not available for {self.name}. Can't use orderflow feature."
+            )
+
+    def validate_freqai(self, config: Config) -> None:
+        freqai_enabled = config.get("freqai", {}).get("enabled", False)
+        if freqai_enabled and not self._ft_has["ohlcv_has_history"]:
+            raise ConfigurationError(
+                f"Historic OHLCV data not available for {self.name}. Can't use freqAI."
+            )
+
+    def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> int:
+        """
+        Checks if required startup_candles is more than ohlcv_candle_limit().
+        Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
+        """
+
+        candle_limit = self.ohlcv_candle_limit(
+            timeframe,
+            self._config["candle_type_def"],
+            dt_ts(date_minus_candles(timeframe, startup_candles)) if timeframe else None,
+        )
+        # Require one more candle - to account for the still open candle.
+        candle_count = startup_candles + 1
+        # Allow 5 calls to the exchange per pair
+        required_candle_call_count = int(
+            (candle_count / candle_limit) + (0 if candle_count % candle_limit == 0 else 1)
+        )
+        if self._ft_has["ohlcv_has_history"]:
+            if required_candle_call_count > 5:
+                # Only allow 5 calls per pair to somewhat limit the impact
+                raise ConfigurationError(
+                    f"This strategy requires {startup_candles} candles to start, "
+                    "which is more than 5x "
+                    f"the amount of candles {self.name} provides for {timeframe}."
+                )
+        elif required_candle_call_count > 1:
+            raise ConfigurationError(
+                f"This strategy requires {startup_candles} candles to start, which is more than "
+                f"the amount of candles {self.name} provides for {timeframe}."
+            )
+        if required_candle_call_count > 1:
+            logger.warning(
+                f"Using {required_candle_call_count} calls to get OHLCV. "
+                f"This can result in slower operations for the bot. Please check "
+                f"if you really need {startup_candles} candles for your strategy"
+            )
+        return required_candle_call_count
+
+    def validate_trading_mode_and_margin_mode(
+        self,
+        trading_mode: TradingMode,
+        margin_mode: MarginMode | None,  # Only None when trading_mode = TradingMode.SPOT
+    ):
+        """
+        Checks if freqtrade can perform trades using the configured
+        trading mode(Margin, Futures) and MarginMode(Cross, Isolated)
+        Throws OperationalException:
+            If the trading_mode/margin_mode type are not supported by freqtrade on this exchange
+        """
+        if trading_mode != TradingMode.SPOT and (
+            (trading_mode, margin_mode) not in self._supported_trading_mode_margin_pairs
+        ):
+            mm_value = margin_mode and margin_mode.value
+            raise OperationalException(
+                f"Freqtrade does not support {mm_value} {trading_mode} on {self.name}"
+            )
+
+    def get_option(self, param: str, default: Any | None = None) -> Any:
+        """
+        Get parameter value from _ft_has
+        """
+        return self._ft_has.get(param, default)
+
+    def exchange_has(self, endpoint: str) -> bool:
+        """
+        Checks if exchange implements a specific API endpoint.
+        Wrapper around ccxt 'has' attribute
+        :param endpoint: Name of endpoint (e.g. 'fetchOHLCV', 'fetchTickers')
+        :return: bool
+        """
+        if endpoint in self._ft_has.get("exchange_has_overrides", {}):
+            return self._ft_has["exchange_has_overrides"][endpoint]
+        return endpoint in self._api_async.has and self._api_async.has[endpoint]
+
+    def features(
+        self, market_type: Literal["spot", "futures"], endpoint, attribute, default: T
+    ) -> T:
+        """
+        Returns the exchange features for the given markettype
+        https://docs.ccxt.com/#/README?id=features
+        attributes are in a nested dict, with spot and swap.linear
+        e.g. spot.fetchOHLCV.limit
+             swap.linear.fetchOHLCV.limit
+        """
+        feat = (
+            self._api_async.features.get("spot", {})
+            if market_type == "spot"
+            else self._api_async.features.get("swap", {}).get("linear", {})
+        )
+
+        return feat.get(endpoint, {}).get(attribute, default)
+
+    def get_precision_amount(self, pair: str) -> float | None:
+        """
+        Returns the amount precision of the exchange.
+        :param pair: Pair to get precision for
+        :return: precision for amount or None. Must be used in combination with precisionMode
+        """
+        return self.markets.get(pair, {}).get("precision", {}).get("amount", None)
+
+    def get_precision_price(self, pair: str) -> float | None:
+        """
+        Returns the price precision of the exchange.
+        :param pair: Pair to get precision for
+        :return: precision for price or None. Must be used in combination with precisionMode
+        """
+        return self.markets.get(pair, {}).get("precision", {}).get("price", None)
+
+    def amount_to_precision(self, pair: str, amount: float) -> float:
+        """
+        Returns the amount to buy or sell to a precision the Exchange accepts
+
+        """
+        return amount_to_precision(amount, self.get_precision_amount(pair), self.precisionMode)
+
+    def price_to_precision(self, pair: str, price: float, *, rounding_mode: int = ROUND) -> float:
+        """
+        Returns the price rounded to the precision the Exchange accepts.
+        The default price_rounding_mode in conf is ROUND.
+        For stoploss calculations, must use ROUND_UP for longs, and ROUND_DOWN for shorts.
+        """
+        return price_to_precision(
+            price,
+            self.get_precision_price(pair),
+            self.precision_mode_price,
+            rounding_mode=rounding_mode,
+        )
+
+    def price_get_one_pip(self, pair: str, price: float) -> float:
+        """
+        Gets the "1 pip" value for this pair.
+        Used in PriceFilter to calculate the 1pip movements.
+        """
+        precision = self.markets[pair]["precision"]["price"]
+        if self.precisionMode == TICK_SIZE:
+            return precision
+        else:
+            return 1 / pow(10, precision)
+
+    def get_min_pair_stake_amount(
+        self, pair: str, price: float, stoploss: float, leverage: float | None = 1.0
+    ) -> float | None:
+        return self._get_stake_amount_limit(pair, price, stoploss, "min", leverage)
+
+    def get_max_pair_stake_amount(self, pair: str, price: float, leverage: float = 1.0) -> float:
+        max_stake_amount = self._get_stake_amount_limit(pair, price, 0.0, "max", leverage)
+        if max_stake_amount is None:
+            # * Should never be executed
+            raise OperationalException(
+                f"{self.name}.get_max_pair_stake_amount should never set max_stake_amount to None"
+            )
+        return max_stake_amount
+
+    def _get_stake_amount_limit(
+        self,
+        pair: str,
+        price: float,
+        stoploss: float,
+        limit: Literal["min", "max"],
+        leverage: float | None = 1.0,
+    ) -> float | None:
+        isMin = limit == "min"
+
+        try:
+            market = self.markets[pair]
+        except KeyError:
+            raise ValueError(f"Can't get market information for symbol {pair}")
+
+        if isMin:
+            # reserve some percent defined in config (5% default) + stoploss
+            margin_reserve: float = 1.0 + self._config.get(
+                "amount_reserve_percent", DEFAULT_AMOUNT_RESERVE_PERCENT
+            )
+            stoploss_reserve = margin_reserve / (1 - abs(stoploss)) if abs(stoploss) != 1 else 1.5
+            # it should not be more than 50%
+            stoploss_reserve = max(min(stoploss_reserve, 1.5), 1)
+        else:
+            margin_reserve = 1.0
+            stoploss_reserve = 1.0
+
+        stake_limits = []
+        limits = market["limits"]
+        if limits["cost"][limit] is not None:
+            stake_limits.append(
+                self._contracts_to_amount(pair, limits["cost"][limit]) * stoploss_reserve
+            )
+
+        if limits["amount"][limit] is not None:
+            stake_limits.append(
+                self._contracts_to_amount(pair, limits["amount"][limit]) * price * margin_reserve
+            )
+
+        if not stake_limits:
+            return None if isMin else float("inf")
+
+        # The value returned should satisfy both limits: for amount (base currency) and
+        # for cost (quote, stake currency), so max() is used here.
+        # See also #2575 at github.
+        return self._get_stake_amount_considering_leverage(
+            max(stake_limits) if isMin else min(stake_limits), leverage or 1.0
+        )
+
+    def _get_stake_amount_considering_leverage(self, stake_amount: float, leverage: float) -> float:
+        """
+        Takes the minimum stake amount for a pair with no leverage and returns the minimum
+        stake amount when leverage is considered
+        :param stake_amount: The stake amount for a pair before leverage is considered
+        :param leverage: The amount of leverage being used on the current trade
+        """
+        return stake_amount / leverage
+
+    # Dry-run methods
+
+    def create_dry_run_order(
+        self,
+        pair: str,
+        ordertype: str,
+        side: BuySell,
+        amount: float,
+        rate: float,
+        leverage: float,
+        params: dict | None = None,
+        stop_loss: bool = False,
+    ) -> CcxtOrder:
+        now = dt_now()
+        order_id = f"dry_run_{side}_{pair}_{now.timestamp()}"
+        # Rounding here must respect to contract sizes
+        _amount = self._contracts_to_amount(
+            pair, self.amount_to_precision(pair, self._amount_to_contracts(pair, amount))
+        )
+        dry_order: CcxtOrder = {
+            "id": order_id,
+            "symbol": pair,
+            "price": rate,
+            "average": rate,
+            "amount": _amount,
+            "cost": _amount * rate,
+            "type": ordertype,
+            "side": side,
+            "filled": 0,
+            "remaining": _amount,
+            "datetime": now.strftime("%Y-%m-%dT%H:%M:%S.%fZ"),
+            "timestamp": dt_ts(now),
+            "status": "open",
+            "fee": None,
+            "info": {},
+        }
+        if stop_loss:
+            dry_order["info"] = {"stopPrice": dry_order["price"]}
+            dry_order[self._ft_has["stop_price_prop"]] = dry_order["price"]
+            # Workaround to avoid filling stoploss orders immediately
+            dry_order["ft_order_type"] = "stoploss"
+        orderbook: OrderBook | None = None
+        if self.exchange_has("fetchL2OrderBook"):
+            orderbook = self.fetch_l2_order_book(pair, 20)
+        if ordertype == "limit" and orderbook:
+            # Allow a 1% price difference
+            allowed_diff = 0.01
+            if self._dry_is_price_crossed(pair, side, rate, orderbook, allowed_diff):
+                logger.info(
+                    f"Converted order {pair} to market order due to price {rate} crossing spread "
+                    f"by more than {allowed_diff:.2%}."
+                )
+                dry_order["type"] = "market"
+
+        if dry_order["type"] == "market" and not dry_order.get("ft_order_type"):
+            # Update market order pricing
+            average = self.get_dry_market_fill_price(pair, side, amount, rate, orderbook)
+            dry_order.update(
+                {
+                    "average": average,
+                    "filled": _amount,
+                    "remaining": 0.0,
+                    "status": "closed",
+                    "cost": (_amount * average),
+                }
+            )
+            # market orders will always incurr taker fees
+            dry_order = self.add_dry_order_fee(pair, dry_order, "taker")
+
+        dry_order = self.check_dry_limit_order_filled(
+            dry_order, immediate=True, orderbook=orderbook
+        )
+
+        self._dry_run_open_orders[dry_order["id"]] = dry_order
+        # Copy order and close it - so the returned order is open unless it's a market order
+        return dry_order
+
+    def add_dry_order_fee(
+        self,
+        pair: str,
+        dry_order: CcxtOrder,
+        taker_or_maker: MakerTaker,
+    ) -> CcxtOrder:
+        fee = self.get_fee(pair, taker_or_maker=taker_or_maker)
+        dry_order.update(
+            {
+                "fee": {
+                    "currency": self.get_pair_quote_currency(pair),
+                    "cost": dry_order["cost"] * fee,
+                    "rate": fee,
+                }
+            }
+        )
+        return dry_order
+
+    def get_dry_market_fill_price(
+        self, pair: str, side: str, amount: float, rate: float, orderbook: OrderBook | None
+    ) -> float:
+        """
+        Get the market order fill price based on orderbook interpolation
+        """
+        if self.exchange_has("fetchL2OrderBook"):
+            if not orderbook:
+                orderbook = self.fetch_l2_order_book(pair, 20)
+            ob_type: OBLiteral = "asks" if side == "buy" else "bids"
+            slippage = 0.05
+            max_slippage_val = rate * ((1 + slippage) if side == "buy" else (1 - slippage))
+
+            remaining_amount = amount
+            filled_value = 0.0
+            book_entry_price = 0.0
+            for book_entry in orderbook[ob_type]:
+                book_entry_price = book_entry[0]
+                book_entry_coin_volume = book_entry[1]
+                if remaining_amount > 0:
+                    if remaining_amount < book_entry_coin_volume:
+                        # Orderbook at this slot bigger than remaining amount
+                        filled_value += remaining_amount * book_entry_price
+                        break
+                    else:
+                        filled_value += book_entry_coin_volume * book_entry_price
+                    remaining_amount -= book_entry_coin_volume
+                else:
+                    break
+            else:
+                # If remaining_amount wasn't consumed completely (break was not called)
+                filled_value += remaining_amount * book_entry_price
+            forecast_avg_filled_price = max(filled_value, 0) / amount
+            # Limit max. slippage to specified value
+            if side == "buy":
+                forecast_avg_filled_price = min(forecast_avg_filled_price, max_slippage_val)
+
+            else:
+                forecast_avg_filled_price = max(forecast_avg_filled_price, max_slippage_val)
+
+            return self.price_to_precision(pair, forecast_avg_filled_price)
+
+        return rate
+
+    def _dry_is_price_crossed(
+        self,
+        pair: str,
+        side: str,
+        limit: float,
+        orderbook: OrderBook | None = None,
+        offset: float = 0.0,
+    ) -> bool:
+        if not self.exchange_has("fetchL2OrderBook"):
+            return True
+        if not orderbook:
+            orderbook = self.fetch_l2_order_book(pair, 1)
+        try:
+            if side == "buy":
+                price = orderbook["asks"][0][0]
+                if limit * (1 - offset) >= price:
+                    return True
+            else:
+                price = orderbook["bids"][0][0]
+                if limit * (1 + offset) <= price:
+                    return True
+        except IndexError:
+            # Ignore empty orderbooks when filling - can be filled with the next iteration.
+            pass
+        return False
+
+    def check_dry_limit_order_filled(
+        self, order: CcxtOrder, immediate: bool = False, orderbook: OrderBook | None = None
+    ) -> CcxtOrder:
+        """
+        Check dry-run limit order fill and update fee (if it filled).
+        """
+        if (
+            order["status"] != "closed"
+            and order["type"] in ["limit"]
+            and not order.get("ft_order_type")
+        ):
+            pair = order["symbol"]
+            if self._dry_is_price_crossed(pair, order["side"], order["price"], orderbook):
+                order.update(
+                    {
+                        "status": "closed",
+                        "filled": order["amount"],
+                        "remaining": 0,
+                    }
+                )
+
+                self.add_dry_order_fee(
+                    pair,
+                    order,
+                    "taker" if immediate else "maker",
+                )
+
+        return order
+
+    def fetch_dry_run_order(self, order_id) -> CcxtOrder:
+        """
+        Return dry-run order
+        Only call if running in dry-run mode.
+        """
+        try:
+            order = self._dry_run_open_orders[order_id]
+            order = self.check_dry_limit_order_filled(order)
+            return order
+        except KeyError as e:
+            from freqtrade.persistence import Order
+
+            order = Order.order_by_id(order_id)
+            if order:
+                ccxt_order = order.to_ccxt_object(self._ft_has["stop_price_prop"])
+                self._dry_run_open_orders[order_id] = ccxt_order
+                return ccxt_order
+            # Gracefully handle errors with dry-run orders.
+            raise InvalidOrderException(
+                f"Tried to get an invalid dry-run-order (id: {order_id}). Message: {e}"
+            ) from e
+
+    # Order handling
+
+    def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
+        if self.trading_mode != TradingMode.SPOT:
+            self.set_margin_mode(pair, self.margin_mode, accept_fail)
+            self._set_leverage(leverage, pair, accept_fail)
+
+    def _get_params(
+        self,
+        side: BuySell,
+        ordertype: str,
+        leverage: float,
+        reduceOnly: bool,
+        time_in_force: str = "GTC",
+    ) -> dict:
+        params = self._params.copy()
+        if time_in_force != "GTC" and ordertype != "market":
+            params.update({"timeInForce": time_in_force.upper()})
+        if reduceOnly:
+            params.update({"reduceOnly": True})
+        return params
+
+    def _order_needs_price(self, side: BuySell, ordertype: str) -> bool:
+        return (
+            ordertype != "market"
+            or (side == "buy" and self._api.options.get("createMarketBuyOrderRequiresPrice", False))
+            or self._ft_has.get("marketOrderRequiresPrice", False)
+        )
+
+    def create_order(
+        self,
+        *,
+        pair: str,
+        ordertype: str,
+        side: BuySell,
+        amount: float,
+        rate: float,
+        leverage: float,
+        reduceOnly: bool = False,
+        time_in_force: str = "GTC",
+    ) -> CcxtOrder:
+        if self._config["dry_run"]:
+            dry_order = self.create_dry_run_order(
+                pair, ordertype, side, amount, self.price_to_precision(pair, rate), leverage
+            )
+            return dry_order
+
+        params = self._get_params(side, ordertype, leverage, reduceOnly, time_in_force)
+
+        try:
+            # Set the precision for amount and price(rate) as accepted by the exchange
+            amount = self.amount_to_precision(pair, self._amount_to_contracts(pair, amount))
+            needs_price = self._order_needs_price(side, ordertype)
+            rate_for_order = self.price_to_precision(pair, rate) if needs_price else None
+
+            if not reduceOnly:
+                self._lev_prep(pair, leverage, side)
+
+            order = self._api.create_order(
+                pair,
+                ordertype,
+                side,
+                amount,
+                rate_for_order,
+                params,
+            )
+            if order.get("status") is None:
+                # Map empty status to open.
+                order["status"] = "open"
+
+            if order.get("type") is None:
+                order["type"] = ordertype
+
+            self._log_exchange_response("create_order", order)
+            order = self._order_contracts_to_amount(order)
+            return order
+
+        except ccxt.InsufficientFunds as e:
+            raise InsufficientFundsError(
+                f"Insufficient funds to create {ordertype} {side} order on market {pair}. "
+                f"Tried to {side} amount {amount} at rate {rate}."
+                f"Message: {e}"
+            ) from e
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(
+                f"Could not create {ordertype} {side} order on market {pair}. "
+                f"Tried to {side} amount {amount} at rate {rate}. "
+                f"Message: {e}"
+            ) from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not place {side} order due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def stoploss_adjust(self, stop_loss: float, order: CcxtOrder, side: str) -> bool:
+        """
+        Verify stop_loss against stoploss-order value (limit or price)
+        Returns True if adjustment is necessary.
+        """
+        if not self._ft_has.get("stoploss_on_exchange"):
+            raise OperationalException(f"stoploss is not implemented for {self.name}.")
+        price_param = self._ft_has["stop_price_prop"]
+        return order.get(price_param, None) is None or (
+            (side == "sell" and stop_loss > float(order[price_param]))
+            or (side == "buy" and stop_loss < float(order[price_param]))
+        )
+
+    def _get_stop_order_type(self, user_order_type) -> tuple[str, str]:
+        available_order_Types: dict[str, str] = self._ft_has["stoploss_order_types"]
+
+        if user_order_type in available_order_Types.keys():
+            ordertype = available_order_Types[user_order_type]
+        else:
+            # Otherwise pick only one available
+            ordertype = next(iter(available_order_Types.values()))
+            user_order_type = next(iter(available_order_Types.keys()))
+        return ordertype, user_order_type
+
+    def _get_stop_limit_rate(self, stop_price: float, order_types: dict, side: str) -> float:
+        # Limit price threshold: As limit price should always be below stop-price
+        limit_price_pct = order_types.get("stoploss_on_exchange_limit_ratio", 0.99)
+        if side == "sell":
+            limit_rate = stop_price * limit_price_pct
+        else:
+            limit_rate = stop_price * (2 - limit_price_pct)
+
+        bad_stop_price = (stop_price < limit_rate) if side == "sell" else (stop_price > limit_rate)
+        # Ensure rate is less than stop price
+        if bad_stop_price:
+            # This can for example happen if the stop / liquidation price is set to 0
+            # Which is possible if a market-order closes right away.
+            # The InvalidOrderException will bubble up to exit_positions, where it will be
+            # handled gracefully.
+            raise InvalidOrderException(
+                "In stoploss limit order, stop price should be more than limit price. "
+                f"Stop price: {stop_price}, Limit price: {limit_rate}, "
+                f"Limit Price pct: {limit_price_pct}"
+            )
+        return limit_rate
+
+    def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> dict:
+        params = self._params.copy()
+        # Verify if stopPrice works for your exchange, else configure stop_price_param
+        params.update({self._ft_has["stop_price_param"]: stop_price})
+        return params
+
+    @retrier(retries=0)
+    def create_stoploss(
+        self,
+        pair: str,
+        amount: float,
+        stop_price: float,
+        order_types: dict,
+        side: BuySell,
+        leverage: float,
+    ) -> CcxtOrder:
+        """
+        creates a stoploss order.
+        requires `_ft_has['stoploss_order_types']` to be set as a dict mapping limit and market
+            to the corresponding exchange type.
+
+        The precise ordertype is determined by the order_types dict or exchange default.
+
+        The exception below should never raise, since we disallow
+        starting the bot in validate_ordertypes()
+
+        This may work with a limited number of other exchanges, but correct working
+            needs to be tested individually.
+        WARNING: setting `stoploss_on_exchange` to True will NOT auto-enable stoploss on exchange.
+            `stoploss_adjust` must still be implemented for this to work.
+        """
+        if not self._ft_has["stoploss_on_exchange"]:
+            raise OperationalException(f"stoploss is not implemented for {self.name}.")
+
+        user_order_type = order_types.get("stoploss", "market")
+        ordertype, user_order_type = self._get_stop_order_type(user_order_type)
+        round_mode = ROUND_DOWN if side == "buy" else ROUND_UP
+        stop_price_norm = self.price_to_precision(pair, stop_price, rounding_mode=round_mode)
+        limit_rate = None
+        if user_order_type == "limit":
+            limit_rate = self._get_stop_limit_rate(stop_price, order_types, side)
+            limit_rate = self.price_to_precision(pair, limit_rate, rounding_mode=round_mode)
+
+        if self._config["dry_run"]:
+            dry_order = self.create_dry_run_order(
+                pair,
+                ordertype,
+                side,
+                amount,
+                stop_price_norm,
+                stop_loss=True,
+                leverage=leverage,
+            )
+            return dry_order
+
+        try:
+            params = self._get_stop_params(
+                side=side, ordertype=ordertype, stop_price=stop_price_norm
+            )
+            if self.trading_mode == TradingMode.FUTURES:
+                params["reduceOnly"] = True
+                if "stoploss_price_type" in order_types and "stop_price_type_field" in self._ft_has:
+                    price_type = self._ft_has["stop_price_type_value_mapping"][
+                        order_types.get("stoploss_price_type", PriceType.LAST)
+                    ]
+                    params[self._ft_has["stop_price_type_field"]] = price_type
+
+            amount = self.amount_to_precision(pair, self._amount_to_contracts(pair, amount))
+
+            self._lev_prep(pair, leverage, side, accept_fail=True)
+            order = self._api.create_order(
+                symbol=pair,
+                type=ordertype,
+                side=side,
+                amount=amount,
+                price=limit_rate,
+                params=params,
+            )
+            self._log_exchange_response("create_stoploss_order", order)
+            order = self._order_contracts_to_amount(order)
+            logger.info(
+                f"stoploss {user_order_type} order added for {pair}. "
+                f"stop price: {stop_price}. limit: {limit_rate}"
+            )
+            return order
+        except ccxt.InsufficientFunds as e:
+            raise InsufficientFundsError(
+                f"Insufficient funds to create {ordertype} {side} order on market {pair}. "
+                f"Tried to {side} amount {amount} at rate {limit_rate} with "
+                f"stop-price {stop_price_norm}. Message: {e}"
+            ) from e
+        except (ccxt.InvalidOrder, ccxt.BadRequest, ccxt.OperationRejected) as e:
+            # Errors:
+            # `Order would trigger immediately.`
+            raise InvalidOrderException(
+                f"Could not create {ordertype} {side} order on market {pair}. "
+                f"Tried to {side} amount {amount} at rate {limit_rate} with "
+                f"stop-price {stop_price_norm}. Message: {e}"
+            ) from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not place stoploss order due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def fetch_order_emulated(self, order_id: str, pair: str, params: dict) -> CcxtOrder:
+        """
+        Emulated fetch_order if the exchange doesn't support fetch_order, but requires separate
+        calls for open and closed orders.
+        """
+        try:
+            order = self._api.fetch_open_order(order_id, pair, params=params)
+            self._log_exchange_response("fetch_open_order", order)
+            order = self._order_contracts_to_amount(order)
+            return order
+        except ccxt.OrderNotFound:
+            try:
+                order = self._api.fetch_closed_order(order_id, pair, params=params)
+                self._log_exchange_response("fetch_closed_order", order)
+                order = self._order_contracts_to_amount(order)
+                return order
+            except ccxt.OrderNotFound as e:
+                raise RetryableOrderError(
+                    f"Order not found (pair: {pair} id: {order_id}). Message: {e}"
+                ) from e
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(
+                f"Tried to get an invalid order (pair: {pair} id: {order_id}). Message: {e}"
+            ) from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get order due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
+    def fetch_order(self, order_id: str, pair: str, params: dict | None = None) -> CcxtOrder:
+        if self._config["dry_run"]:
+            return self.fetch_dry_run_order(order_id)
+        if params is None:
+            params = {}
+        try:
+            if not self.exchange_has("fetchOrder"):
+                return self.fetch_order_emulated(order_id, pair, params)
+            order = self._api.fetch_order(order_id, pair, params=params)
+            self._log_exchange_response("fetch_order", order)
+            order = self._order_contracts_to_amount(order)
+            return order
+        except ccxt.OrderNotFound as e:
+            raise RetryableOrderError(
+                f"Order not found (pair: {pair} id: {order_id}). Message: {e}"
+            ) from e
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(
+                f"Tried to get an invalid order (pair: {pair} id: {order_id}). Message: {e}"
+            ) from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get order due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def fetch_stoploss_order(
+        self, order_id: str, pair: str, params: dict | None = None
+    ) -> CcxtOrder:
+        return self.fetch_order(order_id, pair, params)
+
+    def fetch_order_or_stoploss_order(
+        self, order_id: str, pair: str, stoploss_order: bool = False
+    ) -> CcxtOrder:
+        """
+        Simple wrapper calling either fetch_order or fetch_stoploss_order depending on
+        the stoploss_order parameter
+        :param order_id: OrderId to fetch order
+        :param pair: Pair corresponding to order_id
+        :param stoploss_order: If true, uses fetch_stoploss_order, otherwise fetch_order.
+        """
+        if stoploss_order:
+            return self.fetch_stoploss_order(order_id, pair)
+        return self.fetch_order(order_id, pair)
+
+    def check_order_canceled_empty(self, order: CcxtOrder) -> bool:
+        """
+        Verify if an order has been cancelled without being partially filled
+        :param order: Order dict as returned from fetch_order()
+        :return: True if order has been cancelled without being filled, False otherwise.
+        """
+        return order.get("status") in NON_OPEN_EXCHANGE_STATES and order.get("filled") == 0.0
+
+    @retrier
+    def cancel_order(self, order_id: str, pair: str, params: dict | None = None) -> dict[str, Any]:
+        if self._config["dry_run"]:
+            try:
+                order = self.fetch_dry_run_order(order_id)
+
+                order.update({"status": "canceled", "filled": 0.0, "remaining": order["amount"]})
+                return order
+            except InvalidOrderException:
+                return {}
+
+        if params is None:
+            params = {}
+        try:
+            order = self._api.cancel_order(order_id, pair, params=params)
+            self._log_exchange_response("cancel_order", order)
+            order = self._order_contracts_to_amount(order)
+            return order
+        except ccxt.InvalidOrder as e:
+            raise InvalidOrderException(f"Could not cancel order. Message: {e}") from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not cancel order due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def cancel_stoploss_order(self, order_id: str, pair: str, params: dict | None = None) -> dict:
+        return self.cancel_order(order_id, pair, params)
+
+    def is_cancel_order_result_suitable(self, corder) -> TypeGuard[CcxtOrder]:
+        if not isinstance(corder, dict):
+            return False
+
+        required = ("fee", "status", "amount")
+        return all(corder.get(k, None) is not None for k in required)
+
+    def cancel_order_with_result(self, order_id: str, pair: str, amount: float) -> CcxtOrder:
+        """
+        Cancel order returning a result.
+        Creates a fake result if cancel order returns a non-usable result
+        and fetch_order does not work (certain exchanges don't return cancelled orders)
+        :param order_id: Orderid to cancel
+        :param pair: Pair corresponding to order_id
+        :param amount: Amount to use for fake response
+        :return: Result from either cancel_order if usable, or fetch_order
+        """
+        try:
+            corder = self.cancel_order(order_id, pair)
+            if self.is_cancel_order_result_suitable(corder):
+                return corder
+        except InvalidOrderException:
+            logger.warning(f"Could not cancel order {order_id} for {pair}.")
+        try:
+            order = self.fetch_order(order_id, pair)
+        except InvalidOrderException:
+            logger.warning(f"Could not fetch cancelled order {order_id}.")
+            order = {
+                "id": order_id,
+                "status": "canceled",
+                "amount": amount,
+                "filled": 0.0,
+                "fee": {},
+                "info": {},
+            }
+
+        return order
+
+    def cancel_stoploss_order_with_result(
+        self, order_id: str, pair: str, amount: float
+    ) -> CcxtOrder:
+        """
+        Cancel stoploss order returning a result.
+        Creates a fake result if cancel order returns a non-usable result
+        and fetch_order does not work (certain exchanges don't return cancelled orders)
+        :param order_id: stoploss-order-id to cancel
+        :param pair: Pair corresponding to order_id
+        :param amount: Amount to use for fake response
+        :return: Result from either cancel_order if usable, or fetch_order
+        """
+        corder = self.cancel_stoploss_order(order_id, pair)
+        if self.is_cancel_order_result_suitable(corder):
+            return corder
+        try:
+            order = self.fetch_stoploss_order(order_id, pair)
+        except InvalidOrderException:
+            logger.warning(f"Could not fetch cancelled stoploss order {order_id}.")
+            order = {"id": order_id, "fee": {}, "status": "canceled", "amount": amount, "info": {}}
+
+        return order
+
+    @retrier
+    def get_balances(self) -> CcxtBalances:
+        try:
+            balances = self._api.fetch_balance()
+            # Remove additional info from ccxt results
+            balances.pop("info", None)
+            balances.pop("free", None)
+            balances.pop("total", None)
+            balances.pop("used", None)
+
+            self._log_exchange_response("fetch_balances", balances)
+            return balances
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get balance due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @retrier
+    def fetch_positions(self, pair: str | None = None) -> list[CcxtPosition]:
+        """
+        Fetch positions from the exchange.
+        If no pair is given, all positions are returned.
+        :param pair: Pair for the query
+        """
+        if self._config["dry_run"] or self.trading_mode != TradingMode.FUTURES:
+            return []
+        try:
+            symbols = []
+            if pair:
+                symbols.append(pair)
+            positions: list[CcxtPosition] = self._api.fetch_positions(symbols)
+            self._log_exchange_response("fetch_positions", positions)
+            return positions
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get positions due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def _fetch_orders_emulate(self, pair: str, since_ms: int) -> list[CcxtOrder]:
+        orders = []
+        if self.exchange_has("fetchClosedOrders"):
+            orders = self._api.fetch_closed_orders(pair, since=since_ms)
+            if self.exchange_has("fetchOpenOrders"):
+                orders_open = self._api.fetch_open_orders(pair, since=since_ms)
+                orders.extend(orders_open)
+        return orders
+
+    @retrier(retries=0)
+    def fetch_orders(
+        self, pair: str, since: datetime, params: dict | None = None
+    ) -> list[CcxtOrder]:
+        """
+        Fetch all orders for a pair "since"
+        :param pair: Pair for the query
+        :param since: Starting time for the query
+        """
+        if self._config["dry_run"]:
+            return []
+
+        try:
+            since_ms = int((since.timestamp() - 10) * 1000)
+
+            if self.exchange_has("fetchOrders"):
+                if not params:
+                    params = {}
+                try:
+                    orders: list[CcxtOrder] = self._api.fetch_orders(
+                        pair, since=since_ms, params=params
+                    )
+                except ccxt.NotSupported:
+                    # Some exchanges don't support fetchOrders
+                    # attempt to fetch open and closed orders separately
+                    orders = self._fetch_orders_emulate(pair, since_ms)
+            else:
+                orders = self._fetch_orders_emulate(pair, since_ms)
+            self._log_exchange_response("fetch_orders", orders)
+            orders = [self._order_contracts_to_amount(o) for o in orders]
+            return orders
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not fetch positions due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @retrier
+    def fetch_trading_fees(self) -> dict[str, Any]:
+        """
+        Fetch user account trading fees
+        Can be cached, should not update often.
+        """
+        if (
+            self._config["dry_run"]
+            or self.trading_mode != TradingMode.FUTURES
+            or not self.exchange_has("fetchTradingFees")
+        ):
+            return {}
+        try:
+            trading_fees: dict[str, Any] = self._api.fetch_trading_fees()
+            self._log_exchange_response("fetch_trading_fees", trading_fees)
+            return trading_fees
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not fetch trading fees due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @retrier
+    def fetch_bids_asks(self, symbols: list[str] | None = None, *, cached: bool = False) -> dict:
+        """
+        :param symbols: List of symbols to fetch
+        :param cached: Allow cached result
+        :return: fetch_bids_asks result
+        """
+        if not self.exchange_has("fetchBidsAsks"):
+            return {}
+        if cached:
+            with self._cache_lock:
+                tickers = self._fetch_tickers_cache.get("fetch_bids_asks")
+            if tickers:
+                return tickers
+        try:
+            tickers = self._api.fetch_bids_asks(symbols)
+            with self._cache_lock:
+                self._fetch_tickers_cache["fetch_bids_asks"] = tickers
+            return tickers
+        except ccxt.NotSupported as e:
+            raise OperationalException(
+                f"Exchange {self._api.name} does not support fetching bids/asks in batch. "
+                f"Message: {e}"
+            ) from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not load bids/asks due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @retrier
+    def get_tickers(
+        self,
+        symbols: list[str] | None = None,
+        *,
+        cached: bool = False,
+        market_type: TradingMode | None = None,
+    ) -> Tickers:
+        """
+        :param symbols: List of symbols to fetch
+        :param cached: Allow cached result
+        :param market_type: Market type to fetch - either spot or futures.
+        :return: fetch_tickers result
+        """
+        tickers: Tickers
+        if not self.exchange_has("fetchTickers"):
+            return {}
+        cache_key = f"fetch_tickers_{market_type}" if market_type else "fetch_tickers"
+        if cached:
+            with self._cache_lock:
+                tickers = self._fetch_tickers_cache.get(cache_key)  # type: ignore
+            if tickers:
+                return tickers
+        try:
+            # Re-map futures to swap
+            market_types = {
+                TradingMode.FUTURES: "swap",
+            }
+            params = {"type": market_types.get(market_type, market_type)} if market_type else {}
+            tickers = self._api.fetch_tickers(symbols, params)
+            with self._cache_lock:
+                self._fetch_tickers_cache[cache_key] = tickers
+            return tickers
+        except ccxt.NotSupported as e:
+            raise OperationalException(
+                f"Exchange {self._api.name} does not support fetching tickers in batch. "
+                f"Message: {e}"
+            ) from e
+        except ccxt.BadSymbol as e:
+            logger.warning(
+                f"Could not load tickers due to {e.__class__.__name__}. Message: {e} ."
+                "Reloading markets."
+            )
+            self.reload_markets(True)
+            # Re-raise exception to repeat the call.
+            raise TemporaryError from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not load tickers due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def get_proxy_coin(self) -> str:
+        """
+        Get the proxy coin for the given coin
+        Falls back to the stake currency if no proxy coin is found
+        :return: Proxy coin or stake currency
+        """
+        return self._config["stake_currency"]
+
+    def get_conversion_rate(self, coin: str, currency: str) -> float | None:
+        """
+        Quick and cached way to get conversion rate one currency to the other.
+        Can then be used as "rate * amount" to convert between currencies.
+        :param coin: Coin to convert
+        :param currency: Currency to convert to
+        :returns: Conversion rate from coin to currency
+        :raises: ExchangeErrors
+        """
+
+        if (proxy_coin := self._ft_has["proxy_coin_mapping"].get(coin, None)) is not None:
+            coin = proxy_coin
+        if (proxy_currency := self._ft_has["proxy_coin_mapping"].get(currency, None)) is not None:
+            currency = proxy_currency
+        if coin == currency:
+            return 1.0
+        tickers = self.get_tickers(cached=True)
+        try:
+            for pair in self.get_valid_pair_combination(coin, currency):
+                ticker: Ticker | None = tickers.get(pair, None)
+                if not ticker:
+                    tickers_other: Tickers = self.get_tickers(
+                        cached=True,
+                        market_type=(
+                            TradingMode.SPOT
+                            if self.trading_mode != TradingMode.SPOT
+                            else TradingMode.FUTURES
+                        ),
+                    )
+                    ticker = tickers_other.get(pair, None)
+                if ticker:
+                    rate: float | None = safe_value_fallback2(ticker, ticker, "last", "ask", None)
+                    if rate and pair.startswith(currency) and not pair.endswith(currency):
+                        rate = 1.0 / rate
+                    return rate
+        except ValueError:
+            return None
+        return None
+
+    @retrier
+    def fetch_ticker(self, pair: str) -> Ticker:
+        try:
+            if pair not in self.markets or self.markets[pair].get("active", False) is False:
+                raise ExchangeError(f"Pair {pair} not available")
+            data: Ticker = self._api.fetch_ticker(pair)
+            return data
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not load ticker due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @staticmethod
+    def get_next_limit_in_list(
+        limit: int,
+        limit_range: list[int] | None,
+        range_required: bool = True,
+        upper_limit: int | None = None,
+    ):
+        """
+        Get next greater value in the list.
+        Used by fetch_l2_order_book if the api only supports a limited range
+        if both limit_range and upper_limit is provided, limit_range wins.
+        """
+        if not limit_range:
+            return min(limit, upper_limit) if upper_limit else limit
+
+        result = min([x for x in limit_range if limit <= x] + [max(limit_range)])
+        if not range_required and limit > result:
+            # Range is not required - we can use None as parameter.
+            return None
+        return result
+
+    @retrier
+    def fetch_l2_order_book(self, pair: str, limit: int = 100) -> OrderBook:
+        """
+        Get L2 order book from exchange.
+        Can be limited to a certain amount (if supported).
+        Returns a dict in the format
+        {'asks': [price, volume], 'bids': [price, volume]}
+        """
+        limit1 = self.get_next_limit_in_list(
+            limit,
+            self._ft_has["l2_limit_range"],
+            self._ft_has["l2_limit_range_required"],
+            self._ft_has["l2_limit_upper"],
+        )
+        try:
+            return self._api.fetch_l2_order_book(pair, limit1)
+        except ccxt.NotSupported as e:
+            raise OperationalException(
+                f"Exchange {self._api.name} does not support fetching order book. Message: {e}"
+            ) from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get order book due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def _get_price_side(self, side: str, is_short: bool, conf_strategy: dict) -> BidAsk:
+        price_side = conf_strategy["price_side"]
+
+        if price_side in ("same", "other"):
+            price_map = {
+                ("entry", "long", "same"): "bid",
+                ("entry", "long", "other"): "ask",
+                ("entry", "short", "same"): "ask",
+                ("entry", "short", "other"): "bid",
+                ("exit", "long", "same"): "ask",
+                ("exit", "long", "other"): "bid",
+                ("exit", "short", "same"): "bid",
+                ("exit", "short", "other"): "ask",
+            }
+            price_side = price_map[(side, "short" if is_short else "long", price_side)]
+        return price_side
+
+    def get_rate(
+        self,
+        pair: str,
+        refresh: bool,
+        side: EntryExit,
+        is_short: bool,
+        order_book: OrderBook | None = None,
+        ticker: Ticker | None = None,
+    ) -> float:
+        """
+        Calculates bid/ask target
+        bid rate - between current ask price and last price
+        ask rate - either using ticker bid or first bid based on orderbook
+        or remain static in any other case since it's not updating.
+        :param pair: Pair to get rate for
+        :param refresh: allow cached data
+        :param side: "buy" or "sell"
+        :return: float: Price
+        :raises PricingError if orderbook price could not be determined.
+        """
+        name = side.capitalize()
+        strat_name = "entry_pricing" if side == "entry" else "exit_pricing"
+
+        cache_rate: TTLCache = self._entry_rate_cache if side == "entry" else self._exit_rate_cache
+        if not refresh:
+            with self._cache_lock:
+                rate = cache_rate.get(pair)
+            # Check if cache has been invalidated
+            if rate:
+                logger.debug(f"Using cached {side} rate for {pair}.")
+                return rate
+
+        conf_strategy = self._config.get(strat_name, {})
+
+        price_side = self._get_price_side(side, is_short, conf_strategy)
+
+        if conf_strategy.get("use_order_book", False):
+            order_book_top = conf_strategy.get("order_book_top", 1)
+            if order_book is None:
+                order_book = self.fetch_l2_order_book(pair, order_book_top)
+            rate = self._get_rate_from_ob(pair, side, order_book, name, price_side, order_book_top)
+        else:
+            logger.debug(f"Using Last {price_side.capitalize()} / Last Price")
+            if ticker is None:
+                ticker = self.fetch_ticker(pair)
+            rate = self._get_rate_from_ticker(side, ticker, conf_strategy, price_side)
+
+        if rate is None:
+            raise PricingError(f"{name}-Rate for {pair} was empty.")
+        with self._cache_lock:
+            cache_rate[pair] = rate
+
+        return rate
+
+    def _get_rate_from_ticker(
+        self, side: EntryExit, ticker: Ticker, conf_strategy: dict[str, Any], price_side: BidAsk
+    ) -> float | None:
+        """
+        Get rate from ticker.
+        """
+        ticker_rate = ticker[price_side]
+        if ticker["last"] and ticker_rate:
+            if side == "entry" and ticker_rate > ticker["last"]:
+                balance = conf_strategy.get("price_last_balance", 0.0)
+                ticker_rate = ticker_rate + balance * (ticker["last"] - ticker_rate)
+            elif side == "exit" and ticker_rate < ticker["last"]:
+                balance = conf_strategy.get("price_last_balance", 0.0)
+                ticker_rate = ticker_rate - balance * (ticker_rate - ticker["last"])
+        rate = ticker_rate
+        return rate
+
+    def _get_rate_from_ob(
+        self,
+        pair: str,
+        side: EntryExit,
+        order_book: OrderBook,
+        name: str,
+        price_side: BidAsk,
+        order_book_top: int,
+    ) -> float:
+        """
+        Get rate from orderbook
+        :raises: PricingError if rate could not be determined.
+        """
+        logger.debug("order_book %s", order_book)
+        # top 1 = index 0
+        try:
+            obside: OBLiteral = "bids" if price_side == "bid" else "asks"
+            rate = order_book[obside][order_book_top - 1][0]
+        except (IndexError, KeyError) as e:
+            logger.warning(
+                f"{pair} - {name} Price at location {order_book_top} from orderbook "
+                f"could not be determined. Orderbook: {order_book}"
+            )
+            raise PricingError from e
+        logger.debug(
+            f"{pair} - {name} price from orderbook {price_side.capitalize()}"
+            f"side - top {order_book_top} order book {side} rate {rate:.8f}"
+        )
+        return rate
+
+    def get_rates(self, pair: str, refresh: bool, is_short: bool) -> tuple[float, float]:
+        entry_rate = None
+        exit_rate = None
+        if not refresh:
+            with self._cache_lock:
+                entry_rate = self._entry_rate_cache.get(pair)
+                exit_rate = self._exit_rate_cache.get(pair)
+            if entry_rate:
+                logger.debug(f"Using cached buy rate for {pair}.")
+            if exit_rate:
+                logger.debug(f"Using cached sell rate for {pair}.")
+
+        entry_pricing = self._config.get("entry_pricing", {})
+        exit_pricing = self._config.get("exit_pricing", {})
+        order_book = ticker = None
+        if not entry_rate and entry_pricing.get("use_order_book", False):
+            order_book_top = max(
+                entry_pricing.get("order_book_top", 1), exit_pricing.get("order_book_top", 1)
+            )
+            order_book = self.fetch_l2_order_book(pair, order_book_top)
+            entry_rate = self.get_rate(pair, refresh, "entry", is_short, order_book=order_book)
+        elif not entry_rate:
+            ticker = self.fetch_ticker(pair)
+            entry_rate = self.get_rate(pair, refresh, "entry", is_short, ticker=ticker)
+        if not exit_rate:
+            exit_rate = self.get_rate(
+                pair, refresh, "exit", is_short, order_book=order_book, ticker=ticker
+            )
+        return entry_rate, exit_rate
+
+    # Fee handling
+
+    @retrier
+    def get_trades_for_order(
+        self, order_id: str, pair: str, since: datetime, params: dict | None = None
+    ) -> list:
+        """
+        Fetch Orders using the "fetch_my_trades" endpoint and filter them by order-id.
+        The "since" argument passed in is coming from the database and is in UTC,
+        as timezone-native datetime object.
+        From the python documentation:
+            > Naive datetime instances are assumed to represent local time
+        Therefore, calling "since.timestamp()" will get the UTC timestamp, after applying the
+        transformation from local timezone to UTC.
+        This works for timezones UTC+ since then the result will contain trades from a few hours
+        instead of from the last 5 seconds, however fails for UTC- timezones,
+        since we're then asking for trades with a "since" argument in the future.
+
+        :param order_id order_id: Order-id as given when creating the order
+        :param pair: Pair the order is for
+        :param since: datetime object of the order creation time. Assumes object is in UTC.
+        """
+        if self._config["dry_run"]:
+            return []
+        if not self.exchange_has("fetchMyTrades"):
+            return []
+        try:
+            # Allow 5s offset to catch slight time offsets (discovered in #1185)
+            # since needs to be int in milliseconds
+            _params = params if params else {}
+            my_trades = self._api.fetch_my_trades(
+                pair,
+                int((since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000),
+                params=_params,
+            )
+            matched_trades = [trade for trade in my_trades if trade["order"] == order_id]
+
+            self._log_exchange_response("get_trades_for_order", matched_trades)
+
+            matched_trades = self._trades_contracts_to_amount(matched_trades)
+
+            return matched_trades
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get trades due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def get_order_id_conditional(self, order: CcxtOrder) -> str:
+        return order["id"]
+
+    @retrier
+    def get_fee(
+        self,
+        symbol: str,
+        order_type: str = "",
+        side: str = "",
+        amount: float = 1,
+        price: float = 1,
+        taker_or_maker: MakerTaker = "maker",
+    ) -> float:
+        """
+        Retrieve fee from exchange
+        :param symbol: Pair
+        :param order_type: Type of order (market, limit, ...)
+        :param side: Side of order (buy, sell)
+        :param amount: Amount of order
+        :param price: Price of order
+        :param taker_or_maker: 'maker' or 'taker' (ignored if "type" is provided)
+        """
+        if order_type and order_type == "market":
+            taker_or_maker = "taker"
+        try:
+            if self._config["dry_run"] and self._config.get("fee", None) is not None:
+                return self._config["fee"]
+            # validate that markets are loaded before trying to get fee
+            if self._api.markets is None or len(self._api.markets) == 0:
+                self._api.load_markets(params={})
+
+            return self._api.calculate_fee(
+                symbol=symbol,
+                type=order_type,
+                side=side,
+                amount=amount,
+                price=price,
+                takerOrMaker=taker_or_maker,
+            )["rate"]
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get fee info due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @staticmethod
+    def order_has_fee(order: CcxtOrder) -> bool:
+        """
+        Verifies if the passed in order dict has the needed keys to extract fees,
+        and that these keys (currency, cost) are not empty.
+        :param order: Order or trade (one trade) dict
+        :return: True if the fee substructure contains currency and cost, false otherwise
+        """
+        if not isinstance(order, dict):
+            return False
+        return (
+            "fee" in order
+            and order["fee"] is not None
+            and (order["fee"].keys() >= {"currency", "cost"})
+            and order["fee"]["currency"] is not None
+            and order["fee"]["cost"] is not None
+        )
+
+    def calculate_fee_rate(
+        self, fee: dict, symbol: str, cost: float, amount: float
+    ) -> float | None:
+        """
+        Calculate fee rate if it's not given by the exchange.
+        :param fee: ccxt Fee dict - must contain cost / currency / rate
+        :param symbol: Symbol of the order
+        :param cost: Total cost of the order
+        :param amount: Amount of the order
+        """
+        if fee.get("rate") is not None:
+            return fee.get("rate")
+        fee_curr = fee.get("currency")
+        if fee_curr is None:
+            return None
+        fee_cost = float(fee["cost"])
+
+        # Calculate fee based on order details
+        if fee_curr == self.get_pair_base_currency(symbol):
+            # Base currency - divide by amount
+            return round(fee_cost / amount, 8)
+        elif fee_curr == self.get_pair_quote_currency(symbol):
+            # Quote currency - divide by cost
+            return round(fee_cost / cost, 8) if cost else None
+        else:
+            # If Fee currency is a different currency
+            if not cost:
+                # If cost is None or 0.0 -> falsy, return None
+                return None
+            try:
+                fee_to_quote_rate = self.get_conversion_rate(
+                    fee_curr, self._config["stake_currency"]
+                )
+                if not fee_to_quote_rate:
+                    raise ValueError("Conversion rate not found.")
+            except (ValueError, ExchangeError):
+                fee_to_quote_rate = self._config["exchange"].get("unknown_fee_rate", None)
+                if not fee_to_quote_rate:
+                    return None
+            return round((fee_cost * fee_to_quote_rate) / cost, 8)
+
+    def extract_cost_curr_rate(
+        self, fee: dict[str, Any], symbol: str, cost: float, amount: float
+    ) -> tuple[float, str, float | None]:
+        """
+        Extract tuple of cost, currency, rate.
+        Requires order_has_fee to run first!
+        :param fee: ccxt Fee dict - must contain cost / currency / rate
+        :param symbol: Symbol of the order
+        :param cost: Total cost of the order
+        :param amount: Amount of the order
+        :return: Tuple with cost, currency, rate of the given fee dict
+        """
+        return (
+            float(fee["cost"]),
+            fee["currency"],
+            self.calculate_fee_rate(fee, symbol, cost, amount),
+        )
+
+    # Historic data
+
+    def get_historic_ohlcv(
+        self,
+        pair: str,
+        timeframe: str,
+        since_ms: int,
+        candle_type: CandleType,
+        is_new_pair: bool = False,
+        until_ms: int | None = None,
+    ) -> DataFrame:
+        """
+        Get candle history using asyncio and returns the list of candles.
+        Handles all async work for this.
+        Async over one pair, assuming we get `self.ohlcv_candle_limit()` candles per call.
+        :param pair: Pair to download
+        :param timeframe: Timeframe to get data for
+        :param since_ms: Timestamp in milliseconds to get history from
+        :param candle_type: '', mark, index, premiumIndex, or funding_rate
+        :param is_new_pair: used by binance subclass to allow "fast" new pair downloading
+        :param until_ms: Timestamp in milliseconds to get history up to
+        :return: Dataframe with candle (OHLCV) data
+        """
+        with self._loop_lock:
+            pair, _, _, data, _ = self.loop.run_until_complete(
+                self._async_get_historic_ohlcv(
+                    pair=pair,
+                    timeframe=timeframe,
+                    since_ms=since_ms,
+                    until_ms=until_ms,
+                    candle_type=candle_type,
+                    raise_=True,
+                )
+            )
+        logger.debug(f"Downloaded data for {pair} from ccxt with length {len(data)}.")
+        return ohlcv_to_dataframe(data, timeframe, pair, fill_missing=False, drop_incomplete=True)
+
+    async def _async_get_historic_ohlcv(
+        self,
+        pair: str,
+        timeframe: str,
+        since_ms: int,
+        candle_type: CandleType,
+        raise_: bool = False,
+        until_ms: int | None = None,
+    ) -> OHLCVResponse:
+        """
+        Download historic ohlcv
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+
+        one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
+            timeframe, candle_type, since_ms
+        )
+        logger.debug(
+            "one_call: %s msecs (%s)",
+            one_call,
+            dt_humanize_delta(dt_now() - timedelta(milliseconds=one_call)),
+        )
+        input_coroutines = [
+            self._async_get_candle_history(pair, timeframe, candle_type, since)
+            for since in range(since_ms, until_ms or dt_ts(), one_call)
+        ]
+
+        data: list = []
+        # Chunk requests into batches of 100 to avoid overwhelming ccxt Throttling
+        for input_coro in chunks(input_coroutines, 100):
+            results = await asyncio.gather(*input_coro, return_exceptions=True)
+            for res in results:
+                if isinstance(res, BaseException):
+                    logger.warning(f"Async code raised an exception: {repr(res)}")
+                    if raise_:
+                        raise res
+                    continue
+                else:
+                    # Deconstruct tuple if it's not an exception
+                    p, _, c, new_data, _ = res
+                    if p == pair and c == candle_type:
+                        data.extend(new_data)
+        # Sort data again after extending the result - above calls return in "async order"
+        data = sorted(data, key=lambda x: x[0])
+        return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
+
+    def _build_coroutine(
+        self,
+        pair: str,
+        timeframe: str,
+        candle_type: CandleType,
+        since_ms: int | None,
+        cache: bool,
+    ) -> Coroutine[Any, Any, OHLCVResponse]:
+        not_all_data = cache and self.required_candle_call_count > 1
+        if cache and candle_type in (CandleType.SPOT, CandleType.FUTURES):
+            if self._has_watch_ohlcv and self._exchange_ws:
+                # Subscribe to websocket
+                self._exchange_ws.schedule_ohlcv(pair, timeframe, candle_type)
+
+        if cache and (pair, timeframe, candle_type) in self._klines:
+            candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
+            min_ts = dt_ts(date_minus_candles(timeframe, candle_limit - 5))
+
+            if self._exchange_ws:
+                candle_ts = dt_ts(timeframe_to_prev_date(timeframe))
+                prev_candle_ts = dt_ts(date_minus_candles(timeframe, 1))
+                candles = self._exchange_ws.ohlcvs(pair, timeframe)
+                half_candle = int(candle_ts - (candle_ts - prev_candle_ts) * 0.5)
+                last_refresh_time = int(
+                    self._exchange_ws.klines_last_refresh.get((pair, timeframe, candle_type), 0)
+                )
+
+                if (
+                    candles
+                    and candles[-1][0] >= prev_candle_ts
+                    and last_refresh_time >= half_candle
+                ):
+                    # Usable result, candle contains the previous candle.
+                    # Also, we check if the last refresh time is no more than half the candle ago.
+                    logger.debug(f"reuse watch result for {pair}, {timeframe}, {last_refresh_time}")
+
+                    return self._exchange_ws.get_ohlcv(pair, timeframe, candle_type, candle_ts)
+                logger.info(
+                    f"Couldn't reuse watch for {pair}, {timeframe}, falling back to REST api. "
+                    f"{candle_ts < last_refresh_time}, {candle_ts}, {last_refresh_time}, "
+                    f"{format_ms_time(candle_ts)}, {format_ms_time(last_refresh_time)} "
+                )
+
+            # Check if 1 call can get us updated candles without hole in the data.
+            if min_ts < self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0):
+                # Cache can be used - do one-off call.
+                not_all_data = False
+            else:
+                # Time jump detected, evict cache
+                logger.info(
+                    f"Time jump detected. Evicting cache for {pair}, {timeframe}, {candle_type}"
+                )
+                del self._klines[(pair, timeframe, candle_type)]
+
+        if not since_ms and (self._ft_has["ohlcv_require_since"] or not_all_data):
+            # Multiple calls for one pair - to get more history
+            one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
+                timeframe, candle_type, since_ms
+            )
+            move_to = one_call * self.required_candle_call_count
+            now = timeframe_to_next_date(timeframe)
+            since_ms = dt_ts(now - timedelta(seconds=move_to // 1000))
+
+        if since_ms:
+            return self._async_get_historic_ohlcv(
+                pair, timeframe, since_ms=since_ms, raise_=True, candle_type=candle_type
+            )
+        else:
+            # One call ... "regular" refresh
+            return self._async_get_candle_history(
+                pair, timeframe, since_ms=since_ms, candle_type=candle_type
+            )
+
+    def _build_ohlcv_dl_jobs(
+        self, pair_list: ListPairsWithTimeframes, since_ms: int | None, cache: bool
+    ) -> tuple[list[Coroutine], list[PairWithTimeframe]]:
+        """
+        Build Coroutines to execute as part of refresh_latest_ohlcv
+        """
+        input_coroutines: list[Coroutine[Any, Any, OHLCVResponse]] = []
+        cached_pairs = []
+        for pair, timeframe, candle_type in set(pair_list):
+            if timeframe not in self.timeframes and candle_type in (
+                CandleType.SPOT,
+                CandleType.FUTURES,
+            ):
+                logger.warning(
+                    f"Cannot download ({pair}, {timeframe}) combination as this timeframe is "
+                    f"not available on {self.name}. Available timeframes are "
+                    f"{', '.join(self.timeframes)}."
+                )
+                continue
+
+            if (
+                (pair, timeframe, candle_type) not in self._klines
+                or not cache
+                or self._now_is_time_to_refresh(pair, timeframe, candle_type)
+            ):
+                input_coroutines.append(
+                    self._build_coroutine(pair, timeframe, candle_type, since_ms, cache)
+                )
+
+            else:
+                logger.debug(
+                    f"Using cached candle (OHLCV) data for {pair}, {timeframe}, {candle_type} ..."
+                )
+                cached_pairs.append((pair, timeframe, candle_type))
+
+        return input_coroutines, cached_pairs
+
+    def _process_ohlcv_df(
+        self,
+        pair: str,
+        timeframe: str,
+        c_type: CandleType,
+        ticks: list[list],
+        cache: bool,
+        drop_incomplete: bool,
+    ) -> DataFrame:
+        # keeping last candle time as last refreshed time of the pair
+        if ticks and cache:
+            idx = -2 if drop_incomplete and len(ticks) > 1 else -1
+            self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[idx][0]
+        # keeping parsed dataframe in cache
+        ohlcv_df = ohlcv_to_dataframe(
+            ticks, timeframe, pair=pair, fill_missing=True, drop_incomplete=drop_incomplete
+        )
+        if cache:
+            if (pair, timeframe, c_type) in self._klines:
+                old = self._klines[(pair, timeframe, c_type)]
+                # Reassign so we return the updated, combined df
+                ohlcv_df = clean_ohlcv_dataframe(
+                    concat([old, ohlcv_df], axis=0),
+                    timeframe,
+                    pair,
+                    fill_missing=True,
+                    drop_incomplete=False,
+                )
+                candle_limit = self.ohlcv_candle_limit(timeframe, self._config["candle_type_def"])
+                # Age out old candles
+                ohlcv_df = ohlcv_df.tail(candle_limit + self._startup_candle_count)
+                ohlcv_df = ohlcv_df.reset_index(drop=True)
+                self._klines[(pair, timeframe, c_type)] = ohlcv_df
+            else:
+                self._klines[(pair, timeframe, c_type)] = ohlcv_df
+        return ohlcv_df
+
+    def refresh_latest_ohlcv(
+        self,
+        pair_list: ListPairsWithTimeframes,
+        *,
+        since_ms: int | None = None,
+        cache: bool = True,
+        drop_incomplete: bool | None = None,
+    ) -> dict[PairWithTimeframe, DataFrame]:
+        """
+        Refresh in-memory OHLCV asynchronously and set `_klines` with the result
+        Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
+        Only used in the dataprovider.refresh() method.
+        :param pair_list: List of 2 element tuples containing pair, interval to refresh
+        :param since_ms: time since when to download, in milliseconds
+        :param cache: Assign result to _klines. Useful for one-off downloads like for pairlists
+        :param drop_incomplete: Control candle dropping.
+            Specifying None defaults to _ohlcv_partial_candle
+        :return: Dict of [{(pair, timeframe): Dataframe}]
+        """
+        logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
+
+        # Gather coroutines to run
+        ohlcv_dl_jobs, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
+
+        results_df = {}
+        # Chunk requests into batches of 100 to avoid overwhelming ccxt Throttling
+        for dl_jobs_batch in chunks(ohlcv_dl_jobs, 100):
+
+            async def gather_coroutines(coro):
+                return await asyncio.gather(*coro, return_exceptions=True)
+
+            with self._loop_lock:
+                results = self.loop.run_until_complete(gather_coroutines(dl_jobs_batch))
+
+            for res in results:
+                if isinstance(res, Exception):
+                    logger.warning(f"Async code raised an exception: {repr(res)}")
+                    continue
+                # Deconstruct tuple (has 5 elements)
+                pair, timeframe, c_type, ticks, drop_hint = res
+                drop_incomplete_ = drop_hint if drop_incomplete is None else drop_incomplete
+                ohlcv_df = self._process_ohlcv_df(
+                    pair, timeframe, c_type, ticks, cache, drop_incomplete_
+                )
+
+                results_df[(pair, timeframe, c_type)] = ohlcv_df
+
+        # Return cached klines
+        for pair, timeframe, c_type in cached_pairs:
+            results_df[(pair, timeframe, c_type)] = self.klines(
+                (pair, timeframe, c_type), copy=False
+            )
+
+        return results_df
+
+    def refresh_ohlcv_with_cache(
+        self, pairs: list[PairWithTimeframe], since_ms: int
+    ) -> dict[PairWithTimeframe, DataFrame]:
+        """
+        Refresh ohlcv data for all pairs in needed_pairs if necessary.
+        Caches data with expiring per timeframe.
+        Should only be used for pairlists which need "on time" expirarion, and no longer cache.
+        """
+
+        timeframes = {p[1] for p in pairs}
+        for timeframe in timeframes:
+            if (timeframe, since_ms) not in self._expiring_candle_cache:
+                timeframe_in_sec = timeframe_to_seconds(timeframe)
+                # Initialise cache
+                self._expiring_candle_cache[(timeframe, since_ms)] = PeriodicCache(
+                    ttl=timeframe_in_sec, maxsize=1000
+                )
+
+        # Get candles from cache
+        candles = {
+            c: self._expiring_candle_cache[(c[1], since_ms)].get(c, None)
+            for c in pairs
+            if c in self._expiring_candle_cache[(c[1], since_ms)]
+        }
+        pairs_to_download = [p for p in pairs if p not in candles]
+        if pairs_to_download:
+            candles = self.refresh_latest_ohlcv(pairs_to_download, since_ms=since_ms, cache=False)
+            for c, val in candles.items():
+                self._expiring_candle_cache[(c[1], since_ms)][c] = val
+        return candles
+
+    def _now_is_time_to_refresh(self, pair: str, timeframe: str, candle_type: CandleType) -> bool:
+        # Timeframe in seconds
+        interval_in_sec = timeframe_to_msecs(timeframe)
+        plr = self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0) + interval_in_sec
+        # current,active candle open date
+        now = dt_ts(timeframe_to_prev_date(timeframe))
+        return plr < now
+
+    @retrier_async
+    async def _async_get_candle_history(
+        self,
+        pair: str,
+        timeframe: str,
+        candle_type: CandleType,
+        since_ms: int | None = None,
+    ) -> OHLCVResponse:
+        """
+        Asynchronously get candle history data using fetch_ohlcv
+        :param candle_type: '', mark, index, premiumIndex, or funding_rate
+        returns tuple: (pair, timeframe, ohlcv_list)
+        """
+        try:
+            # Fetch OHLCV asynchronously
+            s = "(" + dt_from_ts(since_ms).isoformat() + ") " if since_ms is not None else ""
+            logger.debug(
+                "Fetching pair %s, %s, interval %s, since %s %s...",
+                pair,
+                candle_type,
+                timeframe,
+                since_ms,
+                s,
+            )
+            params = deepcopy(self._ft_has.get("ohlcv_params", {}))
+            candle_limit = self.ohlcv_candle_limit(
+                timeframe, candle_type=candle_type, since_ms=since_ms
+            )
+
+            if candle_type and candle_type != CandleType.SPOT:
+                params.update({"price": candle_type.value})
+            if candle_type != CandleType.FUNDING_RATE:
+                data = await self._api_async.fetch_ohlcv(
+                    pair, timeframe=timeframe, since=since_ms, limit=candle_limit, params=params
+                )
+            else:
+                # Funding rate
+                data = await self._fetch_funding_rate_history(
+                    pair=pair,
+                    timeframe=timeframe,
+                    limit=candle_limit,
+                    since_ms=since_ms,
+                )
+            # Some exchanges sort OHLCV in ASC order and others in DESC.
+            # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
+            # while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
+            # Only sort if necessary to save computing time
+            try:
+                if data and data[0][0] > data[-1][0]:
+                    data = sorted(data, key=lambda x: x[0])
+            except IndexError:
+                logger.exception("Error loading %s. Result was %s.", pair, data)
+                return pair, timeframe, candle_type, [], self._ohlcv_partial_candle
+            logger.debug("Done fetching pair %s, %s interval %s...", pair, candle_type, timeframe)
+            return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
+
+        except ccxt.NotSupported as e:
+            raise OperationalException(
+                f"Exchange {self._api.name} does not support fetching historical "
+                f"candle (OHLCV) data. Message: {e}"
+            ) from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not fetch historical candle (OHLCV) data "
+                f"for {pair}, {timeframe}, {candle_type} due to {e.__class__.__name__}. "
+                f"Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(
+                f"Could not fetch historical candle (OHLCV) data for "
+                f"{pair}, {timeframe}, {candle_type}. Message: {e}"
+            ) from e
+
+    async def _fetch_funding_rate_history(
+        self,
+        pair: str,
+        timeframe: str,
+        limit: int,
+        since_ms: int | None = None,
+    ) -> list[list]:
+        """
+        Fetch funding rate history - used to selectively override this by subclasses.
+        """
+        # Funding rate
+        data = await self._api_async.fetch_funding_rate_history(pair, since=since_ms, limit=limit)
+        # Convert funding rate to candle pattern
+        data = [[x["timestamp"], x["fundingRate"], 0, 0, 0, 0] for x in data]
+        return data
+
+    # fetch Trade data stuff
+
+    def needed_candle_for_trades_ms(self, timeframe: str, candle_type: CandleType) -> int:
+        candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
+        tf_s = timeframe_to_seconds(timeframe)
+        candles_fetched = candle_limit * self.required_candle_call_count
+
+        max_candles = self._config["orderflow"]["max_candles"]
+
+        required_candles = min(max_candles, candles_fetched)
+        move_to = (
+            tf_s * candle_limit * required_candles
+            if required_candles > candle_limit
+            else (max_candles + 1) * tf_s
+        )
+
+        now = timeframe_to_next_date(timeframe)
+        return int((now - timedelta(seconds=move_to)).timestamp() * 1000)
+
+    def _process_trades_df(
+        self,
+        pair: str,
+        timeframe: str,
+        c_type: CandleType,
+        ticks: list[list],
+        cache: bool,
+        first_required_candle_date: int,
+    ) -> DataFrame:
+        # keeping parsed dataframe in cache
+        trades_df = trades_list_to_df(ticks, True)
+
+        if cache:
+            if (pair, timeframe, c_type) in self._trades:
+                old = self._trades[(pair, timeframe, c_type)]
+                # Reassign so we return the updated, combined df
+                combined_df = concat([old, trades_df], axis=0)
+                logger.debug(f"Clean duplicated ticks from Trades data {pair}")
+                trades_df = DataFrame(
+                    trades_df_remove_duplicates(combined_df), columns=combined_df.columns
+                )
+                # Age out old candles
+                trades_df = trades_df[first_required_candle_date < trades_df["timestamp"]]
+                trades_df = trades_df.reset_index(drop=True)
+            self._trades[(pair, timeframe, c_type)] = trades_df
+        return trades_df
+
+    async def _build_trades_dl_jobs(
+        self, pairwt: PairWithTimeframe, data_handler, cache: bool
+    ) -> tuple[PairWithTimeframe, DataFrame | None]:
+        """
+        Build coroutines to refresh trades for (they're then called through async.gather)
+        """
+        pair, timeframe, candle_type = pairwt
+        since_ms = None
+        new_ticks: list = []
+        all_stored_ticks_df = DataFrame(columns=[*DEFAULT_TRADES_COLUMNS, "date"])
+        first_candle_ms = self.needed_candle_for_trades_ms(timeframe, candle_type)
+        # refresh, if
+        # a. not in _trades
+        # b. no cache used
+        # c. need new data
+        is_in_cache = (pair, timeframe, candle_type) in self._trades
+        if (
+            not is_in_cache
+            or not cache
+            or self._now_is_time_to_refresh_trades(pair, timeframe, candle_type)
+        ):
+            logger.debug(f"Refreshing TRADES data for {pair}")
+            # fetch trades since latest _trades and
+            # store together with existing trades
+            try:
+                until = None
+                from_id = None
+                if is_in_cache:
+                    from_id = self._trades[(pair, timeframe, candle_type)].iloc[-1]["id"]
+                    until = dt_ts()  # now
+
+                else:
+                    until = int(timeframe_to_prev_date(timeframe).timestamp()) * 1000
+                    all_stored_ticks_df = data_handler.trades_load(
+                        f"{pair}-cached", self.trading_mode
+                    )
+
+                    if not all_stored_ticks_df.empty:
+                        if (
+                            all_stored_ticks_df.iloc[-1]["timestamp"] > first_candle_ms
+                            and all_stored_ticks_df.iloc[0]["timestamp"] <= first_candle_ms
+                        ):
+                            # Use cache and populate further
+                            last_cached_ms = all_stored_ticks_df.iloc[-1]["timestamp"]
+                            from_id = all_stored_ticks_df.iloc[-1]["id"]
+                            # only use cached if it's closer than first_candle_ms
+                            since_ms = (
+                                last_cached_ms
+                                if last_cached_ms > first_candle_ms
+                                else first_candle_ms
+                            )
+                        else:
+                            # Skip cache, it's too old
+                            all_stored_ticks_df = DataFrame(
+                                columns=[*DEFAULT_TRADES_COLUMNS, "date"]
+                            )
+
+                # from_id overrules with exchange set to id paginate
+                [_, new_ticks] = await self._async_get_trade_history(
+                    pair,
+                    since=since_ms if since_ms else first_candle_ms,
+                    until=until,
+                    from_id=from_id,
+                )
+
+            except Exception:
+                logger.exception(f"Refreshing TRADES data for {pair} failed")
+                return pairwt, None
+
+            if new_ticks:
+                all_stored_ticks_list = all_stored_ticks_df[DEFAULT_TRADES_COLUMNS].values.tolist()
+                all_stored_ticks_list.extend(new_ticks)
+                trades_df = self._process_trades_df(
+                    pair,
+                    timeframe,
+                    candle_type,
+                    all_stored_ticks_list,
+                    cache,
+                    first_required_candle_date=first_candle_ms,
+                )
+                data_handler.trades_store(
+                    f"{pair}-cached", trades_df[DEFAULT_TRADES_COLUMNS], self.trading_mode
+                )
+                return pairwt, trades_df
+            else:
+                logger.error(f"No new ticks for {pair}")
+        return pairwt, None
+
+    def refresh_latest_trades(
+        self,
+        pair_list: ListPairsWithTimeframes,
+        *,
+        cache: bool = True,
+    ) -> dict[PairWithTimeframe, DataFrame]:
+        """
+        Refresh in-memory TRADES asynchronously and set `_trades` with the result
+        Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
+        Only used in the dataprovider.refresh() method.
+        :param pair_list: List of 3 element tuples containing (pair, timeframe, candle_type)
+        :param cache: Assign result to _trades. Useful for one-off downloads like for pairlists
+        :return: Dict of [{(pair, timeframe): Dataframe}]
+        """
+        from freqtrade.data.history import get_datahandler
+
+        data_handler = get_datahandler(
+            self._config["datadir"], data_format=self._config["dataformat_trades"]
+        )
+        logger.debug("Refreshing TRADES data for %d pairs", len(pair_list))
+        results_df = {}
+        trades_dl_jobs = []
+        for pair_wt in set(pair_list):
+            trades_dl_jobs.append(self._build_trades_dl_jobs(pair_wt, data_handler, cache))
+
+        async def gather_coroutines(coro):
+            return await asyncio.gather(*coro, return_exceptions=True)
+
+        for dl_job_chunk in chunks(trades_dl_jobs, 100):
+            with self._loop_lock:
+                results = self.loop.run_until_complete(gather_coroutines(dl_job_chunk))
+
+            for res in results:
+                if isinstance(res, Exception):
+                    logger.warning(f"Async code raised an exception: {repr(res)}")
+                    continue
+                pairwt, trades_df = res
+                if trades_df is not None:
+                    results_df[pairwt] = trades_df
+
+        return results_df
+
+    def _now_is_time_to_refresh_trades(
+        self, pair: str, timeframe: str, candle_type: CandleType
+    ) -> bool:  # Timeframe in seconds
+        trades = self.trades((pair, timeframe, candle_type), False)
+        pair_last_refreshed = int(trades.iloc[-1]["timestamp"])
+        full_candle = (
+            int(timeframe_to_next_date(timeframe, dt_from_ts(pair_last_refreshed)).timestamp())
+            * 1000
+        )
+        now = dt_ts()
+        return full_candle <= now
+
+    # Fetch historic trades
+
+    @retrier_async
+    async def _async_fetch_trades(
+        self, pair: str, since: int | None = None, params: dict | None = None
+    ) -> tuple[list[list], Any]:
+        """
+        Asynchronously gets trade history using fetch_trades.
+        Handles exchange errors, does one call to the exchange.
+        :param pair: Pair to fetch trade data for
+        :param since: Since as integer timestamp in milliseconds
+        returns: List of dicts containing trades, the next iteration value (new "since" or trade_id)
+        """
+        try:
+            trades_limit = self._max_trades_limit
+            # fetch trades asynchronously
+            if params:
+                logger.debug("Fetching trades for pair %s, params: %s ", pair, params)
+                trades = await self._api_async.fetch_trades(pair, params=params, limit=trades_limit)
+            else:
+                logger.debug(
+                    "Fetching trades for pair %s, since %s %s...",
+                    pair,
+                    since,
+                    "(" + dt_from_ts(since).isoformat() + ") " if since is not None else "",
+                )
+                trades = await self._api_async.fetch_trades(pair, since=since, limit=trades_limit)
+            trades = self._trades_contracts_to_amount(trades)
+            pagination_value = self._get_trade_pagination_next_value(trades)
+            return trades_dict_to_list(trades), pagination_value
+        except ccxt.NotSupported as e:
+            raise OperationalException(
+                f"Exchange {self._api.name} does not support fetching historical trade data."
+                f"Message: {e}"
+            ) from e
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not load trade history due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(f"Could not fetch trade data. Msg: {e}") from e
+
+    def _valid_trade_pagination_id(self, pair: str, from_id: str) -> bool:
+        """
+        Verify trade-pagination id is valid.
+        Workaround for odd Kraken issue where ID is sometimes wrong.
+        """
+        return True
+
+    def _get_trade_pagination_next_value(self, trades: list[dict]):
+        """
+        Extract pagination id for the next "from_id" value
+        Applies only to fetch_trade_history by id.
+        """
+        if not trades:
+            return None
+        if self._trades_pagination == "id":
+            return trades[-1].get("id")
+        else:
+            return trades[-1].get("timestamp")
+
+    async def _async_get_trade_history_id_startup(
+        self, pair: str, since: int
+    ) -> tuple[list[list], str]:
+        """
+        override for initial trade_history_id call
+        """
+        return await self._async_fetch_trades(pair, since=since)
+
+    async def _async_get_trade_history_id(
+        self, pair: str, *, until: int, since: int, from_id: str | None = None
+    ) -> tuple[str, list[list]]:
+        """
+        Asynchronously gets trade history using fetch_trades
+        use this when exchange uses id-based iteration (check `self._trades_pagination`)
+        :param pair: Pair to fetch trade data for
+        :param since: Since as integer timestamp in milliseconds
+        :param until: Until as integer timestamp in milliseconds
+        :param from_id: Download data starting with ID (if id is known). Ignores "since" if set.
+        returns tuple: (pair, trades-list)
+        """
+
+        trades: list[list] = []
+        # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
+        # DEFAULT_TRADES_COLUMNS: 1 -> id
+        has_overlap = self._ft_has.get("trades_pagination_overlap", True)
+        # Skip last trade by default since its the key for the next call
+        x = slice(None, -1) if has_overlap else slice(None)
+
+        if not from_id or not self._valid_trade_pagination_id(pair, from_id):
+            # Fetch first elements using timebased method to get an ID to paginate on
+            # Depending on the Exchange, this can introduce a drift at the start of the interval
+            # of up to an hour.
+            # e.g. Binance returns the "last 1000" candles within a 1h time interval
+            # - so we will miss the first trades.
+            t, from_id = await self._async_get_trade_history_id_startup(pair, since=since)
+            trades.extend(t[x])
+        while True:
+            try:
+                t, from_id_next = await self._async_fetch_trades(
+                    pair, params={self._trades_pagination_arg: from_id}
+                )
+                if t:
+                    trades.extend(t[x])
+                    if from_id == from_id_next or t[-1][0] > until:
+                        logger.debug(
+                            f"Stopping because from_id did not change. Reached {t[-1][0]} > {until}"
+                        )
+                        # Reached the end of the defined-download period - add last trade as well.
+                        if has_overlap:
+                            trades.extend(t[-1:])
+                        break
+
+                    from_id = from_id_next
+                else:
+                    logger.debug("Stopping as no more trades were returned.")
+                    break
+            except asyncio.CancelledError:
+                logger.debug("Async operation Interrupted, breaking trades DL loop.")
+                break
+
+        return (pair, trades)
+
+    async def _async_get_trade_history_time(
+        self, pair: str, until: int, since: int
+    ) -> tuple[str, list[list]]:
+        """
+        Asynchronously gets trade history using fetch_trades,
+        when the exchange uses time-based iteration (check `self._trades_pagination`)
+        :param pair: Pair to fetch trade data for
+        :param since: Since as integer timestamp in milliseconds
+        :param until: Until as integer timestamp in milliseconds
+        returns tuple: (pair, trades-list)
+        """
+
+        trades: list[list] = []
+        # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
+        # DEFAULT_TRADES_COLUMNS: 1 -> id
+        while True:
+            try:
+                t, since_next = await self._async_fetch_trades(pair, since=since)
+                if t:
+                    # No more trades to download available at the exchange,
+                    # So we repeatedly get the same trade over and over again.
+                    if since == since_next and len(t) == 1:
+                        logger.debug("Stopping because no more trades are available.")
+                        break
+                    since = since_next
+                    trades.extend(t)
+                    # Reached the end of the defined-download period
+                    if until and since_next > until:
+                        logger.debug(f"Stopping because until was reached. {since_next} > {until}")
+                        break
+                else:
+                    logger.debug("Stopping as no more trades were returned.")
+                    break
+            except asyncio.CancelledError:
+                logger.debug("Async operation Interrupted, breaking trades DL loop.")
+                break
+
+        return (pair, trades)
+
+    async def _async_get_trade_history(
+        self,
+        pair: str,
+        since: int,
+        until: int | None = None,
+        from_id: str | None = None,
+    ) -> tuple[str, list[list]]:
+        """
+        Async wrapper handling downloading trades using either time or id based methods.
+        """
+
+        logger.debug(
+            f"_async_get_trade_history(), pair: {pair}, "
+            f"since: {since}, until: {until}, from_id: {from_id}"
+        )
+
+        if until is None:
+            until = ccxt.Exchange.milliseconds()
+            logger.debug(f"Exchange milliseconds: {until}")
+
+        if self._trades_pagination == "time":
+            return await self._async_get_trade_history_time(pair=pair, since=since, until=until)
+        elif self._trades_pagination == "id":
+            return await self._async_get_trade_history_id(
+                pair=pair, since=since, until=until, from_id=from_id
+            )
+        else:
+            raise OperationalException(
+                f"Exchange {self.name} does use neither time, nor id based pagination"
+            )
+
+    def get_historic_trades(
+        self,
+        pair: str,
+        since: int,
+        until: int | None = None,
+        from_id: str | None = None,
+    ) -> tuple[str, list]:
+        """
+        Get trade history data using asyncio.
+        Handles all async work and returns the list of candles.
+        Async over one pair, assuming we get `self.ohlcv_candle_limit()` candles per call.
+        :param pair: Pair to download
+        :param since: Timestamp in milliseconds to get history from
+        :param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
+        :param from_id: Download data starting with ID (if id is known)
+        :returns List of trade data
+        """
+        if not self.exchange_has("fetchTrades"):
+            raise OperationalException("This exchange does not support downloading Trades.")
+
+        with self._loop_lock:
+            task = asyncio.ensure_future(
+                self._async_get_trade_history(pair=pair, since=since, until=until, from_id=from_id)
+            )
+
+            for sig in [signal.SIGINT, signal.SIGTERM]:
+                try:
+                    self.loop.add_signal_handler(sig, task.cancel)
+                except NotImplementedError:
+                    # Not all platforms implement signals (e.g. windows)
+                    pass
+            return self.loop.run_until_complete(task)
+
+    @retrier
+    def _get_funding_fees_from_exchange(self, pair: str, since: datetime | int) -> float:
+        """
+        Returns the sum of all funding fees that were exchanged for a pair within a timeframe
+        Dry-run handling happens as part of _calculate_funding_fees.
+        :param pair: (e.g. ADA/USDT)
+        :param since: The earliest time of consideration for calculating funding fees,
+            in unix time or as a datetime
+        """
+        if not self.exchange_has("fetchFundingHistory"):
+            raise OperationalException(
+                f"fetch_funding_history() is not available using {self.name}"
+            )
+
+        if type(since) is datetime:
+            since = dt_ts(since)
+
+        try:
+            funding_history = self._api.fetch_funding_history(symbol=pair, since=since)
+            self._log_exchange_response(
+                "funding_history", funding_history, add_info=f"pair: {pair}, since: {since}"
+            )
+            return sum(fee["amount"] for fee in funding_history)
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get funding fees due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @retrier
+    def get_leverage_tiers(self) -> dict[str, list[dict]]:
+        try:
+            return self._api.fetch_leverage_tiers()
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not load leverage tiers due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    @retrier_async
+    async def get_market_leverage_tiers(self, symbol: str) -> tuple[str, list[dict]]:
+        """Leverage tiers per symbol"""
+        try:
+            tier = await self._api_async.fetch_market_leverage_tiers(symbol)
+            return symbol, tier
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not load leverage tiers for {symbol}"
+                f" due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def load_leverage_tiers(self) -> dict[str, list[dict]]:
+        if self.trading_mode == TradingMode.FUTURES:
+            if self.exchange_has("fetchLeverageTiers"):
+                # Fetch all leverage tiers at once
+                return self.get_leverage_tiers()
+            elif self.exchange_has("fetchMarketLeverageTiers"):
+                # Must fetch the leverage tiers for each market separately
+                # * This is slow(~45s) on Okx, makes ~90 api calls to load all linear swap markets
+                markets = self.markets
+
+                symbols = [
+                    symbol
+                    for symbol, market in markets.items()
+                    if (
+                        self.market_is_future(market)
+                        and market["quote"] == self._config["stake_currency"]
+                    )
+                ]
+
+                tiers: dict[str, list[dict]] = {}
+
+                tiers_cached = self.load_cached_leverage_tiers(self._config["stake_currency"])
+                if tiers_cached:
+                    tiers = tiers_cached
+
+                coros = [
+                    self.get_market_leverage_tiers(symbol)
+                    for symbol in sorted(symbols)
+                    if symbol not in tiers
+                ]
+
+                # Be verbose here, as this delays startup by ~1 minute.
+                if coros:
+                    logger.info(
+                        f"Initializing leverage_tiers for {len(symbols)} markets. "
+                        "This will take about a minute."
+                    )
+                else:
+                    logger.info("Using cached leverage_tiers.")
+
+                async def gather_results(input_coro):
+                    return await asyncio.gather(*input_coro, return_exceptions=True)
+
+                for input_coro in chunks(coros, 100):
+                    with self._loop_lock:
+                        results = self.loop.run_until_complete(gather_results(input_coro))
+
+                    for res in results:
+                        if isinstance(res, Exception):
+                            logger.warning(f"Leverage tier exception: {repr(res)}")
+                            continue
+                        symbol, tier = res
+                        tiers[symbol] = tier
+                if len(coros) > 0:
+                    self.cache_leverage_tiers(tiers, self._config["stake_currency"])
+                logger.info(f"Done initializing {len(symbols)} markets.")
+
+                return tiers
+        return {}
+
+    def cache_leverage_tiers(self, tiers: dict[str, list[dict]], stake_currency: str) -> None:
+        filename = self._config["datadir"] / "futures" / f"leverage_tiers_{stake_currency}.json"
+        if not filename.parent.is_dir():
+            filename.parent.mkdir(parents=True)
+        data = {
+            "updated": datetime.now(timezone.utc),
+            "data": tiers,
+        }
+        file_dump_json(filename, data)
+
+    def load_cached_leverage_tiers(
+        self, stake_currency: str, cache_time: timedelta | None = None
+    ) -> dict[str, list[dict]] | None:
+        """
+        Load cached leverage tiers from disk
+        :param cache_time: The maximum age of the cache before it is considered outdated
+        """
+        if not cache_time:
+            # Default to 4 weeks
+            cache_time = timedelta(weeks=4)
+        filename = self._config["datadir"] / "futures" / f"leverage_tiers_{stake_currency}.json"
+        if filename.is_file():
+            try:
+                tiers = file_load_json(filename)
+                updated = tiers.get("updated")
+                if updated:
+                    updated_dt = parser.parse(updated)
+                    if updated_dt < datetime.now(timezone.utc) - cache_time:
+                        logger.info("Cached leverage tiers are outdated. Will update.")
+                        return None
+                return tiers.get("data")
+            except Exception:
+                logger.exception("Error loading cached leverage tiers. Refreshing.")
+        return None
+
+    def fill_leverage_tiers(self) -> None:
+        """
+        Assigns property _leverage_tiers to a dictionary of information about the leverage
+        allowed on each pair
+        """
+        leverage_tiers = self.load_leverage_tiers()
+        for pair, tiers in leverage_tiers.items():
+            pair_tiers = []
+            for tier in tiers:
+                pair_tiers.append(self.parse_leverage_tier(tier))
+            self._leverage_tiers[pair] = pair_tiers
+
+    def parse_leverage_tier(self, tier) -> dict:
+        info = tier.get("info", {})
+        return {
+            "minNotional": tier["minNotional"],
+            "maxNotional": tier["maxNotional"],
+            "maintenanceMarginRate": tier["maintenanceMarginRate"],
+            "maxLeverage": tier["maxLeverage"],
+            "maintAmt": float(info["cum"]) if "cum" in info else None,
+        }
+
+    def get_max_leverage(self, pair: str, stake_amount: float | None) -> float:
+        """
+        Returns the maximum leverage that a pair can be traded at
+        :param pair: The base/quote currency pair being traded
+        :stake_amount: The total value of the traders margin_mode in quote currency
+        """
+
+        if self.trading_mode == TradingMode.SPOT:
+            return 1.0
+
+        if self.trading_mode == TradingMode.FUTURES:
+            # Checks and edge cases
+            if stake_amount is None:
+                raise OperationalException(
+                    f"{self.name}.get_max_leverage requires argument stake_amount"
+                )
+
+            if pair not in self._leverage_tiers:
+                # Maybe raise exception because it can't be traded on futures?
+                return 1.0
+
+            pair_tiers = self._leverage_tiers[pair]
+
+            if stake_amount == 0:
+                return self._leverage_tiers[pair][0]["maxLeverage"]  # Max lev for lowest amount
+
+            for tier_index in range(len(pair_tiers)):
+                tier = pair_tiers[tier_index]
+                lev = tier["maxLeverage"]
+
+                if tier_index < len(pair_tiers) - 1:
+                    next_tier = pair_tiers[tier_index + 1]
+                    next_floor = next_tier["minNotional"] / next_tier["maxLeverage"]
+                    if next_floor > stake_amount:  # Next tier min too high for stake amount
+                        return min((tier["maxNotional"] / stake_amount), lev)
+                        #
+                        # With the two leverage tiers below,
+                        # - a stake amount of 150 would mean a max leverage of (10000 / 150) = 66.66
+                        # - stakes below 133.33 = max_lev of 75
+                        # - stakes between 133.33-200 = max_lev of 10000/stake = 50.01-74.99
+                        # - stakes from 200 + 1000 = max_lev of 50
+                        #
+                        # {
+                        #     "min": 0,      # stake = 0.0
+                        #     "max": 10000,  # max_stake@75 = 10000/75 = 133.33333333333334
+                        #     "lev": 75,
+                        # },
+                        # {
+                        #     "min": 10000,  # stake = 200.0
+                        #     "max": 50000,  # max_stake@50 = 50000/50 = 1000.0
+                        #     "lev": 50,
+                        # }
+                        #
+
+                else:  # if on the last tier
+                    if stake_amount > tier["maxNotional"]:
+                        # If stake is > than max tradeable amount
+                        raise InvalidOrderException(f"Amount {stake_amount} too high for {pair}")
+                    else:
+                        return tier["maxLeverage"]
+
+            raise OperationalException(
+                "Looped through all tiers without finding a max leverage. Should never be reached"
+            )
+
+        elif self.trading_mode == TradingMode.MARGIN:  # Search markets.limits for max lev
+            market = self.markets[pair]
+            if market["limits"]["leverage"]["max"] is not None:
+                return market["limits"]["leverage"]["max"]
+            else:
+                return 1.0  # Default if max leverage cannot be found
+        else:
+            return 1.0
+
+    @retrier
+    def _set_leverage(
+        self,
+        leverage: float,
+        pair: str | None = None,
+        accept_fail: bool = False,
+    ):
+        """
+        Set's the leverage before making a trade, in order to not
+        have the same leverage on every trade
+        """
+        if self._config["dry_run"] or not self.exchange_has("setLeverage"):
+            # Some exchanges only support one margin_mode type
+            return
+        if self._ft_has.get("floor_leverage", False) is True:
+            # Rounding for binance ...
+            leverage = floor(leverage)
+        try:
+            res = self._api.set_leverage(symbol=pair, leverage=leverage)
+            self._log_exchange_response("set_leverage", res)
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.BadRequest, ccxt.OperationRejected, ccxt.InsufficientFunds) as e:
+            if not accept_fail:
+                raise TemporaryError(
+                    f"Could not set leverage due to {e.__class__.__name__}. Message: {e}"
+                ) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not set leverage due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def get_interest_rate(self) -> float:
+        """
+        Retrieve interest rate - necessary for Margin trading.
+        Should not call the exchange directly when used from backtesting.
+        """
+        return 0.0
+
+    def funding_fee_cutoff(self, open_date: datetime) -> bool:
+        """
+        Funding fees are only charged at full hours (usually every 4-8h).
+        Therefore a trade opening at 10:00:01 will not be charged a funding fee until the next hour.
+        :param open_date: The open date for a trade
+        :return: True if the date falls on a full hour, False otherwise
+        """
+        return open_date.minute == 0 and open_date.second == 0
+
+    @retrier
+    def set_margin_mode(
+        self,
+        pair: str,
+        margin_mode: MarginMode,
+        accept_fail: bool = False,
+        params: dict | None = None,
+    ):
+        """
+        Set's the margin mode on the exchange to cross or isolated for a specific pair
+        :param pair: base/quote currency pair (e.g. "ADA/USDT")
+        """
+        if self._config["dry_run"] or not self.exchange_has("setMarginMode"):
+            # Some exchanges only support one margin_mode type
+            return
+
+        if params is None:
+            params = {}
+        try:
+            res = self._api.set_margin_mode(margin_mode.value, pair, params)
+            self._log_exchange_response("set_margin_mode", res)
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.BadRequest, ccxt.OperationRejected) as e:
+            if not accept_fail:
+                raise TemporaryError(
+                    f"Could not set margin mode due to {e.__class__.__name__}. Message: {e}"
+                ) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not set margin mode due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def _fetch_and_calculate_funding_fees(
+        self,
+        pair: str,
+        amount: float,
+        is_short: bool,
+        open_date: datetime,
+        close_date: datetime | None = None,
+    ) -> float:
+        """
+        Fetches and calculates the sum of all funding fees that occurred for a pair
+        during a futures trade.
+        Only used during dry-run or if the exchange does not provide a funding_rates endpoint.
+        :param pair: The quote/base pair of the trade
+        :param amount: The quantity of the trade
+        :param is_short: trade direction
+        :param open_date: The date and time that the trade started
+        :param close_date: The date and time that the trade ended
+        """
+
+        if self.funding_fee_cutoff(open_date):
+            # Shift back to 1h candle to avoid missing funding fees
+            # Only really relevant for trades very close to the full hour
+            open_date = timeframe_to_prev_date("1h", open_date)
+        timeframe = self._ft_has["mark_ohlcv_timeframe"]
+        timeframe_ff = self._ft_has["funding_fee_timeframe"]
+        mark_price_type = CandleType.from_string(self._ft_has["mark_ohlcv_price"])
+
+        if not close_date:
+            close_date = datetime.now(timezone.utc)
+        since_ms = dt_ts(timeframe_to_prev_date(timeframe, open_date))
+
+        mark_comb: PairWithTimeframe = (pair, timeframe, mark_price_type)
+        funding_comb: PairWithTimeframe = (pair, timeframe_ff, CandleType.FUNDING_RATE)
+
+        candle_histories = self.refresh_latest_ohlcv(
+            [mark_comb, funding_comb],
+            since_ms=since_ms,
+            cache=False,
+            drop_incomplete=False,
+        )
+        try:
+            # we can't assume we always get histories - for example during exchange downtimes
+            funding_rates = candle_histories[funding_comb]
+            mark_rates = candle_histories[mark_comb]
+        except KeyError:
+            raise ExchangeError("Could not find funding rates.") from None
+
+        funding_mark_rates = self.combine_funding_and_mark(funding_rates, mark_rates)
+
+        return self.calculate_funding_fees(
+            funding_mark_rates,
+            amount=amount,
+            is_short=is_short,
+            open_date=open_date,
+            close_date=close_date,
+        )
+
+    @staticmethod
+    def combine_funding_and_mark(
+        funding_rates: DataFrame, mark_rates: DataFrame, futures_funding_rate: int | None = None
+    ) -> DataFrame:
+        """
+        Combine funding-rates and mark-rates dataframes
+        :param funding_rates: Dataframe containing Funding rates (Type FUNDING_RATE)
+        :param mark_rates: Dataframe containing Mark rates (Type mark_ohlcv_price)
+        :param futures_funding_rate: Fake funding rate to use if funding_rates are not available
+        """
+        if futures_funding_rate is None:
+            return mark_rates.merge(
+                funding_rates, on="date", how="inner", suffixes=["_mark", "_fund"]
+            )
+        else:
+            if len(funding_rates) == 0:
+                # No funding rate candles - full fillup with fallback variable
+                mark_rates["open_fund"] = futures_funding_rate
+                return mark_rates.rename(
+                    columns={
+                        "open": "open_mark",
+                        "close": "close_mark",
+                        "high": "high_mark",
+                        "low": "low_mark",
+                        "volume": "volume_mark",
+                    }
+                )
+
+            else:
+                # Fill up missing funding_rate candles with fallback value
+                combined = mark_rates.merge(
+                    funding_rates, on="date", how="left", suffixes=["_mark", "_fund"]
+                )
+                combined["open_fund"] = combined["open_fund"].fillna(futures_funding_rate)
+                return combined
+
+    def calculate_funding_fees(
+        self,
+        df: DataFrame,
+        amount: float,
+        is_short: bool,
+        open_date: datetime,
+        close_date: datetime,
+        time_in_ratio: float | None = None,
+    ) -> float:
+        """
+        calculates the sum of all funding fees that occurred for a pair during a futures trade
+        :param df: Dataframe containing combined funding and mark rates
+                   as `open_fund` and `open_mark`.
+        :param amount: The quantity of the trade
+        :param is_short: trade direction
+        :param open_date: The date and time that the trade started
+        :param close_date: The date and time that the trade ended
+        :param time_in_ratio: Not used by most exchange classes
+        """
+        fees: float = 0
+
+        if not df.empty:
+            df1 = df[(df["date"] >= open_date) & (df["date"] <= close_date)]
+            fees = sum(df1["open_fund"] * df1["open_mark"] * amount)
+        if isnan(fees):
+            fees = 0.0
+        # Negate fees for longs as funding_fees expects it this way based on live endpoints.
+        return fees if is_short else -fees
+
+    def get_funding_fees(
+        self, pair: str, amount: float, is_short: bool, open_date: datetime
+    ) -> float:
+        """
+        Fetch funding fees, either from the exchange (live) or calculates them
+        based on funding rate/mark price history
+        :param pair: The quote/base pair of the trade
+        :param is_short: trade direction
+        :param amount: Trade amount
+        :param open_date: Open date of the trade
+        :return: funding fee since open_date
+        """
+        if self.trading_mode == TradingMode.FUTURES:
+            try:
+                if self._config["dry_run"]:
+                    funding_fees = self._fetch_and_calculate_funding_fees(
+                        pair, amount, is_short, open_date
+                    )
+                else:
+                    funding_fees = self._get_funding_fees_from_exchange(pair, open_date)
+                return funding_fees
+            except ExchangeError:
+                logger.warning(f"Could not update funding fees for {pair}.")
+
+        return 0.0
+
+    def get_liquidation_price(
+        self,
+        pair: str,
+        # Dry-run
+        open_rate: float,  # Entry price of position
+        is_short: bool,
+        amount: float,  # Absolute value of position size
+        stake_amount: float,
+        leverage: float,
+        wallet_balance: float,
+        open_trades: list | None = None,
+    ) -> float | None:
+        """
+        Set's the margin mode on the exchange to cross or isolated for a specific pair
+        """
+        if self.trading_mode == TradingMode.SPOT:
+            return None
+        elif self.trading_mode != TradingMode.FUTURES:
+            raise OperationalException(
+                f"{self.name} does not support {self.margin_mode} {self.trading_mode}"
+            )
+
+        liquidation_price = None
+        if self._config["dry_run"] or not self.exchange_has("fetchPositions"):
+            liquidation_price = self.dry_run_liquidation_price(
+                pair=pair,
+                open_rate=open_rate,
+                is_short=is_short,
+                amount=amount,
+                leverage=leverage,
+                stake_amount=stake_amount,
+                wallet_balance=wallet_balance,
+                open_trades=open_trades or [],
+            )
+        else:
+            positions = self.fetch_positions(pair)
+            if len(positions) > 0:
+                pos = positions[0]
+                liquidation_price = pos["liquidationPrice"]
+
+        if liquidation_price is not None:
+            buffer_amount = abs(open_rate - liquidation_price) * self.liquidation_buffer
+            liquidation_price_buffer = (
+                liquidation_price - buffer_amount if is_short else liquidation_price + buffer_amount
+            )
+            return max(liquidation_price_buffer, 0.0)
+        else:
+            return None
+
+    def dry_run_liquidation_price(
+        self,
+        pair: str,
+        open_rate: float,
+        is_short: bool,
+        amount: float,
+        stake_amount: float,
+        leverage: float,
+        wallet_balance: float,
+        open_trades: list,
+    ) -> float | None:
+        """
+        Important: Must be fetching data from cached values as this is used by backtesting!
+        PERPETUAL:
+         gate: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
+         > Liquidation Price = (Entry Price ± Margin / Contract Multiplier / Size) /
+                                [ 1 ± (Maintenance Margin Ratio + Taker Rate)]
+            Wherein, "+" or "-" depends on whether the contract goes long or short:
+            "-" for long, and "+" for short.
+
+         okx: https://www.okx.com/support/hc/en-us/articles/
+            360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
+
+        :param pair: Pair to calculate liquidation price for
+        :param open_rate: Entry price of position
+        :param is_short: True if the trade is a short, false otherwise
+        :param amount: Absolute value of position size incl. leverage (in base currency)
+        :param stake_amount: Stake amount - Collateral in settle currency.
+        :param leverage: Leverage used for this position.
+        :param wallet_balance: Amount of margin_mode in the wallet being used to trade
+            Cross-Margin Mode: crossWalletBalance
+            Isolated-Margin Mode: isolatedWalletBalance
+        :param open_trades: List of other open trades in the same wallet
+        """
+
+        market = self.markets[pair]
+        taker_fee_rate = market["taker"]
+        mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, stake_amount)
+
+        if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.ISOLATED:
+            if market["inverse"]:
+                raise OperationalException("Freqtrade does not yet support inverse contracts")
+
+            value = wallet_balance / amount
+
+            mm_ratio_taker = mm_ratio + taker_fee_rate
+            if is_short:
+                return (open_rate + value) / (1 + mm_ratio_taker)
+            else:
+                return (open_rate - value) / (1 - mm_ratio_taker)
+        else:
+            raise OperationalException(
+                "Freqtrade only supports isolated futures for leverage trading"
+            )
+
+    def get_maintenance_ratio_and_amt(
+        self,
+        pair: str,
+        notional_value: float,
+    ) -> tuple[float, float | None]:
+        """
+        Important: Must be fetching data from cached values as this is used by backtesting!
+        :param pair: Market symbol
+        :param notional_value: The total trade amount in quote currency
+        :return: (maintenance margin ratio, maintenance amount)
+        """
+
+        if (
+            self._config.get("runmode") in OPTIMIZE_MODES
+            or self.exchange_has("fetchLeverageTiers")
+            or self.exchange_has("fetchMarketLeverageTiers")
+        ):
+            if pair not in self._leverage_tiers:
+                raise InvalidOrderException(
+                    f"Maintenance margin rate for {pair} is unavailable for {self.name}"
+                )
+
+            pair_tiers = self._leverage_tiers[pair]
+
+            for tier in reversed(pair_tiers):
+                if notional_value >= tier["minNotional"]:
+                    return (tier["maintenanceMarginRate"], tier["maintAmt"])
+
+            raise ExchangeError("nominal value can not be lower than 0")
+            # The lowest notional_floor for any pair in fetch_leverage_tiers is always 0 because it
+            # describes the min amt for a tier, and the lowest tier will always go down to 0
+        else:
+            raise ExchangeError(f"Cannot get maintenance ratio using {self.name}")
diff --git a/freqtrade/exchange/exchange_types.py b/freqtrade/exchange/exchange_types.py
new file mode 100644
index 000000000..fed6bff70
--- /dev/null
+++ b/freqtrade/exchange/exchange_types.py
@@ -0,0 +1,105 @@
+from typing import Any, Literal, TypedDict
+
+from freqtrade.enums import CandleType
+
+
+class FtHas(TypedDict, total=False):
+    order_time_in_force: list[str]
+    exchange_has_overrides: dict[str, bool]
+    marketOrderRequiresPrice: bool
+
+    # Stoploss on exchange
+    stoploss_on_exchange: bool
+    stop_price_param: str
+    stop_price_prop: Literal["stopPrice", "stopLossPrice"]
+    stop_price_type_field: str
+    stop_price_type_value_mapping: dict
+    stoploss_order_types: dict[str, str]
+    # ohlcv
+    ohlcv_params: dict
+    ohlcv_candle_limit: int
+    ohlcv_has_history: bool
+    ohlcv_partial_candle: bool
+    ohlcv_require_since: bool
+    ohlcv_volume_currency: str
+    ohlcv_candle_limit_per_timeframe: dict[str, int]
+    # Tickers
+    tickers_have_quoteVolume: bool
+    tickers_have_percentage: bool
+    tickers_have_bid_ask: bool
+    tickers_have_price: bool
+    # Trades
+    trades_limit: int
+    trades_pagination: str
+    trades_pagination_arg: str
+    trades_has_history: bool
+    trades_pagination_overlap: bool
+    # Orderbook
+    l2_limit_range: list[int] | None
+    l2_limit_range_required: bool
+    l2_limit_upper: int | None
+    # Futures
+    ccxt_futures_name: str  # usually swap
+    mark_ohlcv_price: str
+    mark_ohlcv_timeframe: str
+    funding_fee_timeframe: str
+    funding_fee_candle_limit: int
+    floor_leverage: bool
+    uses_leverage_tiers: bool
+    needs_trading_fees: bool
+    order_props_in_contracts: list[Literal["amount", "cost", "filled", "remaining"]]
+
+    proxy_coin_mapping: dict[str, str]
+
+    # Websocket control
+    ws_enabled: bool
+
+
+class Ticker(TypedDict):
+    symbol: str
+    ask: float | None
+    askVolume: float | None
+    bid: float | None
+    bidVolume: float | None
+    last: float | None
+    quoteVolume: float | None
+    baseVolume: float | None
+    percentage: float | None
+    # Several more - only listing required.
+
+
+Tickers = dict[str, Ticker]
+
+
+class OrderBook(TypedDict):
+    symbol: str
+    bids: list[tuple[float, float]]
+    asks: list[tuple[float, float]]
+    timestamp: int | None
+    datetime: str | None
+    nonce: int | None
+
+
+class CcxtBalance(TypedDict):
+    free: float
+    used: float
+    total: float
+
+
+CcxtBalances = dict[str, CcxtBalance]
+
+
+class CcxtPosition(TypedDict):
+    symbol: str
+    side: str
+    contracts: float
+    leverage: float
+    collateral: float | None
+    initialMargin: float | None
+    liquidationPrice: float | None
+
+
+CcxtOrder = dict[str, Any]
+
+# pair, timeframe, candleType, OHLCV, drop last?,
+OHLCVResponse = tuple[str, str, CandleType, list, bool]
diff --git a/freqtrade/exchange/exchange_utils.py b/freqtrade/exchange/exchange_utils.py
new file mode 100644
index 000000000..ad92033fb
--- /dev/null
+++ b/freqtrade/exchange/exchange_utils.py
@@ -0,0 +1,349 @@
+"""
+Exchange support utils
+"""
+
+import inspect
+from datetime import datetime, timedelta, timezone
+from math import ceil, floor, isnan
+from typing import Any
+
+import ccxt
+from ccxt import (
+    DECIMAL_PLACES,
+    ROUND,
+    ROUND_DOWN,
+    ROUND_UP,
+    SIGNIFICANT_DIGITS,
+    TICK_SIZE,
+    TRUNCATE,
+    decimal_to_precision,
+)
+
+from freqtrade.exchange.common import (
+    BAD_EXCHANGES,
+    EXCHANGE_HAS_OPTIONAL,
+    EXCHANGE_HAS_REQUIRED,
+    MAP_EXCHANGE_CHILDCLASS,
+    SUPPORTED_EXCHANGES,
+)
+from freqtrade.exchange.exchange_utils_timeframe import timeframe_to_minutes, timeframe_to_prev_date
+from freqtrade.ft_types import ValidExchangesType
+from freqtrade.util import FtPrecise
+
+
+CcxtModuleType = Any
+
+
+def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType | None = None) -> bool:
+    return exchange_name in ccxt_exchanges(ccxt_module)
+
+
+def ccxt_exchanges(ccxt_module: CcxtModuleType | None = None) -> list[str]:
+    """
+    Return the list of all exchanges known to ccxt
+    """
+    return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
+
+
+def available_exchanges(ccxt_module: CcxtModuleType | None = None) -> list[str]:
+    """
+    Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
+    """
+    exchanges = ccxt_exchanges(ccxt_module)
+    return [x for x in exchanges if validate_exchange(x)[0]]
+
+
+def validate_exchange(exchange: str) -> tuple[bool, str, ccxt.Exchange | None]:
+    """
+    returns: can_use, reason, exchange_object
+        with Reason including both missing and missing_opt
+    """
+    try:
+        ex_mod = getattr(ccxt.pro, exchange.lower())()
+    except AttributeError:
+        ex_mod = getattr(ccxt.async_support, exchange.lower())()
+
+    if not ex_mod or not ex_mod.has:
+        return False, "", None
+
+    result = True
+    reason = ""
+    missing = [
+        k
+        for k, v in EXCHANGE_HAS_REQUIRED.items()
+        if ex_mod.has.get(k) is not True and not (all(ex_mod.has.get(x) for x in v))
+    ]
+    if missing:
+        result = False
+        reason += f"missing: {', '.join(missing)}"
+
+    missing_opt = [k for k in EXCHANGE_HAS_OPTIONAL if not ex_mod.has.get(k)]
+
+    if exchange.lower() in BAD_EXCHANGES:
+        result = False
+        reason = BAD_EXCHANGES.get(exchange.lower(), "")
+
+    if missing_opt:
+        reason += f"{'. ' if reason else ''}missing opt: {', '.join(missing_opt)}. "
+
+    return result, reason, ex_mod
+
+
+def _build_exchange_list_entry(
+    exchange_name: str, exchangeClasses: dict[str, Any]
+) -> ValidExchangesType:
+    exchange_name = exchange_name.lower()
+    valid, comment, ex_mod = validate_exchange(exchange_name)
+    mapped_exchange_name = MAP_EXCHANGE_CHILDCLASS.get(exchange_name, exchange_name).lower()
+    is_alias = getattr(ex_mod, "alias", False)
+    result: ValidExchangesType = {
+        "name": getattr(ex_mod, "name", exchange_name),
+        "classname": exchange_name,
+        "valid": valid,
+        "supported": mapped_exchange_name in SUPPORTED_EXCHANGES and not is_alias,
+        "comment": comment,
+        "dex": getattr(ex_mod, "dex", False),
+        "is_alias": is_alias,
+        "alias_for": inspect.getmro(ex_mod.__class__)[1]().id
+        if getattr(ex_mod, "alias", False)
+        else None,
+        "trade_modes": [{"trading_mode": "spot", "margin_mode": ""}],
+    }
+    if resolved := exchangeClasses.get(mapped_exchange_name):
+        supported_modes = [{"trading_mode": "spot", "margin_mode": ""}] + [
+            {"trading_mode": tm.value, "margin_mode": mm.value}
+            for tm, mm in resolved["class"]._supported_trading_mode_margin_pairs
+        ]
+        result.update(
+            {
+                "trade_modes": supported_modes,
+            }
+        )
+
+    return result
+
+
+def list_available_exchanges(all_exchanges: bool) -> list[ValidExchangesType]:
+    """
+    :return: List of tuples with exchangename, valid, reason.
+    """
+    exchanges = ccxt_exchanges() if all_exchanges else available_exchanges()
+    from freqtrade.resolvers.exchange_resolver import ExchangeResolver
+
+    subclassed = {e["name"].lower(): e for e in ExchangeResolver.search_all_objects({}, False)}
+
+    exchanges_valid: list[ValidExchangesType] = [
+        _build_exchange_list_entry(e, subclassed) for e in exchanges
+    ]
+
+    return exchanges_valid
+
+
+def date_minus_candles(timeframe: str, candle_count: int, date: datetime | None = None) -> datetime:
+    """
+    subtract X candles from a date.
+    :param timeframe: timeframe in string format (e.g. "5m")
+    :param candle_count: Amount of candles to subtract.
+    :param date: date to use. Defaults to now(utc)
+
+    """
+    if not date:
+        date = datetime.now(timezone.utc)
+
+    tf_min = timeframe_to_minutes(timeframe)
+    new_date = timeframe_to_prev_date(timeframe, date) - timedelta(minutes=tf_min * candle_count)
+    return new_date
+
+
+def market_is_active(market: dict) -> bool:
+    """
+    Return True if the market is active.
+    """
+    # "It's active, if the active flag isn't explicitly set to false. If it's missing or
+    # true then it's true. If it's undefined, then it's most likely true, but not 100% )"
+    # See https://github.com/ccxt/ccxt/issues/4874,
+    # https://github.com/ccxt/ccxt/issues/4075#issuecomment-434760520
+    return market.get("active", True) is not False
+
+
+def amount_to_contracts(amount: float, contract_size: float | None) -> float:
+    """
+    Convert amount to contracts.
+    :param amount: amount to convert
+    :param contract_size: contract size - taken from exchange.get_contract_size(pair)
+    :return: num-contracts
+    """
+    if contract_size and contract_size != 1:
+        return float(FtPrecise(amount) / FtPrecise(contract_size))
+    else:
+        return amount
+
+
+def contracts_to_amount(num_contracts: float, contract_size: float | None) -> float:
+    """
+    Takes num-contracts and converts it to contract size
+    :param num_contracts: number of contracts
+    :param contract_size: contract size - taken from exchange.get_contract_size(pair)
+    :return: Amount
+    """
+
+    if contract_size and contract_size != 1:
+        return float(FtPrecise(num_contracts) * FtPrecise(contract_size))
+    else:
+        return num_contracts
+
+
+def amount_to_precision(
+    amount: float, amount_precision: float | None, precisionMode: int | None
+) -> float:
+    """
+    Returns the amount to buy or sell to a precision the Exchange accepts
+    Re-implementation of ccxt internal methods - ensuring we can test the result is correct
+    based on our definitions.
+    :param amount: amount to truncate
+    :param amount_precision: amount precision to use.
+                             should be retrieved from markets[pair]['precision']['amount']
+    :param precisionMode: precision mode to use. Should be used from precisionMode
+                          one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
+    :return: truncated amount
+    """
+    if amount_precision is not None and precisionMode is not None:
+        precision = int(amount_precision) if precisionMode != TICK_SIZE else amount_precision
+        # precision must be an int for non-ticksize inputs.
+        amount = float(
+            decimal_to_precision(
+                amount,
+                rounding_mode=TRUNCATE,
+                precision=precision,
+                counting_mode=precisionMode,
+            )
+        )
+
+    return amount
+
+
+def amount_to_contract_precision(
+    amount,
+    amount_precision: float | None,
+    precisionMode: int | None,
+    contract_size: float | None,
+) -> float:
+    """
+    Returns the amount to buy or sell to a precision the Exchange accepts
+    including calculation to and from contracts.
+    Re-implementation of ccxt internal methods - ensuring we can test the result is correct
+    based on our definitions.
+    :param amount: amount to truncate
+    :param amount_precision: amount precision to use.
+                             should be retrieved from markets[pair]['precision']['amount']
+    :param precisionMode: precision mode to use. Should be used from precisionMode
+                          one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
+    :param contract_size: contract size - taken from exchange.get_contract_size(pair)
+    :return: truncated amount
+    """
+    if amount_precision is not None and precisionMode is not None:
+        contracts = amount_to_contracts(amount, contract_size)
+        amount_p = amount_to_precision(contracts, amount_precision, precisionMode)
+        return contracts_to_amount(amount_p, contract_size)
+    return amount
+
+
+def __price_to_precision_significant_digits(
+    price: float,
+    price_precision: float,
+    *,
+    rounding_mode: int = ROUND,
+) -> float:
+    """
+    Implementation of ROUND_UP/Round_down for significant digits mode.
+    """
+    from decimal import ROUND_DOWN as dec_ROUND_DOWN
+    from decimal import ROUND_UP as dec_ROUND_UP
+    from decimal import Decimal
+
+    dec = Decimal(str(price))
+    string = f"{dec:f}"
+    precision = round(price_precision)
+
+    q = precision - dec.adjusted() - 1
+    sigfig = Decimal("10") ** -q
+    if q < 0:
+        string_to_precision = string[:precision]
+        # string_to_precision is '' when we have zero precision
+        below = sigfig * Decimal(string_to_precision if string_to_precision else "0")
+        above = below + sigfig
+        res = above if rounding_mode == ROUND_UP else below
+        precise = f"{res:f}"
+    else:
+        precise = "{:f}".format(
+            dec.quantize(
+                sigfig, rounding=dec_ROUND_DOWN if rounding_mode == ROUND_DOWN else dec_ROUND_UP
+            )
+        )
+    return float(precise)
+
+
+def price_to_precision(
+    price: float,
+    price_precision: float | None,
+    precisionMode: int | None,
+    *,
+    rounding_mode: int = ROUND,
+) -> float:
+    """
+    Returns the price rounded to the precision the Exchange accepts.
+    Partial Re-implementation of ccxt internal method decimal_to_precision(),
+    which does not support rounding up.
+    For stoploss calculations, must use ROUND_UP for longs, and ROUND_DOWN for shorts.
+
+    TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
+    align with amount_to_precision().
+    :param price: price to convert
+    :param price_precision: price precision to use. Used from markets[pair]['precision']['price']
+    :param precisionMode: precision mode to use. Should be used from precisionMode
+                          one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
+    :param rounding_mode: rounding mode to use. Defaults to ROUND
+    :return: price rounded up to the precision the Exchange accepts
+    """
+    if price_precision is not None and precisionMode is not None and not isnan(price):
+        if rounding_mode not in (ROUND_UP, ROUND_DOWN):
+            # Use CCXT code where possible.
+            return float(
+                decimal_to_precision(
+                    price,
+                    rounding_mode=rounding_mode,
+                    precision=int(price_precision)
+                    if precisionMode != TICK_SIZE
+                    else price_precision,
+                    counting_mode=precisionMode,
+                )
+            )
+
+        if precisionMode == TICK_SIZE:
+            precision = FtPrecise(price_precision)
+            price_str = FtPrecise(price)
+            missing = price_str % precision
+            if not missing == FtPrecise("0"):
+                if rounding_mode == ROUND_UP:
+                    res = price_str - missing + precision
+                elif rounding_mode == ROUND_DOWN:
+                    res = price_str - missing
+                return round(float(str(res)), 14)
+            return price
+        elif precisionMode == DECIMAL_PLACES:
+            ndigits = round(price_precision)
+            ticks = price * (10**ndigits)
+            if rounding_mode == ROUND_UP:
+                return ceil(ticks) / (10**ndigits)
+            if rounding_mode == ROUND_DOWN:
+                return floor(ticks) / (10**ndigits)
+
+            raise ValueError(f"Unknown rounding_mode {rounding_mode}")
+        elif precisionMode == SIGNIFICANT_DIGITS:
+            if rounding_mode in (ROUND_UP, ROUND_DOWN):
+                return __price_to_precision_significant_digits(
+                    price, price_precision, rounding_mode=rounding_mode
+                )
+
+        raise ValueError(f"Unknown precisionMode {precisionMode}")
+    return price
diff --git a/freqtrade/exchange/exchange_utils_timeframe.py b/freqtrade/exchange/exchange_utils_timeframe.py
new file mode 100644
index 000000000..494df58f2
--- /dev/null
+++ b/freqtrade/exchange/exchange_utils_timeframe.py
@@ -0,0 +1,78 @@
+from datetime import datetime, timezone
+
+import ccxt
+from ccxt import ROUND_DOWN, ROUND_UP
+
+from freqtrade.util.datetime_helpers import dt_from_ts, dt_ts
+
+
+def timeframe_to_seconds(timeframe: str) -> int:
+    """
+    Translates the timeframe interval value written in the human readable
+    form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
+    of seconds for one timeframe interval.
+    """
+    return ccxt.Exchange.parse_timeframe(timeframe)
+
+
+def timeframe_to_minutes(timeframe: str) -> int:
+    """
+    Same as timeframe_to_seconds, but returns minutes.
+    """
+    return ccxt.Exchange.parse_timeframe(timeframe) // 60
+
+
+def timeframe_to_msecs(timeframe: str) -> int:
+    """
+    Same as timeframe_to_seconds, but returns milliseconds.
+    """
+    return ccxt.Exchange.parse_timeframe(timeframe) * 1000
+
+
+def timeframe_to_resample_freq(timeframe: str) -> str:
+    """
+    Translates the timeframe interval value written in the human readable
+    form ('1m', '5m', '1h', '1d', '1w', etc.) to the resample frequency
+    used by pandas ('1T', '5T', '1H', '1D', '1W', etc.)
+    """
+    if timeframe == "1y":
+        return "1YS"
+    timeframe_seconds = timeframe_to_seconds(timeframe)
+    timeframe_minutes = timeframe_seconds // 60
+    resample_interval = f"{timeframe_seconds}s"
+    if 10000 < timeframe_minutes < 43200:
+        resample_interval = "1W-MON"
+    elif timeframe_minutes >= 43200 and timeframe_minutes < 525600:
+        # Monthly candles need special treatment to stick to the 1st of the month
+        resample_interval = f"{timeframe}S"
+    elif timeframe_minutes > 43200:
+        resample_interval = timeframe
+    return resample_interval
+
+
+def timeframe_to_prev_date(timeframe: str, date: datetime | None = None) -> datetime:
+    """
+    Use Timeframe and determine the candle start date for this date.
+    Does not round when given a candle start date.
+    :param timeframe: timeframe in string format (e.g. "5m")
+    :param date: date to use. Defaults to now(utc)
+    :returns: date of previous candle (with utc timezone)
+    """
+    if not date:
+        date = datetime.now(timezone.utc)
+
+    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, dt_ts(date), ROUND_DOWN) // 1000
+    return dt_from_ts(new_timestamp)
+
+
+def timeframe_to_next_date(timeframe: str, date: datetime | None = None) -> datetime:
+    """
+    Use Timeframe and determine next candle.
+    :param timeframe: timeframe in string format (e.g. "5m")
+    :param date: date to use. Defaults to now(utc)
+    :returns: date of next candle (with utc timezone)
+    """
+    if not date:
+        date = datetime.now(timezone.utc)
+    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, dt_ts(date), ROUND_UP) // 1000
+    return dt_from_ts(new_timestamp)
diff --git a/freqtrade/exchange/exchange_ws.py b/freqtrade/exchange/exchange_ws.py
new file mode 100644
index 000000000..cbc9772a3
--- /dev/null
+++ b/freqtrade/exchange/exchange_ws.py
@@ -0,0 +1,227 @@
+import asyncio
+import logging
+import time
+from copy import deepcopy
+from functools import partial
+from threading import Thread
+
+import ccxt
+
+from freqtrade.constants import Config, PairWithTimeframe
+from freqtrade.enums.candletype import CandleType
+from freqtrade.exceptions import TemporaryError
+from freqtrade.exchange.common import retrier
+from freqtrade.exchange.exchange import timeframe_to_seconds
+from freqtrade.exchange.exchange_types import OHLCVResponse
+from freqtrade.util import dt_ts, format_ms_time, format_ms_time_det
+
+
+logger = logging.getLogger(__name__)
+
+
+class ExchangeWS:
+    def __init__(self, config: Config, ccxt_object: ccxt.Exchange) -> None:
+        self.config = config
+        self._ccxt_object = ccxt_object
+        self._background_tasks: set[asyncio.Task] = set()
+
+        self._klines_watching: set[PairWithTimeframe] = set()
+        self._klines_scheduled: set[PairWithTimeframe] = set()
+        self.klines_last_refresh: dict[PairWithTimeframe, float] = {}
+        self.klines_last_request: dict[PairWithTimeframe, float] = {}
+        self._thread = Thread(name="ccxt_ws", target=self._start_forever)
+        self._thread.start()
+        self.__cleanup_called = False
+
+    def _start_forever(self) -> None:
+        self._loop = asyncio.new_event_loop()
+        try:
+            self._loop.run_forever()
+        finally:
+            if self._loop.is_running():
+                self._loop.stop()
+
+    def cleanup(self) -> None:
+        logger.debug("Cleanup called - stopping")
+        self._klines_watching.clear()
+        for task in self._background_tasks:
+            task.cancel()
+        if hasattr(self, "_loop") and not self._loop.is_closed():
+            self.reset_connections()
+
+            self._loop.call_soon_threadsafe(self._loop.stop)
+            time.sleep(0.1)
+            if not self._loop.is_closed():
+                self._loop.close()
+
+        self._thread.join()
+        logger.debug("Stopped")
+
+    def reset_connections(self) -> None:
+        """
+        Reset all connections - avoids "connection-reset" errors that happen after ~9 days
+        """
+        if hasattr(self, "_loop") and not self._loop.is_closed():
+            logger.info("Resetting WS connections.")
+            asyncio.run_coroutine_threadsafe(self._cleanup_async(), loop=self._loop)
+            while not self.__cleanup_called:
+                time.sleep(0.1)
+        self.__cleanup_called = False
+
+    async def _cleanup_async(self) -> None:
+        try:
+            await self._ccxt_object.close()
+            # Clear the cache.
+            # Not doing this will cause problems on startup with dynamic pairlists
+            self._ccxt_object.ohlcvs.clear()
+        except Exception:
+            logger.exception("Exception in _cleanup_async")
+        finally:
+            self.__cleanup_called = True
+
+    def _pop_history(self, paircomb: PairWithTimeframe) -> None:
+        """
+        Remove history for a pair/timeframe combination from ccxt cache
+        """
+        self._ccxt_object.ohlcvs.get(paircomb[0], {}).pop(paircomb[1], None)
+        self.klines_last_refresh.pop(paircomb, None)
+
+    @retrier(retries=3)
+    def ohlcvs(self, pair: str, timeframe: str) -> list[list]:
+        """
+        Returns a copy of the klines for a pair/timeframe combination
+        Note: this will only contain the data received from the websocket
+            so the data will build up over time.
+        """
+        try:
+            return deepcopy(self._ccxt_object.ohlcvs.get(pair, {}).get(timeframe, []))
+        except RuntimeError as e:
+            # Capture runtime errors and retry
+            # TemporaryError does not cause backoff - so we're essentially retrying immediately
+            raise TemporaryError(f"Error deepcopying: {e}") from e
+
+    def cleanup_expired(self) -> None:
+        """
+        Remove pairs from watchlist if they've not been requested within
+        the last timeframe (+ offset)
+        """
+        changed = False
+        for p in list(self._klines_watching):
+            _, timeframe, _ = p
+            timeframe_s = timeframe_to_seconds(timeframe)
+            last_refresh = self.klines_last_request.get(p, 0)
+            if last_refresh > 0 and (dt_ts() - last_refresh) > ((timeframe_s + 20) * 1000):
+                logger.info(f"Removing {p} from websocket watchlist.")
+                self._klines_watching.discard(p)
+                # Pop history to avoid getting stale data
+                self._pop_history(p)
+                changed = True
+        if changed:
+            logger.info(f"Removal done: new watch list ({len(self._klines_watching)})")
+
+    async def _schedule_while_true(self) -> None:
+        # For the ones we should be watching
+        for p in self._klines_watching:
+            # Check if they're already scheduled
+            if p not in self._klines_scheduled:
+                self._klines_scheduled.add(p)
+                pair, timeframe, candle_type = p
+                task = asyncio.create_task(
+                    self._continuously_async_watch_ohlcv(pair, timeframe, candle_type)
+                )
+                self._background_tasks.add(task)
+                task.add_done_callback(
+                    partial(
+                        self._continuous_stopped,
+                        pair=pair,
+                        timeframe=timeframe,
+                        candle_type=candle_type,
+                    )
+                )
+
+    async def _unwatch_ohlcv(self, pair: str, timeframe: str, candle_type: CandleType) -> None:
+        try:
+            await self._ccxt_object.un_watch_ohlcv_for_symbols([[pair, timeframe]])
+        except ccxt.NotSupported as e:
+            logger.debug("un_watch_ohlcv_for_symbols not supported: %s", e)
+            pass
+        except Exception:
+            logger.exception("Exception in _unwatch_ohlcv")
+
+    def _continuous_stopped(
+        self, task: asyncio.Task, pair: str, timeframe: str, candle_type: CandleType
+    ):
+        self._background_tasks.discard(task)
+        result = "done"
+        if task.cancelled():
+            result = "cancelled"
+        else:
+            if (result1 := task.result()) is not None:
+                result = str(result1)
+
+        logger.info(f"{pair}, {timeframe}, {candle_type} - Task finished - {result}")
+        asyncio.run_coroutine_threadsafe(
+            self._unwatch_ohlcv(pair, timeframe, candle_type), loop=self._loop
+        )
+
+        self._klines_scheduled.discard((pair, timeframe, candle_type))
+        self._pop_history((pair, timeframe, candle_type))
+
+    async def _continuously_async_watch_ohlcv(
+        self, pair: str, timeframe: str, candle_type: CandleType
+    ) -> None:
+        try:
+            while (pair, timeframe, candle_type) in self._klines_watching:
+                start = dt_ts()
+                data = await self._ccxt_object.watch_ohlcv(pair, timeframe)
+                self.klines_last_refresh[(pair, timeframe, candle_type)] = dt_ts()
+                logger.debug(
+                    f"watch done {pair}, {timeframe}, data {len(data)} "
+                    f"in {(dt_ts() - start) / 1000:.3f}s"
+                )
+        except ccxt.ExchangeClosedByUser:
+            logger.debug("Exchange connection closed by user")
+        except ccxt.BaseError:
+            logger.exception(f"Exception in continuously_async_watch_ohlcv for {pair}, {timeframe}")
+        finally:
+            self._klines_watching.discard((pair, timeframe, candle_type))
+
+    def schedule_ohlcv(self, pair: str, timeframe: str, candle_type: CandleType) -> None:
+        """
+        Schedule a pair/timeframe combination to be watched
+        """
+        self._klines_watching.add((pair, timeframe, candle_type))
+        self.klines_last_request[(pair, timeframe, candle_type)] = dt_ts()
+        # asyncio.run_coroutine_threadsafe(self.schedule_schedule(), loop=self._loop)
+        asyncio.run_coroutine_threadsafe(self._schedule_while_true(), loop=self._loop)
+        self.cleanup_expired()
+
+    async def get_ohlcv(
+        self,
+        pair: str,
+        timeframe: str,
+        candle_type: CandleType,
+        candle_ts: int,
+    ) -> OHLCVResponse:
+        """
+        Returns cached klines from ccxt's "watch" cache.
+        :param candle_ts: timestamp of the end-time of the candle we expect.
+        """
+        # Deepcopy the response - as it might be modified in the background as new messages arrive
+        candles = self.ohlcvs(pair, timeframe)
+        refresh_date = self.klines_last_refresh[(pair, timeframe, candle_type)]
+        received_ts = candles[-1][0] if candles else 0
+        drop_hint = received_ts >= candle_ts
+        if received_ts > refresh_date:
+            logger.warning(
+                f"{pair}, {timeframe} - Candle date > last refresh "
+                f"({format_ms_time(received_ts)} > {format_ms_time_det(refresh_date)}). "
+                "This usually suggests a problem with time synchronization."
+            )
+        logger.debug(
+            f"watch result for {pair}, {timeframe} with length {len(candles)}, "
+            f"r_ts={format_ms_time(received_ts)}, "
+            f"lref={format_ms_time_det(refresh_date)}, "
+            f"candle_ts={format_ms_time(candle_ts)}, {drop_hint=}"
+        )
+        return pair, timeframe, candle_type, candles, drop_hint
diff --git a/freqtrade/exchange/gate.py b/freqtrade/exchange/gate.py
new file mode 100644
index 000000000..49f0ee74b
--- /dev/null
+++ b/freqtrade/exchange/gate.py
@@ -0,0 +1,161 @@
+"""Gate.io exchange subclass"""
+
+import logging
+from datetime import datetime
+
+import ccxt
+
+from freqtrade.constants import BuySell
+from freqtrade.enums import MarginMode, PriceType, TradingMode
+from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.common import retrier
+from freqtrade.exchange.exchange_types import CcxtOrder, FtHas
+from freqtrade.misc import safe_value_fallback2
+
+
+logger = logging.getLogger(__name__)
+
+
+class Gate(Exchange):
+    """
+    Gate.io exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    unified_account = False
+
+    _ft_has: FtHas = {
+        "order_time_in_force": ["GTC", "IOC"],
+        "stoploss_on_exchange": True,
+        "stoploss_order_types": {"limit": "limit"},
+        "stop_price_param": "stopPrice",
+        "stop_price_prop": "stopPrice",
+        "l2_limit_upper": 1000,
+        "marketOrderRequiresPrice": True,
+        "trades_has_history": False,  # Endpoint would support this - but ccxt doesn't.
+    }
+
+    _ft_has_futures: FtHas = {
+        "needs_trading_fees": True,
+        "marketOrderRequiresPrice": False,
+        "funding_fee_candle_limit": 90,
+        "stop_price_type_field": "price_type",
+        "l2_limit_upper": 300,
+        "stop_price_type_value_mapping": {
+            PriceType.LAST: 0,
+            PriceType.MARK: 1,
+            PriceType.INDEX: 2,
+        },
+    }
+
+    _supported_trading_mode_margin_pairs: list[tuple[TradingMode, MarginMode]] = [
+        # TradingMode.SPOT always supported and not required in this list
+        # (TradingMode.MARGIN, MarginMode.CROSS),
+        # (TradingMode.FUTURES, MarginMode.CROSS),
+        (TradingMode.FUTURES, MarginMode.ISOLATED)
+    ]
+
+    @retrier
+    def additional_exchange_init(self) -> None:
+        """
+        Additional exchange initialization logic.
+        .api will be available at this point.
+        Must be overridden in child methods if required.
+        """
+        try:
+            if not self._config["dry_run"]:
+                # TODO: This should work with 4.4.34 and later.
+                self._api.load_unified_status()
+                is_unified = self._api.options.get("unifiedAccount")
+
+                # Returns a tuple of bools, first for margin, second for Account
+                if is_unified:
+                    self.unified_account = True
+                    logger.info("Gate: Unified account.")
+                else:
+                    self.unified_account = False
+                    logger.info("Gate: Classic account.")
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def _get_params(
+        self,
+        side: BuySell,
+        ordertype: str,
+        leverage: float,
+        reduceOnly: bool,
+        time_in_force: str = "GTC",
+    ) -> dict:
+        params = super()._get_params(
+            side=side,
+            ordertype=ordertype,
+            leverage=leverage,
+            reduceOnly=reduceOnly,
+            time_in_force=time_in_force,
+        )
+        if ordertype == "market" and self.trading_mode == TradingMode.FUTURES:
+            params["type"] = "market"
+            params.update({"timeInForce": "IOC"})
+        return params
+
+    def get_trades_for_order(
+        self, order_id: str, pair: str, since: datetime, params: dict | None = None
+    ) -> list:
+        trades = super().get_trades_for_order(order_id, pair, since, params)
+
+        if self.trading_mode == TradingMode.FUTURES:
+            # Futures usually don't contain fees in the response.
+            # As such, futures orders on gate will not contain a fee, which causes
+            # a repeated "update fee" cycle and wrong calculations.
+            # Therefore we patch the response with fees if it's not available.
+            # An alternative also containing fees would be
+            # privateFuturesGetSettleAccountBook({"settle": "usdt"})
+            pair_fees = self._trading_fees.get(pair, {})
+            if pair_fees:
+                for idx, trade in enumerate(trades):
+                    fee = trade.get("fee", {})
+                    if fee and fee.get("cost") is None:
+                        takerOrMaker = trade.get("takerOrMaker", "taker")
+                        if pair_fees.get(takerOrMaker) is not None:
+                            trades[idx]["fee"] = {
+                                "currency": self.get_pair_quote_currency(pair),
+                                "cost": trade["cost"] * pair_fees[takerOrMaker],
+                                "rate": pair_fees[takerOrMaker],
+                            }
+        return trades
+
+    def get_order_id_conditional(self, order: CcxtOrder) -> str:
+        return safe_value_fallback2(order, order, "id_stop", "id")
+
+    def fetch_stoploss_order(
+        self, order_id: str, pair: str, params: dict | None = None
+    ) -> CcxtOrder:
+        order = self.fetch_order(order_id=order_id, pair=pair, params={"stop": True})
+        if order.get("status", "open") == "closed":
+            # Places a real order - which we need to fetch explicitly.
+            val = "trade_id" if self.trading_mode == TradingMode.FUTURES else "fired_order_id"
+
+            if new_orderid := order.get("info", {}).get(val):
+                order1 = self.fetch_order(order_id=new_orderid, pair=pair, params=params)
+                order1["id_stop"] = order1["id"]
+                order1["id"] = order_id
+                order1["type"] = "stoploss"
+                order1["stopPrice"] = order.get("stopPrice")
+                order1["status_stop"] = "triggered"
+
+                return order1
+        return order
+
+    def cancel_stoploss_order(self, order_id: str, pair: str, params: dict | None = None) -> dict:
+        return self.cancel_order(order_id=order_id, pair=pair, params={"stop": True})
diff --git a/freqtrade/exchange/hitbtc.py b/freqtrade/exchange/hitbtc.py
new file mode 100644
index 000000000..d37c7f12e
--- /dev/null
+++ b/freqtrade/exchange/hitbtc.py
@@ -0,0 +1,22 @@
+import logging
+
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import FtHas
+
+
+logger = logging.getLogger(__name__)
+
+
+class Hitbtc(Exchange):
+    """
+    Hitbtc exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    _ft_has: FtHas = {
+        "ohlcv_candle_limit": 1000,
+    }
diff --git a/freqtrade/exchange/htx.py b/freqtrade/exchange/htx.py
new file mode 100644
index 000000000..9b112ff45
--- /dev/null
+++ b/freqtrade/exchange/htx.py
@@ -0,0 +1,41 @@
+"""HTX exchange subclass"""
+
+import logging
+
+from freqtrade.constants import BuySell
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import FtHas
+
+
+logger = logging.getLogger(__name__)
+
+
+class Htx(Exchange):
+    """
+    HTX exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    _ft_has: FtHas = {
+        "stoploss_on_exchange": True,
+        "stop_price_param": "stopPrice",
+        "stop_price_prop": "stopPrice",
+        "stoploss_order_types": {"limit": "stop-limit"},
+        "l2_limit_range": [5, 10, 20],
+        "l2_limit_range_required": False,
+        "ohlcv_candle_limit_per_timeframe": {
+            "1w": 500,
+            "1M": 500,
+        },
+        "trades_has_history": False,  # Endpoint doesn't have a "since" parameter
+    }
+
+    def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> dict:
+        params = self._params.copy()
+        params.update(
+            {
+                "stopPrice": stop_price,
+                "operator": "lte",
+            }
+        )
+        return params
diff --git a/freqtrade/exchange/hyperliquid.py b/freqtrade/exchange/hyperliquid.py
new file mode 100644
index 000000000..b6ec23942
--- /dev/null
+++ b/freqtrade/exchange/hyperliquid.py
@@ -0,0 +1,208 @@
+"""Hyperliquid exchange subclass"""
+
+import logging
+from copy import deepcopy
+from datetime import datetime
+
+from freqtrade.constants import BuySell
+from freqtrade.enums import MarginMode, TradingMode
+from freqtrade.exceptions import ExchangeError, OperationalException
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import CcxtOrder, FtHas
+from freqtrade.util.datetime_helpers import dt_from_ts
+
+
+logger = logging.getLogger(__name__)
+
+
+class Hyperliquid(Exchange):
+    """Hyperliquid exchange class.
+    Contains adjustments needed for Freqtrade to work with this exchange.
+    """
+
+    _ft_has: FtHas = {
+        "ohlcv_has_history": False,
+        "l2_limit_range": [20],
+        "trades_has_history": False,
+        "tickers_have_bid_ask": False,
+        "stoploss_on_exchange": False,
+        "exchange_has_overrides": {"fetchTrades": False},
+        "marketOrderRequiresPrice": True,
+    }
+    _ft_has_futures: FtHas = {
+        "stoploss_on_exchange": True,
+        "stoploss_order_types": {"limit": "limit"},
+        "stop_price_prop": "stopPrice",
+        "funding_fee_timeframe": "1h",
+        "funding_fee_candle_limit": 500,
+        "uses_leverage_tiers": False,
+    }
+
+    _supported_trading_mode_margin_pairs: list[tuple[TradingMode, MarginMode]] = [
+        (TradingMode.FUTURES, MarginMode.ISOLATED)
+    ]
+
+    @property
+    def _ccxt_config(self) -> dict:
+        # ccxt Hyperliquid defaults to swap
+        config = {}
+        if self.trading_mode == TradingMode.SPOT:
+            config.update({"options": {"defaultType": "spot"}})
+        config.update(super()._ccxt_config)
+        return config
+
+    def get_max_leverage(self, pair: str, stake_amount: float | None) -> float:
+        # There are no leverage tiers
+        if self.trading_mode == TradingMode.FUTURES:
+            return self.markets[pair]["limits"]["leverage"]["max"]
+        else:
+            return 1.0
+
+    def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
+        if self.trading_mode != TradingMode.SPOT:
+            # Hyperliquid expects leverage to be an int
+            leverage = int(leverage)
+            # Hyperliquid needs the parameter leverage.
+            # Don't use _set_leverage(), as this sets margin back to cross
+            self.set_margin_mode(pair, self.margin_mode, params={"leverage": leverage})
+
+    def dry_run_liquidation_price(
+        self,
+        pair: str,
+        open_rate: float,  # Entry price of position
+        is_short: bool,
+        amount: float,
+        stake_amount: float,
+        leverage: float,
+        wallet_balance: float,  # Or margin balance
+        open_trades: list,
+    ) -> float | None:
+        """
+        Optimized
+        Docs: https://hyperliquid.gitbook.io/hyperliquid-docs/trading/liquidations
+        Below can be done in fewer lines of code, but like this it matches the documentation.
+
+        Tested with 196 unique ccxt fetch_positions() position outputs
+        - Only first output per position where pnl=0.0
+        - Compare against returned liquidation price
+        Positions: 197 Average deviation: 0.00028980% Max deviation: 0.01309453%
+        Positions info:
+        {'leverage': {1.0: 23, 2.0: 155, 3.0: 8, 4.0: 7, 5.0: 4},
+        'side': {'long': 133, 'short': 64},
+        'symbol': {'BTC/USDC:USDC': 81,
+                   'DOGE/USDC:USDC': 20,
+                   'ETH/USDC:USDC': 53,
+                   'SOL/USDC:USDC': 43}}
+        """
+        # Defining/renaming variables to match the documentation
+        isolated_margin = wallet_balance
+        position_size = amount
+        price = open_rate
+        position_value = price * position_size
+        max_leverage = self.markets[pair]["limits"]["leverage"]["max"]
+
+        # Docs: The maintenance margin is half of the initial margin at max leverage,
+        #       which varies from 3-50x. In other words, the maintenance margin is between 1%
+        #       (for 50x max leverage assets) and 16.7% (for 3x max leverage assets)
+        #       depending on the asset
+        # The key thing here is 'Half of the initial margin at max leverage'.
+        # A bit ambiguous, but this interpretation leads to accurate results:
+        #       1. Start from the position value
+        #       2. Assume max leverage, calculate the initial margin by dividing the position value
+        #          by the max leverage
+        #       3. Divide this by 2
+        maintenance_margin_required = position_value / max_leverage / 2
+
+        # Docs: margin_available (isolated) = isolated_margin - maintenance_margin_required
+        margin_available = isolated_margin - maintenance_margin_required
+
+        # Docs: The maintenance margin is half of the initial margin at max leverage
+        # The docs don't explicitly specify maintenance leverage, but this works.
+        # Double because of the statement 'half of the initial margin at max leverage'
+        maintenance_leverage = max_leverage * 2
+
+        # Docs: l = 1 / MAINTENANCE_LEVERAGE (Using 'll' to comply with PEP8: E741)
+        ll = 1 / maintenance_leverage
+
+        # Docs: side = 1 for long and -1 for short
+        side = -1 if is_short else 1
+
+        # Docs: liq_price = price - side * margin_available / position_size / (1 - l * side)
+        liq_price = price - side * margin_available / position_size / (1 - ll * side)
+
+        if self.trading_mode == TradingMode.FUTURES:
+            return liq_price
+        else:
+            raise OperationalException(
+                "Freqtrade only supports isolated futures for leverage trading"
+            )
+
+    def get_funding_fees(
+        self, pair: str, amount: float, is_short: bool, open_date: datetime
+    ) -> float:
+        """
+        Fetch funding fees, either from the exchange (live) or calculates them
+        based on funding rate/mark price history
+        :param pair: The quote/base pair of the trade
+        :param is_short: trade direction
+        :param amount: Trade amount
+        :param open_date: Open date of the trade
+        :return: funding fee since open_date
+        :raises: ExchangeError if something goes wrong.
+        """
+        # Hyperliquid does not have fetchFundingHistory
+        if self.trading_mode == TradingMode.FUTURES:
+            try:
+                return self._fetch_and_calculate_funding_fees(pair, amount, is_short, open_date)
+            except ExchangeError:
+                logger.warning(f"Could not update funding fees for {pair}.")
+        return 0.0
+
+    def _adjust_hyperliquid_order(
+        self,
+        order: dict,
+    ) -> dict:
+        """
+        Adjusts order response for Hyperliquid
+        :param order: Order response from Hyperliquid
+        :return: Adjusted order response
+        """
+        if (
+            order["average"] is None
+            and order["status"] in ("canceled", "closed")
+            and order["filled"] > 0
+        ):
+            # Hyperliquid does not fill the average price in the order response
+            # Fetch trades to calculate the average price to have the actual price
+            # the order was executed at
+            trades = self.get_trades_for_order(
+                order["id"], order["symbol"], since=dt_from_ts(order["timestamp"])
+            )
+
+            if trades:
+                total_amount = sum(t["amount"] for t in trades)
+                order["average"] = (
+                    sum(t["price"] * t["amount"] for t in trades) / total_amount
+                    if total_amount
+                    else None
+                )
+        return order
+
+    def fetch_order(self, order_id: str, pair: str, params: dict | None = None) -> CcxtOrder:
+        order = super().fetch_order(order_id, pair, params)
+
+        order = self._adjust_hyperliquid_order(order)
+        self._log_exchange_response("fetch_order2", order)
+
+        return order
+
+    def fetch_orders(
+        self, pair: str, since: datetime, params: dict | None = None
+    ) -> list[CcxtOrder]:
+        orders = super().fetch_orders(pair, since, params)
+        for idx, order in enumerate(deepcopy(orders)):
+            order2 = self._adjust_hyperliquid_order(order)
+            orders[idx] = order2
+
+        self._log_exchange_response("fetch_orders2", orders)
+        return orders
diff --git a/freqtrade/exchange/idex.py b/freqtrade/exchange/idex.py
new file mode 100644
index 000000000..9c750f64d
--- /dev/null
+++ b/freqtrade/exchange/idex.py
@@ -0,0 +1,20 @@
+"""Idex exchange subclass"""
+
+import logging
+
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import FtHas
+
+
+logger = logging.getLogger(__name__)
+
+
+class Idex(Exchange):
+    """
+    Idex exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    _ft_has: FtHas = {
+        "ohlcv_candle_limit": 1000,
+    }
diff --git a/freqtrade/exchange/interface.py b/freqtrade/exchange/interface.py
deleted file mode 100644
index 6121a98b3..000000000
--- a/freqtrade/exchange/interface.py
+++ /dev/null
@@ -1,172 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
-
-
-class Exchange(ABC):
-    @property
-    def name(self) -> str:
-        """
-        Name of the exchange.
-        :return: str representation of the class name
-        """
-        return self.__class__.__name__
-
-    @property
-    def fee(self) -> float:
-        """
-        Fee for placing an order
-        :return: percentage in float
-        """
-
-    @abstractmethod
-    def buy(self, pair: str, rate: float, amount: float) -> str:
-        """
-        Places a limit buy order.
-        :param pair: Pair as str, format: BTC_ETH
-        :param rate: Rate limit for order
-        :param amount: The amount to purchase
-        :return: order_id of the placed buy order
-        """
-
-    @abstractmethod
-    def sell(self, pair: str, rate: float, amount: float) -> str:
-        """
-        Places a limit sell order.
-        :param pair: Pair as str, format: BTC_ETH
-        :param rate: Rate limit for order
-        :param amount: The amount to sell
-        :return: order_id of the placed sell order
-        """
-
-    @abstractmethod
-    def get_balance(self, currency: str) -> float:
-        """
-        Gets account balance.
-        :param currency: Currency as str, format: BTC
-        :return: float
-        """
-
-    @abstractmethod
-    def get_balances(self) -> List[dict]:
-        """
-        Gets account balances across currencies
-        :return: List of dicts, format: [
-          {
-            'Currency': str,
-            'Balance': float,
-            'Available': float,
-            'Pending': float,
-          }
-          ...
-        ]
-        """
-
-    @abstractmethod
-    def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
-        """
-        Gets ticker for given pair.
-        :param pair: Pair as str, format: BTC_ETC
-        :param refresh: Shall we query a new value or a cached value is enough
-        :return: dict, format: {
-            'bid': float,
-            'ask': float,
-            'last': float
-        }
-        """
-
-    @abstractmethod
-    def get_ticker_history(self, pair: str, tick_interval: int) -> List[Dict]:
-        """
-        Gets ticker history for given pair.
-        :param pair: Pair as str, format: BTC_ETC
-        :param tick_interval: ticker interval in minutes
-        :return: list, format: [
-            {
-                'O': float,       (Open)
-                'H': float,       (High)
-                'L': float,       (Low)
-                'C': float,       (Close)
-                'V': float,       (Volume)
-                'T': datetime,    (Time)
-                'BV': float,      (Base Volume)
-            },
-            ...
-        ]
-        """
-
-    def get_order(self, order_id: str) -> Dict:
-        """
-        Get order details for the given order_id.
-        :param order_id: ID as str
-        :return: dict, format: {
-            'id': str,
-            'type': str,
-            'pair': str,
-            'opened': str ISO 8601 datetime,
-            'closed': str ISO 8601 datetime,
-            'rate': float,
-            'amount': float,
-            'remaining': int
-        }
-        """
-
-    @abstractmethod
-    def cancel_order(self, order_id: str) -> None:
-        """
-        Cancels order for given order_id.
-        :param order_id: ID as str
-        :return: None
-        """
-
-    @abstractmethod
-    def get_pair_detail_url(self, pair: str) -> str:
-        """
-        Returns the market detail url for the given pair.
-        :param pair: Pair as str, format: BTC_ETC
-        :return: URL as str
-        """
-
-    @abstractmethod
-    def get_markets(self) -> List[str]:
-        """
-        Returns all available markets.
-        :return: List of all available pairs
-        """
-
-    @abstractmethod
-    def get_market_summaries(self) -> List[Dict]:
-        """
-        Returns a 24h market summary for all available markets
-        :return: list, format: [
-            {
-                'MarketName': str,
-                'High': float,
-                'Low': float,
-                'Volume': float,
-                'Last': float,
-                'TimeStamp': datetime,
-                'BaseVolume': float,
-                'Bid': float,
-                'Ask': float,
-                'OpenBuyOrders': int,
-                'OpenSellOrders': int,
-                'PrevDay': float,
-                'Created': datetime
-            },
-            ...
-        ]
-        """
-
-    @abstractmethod
-    def get_wallet_health(self) -> List[Dict]:
-        """
-        Returns a list of all wallet health information
-        :return: list, format: [
-            {
-                'Currency': str,
-                'IsActive': bool,
-                'LastChecked': str,
-                'Notice': str
-            },
-            ...
-        """
diff --git a/freqtrade/exchange/kraken.py b/freqtrade/exchange/kraken.py
new file mode 100644
index 000000000..d82725bbb
--- /dev/null
+++ b/freqtrade/exchange/kraken.py
@@ -0,0 +1,218 @@
+"""Kraken exchange subclass"""
+
+import logging
+from datetime import datetime
+from typing import Any
+
+import ccxt
+from pandas import DataFrame
+
+from freqtrade.constants import BuySell
+from freqtrade.enums import MarginMode, TradingMode
+from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.common import retrier
+from freqtrade.exchange.exchange_types import CcxtBalances, FtHas, Tickers
+
+
+logger = logging.getLogger(__name__)
+
+
+class Kraken(Exchange):
+    _params: dict = {"trading_agreement": "agree"}
+    _ft_has: FtHas = {
+        "stoploss_on_exchange": True,
+        "stop_price_param": "stopLossPrice",
+        "stop_price_prop": "stopLossPrice",
+        "stoploss_order_types": {"limit": "limit", "market": "market"},
+        "order_time_in_force": ["GTC", "IOC", "PO"],
+        "ohlcv_has_history": False,
+        "trades_pagination": "id",
+        "trades_pagination_arg": "since",
+        "trades_pagination_overlap": False,
+        "trades_has_history": True,
+        "mark_ohlcv_timeframe": "4h",
+    }
+
+    _supported_trading_mode_margin_pairs: list[tuple[TradingMode, MarginMode]] = [
+        # TradingMode.SPOT always supported and not required in this list
+        # (TradingMode.MARGIN, MarginMode.CROSS),
+        # (TradingMode.FUTURES, MarginMode.CROSS)
+    ]
+
+    def market_is_tradable(self, market: dict[str, Any]) -> bool:
+        """
+        Check if the market symbol is tradable by Freqtrade.
+        Default checks + check if pair is darkpool pair.
+        """
+        parent_check = super().market_is_tradable(market)
+
+        return parent_check and market.get("darkpool", False) is False
+
+    def get_tickers(
+        self,
+        symbols: list[str] | None = None,
+        *,
+        cached: bool = False,
+        market_type: TradingMode | None = None,
+    ) -> Tickers:
+        # Only fetch tickers for current stake currency
+        # Otherwise the request for kraken becomes too large.
+        symbols = list(self.get_markets(quote_currencies=[self._config["stake_currency"]]))
+        return super().get_tickers(symbols=symbols, cached=cached, market_type=market_type)
+
+    def consolidate_balances(self, balances: CcxtBalances) -> CcxtBalances:
+        """
+        Consolidate balances for the same currency.
+        Kraken returns ".F" balances if rewards is enabled.
+        """
+        consolidated: CcxtBalances = {}
+        for currency, balance in balances.items():
+            base_currency = currency[:-2] if currency.endswith(".F") else currency
+
+            if base_currency in consolidated:
+                consolidated[base_currency]["free"] += balance["free"]
+                consolidated[base_currency]["used"] += balance["used"]
+                consolidated[base_currency]["total"] += balance["total"]
+            else:
+                consolidated[base_currency] = balance
+        return consolidated
+
+    @retrier
+    def get_balances(self) -> CcxtBalances:
+        if self._config["dry_run"]:
+            return {}
+
+        try:
+            balances = self._api.fetch_balance()
+            # Remove additional info from ccxt results
+            balances.pop("info", None)
+            balances.pop("free", None)
+            balances.pop("total", None)
+            balances.pop("used", None)
+            self._log_exchange_response("fetch_balances", balances)
+
+            # Consolidate balances
+            balances = self.consolidate_balances(balances)
+
+            orders = self._api.fetch_open_orders()
+            order_list = [
+                (
+                    x["symbol"].split("/")[0 if x["side"] == "sell" else 1],
+                    x["remaining"] if x["side"] == "sell" else x["remaining"] * x["price"],
+                    # Don't remove the below comment, this can be important for debugging
+                    # x["side"], x["amount"],
+                )
+                for x in orders
+                if x["remaining"] is not None and (x["side"] == "sell" or x["price"] is not None)
+            ]
+            for bal in balances:
+                if not isinstance(balances[bal], dict):
+                    continue
+                balances[bal]["used"] = sum(order[1] for order in order_list if order[0] == bal)
+                balances[bal]["free"] = balances[bal]["total"] - balances[bal]["used"]
+
+            self._log_exchange_response("fetch_balances2", balances)
+            return balances
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get balance due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def _set_leverage(
+        self,
+        leverage: float,
+        pair: str | None = None,
+        accept_fail: bool = False,
+    ):
+        """
+        Kraken set's the leverage as an option in the order object, so we need to
+        add it to params
+        """
+        return
+
+    def _get_params(
+        self,
+        side: BuySell,
+        ordertype: str,
+        leverage: float,
+        reduceOnly: bool,
+        time_in_force: str = "GTC",
+    ) -> dict:
+        params = super()._get_params(
+            side=side,
+            ordertype=ordertype,
+            leverage=leverage,
+            reduceOnly=reduceOnly,
+            time_in_force=time_in_force,
+        )
+        if leverage > 1.0:
+            params["leverage"] = round(leverage)
+        if time_in_force == "PO":
+            params.pop("timeInForce", None)
+            params["postOnly"] = True
+        return params
+
+    def calculate_funding_fees(
+        self,
+        df: DataFrame,
+        amount: float,
+        is_short: bool,
+        open_date: datetime,
+        close_date: datetime,
+        time_in_ratio: float | None = None,
+    ) -> float:
+        """
+        # ! This method will always error when run by Freqtrade because time_in_ratio is never
+        # ! passed to _get_funding_fee. For kraken futures to work in dry run and backtesting
+        # ! functionality must be added that passes the parameter time_in_ratio to
+        # ! _get_funding_fee when using Kraken
+        calculates the sum of all funding fees that occurred for a pair during a futures trade
+        :param df: Dataframe containing combined funding and mark rates
+                   as `open_fund` and `open_mark`.
+        :param amount: The quantity of the trade
+        :param is_short: trade direction
+        :param open_date: The date and time that the trade started
+        :param close_date: The date and time that the trade ended
+        :param time_in_ratio: Not used by most exchange classes
+        """
+        if not time_in_ratio:
+            raise OperationalException(
+                f"time_in_ratio is required for {self.name}._get_funding_fee"
+            )
+        fees: float = 0
+
+        if not df.empty:
+            df = df[(df["date"] >= open_date) & (df["date"] <= close_date)]
+            fees = sum(df["open_fund"] * df["open_mark"] * amount * time_in_ratio)
+
+        return fees if is_short else -fees
+
+    def _get_trade_pagination_next_value(self, trades: list[dict]):
+        """
+        Extract pagination id for the next "from_id" value
+        Applies only to fetch_trade_history by id.
+        """
+        if len(trades) > 0:
+            if isinstance(trades[-1].get("info"), list) and len(trades[-1].get("info", [])) > 7:
+                # Trade response's "last" value.
+                return trades[-1].get("info", [])[-1]
+            # Fall back to timestamp if info is somehow empty.
+            return trades[-1].get("timestamp")
+        return None
+
+    def _valid_trade_pagination_id(self, pair: str, from_id: str) -> bool:
+        """
+        Verify trade-pagination id is valid.
+        Workaround for odd Kraken issue where ID is sometimes wrong.
+        """
+        # Regular id's are in timestamp format 1705443695120072285
+        # If the id is smaller than 19 characters, it's not a valid timestamp.
+        if len(from_id) >= 19:
+            return True
+        logger.debug(f"{pair} - trade-pagination id is not valid. Fallback to timestamp.")
+        return False
diff --git a/freqtrade/exchange/kucoin.py b/freqtrade/exchange/kucoin.py
new file mode 100644
index 000000000..17afbd63a
--- /dev/null
+++ b/freqtrade/exchange/kucoin.py
@@ -0,0 +1,67 @@
+"""Kucoin exchange subclass."""
+
+import logging
+
+from freqtrade.constants import BuySell
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import CcxtOrder, FtHas
+
+
+logger = logging.getLogger(__name__)
+
+
+class Kucoin(Exchange):
+    """Kucoin exchange class.
+
+    Contains adjustments needed for Freqtrade to work with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    _ft_has: FtHas = {
+        "stoploss_on_exchange": True,
+        "stop_price_param": "stopPrice",
+        "stop_price_prop": "stopPrice",
+        "stoploss_order_types": {"limit": "limit", "market": "market"},
+        "l2_limit_range": [20, 100],
+        "l2_limit_range_required": False,
+        "order_time_in_force": ["GTC", "FOK", "IOC"],
+    }
+
+    def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> dict:
+        params = self._params.copy()
+        params.update({"stopPrice": stop_price, "stop": "loss"})
+        return params
+
+    def create_order(
+        self,
+        *,
+        pair: str,
+        ordertype: str,
+        side: BuySell,
+        amount: float,
+        rate: float,
+        leverage: float,
+        reduceOnly: bool = False,
+        time_in_force: str = "GTC",
+    ) -> CcxtOrder:
+        res = super().create_order(
+            pair=pair,
+            ordertype=ordertype,
+            side=side,
+            amount=amount,
+            rate=rate,
+            leverage=leverage,
+            reduceOnly=reduceOnly,
+            time_in_force=time_in_force,
+        )
+        # Kucoin returns only the order-id.
+        # ccxt returns status = 'closed' at the moment - which is information ccxt invented.
+        # Since we rely on status heavily, we must set it to 'open' here.
+        # ref: https://github.com/ccxt/ccxt/pull/16674, (https://github.com/ccxt/ccxt/pull/16553)
+        if not self._config["dry_run"]:
+            res["type"] = ordertype
+            res["status"] = "open"
+        return res
diff --git a/freqtrade/exchange/lbank.py b/freqtrade/exchange/lbank.py
new file mode 100644
index 000000000..02156d2c2
--- /dev/null
+++ b/freqtrade/exchange/lbank.py
@@ -0,0 +1,21 @@
+"""Lbank exchange subclass"""
+
+import logging
+
+from freqtrade.exchange import Exchange
+from freqtrade.exchange.exchange_types import FtHas
+
+
+logger = logging.getLogger(__name__)
+
+
+class Lbank(Exchange):
+    """
+    Lbank exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    _ft_has: FtHas = {
+        "ohlcv_candle_limit": 1998,  # lower than the allowed 2000 to avoid current_candle issue
+        "trades_has_history": False,
+    }
diff --git a/freqtrade/exchange/okx.py b/freqtrade/exchange/okx.py
new file mode 100644
index 000000000..e9784f12e
--- /dev/null
+++ b/freqtrade/exchange/okx.py
@@ -0,0 +1,288 @@
+import logging
+from datetime import timedelta
+
+import ccxt
+
+from freqtrade.constants import BuySell
+from freqtrade.enums import CandleType, MarginMode, PriceType, TradingMode
+from freqtrade.exceptions import (
+    DDosProtection,
+    OperationalException,
+    RetryableOrderError,
+    TemporaryError,
+)
+from freqtrade.exchange import Exchange, date_minus_candles
+from freqtrade.exchange.common import API_RETRY_COUNT, retrier
+from freqtrade.exchange.exchange_types import CcxtOrder, FtHas
+from freqtrade.misc import safe_value_fallback2
+from freqtrade.util import dt_now, dt_ts
+
+
+logger = logging.getLogger(__name__)
+
+
+class Okx(Exchange):
+    """Okx exchange class.
+
+    Contains adjustments needed for Freqtrade to work with this exchange.
+    """
+
+    _ft_has: FtHas = {
+        "ohlcv_candle_limit": 100,  # Warning, special case with data prior to X months
+        "mark_ohlcv_timeframe": "4h",
+        "funding_fee_timeframe": "8h",
+        "stoploss_order_types": {"limit": "limit"},
+        "stoploss_on_exchange": True,
+        "trades_has_history": False,  # Endpoint doesn't have a "since" parameter
+        "ws_enabled": True,
+    }
+    _ft_has_futures: FtHas = {
+        "tickers_have_quoteVolume": False,
+        "stop_price_type_field": "slTriggerPxType",
+        "stop_price_type_value_mapping": {
+            PriceType.LAST: "last",
+            PriceType.MARK: "index",
+            PriceType.INDEX: "mark",
+        },
+        "ws_enabled": True,
+    }
+
+    _supported_trading_mode_margin_pairs: list[tuple[TradingMode, MarginMode]] = [
+        # TradingMode.SPOT always supported and not required in this list
+        # (TradingMode.MARGIN, MarginMode.CROSS),
+        # (TradingMode.FUTURES, MarginMode.CROSS),
+        (TradingMode.FUTURES, MarginMode.ISOLATED),
+    ]
+
+    net_only = True
+
+    _ccxt_params: dict = {"options": {"brokerId": "ffb5405ad327SUDE"}}
+
+    def ohlcv_candle_limit(
+        self, timeframe: str, candle_type: CandleType, since_ms: int | None = None
+    ) -> int:
+        """
+        Exchange ohlcv candle limit
+        OKX has the following behaviour:
+        * 300 candles for up-to-date data
+        * 100 candles for historic data
+        * 100 candles for additional candles (not futures or spot).
+        :param timeframe: Timeframe to check
+        :param candle_type: Candle-type
+        :param since_ms: Starting timestamp
+        :return: Candle limit as integer
+        """
+        if candle_type in (CandleType.FUTURES, CandleType.SPOT) and (
+            not since_ms or since_ms > (date_minus_candles(timeframe, 300).timestamp() * 1000)
+        ):
+            return 300
+
+        return super().ohlcv_candle_limit(timeframe, candle_type, since_ms)
+
+    @retrier
+    def additional_exchange_init(self) -> None:
+        """
+        Additional exchange initialization logic.
+        .api will be available at this point.
+        Must be overridden in child methods if required.
+        """
+        try:
+            if self.trading_mode == TradingMode.FUTURES and not self._config["dry_run"]:
+                accounts = self._api.fetch_accounts()
+                self._log_exchange_response("fetch_accounts", accounts)
+                if len(accounts) > 0:
+                    self.net_only = accounts[0].get("info", {}).get("posMode") == "net_mode"
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    def _get_posSide(self, side: BuySell, reduceOnly: bool):
+        if self.net_only:
+            return "net"
+        if not reduceOnly:
+            # Enter
+            return "long" if side == "buy" else "short"
+        else:
+            # Exit
+            return "long" if side == "sell" else "short"
+
+    def _get_params(
+        self,
+        side: BuySell,
+        ordertype: str,
+        leverage: float,
+        reduceOnly: bool,
+        time_in_force: str = "GTC",
+    ) -> dict:
+        params = super()._get_params(
+            side=side,
+            ordertype=ordertype,
+            leverage=leverage,
+            reduceOnly=reduceOnly,
+            time_in_force=time_in_force,
+        )
+        if self.trading_mode == TradingMode.FUTURES and self.margin_mode:
+            params["tdMode"] = self.margin_mode.value
+            params["posSide"] = self._get_posSide(side, reduceOnly)
+        return params
+
+    def __fetch_leverage_already_set(self, pair: str, leverage: float, side: BuySell) -> bool:
+        try:
+            res_lev = self._api.fetch_leverage(
+                symbol=pair,
+                params={
+                    "mgnMode": self.margin_mode.value,
+                    "posSide": self._get_posSide(side, False),
+                },
+            )
+            self._log_exchange_response("get_leverage", res_lev)
+            already_set = all(float(x["lever"]) == leverage for x in res_lev["data"])
+            return already_set
+
+        except ccxt.BaseError:
+            # Assume all errors as "not set yet"
+            return False
+
+    @retrier
+    def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
+        if self.trading_mode != TradingMode.SPOT and self.margin_mode is not None:
+            try:
+                res = self._api.set_leverage(
+                    leverage=leverage,
+                    symbol=pair,
+                    params={
+                        "mgnMode": self.margin_mode.value,
+                        "posSide": self._get_posSide(side, False),
+                    },
+                )
+                self._log_exchange_response("set_leverage", res)
+
+            except ccxt.DDoSProtection as e:
+                raise DDosProtection(e) from e
+            except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+                already_set = self.__fetch_leverage_already_set(pair, leverage, side)
+                if not already_set:
+                    raise TemporaryError(
+                        f"Could not set leverage due to {e.__class__.__name__}. Message: {e}"
+                    ) from e
+            except ccxt.BaseError as e:
+                raise OperationalException(e) from e
+
+    def get_max_pair_stake_amount(self, pair: str, price: float, leverage: float = 1.0) -> float:
+        if self.trading_mode == TradingMode.SPOT:
+            return float("inf")  # Not actually inf, but this probably won't matter for SPOT
+
+        if pair not in self._leverage_tiers:
+            return float("inf")
+
+        pair_tiers = self._leverage_tiers[pair]
+        return pair_tiers[-1]["maxNotional"] / leverage
+
+    def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> dict:
+        params = super()._get_stop_params(side, ordertype, stop_price)
+        if self.trading_mode == TradingMode.FUTURES and self.margin_mode:
+            params["tdMode"] = self.margin_mode.value
+            params["posSide"] = self._get_posSide(side, True)
+        return params
+
+    def _convert_stop_order(self, pair: str, order_id: str, order: CcxtOrder) -> CcxtOrder:
+        if (
+            order.get("status", "open") == "closed"
+            and (real_order_id := order.get("info", {}).get("ordId")) is not None
+        ):
+            # Once a order triggered, we fetch the regular followup order.
+            order_reg = self.fetch_order(real_order_id, pair)
+            self._log_exchange_response("fetch_stoploss_order1", order_reg)
+            order_reg["id_stop"] = order_reg["id"]
+            order_reg["id"] = order_id
+            order_reg["type"] = "stoploss"
+            order_reg["status_stop"] = "triggered"
+            return order_reg
+        order = self._order_contracts_to_amount(order)
+        order["type"] = "stoploss"
+        return order
+
+    @retrier(retries=API_RETRY_COUNT)
+    def fetch_stoploss_order(
+        self, order_id: str, pair: str, params: dict | None = None
+    ) -> CcxtOrder:
+        if self._config["dry_run"]:
+            return self.fetch_dry_run_order(order_id)
+
+        try:
+            params1 = {"stop": True}
+            order_reg = self._api.fetch_order(order_id, pair, params=params1)
+            self._log_exchange_response("fetch_stoploss_order", order_reg)
+            return self._convert_stop_order(pair, order_id, order_reg)
+        except (ccxt.OrderNotFound, ccxt.InvalidOrder):
+            pass
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f"Could not get order due to {e.__class__.__name__}. Message: {e}"
+            ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+        return self._fetch_stop_order_fallback(order_id, pair)
+
+    def _fetch_stop_order_fallback(self, order_id: str, pair: str) -> CcxtOrder:
+        params2 = {"stop": True, "ordType": "conditional"}
+        for method in (
+            self._api.fetch_open_orders,
+            self._api.fetch_closed_orders,
+            self._api.fetch_canceled_orders,
+        ):
+            try:
+                orders = method(pair, params=params2)
+                orders_f = [order for order in orders if order["id"] == order_id]
+                if orders_f:
+                    order = orders_f[0]
+                    return self._convert_stop_order(pair, order_id, order)
+            except (ccxt.OrderNotFound, ccxt.InvalidOrder):
+                pass
+            except ccxt.DDoSProtection as e:
+                raise DDosProtection(e) from e
+            except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+                raise TemporaryError(
+                    f"Could not get order due to {e.__class__.__name__}. Message: {e}"
+                ) from e
+            except ccxt.BaseError as e:
+                raise OperationalException(e) from e
+        raise RetryableOrderError(f"StoplossOrder not found (pair: {pair} id: {order_id}).")
+
+    def get_order_id_conditional(self, order: CcxtOrder) -> str:
+        if order.get("type", "") == "stop":
+            return safe_value_fallback2(order, order, "id_stop", "id")
+        return order["id"]
+
+    def cancel_stoploss_order(self, order_id: str, pair: str, params: dict | None = None) -> dict:
+        params1 = {"stop": True}
+        # 'ordType': 'conditional'
+        #
+        return self.cancel_order(
+            order_id=order_id,
+            pair=pair,
+            params=params1,
+        )
+
+    def _fetch_orders_emulate(self, pair: str, since_ms: int) -> list[CcxtOrder]:
+        orders = []
+
+        orders = self._api.fetch_closed_orders(pair, since=since_ms)
+        if since_ms < dt_ts(dt_now() - timedelta(days=6, hours=23)):
+            # Regular fetch_closed_orders only returns 7 days of data.
+            # Force usage of "archive" endpoint, which returns 3 months of data.
+            params = {"method": "privateGetTradeOrdersHistoryArchive"}
+            orders_hist = self._api.fetch_closed_orders(pair, since=since_ms, params=params)
+            orders.extend(orders_hist)
+
+        orders_open = self._api.fetch_open_orders(pair, since=since_ms)
+        orders.extend(orders_open)
+        return orders
diff --git a/freqtrade/fiat_convert.py b/freqtrade/fiat_convert.py
deleted file mode 100644
index 4874247db..000000000
--- a/freqtrade/fiat_convert.py
+++ /dev/null
@@ -1,192 +0,0 @@
-"""
-Module that define classes to convert Crypto-currency to FIAT
-e.g BTC to USD
-"""
-
-import logging
-import time
-from coinmarketcap import Market
-
-logger = logging.getLogger(__name__)
-
-
-class CryptoFiat():
-    """
-    Object to describe what is the price of Crypto-currency in a FIAT
-    """
-    # Constants
-    CACHE_DURATION = 6 * 60 * 60  # 6 hours
-
-    def __init__(self, crypto_symbol: str, fiat_symbol: str, price: float) -> None:
-        """
-        Create an object that will contains the price for a crypto-currency in fiat
-        :param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
-        :param fiat_symbol: FIAT currency you want to convert to (e.g USD)
-        :param price: Price in FIAT
-        """
-
-        # Public attributes
-        self.crypto_symbol = None
-        self.fiat_symbol = None
-        self.price = 0.0
-
-        # Private attributes
-        self._expiration = 0
-
-        self.crypto_symbol = crypto_symbol.upper()
-        self.fiat_symbol = fiat_symbol.upper()
-        self.set_price(price=price)
-
-    def set_price(self, price: float) -> None:
-        """
-        Set the price of the Crypto-currency in FIAT and set the expiration time
-        :param price: Price of the current Crypto currency in the fiat
-        :return: None
-        """
-        self.price = price
-        self._expiration = time.time() + self.CACHE_DURATION
-
-    def is_expired(self) -> bool:
-        """
-        Return if the current price is still valid or needs to be refreshed
-        :return: bool, true the price is expired and needs to be refreshed, false the price is
-         still valid
-        """
-        return self._expiration - time.time() <= 0
-
-
-class CryptoToFiatConverter(object):
-    """
-    Main class to initiate Crypto to FIAT.
-    This object contains a list of pair Crypto, FIAT
-    This object is also a Singleton
-    """
-    __instance = None
-    _coinmarketcap = None
-
-    # Constants
-    SUPPORTED_FIAT = [
-        "AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
-        "EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
-        "KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
-        "RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD"
-    ]
-
-    CRYPTOMAP = {
-        'BTC': 'bitcoin',
-        'ETH': 'ethereum',
-        'USDT': 'thether'
-    }
-
-    def __new__(cls):
-        if CryptoToFiatConverter.__instance is None:
-            CryptoToFiatConverter.__instance = object.__new__(cls)
-            try:
-                CryptoToFiatConverter._coinmarketcap = Market()
-            except BaseException:
-                CryptoToFiatConverter._coinmarketcap = None
-        return CryptoToFiatConverter.__instance
-
-    def __init__(self) -> None:
-        self._pairs = []
-
-    def convert_amount(self, crypto_amount: float, crypto_symbol: str, fiat_symbol: str) -> float:
-        """
-        Convert an amount of crypto-currency to fiat
-        :param crypto_amount: amount of crypto-currency to convert
-        :param crypto_symbol: crypto-currency used
-        :param fiat_symbol: fiat to convert to
-        :return: float, value in fiat of the crypto-currency amount
-        """
-        price = self.get_price(crypto_symbol=crypto_symbol, fiat_symbol=fiat_symbol)
-        return float(crypto_amount) * float(price)
-
-    def get_price(self, crypto_symbol: str, fiat_symbol: str) -> float:
-        """
-        Return the price of the Crypto-currency in Fiat
-        :param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
-        :param fiat_symbol: FIAT currency you want to convert to (e.g USD)
-        :return: Price in FIAT
-        """
-        crypto_symbol = crypto_symbol.upper()
-        fiat_symbol = fiat_symbol.upper()
-
-        # Check if the fiat convertion you want is supported
-        if not self._is_supported_fiat(fiat=fiat_symbol):
-            raise ValueError('The fiat {} is not supported.'.format(fiat_symbol))
-
-        # Get the pair that interest us and return the price in fiat
-        for pair in self._pairs:
-            if pair.crypto_symbol == crypto_symbol and pair.fiat_symbol == fiat_symbol:
-                # If the price is expired we refresh it, avoid to call the API all the time
-                if pair.is_expired():
-                    pair.set_price(
-                        price=self._find_price(
-                            crypto_symbol=pair.crypto_symbol,
-                            fiat_symbol=pair.fiat_symbol
-                        )
-                    )
-
-                # return the last price we have for this pair
-                return pair.price
-
-        # The pair does not exist, so we create it and return the price
-        return self._add_pair(
-            crypto_symbol=crypto_symbol,
-            fiat_symbol=fiat_symbol,
-            price=self._find_price(
-                crypto_symbol=crypto_symbol,
-                fiat_symbol=fiat_symbol
-            )
-        )
-
-    def _add_pair(self, crypto_symbol: str, fiat_symbol: str, price: float) -> float:
-        """
-        :param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
-        :param fiat_symbol: FIAT currency you want to convert to (e.g USD)
-        :return: price in FIAT
-        """
-        self._pairs.append(
-            CryptoFiat(
-                crypto_symbol=crypto_symbol,
-                fiat_symbol=fiat_symbol,
-                price=price
-            )
-        )
-
-        return price
-
-    def _is_supported_fiat(self, fiat: str) -> bool:
-        """
-        Check if the FIAT your want to convert to is supported
-        :param fiat: FIAT to check (e.g USD)
-        :return: bool, True supported, False not supported
-        """
-
-        fiat = fiat.upper()
-
-        return fiat in self.SUPPORTED_FIAT
-
-    def _find_price(self, crypto_symbol: str, fiat_symbol: str) -> float:
-        """
-        Call CoinMarketCap API to retrieve the price in the FIAT
-        :param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
-        :param fiat_symbol: FIAT currency you want to convert to (e.g USD)
-        :return: float, price of the crypto-currency in Fiat
-        """
-        # Check if the fiat convertion you want is supported
-        if not self._is_supported_fiat(fiat=fiat_symbol):
-            raise ValueError('The fiat {} is not supported.'.format(fiat_symbol))
-
-        if crypto_symbol not in self.CRYPTOMAP:
-            raise ValueError(
-                'The crypto symbol {} is not supported.'.format(crypto_symbol))
-        try:
-            return float(
-                self._coinmarketcap.ticker(
-                    currency=self.CRYPTOMAP[crypto_symbol],
-                    convert=fiat_symbol
-                )[0]['price_' + fiat_symbol.lower()]
-            )
-        except BaseException:
-            return 0.0
diff --git a/freqtrade/freqai/RL/Base3ActionRLEnv.py b/freqtrade/freqai/RL/Base3ActionRLEnv.py
new file mode 100644
index 000000000..c0e7e750d
--- /dev/null
+++ b/freqtrade/freqai/RL/Base3ActionRLEnv.py
@@ -0,0 +1,140 @@
+import logging
+from enum import Enum
+
+from gymnasium import spaces
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
+
+
+logger = logging.getLogger(__name__)
+
+
+class Actions(Enum):
+    Neutral = 0
+    Buy = 1
+    Sell = 2
+
+
+class Base3ActionRLEnv(BaseEnvironment):
+    """
+    Base class for a 3 action environment
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.actions = Actions
+
+    def set_action_space(self):
+        self.action_space = spaces.Discrete(len(Actions))
+
+    def step(self, action: int):
+        """
+        Logic for a single step (incrementing one candle in time)
+        by the agent
+        :param: action: int = the action type that the agent plans
+            to take for the current step.
+        :returns:
+            observation = current state of environment
+            step_reward = the reward from `calculate_reward()`
+            _done = if the agent "died" or if the candles finished
+            info = dict passed back to openai gym lib
+        """
+        self._done = False
+        self._current_tick += 1
+
+        if self._current_tick == self._end_tick:
+            self._done = True
+
+        self._update_unrealized_total_profit()
+        step_reward = self.calculate_reward(action)
+        self.total_reward += step_reward
+        self.tensorboard_log(self.actions._member_names_[action], category="actions")
+
+        trade_type = None
+        if self.is_tradesignal(action):
+            if action == Actions.Buy.value:
+                if self._position == Positions.Short:
+                    self._update_total_profit()
+                self._position = Positions.Long
+                trade_type = "long"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Sell.value and self.can_short:
+                if self._position == Positions.Long:
+                    self._update_total_profit()
+                self._position = Positions.Short
+                trade_type = "short"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Sell.value and not self.can_short:
+                self._update_total_profit()
+                self._position = Positions.Neutral
+                trade_type = "exit"
+                self._last_trade_tick = None
+            else:
+                print("case not defined")
+
+            if trade_type is not None:
+                self.trade_history.append(
+                    {
+                        "price": self.current_price(),
+                        "index": self._current_tick,
+                        "type": trade_type,
+                        "profit": self.get_unrealized_profit(),
+                    }
+                )
+
+        if (
+            self._total_profit < self.max_drawdown
+            or self._total_unrealized_profit < self.max_drawdown
+        ):
+            self._done = True
+
+        self._position_history.append(self._position)
+
+        info = dict(
+            tick=self._current_tick,
+            action=action,
+            total_reward=self.total_reward,
+            total_profit=self._total_profit,
+            position=self._position.value,
+            trade_duration=self.get_trade_duration(),
+            current_profit_pct=self.get_unrealized_profit(),
+        )
+
+        observation = self._get_observation()
+
+        # user can play with time if they want
+        truncated = False
+
+        self._update_history(info)
+
+        return observation, step_reward, self._done, truncated, info
+
+    def is_tradesignal(self, action: int) -> bool:
+        """
+        Determine if the signal is a trade signal
+        e.g.: agent wants a Actions.Buy while it is in a Positions.short
+        """
+        return (
+            (action == Actions.Buy.value and self._position == Positions.Neutral)
+            or (action == Actions.Sell.value and self._position == Positions.Long)
+            or (
+                action == Actions.Sell.value
+                and self._position == Positions.Neutral
+                and self.can_short
+            )
+            or (
+                action == Actions.Buy.value and self._position == Positions.Short and self.can_short
+            )
+        )
+
+    def _is_valid(self, action: int) -> bool:
+        """
+        Determine if the signal is valid.
+        e.g.: agent wants a Actions.Sell while it is in a Positions.Long
+        """
+        if self.can_short:
+            return action in [Actions.Buy.value, Actions.Sell.value, Actions.Neutral.value]
+        else:
+            if action == Actions.Sell.value and self._position != Positions.Long:
+                return False
+            return True
diff --git a/freqtrade/freqai/RL/Base4ActionRLEnv.py b/freqtrade/freqai/RL/Base4ActionRLEnv.py
new file mode 100644
index 000000000..0d2d74cb7
--- /dev/null
+++ b/freqtrade/freqai/RL/Base4ActionRLEnv.py
@@ -0,0 +1,144 @@
+import logging
+from enum import Enum
+
+from gymnasium import spaces
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
+
+
+logger = logging.getLogger(__name__)
+
+
+class Actions(Enum):
+    Neutral = 0
+    Exit = 1
+    Long_enter = 2
+    Short_enter = 3
+
+
+class Base4ActionRLEnv(BaseEnvironment):
+    """
+    Base class for a 4 action environment
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.actions = Actions
+
+    def set_action_space(self):
+        self.action_space = spaces.Discrete(len(Actions))
+
+    def step(self, action: int):
+        """
+        Logic for a single step (incrementing one candle in time)
+        by the agent
+        :param: action: int = the action type that the agent plans
+            to take for the current step.
+        :returns:
+            observation = current state of environment
+            step_reward = the reward from `calculate_reward()`
+            _done = if the agent "died" or if the candles finished
+            info = dict passed back to openai gym lib
+        """
+        self._done = False
+        self._current_tick += 1
+
+        if self._current_tick == self._end_tick:
+            self._done = True
+
+        self._update_unrealized_total_profit()
+        step_reward = self.calculate_reward(action)
+        self.total_reward += step_reward
+        self.tensorboard_log(self.actions._member_names_[action], category="actions")
+
+        trade_type = None
+        if self.is_tradesignal(action):
+            if action == Actions.Neutral.value:
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+                self._last_trade_tick = None
+            elif action == Actions.Long_enter.value:
+                self._position = Positions.Long
+                trade_type = "enter_long"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Short_enter.value:
+                self._position = Positions.Short
+                trade_type = "enter_short"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Exit.value:
+                self._update_total_profit()
+                self._position = Positions.Neutral
+                trade_type = "exit"
+                self._last_trade_tick = None
+            else:
+                print("case not defined")
+
+            if trade_type is not None:
+                self.trade_history.append(
+                    {
+                        "price": self.current_price(),
+                        "index": self._current_tick,
+                        "type": trade_type,
+                        "profit": self.get_unrealized_profit(),
+                    }
+                )
+
+        if (
+            self._total_profit < self.max_drawdown
+            or self._total_unrealized_profit < self.max_drawdown
+        ):
+            self._done = True
+
+        self._position_history.append(self._position)
+
+        info = dict(
+            tick=self._current_tick,
+            action=action,
+            total_reward=self.total_reward,
+            total_profit=self._total_profit,
+            position=self._position.value,
+            trade_duration=self.get_trade_duration(),
+            current_profit_pct=self.get_unrealized_profit(),
+        )
+
+        observation = self._get_observation()
+
+        # user can play with time if they want
+        truncated = False
+
+        self._update_history(info)
+
+        return observation, step_reward, self._done, truncated, info
+
+    def is_tradesignal(self, action: int) -> bool:
+        """
+        Determine if the signal is a trade signal
+        e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
+        """
+        return not (
+            (action == Actions.Neutral.value and self._position == Positions.Neutral)
+            or (action == Actions.Neutral.value and self._position == Positions.Short)
+            or (action == Actions.Neutral.value and self._position == Positions.Long)
+            or (action == Actions.Short_enter.value and self._position == Positions.Short)
+            or (action == Actions.Short_enter.value and self._position == Positions.Long)
+            or (action == Actions.Exit.value and self._position == Positions.Neutral)
+            or (action == Actions.Long_enter.value and self._position == Positions.Long)
+            or (action == Actions.Long_enter.value and self._position == Positions.Short)
+        )
+
+    def _is_valid(self, action: int) -> bool:
+        """
+        Determine if the signal is valid.
+        e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
+        """
+        # Agent should only try to exit if it is in position
+        if action == Actions.Exit.value:
+            if self._position not in (Positions.Short, Positions.Long):
+                return False
+
+        # Agent should only try to enter if it is not in position
+        if action in (Actions.Short_enter.value, Actions.Long_enter.value):
+            if self._position != Positions.Neutral:
+                return False
+
+        return True
diff --git a/freqtrade/freqai/RL/Base5ActionRLEnv.py b/freqtrade/freqai/RL/Base5ActionRLEnv.py
new file mode 100644
index 000000000..2ae7f6b5a
--- /dev/null
+++ b/freqtrade/freqai/RL/Base5ActionRLEnv.py
@@ -0,0 +1,153 @@
+import logging
+from enum import Enum
+
+from gymnasium import spaces
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
+
+
+logger = logging.getLogger(__name__)
+
+
+class Actions(Enum):
+    Neutral = 0
+    Long_enter = 1
+    Long_exit = 2
+    Short_enter = 3
+    Short_exit = 4
+
+
+class Base5ActionRLEnv(BaseEnvironment):
+    """
+    Base class for a 5 action environment
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.actions = Actions
+
+    def set_action_space(self):
+        self.action_space = spaces.Discrete(len(Actions))
+
+    def step(self, action: int):
+        """
+        Logic for a single step (incrementing one candle in time)
+        by the agent
+        :param: action: int = the action type that the agent plans
+            to take for the current step.
+        :returns:
+            observation = current state of environment
+            step_reward = the reward from `calculate_reward()`
+            _done = if the agent "died" or if the candles finished
+            info = dict passed back to openai gym lib
+        """
+        self._done = False
+        self._current_tick += 1
+
+        if self._current_tick == self._end_tick:
+            self._done = True
+
+        self._update_unrealized_total_profit()
+        step_reward = self.calculate_reward(action)
+        self.total_reward += step_reward
+        self.tensorboard_log(self.actions._member_names_[action], category="actions")
+
+        trade_type = None
+        if self.is_tradesignal(action):
+            if action == Actions.Neutral.value:
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+                self._last_trade_tick = None
+            elif action == Actions.Long_enter.value:
+                self._position = Positions.Long
+                trade_type = "enter_long"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Short_enter.value:
+                self._position = Positions.Short
+                trade_type = "enter_short"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Long_exit.value:
+                self._update_total_profit()
+                self._position = Positions.Neutral
+                trade_type = "exit_long"
+                self._last_trade_tick = None
+            elif action == Actions.Short_exit.value:
+                self._update_total_profit()
+                self._position = Positions.Neutral
+                trade_type = "exit_short"
+                self._last_trade_tick = None
+            else:
+                print("case not defined")
+
+            if trade_type is not None:
+                self.trade_history.append(
+                    {
+                        "price": self.current_price(),
+                        "index": self._current_tick,
+                        "type": trade_type,
+                        "profit": self.get_unrealized_profit(),
+                    }
+                )
+
+        if (
+            self._total_profit < self.max_drawdown
+            or self._total_unrealized_profit < self.max_drawdown
+        ):
+            self._done = True
+
+        self._position_history.append(self._position)
+
+        info = dict(
+            tick=self._current_tick,
+            action=action,
+            total_reward=self.total_reward,
+            total_profit=self._total_profit,
+            position=self._position.value,
+            trade_duration=self.get_trade_duration(),
+            current_profit_pct=self.get_unrealized_profit(),
+        )
+
+        observation = self._get_observation()
+        # user can play with time if they want
+        truncated = False
+
+        self._update_history(info)
+
+        return observation, step_reward, self._done, truncated, info
+
+    def is_tradesignal(self, action: int) -> bool:
+        """
+        Determine if the signal is a trade signal
+        e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
+        """
+        return not (
+            (action == Actions.Neutral.value and self._position == Positions.Neutral)
+            or (action == Actions.Neutral.value and self._position == Positions.Short)
+            or (action == Actions.Neutral.value and self._position == Positions.Long)
+            or (action == Actions.Short_enter.value and self._position == Positions.Short)
+            or (action == Actions.Short_enter.value and self._position == Positions.Long)
+            or (action == Actions.Short_exit.value and self._position == Positions.Long)
+            or (action == Actions.Short_exit.value and self._position == Positions.Neutral)
+            or (action == Actions.Long_enter.value and self._position == Positions.Long)
+            or (action == Actions.Long_enter.value and self._position == Positions.Short)
+            or (action == Actions.Long_exit.value and self._position == Positions.Short)
+            or (action == Actions.Long_exit.value and self._position == Positions.Neutral)
+        )
+
+    def _is_valid(self, action: int) -> bool:
+        # trade signal
+        """
+        Determine if the signal is valid.
+        e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
+        """
+        # Agent should only try to exit if it is in position
+        if action in (Actions.Short_exit.value, Actions.Long_exit.value):
+            if self._position not in (Positions.Short, Positions.Long):
+                return False
+
+        # Agent should only try to enter if it is not in position
+        if action in (Actions.Short_enter.value, Actions.Long_enter.value):
+            if self._position != Positions.Neutral:
+                return False
+
+        return True
diff --git a/freqtrade/freqai/RL/BaseEnvironment.py b/freqtrade/freqai/RL/BaseEnvironment.py
new file mode 100644
index 000000000..d33cf1393
--- /dev/null
+++ b/freqtrade/freqai/RL/BaseEnvironment.py
@@ -0,0 +1,418 @@
+import logging
+import random
+from abc import abstractmethod
+from enum import Enum
+
+import gymnasium as gym
+import numpy as np
+import pandas as pd
+from gymnasium import spaces
+from gymnasium.utils import seeding
+from pandas import DataFrame
+
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseActions(Enum):
+    """
+    Default action space, mostly used for type handling.
+    """
+
+    Neutral = 0
+    Long_enter = 1
+    Long_exit = 2
+    Short_enter = 3
+    Short_exit = 4
+
+
+class Positions(Enum):
+    Short = 0
+    Long = 1
+    Neutral = 0.5
+
+    def opposite(self):
+        return Positions.Short if self == Positions.Long else Positions.Long
+
+
+class BaseEnvironment(gym.Env):
+    """
+    Base class for environments. This class is agnostic to action count.
+    Inherited classes customize this to include varying action counts/types,
+    See RL/Base5ActionRLEnv.py and RL/Base4ActionRLEnv.py
+    """
+
+    def __init__(
+        self,
+        *,
+        df: DataFrame,
+        prices: DataFrame,
+        reward_kwargs: dict,
+        window_size=10,
+        starting_point=True,
+        id: str = "baseenv-1",  # noqa: A002
+        seed: int = 1,
+        config: dict,
+        live: bool = False,
+        fee: float = 0.0015,
+        can_short: bool = False,
+        pair: str = "",
+        df_raw: DataFrame,
+    ):
+        """
+        Initializes the training/eval environment.
+        :param df: dataframe of features
+        :param prices: dataframe of prices to be used in the training environment
+        :param window_size: size of window (temporal) to pass to the agent
+        :param reward_kwargs: extra config settings assigned by user in `rl_config`
+        :param starting_point: start at edge of window or not
+        :param id: string id of the environment (used in backend for multiprocessed env)
+        :param seed: Sets the seed of the environment higher in the gym.Env object
+        :param config: Typical user configuration file
+        :param live: Whether or not this environment is active in dry/live/backtesting
+        :param fee: The fee to use for environmental interactions.
+        :param can_short: Whether or not the environment can short
+        """
+        self.config: dict = config
+        self.rl_config: dict = config["freqai"]["rl_config"]
+        self.add_state_info: bool = self.rl_config.get("add_state_info", False)
+        self.id: str = id
+        self.max_drawdown: float = 1 - self.rl_config.get("max_training_drawdown_pct", 0.8)
+        self.compound_trades: bool = config["stake_amount"] == "unlimited"
+        self.pair: str = pair
+        self.raw_features: DataFrame = df_raw
+        if self.config.get("fee", None) is not None:
+            self.fee = self.config["fee"]
+        else:
+            self.fee = fee
+
+        # set here to default 5Ac, but all children envs can override this
+        self.actions: type[Enum] = BaseActions
+        self.tensorboard_metrics: dict = {}
+        self.can_short: bool = can_short
+        self.live: bool = live
+        if not self.live and self.add_state_info:
+            raise OperationalException(
+                "`add_state_info` is not available in backtesting. Change "
+                "parameter to false in your rl_config. See `add_state_info` "
+                "docs for more info."
+            )
+        self.seed(seed)
+        self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
+
+    def reset_env(
+        self,
+        df: DataFrame,
+        prices: DataFrame,
+        window_size: int,
+        reward_kwargs: dict,
+        starting_point=True,
+    ):
+        """
+        Resets the environment when the agent fails (in our case, if the drawdown
+        exceeds the user set max_training_drawdown_pct)
+        :param df: dataframe of features
+        :param prices: dataframe of prices to be used in the training environment
+        :param window_size: size of window (temporal) to pass to the agent
+        :param reward_kwargs: extra config settings assigned by user in `rl_config`
+        :param starting_point: start at edge of window or not
+        """
+        self.signal_features: DataFrame = df
+        self.prices: DataFrame = prices
+        self.window_size: int = window_size
+        self.starting_point: bool = starting_point
+        self.rr: float = reward_kwargs["rr"]
+        self.profit_aim: float = reward_kwargs["profit_aim"]
+
+        # # spaces
+        if self.add_state_info:
+            self.total_features = self.signal_features.shape[1] + 3
+        else:
+            self.total_features = self.signal_features.shape[1]
+        self.shape = (window_size, self.total_features)
+        self.set_action_space()
+        self.observation_space = spaces.Box(low=-1, high=1, shape=self.shape, dtype=np.float32)
+
+        # episode
+        self._start_tick: int = self.window_size
+        self._end_tick: int = len(self.prices) - 1
+        self._done: bool = False
+        self._current_tick: int = self._start_tick
+        self._last_trade_tick: int | None = None
+        self._position = Positions.Neutral
+        self._position_history: list = [None]
+        self.total_reward: float = 0
+        self._total_profit: float = 1
+        self._total_unrealized_profit: float = 1
+        self.history: dict = {}
+        self.trade_history: list = []
+
+    def get_attr(self, attr: str):
+        """
+        Returns the attribute of the environment
+        :param attr: attribute to return
+        :return: attribute
+        """
+        return getattr(self, attr)
+
+    @abstractmethod
+    def set_action_space(self):
+        """
+        Unique to the environment action count. Must be inherited.
+        """
+
+    def action_masks(self) -> list[bool]:
+        return [self._is_valid(action.value) for action in self.actions]
+
+    def seed(self, seed: int = 1):
+        self.np_random, seed = seeding.np_random(seed)
+        return [seed]
+
+    def tensorboard_log(
+        self,
+        metric: str,
+        value: int | float | None = None,
+        inc: bool | None = None,
+        category: str = "custom",
+    ):
+        """
+        Function builds the tensorboard_metrics dictionary
+        to be parsed by the TensorboardCallback. This
+        function is designed for tracking incremented objects,
+        events, actions inside the training environment.
+        For example, a user can call this to track the
+        frequency of occurrence of an `is_valid` call in
+        their `calculate_reward()`:
+
+        def calculate_reward(self, action: int) -> float:
+            if not self._is_valid(action):
+                self.tensorboard_log("invalid")
+                return -2
+
+        :param metric: metric to be tracked and incremented
+        :param value: `metric` value
+        :param inc: (deprecated) sets whether the `value` is incremented or not
+        :param category: `metric` category
+        """
+        increment = True if value is None else False
+        value = 1 if increment else value
+
+        if category not in self.tensorboard_metrics:
+            self.tensorboard_metrics[category] = {}
+
+        if not increment or metric not in self.tensorboard_metrics[category]:
+            self.tensorboard_metrics[category][metric] = value
+        else:
+            self.tensorboard_metrics[category][metric] += value
+
+    def reset_tensorboard_log(self):
+        self.tensorboard_metrics = {}
+
+    def reset(self, seed=None):
+        """
+        Reset is called at the beginning of every episode
+        """
+        self.reset_tensorboard_log()
+
+        self._done = False
+
+        if self.starting_point is True:
+            if self.rl_config.get("randomize_starting_position", False):
+                length_of_data = int(self._end_tick / 4)
+                start_tick = random.randint(self.window_size + 1, length_of_data)
+                self._start_tick = start_tick
+            self._position_history = (self._start_tick * [None]) + [self._position]
+        else:
+            self._position_history = (self.window_size * [None]) + [self._position]
+
+        self._current_tick = self._start_tick
+        self._last_trade_tick = None
+        self._position = Positions.Neutral
+
+        self.total_reward = 0.0
+        self._total_profit = 1.0  # unit
+        self.history = {}
+        self.trade_history = []
+        self.portfolio_log_returns = np.zeros(len(self.prices))
+
+        self._profits = [(self._start_tick, 1)]
+        self.close_trade_profit = []
+        self._total_unrealized_profit = 1
+
+        return self._get_observation(), self.history
+
+    @abstractmethod
+    def step(self, action: int):
+        """
+        Step depends on action types, this must be inherited.
+        """
+        return
+
+    def _get_observation(self):
+        """
+        This may or may not be independent of action types, user can inherit
+        this in their custom "MyRLEnv"
+        """
+        features_window = self.signal_features[
+            (self._current_tick - self.window_size) : self._current_tick
+        ]
+        if self.add_state_info:
+            features_and_state = DataFrame(
+                np.zeros((len(features_window), 3)),
+                columns=["current_profit_pct", "position", "trade_duration"],
+                index=features_window.index,
+            )
+
+            features_and_state["current_profit_pct"] = self.get_unrealized_profit()
+            features_and_state["position"] = self._position.value
+            features_and_state["trade_duration"] = self.get_trade_duration()
+            features_and_state = pd.concat([features_window, features_and_state], axis=1)
+            return features_and_state
+        else:
+            return features_window
+
+    def get_trade_duration(self):
+        """
+        Get the trade duration if the agent is in a trade
+        """
+        if self._last_trade_tick is None:
+            return 0
+        else:
+            return self._current_tick - self._last_trade_tick
+
+    def get_unrealized_profit(self):
+        """
+        Get the unrealized profit if the agent is in a trade
+        """
+        if self._last_trade_tick is None:
+            return 0.0
+
+        if self._position == Positions.Neutral:
+            return 0.0
+        elif self._position == Positions.Short:
+            current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
+            last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
+            return (last_trade_price - current_price) / last_trade_price
+        elif self._position == Positions.Long:
+            current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
+            last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
+            return (current_price - last_trade_price) / last_trade_price
+        else:
+            return 0.0
+
+    @abstractmethod
+    def is_tradesignal(self, action: int) -> bool:
+        """
+        Determine if the signal is a trade signal. This is
+        unique to the actions in the environment, and therefore must be
+        inherited.
+        """
+        return True
+
+    def _is_valid(self, action: int) -> bool:
+        """
+        Determine if the signal is valid.This is
+        unique to the actions in the environment, and therefore must be
+        inherited.
+        """
+        return True
+
+    def add_entry_fee(self, price):
+        return price * (1 + self.fee)
+
+    def add_exit_fee(self, price):
+        return price / (1 + self.fee)
+
+    def _update_history(self, info):
+        if not self.history:
+            self.history = {key: [] for key in info.keys()}
+
+        for key, value in info.items():
+            self.history[key].append(value)
+
+    @abstractmethod
+    def calculate_reward(self, action: int) -> float:
+        """
+        An example reward function. This is the one function that users will likely
+        wish to inject their own creativity into.
+
+        Warning!
+        This is function is a showcase of functionality designed to show as many possible
+        environment control features as possible. It is also designed to run quickly
+        on small computers. This is a benchmark, it is *not* for live production.
+
+        :param action: int = The action made by the agent for the current candle.
+        :return:
+        float = the reward to give to the agent for current step (used for optimization
+            of weights in NN)
+        """
+
+    def _update_unrealized_total_profit(self):
+        """
+        Update the unrealized total profit in case of episode end.
+        """
+        if self._position in (Positions.Long, Positions.Short):
+            pnl = self.get_unrealized_profit()
+            if self.compound_trades:
+                # assumes unit stake and compounding
+                unrl_profit = self._total_profit * (1 + pnl)
+            else:
+                # assumes unit stake and no compounding
+                unrl_profit = self._total_profit + pnl
+            self._total_unrealized_profit = unrl_profit
+
+    def _update_total_profit(self):
+        pnl = self.get_unrealized_profit()
+        if self.compound_trades:
+            # assumes unit stake and compounding
+            self._total_profit = self._total_profit * (1 + pnl)
+        else:
+            # assumes unit stake and no compounding
+            self._total_profit += pnl
+
+    def current_price(self) -> float:
+        return self.prices.iloc[self._current_tick].open
+
+    def get_actions(self) -> type[Enum]:
+        """
+        Used by SubprocVecEnv to get actions from
+        initialized env for tensorboard callback
+        """
+        return self.actions
+
+    # Keeping around in case we want to start building more complex environment
+    # templates in the future.
+    # def most_recent_return(self):
+    #     """
+    #     Calculate the tick to tick return if in a trade.
+    #     Return is generated from rising prices in Long
+    #     and falling prices in Short positions.
+    #     The actions Sell/Buy or Hold during a Long position trigger the sell/buy-fee.
+    #     """
+    #     # Long positions
+    #     if self._position == Positions.Long:
+    #         current_price = self.prices.iloc[self._current_tick].open
+    #         previous_price = self.prices.iloc[self._current_tick - 1].open
+
+    #         if (self._position_history[self._current_tick - 1] == Positions.Short
+    #                 or self._position_history[self._current_tick - 1] == Positions.Neutral):
+    #             previous_price = self.add_entry_fee(previous_price)
+
+    #         return np.log(current_price) - np.log(previous_price)
+
+    #     # Short positions
+    #     if self._position == Positions.Short:
+    #         current_price = self.prices.iloc[self._current_tick].open
+    #         previous_price = self.prices.iloc[self._current_tick - 1].open
+    #         if (self._position_history[self._current_tick - 1] == Positions.Long
+    #                 or self._position_history[self._current_tick - 1] == Positions.Neutral):
+    #             previous_price = self.add_exit_fee(previous_price)
+
+    #         return np.log(previous_price) - np.log(current_price)
+
+    #     return 0
+
+    # def update_portfolio_log_returns(self, action):
+    #     self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)
diff --git a/freqtrade/freqai/RL/BaseReinforcementLearningModel.py b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
new file mode 100644
index 000000000..70fd2d3b2
--- /dev/null
+++ b/freqtrade/freqai/RL/BaseReinforcementLearningModel.py
@@ -0,0 +1,510 @@
+import copy
+import importlib
+import logging
+from abc import abstractmethod
+from collections.abc import Callable
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import gymnasium as gym
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+import torch as th
+import torch.multiprocessing
+from pandas import DataFrame
+from sb3_contrib.common.maskable.callbacks import MaskableEvalCallback
+from sb3_contrib.common.maskable.utils import is_masking_supported
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.utils import set_random_seed
+from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment, Positions
+from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
+from freqtrade.persistence import Trade
+
+
+logger = logging.getLogger(__name__)
+
+torch.multiprocessing.set_sharing_strategy("file_system")
+
+SB3_MODELS = ["PPO", "A2C", "DQN"]
+SB3_CONTRIB_MODELS = ["TRPO", "ARS", "RecurrentPPO", "MaskablePPO", "QRDQN"]
+
+
+class BaseReinforcementLearningModel(IFreqaiModel):
+    """
+    User created Reinforcement Learning Model prediction class
+    """
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(config=kwargs["config"])
+        self.max_threads = min(
+            self.freqai_info["rl_config"].get("cpu_count", 1),
+            max(int(self.max_system_threads / 2), 1),
+        )
+        th.set_num_threads(self.max_threads)
+        self.reward_params = self.freqai_info["rl_config"]["model_reward_parameters"]
+        self.train_env: VecMonitor | SubprocVecEnv | gym.Env = gym.Env()
+        self.eval_env: VecMonitor | SubprocVecEnv | gym.Env = gym.Env()
+        self.eval_callback: MaskableEvalCallback | None = None
+        self.model_type = self.freqai_info["rl_config"]["model_type"]
+        self.rl_config = self.freqai_info["rl_config"]
+        self.df_raw: DataFrame = DataFrame()
+        self.continual_learning = self.freqai_info.get("continual_learning", False)
+        if self.model_type in SB3_MODELS:
+            import_str = "stable_baselines3"
+        elif self.model_type in SB3_CONTRIB_MODELS:
+            import_str = "sb3_contrib"
+        else:
+            raise OperationalException(
+                f"{self.model_type} not available in stable_baselines3 or "
+                f"sb3_contrib. please choose one of {SB3_MODELS} or "
+                f"{SB3_CONTRIB_MODELS}"
+            )
+
+        mod = importlib.import_module(import_str, self.model_type)
+        self.MODELCLASS = getattr(mod, self.model_type)
+        self.policy_type = self.freqai_info["rl_config"]["policy_type"]
+        self.unset_outlier_removal()
+        self.net_arch = self.rl_config.get("net_arch", [128, 128])
+        self.dd.model_type = import_str
+        self.tensorboard_callback: TensorboardCallback = TensorboardCallback(
+            verbose=1, actions=BaseActions
+        )
+
+    def unset_outlier_removal(self):
+        """
+        If user has activated any function that may remove training points, this
+        function will set them to false and warn them
+        """
+        if self.ft_params.get("use_SVM_to_remove_outliers", False):
+            self.ft_params.update({"use_SVM_to_remove_outliers": False})
+            logger.warning("User tried to use SVM with RL. Deactivating SVM.")
+        if self.ft_params.get("use_DBSCAN_to_remove_outliers", False):
+            self.ft_params.update({"use_DBSCAN_to_remove_outliers": False})
+            logger.warning("User tried to use DBSCAN with RL. Deactivating DBSCAN.")
+        if self.ft_params.get("DI_threshold", False):
+            self.ft_params.update({"DI_threshold": False})
+            logger.warning("User tried to use DI_threshold with RL. Deactivating DI_threshold.")
+        if self.freqai_info["data_split_parameters"].get("shuffle", False):
+            self.freqai_info["data_split_parameters"].update({"shuffle": False})
+            logger.warning("User tried to shuffle training data. Setting shuffle to False")
+
+    def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        Filter the training data and train a model to it. Train makes heavy use of the datakitchen
+        for storing, saving, loading, and analyzing the data.
+        :param unfiltered_df: Full dataframe for the current training period
+        :param metadata: pair metadata from strategy.
+        :returns:
+        :model: Trained model which can be used to inference (self.predict)
+        """
+
+        logger.info(f"--------------------Starting training {pair} --------------------")
+
+        features_filtered, labels_filtered = dk.filter_features(
+            unfiltered_df,
+            dk.training_features_list,
+            dk.label_list,
+            training_filter=True,
+        )
+
+        dd: dict[str, Any] = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        self.df_raw = copy.deepcopy(dd["train_features"])
+        dk.fit_labels()  # FIXME useless for now, but just satiating append methods
+
+        # normalize all data based on train_dataset only
+        prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
+
+        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
+
+        (dd["train_features"], dd["train_labels"], dd["train_weights"]) = (
+            dk.feature_pipeline.fit_transform(
+                dd["train_features"], dd["train_labels"], dd["train_weights"]
+            )
+        )
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            (dd["test_features"], dd["test_labels"], dd["test_weights"]) = (
+                dk.feature_pipeline.transform(
+                    dd["test_features"], dd["test_labels"], dd["test_weights"]
+                )
+            )
+
+        logger.info(
+            f"Training model on {len(dk.data_dictionary['train_features'].columns)}"
+            f" features and {len(dd['train_features'])} data points"
+        )
+
+        self.set_train_and_eval_environments(dd, prices_train, prices_test, dk)
+
+        model = self.fit(dd, dk)
+
+        logger.info(f"--------------------done training {pair}--------------------")
+
+        return model
+
+    def set_train_and_eval_environments(
+        self,
+        data_dictionary: dict[str, DataFrame],
+        prices_train: DataFrame,
+        prices_test: DataFrame,
+        dk: FreqaiDataKitchen,
+    ):
+        """
+        User can override this if they are using a custom MyRLEnv
+        :param data_dictionary: dict = common data dictionary containing train and test
+            features/labels/weights.
+        :param prices_train/test: DataFrame = dataframe comprised of the prices to be used in the
+            environment during training or testing
+        :param dk: FreqaiDataKitchen = the datakitchen for the current pair
+        """
+        train_df = data_dictionary["train_features"]
+        test_df = data_dictionary["test_features"]
+
+        env_info = self.pack_env_dict(dk.pair)
+
+        self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, **env_info)
+        self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test, **env_info))
+        self.eval_callback = MaskableEvalCallback(
+            self.eval_env,
+            deterministic=True,
+            render=False,
+            eval_freq=len(train_df),
+            best_model_save_path=str(dk.data_path),
+            use_masking=(self.model_type == "MaskablePPO" and is_masking_supported(self.eval_env)),
+        )
+
+        actions = self.train_env.get_actions()
+        self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
+
+    def pack_env_dict(self, pair: str) -> dict[str, Any]:
+        """
+        Create dictionary of environment arguments
+        """
+        env_info = {
+            "window_size": self.CONV_WIDTH,
+            "reward_kwargs": self.reward_params,
+            "config": self.config,
+            "live": self.live,
+            "can_short": self.can_short,
+            "pair": pair,
+            "df_raw": self.df_raw,
+        }
+        if self.data_provider:
+            env_info["fee"] = self.data_provider._exchange.get_fee(  # type: ignore
+                symbol=self.data_provider.current_whitelist()[0]
+            )
+
+        return env_info
+
+    @abstractmethod
+    def fit(self, data_dictionary: dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
+        """
+        Agent customizations and abstract Reinforcement Learning customizations
+        go in here. Abstract method, so this function must be overridden by
+        user class.
+        """
+        return
+
+    def get_state_info(self, pair: str) -> tuple[float, float, int]:
+        """
+        State info during dry/live (not backtesting) which is fed back
+        into the model.
+        :param pair: str = COIN/STAKE to get the environment information for
+        :return:
+        :market_side: float = representing short, long, or neutral for
+            pair
+        :current_profit: float = unrealized profit of the current trade
+        :trade_duration: int = the number of candles that the trade has
+            been open for
+        """
+        open_trades = Trade.get_trades_proxy(is_open=True)
+        market_side = 0.5
+        current_profit: float = 0
+        trade_duration = 0
+        for trade in open_trades:
+            if trade.pair == pair:
+                if self.data_provider._exchange is None:  # type: ignore
+                    logger.error("No exchange available.")
+                    return 0, 0, 0
+                else:
+                    current_rate = self.data_provider._exchange.get_rate(  # type: ignore
+                        pair, refresh=False, side="exit", is_short=trade.is_short
+                    )
+
+                now = datetime.now(timezone.utc).timestamp()
+                trade_duration = int((now - trade.open_date_utc.timestamp()) / self.base_tf_seconds)
+                current_profit = trade.calc_profit_ratio(current_rate)
+                if trade.is_short:
+                    market_side = 0
+                else:
+                    market_side = 1
+
+        return market_side, current_profit, int(trade_duration)
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_dataframe: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_df)
+        filtered_dataframe, _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
+        )
+
+        dk.data_dictionary["prediction_features"] = self.drop_ohlc_from_df(filtered_dataframe, dk)
+
+        dk.data_dictionary["prediction_features"], _, _ = dk.feature_pipeline.transform(
+            dk.data_dictionary["prediction_features"], outlier_check=True
+        )
+
+        pred_df = self.rl_model_predict(dk.data_dictionary["prediction_features"], dk, self.model)
+        pred_df.fillna(0, inplace=True)
+
+        return (pred_df, dk.do_predict)
+
+    def rl_model_predict(
+        self, dataframe: DataFrame, dk: FreqaiDataKitchen, model: Any
+    ) -> DataFrame:
+        """
+        A helper function to make predictions in the Reinforcement learning module.
+        :param dataframe: DataFrame = the dataframe of features to make the predictions on
+        :param dk: FreqaiDatakitchen = data kitchen for the current pair
+        :param model: Any = the trained model used to inference the features.
+        """
+        output = pd.DataFrame(np.zeros(len(dataframe)), columns=dk.label_list)
+
+        def _predict(window):
+            observations = dataframe.iloc[window.index]
+            if self.live and self.rl_config.get("add_state_info", False):
+                market_side, current_profit, trade_duration = self.get_state_info(dk.pair)
+                observations["current_profit_pct"] = current_profit
+                observations["position"] = market_side
+                observations["trade_duration"] = trade_duration
+            res, _ = model.predict(observations, deterministic=True)
+            return res
+
+        output = output.rolling(window=self.CONV_WIDTH).apply(_predict)
+
+        return output
+
+    def build_ohlc_price_dataframes(
+        self, data_dictionary: dict, pair: str, dk: FreqaiDataKitchen
+    ) -> tuple[DataFrame, DataFrame]:
+        """
+        Builds the train prices and test prices for the environment.
+        """
+
+        pair = pair.replace(":", "")
+        train_df = data_dictionary["train_features"]
+        test_df = data_dictionary["test_features"]
+
+        # price data for model training and evaluation
+        tf = self.config["timeframe"]
+        rename_dict = {
+            "%-raw_open": "open",
+            "%-raw_low": "low",
+            "%-raw_high": " high",
+            "%-raw_close": "close",
+        }
+        rename_dict_old = {
+            f"%-{pair}raw_open_{tf}": "open",
+            f"%-{pair}raw_low_{tf}": "low",
+            f"%-{pair}raw_high_{tf}": " high",
+            f"%-{pair}raw_close_{tf}": "close",
+        }
+
+        prices_train = train_df.filter(rename_dict.keys(), axis=1)
+        prices_train_old = train_df.filter(rename_dict_old.keys(), axis=1)
+        if prices_train.empty or not prices_train_old.empty:
+            if not prices_train_old.empty:
+                prices_train = prices_train_old
+                rename_dict = rename_dict_old
+            logger.warning(
+                "Reinforcement learning module didn't find the correct raw prices "
+                "assigned in feature_engineering_standard(). "
+                "Please assign them with:\n"
+                'dataframe["%-raw_close"] = dataframe["close"]\n'
+                'dataframe["%-raw_open"] = dataframe["open"]\n'
+                'dataframe["%-raw_high"] = dataframe["high"]\n'
+                'dataframe["%-raw_low"] = dataframe["low"]\n'
+                "inside `feature_engineering_standard()"
+            )
+        elif prices_train.empty:
+            raise OperationalException(
+                "No prices found, please follow log warning instructions to correct the strategy."
+            )
+
+        prices_train.rename(columns=rename_dict, inplace=True)
+        prices_train.reset_index(drop=True)
+
+        prices_test = test_df.filter(rename_dict.keys(), axis=1)
+        prices_test.rename(columns=rename_dict, inplace=True)
+        prices_test.reset_index(drop=True)
+
+        train_df = self.drop_ohlc_from_df(train_df, dk)
+        test_df = self.drop_ohlc_from_df(test_df, dk)
+
+        return prices_train, prices_test
+
+    def drop_ohlc_from_df(self, df: DataFrame, dk: FreqaiDataKitchen):
+        """
+        Given a dataframe, drop the ohlc data
+        """
+        drop_list = ["%-raw_open", "%-raw_low", "%-raw_high", "%-raw_close"]
+
+        if self.rl_config["drop_ohlc_from_features"]:
+            df.drop(drop_list, axis=1, inplace=True)
+            feature_list = dk.training_features_list
+            dk.training_features_list = [e for e in feature_list if e not in drop_list]
+
+        return df
+
+    def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
+        """
+        Can be used by user if they are trying to limit_ram_usage *and*
+        perform continual learning.
+        For now, this is unused.
+        """
+        exists = Path(dk.data_path / f"{dk.model_filename}_model").is_file()
+        if exists:
+            model = self.MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
+        else:
+            logger.info("No model file on disk to continue learning from.")
+
+        return model
+
+    def _on_stop(self):
+        """
+        Hook called on bot shutdown. Close SubprocVecEnv subprocesses for clean shutdown.
+        """
+
+        if self.train_env:
+            self.train_env.close()
+
+        if self.eval_env:
+            self.eval_env.close()
+
+    # Nested class which can be overridden by user to customize further
+    class MyRLEnv(Base5ActionRLEnv):
+        """
+        User can override any function in BaseRLEnv and gym.Env. Here the user
+        sets a custom reward based on profit and trade duration.
+        """
+
+        def calculate_reward(self, action: int) -> float:  # noqa: C901
+            """
+            An example reward function. This is the one function that users will likely
+            wish to inject their own creativity into.
+
+            Warning!
+            This is function is a showcase of functionality designed to show as many possible
+            environment control features as possible. It is also designed to run quickly
+            on small computers. This is a benchmark, it is *not* for live production.
+
+            :param action: int = The action made by the agent for the current candle.
+            :return:
+            float = the reward to give to the agent for current step (used for optimization
+                of weights in NN)
+            """
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+
+            pnl = self.get_unrealized_profit()
+            factor = 100.0
+
+            # you can use feature values from dataframe
+            rsi_now = self.raw_features[
+                f"%-rsi-period-10_shift-1_{self.pair}_{self.config['timeframe']}"
+            ].iloc[self._current_tick]
+
+            # reward agent for entering trades
+            if (
+                action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                and self._position == Positions.Neutral
+            ):
+                if rsi_now < 40:
+                    factor = 40 / rsi_now
+                else:
+                    factor = 1
+                return 25 * factor
+
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+
+            max_trade_duration = self.rl_config.get("max_trade_duration_candles", 300)
+            if self._last_trade_tick:
+                trade_duration = self._current_tick - self._last_trade_tick
+            else:
+                trade_duration = 0
+
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+
+            # discourage sitting in position
+            if (
+                self._position in (Positions.Short, Positions.Long)
+                and action == Actions.Neutral.value
+            ):
+                return -1 * trade_duration / max_trade_duration
+
+            # close long
+            if action == Actions.Long_exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
+                return float(pnl * factor)
+
+            # close short
+            if action == Actions.Short_exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
+                return float(pnl * factor)
+
+            return 0.0
+
+
+def make_env(
+    MyRLEnv: type[BaseEnvironment],
+    env_id: str,
+    rank: int,
+    seed: int,
+    train_df: DataFrame,
+    price: DataFrame,
+    env_info: dict[str, Any],
+) -> Callable:
+    """
+    Utility function for multiprocessed env.
+
+    :param env_id: (str) the environment ID
+    :param num_env: (int) the number of environment you wish to have in subprocesses
+    :param seed: (int) the initial seed for RNG
+    :param rank: (int) index of the subprocess
+    :param env_info: (dict) all required arguments to instantiate the environment.
+    :return: (Callable)
+    """
+
+    def _init() -> gym.Env:
+        env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank, **env_info)
+
+        return env
+
+    set_random_seed(seed)
+    return _init
diff --git a/freqtrade/tests/__init__.py b/freqtrade/freqai/RL/__init__.py
similarity index 100%
rename from freqtrade/tests/__init__.py
rename to freqtrade/freqai/RL/__init__.py
diff --git a/user_data/strategies/__init__.py b/freqtrade/freqai/__init__.py
similarity index 100%
rename from user_data/strategies/__init__.py
rename to freqtrade/freqai/__init__.py
diff --git a/freqtrade/freqai/base_models/BaseClassifierModel.py b/freqtrade/freqai/base_models/BaseClassifierModel.py
new file mode 100644
index 000000000..fe3254792
--- /dev/null
+++ b/freqtrade/freqai/base_models/BaseClassifierModel.py
@@ -0,0 +1,129 @@
+import logging
+from time import time
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+from pandas import DataFrame
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseClassifierModel(IFreqaiModel):
+    """
+    Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
+    User *must* inherit from this class and set fit(). See example scripts
+    such as prediction_models/CatboostClassifier.py for guidance.
+    """
+
+    def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        Filter the training data and train a model to it. Train makes heavy use of the datakitchen
+        for storing, saving, loading, and analyzing the data.
+        :param unfiltered_df: Full dataframe for the current training period
+        :param metadata: pair metadata from strategy.
+        :return:
+        :model: Trained model which can be used to inference (self.predict)
+        """
+
+        logger.info(f"-------------------- Starting training {pair} --------------------")
+
+        start_time = time()
+
+        # filter the features requested by user in the configuration file and elegantly handle NaNs
+        features_filtered, labels_filtered = dk.filter_features(
+            unfiltered_df,
+            dk.training_features_list,
+            dk.label_list,
+            training_filter=True,
+        )
+
+        start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
+        end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
+        logger.info(
+            f"-------------------- Training on data from {start_date} to "
+            f"{end_date} --------------------"
+        )
+        # split data into train/test data.
+        dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
+            dk.fit_labels()
+        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
+
+        (dd["train_features"], dd["train_labels"], dd["train_weights"]) = (
+            dk.feature_pipeline.fit_transform(
+                dd["train_features"], dd["train_labels"], dd["train_weights"]
+            )
+        )
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            (dd["test_features"], dd["test_labels"], dd["test_weights"]) = (
+                dk.feature_pipeline.transform(
+                    dd["test_features"], dd["test_labels"], dd["test_weights"]
+                )
+            )
+
+        logger.info(
+            f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
+        )
+        logger.info(f"Training model on {len(dd['train_features'])} data points")
+
+        model = self.fit(dd, dk)
+
+        end_time = time()
+
+        logger.info(
+            f"-------------------- Done training {pair} "
+            f"({end_time - start_time:.2f} secs) --------------------"
+        )
+
+        return model
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_df)
+        filtered_df, _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
+        )
+
+        dk.data_dictionary["prediction_features"] = filtered_df
+
+        dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
+            dk.data_dictionary["prediction_features"], outlier_check=True
+        )
+
+        predictions = self.model.predict(dk.data_dictionary["prediction_features"])
+        if self.CONV_WIDTH == 1:
+            predictions = np.reshape(predictions, (-1, len(dk.label_list)))
+
+        pred_df = DataFrame(predictions, columns=dk.label_list)
+
+        predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"])
+        if self.CONV_WIDTH == 1:
+            predictions_prob = np.reshape(predictions_prob, (-1, len(self.model.classes_)))
+        pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_)
+
+        pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
+
+        if dk.feature_pipeline["di"]:
+            dk.DI_values = dk.feature_pipeline["di"].di_values
+        else:
+            dk.DI_values = np.zeros(outliers.shape[0])
+        dk.do_predict = outliers
+
+        return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/base_models/BasePyTorchClassifier.py b/freqtrade/freqai/base_models/BasePyTorchClassifier.py
new file mode 100644
index 000000000..b1db396ae
--- /dev/null
+++ b/freqtrade/freqai/base_models/BasePyTorchClassifier.py
@@ -0,0 +1,214 @@
+import logging
+from time import time
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+import torch
+from pandas import DataFrame
+from torch.nn import functional as F
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class BasePyTorchClassifier(BasePyTorchModel):
+    """
+    A PyTorch implementation of a classifier.
+    User must implement fit method
+
+    Important!
+
+    - User must declare the target class names in the strategy,
+    under IStrategy.set_freqai_targets method.
+
+    for example, in your strategy:
+    ```
+        def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+            self.freqai.class_names = ["down", "up"]
+            dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
+                                                  dataframe["close"], 'up', 'down')
+
+            return dataframe
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.class_name_to_index = {}
+        self.index_to_class_name = {}
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param dk: dk: The datakitchen object
+        :param unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        :raises ValueError: if 'class_names' doesn't exist in model meta_data.
+        """
+
+        class_names = self.model.model_meta_data.get("class_names", None)
+        if not class_names:
+            raise ValueError(
+                "Missing class names. self.model.model_meta_data['class_names'] is None."
+            )
+
+        if not self.class_name_to_index:
+            self.init_class_names_to_index_mapping(class_names)
+
+        dk.find_features(unfiltered_df)
+        filtered_df, _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
+        )
+
+        dk.data_dictionary["prediction_features"] = filtered_df
+
+        dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
+            dk.data_dictionary["prediction_features"], outlier_check=True
+        )
+
+        x = self.data_convertor.convert_x(
+            dk.data_dictionary["prediction_features"], device=self.device
+        )
+        self.model.model.eval()
+        logits = self.model.model(x)
+        probs = F.softmax(logits, dim=-1)
+        predicted_classes = torch.argmax(probs, dim=-1)
+        predicted_classes_str = self.decode_class_names(predicted_classes)
+        # used .tolist to convert probs into an iterable, in this way Tensors
+        # are automatically moved to the CPU first if necessary.
+        pred_df_prob = DataFrame(probs.detach().tolist(), columns=class_names)
+        pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
+        pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
+
+        if dk.feature_pipeline["di"]:
+            dk.DI_values = dk.feature_pipeline["di"].di_values
+        else:
+            dk.DI_values = np.zeros(outliers.shape[0])
+        dk.do_predict = outliers
+
+        return (pred_df, dk.do_predict)
+
+    def encode_class_names(
+        self,
+        data_dictionary: dict[str, pd.DataFrame],
+        dk: FreqaiDataKitchen,
+        class_names: list[str],
+    ):
+        """
+        encode class name, str -> int
+        assuming first column of *_labels data frame to be the target column
+        containing the class names
+        """
+
+        target_column_name = dk.label_list[0]
+        for split in self.splits:
+            label_df = data_dictionary[f"{split}_labels"]
+            self.assert_valid_class_names(label_df[target_column_name], class_names)
+            label_df[target_column_name] = list(
+                map(lambda x: self.class_name_to_index[x], label_df[target_column_name])
+            )
+
+    @staticmethod
+    def assert_valid_class_names(target_column: pd.Series, class_names: list[str]):
+        non_defined_labels = set(target_column) - set(class_names)
+        if len(non_defined_labels) != 0:
+            raise OperationalException(
+                f"Found non defined labels: {non_defined_labels}, ",
+                f"expecting labels: {class_names}",
+            )
+
+    def decode_class_names(self, class_ints: torch.Tensor) -> list[str]:
+        """
+        decode class name, int -> str
+        """
+
+        return list(map(lambda x: self.index_to_class_name[x.item()], class_ints))
+
+    def init_class_names_to_index_mapping(self, class_names):
+        self.class_name_to_index = {s: i for i, s in enumerate(class_names)}
+        self.index_to_class_name = {i: s for i, s in enumerate(class_names)}
+        logger.info(f"encoded class name to index: {self.class_name_to_index}")
+
+    def convert_label_column_to_int(
+        self,
+        data_dictionary: dict[str, pd.DataFrame],
+        dk: FreqaiDataKitchen,
+        class_names: list[str],
+    ):
+        self.init_class_names_to_index_mapping(class_names)
+        self.encode_class_names(data_dictionary, dk, class_names)
+
+    def get_class_names(self) -> list[str]:
+        if not self.class_names:
+            raise ValueError(
+                "self.class_names is empty, "
+                "set self.freqai.class_names = ['class a', 'class b', 'class c'] "
+                "inside IStrategy.set_freqai_targets method."
+            )
+
+        return self.class_names
+
+    def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        Filter the training data and train a model to it. Train makes heavy use of the datakitchen
+        for storing, saving, loading, and analyzing the data.
+        :param unfiltered_df: Full dataframe for the current training period
+        :return:
+        :model: Trained model which can be used to inference (self.predict)
+        """
+
+        logger.info(f"-------------------- Starting training {pair} --------------------")
+
+        start_time = time()
+
+        features_filtered, labels_filtered = dk.filter_features(
+            unfiltered_df,
+            dk.training_features_list,
+            dk.label_list,
+            training_filter=True,
+        )
+
+        # split data into train/test data.
+        dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
+            dk.fit_labels()
+
+        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
+
+        (dd["train_features"], dd["train_labels"], dd["train_weights"]) = (
+            dk.feature_pipeline.fit_transform(
+                dd["train_features"], dd["train_labels"], dd["train_weights"]
+            )
+        )
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            (dd["test_features"], dd["test_labels"], dd["test_weights"]) = (
+                dk.feature_pipeline.transform(
+                    dd["test_features"], dd["test_labels"], dd["test_weights"]
+                )
+            )
+
+        logger.info(
+            f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
+        )
+        logger.info(f"Training model on {len(dd['train_features'])} data points")
+
+        model = self.fit(dd, dk)
+        end_time = time()
+
+        logger.info(
+            f"-------------------- Done training {pair} "
+            f"({end_time - start_time:.2f} secs) --------------------"
+        )
+
+        return model
diff --git a/freqtrade/freqai/base_models/BasePyTorchModel.py b/freqtrade/freqai/base_models/BasePyTorchModel.py
new file mode 100644
index 000000000..51de5fc00
--- /dev/null
+++ b/freqtrade/freqai/base_models/BasePyTorchModel.py
@@ -0,0 +1,39 @@
+import logging
+from abc import ABC, abstractmethod
+
+import torch
+
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
+
+
+logger = logging.getLogger(__name__)
+
+
+class BasePyTorchModel(IFreqaiModel, ABC):
+    """
+    Base class for PyTorch type models.
+    User *must* inherit from this class and set fit() and predict() and
+    data_convertor property.
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(config=kwargs["config"])
+        self.dd.model_type = "pytorch"
+        self.device = (
+            "mps"
+            if torch.backends.mps.is_available() and torch.backends.mps.is_built()
+            else ("cuda" if torch.cuda.is_available() else "cpu")
+        )
+        test_size = self.freqai_info.get("data_split_parameters", {}).get("test_size")
+        self.splits = ["train", "test"] if test_size != 0 else ["train"]
+        self.window_size = self.freqai_info.get("conv_width", 1)
+
+    @property
+    @abstractmethod
+    def data_convertor(self) -> PyTorchDataConvertor:
+        """
+        a class responsible for converting `*_features` & `*_labels` pandas dataframes
+        to pytorch tensors.
+        """
+        raise NotImplementedError("Abstract property")
diff --git a/freqtrade/freqai/base_models/BasePyTorchRegressor.py b/freqtrade/freqai/base_models/BasePyTorchRegressor.py
new file mode 100644
index 000000000..6e1db22c7
--- /dev/null
+++ b/freqtrade/freqai/base_models/BasePyTorchRegressor.py
@@ -0,0 +1,117 @@
+import logging
+from time import time
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from pandas import DataFrame
+
+from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class BasePyTorchRegressor(BasePyTorchModel):
+    """
+    A PyTorch implementation of a regressor.
+    User must implement fit method
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_df)
+        filtered_df, _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
+        )
+        dk.data_dictionary["prediction_features"] = filtered_df
+
+        dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
+            dk.data_dictionary["prediction_features"], outlier_check=True
+        )
+
+        x = self.data_convertor.convert_x(
+            dk.data_dictionary["prediction_features"], device=self.device
+        )
+        self.model.model.eval()
+        y = self.model.model(x)
+        pred_df = DataFrame(y.detach().tolist(), columns=[dk.label_list[0]])
+        pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)
+
+        if dk.feature_pipeline["di"]:
+            dk.DI_values = dk.feature_pipeline["di"].di_values
+        else:
+            dk.DI_values = np.zeros(outliers.shape[0])
+        dk.do_predict = outliers
+        return (pred_df, dk.do_predict)
+
+    def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        Filter the training data and train a model to it. Train makes heavy use of the datakitchen
+        for storing, saving, loading, and analyzing the data.
+        :param unfiltered_df: Full dataframe for the current training period
+        :return:
+        :model: Trained model which can be used to inference (self.predict)
+        """
+
+        logger.info(f"-------------------- Starting training {pair} --------------------")
+
+        start_time = time()
+
+        features_filtered, labels_filtered = dk.filter_features(
+            unfiltered_df,
+            dk.training_features_list,
+            dk.label_list,
+            training_filter=True,
+        )
+
+        # split data into train/test data.
+        dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
+            dk.fit_labels()
+        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
+        dk.label_pipeline = self.define_label_pipeline(threads=dk.thread_count)
+
+        (dd["train_features"], dd["train_labels"], dd["train_weights"]) = (
+            dk.feature_pipeline.fit_transform(
+                dd["train_features"], dd["train_labels"], dd["train_weights"]
+            )
+        )
+        dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            (dd["test_features"], dd["test_labels"], dd["test_weights"]) = (
+                dk.feature_pipeline.transform(
+                    dd["test_features"], dd["test_labels"], dd["test_weights"]
+                )
+            )
+            dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
+
+        logger.info(
+            f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
+        )
+        logger.info(f"Training model on {len(dd['train_features'])} data points")
+
+        model = self.fit(dd, dk)
+        end_time = time()
+
+        logger.info(
+            f"-------------------- Done training {pair} "
+            f"({end_time - start_time:.2f} secs) --------------------"
+        )
+
+        return model
diff --git a/freqtrade/freqai/base_models/BaseRegressionModel.py b/freqtrade/freqai/base_models/BaseRegressionModel.py
new file mode 100644
index 000000000..495ee5e39
--- /dev/null
+++ b/freqtrade/freqai/base_models/BaseRegressionModel.py
@@ -0,0 +1,123 @@
+import logging
+from time import time
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from pandas import DataFrame
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseRegressionModel(IFreqaiModel):
+    """
+    Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
+    User *must* inherit from this class and set fit(). See example scripts
+    such as prediction_models/CatboostRegressor.py for guidance.
+    """
+
+    def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        Filter the training data and train a model to it. Train makes heavy use of the datakitchen
+        for storing, saving, loading, and analyzing the data.
+        :param unfiltered_df: Full dataframe for the current training period
+        :param metadata: pair metadata from strategy.
+        :return:
+        :model: Trained model which can be used to inference (self.predict)
+        """
+
+        logger.info(f"-------------------- Starting training {pair} --------------------")
+
+        start_time = time()
+
+        # filter the features requested by user in the configuration file and elegantly handle NaNs
+        features_filtered, labels_filtered = dk.filter_features(
+            unfiltered_df,
+            dk.training_features_list,
+            dk.label_list,
+            training_filter=True,
+        )
+
+        start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
+        end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
+        logger.info(
+            f"-------------------- Training on data from {start_date} to "
+            f"{end_date} --------------------"
+        )
+        # split data into train/test data.
+        dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
+            dk.fit_labels()
+        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
+        dk.label_pipeline = self.define_label_pipeline(threads=dk.thread_count)
+
+        (dd["train_features"], dd["train_labels"], dd["train_weights"]) = (
+            dk.feature_pipeline.fit_transform(
+                dd["train_features"], dd["train_labels"], dd["train_weights"]
+            )
+        )
+        dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            (dd["test_features"], dd["test_labels"], dd["test_weights"]) = (
+                dk.feature_pipeline.transform(
+                    dd["test_features"], dd["test_labels"], dd["test_weights"]
+                )
+            )
+            dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
+
+        logger.info(
+            f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
+        )
+        logger.info(f"Training model on {len(dd['train_features'])} data points")
+
+        model = self.fit(dd, dk)
+
+        end_time = time()
+
+        logger.info(
+            f"-------------------- Done training {pair} "
+            f"({end_time - start_time:.2f} secs) --------------------"
+        )
+
+        return model
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_df)
+        dk.data_dictionary["prediction_features"], _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
+        )
+
+        dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
+            dk.data_dictionary["prediction_features"], outlier_check=True
+        )
+
+        predictions = self.model.predict(dk.data_dictionary["prediction_features"])
+        if self.CONV_WIDTH == 1:
+            predictions = np.reshape(predictions, (-1, len(dk.label_list)))
+
+        pred_df = DataFrame(predictions, columns=dk.label_list)
+
+        pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)
+        if dk.feature_pipeline["di"]:
+            dk.DI_values = dk.feature_pipeline["di"].di_values
+        else:
+            dk.DI_values = np.zeros(outliers.shape[0])
+        dk.do_predict = outliers
+
+        return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/base_models/FreqaiMultiOutputClassifier.py b/freqtrade/freqai/base_models/FreqaiMultiOutputClassifier.py
new file mode 100644
index 000000000..ba6c36eac
--- /dev/null
+++ b/freqtrade/freqai/base_models/FreqaiMultiOutputClassifier.py
@@ -0,0 +1,87 @@
+import numpy as np
+from sklearn.base import is_classifier
+from sklearn.multioutput import MultiOutputClassifier, _fit_estimator
+from sklearn.utils.multiclass import check_classification_targets
+from sklearn.utils.parallel import Parallel, delayed
+from sklearn.utils.validation import has_fit_parameter, validate_data
+
+from freqtrade.exceptions import OperationalException
+
+
+class FreqaiMultiOutputClassifier(MultiOutputClassifier):
+    def fit(self, X, y, sample_weight=None, fit_params=None):
+        """Fit the model to data, separately for each output variable.
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
+            Multi-output targets. An indicator matrix turns on multilabel
+            estimation.
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights. If `None`, then samples are equally weighted.
+            Only supported if the underlying classifier supports sample
+            weights.
+        fit_params : A list of dicts for the fit_params
+            Parameters passed to the ``estimator.fit`` method of each step.
+            Each dict may contain same or different values (e.g. different
+            eval_sets or init_models)
+            .. versionadded:: 0.23
+        Returns
+        -------
+        self : object
+            Returns a fitted instance.
+        """
+
+        if not hasattr(self.estimator, "fit"):
+            raise ValueError("The base estimator should implement a fit method")
+
+        y = validate_data(self, X="no_validation", y=y, multi_output=True)
+
+        if is_classifier(self):
+            check_classification_targets(y)
+
+        if y.ndim == 1:
+            raise ValueError(
+                "y must have at least two dimensions for multi-output regression but has only one."
+            )
+
+        if sample_weight is not None and not has_fit_parameter(self.estimator, "sample_weight"):
+            raise ValueError("Underlying estimator does not support sample weights.")
+
+        if not fit_params:
+            fit_params = [None] * y.shape[1]
+
+        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+            delayed(_fit_estimator)(self.estimator, X, y[:, i], sample_weight, **fit_params[i])
+            for i in range(y.shape[1])
+        )
+
+        self.classes_ = []
+        for estimator in self.estimators_:
+            self.classes_.extend(estimator.classes_)
+        if len(set(self.classes_)) != len(self.classes_):
+            raise OperationalException(
+                f"Class labels must be unique across targets: {self.classes_}"
+            )
+
+        if hasattr(self.estimators_[0], "n_features_in_"):
+            self.n_features_in_ = self.estimators_[0].n_features_in_
+        if hasattr(self.estimators_[0], "feature_names_in_"):
+            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+
+        return self
+
+    def predict_proba(self, X):
+        """
+        Get predict_proba and stack arrays horizontally
+        """
+        results = np.hstack(super().predict_proba(X))
+        return np.squeeze(results)
+
+    def predict(self, X):
+        """
+        Get predict and squeeze into 2D array
+        """
+        results = super().predict(X)
+        return np.squeeze(results)
diff --git a/freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py b/freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
new file mode 100644
index 000000000..15c8db256
--- /dev/null
+++ b/freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
@@ -0,0 +1,52 @@
+from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
+from sklearn.utils.parallel import Parallel, delayed
+from sklearn.utils.validation import has_fit_parameter, validate_data
+
+
+class FreqaiMultiOutputRegressor(MultiOutputRegressor):
+    def fit(self, X, y, sample_weight=None, fit_params=None):
+        """Fit the model to data, separately for each output variable.
+        :param X: {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input data.
+        :param y: {array-like, sparse matrix} of shape (n_samples, n_outputs)
+            Multi-output targets. An indicator matrix turns on multilabel
+            estimation.
+        :param sample_weight: array-like of shape (n_samples,), default=None
+            Sample weights. If `None`, then samples are equally weighted.
+            Only supported if the underlying regressor supports sample
+            weights.
+
+        :param fit_params: A list of dicts for the fit_params
+            Parameters passed to the ``estimator.fit`` method of each step.
+            Each dict may contain same or different values (e.g. different
+            eval_sets or init_models)
+
+        """
+
+        if not hasattr(self.estimator, "fit"):
+            raise ValueError("The base estimator should implement a fit method")
+
+        y = validate_data(self, X="no_validation", y=y, multi_output=True)
+
+        if y.ndim == 1:
+            raise ValueError(
+                "y must have at least two dimensions for multi-output regression but has only one."
+            )
+
+        if sample_weight is not None and not has_fit_parameter(self.estimator, "sample_weight"):
+            raise ValueError("Underlying estimator does not support sample weights.")
+
+        if not fit_params:
+            fit_params = [None] * y.shape[1]
+
+        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+            delayed(_fit_estimator)(self.estimator, X, y[:, i], sample_weight, **fit_params[i])
+            for i in range(y.shape[1])
+        )
+
+        if hasattr(self.estimators_[0], "n_features_in_"):
+            self.n_features_in_ = self.estimators_[0].n_features_in_
+        if hasattr(self.estimators_[0], "feature_names_in_"):
+            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+
+        return
diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py
new file mode 100644
index 000000000..cf3e828bf
--- /dev/null
+++ b/freqtrade/freqai/data_drawer.py
@@ -0,0 +1,765 @@
+import collections
+import importlib
+import logging
+import re
+import shutil
+import threading
+import warnings
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from typing import Any, TypedDict
+
+import numpy as np
+import pandas as pd
+import psutil
+import rapidjson
+from joblib.externals import cloudpickle
+from numpy.typing import NDArray
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
+from freqtrade.data.history import load_pair_history
+from freqtrade.enums import CandleType
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.strategy.interface import IStrategy
+
+
+logger = logging.getLogger(__name__)
+
+FEATURE_PIPELINE = "feature_pipeline"
+LABEL_PIPELINE = "label_pipeline"
+TRAINDF = "trained_df"
+METADATA = "metadata"
+
+METADATA_NUMBER_MODE = rapidjson.NM_NATIVE | rapidjson.NM_NAN
+
+
+class pair_info(TypedDict):
+    model_filename: str
+    trained_timestamp: int
+    data_path: str
+    extras: dict
+
+
+class FreqaiDataDrawer:
+    """
+    Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving
+    /loading to/from disk.
+    This object remains persistent throughout live/dry.
+
+    Record of contribution:
+    FreqAI was developed by a group of individuals who all contributed specific skillsets to the
+    project.
+
+    Conception and software development:
+    Robert Caulk @robcaulk
+
+    Theoretical brainstorming:
+    Elin Törnquist @th0rntwig
+
+    Code review, software architecture brainstorming:
+    @xmatthias
+
+    Beta testing and bug reporting:
+    @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
+    Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
+    """
+
+    def __init__(self, full_path: Path, config: Config):
+        self.config = config
+        self.freqai_info = config.get("freqai", {})
+        # dictionary holding all pair metadata necessary to load in from disk
+        self.pair_dict: dict[str, pair_info] = {}
+        # dictionary holding all actively inferenced models in memory given a model filename
+        self.model_dictionary: dict[str, Any] = {}
+        # all additional metadata that we want to keep in ram
+        self.meta_data_dictionary: dict[str, dict[str, Any]] = {}
+        self.model_return_values: dict[str, DataFrame] = {}
+        self.historic_data: dict[str, dict[str, DataFrame]] = {}
+        self.historic_predictions: dict[str, DataFrame] = {}
+        self.full_path = full_path
+        self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
+        self.historic_predictions_bkp_path = Path(
+            self.full_path / "historic_predictions.backup.pkl"
+        )
+        self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
+        self.global_metadata_path = Path(self.full_path / "global_metadata.json")
+        self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
+        self.load_drawer_from_disk()
+        self.load_historic_predictions_from_disk()
+        self.metric_tracker: dict[str, dict[str, dict[str, list]]] = {}
+        self.load_metric_tracker_from_disk()
+        self.training_queue: dict[str, int] = {}
+        self.history_lock = threading.Lock()
+        self.save_lock = threading.Lock()
+        self.pair_dict_lock = threading.Lock()
+        self.metric_tracker_lock = threading.Lock()
+        self.old_DBSCAN_eps: dict[str, float] = {}
+        self.empty_pair_dict: pair_info = {
+            "model_filename": "",
+            "trained_timestamp": 0,
+            "data_path": "",
+            "extras": {},
+        }
+        self.model_type = self.freqai_info.get("model_save_type", "joblib")
+
+    def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
+        """
+        General utility for adding and updating custom metrics. Typically used
+        for adding training performance, train timings, inferenc timings, cpu loads etc.
+        """
+        with self.metric_tracker_lock:
+            if pair not in self.metric_tracker:
+                self.metric_tracker[pair] = {}
+            if metric not in self.metric_tracker[pair]:
+                self.metric_tracker[pair][metric] = {"timestamp": [], "value": []}
+
+            timestamp = int(datetime.now(timezone.utc).timestamp())
+            self.metric_tracker[pair][metric]["value"].append(value)
+            self.metric_tracker[pair][metric]["timestamp"].append(timestamp)
+
+    def collect_metrics(self, time_spent: float, pair: str):
+        """
+        Add metrics to the metric tracker dictionary
+        """
+        load1, load5, load15 = psutil.getloadavg()
+        cpus = psutil.cpu_count()
+        self.update_metric_tracker("train_time", time_spent, pair)
+        self.update_metric_tracker("cpu_load1min", load1 / cpus, pair)
+        self.update_metric_tracker("cpu_load5min", load5 / cpus, pair)
+        self.update_metric_tracker("cpu_load15min", load15 / cpus, pair)
+
+    def load_global_metadata_from_disk(self):
+        """
+        Locate and load a previously saved global metadata in present model folder.
+        """
+        exists = self.global_metadata_path.is_file()
+        if exists:
+            with self.global_metadata_path.open("r") as fp:
+                metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
+                return metatada_dict
+        return {}
+
+    def load_drawer_from_disk(self):
+        """
+        Locate and load a previously saved data drawer full of all pair model metadata in
+        present model folder.
+        Load any existing metric tracker that may be present.
+        """
+        exists = self.pair_dictionary_path.is_file()
+        if exists:
+            with self.pair_dictionary_path.open("r") as fp:
+                self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
+        else:
+            logger.info("Could not find existing datadrawer, starting from scratch")
+
+    def load_metric_tracker_from_disk(self):
+        """
+        Tries to load an existing metrics dictionary if the user
+        wants to collect metrics.
+        """
+        if self.freqai_info.get("write_metrics_to_disk", False):
+            exists = self.metric_tracker_path.is_file()
+            if exists:
+                with self.metric_tracker_path.open("r") as fp:
+                    self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
+                logger.info("Loading existing metric tracker from disk.")
+            else:
+                logger.info("Could not find existing metric tracker, starting from scratch")
+
+    def load_historic_predictions_from_disk(self):
+        """
+        Locate and load a previously saved historic predictions.
+        :return: bool - whether or not the drawer was located
+        """
+        exists = self.historic_predictions_path.is_file()
+        if exists:
+            try:
+                with self.historic_predictions_path.open("rb") as fp:
+                    self.historic_predictions = cloudpickle.load(fp)
+                logger.info(
+                    f"Found existing historic predictions at {self.full_path}, but beware "
+                    "that statistics may be inaccurate if the bot has been offline for "
+                    "an extended period of time."
+                )
+            except EOFError:
+                logger.warning(
+                    "Historical prediction file was corrupted. Trying to load backup file."
+                )
+                with self.historic_predictions_bkp_path.open("rb") as fp:
+                    self.historic_predictions = cloudpickle.load(fp)
+                logger.warning("FreqAI successfully loaded the backup historical predictions file.")
+
+        else:
+            logger.info("Could not find existing historic_predictions, starting from scratch")
+
+        return exists
+
+    def save_historic_predictions_to_disk(self):
+        """
+        Save historic predictions pickle to disk
+        """
+        with self.historic_predictions_path.open("wb") as fp:
+            cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
+
+        # create a backup
+        shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
+
+    def save_metric_tracker_to_disk(self):
+        """
+        Save metric tracker of all pair metrics collected.
+        """
+        with self.save_lock:
+            with self.metric_tracker_path.open("w") as fp:
+                rapidjson.dump(
+                    self.metric_tracker,
+                    fp,
+                    default=self.np_encoder,
+                    number_mode=rapidjson.NM_NATIVE,
+                )
+
+    def save_drawer_to_disk(self) -> None:
+        """
+        Save data drawer full of all pair model metadata in present model folder.
+        """
+        with self.save_lock:
+            with self.pair_dictionary_path.open("w") as fp:
+                rapidjson.dump(
+                    self.pair_dict, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE
+                )
+
+    def save_global_metadata_to_disk(self, metadata: dict[str, Any]):
+        """
+        Save global metadata json to disk
+        """
+        with self.save_lock:
+            with self.global_metadata_path.open("w") as fp:
+                rapidjson.dump(
+                    metadata, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE
+                )
+
+    def np_encoder(self, obj):
+        if isinstance(obj, np.generic):
+            return obj.item()
+
+    def get_pair_dict_info(self, pair: str) -> tuple[str, int]:
+        """
+        Locate and load existing model metadata from persistent storage. If not located,
+        create a new one and append the current pair to it and prepare it for its first
+        training
+        :param pair: str: pair to lookup
+        :return:
+            model_filename: str = unique filename used for loading persistent objects from disk
+            trained_timestamp: int = the last time the coin was trained
+        """
+
+        pair_dict = self.pair_dict.get(pair)
+
+        if pair_dict:
+            model_filename = pair_dict["model_filename"]
+            trained_timestamp = pair_dict["trained_timestamp"]
+        else:
+            self.pair_dict[pair] = self.empty_pair_dict.copy()
+            model_filename = ""
+            trained_timestamp = 0
+
+        return model_filename, trained_timestamp
+
+    def set_pair_dict_info(self, metadata: dict) -> None:
+        pair_in_dict = self.pair_dict.get(metadata["pair"])
+        if pair_in_dict:
+            return
+        else:
+            self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
+            return
+
+    def set_initial_return_values(
+        self, pair: str, pred_df: DataFrame, dataframe: DataFrame
+    ) -> None:
+        """
+        Set the initial return values to the historical predictions dataframe. This avoids needing
+        to repredict on historical candles, and also stores historical predictions despite
+        retrainings (so stored predictions are true predictions, not just inferencing on trained
+        data).
+
+        We also aim to keep the date from historical predictions so that the FreqUI displays
+        zeros during any downtime (between FreqAI reloads).
+        """
+
+        new_pred = pred_df.copy()
+        # set new_pred values to nans (we want to signal to user that there was nothing
+        # historically made during downtime. The newest pred will get appended later in
+        # append_model_predictions)
+
+        new_pred["date_pred"] = dataframe["date"]
+        # set everything to nan except date_pred
+        columns_to_nan = new_pred.columns.difference(["date_pred", "date"])
+        new_pred[columns_to_nan] = None
+
+        hist_preds = self.historic_predictions[pair].copy()
+
+        # ensure both dataframes have the same date format so they can be merged
+        new_pred["date_pred"] = pd.to_datetime(new_pred["date_pred"])
+        hist_preds["date_pred"] = pd.to_datetime(hist_preds["date_pred"])
+
+        # find the closest common date between new_pred and historic predictions
+        # and cut off the new_pred dataframe at that date
+        common_dates = pd.merge(new_pred, hist_preds, on="date_pred", how="inner")
+        if len(common_dates.index) > 0:
+            new_pred = new_pred.iloc[len(common_dates) :]
+        else:
+            logger.warning(
+                "No common dates found between new predictions and historic "
+                "predictions. You likely left your FreqAI instance offline "
+                f"for more than {len(dataframe.index)} candles."
+            )
+
+        # Pandas warns that its keeping dtypes of non NaN columns...
+        # yea we know and we already want that behavior. Ignoring.
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=FutureWarning)
+            # reindex new_pred columns to match the historic predictions dataframe
+            new_pred_reindexed = new_pred.reindex(columns=hist_preds.columns)
+            df_concat = pd.concat([hist_preds, new_pred_reindexed], ignore_index=True)
+
+        # any missing values will get zeroed out so users can see the exact
+        # downtime in FreqUI
+        df_concat = df_concat.fillna(0)
+        self.historic_predictions[pair] = df_concat
+        self.model_return_values[pair] = df_concat.tail(len(dataframe.index)).reset_index(drop=True)
+
+    def append_model_predictions(
+        self,
+        pair: str,
+        predictions: DataFrame,
+        do_preds: NDArray[np.int_],
+        dk: FreqaiDataKitchen,
+        strat_df: DataFrame,
+    ) -> None:
+        """
+        Append model predictions to historic predictions dataframe, then set the
+        strategy return dataframe to the tail of the historic predictions. The length of
+        the tail is equivalent to the length of the dataframe that entered FreqAI from
+        the strategy originally. Doing this allows FreqUI to always display the correct
+        historic predictions.
+        """
+
+        len_df = len(strat_df)
+        index = self.historic_predictions[pair].index[-1:]
+        columns = self.historic_predictions[pair].columns
+
+        zeros_df = pd.DataFrame(np.zeros((1, len(columns))), index=index, columns=columns)
+        self.historic_predictions[pair] = pd.concat(
+            [self.historic_predictions[pair], zeros_df], ignore_index=True, axis=0
+        )
+        df = self.historic_predictions[pair]
+
+        # model outputs and associated statistics
+        for label in predictions.columns:
+            label_loc = df.columns.get_loc(label)
+            pred_label_loc = predictions.columns.get_loc(label)
+            df.iloc[-1, label_loc] = predictions.iloc[-1, pred_label_loc]
+            if df[label].dtype == object:
+                continue
+            label_mean_loc = df.columns.get_loc(f"{label}_mean")
+            label_std_loc = df.columns.get_loc(f"{label}_std")
+            df.iloc[-1, label_mean_loc] = dk.data["labels_mean"][label]
+            df.iloc[-1, label_std_loc] = dk.data["labels_std"][label]
+
+        # outlier indicators
+        do_predict_loc = df.columns.get_loc("do_predict")
+        df.iloc[-1, do_predict_loc] = do_preds[-1]
+        if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
+            DI_values_loc = df.columns.get_loc("DI_values")
+            df.iloc[-1, DI_values_loc] = dk.DI_values[-1]
+
+        # extra values the user added within custom prediction model
+        if dk.data["extra_returns_per_train"]:
+            rets = dk.data["extra_returns_per_train"]
+            for return_str in rets:
+                return_loc = df.columns.get_loc(return_str)
+                df.iloc[-1, return_loc] = rets[return_str]
+
+        high_price_loc = df.columns.get_loc("high_price")
+        high_loc = strat_df.columns.get_loc("high")
+        df.iloc[-1, high_price_loc] = strat_df.iloc[-1, high_loc]
+        low_price_loc = df.columns.get_loc("low_price")
+        low_loc = strat_df.columns.get_loc("low")
+        df.iloc[-1, low_price_loc] = strat_df.iloc[-1, low_loc]
+        close_price_loc = df.columns.get_loc("close_price")
+        close_loc = strat_df.columns.get_loc("close")
+        df.iloc[-1, close_price_loc] = strat_df.iloc[-1, close_loc]
+        date_pred_loc = df.columns.get_loc("date_pred")
+        date_loc = strat_df.columns.get_loc("date")
+        df.iloc[-1, date_pred_loc] = strat_df.iloc[-1, date_loc]
+
+        self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True)
+
+    def attach_return_values_to_return_dataframe(
+        self, pair: str, dataframe: DataFrame
+    ) -> DataFrame:
+        """
+        Attach the return values to the strat dataframe
+        :param dataframe: DataFrame = strategy dataframe
+        :return: DataFrame = strat dataframe with return values attached
+        """
+        df = self.model_return_values[pair]
+        to_keep = [col for col in dataframe.columns if not col.startswith("&")]
+        dataframe = pd.concat([dataframe[to_keep], df], axis=1)
+        return dataframe
+
+    def return_null_values_to_strategy(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> None:
+        """
+        Build 0 filled dataframe to return to strategy
+        """
+
+        dk.find_features(dataframe)
+        dk.find_labels(dataframe)
+
+        full_labels = dk.label_list + dk.unique_class_list
+
+        for label in full_labels:
+            dataframe[label] = 0
+            dataframe[f"{label}_mean"] = 0
+            dataframe[f"{label}_std"] = 0
+
+        dataframe["do_predict"] = 0
+
+        if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
+            dataframe["DI_values"] = 0
+
+        if dk.data["extra_returns_per_train"]:
+            rets = dk.data["extra_returns_per_train"]
+            for return_str in rets:
+                dataframe[return_str] = 0
+
+        dk.return_dataframe = dataframe
+
+    def purge_old_models(self) -> None:
+        num_keep = self.freqai_info["purge_old_models"]
+        if not num_keep:
+            return
+        elif isinstance(num_keep, bool):
+            num_keep = 2
+
+        model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
+
+        pattern = re.compile(r"sub-train-(\w+)_(\d{10})")
+
+        delete_dict: dict[str, Any] = {}
+
+        for directory in model_folders:
+            result = pattern.match(str(directory.name))
+            if result is None:
+                continue
+            coin = result.group(1)
+            timestamp = result.group(2)
+
+            if coin not in delete_dict:
+                delete_dict[coin] = {}
+                delete_dict[coin]["num_folders"] = 1
+                delete_dict[coin]["timestamps"] = {int(timestamp): directory}
+            else:
+                delete_dict[coin]["num_folders"] += 1
+                delete_dict[coin]["timestamps"][int(timestamp)] = directory
+
+        for coin in delete_dict:
+            if delete_dict[coin]["num_folders"] > num_keep:
+                sorted_dict = collections.OrderedDict(
+                    sorted(delete_dict[coin]["timestamps"].items())
+                )
+                num_delete = len(sorted_dict) - num_keep
+                deleted = 0
+                for k, v in sorted_dict.items():
+                    if deleted >= num_delete:
+                        break
+                    logger.info(f"Freqai purging old model file {v}")
+                    shutil.rmtree(v)
+                    deleted += 1
+
+    def save_metadata(self, dk: FreqaiDataKitchen) -> None:
+        """
+        Saves only metadata for backtesting studies if user prefers
+        not to save model data. This saves tremendous amounts of space
+        for users generating huge studies.
+        This is only active when `save_backtest_models`: false (not default)
+        """
+        if not dk.data_path.is_dir():
+            dk.data_path.mkdir(parents=True, exist_ok=True)
+
+        save_path = Path(dk.data_path)
+
+        dk.data["data_path"] = str(dk.data_path)
+        dk.data["model_filename"] = str(dk.model_filename)
+        dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
+        dk.data["label_list"] = dk.label_list
+
+        with (save_path / f"{dk.model_filename}_{METADATA}.json").open("w") as fp:
+            rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=METADATA_NUMBER_MODE)
+
+        return
+
+    def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
+        """
+        Saves all data associated with a model for a single sub-train time range
+        :param model: User trained model which can be reused for inferencing to generate
+                      predictions
+        """
+
+        if not dk.data_path.is_dir():
+            dk.data_path.mkdir(parents=True, exist_ok=True)
+
+        save_path = Path(dk.data_path)
+
+        # Save the trained model
+        if self.model_type == "joblib":
+            with (save_path / f"{dk.model_filename}_model.joblib").open("wb") as fp:
+                cloudpickle.dump(model, fp)
+        elif self.model_type == "keras":
+            model.save(save_path / f"{dk.model_filename}_model.h5")
+        elif self.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]:
+            model.save(save_path / f"{dk.model_filename}_model.zip")
+
+        dk.data["data_path"] = str(dk.data_path)
+        dk.data["model_filename"] = str(dk.model_filename)
+        dk.data["training_features_list"] = dk.training_features_list
+        dk.data["label_list"] = dk.label_list
+        # store the metadata
+        with (save_path / f"{dk.model_filename}_{METADATA}.json").open("w") as fp:
+            rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=METADATA_NUMBER_MODE)
+
+        # save the pipelines to pickle files
+        with (save_path / f"{dk.model_filename}_{FEATURE_PIPELINE}.pkl").open("wb") as fp:
+            cloudpickle.dump(dk.feature_pipeline, fp)
+
+        with (save_path / f"{dk.model_filename}_{LABEL_PIPELINE}.pkl").open("wb") as fp:
+            cloudpickle.dump(dk.label_pipeline, fp)
+
+        # save the train data to file for post processing if desired
+        dk.data_dictionary["train_features"].to_pickle(
+            save_path / f"{dk.model_filename}_{TRAINDF}.pkl"
+        )
+
+        dk.data_dictionary["train_dates"].to_pickle(
+            save_path / f"{dk.model_filename}_trained_dates_df.pkl"
+        )
+
+        self.model_dictionary[coin] = model
+        self.pair_dict[coin]["model_filename"] = dk.model_filename
+        self.pair_dict[coin]["data_path"] = str(dk.data_path)
+
+        if coin not in self.meta_data_dictionary:
+            self.meta_data_dictionary[coin] = {}
+        self.meta_data_dictionary[coin][METADATA] = dk.data
+        self.meta_data_dictionary[coin][FEATURE_PIPELINE] = dk.feature_pipeline
+        self.meta_data_dictionary[coin][LABEL_PIPELINE] = dk.label_pipeline
+        self.save_drawer_to_disk()
+
+        return
+
+    def load_metadata(self, dk: FreqaiDataKitchen) -> None:
+        """
+        Load only metadata into datakitchen to increase performance during
+        presaved backtesting (prediction file loading).
+        """
+        with (dk.data_path / f"{dk.model_filename}_{METADATA}.json").open("r") as fp:
+            dk.data = rapidjson.load(fp, number_mode=METADATA_NUMBER_MODE)
+            dk.training_features_list = dk.data["training_features_list"]
+            dk.label_list = dk.data["label_list"]
+
+    def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
+        """
+        loads all data required to make a prediction on a sub-train time range
+        :returns:
+        :model: User trained model which can be inferenced for new predictions
+        """
+
+        if not self.pair_dict[coin]["model_filename"]:
+            return None
+
+        if dk.live:
+            dk.model_filename = self.pair_dict[coin]["model_filename"]
+            dk.data_path = Path(self.pair_dict[coin]["data_path"])
+
+        if coin in self.meta_data_dictionary:
+            dk.data = self.meta_data_dictionary[coin][METADATA]
+            dk.feature_pipeline = self.meta_data_dictionary[coin][FEATURE_PIPELINE]
+            dk.label_pipeline = self.meta_data_dictionary[coin][LABEL_PIPELINE]
+        else:
+            with (dk.data_path / f"{dk.model_filename}_{METADATA}.json").open("r") as fp:
+                dk.data = rapidjson.load(fp, number_mode=METADATA_NUMBER_MODE)
+
+            with (dk.data_path / f"{dk.model_filename}_{FEATURE_PIPELINE}.pkl").open("rb") as fp:
+                dk.feature_pipeline = cloudpickle.load(fp)
+            with (dk.data_path / f"{dk.model_filename}_{LABEL_PIPELINE}.pkl").open("rb") as fp:
+                dk.label_pipeline = cloudpickle.load(fp)
+
+        dk.training_features_list = dk.data["training_features_list"]
+        dk.label_list = dk.data["label_list"]
+
+        # try to access model in memory instead of loading object from disk to save time
+        if dk.live and coin in self.model_dictionary:
+            model = self.model_dictionary[coin]
+        elif self.model_type == "joblib":
+            with (dk.data_path / f"{dk.model_filename}_model.joblib").open("rb") as fp:
+                model = cloudpickle.load(fp)
+        elif "stable_baselines" in self.model_type or "sb3_contrib" == self.model_type:
+            mod = importlib.import_module(
+                self.model_type, self.freqai_info["rl_config"]["model_type"]
+            )
+            MODELCLASS = getattr(mod, self.freqai_info["rl_config"]["model_type"])
+            model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
+        elif self.model_type == "pytorch":
+            import torch
+
+            zipfile = torch.load(dk.data_path / f"{dk.model_filename}_model.zip")
+            model = zipfile["pytrainer"]
+            model = model.load_from_checkpoint(zipfile)
+
+        if not model:
+            raise OperationalException(
+                f"Unable to load model, ensure model exists at {dk.data_path} "
+            )
+
+        # load it into ram if it was loaded from disk
+        if coin not in self.model_dictionary:
+            self.model_dictionary[coin] = model
+
+        return model
+
+    def update_historic_data(self, strategy: IStrategy, dk: FreqaiDataKitchen) -> None:
+        """
+        Append new candles to our stores historic data (in memory) so that
+        we do not need to load candle history from disk and we dont need to
+        pinging exchange multiple times for the same candle.
+        :param dataframe: DataFrame = strategy provided dataframe
+        """
+        feat_params = self.freqai_info["feature_parameters"]
+        with self.history_lock:
+            history_data = self.historic_data
+
+            for pair in dk.all_pairs:
+                for tf in feat_params.get("include_timeframes"):
+                    hist_df = history_data[pair][tf]
+                    # check if newest candle is already appended
+                    df_dp = strategy.dp.get_pair_dataframe(pair, tf)
+                    if len(df_dp.index) == 0:
+                        continue
+                    if str(hist_df.iloc[-1]["date"]) == str(df_dp.iloc[-1:]["date"].iloc[-1]):
+                        continue
+
+                    try:
+                        index = df_dp.loc[df_dp["date"] == hist_df.iloc[-1]["date"]].index[0] + 1
+                    except IndexError:
+                        if hist_df.iloc[-1]["date"] < df_dp["date"].iloc[0]:
+                            raise OperationalException(
+                                "In memory historical data is older than "
+                                f"oldest DataProvider candle for {pair} on "
+                                f"timeframe {tf}"
+                            )
+                        else:
+                            index = -1
+                            logger.warning(
+                                f"No common dates in historical data and dataprovider for {pair}. "
+                                f"Appending latest dataprovider candle to historical data "
+                                "but please be aware that there is likely a gap in the historical "
+                                "data. \n"
+                                f"Historical data ends at {hist_df.iloc[-1]['date']} "
+                                f"while dataprovider starts at {df_dp['date'].iloc[0]} and"
+                                f"ends at {df_dp['date'].iloc[0]}."
+                            )
+
+                    history_data[pair][tf] = pd.concat(
+                        [
+                            hist_df,
+                            df_dp.iloc[index:],
+                        ],
+                        ignore_index=True,
+                        axis=0,
+                    )
+
+            self.current_candle = history_data[dk.pair][self.config["timeframe"]].iloc[-1]["date"]
+
+    def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
+        """
+        Load pair histories for all whitelist and corr_pairlist pairs.
+        Only called once upon startup of bot.
+        :param timerange: TimeRange = full timerange required to populate all indicators
+                          for training according to user defined train_period_days
+        """
+        history_data = self.historic_data
+
+        for pair in dk.all_pairs:
+            if pair not in history_data:
+                history_data[pair] = {}
+            for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
+                history_data[pair][tf] = load_pair_history(
+                    datadir=self.config["datadir"],
+                    timeframe=tf,
+                    pair=pair,
+                    timerange=timerange,
+                    data_format=self.config.get("dataformat_ohlcv", "feather"),
+                    candle_type=self.config.get("candle_type_def", CandleType.SPOT),
+                )
+
+    def get_base_and_corr_dataframes(
+        self, timerange: TimeRange, pair: str, dk: FreqaiDataKitchen
+    ) -> tuple[dict[Any, Any], dict[Any, Any]]:
+        """
+        Searches through our historic_data in memory and returns the dataframes relevant
+        to the present pair.
+        :param timerange: TimeRange = full timerange required to populate all indicators
+                          for training according to user defined train_period_days
+        :param metadata: dict = strategy furnished pair metadata
+        """
+        with self.history_lock:
+            corr_dataframes: dict[Any, Any] = {}
+            base_dataframes: dict[Any, Any] = {}
+            historic_data = self.historic_data
+            pairs = self.freqai_info["feature_parameters"].get("include_corr_pairlist", [])
+
+            for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
+                base_dataframes[tf] = dk.slice_dataframe(
+                    timerange, historic_data[pair][tf]
+                ).reset_index(drop=True)
+                if pairs:
+                    for p in pairs:
+                        if pair in p:
+                            continue  # dont repeat anything from whitelist
+                        if p not in corr_dataframes:
+                            corr_dataframes[p] = {}
+                        corr_dataframes[p][tf] = dk.slice_dataframe(
+                            timerange, historic_data[p][tf]
+                        ).reset_index(drop=True)
+
+        return corr_dataframes, base_dataframes
+
+    def get_timerange_from_live_historic_predictions(self) -> TimeRange:
+        """
+        Returns timerange information based on historic predictions file
+        :return: timerange calculated from saved live data
+        """
+        if not self.historic_predictions_path.is_file():
+            raise OperationalException(
+                "Historic predictions not found. Historic predictions data is required "
+                "to run backtest with the freqai-backtest-live-models option "
+            )
+
+        self.load_historic_predictions_from_disk()
+
+        all_pairs_end_dates = []
+        for pair in self.historic_predictions:
+            pair_historic_data = self.historic_predictions[pair]
+            all_pairs_end_dates.append(pair_historic_data.date_pred.max())
+
+        global_metadata = self.load_global_metadata_from_disk()
+        start_date = datetime.fromtimestamp(int(global_metadata["start_dry_live_date"]))
+        end_date = max(all_pairs_end_dates)
+        # add 1 day to string timerange to ensure BT module will load all dataframe data
+        end_date = end_date + timedelta(days=1)
+        backtesting_timerange = TimeRange(
+            "date", "date", int(start_date.timestamp()), int(end_date.timestamp())
+        )
+        return backtesting_timerange
diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
new file mode 100644
index 000000000..6cb52eb29
--- /dev/null
+++ b/freqtrade/freqai/data_kitchen.py
@@ -0,0 +1,1029 @@
+import copy
+import inspect
+import logging
+import random
+import shutil
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+import psutil
+from datasieve.pipeline import Pipeline
+from pandas import DataFrame
+from sklearn.model_selection import train_test_split
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DOCS_LINK, Config
+from freqtrade.data.converter import reduce_dataframe_footprint
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.strategy import merge_informative_pair
+from freqtrade.strategy.interface import IStrategy
+
+
+pd.set_option("future.no_silent_downcasting", True)
+
+SECONDS_IN_DAY = 86400
+SECONDS_IN_HOUR = 3600
+
+logger = logging.getLogger(__name__)
+
+
+class FreqaiDataKitchen:
+    """
+    Class designed to analyze data for a single pair. Employed by the IFreqaiModel class.
+    Functionalities include holding, saving, loading, and analyzing the data.
+
+    This object is not persistent, it is reinstantiated for each coin, each time the coin
+    model needs to be inferenced or trained.
+
+    Record of contribution:
+    FreqAI was developed by a group of individuals who all contributed specific skillsets to the
+    project.
+
+    Conception and software development:
+    Robert Caulk @robcaulk
+
+    Theoretical brainstorming:
+    Elin Törnquist @th0rntwig
+
+    Code review, software architecture brainstorming:
+    @xmatthias
+
+    Beta testing and bug reporting:
+    @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
+    Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
+    """
+
+    def __init__(
+        self,
+        config: Config,
+        live: bool = False,
+        pair: str = "",
+    ):
+        self.data: dict[str, Any] = {}
+        self.data_dictionary: dict[str, DataFrame] = {}
+        self.config = config
+        self.freqai_config: dict[str, Any] = config["freqai"]
+        self.full_df: DataFrame = DataFrame()
+        self.append_df: DataFrame = DataFrame()
+        self.data_path = Path()
+        self.label_list: list = []
+        self.training_features_list: list = []
+        self.model_filename: str = ""
+        self.backtesting_results_path = Path()
+        self.backtest_predictions_folder: str = "backtesting_predictions"
+        self.live = live
+        self.pair = pair
+        self.keras: bool = self.freqai_config.get("keras", False)
+        self.set_all_pairs()
+        self.backtest_live_models = config.get("freqai_backtest_live_models", False)
+        self.feature_pipeline = Pipeline()
+        self.label_pipeline = Pipeline()
+        self.DI_values: npt.NDArray = np.array([])
+
+        if not self.live:
+            self.full_path = self.get_full_models_path(self.config)
+
+            if not self.backtest_live_models:
+                self.full_timerange = self.create_fulltimerange(
+                    self.config["timerange"], self.freqai_config.get("train_period_days", 0)
+                )
+                (self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
+                    self.full_timerange,
+                    config["freqai"]["train_period_days"],
+                    config["freqai"]["backtest_period_days"],
+                )
+
+        self.data["extra_returns_per_train"] = self.freqai_config.get("extra_returns_per_train", {})
+        if not self.freqai_config.get("data_kitchen_thread_count", 0):
+            self.thread_count = max(int(psutil.cpu_count() * 2 - 2), 1)
+        else:
+            self.thread_count = self.freqai_config["data_kitchen_thread_count"]
+        self.train_dates: DataFrame = pd.DataFrame()
+        self.unique_classes: dict[str, list] = {}
+        self.unique_class_list: list = []
+        self.backtest_live_models_data: dict[str, Any] = {}
+
+    def set_paths(
+        self,
+        pair: str,
+        trained_timestamp: int | None = None,
+    ) -> None:
+        """
+        Set the paths to the data for the present coin/botloop
+        :param metadata: dict = strategy furnished pair metadata
+        :param trained_timestamp: int = timestamp of most recent training
+        """
+        self.full_path = self.get_full_models_path(self.config)
+        self.data_path = Path(
+            self.full_path / f"sub-train-{pair.split('/')[0]}_{trained_timestamp}"
+        )
+
+        return
+
+    def make_train_test_datasets(
+        self, filtered_dataframe: DataFrame, labels: DataFrame
+    ) -> dict[Any, Any]:
+        """
+        Given the dataframe for the full history for training, split the data into
+        training and test data according to user specified parameters in configuration
+        file.
+        :param filtered_dataframe: cleaned dataframe ready to be split.
+        :param labels: cleaned labels ready to be split.
+        """
+        feat_dict = self.freqai_config["feature_parameters"]
+
+        if "shuffle" not in self.freqai_config["data_split_parameters"]:
+            self.freqai_config["data_split_parameters"].update({"shuffle": False})
+
+        weights: npt.ArrayLike
+        if feat_dict.get("weight_factor", 0) > 0:
+            weights = self.set_weights_higher_recent(len(filtered_dataframe))
+        else:
+            weights = np.ones(len(filtered_dataframe))
+
+        if self.freqai_config.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            (
+                train_features,
+                test_features,
+                train_labels,
+                test_labels,
+                train_weights,
+                test_weights,
+            ) = train_test_split(
+                filtered_dataframe[: filtered_dataframe.shape[0]],
+                labels,
+                weights,
+                **self.config["freqai"]["data_split_parameters"],
+            )
+        else:
+            test_labels = np.zeros(2)
+            test_features = pd.DataFrame()
+            test_weights = np.zeros(2)
+            train_features = filtered_dataframe
+            train_labels = labels
+            train_weights = weights
+
+        if feat_dict["shuffle_after_split"]:
+            rint1 = random.randint(0, 100)
+            rint2 = random.randint(0, 100)
+            train_features = train_features.sample(frac=1, random_state=rint1).reset_index(
+                drop=True
+            )
+            train_labels = train_labels.sample(frac=1, random_state=rint1).reset_index(drop=True)
+            train_weights = (
+                pd.DataFrame(train_weights)
+                .sample(frac=1, random_state=rint1)
+                .reset_index(drop=True)
+                .to_numpy()[:, 0]
+            )
+            test_features = test_features.sample(frac=1, random_state=rint2).reset_index(drop=True)
+            test_labels = test_labels.sample(frac=1, random_state=rint2).reset_index(drop=True)
+            test_weights = (
+                pd.DataFrame(test_weights)
+                .sample(frac=1, random_state=rint2)
+                .reset_index(drop=True)
+                .to_numpy()[:, 0]
+            )
+
+        # Simplest way to reverse the order of training and test data:
+        if self.freqai_config["feature_parameters"].get("reverse_train_test_order", False):
+            return self.build_data_dictionary(
+                test_features,
+                train_features,
+                test_labels,
+                train_labels,
+                test_weights,
+                train_weights,
+            )
+        else:
+            return self.build_data_dictionary(
+                train_features,
+                test_features,
+                train_labels,
+                test_labels,
+                train_weights,
+                test_weights,
+            )
+
+    def filter_features(
+        self,
+        unfiltered_df: DataFrame,
+        training_feature_list: list,
+        label_list: list | None = None,
+        training_filter: bool = True,
+    ) -> tuple[DataFrame, DataFrame]:
+        """
+        Filter the unfiltered dataframe to extract the user requested features/labels and properly
+        remove all NaNs. Any row with a NaN is removed from training dataset or replaced with
+        0s in the prediction dataset. However, prediction dataset do_predict will reflect any
+        row that had a NaN and will shield user from that prediction.
+
+        :param unfiltered_df: the full dataframe for the present training period
+        :param training_feature_list: list, the training feature list constructed by
+                                      self.build_feature_list() according to user specified
+                                      parameters in the configuration file.
+        :param labels: the labels for the dataset
+        :param training_filter: boolean which lets the function know if it is training data or
+                                prediction data to be filtered.
+        :returns:
+        :filtered_df: dataframe cleaned of NaNs and only containing the user
+        requested feature set.
+        :labels: labels cleaned of NaNs.
+        """
+        filtered_df = unfiltered_df.filter(training_feature_list, axis=1)
+        filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
+
+        drop_index = pd.isnull(filtered_df).any(axis=1)  # get the rows that have NaNs,
+        drop_index = drop_index.replace(True, 1).replace(False, 0).infer_objects(copy=False)
+        if training_filter:
+            # we don't care about total row number (total no. datapoints) in training, we only care
+            # about removing any row with NaNs
+            # if labels has multiple columns (user wants to train multiple modelEs), we detect here
+            labels = unfiltered_df.filter(label_list or [], axis=1)
+            drop_index_labels = pd.isnull(labels).any(axis=1)
+            drop_index_labels = (
+                drop_index_labels.replace(True, 1).replace(False, 0).infer_objects(copy=False)
+            )
+            dates = unfiltered_df["date"]
+            filtered_df = filtered_df[
+                (drop_index == 0) & (drop_index_labels == 0)
+            ]  # dropping values
+            labels = labels[
+                (drop_index == 0) & (drop_index_labels == 0)
+            ]  # assuming the labels depend entirely on the dataframe here.
+            self.train_dates = dates[(drop_index == 0) & (drop_index_labels == 0)]
+            logger.info(
+                f"{self.pair}: dropped {len(unfiltered_df) - len(filtered_df)} training points"
+                f" due to NaNs in populated dataset {len(unfiltered_df)}."
+            )
+            if len(filtered_df) == 0 and not self.live:
+                raise OperationalException(
+                    f"{self.pair}: all training data dropped due to NaNs. "
+                    "You likely did not download enough training data prior "
+                    "to your backtest timerange. Hint:\n"
+                    f"{DOCS_LINK}/freqai-running/"
+                    "#downloading-data-to-cover-the-full-backtest-period"
+                )
+            if (1 - len(filtered_df) / len(unfiltered_df)) > 0.1 and self.live:
+                worst_indicator = str(unfiltered_df.count().idxmin())
+                logger.warning(
+                    f" {(1 - len(filtered_df) / len(unfiltered_df)) * 100:.0f} percent "
+                    " of training data dropped due to NaNs, model may perform inconsistent "
+                    f"with expectations. Verify {worst_indicator}"
+                )
+            self.data["filter_drop_index_training"] = drop_index
+
+        else:
+            # we are backtesting so we need to preserve row number to send back to strategy,
+            # so now we use do_predict to avoid any prediction based on a NaN
+            drop_index = pd.isnull(filtered_df).any(axis=1)
+            self.data["filter_drop_index_prediction"] = drop_index
+            filtered_df.fillna(0, inplace=True)
+            # replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction
+            # that was based on a single NaN is ultimately protected from buys with do_predict
+            drop_index = ~drop_index
+            self.do_predict = np.array(drop_index.replace(True, 1).replace(False, 0))
+            if (len(self.do_predict) - self.do_predict.sum()) > 0:
+                logger.info(
+                    "dropped %s of %s prediction data points due to NaNs.",
+                    len(self.do_predict) - self.do_predict.sum(),
+                    len(filtered_df),
+                )
+            labels = []
+
+        return filtered_df, labels
+
+    def build_data_dictionary(
+        self,
+        train_df: DataFrame,
+        test_df: DataFrame,
+        train_labels: DataFrame,
+        test_labels: DataFrame,
+        train_weights: Any,
+        test_weights: Any,
+    ) -> dict:
+        self.data_dictionary = {
+            "train_features": train_df,
+            "test_features": test_df,
+            "train_labels": train_labels,
+            "test_labels": test_labels,
+            "train_weights": train_weights,
+            "test_weights": test_weights,
+            "train_dates": self.train_dates,
+        }
+
+        return self.data_dictionary
+
+    def split_timerange(
+        self, tr: str, train_split: int = 28, bt_split: float = 7
+    ) -> tuple[list, list]:
+        """
+        Function which takes a single time range (tr) and splits it
+        into sub timeranges to train and backtest on based on user input
+        tr: str, full timerange to train on
+        train_split: the period length for the each training (days). Specified in user
+        configuration file
+        bt_split: the backtesting length (days). Specified in user configuration file
+        """
+
+        if not isinstance(train_split, int) or train_split < 1:
+            raise OperationalException(
+                f"train_period_days must be an integer greater than 0. Got {train_split}."
+            )
+        train_period_days = train_split * SECONDS_IN_DAY
+        bt_period = bt_split * SECONDS_IN_DAY
+
+        full_timerange = TimeRange.parse_timerange(tr)
+        config_timerange = TimeRange.parse_timerange(self.config["timerange"])
+        if config_timerange.stopts == 0:
+            config_timerange.stopts = int(datetime.now(tz=timezone.utc).timestamp())
+        timerange_train = copy.deepcopy(full_timerange)
+        timerange_backtest = copy.deepcopy(full_timerange)
+
+        tr_training_list = []
+        tr_backtesting_list = []
+        tr_training_list_timerange = []
+        tr_backtesting_list_timerange = []
+        first = True
+
+        while True:
+            if not first:
+                timerange_train.startts = timerange_train.startts + int(bt_period)
+            timerange_train.stopts = timerange_train.startts + train_period_days
+
+            first = False
+            tr_training_list.append(timerange_train.timerange_str)
+            tr_training_list_timerange.append(copy.deepcopy(timerange_train))
+
+            # associated backtest period
+            timerange_backtest.startts = timerange_train.stopts
+            timerange_backtest.stopts = timerange_backtest.startts + int(bt_period)
+
+            if timerange_backtest.stopts > config_timerange.stopts:
+                timerange_backtest.stopts = config_timerange.stopts
+
+            tr_backtesting_list.append(timerange_backtest.timerange_str)
+            tr_backtesting_list_timerange.append(copy.deepcopy(timerange_backtest))
+
+            # ensure we are predicting on exactly same amount of data as requested by user defined
+            #  --timerange
+            if timerange_backtest.stopts == config_timerange.stopts:
+                break
+
+        # print(tr_training_list, tr_backtesting_list)
+        return tr_training_list_timerange, tr_backtesting_list_timerange
+
+    def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame:
+        """
+        Given a full dataframe, extract the user desired window
+        :param tr: timerange string that we wish to extract from df
+        :param df: Dataframe containing all candles to run the entire backtest. Here
+                   it is sliced down to just the present training period.
+        """
+        if not self.live:
+            df = df.loc[(df["date"] >= timerange.startdt) & (df["date"] < timerange.stopdt), :]
+        else:
+            df = df.loc[df["date"] >= timerange.startdt, :]
+
+        return df
+
+    def find_features(self, dataframe: DataFrame) -> None:
+        """
+        Find features in the strategy provided dataframe
+        :param dataframe: DataFrame = strategy provided dataframe
+        :return:
+        features: list = the features to be used for training/prediction
+        """
+        column_names = dataframe.columns
+        features = [c for c in column_names if "%" in c]
+
+        if not features:
+            raise OperationalException("Could not find any features!")
+
+        self.training_features_list = features
+
+    def find_labels(self, dataframe: DataFrame) -> None:
+        column_names = dataframe.columns
+        labels = [c for c in column_names if "&" in c]
+        self.label_list = labels
+
+    def set_weights_higher_recent(self, num_weights: int) -> npt.ArrayLike:
+        """
+        Set weights so that recent data is more heavily weighted during
+        training than older data.
+        """
+        wfactor = self.config["freqai"]["feature_parameters"]["weight_factor"]
+        weights = np.exp(-np.arange(num_weights) / (wfactor * num_weights))[::-1]
+        return weights
+
+    def get_predictions_to_append(
+        self, predictions: DataFrame, do_predict: npt.ArrayLike, dataframe_backtest: DataFrame
+    ) -> DataFrame:
+        """
+        Get backtest prediction from current backtest period
+        """
+
+        append_df = DataFrame()
+        for label in predictions.columns:
+            append_df[label] = predictions[label]
+            if append_df[label].dtype == object:
+                continue
+            if "labels_mean" in self.data:
+                append_df[f"{label}_mean"] = self.data["labels_mean"][label]
+            if "labels_std" in self.data:
+                append_df[f"{label}_std"] = self.data["labels_std"][label]
+
+        for extra_col in self.data["extra_returns_per_train"]:
+            append_df[f"{extra_col}"] = self.data["extra_returns_per_train"][extra_col]
+
+        append_df["do_predict"] = do_predict
+        if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
+            append_df["DI_values"] = self.DI_values
+
+        user_cols = [col for col in dataframe_backtest.columns if col.startswith("%%")]
+        cols = ["date"]
+        cols.extend(user_cols)
+
+        dataframe_backtest.reset_index(drop=True, inplace=True)
+        merged_df = pd.concat([dataframe_backtest[cols], append_df], axis=1)
+        return merged_df
+
+    def append_predictions(self, append_df: DataFrame) -> None:
+        """
+        Append backtest prediction from current backtest period to all previous periods
+        """
+
+        if self.full_df.empty:
+            self.full_df = append_df
+        else:
+            self.full_df = pd.concat([self.full_df, append_df], axis=0, ignore_index=True)
+
+    def fill_predictions(self, dataframe):
+        """
+        Back fill values to before the backtesting range so that the dataframe matches size
+        when it goes back to the strategy. These rows are not included in the backtest.
+        """
+        to_keep = [
+            col for col in dataframe.columns if not col.startswith("&") and not col.startswith("%%")
+        ]
+        self.return_dataframe = pd.merge(dataframe[to_keep], self.full_df, how="left", on="date")
+        self.return_dataframe[self.full_df.columns] = self.return_dataframe[
+            self.full_df.columns
+        ].fillna(value=0)
+        self.full_df = DataFrame()
+
+        return
+
+    def create_fulltimerange(self, backtest_tr: str, backtest_period_days: int) -> str:
+        if not isinstance(backtest_period_days, int):
+            raise OperationalException("backtest_period_days must be an integer")
+
+        if backtest_period_days < 0:
+            raise OperationalException("backtest_period_days must be positive")
+
+        backtest_timerange = TimeRange.parse_timerange(backtest_tr)
+
+        if backtest_timerange.stopts == 0:
+            # typically open ended time ranges do work, however, there are some edge cases where
+            # it does not. accommodating these kinds of edge cases just to allow open-ended
+            # timerange is not high enough priority to warrant the effort. It is safer for now
+            # to simply ask user to add their end date
+            raise OperationalException(
+                "FreqAI backtesting does not allow open ended timeranges. "
+                "Please indicate the end date of your desired backtesting. "
+                "timerange."
+            )
+            # backtest_timerange.stopts = int(
+            #     datetime.now(tz=timezone.utc).timestamp()
+            # )
+
+        backtest_timerange.startts = (
+            backtest_timerange.startts - backtest_period_days * SECONDS_IN_DAY
+        )
+        full_timerange = backtest_timerange.timerange_str
+        config_path = Path(self.config["config_files"][0])
+
+        if not self.full_path.is_dir():
+            self.full_path.mkdir(parents=True, exist_ok=True)
+            shutil.copy(
+                config_path.resolve(),
+                Path(self.full_path / config_path.parts[-1]),
+            )
+
+        return full_timerange
+
+    def check_if_model_expired(self, trained_timestamp: int) -> bool:
+        """
+        A model age checker to determine if the model is trustworthy based on user defined
+        `expiration_hours` in the configuration file.
+        :param trained_timestamp: int = The time of training for the most recent model.
+        :return:
+            bool = If the model is expired or not.
+        """
+        time = datetime.now(tz=timezone.utc).timestamp()
+        elapsed_time = (time - trained_timestamp) / 3600  # hours
+        max_time = self.freqai_config.get("expiration_hours", 0)
+        if max_time > 0:
+            return elapsed_time > max_time
+        else:
+            return False
+
+    def check_if_new_training_required(
+        self, trained_timestamp: int
+    ) -> tuple[bool, TimeRange, TimeRange]:
+        time = datetime.now(tz=timezone.utc).timestamp()
+        trained_timerange = TimeRange()
+        data_load_timerange = TimeRange()
+
+        timeframes = self.freqai_config["feature_parameters"].get("include_timeframes")
+
+        max_tf_seconds = 0
+        for tf in timeframes:
+            secs = timeframe_to_seconds(tf)
+            if secs > max_tf_seconds:
+                max_tf_seconds = secs
+
+        # We notice that users like to use exotic indicators where
+        # they do not know the required timeperiod. Here we include a factor
+        # of safety by multiplying the user considered "max" by 2.
+        max_period = self.config.get("startup_candle_count", 20) * 2
+        additional_seconds = max_period * max_tf_seconds
+
+        if trained_timestamp != 0:
+            elapsed_time = (time - trained_timestamp) / SECONDS_IN_HOUR
+            retrain = elapsed_time > self.freqai_config.get("live_retrain_hours", 0)
+            if retrain:
+                trained_timerange.startts = int(
+                    time - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
+                )
+                trained_timerange.stopts = int(time)
+                # we want to load/populate indicators on more data than we plan to train on so
+                # because most of the indicators have a rolling timeperiod, and are thus NaNs
+                # unless they have data further back in time before the start of the train period
+                data_load_timerange.startts = int(
+                    time
+                    - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
+                    - additional_seconds
+                )
+                data_load_timerange.stopts = int(time)
+        else:  # user passed no live_trained_timerange in config
+            trained_timerange.startts = int(
+                time - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
+            )
+            trained_timerange.stopts = int(time)
+
+            data_load_timerange.startts = int(
+                time
+                - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
+                - additional_seconds
+            )
+            data_load_timerange.stopts = int(time)
+            retrain = True
+
+        return retrain, trained_timerange, data_load_timerange
+
+    def set_new_model_names(self, pair: str, timestamp_id: int):
+        coin, _ = pair.split("/")
+        self.data_path = Path(self.full_path / f"sub-train-{pair.split('/')[0]}_{timestamp_id}")
+
+        self.model_filename = f"cb_{coin.lower()}_{timestamp_id}"
+
+    def set_all_pairs(self) -> None:
+        self.all_pairs = copy.deepcopy(
+            self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
+        )
+        for pair in self.config.get("exchange", "").get("pair_whitelist"):
+            if pair not in self.all_pairs:
+                self.all_pairs.append(pair)
+
+    def extract_corr_pair_columns_from_populated_indicators(
+        self, dataframe: DataFrame
+    ) -> dict[str, DataFrame]:
+        """
+        Find the columns of the dataframe corresponding to the corr_pairlist, save them
+        in a dictionary to be reused and attached to other pairs.
+
+        :param dataframe: fully populated dataframe (current pair + corr_pairs)
+        :return: corr_dataframes, dictionary of dataframes to be attached
+                 to other pairs in same candle.
+        """
+        corr_dataframes: dict[str, DataFrame] = {}
+        pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
+
+        for pair in pairs:
+            pair = pair.replace(":", "")  # lightgbm does not like colons
+            pair_cols = [
+                col for col in dataframe.columns if col.startswith("%") and f"{pair}_" in col
+            ]
+
+            if pair_cols:
+                pair_cols.insert(0, "date")
+                corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
+
+        return corr_dataframes
+
+    def attach_corr_pair_columns(
+        self, dataframe: DataFrame, corr_dataframes: dict[str, DataFrame], current_pair: str
+    ) -> DataFrame:
+        """
+        Attach the existing corr_pair dataframes to the current pair dataframe before training
+
+        :param dataframe: current pair strategy dataframe, indicators populated already
+        :param corr_dataframes: dictionary of saved dataframes from earlier in the same candle
+        :param current_pair: current pair to which we will attach corr pair dataframe
+        :return:
+        :dataframe: current pair dataframe of populated indicators, concatenated with corr_pairs
+                    ready for training
+        """
+        pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
+        current_pair = current_pair.replace(":", "")
+        for pair in pairs:
+            pair = pair.replace(":", "")  # lightgbm does not work with colons
+            if current_pair != pair:
+                dataframe = dataframe.merge(corr_dataframes[pair], how="left", on="date")
+
+        return dataframe
+
+    def get_pair_data_for_features(
+        self,
+        pair: str,
+        tf: str,
+        strategy: IStrategy,
+        corr_dataframes: dict,
+        base_dataframes: dict,
+        is_corr_pairs: bool = False,
+    ) -> DataFrame:
+        """
+        Get the data for the pair. If it's not in the dictionary, get it from the data provider
+        :param pair: str = pair to get data for
+        :param tf: str = timeframe to get data for
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+                                (for user defined timeframes)
+        :param base_dataframes: dict = dict containing the current pair dataframes
+                                (for user defined timeframes)
+        :param is_corr_pairs: bool = whether the pair is a corr pair or not
+        :return: dataframe = dataframe containing the pair data
+        """
+        if is_corr_pairs:
+            dataframe = corr_dataframes[pair][tf]
+            if not dataframe.empty:
+                return dataframe
+            else:
+                dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
+                return dataframe
+        else:
+            dataframe = base_dataframes[tf]
+            if not dataframe.empty:
+                return dataframe
+            else:
+                dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
+                return dataframe
+
+    def merge_features(
+        self, df_main: DataFrame, df_to_merge: DataFrame, tf: str, timeframe_inf: str, suffix: str
+    ) -> DataFrame:
+        """
+        Merge the features of the dataframe and remove HLCV and date added columns
+        :param df_main: DataFrame = main dataframe
+        :param df_to_merge: DataFrame = dataframe to merge
+        :param tf: str = timeframe of the main dataframe
+        :param timeframe_inf: str = timeframe of the dataframe to merge
+        :param suffix: str = suffix to add to the columns of the dataframe to merge
+        :return: dataframe = merged dataframe
+        """
+        dataframe = merge_informative_pair(
+            df_main,
+            df_to_merge,
+            tf,
+            timeframe_inf=timeframe_inf,
+            append_timeframe=False,
+            suffix=suffix,
+            ffill=True,
+        )
+        skip_columns = [
+            (f"{s}_{suffix}") for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        dataframe = dataframe.drop(columns=skip_columns)
+        return dataframe
+
+    def populate_features(
+        self,
+        dataframe: DataFrame,
+        pair: str,
+        strategy: IStrategy,
+        corr_dataframes: dict,
+        base_dataframes: dict,
+        is_corr_pairs: bool = False,
+    ) -> DataFrame:
+        """
+        Use the user defined strategy functions for populating features
+        :param dataframe: DataFrame = dataframe to populate
+        :param pair: str = pair to populate
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+        :param base_dataframes: dict = dict containing the current pair dataframes
+        :param is_corr_pairs: bool = whether the pair is a corr pair or not
+        :return: dataframe = populated dataframe
+        """
+        tfs: list[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
+
+        for tf in tfs:
+            metadata = {"pair": pair, "tf": tf}
+            informative_df = self.get_pair_data_for_features(
+                pair, tf, strategy, corr_dataframes, base_dataframes, is_corr_pairs
+            )
+            informative_copy = informative_df.copy()
+
+            logger.debug(f"Populating features for {pair} {tf}")
+
+            for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]:
+                df_features = strategy.feature_engineering_expand_all(
+                    informative_copy.copy(), t, metadata=metadata
+                )
+                suffix = f"{t}"
+                informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix)
+
+            generic_df = strategy.feature_engineering_expand_basic(
+                informative_copy.copy(), metadata=metadata
+            )
+            suffix = "gen"
+
+            informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix)
+
+            indicators = [col for col in informative_df if col.startswith("%")]
+            for n in range(self.freqai_config["feature_parameters"]["include_shifted_candles"] + 1):
+                if n == 0:
+                    continue
+                df_shift = informative_df[indicators].shift(n)
+                df_shift = df_shift.add_suffix("_shift-" + str(n))
+                informative_df = pd.concat((informative_df, df_shift), axis=1)
+
+            dataframe = self.merge_features(
+                dataframe.copy(), informative_df, self.config["timeframe"], tf, f"{pair}_{tf}"
+            )
+
+        return dataframe
+
+    def use_strategy_to_populate_indicators(  # noqa: C901
+        self,
+        strategy: IStrategy,
+        corr_dataframes: dict[str, DataFrame] | None = None,
+        base_dataframes: dict[str, dict[str, DataFrame]] | None = None,
+        pair: str = "",
+        prediction_dataframe: DataFrame | None = None,
+        do_corr_pairs: bool = True,
+    ) -> DataFrame:
+        """
+        Use the user defined strategy for populating indicators during retrain
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+                                (for user defined timeframes)
+        :param base_dataframes: dict = dict containing the current pair dataframes
+                                (for user defined timeframes)
+        :param pair: str = pair to populate
+        :param prediction_dataframe: DataFrame = dataframe containing the pair data
+        used for prediction
+        :param do_corr_pairs: bool = whether to populate corr pairs or not
+        :return:
+        dataframe: DataFrame = dataframe containing populated indicators
+        """
+        if not corr_dataframes:
+            corr_dataframes = {}
+        if not base_dataframes:
+            base_dataframes = {}
+
+        # check if the user is using the deprecated populate_any_indicators function
+        new_version = inspect.getsource(strategy.populate_any_indicators) == (
+            inspect.getsource(IStrategy.populate_any_indicators)
+        )
+
+        if not new_version:
+            raise OperationalException(
+                "You are using the `populate_any_indicators()` function"
+                " which was deprecated on March 1, 2023. Please refer "
+                "to the strategy migration guide to use the new "
+                "feature_engineering_* methods: \n"
+                f"{DOCS_LINK}/strategy_migration/#freqai-strategy \n"
+                "And the feature_engineering_* documentation: \n"
+                f"{DOCS_LINK}/freqai-feature-engineering/"
+            )
+
+        tfs: list[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
+        pairs: list[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
+
+        for tf in tfs:
+            if tf not in base_dataframes:
+                base_dataframes[tf] = pd.DataFrame()
+            for p in pairs:
+                if p not in corr_dataframes:
+                    corr_dataframes[p] = {}
+                if tf not in corr_dataframes[p]:
+                    corr_dataframes[p][tf] = pd.DataFrame()
+
+        if prediction_dataframe is not None and not prediction_dataframe.empty:
+            dataframe = prediction_dataframe.copy()
+            base_dataframes[self.config["timeframe"]] = dataframe.copy()
+        else:
+            dataframe = base_dataframes[self.config["timeframe"]].copy()
+
+        corr_pairs: list[str] = self.freqai_config["feature_parameters"].get(
+            "include_corr_pairlist", []
+        )
+        dataframe = self.populate_features(
+            dataframe.copy(), pair, strategy, corr_dataframes, base_dataframes
+        )
+        metadata = {"pair": pair}
+        dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
+        # ensure corr pairs are always last
+        for corr_pair in corr_pairs:
+            if pair == corr_pair:
+                continue  # dont repeat anything from whitelist
+            if corr_pairs and do_corr_pairs:
+                dataframe = self.populate_features(
+                    dataframe.copy(), corr_pair, strategy, corr_dataframes, base_dataframes, True
+                )
+
+        if self.live:
+            dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
+            dataframe = self.remove_special_chars_from_feature_names(dataframe)
+
+            self.get_unique_classes_from_labels(dataframe)
+
+        if self.config.get("reduce_df_footprint", False):
+            dataframe = reduce_dataframe_footprint(dataframe)
+
+        return dataframe
+
+    def fit_labels(self) -> None:
+        """
+        Fit the labels with a gaussian distribution
+        """
+        import scipy as spy
+
+        self.data["labels_mean"], self.data["labels_std"] = {}, {}
+        for label in self.data_dictionary["train_labels"].columns:
+            if self.data_dictionary["train_labels"][label].dtype == object:
+                continue
+            f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
+            self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
+
+        # in case targets are classifications
+        for label in self.unique_class_list:
+            self.data["labels_mean"][label], self.data["labels_std"][label] = 0, 0
+
+        return
+
+    def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Remove the features from the dataframe before returning it to strategy. This keeps it
+        compact for Frequi purposes.
+        """
+        to_keep = [
+            col for col in dataframe.columns if not col.startswith("%") or col.startswith("%%")
+        ]
+        return dataframe[to_keep]
+
+    def get_unique_classes_from_labels(self, dataframe: DataFrame) -> None:
+        # self.find_features(dataframe)
+        self.find_labels(dataframe)
+
+        for key in self.label_list:
+            if dataframe[key].dtype == object:
+                self.unique_classes[key] = dataframe[key].dropna().unique()
+
+        if self.unique_classes:
+            for label in self.unique_classes:
+                self.unique_class_list += list(self.unique_classes[label])
+
+    def save_backtesting_prediction(self, append_df: DataFrame) -> None:
+        """
+        Save prediction dataframe from backtesting to feather file format
+        :param append_df: dataframe for backtesting period
+        """
+        full_predictions_folder = Path(self.full_path / self.backtest_predictions_folder)
+        if not full_predictions_folder.is_dir():
+            full_predictions_folder.mkdir(parents=True, exist_ok=True)
+
+        append_df.to_feather(self.backtesting_results_path)
+
+    def get_backtesting_prediction(self) -> DataFrame:
+        """
+        Get prediction dataframe from feather file format
+        """
+        append_df = pd.read_feather(self.backtesting_results_path)
+        return append_df
+
+    def check_if_backtest_prediction_is_valid(self, len_backtest_df: int) -> bool:
+        """
+        Check if a backtesting prediction already exists and if the predictions
+        to append have the same size as the backtesting dataframe slice
+        :param length_backtesting_dataframe: Length of backtesting dataframe slice
+        :return:
+        :boolean: whether the prediction file is valid.
+        """
+        path_to_predictionfile = Path(
+            self.full_path
+            / self.backtest_predictions_folder
+            / f"{self.model_filename}_prediction.feather"
+        )
+        self.backtesting_results_path = path_to_predictionfile
+
+        file_exists = path_to_predictionfile.is_file()
+
+        if file_exists:
+            append_df = self.get_backtesting_prediction()
+            if len(append_df) == len_backtest_df and "date" in append_df:
+                logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
+                return True
+            else:
+                logger.info(
+                    "A new backtesting prediction file is required. "
+                    "(Number of predictions is different from dataframe length or "
+                    "old prediction file version)."
+                )
+                return False
+        else:
+            logger.info(f"Could not find backtesting prediction file at {path_to_predictionfile}")
+            return False
+
+    def get_full_models_path(self, config: Config) -> Path:
+        """
+        Returns default FreqAI model path
+        :param config: Configuration dictionary
+        """
+        freqai_config: dict[str, Any] = config["freqai"]
+        return Path(config["user_data_dir"] / "models" / str(freqai_config.get("identifier")))
+
+    def remove_special_chars_from_feature_names(self, dataframe: pd.DataFrame) -> pd.DataFrame:
+        """
+        Remove all special characters from feature strings (:)
+        :param dataframe: the dataframe that just finished indicator population. (unfiltered)
+        :return: dataframe with cleaned feature names
+        """
+
+        spec_chars = [":"]
+        for c in spec_chars:
+            dataframe.columns = dataframe.columns.str.replace(c, "")
+
+        return dataframe
+
+    def buffer_timerange(self, timerange: TimeRange):
+        """
+        Buffer the start and end of the timerange. This is used *after* the indicators
+        are populated.
+
+        The main example use is when predicting maxima and minima, the argrelextrema
+        function  cannot know the maxima/minima at the edges of the timerange. To improve
+        model accuracy, it is best to compute argrelextrema on the full timerange
+        and then use this function to cut off the edges (buffer) by the kernel.
+
+        In another case, if the targets are set to a shifted price movement, this
+        buffer is unnecessary because the shifted candles at the end of the timerange
+        will be NaN and FreqAI will automatically cut those off of the training
+        dataset.
+        """
+        buffer = self.freqai_config["feature_parameters"]["buffer_train_data_candles"]
+        if buffer:
+            timerange.stopts -= buffer * timeframe_to_seconds(self.config["timeframe"])
+            timerange.startts += buffer * timeframe_to_seconds(self.config["timeframe"])
+
+        return timerange
+
+    # deprecated functions
+    def normalize_data(self, data_dictionary: dict) -> dict[Any, Any]:
+        """
+        Deprecation warning, migration assistance
+        """
+        logger.warning(
+            f"Your custom IFreqaiModel relies on the deprecated"
+            " data pipeline. Please update your model to use the new data pipeline."
+            " This can be achieved by following the migration guide at "
+            f"{DOCS_LINK}/strategy_migration/#freqai-new-data-pipeline "
+            "We added a basic pipeline for you, but this will be removed "
+            "in a future version."
+        )
+
+        return data_dictionary
+
+    def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame:
+        """
+        Deprecation warning, migration assistance
+        """
+        logger.warning(
+            f"Your custom IFreqaiModel relies on the deprecated"
+            " data pipeline. Please update your model to use the new data pipeline."
+            " This can be achieved by following the migration guide at "
+            f"{DOCS_LINK}/strategy_migration/#freqai-new-data-pipeline "
+            "We added a basic pipeline for you, but this will be removed "
+            "in a future version."
+        )
+
+        pred_df, _, _ = self.label_pipeline.inverse_transform(df)
+
+        return pred_df
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
new file mode 100644
index 000000000..7b9de5ce2
--- /dev/null
+++ b/freqtrade/freqai/freqai_interface.py
@@ -0,0 +1,1043 @@
+import logging
+import threading
+import time
+from abc import ABC, abstractmethod
+from collections import deque
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Literal
+
+import datasieve.transforms as ds
+import numpy as np
+import pandas as pd
+import psutil
+from datasieve.pipeline import Pipeline
+from datasieve.transforms import SKLearnWrapper
+from numpy.typing import NDArray
+from pandas import DataFrame
+from sklearn.preprocessing import MinMaxScaler
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DOCS_LINK, Config
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.freqai.data_drawer import FreqaiDataDrawer
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.utils import get_tb_logger, plot_feature_importance, record_params
+from freqtrade.strategy.interface import IStrategy
+
+
+pd.options.mode.chained_assignment = None
+logger = logging.getLogger(__name__)
+
+
+class IFreqaiModel(ABC):
+    """
+    Class containing all tools for training and prediction in the strategy.
+    Base*PredictionModels inherit from this class.
+
+    Record of contribution:
+    FreqAI was developed by a group of individuals who all contributed specific skillsets to the
+    project.
+
+    Conception and software development:
+    Robert Caulk @robcaulk
+
+    Theoretical brainstorming:
+    Elin Törnquist @th0rntwig
+
+    Code review, software architecture brainstorming:
+    @xmatthias
+
+    Beta testing and bug reporting:
+    @bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
+    Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
+    """
+
+    def __init__(self, config: Config) -> None:
+        self.config = config
+        self.assert_config(self.config)
+        self.freqai_info: dict[str, Any] = config["freqai"]
+        self.data_split_parameters: dict[str, Any] = config.get("freqai", {}).get(
+            "data_split_parameters", {}
+        )
+        self.model_training_parameters: dict[str, Any] = config.get("freqai", {}).get(
+            "model_training_parameters", {}
+        )
+        self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
+        self.retrain = False
+        self.first = True
+        self.set_full_path()
+        self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
+        if self.save_backtest_models:
+            logger.info("Backtesting module configured to save all models.")
+
+        self.dd = FreqaiDataDrawer(Path(self.full_path), self.config)
+        # set current candle to arbitrary historical date
+        self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
+        self.dd.current_candle = self.current_candle
+        self.scanning = False
+        self.ft_params = self.freqai_info["feature_parameters"]
+        self.corr_pairlist: list[str] = self.ft_params.get("include_corr_pairlist", [])
+        self.keras: bool = self.freqai_info.get("keras", False)
+        if self.keras and self.ft_params.get("DI_threshold", 0):
+            self.ft_params["DI_threshold"] = 0
+            logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
+
+        self.CONV_WIDTH = self.freqai_info.get("conv_width", 1)
+        self.class_names: list[str] = []  # used in classification subclasses
+        self.pair_it = 0
+        self.pair_it_train = 0
+        self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
+        self.train_queue = self._set_train_queue()
+        self.inference_time: float = 0
+        self.train_time: float = 0
+        self.begin_time: float = 0
+        self.begin_time_train: float = 0
+        self.base_tf_seconds = timeframe_to_seconds(self.config["timeframe"])
+        self.continual_learning = self.freqai_info.get("continual_learning", False)
+        self.plot_features = self.ft_params.get("plot_feature_importances", 0)
+        self.corr_dataframes: dict[str, DataFrame] = {}
+        # get_corr_dataframes is controlling the caching of corr_dataframes
+        # for improved performance. Careful with this boolean.
+        self.get_corr_dataframes: bool = True
+        self._threads: list[threading.Thread] = []
+        self._stop_event = threading.Event()
+        self.metadata: dict[str, Any] = self.dd.load_global_metadata_from_disk()
+        self.data_provider: DataProvider | None = None
+        self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
+        self.can_short = True  # overridden in start() with strategy.can_short
+        self.model: Any = None
+        if self.ft_params.get("principal_component_analysis", False) and self.continual_learning:
+            self.ft_params.update({"principal_component_analysis": False})
+            logger.warning("User tried to use PCA with continual learning. Deactivating PCA.")
+        self.activate_tensorboard: bool = self.freqai_info.get("activate_tensorboard", True)
+
+        record_params(config, self.full_path)
+
+    def __getstate__(self):
+        """
+        Return an empty state to be pickled in hyperopt
+        """
+        return {}
+
+    def assert_config(self, config: Config) -> None:
+        if not config.get("freqai", {}):
+            raise OperationalException("No freqai parameters found in configuration file.")
+
+    def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
+        """
+        Entry point to the FreqaiModel from a specific pair, it will train a new model if
+        necessary before making the prediction.
+
+        :param dataframe: Full dataframe coming from strategy - it contains entire
+                           backtesting timerange + additional historical data necessary to train
+        the model.
+        :param metadata: pair metadata coming from strategy.
+        :param strategy: Strategy to train on
+        """
+        self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
+        self.dd.set_pair_dict_info(metadata)
+        self.data_provider = strategy.dp
+        self.can_short = strategy.can_short
+
+        if self.live:
+            self.inference_timer("start")
+            self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
+            dk = self.start_live(dataframe, metadata, strategy, self.dk)
+            dataframe = dk.remove_features_from_df(dk.return_dataframe)
+
+        # For backtesting, each pair enters and then gets trained for each window along the
+        # sliding window defined by "train_period_days" (training window) and "live_retrain_hours"
+        # (backtest window, i.e. window immediately following the training window).
+        # FreqAI slides the window and sequentially builds the backtesting results before returning
+        # the concatenated results for the full backtesting period back to the strategy.
+        else:
+            self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
+            if not self.config.get("freqai_backtest_live_models", False):
+                logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
+                dk = self.start_backtesting(dataframe, metadata, self.dk, strategy)
+                dataframe = dk.remove_features_from_df(dk.return_dataframe)
+            else:
+                logger.info("Backtesting using historic predictions (live models)")
+                dk = self.start_backtesting_from_historic_predictions(dataframe, metadata, self.dk)
+                dataframe = dk.return_dataframe
+
+        self.clean_up()
+        if self.live:
+            self.inference_timer("stop", metadata["pair"])
+
+        return dataframe
+
+    def clean_up(self):
+        """
+        Objects that should be handled by GC already between coins, but
+        are explicitly shown here to help demonstrate the non-persistence of these
+        objects.
+        """
+        self.model = None
+        self.dk = None
+
+    def _on_stop(self):
+        """
+        Callback for Subclasses to override to include logic for shutting down resources
+        when SIGINT is sent.
+        """
+        self.dd.save_historic_predictions_to_disk()
+        return
+
+    def shutdown(self):
+        """
+        Cleans up threads on Shutdown, set stop event. Join threads to wait
+        for current training iteration.
+        """
+        logger.info("Stopping FreqAI")
+        self._stop_event.set()
+
+        self.data_provider = None
+        self._on_stop()
+
+        if self.freqai_info.get("wait_for_training_iteration_on_reload", True):
+            logger.info("Waiting on Training iteration")
+            for _thread in self._threads:
+                _thread.join()
+        else:
+            logger.warning(
+                "Breaking current training iteration because "
+                "you set wait_for_training_iteration_on_reload to "
+                " False."
+            )
+
+    def start_scanning(self, *args, **kwargs) -> None:
+        """
+        Start `self._start_scanning` in a separate thread
+        """
+        _thread = threading.Thread(target=self._start_scanning, args=args, kwargs=kwargs)
+        self._threads.append(_thread)
+        _thread.start()
+
+    def _start_scanning(self, strategy: IStrategy) -> None:
+        """
+        Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
+        to improve model youth. This function is agnostic to data preparation/collection/storage,
+        it simply trains on what ever data is available in the self.dd.
+        :param strategy: IStrategy = The user defined strategy class
+        """
+        while not self._stop_event.is_set():
+            time.sleep(1)
+            pair = self.train_queue[0]
+
+            # ensure pair is available in dp
+            if pair not in strategy.dp.current_whitelist():
+                self.train_queue.popleft()
+                logger.warning(f"{pair} not in current whitelist, removing from train queue.")
+                continue
+
+            (_, trained_timestamp) = self.dd.get_pair_dict_info(pair)
+
+            dk = FreqaiDataKitchen(self.config, self.live, pair)
+            (
+                retrain,
+                new_trained_timerange,
+                data_load_timerange,
+            ) = dk.check_if_new_training_required(trained_timestamp)
+
+            if retrain:
+                self.train_timer("start")
+                dk.set_paths(pair, new_trained_timerange.stopts)
+                try:
+                    self.extract_data_and_train_model(
+                        new_trained_timerange, pair, strategy, dk, data_load_timerange
+                    )
+                except Exception as msg:
+                    logger.exception(
+                        f"Training {pair} raised exception {msg.__class__.__name__}. "
+                        f"Message: {msg}, skipping."
+                    )
+
+                self.train_timer("stop", pair)
+
+                # only rotate the queue after the first has been trained.
+                self.train_queue.rotate(-1)
+
+                self.dd.save_historic_predictions_to_disk()
+                if self.freqai_info.get("write_metrics_to_disk", False):
+                    self.dd.save_metric_tracker_to_disk()
+
+    def start_backtesting(
+        self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
+    ) -> FreqaiDataKitchen:
+        """
+        The main broad execution for backtesting. For backtesting, each pair enters and then gets
+        trained for each window along the sliding window defined by "train_period_days"
+        (training window) and "backtest_period_days" (backtest window, i.e. window immediately
+        following the training window). FreqAI slides the window and sequentially builds
+        the backtesting results before returning the concatenated results for the full
+        backtesting period back to the strategy.
+        :param dataframe: DataFrame = strategy passed dataframe
+        :param metadata: Dict = pair metadata
+        :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        :param strategy: Strategy to train on
+        :return:
+            FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        """
+
+        self.pair_it += 1
+        train_it = 0
+        pair = metadata["pair"]
+        populate_indicators = True
+        check_features = True
+        # Loop enforcing the sliding window training/backtesting paradigm
+        # tr_train is the training time range e.g. 1 historical month
+        # tr_backtest is the backtesting time range e.g. the week directly
+        # following tr_train. Both of these windows slide through the
+        # entire backtest
+        for tr_train, tr_backtest in zip(
+            dk.training_timeranges, dk.backtesting_timeranges, strict=False
+        ):
+            (_, _) = self.dd.get_pair_dict_info(pair)
+            train_it += 1
+            total_trains = len(dk.backtesting_timeranges)
+            self.training_timerange = tr_train
+            len_backtest_df = len(
+                dataframe.loc[
+                    (dataframe["date"] >= tr_backtest.startdt)
+                    & (dataframe["date"] < tr_backtest.stopdt),
+                    :,
+                ]
+            )
+
+            if not self.ensure_data_exists(len_backtest_df, tr_backtest, pair):
+                continue
+
+            self.log_backtesting_progress(tr_train, pair, train_it, total_trains)
+
+            timestamp_model_id = int(tr_train.stopts)
+            if dk.backtest_live_models:
+                timestamp_model_id = int(tr_backtest.startts)
+
+            dk.set_paths(pair, timestamp_model_id)
+
+            dk.set_new_model_names(pair, timestamp_model_id)
+
+            if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
+                if check_features:
+                    self.dd.load_metadata(dk)
+                    df_fts = self.dk.use_strategy_to_populate_indicators(
+                        strategy, prediction_dataframe=dataframe.tail(1), pair=pair
+                    )
+                    df_fts = dk.remove_special_chars_from_feature_names(df_fts)
+                    dk.find_features(df_fts)
+                    self.check_if_feature_list_matches_strategy(dk)
+                    check_features = False
+                append_df = dk.get_backtesting_prediction()
+                dk.append_predictions(append_df)
+            else:
+                if populate_indicators:
+                    dataframe = self.dk.use_strategy_to_populate_indicators(
+                        strategy, prediction_dataframe=dataframe, pair=pair
+                    )
+                    populate_indicators = False
+
+                dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
+                dataframe_base_train = strategy.set_freqai_targets(
+                    dataframe_base_train, metadata=metadata
+                )
+                dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :]
+                dataframe_base_backtest = strategy.set_freqai_targets(
+                    dataframe_base_backtest, metadata=metadata
+                )
+
+                tr_train = dk.buffer_timerange(tr_train)
+
+                dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
+                dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
+
+                dataframe_train = dk.remove_special_chars_from_feature_names(dataframe_train)
+                dataframe_backtest = dk.remove_special_chars_from_feature_names(dataframe_backtest)
+                dk.get_unique_classes_from_labels(dataframe_train)
+
+                if not self.model_exists(dk):
+                    dk.find_features(dataframe_train)
+                    dk.find_labels(dataframe_train)
+
+                    try:
+                        self.tb_logger = get_tb_logger(
+                            self.dd.model_type, dk.data_path, self.activate_tensorboard
+                        )
+                        self.model = self.train(dataframe_train, pair, dk)
+                        self.tb_logger.close()
+                    except Exception as msg:
+                        logger.warning(
+                            f"Training {pair} raised exception {msg.__class__.__name__}. "
+                            f"Message: {msg}, skipping.",
+                            exc_info=True,
+                        )
+                        self.model = None
+
+                    self.dd.pair_dict[pair]["trained_timestamp"] = int(tr_train.stopts)
+                    if self.plot_features and self.model is not None:
+                        plot_feature_importance(self.model, pair, dk, self.plot_features)
+                    if self.save_backtest_models and self.model is not None:
+                        logger.info("Saving backtest model to disk.")
+                        self.dd.save_data(self.model, pair, dk)
+                    else:
+                        logger.info("Saving metadata to disk.")
+                        self.dd.save_metadata(dk)
+                else:
+                    self.model = self.dd.load_data(pair, dk)
+
+                pred_df, do_preds = self.predict(dataframe_backtest, dk)
+                append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest)
+                dk.append_predictions(append_df)
+                dk.save_backtesting_prediction(append_df)
+
+        self.backtesting_fit_live_predictions(dk)
+        dk.fill_predictions(dataframe)
+
+        return dk
+
+    def start_live(
+        self, dataframe: DataFrame, metadata: dict, strategy: IStrategy, dk: FreqaiDataKitchen
+    ) -> FreqaiDataKitchen:
+        """
+        The main broad execution for dry/live. This function will check if a retraining should be
+        performed, and if so, retrain and reset the model.
+        :param dataframe: DataFrame = strategy passed dataframe
+        :param metadata: Dict = pair metadata
+        :param strategy: IStrategy = currently employed strategy
+        dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        :returns:
+        dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        """
+
+        if not strategy.process_only_new_candles:
+            raise OperationalException(
+                "You are trying to use a FreqAI strategy with "
+                "process_only_new_candles = False. This is not supported "
+                "by FreqAI, and it is therefore aborting."
+            )
+
+        # get the model metadata associated with the current pair
+        (_, trained_timestamp) = self.dd.get_pair_dict_info(metadata["pair"])
+
+        # append the historic data once per round
+        if self.dd.historic_data:
+            self.dd.update_historic_data(strategy, dk)
+            logger.debug(f"Updating historic data on pair {metadata['pair']}")
+            self.track_current_candle()
+
+        (_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
+            trained_timestamp
+        )
+        dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
+
+        # load candle history into memory if it is not yet.
+        if not self.dd.historic_data:
+            self.dd.load_all_pair_histories(data_load_timerange, dk)
+
+        if not self.scanning:
+            self.scanning = True
+            self.start_scanning(strategy)
+
+        # load the model and associated data into the data kitchen
+        self.model = self.dd.load_data(metadata["pair"], dk)
+
+        dataframe = dk.use_strategy_to_populate_indicators(
+            strategy,
+            prediction_dataframe=dataframe,
+            pair=metadata["pair"],
+            do_corr_pairs=self.get_corr_dataframes,
+        )
+
+        if not self.model:
+            logger.warning(
+                f"No model ready for {metadata['pair']}, returning null values to strategy."
+            )
+            self.dd.return_null_values_to_strategy(dataframe, dk)
+            return dk
+
+        if self.corr_pairlist:
+            dataframe = self.cache_corr_pairlist_dfs(dataframe, dk)
+
+        dk.find_labels(dataframe)
+
+        self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
+
+        return dk
+
+    def build_strategy_return_arrays(
+        self, dataframe: DataFrame, dk: FreqaiDataKitchen, pair: str, trained_timestamp: int
+    ) -> None:
+        # hold the historical predictions in memory so we are sending back
+        # correct array to strategy
+
+        if pair not in self.dd.model_return_values:
+            # first predictions are made on entire historical candle set coming from strategy. This
+            # allows FreqUI to show full return values.
+            pred_df, do_preds = self.predict(dataframe, dk)
+            if pair not in self.dd.historic_predictions:
+                self.set_initial_historic_predictions(pred_df, dk, pair, dataframe)
+            self.dd.set_initial_return_values(pair, pred_df, dataframe)
+
+            dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
+            return
+        elif self.dk.check_if_model_expired(trained_timestamp):
+            pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
+            do_preds = np.ones(2, dtype=np.int_) * 2
+            dk.DI_values = np.zeros(2)
+            logger.warning(
+                f"Model expired for {pair}, returning null values to strategy. Strategy "
+                "construction should take care to consider this event with "
+                "prediction == 0 and do_predict == 2"
+            )
+        else:
+            # remaining predictions are made only on the most recent candles for performance and
+            # historical accuracy reasons.
+            pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH :], dk, first=False)
+
+        if self.freqai_info.get("fit_live_predictions_candles", 0) and self.live:
+            self.fit_live_predictions(dk, pair)
+        self.dd.append_model_predictions(pair, pred_df, do_preds, dk, dataframe)
+        dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
+
+        return
+
+    def check_if_feature_list_matches_strategy(self, dk: FreqaiDataKitchen) -> None:
+        """
+        Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
+        to a folder holding existing models.
+        :param dataframe: DataFrame = strategy provided dataframe
+        :param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
+                   current coin/bot loop
+        """
+
+        if "training_features_list_raw" in dk.data:
+            feature_list = dk.data["training_features_list_raw"]
+        else:
+            feature_list = dk.data["training_features_list"]
+
+        if dk.training_features_list != feature_list:
+            raise OperationalException(
+                "Trying to access pretrained model with `identifier` "
+                "but found different features furnished by current strategy. "
+                "Change `identifier` to train from scratch, or ensure the "
+                "strategy is furnishing the same features as the pretrained "
+                "model. In case of --strategy-list, please be aware that FreqAI "
+                "requires all strategies to maintain identical "
+                "feature_engineering_* functions"
+            )
+
+    def define_data_pipeline(self, threads=-1) -> Pipeline:
+        ft_params = self.freqai_info["feature_parameters"]
+        pipe_steps = [
+            ("const", ds.VarianceThreshold(threshold=0)),
+            ("scaler", SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))),
+        ]
+
+        if ft_params.get("principal_component_analysis", False):
+            pipe_steps.append(("pca", ds.PCA(n_components=0.999)))
+            pipe_steps.append(
+                ("post-pca-scaler", SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1))))
+            )
+
+        if ft_params.get("use_SVM_to_remove_outliers", False):
+            svm_params = ft_params.get("svm_params", {"shuffle": False, "nu": 0.01})
+            pipe_steps.append(("svm", ds.SVMOutlierExtractor(**svm_params)))
+
+        di = ft_params.get("DI_threshold", 0)
+        if di:
+            pipe_steps.append(("di", ds.DissimilarityIndex(di_threshold=di, n_jobs=threads)))
+
+        if ft_params.get("use_DBSCAN_to_remove_outliers", False):
+            pipe_steps.append(("dbscan", ds.DBSCAN(n_jobs=threads)))
+
+        sigma = self.freqai_info["feature_parameters"].get("noise_standard_deviation", 0)
+        if sigma:
+            pipe_steps.append(("noise", ds.Noise(sigma=sigma)))
+
+        return Pipeline(pipe_steps)
+
+    def define_label_pipeline(self, threads=-1) -> Pipeline:
+        label_pipeline = Pipeline([("scaler", SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1))))])
+
+        return label_pipeline
+
+    def model_exists(self, dk: FreqaiDataKitchen) -> bool:
+        """
+        Given a pair and path, check if a model already exists
+        :param pair: pair e.g. BTC/USD
+        :param path: path to model
+        :return:
+        :boolean: whether the model file exists or not.
+        """
+        if self.dd.model_type == "joblib":
+            file_type = ".joblib"
+        elif self.dd.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]:
+            file_type = ".zip"
+
+        path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model{file_type}")
+        file_exists = path_to_modelfile.is_file()
+        if file_exists:
+            logger.info("Found model at %s", dk.data_path / dk.model_filename)
+        else:
+            logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
+        return file_exists
+
+    def set_full_path(self) -> None:
+        """
+        Creates and sets the full path for the identifier
+        """
+        self.full_path = Path(self.config["user_data_dir"] / "models" / f"{self.identifier}")
+        self.full_path.mkdir(parents=True, exist_ok=True)
+
+    def extract_data_and_train_model(
+        self,
+        new_trained_timerange: TimeRange,
+        pair: str,
+        strategy: IStrategy,
+        dk: FreqaiDataKitchen,
+        data_load_timerange: TimeRange,
+    ):
+        """
+        Retrieve data and train model.
+        :param new_trained_timerange: TimeRange = the timerange to train the model on
+        :param metadata: dict = strategy provided metadata
+        :param strategy: IStrategy = user defined strategy object
+        :param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
+        :param data_load_timerange: TimeRange = the amount of data to be loaded
+                                    for populating indicators
+                                    (larger than new_trained_timerange so that
+                                    new_trained_timerange does not contain any NaNs)
+        """
+
+        corr_dataframes, base_dataframes = self.dd.get_base_and_corr_dataframes(
+            data_load_timerange, pair, dk
+        )
+
+        unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
+            strategy, corr_dataframes=corr_dataframes, base_dataframes=base_dataframes, pair=pair
+        )
+
+        trained_timestamp = new_trained_timerange.stopts
+
+        buffered_timerange = dk.buffer_timerange(new_trained_timerange)
+
+        unfiltered_dataframe = dk.slice_dataframe(buffered_timerange, unfiltered_dataframe)
+
+        # find the features indicated by strategy and store in datakitchen
+        dk.find_features(unfiltered_dataframe)
+        dk.find_labels(unfiltered_dataframe)
+
+        self.tb_logger = get_tb_logger(self.dd.model_type, dk.data_path, self.activate_tensorboard)
+        model = self.train(unfiltered_dataframe, pair, dk)
+        self.tb_logger.close()
+
+        self.dd.pair_dict[pair]["trained_timestamp"] = trained_timestamp
+        dk.set_new_model_names(pair, trained_timestamp)
+        self.dd.save_data(model, pair, dk)
+
+        if self.plot_features:
+            plot_feature_importance(model, pair, dk, self.plot_features)
+
+        self.dd.purge_old_models()
+
+    def set_initial_historic_predictions(
+        self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str, strat_df: DataFrame
+    ) -> None:
+        """
+        This function is called only if the datadrawer failed to load an
+        existing set of historic predictions. In this case, it builds
+        the structure and sets fake predictions off the first training
+        data. After that, FreqAI will append new real predictions to the
+        set of historic predictions.
+
+        These values are used to generate live statistics which can be used
+        in the strategy for adaptive values. E.g. &*_mean/std are quantities
+        that can computed based on live predictions from the set of historical
+        predictions. Those values can be used in the user strategy to better
+        assess prediction rarity, and thus wait for probabilistically favorable
+        entries relative to the live historical predictions.
+
+        If the user reuses an identifier on a subsequent instance,
+        this function will not be called. In that case, "real" predictions
+        will be appended to the loaded set of historic predictions.
+        :param pred_df: DataFrame = the dataframe containing the predictions coming
+            out of a model
+        :param dk: FreqaiDataKitchen = object containing methods for data analysis
+        :param pair: str = current pair
+        :param strat_df: DataFrame = dataframe coming from strategy
+        """
+
+        self.dd.historic_predictions[pair] = pred_df
+        hist_preds_df = self.dd.historic_predictions[pair]
+
+        self.set_start_dry_live_date(strat_df)
+
+        for label in hist_preds_df.columns:
+            if hist_preds_df[label].dtype == object:
+                continue
+            hist_preds_df[f"{label}_mean"] = 0
+            hist_preds_df[f"{label}_std"] = 0
+
+        hist_preds_df["do_predict"] = 0
+
+        if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
+            hist_preds_df["DI_values"] = 0
+
+        for return_str in dk.data["extra_returns_per_train"]:
+            hist_preds_df[return_str] = dk.data["extra_returns_per_train"][return_str]
+
+        hist_preds_df["high_price"] = strat_df["high"]
+        hist_preds_df["low_price"] = strat_df["low"]
+        hist_preds_df["close_price"] = strat_df["close"]
+        hist_preds_df["date_pred"] = strat_df["date"]
+
+    def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
+        """
+        Fit the labels with a gaussian distribution
+        """
+        import scipy as spy
+
+        # add classes from classifier label types if used
+        full_labels = dk.label_list + dk.unique_class_list
+
+        num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
+        dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
+        for label in full_labels:
+            if self.dd.historic_predictions[dk.pair][label].dtype == object:
+                continue
+            f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
+            dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
+
+        return
+
+    def inference_timer(self, do: Literal["start", "stop"] = "start", pair: str = ""):
+        """
+        Timer designed to track the cumulative time spent in FreqAI for one pass through
+        the whitelist. This will check if the time spent is more than 1/4 the time
+        of a single candle, and if so, it will warn the user of degraded performance
+        """
+        if do == "start":
+            self.pair_it += 1
+            self.begin_time = time.time()
+        elif do == "stop":
+            end = time.time()
+            time_spent = end - self.begin_time
+            if self.freqai_info.get("write_metrics_to_disk", False):
+                self.dd.update_metric_tracker("inference_time", time_spent, pair)
+            self.inference_time += time_spent
+            if self.pair_it == self.total_pairs:
+                logger.info(
+                    f"Total time spent inferencing pairlist {self.inference_time:.2f} seconds"
+                )
+                self.pair_it = 0
+                self.inference_time = 0
+        return
+
+    def train_timer(self, do: Literal["start", "stop"] = "start", pair: str = ""):
+        """
+        Timer designed to track the cumulative time spent training the full pairlist in
+        FreqAI.
+        """
+        if do == "start":
+            self.pair_it_train += 1
+            self.begin_time_train = time.time()
+        elif do == "stop":
+            end = time.time()
+            time_spent = end - self.begin_time_train
+            if self.freqai_info.get("write_metrics_to_disk", False):
+                self.dd.collect_metrics(time_spent, pair)
+
+            self.train_time += time_spent
+            if self.pair_it_train == self.total_pairs:
+                logger.info(f"Total time spent training pairlist {self.train_time:.2f} seconds")
+                self.pair_it_train = 0
+                self.train_time = 0
+        return
+
+    def get_init_model(self, pair: str) -> Any:
+        if pair not in self.dd.model_dictionary or not self.continual_learning:
+            init_model = None
+        else:
+            init_model = self.dd.model_dictionary[pair]
+
+        return init_model
+
+    def _set_train_queue(self):
+        """
+        Sets train queue from existing train timestamps if they exist
+        otherwise it sets the train queue based on the provided whitelist.
+        """
+        current_pairlist = self.config.get("exchange", {}).get("pair_whitelist")
+        if not self.dd.pair_dict:
+            logger.info(f"Set fresh train queue from whitelist. Queue: {current_pairlist}")
+            return deque(current_pairlist)
+
+        best_queue = deque()
+
+        pair_dict_sorted = sorted(
+            self.dd.pair_dict.items(), key=lambda k: k[1]["trained_timestamp"]
+        )
+        for pair in pair_dict_sorted:
+            if pair[0] in current_pairlist:
+                best_queue.append(pair[0])
+        for pair in current_pairlist:
+            if pair not in best_queue:
+                best_queue.appendleft(pair)
+
+        logger.info(
+            f"Set existing queue from trained timestamps. Best approximation queue: {best_queue}"
+        )
+        return best_queue
+
+    def cache_corr_pairlist_dfs(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
+        """
+        Cache the corr_pairlist dfs to speed up performance for subsequent pairs during the
+        current candle.
+        :param dataframe: strategy fed dataframe
+        :param dk: datakitchen object for current asset
+        :return: dataframe to attach/extract cached corr_pair dfs to/from.
+        """
+
+        if self.get_corr_dataframes:
+            self.corr_dataframes = dk.extract_corr_pair_columns_from_populated_indicators(dataframe)
+            if not self.corr_dataframes:
+                logger.warning(
+                    "Couldn't cache corr_pair dataframes for improved performance. "
+                    "Consider ensuring that the full coin/stake, e.g. XYZ/USD, "
+                    "is included in the column names when you are creating features "
+                    "in `feature_engineering_*` functions."
+                )
+            self.get_corr_dataframes = not bool(self.corr_dataframes)
+        elif self.corr_dataframes:
+            dataframe = dk.attach_corr_pair_columns(dataframe, self.corr_dataframes, dk.pair)
+
+        return dataframe
+
+    def track_current_candle(self):
+        """
+        Checks if the latest candle appended by the datadrawer is
+        equivalent to the latest candle seen by FreqAI. If not, it
+        asks to refresh the cached corr_dfs, and resets the pair
+        counter.
+        """
+        if self.dd.current_candle > self.current_candle:
+            self.get_corr_dataframes = True
+            self.pair_it = 1
+            self.current_candle = self.dd.current_candle
+
+    def ensure_data_exists(
+        self, len_dataframe_backtest: int, tr_backtest: TimeRange, pair: str
+    ) -> bool:
+        """
+        Check if the dataframe is empty, if not, report useful information to user.
+        :param len_dataframe_backtest: the len of backtesting dataframe
+        :param tr_backtest: current backtesting timerange.
+        :param pair: current pair
+        :return: if the data exists or not
+        """
+        if self.config.get("freqai_backtest_live_models", False) and len_dataframe_backtest == 0:
+            logger.info(
+                f"No data found for pair {pair} from "
+                f"from {tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. "
+                "Probably more than one training within the same candle period."
+            )
+            return False
+        return True
+
+    def log_backtesting_progress(
+        self, tr_train: TimeRange, pair: str, train_it: int, total_trains: int
+    ):
+        """
+        Log the backtesting progress so user knows how many pairs have been trained and
+        how many more pairs/trains remain.
+        :param tr_train: the training timerange
+        :param train_it: the train iteration for the current pair (the sliding window progress)
+        :param pair: the current pair
+        :param total_trains: total trains (total number of slides for the sliding window)
+        """
+        if not self.config.get("freqai_backtest_live_models", False):
+            logger.info(
+                f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
+                f" from {tr_train.start_fmt} "
+                f"to {tr_train.stop_fmt}, {train_it}/{total_trains} "
+                "trains"
+            )
+
+    def backtesting_fit_live_predictions(self, dk: FreqaiDataKitchen):
+        """
+        Apply fit_live_predictions function in backtesting with a dummy historic_predictions
+        The loop is required to simulate dry/live operation, as it is not possible to predict
+        the type of logic implemented by the user.
+        :param dk: datakitchen object
+        """
+        fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 0)
+        if fit_live_predictions_candles:
+            logger.info("Applying fit_live_predictions in backtesting")
+            label_columns = [
+                col
+                for col in dk.full_df.columns
+                if (
+                    col.startswith("&")
+                    and not (col.startswith("&") and col.endswith("_mean"))
+                    and not (col.startswith("&") and col.endswith("_std"))
+                    and col not in self.dk.data["extra_returns_per_train"]
+                )
+            ]
+
+            for index in range(len(dk.full_df)):
+                if index >= fit_live_predictions_candles:
+                    self.dd.historic_predictions[self.dk.pair] = dk.full_df.iloc[
+                        index - fit_live_predictions_candles : index
+                    ]
+                    self.fit_live_predictions(self.dk, self.dk.pair)
+                    for label in label_columns:
+                        if dk.full_df[label].dtype == object:
+                            continue
+                        if "labels_mean" in self.dk.data:
+                            dk.full_df.at[index, f"{label}_mean"] = self.dk.data["labels_mean"][
+                                label
+                            ]
+                        if "labels_std" in self.dk.data:
+                            dk.full_df.at[index, f"{label}_std"] = self.dk.data["labels_std"][label]
+
+                    for extra_col in self.dk.data["extra_returns_per_train"]:
+                        dk.full_df.at[index, f"{extra_col}"] = self.dk.data[
+                            "extra_returns_per_train"
+                        ][extra_col]
+
+        return
+
+    def update_metadata(self, metadata: dict[str, Any]):
+        """
+        Update global metadata and save the updated json file
+        :param metadata: new global metadata dict
+        """
+        self.dd.save_global_metadata_to_disk(metadata)
+        self.metadata = metadata
+
+    def set_start_dry_live_date(self, live_dataframe: DataFrame):
+        key_name = "start_dry_live_date"
+        if key_name not in self.metadata:
+            metadata = self.metadata
+            metadata[key_name] = int(
+                pd.to_datetime(live_dataframe.tail(1)["date"].values[0]).timestamp()
+            )
+            self.update_metadata(metadata)
+
+    def start_backtesting_from_historic_predictions(
+        self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
+    ) -> FreqaiDataKitchen:
+        """
+        :param dataframe: DataFrame = strategy passed dataframe
+        :param metadata: Dict = pair metadata
+        :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        :return:
+            FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        """
+        pair = metadata["pair"]
+        dk.return_dataframe = dataframe
+        saved_dataframe = self.dd.historic_predictions[pair]
+        columns_to_drop = list(
+            set(saved_dataframe.columns).intersection(dk.return_dataframe.columns)
+        )
+        dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
+        dk.return_dataframe = pd.merge(
+            dk.return_dataframe, saved_dataframe, how="left", left_on="date", right_on="date_pred"
+        )
+        return dk
+
+    # Following methods which are overridden by user made prediction models.
+    # See freqai/prediction_models/CatboostPredictionModel.py for an example.
+
+    @abstractmethod
+    def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        Filter the training data and train a model to it. Train makes heavy use of the datahandler
+        for storing, saving, loading, and analyzing the data.
+        :param unfiltered_df: Full dataframe for the current training period
+        :param metadata: pair metadata from strategy.
+        :return: Trained model which can be used to inference (self.predict)
+        """
+
+    @abstractmethod
+    def fit(self, data_dictionary: dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        Most regressors use the same function names and arguments e.g. user
+        can drop in LGBMRegressor in place of CatBoostRegressor and all data
+        management will be properly handled by Freqai.
+        :param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
+        """
+
+        return
+
+    @abstractmethod
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_df: Full dataframe for the current backtest period.
+        :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        :param first: boolean = whether this is the first prediction or not.
+        :return:
+        :predictions: np.array of predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
+        """
+
+    # deprecated functions
+    def data_cleaning_train(self, dk: FreqaiDataKitchen, pair: str):
+        """
+        throw deprecation warning if this function is called
+        """
+        logger.warning(
+            f"Your model {self.__class__.__name__} relies on the deprecated"
+            " data pipeline. Please update your model to use the new data pipeline."
+            " This can be achieved by following the migration guide at "
+            f"{DOCS_LINK}/strategy_migration/#freqai-new-data-pipeline"
+        )
+        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
+        dd = dk.data_dictionary
+        (dd["train_features"], dd["train_labels"], dd["train_weights"]) = (
+            dk.feature_pipeline.fit_transform(
+                dd["train_features"], dd["train_labels"], dd["train_weights"]
+            )
+        )
+
+        (dd["test_features"], dd["test_labels"], dd["test_weights"]) = (
+            dk.feature_pipeline.transform(
+                dd["test_features"], dd["test_labels"], dd["test_weights"]
+            )
+        )
+
+        dk.label_pipeline = self.define_label_pipeline(threads=dk.thread_count)
+
+        dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
+        dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
+        return
+
+    def data_cleaning_predict(self, dk: FreqaiDataKitchen, pair: str):
+        """
+        throw deprecation warning if this function is called
+        """
+        logger.warning(
+            f"Your model {self.__class__.__name__} relies on the deprecated"
+            " data pipeline. Please update your model to use the new data pipeline."
+            " This can be achieved by following the migration guide at "
+            f"{DOCS_LINK}/strategy_migration/#freqai-new-data-pipeline"
+        )
+        dd = dk.data_dictionary
+        dd["predict_features"], outliers, _ = dk.feature_pipeline.transform(
+            dd["predict_features"], outlier_check=True
+        )
+        if self.freqai_info.get("DI_threshold", 0) > 0:
+            dk.DI_values = dk.feature_pipeline["di"].di_values
+        else:
+            dk.DI_values = np.zeros(outliers.shape[0])
+        dk.do_predict = outliers
+        return
diff --git a/freqtrade/freqai/prediction_models/CatboostClassifier.py b/freqtrade/freqai/prediction_models/CatboostClassifier.py
new file mode 100644
index 000000000..632dc781e
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/CatboostClassifier.py
@@ -0,0 +1,61 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from catboost import CatBoostClassifier, Pool
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class CatboostClassifier(BaseClassifierModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        train_data = Pool(
+            data=data_dictionary["train_features"],
+            label=data_dictionary["train_labels"],
+            weight=data_dictionary["train_weights"],
+        )
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            test_data = None
+        else:
+            test_data = Pool(
+                data=data_dictionary["test_features"],
+                label=data_dictionary["test_labels"],
+                weight=data_dictionary["test_weights"],
+            )
+
+        cbr = CatBoostClassifier(
+            allow_writing_files=True,
+            loss_function="MultiClass",
+            train_dir=Path(dk.data_path),
+            **self.model_training_parameters,
+        )
+
+        init_model = self.get_init_model(dk.pair)
+
+        cbr.fit(
+            X=train_data,
+            eval_set=test_data,
+            init_model=init_model,
+        )
+
+        return cbr
diff --git a/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py b/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py
new file mode 100644
index 000000000..a277bc2d7
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py
@@ -0,0 +1,79 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from catboost import CatBoostClassifier, Pool
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class CatboostClassifierMultiTarget(BaseClassifierModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        cbc = CatBoostClassifier(
+            allow_writing_files=True,
+            loss_function="MultiClass",
+            train_dir=Path(dk.data_path),
+            **self.model_training_parameters,
+        )
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        sample_weight = data_dictionary["train_weights"]
+
+        eval_sets = [None] * y.shape[1]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            eval_sets = [None] * data_dictionary["test_labels"].shape[1]
+
+            for i in range(data_dictionary["test_labels"].shape[1]):
+                eval_sets[i] = Pool(
+                    data=data_dictionary["test_features"],
+                    label=data_dictionary["test_labels"].iloc[:, i],
+                    weight=data_dictionary["test_weights"],
+                )
+
+        init_model = self.get_init_model(dk.pair)
+
+        if init_model:
+            init_models = init_model.estimators_
+        else:
+            init_models = [None] * y.shape[1]
+
+        fit_params = []
+        for i in range(len(eval_sets)):
+            fit_params.append(
+                {
+                    "eval_set": eval_sets[i],
+                    "init_model": init_models[i],
+                }
+            )
+
+        model = FreqaiMultiOutputClassifier(estimator=cbc)
+        thread_training = self.freqai_info.get("multitarget_parallel_training", False)
+        if thread_training:
+            model.n_jobs = y.shape[1]
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
+
+        return model
diff --git a/freqtrade/freqai/prediction_models/CatboostRegressor.py b/freqtrade/freqai/prediction_models/CatboostRegressor.py
new file mode 100644
index 000000000..fb2727b33
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/CatboostRegressor.py
@@ -0,0 +1,60 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from catboost import CatBoostRegressor, Pool
+
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class CatboostRegressor(BaseRegressionModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        train_data = Pool(
+            data=data_dictionary["train_features"],
+            label=data_dictionary["train_labels"],
+            weight=data_dictionary["train_weights"],
+        )
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            test_data = None
+        else:
+            test_data = Pool(
+                data=data_dictionary["test_features"],
+                label=data_dictionary["test_labels"],
+                weight=data_dictionary["test_weights"],
+            )
+
+        init_model = self.get_init_model(dk.pair)
+
+        model = CatBoostRegressor(
+            allow_writing_files=True,
+            train_dir=Path(dk.data_path),
+            **self.model_training_parameters,
+        )
+
+        model.fit(
+            X=train_data,
+            eval_set=test_data,
+            init_model=init_model,
+        )
+
+        return model
diff --git a/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py b/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
new file mode 100644
index 000000000..ad3ddcd9b
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
@@ -0,0 +1,78 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from catboost import CatBoostRegressor, Pool
+
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class CatboostRegressorMultiTarget(BaseRegressionModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        cbr = CatBoostRegressor(
+            allow_writing_files=True,
+            train_dir=Path(dk.data_path),
+            **self.model_training_parameters,
+        )
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        sample_weight = data_dictionary["train_weights"]
+
+        eval_sets = [None] * y.shape[1]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            eval_sets = [None] * data_dictionary["test_labels"].shape[1]
+
+            for i in range(data_dictionary["test_labels"].shape[1]):
+                eval_sets[i] = Pool(
+                    data=data_dictionary["test_features"],
+                    label=data_dictionary["test_labels"].iloc[:, i],
+                    weight=data_dictionary["test_weights"],
+                )
+
+        init_model = self.get_init_model(dk.pair)
+
+        if init_model:
+            init_models = init_model.estimators_
+        else:
+            init_models = [None] * y.shape[1]
+
+        fit_params = []
+        for i in range(len(eval_sets)):
+            fit_params.append(
+                {
+                    "eval_set": eval_sets[i],
+                    "init_model": init_models[i],
+                }
+            )
+
+        model = FreqaiMultiOutputRegressor(estimator=cbr)
+        thread_training = self.freqai_info.get("multitarget_parallel_training", False)
+        if thread_training:
+            model.n_jobs = y.shape[1]
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
+
+        return model
diff --git a/freqtrade/freqai/prediction_models/LightGBMClassifier.py b/freqtrade/freqai/prediction_models/LightGBMClassifier.py
new file mode 100644
index 000000000..e17f7417c
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/LightGBMClassifier.py
@@ -0,0 +1,58 @@
+import logging
+from typing import Any
+
+from lightgbm import LGBMClassifier
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class LightGBMClassifier(BaseClassifierModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+            test_weights = None
+        else:
+            eval_set = [
+                (
+                    data_dictionary["test_features"].to_numpy(),
+                    data_dictionary["test_labels"].to_numpy()[:, 0],
+                )
+            ]
+            test_weights = data_dictionary["test_weights"]
+        X = data_dictionary["train_features"].to_numpy()
+        y = data_dictionary["train_labels"].to_numpy()[:, 0]
+        train_weights = data_dictionary["train_weights"]
+
+        init_model = self.get_init_model(dk.pair)
+
+        model = LGBMClassifier(**self.model_training_parameters)
+        model.fit(
+            X=X,
+            y=y,
+            eval_set=eval_set,
+            sample_weight=train_weights,
+            eval_sample_weight=[test_weights],
+            init_model=init_model,
+        )
+
+        return model
diff --git a/freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py b/freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py
new file mode 100644
index 000000000..9fb775614
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py
@@ -0,0 +1,72 @@
+import logging
+from typing import Any
+
+from lightgbm import LGBMClassifier
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class LightGBMClassifierMultiTarget(BaseClassifierModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        lgb = LGBMClassifier(**self.model_training_parameters)
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+        sample_weight = data_dictionary["train_weights"]
+
+        eval_weights = None
+        eval_sets = [None] * y.shape[1]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            eval_weights = [data_dictionary["test_weights"]]
+            eval_sets = [(None, None)] * data_dictionary["test_labels"].shape[1]  # type: ignore
+            for i in range(data_dictionary["test_labels"].shape[1]):
+                eval_sets[i] = (  # type: ignore
+                    data_dictionary["test_features"],
+                    data_dictionary["test_labels"].iloc[:, i],
+                )
+
+        init_model = self.get_init_model(dk.pair)
+        if init_model:
+            init_models = init_model.estimators_
+        else:
+            init_models = [None] * y.shape[1]
+
+        fit_params = []
+        for i in range(len(eval_sets)):
+            fit_params.append(
+                {
+                    "eval_set": eval_sets[i],
+                    "eval_sample_weight": eval_weights,
+                    "init_model": init_models[i],
+                }
+            )
+
+        model = FreqaiMultiOutputClassifier(estimator=lgb)
+        thread_training = self.freqai_info.get("multitarget_parallel_training", False)
+        if thread_training:
+            model.n_jobs = y.shape[1]
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
+
+        return model
diff --git a/freqtrade/freqai/prediction_models/LightGBMRegressor.py b/freqtrade/freqai/prediction_models/LightGBMRegressor.py
new file mode 100644
index 000000000..d55cd0ca2
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/LightGBMRegressor.py
@@ -0,0 +1,54 @@
+import logging
+from typing import Any
+
+from lightgbm import LGBMRegressor
+
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class LightGBMRegressor(BaseRegressionModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+            eval_weights = None
+        else:
+            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
+            eval_weights = data_dictionary["test_weights"]
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+        train_weights = data_dictionary["train_weights"]
+
+        init_model = self.get_init_model(dk.pair)
+
+        model = LGBMRegressor(**self.model_training_parameters)
+
+        model.fit(
+            X=X,
+            y=y,
+            eval_set=eval_set,
+            sample_weight=train_weights,
+            eval_sample_weight=[eval_weights],
+            init_model=init_model,
+        )
+
+        return model
diff --git a/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py b/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
new file mode 100644
index 000000000..c4669a79d
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
@@ -0,0 +1,74 @@
+import logging
+from typing import Any
+
+from lightgbm import LGBMRegressor
+
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class LightGBMRegressorMultiTarget(BaseRegressionModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        lgb = LGBMRegressor(**self.model_training_parameters)
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+        sample_weight = data_dictionary["train_weights"]
+
+        eval_weights = None
+        eval_sets = [None] * y.shape[1]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
+            eval_weights = [data_dictionary["test_weights"]]
+            eval_sets = [(None, None)] * data_dictionary["test_labels"].shape[1]  # type: ignore
+            for i in range(data_dictionary["test_labels"].shape[1]):
+                eval_sets[i] = [  # type: ignore
+                    (
+                        data_dictionary["test_features"],
+                        data_dictionary["test_labels"].iloc[:, i],
+                    )
+                ]
+
+        init_model = self.get_init_model(dk.pair)
+        if init_model:
+            init_models = init_model.estimators_
+        else:
+            init_models = [None] * y.shape[1]
+
+        fit_params = []
+        for i in range(len(eval_sets)):
+            fit_params.append(
+                {
+                    "eval_set": eval_sets[i],
+                    "eval_sample_weight": eval_weights,
+                    "init_model": init_models[i],
+                }
+            )
+
+        model = FreqaiMultiOutputRegressor(estimator=lgb)
+        thread_training = self.freqai_info.get("multitarget_parallel_training", False)
+        if thread_training:
+            model.n_jobs = y.shape[1]
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
+
+        return model
diff --git a/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py b/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
new file mode 100644
index 000000000..d291ade7f
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
@@ -0,0 +1,90 @@
+from typing import Any
+
+import torch
+
+from freqtrade.freqai.base_models.BasePyTorchClassifier import BasePyTorchClassifier
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.torch.PyTorchDataConvertor import (
+    DefaultPyTorchDataConvertor,
+    PyTorchDataConvertor,
+)
+from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel
+from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer
+
+
+class PyTorchMLPClassifier(BasePyTorchClassifier):
+    """
+    This class implements the fit method of IFreqaiModel.
+    in the fit method we initialize the model and trainer objects.
+    the only requirement from the model is to be aligned to PyTorchClassifier
+    predict method that expects the model to predict a tensor of type long.
+
+    parameters are passed via `model_training_parameters` under the freqai
+    section in the config file. e.g:
+    {
+        ...
+        "freqai": {
+            ...
+            "model_training_parameters" : {
+                "learning_rate": 3e-4,
+                "trainer_kwargs": {
+                    "n_steps": 5000,
+                    "batch_size": 64,
+                    "n_epochs": null,
+                },
+                "model_kwargs": {
+                    "hidden_dim": 512,
+                    "dropout_percent": 0.2,
+                    "n_layer": 1,
+                },
+            }
+        }
+    }
+    """
+
+    @property
+    def data_convertor(self) -> PyTorchDataConvertor:
+        return DefaultPyTorchDataConvertor(
+            target_tensor_type=torch.long, squeeze_target_tensor=True
+        )
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        config = self.freqai_info.get("model_training_parameters", {})
+        self.learning_rate: float = config.get("learning_rate", 3e-4)
+        self.model_kwargs: dict[str, Any] = config.get("model_kwargs", {})
+        self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs", {})
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        :raises ValueError: If self.class_names is not defined in the parent class.
+        """
+
+        class_names = self.get_class_names()
+        self.convert_label_column_to_int(data_dictionary, dk, class_names)
+        n_features = data_dictionary["train_features"].shape[-1]
+        model = PyTorchMLPModel(
+            input_dim=n_features, output_dim=len(class_names), **self.model_kwargs
+        )
+        model.to(self.device)
+        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
+        criterion = torch.nn.CrossEntropyLoss()
+        # check if continual_learning is activated, and retrieve the model to continue training
+        trainer = self.get_init_model(dk.pair)
+        if trainer is None:
+            trainer = PyTorchModelTrainer(
+                model=model,
+                optimizer=optimizer,
+                criterion=criterion,
+                model_meta_data={"class_names": class_names},
+                device=self.device,
+                data_convertor=self.data_convertor,
+                tb_logger=self.tb_logger,
+                **self.trainer_kwargs,
+            )
+        trainer.fit(data_dictionary, self.splits)
+        return trainer
diff --git a/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py b/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
new file mode 100644
index 000000000..e5ff6d969
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
@@ -0,0 +1,83 @@
+from typing import Any
+
+import torch
+
+from freqtrade.freqai.base_models.BasePyTorchRegressor import BasePyTorchRegressor
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.torch.PyTorchDataConvertor import (
+    DefaultPyTorchDataConvertor,
+    PyTorchDataConvertor,
+)
+from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel
+from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer
+
+
+class PyTorchMLPRegressor(BasePyTorchRegressor):
+    """
+    This class implements the fit method of IFreqaiModel.
+    in the fit method we initialize the model and trainer objects.
+    the only requirement from the model is to be aligned to PyTorchRegressor
+    predict method that expects the model to predict tensor of type float.
+    the trainer defines the training loop.
+
+    parameters are passed via `model_training_parameters` under the freqai
+    section in the config file. e.g:
+    {
+        ...
+        "freqai": {
+            ...
+            "model_training_parameters" : {
+                "learning_rate": 3e-4,
+                "trainer_kwargs": {
+                    "n_steps": 5000,
+                    "batch_size": 64,
+                    "n_epochs": null,
+                },
+                "model_kwargs": {
+                    "hidden_dim": 512,
+                    "dropout_percent": 0.2,
+                    "n_layer": 1,
+                },
+            }
+        }
+    }
+    """
+
+    @property
+    def data_convertor(self) -> PyTorchDataConvertor:
+        return DefaultPyTorchDataConvertor(target_tensor_type=torch.float)
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        config = self.freqai_info.get("model_training_parameters", {})
+        self.learning_rate: float = config.get("learning_rate", 3e-4)
+        self.model_kwargs: dict[str, Any] = config.get("model_kwargs", {})
+        self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs", {})
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        n_features = data_dictionary["train_features"].shape[-1]
+        model = PyTorchMLPModel(input_dim=n_features, output_dim=1, **self.model_kwargs)
+        model.to(self.device)
+        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
+        criterion = torch.nn.MSELoss()
+        # check if continual_learning is activated, and retrieve the model to continue training
+        trainer = self.get_init_model(dk.pair)
+        if trainer is None:
+            trainer = PyTorchModelTrainer(
+                model=model,
+                optimizer=optimizer,
+                criterion=criterion,
+                device=self.device,
+                data_convertor=self.data_convertor,
+                tb_logger=self.tb_logger,
+                **self.trainer_kwargs,
+            )
+        trainer.fit(data_dictionary, self.splits)
+        return trainer
diff --git a/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
new file mode 100644
index 000000000..19c4ecaf3
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
@@ -0,0 +1,155 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+import torch
+
+from freqtrade.freqai.base_models.BasePyTorchRegressor import BasePyTorchRegressor
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.torch.PyTorchDataConvertor import (
+    DefaultPyTorchDataConvertor,
+    PyTorchDataConvertor,
+)
+from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchTransformerTrainer
+from freqtrade.freqai.torch.PyTorchTransformerModel import PyTorchTransformerModel
+
+
+class PyTorchTransformerRegressor(BasePyTorchRegressor):
+    """
+    This class implements the fit method of IFreqaiModel.
+    in the fit method we initialize the model and trainer objects.
+    the only requirement from the model is to be aligned to PyTorchRegressor
+    predict method that expects the model to predict tensor of type float.
+    the trainer defines the training loop.
+
+    parameters are passed via `model_training_parameters` under the freqai
+    section in the config file. e.g:
+    {
+        ...
+        "freqai": {
+            ...
+            "conv_width": 30,  // PyTorchTransformer is based on windowing
+            "feature_parameters": {
+                ...
+                "include_shifted_candles": 0,  // which removes the need for shifted candles
+                ...
+            },
+            "model_training_parameters" : {
+                "learning_rate": 3e-4,
+                "trainer_kwargs": {
+                    "n_steps": 5000,
+                    "batch_size": 64,
+                    "n_epochs": null
+                },
+                "model_kwargs": {
+                    "hidden_dim": 512,
+                    "dropout_percent": 0.2,
+                    "n_layer": 1,
+                },
+            }
+        }
+    }
+    """
+
+    @property
+    def data_convertor(self) -> PyTorchDataConvertor:
+        return DefaultPyTorchDataConvertor(target_tensor_type=torch.float)
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        config = self.freqai_info.get("model_training_parameters", {})
+        self.learning_rate: float = config.get("learning_rate", 3e-4)
+        self.model_kwargs: dict[str, Any] = config.get("model_kwargs", {})
+        self.trainer_kwargs: dict[str, Any] = config.get("trainer_kwargs", {})
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        n_features = data_dictionary["train_features"].shape[-1]
+        n_labels = data_dictionary["train_labels"].shape[-1]
+        model = PyTorchTransformerModel(
+            input_dim=n_features,
+            output_dim=n_labels,
+            time_window=self.window_size,
+            **self.model_kwargs,
+        )
+        model.to(self.device)
+        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
+        criterion = torch.nn.MSELoss()
+        # check if continual_learning is activated, and retrieve the model to continue training
+        trainer = self.get_init_model(dk.pair)
+        if trainer is None:
+            trainer = PyTorchTransformerTrainer(
+                model=model,
+                optimizer=optimizer,
+                criterion=criterion,
+                device=self.device,
+                data_convertor=self.data_convertor,
+                window_size=self.window_size,
+                tb_logger=self.tb_logger,
+                **self.trainer_kwargs,
+            )
+        trainer.fit(data_dictionary, self.splits)
+        return trainer
+
+    def predict(
+        self, unfiltered_df: pd.DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[pd.DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_df)
+        dk.data_dictionary["prediction_features"], _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
+        )
+
+        dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
+            dk.data_dictionary["prediction_features"], outlier_check=True
+        )
+
+        x = self.data_convertor.convert_x(
+            dk.data_dictionary["prediction_features"], device=self.device
+        )
+        # if user is asking for multiple predictions, slide the window
+        # along the tensor
+        x = x.unsqueeze(0)
+        # create empty torch tensor
+        self.model.model.eval()
+        yb = torch.empty(0).to(self.device)
+        if x.shape[1] > self.window_size:
+            ws = self.window_size
+            for i in range(0, x.shape[1] - ws):
+                xb = x[:, i : i + ws, :].to(self.device)
+                y = self.model.model(xb)
+                yb = torch.cat((yb, y), dim=1)
+        else:
+            yb = self.model.model(x)
+
+        yb = yb.cpu().squeeze(0)
+        pred_df = pd.DataFrame(yb.detach().numpy(), columns=dk.label_list)
+        pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)
+
+        if self.ft_params.get("DI_threshold", 0) > 0:
+            dk.DI_values = dk.feature_pipeline["di"].di_values
+        else:
+            dk.DI_values = np.zeros(outliers.shape[0])
+        dk.do_predict = outliers
+
+        if x.shape[1] > 1:
+            zeros_df = pd.DataFrame(
+                np.zeros((x.shape[1] - len(pred_df), len(pred_df.columns))), columns=pred_df.columns
+            )
+            pred_df = pd.concat([zeros_df, pred_df], axis=0, ignore_index=True)
+        return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/prediction_models/ReinforcementLearner.py b/freqtrade/freqai/prediction_models/ReinforcementLearner.py
new file mode 100644
index 000000000..87c3450dc
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/ReinforcementLearner.py
@@ -0,0 +1,171 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+import torch as th
+from stable_baselines3.common.callbacks import ProgressBarCallback
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment
+from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
+
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearner(BaseReinforcementLearningModel):
+    """
+    Reinforcement Learning Model prediction model.
+
+    Users can inherit from this class to make their own RL model with custom
+    environment/training controls. Define the file as follows:
+
+    ```
+    from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
+
+    class MyCoolRLModel(ReinforcementLearner):
+    ```
+
+    Save the file to `user_data/freqaimodels`, then run it with:
+
+    freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
+
+    Here the users can override any of the functions
+    available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
+    is where the user overrides `MyRLEnv` (see below), to define custom
+    `calculate_reward()` function, or to override any other parts of the environment.
+
+    This class also allows users to override any other part of the IFreqaiModel tree.
+    For example, the user can override `def fit()` or `def train()` or `def predict()`
+    to take fine-tuned control over these processes.
+
+    Another common override may be `def data_cleaning_predict()` where the user can
+    take fine-tuned control over the data handling pipeline.
+    """
+
+    def fit(self, data_dictionary: dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
+        """
+        User customizable fit method
+        :param data_dictionary: dict = common data dictionary containing all train/test
+            features/labels/weights.
+        :param dk: FreqaiDatakitchen = data kitchen for current pair.
+        :return:
+        model Any = trained model to be used for inference in dry/live/backtesting
+        """
+        train_df = data_dictionary["train_features"]
+        total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
+
+        policy_kwargs = dict(activation_fn=th.nn.ReLU, net_arch=self.net_arch)
+
+        if self.activate_tensorboard:
+            tb_path = Path(dk.full_path / "tensorboard" / dk.pair.split("/")[0])
+        else:
+            tb_path = None
+
+        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
+            model = self.MODELCLASS(
+                self.policy_type,
+                self.train_env,
+                policy_kwargs=policy_kwargs,
+                tensorboard_log=tb_path,
+                **self.freqai_info.get("model_training_parameters", {}),
+            )
+        else:
+            logger.info(
+                "Continual training activated - starting training from previously trained agent."
+            )
+            model = self.dd.model_dictionary[dk.pair]
+            model.set_env(self.train_env)
+        callbacks: list[Any] = [self.eval_callback, self.tensorboard_callback]
+        progressbar_callback: ProgressBarCallback | None = None
+        if self.rl_config.get("progress_bar", False):
+            progressbar_callback = ProgressBarCallback()
+            callbacks.insert(0, progressbar_callback)
+
+        try:
+            model.learn(
+                total_timesteps=int(total_timesteps),
+                callback=callbacks,
+            )
+        finally:
+            if progressbar_callback:
+                progressbar_callback.on_training_end()
+
+        if Path(dk.data_path / "best_model.zip").is_file():
+            logger.info("Callback found a best model.")
+            best_model = self.MODELCLASS.load(dk.data_path / "best_model")
+            return best_model
+
+        logger.info("Couldn't find best model, using final model instead.")
+
+        return model
+
+    MyRLEnv: type[BaseEnvironment]
+
+    class MyRLEnv(Base5ActionRLEnv):  # type: ignore[no-redef]
+        """
+        User can override any function in BaseRLEnv and gym.Env. Here the user
+        sets a custom reward based on profit and trade duration.
+        """
+
+        def calculate_reward(self, action: int) -> float:
+            """
+            An example reward function. This is the one function that users will likely
+            wish to inject their own creativity into.
+
+                        Warning!
+            This is function is a showcase of functionality designed to show as many possible
+            environment control features as possible. It is also designed to run quickly
+            on small computers. This is a benchmark, it is *not* for live production.
+
+            :param action: int = The action made by the agent for the current candle.
+            :return:
+            float = the reward to give to the agent for current step (used for optimization
+                of weights in NN)
+            """
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                self.tensorboard_log("invalid", category="actions")
+                return -2
+
+            pnl = self.get_unrealized_profit()
+            factor = 100.0
+
+            # reward agent for entering trades
+            if action == Actions.Long_enter.value and self._position == Positions.Neutral:
+                return 25
+            if action == Actions.Short_enter.value and self._position == Positions.Neutral:
+                return 25
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+
+            max_trade_duration = self.rl_config.get("max_trade_duration_candles", 300)
+            trade_duration = self._current_tick - self._last_trade_tick  # type: ignore
+
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+
+            # discourage sitting in position
+            if (
+                self._position in (Positions.Short, Positions.Long)
+                and action == Actions.Neutral.value
+            ):
+                return -1 * trade_duration / max_trade_duration
+
+            # close long
+            if action == Actions.Long_exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
+                return float(pnl * factor)
+
+            # close short
+            if action == Actions.Short_exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
+                return float(pnl * factor)
+
+            return 0.0
diff --git a/freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py b/freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
new file mode 100644
index 000000000..9b2ba5191
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
@@ -0,0 +1,85 @@
+import logging
+from typing import Any
+
+from pandas import DataFrame
+from sb3_contrib.common.maskable.callbacks import MaskableEvalCallback
+from sb3_contrib.common.maskable.utils import is_masking_supported
+from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
+from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
+from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
+
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearner_multiproc(ReinforcementLearner):
+    """
+    Demonstration of how to build vectorized environments
+    """
+
+    def set_train_and_eval_environments(
+        self,
+        data_dictionary: dict[str, Any],
+        prices_train: DataFrame,
+        prices_test: DataFrame,
+        dk: FreqaiDataKitchen,
+    ):
+        """
+        User can override this if they are using a custom MyRLEnv
+        :param data_dictionary: dict = common data dictionary containing train and test
+            features/labels/weights.
+        :param prices_train/test: DataFrame = dataframe comprised of the prices to be used in
+            the environment during training
+        or testing
+        :param dk: FreqaiDataKitchen = the datakitchen for the current pair
+        """
+        train_df = data_dictionary["train_features"]
+        test_df = data_dictionary["test_features"]
+
+        if self.train_env:
+            self.train_env.close()
+        if self.eval_env:
+            self.eval_env.close()
+
+        env_info = self.pack_env_dict(dk.pair)
+
+        eval_freq = len(train_df) // self.max_threads
+
+        env_id = "train_env"
+        self.train_env = VecMonitor(
+            SubprocVecEnv(
+                [
+                    make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train, env_info=env_info)
+                    for i in range(self.max_threads)
+                ]
+            )
+        )
+
+        eval_env_id = "eval_env"
+        self.eval_env = VecMonitor(
+            SubprocVecEnv(
+                [
+                    make_env(
+                        self.MyRLEnv, eval_env_id, i, 1, test_df, prices_test, env_info=env_info
+                    )
+                    for i in range(self.max_threads)
+                ]
+            )
+        )
+
+        self.eval_callback = MaskableEvalCallback(
+            self.eval_env,
+            deterministic=True,
+            render=False,
+            eval_freq=eval_freq,
+            best_model_save_path=str(dk.data_path),
+            use_masking=(self.model_type == "MaskablePPO" and is_masking_supported(self.eval_env)),
+        )
+
+        # TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
+        # IT WILL RETURN FALSE INFORMATION, NEVERTHELESS NOT THREAD SAFE WITH SB3!!!
+        actions = self.train_env.env_method("get_actions")[0]
+        self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
diff --git a/freqtrade/freqai/prediction_models/SKLearnRandomForestClassifier.py b/freqtrade/freqai/prediction_models/SKLearnRandomForestClassifier.py
new file mode 100644
index 000000000..b008a8ecd
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/SKLearnRandomForestClassifier.py
@@ -0,0 +1,84 @@
+import logging
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+from pandas import DataFrame
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class SKLearnRandomForestClassifier(BaseClassifierModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        X = data_dictionary["train_features"].to_numpy()
+        y = data_dictionary["train_labels"].to_numpy()[:, 0]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+        else:
+            test_features = data_dictionary["test_features"].to_numpy()
+            test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
+
+            eval_set = (test_features, test_labels)
+
+        if self.freqai_info.get("continual_learning", False):
+            logger.warning(
+                "Continual learning is not supported for SKLearnRandomForestClassifier, ignoring."
+            )
+
+        train_weights = data_dictionary["train_weights"]
+
+        model = RandomForestClassifier(**self.model_training_parameters)
+
+        model.fit(X=X, y=y, sample_weight=train_weights)
+        if eval_set:
+            logger.info("Score: %s", model.score(eval_set[0], eval_set[1]))
+
+        return model
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param  unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        (pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
+
+        le = LabelEncoder()
+        label = dk.label_list[0]
+        labels_before = list(dk.data["labels_std"].keys())
+        labels_after = le.fit_transform(labels_before).tolist()
+        pred_df[label] = le.inverse_transform(pred_df[label])
+        pred_df = pred_df.rename(
+            columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))}
+        )
+
+        return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/prediction_models/XGBoostClassifier.py b/freqtrade/freqai/prediction_models/XGBoostClassifier.py
new file mode 100644
index 000000000..48634ce4d
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/XGBoostClassifier.py
@@ -0,0 +1,88 @@
+import logging
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+from pandas import DataFrame
+from pandas.api.types import is_integer_dtype
+from sklearn.preprocessing import LabelEncoder
+from xgboost import XGBClassifier
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostClassifier(BaseClassifierModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        X = data_dictionary["train_features"].to_numpy()
+        y = data_dictionary["train_labels"].to_numpy()[:, 0]
+
+        le = LabelEncoder()
+        if not is_integer_dtype(y):
+            y = pd.Series(le.fit_transform(y), dtype="int64")
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+        else:
+            test_features = data_dictionary["test_features"].to_numpy()
+            test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
+
+            if not is_integer_dtype(test_labels):
+                test_labels = pd.Series(le.transform(test_labels), dtype="int64")
+
+            eval_set = [(test_features, test_labels)]
+
+        train_weights = data_dictionary["train_weights"]
+
+        init_model = self.get_init_model(dk.pair)
+
+        model = XGBClassifier(**self.model_training_parameters)
+
+        model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights, xgb_model=init_model)
+
+        return model
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        (pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
+
+        le = LabelEncoder()
+        label = dk.label_list[0]
+        labels_before = list(dk.data["labels_std"].keys())
+        labels_after = le.fit_transform(labels_before).tolist()
+        pred_df[label] = le.inverse_transform(pred_df[label])
+        pred_df = pred_df.rename(
+            columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))}
+        )
+
+        return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py b/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py
new file mode 100644
index 000000000..6760ad285
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py
@@ -0,0 +1,88 @@
+import logging
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+from pandas import DataFrame
+from pandas.api.types import is_integer_dtype
+from sklearn.preprocessing import LabelEncoder
+from xgboost import XGBRFClassifier
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostRFClassifier(BaseClassifierModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        X = data_dictionary["train_features"].to_numpy()
+        y = data_dictionary["train_labels"].to_numpy()[:, 0]
+
+        le = LabelEncoder()
+        if not is_integer_dtype(y):
+            y = pd.Series(le.fit_transform(y), dtype="int64")
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+        else:
+            test_features = data_dictionary["test_features"].to_numpy()
+            test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
+
+            if not is_integer_dtype(test_labels):
+                test_labels = pd.Series(le.transform(test_labels), dtype="int64")
+
+            eval_set = [(test_features, test_labels)]
+
+        train_weights = data_dictionary["train_weights"]
+
+        init_model = self.get_init_model(dk.pair)
+
+        model = XGBRFClassifier(**self.model_training_parameters)
+
+        model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights, xgb_model=init_model)
+
+        return model
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param  unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        (pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
+
+        le = LabelEncoder()
+        label = dk.label_list[0]
+        labels_before = list(dk.data["labels_std"].keys())
+        labels_after = le.fit_transform(labels_before).tolist()
+        pred_df[label] = le.inverse_transform(pred_df[label])
+        pred_df = pred_df.rename(
+            columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))}
+        )
+
+        return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/prediction_models/XGBoostRFRegressor.py b/freqtrade/freqai/prediction_models/XGBoostRFRegressor.py
new file mode 100644
index 000000000..140186075
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/XGBoostRFRegressor.py
@@ -0,0 +1,64 @@
+import logging
+from typing import Any
+
+from xgboost import XGBRFRegressor
+
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostRFRegressor(BaseRegressionModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+            eval_weights = None
+        else:
+            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
+            eval_weights = [data_dictionary["test_weights"]]
+
+        sample_weight = data_dictionary["train_weights"]
+
+        xgb_model = self.get_init_model(dk.pair)
+
+        model = XGBRFRegressor(**self.model_training_parameters)
+
+        # Callbacks are not supported for XGBRFRegressor, and version 2.1.x started to throw
+        # the following error:
+        # NotImplementedError: `early_stopping_rounds` and `callbacks` are not implemented
+        # for random forest.
+
+        # model.set_params(callbacks=[TBCallback(dk.data_path)])
+        model.fit(
+            X=X,
+            y=y,
+            sample_weight=sample_weight,
+            eval_set=eval_set,
+            sample_weight_eval_set=eval_weights,
+            xgb_model=xgb_model,
+        )
+        # set the callbacks to empty so that we can serialize to disk later
+        # model.set_params(callbacks=[])
+
+        return model
diff --git a/freqtrade/freqai/prediction_models/XGBoostRegressor.py b/freqtrade/freqai/prediction_models/XGBoostRegressor.py
new file mode 100644
index 000000000..f30aca65d
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/XGBoostRegressor.py
@@ -0,0 +1,60 @@
+import logging
+from typing import Any
+
+from xgboost import XGBRegressor
+
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.tensorboard import TBCallback
+
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostRegressor(BaseRegressionModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+            eval_weights = None
+        else:
+            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"]), (X, y)]
+            eval_weights = [data_dictionary["test_weights"], data_dictionary["train_weights"]]
+
+        sample_weight = data_dictionary["train_weights"]
+
+        xgb_model = self.get_init_model(dk.pair)
+
+        model = XGBRegressor(**self.model_training_parameters)
+
+        model.set_params(callbacks=[TBCallback(dk.data_path)])
+        model.fit(
+            X=X,
+            y=y,
+            sample_weight=sample_weight,
+            eval_set=eval_set,
+            sample_weight_eval_set=eval_weights,
+            xgb_model=xgb_model,
+        )
+        # set the callbacks to empty so that we can serialize to disk later
+        model.set_params(callbacks=[])
+
+        return model
diff --git a/freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py b/freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py
new file mode 100644
index 000000000..5cee0148d
--- /dev/null
+++ b/freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py
@@ -0,0 +1,61 @@
+import logging
+from typing import Any
+
+from xgboost import XGBRegressor
+
+from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.tensorboard import TBCallback
+
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostRegressorMultiTarget(BaseRegressionModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    This is an exact copy of XGBoostRegressor kept for compatibility reasons.
+    """
+
+    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
+            eval_set = None
+            eval_weights = None
+        else:
+            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"]), (X, y)]
+            eval_weights = [data_dictionary["test_weights"], data_dictionary["train_weights"]]
+
+        sample_weight = data_dictionary["train_weights"]
+
+        xgb_model = self.get_init_model(dk.pair)
+
+        model = XGBRegressor(**self.model_training_parameters)
+
+        model.set_params(callbacks=[TBCallback(dk.data_path)])
+        model.fit(
+            X=X,
+            y=y,
+            sample_weight=sample_weight,
+            eval_set=eval_set,
+            sample_weight_eval_set=eval_weights,
+            xgb_model=xgb_model,
+        )
+        # set the callbacks to empty so that we can serialize to disk later
+        model.set_params(callbacks=[])
+
+        return model
diff --git a/docs/.gitkeep b/freqtrade/freqai/prediction_models/__init__.py
similarity index 100%
rename from docs/.gitkeep
rename to freqtrade/freqai/prediction_models/__init__.py
diff --git a/freqtrade/freqai/tensorboard/TensorboardCallback.py b/freqtrade/freqai/tensorboard/TensorboardCallback.py
new file mode 100644
index 000000000..0ffbb5838
--- /dev/null
+++ b/freqtrade/freqai/tensorboard/TensorboardCallback.py
@@ -0,0 +1,61 @@
+from enum import Enum
+from typing import Any
+
+from stable_baselines3.common.callbacks import BaseCallback
+from stable_baselines3.common.logger import HParam
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions
+
+
+class TensorboardCallback(BaseCallback):
+    """
+    Custom callback for plotting additional values in tensorboard and
+    episodic summary reports.
+    """
+
+    def __init__(self, verbose=1, actions: type[Enum] = BaseActions):
+        super().__init__(verbose)
+        self.model: Any = None
+        self.actions: type[Enum] = actions
+
+    def _on_training_start(self) -> None:
+        hparam_dict = {
+            "algorithm": self.model.__class__.__name__,
+            "learning_rate": self.model.learning_rate,
+            # "gamma": self.model.gamma,
+            # "gae_lambda": self.model.gae_lambda,
+            # "batch_size": self.model.batch_size,
+            # "n_steps": self.model.n_steps,
+        }
+        metric_dict: dict[str, float | int] = {
+            "eval/mean_reward": 0,
+            "rollout/ep_rew_mean": 0,
+            "rollout/ep_len_mean": 0,
+            "train/value_loss": 0,
+            "train/explained_variance": 0,
+        }
+        self.logger.record(
+            "hparams",
+            HParam(hparam_dict, metric_dict),
+            exclude=("stdout", "log", "json", "csv"),
+        )
+
+    def _on_step(self) -> bool:
+        local_info = self.locals["infos"][0]
+
+        if hasattr(self.training_env, "envs"):
+            tensorboard_metrics = self.training_env.envs[0].unwrapped.tensorboard_metrics
+
+        else:
+            # For RL-multiproc - usage of [0] might need to be evaluated
+            tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
+
+        for metric in local_info:
+            if metric not in ["episode", "terminal_observation"]:
+                self.logger.record(f"info/{metric}", local_info[metric])
+
+        for category in tensorboard_metrics:
+            for metric in tensorboard_metrics[category]:
+                self.logger.record(f"{category}/{metric}", tensorboard_metrics[category][metric])
+
+        return True
diff --git a/freqtrade/freqai/tensorboard/__init__.py b/freqtrade/freqai/tensorboard/__init__.py
new file mode 100644
index 000000000..183c25b22
--- /dev/null
+++ b/freqtrade/freqai/tensorboard/__init__.py
@@ -0,0 +1,16 @@
+# ensure users can still use a non-torch freqai version
+try:
+    from freqtrade.freqai.tensorboard.tensorboard import TensorBoardCallback, TensorboardLogger
+
+    TBLogger = TensorboardLogger
+    TBCallback = TensorBoardCallback
+except ModuleNotFoundError:
+    from freqtrade.freqai.tensorboard.base_tensorboard import (
+        BaseTensorBoardCallback,
+        BaseTensorboardLogger,
+    )
+
+    TBLogger = BaseTensorboardLogger  # type: ignore
+    TBCallback = BaseTensorBoardCallback  # type: ignore
+
+__all__ = ("TBLogger", "TBCallback")
diff --git a/freqtrade/freqai/tensorboard/base_tensorboard.py b/freqtrade/freqai/tensorboard/base_tensorboard.py
new file mode 100644
index 000000000..73a97230f
--- /dev/null
+++ b/freqtrade/freqai/tensorboard/base_tensorboard.py
@@ -0,0 +1,30 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from xgboost.callback import TrainingCallback
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseTensorboardLogger:
+    def __init__(self, logdir: Path, activate: bool = True):
+        pass
+
+    def log_scalar(self, tag: str, scalar_value: Any, step: int):
+        return
+
+    def close(self):
+        return
+
+
+class BaseTensorBoardCallback(TrainingCallback):
+    def __init__(self, logdir: Path, activate: bool = True):
+        pass
+
+    def after_iteration(self, model, epoch: int, evals_log: TrainingCallback.EvalsLog) -> bool:
+        return False
+
+    def after_training(self, model):
+        return model
diff --git a/freqtrade/freqai/tensorboard/tensorboard.py b/freqtrade/freqai/tensorboard/tensorboard.py
new file mode 100644
index 000000000..76bd5c2f8
--- /dev/null
+++ b/freqtrade/freqai/tensorboard/tensorboard.py
@@ -0,0 +1,61 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from torch.utils.tensorboard import SummaryWriter
+from xgboost import callback
+
+from freqtrade.freqai.tensorboard.base_tensorboard import (
+    BaseTensorBoardCallback,
+    BaseTensorboardLogger,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+class TensorboardLogger(BaseTensorboardLogger):
+    def __init__(self, logdir: Path, activate: bool = True):
+        self.activate = activate
+        if self.activate:
+            self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")
+
+    def log_scalar(self, tag: str, scalar_value: Any, step: int):
+        if self.activate:
+            self.writer.add_scalar(tag, scalar_value, step)
+
+    def close(self):
+        if self.activate:
+            self.writer.flush()
+            self.writer.close()
+
+
+class TensorBoardCallback(BaseTensorBoardCallback):
+    def __init__(self, logdir: Path, activate: bool = True):
+        self.activate = activate
+        if self.activate:
+            self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")
+
+    def after_iteration(
+        self, model, epoch: int, evals_log: callback.TrainingCallback.EvalsLog
+    ) -> bool:
+        if not self.activate:
+            return False
+        if not evals_log:
+            return False
+
+        evals = ["validation", "train"]
+        for metric, eval_ in zip(evals_log.items(), evals, strict=False):
+            for metric_name, log in metric[1].items():
+                score = log[-1][0] if isinstance(log[-1], tuple) else log[-1]
+                self.writer.add_scalar(f"{eval_}-{metric_name}", score, epoch)
+
+        return False
+
+    def after_training(self, model):
+        if not self.activate:
+            return model
+        self.writer.flush()
+        self.writer.close()
+
+        return model
diff --git a/freqtrade/freqai/torch/PyTorchDataConvertor.py b/freqtrade/freqai/torch/PyTorchDataConvertor.py
new file mode 100644
index 000000000..5b3f249c3
--- /dev/null
+++ b/freqtrade/freqai/torch/PyTorchDataConvertor.py
@@ -0,0 +1,57 @@
+from abc import ABC, abstractmethod
+
+import pandas as pd
+import torch
+
+
+class PyTorchDataConvertor(ABC):
+    """
+    This class is responsible for converting `*_features` & `*_labels` pandas dataframes
+    to pytorch tensors.
+    """
+
+    @abstractmethod
+    def convert_x(self, df: pd.DataFrame, device: str) -> torch.Tensor:
+        """
+        :param df: "*_features" dataframe.
+        :param device: The device to use for training (e.g. 'cpu', 'cuda').
+        """
+
+    @abstractmethod
+    def convert_y(self, df: pd.DataFrame, device: str) -> torch.Tensor:
+        """
+        :param df: "*_labels" dataframe.
+        :param device: The device to use for training (e.g. 'cpu', 'cuda').
+        """
+
+
+class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
+    """
+    A default conversion that keeps features dataframe shapes.
+    """
+
+    def __init__(
+        self,
+        target_tensor_type: torch.dtype = torch.float32,
+        squeeze_target_tensor: bool = False,
+    ):
+        """
+        :param target_tensor_type: type of target tensor, for classification use
+            torch.long, for regressor use torch.float or torch.double.
+        :param squeeze_target_tensor: controls the target shape, used for loss functions
+            that requires 0D or 1D.
+        """
+        self._target_tensor_type = target_tensor_type
+        self._squeeze_target_tensor = squeeze_target_tensor
+
+    def convert_x(self, df: pd.DataFrame, device: str) -> torch.Tensor:
+        numpy_arrays = df.values
+        x = torch.tensor(numpy_arrays, device=device, dtype=torch.float32)
+        return x
+
+    def convert_y(self, df: pd.DataFrame, device: str) -> torch.Tensor:
+        numpy_arrays = df.values
+        y = torch.tensor(numpy_arrays, device=device, dtype=self._target_tensor_type)
+        if self._squeeze_target_tensor:
+            y = y.squeeze()
+        return y
diff --git a/freqtrade/freqai/torch/PyTorchMLPModel.py b/freqtrade/freqai/torch/PyTorchMLPModel.py
new file mode 100644
index 000000000..0093388f8
--- /dev/null
+++ b/freqtrade/freqai/torch/PyTorchMLPModel.py
@@ -0,0 +1,96 @@
+import logging
+
+import torch
+from torch import nn
+
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchMLPModel(nn.Module):
+    """
+    A multi-layer perceptron (MLP) model implemented using PyTorch.
+
+    This class mainly serves as a simple example for the integration of PyTorch model's
+    to freqai. It is not optimized at all and should not be used for production purposes.
+
+    :param input_dim: The number of input features. This parameter specifies the number
+        of features in the input data that the MLP will use to make predictions.
+    :param output_dim: The number of output classes. This parameter specifies the number
+        of classes that the MLP will predict.
+    :param hidden_dim: The number of hidden units in each layer. This parameter controls
+        the complexity of the MLP and determines how many nonlinear relationships the MLP
+        can represent. Increasing the number of hidden units can increase the capacity of
+        the MLP to model complex patterns, but it also increases the risk of overfitting
+        the training data. Default: 256
+    :param dropout_percent: The dropout rate for regularization. This parameter specifies
+        the probability of dropping out a neuron during training to prevent overfitting.
+        The dropout rate should be tuned carefully to balance between underfitting and
+        overfitting. Default: 0.2
+    :param n_layer: The number of layers in the MLP. This parameter specifies the number
+        of layers in the MLP architecture. Adding more layers to the MLP can increase its
+        capacity to model complex patterns, but it also increases the risk of overfitting
+        the training data. Default: 1
+
+    :returns: The output of the MLP, with shape (batch_size, output_dim)
+    """
+
+    def __init__(self, input_dim: int, output_dim: int, **kwargs):
+        super().__init__()
+        hidden_dim: int = kwargs.get("hidden_dim", 256)
+        dropout_percent: int = kwargs.get("dropout_percent", 0.2)
+        n_layer: int = kwargs.get("n_layer", 1)
+        self.input_layer = nn.Linear(input_dim, hidden_dim)
+        self.blocks = nn.Sequential(*[Block(hidden_dim, dropout_percent) for _ in range(n_layer)])
+        self.output_layer = nn.Linear(hidden_dim, output_dim)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(p=dropout_percent)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x: torch.Tensor = tensors[0]
+        x = self.relu(self.input_layer(x))
+        x = self.dropout(x)
+        x = self.blocks(x)
+        x = self.output_layer(x)
+        return x
+
+
+class Block(nn.Module):
+    """
+    A building block for a multi-layer perceptron (MLP).
+
+    :param hidden_dim: The number of hidden units in the feedforward network.
+    :param dropout_percent: The dropout rate for regularization.
+
+    :returns: torch.Tensor. with shape (batch_size, hidden_dim)
+    """
+
+    def __init__(self, hidden_dim: int, dropout_percent: int):
+        super().__init__()
+        self.ff = FeedForward(hidden_dim)
+        self.dropout = nn.Dropout(p=dropout_percent)
+        self.ln = nn.LayerNorm(hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.ff(self.ln(x))
+        x = self.dropout(x)
+        return x
+
+
+class FeedForward(nn.Module):
+    """
+    A simple fully-connected feedforward neural network block.
+
+    :param hidden_dim: The number of hidden units in the block.
+    :return: torch.Tensor. with shape (batch_size, hidden_dim)
+    """
+
+    def __init__(self, hidden_dim: int):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU(),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.net(x)
diff --git a/freqtrade/freqai/torch/PyTorchModelTrainer.py b/freqtrade/freqai/torch/PyTorchModelTrainer.py
new file mode 100644
index 000000000..92e9effdd
--- /dev/null
+++ b/freqtrade/freqai/torch/PyTorchModelTrainer.py
@@ -0,0 +1,224 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+import torch
+from torch import nn
+from torch.optim import Optimizer
+from torch.utils.data import DataLoader, TensorDataset
+
+from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
+from freqtrade.freqai.torch.PyTorchTrainerInterface import PyTorchTrainerInterface
+
+from .datasets import WindowDataset
+
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchModelTrainer(PyTorchTrainerInterface):
+    def __init__(
+        self,
+        model: nn.Module,
+        optimizer: Optimizer,
+        criterion: nn.Module,
+        device: str,
+        data_convertor: PyTorchDataConvertor,
+        model_meta_data: dict[str, Any] | None = None,
+        window_size: int = 1,
+        tb_logger: Any = None,
+        **kwargs,
+    ):
+        """
+        :param model: The PyTorch model to be trained.
+        :param optimizer: The optimizer to use for training.
+        :param criterion: The loss function to use for training.
+        :param device: The device to use for training (e.g. 'cpu', 'cuda').
+        :param init_model: A dictionary containing the initial model/optimizer
+            state_dict and model_meta_data saved by self.save() method.
+        :param model_meta_data: Additional metadata about the model (optional).
+        :param data_convertor: converter from pd.DataFrame to torch.tensor.
+        :param n_steps: used to calculate n_epochs. The number of training iterations to run.
+            iteration here refers to the number of times optimizer.step() is called.
+            ignored if n_epochs is set.
+        :param n_epochs: The maximum number batches to use for evaluation.
+        :param batch_size: The size of the batches to use during training.
+        """
+        if model_meta_data is None:
+            model_meta_data = {}
+        self.model = model
+        self.optimizer = optimizer
+        self.criterion = criterion
+        self.model_meta_data = model_meta_data
+        self.device = device
+        self.n_epochs: int | None = kwargs.get("n_epochs", 10)
+        self.n_steps: int | None = kwargs.get("n_steps", None)
+        if self.n_steps is None and not self.n_epochs:
+            raise Exception("Either `n_steps` or `n_epochs` should be set.")
+
+        self.batch_size: int = kwargs.get("batch_size", 64)
+        self.data_convertor = data_convertor
+        self.window_size: int = window_size
+        self.tb_logger = tb_logger
+        self.test_batch_counter = 0
+
+    def fit(self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]):
+        """
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+        all the training and test data/labels.
+        :param splits: splits to use in training, splits must contain "train",
+        optional "test" could be added by setting freqai.data_split_parameters.test_size > 0
+        in the config file.
+
+         - Calculates the predicted output for the batch using the PyTorch model.
+         - Calculates the loss between the predicted and actual output using a loss function.
+         - Computes the gradients of the loss with respect to the model's parameters using
+           backpropagation.
+         - Updates the model's parameters using an optimizer.
+        """
+        self.model.train()
+
+        data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
+        n_obs = len(data_dictionary["train_features"])
+        n_epochs = self.n_epochs or self.calc_n_epochs(n_obs=n_obs)
+        batch_counter = 0
+        for _ in range(n_epochs):
+            for _, batch_data in enumerate(data_loaders_dictionary["train"]):
+                xb, yb = batch_data
+                xb = xb.to(self.device)
+                yb = yb.to(self.device)
+                yb_pred = self.model(xb)
+                loss = self.criterion(yb_pred, yb)
+
+                self.optimizer.zero_grad(set_to_none=True)
+                loss.backward()
+                self.optimizer.step()
+                self.tb_logger.log_scalar("train_loss", loss.item(), batch_counter)
+                batch_counter += 1
+
+            # evaluation
+            if "test" in splits:
+                self.estimate_loss(data_loaders_dictionary, "test")
+
+    @torch.no_grad()
+    def estimate_loss(
+        self,
+        data_loader_dictionary: dict[str, DataLoader],
+        split: str,
+    ) -> None:
+        self.model.eval()
+        for _, batch_data in enumerate(data_loader_dictionary[split]):
+            xb, yb = batch_data
+            xb = xb.to(self.device)
+            yb = yb.to(self.device)
+
+            yb_pred = self.model(xb)
+            loss = self.criterion(yb_pred, yb)
+            self.tb_logger.log_scalar(f"{split}_loss", loss.item(), self.test_batch_counter)
+            self.test_batch_counter += 1
+
+        self.model.train()
+
+    def create_data_loaders_dictionary(
+        self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]
+    ) -> dict[str, DataLoader]:
+        """
+        Converts the input data to PyTorch tensors using a data loader.
+        """
+        data_loader_dictionary = {}
+        for split in splits:
+            x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"], self.device)
+            y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device)
+            dataset = TensorDataset(x, y)
+            data_loader = DataLoader(
+                dataset,
+                batch_size=self.batch_size,
+                shuffle=True,
+                drop_last=True,
+                num_workers=0,
+            )
+            data_loader_dictionary[split] = data_loader
+
+        return data_loader_dictionary
+
+    def calc_n_epochs(self, n_obs: int) -> int:
+        """
+        Calculates the number of epochs required to reach the maximum number
+        of iterations specified in the model training parameters.
+
+        the motivation here is that `n_steps` is easier to optimize and keep stable,
+        across different n_obs - the number of data points.
+        """
+        if not isinstance(self.n_steps, int):
+            raise ValueError("Either `n_steps` or `n_epochs` should be set.")
+        n_batches = n_obs // self.batch_size
+        n_epochs = max(self.n_steps // n_batches, 1)
+        if n_epochs <= 10:
+            logger.warning(
+                f"Setting low n_epochs: {n_epochs}. "
+                f"Please consider increasing `n_steps` hyper-parameter."
+            )
+
+        return n_epochs
+
+    def save(self, path: Path):
+        """
+        - Saving any nn.Module state_dict
+        - Saving model_meta_data, this dict should contain any additional data that the
+          user needs to store. e.g. class_names for classification models.
+        """
+
+        torch.save(
+            {
+                "model_state_dict": self.model.state_dict(),
+                "optimizer_state_dict": self.optimizer.state_dict(),
+                "model_meta_data": self.model_meta_data,
+                "pytrainer": self,
+            },
+            path,
+        )
+
+    def load(self, path: Path):
+        checkpoint = torch.load(path)
+        return self.load_from_checkpoint(checkpoint)
+
+    def load_from_checkpoint(self, checkpoint: dict):
+        """
+        when using continual_learning, DataDrawer will load the dictionary
+        (containing state dicts and model_meta_data) by calling torch.load(path).
+        you can access this dict from any class that inherits IFreqaiModel by calling
+        get_init_model method.
+        """
+        self.model.load_state_dict(checkpoint["model_state_dict"])
+        self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+        self.model_meta_data = checkpoint["model_meta_data"]
+        return self
+
+
+class PyTorchTransformerTrainer(PyTorchModelTrainer):
+    """
+    Creating a trainer for the Transformer model.
+    """
+
+    def create_data_loaders_dictionary(
+        self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]
+    ) -> dict[str, DataLoader]:
+        """
+        Converts the input data to PyTorch tensors using a data loader.
+        """
+        data_loader_dictionary = {}
+        for split in splits:
+            x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"], self.device)
+            y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device)
+            dataset = WindowDataset(x, y, self.window_size)
+            data_loader = DataLoader(
+                dataset,
+                batch_size=self.batch_size,
+                shuffle=False,
+                drop_last=True,
+                num_workers=0,
+            )
+            data_loader_dictionary[split] = data_loader
+
+        return data_loader_dictionary
diff --git a/freqtrade/freqai/torch/PyTorchTrainerInterface.py b/freqtrade/freqai/torch/PyTorchTrainerInterface.py
new file mode 100644
index 000000000..193540205
--- /dev/null
+++ b/freqtrade/freqai/torch/PyTorchTrainerInterface.py
@@ -0,0 +1,51 @@
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+import pandas as pd
+import torch
+from torch import nn
+
+
+class PyTorchTrainerInterface(ABC):
+    @abstractmethod
+    def fit(self, data_dictionary: dict[str, pd.DataFrame], splits: list[str]) -> None:
+        """
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+        all the training and test data/labels.
+        :param splits: splits to use in training, splits must contain "train",
+        optional "test" could be added by setting freqai.data_split_parameters.test_size > 0
+        in the config file.
+
+         - Calculates the predicted output for the batch using the PyTorch model.
+         - Calculates the loss between the predicted and actual output using a loss function.
+         - Computes the gradients of the loss with respect to the model's parameters using
+           backpropagation.
+         - Updates the model's parameters using an optimizer.
+        """
+
+    @abstractmethod
+    def save(self, path: Path) -> None:
+        """
+        - Saving any nn.Module state_dict
+        - Saving model_meta_data, this dict should contain any additional data that the
+          user needs to store. e.g class_names for classification models.
+        """
+
+    def load(self, path: Path) -> nn.Module:
+        """
+        :param path: path to zip file.
+        :returns: pytorch model.
+        """
+        checkpoint = torch.load(path)
+        return self.load_from_checkpoint(checkpoint)
+
+    @abstractmethod
+    def load_from_checkpoint(self, checkpoint: dict) -> nn.Module:
+        """
+        when using continual_learning, DataDrawer will load the dictionary
+        (containing state dicts and model_meta_data) by calling torch.load(path).
+        you can access this dict from any class that inherits IFreqaiModel by calling
+        get_init_model method.
+        :checkpoint checkpoint: dict containing the model & optimizer state dicts,
+        model_meta_data, etc..
+        """
diff --git a/freqtrade/freqai/torch/PyTorchTransformerModel.py b/freqtrade/freqai/torch/PyTorchTransformerModel.py
new file mode 100644
index 000000000..ae57b8ffd
--- /dev/null
+++ b/freqtrade/freqai/torch/PyTorchTransformerModel.py
@@ -0,0 +1,102 @@
+import math
+
+import torch
+from torch import nn
+
+
+"""
+The architecture is based on the paper “Attention Is All You Need”.
+Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
+Lukasz Kaiser, and Illia Polosukhin. 2017.
+"""
+
+
+class PyTorchTransformerModel(nn.Module):
+    """
+    A transformer approach to time series modeling using positional encoding.
+    The architecture is based on the paper “Attention Is All You Need”.
+    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
+    Lukasz Kaiser, and Illia Polosukhin. 2017.
+    """
+
+    def __init__(
+        self,
+        input_dim: int = 7,
+        output_dim: int = 7,
+        hidden_dim=1024,
+        n_layer=2,
+        dropout_percent=0.1,
+        time_window=10,
+        nhead=8,
+    ):
+        super().__init__()
+        self.time_window = time_window
+        # ensure the input dimension to the transformer is divisible by nhead
+        self.dim_val = input_dim - (input_dim % nhead)
+        self.input_net = nn.Sequential(
+            nn.Dropout(dropout_percent), nn.Linear(input_dim, self.dim_val)
+        )
+
+        # Encode the timeseries with Positional encoding
+        self.positional_encoding = PositionalEncoding(d_model=self.dim_val, max_len=self.dim_val)
+
+        # Define the encoder block of the Transformer
+        self.encoder_layer = nn.TransformerEncoderLayer(
+            d_model=self.dim_val, nhead=nhead, dropout=dropout_percent, batch_first=True
+        )
+        self.transformer = nn.TransformerEncoder(self.encoder_layer, num_layers=n_layer)
+
+        # the pseudo decoding FC
+        self.output_net = nn.Sequential(
+            nn.Linear(self.dim_val * time_window, int(hidden_dim)),
+            nn.ReLU(),
+            nn.Dropout(dropout_percent),
+            nn.Linear(int(hidden_dim), int(hidden_dim / 2)),
+            nn.ReLU(),
+            nn.Dropout(dropout_percent),
+            nn.Linear(int(hidden_dim / 2), int(hidden_dim / 4)),
+            nn.ReLU(),
+            nn.Dropout(dropout_percent),
+            nn.Linear(int(hidden_dim / 4), output_dim),
+        )
+
+    def forward(self, x, mask=None, add_positional_encoding=True):
+        """
+        Args:
+            x: Input features of shape [Batch, SeqLen, input_dim]
+            mask: Mask to apply on the attention outputs (optional)
+            add_positional_encoding: If True, we add the positional encoding to the input.
+                                      Might not be desired for some tasks.
+        """
+        x = self.input_net(x)
+        if add_positional_encoding:
+            x = self.positional_encoding(x)
+        x = self.transformer(x, mask=mask)
+        x = x.reshape(-1, 1, self.time_window * x.shape[-1])
+        x = self.output_net(x)
+        return x
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        """
+        Args
+            d_model: Hidden dimensionality of the input.
+            max_len: Maximum length of a sequence to expect.
+        """
+        super().__init__()
+
+        # Create matrix of [SeqLen, HiddenDim] representing the positional encoding
+        # for max_len inputs
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+
+        self.register_buffer("pe", pe, persistent=False)
+
+    def forward(self, x):
+        x = x + self.pe[:, : x.size(1)]
+        return x
diff --git a/freqtrade/freqai/torch/__init__.py b/freqtrade/freqai/torch/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/freqtrade/freqai/torch/datasets.py b/freqtrade/freqai/torch/datasets.py
new file mode 100644
index 000000000..8ddb3b698
--- /dev/null
+++ b/freqtrade/freqai/torch/datasets.py
@@ -0,0 +1,19 @@
+import torch
+
+
+class WindowDataset(torch.utils.data.Dataset):
+    def __init__(self, xs, ys, window_size):
+        self.xs = xs
+        self.ys = ys
+        self.window_size = window_size
+
+    def __len__(self):
+        return len(self.xs) - self.window_size
+
+    def __getitem__(self, index):
+        idx_rev = len(self.xs) - self.window_size - index - 1
+        window_x = self.xs[idx_rev : idx_rev + self.window_size, :]
+        # Beware of indexing, these two window_x and window_y are aimed at the same row!
+        # this is what happens when you use :
+        window_y = self.ys[idx_rev + self.window_size - 1, :].unsqueeze(0)
+        return window_x, window_y
diff --git a/freqtrade/freqai/utils.py b/freqtrade/freqai/utils.py
new file mode 100644
index 000000000..d2a15046d
--- /dev/null
+++ b/freqtrade/freqai/utils.py
@@ -0,0 +1,205 @@
+import logging
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import rapidjson
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.freqai.data_drawer import FreqaiDataDrawer
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
+
+
+logger = logging.getLogger(__name__)
+
+
+def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
+    """
+    Called only once upon start of bot to download the necessary data for
+    populating indicators and training the model.
+    :param timerange: TimeRange = The full data timerange for populating the indicators
+                                    and training the model.
+    :param dp: DataProvider instance attached to the strategy
+    """
+
+    if dp._exchange is None:
+        raise OperationalException("No exchange object found.")
+    markets = [
+        p
+        for p in dp._exchange.get_markets(
+            tradable_only=True, active_only=not config.get("include_inactive")
+        ).keys()
+    ]
+
+    all_pairs = dynamic_expand_pairlist(config, markets)
+
+    timerange = get_required_data_timerange(config)
+
+    new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
+
+    refresh_backtest_ohlcv_data(
+        dp._exchange,
+        pairs=all_pairs,
+        timeframes=config["freqai"]["feature_parameters"].get("include_timeframes"),
+        datadir=config["datadir"],
+        timerange=timerange,
+        new_pairs_days=new_pairs_days,
+        erase=False,
+        data_format=config.get("dataformat_ohlcv", "feather"),
+        trading_mode=config.get("trading_mode", "spot"),
+        prepend=config.get("prepend_data", False),
+    )
+
+
+def get_required_data_timerange(config: Config) -> TimeRange:
+    """
+    Used to compute the required data download time range
+    for auto data-download in FreqAI
+    """
+    time = datetime.now(tz=timezone.utc).timestamp()
+
+    timeframes = config["freqai"]["feature_parameters"].get("include_timeframes")
+
+    max_tf_seconds = 0
+    for tf in timeframes:
+        secs = timeframe_to_seconds(tf)
+        if secs > max_tf_seconds:
+            max_tf_seconds = secs
+
+    startup_candles = config.get("startup_candle_count", 0)
+    indicator_periods = config["freqai"]["feature_parameters"]["indicator_periods_candles"]
+
+    # factor the max_period as a factor of safety.
+    max_period = int(max(startup_candles, max(indicator_periods)) * 1.5)
+    config["startup_candle_count"] = max_period
+    logger.info(f"FreqAI auto-downloader using {max_period} startup candles.")
+
+    additional_seconds = max_period * max_tf_seconds
+
+    startts = int(time - config["freqai"].get("train_period_days", 0) * 86400 - additional_seconds)
+    stopts = int(time)
+    data_load_timerange = TimeRange("date", "date", startts, stopts)
+
+    return data_load_timerange
+
+
+def plot_feature_importance(
+    model: Any, pair: str, dk: FreqaiDataKitchen, count_max: int = 25
+) -> None:
+    """
+    Plot Best and worst features by importance for a single sub-train.
+    :param model: Any = A model which was `fit` using a common library
+                        such as catboost or lightgbm
+    :param pair: str = pair e.g. BTC/USD
+    :param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
+    :param count_max: int = the amount of features to be loaded per column
+    """
+    from freqtrade.plot.plotting import go, make_subplots, store_plot_file
+
+    # Extract feature importance from model
+    models = {}
+    if "FreqaiMultiOutputRegressor" in str(model.__class__):
+        for estimator, label in zip(model.estimators_, dk.label_list, strict=False):
+            models[label] = estimator
+    else:
+        models[dk.label_list[0]] = model
+
+    for label in models:
+        mdl = models[label]
+        if "catboost.core" in str(mdl.__class__):
+            feature_importance = mdl.get_feature_importance()
+        elif "lightgbm.sklearn" in str(mdl.__class__):
+            feature_importance = mdl.feature_importances_
+        elif "xgb" in str(mdl.__class__):
+            feature_importance = mdl.feature_importances_
+        else:
+            logger.info("Model type does not support generating feature importances.")
+            return
+
+        # Data preparation
+        fi_df = pd.DataFrame(
+            {
+                "feature_names": np.array(dk.data_dictionary["train_features"].columns),
+                "feature_importance": np.array(feature_importance),
+            }
+        )
+        fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
+        fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
+
+        # Plotting
+        def add_feature_trace(fig, fi_df, col):
+            return fig.add_trace(
+                go.Bar(
+                    x=fi_df["feature_importance"],
+                    y=fi_df["feature_names"],
+                    orientation="h",
+                    showlegend=False,
+                ),
+                row=1,
+                col=col,
+            )
+
+        fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
+        fig = add_feature_trace(fig, fi_df_top, 1)
+        fig = add_feature_trace(fig, fi_df_worst, 2)
+        fig.update_layout(title_text=f"Best and worst features by importance {pair}")
+        label = label.replace("&", "").replace("%", "")  # escape two FreqAI specific characters
+        store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)
+
+
+def record_params(config: dict[str, Any], full_path: Path) -> None:
+    """
+    Records run params in the full path for reproducibility
+    """
+    params_record_path = full_path / "run_params.json"
+
+    run_params = {
+        "freqai": config.get("freqai", {}),
+        "timeframe": config.get("timeframe"),
+        "stake_amount": config.get("stake_amount"),
+        "stake_currency": config.get("stake_currency"),
+        "max_open_trades": config.get("max_open_trades"),
+        "pairs": config.get("exchange", {}).get("pair_whitelist"),
+    }
+
+    with params_record_path.open("w") as handle:
+        rapidjson.dump(
+            run_params,
+            handle,
+            indent=4,
+            default=str,
+            number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN,
+        )
+
+
+def get_timerange_backtest_live_models(config: Config) -> str:
+    """
+    Returns a formatted timerange for backtest live/ready models
+    :param config: Configuration dictionary
+
+    :return: a string timerange (format example: '20220801-20220822')
+    """
+    dk = FreqaiDataKitchen(config)
+    models_path = dk.get_full_models_path(config)
+    dd = FreqaiDataDrawer(models_path, config)
+    timerange = dd.get_timerange_from_live_historic_predictions()
+    return timerange.timerange_str
+
+
+def get_tb_logger(model_type: str, path: Path, activate: bool) -> Any:
+    if model_type == "pytorch" and activate:
+        from freqtrade.freqai.tensorboard import TBLogger
+
+        return TBLogger(path, activate)
+    else:
+        from freqtrade.freqai.tensorboard.base_tensorboard import BaseTensorboardLogger
+
+        return BaseTensorboardLogger(path, activate)
diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py
new file mode 100644
index 000000000..63357dcd8
--- /dev/null
+++ b/freqtrade/freqtradebot.py
@@ -0,0 +1,2631 @@
+"""
+Freqtrade is the main module of this bot. It contains the class Freqtrade()
+"""
+
+import logging
+import traceback
+from copy import deepcopy
+from datetime import datetime, time, timedelta, timezone
+from math import isclose
+from threading import Lock
+from time import sleep
+from typing import Any
+
+from schedule import Scheduler
+
+from freqtrade import constants
+from freqtrade.configuration import validate_config_consistency
+from freqtrade.constants import BuySell, Config, EntryExecuteMode, ExchangeConfig, LongShort
+from freqtrade.data.converter import order_book_to_dataframe
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.edge import Edge
+from freqtrade.enums import (
+    ExitCheckTuple,
+    ExitType,
+    MarginMode,
+    RPCMessageType,
+    SignalDirection,
+    State,
+    TradingMode,
+)
+from freqtrade.exceptions import (
+    DependencyException,
+    ExchangeError,
+    InsufficientFundsError,
+    InvalidOrderException,
+    PricingError,
+)
+from freqtrade.exchange import (
+    ROUND_DOWN,
+    ROUND_UP,
+    remove_exchange_credentials,
+    timeframe_to_minutes,
+    timeframe_to_next_date,
+    timeframe_to_seconds,
+)
+from freqtrade.exchange.exchange_types import CcxtOrder
+from freqtrade.leverage.liquidation_price import update_liquidation_prices
+from freqtrade.misc import safe_value_fallback, safe_value_fallback2
+from freqtrade.mixins import LoggingMixin
+from freqtrade.persistence import Order, PairLocks, Trade, init_db
+from freqtrade.persistence.key_value_store import set_startup_time
+from freqtrade.plugins.pairlistmanager import PairListManager
+from freqtrade.plugins.protectionmanager import ProtectionManager
+from freqtrade.resolvers import ExchangeResolver, StrategyResolver
+from freqtrade.rpc import RPCManager
+from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer
+from freqtrade.rpc.rpc_types import (
+    ProfitLossStr,
+    RPCCancelMsg,
+    RPCEntryMsg,
+    RPCExitCancelMsg,
+    RPCExitMsg,
+    RPCProtectionMsg,
+)
+from freqtrade.strategy.interface import IStrategy
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util import FtPrecise, MeasureTime, PeriodicCache, dt_from_ts, dt_now
+from freqtrade.util.migrations.binance_mig import migrate_binance_futures_names
+from freqtrade.wallets import Wallets
+
+
+logger = logging.getLogger(__name__)
+
+
+class FreqtradeBot(LoggingMixin):
+    """
+    Freqtrade is the main class of the bot.
+    This is from here the bot start its logic.
+    """
+
+    def __init__(self, config: Config) -> None:
+        """
+        Init all variables and objects the bot needs to work
+        :param config: configuration dict, you can use Configuration.get_config()
+        to get the config dict.
+        """
+        self.active_pair_whitelist: list[str] = []
+
+        # Init bot state
+        self.state = State.STOPPED
+
+        # Init objects
+        self.config = config
+        exchange_config: ExchangeConfig = deepcopy(config["exchange"])
+        # Remove credentials from original exchange config to avoid accidental credential exposure
+        remove_exchange_credentials(config["exchange"], True)
+
+        self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
+
+        # Check config consistency here since strategies can set certain options
+        validate_config_consistency(config)
+
+        self.exchange = ExchangeResolver.load_exchange(
+            self.config, exchange_config=exchange_config, load_leverage_tiers=True
+        )
+
+        init_db(self.config["db_url"])
+
+        self.wallets = Wallets(self.config, self.exchange)
+
+        PairLocks.timeframe = self.config["timeframe"]
+
+        self.trading_mode: TradingMode = self.config.get("trading_mode", TradingMode.SPOT)
+        self.margin_mode: MarginMode = self.config.get("margin_mode", MarginMode.NONE)
+        self.last_process: datetime | None = None
+
+        # RPC runs in separate threads, can start handling external commands just after
+        # initialization, even before Freqtradebot has a chance to start its throttling,
+        # so anything in the Freqtradebot instance should be ready (initialized), including
+        # the initial state of the bot.
+        # Keep this at the end of this initialization method.
+        self.rpc: RPCManager = RPCManager(self)
+
+        self.dataprovider = DataProvider(self.config, self.exchange, rpc=self.rpc)
+        self.pairlists = PairListManager(self.exchange, self.config, self.dataprovider)
+
+        self.dataprovider.add_pairlisthandler(self.pairlists)
+
+        # Attach Dataprovider to strategy instance
+        self.strategy.dp = self.dataprovider
+        # Attach Wallets to strategy instance
+        self.strategy.wallets = self.wallets
+
+        # Initializing Edge only if enabled
+        self.edge = (
+            Edge(self.config, self.exchange, self.strategy)
+            if self.config.get("edge", {}).get("enabled", False)
+            else None
+        )
+
+        # Init ExternalMessageConsumer if enabled
+        self.emc = (
+            ExternalMessageConsumer(self.config, self.dataprovider)
+            if self.config.get("external_message_consumer", {}).get("enabled", False)
+            else None
+        )
+
+        logger.info("Starting initial pairlist refresh")
+        with MeasureTime(
+            lambda duration, _: logger.info(f"Initial Pairlist refresh took {duration:.2f}s"), 0
+        ):
+            self.active_pair_whitelist = self._refresh_active_whitelist()
+
+        # Set initial bot state from config
+        initial_state = self.config.get("initial_state")
+        self.state = State[initial_state.upper()] if initial_state else State.STOPPED
+
+        # Protect exit-logic from forcesell and vice versa
+        self._exit_lock = Lock()
+        timeframe_secs = timeframe_to_seconds(self.strategy.timeframe)
+        self._exit_reason_cache = PeriodicCache(100, ttl=timeframe_secs)
+        LoggingMixin.__init__(self, logger, timeframe_secs)
+
+        self._schedule = Scheduler()
+
+        if self.trading_mode == TradingMode.FUTURES:
+
+            def update():
+                self.update_funding_fees()
+                self.update_all_liquidation_prices()
+                self.wallets.update()
+
+            # This would be more efficient if scheduled in utc time, and performed at each
+            # funding interval, specified by funding_fee_times on the exchange classes
+            # However, this reduces the precision - and might therefore lead to problems.
+            for time_slot in range(0, 24):
+                for minutes in [1, 31]:
+                    t = str(time(time_slot, minutes, 2))
+                    self._schedule.every().day.at(t).do(update)
+
+        self._schedule.every().day.at("00:02").do(self.exchange.ws_connection_reset)
+
+        self.strategy.ft_bot_start()
+        # Initialize protections AFTER bot start - otherwise parameters are not loaded.
+        self.protections = ProtectionManager(self.config, self.strategy.protections)
+
+        def log_took_too_long(duration: float, time_limit: float):
+            logger.warning(
+                f"Strategy analysis took {duration:.2f}s, more than 25% of the timeframe "
+                f"({time_limit:.2f}s). This can lead to delayed orders and missed signals."
+                "Consider either reducing the amount of work your strategy performs "
+                "or reduce the amount of pairs in the Pairlist."
+            )
+
+        self._measure_execution = MeasureTime(log_took_too_long, timeframe_secs * 0.25)
+
+    def notify_status(self, msg: str, msg_type=RPCMessageType.STATUS) -> None:
+        """
+        Public method for users of this class (worker, etc.) to send notifications
+        via RPC about changes in the bot status.
+        """
+        self.rpc.send_msg({"type": msg_type, "status": msg})
+
+    def cleanup(self) -> None:
+        """
+        Cleanup pending resources on an already stopped bot
+        :return: None
+        """
+        logger.info("Cleaning up modules ...")
+        try:
+            # Wrap db activities in shutdown to avoid problems if database is gone,
+            # and raises further exceptions.
+            if self.config["cancel_open_orders_on_exit"]:
+                self.cancel_all_open_orders()
+
+            self.check_for_open_trades()
+        except Exception as e:
+            logger.warning(f"Exception during cleanup: {e.__class__.__name__} {e}")
+
+        finally:
+            self.strategy.ft_bot_cleanup()
+
+        self.rpc.cleanup()
+        if self.emc:
+            self.emc.shutdown()
+        self.exchange.close()
+        try:
+            Trade.commit()
+        except Exception:
+            # Exceptions here will be happening if the db disappeared.
+            # At which point we can no longer commit anyway.
+            logger.exception("Error during cleanup")
+
+    def startup(self) -> None:
+        """
+        Called on startup and after reloading the bot - triggers notifications and
+        performs startup tasks
+        """
+        migrate_binance_futures_names(self.config)
+        set_startup_time()
+
+        self.rpc.startup_messages(self.config, self.pairlists, self.protections)
+        # Update older trades with precision and precision mode
+        self.startup_backpopulate_precision()
+        if not self.edge:
+            # Adjust stoploss if it was changed
+            Trade.stoploss_reinitialization(self.strategy.stoploss)
+
+        # Only update open orders on startup
+        # This will update the database after the initial migration
+        self.startup_update_open_orders()
+        self.update_all_liquidation_prices()
+        self.update_funding_fees()
+
+    def process(self) -> None:
+        """
+        Queries the persistence layer for open trades and handles them,
+        otherwise a new trade is created.
+        :return: True if one or more trades has been created or closed, False otherwise
+        """
+
+        # Check whether markets have to be reloaded and reload them when it's needed
+        self.exchange.reload_markets()
+
+        self.update_trades_without_assigned_fees()
+
+        # Query trades from persistence layer
+        trades: list[Trade] = Trade.get_open_trades()
+
+        self.active_pair_whitelist = self._refresh_active_whitelist(trades)
+
+        # Refreshing candles
+        self.dataprovider.refresh(
+            self.pairlists.create_pair_list(self.active_pair_whitelist),
+            self.strategy.gather_informative_pairs(),
+        )
+
+        strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)(
+            current_time=datetime.now(timezone.utc)
+        )
+
+        with self._measure_execution:
+            self.strategy.analyze(self.active_pair_whitelist)
+
+        with self._exit_lock:
+            # Check for exchange cancellations, timeouts and user requested replace
+            self.manage_open_orders()
+
+        # Protect from collisions with force_exit.
+        # Without this, freqtrade may try to recreate stoploss_on_exchange orders
+        # while exiting is in process, since telegram messages arrive in an different thread.
+        with self._exit_lock:
+            trades = Trade.get_open_trades()
+            # First process current opened trades (positions)
+            self.exit_positions(trades)
+            Trade.commit()
+
+        # Check if we need to adjust our current positions before attempting to enter new trades.
+        if self.strategy.position_adjustment_enable:
+            with self._exit_lock:
+                self.process_open_trade_positions()
+
+        # Then looking for entry opportunities
+        if self.state == State.RUNNING and self.get_free_open_trades():
+            self.enter_positions()
+        self._schedule.run_pending()
+        Trade.commit()
+        self.rpc.process_msg_queue(self.dataprovider._msg_queue)
+        self.last_process = datetime.now(timezone.utc)
+
+    def process_stopped(self) -> None:
+        """
+        Close all orders that were left open
+        """
+        if self.config["cancel_open_orders_on_exit"]:
+            self.cancel_all_open_orders()
+
+    def check_for_open_trades(self):
+        """
+        Notify the user when the bot is stopped (not reloaded)
+        and there are still open trades active.
+        """
+        open_trades = Trade.get_open_trades()
+
+        if len(open_trades) != 0 and self.state != State.RELOAD_CONFIG:
+            msg = {
+                "type": RPCMessageType.WARNING,
+                "status": f"{len(open_trades)} open trades active.\n\n"
+                f"Handle these trades manually on {self.exchange.name}, "
+                f"or '/start' the bot again and use '/stopentry' "
+                f"to handle open trades gracefully. \n"
+                f"{'Note: Trades are simulated (dry run).' if self.config['dry_run'] else ''}",
+            }
+            self.rpc.send_msg(msg)
+
+    def _refresh_active_whitelist(self, trades: list[Trade] | None = None) -> list[str]:
+        """
+        Refresh active whitelist from pairlist or edge and extend it with
+        pairs that have open trades.
+        """
+        # Refresh whitelist
+        _prev_whitelist = self.pairlists.whitelist
+        self.pairlists.refresh_pairlist()
+        _whitelist = self.pairlists.whitelist
+
+        # Calculating Edge positioning
+        if self.edge:
+            self.edge.calculate(_whitelist)
+            _whitelist = self.edge.adjust(_whitelist)
+
+        if trades:
+            # Extend active-pair whitelist with pairs of open trades
+            # It ensures that candle (OHLCV) data are downloaded for open trades as well
+            _whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
+
+        # Called last to include the included pairs
+        if _prev_whitelist != _whitelist:
+            self.rpc.send_msg({"type": RPCMessageType.WHITELIST, "data": _whitelist})
+
+        return _whitelist
+
+    def get_free_open_trades(self) -> int:
+        """
+        Return the number of free open trades slots or 0 if
+        max number of open trades reached
+        """
+        open_trades = Trade.get_open_trade_count()
+        return max(0, self.config["max_open_trades"] - open_trades)
+
+    def update_all_liquidation_prices(self) -> None:
+        if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.CROSS:
+            # Update liquidation prices for all trades in cross margin mode
+            update_liquidation_prices(
+                exchange=self.exchange,
+                wallets=self.wallets,
+                stake_currency=self.config["stake_currency"],
+                dry_run=self.config["dry_run"],
+            )
+
+    def update_funding_fees(self) -> None:
+        if self.trading_mode == TradingMode.FUTURES:
+            trades: list[Trade] = Trade.get_open_trades()
+            for trade in trades:
+                trade.set_funding_fees(
+                    self.exchange.get_funding_fees(
+                        pair=trade.pair,
+                        amount=trade.amount,
+                        is_short=trade.is_short,
+                        open_date=trade.date_last_filled_utc,
+                    )
+                )
+
+    def startup_backpopulate_precision(self) -> None:
+        trades = Trade.get_trades([Trade.contract_size.is_(None)])
+        for trade in trades:
+            if trade.exchange != self.exchange.id:
+                continue
+            trade.precision_mode = self.exchange.precisionMode
+            trade.precision_mode_price = self.exchange.precision_mode_price
+            trade.amount_precision = self.exchange.get_precision_amount(trade.pair)
+            trade.price_precision = self.exchange.get_precision_price(trade.pair)
+            trade.contract_size = self.exchange.get_contract_size(trade.pair)
+        Trade.commit()
+
+    def startup_update_open_orders(self):
+        """
+        Updates open orders based on order list kept in the database.
+        Mainly updates the state of orders - but may also close trades
+        """
+        if self.config["dry_run"] or self.config["exchange"].get("skip_open_order_update", False):
+            # Updating open orders in dry-run does not make sense and will fail.
+            return
+
+        orders = Order.get_open_orders()
+        logger.info(f"Updating {len(orders)} open orders.")
+        for order in orders:
+            try:
+                fo = self.exchange.fetch_order_or_stoploss_order(
+                    order.order_id, order.ft_pair, order.ft_order_side == "stoploss"
+                )
+                if not order.trade:
+                    # This should not happen, but it does if trades were deleted manually.
+                    # This can only incur on sqlite, which doesn't enforce foreign constraints.
+                    logger.warning(
+                        f"Order {order.order_id} has no trade attached. "
+                        "This may suggest a database corruption. "
+                        f"The expected trade ID is {order.ft_trade_id}. Ignoring this order."
+                    )
+                    continue
+                self.update_trade_state(
+                    order.trade,
+                    order.order_id,
+                    fo,
+                    stoploss_order=(order.ft_order_side == "stoploss"),
+                )
+
+            except InvalidOrderException as e:
+                logger.warning(f"Error updating Order {order.order_id} due to {e}.")
+                if order.order_date_utc - timedelta(days=5) < datetime.now(timezone.utc):
+                    logger.warning(
+                        "Order is older than 5 days. Assuming order was fully cancelled."
+                    )
+                    fo = order.to_ccxt_object()
+                    fo["status"] = "canceled"
+                    self.handle_cancel_order(
+                        fo, order, order.trade, constants.CANCEL_REASON["TIMEOUT"]
+                    )
+
+            except ExchangeError as e:
+                logger.warning(f"Error updating Order {order.order_id} due to {e}")
+
+    def update_trades_without_assigned_fees(self) -> None:
+        """
+        Update closed trades without close fees assigned.
+        Only acts when Orders are in the database, otherwise the last order-id is unknown.
+        """
+        if self.config["dry_run"]:
+            # Updating open orders in dry-run does not make sense and will fail.
+            return
+
+        trades: list[Trade] = Trade.get_closed_trades_without_assigned_fees()
+        for trade in trades:
+            if not trade.is_open and not trade.fee_updated(trade.exit_side):
+                # Get sell fee
+                order = trade.select_order(trade.exit_side, False, only_filled=True)
+                if not order:
+                    order = trade.select_order("stoploss", False)
+                if order:
+                    logger.info(
+                        f"Updating {trade.exit_side}-fee on trade {trade}"
+                        f"for order {order.order_id}."
+                    )
+                    self.update_trade_state(
+                        trade,
+                        order.order_id,
+                        stoploss_order=order.ft_order_side == "stoploss",
+                        send_msg=False,
+                    )
+
+        trades = Trade.get_open_trades_without_assigned_fees()
+        for trade in trades:
+            with self._exit_lock:
+                if trade.is_open and not trade.fee_updated(trade.entry_side):
+                    order = trade.select_order(trade.entry_side, False, only_filled=True)
+                    open_order = trade.select_order(trade.entry_side, True)
+                    if order and open_order is None:
+                        logger.info(
+                            f"Updating {trade.entry_side}-fee on trade {trade}"
+                            f"for order {order.order_id}."
+                        )
+                        self.update_trade_state(trade, order.order_id, send_msg=False)
+
+    def handle_insufficient_funds(self, trade: Trade):
+        """
+        Try refinding a lost trade.
+        Only used when InsufficientFunds appears on exit orders (stoploss or long sell/short buy).
+        Tries to walk the stored orders and updates the trade state if necessary.
+        """
+        logger.info(f"Trying to refind lost order for {trade}")
+        for order in trade.orders:
+            logger.info(f"Trying to refind {order}")
+            fo = None
+            if not order.ft_is_open:
+                logger.debug(f"Order {order} is no longer open.")
+                continue
+            try:
+                fo = self.exchange.fetch_order_or_stoploss_order(
+                    order.order_id, order.ft_pair, order.ft_order_side == "stoploss"
+                )
+                if fo:
+                    logger.info(f"Found {order} for trade {trade}.")
+                    self.update_trade_state(
+                        trade, order.order_id, fo, stoploss_order=order.ft_order_side == "stoploss"
+                    )
+
+            except ExchangeError:
+                logger.warning(f"Error updating {order.order_id}.")
+
+    def handle_onexchange_order(self, trade: Trade) -> bool:
+        """
+        Try refinding a order that is not in the database.
+        Only used balance disappeared, which would make exiting impossible.
+        :return: True if the trade was deleted, False otherwise
+        """
+        try:
+            orders = self.exchange.fetch_orders(
+                trade.pair, trade.open_date_utc - timedelta(seconds=10)
+            )
+            prev_exit_reason = trade.exit_reason
+            prev_trade_state = trade.is_open
+            prev_trade_amount = trade.amount
+            for order in orders:
+                trade_order = [o for o in trade.orders if o.order_id == order["id"]]
+
+                if trade_order:
+                    # We knew this order, but didn't have it updated properly
+                    order_obj = trade_order[0]
+                else:
+                    logger.info(f"Found previously unknown order {order['id']} for {trade.pair}.")
+
+                    order_obj = Order.parse_from_ccxt_object(order, trade.pair, order["side"])
+                    order_obj.order_filled_date = dt_from_ts(
+                        safe_value_fallback(order, "lastTradeTimestamp", "timestamp")
+                    )
+                    trade.orders.append(order_obj)
+                    Trade.commit()
+                    trade.exit_reason = ExitType.SOLD_ON_EXCHANGE.value
+
+                self.update_trade_state(trade, order["id"], order, send_msg=False)
+
+                logger.info(f"handled order {order['id']}")
+
+            # Refresh trade from database
+            Trade.session.refresh(trade)
+            if not trade.is_open:
+                # Trade was just closed
+                trade.close_date = trade.date_last_filled_utc
+                self.order_close_notify(
+                    trade,
+                    order_obj,
+                    order_obj.ft_order_side == "stoploss",
+                    send_msg=prev_trade_state != trade.is_open,
+                )
+            else:
+                trade.exit_reason = prev_exit_reason
+                total = (
+                    self.wallets.get_owned(trade.pair, trade.base_currency)
+                    if trade.base_currency
+                    else 0
+                )
+                if total < trade.amount:
+                    if trade.fully_canceled_entry_order_count == len(trade.orders):
+                        logger.warning(
+                            f"Trade only had fully canceled entry orders. "
+                            f"Removing {trade} from database."
+                        )
+
+                        self._notify_enter_cancel(
+                            trade,
+                            order_type=self.strategy.order_types["entry"],
+                            reason=constants.CANCEL_REASON["FULLY_CANCELLED"],
+                        )
+                        trade.delete()
+                        return True
+                    if total > trade.amount * 0.98:
+                        logger.warning(
+                            f"{trade} has a total of {trade.amount} {trade.base_currency}, "
+                            f"but the Wallet shows a total of {total} {trade.base_currency}. "
+                            f"Adjusting trade amount to {total}. "
+                            "This may however lead to further issues."
+                        )
+                        trade.amount = total
+                    else:
+                        logger.warning(
+                            f"{trade} has a total of {trade.amount} {trade.base_currency}, "
+                            f"but the Wallet shows a total of {total} {trade.base_currency}. "
+                            "Refusing to adjust as the difference is too large. "
+                            "This may however lead to further issues."
+                        )
+                if prev_trade_amount != trade.amount:
+                    # Cancel stoploss on exchange if the amount changed
+                    trade = self.cancel_stoploss_on_exchange(trade)
+            Trade.commit()
+
+        except ExchangeError:
+            logger.warning("Error finding onexchange order.")
+        except Exception:
+            # catching https://github.com/freqtrade/freqtrade/issues/9025
+            logger.warning("Error finding onexchange order", exc_info=True)
+        return False
+
+    #
+    # enter positions / open trades logic and methods
+    #
+
+    def enter_positions(self) -> int:
+        """
+        Tries to execute entry orders for new trades (positions)
+        """
+        trades_created = 0
+
+        whitelist = deepcopy(self.active_pair_whitelist)
+        if not whitelist:
+            self.log_once("Active pair whitelist is empty.", logger.info)
+            return trades_created
+        # Remove pairs for currently opened trades from the whitelist
+        for trade in Trade.get_open_trades():
+            if trade.pair in whitelist:
+                whitelist.remove(trade.pair)
+                logger.debug("Ignoring %s in pair whitelist", trade.pair)
+
+        if not whitelist:
+            self.log_once(
+                "No currency pair in active pair whitelist, but checking to exit open trades.",
+                logger.info,
+            )
+            return trades_created
+        if PairLocks.is_global_lock(side="*"):
+            # This only checks for total locks (both sides).
+            # per-side locks will be evaluated by `is_pair_locked` within create_trade,
+            # once the direction for the trade is clear.
+            lock = PairLocks.get_pair_longest_lock("*")
+            if lock:
+                self.log_once(
+                    f"Global pairlock active until "
+                    f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)}. "
+                    f"Not creating new trades, reason: {lock.reason}.",
+                    logger.info,
+                )
+            else:
+                self.log_once("Global pairlock active. Not creating new trades.", logger.info)
+            return trades_created
+        # Create entity and execute trade for each pair from whitelist
+        for pair in whitelist:
+            try:
+                with self._exit_lock:
+                    trades_created += self.create_trade(pair)
+            except DependencyException as exception:
+                logger.warning("Unable to create trade for %s: %s", pair, exception)
+
+        if not trades_created:
+            logger.debug("Found no enter signals for whitelisted currencies. Trying again...")
+
+        return trades_created
+
+    def create_trade(self, pair: str) -> bool:
+        """
+        Check the implemented trading strategy for entry signals.
+
+        If the pair triggers the enter signal a new trade record gets created
+        and the entry-order opening the trade gets issued towards the exchange.
+
+        :return: True if a trade has been created.
+        """
+        logger.debug(f"create_trade for pair {pair}")
+
+        analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(pair, self.strategy.timeframe)
+        nowtime = analyzed_df.iloc[-1]["date"] if len(analyzed_df) > 0 else None
+
+        # get_free_open_trades is checked before create_trade is called
+        # but it is still used here to prevent opening too many trades within one iteration
+        if not self.get_free_open_trades():
+            logger.debug(f"Can't open a new trade for {pair}: max number of trades is reached.")
+            return False
+
+        # running get_signal on historical data fetched
+        (signal, enter_tag) = self.strategy.get_entry_signal(
+            pair, self.strategy.timeframe, analyzed_df
+        )
+
+        if signal:
+            if self.strategy.is_pair_locked(pair, candle_date=nowtime, side=signal):
+                lock = PairLocks.get_pair_longest_lock(pair, nowtime, signal)
+                if lock:
+                    self.log_once(
+                        f"Pair {pair} {lock.side} is locked until "
+                        f"{lock.lock_end_time.strftime(constants.DATETIME_PRINT_FORMAT)} "
+                        f"due to {lock.reason}.",
+                        logger.info,
+                    )
+                else:
+                    self.log_once(f"Pair {pair} is currently locked.", logger.info)
+                return False
+            stake_amount = self.wallets.get_trade_stake_amount(
+                pair, self.config["max_open_trades"], self.edge
+            )
+
+            bid_check_dom = self.config.get("entry_pricing", {}).get("check_depth_of_market", {})
+            if (bid_check_dom.get("enabled", False)) and (
+                bid_check_dom.get("bids_to_ask_delta", 0) > 0
+            ):
+                if self._check_depth_of_market(pair, bid_check_dom, side=signal):
+                    return self.execute_entry(
+                        pair,
+                        stake_amount,
+                        enter_tag=enter_tag,
+                        is_short=(signal == SignalDirection.SHORT),
+                    )
+                else:
+                    return False
+
+            return self.execute_entry(
+                pair, stake_amount, enter_tag=enter_tag, is_short=(signal == SignalDirection.SHORT)
+            )
+        else:
+            return False
+
+    #
+    # Modify positions / DCA logic and methods
+    #
+    def process_open_trade_positions(self):
+        """
+        Tries to execute additional buy or sell orders for open trades (positions)
+        """
+        # Walk through each pair and check if it needs changes
+        for trade in Trade.get_open_trades():
+            # If there is any open orders, wait for them to finish.
+            # TODO Remove to allow mul open orders
+            if trade.has_open_position or trade.has_open_orders:
+                # Do a wallets update (will be ratelimited to once per hour)
+                self.wallets.update(False)
+                try:
+                    self.check_and_call_adjust_trade_position(trade)
+                except DependencyException as exception:
+                    logger.warning(
+                        f"Unable to adjust position of trade for {trade.pair}: {exception}"
+                    )
+
+    def check_and_call_adjust_trade_position(self, trade: Trade):
+        """
+        Check the implemented trading strategy for adjustment command.
+        If the strategy triggers the adjustment, a new order gets issued.
+        Once that completes, the existing trade is modified to match new data.
+        """
+        current_entry_rate, current_exit_rate = self.exchange.get_rates(
+            trade.pair, True, trade.is_short
+        )
+
+        current_entry_profit = trade.calc_profit_ratio(current_entry_rate)
+        current_exit_profit = trade.calc_profit_ratio(current_exit_rate)
+
+        min_entry_stake = self.exchange.get_min_pair_stake_amount(
+            trade.pair, current_entry_rate, 0.0
+        )
+        min_exit_stake = self.exchange.get_min_pair_stake_amount(
+            trade.pair, current_exit_rate, self.strategy.stoploss
+        )
+        max_entry_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_entry_rate)
+        stake_available = self.wallets.get_available_stake_amount()
+        logger.debug(f"Calling adjust_trade_position for pair {trade.pair}")
+        stake_amount, order_tag = self.strategy._adjust_trade_position_internal(
+            trade=trade,
+            current_time=datetime.now(timezone.utc),
+            current_rate=current_entry_rate,
+            current_profit=current_entry_profit,
+            min_stake=min_entry_stake,
+            max_stake=min(max_entry_stake, stake_available),
+            current_entry_rate=current_entry_rate,
+            current_exit_rate=current_exit_rate,
+            current_entry_profit=current_entry_profit,
+            current_exit_profit=current_exit_profit,
+        )
+
+        if stake_amount is not None and stake_amount > 0.0:
+            if self.state == State.PAUSED:
+                logger.debug("Position adjustment aborted because the bot is in PAUSED state")
+                return
+
+            # We should increase our position
+            if self.strategy.max_entry_position_adjustment > -1:
+                count_of_entries = trade.nr_of_successful_entries
+                if count_of_entries > self.strategy.max_entry_position_adjustment:
+                    logger.debug(f"Max adjustment entries for {trade.pair} has been reached.")
+                    return
+                else:
+                    logger.debug("Max adjustment entries is set to unlimited.")
+
+            self.execute_entry(
+                trade.pair,
+                stake_amount,
+                price=current_entry_rate,
+                trade=trade,
+                is_short=trade.is_short,
+                mode="pos_adjust",
+                enter_tag=order_tag,
+            )
+
+        if stake_amount is not None and stake_amount < 0.0:
+            # We should decrease our position
+            amount = self.exchange.amount_to_contract_precision(
+                trade.pair,
+                abs(
+                    float(
+                        FtPrecise(stake_amount)
+                        * FtPrecise(trade.amount)
+                        / FtPrecise(trade.stake_amount)
+                    )
+                ),
+            )
+
+            if amount == 0.0:
+                logger.info(
+                    f"Wanted to exit of {stake_amount} amount, "
+                    "but exit amount is now 0.0 due to exchange limits - not exiting."
+                )
+                return
+
+            remaining = (trade.amount - amount) * current_exit_rate
+            if min_exit_stake and remaining != 0 and remaining < min_exit_stake:
+                logger.info(
+                    f"Remaining amount of {remaining} would be smaller "
+                    f"than the minimum of {min_exit_stake}."
+                )
+                return
+
+            self.execute_trade_exit(
+                trade,
+                current_exit_rate,
+                exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
+                sub_trade_amt=amount,
+                exit_tag=order_tag,
+            )
+
+    def _check_depth_of_market(self, pair: str, conf: dict, side: SignalDirection) -> bool:
+        """
+        Checks depth of market before executing an entry
+        """
+        conf_bids_to_ask_delta = conf.get("bids_to_ask_delta", 0)
+        logger.info(f"Checking depth of market for {pair} ...")
+        order_book = self.exchange.fetch_l2_order_book(pair, 1000)
+        order_book_data_frame = order_book_to_dataframe(order_book["bids"], order_book["asks"])
+        order_book_bids = order_book_data_frame["b_size"].sum()
+        order_book_asks = order_book_data_frame["a_size"].sum()
+
+        entry_side = order_book_bids if side == SignalDirection.LONG else order_book_asks
+        exit_side = order_book_asks if side == SignalDirection.LONG else order_book_bids
+        bids_ask_delta = entry_side / exit_side
+
+        bids = f"Bids: {order_book_bids}"
+        asks = f"Asks: {order_book_asks}"
+        delta = f"Delta: {bids_ask_delta}"
+
+        logger.info(
+            f"{bids}, {asks}, {delta}, Direction: {side.value} "
+            f"Bid Price: {order_book['bids'][0][0]}, Ask Price: {order_book['asks'][0][0]}, "
+            f"Immediate Bid Quantity: {order_book['bids'][0][1]}, "
+            f"Immediate Ask Quantity: {order_book['asks'][0][1]}."
+        )
+        if bids_ask_delta >= conf_bids_to_ask_delta:
+            logger.info(f"Bids to asks delta for {pair} DOES satisfy condition.")
+            return True
+        else:
+            logger.info(f"Bids to asks delta for {pair} does not satisfy condition.")
+            return False
+
+    def execute_entry(
+        self,
+        pair: str,
+        stake_amount: float,
+        price: float | None = None,
+        *,
+        is_short: bool = False,
+        ordertype: str | None = None,
+        enter_tag: str | None = None,
+        trade: Trade | None = None,
+        mode: EntryExecuteMode = "initial",
+        leverage_: float | None = None,
+    ) -> bool:
+        """
+        Executes an entry for the given pair
+        :param pair: pair for which we want to create a LIMIT order
+        :param stake_amount: amount of stake-currency for the pair
+        :return: True if an entry order is created, False if it fails.
+        :raise: DependencyException or it's subclasses like ExchangeError.
+        """
+        time_in_force = self.strategy.order_time_in_force["entry"]
+
+        side: BuySell = "sell" if is_short else "buy"
+        name = "Short" if is_short else "Long"
+        trade_side: LongShort = "short" if is_short else "long"
+        pos_adjust = trade is not None
+
+        enter_limit_requested, stake_amount, leverage = self.get_valid_enter_price_and_stake(
+            pair, price, stake_amount, trade_side, enter_tag, trade, mode, leverage_
+        )
+
+        if not stake_amount:
+            return False
+
+        msg = (
+            f"Position adjust: about to create a new order for {pair} with stake_amount: "
+            f"{stake_amount} and price: {enter_limit_requested} for {trade}"
+            if mode == "pos_adjust"
+            else (
+                f"Replacing {side} order: about create a new order for {pair} with stake_amount: "
+                f"{stake_amount} and price: {enter_limit_requested} ..."
+                if mode == "replace"
+                else f"{name} signal found: about create a new trade for {pair} with stake_amount: "
+                f"{stake_amount} and price: {enter_limit_requested} ..."
+            )
+        )
+        logger.info(msg)
+        amount = (stake_amount / enter_limit_requested) * leverage
+        order_type = ordertype or self.strategy.order_types["entry"]
+
+        if mode == "initial" and not strategy_safe_wrapper(
+            self.strategy.confirm_trade_entry, default_retval=True
+        )(
+            pair=pair,
+            order_type=order_type,
+            amount=amount,
+            rate=enter_limit_requested,
+            time_in_force=time_in_force,
+            current_time=datetime.now(timezone.utc),
+            entry_tag=enter_tag,
+            side=trade_side,
+        ):
+            logger.info(f"User denied entry for {pair}.")
+            return False
+
+        if trade and self.handle_similar_open_order(trade, enter_limit_requested, amount, side):
+            return False
+
+        order = self.exchange.create_order(
+            pair=pair,
+            ordertype=order_type,
+            side=side,
+            amount=amount,
+            rate=enter_limit_requested,
+            reduceOnly=False,
+            time_in_force=time_in_force,
+            leverage=leverage,
+        )
+        order_obj = Order.parse_from_ccxt_object(order, pair, side, amount, enter_limit_requested)
+        order_obj.ft_order_tag = enter_tag
+        order_id = order["id"]
+        order_status = order.get("status")
+        logger.info(f"Order {order_id} was created for {pair} and status is {order_status}.")
+
+        # we assume the order is executed at the price requested
+        enter_limit_filled_price = enter_limit_requested
+        amount_requested = amount
+
+        if order_status == "expired" or order_status == "rejected":
+            # return false if the order is not filled
+            if float(order["filled"]) == 0:
+                logger.warning(
+                    f"{name} {time_in_force} order with time in force {order_type} "
+                    f"for {pair} is {order_status} by {self.exchange.name}."
+                    " zero amount is fulfilled."
+                )
+                return False
+            else:
+                # the order is partially fulfilled
+                # in case of IOC orders we can check immediately
+                # if the order is fulfilled fully or partially
+                logger.warning(
+                    "%s %s order with time in force %s for %s is %s by %s."
+                    " %s amount fulfilled out of %s (%s remaining which is canceled).",
+                    name,
+                    time_in_force,
+                    order_type,
+                    pair,
+                    order_status,
+                    self.exchange.name,
+                    order["filled"],
+                    order["amount"],
+                    order["remaining"],
+                )
+                amount = safe_value_fallback(order, "filled", "amount", amount)
+                enter_limit_filled_price = safe_value_fallback(
+                    order, "average", "price", enter_limit_filled_price
+                )
+
+        # in case of FOK the order may be filled immediately and fully
+        elif order_status == "closed":
+            amount = safe_value_fallback(order, "filled", "amount", amount)
+            enter_limit_filled_price = safe_value_fallback(
+                order, "average", "price", enter_limit_requested
+            )
+
+        # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
+        fee = self.exchange.get_fee(symbol=pair, taker_or_maker="maker")
+        base_currency = self.exchange.get_pair_base_currency(pair)
+        open_date = datetime.now(timezone.utc)
+
+        funding_fees = self.exchange.get_funding_fees(
+            pair=pair,
+            amount=amount + trade.amount if trade else amount,
+            is_short=is_short,
+            open_date=trade.date_last_filled_utc if trade else open_date,
+        )
+
+        # This is a new trade
+        if trade is None:
+            trade = Trade(
+                pair=pair,
+                base_currency=base_currency,
+                stake_currency=self.config["stake_currency"],
+                stake_amount=stake_amount,
+                amount=0,
+                is_open=True,
+                amount_requested=amount_requested,
+                fee_open=fee,
+                fee_close=fee,
+                open_rate=enter_limit_filled_price,
+                open_rate_requested=enter_limit_requested,
+                open_date=open_date,
+                exchange=self.exchange.id,
+                strategy=self.strategy.get_strategy_name(),
+                enter_tag=enter_tag,
+                timeframe=timeframe_to_minutes(self.config["timeframe"]),
+                leverage=leverage,
+                is_short=is_short,
+                trading_mode=self.trading_mode,
+                funding_fees=funding_fees,
+                amount_precision=self.exchange.get_precision_amount(pair),
+                price_precision=self.exchange.get_precision_price(pair),
+                precision_mode=self.exchange.precisionMode,
+                precision_mode_price=self.exchange.precision_mode_price,
+                contract_size=self.exchange.get_contract_size(pair),
+            )
+            stoploss = self.strategy.stoploss if not self.edge else self.edge.get_stoploss(pair)
+            trade.adjust_stop_loss(trade.open_rate, stoploss, initial=True)
+
+        else:
+            # This is additional entry, we reset fee_open_currency so timeout checking can work
+            trade.is_open = True
+            trade.fee_open_currency = None
+            trade.set_funding_fees(funding_fees)
+
+        trade.orders.append(order_obj)
+        trade.recalc_trade_from_orders()
+        Trade.session.add(trade)
+        Trade.commit()
+
+        # Updating wallets
+        self.wallets.update()
+
+        self._notify_enter(trade, order_obj, order_type, sub_trade=pos_adjust)
+
+        if pos_adjust:
+            if order_status == "closed":
+                logger.info(f"DCA order closed, trade should be up to date: {trade}")
+                trade = self.cancel_stoploss_on_exchange(trade)
+            else:
+                logger.info(f"DCA order {order_status}, will wait for resolution: {trade}")
+
+        # Update fees if order is non-opened
+        if order_status in constants.NON_OPEN_EXCHANGE_STATES:
+            fully_canceled = self.update_trade_state(trade, order_id, order)
+            if fully_canceled and mode != "replace":
+                # Fully canceled orders, may happen with some time in force setups (IOC).
+                # Should be handled immediately.
+                self.handle_cancel_enter(
+                    trade, order, order_obj, constants.CANCEL_REASON["TIMEOUT"]
+                )
+
+        return True
+
+    def cancel_stoploss_on_exchange(self, trade: Trade) -> Trade:
+        # First cancelling stoploss on exchange ...
+        for oslo in trade.open_sl_orders:
+            try:
+                logger.info(f"Cancelling stoploss on exchange for {trade} order: {oslo.order_id}")
+                co = self.exchange.cancel_stoploss_order_with_result(
+                    oslo.order_id, trade.pair, trade.amount
+                )
+                self.update_trade_state(trade, oslo.order_id, co, stoploss_order=True)
+            except InvalidOrderException:
+                logger.exception(
+                    f"Could not cancel stoploss order {oslo.order_id} for pair {trade.pair}"
+                )
+        return trade
+
+    def get_valid_enter_price_and_stake(
+        self,
+        pair: str,
+        price: float | None,
+        stake_amount: float,
+        trade_side: LongShort,
+        entry_tag: str | None,
+        trade: Trade | None,
+        mode: EntryExecuteMode,
+        leverage_: float | None,
+    ) -> tuple[float, float, float]:
+        """
+        Validate and eventually adjust (within limits) limit, amount and leverage
+        :return: Tuple with (price, amount, leverage)
+        """
+
+        if price:
+            enter_limit_requested = price
+        else:
+            # Calculate price
+            enter_limit_requested = self.exchange.get_rate(
+                pair, side="entry", is_short=(trade_side == "short"), refresh=True
+            )
+        if mode != "replace":
+            # Don't call custom_entry_price in order-adjust scenario
+            custom_entry_price = strategy_safe_wrapper(
+                self.strategy.custom_entry_price, default_retval=enter_limit_requested
+            )(
+                pair=pair,
+                trade=trade,
+                current_time=datetime.now(timezone.utc),
+                proposed_rate=enter_limit_requested,
+                entry_tag=entry_tag,
+                side=trade_side,
+            )
+
+            enter_limit_requested = self.get_valid_price(custom_entry_price, enter_limit_requested)
+
+        if not enter_limit_requested:
+            raise PricingError("Could not determine entry price.")
+
+        if self.trading_mode != TradingMode.SPOT and trade is None:
+            max_leverage = self.exchange.get_max_leverage(pair, stake_amount)
+            if leverage_:
+                leverage = leverage_
+            else:
+                leverage = strategy_safe_wrapper(self.strategy.leverage, default_retval=1.0)(
+                    pair=pair,
+                    current_time=datetime.now(timezone.utc),
+                    current_rate=enter_limit_requested,
+                    proposed_leverage=1.0,
+                    max_leverage=max_leverage,
+                    side=trade_side,
+                    entry_tag=entry_tag,
+                )
+            # Cap leverage between 1.0 and max_leverage.
+            leverage = min(max(leverage, 1.0), max_leverage)
+        else:
+            # Changing leverage currently not possible
+            leverage = trade.leverage if trade else 1.0
+
+        # Min-stake-amount should actually include Leverage - this way our "minimal"
+        # stake- amount might be higher than necessary.
+        # We do however also need min-stake to determine leverage, therefore this is ignored as
+        # edge-case for now.
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(
+            pair,
+            enter_limit_requested,
+            self.strategy.stoploss if not mode == "pos_adjust" else 0.0,
+            leverage,
+        )
+        max_stake_amount = self.exchange.get_max_pair_stake_amount(
+            pair, enter_limit_requested, leverage
+        )
+
+        if not self.edge and trade is None:
+            stake_available = self.wallets.get_available_stake_amount()
+            stake_amount = strategy_safe_wrapper(
+                self.strategy.custom_stake_amount, default_retval=stake_amount
+            )(
+                pair=pair,
+                current_time=datetime.now(timezone.utc),
+                current_rate=enter_limit_requested,
+                proposed_stake=stake_amount,
+                min_stake=min_stake_amount,
+                max_stake=min(max_stake_amount, stake_available),
+                leverage=leverage,
+                entry_tag=entry_tag,
+                side=trade_side,
+            )
+
+        stake_amount = self.wallets.validate_stake_amount(
+            pair=pair,
+            stake_amount=stake_amount,
+            min_stake_amount=min_stake_amount,
+            max_stake_amount=max_stake_amount,
+            trade_amount=trade.stake_amount if trade else None,
+        )
+
+        return enter_limit_requested, stake_amount, leverage
+
+    def _notify_enter(
+        self,
+        trade: Trade,
+        order: Order,
+        order_type: str | None,
+        fill: bool = False,
+        sub_trade: bool = False,
+    ) -> None:
+        """
+        Sends rpc notification when a entry order occurred.
+        """
+        open_rate = order.safe_price
+
+        if open_rate is None:
+            open_rate = trade.open_rate
+
+        current_rate = self.exchange.get_rate(
+            trade.pair, side="entry", is_short=trade.is_short, refresh=False
+        )
+        stake_amount = trade.stake_amount
+        if not fill and trade.nr_of_successful_entries > 0:
+            # If we have open orders, we need to add the stake amount of the open orders
+            # as it's not yet included in the trade.stake_amount
+            stake_amount += sum(
+                o.stake_amount for o in trade.open_orders if o.ft_order_side == trade.entry_side
+            )
+
+        msg: RPCEntryMsg = {
+            "trade_id": trade.id,
+            "type": RPCMessageType.ENTRY_FILL if fill else RPCMessageType.ENTRY,
+            "buy_tag": trade.enter_tag,
+            "enter_tag": trade.enter_tag,
+            "exchange": trade.exchange.capitalize(),
+            "pair": trade.pair,
+            "leverage": trade.leverage if trade.leverage else None,
+            "direction": "Short" if trade.is_short else "Long",
+            "limit": open_rate,  # Deprecated (?)
+            "open_rate": open_rate,
+            "order_type": order_type or "unknown",
+            "stake_amount": stake_amount,
+            "stake_currency": self.config["stake_currency"],
+            "base_currency": self.exchange.get_pair_base_currency(trade.pair),
+            "quote_currency": self.exchange.get_pair_quote_currency(trade.pair),
+            "fiat_currency": self.config.get("fiat_display_currency", None),
+            "amount": order.safe_amount_after_fee if fill else (order.safe_amount or trade.amount),
+            "open_date": trade.open_date_utc or datetime.now(timezone.utc),
+            "current_rate": current_rate,
+            "sub_trade": sub_trade,
+        }
+
+        # Send the message
+        self.rpc.send_msg(msg)
+
+    def _notify_enter_cancel(
+        self, trade: Trade, order_type: str, reason: str, sub_trade: bool = False
+    ) -> None:
+        """
+        Sends rpc notification when a entry order cancel occurred.
+        """
+        current_rate = self.exchange.get_rate(
+            trade.pair, side="entry", is_short=trade.is_short, refresh=False
+        )
+
+        msg: RPCCancelMsg = {
+            "trade_id": trade.id,
+            "type": RPCMessageType.ENTRY_CANCEL,
+            "buy_tag": trade.enter_tag,
+            "enter_tag": trade.enter_tag,
+            "exchange": trade.exchange.capitalize(),
+            "pair": trade.pair,
+            "leverage": trade.leverage,
+            "direction": "Short" if trade.is_short else "Long",
+            "limit": trade.open_rate,
+            "order_type": order_type,
+            "stake_amount": trade.stake_amount,
+            "open_rate": trade.open_rate,
+            "stake_currency": self.config["stake_currency"],
+            "base_currency": self.exchange.get_pair_base_currency(trade.pair),
+            "quote_currency": self.exchange.get_pair_quote_currency(trade.pair),
+            "fiat_currency": self.config.get("fiat_display_currency", None),
+            "amount": trade.amount,
+            "open_date": trade.open_date,
+            "current_rate": current_rate,
+            "reason": reason,
+            "sub_trade": sub_trade,
+        }
+
+        # Send the message
+        self.rpc.send_msg(msg)
+
+    #
+    # SELL / exit positions / close trades logic and methods
+    #
+
+    def exit_positions(self, trades: list[Trade]) -> int:
+        """
+        Tries to execute exit orders for open trades (positions)
+        """
+        trades_closed = 0
+        for trade in trades:
+            if (
+                not trade.has_open_orders
+                and not trade.has_open_sl_orders
+                and not self.wallets.check_exit_amount(trade)
+            ):
+                logger.warning(
+                    f"Not enough {trade.safe_base_currency} in wallet to exit {trade}. "
+                    "Trying to recover."
+                )
+                if self.handle_onexchange_order(trade):
+                    # Trade was deleted. Don't continue.
+                    continue
+
+            try:
+                try:
+                    if self.strategy.order_types.get(
+                        "stoploss_on_exchange"
+                    ) and self.handle_stoploss_on_exchange(trade):
+                        trades_closed += 1
+                        Trade.commit()
+                        continue
+
+                except InvalidOrderException as exception:
+                    logger.warning(
+                        f"Unable to handle stoploss on exchange for {trade.pair}: {exception}"
+                    )
+                # Check if we can exit our current position for this trade
+                if trade.has_open_position and trade.is_open and self.handle_trade(trade):
+                    trades_closed += 1
+
+            except DependencyException as exception:
+                logger.warning(f"Unable to exit trade {trade.pair}: {exception}")
+
+        # Updating wallets if any trade occurred
+        if trades_closed:
+            self.wallets.update()
+
+        return trades_closed
+
+    def handle_trade(self, trade: Trade) -> bool:
+        """
+        Exits the current pair if the threshold is reached and updates the trade record.
+        :return: True if trade has been sold/exited_short, False otherwise
+        """
+        if not trade.is_open:
+            raise DependencyException(f"Attempt to handle closed trade: {trade}")
+
+        logger.debug("Handling %s ...", trade)
+
+        (enter, exit_) = (False, False)
+        exit_tag = None
+        exit_signal_type = "exit_short" if trade.is_short else "exit_long"
+
+        if self.config.get("use_exit_signal", True) or self.config.get(
+            "ignore_roi_if_entry_signal", False
+        ):
+            analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(
+                trade.pair, self.strategy.timeframe
+            )
+
+            (enter, exit_, exit_tag) = self.strategy.get_exit_signal(
+                trade.pair, self.strategy.timeframe, analyzed_df, is_short=trade.is_short
+            )
+
+        logger.debug("checking exit")
+        exit_rate = self.exchange.get_rate(
+            trade.pair, side="exit", is_short=trade.is_short, refresh=True
+        )
+        if self._check_and_execute_exit(trade, exit_rate, enter, exit_, exit_tag):
+            return True
+
+        logger.debug(f"Found no {exit_signal_type} signal for %s.", trade)
+        return False
+
+    def _check_and_execute_exit(
+        self, trade: Trade, exit_rate: float, enter: bool, exit_: bool, exit_tag: str | None
+    ) -> bool:
+        """
+        Check and execute trade exit
+        """
+        exits: list[ExitCheckTuple] = self.strategy.should_exit(
+            trade,
+            exit_rate,
+            datetime.now(timezone.utc),
+            enter=enter,
+            exit_=exit_,
+            force_stoploss=self.edge.get_stoploss(trade.pair) if self.edge else 0,
+        )
+        for should_exit in exits:
+            if should_exit.exit_flag:
+                exit_tag1 = exit_tag if should_exit.exit_type == ExitType.EXIT_SIGNAL else None
+                if trade.has_open_orders:
+                    if prev_eval := self._exit_reason_cache.get(
+                        f"{trade.pair}_{trade.id}_{exit_tag1 or should_exit.exit_reason}", None
+                    ):
+                        logger.debug(
+                            f"Exit reason already seen this candle, first seen at {prev_eval}"
+                        )
+                        continue
+
+                logger.info(
+                    f"Exit for {trade.pair} detected. Reason: {should_exit.exit_type}"
+                    f"{f' Tag: {exit_tag1}' if exit_tag1 is not None else ''}"
+                )
+                exited = self.execute_trade_exit(trade, exit_rate, should_exit, exit_tag=exit_tag1)
+                if exited:
+                    return True
+        return False
+
+    def create_stoploss_order(self, trade: Trade, stop_price: float) -> bool:
+        """
+        Abstracts creating stoploss orders from the logic.
+        Handles errors and updates the trade database object.
+        Force-sells the pair (using EmergencySell reason) in case of Problems creating the order.
+        :return: True if the order succeeded, and False in case of problems.
+        """
+        try:
+            stoploss_order = self.exchange.create_stoploss(
+                pair=trade.pair,
+                amount=trade.amount,
+                stop_price=stop_price,
+                order_types=self.strategy.order_types,
+                side=trade.exit_side,
+                leverage=trade.leverage,
+            )
+
+            order_obj = Order.parse_from_ccxt_object(
+                stoploss_order, trade.pair, "stoploss", trade.amount, stop_price
+            )
+            trade.orders.append(order_obj)
+            return True
+        except InsufficientFundsError as e:
+            logger.warning(f"Unable to place stoploss order {e}.")
+            # Try to figure out what went wrong
+            self.handle_insufficient_funds(trade)
+
+        except InvalidOrderException as e:
+            logger.error(f"Unable to place a stoploss order on exchange. {e}")
+            logger.warning("Exiting the trade forcefully")
+            self.emergency_exit(trade, stop_price)
+
+        except ExchangeError:
+            logger.exception("Unable to place a stoploss order on exchange.")
+        return False
+
+    def handle_stoploss_on_exchange(self, trade: Trade) -> bool:
+        """
+        Check if trade is fulfilled in which case the stoploss
+        on exchange should be added immediately if stoploss on exchange
+        is enabled.
+        # TODO: liquidation price always on exchange, even without stoploss_on_exchange
+        # Therefore fetching account liquidations for open pairs may make sense.
+        """
+
+        logger.debug("Handling stoploss on exchange %s ...", trade)
+
+        stoploss_orders = []
+        for slo in trade.open_sl_orders:
+            stoploss_order = None
+            try:
+                # First we check if there is already a stoploss on exchange
+                stoploss_order = (
+                    self.exchange.fetch_stoploss_order(slo.order_id, trade.pair)
+                    if slo.order_id
+                    else None
+                )
+            except InvalidOrderException as exception:
+                logger.warning("Unable to fetch stoploss order: %s", exception)
+
+            if stoploss_order:
+                stoploss_orders.append(stoploss_order)
+                self.update_trade_state(trade, slo.order_id, stoploss_order, stoploss_order=True)
+
+            # We check if stoploss order is fulfilled
+            if stoploss_order and stoploss_order["status"] in ("closed", "triggered"):
+                trade.exit_reason = ExitType.STOPLOSS_ON_EXCHANGE.value
+                self._notify_exit(trade, "stoploss", True)
+                self.handle_protections(trade.pair, trade.trade_direction)
+                return True
+
+        if not trade.has_open_position or not trade.is_open:
+            # The trade can be closed already (sell-order fill confirmation came in this iteration)
+            return False
+
+        # If enter order is fulfilled but there is no stoploss, we add a stoploss on exchange
+        if len(stoploss_orders) == 0:
+            stop_price = trade.stoploss_or_liquidation
+            if self.edge:
+                stoploss = self.edge.get_stoploss(pair=trade.pair)
+                stop_price = (
+                    trade.open_rate * (1 - stoploss)
+                    if trade.is_short
+                    else trade.open_rate * (1 + stoploss)
+                )
+
+            if self.create_stoploss_order(trade=trade, stop_price=stop_price):
+                # The above will return False if the placement failed and the trade was force-sold.
+                # in which case the trade will be closed - which we must check below.
+                return False
+
+        self.manage_trade_stoploss_orders(trade, stoploss_orders)
+
+        return False
+
+    def handle_trailing_stoploss_on_exchange(self, trade: Trade, order: CcxtOrder) -> None:
+        """
+        Check to see if stoploss on exchange should be updated
+        in case of trailing stoploss on exchange
+        :param trade: Corresponding Trade
+        :param order: Current on exchange stoploss order
+        :return: None
+        """
+        stoploss_norm = self.exchange.price_to_precision(
+            trade.pair,
+            trade.stoploss_or_liquidation,
+            rounding_mode=ROUND_DOWN if trade.is_short else ROUND_UP,
+        )
+
+        if self.exchange.stoploss_adjust(stoploss_norm, order, side=trade.exit_side):
+            # we check if the update is necessary
+            update_beat = self.strategy.order_types.get("stoploss_on_exchange_interval", 60)
+            upd_req = datetime.now(timezone.utc) - timedelta(seconds=update_beat)
+            if trade.stoploss_last_update_utc and upd_req >= trade.stoploss_last_update_utc:
+                # cancelling the current stoploss on exchange first
+                logger.info(
+                    f"Cancelling current stoploss on exchange for pair {trade.pair} "
+                    f"(orderid:{order['id']}) in order to add another one ..."
+                )
+
+                self.cancel_stoploss_on_exchange(trade)
+                if not trade.is_open:
+                    logger.warning(
+                        f"Trade {trade} is closed, not creating trailing stoploss order."
+                    )
+                    return
+
+                # Create new stoploss order
+                if not self.create_stoploss_order(trade=trade, stop_price=stoploss_norm):
+                    logger.warning(
+                        f"Could not create trailing stoploss order for pair {trade.pair}."
+                    )
+
+    def manage_trade_stoploss_orders(self, trade: Trade, stoploss_orders: list[CcxtOrder]):
+        """
+        Perform required actions according to existing stoploss orders of trade
+        :param trade: Corresponding Trade
+        :param stoploss_orders: Current on exchange stoploss orders
+        :return: None
+        """
+        # If all stoploss ordered are canceled for some reason we add it again
+        canceled_sl_orders = [
+            o for o in stoploss_orders if o["status"] in ("canceled", "cancelled")
+        ]
+        if (
+            trade.is_open
+            and len(stoploss_orders) > 0
+            and len(stoploss_orders) == len(canceled_sl_orders)
+        ):
+            if self.create_stoploss_order(trade=trade, stop_price=trade.stoploss_or_liquidation):
+                return False
+            else:
+                logger.warning("All Stoploss orders are cancelled, but unable to recreate one.")
+
+        active_sl_orders = [o for o in stoploss_orders if o not in canceled_sl_orders]
+        if len(active_sl_orders) > 0:
+            last_active_sl_order = active_sl_orders[-1]
+            # Finally we check if stoploss on exchange should be moved up because of trailing.
+            # Triggered Orders are now real orders - so don't replace stoploss anymore
+            if (
+                trade.is_open
+                and last_active_sl_order.get("status_stop") != "triggered"
+                and (
+                    self.config.get("trailing_stop", False)
+                    or self.config.get("use_custom_stoploss", False)
+                )
+            ):
+                # if trailing stoploss is enabled we check if stoploss value has changed
+                # in which case we cancel stoploss order and put another one with new
+                # value immediately
+                self.handle_trailing_stoploss_on_exchange(trade, last_active_sl_order)
+
+        return
+
+    def manage_open_orders(self) -> None:
+        """
+        Management of open orders on exchange. Unfilled orders might be cancelled if timeout
+        was met or replaced if there's a new candle and user has requested it.
+        Timeout setting takes priority over limit order adjustment request.
+        :return: None
+        """
+        for trade in Trade.get_open_trades():
+            open_order: Order
+            for open_order in trade.open_orders:
+                try:
+                    order = self.exchange.fetch_order(open_order.order_id, trade.pair)
+
+                except ExchangeError:
+                    logger.info(
+                        "Cannot query order for %s due to %s", trade, traceback.format_exc()
+                    )
+                    continue
+
+                fully_cancelled = self.update_trade_state(trade, open_order.order_id, order)
+                not_closed = order["status"] == "open" or fully_cancelled
+
+                if not_closed:
+                    if fully_cancelled or (
+                        open_order
+                        and self.strategy.ft_check_timed_out(
+                            trade, open_order, datetime.now(timezone.utc)
+                        )
+                    ):
+                        self.handle_cancel_order(
+                            order, open_order, trade, constants.CANCEL_REASON["TIMEOUT"]
+                        )
+                    else:
+                        self.replace_order(order, open_order, trade)
+
+    def handle_cancel_order(
+        self, order: CcxtOrder, order_obj: Order, trade: Trade, reason: str, replacing: bool = False
+    ) -> bool:
+        """
+        Check if current analyzed order timed out and cancel if necessary.
+        :param order: Order dict grabbed with exchange.fetch_order()
+        :param order_obj: Order object from the database.
+        :param trade: Trade object.
+        :return: True if the order was canceled, False otherwise.
+        """
+        if order["side"] == trade.entry_side:
+            return self.handle_cancel_enter(trade, order, order_obj, reason, replacing)
+        else:
+            canceled = self.handle_cancel_exit(trade, order, order_obj, reason)
+            if not replacing:
+                canceled_count = trade.get_canceled_exit_order_count()
+                max_timeouts = self.config.get("unfilledtimeout", {}).get("exit_timeout_count", 0)
+                if canceled and max_timeouts > 0 and canceled_count >= max_timeouts:
+                    logger.warning(
+                        f"Emergency exiting trade {trade}, as the exit order "
+                        f"timed out {max_timeouts} times. force selling {order['amount']}."
+                    )
+                    self.emergency_exit(trade, order["price"], order["amount"])
+            return canceled
+
+    def emergency_exit(
+        self, trade: Trade, price: float, sub_trade_amt: float | None = None
+    ) -> None:
+        try:
+            self.execute_trade_exit(
+                trade,
+                price,
+                exit_check=ExitCheckTuple(exit_type=ExitType.EMERGENCY_EXIT),
+                sub_trade_amt=sub_trade_amt,
+            )
+        except DependencyException as exception:
+            logger.warning(f"Unable to emergency exit trade {trade.pair}: {exception}")
+
+    def replace_order_failed(self, trade: Trade, msg: str) -> None:
+        """
+        Order replacement fail handling.
+        Deletes the trade if necessary.
+        :param trade: Trade object.
+        :param msg: Error message.
+        """
+        logger.warning(msg)
+        if trade.nr_of_successful_entries == 0:
+            # this is the first entry and we didn't get filled yet, delete trade
+            logger.warning(f"Removing {trade} from database.")
+            self._notify_enter_cancel(
+                trade,
+                order_type=self.strategy.order_types["entry"],
+                reason=constants.CANCEL_REASON["REPLACE_FAILED"],
+            )
+            trade.delete()
+
+    def replace_order(self, order: CcxtOrder, order_obj: Order | None, trade: Trade) -> None:
+        """
+        Check if current analyzed entry order should be replaced or simply cancelled.
+        To simply cancel the existing order(no replacement) adjust_order_price() should return None
+        To maintain existing order adjust_order_price() should return order_obj.price
+        To replace existing order adjust_order_price() should return desired price for limit order
+        :param order: Order dict grabbed with exchange.fetch_order()
+        :param order_obj: Order object.
+        :param trade: Trade object.
+        :return: None
+        """
+        analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(
+            trade.pair, self.strategy.timeframe
+        )
+        latest_candle_open_date = analyzed_df.iloc[-1]["date"] if len(analyzed_df) > 0 else None
+        latest_candle_close_date = timeframe_to_next_date(
+            self.strategy.timeframe, latest_candle_open_date
+        )
+        # Check if new candle
+        if order_obj and latest_candle_close_date > order_obj.order_date_utc:
+            is_entry = order_obj.side == trade.entry_side
+            # New candle
+            proposed_rate = self.exchange.get_rate(
+                trade.pair,
+                side="entry" if is_entry else "exit",
+                is_short=trade.is_short,
+                refresh=True,
+            )
+            adjusted_price = strategy_safe_wrapper(
+                self.strategy.adjust_order_price, default_retval=order_obj.safe_placement_price
+            )(
+                trade=trade,
+                order=order_obj,
+                pair=trade.pair,
+                current_time=datetime.now(timezone.utc),
+                proposed_rate=proposed_rate,
+                current_order_rate=order_obj.safe_placement_price,
+                entry_tag=trade.enter_tag,
+                side=trade.trade_direction,
+                is_entry=is_entry,
+            )
+
+            replacing = True
+            cancel_reason = constants.CANCEL_REASON["REPLACE"]
+            if not adjusted_price:
+                replacing = False
+                cancel_reason = constants.CANCEL_REASON["USER_CANCEL"]
+
+            if order_obj.safe_placement_price != adjusted_price:
+                self.handle_replace_order(
+                    order,
+                    order_obj,
+                    trade,
+                    adjusted_price,
+                    is_entry,
+                    cancel_reason,
+                    replacing=replacing,
+                )
+
+    def handle_replace_order(
+        self,
+        order: CcxtOrder | None,
+        order_obj: Order,
+        trade: Trade,
+        new_order_price: float | None,
+        is_entry: bool,
+        cancel_reason: str,
+        replacing: bool = False,
+    ) -> None:
+        """
+        Cancel existing order if new price is supplied, and if the cancel is successful,
+        places a new order with the remaining capital.
+        """
+        if not order:
+            order = self.exchange.fetch_order(order_obj.order_id, trade.pair)
+        res = self.handle_cancel_order(order, order_obj, trade, cancel_reason, replacing=replacing)
+        if not res:
+            self.replace_order_failed(
+                trade, f"Could not fully cancel order for {trade}, therefore not replacing."
+            )
+            return
+        if new_order_price:
+            # place new order only if new price is supplied
+            try:
+                if is_entry:
+                    succeeded = self.execute_entry(
+                        pair=trade.pair,
+                        stake_amount=(
+                            order_obj.safe_remaining * order_obj.safe_price / trade.leverage
+                        ),
+                        price=new_order_price,
+                        trade=trade,
+                        is_short=trade.is_short,
+                        mode="replace",
+                    )
+                else:
+                    succeeded = self.execute_trade_exit(
+                        trade,
+                        new_order_price,
+                        exit_check=ExitCheckTuple(
+                            exit_type=ExitType.CUSTOM_EXIT,
+                            exit_reason=order_obj.ft_order_tag or "order_replaced",
+                        ),
+                        ordertype="limit",
+                        sub_trade_amt=order_obj.safe_remaining,
+                    )
+                if not succeeded:
+                    self.replace_order_failed(trade, f"Could not replace order for {trade}.")
+            except DependencyException as exception:
+                logger.warning(f"Unable to replace order for {trade.pair}: {exception}")
+                self.replace_order_failed(trade, f"Could not replace order for {trade}.")
+
+    def cancel_open_orders_of_trade(
+        self, trade: Trade, sides: list[str], reason: str, replacing: bool = False
+    ) -> None:
+        """
+        Cancel trade orders of specified sides that are currently open
+        :param trade: Trade object of the trade we're analyzing
+        :param reason: The reason for that cancellation
+        :param sides: The sides where cancellation should take place
+        :return: None
+        """
+
+        for open_order in trade.open_orders:
+            try:
+                order = self.exchange.fetch_order(open_order.order_id, trade.pair)
+            except ExchangeError:
+                logger.info("Can't query order for %s due to %s", trade, traceback.format_exc())
+                continue
+
+            if order["side"] in sides:
+                if order["side"] == trade.entry_side:
+                    self.handle_cancel_enter(trade, order, open_order, reason, replacing)
+
+                elif order["side"] == trade.exit_side:
+                    self.handle_cancel_exit(trade, order, open_order, reason)
+
+    def cancel_all_open_orders(self) -> None:
+        """
+        Cancel all orders that are currently open
+        :return: None
+        """
+
+        for trade in Trade.get_open_trades():
+            self.cancel_open_orders_of_trade(
+                trade, [trade.entry_side, trade.exit_side], constants.CANCEL_REASON["ALL_CANCELLED"]
+            )
+
+        Trade.commit()
+
+    def handle_similar_open_order(
+        self, trade: Trade, price: float, amount: float, side: str
+    ) -> bool:
+        """
+        Keep existing open order if same amount and side otherwise cancel
+        :param trade: Trade object of the trade we're analyzing
+        :param price: Limit price of the potential new order
+        :param amount: Quantity of assets of the potential new order
+        :param side: Side of the potential new order
+        :return: True if an existing similar order was found
+        """
+        if trade.has_open_orders:
+            oo = trade.select_order(side, True)
+            if oo is not None:
+                if price == oo.price and side == oo.side and amount == oo.amount:
+                    logger.info(
+                        f"A similar open order was found for {trade.pair}. "
+                        f"Keeping existing {trade.exit_side} order. {price=},  {amount=}"
+                    )
+                    return True
+            # cancel open orders of this trade if order is different
+            self.cancel_open_orders_of_trade(
+                trade,
+                [trade.entry_side, trade.exit_side],
+                constants.CANCEL_REASON["REPLACE"],
+                True,
+            )
+            Trade.commit()
+            return False
+
+        return False
+
+    def handle_cancel_enter(
+        self,
+        trade: Trade,
+        order: CcxtOrder,
+        order_obj: Order,
+        reason: str,
+        replacing: bool | None = False,
+    ) -> bool:
+        """
+        entry cancel - cancel order
+        :param order_obj: Order object from the database.
+        :param replacing: Replacing order - prevent trade deletion.
+        :return: True if trade was fully cancelled
+        """
+        was_trade_fully_canceled = False
+        order_id = order_obj.order_id
+        side = trade.entry_side.capitalize()
+
+        if order["status"] not in constants.NON_OPEN_EXCHANGE_STATES:
+            filled_val: float = order.get("filled", 0.0) or 0.0
+            filled_stake = filled_val * trade.open_rate
+            minstake = self.exchange.get_min_pair_stake_amount(
+                trade.pair, trade.open_rate, self.strategy.stoploss
+            )
+
+            if filled_val > 0 and minstake and filled_stake < minstake:
+                logger.warning(
+                    f"Order {order_id} for {trade.pair} not cancelled, "
+                    f"as the filled amount of {filled_val} would result in an unexitable trade."
+                )
+                return False
+            corder = self.exchange.cancel_order_with_result(order_id, trade.pair, trade.amount)
+            order_obj.ft_cancel_reason = reason
+            # if replacing, retry fetching the order 3 times if the status is not what we need
+            if replacing:
+                retry_count = 0
+                while (
+                    corder.get("status") not in constants.NON_OPEN_EXCHANGE_STATES
+                    and retry_count < 3
+                ):
+                    sleep(0.5)
+                    corder = self.exchange.fetch_order(order_id, trade.pair)
+                    retry_count += 1
+
+            # Avoid race condition where the order could not be cancelled coz its already filled.
+            # Simply bailing here is the only safe way - as this order will then be
+            # handled in the next iteration.
+            if corder.get("status") not in constants.NON_OPEN_EXCHANGE_STATES:
+                logger.warning(f"Order {order_id} for {trade.pair} not cancelled.")
+                return False
+        else:
+            # Order was cancelled already, so we can reuse the existing dict
+            corder = order
+            if order_obj.ft_cancel_reason is None:
+                order_obj.ft_cancel_reason = constants.CANCEL_REASON["CANCELLED_ON_EXCHANGE"]
+
+        logger.info(f"{side} order {order_obj.ft_cancel_reason} for {trade}.")
+
+        # Using filled to determine the filled amount
+        filled_amount = safe_value_fallback2(corder, order, "filled", "filled")
+        if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
+            was_trade_fully_canceled = True
+            # if trade is not partially completed and it's the only order, just delete the trade
+            open_order_count = len(
+                [order for order in trade.orders if order.ft_is_open and order.order_id != order_id]
+            )
+            if open_order_count < 1 and trade.nr_of_successful_entries == 0 and not replacing:
+                logger.info(f"{side} order fully cancelled. Removing {trade} from database.")
+                trade.delete()
+                order_obj.ft_cancel_reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
+            else:
+                self.update_trade_state(trade, order_id, corder)
+                logger.info(f"{side} Order timeout for {trade}.")
+        else:
+            # update_trade_state (and subsequently recalc_trade_from_orders) will handle updates
+            # to the trade object
+            self.update_trade_state(trade, order_id, corder)
+
+            logger.info(
+                f"Partial {trade.entry_side} order timeout for {trade}. Filled: {filled_amount}, "
+                f"total: {order_obj.ft_amount}"
+            )
+            order_obj.ft_cancel_reason += f", {constants.CANCEL_REASON['PARTIALLY_FILLED']}"
+
+        self.wallets.update()
+        self._notify_enter_cancel(
+            trade, order_type=self.strategy.order_types["entry"], reason=order_obj.ft_cancel_reason
+        )
+        return was_trade_fully_canceled
+
+    def handle_cancel_exit(
+        self, trade: Trade, order: CcxtOrder, order_obj: Order, reason: str
+    ) -> bool:
+        """
+        exit order cancel - cancel order and update trade
+        :return: True if exit order was cancelled, false otherwise
+        """
+        order_id = order_obj.order_id
+        cancelled = False
+        # Cancelled orders may have the status of 'canceled' or 'closed'
+        if order["status"] not in constants.NON_OPEN_EXCHANGE_STATES:
+            filled_amt: float = order.get("filled", 0.0) or 0.0
+            # Filled val is in quote currency (after leverage)
+            filled_rem_stake = trade.stake_amount - (filled_amt * trade.open_rate / trade.leverage)
+            minstake = self.exchange.get_min_pair_stake_amount(
+                trade.pair, trade.open_rate, self.strategy.stoploss
+            )
+            # Double-check remaining amount
+            if filled_amt > 0:
+                reason = constants.CANCEL_REASON["PARTIALLY_FILLED"]
+                if minstake and filled_rem_stake < minstake:
+                    logger.warning(
+                        f"Order {order_id} for {trade.pair} not cancelled, as "
+                        f"the filled amount of {filled_amt} would result in an unexitable trade."
+                    )
+                    reason = constants.CANCEL_REASON["PARTIALLY_FILLED_KEEP_OPEN"]
+
+                    self._notify_exit_cancel(
+                        trade,
+                        order_type=self.strategy.order_types["exit"],
+                        reason=reason,
+                        order_id=order["id"],
+                        sub_trade=trade.amount != order["amount"],
+                    )
+                    return False
+            order_obj.ft_cancel_reason = reason
+            try:
+                order = self.exchange.cancel_order_with_result(
+                    order["id"], trade.pair, trade.amount
+                )
+            except InvalidOrderException:
+                logger.exception(f"Could not cancel {trade.exit_side} order {order_id}")
+                return False
+
+            # Set exit_reason for fill message
+            exit_reason_prev = trade.exit_reason
+            trade.exit_reason = trade.exit_reason + f", {reason}" if trade.exit_reason else reason
+            # Order might be filled above in odd timing issues.
+            if order.get("status") in ("canceled", "cancelled"):
+                trade.exit_reason = None
+            else:
+                trade.exit_reason = exit_reason_prev
+            cancelled = True
+        else:
+            if order_obj.ft_cancel_reason is None:
+                order_obj.ft_cancel_reason = constants.CANCEL_REASON["CANCELLED_ON_EXCHANGE"]
+            trade.exit_reason = None
+
+        self.update_trade_state(trade, order["id"], order)
+
+        logger.info(
+            f"{trade.exit_side.capitalize()} order {order_obj.ft_cancel_reason} for {trade}."
+        )
+        trade.close_rate = None
+        trade.close_rate_requested = None
+
+        self._notify_exit_cancel(
+            trade,
+            order_type=self.strategy.order_types["exit"],
+            reason=order_obj.ft_cancel_reason,
+            order_id=order["id"],
+            sub_trade=trade.amount != order["amount"],
+        )
+        return cancelled
+
+    def _safe_exit_amount(self, trade: Trade, pair: str, amount: float) -> float:
+        """
+        Get sellable amount.
+        Should be trade.amount - but will fall back to the available amount if necessary.
+        This should cover cases where get_real_amount() was not able to update the amount
+        for whatever reason.
+        :param trade: Trade we're working with
+        :param pair: Pair we're trying to sell
+        :param amount: amount we expect to be available
+        :return: amount to sell
+        :raise: DependencyException: if available balance is not within 2% of the available amount.
+        """
+        # Update wallets to ensure amounts tied up in a stoploss is now free!
+        self.wallets.update()
+        if self.trading_mode == TradingMode.FUTURES:
+            # A safe exit amount isn't needed for futures, you can just exit/close the position
+            return amount
+
+        trade_base_currency = self.exchange.get_pair_base_currency(pair)
+        # Free + Used - open orders will eventually still be canceled.
+        wallet_amount = self.wallets.get_free(trade_base_currency) + self.wallets.get_used(
+            trade_base_currency
+        )
+
+        logger.debug(f"{pair} - Wallet: {wallet_amount} - Trade-amount: {amount}")
+        if wallet_amount >= amount:
+            return amount
+        elif wallet_amount > amount * 0.98:
+            logger.info(f"{pair} - Falling back to wallet-amount {wallet_amount} -> {amount}.")
+            trade.amount = wallet_amount
+            return wallet_amount
+        else:
+            raise DependencyException(
+                f"Not enough amount to exit trade. Trade-amount: {amount}, Wallet: {wallet_amount}"
+            )
+
+    def execute_trade_exit(
+        self,
+        trade: Trade,
+        limit: float,
+        exit_check: ExitCheckTuple,
+        *,
+        exit_tag: str | None = None,
+        ordertype: str | None = None,
+        sub_trade_amt: float | None = None,
+    ) -> bool:
+        """
+        Executes a trade exit for the given trade and limit
+        :param trade: Trade instance
+        :param limit: limit rate for the sell order
+        :param exit_check: CheckTuple with signal and reason
+        :return: True if it succeeds False
+        """
+        trade.set_funding_fees(
+            self.exchange.get_funding_fees(
+                pair=trade.pair,
+                amount=trade.amount,
+                is_short=trade.is_short,
+                open_date=trade.date_last_filled_utc,
+            )
+        )
+
+        exit_type = "exit"
+        exit_reason = exit_tag or exit_check.exit_reason
+        if exit_check.exit_type in (
+            ExitType.STOP_LOSS,
+            ExitType.TRAILING_STOP_LOSS,
+            ExitType.LIQUIDATION,
+        ):
+            exit_type = "stoploss"
+
+        # set custom_exit_price if available
+        proposed_limit_rate = limit
+        current_profit = trade.calc_profit_ratio(limit)
+        custom_exit_price = strategy_safe_wrapper(
+            self.strategy.custom_exit_price, default_retval=proposed_limit_rate
+        )(
+            pair=trade.pair,
+            trade=trade,
+            current_time=datetime.now(timezone.utc),
+            proposed_rate=proposed_limit_rate,
+            current_profit=current_profit,
+            exit_tag=exit_reason,
+        )
+
+        limit = self.get_valid_price(custom_exit_price, proposed_limit_rate)
+
+        # First cancelling stoploss on exchange ...
+        trade = self.cancel_stoploss_on_exchange(trade)
+
+        order_type = ordertype or self.strategy.order_types[exit_type]
+        if exit_check.exit_type == ExitType.EMERGENCY_EXIT:
+            # Emergency sells (default to market!)
+            order_type = self.strategy.order_types.get("emergency_exit", "market")
+
+        amount = self._safe_exit_amount(trade, trade.pair, sub_trade_amt or trade.amount)
+        time_in_force = self.strategy.order_time_in_force["exit"]
+
+        if (
+            exit_check.exit_type != ExitType.LIQUIDATION
+            and not sub_trade_amt
+            and not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
+                pair=trade.pair,
+                trade=trade,
+                order_type=order_type,
+                amount=amount,
+                rate=limit,
+                time_in_force=time_in_force,
+                exit_reason=exit_reason,
+                sell_reason=exit_reason,  # sellreason -> compatibility
+                current_time=datetime.now(timezone.utc),
+            )
+        ):
+            logger.info(f"User denied exit for {trade.pair}.")
+            return False
+
+        if trade.has_open_orders:
+            if self.handle_similar_open_order(trade, limit, amount, trade.exit_side):
+                return False
+
+        try:
+            # Execute sell and update trade record
+            order = self.exchange.create_order(
+                pair=trade.pair,
+                ordertype=order_type,
+                side=trade.exit_side,
+                amount=amount,
+                rate=limit,
+                leverage=trade.leverage,
+                reduceOnly=self.trading_mode == TradingMode.FUTURES,
+                time_in_force=time_in_force,
+            )
+        except InsufficientFundsError as e:
+            logger.warning(f"Unable to place order {e}.")
+            # Try to figure out what went wrong
+            self.handle_insufficient_funds(trade)
+            return False
+
+        self._exit_reason_cache[f"{trade.pair}_{trade.id}_{exit_reason}"] = dt_now()
+        order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side, amount, limit)
+        order_obj.ft_order_tag = exit_reason
+        trade.orders.append(order_obj)
+
+        trade.exit_order_status = ""
+        trade.close_rate_requested = limit
+        trade.exit_reason = exit_reason
+
+        self._notify_exit(trade, order_type, sub_trade=bool(sub_trade_amt), order=order_obj)
+        # In case of market sell orders the order can be closed immediately
+        if order.get("status", "unknown") in ("closed", "expired"):
+            self.update_trade_state(trade, order_obj.order_id, order)
+        Trade.commit()
+
+        return True
+
+    def _notify_exit(
+        self,
+        trade: Trade,
+        order_type: str | None,
+        fill: bool = False,
+        sub_trade: bool = False,
+        order: Order | None = None,
+    ) -> None:
+        """
+        Sends rpc notification when a sell occurred.
+        """
+        # Use cached rates here - it was updated seconds ago.
+        current_rate = (
+            self.exchange.get_rate(trade.pair, side="exit", is_short=trade.is_short, refresh=False)
+            if not fill
+            else None
+        )
+
+        # second condition is for mypy only; order will always be passed during sub trade
+        if sub_trade and order is not None:
+            amount = order.safe_filled if fill else order.safe_amount
+            order_rate: float = order.safe_price
+
+            profit = trade.calculate_profit(order_rate, amount, trade.open_rate)
+        else:
+            order_rate = trade.safe_close_rate
+            profit = trade.calculate_profit(rate=order_rate)
+            amount = trade.amount
+        gain: ProfitLossStr = "profit" if profit.profit_ratio > 0 else "loss"
+
+        msg: RPCExitMsg = {
+            "type": (RPCMessageType.EXIT_FILL if fill else RPCMessageType.EXIT),
+            "trade_id": trade.id,
+            "exchange": trade.exchange.capitalize(),
+            "pair": trade.pair,
+            "leverage": trade.leverage,
+            "direction": "Short" if trade.is_short else "Long",
+            "gain": gain,
+            "limit": order_rate,  # Deprecated
+            "order_rate": order_rate,
+            "order_type": order_type or "unknown",
+            "amount": amount,
+            "open_rate": trade.open_rate,
+            "close_rate": order_rate,
+            "current_rate": current_rate,
+            "profit_amount": profit.profit_abs,
+            "profit_ratio": profit.profit_ratio,
+            "buy_tag": trade.enter_tag,
+            "enter_tag": trade.enter_tag,
+            "exit_reason": trade.exit_reason,
+            "open_date": trade.open_date_utc,
+            "close_date": trade.close_date_utc or datetime.now(timezone.utc),
+            "stake_amount": trade.stake_amount,
+            "stake_currency": self.config["stake_currency"],
+            "base_currency": self.exchange.get_pair_base_currency(trade.pair),
+            "quote_currency": self.exchange.get_pair_quote_currency(trade.pair),
+            "fiat_currency": self.config.get("fiat_display_currency"),
+            "sub_trade": sub_trade,
+            "cumulative_profit": trade.realized_profit,
+            "final_profit_ratio": trade.close_profit if not trade.is_open else None,
+            "is_final_exit": trade.is_open is False,
+        }
+
+        # Send the message
+        self.rpc.send_msg(msg)
+
+    def _notify_exit_cancel(
+        self, trade: Trade, order_type: str, reason: str, order_id: str, sub_trade: bool = False
+    ) -> None:
+        """
+        Sends rpc notification when a sell cancel occurred.
+        """
+        if trade.exit_order_status == reason:
+            return
+        else:
+            trade.exit_order_status = reason
+
+        order_or_none = trade.select_order_by_order_id(order_id)
+        order = self.order_obj_or_raise(order_id, order_or_none)
+
+        profit_rate: float = trade.safe_close_rate
+        profit = trade.calculate_profit(rate=profit_rate)
+        current_rate = self.exchange.get_rate(
+            trade.pair, side="exit", is_short=trade.is_short, refresh=False
+        )
+        gain: ProfitLossStr = "profit" if profit.profit_ratio > 0 else "loss"
+
+        msg: RPCExitCancelMsg = {
+            "type": RPCMessageType.EXIT_CANCEL,
+            "trade_id": trade.id,
+            "exchange": trade.exchange.capitalize(),
+            "pair": trade.pair,
+            "leverage": trade.leverage,
+            "direction": "Short" if trade.is_short else "Long",
+            "gain": gain,
+            "limit": profit_rate or 0,
+            "order_type": order_type,
+            "amount": order.safe_amount_after_fee,
+            "open_rate": trade.open_rate,
+            "current_rate": current_rate,
+            "profit_amount": profit.profit_abs,
+            "profit_ratio": profit.profit_ratio,
+            "buy_tag": trade.enter_tag,
+            "enter_tag": trade.enter_tag,
+            "exit_reason": trade.exit_reason,
+            "open_date": trade.open_date,
+            "close_date": trade.close_date or datetime.now(timezone.utc),
+            "stake_currency": self.config["stake_currency"],
+            "base_currency": self.exchange.get_pair_base_currency(trade.pair),
+            "quote_currency": self.exchange.get_pair_quote_currency(trade.pair),
+            "fiat_currency": self.config.get("fiat_display_currency", None),
+            "reason": reason,
+            "sub_trade": sub_trade,
+            "stake_amount": trade.stake_amount,
+        }
+
+        # Send the message
+        self.rpc.send_msg(msg)
+
+    def order_obj_or_raise(self, order_id: str, order_obj: Order | None) -> Order:
+        if not order_obj:
+            raise DependencyException(
+                f"Order_obj not found for {order_id}. This should not have happened."
+            )
+        return order_obj
+
+    #
+    # Common update trade state methods
+    #
+
+    def update_trade_state(
+        self,
+        trade: Trade,
+        order_id: str | None,
+        action_order: CcxtOrder | None = None,
+        *,
+        stoploss_order: bool = False,
+        send_msg: bool = True,
+    ) -> bool:
+        """
+        Checks trades with open orders and updates the amount if necessary
+        Handles closing both buy and sell orders.
+        :param trade: Trade object of the trade we're analyzing
+        :param order_id: Order-id of the order we're analyzing
+        :param action_order: Already acquired order object
+        :param send_msg: Send notification - should always be True except in "recovery" methods
+        :return: True if order has been cancelled without being filled partially, False otherwise
+        """
+        if not order_id:
+            logger.warning(f"Orderid for trade {trade} is empty.")
+            return False
+
+        # Update trade with order values
+        if not stoploss_order:
+            logger.info(f"Found open order for {trade}")
+        try:
+            order = action_order or self.exchange.fetch_order_or_stoploss_order(
+                order_id, trade.pair, stoploss_order
+            )
+        except InvalidOrderException as exception:
+            logger.warning("Unable to fetch order %s: %s", order_id, exception)
+            return False
+
+        trade.update_order(order)
+
+        if self.exchange.check_order_canceled_empty(order):
+            # Trade has been cancelled on exchange
+            # Handling of this will happen in handle_cancel_order.
+            return True
+
+        order_obj_or_none = trade.select_order_by_order_id(order_id)
+        order_obj = self.order_obj_or_raise(order_id, order_obj_or_none)
+
+        self.handle_order_fee(trade, order_obj, order)
+
+        trade.update_trade(order_obj, not send_msg)
+
+        trade = self._update_trade_after_fill(trade, order_obj, send_msg)
+        Trade.commit()
+
+        self.order_close_notify(trade, order_obj, stoploss_order, send_msg)
+
+        return False
+
+    def _update_trade_after_fill(self, trade: Trade, order: Order, send_msg: bool) -> Trade:
+        if order.status in constants.NON_OPEN_EXCHANGE_STATES:
+            strategy_safe_wrapper(self.strategy.order_filled, default_retval=None)(
+                pair=trade.pair, trade=trade, order=order, current_time=datetime.now(timezone.utc)
+            )
+            # If a entry order was closed, force update on stoploss on exchange
+            if order.ft_order_side == trade.entry_side:
+                if send_msg:
+                    # Don't cancel stoploss in recovery modes immediately
+                    trade = self.cancel_stoploss_on_exchange(trade)
+                if not self.edge:
+                    # TODO: should shorting/leverage be supported by Edge,
+                    # then this will need to be fixed.
+                    trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
+            if (
+                order.ft_order_side == trade.entry_side
+                or (trade.amount > 0 and trade.is_open)
+                or self.margin_mode == MarginMode.CROSS
+            ):
+                # Must also run for partial exits
+                # TODO: Margin will need to use interest_rate as well.
+                # interest_rate = self.exchange.get_interest_rate()
+                update_liquidation_prices(
+                    trade,
+                    exchange=self.exchange,
+                    wallets=self.wallets,
+                    stake_currency=self.config["stake_currency"],
+                    dry_run=self.config["dry_run"],
+                )
+                if self.strategy.use_custom_stoploss:
+                    current_rate = self.exchange.get_rate(
+                        trade.pair, side="exit", is_short=trade.is_short, refresh=True
+                    )
+                    profit = trade.calc_profit_ratio(current_rate)
+                    self.strategy.ft_stoploss_adjust(
+                        current_rate, trade, datetime.now(timezone.utc), profit, 0, after_fill=True
+                    )
+            # Updating wallets when order is closed
+            self.wallets.update()
+        return trade
+
+    def order_close_notify(self, trade: Trade, order: Order, stoploss_order: bool, send_msg: bool):
+        """send "fill" notifications"""
+
+        if order.ft_order_side == trade.exit_side:
+            # Exit notification
+            if send_msg and not stoploss_order and order.order_id not in trade.open_orders_ids:
+                self._notify_exit(
+                    trade, order.order_type, fill=True, sub_trade=trade.is_open, order=order
+                )
+            if not trade.is_open:
+                self.handle_protections(trade.pair, trade.trade_direction)
+        elif send_msg and order.order_id not in trade.open_orders_ids and not stoploss_order:
+            sub_trade = not isclose(
+                order.safe_amount_after_fee, trade.amount, abs_tol=constants.MATH_CLOSE_PREC
+            )
+            # Enter fill
+            self._notify_enter(trade, order, order.order_type, fill=True, sub_trade=sub_trade)
+
+    def handle_protections(self, pair: str, side: LongShort) -> None:
+        # Lock pair for one candle to prevent immediate re-entries
+        self.strategy.lock_pair(pair, datetime.now(timezone.utc), reason="Auto lock", side=side)
+        prot_trig = self.protections.stop_per_pair(pair, side=side)
+        if prot_trig:
+            msg: RPCProtectionMsg = {
+                "type": RPCMessageType.PROTECTION_TRIGGER,
+                "base_currency": self.exchange.get_pair_base_currency(prot_trig.pair),
+                **prot_trig.to_json(),  # type: ignore
+            }
+            self.rpc.send_msg(msg)
+
+        prot_trig_glb = self.protections.global_stop(side=side)
+        if prot_trig_glb:
+            msg = {
+                "type": RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
+                "base_currency": self.exchange.get_pair_base_currency(prot_trig_glb.pair),
+                **prot_trig_glb.to_json(),  # type: ignore
+            }
+            self.rpc.send_msg(msg)
+
+    def apply_fee_conditional(
+        self,
+        trade: Trade,
+        trade_base_currency: str,
+        amount: float,
+        fee_abs: float,
+        order_obj: Order,
+    ) -> float | None:
+        """
+        Applies the fee to amount (either from Order or from Trades).
+        Can eat into dust if more than the required asset is available.
+        In case of trade adjustment orders, trade.amount will not have been adjusted yet.
+        Can't happen in Futures mode - where Fees are always in settlement currency,
+        never in base currency.
+        """
+        self.wallets.update()
+        amount_ = trade.amount
+        if order_obj.ft_order_side == trade.exit_side or order_obj.ft_order_side == "stoploss":
+            # check against remaining amount!
+            amount_ = trade.amount - amount
+
+        if trade.nr_of_successful_entries >= 1 and order_obj.ft_order_side == trade.entry_side:
+            # In case of re-entry's, trade.amount doesn't contain the amount of the last entry.
+            amount_ = trade.amount + amount
+
+        if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount_:
+            # Eat into dust if we own more than base currency
+            logger.info(
+                f"Fee amount for {trade} was in base currency - Eating Fee {fee_abs} into dust."
+            )
+        elif fee_abs != 0:
+            logger.info(f"Applying fee on amount for {trade}, fee={fee_abs}.")
+            return fee_abs
+        return None
+
+    def handle_order_fee(self, trade: Trade, order_obj: Order, order: CcxtOrder) -> None:
+        # Try update amount (binance-fix)
+        try:
+            fee_abs = self.get_real_amount(trade, order, order_obj)
+            if fee_abs is not None:
+                order_obj.ft_fee_base = fee_abs
+        except DependencyException as exception:
+            logger.warning("Could not update trade amount: %s", exception)
+
+    def get_real_amount(self, trade: Trade, order: CcxtOrder, order_obj: Order) -> float | None:
+        """
+        Detect and update trade fee.
+        Calls trade.update_fee() upon correct detection.
+        Returns modified amount if the fee was taken from the destination currency.
+        Necessary for exchanges which charge fees in base currency (e.g. binance)
+        :return: Absolute fee to apply for this order or None
+        """
+        # Init variables
+        order_amount = safe_value_fallback(order, "filled", "amount")
+        # Only run for closed orders
+        if (
+            trade.fee_updated(order.get("side", ""))
+            or order["status"] == "open"
+            or order_obj.ft_fee_base
+        ):
+            return None
+
+        trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
+        # use fee from order-dict if possible
+        if self.exchange.order_has_fee(order):
+            fee_cost, fee_currency, fee_rate = self.exchange.extract_cost_curr_rate(
+                order["fee"], order["symbol"], order["cost"], order_obj.safe_filled
+            )
+            logger.info(
+                f"Fee for Trade {trade} [{order_obj.ft_order_side}]: "
+                f"{fee_cost:.8g} {fee_currency} - rate: {fee_rate}"
+            )
+            if fee_rate is None or fee_rate < 0.02:
+                # Reject all fees that report as > 2%.
+                # These are most likely caused by a parsing bug in ccxt
+                # due to multiple trades (https://github.com/ccxt/ccxt/issues/8025)
+                trade.update_fee(fee_cost, fee_currency, fee_rate, order.get("side", ""))
+                if trade_base_currency == fee_currency:
+                    # Apply fee to amount
+                    return self.apply_fee_conditional(
+                        trade,
+                        trade_base_currency,
+                        amount=order_amount,
+                        fee_abs=fee_cost,
+                        order_obj=order_obj,
+                    )
+                return None
+        return self.fee_detection_from_trades(
+            trade, order, order_obj, order_amount, order.get("trades", [])
+        )
+
+    def _trades_valid_for_fee(self, trades: list[dict[str, Any]]) -> bool:
+        """
+        Check if trades are valid for fee detection.
+        :return: True if trades are valid for fee detection, False otherwise
+        """
+        if not trades:
+            return False
+        # We expect amount and cost to be present in all trade objects.
+        if any(trade.get("amount") is None or trade.get("cost") is None for trade in trades):
+            return False
+        return True
+
+    def fee_detection_from_trades(
+        self, trade: Trade, order: CcxtOrder, order_obj: Order, order_amount: float, trades: list
+    ) -> float | None:
+        """
+        fee-detection fallback to Trades.
+        Either uses provided trades list or the result of fetch_my_trades to get correct fee.
+        """
+        if not self._trades_valid_for_fee(trades):
+            trades = self.exchange.get_trades_for_order(
+                self.exchange.get_order_id_conditional(order), trade.pair, order_obj.order_date
+            )
+
+        if len(trades) == 0:
+            logger.info("Applying fee on amount for %s failed: myTrade-dict empty found", trade)
+            return None
+        fee_currency = None
+        amount = 0
+        fee_abs = 0.0
+        fee_cost = 0.0
+        trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
+        fee_rate_array: list[float] = []
+        for exectrade in trades:
+            amount += exectrade["amount"]
+            if self.exchange.order_has_fee(exectrade):
+                # Prefer singular fee
+                fees = [exectrade["fee"]]
+            else:
+                fees = exectrade.get("fees", [])
+            for fee in fees:
+                fee_cost_, fee_currency, fee_rate_ = self.exchange.extract_cost_curr_rate(
+                    fee, exectrade["symbol"], exectrade["cost"], exectrade["amount"]
+                )
+                fee_cost += fee_cost_
+                if fee_rate_ is not None:
+                    fee_rate_array.append(fee_rate_)
+                # only applies if fee is in quote currency!
+                if trade_base_currency == fee_currency:
+                    fee_abs += fee_cost_
+        # Ensure at least one trade was found:
+        if fee_currency:
+            # fee_rate should use mean
+            fee_rate = sum(fee_rate_array) / float(len(fee_rate_array)) if fee_rate_array else None
+            if fee_rate is not None and fee_rate < 0.02:
+                # Only update if fee-rate is < 2%
+                trade.update_fee(fee_cost, fee_currency, fee_rate, order.get("side", ""))
+            else:
+                logger.warning(
+                    f"Not updating {order.get('side', '')}-fee - rate: {fee_rate}, {fee_currency}."
+                )
+
+        if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC):
+            # * Leverage could be a cause for this warning
+            logger.warning(f"Amount {amount} does not match amount {trade.amount}")
+            raise DependencyException("Half bought? Amounts don't match")
+
+        if fee_abs != 0:
+            return self.apply_fee_conditional(
+                trade, trade_base_currency, amount=amount, fee_abs=fee_abs, order_obj=order_obj
+            )
+        return None
+
+    def get_valid_price(self, custom_price: float, proposed_price: float) -> float:
+        """
+        Return the valid price.
+        Check if the custom price is of the good type if not return proposed_price
+        :return: valid price for the order
+        """
+        if custom_price:
+            try:
+                valid_custom_price = float(custom_price)
+            except ValueError:
+                valid_custom_price = proposed_price
+        else:
+            valid_custom_price = proposed_price
+
+        cust_p_max_dist_r = self.config.get("custom_price_max_distance_ratio", 0.02)
+        min_custom_price_allowed = proposed_price - (proposed_price * cust_p_max_dist_r)
+        max_custom_price_allowed = proposed_price + (proposed_price * cust_p_max_dist_r)
+
+        # Bracket between min_custom_price_allowed and max_custom_price_allowed
+        final_price = max(
+            min(valid_custom_price, max_custom_price_allowed), min_custom_price_allowed
+        )
+
+        # Log a warning if the custom price was adjusted by clamping.
+        if final_price != valid_custom_price:
+            logger.info(
+                f"Custom price adjusted from {valid_custom_price} to {final_price} based on "
+                "custom_price_max_distance_ratio of {cust_p_max_dist_r}."
+            )
+
+        return final_price
diff --git a/freqtrade/ft_types/__init__.py b/freqtrade/ft_types/__init__.py
new file mode 100644
index 000000000..1915a8549
--- /dev/null
+++ b/freqtrade/ft_types/__init__.py
@@ -0,0 +1,10 @@
+# flake8: noqa: F401
+from freqtrade.ft_types.backtest_result_type import (
+    BacktestContentType,
+    BacktestContentTypeIcomplete,
+    BacktestHistoryEntryType,
+    BacktestMetadataType,
+    BacktestResultType,
+    get_BacktestResultType_default,
+)
+from freqtrade.ft_types.valid_exchanges_type import ValidExchangesType
diff --git a/freqtrade/ft_types/backtest_result_type.py b/freqtrade/ft_types/backtest_result_type.py
new file mode 100644
index 000000000..b253231a1
--- /dev/null
+++ b/freqtrade/ft_types/backtest_result_type.py
@@ -0,0 +1,61 @@
+from copy import deepcopy
+from typing import Any, cast
+
+from pandas import DataFrame
+from typing_extensions import TypedDict
+
+from freqtrade.constants import Config
+
+
+class BacktestMetadataType(TypedDict):
+    run_id: str
+    backtest_start_time: int
+
+
+class BacktestResultType(TypedDict):
+    metadata: dict[str, Any]  # BacktestMetadataType
+    strategy: dict[str, Any]
+    strategy_comparison: list[Any]
+
+
+def get_BacktestResultType_default() -> BacktestResultType:
+    return cast(
+        BacktestResultType,
+        deepcopy(
+            {
+                "metadata": {},
+                "strategy": {},
+                "strategy_comparison": [],
+            }
+        ),
+    )
+
+
+class BacktestHistoryEntryType(BacktestMetadataType):
+    filename: str
+    strategy: str
+    notes: str
+    backtest_start_ts: int | None
+    backtest_end_ts: int | None
+    timeframe: str | None
+    timeframe_detail: str | None
+
+
+class BacktestContentTypeIcomplete(TypedDict, total=False):
+    results: DataFrame
+    config: Config
+    locks: Any
+    rejected_signals: int
+    timedout_entry_orders: int
+    timedout_exit_orders: int
+    canceled_trade_entries: int
+    canceled_entry_orders: int
+    replaced_entry_orders: int
+    final_balance: float
+    backtest_start_time: int
+    backtest_end_time: int
+    run_id: str
+
+
+class BacktestContentType(BacktestContentTypeIcomplete, total=True):
+    pass
diff --git a/freqtrade/ft_types/valid_exchanges_type.py b/freqtrade/ft_types/valid_exchanges_type.py
new file mode 100644
index 000000000..d6b93f4b2
--- /dev/null
+++ b/freqtrade/ft_types/valid_exchanges_type.py
@@ -0,0 +1,20 @@
+# Used for list-exchanges
+
+from typing_extensions import TypedDict
+
+
+class TradeModeType(TypedDict):
+    trading_mode: str
+    margin_mode: str
+
+
+class ValidExchangesType(TypedDict):
+    name: str
+    classname: str
+    valid: bool
+    supported: bool
+    comment: str
+    dex: bool
+    is_alias: bool
+    alias_for: str | None
+    trade_modes: list[TradeModeType]
diff --git a/freqtrade/indicator_helpers.py b/freqtrade/indicator_helpers.py
deleted file mode 100644
index c3cc42e6a..000000000
--- a/freqtrade/indicator_helpers.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from math import exp, pi, sqrt, cos
-
-import numpy
-import talib as ta
-from pandas import Series
-
-
-def went_up(series: Series) -> Series:
-    return series > series.shift(1)
-
-
-def went_down(series: Series) -> Series:
-    return series < series.shift(1)
-
-
-def ehlers_super_smoother(series: Series, smoothing: float = 6):
-    magic = pi * sqrt(2) / smoothing
-    a1 = exp(-magic)
-    coeff2 = 2 * a1 * cos(magic)
-    coeff3 = -a1 * a1
-    coeff1 = (1 - coeff2 - coeff3) / 2
-
-    filtered = series.copy()
-
-    for i in range(2, len(series)):
-        filtered.iloc[i] = coeff1 * (series.iloc[i] + series.iloc[i-1]) + \
-            coeff2 * filtered.iloc[i-1] + coeff3 * filtered.iloc[i-2]
-
-    return filtered
-
-
-def fishers_inverse(series: Series, smoothing: float = 0):
-    """ Does a smoothed fishers inverse transformation.
-        Can be used with any oscillator that goes from 0 to 100 like RSI or MFI """
-    v1 = 0.1 * (series - 50)
-    if smoothing > 0:
-        v2 = ta.WMA(v1.values, timeperiod=smoothing)
-    else:
-        v2 = v1
-    return (numpy.exp(2 * v2)-1) / (numpy.exp(2 * v2) + 1)
diff --git a/freqtrade/leverage/__init__.py b/freqtrade/leverage/__init__.py
new file mode 100644
index 000000000..d4526dbec
--- /dev/null
+++ b/freqtrade/leverage/__init__.py
@@ -0,0 +1 @@
+from freqtrade.leverage.interest import interest  # noqa: F401
diff --git a/freqtrade/leverage/interest.py b/freqtrade/leverage/interest.py
new file mode 100644
index 000000000..f409f2b94
--- /dev/null
+++ b/freqtrade/leverage/interest.py
@@ -0,0 +1,36 @@
+from math import ceil
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.util import FtPrecise
+
+
+one = FtPrecise(1.0)
+four = FtPrecise(4.0)
+twenty_four = FtPrecise(24.0)
+
+
+def interest(
+    exchange_name: str, borrowed: FtPrecise, rate: FtPrecise, hours: FtPrecise
+) -> FtPrecise:
+    """
+    Equation to calculate interest on margin trades
+
+    :param exchange_name: The exchanged being trading on
+    :param borrowed: The amount of currency being borrowed
+    :param rate: The rate of interest (i.e daily interest rate)
+    :param hours: The time in hours that the currency has been borrowed for
+
+    Raises:
+        OperationalException: Raised if freqtrade does
+        not support margin trading for this exchange
+
+    Returns: The amount of interest owed (currency matches borrowed)
+    """
+    exchange_name = exchange_name.lower()
+    if exchange_name == "binance":
+        return borrowed * rate * FtPrecise(ceil(hours)) / twenty_four
+    elif exchange_name == "kraken":
+        # Rounded based on https://kraken-fees-calculator.github.io/
+        return borrowed * rate * (one + FtPrecise(ceil(hours / four)))
+    else:
+        raise OperationalException(f"Leverage not available on {exchange_name} with freqtrade")
diff --git a/freqtrade/leverage/liquidation_price.py b/freqtrade/leverage/liquidation_price.py
new file mode 100644
index 000000000..af34f43fd
--- /dev/null
+++ b/freqtrade/leverage/liquidation_price.py
@@ -0,0 +1,69 @@
+import logging
+
+from freqtrade.enums import MarginMode
+from freqtrade.exceptions import DependencyException
+from freqtrade.exchange import Exchange
+from freqtrade.persistence import LocalTrade, Trade
+from freqtrade.wallets import Wallets
+
+
+logger = logging.getLogger(__name__)
+
+
+def update_liquidation_prices(
+    trade: LocalTrade | None = None,
+    *,
+    exchange: Exchange,
+    wallets: Wallets,
+    stake_currency: str,
+    dry_run: bool = False,
+):
+    """
+    Update trade liquidation price in isolated margin mode.
+    Updates liquidation price for all trades in cross margin mode.
+    """
+    try:
+        if exchange.margin_mode == MarginMode.CROSS:
+            total_wallet_stake = 0.0
+            if dry_run:
+                # Parameters only needed for cross margin
+                total_wallet_stake = wallets.get_collateral()
+                logger.info(
+                    "Updating liquidation price for all open trades. "
+                    f"Collateral {total_wallet_stake} {stake_currency}."
+                )
+
+            open_trades: list[Trade] = Trade.get_open_trades()
+            for t in open_trades:
+                if t.has_open_position:
+                    # TODO: This should be done in a batch update
+                    t.set_liquidation_price(
+                        exchange.get_liquidation_price(
+                            pair=t.pair,
+                            open_rate=t.open_rate,
+                            is_short=t.is_short,
+                            amount=t.amount,
+                            stake_amount=t.stake_amount,
+                            leverage=t.leverage,
+                            wallet_balance=total_wallet_stake,
+                            open_trades=open_trades,
+                        )
+                    )
+        elif trade:
+            trade.set_liquidation_price(
+                exchange.get_liquidation_price(
+                    pair=trade.pair,
+                    open_rate=trade.open_rate,
+                    is_short=trade.is_short,
+                    amount=trade.amount,
+                    stake_amount=trade.stake_amount,
+                    leverage=trade.leverage,
+                    wallet_balance=trade.stake_amount,
+                )
+            )
+        else:
+            raise DependencyException(
+                "Trade object is required for updating liquidation price in isolated margin mode."
+            )
+    except DependencyException:
+        logger.warning("Unable to calculate liquidation price")
diff --git a/freqtrade/loggers/__init__.py b/freqtrade/loggers/__init__.py
new file mode 100644
index 000000000..e39dcdd45
--- /dev/null
+++ b/freqtrade/loggers/__init__.py
@@ -0,0 +1,231 @@
+import logging
+import logging.config
+import os
+from copy import deepcopy
+from logging import Formatter
+from pathlib import Path
+from typing import Any
+
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.loggers.buffering_handler import FTBufferingHandler
+from freqtrade.loggers.ft_rich_handler import FtRichHandler
+from freqtrade.loggers.rich_console import get_rich_console
+
+
+# from freqtrade.loggers.std_err_stream_handler import FTStdErrStreamHandler
+
+
+logger = logging.getLogger(__name__)
+LOGFORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+
+# Initialize bufferhandler - will be used for /log endpoints
+bufferHandler = FTBufferingHandler(1000)
+bufferHandler.setFormatter(Formatter(LOGFORMAT))
+
+
+error_console = get_rich_console(stderr=True, color_system=None)
+
+
+def get_existing_handlers(handlertype):
+    """
+    Returns Existing handler or None (if the handler has not yet been added to the root handlers).
+    """
+    return next((h for h in logging.root.handlers if isinstance(h, handlertype)), None)
+
+
+def setup_logging_pre() -> None:
+    """
+    Early setup for logging.
+    Uses INFO loglevel and only the Streamhandler.
+    Early messages (before proper logging setup) will therefore only be sent to additional
+    logging handlers after the real initialization, because we don't know which
+    ones the user desires beforehand.
+    """
+    rh = FtRichHandler(console=error_console)
+    rh.setFormatter(Formatter("%(message)s"))
+    logging.basicConfig(
+        level=logging.INFO,
+        format=LOGFORMAT,
+        handlers=[
+            # FTStdErrStreamHandler(),
+            rh,
+            bufferHandler,
+        ],
+    )
+
+
+FT_LOGGING_CONFIG = {
+    "version": 1,
+    # "incremental": True,
+    # "disable_existing_loggers": False,
+    "formatters": {
+        "basic": {"format": "%(message)s"},
+        "standard": {
+            "format": LOGFORMAT,
+        },
+    },
+    "handlers": {
+        "console": {
+            "class": "freqtrade.loggers.ft_rich_handler.FtRichHandler",
+            "formatter": "basic",
+        },
+    },
+    "root": {
+        "handlers": [
+            "console",
+            # "file",
+        ],
+        "level": "INFO",
+    },
+}
+
+
+def _set_log_levels(
+    log_config: dict[str, Any], verbosity: int = 0, api_verbosity: str = "info"
+) -> None:
+    """
+    Set the logging level for the different loggers
+    """
+    if "loggers" not in log_config:
+        log_config["loggers"] = {}
+
+    # Set default levels for third party libraries
+    third_party_loggers = {
+        "freqtrade": logging.INFO if verbosity <= 1 else logging.DEBUG,
+        "requests": logging.INFO if verbosity <= 1 else logging.DEBUG,
+        "urllib3": logging.INFO if verbosity <= 1 else logging.DEBUG,
+        "httpcore": logging.INFO if verbosity <= 1 else logging.DEBUG,
+        "ccxt.base.exchange": logging.INFO if verbosity <= 2 else logging.DEBUG,
+        "telegram": logging.INFO,
+        "httpx": logging.WARNING,
+        "werkzeug": logging.ERROR if api_verbosity == "error" else logging.INFO,
+    }
+
+    # Add third party loggers to the configuration
+    for logger_name, level in third_party_loggers.items():
+        if logger_name not in log_config["loggers"]:
+            log_config["loggers"][logger_name] = {
+                "level": logging.getLevelName(level),
+                "propagate": True,
+            }
+
+
+def _add_root_handler(log_config: dict[str, Any], handler_name: str):
+    if handler_name not in log_config["root"]["handlers"]:
+        log_config["root"]["handlers"].append(handler_name)
+
+
+def _add_formatter(log_config: dict[str, Any], format_name: str, format_: str):
+    if format_name not in log_config["formatters"]:
+        log_config["formatters"][format_name] = {"format": format_}
+
+
+def _create_log_config(config: Config) -> dict[str, Any]:
+    # Get log_config from user config or use default
+    log_config = config.get("log_config", deepcopy(FT_LOGGING_CONFIG))
+
+    if logfile := config.get("logfile"):
+        s = logfile.split(":")
+        if s[0] == "syslog":
+            logger.warning(
+                "DEPRECATED: Configuring syslog logging via command line is deprecated."
+                "Please use the log_config option in the configuration file instead."
+            )
+            # Add syslog handler to the config
+            log_config["handlers"]["syslog"] = {
+                "class": "logging.handlers.SysLogHandler",
+                "formatter": "syslog_format",
+                "address": (s[1], int(s[2])) if len(s) > 2 else s[1] if len(s) > 1 else "/dev/log",
+            }
+
+            _add_formatter(log_config, "syslog_format", "%(name)s - %(levelname)s - %(message)s")
+            _add_root_handler(log_config, "syslog")
+
+        elif s[0] == "journald":  # pragma: no cover
+            # Check if we have the module available
+            logger.warning(
+                "DEPRECATED: Configuring Journald logging via command line is deprecated."
+                "Please use the log_config option in the configuration file instead."
+            )
+            try:
+                from cysystemd.journal import JournaldLogHandler  # noqa: F401
+            except ImportError:
+                raise OperationalException(
+                    "You need the cysystemd python package be installed in "
+                    "order to use logging to journald."
+                )
+
+            # Add journald handler to the config
+            log_config["handlers"]["journald"] = {
+                "class": "cysystemd.journal.JournaldLogHandler",
+                "formatter": "journald_format",
+            }
+
+            _add_formatter(log_config, "journald_format", "%(name)s - %(levelname)s - %(message)s")
+            _add_root_handler(log_config, "journald")
+
+        else:
+            # Regular file logging
+            # Update existing file handler configuration
+            if "file" in log_config["handlers"]:
+                log_config["handlers"]["file"]["filename"] = logfile
+            else:
+                log_config["handlers"]["file"] = {
+                    "class": "logging.handlers.RotatingFileHandler",
+                    "formatter": "standard",
+                    "filename": logfile,
+                    "maxBytes": 1024 * 1024 * 10,  # 10Mb
+                    "backupCount": 10,
+                }
+            _add_root_handler(log_config, "file")
+
+    # Dynamically update some handlers
+    for handler_config in log_config.get("handlers", {}).values():
+        if handler_config.get("class") == "freqtrade.loggers.ft_rich_handler.FtRichHandler":
+            handler_config["console"] = error_console
+        elif handler_config.get("class") == "logging.handlers.RotatingFileHandler":
+            logfile_path = Path(handler_config["filename"])
+            try:
+                # Create parent for filehandler
+                logfile_path.parent.mkdir(parents=True, exist_ok=True)
+            except PermissionError:
+                raise OperationalException(
+                    f'Failed to create or access log file "{logfile_path.absolute()}". '
+                    "Please make sure you have the write permission to the log file or its parent "
+                    "directories. If you're running freqtrade using docker, you see this error "
+                    "message probably because you've logged in as the root user, please switch to "
+                    "non-root user, delete and recreate the directories you need, and then try "
+                    "again."
+                )
+    return log_config
+
+
+def setup_logging(config: Config) -> None:
+    """
+    Process -v/--verbose, --logfile options
+    """
+    verbosity = config["verbosity"]
+    if os.environ.get("PYTEST_VERSION") is None or config.get("ft_tests_force_logging"):
+        log_config = _create_log_config(config)
+        _set_log_levels(
+            log_config, verbosity, config.get("api_server", {}).get("verbosity", "info")
+        )
+
+        logging.config.dictConfig(log_config)
+
+    # Add buffer handler to root logger
+    if bufferHandler not in logging.root.handlers:
+        logging.root.addHandler(bufferHandler)
+
+    # Set color system for console output
+    if config.get("print_colorized", True):
+        logger.info("Enabling colorized output.")
+        error_console._color_system = error_console._detect_color_system()
+
+    logging.info("Logfile configured")
+
+    # Set verbosity levels
+    logging.root.setLevel(logging.INFO if verbosity < 1 else logging.DEBUG)
+
+    logger.info("Verbosity set to %s", verbosity)
diff --git a/freqtrade/loggers/buffering_handler.py b/freqtrade/loggers/buffering_handler.py
new file mode 100644
index 000000000..02409708a
--- /dev/null
+++ b/freqtrade/loggers/buffering_handler.py
@@ -0,0 +1,16 @@
+from logging.handlers import BufferingHandler
+
+
+class FTBufferingHandler(BufferingHandler):
+    def flush(self):
+        """
+        Override Flush behaviour - we keep half of the configured capacity
+        otherwise, we have moments with "empty" logs.
+        """
+        self.acquire()
+        try:
+            # Keep half of the records in buffer.
+            records_to_keep = -int(self.capacity / 2)
+            self.buffer = self.buffer[records_to_keep:]
+        finally:
+            self.release()
diff --git a/freqtrade/loggers/ft_rich_handler.py b/freqtrade/loggers/ft_rich_handler.py
new file mode 100644
index 000000000..dba402f17
--- /dev/null
+++ b/freqtrade/loggers/ft_rich_handler.py
@@ -0,0 +1,44 @@
+from datetime import datetime
+from logging import Handler
+
+from rich._null_file import NullFile
+from rich.console import Console
+from rich.text import Text
+
+
+class FtRichHandler(Handler):
+    """
+    Basic colorized logging handler using Rich.
+    Does not support all features of the standard logging handler, and uses a hard-coded log format
+    """
+
+    def __init__(self, console: Console, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self._console = console
+
+    def emit(self, record):
+        try:
+            msg = self.format(record)
+            # Format log message
+            log_time = Text(
+                datetime.fromtimestamp(record.created).strftime("%Y-%m-%d %H:%M:%S,%f")[:-3],
+            )
+            name = Text(record.name, style="violet")
+            log_level = Text(record.levelname, style=f"logging.level.{record.levelname.lower()}")
+            gray_sep = Text(" - ", style="gray46")
+
+            if isinstance(self._console.file, NullFile):
+                # Handles pythonw, where stdout/stderr are null, and we return NullFile
+                # instance from Console.file. In this case, we still want to make a log record
+                # even though we won't be writing anything to a file.
+                self.handleError(record)
+                return
+
+            self._console.print(
+                Text() + log_time + gray_sep + name + gray_sep + log_level + gray_sep + msg
+            )
+
+        except RecursionError:
+            raise
+        except Exception:
+            self.handleError(record)
diff --git a/freqtrade/loggers/json_formatter.py b/freqtrade/loggers/json_formatter.py
new file mode 100644
index 000000000..a74922a41
--- /dev/null
+++ b/freqtrade/loggers/json_formatter.py
@@ -0,0 +1,74 @@
+import json
+import logging
+
+
+class JsonFormatter(logging.Formatter):
+    """
+    Formatter that outputs JSON strings after parsing the LogRecord.
+
+    @param dict fmt_dict: Key: logging format attribute pairs. Defaults to {"message": "message"}.
+    @param str time_format: time.strftime() format string. Default: "%Y-%m-%dT%H:%M:%S"
+    @param str msec_format: Microsecond formatting. Appended at the end. Default: "%s.%03dZ"
+    """
+
+    def __init__(
+        self,
+        fmt_dict: dict | None = None,
+        time_format: str = "%Y-%m-%dT%H:%M:%S",
+        msec_format: str = "%s.%03dZ",
+    ):
+        self.fmt_dict = (
+            fmt_dict
+            if fmt_dict is not None
+            else {
+                "timestamp": "asctime",
+                "level": "levelname",
+                "logger": "name",
+                "message": "message",
+            }
+        )
+        self.default_time_format = time_format
+        self.default_msec_format = msec_format
+        self.datefmt = None
+
+    def usesTime(self) -> bool:
+        """
+        Look for the attribute in the format dict values instead of the fmt string.
+        """
+        return "asctime" in self.fmt_dict.values()
+
+    def formatMessage(self, record) -> str:
+        raise NotImplementedError()
+
+    def formatMessageDict(self, record) -> dict:
+        """
+        Return a dictionary of the relevant LogRecord attributes instead of a string.
+        KeyError is raised if an unknown attribute is provided in the fmt_dict.
+        """
+        return {fmt_key: record.__dict__[fmt_val] for fmt_key, fmt_val in self.fmt_dict.items()}
+
+    def format(self, record) -> str:
+        """
+        Mostly the same as the parent's class method, the difference being that a dict is
+        manipulated and dumped as JSON instead of a string.
+        """
+        record.message = record.getMessage()
+
+        if self.usesTime():
+            record.asctime = self.formatTime(record, self.datefmt)
+
+        message_dict = self.formatMessageDict(record)
+
+        if record.exc_info:
+            # Cache the traceback text to avoid converting it multiple times
+            # (it's constant anyway)
+            if not record.exc_text:
+                record.exc_text = self.formatException(record.exc_info)
+
+        if record.exc_text:
+            message_dict["exc_info"] = record.exc_text
+
+        if record.stack_info:
+            message_dict["stack_info"] = self.formatStack(record.stack_info)
+
+        return json.dumps(message_dict, default=str)
diff --git a/freqtrade/loggers/rich_console.py b/freqtrade/loggers/rich_console.py
new file mode 100644
index 000000000..f3ecd5556
--- /dev/null
+++ b/freqtrade/loggers/rich_console.py
@@ -0,0 +1,26 @@
+import sys
+from shutil import get_terminal_size
+
+from rich.console import Console
+
+
+def console_width() -> int | None:
+    """
+    Get the width of the console
+    """
+    if any(module in ["pytest", "ipykernel"] for module in sys.modules):
+        return 200
+
+    width, _ = get_terminal_size((1, 24))
+    # Fall back to 200 if terminal size is not available.
+    # This is determined by assuming an insane width of 1char, which is unlikely.
+    w = None if width > 1 else 200
+    return w
+
+
+def get_rich_console(**kwargs) -> Console:
+    """
+    Get a rich console with default settings
+    """
+    kwargs["width"] = kwargs.get("width", console_width())
+    return Console(**kwargs)
diff --git a/freqtrade/loggers/set_log_levels.py b/freqtrade/loggers/set_log_levels.py
new file mode 100644
index 000000000..d794c5ebc
--- /dev/null
+++ b/freqtrade/loggers/set_log_levels.py
@@ -0,0 +1,31 @@
+import logging
+
+
+logger = logging.getLogger(__name__)
+
+
+__BIAS_TESTER_LOGGERS = [
+    "freqtrade.resolvers",
+    "freqtrade.strategy.hyper",
+    "freqtrade.configuration.config_validation",
+]
+
+
+def reduce_verbosity_for_bias_tester() -> None:
+    """
+    Reduce verbosity for bias tester.
+    It loads the same strategy several times, which would spam the log.
+    """
+    logger.info("Reducing verbosity for bias tester.")
+    for logger_name in __BIAS_TESTER_LOGGERS:
+        logging.getLogger(logger_name).setLevel(logging.WARNING)
+
+
+def restore_verbosity_for_bias_tester() -> None:
+    """
+    Restore verbosity after bias tester.
+    """
+    logger.info("Restoring log verbosity.")
+    log_level = logging.NOTSET
+    for logger_name in __BIAS_TESTER_LOGGERS:
+        logging.getLogger(logger_name).setLevel(log_level)
diff --git a/freqtrade/loggers/std_err_stream_handler.py b/freqtrade/loggers/std_err_stream_handler.py
new file mode 100644
index 000000000..d7f7e4052
--- /dev/null
+++ b/freqtrade/loggers/std_err_stream_handler.py
@@ -0,0 +1,26 @@
+import sys
+from logging import Handler
+
+
+class FTStdErrStreamHandler(Handler):
+    def flush(self):
+        """
+        Override Flush behaviour - we keep half of the configured capacity
+        otherwise, we have moments with "empty" logs.
+        """
+        self.acquire()
+        try:
+            sys.stderr.flush()
+        finally:
+            self.release()
+
+    def emit(self, record):
+        try:
+            msg = self.format(record)
+            # Don't keep a reference to stderr - this can be problematic with progressbars.
+            sys.stderr.write(msg + "\n")
+            self.flush()
+        except RecursionError:
+            raise
+        except Exception:
+            self.handleError(record)
diff --git a/freqtrade/main.py b/freqtrade/main.py
index 327d89a59..d4aab3468 100755
--- a/freqtrade/main.py
+++ b/freqtrade/main.py
@@ -1,564 +1,79 @@
 #!/usr/bin/env python3
-import copy
-import json
+"""
+Main Freqtrade bot script.
+Read the documentation to know what cli arguments you need.
+"""
+
 import logging
 import sys
-import time
-import traceback
-from datetime import datetime
-from typing import Dict, List, Optional, Any
-
-import arrow
-import requests
-from cachetools import cached, TTLCache
-
-from freqtrade import (DependencyException, OperationalException, __version__,
-                       exchange, persistence, rpc)
-from freqtrade.analyze import get_signal
-from freqtrade.fiat_convert import CryptoToFiatConverter
-from freqtrade.misc import (State, get_state, load_config, parse_args,
-                            throttle, update_state)
-from freqtrade.persistence import Trade
-from freqtrade.strategy.strategy import Strategy
-
-logger = logging.getLogger('freqtrade')
-
-_CONF: Dict[str, Any] = {}
+from typing import Any
 
 
-def refresh_whitelist(whitelist: List[str]) -> List[str]:
+# check min. python version
+if sys.version_info < (3, 10):  # pragma: no cover  # noqa: UP036
+    sys.exit("Freqtrade requires Python version >= 3.10")
+
+from freqtrade import __version__
+from freqtrade.commands import Arguments
+from freqtrade.constants import DOCS_LINK
+from freqtrade.exceptions import ConfigurationError, FreqtradeException, OperationalException
+from freqtrade.loggers import setup_logging_pre
+from freqtrade.system import asyncio_setup, gc_set_threshold, print_version_info
+
+
+logger = logging.getLogger("freqtrade")
+
+
+def main(sysargv: list[str] | None = None) -> None:
     """
-    Check wallet health and remove pair from whitelist if necessary
-    :param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to trade
-    :return: the list of pairs the user wants to trade without the one unavailable or black_listed
+    This function will initiate the bot and start the trading loop.
+    :return: None
     """
-    sanitized_whitelist = whitelist
-    health = exchange.get_wallet_health()
-    known_pairs = set()
-    for status in health:
-        pair = '{}_{}'.format(_CONF['stake_currency'], status['Currency'])
-        # pair is not int the generated dynamic market, or in the blacklist ... ignore it
-        if pair not in whitelist or pair in _CONF['exchange'].get('pair_blacklist', []):
-            continue
-        # else the pair is valid
-        known_pairs.add(pair)
-        # Market is not active
-        if not status['IsActive']:
-            sanitized_whitelist.remove(pair)
-            logger.info(
-                'Ignoring %s from whitelist (reason: %s).',
-                pair, status.get('Notice') or 'wallet is not active'
-            )
 
-    # We need to remove pairs that are unknown
-    final_list = [x for x in sanitized_whitelist if x in known_pairs]
-    return final_list
-
-
-def process_maybe_execute_buy(interval: int) -> bool:
-    """
-    Tries to execute a buy trade in a safe way
-    :return: True if executed
-    """
+    return_code: Any = 1
     try:
-        # Create entity and execute trade
-        if create_trade(float(_CONF['stake_amount']), interval):
-            return True
-
-        logger.info('Found no buy signals for whitelisted currencies. Trying again..')
-        return False
-    except DependencyException as exception:
-        logger.warning('Unable to create trade: %s', exception)
-        return False
-
-
-def process_maybe_execute_sell(trade: Trade, interval: int) -> bool:
-    """
-    Tries to execute a sell trade
-    :return: True if executed
-    """
-    # Get order details for actual price per unit
-    if trade.open_order_id:
-        # Update trade with order values
-        logger.info('Found open order for %s', trade)
-        trade.update(exchange.get_order(trade.open_order_id))
-
-    if trade.is_open and trade.open_order_id is None:
-        # Check if we can sell our current pair
-        return handle_trade(trade, interval)
-    return False
-
-
-def _process(interval: int, nb_assets: Optional[int] = 0) -> bool:
-    """
-    Queries the persistence layer for open trades and handles them,
-    otherwise a new trade is created.
-    :param: nb_assets: the maximum number of pairs to be traded at the same time
-    :return: True if one or more trades has been created or closed, False otherwise
-    """
-    state_changed = False
-    try:
-        # Refresh whitelist based on wallet maintenance
-        sanitized_list = refresh_whitelist(
-            gen_pair_whitelist(
-                _CONF['stake_currency']
-            ) if nb_assets else _CONF['exchange']['pair_whitelist']
-        )
-
-        # Keep only the subsets of pairs wanted (up to nb_assets)
-        final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list
-        _CONF['exchange']['pair_whitelist'] = final_list
-
-        # Query trades from persistence layer
-        trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-
-        # First process current opened trades
-        for trade in trades:
-            state_changed |= process_maybe_execute_sell(trade, interval)
-
-        # Then looking for buy opportunities
-        if len(trades) < _CONF['max_open_trades']:
-            state_changed = process_maybe_execute_buy(interval)
-
-        if 'unfilledtimeout' in _CONF:
-            # Check and handle any timed out open orders
-            check_handle_timedout(_CONF['unfilledtimeout'])
-            Trade.session.flush()
-
-    except (requests.exceptions.RequestException, json.JSONDecodeError) as error:
-        logger.warning('%s, retrying in 30 seconds...', error)
-        time.sleep(30)
-    except OperationalException:
-        rpc.send_msg('*Status:* OperationalException:\n```\n{traceback}```{hint}'.format(
-            traceback=traceback.format_exc(),
-            hint='Issue `/start` if you think it is safe to restart.'
-        ))
-        logger.exception('OperationalException. Stopping trader ...')
-        update_state(State.STOPPED)
-    return state_changed
-
-
-# FIX: 20180110, why is cancel.order unconditionally here, whereas
-#                it is conditionally called in the
-#                handle_timedout_limit_sell()?
-def handle_timedout_limit_buy(trade: Trade, order: Dict) -> bool:
-    """Buy timeout - cancel order
-    :return: True if order was fully cancelled
-    """
-    exchange.cancel_order(trade.open_order_id)
-    if order['remaining'] == order['amount']:
-        # if trade is not partially completed, just delete the trade
-        Trade.session.delete(trade)
-        # FIX? do we really need to flush, caller of
-        #      check_handle_timedout will flush afterwards
-        Trade.session.flush()
-        logger.info('Buy order timeout for %s.', trade)
-        rpc.send_msg('*Timeout:* Unfilled buy order for {} cancelled'.format(
-                     trade.pair.replace('_', '/')))
-        return True
-
-    # if trade is partially complete, edit the stake details for the trade
-    # and close the order
-    trade.amount = order['amount'] - order['remaining']
-    trade.stake_amount = trade.amount * trade.open_rate
-    trade.open_order_id = None
-    logger.info('Partial buy order timeout for %s.', trade)
-    rpc.send_msg('*Timeout:* Remaining buy order for {} cancelled'.format(
-                 trade.pair.replace('_', '/')))
-    return False
-
-
-# FIX: 20180110, should cancel_order() be cond. or unconditionally called?
-def handle_timedout_limit_sell(trade: Trade, order: Dict) -> bool:
-    """
-    Sell timeout - cancel order and update trade
-    :return: True if order was fully cancelled
-    """
-    if order['remaining'] == order['amount']:
-        # if trade is not partially completed, just cancel the trade
-        exchange.cancel_order(trade.open_order_id)
-        trade.close_rate = None
-        trade.close_profit = None
-        trade.close_date = None
-        trade.is_open = True
-        trade.open_order_id = None
-        rpc.send_msg('*Timeout:* Unfilled sell order for {} cancelled'.format(
-                     trade.pair.replace('_', '/')))
-        logger.info('Sell order timeout for %s.', trade)
-        return True
-
-    # TODO: figure out how to handle partially complete sell orders
-    return False
-
-
-def check_handle_timedout(timeoutvalue: int) -> None:
-    """
-    Check if any orders are timed out and cancel if neccessary
-    :param timeoutvalue: Number of minutes until order is considered timed out
-    :return: None
-    """
-    timeoutthreashold = arrow.utcnow().shift(minutes=-timeoutvalue).datetime
-
-    for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
-        try:
-            order = exchange.get_order(trade.open_order_id)
-        except requests.exceptions.RequestException:
-            logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
-            continue
-        ordertime = arrow.get(order['opened'])
-
-        # Check if trade is still actually open
-        if int(order['remaining']) == 0:
-            continue
-
-        if order['type'] == "LIMIT_BUY" and ordertime < timeoutthreashold:
-            handle_timedout_limit_buy(trade, order)
-        elif order['type'] == "LIMIT_SELL" and ordertime < timeoutthreashold:
-            handle_timedout_limit_sell(trade, order)
-
-
-def execute_sell(trade: Trade, limit: float) -> None:
-    """
-    Executes a limit sell for the given trade and limit
-    :param trade: Trade instance
-    :param limit: limit rate for the sell order
-    :return: None
-    """
-    # Execute sell and update trade record
-    order_id = exchange.sell(str(trade.pair), limit, trade.amount)
-    trade.open_order_id = order_id
-
-    fmt_exp_profit = round(trade.calc_profit_percent(rate=limit) * 100, 2)
-    profit_trade = trade.calc_profit(rate=limit)
-    current_rate = exchange.get_ticker(trade.pair, False)['bid']
-    profit = trade.calc_profit_percent(current_rate)
-
-    message = """*{exchange}:* Selling
-*Current Pair:* [{pair}]({pair_url})
-*Limit:* `{limit}`
-*Amount:* `{amount}`
-*Open Rate:* `{open_rate:.8f}`
-*Current Rate:* `{current_rate:.8f}`
-*Profit:* `{profit:.2f}%`
-    """.format(
-        exchange=trade.exchange,
-        pair=trade.pair,
-        pair_url=exchange.get_pair_detail_url(trade.pair),
-        limit=limit,
-        open_rate=trade.open_rate,
-        current_rate=current_rate,
-        amount=round(trade.amount, 8),
-        profit=round(profit * 100, 2),
-    )
-
-    # For regular case, when the configuration exists
-    if 'stake_currency' in _CONF and 'fiat_display_currency' in _CONF:
-        fiat_converter = CryptoToFiatConverter()
-        profit_fiat = fiat_converter.convert_amount(
-            profit_trade,
-            _CONF['stake_currency'],
-            _CONF['fiat_display_currency']
-        )
-        message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f} {coin}`' \
-                   '` / {profit_fiat:.3f} {fiat})`'.format(
-                       gain="profit" if fmt_exp_profit > 0 else "loss",
-                       profit_percent=fmt_exp_profit,
-                       profit_coin=profit_trade,
-                       coin=_CONF['stake_currency'],
-                       profit_fiat=profit_fiat,
-                       fiat=_CONF['fiat_display_currency'],
-                   )
-    # Because telegram._forcesell does not have the configuration
-    # Ignore the FIAT value and does not show the stake_currency as well
-    else:
-        message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f})`'.format(
-            gain="profit" if fmt_exp_profit > 0 else "loss",
-            profit_percent=fmt_exp_profit,
-            profit_coin=profit_trade
-        )
-
-    # Send the message
-    rpc.send_msg(message)
-    Trade.session.flush()
-
-
-def min_roi_reached(trade: Trade, current_rate: float, current_time: datetime) -> bool:
-    """
-    Based an earlier trade and current price and ROI configuration, decides whether bot should sell
-    :return True if bot should sell at current rate
-    """
-    strategy = Strategy()
-
-    current_profit = trade.calc_profit_percent(current_rate)
-    if strategy.stoploss is not None and current_profit < strategy.stoploss:
-        logger.debug('Stop loss hit.')
-        return True
-
-    # Check if time matches and current rate is above threshold
-    time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
-    for duration, threshold in strategy.minimal_roi.items():
-        if time_diff <= duration:
-            return False
-        if current_profit > threshold:
-            return True
-    return False
-
-
-def should_sell(trade: Trade, rate: float, date: datetime, buy: bool, sell: bool) -> bool:
-    """
-    This function evaluate if on the condition required to trigger a sell has been reached
-    if the threshold is reached and updates the trade record.
-    :return: True if trade should be sold, False otherwise
-    """
-    # Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
-    if min_roi_reached(trade, rate, date):
-        logger.debug('Required profit reached. Selling..')
-        return True
-
-    # Experimental: Check if the trade is profitable before selling it (avoid selling at loss)
-    if _CONF.get('experimental', {}).get('sell_profit_only', False):
-        logger.debug('Checking if trade is profitable..')
-        if trade.calc_profit(rate=rate) <= 0:
-            return False
-
-    if sell and not buy and _CONF.get('experimental', {}).get('use_sell_signal', False):
-        logger.debug('Sell signal received. Selling..')
-        return True
-
-    return False
-
-
-def handle_trade(trade: Trade, interval: int) -> bool:
-    """
-    Sells the current pair if the threshold is reached and updates the trade record.
-    :return: True if trade has been sold, False otherwise
-    """
-    if not trade.is_open:
-        raise ValueError('attempt to handle closed trade: {}'.format(trade))
-
-    logger.debug('Handling %s ...', trade)
-    current_rate = exchange.get_ticker(trade.pair)['bid']
-
-    (buy, sell) = (False, False)
-
-    if _CONF.get('experimental', {}).get('use_sell_signal'):
-        (buy, sell) = get_signal(trade.pair, interval)
-
-    if should_sell(trade, current_rate, datetime.utcnow(), buy, sell):
-        execute_sell(trade, current_rate)
-        return True
-
-    return False
-
-
-def get_target_bid(ticker: Dict[str, float]) -> float:
-    """ Calculates bid target between current ask price and last price """
-    if ticker['ask'] < ticker['last']:
-        return ticker['ask']
-    balance = _CONF['bid_strategy']['ask_last_balance']
-    return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
-
-
-def create_trade(stake_amount: float, interval: int) -> bool:
-    """
-    Checks the implemented trading indicator(s) for a randomly picked pair,
-    if one pair triggers the buy_signal a new trade record gets created
-    :param stake_amount: amount of btc to spend
-    :return: True if a trade object has been created and persisted, False otherwise
-    """
-    logger.info(
-        'Checking buy signals to create a new trade with stake_amount: %f ...',
-        stake_amount
-    )
-    whitelist = copy.deepcopy(_CONF['exchange']['pair_whitelist'])
-    # Check if stake_amount is fulfilled
-    if exchange.get_balance(_CONF['stake_currency']) < stake_amount:
-        raise DependencyException(
-            'stake amount is not fulfilled (currency={})'.format(_CONF['stake_currency'])
-        )
-
-    # Remove currently opened and latest pairs from whitelist
-    for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
-        if trade.pair in whitelist:
-            whitelist.remove(trade.pair)
-            logger.debug('Ignoring %s in pair whitelist', trade.pair)
-    if not whitelist:
-        raise DependencyException('No currency pairs in whitelist')
-
-    # Pick pair based on StochRSI buy signals
-    for _pair in whitelist:
-        (buy, sell) = get_signal(_pair, interval)
-        if buy and not sell:
-            pair = _pair
-            break
-    else:
-        return False
-
-    # Calculate amount
-    buy_limit = get_target_bid(exchange.get_ticker(pair))
-    amount = stake_amount / buy_limit
-
-    order_id = exchange.buy(pair, buy_limit, amount)
-
-    fiat_converter = CryptoToFiatConverter()
-    stake_amount_fiat = fiat_converter.convert_amount(
-        stake_amount,
-        _CONF['stake_currency'],
-        _CONF['fiat_display_currency']
-    )
-
-    # Create trade entity and return
-    rpc.send_msg('*{}:* Buying [{}]({}) with limit `{:.8f} ({:.6f} {}, {:.3f} {})` '.format(
-        exchange.get_name().upper(),
-        pair.replace('_', '/'),
-        exchange.get_pair_detail_url(pair),
-        buy_limit, stake_amount, _CONF['stake_currency'],
-        stake_amount_fiat, _CONF['fiat_display_currency']
-    ))
-    # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
-    trade = Trade(
-        pair=pair,
-        stake_amount=stake_amount,
-        amount=amount,
-        fee=exchange.get_fee(),
-        open_rate=buy_limit,
-        open_date=datetime.utcnow(),
-        exchange=exchange.get_name().upper(),
-        open_order_id=order_id
-    )
-    Trade.session.add(trade)
-    Trade.session.flush()
-    return True
-
-
-def init(config: dict, db_url: Optional[str] = None) -> None:
-    """
-    Initializes all modules and updates the config
-    :param config: config as dict
-    :param db_url: database connector string for sqlalchemy (Optional)
-    :return: None
-    """
-    # Initialize all modules
-    rpc.init(config)
-    persistence.init(config, db_url)
-    exchange.init(config)
-
-    strategy = Strategy()
-    strategy.init(config)
-
-    # Set initial application state
-    initial_state = config.get('initial_state')
-    if initial_state:
-        update_state(State[initial_state.upper()])
-    else:
-        update_state(State.STOPPED)
-
-
-@cached(TTLCache(maxsize=1, ttl=1800))
-def gen_pair_whitelist(base_currency: str, key: str = 'BaseVolume') -> List[str]:
-    """
-    Updates the whitelist with with a dynamically generated list
-    :param base_currency: base currency as str
-    :param key: sort key (defaults to 'BaseVolume')
-    :return: List of pairs
-    """
-    summaries = sorted(
-        (s for s in exchange.get_market_summaries() if s['MarketName'].startswith(base_currency)),
-        key=lambda s: s.get(key) or 0.0,
-        reverse=True
-    )
-
-    return [s['MarketName'].replace('-', '_') for s in summaries]
-
-
-def cleanup() -> None:
-    """
-    Cleanup the application state und finish all pending tasks
-    :return: None
-    """
-    rpc.send_msg('*Status:* `Stopping trader...`')
-    logger.info('Stopping trader and cleaning up modules...')
-    update_state(State.STOPPED)
-    persistence.cleanup()
-    rpc.cleanup()
-    exit(0)
-
-
-def main(sysargv=sys.argv[1:]) -> int:
-    """
-    Loads and validates the config and handles the main loop
-    :return: None
-    """
-    global _CONF
-    args = parse_args(sysargv,
-                      'Simple High Frequency Trading Bot for crypto currencies')
-
-    # A subcommand has been issued
-    if hasattr(args, 'func'):
-        args.func(args)
-        return 0
-
-    # Initialize logger
-    logging.basicConfig(
-        level=args.loglevel,
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    )
-
-    logger.info(
-        'Starting freqtrade %s (loglevel=%s)',
-        __version__,
-        logging.getLevelName(args.loglevel)
-    )
-
-    # Load and validate configuration
-    _CONF = load_config(args.config)
-
-    # Add the strategy file to use
-    _CONF.update({'strategy': args.strategy})
-
-    # Initialize all modules and start main loop
-    if args.dynamic_whitelist:
-        logger.info('Using dynamically generated whitelist. (--dynamic-whitelist detected)')
-
-    # If the user ask for Dry run with a local DB instead of memory
-    if args.dry_run_db:
-        if _CONF.get('dry_run', False):
-            _CONF.update({'dry_run_db': True})
-            logger.info(
-                'Dry_run will use the DB file: "tradesv3.dry_run.sqlite". (--dry_run_db detected)'
-            )
+        setup_logging_pre()
+        asyncio_setup()
+        arguments = Arguments(sysargv)
+        args = arguments.get_parsed_arg()
+
+        # Call subcommand.
+        if args.get("version") or args.get("version_main"):
+            print_version_info()
+            return_code = 0
+        elif "func" in args:
+            logger.info(f"freqtrade {__version__}")
+            gc_set_threshold()
+            return_code = args["func"](args)
         else:
-            logger.info('Dry run is disabled. (--dry_run_db ignored)')
+            # No subcommand was issued.
+            raise OperationalException(
+                "Usage of Freqtrade requires a subcommand to be specified.\n"
+                "To have the bot executing trades in live/dry-run modes, "
+                "depending on the value of the `dry_run` setting in the config, run Freqtrade "
+                "as `freqtrade trade [options...]`.\n"
+                "To see the full list of options available, please use "
+                "`freqtrade --help` or `freqtrade <command> --help`."
+            )
 
-    try:
-        init(_CONF)
-        old_state = None
-
-        while True:
-            new_state = get_state()
-            # Log state transition
-            if new_state != old_state:
-                rpc.send_msg('*Status:* `{}`'.format(new_state.name.lower()))
-                logger.info('Changing state to: %s', new_state.name)
-
-            if new_state == State.STOPPED:
-                time.sleep(1)
-            elif new_state == State.RUNNING:
-                throttle(
-                    _process,
-                    min_secs=_CONF['internals'].get('process_throttle_secs', 10),
-                    nb_assets=args.dynamic_whitelist,
-                    interval=int(_CONF.get('ticker_interval', 5))
-                )
-            old_state = new_state
+    except SystemExit as e:  # pragma: no cover
+        return_code = e
     except KeyboardInterrupt:
-        logger.info('SIGINT received, aborting ...')
-    except BaseException:
-        logger.exception('Fatal exception!')
+        logger.info("SIGINT received, aborting ...")
+        return_code = 0
+    except ConfigurationError as e:
+        logger.error(
+            f"Configuration error: {e}\n"
+            f"Please make sure to review the documentation at {DOCS_LINK}."
+        )
+    except FreqtradeException as e:
+        logger.error(str(e))
+        return_code = 2
+    except Exception:
+        logger.exception("Fatal exception!")
     finally:
-        cleanup()
-    return 0
+        sys.exit(return_code)
 
 
-if __name__ == '__main__':
-    main(sys.argv[1:])
+if __name__ == "__main__":  # pragma: no cover
+    main()
diff --git a/freqtrade/misc.py b/freqtrade/misc.py
index 91498a189..a6dbed85e 100644
--- a/freqtrade/misc.py
+++ b/freqtrade/misc.py
@@ -1,435 +1,238 @@
-import argparse
-import enum
-import json
+"""
+Various tool function for Freqtrade and scripts
+"""
+
+import gzip
 import logging
-import time
-import os
-import re
-from datetime import datetime
-from typing import Any, Callable, Dict, List
+from collections.abc import Iterator, Mapping
+from io import StringIO
+from pathlib import Path
+from typing import Any, TextIO
+from urllib.parse import urlparse
 
-import numpy as np
-from jsonschema import Draft4Validator, validate
-from jsonschema.exceptions import ValidationError, best_match
-from wrapt import synchronized
+import pandas as pd
+import rapidjson
+
+from freqtrade.enums import SignalTagType, SignalType
 
-from freqtrade import __version__
 
 logger = logging.getLogger(__name__)
 
 
-class State(enum.Enum):
-    RUNNING = 0
-    STOPPED = 1
-
-
-# Current application state
-_STATE = State.STOPPED
-
-
-############################################
-# Used by scripts                          #
-# Matplotlib doesn't support ::datetime64, #
-# so we need to convert it into ::datetime #
-############################################
-
-def datesarray_to_datetimearray(dates):
+def dump_json_to_file(file_obj: TextIO, data: Any) -> None:
     """
-    Convert an pandas-array of timestamps into
-    An numpy-array of datetimes
-    :return: numpy-array of datetime
+    Dump JSON data into a file object
+    :param file_obj: File object to write to
+    :param data: JSON Data to save
     """
-    times = []
-    dates = dates.astype(datetime)
-    for i in range(0, dates.size):
-        date = dates[i].to_pydatetime()
-        times.append(date)
-    return np.array(times)
+    rapidjson.dump(data, file_obj, default=str, number_mode=rapidjson.NM_NATIVE)
 
 
-def common_datearray(dfs):
-    alldates = {}
-    for pair, pair_data in dfs.items():
-        dates = datesarray_to_datetimearray(pair_data['date'])
-        for date in dates:
-            alldates[date] = 1
-    lst = []
-    for date, _ in alldates.items():
-        lst.append(date)
-    arr = np.array(lst)
-    return np.sort(arr, axis=0)
-
-
-def file_dump_json(filename, data) -> None:
-    with open(filename, 'w') as fp:
-        json.dump(data, fp)
-
-
-@synchronized
-def update_state(state: State) -> None:
+def file_dump_json(filename: Path, data: Any, is_zip: bool = False, log: bool = True) -> None:
     """
-    Updates the application state
-    :param state: new state
-    :return: None
-    """
-    global _STATE
-    _STATE = state
-
-
-@synchronized
-def get_state() -> State:
-    """
-    Gets the current application state
+    Dump JSON data into a file
+    :param filename: file to create
+    :param is_zip: if file should be zip
+    :param data: JSON Data to save
     :return:
     """
-    return _STATE
+
+    if is_zip:
+        if filename.suffix != ".gz":
+            filename = filename.with_suffix(".gz")
+        if log:
+            logger.info(f'dumping json to "{filename}"')
+
+        with gzip.open(filename, "wt", encoding="utf-8") as fpz:
+            dump_json_to_file(fpz, data)
+    else:
+        if log:
+            logger.info(f'dumping json to "{filename}"')
+        with filename.open("w") as fp:
+            dump_json_to_file(fp, data)
+
+    logger.debug(f'done json to "{filename}"')
 
 
-def load_config(path: str) -> Dict:
+def json_load(datafile: TextIO) -> Any:
     """
-    Loads a config file from the given path
-    :param path: path as str
-    :return: configuration as dictionary
+    load data with rapidjson
+    Use this to have a consistent experience,
+    set number_mode to "NM_NATIVE" for greatest speed
     """
-    with open(path) as file:
-        conf = json.load(file)
-    if 'internals' not in conf:
-        conf['internals'] = {}
-    logger.info('Validating configuration ...')
-    try:
-        validate(conf, CONF_SCHEMA)
-        return conf
-    except ValidationError as exception:
-        logger.fatal('Invalid configuration. See config.json.example. Reason: %s', exception)
-        raise ValidationError(
-            best_match(Draft4Validator(CONF_SCHEMA).iter_errors(conf)).message
-        )
+    return rapidjson.load(datafile, number_mode=rapidjson.NM_NATIVE)
 
 
-def throttle(func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any:
-    """
-    Throttles the given callable that it
-    takes at least `min_secs` to finish execution.
-    :param func: Any callable
-    :param min_secs: minimum execution time in seconds
-    :return: Any
-    """
-    start = time.time()
-    result = func(*args, **kwargs)
-    end = time.time()
-    duration = max(min_secs - (end - start), 0.0)
-    logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
-    time.sleep(duration)
-    return result
-
-
-def common_args_parser(description: str):
-    """
-    Parses given common arguments and returns them as a parsed object.
-    """
-    parser = argparse.ArgumentParser(
-        description=description
-    )
-    parser.add_argument(
-        '-v', '--verbose',
-        help='be verbose',
-        action='store_const',
-        dest='loglevel',
-        const=logging.DEBUG,
-        default=logging.INFO,
-    )
-    parser.add_argument(
-        '--version',
-        action='version',
-        version='%(prog)s {}'.format(__version__),
-    )
-    parser.add_argument(
-        '-c', '--config',
-        help='specify configuration file (default: %(default)s)',
-        dest='config',
-        default='config.json',
-        type=str,
-        metavar='PATH',
-    )
-    parser.add_argument(
-        '-d', '--datadir',
-        help='path to backtest data (default: %(default)s',
-        dest='datadir',
-        default=os.path.join('freqtrade', 'tests', 'testdata'),
-        type=str,
-        metavar='PATH',
-    )
-    parser.add_argument(
-        '-s', '--strategy',
-        help='specify strategy file (default: %(default)s)',
-        dest='strategy',
-        default='default_strategy',
-        type=str,
-        metavar='PATH',
-    )
-    return parser
-
-
-def parse_args(args: List[str], description: str):
-    """
-    Parses given arguments and returns an argparse Namespace instance.
-    Returns None if a sub command has been selected and executed.
-    """
-    parser = common_args_parser(description)
-    parser.add_argument(
-        '--dry-run-db',
-        help='Force dry run to use a local DB "tradesv3.dry_run.sqlite" \
-             instead of memory DB. Work only if dry_run is enabled.',
-        action='store_true',
-        dest='dry_run_db',
-    )
-    parser.add_argument(
-        '--dynamic-whitelist',
-        help='dynamically generate and update whitelist \
-             based on 24h BaseVolume (Default 20 currencies)',  # noqa
-        dest='dynamic_whitelist',
-        const=20,
-        type=int,
-        metavar='INT',
-        nargs='?',
-    )
-
-    build_subcommands(parser)
-    return parser.parse_args(args)
-
-
-def scripts_options(parser: argparse.ArgumentParser) -> None:
-    parser.add_argument(
-        '-p', '--pair',
-        help='Show profits for only this pairs. Pairs are comma-separated.',
-        dest='pair',
-        default=None
-    )
-
-
-def optimizer_shared_options(parser: argparse.ArgumentParser) -> None:
-    parser.add_argument(
-        '-i', '--ticker-interval',
-        help='specify ticker interval in minutes (1, 5, 30, 60, 1440)',
-        dest='ticker_interval',
-        type=int,
-        metavar='INT',
-    )
-    parser.add_argument(
-        '--realistic-simulation',
-        help='uses max_open_trades from config to simulate real world limitations',
-        action='store_true',
-        dest='realistic_simulation',
-    )
-    parser.add_argument(
-        '--timerange',
-        help='Specify what timerange of data to use.',
-        default=None,
-        type=str,
-        dest='timerange',
-    )
-
-
-def backtesting_options(parser: argparse.ArgumentParser) -> None:
-    parser.add_argument(
-        '-l', '--live',
-        action='store_true',
-        dest='live',
-        help='using live data',
-    )
-    parser.add_argument(
-        '-r', '--refresh-pairs-cached',
-        help='refresh the pairs files in tests/testdata with the latest data from Bittrex. \
-              Use it if you want to run your backtesting with up-to-date data.',
-        action='store_true',
-        dest='refresh_pairs',
-    )
-    parser.add_argument(
-        '--export',
-        help='Export backtest results, argument are: trades\
-              Example --export=trades',
-        type=str,
-        default=None,
-        dest='export',
-    )
-
-
-def hyperopt_options(parser: argparse.ArgumentParser) -> None:
-    parser.add_argument(
-        '-e', '--epochs',
-        help='specify number of epochs (default: %(default)d)',
-        dest='epochs',
-        default=100,
-        type=int,
-        metavar='INT',
-    )
-    parser.add_argument(
-        '--use-mongodb',
-        help='parallelize evaluations with mongodb (requires mongod in PATH)',
-        dest='mongodb',
-        action='store_true',
-    )
-    parser.add_argument(
-        '-s', '--spaces',
-        help='Specify which parameters to hyperopt. Space separate list. \
-              Default: %(default)s',
-        choices=['all', 'buy', 'roi', 'stoploss'],
-        default='all',
-        nargs='+',
-        dest='spaces',
-    )
-
-
-def parse_timerange(text):
-    if text is None:
+def file_load_json(file: Path):
+    if file.suffix != ".gz":
+        gzipfile = file.with_suffix(file.suffix + ".gz")
+    else:
+        gzipfile = file
+    # Try gzip file first, otherwise regular json file.
+    if gzipfile.is_file():
+        logger.debug(f"Loading historical data from file {gzipfile}")
+        with gzip.open(gzipfile, "rt", encoding="utf-8") as datafile:
+            pairdata = json_load(datafile)
+    elif file.is_file():
+        logger.debug(f"Loading historical data from file {file}")
+        with file.open() as datafile:
+            pairdata = json_load(datafile)
+    else:
         return None
-    syntax = [(r'^-(\d{8})$', (None, 'date')),
-              (r'^(\d{8})-$', ('date', None)),
-              (r'^(\d{8})-(\d{8})$', ('date', 'date')),
-              (r'^(-\d+)$', (None, 'line')),
-              (r'^(\d+)-$', ('line', None)),
-              (r'^(\d+)-(\d+)$', ('index', 'index'))]
-    for rex, stype in syntax:
-        # Apply the regular expression to text
-        match = re.match(rex, text)
-        if match:  # Regex has matched
-            rvals = match.groups()
-            index = 0
-            start = None
-            stop = None
-            if stype[0]:
-                start = rvals[index]
-                if stype[0] != 'date':
-                    start = int(start)
-                index += 1
-            if stype[1]:
-                stop = rvals[index]
-                if stype[1] != 'date':
-                    stop = int(stop)
-            return (stype, start, stop)
-    raise Exception('Incorrect syntax for timerange "%s"' % text)
+    return pairdata
 
 
-def build_subcommands(parser: argparse.ArgumentParser) -> None:
-    """ Builds and attaches all subcommands """
-    from freqtrade.optimize import backtesting, hyperopt
-
-    subparsers = parser.add_subparsers(dest='subparser')
-
-    # Add backtesting subcommand
-    backtesting_cmd = subparsers.add_parser('backtesting', help='backtesting module')
-    backtesting_cmd.set_defaults(func=backtesting.start)
-    optimizer_shared_options(backtesting_cmd)
-    backtesting_options(backtesting_cmd)
-
-    # Add hyperopt subcommand
-    hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
-    hyperopt_cmd.set_defaults(func=hyperopt.start)
-    optimizer_shared_options(hyperopt_cmd)
-    hyperopt_options(hyperopt_cmd)
+def is_file_in_dir(file: Path, directory: Path) -> bool:
+    """
+    Helper function to check if file is in directory.
+    """
+    return file.is_file() and file.parent.samefile(directory)
 
 
-# Required json-schema for user specified config
-CONF_SCHEMA = {
-    'type': 'object',
-    'properties': {
-        'max_open_trades': {'type': 'integer', 'minimum': 0},
-        'ticker_interval': {'type': 'integer', 'enum': [1, 5, 30, 60, 1440]},
-        'stake_currency': {'type': 'string', 'enum': ['BTC', 'ETH', 'USDT']},
-        'stake_amount': {'type': 'number', 'minimum': 0.0005},
-        'fiat_display_currency': {'type': 'string', 'enum': ['AUD', 'BRL', 'CAD', 'CHF',
-                                                             'CLP', 'CNY', 'CZK', 'DKK',
-                                                             'EUR', 'GBP', 'HKD', 'HUF',
-                                                             'IDR', 'ILS', 'INR', 'JPY',
-                                                             'KRW', 'MXN', 'MYR', 'NOK',
-                                                             'NZD', 'PHP', 'PKR', 'PLN',
-                                                             'RUB', 'SEK', 'SGD', 'THB',
-                                                             'TRY', 'TWD', 'ZAR', 'USD']},
-        'dry_run': {'type': 'boolean'},
-        'minimal_roi': {
-            'type': 'object',
-            'patternProperties': {
-                '^[0-9.]+$': {'type': 'number'}
-            },
-            'minProperties': 1
-        },
-        'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True},
-        'unfilledtimeout': {'type': 'integer', 'minimum': 0},
-        'bid_strategy': {
-            'type': 'object',
-            'properties': {
-                'ask_last_balance': {
-                    'type': 'number',
-                    'minimum': 0,
-                    'maximum': 1,
-                    'exclusiveMaximum': False
-                },
-            },
-            'required': ['ask_last_balance']
-        },
-        'exchange': {'$ref': '#/definitions/exchange'},
-        'experimental': {
-            'type': 'object',
-            'properties': {
-                'use_sell_signal': {'type': 'boolean'},
-                'sell_profit_only': {'type': 'boolean'}
-            }
-        },
-        'telegram': {
-            'type': 'object',
-            'properties': {
-                'enabled': {'type': 'boolean'},
-                'token': {'type': 'string'},
-                'chat_id': {'type': 'string'},
-            },
-            'required': ['enabled', 'token', 'chat_id']
-        },
-        'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
-        'internals': {
-            'type': 'object',
-            'properties': {
-                'process_throttle_secs': {'type': 'number'},
-                'interval': {'type': 'integer'}
-            }
-        }
-    },
-    'definitions': {
-        'exchange': {
-            'type': 'object',
-            'properties': {
-                'name': {'type': 'string'},
-                'key': {'type': 'string'},
-                'secret': {'type': 'string'},
-                'pair_whitelist': {
-                    'type': 'array',
-                    'items': {
-                        'type': 'string',
-                        'pattern': '^[0-9A-Z]+_[0-9A-Z]+$'
-                    },
-                    'uniqueItems': True
-                },
-                'pair_blacklist': {
-                    'type': 'array',
-                    'items': {
-                        'type': 'string',
-                        'pattern': '^[0-9A-Z]+_[0-9A-Z]+$'
-                    },
-                    'uniqueItems': True
-                }
-            },
-            'required': ['name', 'key', 'secret', 'pair_whitelist']
-        }
-    },
-    'anyOf': [
-        {'required': ['exchange']}
-    ],
-    'required': [
-        'max_open_trades',
-        'stake_currency',
-        'stake_amount',
-        'fiat_display_currency',
-        'dry_run',
-        'bid_strategy',
-        'telegram'
-    ]
-}
+def pair_to_filename(pair: str) -> str:
+    for ch in ["/", " ", ".", "@", "$", "+", ":"]:
+        pair = pair.replace(ch, "_")
+    return pair
+
+
+def deep_merge_dicts(source, destination, allow_null_overrides: bool = True):
+    """
+    Values from Source override destination, destination is returned (and modified!!)
+    Sample:
+    >>> a = { 'first' : { 'rows' : { 'pass' : 'dog', 'number' : '1' } } }
+    >>> b = { 'first' : { 'rows' : { 'fail' : 'cat', 'number' : '5' } } }
+    >>> merge(b, a) == { 'first' : { 'rows' : { 'pass' : 'dog', 'fail' : 'cat', 'number' : '5' } } }
+    True
+    """
+    for key, value in source.items():
+        if isinstance(value, dict):
+            # get node or create one
+            node = destination.setdefault(key, {})
+            deep_merge_dicts(value, node, allow_null_overrides)
+        elif value is not None or allow_null_overrides:
+            destination[key] = value
+
+    return destination
+
+
+def round_dict(d, n):
+    """
+    Rounds float values in the dict to n digits after the decimal point.
+    """
+    return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
+
+
+DictMap = dict[str, Any] | Mapping[str, Any]
+
+
+def safe_value_fallback(obj: DictMap, key1: str, key2: str | None = None, default_value=None):
+    """
+    Search a value in obj, return this if it's not None.
+    Then search key2 in obj - return that if it's not none - then use default_value.
+    Else falls back to None.
+    """
+    if key1 in obj and obj[key1] is not None:
+        return obj[key1]
+    else:
+        if key2 and key2 in obj and obj[key2] is not None:
+            return obj[key2]
+    return default_value
+
+
+def safe_value_fallback2(dict1: DictMap, dict2: DictMap, key1: str, key2: str, default_value=None):
+    """
+    Search a value in dict1, return this if it's not None.
+    Fall back to dict2 - return key2 from dict2 if it's not None.
+    Else falls back to None.
+
+    """
+    if key1 in dict1 and dict1[key1] is not None:
+        return dict1[key1]
+    else:
+        if key2 in dict2 and dict2[key2] is not None:
+            return dict2[key2]
+    return default_value
+
+
+def plural(num: float, singular: str, plural: str | None = None) -> str:
+    return singular if (num == 1 or num == -1) else plural or singular + "s"
+
+
+def chunks(lst: list[Any], n: int) -> Iterator[list[Any]]:
+    """
+    Split lst into chunks of the size n.
+    :param lst: list to split into chunks
+    :param n: number of max elements per chunk
+    :return: None
+    """
+    for chunk in range(0, len(lst), n):
+        yield (lst[chunk : chunk + n])
+
+
+def parse_db_uri_for_logging(uri: str):
+    """
+    Helper method to parse the DB URI and return the same DB URI with the password censored
+    if it contains it. Otherwise, return the DB URI unchanged
+    :param uri: DB URI to parse for logging
+    """
+    parsed_db_uri = urlparse(uri)
+    if not parsed_db_uri.netloc:  # No need for censoring as no password was provided
+        return uri
+    pwd = parsed_db_uri.netloc.split(":")[1].split("@")[0]
+    return parsed_db_uri.geturl().replace(f":{pwd}@", ":*****@")
+
+
+def dataframe_to_json(dataframe: pd.DataFrame) -> str:
+    """
+    Serialize a DataFrame for transmission over the wire using JSON
+    :param dataframe: A pandas DataFrame
+    :returns: A JSON string of the pandas DataFrame
+    """
+    return dataframe.to_json(orient="split")
+
+
+def json_to_dataframe(data: str) -> pd.DataFrame:
+    """
+    Deserialize JSON into a DataFrame
+    :param data: A JSON string
+    :returns: A pandas DataFrame from the JSON string
+    """
+    dataframe = pd.read_json(StringIO(data), orient="split")
+    if "date" in dataframe.columns:
+        dataframe["date"] = pd.to_datetime(dataframe["date"], unit="ms", utc=True)
+
+    return dataframe
+
+
+def remove_entry_exit_signals(dataframe: pd.DataFrame):
+    """
+    Remove Entry and Exit signals from a DataFrame
+
+    :param dataframe: The DataFrame to remove signals from
+    """
+    dataframe[SignalType.ENTER_LONG.value] = 0
+    dataframe[SignalType.EXIT_LONG.value] = 0
+    dataframe[SignalType.ENTER_SHORT.value] = 0
+    dataframe[SignalType.EXIT_SHORT.value] = 0
+    dataframe[SignalTagType.ENTER_TAG.value] = None
+    dataframe[SignalTagType.EXIT_TAG.value] = None
+
+    return dataframe
+
+
+def append_candles_to_dataframe(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
+    """
+    Append the `right` dataframe to the `left` dataframe
+
+    :param left: The full dataframe you want appended to
+    :param right: The new dataframe containing the data you want appended
+    :returns: The dataframe with the right data in it
+    """
+    if left.iloc[-1]["date"] != right.iloc[-1]["date"]:
+        left = pd.concat([left, right])
+
+    # Only keep the last 1500 candles in memory
+    left = left[-1500:] if len(left) > 1500 else left
+    left.reset_index(drop=True, inplace=True)
+
+    return left
diff --git a/freqtrade/mixins/__init__.py b/freqtrade/mixins/__init__.py
new file mode 100644
index 000000000..c5363c076
--- /dev/null
+++ b/freqtrade/mixins/__init__.py
@@ -0,0 +1 @@
+from freqtrade.mixins.logging_mixin import LoggingMixin  # noqa: F401
diff --git a/freqtrade/mixins/logging_mixin.py b/freqtrade/mixins/logging_mixin.py
new file mode 100644
index 000000000..58399e738
--- /dev/null
+++ b/freqtrade/mixins/logging_mixin.py
@@ -0,0 +1,40 @@
+from collections.abc import Callable
+
+from cachetools import TTLCache, cached
+
+
+class LoggingMixin:
+    """
+    Logging Mixin
+    Shows similar messages only once every `refresh_period`.
+    """
+
+    # Disable output completely
+    show_output = True
+
+    def __init__(self, logger, refresh_period: int = 3600):
+        """
+        :param refresh_period: in seconds - Show identical messages in this intervals
+        """
+        self.logger = logger
+        self.refresh_period = refresh_period
+        self._log_cache: TTLCache = TTLCache(maxsize=1024, ttl=self.refresh_period)
+
+    def log_once(self, message: str, logmethod: Callable) -> None:
+        """
+        Logs message - not more often than "refresh_period" to avoid log spamming
+        Logs the log-message as debug as well to simplify debugging.
+        :param message: String containing the message to be sent to the function.
+        :param logmethod: Function that'll be called. Most likely `logger.info`.
+        :return: None.
+        """
+
+        @cached(cache=self._log_cache)
+        def _log_once(message: str):
+            logmethod(message)
+
+        # Log as debug first
+        self.logger.debug(message)
+        # Call hidden function.
+        if self.show_output:
+            _log_once(message)
diff --git a/freqtrade/optimize/__init__.py b/freqtrade/optimize/__init__.py
index 931c720ee..e69de29bb 100644
--- a/freqtrade/optimize/__init__.py
+++ b/freqtrade/optimize/__init__.py
@@ -1,165 +0,0 @@
-# pragma pylint: disable=missing-docstring
-
-import logging
-import json
-import os
-from typing import Optional, List, Dict
-from pandas import DataFrame
-from freqtrade.exchange import get_ticker_history
-from freqtrade.analyze import populate_indicators, parse_ticker_dataframe
-
-from freqtrade import misc
-from user_data.hyperopt_conf import hyperopt_optimize_conf
-import gzip
-
-logger = logging.getLogger(__name__)
-
-
-def trim_tickerlist(tickerlist, timerange):
-    (stype, start, stop) = timerange
-    if stype == (None, 'line'):
-        return tickerlist[stop:]
-    elif stype == ('line', None):
-        return tickerlist[0:start]
-    elif stype == ('index', 'index'):
-        return tickerlist[start:stop]
-
-    return tickerlist
-
-
-def load_tickerdata_file(datadir, pair, ticker_interval, timerange=None):
-    """
-    Load a pair from file,
-    :return dict OR empty if unsuccesful
-    """
-    path = make_testdata_path(datadir)
-    file = os.path.join(path, '{pair}-{ticker_interval}.json'.format(
-        pair=pair,
-        ticker_interval=ticker_interval,
-    ))
-    gzipfile = file + '.gz'
-
-    # If the file does not exist we download it when None is returned.
-    # If file exists, read the file, load the json
-    if os.path.isfile(gzipfile):
-        logger.debug('Loading ticker data from file %s', gzipfile)
-        with gzip.open(gzipfile) as tickerdata:
-            pairdata = json.load(tickerdata)
-    elif os.path.isfile(file):
-        logger.debug('Loading ticker data from file %s', file)
-        with open(file) as tickerdata:
-            pairdata = json.load(tickerdata)
-    else:
-        return None
-
-    if timerange:
-        pairdata = trim_tickerlist(pairdata, timerange)
-    return pairdata
-
-
-def load_data(datadir: str, ticker_interval: int, pairs: Optional[List[str]] = None,
-              refresh_pairs: Optional[bool] = False, timerange=None) -> Dict[str, List]:
-    """
-    Loads ticker history data for the given parameters
-    :param ticker_interval: ticker interval in minutes
-    :param pairs: list of pairs
-    :return: dict
-    """
-    result = {}
-
-    _pairs = pairs or hyperopt_optimize_conf()['exchange']['pair_whitelist']
-
-    # If the user force the refresh of pairs
-    if refresh_pairs:
-        logger.info('Download data for all pairs and store them in %s', datadir)
-        download_pairs(datadir, _pairs, ticker_interval)
-
-    for pair in _pairs:
-        pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
-        if not pairdata:
-            # download the tickerdata from exchange
-            download_backtesting_testdata(datadir, pair=pair, interval=ticker_interval)
-            # and retry reading the pair
-            pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
-        result[pair] = pairdata
-    return result
-
-
-def tickerdata_to_dataframe(data):
-    preprocessed = preprocess(data)
-    return preprocessed
-
-
-def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
-    """Creates a dataframe and populates indicators for given ticker data"""
-    return {pair: populate_indicators(parse_ticker_dataframe(pair_data))
-            for pair, pair_data in tickerdata.items()}
-
-
-def make_testdata_path(datadir: str) -> str:
-    """Return the path where testdata files are stored"""
-    return datadir or os.path.abspath(os.path.join(os.path.dirname(__file__),
-                                                   '..', 'tests', 'testdata'))
-
-
-def download_pairs(datadir, pairs: List[str], ticker_interval: int) -> bool:
-    """For each pairs passed in parameters, download the ticker intervals"""
-    for pair in pairs:
-        try:
-            download_backtesting_testdata(datadir, pair=pair, interval=ticker_interval)
-        except BaseException:
-            logger.info('Failed to download the pair: "{pair}", Interval: {interval} min'.format(
-                pair=pair,
-                interval=ticker_interval,
-            ))
-            return False
-    return True
-
-
-def file_dump_json(filename, data):
-    with open(filename, "wt") as fp:
-        json.dump(data, fp)
-
-
-# FIX: 20180110, suggest rename interval to tick_interval
-def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) -> bool:
-    """
-    Download the latest 1 and 5 ticker intervals from Bittrex for the pairs passed in parameters
-    Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
-    :param pairs: list of pairs to download
-    :return: bool
-    """
-
-    path = make_testdata_path(datadir)
-    logger.info('Download the pair: "{pair}", Interval: {interval} min'.format(
-        pair=pair,
-        interval=interval,
-    ))
-
-    filepair = pair.replace("-", "_")
-    filename = os.path.join(path, '{pair}-{interval}.json'.format(
-        pair=filepair,
-        interval=interval,
-    ))
-
-    if os.path.isfile(filename):
-        with open(filename, "rt") as fp:
-            data = json.load(fp)
-        logger.debug("Current Start: {}".format(data[1]['T']))
-        logger.debug("Current End: {}".format(data[-1:][0]['T']))
-    else:
-        data = []
-        logger.debug("Current Start: None")
-        logger.debug("Current End: None")
-
-    new_data = get_ticker_history(pair=pair, tick_interval=int(interval))
-    for row in new_data:
-        if row not in data:
-            data.append(row)
-    logger.debug("New Start: {}".format(data[1]['T']))
-    logger.debug("New End: {}".format(data[-1:][0]['T']))
-    data = sorted(data, key=lambda data: data['T'])
-
-    misc.file_dump_json(filename, data)
-
-    return True
diff --git a/freqtrade/optimize/analysis/__init__.py b/freqtrade/optimize/analysis/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/freqtrade/optimize/analysis/lookahead.py b/freqtrade/optimize/analysis/lookahead.py
new file mode 100755
index 000000000..70cb6913d
--- /dev/null
+++ b/freqtrade/optimize/analysis/lookahead.py
@@ -0,0 +1,282 @@
+import logging
+import shutil
+from copy import deepcopy
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+from pandas import DataFrame
+
+from freqtrade.data.history import get_timerange
+from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.loggers.set_log_levels import (
+    reduce_verbosity_for_bias_tester,
+    restore_verbosity_for_bias_tester,
+)
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.optimize.base_analysis import BaseAnalysis, VarHolder
+
+
+logger = logging.getLogger(__name__)
+
+
+class Analysis:
+    def __init__(self) -> None:
+        self.total_signals = 0
+        self.false_entry_signals = 0
+        self.false_exit_signals = 0
+        self.false_indicators: list[str] = []
+        self.has_bias = False
+
+
+class LookaheadAnalysis(BaseAnalysis):
+    def __init__(self, config: dict[str, Any], strategy_obj: dict):
+        super().__init__(config, strategy_obj)
+
+        self.entry_varHolders: list[VarHolder] = []
+        self.exit_varHolders: list[VarHolder] = []
+
+        self.current_analysis = Analysis()
+        self.minimum_trade_amount = config["minimum_trade_amount"]
+        self.targeted_trade_amount = config["targeted_trade_amount"]
+
+    @staticmethod
+    def get_result(backtesting: Backtesting, processed: DataFrame):
+        min_date, max_date = get_timerange(processed)
+
+        result = backtesting.backtest(
+            processed=deepcopy(processed), start_date=min_date, end_date=max_date
+        )
+        return result
+
+    @staticmethod
+    def report_signal(result: dict, column_name: str, checked_timestamp: datetime):
+        df = result["results"]
+        row_count = df[column_name].shape[0]
+
+        if row_count == 0:
+            return False
+        else:
+            df_cut = df[(df[column_name] == checked_timestamp)]
+            if df_cut[column_name].shape[0] == 0:
+                return False
+            else:
+                return True
+        return False
+
+    # analyzes two data frames with processed indicators and shows differences between them.
+    def analyze_indicators(self, full_vars: VarHolder, cut_vars: VarHolder, current_pair: str):
+        # extract dataframes
+        cut_df: DataFrame = cut_vars.indicators[current_pair]
+        full_df: DataFrame = full_vars.indicators[current_pair]
+
+        # cut longer dataframe to length of the shorter
+        full_df_cut = full_df[(full_df.date == cut_vars.compared_dt)].reset_index(drop=True)
+        cut_df_cut = cut_df[(cut_df.date == cut_vars.compared_dt)].reset_index(drop=True)
+
+        # check if dataframes are not empty
+        if full_df_cut.shape[0] != 0 and cut_df_cut.shape[0] != 0:
+            # compare dataframes
+            compare_df = full_df_cut.compare(cut_df_cut)
+
+            if compare_df.shape[0] > 0:
+                for col_name, values in compare_df.items():
+                    col_idx = compare_df.columns.get_loc(col_name)
+                    compare_df_row = compare_df.iloc[0]
+                    # compare_df now comprises tuples with [1] having either 'self' or 'other'
+                    if "other" in col_name[1]:
+                        continue
+                    self_value = compare_df_row.iloc[col_idx]
+                    other_value = compare_df_row.iloc[col_idx + 1]
+
+                    # output differences
+                    if self_value != other_value:
+                        if not self.current_analysis.false_indicators.__contains__(col_name[0]):
+                            self.current_analysis.false_indicators.append(col_name[0])
+                            logger.info(
+                                f"=> found look ahead bias in indicator "
+                                f"{col_name[0]}. "
+                                f"{str(self_value)} != {str(other_value)}"
+                            )
+
+    def prepare_data(self, varholder: VarHolder, pairs_to_load: list[DataFrame]):
+        if "freqai" in self.local_config and "identifier" in self.local_config["freqai"]:
+            # purge previous data if the freqai model is defined
+            # (to be sure nothing is carried over from older backtests)
+            path_to_current_identifier = Path(
+                f"{self.local_config['user_data_dir']}/models/"
+                f"{self.local_config['freqai']['identifier']}"
+            ).resolve()
+            # remove folder and its contents
+            if Path.exists(path_to_current_identifier):
+                shutil.rmtree(path_to_current_identifier)
+
+        prepare_data_config = deepcopy(self.local_config)
+        prepare_data_config["timerange"] = (
+            str(self.dt_to_timestamp(varholder.from_dt))
+            + "-"
+            + str(self.dt_to_timestamp(varholder.to_dt))
+        )
+        prepare_data_config["exchange"]["pair_whitelist"] = pairs_to_load
+
+        if self._fee is not None:
+            # Don't re-calculate fee per pair, as fee might differ per pair.
+            prepare_data_config["fee"] = self._fee
+
+        backtesting = Backtesting(prepare_data_config, self.exchange)
+        self.exchange = backtesting.exchange
+        self._fee = backtesting.fee
+        backtesting._set_strategy(backtesting.strategylist[0])
+
+        varholder.data, varholder.timerange = backtesting.load_bt_data()
+        backtesting.load_bt_data_detail()
+        varholder.timeframe = backtesting.timeframe
+
+        varholder.indicators = backtesting.strategy.advise_all_indicators(varholder.data)
+        varholder.result = self.get_result(backtesting, varholder.indicators)
+
+    def fill_entry_and_exit_varHolders(self, result_row):
+        # entry_varHolder
+        entry_varHolder = VarHolder()
+        self.entry_varHolders.append(entry_varHolder)
+        entry_varHolder.from_dt = self.full_varHolder.from_dt
+        entry_varHolder.compared_dt = result_row["open_date"]
+        # to_dt needs +1 candle since it won't buy on the last candle
+        entry_varHolder.to_dt = result_row["open_date"] + timedelta(
+            minutes=timeframe_to_minutes(self.full_varHolder.timeframe)
+        )
+        self.prepare_data(entry_varHolder, [result_row["pair"]])
+
+        # exit_varHolder
+        exit_varHolder = VarHolder()
+        self.exit_varHolders.append(exit_varHolder)
+        # to_dt needs +1 candle since it will always exit/force-exit trades on the last candle
+        exit_varHolder.from_dt = self.full_varHolder.from_dt
+        exit_varHolder.to_dt = result_row["close_date"] + timedelta(
+            minutes=timeframe_to_minutes(self.full_varHolder.timeframe)
+        )
+        exit_varHolder.compared_dt = result_row["close_date"]
+        self.prepare_data(exit_varHolder, [result_row["pair"]])
+
+    # now we analyze a full trade of full_varholder and look for analyze its bias
+    def analyze_row(self, idx: int, result_row):
+        # if force-sold, ignore this signal since here it will unconditionally exit.
+        if result_row.close_date == self.dt_to_timestamp(self.full_varHolder.to_dt):
+            return
+
+        # keep track of how many signals are processed at total
+        self.current_analysis.total_signals += 1
+
+        # fill entry_varHolder and exit_varHolder
+        self.fill_entry_and_exit_varHolders(result_row)
+
+        # this will trigger a logger-message
+        buy_or_sell_biased: bool = False
+
+        # register if buy signal is broken
+        if not self.report_signal(
+            self.entry_varHolders[idx].result, "open_date", self.entry_varHolders[idx].compared_dt
+        ):
+            self.current_analysis.false_entry_signals += 1
+            buy_or_sell_biased = True
+
+        # register if buy or sell signal is broken
+        if not self.report_signal(
+            self.exit_varHolders[idx].result, "close_date", self.exit_varHolders[idx].compared_dt
+        ):
+            self.current_analysis.false_exit_signals += 1
+            buy_or_sell_biased = True
+
+        if buy_or_sell_biased:
+            logger.info(
+                f"found lookahead-bias in trade "
+                f"pair: {result_row['pair']}, "
+                f"timerange:{result_row['open_date']} - {result_row['close_date']}, "
+                f"idx: {idx}"
+            )
+
+        # check if the indicators themselves contain biased data
+        self.analyze_indicators(self.full_varHolder, self.entry_varHolders[idx], result_row["pair"])
+        self.analyze_indicators(self.full_varHolder, self.exit_varHolders[idx], result_row["pair"])
+
+    def start(self) -> None:
+        super().start()
+
+        reduce_verbosity_for_bias_tester()
+
+        # check if requirements have been met of full_varholder
+        found_signals: int = self.full_varHolder.result["results"].shape[0] + 1
+        if found_signals >= self.targeted_trade_amount:
+            logger.info(
+                f"Found {found_signals} trades, calculating {self.targeted_trade_amount} trades."
+            )
+        elif self.targeted_trade_amount >= found_signals >= self.minimum_trade_amount:
+            logger.info(f"Only found {found_signals} trades. Calculating all available trades.")
+        else:
+            logger.info(
+                f"found {found_signals} trades "
+                f"which is less than minimum_trade_amount {self.minimum_trade_amount}. "
+                f"Cancelling this backtest lookahead bias test."
+            )
+            return
+
+        # now we loop through all signals
+        # starting from the same datetime to avoid miss-reports of bias
+        for idx, result_row in self.full_varHolder.result["results"].iterrows():
+            if self.current_analysis.total_signals == self.targeted_trade_amount:
+                logger.info(f"Found targeted trade amount = {self.targeted_trade_amount} signals.")
+                break
+            if found_signals < self.minimum_trade_amount:
+                logger.info(
+                    f"only found {found_signals} "
+                    f"which is smaller than "
+                    f"minimum trade amount = {self.minimum_trade_amount}. "
+                    f"Exiting this lookahead-analysis"
+                )
+                return None
+            if "force_exit" in result_row["exit_reason"]:
+                logger.info(
+                    "found force-exit in pair: {result_row['pair']}, "
+                    f"timerange:{result_row['open_date']}-{result_row['close_date']}, "
+                    f"idx: {idx}, skipping this one to avoid a false-positive."
+                )
+
+                # just to keep the IDs of both full, entry and exit varholders the same
+                # to achieve a better debugging experience
+                self.entry_varHolders.append(VarHolder())
+                self.exit_varHolders.append(VarHolder())
+                continue
+
+            self.analyze_row(idx, result_row)
+
+        if len(self.entry_varHolders) < self.minimum_trade_amount:
+            logger.info(
+                f"only found {found_signals} after skipping forced exits "
+                f"which is smaller than "
+                f"minimum trade amount = {self.minimum_trade_amount}. "
+                f"Exiting this lookahead-analysis"
+            )
+
+        # Restore verbosity, so it's not too quiet for the next strategy
+        restore_verbosity_for_bias_tester()
+        # check and report signals
+        if self.current_analysis.total_signals < self.local_config["minimum_trade_amount"]:
+            logger.info(
+                f" -> {self.local_config['strategy']} : too few trades. "
+                f"We only found {self.current_analysis.total_signals} trades. "
+                f"Hint: Extend the timerange "
+                f"to get at least {self.local_config['minimum_trade_amount']} "
+                f"or lower the value of minimum_trade_amount."
+            )
+            self.failed_bias_check = True
+        elif (
+            self.current_analysis.false_entry_signals > 0
+            or self.current_analysis.false_exit_signals > 0
+            or len(self.current_analysis.false_indicators) > 0
+        ):
+            logger.info(f" => {self.local_config['strategy']} : bias detected!")
+            self.current_analysis.has_bias = True
+            self.failed_bias_check = False
+        else:
+            logger.info(self.local_config["strategy"] + ": no bias detected")
+            self.failed_bias_check = False
diff --git a/freqtrade/optimize/analysis/lookahead_helpers.py b/freqtrade/optimize/analysis/lookahead_helpers.py
new file mode 100644
index 000000000..631a9549f
--- /dev/null
+++ b/freqtrade/optimize/analysis/lookahead_helpers.py
@@ -0,0 +1,273 @@
+import logging
+import time
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+from rich.text import Text
+
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.analysis.lookahead import LookaheadAnalysis
+from freqtrade.resolvers import StrategyResolver
+from freqtrade.util import get_dry_run_wallet, print_rich_table
+
+
+logger = logging.getLogger(__name__)
+
+
+class LookaheadAnalysisSubFunctions:
+    @staticmethod
+    def text_table_lookahead_analysis_instances(
+        config: dict[str, Any],
+        lookahead_instances: list[LookaheadAnalysis],
+        caption: str | None = None,
+    ):
+        headers = [
+            "filename",
+            "strategy",
+            "has_bias",
+            "total_signals",
+            "biased_entry_signals",
+            "biased_exit_signals",
+            "biased_indicators",
+        ]
+        data = []
+        for inst in lookahead_instances:
+            if config["minimum_trade_amount"] > inst.current_analysis.total_signals:
+                data.append(
+                    [
+                        inst.strategy_obj["location"].parts[-1],
+                        inst.strategy_obj["name"],
+                        "too few trades caught "
+                        f"({inst.current_analysis.total_signals}/{config['minimum_trade_amount']})."
+                        f"Test failed.",
+                    ]
+                )
+            elif inst.failed_bias_check:
+                data.append(
+                    [
+                        inst.strategy_obj["location"].parts[-1],
+                        inst.strategy_obj["name"],
+                        "error while checking",
+                    ]
+                )
+            else:
+                data.append(
+                    [
+                        inst.strategy_obj["location"].parts[-1],
+                        inst.strategy_obj["name"],
+                        Text("Yes", style="bold red")
+                        if inst.current_analysis.has_bias
+                        else Text("No", style="bold green"),
+                        inst.current_analysis.total_signals,
+                        inst.current_analysis.false_entry_signals,
+                        inst.current_analysis.false_exit_signals,
+                        ", ".join(inst.current_analysis.false_indicators),
+                    ]
+                )
+
+        print_rich_table(
+            data, headers, summary="Lookahead Analysis", table_kwargs={"caption": caption}
+        )
+        return data
+
+    @staticmethod
+    def export_to_csv(config: dict[str, Any], lookahead_analysis: list[LookaheadAnalysis]):
+        def add_or_update_row(df, row_data):
+            if (
+                (df["filename"] == row_data["filename"]) & (df["strategy"] == row_data["strategy"])
+            ).any():
+                # Update existing row
+                pd_series = pd.DataFrame([row_data])
+                df.loc[
+                    (df["filename"] == row_data["filename"])
+                    & (df["strategy"] == row_data["strategy"])
+                ] = pd_series
+            else:
+                # Add new row
+                df = pd.concat([df, pd.DataFrame([row_data], columns=df.columns)])
+
+            return df
+
+        if Path(config["lookahead_analysis_exportfilename"]).exists():
+            # Read CSV file into a pandas dataframe
+            csv_df = pd.read_csv(config["lookahead_analysis_exportfilename"])
+        else:
+            # Create a new empty DataFrame with the desired column names and set the index
+            csv_df = pd.DataFrame(
+                columns=[
+                    "filename",
+                    "strategy",
+                    "has_bias",
+                    "total_signals",
+                    "biased_entry_signals",
+                    "biased_exit_signals",
+                    "biased_indicators",
+                ],
+                index=None,
+            )
+
+        for inst in lookahead_analysis:
+            # only update if
+            if (
+                inst.current_analysis.total_signals > config["minimum_trade_amount"]
+                and inst.failed_bias_check is not True
+            ):
+                new_row_data = {
+                    "filename": inst.strategy_obj["location"].parts[-1],
+                    "strategy": inst.strategy_obj["name"],
+                    "has_bias": inst.current_analysis.has_bias,
+                    "total_signals": int(inst.current_analysis.total_signals),
+                    "biased_entry_signals": int(inst.current_analysis.false_entry_signals),
+                    "biased_exit_signals": int(inst.current_analysis.false_exit_signals),
+                    "biased_indicators": ",".join(inst.current_analysis.false_indicators),
+                }
+                csv_df = add_or_update_row(csv_df, new_row_data)
+
+        # Fill NaN values with a default value (e.g., 0)
+        csv_df["total_signals"] = csv_df["total_signals"].astype(int).fillna(0)
+        csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype(int).fillna(0)
+        csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype(int).fillna(0)
+
+        # Convert columns to integers
+        csv_df["total_signals"] = csv_df["total_signals"].astype(int)
+        csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype(int)
+        csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype(int)
+
+        logger.info(f"saving {config['lookahead_analysis_exportfilename']}")
+        csv_df.to_csv(config["lookahead_analysis_exportfilename"], index=False)
+
+    @staticmethod
+    def calculate_config_overrides(config: Config):
+        if config.get("enable_protections", False):
+            # if protections are used globally, they can produce false positives.
+            config["enable_protections"] = False
+            logger.info(
+                "Protections were enabled. "
+                "Disabling protections now "
+                "since they could otherwise produce false positives."
+            )
+        if config["targeted_trade_amount"] < config["minimum_trade_amount"]:
+            # this combo doesn't make any sense.
+            raise OperationalException(
+                "Targeted trade amount can't be smaller than minimum trade amount."
+            )
+        if len(config["pairs"]) > config.get("max_open_trades", 0):
+            logger.info(
+                "Max_open_trades were less than amount of pairs "
+                "or defined in the strategy. "
+                "Set max_open_trades to amount of pairs "
+                "just to avoid false positives."
+            )
+            config["max_open_trades"] = len(config["pairs"])
+
+        min_dry_run_wallet = 1000000000
+        if get_dry_run_wallet(config) < min_dry_run_wallet:
+            logger.info(
+                "Dry run wallet was not set to 1 billion, pushing it up there "
+                "just to avoid false positives"
+            )
+            config["dry_run_wallet"] = min_dry_run_wallet
+
+        if "timerange" not in config:
+            # setting a timerange is enforced here
+            raise OperationalException(
+                "Please set a timerange. "
+                "Usually a few months are enough depending on your needs and strategy."
+            )
+        # fix stake_amount to 10k.
+        # in a combination with a wallet size of 1 billion it should always be able to trade
+        # no matter if they use custom_stake_amount as a small percentage of wallet size
+        # or fixate custom_stake_amount to a certain value.
+        logger.info("fixing stake_amount to 10k")
+        config["stake_amount"] = 10000
+
+        # enforce cache to be 'none', shift it to 'none' if not already
+        # (since the default value is 'day')
+        if config.get("backtest_cache") is None:
+            config["backtest_cache"] = "none"
+        elif config["backtest_cache"] != "none":
+            logger.info(
+                f"backtest_cache = "
+                f"{config['backtest_cache']} detected. "
+                f"Inside lookahead-analysis it is enforced to be 'none'. "
+                f"Changed it to 'none'"
+            )
+            config["backtest_cache"] = "none"
+        return config
+
+    @staticmethod
+    def initialize_single_lookahead_analysis(config: Config, strategy_obj: dict[str, Any]):
+        logger.info(f"Bias test of {Path(strategy_obj['location']).name} started.")
+        start = time.perf_counter()
+        current_instance = LookaheadAnalysis(config, strategy_obj)
+        current_instance.start()
+        elapsed = time.perf_counter() - start
+        logger.info(
+            f"Checking look ahead bias via backtests "
+            f"of {Path(strategy_obj['location']).name} "
+            f"took {elapsed:.0f} seconds."
+        )
+        return current_instance
+
+    @staticmethod
+    def start(config: Config):
+        config = LookaheadAnalysisSubFunctions.calculate_config_overrides(config)
+
+        strategy_objs = StrategyResolver.search_all_objects(
+            config, enum_failed=False, recursive=config.get("recursive_strategy_search", False)
+        )
+
+        lookaheadAnalysis_instances = []
+
+        # unify --strategy and --strategy-list to one list
+        if not (strategy_list := config.get("strategy_list", [])):
+            if config.get("strategy") is None:
+                raise OperationalException(
+                    "No Strategy specified. Please specify a strategy via --strategy or "
+                    "--strategy-list"
+                )
+            strategy_list = [config["strategy"]]
+
+        # check if strategies can be properly loaded, only check them if they can be.
+        for strat in strategy_list:
+            for strategy_obj in strategy_objs:
+                if strategy_obj["name"] == strat and strategy_obj not in strategy_list:
+                    lookaheadAnalysis_instances.append(
+                        LookaheadAnalysisSubFunctions.initialize_single_lookahead_analysis(
+                            config, strategy_obj
+                        )
+                    )
+                    break
+
+        # report the results
+        if lookaheadAnalysis_instances:
+            caption: str | None = None
+            if any(
+                [
+                    any(
+                        [
+                            indicator.startswith("&")
+                            for indicator in inst.current_analysis.false_indicators
+                        ]
+                    )
+                    for inst in lookaheadAnalysis_instances
+                ]
+            ):
+                caption = (
+                    "Any indicators in 'biased_indicators' which are used within "
+                    "set_freqai_targets() can be ignored."
+                )
+            LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances(
+                config, lookaheadAnalysis_instances, caption=caption
+            )
+            if config.get("lookahead_analysis_exportfilename") is not None:
+                LookaheadAnalysisSubFunctions.export_to_csv(config, lookaheadAnalysis_instances)
+        else:
+            logger.error(
+                "There were no strategies specified neither through "
+                "--strategy nor through "
+                "--strategy-list "
+                "or timeframe was not specified."
+            )
diff --git a/freqtrade/optimize/analysis/recursive.py b/freqtrade/optimize/analysis/recursive.py
new file mode 100644
index 000000000..50d1b75d6
--- /dev/null
+++ b/freqtrade/optimize/analysis/recursive.py
@@ -0,0 +1,207 @@
+import logging
+import numbers
+import shutil
+from copy import deepcopy
+from datetime import timedelta
+from pathlib import Path
+from typing import Any
+
+from pandas import DataFrame
+
+from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.loggers.set_log_levels import (
+    reduce_verbosity_for_bias_tester,
+    restore_verbosity_for_bias_tester,
+)
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.optimize.base_analysis import BaseAnalysis, VarHolder
+from freqtrade.resolvers import StrategyResolver
+
+
+logger = logging.getLogger(__name__)
+
+
+def is_number(variable):
+    return isinstance(variable, numbers.Number) and not isinstance(variable, bool)
+
+
+class RecursiveAnalysis(BaseAnalysis):
+    def __init__(self, config: dict[str, Any], strategy_obj: dict):
+        self._startup_candle = list(
+            map(int, config.get("startup_candle", [199, 399, 499, 999, 1999]))
+        )
+
+        super().__init__(config, strategy_obj)
+
+        strat = StrategyResolver.load_strategy(config)
+        self._strat_scc = strat.startup_candle_count
+
+        if self._strat_scc not in self._startup_candle:
+            self._startup_candle.append(self._strat_scc)
+        self._startup_candle.sort()
+
+        self.partial_varHolder_array: list[VarHolder] = []
+        self.partial_varHolder_lookahead_array: list[VarHolder] = []
+
+        self.dict_recursive: dict[str, Any] = dict()
+
+    # For recursive bias check
+    # analyzes two data frames with processed indicators and shows differences between them.
+    def analyze_indicators(self):
+        pair_to_check = self.local_config["pairs"][0]
+        logger.info("Start checking for recursive bias")
+
+        # check and report signals
+        base_last_row = self.full_varHolder.indicators[pair_to_check].iloc[-1]
+
+        for part in self.partial_varHolder_array:
+            part_last_row = part.indicators[pair_to_check].iloc[-1]
+
+            compare_df = base_last_row.compare(part_last_row)
+            if compare_df.shape[0] > 0:
+                # print(compare_df)
+                for col_name, values in compare_df.items():
+                    # print(col_name)
+                    if "other" == col_name:
+                        continue
+                    indicators = values.index
+
+                    for indicator in indicators:
+                        if indicator not in self.dict_recursive:
+                            self.dict_recursive[indicator] = {}
+
+                        values_diff = compare_df.loc[indicator]
+                        values_diff_self = values_diff.loc["self"]
+                        values_diff_other = values_diff.loc["other"]
+
+                        if (
+                            values_diff_self
+                            and values_diff_other
+                            and is_number(values_diff_self)
+                            and is_number(values_diff_other)
+                        ):
+                            diff = (values_diff_other - values_diff_self) / values_diff_self * 100
+                            str_diff = f"{diff:.3f}%"
+                        else:
+                            str_diff = "NaN"
+                        self.dict_recursive[indicator][part.startup_candle] = str_diff
+
+            else:
+                logger.info("No variance on indicator(s) found due to recursive formula.")
+                break
+
+    # For lookahead bias check
+    # analyzes two data frames with processed indicators and shows differences between them.
+    def analyze_indicators_lookahead(self):
+        pair_to_check = self.local_config["pairs"][0]
+        logger.info("Start checking for lookahead bias on indicators only")
+
+        part = self.partial_varHolder_lookahead_array[0]
+        part_last_row = part.indicators[pair_to_check].iloc[-1]
+        date_to_check = part_last_row["date"]
+        index_to_get = self.full_varHolder.indicators[pair_to_check]["date"] == date_to_check
+        base_row_check = self.full_varHolder.indicators[pair_to_check].loc[index_to_get].iloc[-1]
+
+        check_time = part.to_dt.strftime("%Y-%m-%dT%H:%M:%S")
+
+        logger.info(f"Check indicators at {check_time}")
+        # logger.info(f"vs {part_timerange} with {part.startup_candle} startup candle")
+
+        compare_df = base_row_check.compare(part_last_row)
+        if compare_df.shape[0] > 0:
+            # print(compare_df)
+            for col_name, values in compare_df.items():
+                # print(col_name)
+                if "other" == col_name:
+                    continue
+                indicators = values.index
+
+                for indicator in indicators:
+                    logger.info(f"=> found lookahead in indicator {indicator}")
+                    # logger.info("base value {:.5f}".format(values_diff_self))
+                    # logger.info("part value {:.5f}".format(values_diff_other))
+
+        else:
+            logger.info("No lookahead bias on indicators found.")
+
+    def prepare_data(self, varholder: VarHolder, pairs_to_load: list[DataFrame]):
+        if "freqai" in self.local_config and "identifier" in self.local_config["freqai"]:
+            # purge previous data if the freqai model is defined
+            # (to be sure nothing is carried over from older backtests)
+            path_to_current_identifier = Path(
+                f"{self.local_config['user_data_dir']}/models/"
+                f"{self.local_config['freqai']['identifier']}"
+            ).resolve()
+            # remove folder and its contents
+            if Path.exists(path_to_current_identifier):
+                shutil.rmtree(path_to_current_identifier)
+
+        prepare_data_config = deepcopy(self.local_config)
+        prepare_data_config["timerange"] = (
+            str(self.dt_to_timestamp(varholder.from_dt))
+            + "-"
+            + str(self.dt_to_timestamp(varholder.to_dt))
+        )
+        prepare_data_config["exchange"]["pair_whitelist"] = pairs_to_load
+
+        backtesting = Backtesting(prepare_data_config, self.exchange)
+        self.exchange = backtesting.exchange
+        backtesting._set_strategy(backtesting.strategylist[0])
+
+        varholder.data, varholder.timerange = backtesting.load_bt_data()
+        backtesting.load_bt_data_detail()
+        varholder.timeframe = backtesting.timeframe
+
+        varholder.indicators = backtesting.strategy.advise_all_indicators(varholder.data)
+
+    def fill_partial_varholder(self, start_date, startup_candle):
+        logger.info(f"Calculating indicators using startup candle of {startup_candle}.")
+        partial_varHolder = VarHolder()
+
+        partial_varHolder.from_dt = start_date
+        partial_varHolder.to_dt = self.full_varHolder.to_dt
+        partial_varHolder.startup_candle = startup_candle
+
+        self.local_config["startup_candle_count"] = startup_candle
+
+        self.prepare_data(partial_varHolder, self.local_config["pairs"])
+
+        self.partial_varHolder_array.append(partial_varHolder)
+
+    def fill_partial_varholder_lookahead(self, end_date):
+        logger.info("Calculating indicators to test lookahead on indicators.")
+
+        partial_varHolder = VarHolder()
+
+        partial_varHolder.from_dt = self.full_varHolder.from_dt
+        partial_varHolder.to_dt = end_date
+
+        self.prepare_data(partial_varHolder, self.local_config["pairs"])
+
+        self.partial_varHolder_lookahead_array.append(partial_varHolder)
+
+    def start(self) -> None:
+        super().start()
+
+        reduce_verbosity_for_bias_tester()
+        start_date_full = self.full_varHolder.from_dt
+        end_date_full = self.full_varHolder.to_dt
+
+        timeframe_minutes = timeframe_to_minutes(self.full_varHolder.timeframe)
+
+        end_date_partial = start_date_full + timedelta(minutes=int(timeframe_minutes * 10))
+
+        self.fill_partial_varholder_lookahead(end_date_partial)
+
+        # restore_verbosity_for_bias_tester()
+
+        start_date_partial = end_date_full - timedelta(minutes=int(timeframe_minutes))
+
+        for startup_candle in self._startup_candle:
+            self.fill_partial_varholder(start_date_partial, startup_candle)
+
+        # Restore verbosity, so it's not too quiet for the next strategy
+        restore_verbosity_for_bias_tester()
+
+        self.analyze_indicators()
+        self.analyze_indicators_lookahead()
diff --git a/freqtrade/optimize/analysis/recursive_helpers.py b/freqtrade/optimize/analysis/recursive_helpers.py
new file mode 100644
index 000000000..1d2ccdd0b
--- /dev/null
+++ b/freqtrade/optimize/analysis/recursive_helpers.py
@@ -0,0 +1,116 @@
+import logging
+import time
+from pathlib import Path
+from typing import Any
+
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.analysis.recursive import RecursiveAnalysis
+from freqtrade.resolvers import StrategyResolver
+from freqtrade.util import print_rich_table
+
+
+logger = logging.getLogger(__name__)
+
+
+class RecursiveAnalysisSubFunctions:
+    @staticmethod
+    def text_table_recursive_analysis_instances(recursive_instances: list[RecursiveAnalysis]):
+        startups = recursive_instances[0]._startup_candle
+        strat_scc = recursive_instances[0]._strat_scc
+        headers = ["Indicators"]
+        for candle in startups:
+            if candle == strat_scc:
+                headers.append(f"{candle} (from strategy)")
+            else:
+                headers.append(str(candle))
+
+        data = []
+        for inst in recursive_instances:
+            if len(inst.dict_recursive) > 0:
+                for indicator, values in inst.dict_recursive.items():
+                    temp_data = [indicator]
+                    for candle in startups:
+                        temp_data.append(values.get(int(candle), "-"))
+                    data.append(temp_data)
+
+        if len(data) > 0:
+            print_rich_table(data, headers, summary="Recursive Analysis")
+
+            return data
+
+        return data
+
+    @staticmethod
+    def calculate_config_overrides(config: Config):
+        if "timerange" not in config:
+            # setting a timerange is enforced here
+            raise OperationalException(
+                "Please set a timerange. "
+                "A timerange of 5000 candles are enough for recursive analysis."
+            )
+
+        if config.get("backtest_cache") is None:
+            config["backtest_cache"] = "none"
+        elif config["backtest_cache"] != "none":
+            logger.info(
+                f"backtest_cache = "
+                f"{config['backtest_cache']} detected. "
+                f"Inside recursive-analysis it is enforced to be 'none'. "
+                f"Changed it to 'none'"
+            )
+            config["backtest_cache"] = "none"
+        return config
+
+    @staticmethod
+    def initialize_single_recursive_analysis(config: Config, strategy_obj: dict[str, Any]):
+        logger.info(f"Recursive test of {Path(strategy_obj['location']).name} started.")
+        start = time.perf_counter()
+        current_instance = RecursiveAnalysis(config, strategy_obj)
+        current_instance.start()
+        elapsed = time.perf_counter() - start
+        logger.info(
+            f"Checking recursive and indicator-only lookahead bias of indicators "
+            f"of {Path(strategy_obj['location']).name} "
+            f"took {elapsed:.0f} seconds."
+        )
+        return current_instance
+
+    @staticmethod
+    def start(config: Config):
+        config = RecursiveAnalysisSubFunctions.calculate_config_overrides(config)
+
+        strategy_objs = StrategyResolver.search_all_objects(
+            config, enum_failed=False, recursive=config.get("recursive_strategy_search", False)
+        )
+
+        RecursiveAnalysis_instances = []
+
+        # unify --strategy and --strategy-list to one list
+        if not (strategy_list := config.get("strategy_list", [])):
+            if config.get("strategy") is None:
+                raise OperationalException(
+                    "No Strategy specified. Please specify a strategy via --strategy"
+                )
+            strategy_list = [config["strategy"]]
+
+        # check if strategies can be properly loaded, only check them if they can be.
+        for strat in strategy_list:
+            for strategy_obj in strategy_objs:
+                if strategy_obj["name"] == strat and strategy_obj not in strategy_list:
+                    RecursiveAnalysis_instances.append(
+                        RecursiveAnalysisSubFunctions.initialize_single_recursive_analysis(
+                            config, strategy_obj
+                        )
+                    )
+                    break
+
+        # report the results
+        if RecursiveAnalysis_instances:
+            RecursiveAnalysisSubFunctions.text_table_recursive_analysis_instances(
+                RecursiveAnalysis_instances
+            )
+        else:
+            logger.error(
+                "There was no strategy specified through --strategy or timeframe was not specified."
+            )
diff --git a/freqtrade/optimize/backtest_caching.py b/freqtrade/optimize/backtest_caching.py
new file mode 100644
index 000000000..9c3e2f343
--- /dev/null
+++ b/freqtrade/optimize/backtest_caching.py
@@ -0,0 +1,43 @@
+import hashlib
+from copy import deepcopy
+from pathlib import Path
+
+import rapidjson
+
+
+def get_strategy_run_id(strategy) -> str:
+    """
+    Generate unique identification hash for a backtest run. Identical config and strategy file will
+    always return an identical hash.
+    :param strategy: strategy object.
+    :return: hex string id.
+    """
+    digest = hashlib.sha1()  # noqa: S324
+    config = deepcopy(strategy.config)
+
+    # Options that have no impact on results of individual backtest.
+    not_important_keys = ("strategy_list", "original_config", "telegram", "api_server")
+    for k in not_important_keys:
+        if k in config:
+            del config[k]
+
+    # Explicitly allow NaN values (e.g. max_open_trades).
+    # as it does not matter for getting the hash.
+    digest.update(
+        rapidjson.dumps(config, default=str, number_mode=rapidjson.NM_NAN).encode("utf-8")
+    )
+    # Include _ft_params_from_file - so changing parameter files cause cache eviction
+    digest.update(
+        rapidjson.dumps(
+            strategy._ft_params_from_file, default=str, number_mode=rapidjson.NM_NAN
+        ).encode("utf-8")
+    )
+    with Path(strategy.__file__).open("rb") as fp:
+        digest.update(fp.read())
+    return digest.hexdigest().lower()
+
+
+def get_backtest_metadata_filename(filename: Path | str) -> Path:
+    """Return metadata filename for specified backtest results file."""
+    filename = Path(filename)
+    return filename.parent / Path(f"{filename.stem}.meta.json")
diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py
index a0dc8a789..9326c0840 100644
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@@ -1,231 +1,1820 @@
-# pragma pylint: disable=missing-docstring,W0212
+# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
+
+"""
+This module contains the backtesting logic
+"""
 
 import logging
-from typing import Dict, Tuple
+from collections import defaultdict
+from copy import deepcopy
+from datetime import datetime, timedelta
 
-import arrow
-from pandas import DataFrame, Series
-from tabulate import tabulate
+from numpy import nan
+from pandas import DataFrame
+
+from freqtrade import constants
+from freqtrade.configuration import TimeRange, validate_config_consistency
+from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, IntOrInf, LongShort
+from freqtrade.data import history
+from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
+from freqtrade.data.converter import trim_dataframe, trim_dataframes
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.data.metrics import combined_dataframes_with_rel_mean
+from freqtrade.enums import (
+    BacktestState,
+    CandleType,
+    ExitCheckTuple,
+    ExitType,
+    MarginMode,
+    RunMode,
+    TradingMode,
+)
+from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.exchange import (
+    amount_to_contract_precision,
+    price_to_precision,
+    timeframe_to_seconds,
+)
+from freqtrade.exchange.exchange import Exchange
+from freqtrade.ft_types import (
+    BacktestContentType,
+    BacktestContentTypeIcomplete,
+    BacktestResultType,
+    get_BacktestResultType_default,
+)
+from freqtrade.leverage.liquidation_price import update_liquidation_prices
+from freqtrade.mixins import LoggingMixin
+from freqtrade.optimize.backtest_caching import get_strategy_run_id
+from freqtrade.optimize.bt_progress import BTProgress
+from freqtrade.optimize.optimize_reports import (
+    generate_backtest_stats,
+    generate_rejected_signals,
+    generate_trade_signal_candles,
+    show_backtest_results,
+    store_backtest_results,
+)
+from freqtrade.persistence import (
+    CustomDataWrapper,
+    LocalTrade,
+    Order,
+    PairLocks,
+    Trade,
+    disable_database_use,
+    enable_database_use,
+)
+from freqtrade.plugins.pairlistmanager import PairListManager
+from freqtrade.plugins.protectionmanager import ProtectionManager
+from freqtrade.resolvers import ExchangeResolver, StrategyResolver
+from freqtrade.strategy.interface import IStrategy
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util import FtPrecise, dt_now
+from freqtrade.util.migrations import migrate_data
+from freqtrade.wallets import Wallets
 
-import freqtrade.misc as misc
-import freqtrade.optimize as optimize
-from freqtrade import exchange
-from freqtrade.analyze import populate_buy_trend, populate_sell_trend
-from freqtrade.exchange import Bittrex
-from freqtrade.main import should_sell
-from freqtrade.persistence import Trade
-from freqtrade.strategy.strategy import Strategy
 
 logger = logging.getLogger(__name__)
 
+# Indexes for backtest tuples
+DATE_IDX = 0
+OPEN_IDX = 1
+HIGH_IDX = 2
+LOW_IDX = 3
+CLOSE_IDX = 4
+LONG_IDX = 5
+ELONG_IDX = 6  # Exit long
+SHORT_IDX = 7
+ESHORT_IDX = 8  # Exit short
+ENTER_TAG_IDX = 9
+EXIT_TAG_IDX = 10
 
-def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
-    """
-    Get the maximum timeframe for the given backtest data
-    :param data: dictionary with preprocessed backtesting data
-    :return: tuple containing min_date, max_date
-    """
-    all_dates = Series([])
-    for pair_data in data.values():
-        all_dates = all_dates.append(pair_data['date'])
-    all_dates.sort_values(inplace=True)
-    return arrow.get(all_dates.iloc[0]), arrow.get(all_dates.iloc[-1])
+# Every change to this headers list must evaluate further usages of the resulting tuple
+# and eventually change the constants for indexes at the top
+HEADERS = [
+    "date",
+    "open",
+    "high",
+    "low",
+    "close",
+    "enter_long",
+    "exit_long",
+    "enter_short",
+    "exit_short",
+    "enter_tag",
+    "exit_tag",
+]
 
 
-def generate_text_table(
-        data: Dict[str, Dict], results: DataFrame, stake_currency) -> str:
+class Backtesting:
     """
-    Generates and returns a text table for the given backtest data and the results dataframe
-    :return: pretty printed table with tabulate as str
+    Backtesting class, this class contains all the logic to run a backtest
+
+    To run a backtest:
+    backtesting = Backtesting(config)
+    backtesting.start()
     """
-    floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
-    tabular_data = []
-    headers = ['pair', 'buy count', 'avg profit %',
-               'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
-    for pair in data:
-        result = results[results.currency == pair]
-        tabular_data.append([
+
+    def __init__(self, config: Config, exchange: Exchange | None = None) -> None:
+        LoggingMixin.show_output = False
+        self.config = config
+        self.results: BacktestResultType = get_BacktestResultType_default()
+        self.trade_id_counter: int = 0
+        self.order_id_counter: int = 0
+
+        config["dry_run"] = True
+        self.run_ids: dict[str, str] = {}
+        self.strategylist: list[IStrategy] = []
+        self.all_results: dict[str, BacktestContentType] = {}
+        self.analysis_results: dict[str, dict[str, DataFrame]] = {
+            "signals": {},
+            "rejected": {},
+            "exited": {},
+        }
+        self.rejected_dict: dict[str, list] = {}
+
+        self._exchange_name = self.config["exchange"]["name"]
+        if not exchange:
+            exchange = ExchangeResolver.load_exchange(self.config, load_leverage_tiers=True)
+        self.exchange = exchange
+
+        self.dataprovider = DataProvider(self.config, self.exchange)
+
+        if self.config.get("strategy_list"):
+            if self.config.get("freqai", {}).get("enabled", False):
+                logger.warning(
+                    "Using --strategy-list with FreqAI REQUIRES all strategies "
+                    "to have identical feature_engineering_* functions."
+                )
+            for strat in list(self.config["strategy_list"]):
+                stratconf = deepcopy(self.config)
+                stratconf["strategy"] = strat
+                self.strategylist.append(StrategyResolver.load_strategy(stratconf))
+                validate_config_consistency(stratconf)
+
+        else:
+            # No strategy list specified, only one strategy
+            self.strategylist.append(StrategyResolver.load_strategy(self.config))
+            validate_config_consistency(self.config)
+
+        if "timeframe" not in self.config:
+            raise OperationalException(
+                "Timeframe needs to be set in either "
+                "configuration or as cli argument `--timeframe 5m`"
+            )
+        self.timeframe = str(self.config.get("timeframe"))
+        self.timeframe_secs = timeframe_to_seconds(self.timeframe)
+        self.timeframe_min = self.timeframe_secs // 60
+        self.timeframe_td = timedelta(seconds=self.timeframe_secs)
+        self.disable_database_use()
+        self.init_backtest_detail()
+        self.pairlists = PairListManager(self.exchange, self.config, self.dataprovider)
+        self._validate_pairlists_for_backtesting()
+
+        self.dataprovider.add_pairlisthandler(self.pairlists)
+        self.pairlists.refresh_pairlist()
+
+        if len(self.pairlists.whitelist) == 0:
+            raise OperationalException("No pair in whitelist.")
+
+        if config.get("fee", None) is not None:
+            self.fee = config["fee"]
+            logger.info(f"Using fee {self.fee:.4%} from config.")
+        else:
+            fees = [
+                self.exchange.get_fee(
+                    symbol=self.pairlists.whitelist[0],
+                    taker_or_maker=mt,  # type: ignore
+                )
+                for mt in ("taker", "maker")
+            ]
+            self.fee = max(fee for fee in fees if fee is not None)
+            logger.info(f"Using fee {self.fee:.4%} - worst case fee from exchange (lowest tier).")
+        self.precision_mode = self.exchange.precisionMode
+        self.precision_mode_price = self.exchange.precision_mode_price
+
+        if self.config.get("freqai_backtest_live_models", False):
+            from freqtrade.freqai.utils import get_timerange_backtest_live_models
+
+            self.config["timerange"] = get_timerange_backtest_live_models(self.config)
+
+        self.timerange = TimeRange.parse_timerange(
+            None if self.config.get("timerange") is None else str(self.config.get("timerange"))
+        )
+
+        # Get maximum required startup period
+        self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
+        self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
+
+        # Add maximum startup candle count to configuration for informative pairs support
+        self.config["startup_candle_count"] = self.required_startup
+
+        if self.config.get("freqai", {}).get("enabled", False):
+            # For FreqAI, increase the required_startup to includes the training data
+            # This value should NOT be written to startup_candle_count
+            self.required_startup = self.dataprovider.get_required_startup(self.timeframe)
+
+        self.trading_mode: TradingMode = config.get("trading_mode", TradingMode.SPOT)
+        self.margin_mode: MarginMode = config.get("margin_mode", MarginMode.ISOLATED)
+        # strategies which define "can_short=True" will fail to load in Spot mode.
+        self._can_short = self.trading_mode != TradingMode.SPOT
+        self._position_stacking: bool = self.config.get("position_stacking", False)
+        self.enable_protections: bool = self.config.get("enable_protections", False)
+        migrate_data(config, self.exchange)
+
+        self.init_backtest()
+
+    def _validate_pairlists_for_backtesting(self):
+        if "VolumePairList" in self.pairlists.name_list:
+            raise OperationalException(
+                "VolumePairList not allowed for backtesting. Please use StaticPairList instead."
+            )
+
+        if len(self.strategylist) > 1 and "PrecisionFilter" in self.pairlists.name_list:
+            raise OperationalException(
+                "PrecisionFilter not allowed for backtesting multiple strategies."
+            )
+
+    @staticmethod
+    def cleanup():
+        LoggingMixin.show_output = True
+        enable_database_use()
+
+    def init_backtest_detail(self) -> None:
+        # Load detail timeframe if specified
+        self.timeframe_detail = str(self.config.get("timeframe_detail", ""))
+        if self.timeframe_detail:
+            timeframe_detail_secs = timeframe_to_seconds(self.timeframe_detail)
+            self.timeframe_detail_td = timedelta(seconds=timeframe_detail_secs)
+            if self.timeframe_secs <= timeframe_detail_secs:
+                raise OperationalException(
+                    "Detail timeframe must be smaller than strategy timeframe."
+                )
+
+        else:
+            self.timeframe_detail_td = timedelta(seconds=0)
+        self.detail_data: dict[str, DataFrame] = {}
+        self.futures_data: dict[str, DataFrame] = {}
+
+    def init_backtest(self):
+        self.prepare_backtest(False)
+
+        self.wallets = Wallets(self.config, self.exchange, is_backtest=True)
+
+        self.progress = BTProgress()
+        self.abort = False
+
+    def _set_strategy(self, strategy: IStrategy):
+        """
+        Load strategy into backtesting
+        """
+        self.strategy: IStrategy = strategy
+        strategy.dp = self.dataprovider
+        # Attach Wallets to Strategy baseclass
+        strategy.wallets = self.wallets
+        # Set stoploss_on_exchange to false for backtesting,
+        # since a "perfect" stoploss-exit is assumed anyway
+        # And the regular "stoploss" function would not apply to that case
+        self.strategy.order_types["stoploss_on_exchange"] = False
+        # Update can_short flag
+        self._can_short = self.trading_mode != TradingMode.SPOT and strategy.can_short
+
+        self.strategy.ft_bot_start()
+
+    def _load_protections(self, strategy: IStrategy):
+        if self.config.get("enable_protections", False):
+            self.protections = ProtectionManager(self.config, strategy.protections)
+
+    def load_bt_data(self) -> tuple[dict[str, DataFrame], TimeRange]:
+        """
+        Loads backtest data and returns the data combined with the timerange
+        as tuple.
+        """
+        self.progress.init_step(BacktestState.DATALOAD, 1)
+
+        data = history.load_data(
+            datadir=self.config["datadir"],
+            pairs=self.pairlists.whitelist,
+            timeframe=self.timeframe,
+            timerange=self.timerange,
+            startup_candles=self.required_startup,
+            fail_without_data=True,
+            data_format=self.config["dataformat_ohlcv"],
+            candle_type=self.config.get("candle_type_def", CandleType.SPOT),
+        )
+
+        min_date, max_date = history.get_timerange(data)
+
+        logger.info(
+            f"Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} "
+            f"up to {max_date.strftime(DATETIME_PRINT_FORMAT)} "
+            f"({(max_date - min_date).days} days)."
+        )
+
+        # Adjust startts forward if not enough data is available
+        self.timerange.adjust_start_if_necessary(
+            timeframe_to_seconds(self.timeframe), self.required_startup, min_date
+        )
+
+        self.progress.set_new_value(1)
+        return data, self.timerange
+
+    def load_bt_data_detail(self) -> None:
+        """
+        Loads backtest detail data (smaller timeframe) if necessary.
+        """
+        if self.timeframe_detail:
+            self.detail_data = history.load_data(
+                datadir=self.config["datadir"],
+                pairs=self.pairlists.whitelist,
+                timeframe=self.timeframe_detail,
+                timerange=self.timerange,
+                startup_candles=0,
+                fail_without_data=True,
+                data_format=self.config["dataformat_ohlcv"],
+                candle_type=self.config.get("candle_type_def", CandleType.SPOT),
+            )
+        else:
+            self.detail_data = {}
+        if self.trading_mode == TradingMode.FUTURES:
+            funding_fee_timeframe: str = self.exchange.get_option("funding_fee_timeframe")
+            self.funding_fee_timeframe_secs: int = timeframe_to_seconds(funding_fee_timeframe)
+            mark_timeframe: str = self.exchange.get_option("mark_ohlcv_timeframe")
+
+            # Load additional futures data.
+            funding_rates_dict = history.load_data(
+                datadir=self.config["datadir"],
+                pairs=self.pairlists.whitelist,
+                timeframe=funding_fee_timeframe,
+                timerange=self.timerange,
+                startup_candles=0,
+                fail_without_data=True,
+                data_format=self.config["dataformat_ohlcv"],
+                candle_type=CandleType.FUNDING_RATE,
+            )
+
+            # For simplicity, assign to CandleType.Mark (might contain index candles!)
+            mark_rates_dict = history.load_data(
+                datadir=self.config["datadir"],
+                pairs=self.pairlists.whitelist,
+                timeframe=mark_timeframe,
+                timerange=self.timerange,
+                startup_candles=0,
+                fail_without_data=True,
+                data_format=self.config["dataformat_ohlcv"],
+                candle_type=CandleType.from_string(self.exchange.get_option("mark_ohlcv_price")),
+            )
+            # Combine data to avoid combining the data per trade.
+            unavailable_pairs = []
+            uses_leverage_tiers = self.exchange.get_option("uses_leverage_tiers", True)
+            for pair in self.pairlists.whitelist:
+                if uses_leverage_tiers and pair not in self.exchange._leverage_tiers:
+                    unavailable_pairs.append(pair)
+                    continue
+
+                self.futures_data[pair] = self.exchange.combine_funding_and_mark(
+                    funding_rates=funding_rates_dict[pair],
+                    mark_rates=mark_rates_dict[pair],
+                    futures_funding_rate=self.config.get("futures_funding_rate", None),
+                )
+
+            if unavailable_pairs:
+                raise OperationalException(
+                    f"Pairs {', '.join(unavailable_pairs)} got no leverage tiers available. "
+                    "It is therefore impossible to backtest with this pair at the moment."
+                )
+        else:
+            self.futures_data = {}
+
+    def disable_database_use(self):
+        disable_database_use(self.timeframe)
+
+    def prepare_backtest(self, enable_protections):
+        """
+        Backtesting setup method - called once for every call to "backtest()".
+        """
+        self.disable_database_use()
+        PairLocks.reset_locks()
+        Trade.reset_trades()
+        CustomDataWrapper.reset_custom_data()
+        self.rejected_trades = 0
+        self.timedout_entry_orders = 0
+        self.timedout_exit_orders = 0
+        self.canceled_trade_entries = 0
+        self.canceled_entry_orders = 0
+        self.replaced_entry_orders = 0
+        self.canceled_exit_orders = 0
+        self.replaced_exit_orders = 0
+        self.dataprovider.clear_cache()
+        if enable_protections:
+            self._load_protections(self.strategy)
+
+    def check_abort(self):
+        """
+        Check if abort was requested, raise DependencyException if that's the case
+        Only applies to Interactive backtest mode (webserver mode)
+        """
+        if self.abort:
+            self.abort = False
+            raise DependencyException("Stop requested")
+
+    def _get_ohlcv_as_lists(self, processed: dict[str, DataFrame]) -> dict[str, tuple]:
+        """
+        Helper function to convert a processed dataframes into lists for performance reasons.
+
+        Used by backtest() - so keep this optimized for performance.
+
+        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
+        optimize memory usage!
+        """
+
+        data: dict = {}
+        self.progress.init_step(BacktestState.CONVERT, len(processed))
+
+        # Create dict with data
+        for pair in processed.keys():
+            pair_data = processed[pair]
+            self.check_abort()
+            self.progress.increment()
+
+            if not pair_data.empty:
+                # Cleanup from prior runs
+                pair_data.drop(HEADERS[5:] + ["buy", "sell"], axis=1, errors="ignore")
+            df_analyzed = self.strategy.ft_advise_signals(pair_data, {"pair": pair})
+            # Update dataprovider cache
+            self.dataprovider._set_cached_df(
+                pair, self.timeframe, df_analyzed, self.config["candle_type_def"]
+            )
+
+            # Trim startup period from analyzed dataframe
+            df_analyzed = processed[pair] = pair_data = trim_dataframe(
+                df_analyzed, self.timerange, startup_candles=self.required_startup
+            )
+
+            # Create a copy of the dataframe before shifting, that way the entry signal/tag
+            # remains on the correct candle for callbacks.
+            df_analyzed = df_analyzed.copy()
+
+            # To avoid using data from future, we use entry/exit signals shifted
+            # from the previous candle
+            for col in HEADERS[5:]:
+                tag_col = col in ("enter_tag", "exit_tag")
+                if col in df_analyzed.columns:
+                    df_analyzed[col] = (
+                        df_analyzed.loc[:, col]
+                        .replace([nan], [0 if not tag_col else None])
+                        .shift(1)
+                    )
+                elif not df_analyzed.empty:
+                    df_analyzed[col] = 0 if not tag_col else None
+
+            df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
+
+            # Convert from Pandas to list for performance reasons
+            # (Looping Pandas is slow.)
+            data[pair] = df_analyzed[HEADERS].values.tolist() if not df_analyzed.empty else []
+        return data
+
+    def _get_close_rate(
+        self, row: tuple, trade: LocalTrade, exit_: ExitCheckTuple, trade_dur: int
+    ) -> float:
+        """
+        Get close rate for backtesting result
+        """
+        # Special handling if high or low hit STOP_LOSS or ROI
+        if exit_.exit_type in (
+            ExitType.STOP_LOSS,
+            ExitType.TRAILING_STOP_LOSS,
+            ExitType.LIQUIDATION,
+        ):
+            return self._get_close_rate_for_stoploss(row, trade, exit_, trade_dur)
+        elif exit_.exit_type == (ExitType.ROI):
+            return self._get_close_rate_for_roi(row, trade, exit_, trade_dur)
+        else:
+            return row[OPEN_IDX]
+
+    def _get_close_rate_for_stoploss(
+        self, row: tuple, trade: LocalTrade, exit_: ExitCheckTuple, trade_dur: int
+    ) -> float:
+        # our stoploss was already lower than candle high,
+        # possibly due to a cancelled trade exit.
+        # exit at open price.
+        is_short = trade.is_short or False
+        leverage = trade.leverage or 1.0
+        side_1 = -1 if is_short else 1
+        if exit_.exit_type == ExitType.LIQUIDATION and trade.liquidation_price:
+            stoploss_value = trade.liquidation_price
+        else:
+            stoploss_value = trade.stop_loss
+
+        if is_short:
+            if stoploss_value < row[LOW_IDX]:
+                return row[OPEN_IDX]
+        else:
+            if stoploss_value > row[HIGH_IDX]:
+                return row[OPEN_IDX]
+
+        # Special case: trailing triggers within same candle as trade opened. Assume most
+        # pessimistic price movement, which is moving just enough to arm stoploss and
+        # immediately going down to stop price.
+        if exit_.exit_type == ExitType.TRAILING_STOP_LOSS and trade_dur == 0:
+            if (
+                not self.strategy.use_custom_stoploss
+                and self.strategy.trailing_stop
+                and self.strategy.trailing_only_offset_is_reached
+                and self.strategy.trailing_stop_positive_offset is not None
+                and self.strategy.trailing_stop_positive
+            ):
+                # Worst case: price reaches stop_positive_offset and dives down.
+                stop_rate = row[OPEN_IDX] * (
+                    1
+                    + side_1 * abs(self.strategy.trailing_stop_positive_offset)
+                    - side_1 * abs(self.strategy.trailing_stop_positive / leverage)
+                )
+            else:
+                # Worst case: price ticks tiny bit above open and dives down.
+                stop_rate = row[OPEN_IDX] * (
+                    1 - side_1 * abs((trade.stop_loss_pct or 0.0) / leverage)
+                )
+
+            # Limit lower-end to candle low to avoid exits below the low.
+            # This still remains "worst case" - but "worst realistic case".
+            if is_short:
+                return min(row[HIGH_IDX], stop_rate)
+            else:
+                return max(row[LOW_IDX], stop_rate)
+
+        # Set close_rate to stoploss
+        return stoploss_value
+
+    def _get_close_rate_for_roi(
+        self, row: tuple, trade: LocalTrade, exit_: ExitCheckTuple, trade_dur: int
+    ) -> float:
+        is_short = trade.is_short or False
+        leverage = trade.leverage or 1.0
+        side_1 = -1 if is_short else 1
+        roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
+        if roi is not None and roi_entry is not None:
+            if roi == -1 and roi_entry % self.timeframe_min == 0:
+                # When force_exiting with ROI=-1, the roi time will always be equal to trade_dur.
+                # If that entry is a multiple of the timeframe (so on candle open)
+                # - we'll use open instead of close
+                return row[OPEN_IDX]
+
+            # - (Expected abs profit - open_rate - open_fee) / (fee_close -1)
+            roi_rate = trade.open_rate * roi / leverage
+            open_fee_rate = side_1 * trade.open_rate * (1 + side_1 * trade.fee_open)
+            close_rate = -(roi_rate + open_fee_rate) / ((trade.fee_close or 0.0) - side_1 * 1)
+            if is_short:
+                is_new_roi = row[OPEN_IDX] < close_rate
+            else:
+                is_new_roi = row[OPEN_IDX] > close_rate
+            if (
+                trade_dur > 0
+                and trade_dur == roi_entry
+                and roi_entry % self.timeframe_min == 0
+                and is_new_roi
+            ):
+                # new ROI entry came into effect.
+                # use Open rate if open_rate > calculated exit rate
+                return row[OPEN_IDX]
+
+            if trade_dur == 0 and (
+                (
+                    is_short
+                    # Red candle (for longs)
+                    and row[OPEN_IDX] < row[CLOSE_IDX]  # Red candle
+                    and trade.open_rate > row[OPEN_IDX]  # trade-open above open_rate
+                    and close_rate < row[CLOSE_IDX]  # closes below close
+                )
+                or (
+                    not is_short
+                    # green candle (for shorts)
+                    and row[OPEN_IDX] > row[CLOSE_IDX]  # green candle
+                    and trade.open_rate < row[OPEN_IDX]  # trade-open below open_rate
+                    and close_rate > row[CLOSE_IDX]  # closes above close
+                )
+            ):
+                # ROI on opening candles with custom pricing can only
+                # trigger if the entry was at Open or lower wick.
+                # details: https: // github.com/freqtrade/freqtrade/issues/6261
+                # If open_rate is < open, only allow exits below the close on red candles.
+                raise ValueError("Opening candle ROI on red candles.")
+
+            # Use the maximum between close_rate and low as we
+            # cannot exit outside of a candle.
+            # Applies when a new ROI setting comes in place and the whole candle is above that.
+            return min(max(close_rate, row[LOW_IDX]), row[HIGH_IDX])
+
+        else:
+            # This should not be reached...
+            return row[OPEN_IDX]
+
+    def _check_adjust_trade_for_candle(
+        self, trade: LocalTrade, row: tuple, current_time: datetime
+    ) -> LocalTrade:
+        current_rate: float = row[OPEN_IDX]
+        current_profit = trade.calc_profit_ratio(current_rate)
+        min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, current_rate, -0.1)
+        max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_rate)
+        stake_available = self.wallets.get_available_stake_amount()
+        stake_amount, order_tag = self.strategy._adjust_trade_position_internal(
+            trade=trade,  # type: ignore[arg-type]
+            current_time=current_time,
+            current_rate=current_rate,
+            current_profit=current_profit,
+            min_stake=min_stake,
+            max_stake=min(max_stake, stake_available),
+            current_entry_rate=current_rate,
+            current_exit_rate=current_rate,
+            current_entry_profit=current_profit,
+            current_exit_profit=current_profit,
+        )
+
+        # Check if we should increase our position
+        if stake_amount is not None and stake_amount > 0.0:
+            check_adjust_entry = True
+            if self.strategy.max_entry_position_adjustment > -1:
+                entry_count = trade.nr_of_successful_entries
+                check_adjust_entry = entry_count <= self.strategy.max_entry_position_adjustment
+            if check_adjust_entry:
+                pos_trade = self._enter_trade(
+                    trade.pair,
+                    row,
+                    "short" if trade.is_short else "long",
+                    stake_amount,
+                    trade,
+                    entry_tag1=order_tag,
+                )
+                if pos_trade is not None:
+                    self.wallets.update()
+                    return pos_trade
+
+        if stake_amount is not None and stake_amount < 0.0:
+            amount = amount_to_contract_precision(
+                abs(
+                    float(
+                        FtPrecise(stake_amount)
+                        * FtPrecise(trade.amount)
+                        / FtPrecise(trade.stake_amount)
+                    )
+                ),
+                trade.amount_precision,
+                self.precision_mode,
+                trade.contract_size,
+            )
+            if amount == 0.0:
+                return trade
+            remaining = (trade.amount - amount) * current_rate
+            if min_stake and remaining != 0 and remaining < min_stake:
+                # Remaining stake is too low to be sold.
+                return trade
+            exit_ = ExitCheckTuple(ExitType.PARTIAL_EXIT, order_tag)
+            pos_trade = self._get_exit_for_signal(trade, row, exit_, current_time, amount)
+            if pos_trade is not None:
+                order = pos_trade.orders[-1]
+                # If the order was filled and for the full trade amount, we need to close the trade.
+                self._process_exit_order(order, pos_trade, current_time, row, trade.pair)
+                return pos_trade
+
+        return trade
+
+    def _get_order_filled(self, rate: float, row: tuple) -> bool:
+        """Rate is within candle, therefore filled"""
+        return row[LOW_IDX] <= rate <= row[HIGH_IDX]
+
+    def _call_adjust_stop(self, current_date: datetime, trade: LocalTrade, current_rate: float):
+        profit = trade.calc_profit_ratio(current_rate)
+        self.strategy.ft_stoploss_adjust(
+            current_rate,
+            trade,  # type: ignore
+            current_date,
+            profit,
+            0,
+            after_fill=True,
+        )
+
+    def _try_close_open_order(
+        self, order: Order | None, trade: LocalTrade, current_date: datetime, row: tuple
+    ) -> bool:
+        """
+        Check if an order is open and if it should've filled.
+        :return:  True if the order filled.
+        """
+        if order and self._get_order_filled(order.ft_price, row):
+            order.close_bt_order(current_date, trade)
+            self._run_funding_fees(trade, current_date, force=True)
+            strategy_safe_wrapper(self.strategy.order_filled, default_retval=None)(
+                pair=trade.pair,
+                trade=trade,  # type: ignore[arg-type]
+                order=order,
+                current_time=current_date,
+            )
+
+            if self.margin_mode == MarginMode.CROSS or not (
+                order.ft_order_side == trade.exit_side and order.safe_amount == trade.amount
+            ):
+                # trade is still open or we are in cross margin mode and
+                # must update all liquidation prices
+                update_liquidation_prices(
+                    trade,
+                    exchange=self.exchange,
+                    wallets=self.wallets,
+                    stake_currency=self.config["stake_currency"],
+                    dry_run=True,
+                )
+            if not (order.ft_order_side == trade.exit_side and order.safe_amount == trade.amount):
+                self._call_adjust_stop(current_date, trade, order.ft_price)
+            return True
+        return False
+
+    def _process_exit_order(
+        self, order: Order, trade: LocalTrade, current_time: datetime, row: tuple, pair: str
+    ):
+        """
+        Takes an exit order and processes it, potentially closing the trade.
+        """
+        if self._try_close_open_order(order, trade, current_time, row):
+            sub_trade = order.safe_amount_after_fee != trade.amount
+            if sub_trade:
+                trade.recalc_trade_from_orders()
+            else:
+                trade.close_date = current_time
+                trade.close(order.ft_price, show_msg=False)
+
+                LocalTrade.close_bt_trade(trade)
+            self.wallets.update()
+            self.run_protections(pair, current_time, trade.trade_direction)
+
+    def _get_exit_for_signal(
+        self,
+        trade: LocalTrade,
+        row: tuple,
+        exit_: ExitCheckTuple,
+        current_time: datetime,
+        amount: float | None = None,
+    ) -> LocalTrade | None:
+        if exit_.exit_flag:
+            trade.close_date = current_time
+            exit_reason = exit_.exit_reason
+            amount_ = amount if amount is not None else trade.amount
+            trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
+            try:
+                close_rate = self._get_close_rate(row, trade, exit_, trade_dur)
+            except ValueError:
+                return None
+            # call the custom exit price,with default value as previous close_rate
+            current_profit = trade.calc_profit_ratio(close_rate)
+            order_type = self.strategy.order_types["exit"]
+            if exit_.exit_type in (
+                ExitType.EXIT_SIGNAL,
+                ExitType.CUSTOM_EXIT,
+                ExitType.PARTIAL_EXIT,
+            ):
+                # Checks and adds an exit tag, after checking that the length of the
+                # row has the length for an exit tag column
+                if (
+                    len(row) > EXIT_TAG_IDX
+                    and row[EXIT_TAG_IDX] is not None
+                    and len(row[EXIT_TAG_IDX]) > 0
+                    and exit_.exit_type in (ExitType.EXIT_SIGNAL,)
+                ):
+                    exit_reason = row[EXIT_TAG_IDX]
+                # Custom exit pricing only for exit-signals
+                if order_type == "limit":
+                    rate = strategy_safe_wrapper(
+                        self.strategy.custom_exit_price, default_retval=close_rate
+                    )(
+                        pair=trade.pair,
+                        trade=trade,  # type: ignore[arg-type]
+                        current_time=current_time,
+                        proposed_rate=close_rate,
+                        current_profit=current_profit,
+                        exit_tag=exit_reason,
+                    )
+                    if rate is not None and rate != close_rate:
+                        close_rate = price_to_precision(
+                            rate, trade.price_precision, self.precision_mode_price
+                        )
+                    # We can't place orders lower than current low.
+                    # freqtrade does not support this in live, and the order would fill immediately
+                    if trade.is_short:
+                        close_rate = min(close_rate, row[HIGH_IDX])
+                    else:
+                        close_rate = max(close_rate, row[LOW_IDX])
+            # Confirm trade exit:
+            time_in_force = self.strategy.order_time_in_force["exit"]
+
+            if exit_.exit_type not in (
+                ExitType.LIQUIDATION,
+                ExitType.PARTIAL_EXIT,
+            ) and not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
+                pair=trade.pair,
+                trade=trade,  # type: ignore[arg-type]
+                order_type=order_type,
+                amount=amount_,
+                rate=close_rate,
+                time_in_force=time_in_force,
+                sell_reason=exit_reason,  # deprecated
+                exit_reason=exit_reason,
+                current_time=current_time,
+            ):
+                return None
+
+            trade.exit_reason = exit_reason
+
+            return self._exit_trade(trade, row, close_rate, amount_, exit_reason)
+        return None
+
+    def _exit_trade(
+        self,
+        trade: LocalTrade,
+        sell_row: tuple,
+        close_rate: float,
+        amount: float,
+        exit_reason: str | None,
+    ) -> LocalTrade | None:
+        self.order_id_counter += 1
+        exit_candle_time = sell_row[DATE_IDX].to_pydatetime()
+        order_type = self.strategy.order_types["exit"]
+        # amount = amount or trade.amount
+        amount = amount_to_contract_precision(
+            amount or trade.amount, trade.amount_precision, self.precision_mode, trade.contract_size
+        )
+
+        if self.handle_similar_order(trade, close_rate, amount, trade.exit_side, exit_candle_time):
+            return None
+
+        order = Order(
+            id=self.order_id_counter,
+            ft_trade_id=trade.id,
+            order_date=exit_candle_time,
+            order_update_date=exit_candle_time,
+            ft_is_open=True,
+            ft_pair=trade.pair,
+            order_id=str(self.order_id_counter),
+            symbol=trade.pair,
+            ft_order_side=trade.exit_side,
+            side=trade.exit_side,
+            order_type=order_type,
+            status="open",
+            ft_price=close_rate,
+            price=close_rate,
+            average=close_rate,
+            amount=amount,
+            filled=0,
+            remaining=amount,
+            cost=amount * close_rate * (1 + self.fee),
+            ft_order_tag=exit_reason,
+        )
+        order._trade_bt = trade
+        trade.orders.append(order)
+        return trade
+
+    def _check_trade_exit(
+        self, trade: LocalTrade, row: tuple, current_time: datetime
+    ) -> LocalTrade | None:
+        self._run_funding_fees(trade, current_time)
+
+        # Check if we need to adjust our current positions
+        if self.strategy.position_adjustment_enable:
+            trade = self._check_adjust_trade_for_candle(trade, row, current_time)
+
+        if trade.is_open:
+            enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
+            exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
+            exits = self.strategy.should_exit(
+                trade,  # type: ignore
+                row[OPEN_IDX],
+                row[DATE_IDX].to_pydatetime(),
+                enter=enter,
+                exit_=exit_sig,
+                low=row[LOW_IDX],
+                high=row[HIGH_IDX],
+            )
+            for exit_ in exits:
+                t = self._get_exit_for_signal(trade, row, exit_, current_time)
+                if t:
+                    return t
+        return None
+
+    def _run_funding_fees(self, trade: LocalTrade, current_time: datetime, force: bool = False):
+        """
+        Calculate funding fees if necessary and add them to the trade.
+        """
+        if self.trading_mode == TradingMode.FUTURES:
+            if force or (current_time.timestamp() % self.funding_fee_timeframe_secs) == 0:
+                # Funding fee interval.
+                trade.set_funding_fees(
+                    self.exchange.calculate_funding_fees(
+                        self.futures_data[trade.pair],
+                        amount=trade.amount,
+                        is_short=trade.is_short,
+                        open_date=trade.date_last_filled_utc,
+                        close_date=current_time,
+                    )
+                )
+
+    def get_valid_price_and_stake(
+        self,
+        pair: str,
+        row: tuple,
+        propose_rate: float,
+        stake_amount: float,
+        direction: LongShort,
+        current_time: datetime,
+        entry_tag: str | None,
+        trade: LocalTrade | None,
+        order_type: str,
+        price_precision: float | None,
+    ) -> tuple[float, float, float, float]:
+        if order_type == "limit":
+            new_rate = strategy_safe_wrapper(
+                self.strategy.custom_entry_price, default_retval=propose_rate
+            )(
+                pair=pair,
+                trade=trade,  # type: ignore[arg-type]
+                current_time=current_time,
+                proposed_rate=propose_rate,
+                entry_tag=entry_tag,
+                side=direction,
+            )  # default value is the open rate
+            # We can't place orders higher than current high (otherwise it'd be a stop limit entry)
+            # which freqtrade does not support in live.
+            if new_rate is not None and new_rate != propose_rate:
+                propose_rate = price_to_precision(
+                    new_rate, price_precision, self.precision_mode_price
+                )
+            if direction == "short":
+                propose_rate = max(propose_rate, row[LOW_IDX])
+            else:
+                propose_rate = min(propose_rate, row[HIGH_IDX])
+
+        pos_adjust = trade is not None
+        leverage = trade.leverage if trade else 1.0
+        if not pos_adjust:
+            try:
+                stake_amount = self.wallets.get_trade_stake_amount(
+                    pair, self.strategy.max_open_trades, update=False
+                )
+            except DependencyException:
+                return 0, 0, 0, 0
+
+            max_leverage = self.exchange.get_max_leverage(pair, stake_amount)
+            leverage = (
+                strategy_safe_wrapper(self.strategy.leverage, default_retval=1.0)(
+                    pair=pair,
+                    current_time=current_time,
+                    current_rate=row[OPEN_IDX],
+                    proposed_leverage=1.0,
+                    max_leverage=max_leverage,
+                    side=direction,
+                    entry_tag=entry_tag,
+                )
+                if self.trading_mode != TradingMode.SPOT
+                else 1.0
+            )
+            # Cap leverage between 1.0 and max_leverage.
+            leverage = min(max(leverage, 1.0), max_leverage)
+
+        min_stake_amount = (
+            self.exchange.get_min_pair_stake_amount(
+                pair, propose_rate, -0.05 if not pos_adjust else 0.0, leverage=leverage
+            )
+            or 0
+        )
+        max_stake_amount = self.exchange.get_max_pair_stake_amount(
+            pair, propose_rate, leverage=leverage
+        )
+        stake_available = self.wallets.get_available_stake_amount()
+
+        if not pos_adjust:
+            stake_amount = strategy_safe_wrapper(
+                self.strategy.custom_stake_amount, default_retval=stake_amount
+            )(
+                pair=pair,
+                current_time=current_time,
+                current_rate=propose_rate,
+                proposed_stake=stake_amount,
+                min_stake=min_stake_amount,
+                max_stake=min(stake_available, max_stake_amount),
+                leverage=leverage,
+                entry_tag=entry_tag,
+                side=direction,
+            )
+
+        stake_amount_val = self.wallets.validate_stake_amount(
+            pair=pair,
+            stake_amount=stake_amount,
+            min_stake_amount=min_stake_amount,
+            max_stake_amount=max_stake_amount,
+            trade_amount=trade.stake_amount if trade else None,
+        )
+
+        return propose_rate, stake_amount_val, leverage, min_stake_amount
+
+    def _enter_trade(
+        self,
+        pair: str,
+        row: tuple,
+        direction: LongShort,
+        stake_amount: float | None = None,
+        trade: LocalTrade | None = None,
+        requested_rate: float | None = None,
+        requested_stake: float | None = None,
+        entry_tag1: str | None = None,
+    ) -> LocalTrade | None:
+        """
+        :param trade: Trade to adjust - initial entry if None
+        :param requested_rate: Adjusted entry rate
+        :param requested_stake: Stake amount for adjusted orders (`adjust_entry_price`).
+        """
+
+        current_time = row[DATE_IDX].to_pydatetime()
+        entry_tag = entry_tag1 or (row[ENTER_TAG_IDX] if len(row) >= ENTER_TAG_IDX + 1 else None)
+        # let's call the custom entry price, using the open price as default price
+        order_type = self.strategy.order_types["entry"]
+        pos_adjust = trade is not None and requested_rate is None
+
+        stake_amount_ = stake_amount or (trade.stake_amount if trade else 0.0)
+        precision_price = self.exchange.get_precision_price(pair)
+
+        propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake(
             pair,
-            len(result.index),
-            result.profit_percent.mean() * 100.0,
-            result.profit_BTC.sum(),
-            result.duration.mean(),
-            len(result[result.profit_BTC > 0]),
-            len(result[result.profit_BTC < 0])
-        ])
+            row,
+            row[OPEN_IDX],
+            stake_amount_,
+            direction,
+            current_time,
+            entry_tag,
+            trade,
+            order_type,
+            precision_price,
+        )
 
-    # Append Total
-    tabular_data.append([
-        'TOTAL',
-        len(results.index),
-        results.profit_percent.mean() * 100.0,
-        results.profit_BTC.sum(),
-        results.duration.mean(),
-        len(results[results.profit_BTC > 0]),
-        len(results[results.profit_BTC < 0])
-    ])
-    return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
+        # replace proposed rate if another rate was requested
+        propose_rate = requested_rate if requested_rate else propose_rate
+        stake_amount = requested_stake if requested_stake else stake_amount
 
+        if not stake_amount:
+            # In case of pos adjust, still return the original trade
+            # If not pos adjust, trade is None
+            return trade
+        time_in_force = self.strategy.order_time_in_force["entry"]
 
-def get_sell_trade_entry(pair, buy_row, partial_ticker, trade_count_lock, args):
-    stake_amount = args['stake_amount']
-    max_open_trades = args.get('max_open_trades', 0)
-    trade = Trade(open_rate=buy_row.close,
-                  open_date=buy_row.date,
-                  stake_amount=stake_amount,
-                  amount=stake_amount / buy_row.open,
-                  fee=exchange.get_fee()
-                  )
+        if stake_amount and (not min_stake_amount or stake_amount >= min_stake_amount):
+            self.order_id_counter += 1
+            base_currency = self.exchange.get_pair_base_currency(pair)
+            amount_p = (stake_amount / propose_rate) * leverage
 
-    # calculate win/lose forwards from buy point
-    for sell_row in partial_ticker:
-        if max_open_trades > 0:
-            # Increase trade_count_lock for every iteration
-            trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
+            contract_size = self.exchange.get_contract_size(pair)
+            precision_amount = self.exchange.get_precision_amount(pair)
+            amount = amount_to_contract_precision(
+                amount_p, precision_amount, self.precision_mode, contract_size
+            )
+            if not amount:
+                # No amount left after truncating to precision.
+                return trade
+            # Backcalculate actual stake amount.
+            stake_amount = amount * propose_rate / leverage
 
-        buy_signal = sell_row.buy
-        if should_sell(trade, sell_row.close, sell_row.date, buy_signal, sell_row.sell):
-            return sell_row, (pair,
-                              trade.calc_profit_percent(rate=sell_row.close),
-                              trade.calc_profit(rate=sell_row.close),
-                              (sell_row.date - buy_row.date).seconds // 60
-                              ), sell_row.date
-    return None
+            if not pos_adjust:
+                # Confirm trade entry:
+                if not strategy_safe_wrapper(
+                    self.strategy.confirm_trade_entry, default_retval=True
+                )(
+                    pair=pair,
+                    order_type=order_type,
+                    amount=amount,
+                    rate=propose_rate,
+                    time_in_force=time_in_force,
+                    current_time=current_time,
+                    entry_tag=entry_tag,
+                    side=direction,
+                ):
+                    return trade
 
+            is_short = direction == "short"
+            # Necessary for Margin trading. Disabled until support is enabled.
+            # interest_rate = self.exchange.get_interest_rate()
 
-def backtest(args) -> DataFrame:
-    """
-    Implements backtesting functionality
-    :param args: a dict containing:
-        stake_amount: btc amount to use for each trade
-        processed: a processed dictionary with format {pair, data}
-        max_open_trades: maximum number of concurrent trades (default: 0, disabled)
-        realistic: do we try to simulate realistic trades? (default: True)
-        sell_profit_only: sell if profit only
-        use_sell_signal: act on sell-signal
-    :return: DataFrame
-    """
-    headers = ['date', 'buy', 'open', 'close', 'sell']
-    processed = args['processed']
-    max_open_trades = args.get('max_open_trades', 0)
-    realistic = args.get('realistic', False)
-    record = args.get('record', None)
-    records = []
-    trades = []
-    trade_count_lock: dict = {}
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-    for pair, pair_data in processed.items():
-        pair_data['buy'], pair_data['sell'] = 0, 0  # cleanup from previous run
+            if trade is None:
+                # Enter trade
+                self.trade_id_counter += 1
+                trade = LocalTrade(
+                    id=self.trade_id_counter,
+                    pair=pair,
+                    base_currency=base_currency,
+                    stake_currency=self.config["stake_currency"],
+                    open_rate=propose_rate,
+                    open_rate_requested=propose_rate,
+                    open_date=current_time,
+                    stake_amount=stake_amount,
+                    amount=0,
+                    amount_requested=amount,
+                    fee_open=self.fee,
+                    fee_close=self.fee,
+                    is_open=True,
+                    enter_tag=entry_tag,
+                    timeframe=self.timeframe_min,
+                    exchange=self._exchange_name,
+                    is_short=is_short,
+                    trading_mode=self.trading_mode,
+                    leverage=leverage,
+                    # interest_rate=interest_rate,
+                    amount_precision=precision_amount,
+                    price_precision=precision_price,
+                    precision_mode=self.precision_mode,
+                    precision_mode_price=self.precision_mode_price,
+                    contract_size=contract_size,
+                    orders=[],
+                )
+                LocalTrade.add_bt_trade(trade)
+            elif self.handle_similar_order(
+                trade, propose_rate, amount, trade.entry_side, current_time
+            ):
+                return None
 
-        ticker_data = populate_sell_trend(populate_buy_trend(pair_data))[headers]
-        ticker = [x for x in ticker_data.itertuples()]
+            trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
 
-        lock_pair_until = None
-        for index, row in enumerate(ticker):
-            if row.buy == 0 or row.sell == 1:
-                continue  # skip rows where no buy signal or that would immediately sell off
+            order = Order(
+                id=self.order_id_counter,
+                ft_trade_id=trade.id,
+                ft_is_open=True,
+                ft_pair=trade.pair,
+                order_id=str(self.order_id_counter),
+                symbol=trade.pair,
+                ft_order_side=trade.entry_side,
+                side=trade.entry_side,
+                order_type=order_type,
+                status="open",
+                order_date=current_time,
+                order_filled_date=current_time,
+                order_update_date=current_time,
+                ft_price=propose_rate,
+                price=propose_rate,
+                average=propose_rate,
+                amount=amount,
+                filled=0,
+                remaining=amount,
+                cost=amount * propose_rate * (1 + self.fee),
+                ft_order_tag=entry_tag,
+            )
+            order._trade_bt = trade
+            trade.orders.append(order)
+            self._try_close_open_order(order, trade, current_time, row)
+            trade.recalc_trade_from_orders()
 
-            if realistic:
-                if lock_pair_until is not None and row.date <= lock_pair_until:
+        return trade
+
+    def handle_left_open(
+        self, open_trades: dict[str, list[LocalTrade]], data: dict[str, list[tuple]]
+    ) -> None:
+        """
+        Handling of left open trades at the end of backtesting
+        """
+        for pair in open_trades.keys():
+            for trade in list(open_trades[pair]):
+                if (
+                    trade.has_open_orders and trade.nr_of_successful_entries == 0
+                ) or not trade.has_open_position:
+                    # Ignore trade if entry-order did not fill yet
+                    LocalTrade.remove_bt_trade(trade)
                     continue
-            if max_open_trades > 0:
-                # Check if max_open_trades has already been reached for the given date
-                if not trade_count_lock.get(row.date, 0) < max_open_trades:
+
+                exit_row = data[pair][-1]
+                self._exit_trade(
+                    trade, exit_row, exit_row[OPEN_IDX], trade.amount, ExitType.FORCE_EXIT.value
+                )
+                trade.exit_reason = ExitType.FORCE_EXIT.value
+                self._process_exit_order(
+                    trade.orders[-1], trade, exit_row[DATE_IDX].to_pydatetime(), exit_row, pair
+                )
+
+    def trade_slot_available(self, open_trade_count: int) -> bool:
+        # Always allow trades when max_open_trades is enabled.
+        max_open_trades: IntOrInf = self.strategy.max_open_trades
+        if max_open_trades <= 0 or open_trade_count < max_open_trades:
+            return True
+        # Rejected trade
+        self.rejected_trades += 1
+        return False
+
+    def check_for_trade_entry(self, row) -> LongShort | None:
+        enter_long = row[LONG_IDX] == 1
+        exit_long = row[ELONG_IDX] == 1
+        enter_short = self._can_short and row[SHORT_IDX] == 1
+        exit_short = self._can_short and row[ESHORT_IDX] == 1
+
+        if enter_long == 1 and not any([exit_long, enter_short]):
+            # Long
+            return "long"
+        if enter_short == 1 and not any([exit_short, enter_long]):
+            # Short
+            return "short"
+        return None
+
+    def run_protections(self, pair: str, current_time: datetime, side: LongShort):
+        if self.enable_protections:
+            self.protections.stop_per_pair(pair, current_time, side)
+            self.protections.global_stop(current_time, side)
+
+    def manage_open_orders(self, trade: LocalTrade, current_time: datetime, row: tuple) -> bool:
+        """
+        Check if any open order needs to be cancelled or replaced.
+        Returns True if the trade should be deleted.
+        """
+        for order in [o for o in trade.orders if o.ft_is_open]:
+            oc = self.check_order_cancel(trade, order, current_time)
+            if oc:
+                # delete trade due to order timeout
+                return True
+            elif oc is None and self.check_order_replace(trade, order, current_time, row):
+                # delete trade due to user request
+                self.canceled_trade_entries += 1
+                return True
+        # default maintain trade
+        return False
+
+    def cancel_open_orders(self, trade: LocalTrade, current_time: datetime):
+        """
+        Cancel all open orders for the given trade.
+        """
+        for order in [o for o in trade.orders if o.ft_is_open]:
+            if order.side == trade.entry_side:
+                self.canceled_entry_orders += 1
+            elif order.side == trade.exit_side:
+                self.canceled_exit_orders += 1
+            # canceled orders are removed from the trade
+            del trade.orders[trade.orders.index(order)]
+
+    def handle_similar_order(
+        self, trade: LocalTrade, price: float, amount: float, side: str, current_time: datetime
+    ) -> bool:
+        """
+        Handle similar order for the given trade.
+        """
+        if trade.has_open_orders:
+            oo = trade.select_order(side, True)
+            if oo:
+                if (price == oo.price) and (side == oo.side) and (amount == oo.amount):
+                    # logger.info(
+                    #     f"A similar open order was found for {trade.pair}. "
+                    #     f"Keeping existing {trade.exit_side} order. {price=},  {amount=}"
+                    # )
+                    return True
+            self.cancel_open_orders(trade, current_time)
+
+        return False
+
+    def check_order_cancel(
+        self, trade: LocalTrade, order: Order, current_time: datetime
+    ) -> bool | None:
+        """
+        Check if current analyzed order has to be canceled.
+        Returns True if the trade should be Deleted (initial order was canceled),
+                False if it's Canceled
+                None if the order is still active.
+        """
+        timedout = self.strategy.ft_check_timed_out(
+            trade,  # type: ignore[arg-type]
+            order,
+            current_time,
+        )
+        if timedout:
+            if order.side == trade.entry_side:
+                self.timedout_entry_orders += 1
+                if trade.nr_of_successful_entries == 0:
+                    # Remove trade due to entry timeout expiration.
+                    return True
+                else:
+                    # Close additional entry order
+                    del trade.orders[trade.orders.index(order)]
+                    return False
+            if order.side == trade.exit_side:
+                self.timedout_exit_orders += 1
+                # Close exit order and retry exiting on next signal.
+                del trade.orders[trade.orders.index(order)]
+                return False
+        return None
+
+    def check_order_replace(
+        self, trade: LocalTrade, order: Order, current_time, row: tuple
+    ) -> bool:
+        """
+        Check if current analyzed entry order has to be replaced and do so.
+        If user requested cancellation and there are no filled orders in the trade will
+        instruct caller to delete the trade.
+        Returns True if the trade should be deleted.
+        """
+        # only check on new candles for open entry orders
+        if current_time > order.order_date_utc:
+            is_entry = order.side == trade.entry_side
+            requested_rate = strategy_safe_wrapper(
+                self.strategy.adjust_order_price, default_retval=order.ft_price
+            )(
+                trade=trade,  # type: ignore[arg-type]
+                order=order,
+                pair=trade.pair,
+                current_time=current_time,
+                proposed_rate=row[OPEN_IDX],
+                current_order_rate=order.ft_price,
+                entry_tag=trade.enter_tag,
+                side=trade.trade_direction,
+                is_entry=is_entry,
+            )  # default value is current order price
+
+            # cancel existing order whenever a new rate is requested (or None)
+            if requested_rate == order.ft_price:
+                # assumption: there can't be multiple open entry orders at any given time
+                return False
+            else:
+                del trade.orders[trade.orders.index(order)]
+                if is_entry:
+                    self.canceled_entry_orders += 1
+                else:
+                    self.canceled_exit_orders += 1
+
+            # place new order if result was not None
+            if requested_rate:
+                if is_entry:
+                    self._enter_trade(
+                        pair=trade.pair,
+                        row=row,
+                        trade=trade,
+                        requested_rate=requested_rate,
+                        requested_stake=(order.safe_remaining * order.ft_price / trade.leverage),
+                        direction="short" if trade.is_short else "long",
+                    )
+                    self.replaced_entry_orders += 1
+                else:
+                    self._exit_trade(
+                        trade=trade,
+                        sell_row=row,
+                        close_rate=requested_rate,
+                        amount=order.safe_remaining,
+                        exit_reason=order.ft_order_tag,
+                    )
+                    self.replaced_exit_orders += 1
+                # Delete trade if no successful entries happened (if placing the new order failed)
+                if not trade.has_open_orders and is_entry and trade.nr_of_successful_entries == 0:
+                    return True
+            else:
+                # assumption: there can't be multiple open entry orders at any given time
+                return trade.nr_of_successful_entries == 0
+        return False
+
+    def validate_row(
+        self, data: dict, pair: str, row_index: int, current_time: datetime
+    ) -> tuple | None:
+        try:
+            # Row is treated as "current incomplete candle".
+            # entry / exit signals are shifted by 1 to compensate for this.
+            row = data[pair][row_index]
+        except IndexError:
+            # missing Data for one pair at the end.
+            # Warnings for this are shown during data loading
+            return None
+
+        # Waits until the time-counter reaches the start of the data for this pair.
+        if row[DATE_IDX] > current_time:
+            return None
+        return row
+
+    def _collate_rejected(self, pair, row):
+        """
+        Temporarily store rejected signal information for downstream use in backtesting_analysis
+        """
+        # It could be fun to enable hyperopt mode to write
+        # a loss function to reduce rejected signals
+        if (
+            self.config.get("export", "none") == "signals"
+            and self.dataprovider.runmode == RunMode.BACKTEST
+        ):
+            if pair not in self.rejected_dict:
+                self.rejected_dict[pair] = []
+            self.rejected_dict[pair].append([row[DATE_IDX], row[ENTER_TAG_IDX]])
+
+    def backtest_loop(
+        self,
+        row: tuple,
+        pair: str,
+        current_time: datetime,
+        trade_dir: LongShort | None,
+        can_enter: bool,
+    ) -> LongShort | None:
+        """
+        NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
+
+        Backtesting processing for one candle/pair.
+        """
+        exiting_dir: LongShort | None = None
+        if not self._position_stacking and len(LocalTrade.bt_trades_open_pp[pair]) > 0:
+            # position_stacking not supported for now.
+            exiting_dir = "short" if LocalTrade.bt_trades_open_pp[pair][0].is_short else "long"
+
+        for t in list(LocalTrade.bt_trades_open_pp[pair]):
+            # 1. Manage currently open orders of active trades
+            if self.manage_open_orders(t, current_time, row):
+                # Remove trade (initial open order never filled)
+                LocalTrade.remove_bt_trade(t)
+                self.wallets.update()
+
+        # 2. Process entries.
+        # without positionstacking, we can only have one open trade per pair.
+        # max_open_trades must be respected
+        # don't open on the last row
+        # We only open trades on the main candle, not on detail candles
+        if (
+            can_enter
+            and trade_dir is not None
+            and (self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
+            and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
+        ):
+            if self.trade_slot_available(LocalTrade.bt_open_open_trade_count):
+                trade = self._enter_trade(pair, row, trade_dir)
+                if trade:
+                    self.wallets.update()
+            else:
+                self._collate_rejected(pair, row)
+
+        for trade in list(LocalTrade.bt_trades_open_pp[pair]):
+            # 3. Process entry orders.
+            order = trade.select_order(trade.entry_side, is_open=True)
+            if self._try_close_open_order(order, trade, current_time, row):
+                self.wallets.update()
+
+            # 4. Create exit orders (if any)
+            if trade.has_open_position:
+                self._check_trade_exit(trade, row, current_time)  # Place exit order if necessary
+
+            # 5. Process exit orders.
+            order = trade.select_order(trade.exit_side, is_open=True)
+            if order:
+                self._process_exit_order(order, trade, current_time, row, pair)
+
+        if exiting_dir and len(LocalTrade.bt_trades_open_pp[pair]) == 0:
+            return exiting_dir
+        return None
+
+    def get_detail_data(self, pair: str, row: tuple) -> list[tuple] | None:
+        """
+        Spread into detail data
+        """
+        current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
+        exit_candle_end = current_detail_time + self.timeframe_td
+        detail_data = self.detail_data[pair]
+        detail_data = detail_data.loc[
+            (detail_data["date"] >= current_detail_time) & (detail_data["date"] < exit_candle_end)
+        ].copy()
+
+        if len(detail_data) == 0:
+            return None
+        detail_data.loc[:, "enter_long"] = row[LONG_IDX]
+        detail_data.loc[:, "exit_long"] = row[ELONG_IDX]
+        detail_data.loc[:, "enter_short"] = row[SHORT_IDX]
+        detail_data.loc[:, "exit_short"] = row[ESHORT_IDX]
+        detail_data.loc[:, "enter_tag"] = row[ENTER_TAG_IDX]
+        detail_data.loc[:, "exit_tag"] = row[EXIT_TAG_IDX]
+        return detail_data[HEADERS].values.tolist()
+
+    def _time_generator(self, start_date: datetime, end_date: datetime):
+        current_time = start_date + self.timeframe_td
+        while current_time <= end_date:
+            yield current_time
+            current_time += self.timeframe_td
+
+    def _time_generator_det(self, start_date: datetime, end_date: datetime):
+        """
+        Loop for each detail candle.
+        Yields only the start date if no detail timeframe is set.
+        """
+        if not self.timeframe_detail_td:
+            yield start_date, True, False, 0
+            return
+
+        current_time = start_date
+        i = 0
+        while current_time <= end_date:
+            yield current_time, i == 0, True, i
+            i += 1
+            current_time += self.timeframe_detail_td
+
+    def _time_pair_generator_det(self, current_time: datetime, pairs: list[str]):
+        for current_time_det, is_first, has_detail, idx in self._time_generator_det(
+            current_time, current_time + self.timeframe_td
+        ):
+            # Pairs that have open trades should be processed first
+            new_pairlist = list(dict.fromkeys([t.pair for t in LocalTrade.bt_trades_open] + pairs))
+            for pair in new_pairlist:
+                yield current_time_det, is_first, has_detail, idx, pair
+
+    def time_pair_generator(
+        self,
+        start_date: datetime,
+        end_date: datetime,
+        pairs: list[str],
+        data: dict[str, list[tuple]],
+    ):
+        """
+        Backtest time and pair generator
+        :returns: generator of (current_time, pair, row, is_last_row, trade_dir)
+            where is_last_row is a boolean indicating if this is the data end date.
+        """
+        current_time = start_date + self.timeframe_td
+        self.progress.init_step(
+            BacktestState.BACKTEST, int((end_date - start_date) / self.timeframe_td)
+        )
+        # Indexes per pair, so some pairs are allowed to have a missing start.
+        indexes: dict = defaultdict(int)
+
+        for current_time in self._time_generator(start_date, end_date):
+            # Loop for each main candle.
+            self.check_abort()
+            # Reset open trade count for this candle
+            # Critical to avoid exceeding max_open_trades in backtesting
+            # when timeframe-detail is used and trades close within the opening candle.
+            strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)(
+                current_time=current_time
+            )
+            pair_detail_cache: dict[str, list[tuple]] = {}
+            pair_tradedir_cache: dict[str, LongShort | None] = {}
+            pairs_with_open_trades = [t.pair for t in LocalTrade.bt_trades_open]
+
+            for current_time_det, is_first, has_detail, idx, pair in self._time_pair_generator_det(
+                current_time, pairs
+            ):
+                # Loop for each detail candle (if necessary) and pair
+                # Yields only the main date if no detail timeframe is set.
+
+                # Pairs that have open trades should be processed first
+                trade_dir: LongShort | None = None
+                if is_first:
+                    # Main candle
+                    row_index = indexes[pair]
+                    row = self.validate_row(data, pair, row_index, current_time)
+                    if not row:
+                        continue
+
+                    row_index += 1
+                    indexes[pair] = row_index
+                    is_last_row = current_time == end_date
+                    self.dataprovider._set_dataframe_max_index(
+                        pair, self.required_startup + row_index
+                    )
+                    trade_dir = self.check_for_trade_entry(row)
+                    pair_tradedir_cache[pair] = trade_dir
+
+                else:
+                    # Detail candle - from cache.
+                    detail_data = pair_detail_cache.get(pair)
+                    if detail_data is None or len(detail_data) <= idx:
+                        # logger.info(f"skipping {pair}, {current_time_det}, {trade_dir}")
+                        continue
+                    row = detail_data[idx]
+                    trade_dir = pair_tradedir_cache.get(pair)
+
+                    if self.strategy.ignore_expired_candle(
+                        current_time - self.timeframe_td,  # last closed candle is 1 timeframe away.
+                        current_time_det,
+                        self.timeframe_secs,
+                        trade_dir is not None,
+                    ):
+                        # Ignore late entries eventually
+                        trade_dir = None
+
+                self.dataprovider._set_dataframe_max_date(current_time_det)
+
+                pair_has_open_trades = len(LocalTrade.bt_trades_open_pp[pair]) > 0
+                if pair in pairs_with_open_trades and not pair_has_open_trades:
+                    # Pair has had open trades which closed in the current main candle.
+                    # Skip this pair for this timeframe
                     continue
-                trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
+                if pair_has_open_trades and pair not in pairs_with_open_trades:
+                    # auto-lock for pairs that have open trades
+                    # Necessary for detail - to capture trades that open and close within
+                    # the same main candle
+                    pairs_with_open_trades.append(pair)
 
-            ret = get_sell_trade_entry(pair, row, ticker[index+1:], trade_count_lock, args)
-            if ret:
-                row2, trade_entry, next_date = ret
-                lock_pair_until = next_date
-                trades.append(trade_entry)
-                if record:
-                    # Note, need to be json.dump friendly
-                    # record a tuple of pair, current_profit_percent,
-                    # entry-date, duration
-                    records.append((pair, trade_entry[1],
-                                    row.date.strftime('%s'),
-                                    row2.date.strftime('%s'),
-                                    row.date, trade_entry[3]))
-    # For now export inside backtest(), maybe change so that backtest()
-    # returns a tuple like: (dataframe, records, logs, etc)
-    if record and record.find('trades') >= 0:
-        logger.info('Dumping backtest results')
-        misc.file_dump_json('backtest-result.json', records)
-    labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
-    return DataFrame.from_records(trades, columns=labels)
+                if (
+                    is_first
+                    and (trade_dir is not None or pair_has_open_trades)
+                    and has_detail
+                    and pair not in pair_detail_cache
+                    and pair in self.detail_data
+                    and row
+                ):
+                    # Spread candle into detail timeframe and cache that -
+                    # only once per main candle
+                    # and only if we can expect activity.
+                    pair_detail = self.get_detail_data(pair, row)
+                    if pair_detail is not None:
+                        pair_detail_cache[pair] = pair_detail
+                    row = pair_detail_cache[pair][idx]
 
+                is_last_row = current_time_det == end_date
 
-def start(args):
-    # Initialize logger
-    logging.basicConfig(
-        level=args.loglevel,
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    )
+                yield current_time_det, pair, row, is_last_row, trade_dir
+            self.progress.increment()
 
-    exchange._API = Bittrex({'key': '', 'secret': ''})
+    def backtest(
+        self, processed: dict, start_date: datetime, end_date: datetime
+    ) -> BacktestContentTypeIcomplete:
+        """
+        Implement backtesting functionality
 
-    logger.info('Using config: %s ...', args.config)
-    config = misc.load_config(args.config)
+        NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
+        Of course try to not have ugly code. By some accessor are sometime slower than functions.
+        Avoid extensive logging in this method and functions it calls.
 
-    # If -i/--ticker-interval is use we override the configuration parameter
-    # (that will override the strategy configuration)
-    if args.ticker_interval:
-        config.update({'ticker_interval': args.ticker_interval})
+        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
+        optimize memory usage!
+        :param start_date: backtesting timerange start datetime
+        :param end_date: backtesting timerange end datetime
+        :return: DataFrame with trades (results of backtesting)
+        """
+        self.prepare_backtest(self.enable_protections)
+        # Ensure wallets are up-to-date (important for --strategy-list)
+        self.wallets.update()
+        # Use dict of lists with data for performance
+        # (looping lists is a lot faster than pandas DataFrames)
+        data: dict = self._get_ohlcv_as_lists(processed)
 
-    # init the strategy to use
-    config.update({'strategy': args.strategy})
-    strategy = Strategy()
-    strategy.init(config)
+        # Loop timerange and get candle for each pair at that point in time
+        for (
+            current_time,
+            pair,
+            row,
+            is_last_row,
+            trade_dir,
+        ) in self.time_pair_generator(start_date, end_date, list(data.keys()), data):
+            if not self._can_short or trade_dir is None:
+                # No need to reverse position if shorting is disabled or there's no new signal
+                self.backtest_loop(row, pair, current_time, trade_dir, not is_last_row)
+            else:
+                # Conditionally call backtest_loop a 2nd time if shorting is enabled,
+                # a position closed and a new signal in the other direction is available.
 
-    logger.info('Using ticker_interval: %d ...', strategy.ticker_interval)
+                for _ in (0, 1):
+                    a = self.backtest_loop(row, pair, current_time, trade_dir, not is_last_row)
+                    if not a or a == trade_dir:
+                        # the trade didn't close or position change is in the same direction
+                        break
 
-    data = {}
-    pairs = config['exchange']['pair_whitelist']
-    logger.info('Using stake_currency: %s ...', config['stake_currency'])
-    logger.info('Using stake_amount: %s ...', config['stake_amount'])
+        self.handle_left_open(LocalTrade.bt_trades_open_pp, data=data)
+        self.wallets.update()
 
-    if args.live:
-        logger.info('Downloading data for all pairs in whitelist ...')
-        for pair in pairs:
-            data[pair] = exchange.get_ticker_history(pair, strategy.ticker_interval)
-    else:
-        logger.info('Using local backtesting data (using whitelist in given config) ...')
+        results = trade_list_to_dataframe(LocalTrade.bt_trades)
+        return {
+            "results": results,
+            "config": self.strategy.config,
+            "locks": PairLocks.get_all_locks(),
+            "rejected_signals": self.rejected_trades,
+            "timedout_entry_orders": self.timedout_entry_orders,
+            "timedout_exit_orders": self.timedout_exit_orders,
+            "canceled_trade_entries": self.canceled_trade_entries,
+            "canceled_entry_orders": self.canceled_entry_orders,
+            "replaced_entry_orders": self.replaced_entry_orders,
+            "final_balance": self.wallets.get_total(self.strategy.config["stake_currency"]),
+        }
 
-        timerange = misc.parse_timerange(args.timerange)
-        data = optimize.load_data(args.datadir,
-                                  pairs=pairs,
-                                  ticker_interval=strategy.ticker_interval,
-                                  refresh_pairs=args.refresh_pairs,
-                                  timerange=timerange)
-    max_open_trades = 0
-    if args.realistic_simulation:
-        logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
-        max_open_trades = config['max_open_trades']
+    def backtest_one_strategy(
+        self, strat: IStrategy, data: dict[str, DataFrame], timerange: TimeRange
+    ):
+        self.progress.init_step(BacktestState.ANALYZE, 0)
+        strategy_name = strat.get_strategy_name()
+        logger.info(f"Running backtesting for Strategy {strategy_name}")
+        backtest_start_time = dt_now()
+        self._set_strategy(strat)
 
-    # Monkey patch config
-    from freqtrade import main
-    main._CONF = config
+        # need to reprocess data every time to populate signals
+        preprocessed = self.strategy.advise_all_indicators(data)
 
-    preprocessed = optimize.tickerdata_to_dataframe(data)
-    # Print timeframe
-    min_date, max_date = get_timeframe(preprocessed)
-    logger.info('Measuring data from %s up to %s (%s days)..',
-                min_date.isoformat(),
-                max_date.isoformat(),
-                (max_date-min_date).days)
-    # Execute backtest and print results
-    sell_profit_only = config.get('experimental', {}).get('sell_profit_only', False)
-    use_sell_signal = config.get('experimental', {}).get('use_sell_signal', False)
-    results = backtest({'stake_amount': config['stake_amount'],
-                        'processed': preprocessed,
-                        'max_open_trades': max_open_trades,
-                        'realistic': args.realistic_simulation,
-                        'sell_profit_only': sell_profit_only,
-                        'use_sell_signal': use_sell_signal,
-                        'record': args.export
-                        })
-    logger.info(
-        '\n==================================== BACKTESTING REPORT ====================================\n%s',  # noqa
-        generate_text_table(data, results, config['stake_currency'])
-    )
+        # Trim startup period from analyzed dataframe
+        # This only used to determine if trimming would result in an empty dataframe
+        preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)
+
+        if not preprocessed_tmp:
+            raise OperationalException("No data left after adjusting for startup candles.")
+
+        # Use preprocessed_tmp for date generation (the trimmed dataframe).
+        # Backtesting will re-trim the dataframes after entry/exit signal generation.
+        min_date, max_date = history.get_timerange(preprocessed_tmp)
+        logger.info(
+            f"Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} "
+            f"up to {max_date.strftime(DATETIME_PRINT_FORMAT)} "
+            f"({(max_date - min_date).days} days)."
+        )
+        # Execute backtest and store results
+        results = self.backtest(
+            processed=preprocessed,
+            start_date=min_date,
+            end_date=max_date,
+        )
+        backtest_end_time = dt_now()
+        results.update(
+            {
+                "run_id": self.run_ids.get(strategy_name, ""),
+                "backtest_start_time": int(backtest_start_time.timestamp()),
+                "backtest_end_time": int(backtest_end_time.timestamp()),
+            }
+        )
+        self.all_results[strategy_name] = results
+
+        if (
+            self.config.get("export", "none") == "signals"
+            and self.dataprovider.runmode == RunMode.BACKTEST
+        ):
+            signals = generate_trade_signal_candles(preprocessed_tmp, results, "open_date")
+            rejected = generate_rejected_signals(preprocessed_tmp, self.rejected_dict)
+            exited = generate_trade_signal_candles(preprocessed_tmp, results, "close_date")
+
+            self.analysis_results["signals"][strategy_name] = signals
+            self.analysis_results["rejected"][strategy_name] = rejected
+            self.analysis_results["exited"][strategy_name] = exited
+
+        return min_date, max_date
+
+    def _get_min_cached_backtest_date(self):
+        min_backtest_date = None
+        backtest_cache_age = self.config.get("backtest_cache", constants.BACKTEST_CACHE_DEFAULT)
+        if self.timerange.stopts == 0 or self.timerange.stopdt > dt_now():
+            logger.warning("Backtest result caching disabled due to use of open-ended timerange.")
+        elif backtest_cache_age == "day":
+            min_backtest_date = dt_now() - timedelta(days=1)
+        elif backtest_cache_age == "week":
+            min_backtest_date = dt_now() - timedelta(weeks=1)
+        elif backtest_cache_age == "month":
+            min_backtest_date = dt_now() - timedelta(weeks=4)
+        return min_backtest_date
+
+    def load_prior_backtest(self):
+        self.run_ids = {
+            strategy.get_strategy_name(): get_strategy_run_id(strategy)
+            for strategy in self.strategylist
+        }
+
+        # Load previous result that will be updated incrementally.
+        # This can be circumvented in certain instances in combination with downloading more data
+        min_backtest_date = self._get_min_cached_backtest_date()
+        if min_backtest_date is not None:
+            self.results = find_existing_backtest_stats(
+                self.config["user_data_dir"] / "backtest_results", self.run_ids, min_backtest_date
+            )
+
+    def start(self) -> None:
+        """
+        Run backtesting end-to-end
+        """
+        data: dict[str, DataFrame] = {}
+
+        data, timerange = self.load_bt_data()
+        self.load_bt_data_detail()
+        logger.info("Dataload complete. Calculating indicators")
+
+        self.load_prior_backtest()
+
+        for strat in self.strategylist:
+            if self.results and strat.get_strategy_name() in self.results["strategy"]:
+                # When previous result hash matches - reuse that result and skip backtesting.
+                logger.info(f"Reusing result of previous backtest for {strat.get_strategy_name()}")
+                continue
+            min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
+
+        # Update old results with new ones.
+        if len(self.all_results) > 0:
+            results = generate_backtest_stats(
+                data, self.all_results, min_date=min_date, max_date=max_date
+            )
+            if self.results:
+                self.results["metadata"].update(results["metadata"])
+                self.results["strategy"].update(results["strategy"])
+                self.results["strategy_comparison"].extend(results["strategy_comparison"])
+            else:
+                self.results = results
+            dt_appendix = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+            if self.config.get("export", "none") in ("trades", "signals"):
+                combined_res = combined_dataframes_with_rel_mean(data, min_date, max_date)
+                store_backtest_results(
+                    self.config,
+                    self.results,
+                    dt_appendix,
+                    market_change_data=combined_res,
+                    analysis_results=self.analysis_results,
+                    strategy_files={s.get_strategy_name(): s.__file__ for s in self.strategylist},
+                )
+
+        # Results may be mixed up now. Sort them so they follow --strategy-list order.
+        if "strategy_list" in self.config and len(self.results) > 0:
+            self.results["strategy_comparison"] = sorted(
+                self.results["strategy_comparison"],
+                key=lambda c: self.config["strategy_list"].index(c["key"]),
+            )
+            self.results["strategy"] = dict(
+                sorted(
+                    self.results["strategy"].items(),
+                    key=lambda kv: self.config["strategy_list"].index(kv[0]),
+                )
+            )
+
+        if len(self.strategylist) > 0:
+            # Show backtest results
+            show_backtest_results(self.config, self.results)
diff --git a/freqtrade/optimize/base_analysis.py b/freqtrade/optimize/base_analysis.py
new file mode 100644
index 000000000..a2fe53867
--- /dev/null
+++ b/freqtrade/optimize/base_analysis.py
@@ -0,0 +1,64 @@
+import logging
+from copy import deepcopy
+from datetime import datetime, timezone
+from typing import Any
+
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+
+
+logger = logging.getLogger(__name__)
+
+
+class VarHolder:
+    timerange: TimeRange
+    data: DataFrame
+    indicators: dict[str, DataFrame]
+    result: DataFrame
+    compared: DataFrame
+    from_dt: datetime
+    to_dt: datetime
+    compared_dt: datetime
+    timeframe: str
+    startup_candle: int
+
+
+class BaseAnalysis:
+    def __init__(self, config: dict[str, Any], strategy_obj: dict):
+        self.failed_bias_check = True
+        self.full_varHolder = VarHolder()
+        self.exchange: Any | None = None
+        self._fee = None
+
+        # pull variables the scope of the lookahead_analysis-instance
+        self.local_config = deepcopy(config)
+        self.local_config["strategy"] = strategy_obj["name"]
+        self.strategy_obj = strategy_obj
+
+    @staticmethod
+    def dt_to_timestamp(dt: datetime):
+        timestamp = int(dt.replace(tzinfo=timezone.utc).timestamp())
+        return timestamp
+
+    def fill_full_varholder(self):
+        self.full_varHolder = VarHolder()
+
+        # define datetime in human-readable format
+        parsed_timerange = TimeRange.parse_timerange(self.local_config["timerange"])
+
+        if parsed_timerange.startdt is None:
+            self.full_varHolder.from_dt = datetime.fromtimestamp(0, tz=timezone.utc)
+        else:
+            self.full_varHolder.from_dt = parsed_timerange.startdt
+
+        if parsed_timerange.stopdt is None:
+            self.full_varHolder.to_dt = datetime.now(timezone.utc)
+        else:
+            self.full_varHolder.to_dt = parsed_timerange.stopdt
+
+        self.prepare_data(self.full_varHolder, self.local_config["pairs"])
+
+    def start(self) -> None:
+        # first make a single backtest
+        self.fill_full_varholder()
diff --git a/freqtrade/optimize/bt_progress.py b/freqtrade/optimize/bt_progress.py
new file mode 100644
index 000000000..a49fe0d86
--- /dev/null
+++ b/freqtrade/optimize/bt_progress.py
@@ -0,0 +1,34 @@
+from freqtrade.enums import BacktestState
+
+
+class BTProgress:
+    _action: BacktestState = BacktestState.STARTUP
+    _progress: float = 0
+    _max_steps: float = 0
+
+    def __init__(self):
+        pass
+
+    def init_step(self, action: BacktestState, max_steps: float):
+        self._action = action
+        self._max_steps = max_steps
+        self._progress = 0
+
+    def set_new_value(self, new_value: float):
+        self._progress = new_value
+
+    def increment(self):
+        self._progress += 1
+
+    @property
+    def progress(self):
+        """
+        Get progress as ratio, capped to be between 0 and 1 (to avoid small calculation errors).
+        """
+        return max(
+            min(round(self._progress / self._max_steps, 5) if self._max_steps > 0 else 0, 1), 0
+        )
+
+    @property
+    def action(self):
+        return str(self._action)
diff --git a/freqtrade/optimize/edge_cli.py b/freqtrade/optimize/edge_cli.py
new file mode 100644
index 000000000..65dd30efc
--- /dev/null
+++ b/freqtrade/optimize/edge_cli.py
@@ -0,0 +1,55 @@
+# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
+
+"""
+This module contains the edge backtesting interface
+"""
+
+import logging
+
+from freqtrade import constants
+from freqtrade.configuration import TimeRange, validate_config_consistency
+from freqtrade.constants import Config
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.edge import Edge
+from freqtrade.optimize.optimize_reports import generate_edge_table
+from freqtrade.resolvers import ExchangeResolver, StrategyResolver
+
+
+logger = logging.getLogger(__name__)
+
+
+class EdgeCli:
+    """
+    EdgeCli class, this class contains all the logic to run edge backtesting
+
+    To run a edge backtest:
+    edge = EdgeCli(config)
+    edge.start()
+    """
+
+    def __init__(self, config: Config) -> None:
+        self.config = config
+
+        # Ensure using dry-run
+        self.config["dry_run"] = True
+        self.config["stake_amount"] = constants.UNLIMITED_STAKE_AMOUNT
+        self.exchange = ExchangeResolver.load_exchange(self.config)
+        self.strategy = StrategyResolver.load_strategy(self.config)
+        self.strategy.dp = DataProvider(config, self.exchange)
+
+        validate_config_consistency(self.config)
+
+        self.edge = Edge(config, self.exchange, self.strategy)
+        # Set refresh_pairs to false for edge-cli (it must be true for edge)
+        self.edge._refresh_pairs = False
+
+        self.edge._timerange = TimeRange.parse_timerange(
+            None if self.config.get("timerange") is None else str(self.config.get("timerange"))
+        )
+        self.strategy.ft_bot_start()
+
+    def start(self) -> None:
+        result = self.edge.calculate(self.config["exchange"]["pair_whitelist"])
+        if result:
+            print("")  # blank line for readability
+            generate_edge_table(self.edge._cached_pairs)
diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py
deleted file mode 100644
index 8138f4caa..000000000
--- a/freqtrade/optimize/hyperopt.py
+++ /dev/null
@@ -1,561 +0,0 @@
-# pragma pylint: disable=missing-docstring,W0212,W0603
-
-
-import json
-import logging
-import os
-import pickle
-import signal
-import sys
-from functools import reduce
-from math import exp
-from operator import itemgetter
-from typing import Dict, Any, Callable
-
-import numpy
-import talib.abstract as ta
-from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
-from hyperopt.mongoexp import MongoTrials
-from pandas import DataFrame
-
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-# Monkey patch config
-from freqtrade import main  # noqa; noqa
-from freqtrade import exchange, misc, optimize
-from freqtrade.exchange import Bittrex
-from freqtrade.misc import load_config
-from freqtrade.optimize import backtesting
-from freqtrade.optimize.backtesting import backtest
-from freqtrade.strategy.strategy import Strategy
-from user_data.hyperopt_conf import hyperopt_optimize_conf
-
-# Remove noisy log messages
-logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING)
-logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
-
-logger = logging.getLogger(__name__)
-
-# set TARGET_TRADES to suit your number concurrent trades so its realistic to the number of days
-TARGET_TRADES = 600
-TOTAL_TRIES = 0
-_CURRENT_TRIES = 0
-CURRENT_BEST_LOSS = 100
-
-# max average trade duration in minutes
-# if eval ends with higher value, we consider it a failed eval
-MAX_ACCEPTED_TRADE_DURATION = 300
-
-# this is expexted avg profit * expected trade count
-# for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85
-# check that the reported Σ% values do not exceed this!
-EXPECTED_MAX_PROFIT = 3.0
-
-# Configuration and data used by hyperopt
-PROCESSED = None  # optimize.preprocess(optimize.load_data())
-OPTIMIZE_CONFIG = hyperopt_optimize_conf()
-
-# Hyperopt Trials
-TRIALS_FILE = os.path.join('user_data', 'hyperopt_trials.pickle')
-TRIALS = Trials()
-
-main._CONF = OPTIMIZE_CONFIG
-
-
-def populate_indicators(dataframe: DataFrame) -> DataFrame:
-    """
-    Adds several different TA indicators to the given DataFrame
-    """
-    dataframe['adx'] = ta.ADX(dataframe)
-    dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
-    dataframe['cci'] = ta.CCI(dataframe)
-    macd = ta.MACD(dataframe)
-    dataframe['macd'] = macd['macd']
-    dataframe['macdsignal'] = macd['macdsignal']
-    dataframe['macdhist'] = macd['macdhist']
-    dataframe['mfi'] = ta.MFI(dataframe)
-    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
-    dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
-    dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-    dataframe['roc'] = ta.ROC(dataframe)
-    dataframe['rsi'] = ta.RSI(dataframe)
-    # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
-    rsi = 0.1 * (dataframe['rsi'] - 50)
-    dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
-    # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
-    dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
-    # Stoch
-    stoch = ta.STOCH(dataframe)
-    dataframe['slowd'] = stoch['slowd']
-    dataframe['slowk'] = stoch['slowk']
-    # Stoch fast
-    stoch_fast = ta.STOCHF(dataframe)
-    dataframe['fastd'] = stoch_fast['fastd']
-    dataframe['fastk'] = stoch_fast['fastk']
-    # Stoch RSI
-    stoch_rsi = ta.STOCHRSI(dataframe)
-    dataframe['fastd_rsi'] = stoch_rsi['fastd']
-    dataframe['fastk_rsi'] = stoch_rsi['fastk']
-    # Bollinger bands
-    bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-    dataframe['bb_lowerband'] = bollinger['lower']
-    dataframe['bb_middleband'] = bollinger['mid']
-    dataframe['bb_upperband'] = bollinger['upper']
-    # EMA - Exponential Moving Average
-    dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
-    dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
-    dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
-    dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
-    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
-    # SAR Parabolic
-    dataframe['sar'] = ta.SAR(dataframe)
-    # SMA - Simple Moving Average
-    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-    # TEMA - Triple Exponential Moving Average
-    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
-    # Hilbert Transform Indicator - SineWave
-    hilbert = ta.HT_SINE(dataframe)
-    dataframe['htsine'] = hilbert['sine']
-    dataframe['htleadsine'] = hilbert['leadsine']
-
-    # Pattern Recognition - Bullish candlestick patterns
-    # ------------------------------------
-    """
-    # Hammer: values [0, 100]
-    dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
-    # Inverted Hammer: values [0, 100]
-    dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
-    # Dragonfly Doji: values [0, 100]
-    dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
-    # Piercing Line: values [0, 100]
-    dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
-    # Morningstar: values [0, 100]
-    dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
-    # Three White Soldiers: values [0, 100]
-    dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
-    """
-
-    # Pattern Recognition - Bearish candlestick patterns
-    # ------------------------------------
-    """
-    # Hanging Man: values [0, 100]
-    dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
-    # Shooting Star: values [0, 100]
-    dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
-    # Gravestone Doji: values [0, 100]
-    dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
-    # Dark Cloud Cover: values [0, 100]
-    dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
-    # Evening Doji Star: values [0, 100]
-    dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
-    # Evening Star: values [0, 100]
-    dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
-    """
-
-    # Pattern Recognition - Bullish/Bearish candlestick patterns
-    # ------------------------------------
-    """
-    # Three Line Strike: values [0, -100, 100]
-    dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
-    # Spinning Top: values [0, -100, 100]
-    dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
-    # Engulfing: values [0, -100, 100]
-    dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
-    # Harami: values [0, -100, 100]
-    dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
-    # Three Outside Up/Down: values [0, -100, 100]
-    dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
-    # Three Inside Up/Down: values [0, -100, 100]
-    dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
-    """
-
-    # Chart type
-    # ------------------------------------
-    # Heikinashi stategy
-    heikinashi = qtpylib.heikinashi(dataframe)
-    dataframe['ha_open'] = heikinashi['open']
-    dataframe['ha_close'] = heikinashi['close']
-    dataframe['ha_high'] = heikinashi['high']
-    dataframe['ha_low'] = heikinashi['low']
-
-    return dataframe
-
-
-def save_trials(trials, trials_path=TRIALS_FILE):
-    """Save hyperopt trials to file"""
-    logger.info('Saving Trials to \'{}\''.format(trials_path))
-    pickle.dump(trials, open(trials_path, 'wb'))
-
-
-def read_trials(trials_path=TRIALS_FILE):
-    """Read hyperopt trials file"""
-    logger.info('Reading Trials from \'{}\''.format(trials_path))
-    trials = pickle.load(open(trials_path, 'rb'))
-    os.remove(trials_path)
-    return trials
-
-
-def log_trials_result(trials):
-    vals = json.dumps(trials.best_trial['misc']['vals'], indent=4)
-    results = trials.best_trial['result']['result']
-    logger.info('Best result:\n%s\nwith values:\n%s', results, vals)
-
-
-def log_results(results):
-    """ log results if it is better than any previous evaluation """
-    global CURRENT_BEST_LOSS
-
-    if results['loss'] < CURRENT_BEST_LOSS:
-        CURRENT_BEST_LOSS = results['loss']
-        logger.info('{:5d}/{}: {}. Loss {:.5f}'.format(
-            results['current_tries'],
-            results['total_tries'],
-            results['result'],
-            results['loss']))
-    else:
-        print('.', end='')
-        sys.stdout.flush()
-
-
-def calculate_loss(total_profit: float, trade_count: int, trade_duration: float):
-    """ objective function, returns smaller number for more optimal results """
-    trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
-    profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
-    duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
-    return trade_loss + profit_loss + duration_loss
-
-
-def generate_roi_table(params) -> Dict[int, float]:
-    roi_table = {}
-    roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
-    roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
-    roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
-    roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
-
-    return roi_table
-
-
-def roi_space() -> Dict[str, Any]:
-    return {
-        'roi_t1': hp.quniform('roi_t1', 10, 120, 20),
-        'roi_t2': hp.quniform('roi_t2', 10, 60, 15),
-        'roi_t3': hp.quniform('roi_t3', 10, 40, 10),
-        'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01),
-        'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01),
-        'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
-    }
-
-
-def stoploss_space() -> Dict[str, Any]:
-    return {
-        'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
-    }
-
-
-def indicator_space() -> Dict[str, Any]:
-    """
-    Define your Hyperopt space for searching strategy parameters
-    """
-    return {
-        'macd_below_zero': hp.choice('macd_below_zero', [
-            {'enabled': False},
-            {'enabled': True}
-        ]),
-        'mfi': hp.choice('mfi', [
-            {'enabled': False},
-            {'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)}
-        ]),
-        'fastd': hp.choice('fastd', [
-            {'enabled': False},
-            {'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)}
-        ]),
-        'adx': hp.choice('adx', [
-            {'enabled': False},
-            {'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)}
-        ]),
-        'rsi': hp.choice('rsi', [
-            {'enabled': False},
-            {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)}
-        ]),
-        'uptrend_long_ema': hp.choice('uptrend_long_ema', [
-            {'enabled': False},
-            {'enabled': True}
-        ]),
-        'uptrend_short_ema': hp.choice('uptrend_short_ema', [
-            {'enabled': False},
-            {'enabled': True}
-        ]),
-        'over_sar': hp.choice('over_sar', [
-            {'enabled': False},
-            {'enabled': True}
-        ]),
-        'green_candle': hp.choice('green_candle', [
-            {'enabled': False},
-            {'enabled': True}
-        ]),
-        'uptrend_sma': hp.choice('uptrend_sma', [
-            {'enabled': False},
-            {'enabled': True}
-        ]),
-        'trigger': hp.choice('trigger', [
-            {'type': 'lower_bb'},
-            {'type': 'lower_bb_tema'},
-            {'type': 'faststoch10'},
-            {'type': 'ao_cross_zero'},
-            {'type': 'ema3_cross_ema10'},
-            {'type': 'macd_cross_signal'},
-            {'type': 'sar_reversal'},
-            {'type': 'ht_sine'},
-            {'type': 'heiken_reversal_bull'},
-            {'type': 'di_cross'},
-        ]),
-    }
-
-
-def has_space(spaces, space):
-    if space in spaces or 'all' in spaces:
-        return True
-    return False
-
-
-def hyperopt_space(selected_spaces: str) -> Dict[str, Any]:
-    spaces = {}
-    if has_space(selected_spaces, 'buy'):
-        spaces = {**spaces, **indicator_space()}
-    if has_space(selected_spaces, 'roi'):
-        spaces = {**spaces, **roi_space()}
-    if has_space(selected_spaces, 'stoploss'):
-        spaces = {**spaces, **stoploss_space()}
-    return spaces
-
-
-def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
-    """
-    Define the buy strategy parameters to be used by hyperopt
-    """
-    def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
-        conditions = []
-        # GUARDS AND TRENDS
-        if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
-            conditions.append(dataframe['ema50'] > dataframe['ema100'])
-        if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
-            conditions.append(dataframe['macd'] < 0)
-        if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
-            conditions.append(dataframe['ema5'] > dataframe['ema10'])
-        if 'mfi' in params and params['mfi']['enabled']:
-            conditions.append(dataframe['mfi'] < params['mfi']['value'])
-        if 'fastd' in params and params['fastd']['enabled']:
-            conditions.append(dataframe['fastd'] < params['fastd']['value'])
-        if 'adx' in params and params['adx']['enabled']:
-            conditions.append(dataframe['adx'] > params['adx']['value'])
-        if 'rsi' in params and params['rsi']['enabled']:
-            conditions.append(dataframe['rsi'] < params['rsi']['value'])
-        if 'over_sar' in params and params['over_sar']['enabled']:
-            conditions.append(dataframe['close'] > dataframe['sar'])
-        if 'green_candle' in params and params['green_candle']['enabled']:
-            conditions.append(dataframe['close'] > dataframe['open'])
-        if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
-            prevsma = dataframe['sma'].shift(1)
-            conditions.append(dataframe['sma'] > prevsma)
-
-        # TRIGGERS
-        triggers = {
-            'lower_bb': (
-                dataframe['close'] < dataframe['bb_lowerband']
-            ),
-            'lower_bb_tema': (
-                dataframe['tema'] < dataframe['bb_lowerband']
-            ),
-            'faststoch10': (qtpylib.crossed_above(
-                dataframe['fastd'], 10.0
-            )),
-            'ao_cross_zero': (qtpylib.crossed_above(
-                dataframe['ao'], 0.0
-            )),
-            'ema3_cross_ema10': (qtpylib.crossed_above(
-                dataframe['ema3'], dataframe['ema10']
-            )),
-            'macd_cross_signal': (qtpylib.crossed_above(
-                dataframe['macd'], dataframe['macdsignal']
-            )),
-            'sar_reversal': (qtpylib.crossed_above(
-                dataframe['close'], dataframe['sar']
-            )),
-            'ht_sine': (qtpylib.crossed_above(
-                dataframe['htleadsine'], dataframe['htsine']
-            )),
-            'heiken_reversal_bull': (
-                (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
-                (dataframe['ha_low'] == dataframe['ha_open'])
-            ),
-            'di_cross': (qtpylib.crossed_above(
-                dataframe['plus_di'], dataframe['minus_di']
-            )),
-        }
-        conditions.append(triggers.get(params['trigger']['type']))
-
-        dataframe.loc[
-            reduce(lambda x, y: x & y, conditions),
-            'buy'] = 1
-
-        return dataframe
-
-    return populate_buy_trend
-
-
-def generate_optimizer(args):
-    def optimizer(params):
-        global _CURRENT_TRIES
-
-        strategy = Strategy()
-        if has_space(args.spaces, 'roi'):
-            strategy.minimal_roi = generate_roi_table(params)
-
-        if has_space(args.spaces, 'buy'):
-            backtesting.populate_buy_trend = buy_strategy_generator(params)
-
-        if has_space(args.spaces, 'stoploss'):
-            strategy.stoploss = params['stoploss']
-
-        results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'],
-                            'processed': PROCESSED,
-                            'realistic': args.realistic_simulation,
-                            })
-        result_explanation = format_results(results)
-
-        total_profit = results.profit_percent.sum()
-        trade_count = len(results.index)
-        trade_duration = results.duration.mean()
-
-        if trade_count == 0 or trade_duration > MAX_ACCEPTED_TRADE_DURATION:
-            print('.', end='')
-            return {
-                'status': STATUS_FAIL,
-                'loss': float('inf')
-            }
-
-        loss = calculate_loss(total_profit, trade_count, trade_duration)
-
-        _CURRENT_TRIES += 1
-
-        log_results({
-            'loss': loss,
-            'current_tries': _CURRENT_TRIES,
-            'total_tries': TOTAL_TRIES,
-            'result': result_explanation,
-        })
-
-        return {
-            'loss': loss,
-            'status': STATUS_OK,
-            'result': result_explanation,
-        }
-
-    return optimizer
-
-
-def format_results(results: DataFrame):
-    return ('{:6d} trades. Avg profit {: 5.2f}%. '
-            'Total profit {: 11.8f} BTC ({:.4f}Σ%). Avg duration {:5.1f} mins.').format(
-                len(results.index),
-                results.profit_percent.mean() * 100.0,
-                results.profit_BTC.sum(),
-                results.profit_percent.sum(),
-                results.duration.mean(),
-            )
-
-
-def start(args):
-    global TOTAL_TRIES, PROCESSED, TRIALS, _CURRENT_TRIES
-
-    TOTAL_TRIES = args.epochs
-
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    # Initialize logger
-    logging.basicConfig(
-        level=args.loglevel,
-        format='\n%(message)s',
-    )
-
-    logger.info('Using config: %s ...', args.config)
-    config = load_config(args.config)
-    pairs = config['exchange']['pair_whitelist']
-
-    # If -i/--ticker-interval is use we override the configuration parameter
-    # (that will override the strategy configuration)
-    if args.ticker_interval:
-        config.update({'ticker_interval': args.ticker_interval})
-
-    # init the strategy to use
-    config.update({'strategy': args.strategy})
-    strategy = Strategy()
-    strategy.init(config)
-
-    timerange = misc.parse_timerange(args.timerange)
-    data = optimize.load_data(args.datadir, pairs=pairs,
-                              ticker_interval=strategy.ticker_interval,
-                              timerange=timerange)
-    if has_space(args.spaces, 'buy'):
-        optimize.populate_indicators = populate_indicators
-    PROCESSED = optimize.tickerdata_to_dataframe(data)
-
-    if args.mongodb:
-        logger.info('Using mongodb ...')
-        logger.info('Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!')
-
-        db_name = 'freqtrade_hyperopt'
-        TRIALS = MongoTrials('mongo://127.0.0.1:1234/{}/jobs'.format(db_name), exp_key='exp1')
-    else:
-        logger.info('Preparing Trials..')
-        signal.signal(signal.SIGINT, signal_handler)
-        # read trials file if we have one
-        if os.path.exists(TRIALS_FILE):
-            TRIALS = read_trials()
-
-            _CURRENT_TRIES = len(TRIALS.results)
-            TOTAL_TRIES = TOTAL_TRIES + _CURRENT_TRIES
-            logger.info(
-                'Continuing with trials. Current: {}, Total: {}'
-                .format(_CURRENT_TRIES, TOTAL_TRIES))
-
-    try:
-        best_parameters = fmin(
-            fn=generate_optimizer(args),
-            space=hyperopt_space(args.spaces),
-            algo=tpe.suggest,
-            max_evals=TOTAL_TRIES,
-            trials=TRIALS
-        )
-
-        results = sorted(TRIALS.results, key=itemgetter('loss'))
-        best_result = results[0]['result']
-
-    except ValueError:
-        best_parameters = {}
-        best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
-                      'try with more epochs (param: -e).'
-
-    # Improve best parameter logging display
-    if best_parameters:
-        best_parameters = space_eval(
-            hyperopt_space(args.spaces),
-            best_parameters
-        )
-
-    logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
-    if 'roi_t1' in best_parameters:
-        logger.info('ROI table:\n%s', generate_roi_table(best_parameters))
-    logger.info('Best Result:\n%s', best_result)
-
-    # Store trials result to file to resume next time
-    save_trials(TRIALS)
-
-
-def signal_handler(sig, frame):
-    """Hyperopt SIGINT handler"""
-    logger.info('Hyperopt received {}'.format(signal.Signals(sig).name))
-
-    save_trials(TRIALS)
-    log_trials_result(TRIALS)
-    sys.exit(0)
diff --git a/freqtrade/optimize/hyperopt/__init__.py b/freqtrade/optimize/hyperopt/__init__.py
new file mode 100644
index 000000000..7dea075ea
--- /dev/null
+++ b/freqtrade/optimize/hyperopt/__init__.py
@@ -0,0 +1,5 @@
+from freqtrade.optimize.hyperopt.hyperopt import Hyperopt
+from freqtrade.optimize.hyperopt_loss.hyperopt_loss_interface import IHyperOptLoss
+
+
+__all__ = ["Hyperopt", "IHyperOptLoss"]
diff --git a/freqtrade/optimize/hyperopt/hyperopt.py b/freqtrade/optimize/hyperopt/hyperopt.py
new file mode 100644
index 000000000..ef71860a5
--- /dev/null
+++ b/freqtrade/optimize/hyperopt/hyperopt.py
@@ -0,0 +1,331 @@
+# pragma pylint: disable=too-many-instance-attributes, pointless-string-statement
+
+"""
+This module contains the hyperopt logic
+"""
+
+import logging
+import random
+from datetime import datetime
+from math import ceil
+from multiprocessing import Manager
+from pathlib import Path
+from typing import Any
+
+import rapidjson
+from joblib import Parallel, cpu_count, delayed, wrap_non_picklable_objects
+
+from freqtrade.constants import FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
+from freqtrade.enums import HyperoptState
+from freqtrade.exceptions import OperationalException
+from freqtrade.misc import file_dump_json, plural
+from freqtrade.optimize.hyperopt.hyperopt_logger import logging_mp_handle, logging_mp_setup
+from freqtrade.optimize.hyperopt.hyperopt_optimizer import HyperOptimizer
+from freqtrade.optimize.hyperopt.hyperopt_output import HyperoptOutput
+from freqtrade.optimize.hyperopt_tools import (
+    HyperoptStateContainer,
+    HyperoptTools,
+    hyperopt_serializer,
+)
+from freqtrade.util import get_progress_tracker
+
+
+logger = logging.getLogger(__name__)
+
+
+INITIAL_POINTS = 30
+
+# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
+# in the skopt model queue, to optimize memory consumption
+SKOPT_MODEL_QUEUE_SIZE = 10
+
+log_queue: Any
+
+
+class Hyperopt:
+    """
+    Hyperopt class, this class contains all the logic to run a hyperopt simulation
+
+    To start a hyperopt run:
+    hyperopt = Hyperopt(config)
+    hyperopt.start()
+    """
+
+    def __init__(self, config: Config) -> None:
+        self._hyper_out: HyperoptOutput = HyperoptOutput(streaming=True)
+
+        self.config = config
+
+        self.analyze_per_epoch = self.config.get("analyze_per_epoch", False)
+        HyperoptStateContainer.set_state(HyperoptState.STARTUP)
+
+        if self.config.get("hyperopt"):
+            raise OperationalException(
+                "Using separate Hyperopt files has been removed in 2021.9. Please convert "
+                "your existing Hyperopt file to the new Hyperoptable strategy interface"
+            )
+
+        time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        strategy = str(self.config["strategy"])
+        self.results_file: Path = (
+            self.config["user_data_dir"]
+            / "hyperopt_results"
+            / f"strategy_{strategy}_{time_now}.fthypt"
+        )
+        self.data_pickle_file = (
+            self.config["user_data_dir"] / "hyperopt_results" / "hyperopt_tickerdata.pkl"
+        )
+        self.total_epochs = config.get("epochs", 0)
+
+        self.current_best_loss = 100
+
+        self.clean_hyperopt()
+
+        self.num_epochs_saved = 0
+        self.current_best_epoch: dict[str, Any] | None = None
+
+        if HyperoptTools.has_space(self.config, "sell"):
+            # Make sure use_exit_signal is enabled
+            self.config["use_exit_signal"] = True
+
+        self.print_all = self.config.get("print_all", False)
+        self.hyperopt_table_header = 0
+        self.print_json = self.config.get("print_json", False)
+
+        self.hyperopter = HyperOptimizer(self.config)
+
+    @staticmethod
+    def get_lock_filename(config: Config) -> str:
+        return str(config["user_data_dir"] / "hyperopt.lock")
+
+    def clean_hyperopt(self) -> None:
+        """
+        Remove hyperopt pickle files to restart hyperopt.
+        """
+        for f in [self.data_pickle_file, self.results_file]:
+            p = Path(f)
+            if p.is_file():
+                logger.info(f"Removing `{p}`.")
+                p.unlink()
+
+    def _save_result(self, epoch: dict) -> None:
+        """
+        Save hyperopt results to file
+        Store one line per epoch.
+        While not a valid json object - this allows appending easily.
+        :param epoch: result dictionary for this epoch.
+        """
+        epoch[FTHYPT_FILEVERSION] = 2
+        with self.results_file.open("a") as f:
+            rapidjson.dump(
+                epoch,
+                f,
+                default=hyperopt_serializer,
+                number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN,
+            )
+            f.write("\n")
+
+        self.num_epochs_saved += 1
+        logger.debug(
+            f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
+            f"saved to '{self.results_file}'."
+        )
+        # Store hyperopt filename
+        latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
+        file_dump_json(latest_filename, {"latest_hyperopt": str(self.results_file.name)}, log=False)
+
+    def print_results(self, results: dict[str, Any]) -> None:
+        """
+        Log results if it is better than any previous evaluation
+        TODO: this should be moved to HyperoptTools too
+        """
+        is_best = results["is_best"]
+
+        if self.print_all or is_best:
+            self._hyper_out.add_data(
+                self.config,
+                [results],
+                self.total_epochs,
+                self.print_all,
+            )
+
+    def run_optimizer_parallel(self, parallel: Parallel, asked: list[list]) -> list[dict[str, Any]]:
+        """Start optimizer in a parallel way"""
+
+        def optimizer_wrapper(*args, **kwargs):
+            # global log queue. This must happen in the file that initializes Parallel
+            logging_mp_setup(
+                log_queue, logging.INFO if self.config["verbosity"] < 1 else logging.DEBUG
+            )
+
+            return self.hyperopter.generate_optimizer(*args, **kwargs)
+
+        return parallel(delayed(wrap_non_picklable_objects(optimizer_wrapper))(v) for v in asked)
+
+    def _set_random_state(self, random_state: int | None) -> int:
+        return random_state or random.randint(1, 2**16 - 1)  # noqa: S311
+
+    def get_asked_points(self, n_points: int) -> tuple[list[list[Any]], list[bool]]:
+        """
+        Enforce points returned from `self.opt.ask` have not been already evaluated
+
+        Steps:
+        1. Try to get points using `self.opt.ask` first
+        2. Discard the points that have already been evaluated
+        3. Retry using `self.opt.ask` up to 3 times
+        4. If still some points are missing in respect to `n_points`, random sample some points
+        5. Repeat until at least `n_points` points in the `asked_non_tried` list
+        6. Return a list with length truncated at `n_points`
+        """
+
+        def unique_list(a_list):
+            new_list = []
+            for item in a_list:
+                if item not in new_list:
+                    new_list.append(item)
+            return new_list
+
+        i = 0
+        asked_non_tried: list[list[Any]] = []
+        is_random_non_tried: list[bool] = []
+        while i < 5 and len(asked_non_tried) < n_points:
+            if i < 3:
+                self.opt.cache_ = {}
+                asked = unique_list(self.opt.ask(n_points=n_points * 5 if i > 0 else n_points))
+                is_random = [False for _ in range(len(asked))]
+            else:
+                asked = unique_list(self.opt.space.rvs(n_samples=n_points * 5))
+                is_random = [True for _ in range(len(asked))]
+            is_random_non_tried += [
+                rand
+                for x, rand in zip(asked, is_random, strict=False)
+                if x not in self.opt.Xi and x not in asked_non_tried
+            ]
+            asked_non_tried += [
+                x for x in asked if x not in self.opt.Xi and x not in asked_non_tried
+            ]
+            i += 1
+
+        if asked_non_tried:
+            return (
+                asked_non_tried[: min(len(asked_non_tried), n_points)],
+                is_random_non_tried[: min(len(asked_non_tried), n_points)],
+            )
+        else:
+            return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
+
+    def evaluate_result(self, val: dict[str, Any], current: int, is_random: bool):
+        """
+        Evaluate results returned from generate_optimizer
+        """
+        val["current_epoch"] = current
+        val["is_initial_point"] = current <= INITIAL_POINTS
+
+        logger.debug("Optimizer epoch evaluated: %s", val)
+
+        is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
+        # This value is assigned here and not in the optimization method
+        # to keep proper order in the list of results. That's because
+        # evaluations can take different time. Here they are aligned in the
+        # order they will be shown to the user.
+        val["is_best"] = is_best
+        val["is_random"] = is_random
+        self.print_results(val)
+
+        if is_best:
+            self.current_best_loss = val["loss"]
+            self.current_best_epoch = val
+
+        self._save_result(val)
+
+    def _setup_logging_mp_workaround(self) -> None:
+        """
+        Workaround for logging in child processes.
+        local_queue must be a global in the file that initializes Parallel.
+        """
+        global log_queue
+        m = Manager()
+        log_queue = m.Queue()
+
+    def start(self) -> None:
+        self.random_state = self._set_random_state(self.config.get("hyperopt_random_state"))
+        logger.info(f"Using optimizer random state: {self.random_state}")
+        self.hyperopt_table_header = -1
+        self.hyperopter.prepare_hyperopt()
+
+        cpus = cpu_count()
+        logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
+        config_jobs = self.config.get("hyperopt_jobs", -1)
+        logger.info(f"Number of parallel jobs set as: {config_jobs}")
+
+        self.opt = self.hyperopter.get_optimizer(
+            config_jobs, self.random_state, INITIAL_POINTS, SKOPT_MODEL_QUEUE_SIZE
+        )
+        self._setup_logging_mp_workaround()
+        try:
+            with Parallel(n_jobs=config_jobs) as parallel:
+                jobs = parallel._effective_n_jobs()
+                logger.info(f"Effective number of parallel workers used: {jobs}")
+
+                # Define progressbar
+                with get_progress_tracker(cust_callables=[self._hyper_out]) as pbar:
+                    task = pbar.add_task("Epochs", total=self.total_epochs)
+
+                    start = 0
+
+                    if self.analyze_per_epoch:
+                        # First analysis not in parallel mode when using --analyze-per-epoch.
+                        # This allows dataprovider to load it's informative cache.
+                        asked, is_random = self.get_asked_points(n_points=1)
+                        f_val0 = self.hyperopter.generate_optimizer(asked[0])
+                        self.opt.tell(asked, [f_val0["loss"]])
+                        self.evaluate_result(f_val0, 1, is_random[0])
+                        pbar.update(task, advance=1)
+                        start += 1
+
+                    evals = ceil((self.total_epochs - start) / jobs)
+                    for i in range(evals):
+                        # Correct the number of epochs to be processed for the last
+                        # iteration (should not exceed self.total_epochs in total)
+                        n_rest = (i + 1) * jobs - (self.total_epochs - start)
+                        current_jobs = jobs - n_rest if n_rest > 0 else jobs
+
+                        asked, is_random = self.get_asked_points(n_points=current_jobs)
+                        f_val = self.run_optimizer_parallel(parallel, asked)
+                        self.opt.tell(asked, [v["loss"] for v in f_val])
+
+                        for j, val in enumerate(f_val):
+                            # Use human-friendly indexes here (starting from 1)
+                            current = i * jobs + j + 1 + start
+
+                            self.evaluate_result(val, current, is_random[j])
+                            pbar.update(task, advance=1)
+                        logging_mp_handle(log_queue)
+
+        except KeyboardInterrupt:
+            print("User interrupted..")
+
+        logger.info(
+            f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
+            f"saved to '{self.results_file}'."
+        )
+
+        if self.current_best_epoch:
+            HyperoptTools.try_export_params(
+                self.config,
+                self.hyperopter.get_strategy_name(),
+                self.current_best_epoch,
+            )
+
+            HyperoptTools.show_epoch_details(
+                self.current_best_epoch, self.total_epochs, self.print_json
+            )
+        elif self.num_epochs_saved > 0:
+            print(
+                f"No good result found for given optimization function in {self.num_epochs_saved} "
+                f"{plural(self.num_epochs_saved, 'epoch')}."
+            )
+        else:
+            # This is printed when Ctrl+C is pressed quickly, before first epochs have
+            # a chance to be evaluated.
+            print("No epochs evaluated yet, no best result.")
diff --git a/freqtrade/optimize/hyperopt/hyperopt_auto.py b/freqtrade/optimize/hyperopt/hyperopt_auto.py
new file mode 100644
index 000000000..1da80ec02
--- /dev/null
+++ b/freqtrade/optimize/hyperopt/hyperopt_auto.py
@@ -0,0 +1,101 @@
+"""
+HyperOptAuto class.
+This module implements a convenience auto-hyperopt class, which can be used together with strategies
+ that implement IHyperStrategy interface.
+"""
+
+import logging
+from collections.abc import Callable
+from contextlib import suppress
+
+from freqtrade.exceptions import OperationalException
+
+
+with suppress(ImportError):
+    from skopt.space import Dimension
+
+from freqtrade.optimize.hyperopt.hyperopt_interface import EstimatorType, IHyperOpt
+
+
+logger = logging.getLogger(__name__)
+
+
+def _format_exception_message(space: str, ignore_missing_space: bool) -> None:
+    msg = (
+        f"The '{space}' space is included into the hyperoptimization "
+        f"but no parameter for this space was found in your Strategy. "
+    )
+    if ignore_missing_space:
+        logger.warning(msg + "This space will be ignored.")
+    else:
+        raise OperationalException(
+            msg + f"Please make sure to have parameters for this space enabled for optimization "
+            f"or remove the '{space}' space from hyperoptimization."
+        )
+
+
+class HyperOptAuto(IHyperOpt):
+    """
+    This class delegates functionality to Strategy(IHyperStrategy) and Strategy.HyperOpt classes.
+     Most of the time Strategy.HyperOpt class would only implement indicator_space and
+     sell_indicator_space methods, but other hyperopt methods can be overridden as well.
+    """
+
+    def _get_func(self, name) -> Callable:
+        """
+        Return a function defined in Strategy.HyperOpt class, or one defined in super() class.
+        :param name: function name.
+        :return: a requested function.
+        """
+        hyperopt_cls = getattr(self.strategy, "HyperOpt", None)
+        default_func = getattr(super(), name)
+        if hyperopt_cls:
+            return getattr(hyperopt_cls, name, default_func)
+        else:
+            return default_func
+
+    def _generate_indicator_space(self, category):
+        for attr_name, attr in self.strategy.enumerate_parameters(category):
+            if attr.optimize:
+                yield attr.get_space(attr_name)
+
+    def _get_indicator_space(self, category) -> list:
+        # TODO: is this necessary, or can we call "generate_space" directly?
+        indicator_space = list(self._generate_indicator_space(category))
+        if len(indicator_space) > 0:
+            return indicator_space
+        else:
+            _format_exception_message(
+                category, self.config.get("hyperopt_ignore_missing_space", False)
+            )
+            return []
+
+    def buy_indicator_space(self) -> list["Dimension"]:
+        return self._get_indicator_space("buy")
+
+    def sell_indicator_space(self) -> list["Dimension"]:
+        return self._get_indicator_space("sell")
+
+    def protection_space(self) -> list["Dimension"]:
+        return self._get_indicator_space("protection")
+
+    def generate_roi_table(self, params: dict) -> dict[int, float]:
+        return self._get_func("generate_roi_table")(params)
+
+    def roi_space(self) -> list["Dimension"]:
+        return self._get_func("roi_space")()
+
+    def stoploss_space(self) -> list["Dimension"]:
+        return self._get_func("stoploss_space")()
+
+    def generate_trailing_params(self, params: dict) -> dict:
+        return self._get_func("generate_trailing_params")(params)
+
+    def trailing_space(self) -> list["Dimension"]:
+        return self._get_func("trailing_space")()
+
+    def max_open_trades_space(self) -> list["Dimension"]:
+        return self._get_func("max_open_trades_space")()
+
+    def generate_estimator(self, dimensions: list["Dimension"], **kwargs) -> EstimatorType:
+        return self._get_func("generate_estimator")(dimensions=dimensions, **kwargs)
diff --git a/freqtrade/optimize/hyperopt/hyperopt_interface.py b/freqtrade/optimize/hyperopt/hyperopt_interface.py
new file mode 100644
index 000000000..6382d7f8d
--- /dev/null
+++ b/freqtrade/optimize/hyperopt/hyperopt_interface.py
@@ -0,0 +1,217 @@
+"""
+IHyperOpt interface
+This module defines the interface to apply for hyperopt
+"""
+
+import logging
+import math
+from abc import ABC
+from typing import TypeAlias
+
+from sklearn.base import RegressorMixin
+from skopt.space import Categorical, Dimension, Integer
+
+from freqtrade.constants import Config
+from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.misc import round_dict
+from freqtrade.optimize.space import SKDecimal
+from freqtrade.strategy import IStrategy
+
+
+logger = logging.getLogger(__name__)
+
+EstimatorType: TypeAlias = RegressorMixin | str
+
+
+class IHyperOpt(ABC):
+    """
+    Interface for freqtrade hyperopt
+    Defines the mandatory structure must follow any custom hyperopt
+
+    Class attributes you can use:
+        timeframe -> int: value of the timeframe to use for the strategy
+    """
+
+    timeframe: str
+    strategy: IStrategy
+
+    def __init__(self, config: Config) -> None:
+        self.config = config
+
+        # Assign timeframe to be used in hyperopt
+        IHyperOpt.timeframe = str(config["timeframe"])
+
+    def generate_estimator(self, dimensions: list[Dimension], **kwargs) -> EstimatorType:
+        """
+        Return base_estimator.
+        Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
+        inheriting from RegressorMixin (from sklearn).
+        """
+        return "ET"
+
+    def generate_roi_table(self, params: dict) -> dict[int, float]:
+        """
+        Create a ROI table.
+
+        Generates the ROI table that will be used by Hyperopt.
+        You may override it in your custom Hyperopt class.
+        """
+        roi_table = {}
+        roi_table[0] = params["roi_p1"] + params["roi_p2"] + params["roi_p3"]
+        roi_table[params["roi_t3"]] = params["roi_p1"] + params["roi_p2"]
+        roi_table[params["roi_t3"] + params["roi_t2"]] = params["roi_p1"]
+        roi_table[params["roi_t3"] + params["roi_t2"] + params["roi_t1"]] = 0
+
+        return roi_table
+
+    def roi_space(self) -> list[Dimension]:
+        """
+        Create a ROI space.
+
+        Defines values to search for each ROI steps.
+
+        This method implements adaptive roi hyperspace with varied
+        ranges for parameters which automatically adapts to the
+        timeframe used.
+
+        It's used by Freqtrade by default, if no custom roi_space method is defined.
+        """
+
+        # Default scaling coefficients for the roi hyperspace. Can be changed
+        # to adjust resulting ranges of the ROI tables.
+        # Increase if you need wider ranges in the roi hyperspace, decrease if shorter
+        # ranges are needed.
+        roi_t_alpha = 1.0
+        roi_p_alpha = 1.0
+
+        timeframe_min = timeframe_to_minutes(self.timeframe)
+
+        # We define here limits for the ROI space parameters automagically adapted to the
+        # timeframe used by the bot:
+        #
+        # * 'roi_t' (limits for the time intervals in the ROI tables) components
+        #   are scaled linearly.
+        # * 'roi_p' (limits for the ROI value steps) components are scaled logarithmically.
+        #
+        # The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
+        # method for the 5m timeframe.
+        roi_t_scale = timeframe_min / 5
+        roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)
+        roi_limits = {
+            "roi_t1_min": int(10 * roi_t_scale * roi_t_alpha),
+            "roi_t1_max": int(120 * roi_t_scale * roi_t_alpha),
+            "roi_t2_min": int(10 * roi_t_scale * roi_t_alpha),
+            "roi_t2_max": int(60 * roi_t_scale * roi_t_alpha),
+            "roi_t3_min": int(10 * roi_t_scale * roi_t_alpha),
+            "roi_t3_max": int(40 * roi_t_scale * roi_t_alpha),
+            "roi_p1_min": 0.01 * roi_p_scale * roi_p_alpha,
+            "roi_p1_max": 0.04 * roi_p_scale * roi_p_alpha,
+            "roi_p2_min": 0.01 * roi_p_scale * roi_p_alpha,
+            "roi_p2_max": 0.07 * roi_p_scale * roi_p_alpha,
+            "roi_p3_min": 0.01 * roi_p_scale * roi_p_alpha,
+            "roi_p3_max": 0.20 * roi_p_scale * roi_p_alpha,
+        }
+        logger.debug(f"Using roi space limits: {roi_limits}")
+        p = {
+            "roi_t1": roi_limits["roi_t1_min"],
+            "roi_t2": roi_limits["roi_t2_min"],
+            "roi_t3": roi_limits["roi_t3_min"],
+            "roi_p1": roi_limits["roi_p1_min"],
+            "roi_p2": roi_limits["roi_p2_min"],
+            "roi_p3": roi_limits["roi_p3_min"],
+        }
+        logger.info(f"Min roi table: {round_dict(self.generate_roi_table(p), 3)}")
+        p = {
+            "roi_t1": roi_limits["roi_t1_max"],
+            "roi_t2": roi_limits["roi_t2_max"],
+            "roi_t3": roi_limits["roi_t3_max"],
+            "roi_p1": roi_limits["roi_p1_max"],
+            "roi_p2": roi_limits["roi_p2_max"],
+            "roi_p3": roi_limits["roi_p3_max"],
+        }
+        logger.info(f"Max roi table: {round_dict(self.generate_roi_table(p), 3)}")
+
+        return [
+            Integer(roi_limits["roi_t1_min"], roi_limits["roi_t1_max"], name="roi_t1"),
+            Integer(roi_limits["roi_t2_min"], roi_limits["roi_t2_max"], name="roi_t2"),
+            Integer(roi_limits["roi_t3_min"], roi_limits["roi_t3_max"], name="roi_t3"),
+            SKDecimal(
+                roi_limits["roi_p1_min"], roi_limits["roi_p1_max"], decimals=3, name="roi_p1"
+            ),
+            SKDecimal(
+                roi_limits["roi_p2_min"], roi_limits["roi_p2_max"], decimals=3, name="roi_p2"
+            ),
+            SKDecimal(
+                roi_limits["roi_p3_min"], roi_limits["roi_p3_max"], decimals=3, name="roi_p3"
+            ),
+        ]
+
+    def stoploss_space(self) -> list[Dimension]:
+        """
+        Create a stoploss space.
+
+        Defines range of stoploss values to search.
+        You may override it in your custom Hyperopt class.
+        """
+        return [
+            SKDecimal(-0.35, -0.02, decimals=3, name="stoploss"),
+        ]
+
+    def generate_trailing_params(self, params: dict) -> dict:
+        """
+        Create dict with trailing stop parameters.
+        """
+        return {
+            "trailing_stop": params["trailing_stop"],
+            "trailing_stop_positive": params["trailing_stop_positive"],
+            "trailing_stop_positive_offset": (
+                params["trailing_stop_positive"] + params["trailing_stop_positive_offset_p1"]
+            ),
+            "trailing_only_offset_is_reached": params["trailing_only_offset_is_reached"],
+        }
+
+    def trailing_space(self) -> list[Dimension]:
+        """
+        Create a trailing stoploss space.
+
+        You may override it in your custom Hyperopt class.
+        """
+        return [
+            # It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
+            # is used. Otherwise hyperopt will vary other parameters that won't have effect if
+            # trailing_stop is set False.
+            # This parameter is included into the hyperspace dimensions rather than assigning
+            # it explicitly in the code in order to have it printed in the results along with
+            # other 'trailing' hyperspace parameters.
+            Categorical([True], name="trailing_stop"),
+            SKDecimal(0.01, 0.35, decimals=3, name="trailing_stop_positive"),
+            # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
+            # so this intermediate parameter is used as the value of the difference between
+            # them. The value of the 'trailing_stop_positive_offset' is constructed in the
+            # generate_trailing_params() method.
+            # This is similar to the hyperspace dimensions used for constructing the ROI tables.
+            SKDecimal(0.001, 0.1, decimals=3, name="trailing_stop_positive_offset_p1"),
+            Categorical([True, False], name="trailing_only_offset_is_reached"),
+        ]
+
+    def max_open_trades_space(self) -> list[Dimension]:
+        """
+        Create a max open trades space.
+
+        You may override it in your custom Hyperopt class.
+        """
+        return [
+            Integer(-1, 10, name="max_open_trades"),
+        ]
+
+    # This is needed for proper unpickling the class attribute timeframe
+    # which is set to the actual value by the resolver.
+    # Why do I still need such shamanic mantras in modern python?
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["timeframe"] = self.timeframe
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        IHyperOpt.timeframe = state["timeframe"]
diff --git a/freqtrade/optimize/hyperopt/hyperopt_logger.py b/freqtrade/optimize/hyperopt/hyperopt_logger.py
new file mode 100644
index 000000000..d6940ee3a
--- /dev/null
+++ b/freqtrade/optimize/hyperopt/hyperopt_logger.py
@@ -0,0 +1,40 @@
+import logging
+from logging.handlers import QueueHandler
+from multiprocessing import Queue, current_process
+from queue import Empty
+
+
+logger = logging.getLogger(__name__)
+
+
+def logging_mp_setup(log_queue: Queue, verbosity: int):
+    """
+    Setup logging in a child process.
+    Must be called in the child process before logging.
+    log_queue MUST be passed to the child process via inheritance
+        Which essentially means that the log_queue must be a global, created in the same
+        file as Parallel is initialized.
+    """
+    current_proc = current_process().name
+    if current_proc != "MainProcess":
+        h = QueueHandler(log_queue)
+        root = logging.getLogger()
+        root.setLevel(verbosity)
+        root.addHandler(h)
+
+
+def logging_mp_handle(q: Queue):
+    """
+    Handle logging from a child process.
+    Must be called in the parent process to handle log messages from the child process.
+    """
+
+    try:
+        while True:
+            record = q.get(block=False)
+            if record is None:
+                break
+            logger.handle(record)
+
+    except Empty:
+        pass
diff --git a/freqtrade/optimize/hyperopt/hyperopt_optimizer.py b/freqtrade/optimize/hyperopt/hyperopt_optimizer.py
new file mode 100644
index 000000000..d46859ee7
--- /dev/null
+++ b/freqtrade/optimize/hyperopt/hyperopt_optimizer.py
@@ -0,0 +1,443 @@
+"""
+This module contains the hyperopt optimizer class, which needs to be pickled
+and will be sent to the hyperopt worker processes.
+"""
+
+import logging
+import sys
+import warnings
+from datetime import datetime, timezone
+from typing import Any
+
+from joblib import dump, load
+from joblib.externals import cloudpickle
+from pandas import DataFrame
+
+from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
+from freqtrade.data.converter import trim_dataframes
+from freqtrade.data.history import get_timerange
+from freqtrade.data.metrics import calculate_market_change
+from freqtrade.enums import HyperoptState
+from freqtrade.exceptions import OperationalException
+from freqtrade.ft_types import BacktestContentType
+from freqtrade.misc import deep_merge_dicts
+from freqtrade.optimize.backtesting import Backtesting
+
+# Import IHyperOptLoss to allow unpickling classes from these modules
+from freqtrade.optimize.hyperopt.hyperopt_auto import HyperOptAuto
+from freqtrade.optimize.hyperopt_loss.hyperopt_loss_interface import IHyperOptLoss
+from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer, HyperoptTools
+from freqtrade.optimize.optimize_reports import generate_strategy_stats
+from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
+from freqtrade.util.dry_run_wallet import get_dry_run_wallet
+
+
+# Suppress scikit-learn FutureWarnings from skopt
+with warnings.catch_warnings():
+    warnings.filterwarnings("ignore", category=FutureWarning)
+    from skopt import Optimizer
+    from skopt.space import Dimension
+
+logger = logging.getLogger(__name__)
+
+
+MAX_LOSS = 100000  # just a big enough number to be bad result in loss optimization
+
+
+class HyperOptimizer:
+    """
+    HyperoptOptimizer class
+    This class is sent to the hyperopt worker processes.
+    """
+
+    def __init__(self, config: Config) -> None:
+        self.buy_space: list[Dimension] = []
+        self.sell_space: list[Dimension] = []
+        self.protection_space: list[Dimension] = []
+        self.roi_space: list[Dimension] = []
+        self.stoploss_space: list[Dimension] = []
+        self.trailing_space: list[Dimension] = []
+        self.max_open_trades_space: list[Dimension] = []
+        self.dimensions: list[Dimension] = []
+
+        self.config = config
+        self.min_date: datetime
+        self.max_date: datetime
+
+        self.backtesting = Backtesting(self.config)
+        self.pairlist = self.backtesting.pairlists.whitelist
+        self.custom_hyperopt: HyperOptAuto
+        self.analyze_per_epoch = self.config.get("analyze_per_epoch", False)
+
+        if not self.config.get("hyperopt"):
+            self.custom_hyperopt = HyperOptAuto(self.config)
+        else:
+            raise OperationalException(
+                "Using separate Hyperopt files has been removed in 2021.9. Please convert "
+                "your existing Hyperopt file to the new Hyperoptable strategy interface"
+            )
+
+        self.backtesting._set_strategy(self.backtesting.strategylist[0])
+        self.custom_hyperopt.strategy = self.backtesting.strategy
+
+        self.hyperopt_pickle_magic(self.backtesting.strategy.__class__.__bases__)
+        self.custom_hyperoptloss: IHyperOptLoss = HyperOptLossResolver.load_hyperoptloss(
+            self.config
+        )
+        self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
+
+        self.data_pickle_file = (
+            self.config["user_data_dir"] / "hyperopt_results" / "hyperopt_tickerdata.pkl"
+        )
+
+        self.market_change = 0.0
+
+        if HyperoptTools.has_space(self.config, "sell"):
+            # Make sure use_exit_signal is enabled
+            self.config["use_exit_signal"] = True
+
+    def prepare_hyperopt(self) -> None:
+        # Initialize spaces ...
+        self.init_spaces()
+
+        self.prepare_hyperopt_data()
+
+        # We don't need exchange instance anymore while running hyperopt
+        self.backtesting.exchange.close()
+        self.backtesting.exchange._api = None
+        self.backtesting.exchange._api_async = None
+        self.backtesting.exchange.loop = None  # type: ignore
+        self.backtesting.exchange._loop_lock = None  # type: ignore
+        self.backtesting.exchange._cache_lock = None  # type: ignore
+        # self.backtesting.exchange = None  # type: ignore
+        self.backtesting.pairlists = None  # type: ignore
+
+    def get_strategy_name(self) -> str:
+        return self.backtesting.strategy.get_strategy_name()
+
+    def hyperopt_pickle_magic(self, bases: tuple[type, ...]) -> None:
+        """
+        Hyperopt magic to allow strategy inheritance across files.
+        For this to properly work, we need to register the module of the imported class
+        to pickle as value.
+        """
+        for modules in bases:
+            if modules.__name__ != "IStrategy":
+                if mod := sys.modules.get(modules.__module__):
+                    cloudpickle.register_pickle_by_value(mod)
+                self.hyperopt_pickle_magic(modules.__bases__)
+
+    def _get_params_dict(
+        self, dimensions: list[Dimension], raw_params: list[Any]
+    ) -> dict[str, Any]:
+        # Ensure the number of dimensions match
+        # the number of parameters in the list.
+        if len(raw_params) != len(dimensions):
+            raise ValueError("Mismatch in number of search-space dimensions.")
+
+        # Return a dict where the keys are the names of the dimensions
+        # and the values are taken from the list of parameters.
+        return {d.name: v for d, v in zip(dimensions, raw_params, strict=False)}
+
+    def _get_params_details(self, params: dict) -> dict:
+        """
+        Return the params for each space
+        """
+        result: dict = {}
+
+        if HyperoptTools.has_space(self.config, "buy"):
+            result["buy"] = {p.name: params.get(p.name) for p in self.buy_space}
+        if HyperoptTools.has_space(self.config, "sell"):
+            result["sell"] = {p.name: params.get(p.name) for p in self.sell_space}
+        if HyperoptTools.has_space(self.config, "protection"):
+            result["protection"] = {p.name: params.get(p.name) for p in self.protection_space}
+        if HyperoptTools.has_space(self.config, "roi"):
+            result["roi"] = {
+                str(k): v for k, v in self.custom_hyperopt.generate_roi_table(params).items()
+            }
+        if HyperoptTools.has_space(self.config, "stoploss"):
+            result["stoploss"] = {p.name: params.get(p.name) for p in self.stoploss_space}
+        if HyperoptTools.has_space(self.config, "trailing"):
+            result["trailing"] = self.custom_hyperopt.generate_trailing_params(params)
+        if HyperoptTools.has_space(self.config, "trades"):
+            result["max_open_trades"] = {
+                "max_open_trades": (
+                    self.backtesting.strategy.max_open_trades
+                    if self.backtesting.strategy.max_open_trades != float("inf")
+                    else -1
+                )
+            }
+
+        return result
+
+    def _get_no_optimize_details(self) -> dict[str, Any]:
+        """
+        Get non-optimized parameters
+        """
+        result: dict[str, Any] = {}
+        strategy = self.backtesting.strategy
+        if not HyperoptTools.has_space(self.config, "roi"):
+            result["roi"] = {str(k): v for k, v in strategy.minimal_roi.items()}
+        if not HyperoptTools.has_space(self.config, "stoploss"):
+            result["stoploss"] = {"stoploss": strategy.stoploss}
+        if not HyperoptTools.has_space(self.config, "trailing"):
+            result["trailing"] = {
+                "trailing_stop": strategy.trailing_stop,
+                "trailing_stop_positive": strategy.trailing_stop_positive,
+                "trailing_stop_positive_offset": strategy.trailing_stop_positive_offset,
+                "trailing_only_offset_is_reached": strategy.trailing_only_offset_is_reached,
+            }
+        if not HyperoptTools.has_space(self.config, "trades"):
+            result["max_open_trades"] = {"max_open_trades": strategy.max_open_trades}
+        return result
+
+    def init_spaces(self):
+        """
+        Assign the dimensions in the hyperoptimization space.
+        """
+        if HyperoptTools.has_space(self.config, "protection"):
+            # Protections can only be optimized when using the Parameter interface
+            logger.debug("Hyperopt has 'protection' space")
+            # Enable Protections if protection space is selected.
+            self.config["enable_protections"] = True
+            self.backtesting.enable_protections = True
+            self.protection_space = self.custom_hyperopt.protection_space()
+
+        if HyperoptTools.has_space(self.config, "buy"):
+            logger.debug("Hyperopt has 'buy' space")
+            self.buy_space = self.custom_hyperopt.buy_indicator_space()
+
+        if HyperoptTools.has_space(self.config, "sell"):
+            logger.debug("Hyperopt has 'sell' space")
+            self.sell_space = self.custom_hyperopt.sell_indicator_space()
+
+        if HyperoptTools.has_space(self.config, "roi"):
+            logger.debug("Hyperopt has 'roi' space")
+            self.roi_space = self.custom_hyperopt.roi_space()
+
+        if HyperoptTools.has_space(self.config, "stoploss"):
+            logger.debug("Hyperopt has 'stoploss' space")
+            self.stoploss_space = self.custom_hyperopt.stoploss_space()
+
+        if HyperoptTools.has_space(self.config, "trailing"):
+            logger.debug("Hyperopt has 'trailing' space")
+            self.trailing_space = self.custom_hyperopt.trailing_space()
+
+        if HyperoptTools.has_space(self.config, "trades"):
+            logger.debug("Hyperopt has 'trades' space")
+            self.max_open_trades_space = self.custom_hyperopt.max_open_trades_space()
+
+        self.dimensions = (
+            self.buy_space
+            + self.sell_space
+            + self.protection_space
+            + self.roi_space
+            + self.stoploss_space
+            + self.trailing_space
+            + self.max_open_trades_space
+        )
+
+    def assign_params(self, params_dict: dict[str, Any], category: str) -> None:
+        """
+        Assign hyperoptable parameters
+        """
+        for attr_name, attr in self.backtesting.strategy.enumerate_parameters(category):
+            if attr.optimize:
+                # noinspection PyProtectedMember
+                attr.value = params_dict[attr_name]
+
+    def generate_optimizer(self, raw_params: list[Any]) -> dict[str, Any]:
+        """
+        Used Optimize function.
+        Called once per epoch to optimize whatever is configured.
+        Keep this function as optimized as possible!
+        """
+        HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE)
+        backtest_start_time = datetime.now(timezone.utc)
+        params_dict = self._get_params_dict(self.dimensions, raw_params)
+
+        # Apply parameters
+        if HyperoptTools.has_space(self.config, "buy"):
+            self.assign_params(params_dict, "buy")
+
+        if HyperoptTools.has_space(self.config, "sell"):
+            self.assign_params(params_dict, "sell")
+
+        if HyperoptTools.has_space(self.config, "protection"):
+            self.assign_params(params_dict, "protection")
+
+        if HyperoptTools.has_space(self.config, "roi"):
+            self.backtesting.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(
+                params_dict
+            )
+
+        if HyperoptTools.has_space(self.config, "stoploss"):
+            self.backtesting.strategy.stoploss = params_dict["stoploss"]
+
+        if HyperoptTools.has_space(self.config, "trailing"):
+            d = self.custom_hyperopt.generate_trailing_params(params_dict)
+            self.backtesting.strategy.trailing_stop = d["trailing_stop"]
+            self.backtesting.strategy.trailing_stop_positive = d["trailing_stop_positive"]
+            self.backtesting.strategy.trailing_stop_positive_offset = d[
+                "trailing_stop_positive_offset"
+            ]
+            self.backtesting.strategy.trailing_only_offset_is_reached = d[
+                "trailing_only_offset_is_reached"
+            ]
+
+        if HyperoptTools.has_space(self.config, "trades"):
+            if self.config["stake_amount"] == "unlimited" and (
+                params_dict["max_open_trades"] == -1 or params_dict["max_open_trades"] == 0
+            ):
+                # Ignore unlimited max open trades if stake amount is unlimited
+                params_dict.update({"max_open_trades": self.config["max_open_trades"]})
+
+            updated_max_open_trades = (
+                int(params_dict["max_open_trades"])
+                if (params_dict["max_open_trades"] != -1 and params_dict["max_open_trades"] != 0)
+                else float("inf")
+            )
+
+            self.config.update({"max_open_trades": updated_max_open_trades})
+
+            self.backtesting.strategy.max_open_trades = updated_max_open_trades
+
+        with self.data_pickle_file.open("rb") as f:
+            processed = load(f, mmap_mode="r")
+            if self.analyze_per_epoch:
+                # Data is not yet analyzed, rerun populate_indicators.
+                processed = self.advise_and_trim(processed)
+
+        bt_results = self.backtesting.backtest(
+            processed=processed, start_date=self.min_date, end_date=self.max_date
+        )
+        backtest_end_time = datetime.now(timezone.utc)
+        bt_results.update(
+            {
+                "backtest_start_time": int(backtest_start_time.timestamp()),
+                "backtest_end_time": int(backtest_end_time.timestamp()),
+            }
+        )
+
+        return self._get_results_dict(
+            bt_results, self.min_date, self.max_date, params_dict, processed=processed
+        )
+
+    def _get_results_dict(
+        self,
+        backtesting_results: BacktestContentType,
+        min_date: datetime,
+        max_date: datetime,
+        params_dict: dict[str, Any],
+        processed: dict[str, DataFrame],
+    ) -> dict[str, Any]:
+        params_details = self._get_params_details(params_dict)
+
+        strat_stats = generate_strategy_stats(
+            self.pairlist,
+            self.backtesting.strategy.get_strategy_name(),
+            backtesting_results,
+            min_date,
+            max_date,
+            market_change=self.market_change,
+            is_hyperopt=True,
+        )
+        results_explanation = HyperoptTools.format_results_explanation_string(
+            strat_stats, self.config["stake_currency"]
+        )
+
+        not_optimized = self.backtesting.strategy.get_no_optimize_params()
+        not_optimized = deep_merge_dicts(not_optimized, self._get_no_optimize_details())
+
+        trade_count = strat_stats["total_trades"]
+        total_profit = strat_stats["profit_total"]
+
+        # If this evaluation contains too short amount of trades to be
+        # interesting -- consider it as 'bad' (assigned max. loss value)
+        # in order to cast this hyperspace point away from optimization
+        # path. We do not want to optimize 'hodl' strategies.
+        loss: float = MAX_LOSS
+        if trade_count >= self.config["hyperopt_min_trades"]:
+            loss = self.calculate_loss(
+                results=backtesting_results["results"],
+                trade_count=trade_count,
+                min_date=min_date,
+                max_date=max_date,
+                config=self.config,
+                processed=processed,
+                backtest_stats=strat_stats,
+                starting_balance=get_dry_run_wallet(self.config),
+            )
+        return {
+            "loss": loss,
+            "params_dict": params_dict,
+            "params_details": params_details,
+            "params_not_optimized": not_optimized,
+            "results_metrics": strat_stats,
+            "results_explanation": results_explanation,
+            "total_profit": total_profit,
+        }
+
+    def get_optimizer(
+        self,
+        cpu_count: int,
+        random_state: int,
+        initial_points: int,
+        model_queue_size: int,
+    ) -> Optimizer:
+        dimensions = self.dimensions
+        estimator = self.custom_hyperopt.generate_estimator(dimensions=dimensions)
+
+        acq_optimizer = "sampling"
+        if isinstance(estimator, str):
+            if estimator not in ("GP", "RF", "ET", "GBRT"):
+                raise OperationalException(f"Estimator {estimator} not supported.")
+            else:
+                acq_optimizer = "auto"
+
+        logger.info(f"Using estimator {estimator}.")
+        return Optimizer(
+            dimensions,
+            base_estimator=estimator,
+            acq_optimizer=acq_optimizer,
+            n_initial_points=initial_points,
+            acq_optimizer_kwargs={"n_jobs": cpu_count},
+            random_state=random_state,
+            model_queue_size=model_queue_size,
+        )
+
+    def advise_and_trim(self, data: dict[str, DataFrame]) -> dict[str, DataFrame]:
+        preprocessed = self.backtesting.strategy.advise_all_indicators(data)
+
+        # Trim startup period from analyzed dataframe to get correct dates for output.
+        # This is only used to keep track of min/max date after trimming.
+        # The result is NOT returned from this method, actual trimming happens in backtesting.
+        trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
+        self.min_date, self.max_date = get_timerange(trimmed)
+        if not self.market_change:
+            self.market_change = calculate_market_change(trimmed, "close")
+
+        # Real trimming will happen as part of backtesting.
+        return preprocessed
+
+    def prepare_hyperopt_data(self) -> None:
+        HyperoptStateContainer.set_state(HyperoptState.DATALOAD)
+        data, self.timerange = self.backtesting.load_bt_data()
+        self.backtesting.load_bt_data_detail()
+        logger.info("Dataload complete. Calculating indicators")
+
+        if not self.analyze_per_epoch:
+            HyperoptStateContainer.set_state(HyperoptState.INDICATORS)
+
+            preprocessed = self.advise_and_trim(data)
+
+            logger.info(
+                f"Hyperopting with data from "
+                f"{self.min_date.strftime(DATETIME_PRINT_FORMAT)} "
+                f"up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} "
+                f"({(self.max_date - self.min_date).days} days).."
+            )
+            # Store non-trimmed data - will be trimmed after signal generation.
+            dump(preprocessed, self.data_pickle_file)
+        else:
+            dump(data, self.data_pickle_file)
diff --git a/freqtrade/optimize/hyperopt/hyperopt_output.py b/freqtrade/optimize/hyperopt/hyperopt_output.py
new file mode 100644
index 000000000..5327bffc1
--- /dev/null
+++ b/freqtrade/optimize/hyperopt/hyperopt_output.py
@@ -0,0 +1,143 @@
+import sys
+from os import get_terminal_size
+from typing import Any
+
+from rich.align import Align
+from rich.console import Console
+from rich.table import Table
+from rich.text import Text
+
+from freqtrade.constants import Config
+from freqtrade.optimize.optimize_reports import generate_wins_draws_losses
+from freqtrade.util import fmt_coin
+
+
+class HyperoptOutput:
+    def __init__(self, streaming=False) -> None:
+        self._results: list[Any] = []
+        self._streaming = streaming
+        self.__init_table()
+
+    def __call__(self, *args: Any, **kwds: Any) -> Any:
+        return Align.center(self.table)
+
+    def __init_table(self) -> None:
+        """Initialize table"""
+        self.table = Table(
+            title="Hyperopt results",
+        )
+        # Headers
+        self.table.add_column("Best", justify="left")
+        self.table.add_column("Epoch", justify="right")
+        self.table.add_column("Trades", justify="right")
+        self.table.add_column("Win  Draw  Loss  Win%", justify="right")
+        self.table.add_column("Avg profit", justify="right")
+        self.table.add_column("Profit", justify="right")
+        self.table.add_column("Avg duration", justify="right")
+        self.table.add_column("Objective", justify="right")
+        self.table.add_column("Max Drawdown (Acct)", justify="right")
+
+    def print(self, console: Console | None = None, *, print_colorized=True):
+        if not console:
+            console = Console(
+                color_system="auto" if print_colorized else None,
+                width=200 if "pytest" in sys.modules else None,
+            )
+
+        console.print(self.table)
+
+    def add_data(
+        self,
+        config: Config,
+        results: list,
+        total_epochs: int,
+        highlight_best: bool,
+    ) -> None:
+        """Format one or multiple rows and add them"""
+        stake_currency = config["stake_currency"]
+        self._results.extend(results)
+
+        max_rows: int | None = None
+
+        if self._streaming:
+            try:
+                ts = get_terminal_size()
+                # Get terminal size.
+                # Account for header, borders, and for the progress bar.
+                # This assumes that lines don't wrap.
+                if ts.columns < 148:
+                    # If the terminal is too small, we can't display the table properly.
+                    # We will halve the number of rows to display.
+                    max_rows = -(int(ts.lines / 2) - 6)
+                else:
+                    max_rows = -(ts.lines - 6)
+            except OSError:
+                # If we can't get the terminal size, we will just display the last 10 rows.
+                pass
+
+        self.__init_table()
+        for r in self._results[max_rows:]:
+            self.table.add_row(
+                *[
+                    # "Best":
+                    (
+                        ("*" if r["is_initial_point"] or r["is_random"] else "")
+                        + (" Best" if r["is_best"] else "")
+                    ).lstrip(),
+                    # "Epoch":
+                    f"{r['current_epoch']}/{total_epochs}",
+                    # "Trades":
+                    str(r["results_metrics"]["total_trades"]),
+                    # "Win  Draw  Loss  Win%":
+                    generate_wins_draws_losses(
+                        r["results_metrics"]["wins"],
+                        r["results_metrics"]["draws"],
+                        r["results_metrics"]["losses"],
+                    ),
+                    # "Avg profit":
+                    f"{r['results_metrics']['profit_mean']:.2%}"
+                    if r["results_metrics"]["profit_mean"] is not None
+                    else "--",
+                    # "Profit":
+                    Text(
+                        "{} {}".format(
+                            fmt_coin(
+                                r["results_metrics"]["profit_total_abs"],
+                                stake_currency,
+                                keep_trailing_zeros=True,
+                            ),
+                            f"({r['results_metrics']['profit_total']:,.2%})".rjust(10, " "),
+                        )
+                        if r["results_metrics"].get("profit_total_abs", 0) != 0.0
+                        else "--",
+                        style=(
+                            "green"
+                            if r["results_metrics"].get("profit_total_abs", 0) > 0
+                            else "red"
+                        )
+                        if not r["is_best"]
+                        else "",
+                    ),
+                    # "Avg duration":
+                    str(r["results_metrics"]["holding_avg"]),
+                    # "Objective":
+                    f"{r['loss']:,.5f}" if r["loss"] != 100000 else "N/A",
+                    # "Max Drawdown (Acct)":
+                    "{} {}".format(
+                        fmt_coin(
+                            r["results_metrics"]["max_drawdown_abs"],
+                            stake_currency,
+                            keep_trailing_zeros=True,
+                        ),
+                        (f"({r['results_metrics']['max_drawdown_account']:,.2%})").rjust(10, " "),
+                    )
+                    if r["results_metrics"]["max_drawdown_account"] != 0.0
+                    else "--",
+                ],
+                style=" ".join(
+                    [
+                        "bold gold1" if r["is_best"] and highlight_best else "",
+                        "italic " if r["is_initial_point"] else "",
+                    ]
+                ),
+            )
diff --git a/freqtrade/optimize/hyperopt_epoch_filters.py b/freqtrade/optimize/hyperopt_epoch_filters.py
new file mode 100644
index 000000000..5112b062d
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_epoch_filters.py
@@ -0,0 +1,122 @@
+import logging
+
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def hyperopt_filter_epochs(epochs: list, filteroptions: dict, log: bool = True) -> list:
+    """
+    Filter our items from the list of hyperopt results
+    """
+    if filteroptions["only_best"]:
+        epochs = [x for x in epochs if x["is_best"]]
+    if filteroptions["only_profitable"]:
+        epochs = [x for x in epochs if x["results_metrics"].get("profit_total", 0) > 0]
+
+    epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
+
+    epochs = _hyperopt_filter_epochs_duration(epochs, filteroptions)
+
+    epochs = _hyperopt_filter_epochs_profit(epochs, filteroptions)
+
+    epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions)
+    if log:
+        logger.info(
+            f"{len(epochs)} "
+            + ("best " if filteroptions["only_best"] else "")
+            + ("profitable " if filteroptions["only_profitable"] else "")
+            + "epochs found."
+        )
+    return epochs
+
+
+def _hyperopt_filter_epochs_trade(epochs: list, trade_count: int):
+    """
+    Filter epochs with trade-counts > trades
+    """
+    return [x for x in epochs if x["results_metrics"].get("total_trades", 0) > trade_count]
+
+
+def _hyperopt_filter_epochs_trade_count(epochs: list, filteroptions: dict) -> list:
+    if filteroptions["filter_min_trades"] > 0:
+        epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions["filter_min_trades"])
+
+    if filteroptions["filter_max_trades"] > 0:
+        epochs = [
+            x
+            for x in epochs
+            if x["results_metrics"].get("total_trades") < filteroptions["filter_max_trades"]
+        ]
+    return epochs
+
+
+def _hyperopt_filter_epochs_duration(epochs: list, filteroptions: dict) -> list:
+    def get_duration_value(x):
+        # Duration in minutes ...
+        if "holding_avg_s" in x["results_metrics"]:
+            avg = x["results_metrics"]["holding_avg_s"]
+            return avg // 60
+        raise OperationalException(
+            "Holding-average not available. Please omit the filter on average time, "
+            "or rerun hyperopt with this version"
+        )
+
+    if filteroptions["filter_min_avg_time"] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [x for x in epochs if get_duration_value(x) > filteroptions["filter_min_avg_time"]]
+    if filteroptions["filter_max_avg_time"] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [x for x in epochs if get_duration_value(x) < filteroptions["filter_max_avg_time"]]
+
+    return epochs
+
+
+def _hyperopt_filter_epochs_profit(epochs: list, filteroptions: dict) -> list:
+    if filteroptions["filter_min_avg_profit"] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x
+            for x in epochs
+            if x["results_metrics"].get("profit_mean", 0) * 100
+            > filteroptions["filter_min_avg_profit"]
+        ]
+    if filteroptions["filter_max_avg_profit"] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x
+            for x in epochs
+            if x["results_metrics"].get("profit_mean", 0) * 100
+            < filteroptions["filter_max_avg_profit"]
+        ]
+    if filteroptions["filter_min_total_profit"] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x
+            for x in epochs
+            if x["results_metrics"].get("profit_total_abs", 0)
+            > filteroptions["filter_min_total_profit"]
+        ]
+    if filteroptions["filter_max_total_profit"] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+        epochs = [
+            x
+            for x in epochs
+            if x["results_metrics"].get("profit_total_abs", 0)
+            < filteroptions["filter_max_total_profit"]
+        ]
+    return epochs
+
+
+def _hyperopt_filter_epochs_objective(epochs: list, filteroptions: dict) -> list:
+    if filteroptions["filter_min_objective"] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+
+        epochs = [x for x in epochs if x["loss"] < filteroptions["filter_min_objective"]]
+    if filteroptions["filter_max_objective"] is not None:
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
+
+        epochs = [x for x in epochs if x["loss"] > filteroptions["filter_max_objective"]]
+
+    return epochs
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py
new file mode 100644
index 000000000..4f1a82e1e
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py
@@ -0,0 +1,39 @@
+"""
+CalmarHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+from datetime import datetime
+
+from pandas import DataFrame
+
+from freqtrade.data.metrics import calculate_calmar
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class CalmarHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Calmar Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        min_date: datetime,
+        max_date: datetime,
+        starting_balance: float,
+        *args,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Calmar Ratio calculation.
+        """
+        calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance)
+        # print(expected_returns_mean, max_drawdown, calmar_ratio)
+        return -calmar_ratio
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_interface.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_interface.py
new file mode 100644
index 000000000..ddc96d6a0
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_interface.py
@@ -0,0 +1,39 @@
+"""
+IHyperOptLoss interface
+This module defines the interface for the loss-function for hyperopt
+"""
+
+from abc import ABC, abstractmethod
+from datetime import datetime
+from typing import Any
+
+from pandas import DataFrame
+
+from freqtrade.constants import Config
+
+
+class IHyperOptLoss(ABC):
+    """
+    Interface for freqtrade hyperopt Loss functions.
+    Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
+    """
+
+    timeframe: str
+
+    @staticmethod
+    @abstractmethod
+    def hyperopt_loss_function(
+        *,
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        config: Config,
+        processed: dict[str, DataFrame],
+        backtest_stats: dict[str, Any],
+        starting_balance: float,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function, returns smaller number for better results
+        """
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown.py
new file mode 100644
index 000000000..ce1b29cf5
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown.py
@@ -0,0 +1,45 @@
+"""
+MaxDrawDownHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+from datetime import datetime
+
+from pandas import DataFrame
+
+from freqtrade.data.metrics import calculate_max_drawdown
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class MaxDrawDownHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation optimizes for max draw down and profit
+    Less max drawdown more profit -> Lower return value
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        *args,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function.
+
+        Uses profit ratio weighted max_drawdown when drawdown is available.
+        Otherwise directly optimizes profit ratio.
+        """
+        total_profit = results["profit_abs"].sum()
+        try:
+            max_drawdown = calculate_max_drawdown(results, value_col="profit_abs")
+        except ValueError:
+            # No losing trade, therefore no drawdown.
+            return -total_profit
+        return -total_profit / max_drawdown.drawdown_abs
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_per_pair.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_per_pair.py
new file mode 100644
index 000000000..19ce29455
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_per_pair.py
@@ -0,0 +1,59 @@
+"""
+MaxDrawDownPerPairHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+from typing import Any
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class MaxDrawDownPerPairHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation calculates the profit/drawdown ratio per pair and
+    returns the worst result as objective, forcing hyperopt to optimize
+    the parameters for all pairs in the pairlist.
+
+    This way, we prevent one or more pairs with good results from inflating
+    the metrics, while the rest of the pairs with poor results are not
+    represented and therefore not optimized.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(backtest_stats: dict[str, Any], *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for better results.
+        """
+
+        ##############################################
+        # Configurable parameters
+        ##############################################
+        # Minimum acceptable profit/drawdown per pair
+        min_acceptable_profit_dd = 1.0
+        # Penalty when acceptable minimum are not met
+        penalty = 20
+        ##############################################
+
+        score_per_pair = []
+        for p in backtest_stats["results_per_pair"]:
+            if p["key"] != "TOTAL":
+                profit = p.get("profit_total_abs", 0)
+                drawdown = p.get("max_drawdown_abs", 0)
+
+                if drawdown != 0 and profit != 0:
+                    profit_dd = profit / drawdown
+                else:
+                    profit_dd = profit
+
+                if profit_dd < min_acceptable_profit_dd:
+                    score = profit_dd - penalty
+                else:
+                    score = profit_dd
+
+                score_per_pair.append(score)
+
+        return -min(score_per_pair)
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py
new file mode 100644
index 000000000..3cd578cb4
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py
@@ -0,0 +1,43 @@
+"""
+MaxDrawDownRelativeHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+from pandas import DataFrame
+
+from freqtrade.data.metrics import calculate_underwater
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation optimizes for max draw down and profit
+    Less max drawdown more profit -> Lower return value
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame, starting_balance: float, *args, **kwargs
+    ) -> float:
+        """
+        Objective function.
+
+        Uses profit ratio weighted max_drawdown when drawdown is available.
+        Otherwise directly optimizes profit ratio.
+        """
+        total_profit = results["profit_abs"].sum()
+        try:
+            drawdown_df = calculate_underwater(
+                results, value_col="profit_abs", starting_balance=starting_balance
+            )
+            max_drawdown = abs(min(drawdown_df["drawdown"]))
+            relative_drawdown = max(drawdown_df["drawdown_relative"])
+            if max_drawdown == 0:
+                return -total_profit
+            return -total_profit / max_drawdown / relative_drawdown
+        except (Exception, ValueError):
+            return -total_profit
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_multi_metric.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_multi_metric.py
new file mode 100644
index 000000000..0918c2c92
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_multi_metric.py
@@ -0,0 +1,104 @@
+"""
+MultiMetricHyperOptLoss
+
+This module defines the alternative HyperOptLoss class based on:
+  - Profit
+  - Drawdown
+  - Profit Factor
+  - Expectancy Ratio
+  - Winrate
+  - Amount of trades
+
+Possible to change:
+  - `DRAWDOWN_MULT` to penalize drawdown objective for individual needs;
+  - `TARGET_TRADE_AMOUNT` to adjust amount of trades impact.
+  - `EXPECTANCY_CONST` to adjust expectancy ratio impact.
+  - `PF_CONST` to adjust profit factor impact.
+  - `WINRATE_CONST` to adjust winrate impact.
+
+
+DRAWDOWN_MULT variable within the hyperoptloss file can be adjusted to be stricter or more
+              flexible on drawdown purposes. Smaller numbers penalize drawdowns more severely.
+PF_CONST variable adjusts the impact of the Profit Factor on the optimization.
+EXPECTANCY_CONST variable controls the influence of the Expectancy Ratio.
+WINRATE_CONST variable can be adjusted to increase or decrease impact of winrate.
+
+PF_CONST, EXPECTANCY_CONST, WINRATE_CONST all operate in a similar manner:
+        a higher value means that the metric has a lesser impact on the objective,
+        while a lower value means that it has a greater impact.
+TARGET_TRADE_AMOUNT variable sets the minimum number of trades required to avoid penalties.
+            If the trade amount falls below this threshold, the penalty is applied.
+"""
+
+import numpy as np
+from pandas import DataFrame
+
+from freqtrade.data.metrics import calculate_expectancy, calculate_max_drawdown
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+# smaller numbers penalize drawdowns more severely
+DRAWDOWN_MULT = 0.055
+# A very large number to use as a replacement for infinity
+LARGE_NUMBER = 1e6
+# Target trade amount, if higher that TARGET_TRADE_AMOUNT - no penalty
+TARGET_TRADE_AMOUNT = 50
+# Coefficient to adjust impact of expectancy
+EXPECTANCY_CONST = 2.0
+# Coefficient to adjust profit factor impact
+PF_CONST = 1.0
+# Coefficient to adjust winrate impact
+WINRATE_CONST = 1.2
+
+
+class MultiMetricHyperOptLoss(IHyperOptLoss):
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        trade_count: int,
+        starting_balance: float,
+        **kwargs,
+    ) -> float:
+        total_profit = results["profit_abs"].sum()
+
+        # Calculate profit factor
+        winning_profit = results.loc[results["profit_abs"] > 0, "profit_abs"].sum()
+        losing_profit = results.loc[results["profit_abs"] < 0, "profit_abs"].sum()
+        profit_factor = winning_profit / (abs(losing_profit) + 1e-6)
+        log_profit_factor = np.log(profit_factor + PF_CONST)
+
+        # Calculate expectancy
+        _, expectancy_ratio = calculate_expectancy(results)
+        log_expectancy_ratio = np.log(min(10, expectancy_ratio) + EXPECTANCY_CONST)
+
+        # Calculate winrate
+        winning_trades = results.loc[results["profit_abs"] > 0]
+        winrate = len(winning_trades) / len(results)
+        log_winrate_coef = np.log(WINRATE_CONST + winrate)
+
+        # Calculate drawdown
+        try:
+            drawdown = calculate_max_drawdown(
+                results, starting_balance=starting_balance, value_col="profit_abs"
+            )
+            relative_account_drawdown = drawdown.relative_account_drawdown
+        except ValueError:
+            relative_account_drawdown = 0
+
+        # Trade Count Penalty
+        trade_count_penalty = 1.0  # Default: no penalty
+        if trade_count < TARGET_TRADE_AMOUNT:
+            trade_count_penalty = 1 - (abs(trade_count - TARGET_TRADE_AMOUNT) / TARGET_TRADE_AMOUNT)
+            trade_count_penalty = max(trade_count_penalty, 0.1)
+
+        profit_draw_function = total_profit - (relative_account_drawdown * total_profit) * (
+            1 - DRAWDOWN_MULT
+        )
+
+        return -1 * (
+            profit_draw_function
+            * log_profit_factor
+            * log_expectancy_ratio
+            * log_winrate_coef
+            * trade_count_penalty
+        )
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_onlyprofit.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_onlyprofit.py
new file mode 100644
index 000000000..dd4a448d5
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_onlyprofit.py
@@ -0,0 +1,26 @@
+"""
+OnlyProfitHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+from pandas import DataFrame
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class OnlyProfitHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation takes only absolute profit into account, not looking at any other indicator.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int, *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for better results.
+        """
+        total_profit = results["profit_abs"].sum()
+        return -1 * total_profit
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_profit_drawdown.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_profit_drawdown.py
new file mode 100644
index 000000000..af1b33dfb
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_profit_drawdown.py
@@ -0,0 +1,38 @@
+"""
+ProfitDrawDownHyperOptLoss
+
+This module defines the alternative HyperOptLoss class based on Profit &
+Drawdown objective which can be used for Hyperoptimization.
+
+Possible to change `DRAWDOWN_MULT` to penalize drawdown objective for
+individual needs.
+"""
+
+from pandas import DataFrame
+
+from freqtrade.data.metrics import calculate_max_drawdown
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+# smaller numbers penalize drawdowns more severely
+DRAWDOWN_MULT = 0.075
+
+
+class ProfitDrawDownHyperOptLoss(IHyperOptLoss):
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame, starting_balance: float, *args, **kwargs
+    ) -> float:
+        total_profit = results["profit_abs"].sum()
+
+        try:
+            drawdown = calculate_max_drawdown(
+                results, starting_balance=starting_balance, value_col="profit_abs"
+            )
+            relative_account_drawdown = drawdown.relative_account_drawdown
+        except ValueError:
+            relative_account_drawdown = 0
+
+        return -1 * (
+            total_profit - (relative_account_drawdown * total_profit) * (1 - DRAWDOWN_MULT)
+        )
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py
new file mode 100644
index 000000000..20e4ee2b6
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py
@@ -0,0 +1,39 @@
+"""
+SharpeHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+from datetime import datetime
+
+from pandas import DataFrame
+
+from freqtrade.data.metrics import calculate_sharpe
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SharpeHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sharpe Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        min_date: datetime,
+        max_date: datetime,
+        starting_balance: float,
+        *args,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sharpe Ratio calculation.
+        """
+        sharp_ratio = calculate_sharpe(results, min_date, max_date, starting_balance)
+        # print(expected_returns_mean, up_stdev, sharp_ratio)
+        return -sharp_ratio
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe_daily.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe_daily.py
new file mode 100644
index 000000000..ea1efe0e1
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe_daily.py
@@ -0,0 +1,70 @@
+"""
+SharpeHyperOptLossDaily
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+import math
+from datetime import datetime
+
+from pandas import DataFrame, date_range
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SharpeHyperOptLossDaily(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sharpe Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        *args,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sharpe Ratio calculation.
+        """
+        resample_freq = "1D"
+        slippage_per_trade_ratio = 0.0005
+        days_in_year = 365
+        annual_risk_free_rate = 0.0
+        risk_free_rate = annual_risk_free_rate / days_in_year
+
+        # apply slippage per trade to profit_ratio
+        results.loc[:, "profit_ratio_after_slippage"] = (
+            results["profit_ratio"] - slippage_per_trade_ratio
+        )
+
+        # create the index within the min_date and end max_date
+        t_index = date_range(start=min_date, end=max_date, freq=resample_freq, normalize=True)
+
+        sum_daily = (
+            results.resample(resample_freq, on="close_date")
+            .agg({"profit_ratio_after_slippage": "sum"})
+            .reindex(t_index)
+            .fillna(0)
+        )
+
+        total_profit = sum_daily["profit_ratio_after_slippage"] - risk_free_rate
+        expected_returns_mean = total_profit.mean()
+        up_stdev = total_profit.std()
+
+        if up_stdev != 0:
+            sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
+        else:
+            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+            sharp_ratio = -20.0
+
+        # print(t_index, sum_daily, total_profit)
+        # print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
+        return -sharp_ratio
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_short_trade_dur.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_short_trade_dur.py
new file mode 100644
index 000000000..6c6e0a8cb
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_short_trade_dur.py
@@ -0,0 +1,57 @@
+"""
+ShortTradeDurHyperOptLoss
+This module defines the default HyperoptLoss class which is being used for
+Hyperoptimization.
+"""
+
+from math import exp
+
+from pandas import DataFrame
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+# Set TARGET_TRADES to suit your number concurrent trades so its realistic
+# to the number of days
+TARGET_TRADES = 600
+
+# This is assumed to be expected avg profit * expected trade count.
+# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
+# expected max profit = 3.85
+# Check that the reported Σ% values do not exceed this!
+# Note, this is ratio. 3.85 stated above means 385Σ%.
+EXPECTED_MAX_PROFIT = 3.0
+
+# Max average trade duration in minutes.
+# If eval ends with higher value, we consider it a failed eval.
+MAX_ACCEPTED_TRADE_DURATION = 300
+
+
+class ShortTradeDurHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the default loss function for hyperopt
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int, *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for better results
+        This is the Default algorithm
+        Weights are distributed as follows:
+        * 0.4 to trade duration
+        * 0.25: Avoiding trade loss
+        * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
+        """
+        total_profit = results["profit_ratio"].sum()
+        trade_duration = results["trade_duration"].mean()
+
+        trade_loss = 1 - 0.25 * exp(-((trade_count - TARGET_TRADES) ** 2) / 10**5.8)
+        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
+        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
+        result = trade_loss + profit_loss + duration_loss
+        return result
+
+
+# Create an alias for This to allow the legacy Method to work as well.
+class DefaultHyperOptLoss(ShortTradeDurHyperOptLoss):
+    pass
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py
new file mode 100644
index 000000000..935d038e5
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py
@@ -0,0 +1,39 @@
+"""
+SortinoHyperOptLoss
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+from datetime import datetime
+
+from pandas import DataFrame
+
+from freqtrade.data.metrics import calculate_sortino
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SortinoHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sortino Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        min_date: datetime,
+        max_date: datetime,
+        starting_balance: float,
+        *args,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sortino Ratio calculation.
+        """
+        sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
+        # print(expected_returns_mean, down_stdev, sortino_ratio)
+        return -sortino_ratio
diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino_daily.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino_daily.py
new file mode 100644
index 000000000..321b89dc2
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino_daily.py
@@ -0,0 +1,78 @@
+"""
+SortinoHyperOptLossDaily
+
+This module defines the alternative HyperOptLoss class which can be used for
+Hyperoptimization.
+"""
+
+import math
+from datetime import datetime
+
+from pandas import DataFrame, date_range
+
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+class SortinoHyperOptLossDaily(IHyperOptLoss):
+    """
+    Defines the loss function for hyperopt.
+
+    This implementation uses the Sortino Ratio calculation.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        *args,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function, returns smaller number for more optimal results.
+
+        Uses Sortino Ratio calculation.
+
+        Sortino Ratio calculated as described in
+        http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
+        """
+        resample_freq = "1D"
+        slippage_per_trade_ratio = 0.0005
+        days_in_year = 365
+        minimum_acceptable_return = 0.0
+
+        # apply slippage per trade to profit_ratio
+        results.loc[:, "profit_ratio_after_slippage"] = (
+            results["profit_ratio"] - slippage_per_trade_ratio
+        )
+
+        # create the index within the min_date and end max_date
+        t_index = date_range(start=min_date, end=max_date, freq=resample_freq, normalize=True)
+
+        sum_daily = (
+            results.resample(resample_freq, on="close_date")
+            .agg({"profit_ratio_after_slippage": "sum"})
+            .reindex(t_index)
+            .fillna(0)
+        )
+
+        total_profit = sum_daily["profit_ratio_after_slippage"] - minimum_acceptable_return
+        expected_returns_mean = total_profit.mean()
+
+        sum_daily["downside_returns"] = 0.0
+        sum_daily.loc[total_profit < 0, "downside_returns"] = total_profit
+        total_downside = sum_daily["downside_returns"]
+        # Here total_downside contains min(0, P - MAR) values,
+        # where P = sum_daily["profit_ratio_after_slippage"]
+        down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
+
+        if down_stdev != 0:
+            sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
+        else:
+            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+            sortino_ratio = -20.0
+
+        # print(t_index, sum_daily, total_profit)
+        # print(minimum_acceptable_return, expected_returns_mean, down_stdev, sortino_ratio)
+        return -sortino_ratio
diff --git a/freqtrade/optimize/hyperopt_tools.py b/freqtrade/optimize/hyperopt_tools.py
new file mode 100644
index 000000000..ff4747781
--- /dev/null
+++ b/freqtrade/optimize/hyperopt_tools.py
@@ -0,0 +1,447 @@
+import logging
+from collections.abc import Iterator
+from copy import deepcopy
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import rapidjson
+from pandas import isna, json_normalize
+
+from freqtrade.constants import FTHYPT_FILEVERSION, Config
+from freqtrade.enums import HyperoptState
+from freqtrade.exceptions import OperationalException
+from freqtrade.misc import deep_merge_dicts, round_dict, safe_value_fallback2
+from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
+
+
+logger = logging.getLogger(__name__)
+
+NON_OPT_PARAM_APPENDIX = "  # value loaded from strategy"
+
+HYPER_PARAMS_FILE_FORMAT = rapidjson.NM_NATIVE | rapidjson.NM_NAN
+
+
+def hyperopt_serializer(x):
+    if isinstance(x, np.integer):
+        return int(x)
+    if isinstance(x, np.bool_):
+        return bool(x)
+
+    return str(x)
+
+
+class HyperoptStateContainer:
+    """Singleton class to track state of hyperopt"""
+
+    state: HyperoptState = HyperoptState.OPTIMIZE
+
+    @classmethod
+    def set_state(cls, value: HyperoptState):
+        cls.state = value
+
+
+class HyperoptTools:
+    @staticmethod
+    def get_strategy_filename(config: Config, strategy_name: str) -> Path | None:
+        """
+        Get Strategy-location (filename) from strategy_name
+        """
+        from freqtrade.resolvers.strategy_resolver import StrategyResolver
+
+        strategy_objs = StrategyResolver.search_all_objects(
+            config, False, config.get("recursive_strategy_search", False)
+        )
+        strategies = [s for s in strategy_objs if s["name"] == strategy_name]
+        if strategies:
+            strategy = strategies[0]
+
+            return Path(strategy["location"])
+        return None
+
+    @staticmethod
+    def export_params(params, strategy_name: str, filename: Path):
+        """
+        Generate files
+        """
+        final_params = deepcopy(params["params_not_optimized"])
+        final_params = deep_merge_dicts(params["params_details"], final_params)
+        final_params = {
+            "strategy_name": strategy_name,
+            "params": final_params,
+            "ft_stratparam_v": 1,
+            "export_time": datetime.now(timezone.utc),
+        }
+        logger.info(f"Dumping parameters to {filename}")
+        with filename.open("w") as f:
+            rapidjson.dump(
+                final_params,
+                f,
+                indent=2,
+                default=hyperopt_serializer,
+                number_mode=HYPER_PARAMS_FILE_FORMAT,
+            )
+
+    @staticmethod
+    def load_params(filename: Path) -> dict:
+        """
+        Load parameters from file
+        """
+        with filename.open("r") as f:
+            params = rapidjson.load(f, number_mode=HYPER_PARAMS_FILE_FORMAT)
+        return params
+
+    @staticmethod
+    def try_export_params(config: Config, strategy_name: str, params: dict):
+        if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get("disableparamexport", False):
+            # Export parameters ...
+            fn = HyperoptTools.get_strategy_filename(config, strategy_name)
+            if fn:
+                HyperoptTools.export_params(params, strategy_name, fn.with_suffix(".json"))
+            else:
+                logger.warning("Strategy not found, not exporting parameter file.")
+
+    @staticmethod
+    def has_space(config: Config, space: str) -> bool:
+        """
+        Tell if the space value is contained in the configuration
+        """
+        # 'trailing' and 'protection spaces are not included in the 'default' set of spaces
+        if space in ("trailing", "protection", "trades"):
+            return any(s in config["spaces"] for s in [space, "all"])
+        else:
+            return any(s in config["spaces"] for s in [space, "all", "default"])
+
+    @staticmethod
+    def _read_results(results_file: Path, batch_size: int = 10) -> Iterator[list[Any]]:
+        """
+        Stream hyperopt results from file
+        """
+        import rapidjson
+
+        logger.info(f"Reading epochs from '{results_file}'")
+        with results_file.open("r") as f:
+            data = []
+            for line in f:
+                data += [rapidjson.loads(line)]
+                if len(data) >= batch_size:
+                    yield data
+                    data = []
+        yield data
+
+    @staticmethod
+    def _test_hyperopt_results_exist(results_file) -> bool:
+        if results_file.is_file() and results_file.stat().st_size > 0:
+            if results_file.suffix == ".pickle":
+                raise OperationalException(
+                    "Legacy hyperopt results are no longer supported."
+                    "Please rerun hyperopt or use an older version to load this file."
+                )
+            return True
+        else:
+            # No file found.
+            return False
+
+    @staticmethod
+    def load_filtered_results(results_file: Path, config: Config) -> tuple[list, int]:
+        filteroptions = {
+            "only_best": config.get("hyperopt_list_best", False),
+            "only_profitable": config.get("hyperopt_list_profitable", False),
+            "filter_min_trades": config.get("hyperopt_list_min_trades", 0),
+            "filter_max_trades": config.get("hyperopt_list_max_trades", 0),
+            "filter_min_avg_time": config.get("hyperopt_list_min_avg_time"),
+            "filter_max_avg_time": config.get("hyperopt_list_max_avg_time"),
+            "filter_min_avg_profit": config.get("hyperopt_list_min_avg_profit"),
+            "filter_max_avg_profit": config.get("hyperopt_list_max_avg_profit"),
+            "filter_min_total_profit": config.get("hyperopt_list_min_total_profit"),
+            "filter_max_total_profit": config.get("hyperopt_list_max_total_profit"),
+            "filter_min_objective": config.get("hyperopt_list_min_objective"),
+            "filter_max_objective": config.get("hyperopt_list_max_objective"),
+        }
+        if not HyperoptTools._test_hyperopt_results_exist(results_file):
+            # No file found.
+            logger.warning(f"Hyperopt file {results_file} not found.")
+            return [], 0
+
+        epochs = []
+        total_epochs = 0
+        for epochs_tmp in HyperoptTools._read_results(results_file):
+            if total_epochs == 0 and epochs_tmp[0].get("is_best") is None:
+                raise OperationalException(
+                    "The file with HyperoptTools results is incompatible with this version "
+                    "of Freqtrade and cannot be loaded."
+                )
+            total_epochs += len(epochs_tmp)
+            epochs += hyperopt_filter_epochs(epochs_tmp, filteroptions, log=False)
+
+        logger.info(f"Loaded {total_epochs} previous evaluations from disk.")
+
+        # Final filter run ...
+        epochs = hyperopt_filter_epochs(epochs, filteroptions, log=True)
+
+        return epochs, total_epochs
+
+    @staticmethod
+    def show_epoch_details(
+        results,
+        total_epochs: int,
+        print_json: bool,
+        no_header: bool = False,
+        header_str: str | None = None,
+    ) -> None:
+        """
+        Display details of the hyperopt result
+        """
+        params = results.get("params_details", {})
+        non_optimized = results.get("params_not_optimized", {})
+
+        # Default header string
+        if header_str is None:
+            header_str = "Best result"
+
+        if not no_header:
+            explanation_str = HyperoptTools._format_explanation_string(results, total_epochs)
+            print(f"\n{header_str}:\n\n{explanation_str}\n")
+
+        if print_json:
+            result_dict: dict = {}
+            for s in [
+                "buy",
+                "sell",
+                "protection",
+                "roi",
+                "stoploss",
+                "trailing",
+                "max_open_trades",
+            ]:
+                HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
+            print(rapidjson.dumps(result_dict, default=str, number_mode=HYPER_PARAMS_FILE_FORMAT))
+
+        else:
+            HyperoptTools._params_pretty_print(
+                params, "buy", "Buy hyperspace params:", non_optimized
+            )
+            HyperoptTools._params_pretty_print(
+                params, "sell", "Sell hyperspace params:", non_optimized
+            )
+            HyperoptTools._params_pretty_print(
+                params, "protection", "Protection hyperspace params:", non_optimized
+            )
+            HyperoptTools._params_pretty_print(params, "roi", "ROI table:", non_optimized)
+            HyperoptTools._params_pretty_print(params, "stoploss", "Stoploss:", non_optimized)
+            HyperoptTools._params_pretty_print(params, "trailing", "Trailing stop:", non_optimized)
+            HyperoptTools._params_pretty_print(
+                params, "max_open_trades", "Max Open Trades:", non_optimized
+            )
+
+    @staticmethod
+    def _params_update_for_json(result_dict, params, non_optimized, space: str) -> None:
+        if (space in params) or (space in non_optimized):
+            space_params = HyperoptTools._space_params(params, space)
+            space_non_optimized = HyperoptTools._space_params(non_optimized, space)
+            all_space_params = space_params
+
+            # Merge non optimized params if there are any
+            if len(space_non_optimized) > 0:
+                all_space_params = {**space_params, **space_non_optimized}
+
+            if space in ["buy", "sell"]:
+                result_dict.setdefault("params", {}).update(all_space_params)
+            elif space == "roi":
+                # Convert keys in min_roi dict to strings because
+                # rapidjson cannot dump dicts with integer keys...
+                result_dict["minimal_roi"] = {str(k): v for k, v in all_space_params.items()}
+            else:  # 'stoploss', 'trailing'
+                result_dict.update(all_space_params)
+
+    @staticmethod
+    def _params_pretty_print(
+        params, space: str, header: str, non_optimized: dict | None = None
+    ) -> None:
+        if space in params or (non_optimized and space in non_optimized):
+            space_params = HyperoptTools._space_params(params, space, 5)
+            no_params = HyperoptTools._space_params(non_optimized, space, 5)
+            appendix = ""
+            if not space_params and not no_params:
+                # No parameters - don't print
+                return
+            if not space_params:
+                # Not optimized parameters - append string
+                appendix = NON_OPT_PARAM_APPENDIX
+
+            result = f"\n# {header}\n"
+            if space == "stoploss":
+                stoploss = safe_value_fallback2(space_params, no_params, space, space)
+                result += f"stoploss = {stoploss}{appendix}"
+            elif space == "max_open_trades":
+                max_open_trades = safe_value_fallback2(space_params, no_params, space, space)
+                result += f"max_open_trades = {max_open_trades}{appendix}"
+            elif space == "roi":
+                result = result[:-1] + f"{appendix}\n"
+                minimal_roi_result = rapidjson.dumps(
+                    {str(k): v for k, v in (space_params or no_params).items()},
+                    default=str,
+                    indent=4,
+                    number_mode=rapidjson.NM_NATIVE,
+                )
+                result += f"minimal_roi = {minimal_roi_result}"
+            elif space == "trailing":
+                for k, v in (space_params or no_params).items():
+                    result += f"{k} = {v}{appendix}\n"
+
+            else:
+                # Buy / sell parameters
+
+                result += f"{space}_params = {HyperoptTools._pprint_dict(space_params, no_params)}"
+
+            result = result.replace("\n", "\n    ")
+            print(result)
+
+    @staticmethod
+    def _space_params(params, space: str, r: int | None = None) -> dict:
+        d = params.get(space)
+        if d:
+            # Round floats to `r` digits after the decimal point if requested
+            return round_dict(d, r) if r else d
+        return {}
+
+    @staticmethod
+    def _pprint_dict(params, non_optimized, indent: int = 4):
+        """
+        Pretty-print hyperopt results (based on 2 dicts - with add. comment)
+        """
+        p = params.copy()
+        p.update(non_optimized)
+        result = "{\n"
+
+        for k, param in p.items():
+            result += " " * indent + f'"{k}": '
+            result += f'"{param}",' if isinstance(param, str) else f"{param},"
+            if k in non_optimized:
+                result += NON_OPT_PARAM_APPENDIX
+            result += "\n"
+        result += "}"
+        return result
+
+    @staticmethod
+    def is_best_loss(results, current_best_loss: float) -> bool:
+        return bool(results["loss"] < current_best_loss)
+
+    @staticmethod
+    def format_results_explanation_string(results_metrics: dict, stake_currency: str) -> str:
+        """
+        Return the formatted results explanation in a string
+        """
+        return (
+            f"{results_metrics['total_trades']:6d} trades. "
+            f"{results_metrics['wins']}/{results_metrics['draws']}"
+            f"/{results_metrics['losses']} Wins/Draws/Losses. "
+            f"Avg profit {results_metrics['profit_mean']:7.2%}. "
+            f"Median profit {results_metrics['profit_median']:7.2%}. "
+            f"Total profit {results_metrics['profit_total_abs']:11.8f} {stake_currency} "
+            f"({results_metrics['profit_total']:8.2%}). "
+            f"Avg duration {results_metrics['holding_avg']} min."
+        )
+
+    @staticmethod
+    def _format_explanation_string(results, total_epochs) -> str:
+        return (
+            ("*" if results["is_initial_point"] else " ")
+            + f"{results['current_epoch']:5d}/{total_epochs}: "
+            + f"{results['results_explanation']} "
+            + f"Objective: {results['loss']:.5f}"
+        )
+
+    @staticmethod
+    def export_csv_file(config: Config, results: list, csv_file: str) -> None:
+        """
+        Log result to csv-file
+        """
+        if not results:
+            return
+
+        # Verification for overwrite
+        if Path(csv_file).is_file():
+            logger.error(f"CSV file already exists: {csv_file}")
+            return
+
+        try:
+            Path(csv_file).open("w+").close()
+        except OSError:
+            logger.error(f"Failed to create CSV file: {csv_file}")
+            return
+
+        trials = json_normalize(results, max_level=1)
+        trials["Best"] = ""
+
+        base_metrics = [
+            "Best",
+            "current_epoch",
+            "results_metrics.total_trades",
+            "results_metrics.profit_mean",
+            "results_metrics.profit_median",
+            "results_metrics.profit_total",
+            "results_metrics.stake_currency",
+            "results_metrics.profit_total_abs",
+            "results_metrics.holding_avg",
+            "results_metrics.trade_count_long",
+            "results_metrics.trade_count_short",
+            "results_metrics.max_drawdown_abs",
+            "results_metrics.max_drawdown_account",
+            "loss",
+            "is_initial_point",
+            "is_best",
+        ]
+        perc_multi = 100
+
+        param_metrics = [("params_dict." + param) for param in results[0]["params_dict"].keys()]
+        trials = trials[base_metrics + param_metrics]
+
+        base_columns = [
+            "Best",
+            "Epoch",
+            "Trades",
+            "Avg profit",
+            "Median profit",
+            "Total profit",
+            "Stake currency",
+            "Profit",
+            "Avg duration",
+            "Trade count long",
+            "Trade count short",
+            "Max drawdown",
+            "Max drawdown percent",
+            "Objective",
+            "is_initial_point",
+            "is_best",
+        ]
+        param_columns = list(results[0]["params_dict"].keys())
+        trials.columns = base_columns + param_columns
+
+        trials["is_profit"] = False
+        trials.loc[trials["is_initial_point"], "Best"] = "*"
+        trials.loc[trials["is_best"], "Best"] = "Best"
+        trials.loc[trials["is_initial_point"] & trials["is_best"], "Best"] = "* Best"
+        trials.loc[trials["Total profit"] > 0, "is_profit"] = True
+        trials["Epoch"] = trials["Epoch"].astype(str)
+        trials["Trades"] = trials["Trades"].astype(str)
+        trials["Median profit"] = trials["Median profit"] * perc_multi
+
+        trials["Total profit"] = trials["Total profit"].apply(
+            lambda x: f"{x:,.8f}" if x != 0.0 else ""
+        )
+        trials["Profit"] = trials["Profit"].apply(lambda x: f"{x:,.2f}" if not isna(x) else "")
+        trials["Avg profit"] = trials["Avg profit"].apply(
+            lambda x: f"{x * perc_multi:,.2f}%" if not isna(x) else ""
+        )
+        trials["Max drawdown percent"] = trials["Max drawdown percent"].apply(
+            lambda x: f"{x * perc_multi:,.2f}%" if not isna(x) else ""
+        )
+        trials["Objective"] = trials["Objective"].apply(
+            lambda x: f"{x:,.5f}" if x != 100000 else ""
+        )
+
+        trials = trials.drop(columns=["is_initial_point", "is_best", "is_profit"])
+        trials.to_csv(csv_file, index=False, header=True, mode="w", encoding="UTF-8")
+        logger.info(f"CSV file created: {csv_file}")
diff --git a/freqtrade/optimize/optimize_reports/__init__.py b/freqtrade/optimize/optimize_reports/__init__.py
new file mode 100644
index 000000000..c08bae4be
--- /dev/null
+++ b/freqtrade/optimize/optimize_reports/__init__.py
@@ -0,0 +1,27 @@
+# flake8: noqa: F401
+from freqtrade.optimize.optimize_reports.bt_output import (
+    generate_edge_table,
+    generate_wins_draws_losses,
+    show_backtest_result,
+    show_backtest_results,
+    show_sorted_pairlist,
+    text_table_add_metrics,
+    text_table_bt_results,
+    text_table_periodic_breakdown,
+    text_table_strategy,
+    text_table_tags,
+)
+from freqtrade.optimize.optimize_reports.bt_storage import store_backtest_results
+from freqtrade.optimize.optimize_reports.optimize_reports import (
+    generate_all_periodic_breakdown_stats,
+    generate_backtest_stats,
+    generate_daily_stats,
+    generate_pair_metrics,
+    generate_periodic_breakdown_stats,
+    generate_rejected_signals,
+    generate_strategy_comparison,
+    generate_strategy_stats,
+    generate_tag_metrics,
+    generate_trade_signal_candles,
+    generate_trading_stats,
+)
diff --git a/freqtrade/optimize/optimize_reports/bt_output.py b/freqtrade/optimize/optimize_reports/bt_output.py
new file mode 100644
index 000000000..7dea7224b
--- /dev/null
+++ b/freqtrade/optimize/optimize_reports/bt_output.py
@@ -0,0 +1,519 @@
+import logging
+from typing import Any, Literal
+
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config
+from freqtrade.ft_types import BacktestResultType
+from freqtrade.optimize.optimize_reports.optimize_reports import generate_periodic_breakdown_stats
+from freqtrade.util import decimals_per_coin, fmt_coin, print_rich_table
+
+
+logger = logging.getLogger(__name__)
+
+
+def _get_line_floatfmt(stake_currency: str) -> list[str]:
+    """
+    Generate floatformat (goes in line with _generate_result_line())
+    """
+    return ["s", "d", ".2f", f".{decimals_per_coin(stake_currency)}f", ".2f", "d", "s", "s"]
+
+
+def _get_line_header(
+    first_column: str | list[str], stake_currency: str, direction: str = "Trades"
+) -> list[str]:
+    """
+    Generate header lines (goes in line with _generate_result_line())
+    """
+    return [
+        *([first_column] if isinstance(first_column, str) else first_column),
+        direction,
+        "Avg Profit %",
+        f"Tot Profit {stake_currency}",
+        "Tot Profit %",
+        "Avg Duration",
+        "Win  Draw  Loss  Win%",
+    ]
+
+
+def generate_wins_draws_losses(wins, draws, losses):
+    if wins > 0 and losses == 0:
+        wl_ratio = "100"
+    elif wins == 0:
+        wl_ratio = "0"
+    else:
+        wl_ratio = f"{100.0 / (wins + draws + losses) * wins:.1f}" if losses > 0 else "100"
+    return f"{wins:>4}  {draws:>4}  {losses:>4}  {wl_ratio:>4}"
+
+
+def text_table_bt_results(
+    pair_results: list[dict[str, Any]], stake_currency: str, title: str
+) -> None:
+    """
+    Generates and returns a text table for the given backtest data and the results dataframe
+    :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
+    :param stake_currency: stake-currency - used to correctly name headers
+    :param title: Title of the table
+    """
+
+    headers = _get_line_header("Pair", stake_currency, "Trades")
+    output = [
+        [
+            t["key"],
+            t["trades"],
+            t["profit_mean_pct"],
+            f"{t['profit_total_abs']:.{decimals_per_coin(stake_currency)}f}",
+            t["profit_total_pct"],
+            t["duration_avg"],
+            generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]),
+        ]
+        for t in pair_results
+    ]
+    # Ignore type as floatfmt does allow tuples but mypy does not know that
+    print_rich_table(output, headers, summary=title)
+
+
+def text_table_tags(
+    tag_type: Literal["enter_tag", "exit_tag", "mix_tag"],
+    tag_results: list[dict[str, Any]],
+    stake_currency: str,
+) -> None:
+    """
+    Generates and returns a text table for the given backtest data and the results dataframe
+    :param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
+    :param stake_currency: stake-currency - used to correctly name headers
+    """
+    floatfmt = _get_line_floatfmt(stake_currency)
+    fallback: str = ""
+    is_list = False
+    if tag_type == "enter_tag":
+        title = "Enter Tag"
+        headers = _get_line_header(title, stake_currency, "Entries")
+    elif tag_type == "exit_tag":
+        title = "Exit Reason"
+        headers = _get_line_header(title, stake_currency, "Exits")
+        fallback = "exit_reason"
+    else:
+        # Mix tag
+        title = "Mixed Tag"
+        headers = _get_line_header(["Enter Tag", "Exit Reason"], stake_currency, "Trades")
+        floatfmt.insert(0, "s")
+        is_list = True
+
+    output = [
+        [
+            *(
+                (
+                    (t["key"] if isinstance(t["key"], list) else [t["key"], ""])
+                    if is_list
+                    else [t["key"]]
+                )
+                if t.get("key") is not None and len(str(t["key"])) > 0
+                else [t.get(fallback, "OTHER")]
+            ),
+            t["trades"],
+            t["profit_mean_pct"],
+            f"{t['profit_total_abs']:.{decimals_per_coin(stake_currency)}f}",
+            t["profit_total_pct"],
+            t.get("duration_avg"),
+            generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]),
+        ]
+        for t in tag_results
+    ]
+    # Ignore type as floatfmt does allow tuples but mypy does not know that
+    print_rich_table(output, headers, summary=f"{title.upper()} STATS")
+
+
+def text_table_periodic_breakdown(
+    days_breakdown_stats: list[dict[str, Any]], stake_currency: str, period: str
+) -> None:
+    """
+    Generate small table with Backtest results by days
+    :param days_breakdown_stats: Days breakdown metrics
+    :param stake_currency: Stakecurrency used
+    """
+    headers = [
+        period.capitalize(),
+        "Trades",
+        f"Tot Profit {stake_currency}",
+        "Profit Factor",
+        "Win  Draw  Loss  Win%",
+    ]
+    output = [
+        [
+            d["date"],
+            d.get("trades", "N/A"),
+            fmt_coin(d["profit_abs"], stake_currency, False),
+            round(d["profit_factor"], 2) if "profit_factor" in d else "N/A",
+            generate_wins_draws_losses(d["wins"], d["draws"], d.get("losses", d.get("loses", 0))),
+        ]
+        for d in days_breakdown_stats
+    ]
+    print_rich_table(output, headers, summary=f"{period.upper()} BREAKDOWN")
+
+
+def text_table_strategy(strategy_results, stake_currency: str, title: str):
+    """
+    Generate summary table per strategy
+    :param strategy_results: Dict of <Strategyname: DataFrame> containing results for all strategies
+    :param stake_currency: stake-currency - used to correctly name headers
+    """
+    headers = _get_line_header("Strategy", stake_currency, "Trades")
+    # _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
+    # therefore we slip this column in only for strategy summary here.
+    headers.append("Drawdown")
+
+    # Align drawdown string on the center two space separator.
+    if "max_drawdown_account" in strategy_results[0]:
+        drawdown = [f"{t['max_drawdown_account'] * 100:.2f}" for t in strategy_results]
+    else:
+        # Support for prior backtest results
+        drawdown = [f"{t['max_drawdown_per']:.2f}" for t in strategy_results]
+
+    dd_pad_abs = max([len(t["max_drawdown_abs"]) for t in strategy_results])
+    dd_pad_per = max([len(dd) for dd in drawdown])
+    drawdown = [
+        f"{t['max_drawdown_abs']:>{dd_pad_abs}} {stake_currency}  {dd:>{dd_pad_per}}%"
+        for t, dd in zip(strategy_results, drawdown, strict=False)
+    ]
+
+    output = [
+        [
+            t["key"],
+            t["trades"],
+            f"{t['profit_mean_pct']:.2f}",
+            f"{t['profit_total_abs']:.{decimals_per_coin(stake_currency)}f}",
+            t["profit_total_pct"],
+            t["duration_avg"],
+            generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]),
+            drawdown,
+        ]
+        for t, drawdown in zip(strategy_results, drawdown, strict=False)
+    ]
+    print_rich_table(output, headers, summary=title)
+
+
+def text_table_add_metrics(strat_results: dict) -> None:
+    if len(strat_results["trades"]) > 0:
+        best_trade = max(strat_results["trades"], key=lambda x: x["profit_ratio"])
+        worst_trade = min(strat_results["trades"], key=lambda x: x["profit_ratio"])
+
+        short_metrics = (
+            [
+                ("", ""),  # Empty line to improve readability
+                (
+                    "Long / Short",
+                    f"{strat_results.get('trade_count_long', 'total_trades')} / "
+                    f"{strat_results.get('trade_count_short', 0)}",
+                ),
+                ("Total profit Long %", f"{strat_results['profit_total_long']:.2%}"),
+                ("Total profit Short %", f"{strat_results['profit_total_short']:.2%}"),
+                (
+                    "Absolute profit Long",
+                    fmt_coin(
+                        strat_results["profit_total_long_abs"], strat_results["stake_currency"]
+                    ),
+                ),
+                (
+                    "Absolute profit Short",
+                    fmt_coin(
+                        strat_results["profit_total_short_abs"], strat_results["stake_currency"]
+                    ),
+                ),
+            ]
+            if strat_results.get("trade_count_short", 0) > 0
+            else []
+        )
+
+        drawdown_metrics = []
+        if "max_relative_drawdown" in strat_results:
+            # Compatibility to show old hyperopt results
+            drawdown_metrics.append(
+                ("Max % of account underwater", f"{strat_results['max_relative_drawdown']:.2%}")
+            )
+        drawdown_metrics.extend(
+            [
+                (
+                    ("Absolute Drawdown (Account)", f"{strat_results['max_drawdown_account']:.2%}")
+                    if "max_drawdown_account" in strat_results
+                    else ("Drawdown", f"{strat_results['max_drawdown']:.2%}")
+                ),
+                (
+                    "Absolute Drawdown",
+                    fmt_coin(strat_results["max_drawdown_abs"], strat_results["stake_currency"]),
+                ),
+                (
+                    "Drawdown high",
+                    fmt_coin(strat_results["max_drawdown_high"], strat_results["stake_currency"]),
+                ),
+                (
+                    "Drawdown low",
+                    fmt_coin(strat_results["max_drawdown_low"], strat_results["stake_currency"]),
+                ),
+                ("Drawdown Start", strat_results["drawdown_start"]),
+                ("Drawdown End", strat_results["drawdown_end"]),
+            ]
+        )
+
+        entry_adjustment_metrics = (
+            [
+                ("Canceled Trade Entries", strat_results.get("canceled_trade_entries", "N/A")),
+                ("Canceled Entry Orders", strat_results.get("canceled_entry_orders", "N/A")),
+                ("Replaced Entry Orders", strat_results.get("replaced_entry_orders", "N/A")),
+            ]
+            if strat_results.get("canceled_entry_orders", 0) > 0
+            else []
+        )
+
+        trading_mode = (
+            (
+                [
+                    (
+                        "Trading Mode",
+                        (
+                            ""
+                            if not strat_results.get("margin_mode")
+                            or strat_results.get("trading_mode", "spot") == "spot"
+                            else f"{strat_results['margin_mode'].capitalize()} "
+                        )
+                        + f"{strat_results['trading_mode'].capitalize()}",
+                    )
+                ]
+            )
+            if "trading_mode" in strat_results
+            else []
+        )
+
+        # Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
+        # command stores these results and newer version of freqtrade must be able to handle old
+        # results with missing new fields.
+        metrics = [
+            ("Backtesting from", strat_results["backtest_start"]),
+            ("Backtesting to", strat_results["backtest_end"]),
+            *trading_mode,
+            ("Max open trades", strat_results["max_open_trades"]),
+            ("", ""),  # Empty line to improve readability
+            (
+                "Total/Daily Avg Trades",
+                f"{strat_results['total_trades']} / {strat_results['trades_per_day']}",
+            ),
+            (
+                "Starting balance",
+                fmt_coin(strat_results["starting_balance"], strat_results["stake_currency"]),
+            ),
+            (
+                "Final balance",
+                fmt_coin(strat_results["final_balance"], strat_results["stake_currency"]),
+            ),
+            (
+                "Absolute profit ",
+                fmt_coin(strat_results["profit_total_abs"], strat_results["stake_currency"]),
+            ),
+            ("Total profit %", f"{strat_results['profit_total']:.2%}"),
+            ("CAGR %", f"{strat_results['cagr']:.2%}" if "cagr" in strat_results else "N/A"),
+            ("Sortino", f"{strat_results['sortino']:.2f}" if "sortino" in strat_results else "N/A"),
+            ("Sharpe", f"{strat_results['sharpe']:.2f}" if "sharpe" in strat_results else "N/A"),
+            ("Calmar", f"{strat_results['calmar']:.2f}" if "calmar" in strat_results else "N/A"),
+            ("SQN", f"{strat_results['sqn']:.2f}" if "sqn" in strat_results else "N/A"),
+            (
+                "Profit factor",
+                (
+                    f"{strat_results['profit_factor']:.2f}"
+                    if "profit_factor" in strat_results
+                    else "N/A"
+                ),
+            ),
+            (
+                "Expectancy (Ratio)",
+                (
+                    f"{strat_results['expectancy']:.2f} ({strat_results['expectancy_ratio']:.2f})"
+                    if "expectancy_ratio" in strat_results
+                    else "N/A"
+                ),
+            ),
+            (
+                "Avg. daily profit %",
+                f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}",
+            ),
+            (
+                "Avg. stake amount",
+                fmt_coin(strat_results["avg_stake_amount"], strat_results["stake_currency"]),
+            ),
+            (
+                "Total trade volume",
+                fmt_coin(strat_results["total_volume"], strat_results["stake_currency"]),
+            ),
+            *short_metrics,
+            ("", ""),  # Empty line to improve readability
+            (
+                "Best Pair",
+                f"{strat_results['best_pair']['key']} "
+                f"{strat_results['best_pair']['profit_total']:.2%}",
+            ),
+            (
+                "Worst Pair",
+                f"{strat_results['worst_pair']['key']} "
+                f"{strat_results['worst_pair']['profit_total']:.2%}",
+            ),
+            ("Best trade", f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"),
+            ("Worst trade", f"{worst_trade['pair']} {worst_trade['profit_ratio']:.2%}"),
+            (
+                "Best day",
+                fmt_coin(strat_results["backtest_best_day_abs"], strat_results["stake_currency"]),
+            ),
+            (
+                "Worst day",
+                fmt_coin(strat_results["backtest_worst_day_abs"], strat_results["stake_currency"]),
+            ),
+            (
+                "Days win/draw/lose",
+                f"{strat_results['winning_days']} / "
+                f"{strat_results['draw_days']} / {strat_results['losing_days']}",
+            ),
+            ("Avg. Duration Winners", f"{strat_results['winner_holding_avg']}"),
+            ("Avg. Duration Loser", f"{strat_results['loser_holding_avg']}"),
+            (
+                "Max Consecutive Wins / Loss",
+                (
+                    (
+                        f"{strat_results['max_consecutive_wins']} / "
+                        f"{strat_results['max_consecutive_losses']}"
+                    )
+                    if "max_consecutive_losses" in strat_results
+                    else "N/A"
+                ),
+            ),
+            ("Rejected Entry signals", strat_results.get("rejected_signals", "N/A")),
+            (
+                "Entry/Exit Timeouts",
+                f"{strat_results.get('timedout_entry_orders', 'N/A')} / "
+                f"{strat_results.get('timedout_exit_orders', 'N/A')}",
+            ),
+            *entry_adjustment_metrics,
+            ("", ""),  # Empty line to improve readability
+            ("Min balance", fmt_coin(strat_results["csum_min"], strat_results["stake_currency"])),
+            ("Max balance", fmt_coin(strat_results["csum_max"], strat_results["stake_currency"])),
+            *drawdown_metrics,
+            ("Market change", f"{strat_results['market_change']:.2%}"),
+        ]
+        print_rich_table(metrics, ["Metric", "Value"], summary="SUMMARY METRICS", justify="left")
+
+    else:
+        start_balance = fmt_coin(strat_results["starting_balance"], strat_results["stake_currency"])
+        stake_amount = (
+            fmt_coin(strat_results["stake_amount"], strat_results["stake_currency"])
+            if strat_results["stake_amount"] != UNLIMITED_STAKE_AMOUNT
+            else "unlimited"
+        )
+
+        message = (
+            "No trades made. "
+            f"Your starting balance was {start_balance}, "
+            f"and your stake was {stake_amount}."
+        )
+        print(message)
+
+
+def _show_tag_subresults(results: dict[str, Any], stake_currency: str):
+    """
+    Print tag subresults (enter_tag, exit_reason_summary, mix_tag_stats)
+    """
+    if (enter_tags := results.get("results_per_enter_tag")) is not None:
+        text_table_tags("enter_tag", enter_tags, stake_currency)
+
+    if (exit_reasons := results.get("exit_reason_summary")) is not None:
+        text_table_tags("exit_tag", exit_reasons, stake_currency)
+
+    if (mix_tag := results.get("mix_tag_stats")) is not None:
+        text_table_tags("mix_tag", mix_tag, stake_currency)
+
+
+def show_backtest_result(
+    strategy: str, results: dict[str, Any], stake_currency: str, backtest_breakdown: list[str]
+):
+    """
+    Print results for one strategy
+    """
+    # Print results
+    print(f"Result for strategy {strategy}")
+    text_table_bt_results(
+        results["results_per_pair"], stake_currency=stake_currency, title="BACKTESTING REPORT"
+    )
+    text_table_bt_results(
+        results["left_open_trades"], stake_currency=stake_currency, title="LEFT OPEN TRADES REPORT"
+    )
+
+    _show_tag_subresults(results, stake_currency)
+
+    for period in backtest_breakdown:
+        if period in results.get("periodic_breakdown", {}):
+            days_breakdown_stats = results["periodic_breakdown"][period]
+        else:
+            days_breakdown_stats = generate_periodic_breakdown_stats(
+                trade_list=results["trades"], period=period
+            )
+        text_table_periodic_breakdown(
+            days_breakdown_stats=days_breakdown_stats, stake_currency=stake_currency, period=period
+        )
+
+    text_table_add_metrics(results)
+
+    print()
+
+
+def show_backtest_results(config: Config, backtest_stats: BacktestResultType):
+    stake_currency = config["stake_currency"]
+
+    for strategy, results in backtest_stats["strategy"].items():
+        show_backtest_result(
+            strategy, results, stake_currency, config.get("backtest_breakdown", [])
+        )
+
+    if len(backtest_stats["strategy"]) > 0:
+        # Print Strategy summary table
+
+        print(
+            f"Backtested {results['backtest_start']} -> {results['backtest_end']} |"
+            f" Max open trades : {results['max_open_trades']}"
+        )
+        text_table_strategy(
+            backtest_stats["strategy_comparison"], stake_currency, "STRATEGY SUMMARY"
+        )
+
+
+def show_sorted_pairlist(config: Config, backtest_stats: BacktestResultType):
+    if config.get("backtest_show_pair_list", False):
+        for strategy, results in backtest_stats["strategy"].items():
+            print(f"Pairs for Strategy {strategy}: \n[")
+            for result in results["results_per_pair"]:
+                if result["key"] != "TOTAL":
+                    print(f'"{result["key"]}",  // {result["profit_mean"]:.2%}')
+            print("]")
+
+
+def generate_edge_table(results: dict) -> None:
+    tabular_data = []
+    headers = [
+        "Pair",
+        "Stoploss",
+        "Win Rate",
+        "Risk Reward Ratio",
+        "Required Risk Reward",
+        "Expectancy",
+        "Total Number of Trades",
+        "Average Duration (min)",
+    ]
+
+    for result in results.items():
+        if result[1].nb_trades > 0:
+            tabular_data.append(
+                [
+                    result[0],
+                    f"{result[1].stoploss:.10g}",
+                    f"{result[1].winrate:.2f}",
+                    f"{result[1].risk_reward_ratio:.2f}",
+                    f"{result[1].required_risk_reward:.2f}",
+                    f"{result[1].expectancy:.2f}",
+                    result[1].nb_trades,
+                    round(result[1].avg_trade_duration),
+                ]
+            )
+    print_rich_table(tabular_data, headers, summary="EDGE TABLE")
diff --git a/freqtrade/optimize/optimize_reports/bt_storage.py b/freqtrade/optimize/optimize_reports/bt_storage.py
new file mode 100644
index 000000000..1e6a67e81
--- /dev/null
+++ b/freqtrade/optimize/optimize_reports/bt_storage.py
@@ -0,0 +1,140 @@
+import logging
+from io import BytesIO, StringIO
+from pathlib import Path
+from typing import Any
+from zipfile import ZIP_DEFLATED, ZipFile
+
+from pandas import DataFrame
+
+from freqtrade.configuration import sanitize_config
+from freqtrade.constants import LAST_BT_RESULT_FN
+from freqtrade.enums.runmode import RunMode
+from freqtrade.ft_types import BacktestResultType
+from freqtrade.misc import dump_json_to_file, file_dump_json
+from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
+
+
+logger = logging.getLogger(__name__)
+
+
+def file_dump_joblib(file_obj: BytesIO, data: Any, log: bool = True) -> None:
+    """
+    Dump object data into a file
+    :param filename: file to create
+    :param data: Object data to save
+    :return:
+    """
+    import joblib
+
+    joblib.dump(data, file_obj)
+
+
+def _generate_filename(recordfilename: Path, appendix: str, suffix: str) -> Path:
+    """
+    Generates a filename based on the provided parameters.
+    :param recordfilename: Path object, which can either be a filename or a directory.
+    :param appendix: use for the filename. e.g. backtest-result-<datetime>
+    :param suffix: Suffix to use for the file, e.g. .json, .pkl
+    :return: Generated filename as a Path object
+    """
+    if recordfilename.is_dir():
+        filename = (recordfilename / f"backtest-result-{appendix}").with_suffix(suffix)
+    else:
+        filename = Path.joinpath(
+            recordfilename.parent, f"{recordfilename.stem}-{appendix}"
+        ).with_suffix(suffix)
+    return filename
+
+
+def store_backtest_results(
+    config: dict,
+    stats: BacktestResultType,
+    dtappendix: str,
+    *,
+    market_change_data: DataFrame | None = None,
+    analysis_results: dict[str, dict[str, DataFrame]] | None = None,
+    strategy_files: dict[str, str] | None = None,
+) -> Path:
+    """
+    Stores backtest results and analysis data in a zip file, with metadata stored separately
+    for convenience.
+    :param config: Configuration dictionary
+    :param stats: Dataframe containing the backtesting statistics
+    :param dtappendix: Datetime to use for the filename
+    :param market_change_data: Dataframe containing market change data
+    :param analysis_results: Dictionary containing analysis results
+    """
+    recordfilename: Path = config["exportfilename"]
+    zip_filename = _generate_filename(recordfilename, dtappendix, ".zip")
+    base_filename = _generate_filename(recordfilename, dtappendix, "")
+    json_filename = _generate_filename(recordfilename, dtappendix, ".json")
+
+    # Store metadata separately with .json extension
+    file_dump_json(get_backtest_metadata_filename(json_filename), stats["metadata"])
+
+    # Store latest backtest info separately
+    latest_filename = Path.joinpath(zip_filename.parent, LAST_BT_RESULT_FN)
+    file_dump_json(latest_filename, {"latest_backtest": str(zip_filename.name)}, log=False)
+
+    # Create zip file and add the files
+    with ZipFile(zip_filename, "w", ZIP_DEFLATED) as zipf:
+        # Store stats
+        stats_copy = {
+            "strategy": stats["strategy"],
+            "strategy_comparison": stats["strategy_comparison"],
+        }
+        stats_buf = StringIO()
+        dump_json_to_file(stats_buf, stats_copy)
+        zipf.writestr(json_filename.name, stats_buf.getvalue())
+
+        config_buf = StringIO()
+        dump_json_to_file(config_buf, sanitize_config(config["original_config"]))
+        zipf.writestr(f"{base_filename.stem}_config.json", config_buf.getvalue())
+
+        for strategy_name, strategy_file in (strategy_files or {}).items():
+            # Store the strategy file and its parameters
+            strategy_buf = BytesIO()
+            strategy_path = Path(strategy_file)
+            if not strategy_path.is_file():
+                logger.warning(f"Strategy file '{strategy_path}' does not exist. Skipping.")
+                continue
+            with strategy_path.open("rb") as strategy_file_obj:
+                strategy_buf.write(strategy_file_obj.read())
+            strategy_buf.seek(0)
+            zipf.writestr(f"{base_filename.stem}_{strategy_name}.py", strategy_buf.getvalue())
+            strategy_params = strategy_path.with_suffix(".json")
+            if strategy_params.is_file():
+                strategy_params_buf = BytesIO()
+                with strategy_params.open("rb") as strategy_params_obj:
+                    strategy_params_buf.write(strategy_params_obj.read())
+                strategy_params_buf.seek(0)
+                zipf.writestr(
+                    f"{base_filename.stem}_{strategy_name}.json",
+                    strategy_params_buf.getvalue(),
+                )
+
+        # Add market change data if present
+        if market_change_data is not None:
+            market_change_name = f"{base_filename.stem}_market_change.feather"
+            market_change_buf = BytesIO()
+            market_change_data.reset_index().to_feather(
+                market_change_buf, compression_level=9, compression="lz4"
+            )
+            market_change_buf.seek(0)
+            zipf.writestr(market_change_name, market_change_buf.getvalue())
+
+        # Add analysis results if present and running in backtest mode
+        if (
+            config.get("export", "none") == "signals"
+            and analysis_results is not None
+            and config.get("runmode", RunMode.OTHER) == RunMode.BACKTEST
+        ):
+            for name in ["signals", "rejected", "exited"]:
+                if name in analysis_results:
+                    analysis_name = f"{base_filename.stem}_{name}.pkl"
+                    analysis_buf = BytesIO()
+                    file_dump_joblib(analysis_buf, analysis_results[name])
+                    analysis_buf.seek(0)
+                    zipf.writestr(analysis_name, analysis_buf.getvalue())
+
+    return zip_filename
diff --git a/freqtrade/optimize/optimize_reports/optimize_reports.py b/freqtrade/optimize/optimize_reports/optimize_reports.py
new file mode 100644
index 000000000..560e91476
--- /dev/null
+++ b/freqtrade/optimize/optimize_reports/optimize_reports.py
@@ -0,0 +1,674 @@
+import logging
+from copy import deepcopy
+from datetime import datetime, timedelta, timezone
+from typing import Any, Literal
+
+import numpy as np
+from pandas import DataFrame, Series, concat, to_datetime
+
+from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT
+from freqtrade.data.metrics import (
+    calculate_cagr,
+    calculate_calmar,
+    calculate_csum,
+    calculate_expectancy,
+    calculate_market_change,
+    calculate_max_drawdown,
+    calculate_sharpe,
+    calculate_sortino,
+    calculate_sqn,
+)
+from freqtrade.ft_types import (
+    BacktestContentType,
+    BacktestResultType,
+    get_BacktestResultType_default,
+)
+from freqtrade.util import decimals_per_coin, fmt_coin, get_dry_run_wallet
+
+
+logger = logging.getLogger(__name__)
+
+
+def generate_trade_signal_candles(
+    preprocessed_df: dict[str, DataFrame], bt_results: BacktestContentType, date_col: str
+) -> dict[str, DataFrame]:
+    signal_candles_only = {}
+    for pair in preprocessed_df.keys():
+        signal_candles_only_df = DataFrame()
+
+        pairdf = preprocessed_df[pair]
+        resdf = bt_results["results"]
+        pairresults = resdf.loc[(resdf["pair"] == pair)]
+
+        if pairdf.shape[0] > 0:
+            for t, v in pairresults.iterrows():
+                allinds = pairdf.loc[(pairdf["date"] < v[date_col])]
+                signal_inds = allinds.iloc[[-1]]
+                signal_candles_only_df = concat(
+                    [signal_candles_only_df.infer_objects(), signal_inds.infer_objects()]
+                )
+
+            signal_candles_only[pair] = signal_candles_only_df
+    return signal_candles_only
+
+
+def generate_rejected_signals(
+    preprocessed_df: dict[str, DataFrame], rejected_dict: dict[str, DataFrame]
+) -> dict[str, DataFrame]:
+    rejected_candles_only = {}
+    for pair, signals in rejected_dict.items():
+        rejected_signals_only_df = DataFrame()
+        pairdf = preprocessed_df[pair]
+
+        for t in signals:
+            data_df_row = pairdf.loc[(pairdf["date"] == t[0])].copy()
+            data_df_row["pair"] = pair
+            data_df_row["enter_tag"] = t[1]
+
+            rejected_signals_only_df = concat(
+                [rejected_signals_only_df.infer_objects(), data_df_row.infer_objects()]
+            )
+
+        rejected_candles_only[pair] = rejected_signals_only_df
+    return rejected_candles_only
+
+
+def _generate_result_line(
+    result: DataFrame,
+    min_date: datetime,
+    max_date: datetime,
+    starting_balance: float,
+    first_column: str | list[str],
+) -> dict:
+    """
+    Generate one result dict, with "first_column" as key.
+    """
+    profit_sum = result["profit_ratio"].sum()
+    # (end-capital - starting capital) / starting capital
+    profit_total = result["profit_abs"].sum() / starting_balance
+    backtest_days = (max_date - min_date).days or 1
+    final_balance = starting_balance + result["profit_abs"].sum()
+    expectancy, expectancy_ratio = calculate_expectancy(result)
+    winning_profit = result.loc[result["profit_abs"] > 0, "profit_abs"].sum()
+    losing_profit = result.loc[result["profit_abs"] < 0, "profit_abs"].sum()
+    profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
+
+    try:
+        drawdown = calculate_max_drawdown(
+            result, value_col="profit_abs", starting_balance=starting_balance
+        )
+
+    except ValueError:
+        drawdown = None
+
+    return {
+        "key": first_column,
+        "trades": len(result),
+        "profit_mean": result["profit_ratio"].mean() if len(result) > 0 else 0.0,
+        "profit_mean_pct": (
+            round(result["profit_ratio"].mean() * 100.0, 2) if len(result) > 0 else 0.0
+        ),
+        "profit_sum": profit_sum,
+        "profit_sum_pct": round(profit_sum * 100.0, 2),
+        "profit_total_abs": result["profit_abs"].sum(),
+        "profit_total": profit_total,
+        "profit_total_pct": round(profit_total * 100.0, 2),
+        "duration_avg": (
+            str(timedelta(minutes=round(result["trade_duration"].mean())))
+            if not result.empty
+            else "0:00"
+        ),
+        # 'duration_max': str(timedelta(
+        #                     minutes=round(result['trade_duration'].max()))
+        #                     ) if not result.empty else '0:00',
+        # 'duration_min': str(timedelta(
+        #                     minutes=round(result['trade_duration'].min()))
+        #                     ) if not result.empty else '0:00',
+        "wins": len(result[result["profit_abs"] > 0]),
+        "draws": len(result[result["profit_abs"] == 0]),
+        "losses": len(result[result["profit_abs"] < 0]),
+        "winrate": len(result[result["profit_abs"] > 0]) / len(result) if len(result) else 0.0,
+        "cagr": calculate_cagr(backtest_days, starting_balance, final_balance),
+        "expectancy": expectancy,
+        "expectancy_ratio": expectancy_ratio,
+        "sortino": calculate_sortino(result, min_date, max_date, starting_balance),
+        "sharpe": calculate_sharpe(result, min_date, max_date, starting_balance),
+        "calmar": calculate_calmar(result, min_date, max_date, starting_balance),
+        "sqn": calculate_sqn(result, starting_balance),
+        "profit_factor": profit_factor,
+        "max_drawdown_account": drawdown.relative_account_drawdown if drawdown else 0.0,
+        "max_drawdown_abs": drawdown.drawdown_abs if drawdown else 0.0,
+    }
+
+
+def calculate_trade_volume(trades_dict: list[dict[str, Any]]) -> float:
+    # Aggregate the total volume traded from orders.cost.
+    # Orders is a nested dictionary within the trades list.
+
+    return sum(sum(order["cost"] for order in trade.get("orders", [])) for trade in trades_dict)
+
+
+def generate_pair_metrics(  #
+    pairlist: list[str],
+    stake_currency: str,
+    starting_balance: float,
+    results: DataFrame,
+    min_date: datetime,
+    max_date: datetime,
+    skip_nan: bool = False,
+) -> list[dict]:
+    """
+    Generates and returns a list  for the given backtest data and the results dataframe
+    :param pairlist: Pairlist used
+    :param stake_currency: stake-currency - used to correctly name headers
+    :param starting_balance: Starting balance
+    :param results: Dataframe containing the backtest results
+    :param skip_nan: Print "left open" open trades
+    :return: List of Dicts containing the metrics per pair
+    """
+
+    tabular_data = []
+
+    for pair in pairlist:
+        result = results[results["pair"] == pair]
+        if skip_nan and result["profit_abs"].isnull().all():
+            continue
+
+        tabular_data.append(
+            _generate_result_line(result, min_date, max_date, starting_balance, pair)
+        )
+
+    # Sort by total profit %:
+    tabular_data = sorted(tabular_data, key=lambda k: k["profit_total_abs"], reverse=True)
+
+    # Append Total
+    tabular_data.append(
+        _generate_result_line(results, min_date, max_date, starting_balance, "TOTAL")
+    )
+
+    return tabular_data
+
+
+def generate_tag_metrics(
+    tag_type: Literal["enter_tag", "exit_reason"] | list[Literal["enter_tag", "exit_reason"]],
+    starting_balance: float,
+    results: DataFrame,
+    min_date: datetime,
+    max_date: datetime,
+    skip_nan: bool = False,
+) -> list[dict]:
+    """
+    Generates and returns a list of metrics for the given tag trades and the results dataframe
+    :param starting_balance: Starting balance
+    :param results: Dataframe containing the backtest results
+    :param skip_nan: Print "left open" open trades
+    :return: List of Dicts containing the metrics per pair
+    """
+
+    tabular_data = []
+
+    if all(
+        tag in results.columns for tag in (tag_type if isinstance(tag_type, list) else [tag_type])
+    ):
+        for tags, group in results.groupby(tag_type):
+            if skip_nan and group["profit_abs"].isnull().all():
+                continue
+
+            tabular_data.append(
+                _generate_result_line(group, min_date, max_date, starting_balance, tags)
+            )
+
+        # Sort by total profit %:
+        tabular_data = sorted(tabular_data, key=lambda k: k["profit_total_abs"], reverse=True)
+
+        # Append Total
+        tabular_data.append(
+            _generate_result_line(results, min_date, max_date, starting_balance, "TOTAL")
+        )
+        return tabular_data
+    else:
+        return []
+
+
+def generate_strategy_comparison(bt_stats: dict) -> list[dict]:
+    """
+    Generate summary per strategy
+    :param bt_stats: Dict of <Strategyname: DataFrame> containing results for all strategies
+    :return: List of Dicts containing the metrics per Strategy
+    """
+
+    tabular_data = []
+    for strategy, result in bt_stats.items():
+        tabular_data.append(deepcopy(result["results_per_pair"][-1]))
+        # Update "key" to strategy (results_per_pair has it as "Total").
+        tabular_data[-1]["key"] = strategy
+        tabular_data[-1]["max_drawdown_account"] = result["max_drawdown_account"]
+        tabular_data[-1]["max_drawdown_abs"] = fmt_coin(
+            result["max_drawdown_abs"], result["stake_currency"], False
+        )
+    return tabular_data
+
+
+def _get_resample_from_period(period: str) -> str:
+    if period == "day":
+        return "1d"
+    if period == "week":
+        # Weekly defaulting to Monday.
+        return "1W-MON"
+    if period == "month":
+        return "1ME"
+    if period == "year":
+        return "1YE"
+    raise ValueError(f"Period {period} is not supported.")
+
+
+def generate_periodic_breakdown_stats(
+    trade_list: list | DataFrame, period: str
+) -> list[dict[str, Any]]:
+    results = trade_list if not isinstance(trade_list, list) else DataFrame.from_records(trade_list)
+    if len(results) == 0:
+        return []
+    results["close_date"] = to_datetime(results["close_date"], utc=True)
+    resample_period = _get_resample_from_period(period)
+    resampled = results.resample(resample_period, on="close_date")
+    stats = []
+    for name, day in resampled:
+        profit_abs = day["profit_abs"].sum().round(10)
+        wins = sum(day["profit_abs"] > 0)
+        draws = sum(day["profit_abs"] == 0)
+        losses = sum(day["profit_abs"] < 0)
+        trades = wins + draws + losses
+        winning_profit = day.loc[day["profit_abs"] > 0, "profit_abs"].sum()
+        losing_profit = day.loc[day["profit_abs"] < 0, "profit_abs"].sum()
+        profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
+        stats.append(
+            {
+                "date": name.strftime("%d/%m/%Y"),
+                "date_ts": int(name.to_pydatetime().timestamp() * 1000),
+                "profit_abs": profit_abs,
+                "wins": wins,
+                "draws": draws,
+                "losses": losses,
+                "trades": trades,
+                "profit_factor": round(profit_factor, 8),
+            }
+        )
+    return stats
+
+
+def generate_all_periodic_breakdown_stats(trade_list: list) -> dict[str, list]:
+    result = {}
+    for period in BACKTEST_BREAKDOWNS:
+        result[period] = generate_periodic_breakdown_stats(trade_list, period)
+    return result
+
+
+def calc_streak(dataframe: DataFrame) -> tuple[int, int]:
+    """
+    Calculate consecutive win and loss streaks
+    :param dataframe: Dataframe containing the trades dataframe, with profit_ratio column
+    :return: Tuple containing consecutive wins and losses
+    """
+
+    df = Series(np.where(dataframe["profit_ratio"] > 0, "win", "loss")).to_frame("result")
+    df["streaks"] = df["result"].ne(df["result"].shift()).cumsum().rename("streaks")
+    df["counter"] = df["streaks"].groupby(df["streaks"]).cumcount() + 1
+    res = df.groupby(df["result"]).max()
+    #
+    cons_wins = int(res.loc["win", "counter"]) if "win" in res.index else 0
+    cons_losses = int(res.loc["loss", "counter"]) if "loss" in res.index else 0
+    return cons_wins, cons_losses
+
+
+def generate_trading_stats(results: DataFrame) -> dict[str, Any]:
+    """Generate overall trade statistics"""
+    if len(results) == 0:
+        return {
+            "wins": 0,
+            "losses": 0,
+            "draws": 0,
+            "winrate": 0,
+            "holding_avg": timedelta(),
+            "winner_holding_avg": timedelta(),
+            "loser_holding_avg": timedelta(),
+            "max_consecutive_wins": 0,
+            "max_consecutive_losses": 0,
+        }
+
+    winning_trades = results.loc[results["profit_ratio"] > 0]
+    draw_trades = results.loc[results["profit_ratio"] == 0]
+    losing_trades = results.loc[results["profit_ratio"] < 0]
+
+    holding_avg = (
+        timedelta(minutes=round(results["trade_duration"].mean()))
+        if not results.empty
+        else timedelta()
+    )
+    winner_holding_avg = (
+        timedelta(minutes=round(winning_trades["trade_duration"].mean()))
+        if not winning_trades.empty
+        else timedelta()
+    )
+    loser_holding_avg = (
+        timedelta(minutes=round(losing_trades["trade_duration"].mean()))
+        if not losing_trades.empty
+        else timedelta()
+    )
+    winstreak, loss_streak = calc_streak(results)
+
+    return {
+        "wins": len(winning_trades),
+        "losses": len(losing_trades),
+        "draws": len(draw_trades),
+        "winrate": len(winning_trades) / len(results) if len(results) else 0.0,
+        "holding_avg": holding_avg,
+        "holding_avg_s": holding_avg.total_seconds(),
+        "winner_holding_avg": winner_holding_avg,
+        "winner_holding_avg_s": winner_holding_avg.total_seconds(),
+        "loser_holding_avg": loser_holding_avg,
+        "loser_holding_avg_s": loser_holding_avg.total_seconds(),
+        "max_consecutive_wins": winstreak,
+        "max_consecutive_losses": loss_streak,
+    }
+
+
+def generate_daily_stats(results: DataFrame) -> dict[str, Any]:
+    """Generate daily statistics"""
+    if len(results) == 0:
+        return {
+            "backtest_best_day": 0,
+            "backtest_worst_day": 0,
+            "backtest_best_day_abs": 0,
+            "backtest_worst_day_abs": 0,
+            "winning_days": 0,
+            "draw_days": 0,
+            "losing_days": 0,
+            "daily_profit_list": [],
+        }
+    daily_profit_rel = results.resample("1d", on="close_date")["profit_ratio"].sum()
+    daily_profit = results.resample("1d", on="close_date")["profit_abs"].sum().round(10)
+    worst_rel = min(daily_profit_rel)
+    best_rel = max(daily_profit_rel)
+    worst = min(daily_profit)
+    best = max(daily_profit)
+    winning_days = sum(daily_profit > 0)
+    draw_days = sum(daily_profit == 0)
+    losing_days = sum(daily_profit < 0)
+    daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.items()]
+
+    return {
+        "backtest_best_day": best_rel,
+        "backtest_worst_day": worst_rel,
+        "backtest_best_day_abs": best,
+        "backtest_worst_day_abs": worst,
+        "winning_days": winning_days,
+        "draw_days": draw_days,
+        "losing_days": losing_days,
+        "daily_profit": daily_profit_list,
+    }
+
+
+def generate_strategy_stats(
+    pairlist: list[str],
+    strategy: str,
+    content: BacktestContentType,
+    min_date: datetime,
+    max_date: datetime,
+    market_change: float,
+    is_hyperopt: bool = False,
+) -> dict[str, Any]:
+    """
+    :param pairlist: List of pairs to backtest
+    :param strategy: Strategy name
+    :param content: Backtest result data in the format:
+                    {'results: results, 'config: config}}.
+    :param min_date: Backtest start date
+    :param max_date: Backtest end date
+    :param market_change: float indicating the market change
+    :return: Dictionary containing results per strategy and a strategy summary.
+    """
+    results: DataFrame = content["results"]
+    if not isinstance(results, DataFrame):
+        return {}
+    config = content["config"]
+    max_open_trades = min(config["max_open_trades"], len(pairlist))
+    start_balance = get_dry_run_wallet(config)
+    stake_currency = config["stake_currency"]
+
+    pair_results = generate_pair_metrics(
+        pairlist,
+        stake_currency=stake_currency,
+        starting_balance=start_balance,
+        results=results,
+        min_date=min_date,
+        max_date=max_date,
+        skip_nan=False,
+    )
+
+    enter_tag_stats = generate_tag_metrics(
+        "enter_tag",
+        starting_balance=start_balance,
+        results=results,
+        min_date=min_date,
+        max_date=max_date,
+        skip_nan=False,
+    )
+    exit_reason_stats = generate_tag_metrics(
+        "exit_reason",
+        starting_balance=start_balance,
+        results=results,
+        min_date=min_date,
+        max_date=max_date,
+        skip_nan=False,
+    )
+    mix_tag_stats = generate_tag_metrics(
+        ["enter_tag", "exit_reason"],
+        starting_balance=start_balance,
+        results=results,
+        min_date=min_date,
+        max_date=max_date,
+        skip_nan=False,
+    )
+    left_open_results = generate_pair_metrics(
+        pairlist,
+        stake_currency=stake_currency,
+        starting_balance=start_balance,
+        results=results.loc[results["exit_reason"] == "force_exit"],
+        min_date=min_date,
+        max_date=max_date,
+        skip_nan=True,
+    )
+
+    daily_stats = generate_daily_stats(results)
+    trade_stats = generate_trading_stats(results)
+
+    periodic_breakdown = {}
+    if not is_hyperopt:
+        periodic_breakdown = {"periodic_breakdown": generate_all_periodic_breakdown_stats(results)}
+
+    best_pair = (
+        max(
+            [pair for pair in pair_results if pair["key"] != "TOTAL"], key=lambda x: x["profit_sum"]
+        )
+        if len(pair_results) > 1
+        else None
+    )
+    worst_pair = (
+        min(
+            [pair for pair in pair_results if pair["key"] != "TOTAL"], key=lambda x: x["profit_sum"]
+        )
+        if len(pair_results) > 1
+        else None
+    )
+    winning_profit = results.loc[results["profit_abs"] > 0, "profit_abs"].sum()
+    losing_profit = results.loc[results["profit_abs"] < 0, "profit_abs"].sum()
+    profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
+
+    expectancy, expectancy_ratio = calculate_expectancy(results)
+    backtest_days = (max_date - min_date).days or 1
+    trades_dict = results.to_dict(orient="records")
+    strat_stats = {
+        "trades": trades_dict,
+        "locks": [lock.to_json() for lock in content["locks"]],
+        "best_pair": best_pair,
+        "worst_pair": worst_pair,
+        "results_per_pair": pair_results,
+        "results_per_enter_tag": enter_tag_stats,
+        "exit_reason_summary": exit_reason_stats,
+        "mix_tag_stats": mix_tag_stats,
+        "left_open_trades": left_open_results,
+        "total_trades": len(results),
+        "trade_count_long": len(results.loc[~results["is_short"]]),
+        "trade_count_short": len(results.loc[results["is_short"]]),
+        "total_volume": calculate_trade_volume(trades_dict),
+        "avg_stake_amount": results["stake_amount"].mean() if len(results) > 0 else 0,
+        "profit_mean": results["profit_ratio"].mean() if len(results) > 0 else 0,
+        "profit_median": results["profit_ratio"].median() if len(results) > 0 else 0,
+        "profit_total": results["profit_abs"].sum() / start_balance,
+        "profit_total_long": results.loc[~results["is_short"], "profit_abs"].sum() / start_balance,
+        "profit_total_short": results.loc[results["is_short"], "profit_abs"].sum() / start_balance,
+        "profit_total_abs": results["profit_abs"].sum(),
+        "profit_total_long_abs": results.loc[~results["is_short"], "profit_abs"].sum(),
+        "profit_total_short_abs": results.loc[results["is_short"], "profit_abs"].sum(),
+        "cagr": calculate_cagr(backtest_days, start_balance, content["final_balance"]),
+        "expectancy": expectancy,
+        "expectancy_ratio": expectancy_ratio,
+        "sortino": calculate_sortino(results, min_date, max_date, start_balance),
+        "sharpe": calculate_sharpe(results, min_date, max_date, start_balance),
+        "calmar": calculate_calmar(results, min_date, max_date, start_balance),
+        "sqn": calculate_sqn(results, start_balance),
+        "profit_factor": profit_factor,
+        "backtest_start": min_date.strftime(DATETIME_PRINT_FORMAT),
+        "backtest_start_ts": int(min_date.timestamp() * 1000),
+        "backtest_end": max_date.strftime(DATETIME_PRINT_FORMAT),
+        "backtest_end_ts": int(max_date.timestamp() * 1000),
+        "backtest_days": backtest_days,
+        "backtest_run_start_ts": content["backtest_start_time"],
+        "backtest_run_end_ts": content["backtest_end_time"],
+        "trades_per_day": round(len(results) / backtest_days, 2),
+        "market_change": market_change,
+        "pairlist": pairlist,
+        "stake_amount": config["stake_amount"],
+        "stake_currency": config["stake_currency"],
+        "stake_currency_decimals": decimals_per_coin(config["stake_currency"]),
+        "starting_balance": start_balance,
+        "dry_run_wallet": start_balance,
+        "final_balance": content["final_balance"],
+        "rejected_signals": content["rejected_signals"],
+        "timedout_entry_orders": content["timedout_entry_orders"],
+        "timedout_exit_orders": content["timedout_exit_orders"],
+        "canceled_trade_entries": content["canceled_trade_entries"],
+        "canceled_entry_orders": content["canceled_entry_orders"],
+        "replaced_entry_orders": content["replaced_entry_orders"],
+        "max_open_trades": max_open_trades,
+        "max_open_trades_setting": (
+            config["max_open_trades"] if config["max_open_trades"] != float("inf") else -1
+        ),
+        "timeframe": config["timeframe"],
+        "timeframe_detail": config.get("timeframe_detail", ""),
+        "timerange": config.get("timerange", ""),
+        "enable_protections": config.get("enable_protections", False),
+        "strategy_name": strategy,
+        # Parameters relevant for backtesting
+        "stoploss": config["stoploss"],
+        "trailing_stop": config.get("trailing_stop", False),
+        "trailing_stop_positive": config.get("trailing_stop_positive"),
+        "trailing_stop_positive_offset": config.get("trailing_stop_positive_offset", 0.0),
+        "trailing_only_offset_is_reached": config.get("trailing_only_offset_is_reached", False),
+        "use_custom_stoploss": config.get("use_custom_stoploss", False),
+        "minimal_roi": config["minimal_roi"],
+        "use_exit_signal": config["use_exit_signal"],
+        "exit_profit_only": config["exit_profit_only"],
+        "exit_profit_offset": config["exit_profit_offset"],
+        "ignore_roi_if_entry_signal": config["ignore_roi_if_entry_signal"],
+        "trading_mode": config["trading_mode"],
+        "margin_mode": config["margin_mode"],
+        **periodic_breakdown,
+        **daily_stats,
+        **trade_stats,
+    }
+
+    try:
+        drawdown = calculate_max_drawdown(
+            results, value_col="profit_abs", starting_balance=start_balance
+        )
+        # max_relative_drawdown = Underwater
+        underwater = calculate_max_drawdown(
+            results, value_col="profit_abs", starting_balance=start_balance, relative=True
+        )
+
+        strat_stats.update(
+            {
+                "max_drawdown_account": drawdown.relative_account_drawdown,
+                "max_relative_drawdown": underwater.relative_account_drawdown,
+                "max_drawdown_abs": drawdown.drawdown_abs,
+                "drawdown_start": drawdown.high_date.strftime(DATETIME_PRINT_FORMAT),
+                "drawdown_start_ts": drawdown.high_date.timestamp() * 1000,
+                "drawdown_end": drawdown.low_date.strftime(DATETIME_PRINT_FORMAT),
+                "drawdown_end_ts": drawdown.low_date.timestamp() * 1000,
+                "max_drawdown_low": drawdown.low_value,
+                "max_drawdown_high": drawdown.high_value,
+            }
+        )
+
+        csum_min, csum_max = calculate_csum(results, start_balance)
+        strat_stats.update({"csum_min": csum_min, "csum_max": csum_max})
+
+    except ValueError:
+        strat_stats.update(
+            {
+                "max_drawdown_account": 0.0,
+                "max_relative_drawdown": 0.0,
+                "max_drawdown_abs": 0.0,
+                "max_drawdown_low": 0.0,
+                "max_drawdown_high": 0.0,
+                "drawdown_start": datetime(1970, 1, 1, tzinfo=timezone.utc),
+                "drawdown_start_ts": 0,
+                "drawdown_end": datetime(1970, 1, 1, tzinfo=timezone.utc),
+                "drawdown_end_ts": 0,
+                "csum_min": 0,
+                "csum_max": 0,
+            }
+        )
+
+    return strat_stats
+
+
+def generate_backtest_stats(
+    btdata: dict[str, DataFrame],
+    all_results: dict[str, BacktestContentType],
+    min_date: datetime,
+    max_date: datetime,
+) -> BacktestResultType:
+    """
+    :param btdata: Backtest data
+    :param all_results: backtest result - dictionary in the form:
+                     { Strategy: {'results: results, 'config: config}}.
+    :param min_date: Backtest start date
+    :param max_date: Backtest end date
+    :return: Dictionary containing results per strategy and a strategy summary.
+    """
+    result: BacktestResultType = get_BacktestResultType_default()
+    market_change = calculate_market_change(btdata, "close")
+    metadata = {}
+    pairlist = list(btdata.keys())
+    for strategy, content in all_results.items():
+        strat_stats = generate_strategy_stats(
+            pairlist, strategy, content, min_date, max_date, market_change=market_change
+        )
+        metadata[strategy] = {
+            "run_id": content["run_id"],
+            "backtest_start_time": content["backtest_start_time"],
+            "timeframe": content["config"]["timeframe"],
+            "timeframe_detail": content["config"].get("timeframe_detail", None),
+            "backtest_start_ts": int(min_date.timestamp()),
+            "backtest_end_ts": int(max_date.timestamp()),
+        }
+        result["strategy"][strategy] = strat_stats
+
+    strategy_results = generate_strategy_comparison(bt_stats=result["strategy"])
+
+    result["metadata"] = metadata
+    result["strategy_comparison"] = strategy_results
+
+    return result
diff --git a/freqtrade/optimize/space/__init__.py b/freqtrade/optimize/space/__init__.py
new file mode 100644
index 000000000..6c59a4d8f
--- /dev/null
+++ b/freqtrade/optimize/space/__init__.py
@@ -0,0 +1,3 @@
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa: F401
+
+from .decimalspace import SKDecimal  # noqa: F401
diff --git a/freqtrade/optimize/space/decimalspace.py b/freqtrade/optimize/space/decimalspace.py
new file mode 100644
index 000000000..f5c122fb3
--- /dev/null
+++ b/freqtrade/optimize/space/decimalspace.py
@@ -0,0 +1,47 @@
+import numpy as np
+from skopt.space import Integer
+
+
+class SKDecimal(Integer):
+    def __init__(
+        self,
+        low,
+        high,
+        decimals=3,
+        prior="uniform",
+        base=10,
+        transform=None,
+        name=None,
+        dtype=np.int64,
+    ):
+        self.decimals = decimals
+
+        self.pow_dot_one = pow(0.1, self.decimals)
+        self.pow_ten = pow(10, self.decimals)
+
+        _low = int(low * self.pow_ten)
+        _high = int(high * self.pow_ten)
+        # trunc to precision to avoid points out of space
+        self.low_orig = round(_low * self.pow_dot_one, self.decimals)
+        self.high_orig = round(_high * self.pow_dot_one, self.decimals)
+
+        super().__init__(_low, _high, prior, base, transform, name, dtype)
+
+    def __repr__(self):
+        return (
+            f"Decimal(low={self.low_orig}, high={self.high_orig}, decimals={self.decimals}, "
+            f"prior='{self.prior}', transform='{self.transform_}')"
+        )
+
+    def __contains__(self, point):
+        if isinstance(point, list):
+            point = np.array(point)
+        return self.low_orig <= point <= self.high_orig
+
+    def transform(self, Xt):
+        return super().transform([int(v * self.pow_ten) for v in Xt])
+
+    def inverse_transform(self, Xt):
+        res = super().inverse_transform(Xt)
+        # equivalent to [round(x * pow(0.1, self.decimals), self.decimals) for x in res]
+        return [int(v) / self.pow_ten for v in res]
diff --git a/freqtrade/persistence.py b/freqtrade/persistence.py
deleted file mode 100644
index e3043089f..000000000
--- a/freqtrade/persistence.py
+++ /dev/null
@@ -1,213 +0,0 @@
-import logging
-from datetime import datetime
-from decimal import Decimal, getcontext
-from typing import Dict, Optional
-
-import arrow
-from sqlalchemy import (Boolean, Column, DateTime, Float, Integer, String,
-                        create_engine)
-from sqlalchemy.engine import Engine
-from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy.orm.scoping import scoped_session
-from sqlalchemy.orm.session import sessionmaker
-from sqlalchemy.pool import StaticPool
-
-logger = logging.getLogger(__name__)
-
-_CONF = {}
-_DECL_BASE = declarative_base()
-
-
-def init(config: dict, engine: Optional[Engine] = None) -> None:
-    """
-    Initializes this module with the given config,
-    registers all known command handlers
-    and starts polling for message updates
-    :param config: config to use
-    :param engine: database engine for sqlalchemy (Optional)
-    :return: None
-    """
-    _CONF.update(config)
-    if not engine:
-        if _CONF.get('dry_run', False):
-            # the user wants dry run to use a DB
-            if _CONF.get('dry_run_db', False):
-                engine = create_engine('sqlite:///tradesv3.dry_run.sqlite')
-            # Otherwise dry run will store in memory
-            else:
-                engine = create_engine('sqlite://',
-                                       connect_args={'check_same_thread': False},
-                                       poolclass=StaticPool,
-                                       echo=False)
-        else:
-            engine = create_engine('sqlite:///tradesv3.sqlite')
-
-    session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
-    Trade.session = session()
-    Trade.query = session.query_property()
-    _DECL_BASE.metadata.create_all(engine)
-
-    # Clean dry_run DB
-    if _CONF.get('dry_run', False) and _CONF.get('dry_run_db', False):
-        clean_dry_run_db()
-
-
-def cleanup() -> None:
-    """
-    Flushes all pending operations to disk.
-    :return: None
-    """
-    Trade.session.flush()
-
-
-def clean_dry_run_db() -> None:
-    """
-    Remove open_order_id from a Dry_run DB
-    :return: None
-    """
-    for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
-        # Check we are updating only a dry_run order not a prod one
-        if 'dry_run' in trade.open_order_id:
-            trade.open_order_id = None
-
-
-class Trade(_DECL_BASE):
-    __tablename__ = 'trades'
-
-    id = Column(Integer, primary_key=True)
-    exchange = Column(String, nullable=False)
-    pair = Column(String, nullable=False)
-    is_open = Column(Boolean, nullable=False, default=True)
-    fee = Column(Float, nullable=False, default=0.0)
-    open_rate = Column(Float)
-    close_rate = Column(Float)
-    close_profit = Column(Float)
-    stake_amount = Column(Float, nullable=False)
-    amount = Column(Float)
-    open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
-    close_date = Column(DateTime)
-    open_order_id = Column(String)
-
-    def __repr__(self):
-        return 'Trade(id={}, pair={}, amount={:.8f}, open_rate={:.8f}, open_since={})'.format(
-            self.id,
-            self.pair,
-            self.amount,
-            self.open_rate,
-            arrow.get(self.open_date).humanize() if self.is_open else 'closed'
-        )
-
-    def update(self, order: Dict) -> None:
-        """
-        Updates this entity with amount and actual open/close rates.
-        :param order: order retrieved by exchange.get_order()
-        :return: None
-        """
-        # Ignore open and cancelled orders
-        if not order['closed'] or order['rate'] is None:
-            return
-
-        logger.info('Updating trade (id=%d) ...', self.id)
-
-        getcontext().prec = 8  # Bittrex do not go above 8 decimal
-        if order['type'] == 'LIMIT_BUY':
-            # Update open rate and actual amount
-            self.open_rate = Decimal(order['rate'])
-            self.amount = Decimal(order['amount'])
-            logger.info('LIMIT_BUY has been fulfilled for %s.', self)
-            self.open_order_id = None
-        elif order['type'] == 'LIMIT_SELL':
-            self.close(order['rate'])
-        else:
-            raise ValueError('Unknown order type: {}'.format(order['type']))
-        cleanup()
-
-    def close(self, rate: float) -> None:
-        """
-        Sets close_rate to the given rate, calculates total profit
-        and marks trade as closed
-        """
-        self.close_rate = Decimal(rate)
-        self.close_profit = self.calc_profit_percent()
-        self.close_date = datetime.utcnow()
-        self.is_open = False
-        self.open_order_id = None
-        logger.info(
-            'Marking %s as closed as the trade is fulfilled and found no open orders for it.',
-            self
-        )
-
-    def calc_open_trade_price(
-            self,
-            fee: Optional[float] = None) -> float:
-        """
-        Calculate the open_rate in BTC
-        :param fee: fee to use on the open rate (optional).
-        If rate is not set self.fee will be used
-        :return: Price in BTC of the open trade
-        """
-        getcontext().prec = 8
-
-        buy_trade = (Decimal(self.amount) * Decimal(self.open_rate))
-        fees = buy_trade * Decimal(fee or self.fee)
-        return float(buy_trade + fees)
-
-    def calc_close_trade_price(
-            self,
-            rate: Optional[float] = None,
-            fee: Optional[float] = None) -> float:
-        """
-        Calculate the close_rate in BTC
-        :param fee: fee to use on the close rate (optional).
-        If rate is not set self.fee will be used
-        :param rate: rate to compare with (optional).
-        If rate is not set self.close_rate will be used
-        :return: Price in BTC of the open trade
-        """
-        getcontext().prec = 8
-
-        if rate is None and not self.close_rate:
-            return 0.0
-
-        sell_trade = (Decimal(self.amount) * Decimal(rate or self.close_rate))
-        fees = sell_trade * Decimal(fee or self.fee)
-        return float(sell_trade - fees)
-
-    def calc_profit(
-            self,
-            rate: Optional[float] = None,
-            fee: Optional[float] = None) -> float:
-        """
-        Calculate the profit in BTC between Close and Open trade
-        :param fee: fee to use on the close rate (optional).
-        If rate is not set self.fee will be used
-        :param rate: close rate to compare with (optional).
-        If rate is not set self.close_rate will be used
-        :return:  profit in BTC as float
-        """
-        open_trade_price = self.calc_open_trade_price()
-        close_trade_price = self.calc_close_trade_price(
-            rate=(rate or self.close_rate),
-            fee=(fee or self.fee)
-        )
-        return float("{0:.8f}".format(close_trade_price - open_trade_price))
-
-    def calc_profit_percent(
-            self,
-            rate: Optional[float] = None,
-            fee: Optional[float] = None) -> float:
-        """
-        Calculates the profit in percentage (including fee).
-        :param rate: rate to compare with (optional).
-        If rate is not set self.close_rate will be used
-        :return: profit in percentage as float
-        """
-        getcontext().prec = 8
-
-        open_trade_price = self.calc_open_trade_price()
-        close_trade_price = self.calc_close_trade_price(
-            rate=(rate or self.close_rate),
-            fee=(fee or self.fee)
-        )
-
-        return float("{0:.8f}".format((close_trade_price / open_trade_price) - 1))
diff --git a/freqtrade/persistence/__init__.py b/freqtrade/persistence/__init__.py
new file mode 100644
index 000000000..3612544ee
--- /dev/null
+++ b/freqtrade/persistence/__init__.py
@@ -0,0 +1,12 @@
+# flake8: noqa: F401
+
+from freqtrade.persistence.custom_data import CustomDataWrapper
+from freqtrade.persistence.key_value_store import KeyStoreKeys, KeyValueStore
+from freqtrade.persistence.models import init_db
+from freqtrade.persistence.pairlock_middleware import PairLocks
+from freqtrade.persistence.trade_model import LocalTrade, Order, Trade
+from freqtrade.persistence.usedb_context import (
+    FtNoDBContext,
+    disable_database_use,
+    enable_database_use,
+)
diff --git a/freqtrade/persistence/base.py b/freqtrade/persistence/base.py
new file mode 100644
index 000000000..5f5c40dea
--- /dev/null
+++ b/freqtrade/persistence/base.py
@@ -0,0 +1,8 @@
+from sqlalchemy.orm import DeclarativeBase, Session, scoped_session
+
+
+SessionType = scoped_session[Session]
+
+
+class ModelBase(DeclarativeBase):
+    pass
diff --git a/freqtrade/persistence/custom_data.py b/freqtrade/persistence/custom_data.py
new file mode 100644
index 000000000..17c0cbf0a
--- /dev/null
+++ b/freqtrade/persistence/custom_data.py
@@ -0,0 +1,182 @@
+import json
+import logging
+from collections.abc import Sequence
+from datetime import datetime
+from typing import Any, ClassVar
+
+from sqlalchemy import DateTime, ForeignKey, Integer, String, Text, UniqueConstraint, select
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from freqtrade.constants import DATETIME_PRINT_FORMAT
+from freqtrade.persistence.base import ModelBase, SessionType
+from freqtrade.util import dt_now
+
+
+logger = logging.getLogger(__name__)
+
+
+class _CustomData(ModelBase):
+    """
+    CustomData database model
+    Keeps records of metadata as key/value store
+    for trades or global persistent values
+    One to many relationship with Trades:
+      - One trade can have many metadata entries
+      - One metadata entry can only be associated with one Trade
+    """
+
+    __tablename__ = "trade_custom_data"
+    __allow_unmapped__ = True
+    session: ClassVar[SessionType]
+
+    # Uniqueness should be ensured over pair, order_id
+    # its likely that order_id is unique per Pair on some exchanges.
+    __table_args__ = (UniqueConstraint("ft_trade_id", "cd_key", name="_trade_id_cd_key"),)
+
+    id = mapped_column(Integer, primary_key=True)
+    ft_trade_id = mapped_column(Integer, ForeignKey("trades.id"), index=True)
+
+    trade = relationship("Trade", back_populates="custom_data")
+
+    cd_key: Mapped[str] = mapped_column(String(255), nullable=False)
+    cd_type: Mapped[str] = mapped_column(String(25), nullable=False)
+    cd_value: Mapped[str] = mapped_column(Text, nullable=False)
+    created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, default=dt_now)
+    updated_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
+
+    # Empty container value - not persisted, but filled with cd_value on query
+    value: Any = None
+
+    def __repr__(self):
+        create_time = (
+            self.created_at.strftime(DATETIME_PRINT_FORMAT) if self.created_at is not None else None
+        )
+        update_time = (
+            self.updated_at.strftime(DATETIME_PRINT_FORMAT) if self.updated_at is not None else None
+        )
+        return (
+            f"CustomData(id={self.id}, key={self.cd_key}, type={self.cd_type}, "
+            + f"value={self.cd_value}, trade_id={self.ft_trade_id}, created={create_time}, "
+            + f"updated={update_time})"
+        )
+
+    @classmethod
+    def query_cd(
+        cls, key: str | None = None, trade_id: int | None = None
+    ) -> Sequence["_CustomData"]:
+        """
+        Get all CustomData, if trade_id is not specified
+        return will be for generic values not tied to a trade
+        :param trade_id: id of the Trade
+        """
+        filters = []
+        if trade_id is not None:
+            filters.append(_CustomData.ft_trade_id == trade_id)
+        if key is not None:
+            filters.append(_CustomData.cd_key.ilike(key))
+
+        return _CustomData.session.scalars(select(_CustomData).filter(*filters)).all()
+
+
+class CustomDataWrapper:
+    """
+    CustomData middleware class
+    Abstracts the database layer away so it becomes optional - which will be necessary to support
+    backtesting and hyperopt in the future.
+    """
+
+    use_db = True
+    custom_data: list[_CustomData] = []
+    unserialized_types = ["bool", "float", "int", "str"]
+
+    @staticmethod
+    def _convert_custom_data(data: _CustomData) -> _CustomData:
+        if data.cd_type in CustomDataWrapper.unserialized_types:
+            data.value = data.cd_value
+            if data.cd_type == "bool":
+                data.value = data.cd_value.lower() == "true"
+            elif data.cd_type == "int":
+                data.value = int(data.cd_value)
+            elif data.cd_type == "float":
+                data.value = float(data.cd_value)
+        else:
+            data.value = json.loads(data.cd_value)
+        return data
+
+    @staticmethod
+    def reset_custom_data() -> None:
+        """
+        Resets all key-value pairs. Only active for backtesting mode.
+        """
+        if not CustomDataWrapper.use_db:
+            CustomDataWrapper.custom_data = []
+
+    @staticmethod
+    def delete_custom_data(trade_id: int) -> None:
+        _CustomData.session.query(_CustomData).filter(_CustomData.ft_trade_id == trade_id).delete()
+        _CustomData.session.commit()
+
+    @staticmethod
+    def get_custom_data(*, trade_id: int, key: str | None = None) -> list[_CustomData]:
+        if CustomDataWrapper.use_db:
+            filters = [
+                _CustomData.ft_trade_id == trade_id,
+            ]
+            if key is not None:
+                filters.append(_CustomData.cd_key.ilike(key))
+            filtered_custom_data = _CustomData.session.scalars(
+                select(_CustomData).filter(*filters)
+            ).all()
+
+        else:
+            filtered_custom_data = [
+                data_entry
+                for data_entry in CustomDataWrapper.custom_data
+                if (data_entry.ft_trade_id == trade_id)
+            ]
+            if key is not None:
+                filtered_custom_data = [
+                    data_entry
+                    for data_entry in filtered_custom_data
+                    if (data_entry.cd_key.casefold() == key.casefold())
+                ]
+        return [CustomDataWrapper._convert_custom_data(d) for d in filtered_custom_data]
+
+    @staticmethod
+    def set_custom_data(trade_id: int, key: str, value: Any) -> None:
+        value_type = type(value).__name__
+
+        if value_type not in CustomDataWrapper.unserialized_types:
+            try:
+                value_db = json.dumps(value)
+            except TypeError as e:
+                logger.warning(f"could not serialize {key} value due to {e}")
+                return
+        else:
+            value_db = str(value)
+
+        if trade_id is None:
+            trade_id = 0
+
+        custom_data = CustomDataWrapper.get_custom_data(trade_id=trade_id, key=key)
+        if custom_data:
+            data_entry = custom_data[0]
+            data_entry.cd_value = value_db
+            data_entry.updated_at = dt_now()
+        else:
+            data_entry = _CustomData(
+                ft_trade_id=trade_id,
+                cd_key=key,
+                cd_type=value_type,
+                cd_value=value_db,
+                created_at=dt_now(),
+            )
+        data_entry.value = value
+
+        if CustomDataWrapper.use_db and value_db is not None:
+            _CustomData.session.add(data_entry)
+            _CustomData.session.commit()
+        else:
+            if not custom_data:
+                CustomDataWrapper.custom_data.append(data_entry)
+            # Existing data will have updated interactively.
diff --git a/freqtrade/persistence/key_value_store.py b/freqtrade/persistence/key_value_store.py
new file mode 100644
index 000000000..e7b782ac4
--- /dev/null
+++ b/freqtrade/persistence/key_value_store.py
@@ -0,0 +1,209 @@
+from datetime import datetime, timezone
+from enum import Enum
+from typing import ClassVar
+
+from sqlalchemy import String
+from sqlalchemy.orm import Mapped, mapped_column
+
+from freqtrade.persistence.base import ModelBase, SessionType
+
+
+ValueTypes = str | datetime | float | int
+
+
+class ValueTypesEnum(str, Enum):
+    STRING = "str"
+    DATETIME = "datetime"
+    FLOAT = "float"
+    INT = "int"
+
+
+class KeyStoreKeys(str, Enum):
+    BOT_START_TIME = "bot_start_time"
+    STARTUP_TIME = "startup_time"
+
+
+class _KeyValueStoreModel(ModelBase):
+    """
+    Pair Locks database model.
+    """
+
+    __tablename__ = "KeyValueStore"
+    session: ClassVar[SessionType]
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+
+    key: Mapped[KeyStoreKeys] = mapped_column(String(25), nullable=False, index=True)
+
+    value_type: Mapped[ValueTypesEnum] = mapped_column(String(20), nullable=False)
+
+    string_value: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    datetime_value: Mapped[datetime | None]
+    float_value: Mapped[float | None]
+    int_value: Mapped[int | None]
+
+
+class KeyValueStore:
+    """
+    Generic bot-wide, persistent key-value store
+    Can be used to store generic values, e.g. very first bot startup time.
+    Supports the types str, datetime, float and int.
+    """
+
+    @staticmethod
+    def store_value(key: KeyStoreKeys, value: ValueTypes) -> None:
+        """
+        Store the given value for the given key.
+        :param key: Key to store the value for - can be used in get-value to retrieve the key
+        :param value: Value to store - can be str, datetime, float or int
+        """
+        kv = (
+            _KeyValueStoreModel.session.query(_KeyValueStoreModel)
+            .filter(_KeyValueStoreModel.key == key)
+            .first()
+        )
+        if kv is None:
+            kv = _KeyValueStoreModel(key=key)
+        if isinstance(value, str):
+            kv.value_type = ValueTypesEnum.STRING
+            kv.string_value = value
+        elif isinstance(value, datetime):
+            kv.value_type = ValueTypesEnum.DATETIME
+            kv.datetime_value = value
+        elif isinstance(value, float):
+            kv.value_type = ValueTypesEnum.FLOAT
+            kv.float_value = value
+        elif isinstance(value, int):
+            kv.value_type = ValueTypesEnum.INT
+            kv.int_value = value
+        else:
+            raise ValueError(f"Unknown value type {kv.value_type}")
+        _KeyValueStoreModel.session.add(kv)
+        _KeyValueStoreModel.session.commit()
+
+    @staticmethod
+    def delete_value(key: KeyStoreKeys) -> None:
+        """
+        Delete the value for the given key.
+        :param key: Key to delete the value for
+        """
+        kv = (
+            _KeyValueStoreModel.session.query(_KeyValueStoreModel)
+            .filter(_KeyValueStoreModel.key == key)
+            .first()
+        )
+        if kv is not None:
+            _KeyValueStoreModel.session.delete(kv)
+            _KeyValueStoreModel.session.commit()
+
+    @staticmethod
+    def get_value(key: KeyStoreKeys) -> ValueTypes | None:
+        """
+        Get the value for the given key.
+        :param key: Key to get the value for
+        """
+        kv = (
+            _KeyValueStoreModel.session.query(_KeyValueStoreModel)
+            .filter(_KeyValueStoreModel.key == key)
+            .first()
+        )
+        if kv is None:
+            return None
+        if kv.value_type == ValueTypesEnum.STRING:
+            return kv.string_value
+        if kv.value_type == ValueTypesEnum.DATETIME and kv.datetime_value is not None:
+            return kv.datetime_value.replace(tzinfo=timezone.utc)
+        if kv.value_type == ValueTypesEnum.FLOAT:
+            return kv.float_value
+        if kv.value_type == ValueTypesEnum.INT:
+            return kv.int_value
+        # This should never happen unless someone messed with the database manually
+        raise ValueError(f"Unknown value type {kv.value_type}")  # pragma: no cover
+
+    @staticmethod
+    def get_string_value(key: KeyStoreKeys) -> str | None:
+        """
+        Get the value for the given key.
+        :param key: Key to get the value for
+        """
+        kv = (
+            _KeyValueStoreModel.session.query(_KeyValueStoreModel)
+            .filter(
+                _KeyValueStoreModel.key == key,
+                _KeyValueStoreModel.value_type == ValueTypesEnum.STRING,
+            )
+            .first()
+        )
+        if kv is None:
+            return None
+        return kv.string_value
+
+    @staticmethod
+    def get_datetime_value(key: KeyStoreKeys) -> datetime | None:
+        """
+        Get the value for the given key.
+        :param key: Key to get the value for
+        """
+        kv = (
+            _KeyValueStoreModel.session.query(_KeyValueStoreModel)
+            .filter(
+                _KeyValueStoreModel.key == key,
+                _KeyValueStoreModel.value_type == ValueTypesEnum.DATETIME,
+            )
+            .first()
+        )
+        if kv is None or kv.datetime_value is None:
+            return None
+        return kv.datetime_value.replace(tzinfo=timezone.utc)
+
+    @staticmethod
+    def get_float_value(key: KeyStoreKeys) -> float | None:
+        """
+        Get the value for the given key.
+        :param key: Key to get the value for
+        """
+        kv = (
+            _KeyValueStoreModel.session.query(_KeyValueStoreModel)
+            .filter(
+                _KeyValueStoreModel.key == key,
+                _KeyValueStoreModel.value_type == ValueTypesEnum.FLOAT,
+            )
+            .first()
+        )
+        if kv is None:
+            return None
+        return kv.float_value
+
+    @staticmethod
+    def get_int_value(key: KeyStoreKeys) -> int | None:
+        """
+        Get the value for the given key.
+        :param key: Key to get the value for
+        """
+        kv = (
+            _KeyValueStoreModel.session.query(_KeyValueStoreModel)
+            .filter(
+                _KeyValueStoreModel.key == key, _KeyValueStoreModel.value_type == ValueTypesEnum.INT
+            )
+            .first()
+        )
+        if kv is None:
+            return None
+        return kv.int_value
+
+
+def set_startup_time():
+    """
+    sets bot_start_time to the first trade open date - or "now" on new databases.
+    sets startup_time to "now"
+    """
+    st = KeyValueStore.get_value("bot_start_time")
+    if st is None:
+        from freqtrade.persistence import Trade
+
+        t = Trade.session.query(Trade).order_by(Trade.open_date.asc()).first()
+        if t is not None:
+            KeyValueStore.store_value("bot_start_time", t.open_date_utc)
+        else:
+            KeyValueStore.store_value("bot_start_time", datetime.now(timezone.utc))
+    KeyValueStore.store_value("startup_time", datetime.now(timezone.utc))
diff --git a/freqtrade/persistence/migrations.py b/freqtrade/persistence/migrations.py
new file mode 100644
index 000000000..f0aa2f289
--- /dev/null
+++ b/freqtrade/persistence/migrations.py
@@ -0,0 +1,410 @@
+import logging
+
+from sqlalchemy import inspect, select, text, update
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence.trade_model import Order, Trade
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_table_names_for_table(inspector, tabletype) -> list[str]:
+    return [t for t in inspector.get_table_names() if t.startswith(tabletype)]
+
+
+def has_column(columns: list, searchname: str) -> bool:
+    return len(list(filter(lambda x: x["name"] == searchname, columns))) == 1
+
+
+def get_column_def(columns: list, column: str, default: str) -> str:
+    return default if not has_column(columns, column) else column
+
+
+def get_backup_name(tabs: list[str], backup_prefix: str):
+    table_back_name = backup_prefix
+    for i, table_back_name in enumerate(tabs):
+        table_back_name = f"{backup_prefix}{i}"
+        logger.debug(f"trying {table_back_name}")
+
+    return table_back_name
+
+
+def get_last_sequence_ids(engine, trade_back_name: str, order_back_name: str):
+    order_id: int | None = None
+    trade_id: int | None = None
+
+    if engine.name == "postgresql":
+        with engine.begin() as connection:
+            trade_id = connection.execute(text("select nextval('trades_id_seq')")).fetchone()[0]
+            order_id = connection.execute(text("select nextval('orders_id_seq')")).fetchone()[0]
+        with engine.begin() as connection:
+            connection.execute(
+                text(f"ALTER SEQUENCE orders_id_seq rename to {order_back_name}_id_seq_bak")
+            )
+            connection.execute(
+                text(f"ALTER SEQUENCE trades_id_seq rename to {trade_back_name}_id_seq_bak")
+            )
+    return order_id, trade_id
+
+
+def set_sequence_ids(engine, order_id, trade_id, pairlock_id=None):
+    if engine.name == "postgresql":
+        with engine.begin() as connection:
+            if order_id:
+                connection.execute(text(f"ALTER SEQUENCE orders_id_seq RESTART WITH {order_id}"))
+            if trade_id:
+                connection.execute(text(f"ALTER SEQUENCE trades_id_seq RESTART WITH {trade_id}"))
+            if pairlock_id:
+                connection.execute(
+                    text(f"ALTER SEQUENCE pairlocks_id_seq RESTART WITH {pairlock_id}")
+                )
+
+
+def drop_index_on_table(engine, inspector, table_bak_name):
+    with engine.begin() as connection:
+        # drop indexes on backup table in new session
+        for index in inspector.get_indexes(table_bak_name):
+            if engine.name == "mysql":
+                connection.execute(text(f"drop index {index['name']} on {table_bak_name}"))
+            else:
+                connection.execute(text(f"drop index {index['name']}"))
+
+
+def migrate_trades_and_orders_table(
+    decl_base,
+    inspector,
+    engine,
+    trade_back_name: str,
+    cols: list,
+    order_back_name: str,
+    cols_order: list,
+):
+    base_currency = get_column_def(cols, "base_currency", "null")
+    stake_currency = get_column_def(cols, "stake_currency", "null")
+    fee_open = get_column_def(cols, "fee_open", "fee")
+    fee_open_cost = get_column_def(cols, "fee_open_cost", "null")
+    fee_open_currency = get_column_def(cols, "fee_open_currency", "null")
+    fee_close = get_column_def(cols, "fee_close", "fee")
+    fee_close_cost = get_column_def(cols, "fee_close_cost", "null")
+    fee_close_currency = get_column_def(cols, "fee_close_currency", "null")
+    open_rate_requested = get_column_def(cols, "open_rate_requested", "null")
+    close_rate_requested = get_column_def(cols, "close_rate_requested", "null")
+    stop_loss = get_column_def(cols, "stop_loss", "0.0")
+    stop_loss_pct = get_column_def(cols, "stop_loss_pct", "null")
+    initial_stop_loss = get_column_def(cols, "initial_stop_loss", "0.0")
+    initial_stop_loss_pct = get_column_def(cols, "initial_stop_loss_pct", "null")
+    is_stop_loss_trailing = get_column_def(
+        cols,
+        "is_stop_loss_trailing",
+        f"coalesce({stop_loss_pct}, 0.0) <> coalesce({initial_stop_loss_pct}, 0.0)",
+    )
+    max_rate = get_column_def(cols, "max_rate", "0.0")
+    min_rate = get_column_def(cols, "min_rate", "null")
+    exit_reason = get_column_def(cols, "sell_reason", get_column_def(cols, "exit_reason", "null"))
+    strategy = get_column_def(cols, "strategy", "null")
+    enter_tag = get_column_def(cols, "buy_tag", get_column_def(cols, "enter_tag", "null"))
+    realized_profit = get_column_def(cols, "realized_profit", "0.0")
+
+    trading_mode = get_column_def(cols, "trading_mode", "null")
+
+    # Leverage Properties
+    leverage = get_column_def(cols, "leverage", "1.0")
+    liquidation_price = get_column_def(
+        cols, "liquidation_price", get_column_def(cols, "isolated_liq", "null")
+    )
+    # sqlite does not support literals for booleans
+    if engine.name == "postgresql":
+        is_short = get_column_def(cols, "is_short", "false")
+    else:
+        is_short = get_column_def(cols, "is_short", "0")
+
+    # Futures Properties
+    interest_rate = get_column_def(cols, "interest_rate", "0.0")
+    funding_fees = get_column_def(cols, "funding_fees", "0.0")
+    funding_fee_running = get_column_def(cols, "funding_fee_running", "null")
+    max_stake_amount = get_column_def(cols, "max_stake_amount", "stake_amount")
+    record_version = get_column_def(cols, "record_version", "1")
+
+    # If ticker-interval existed use that, else null.
+    if has_column(cols, "ticker_interval"):
+        timeframe = get_column_def(cols, "timeframe", "ticker_interval")
+    else:
+        timeframe = get_column_def(cols, "timeframe", "null")
+
+    open_trade_value = get_column_def(
+        cols, "open_trade_value", f"amount * open_rate * (1 + {fee_open})"
+    )
+    close_profit_abs = get_column_def(
+        cols, "close_profit_abs", f"(amount * close_rate * (1 - {fee_close})) - {open_trade_value}"
+    )
+    exit_order_status = get_column_def(
+        cols, "exit_order_status", get_column_def(cols, "sell_order_status", "null")
+    )
+    amount_requested = get_column_def(cols, "amount_requested", "amount")
+
+    amount_precision = get_column_def(cols, "amount_precision", "null")
+    price_precision = get_column_def(cols, "price_precision", "null")
+    precision_mode = get_column_def(cols, "precision_mode", "null")
+    contract_size = get_column_def(cols, "contract_size", "null")
+    precision_mode_price = get_column_def(
+        cols, "precision_mode_price", get_column_def(cols, "precision_mode", "null")
+    )
+
+    # Schema migration necessary
+    with engine.begin() as connection:
+        connection.execute(text(f"alter table trades rename to {trade_back_name}"))
+
+    drop_index_on_table(engine, inspector, trade_back_name)
+
+    order_id, trade_id = get_last_sequence_ids(engine, trade_back_name, order_back_name)
+
+    drop_orders_table(engine, order_back_name)
+
+    # let SQLAlchemy create the schema as required
+    decl_base.metadata.create_all(engine)
+
+    # Copy data back - following the correct schema
+    with engine.begin() as connection:
+        connection.execute(
+            text(
+                f"""insert into trades
+            (id, exchange, pair, base_currency, stake_currency, is_open,
+            fee_open, fee_open_cost, fee_open_currency,
+            fee_close, fee_close_cost, fee_close_currency, open_rate,
+            open_rate_requested, close_rate, close_rate_requested, close_profit,
+            stake_amount, amount, amount_requested, open_date, close_date,
+            stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
+            is_stop_loss_trailing,
+            max_rate, min_rate, exit_reason, exit_order_status, strategy, enter_tag,
+            timeframe, open_trade_value, close_profit_abs,
+            trading_mode, leverage, liquidation_price, is_short,
+            interest_rate, funding_fees, funding_fee_running, realized_profit,
+            amount_precision, price_precision, precision_mode, precision_mode_price, contract_size,
+            max_stake_amount, record_version
+            )
+        select id, lower(exchange), pair, {base_currency} base_currency,
+            {stake_currency} stake_currency,
+            is_open, {fee_open} fee_open, {fee_open_cost} fee_open_cost,
+            {fee_open_currency} fee_open_currency, {fee_close} fee_close,
+            {fee_close_cost} fee_close_cost, {fee_close_currency} fee_close_currency,
+            open_rate, {open_rate_requested} open_rate_requested, close_rate,
+            {close_rate_requested} close_rate_requested, close_profit,
+            stake_amount, amount, {amount_requested}, open_date, close_date,
+            {stop_loss} stop_loss, {stop_loss_pct} stop_loss_pct,
+            {initial_stop_loss} initial_stop_loss,
+            {initial_stop_loss_pct} initial_stop_loss_pct,
+            {is_stop_loss_trailing} is_stop_loss_trailing,
+            {max_rate} max_rate, {min_rate} min_rate,
+            case when {exit_reason} = 'sell_signal' then 'exit_signal'
+                 when {exit_reason} = 'custom_sell' then 'custom_exit'
+                 when {exit_reason} = 'force_sell' then 'force_exit'
+                 when {exit_reason} = 'emergency_sell' then 'emergency_exit'
+                 else {exit_reason}
+            end exit_reason,
+            {exit_order_status} exit_order_status,
+            {strategy} strategy, {enter_tag} enter_tag, {timeframe} timeframe,
+            {open_trade_value} open_trade_value, {close_profit_abs} close_profit_abs,
+            {trading_mode} trading_mode, {leverage} leverage, {liquidation_price} liquidation_price,
+            {is_short} is_short, {interest_rate} interest_rate,
+            {funding_fees} funding_fees, {funding_fee_running} funding_fee_running,
+            {realized_profit} realized_profit,
+            {amount_precision} amount_precision, {price_precision} price_precision,
+            {precision_mode} precision_mode, {precision_mode_price} precision_mode_price,
+            {contract_size} contract_size, {max_stake_amount} max_stake_amount,
+            {record_version} record_version
+            from {trade_back_name}
+            """
+            )
+        )
+
+    migrate_orders_table(engine, order_back_name, cols_order)
+    set_sequence_ids(engine, order_id, trade_id)
+
+
+def drop_orders_table(engine, table_back_name: str):
+    # Drop and recreate orders table as backup
+    # This drops foreign keys, too.
+
+    with engine.begin() as connection:
+        connection.execute(text(f"create table {table_back_name} as select * from orders"))
+        connection.execute(text("drop table orders"))
+
+
+def migrate_orders_table(engine, table_back_name: str, cols_order: list):
+    ft_fee_base = get_column_def(cols_order, "ft_fee_base", "null")
+    average = get_column_def(cols_order, "average", "null")
+    stop_price = get_column_def(cols_order, "stop_price", "null")
+    funding_fee = get_column_def(cols_order, "funding_fee", "0.0")
+    ft_amount = get_column_def(cols_order, "ft_amount", "coalesce(amount, 0.0)")
+    ft_price = get_column_def(cols_order, "ft_price", "coalesce(price, 0.0)")
+    ft_cancel_reason = get_column_def(cols_order, "ft_cancel_reason", "null")
+    ft_order_tag = get_column_def(cols_order, "ft_order_tag", "null")
+
+    # sqlite does not support literals for booleans
+    with engine.begin() as connection:
+        connection.execute(
+            text(
+                f"""
+            insert into orders (id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
+            status, symbol, order_type, side, price, amount, filled, average, remaining, cost,
+            stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee,
+            ft_amount, ft_price, ft_cancel_reason, ft_order_tag
+            )
+            select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
+            status, symbol, order_type, side, price, amount, filled, {average} average, remaining,
+            cost, {stop_price} stop_price, order_date, order_filled_date,
+            order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee,
+            {ft_amount} ft_amount, {ft_price} ft_price, {ft_cancel_reason} ft_cancel_reason,
+            {ft_order_tag} ft_order_tag
+            from {table_back_name}
+            """
+            )
+        )
+
+
+def migrate_pairlocks_table(decl_base, inspector, engine, pairlock_back_name: str, cols: list):
+    # Schema migration necessary
+    with engine.begin() as connection:
+        connection.execute(text(f"alter table pairlocks rename to {pairlock_back_name}"))
+
+    drop_index_on_table(engine, inspector, pairlock_back_name)
+
+    side = get_column_def(cols, "side", "'*'")
+
+    # let SQLAlchemy create the schema as required
+    decl_base.metadata.create_all(engine)
+    # Copy data back - following the correct schema
+    with engine.begin() as connection:
+        connection.execute(
+            text(
+                f"""insert into pairlocks
+        (id, pair, side, reason, lock_time,
+         lock_end_time, active)
+        select id, pair, {side} side, reason, lock_time,
+         lock_end_time, active
+        from {pairlock_back_name}
+        """
+            )
+        )
+
+
+def set_sqlite_to_wal(engine):
+    if engine.name == "sqlite" and str(engine.url) != "sqlite://":
+        # Set Mode to
+        with engine.begin() as connection:
+            connection.execute(text("PRAGMA journal_mode=wal"))
+
+
+def fix_old_dry_orders(engine):
+    with engine.begin() as connection:
+        # Update current dry-run Orders where
+        # - stoploss order is Open (will be replaced eventually)
+        # 2nd query:
+        # - current Order is open
+        # - current Trade is closed
+        # - current Order trade_id not equal to current Trade.id
+        # - current Order not stoploss
+
+        stmt = (
+            update(Order)
+            .where(
+                Order.ft_is_open.is_(True),
+                Order.ft_order_side == "stoploss",
+                Order.order_id.like("dry%"),
+            )
+            .values(ft_is_open=False)
+        )
+        connection.execute(stmt)
+
+        # Close dry-run orders for closed trades.
+        stmt = (
+            update(Order)
+            .where(
+                Order.ft_is_open.is_(True),
+                Order.ft_trade_id.not_in(select(Trade.id).where(Trade.is_open.is_(True))),
+                Order.ft_order_side != "stoploss",
+                Order.order_id.like("dry%"),
+            )
+            .values(ft_is_open=False)
+        )
+        connection.execute(stmt)
+
+
+def fix_wrong_max_stake_amount(engine):
+    """
+    Fix max_stake_amount for leveraged closed trades
+    This caused record_version to be bumped to 2.
+    """
+    with engine.begin() as connection:
+        stmt = (
+            update(Trade)
+            .where(
+                Trade.record_version < 2,
+                Trade.leverage > 1,
+                Trade.is_open.is_(False),
+                Trade.max_stake_amount != 0,
+            )
+            .values(max_stake_amount=Trade.max_stake_amount / Trade.leverage, record_version=2)
+        )
+        connection.execute(stmt)
+
+
+def check_migrate(engine, decl_base, previous_tables) -> None:
+    """
+    Checks if migration is necessary and migrates if necessary
+    """
+    inspector = inspect(engine)
+
+    cols_trades = inspector.get_columns("trades")
+    cols_orders = inspector.get_columns("orders")
+    cols_pairlocks = inspector.get_columns("pairlocks")
+    tabs = get_table_names_for_table(inspector, "trades")
+    table_back_name = get_backup_name(tabs, "trades_bak")
+    order_tabs = get_table_names_for_table(inspector, "orders")
+    order_table_bak_name = get_backup_name(order_tabs, "orders_bak")
+    pairlock_tabs = get_table_names_for_table(inspector, "pairlocks")
+    pairlock_table_bak_name = get_backup_name(pairlock_tabs, "pairlocks_bak")
+
+    # Check if migration necessary
+    # Migrates both trades and orders table!
+    # if ('orders' not in previous_tables
+    # or not has_column(cols_orders, 'funding_fee')):
+    migrating = False
+    if not has_column(cols_trades, "record_version"):
+        # if not has_column(cols_orders, "ft_order_tag"):
+        migrating = True
+        logger.info(
+            f"Running database migration for trades - "
+            f"backup: {table_back_name}, {order_table_bak_name}"
+        )
+        migrate_trades_and_orders_table(
+            decl_base,
+            inspector,
+            engine,
+            table_back_name,
+            cols_trades,
+            order_table_bak_name,
+            cols_orders,
+        )
+
+    if not has_column(cols_pairlocks, "side"):
+        migrating = True
+        logger.info(f"Running database migration for pairlocks - backup: {pairlock_table_bak_name}")
+
+        migrate_pairlocks_table(
+            decl_base, inspector, engine, pairlock_table_bak_name, cols_pairlocks
+        )
+    if "orders" not in previous_tables and "trades" in previous_tables:
+        raise OperationalException(
+            "Your database seems to be very old. "
+            "Please update to freqtrade 2022.3 to migrate this database or "
+            "start with a fresh database."
+        )
+
+    set_sqlite_to_wal(engine)
+    fix_old_dry_orders(engine)
+    fix_wrong_max_stake_amount(engine)
+
+    if migrating:
+        logger.info("Database migration finished.")
diff --git a/freqtrade/persistence/models.py b/freqtrade/persistence/models.py
new file mode 100644
index 000000000..f35f021cf
--- /dev/null
+++ b/freqtrade/persistence/models.py
@@ -0,0 +1,96 @@
+"""
+This module contains the class to persist trades into SQLite
+"""
+
+import logging
+import threading
+from contextvars import ContextVar
+from typing import Any, Final
+
+from sqlalchemy import create_engine, inspect
+from sqlalchemy.exc import NoSuchModuleError
+from sqlalchemy.orm import scoped_session, sessionmaker
+from sqlalchemy.pool import StaticPool
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence.base import ModelBase
+from freqtrade.persistence.custom_data import _CustomData
+from freqtrade.persistence.key_value_store import _KeyValueStoreModel
+from freqtrade.persistence.migrations import check_migrate
+from freqtrade.persistence.pairlock import PairLock
+from freqtrade.persistence.trade_model import Order, Trade
+
+
+logger = logging.getLogger(__name__)
+
+
+REQUEST_ID_CTX_KEY: Final[str] = "request_id"
+_request_id_ctx_var: ContextVar[str | None] = ContextVar(REQUEST_ID_CTX_KEY, default=None)
+
+
+def get_request_or_thread_id() -> str | None:
+    """
+    Helper method to get either async context (for fastapi requests), or thread id
+    """
+    request_id = _request_id_ctx_var.get()
+    if request_id is None:
+        # when not in request context - use thread id
+        request_id = str(threading.current_thread().ident)
+
+    return request_id
+
+
+_SQL_DOCS_URL = "http://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls"
+
+
+def init_db(db_url: str) -> None:
+    """
+    Initializes this module with the given config,
+    registers all known command handlers
+    and starts polling for message updates
+    :param db_url: Database to use
+    :return: None
+    """
+    kwargs: dict[str, Any] = {}
+
+    if db_url == "sqlite:///":
+        raise OperationalException(
+            f"Bad db-url {db_url}. For in-memory database, please use `sqlite://`."
+        )
+    if db_url == "sqlite://":
+        kwargs.update(
+            {
+                "poolclass": StaticPool,
+            }
+        )
+    # Take care of thread ownership
+    if db_url.startswith("sqlite://"):
+        kwargs.update(
+            {
+                "connect_args": {"check_same_thread": False},
+            }
+        )
+
+    try:
+        engine = create_engine(db_url, future=True, **kwargs)
+    except NoSuchModuleError:
+        raise OperationalException(
+            f"Given value for db_url: '{db_url}' is no valid database URL! (See {_SQL_DOCS_URL})"
+        )
+
+    # https://docs.sqlalchemy.org/en/13/orm/contextual.html#thread-local-scope
+    # Scoped sessions proxy requests to the appropriate thread-local session.
+    # Since we also use fastAPI, we need to make it aware of the request id, too
+    Trade.session = scoped_session(
+        sessionmaker(bind=engine, autoflush=False), scopefunc=get_request_or_thread_id
+    )
+    Order.session = Trade.session
+    PairLock.session = Trade.session
+    _KeyValueStoreModel.session = Trade.session
+    _CustomData.session = scoped_session(
+        sessionmaker(bind=engine, autoflush=True), scopefunc=get_request_or_thread_id
+    )
+
+    previous_tables = inspect(engine).get_table_names()
+    ModelBase.metadata.create_all(engine)
+    check_migrate(engine, decl_base=ModelBase, previous_tables=previous_tables)
diff --git a/freqtrade/persistence/pairlock.py b/freqtrade/persistence/pairlock.py
new file mode 100644
index 000000000..6c98a607d
--- /dev/null
+++ b/freqtrade/persistence/pairlock.py
@@ -0,0 +1,80 @@
+from datetime import datetime, timezone
+from typing import Any, ClassVar
+
+from sqlalchemy import ScalarResult, String, or_, select
+from sqlalchemy.orm import Mapped, mapped_column
+
+from freqtrade.constants import DATETIME_PRINT_FORMAT
+from freqtrade.persistence.base import ModelBase, SessionType
+
+
+class PairLock(ModelBase):
+    """
+    Pair Locks database model.
+    """
+
+    __tablename__ = "pairlocks"
+    session: ClassVar[SessionType]
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+
+    pair: Mapped[str] = mapped_column(String(25), nullable=False, index=True)
+    # lock direction - long, short or * (for both)
+    side: Mapped[str] = mapped_column(String(25), nullable=False, default="*")
+    reason: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    # Time the pair was locked (start time)
+    lock_time: Mapped[datetime] = mapped_column(nullable=False)
+    # Time until the pair is locked (end time)
+    lock_end_time: Mapped[datetime] = mapped_column(nullable=False, index=True)
+
+    active: Mapped[bool] = mapped_column(nullable=False, default=True, index=True)
+
+    def __repr__(self) -> str:
+        lock_time = self.lock_time.strftime(DATETIME_PRINT_FORMAT)
+        lock_end_time = self.lock_end_time.strftime(DATETIME_PRINT_FORMAT)
+        return (
+            f"PairLock(id={self.id}, pair={self.pair}, side={self.side}, lock_time={lock_time}, "
+            f"lock_end_time={lock_end_time}, reason={self.reason}, active={self.active})"
+        )
+
+    @staticmethod
+    def query_pair_locks(
+        pair: str | None, now: datetime, side: str | None = None
+    ) -> ScalarResult["PairLock"]:
+        """
+        Get all currently active locks for this pair
+        :param pair: Pair to check for. Returns all current locks if pair is empty
+        :param now: Datetime object (generated via datetime.now(timezone.utc)).
+        """
+        filters = [
+            PairLock.lock_end_time > now,
+            # Only active locks
+            PairLock.active.is_(True),
+        ]
+        if pair:
+            filters.append(PairLock.pair == pair)
+        if side is not None and side != "*":
+            filters.append(or_(PairLock.side == side, PairLock.side == "*"))
+        elif side is not None:
+            filters.append(PairLock.side == "*")
+
+        return PairLock.session.scalars(select(PairLock).filter(*filters))
+
+    @staticmethod
+    def get_all_locks() -> ScalarResult["PairLock"]:
+        return PairLock.session.scalars(select(PairLock))
+
+    def to_json(self) -> dict[str, Any]:
+        return {
+            "id": self.id,
+            "pair": self.pair,
+            "lock_time": self.lock_time.strftime(DATETIME_PRINT_FORMAT),
+            "lock_timestamp": int(self.lock_time.replace(tzinfo=timezone.utc).timestamp() * 1000),
+            "lock_end_time": self.lock_end_time.strftime(DATETIME_PRINT_FORMAT),
+            "lock_end_timestamp": int(
+                self.lock_end_time.replace(tzinfo=timezone.utc).timestamp() * 1000
+            ),
+            "reason": self.reason,
+            "side": self.side,
+            "active": self.active,
+        }
diff --git a/freqtrade/persistence/pairlock_middleware.py b/freqtrade/persistence/pairlock_middleware.py
new file mode 100644
index 000000000..0adbcc8be
--- /dev/null
+++ b/freqtrade/persistence/pairlock_middleware.py
@@ -0,0 +1,190 @@
+import logging
+from collections.abc import Sequence
+from datetime import datetime, timezone
+
+from sqlalchemy import select
+
+from freqtrade.exchange import timeframe_to_next_date
+from freqtrade.persistence.models import PairLock
+
+
+logger = logging.getLogger(__name__)
+
+
+class PairLocks:
+    """
+    Pairlocks middleware class
+    Abstracts the database layer away so it becomes optional - which will be necessary to support
+    backtesting and hyperopt in the future.
+    """
+
+    use_db = True
+    locks: list[PairLock] = []
+
+    timeframe: str = ""
+
+    @staticmethod
+    def reset_locks() -> None:
+        """
+        Resets all locks. Only active for backtesting mode.
+        """
+        if not PairLocks.use_db:
+            PairLocks.locks = []
+
+    @staticmethod
+    def lock_pair(
+        pair: str,
+        until: datetime,
+        reason: str | None = None,
+        *,
+        now: datetime | None = None,
+        side: str = "*",
+    ) -> PairLock:
+        """
+        Create PairLock from now to "until".
+        Uses database by default, unless PairLocks.use_db is set to False,
+        in which case a list is maintained.
+        :param pair: pair to lock. use '*' to lock all pairs
+        :param until: End time of the lock. Will be rounded up to the next candle.
+        :param reason: Reason string that will be shown as reason for the lock
+        :param now: Current timestamp. Used to determine lock start time.
+        :param side: Side to lock pair, can be 'long', 'short' or '*'
+        """
+        lock = PairLock(
+            pair=pair,
+            lock_time=now or datetime.now(timezone.utc),
+            lock_end_time=timeframe_to_next_date(PairLocks.timeframe, until),
+            reason=reason,
+            side=side,
+            active=True,
+        )
+        if PairLocks.use_db:
+            PairLock.session.add(lock)
+            PairLock.session.commit()
+        else:
+            PairLocks.locks.append(lock)
+        return lock
+
+    @staticmethod
+    def get_pair_locks(
+        pair: str | None, now: datetime | None = None, side: str | None = None
+    ) -> Sequence[PairLock]:
+        """
+        Get all currently active locks for this pair
+        :param pair: Pair to check for. Returns all current locks if pair is empty
+        :param now: Datetime object (generated via datetime.now(timezone.utc)).
+                    defaults to datetime.now(timezone.utc)
+        :param side: Side get locks for, can be 'long', 'short', '*' or None
+        """
+        if not now:
+            now = datetime.now(timezone.utc)
+
+        if PairLocks.use_db:
+            return PairLock.query_pair_locks(pair, now, side).all()
+        else:
+            locks = [
+                lock
+                for lock in PairLocks.locks
+                if (
+                    lock.lock_end_time >= now
+                    and lock.active is True
+                    and (pair is None or lock.pair == pair)
+                    and (side is None or lock.side == "*" or lock.side == side)
+                )
+            ]
+            return locks
+
+    @staticmethod
+    def get_pair_longest_lock(
+        pair: str, now: datetime | None = None, side: str = "*"
+    ) -> PairLock | None:
+        """
+        Get the lock that expires the latest for the pair given.
+        """
+        locks = PairLocks.get_pair_locks(pair, now, side=side)
+        locks = sorted(locks, key=lambda lock: lock.lock_end_time, reverse=True)
+        return locks[0] if locks else None
+
+    @staticmethod
+    def unlock_pair(pair: str, now: datetime | None = None, side: str = "*") -> None:
+        """
+        Release all locks for this pair.
+        :param pair: Pair to unlock
+        :param now: Datetime object (generated via datetime.now(timezone.utc)).
+            defaults to datetime.now(timezone.utc)
+        """
+        if not now:
+            now = datetime.now(timezone.utc)
+
+        logger.info(f"Releasing all locks for {pair}.")
+        locks = PairLocks.get_pair_locks(pair, now, side=side)
+        for lock in locks:
+            lock.active = False
+        if PairLocks.use_db:
+            PairLock.session.commit()
+
+    @staticmethod
+    def unlock_reason(reason: str, now: datetime | None = None) -> None:
+        """
+        Release all locks for this reason.
+        :param reason: Which reason to unlock
+        :param now: Datetime object (generated via datetime.now(timezone.utc)).
+            defaults to datetime.now(timezone.utc)
+        """
+        if not now:
+            now = datetime.now(timezone.utc)
+
+        if PairLocks.use_db:
+            # used in live modes
+            logger.info(f"Releasing all locks with reason '{reason}':")
+            filters = [
+                PairLock.lock_end_time > now,
+                PairLock.active.is_(True),
+                PairLock.reason == reason,
+            ]
+            locks = PairLock.session.scalars(select(PairLock).filter(*filters)).all()
+            for lock in locks:
+                logger.info(f"Releasing lock for {lock.pair} with reason '{reason}'.")
+                lock.active = False
+            PairLock.session.commit()
+        else:
+            # used in backtesting mode; don't show log messages for speed
+            locksb = PairLocks.get_pair_locks(None)
+            for lock in locksb:
+                if lock.reason == reason:
+                    lock.active = False
+
+    @staticmethod
+    def is_global_lock(now: datetime | None = None, side: str = "*") -> bool:
+        """
+        :param now: Datetime object (generated via datetime.now(timezone.utc)).
+            defaults to datetime.now(timezone.utc)
+        """
+        if not now:
+            now = datetime.now(timezone.utc)
+
+        return len(PairLocks.get_pair_locks("*", now, side)) > 0
+
+    @staticmethod
+    def is_pair_locked(pair: str, now: datetime | None = None, side: str = "*") -> bool:
+        """
+        :param pair: Pair to check for
+        :param now: Datetime object (generated via datetime.now(timezone.utc)).
+            defaults to datetime.now(timezone.utc)
+        """
+        if not now:
+            now = datetime.now(timezone.utc)
+
+        return len(PairLocks.get_pair_locks(pair, now, side)) > 0 or PairLocks.is_global_lock(
+            now, side
+        )
+
+    @staticmethod
+    def get_all_locks() -> Sequence[PairLock]:
+        """
+        Return all locks, also locks with expired end date
+        """
+        if PairLocks.use_db:
+            return PairLock.get_all_locks().all()
+        else:
+            return PairLocks.locks
diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py
new file mode 100644
index 000000000..5f24db69d
--- /dev/null
+++ b/freqtrade/persistence/trade_model.py
@@ -0,0 +1,2146 @@
+"""
+This module contains the class to persist trades into SQLite
+"""
+
+import logging
+from collections import defaultdict
+from collections.abc import Sequence
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from math import isclose
+from typing import Any, ClassVar, Optional, cast
+
+from sqlalchemy import (
+    Enum,
+    Float,
+    ForeignKey,
+    Integer,
+    ScalarResult,
+    Select,
+    String,
+    UniqueConstraint,
+    case,
+    desc,
+    func,
+    select,
+)
+from sqlalchemy.orm import Mapped, lazyload, mapped_column, relationship, validates
+from typing_extensions import Self
+
+from freqtrade.constants import (
+    CANCELED_EXCHANGE_STATES,
+    CUSTOM_TAG_MAX_LENGTH,
+    DATETIME_PRINT_FORMAT,
+    MATH_CLOSE_PREC,
+    NON_OPEN_EXCHANGE_STATES,
+    BuySell,
+    LongShort,
+)
+from freqtrade.enums import ExitType, TradingMode
+from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.exchange import (
+    ROUND_DOWN,
+    ROUND_UP,
+    amount_to_contract_precision,
+    price_to_precision,
+)
+from freqtrade.exchange.exchange_types import CcxtOrder
+from freqtrade.leverage import interest
+from freqtrade.misc import safe_value_fallback
+from freqtrade.persistence.base import ModelBase, SessionType
+from freqtrade.persistence.custom_data import CustomDataWrapper, _CustomData
+from freqtrade.util import FtPrecise, dt_from_ts, dt_now, dt_ts, dt_ts_none
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ProfitStruct:
+    profit_abs: float
+    profit_ratio: float
+    total_profit: float
+    total_profit_ratio: float
+
+
+class Order(ModelBase):
+    """
+    Order database model
+    Keeps a record of all orders placed on the exchange
+
+    One to many relationship with Trades:
+      - One trade can have many orders
+      - One Order can only be associated with one Trade
+
+    Mirrors CCXT Order structure
+    """
+
+    __tablename__ = "orders"
+    __allow_unmapped__ = True
+    session: ClassVar[SessionType]
+
+    # Uniqueness should be ensured over pair, order_id
+    # its likely that order_id is unique per Pair on some exchanges.
+    __table_args__ = (UniqueConstraint("ft_pair", "order_id", name="_order_pair_order_id"),)
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    ft_trade_id: Mapped[int] = mapped_column(Integer, ForeignKey("trades.id"), index=True)
+
+    _trade_live: Mapped["Trade"] = relationship("Trade", back_populates="orders", lazy="immediate")
+    _trade_bt: "LocalTrade" = None  # type: ignore
+
+    # order_side can only be 'buy', 'sell' or 'stoploss'
+    ft_order_side: Mapped[str] = mapped_column(String(25), nullable=False)
+    ft_pair: Mapped[str] = mapped_column(String(25), nullable=False)
+    ft_is_open: Mapped[bool] = mapped_column(nullable=False, default=True, index=True)
+    ft_amount: Mapped[float] = mapped_column(Float(), nullable=False)
+    ft_price: Mapped[float] = mapped_column(Float(), nullable=False)
+    ft_cancel_reason: Mapped[str] = mapped_column(String(CUSTOM_TAG_MAX_LENGTH), nullable=True)
+
+    order_id: Mapped[str] = mapped_column(String(255), nullable=False, index=True)
+    status: Mapped[str | None] = mapped_column(String(255), nullable=True)
+    symbol: Mapped[str | None] = mapped_column(String(25), nullable=True)
+    order_type: Mapped[str | None] = mapped_column(String(50), nullable=True)
+    side: Mapped[str] = mapped_column(String(25), nullable=True)
+    price: Mapped[float | None] = mapped_column(Float(), nullable=True)
+    average: Mapped[float | None] = mapped_column(Float(), nullable=True)
+    amount: Mapped[float | None] = mapped_column(Float(), nullable=True)
+    filled: Mapped[float | None] = mapped_column(Float(), nullable=True)
+    remaining: Mapped[float | None] = mapped_column(Float(), nullable=True)
+    cost: Mapped[float | None] = mapped_column(Float(), nullable=True)
+    stop_price: Mapped[float | None] = mapped_column(Float(), nullable=True)
+    order_date: Mapped[datetime] = mapped_column(nullable=True, default=dt_now)
+    order_filled_date: Mapped[datetime | None] = mapped_column(nullable=True)
+    order_update_date: Mapped[datetime | None] = mapped_column(nullable=True)
+    funding_fee: Mapped[float | None] = mapped_column(Float(), nullable=True)
+
+    ft_fee_base: Mapped[float | None] = mapped_column(Float(), nullable=True)
+    ft_order_tag: Mapped[str | None] = mapped_column(String(CUSTOM_TAG_MAX_LENGTH), nullable=True)
+
+    @property
+    def order_date_utc(self) -> datetime:
+        """Order-date with UTC timezoneinfo"""
+        return self.order_date.replace(tzinfo=timezone.utc)
+
+    @property
+    def order_filled_utc(self) -> datetime | None:
+        """last order-date with UTC timezoneinfo"""
+        return (
+            self.order_filled_date.replace(tzinfo=timezone.utc) if self.order_filled_date else None
+        )
+
+    @property
+    def safe_amount(self) -> float:
+        return self.amount or self.ft_amount
+
+    @property
+    def safe_placement_price(self) -> float:
+        """Price at which the order was placed"""
+        return self.price or self.stop_price or self.ft_price
+
+    @property
+    def safe_price(self) -> float:
+        return self.average or self.price or self.stop_price or self.ft_price
+
+    @property
+    def safe_filled(self) -> float:
+        return self.filled if self.filled is not None else 0.0
+
+    @property
+    def safe_cost(self) -> float:
+        return self.cost or 0.0
+
+    @property
+    def safe_remaining(self) -> float:
+        return (
+            self.remaining
+            if self.remaining is not None
+            else self.safe_amount - (self.filled or 0.0)
+        )
+
+    @property
+    def safe_fee_base(self) -> float:
+        return self.ft_fee_base or 0.0
+
+    @property
+    def safe_amount_after_fee(self) -> float:
+        return self.safe_filled - self.safe_fee_base
+
+    @property
+    def trade(self) -> "LocalTrade":
+        return self._trade_bt or self._trade_live
+
+    @property
+    def stake_amount(self) -> float:
+        """Amount in stake currency used for this order"""
+        return float(
+            FtPrecise(self.safe_amount)
+            * FtPrecise(self.safe_price)
+            / FtPrecise(self.trade.leverage)
+        )
+
+    @property
+    def stake_amount_filled(self) -> float:
+        """Filled Amount in stake currency used for this order"""
+        return float(
+            FtPrecise(self.safe_filled)
+            * FtPrecise(self.safe_price)
+            / FtPrecise(self.trade.leverage)
+        )
+
+    def __repr__(self):
+        return (
+            f"Order(id={self.id}, trade={self.ft_trade_id}, order_id={self.order_id}, "
+            f"side={self.side}, filled={self.safe_filled}, price={self.safe_price}, "
+            f"amount={self.amount}, "
+            f"status={self.status}, date={self.order_date_utc:{DATETIME_PRINT_FORMAT}})"
+        )
+
+    def update_from_ccxt_object(self, order):
+        """
+        Update Order from ccxt response
+        Only updates if fields are available from ccxt -
+        """
+        if self.order_id != str(order["id"]):
+            raise DependencyException("Order-id's don't match")
+
+        self.status = safe_value_fallback(order, "status", default_value=self.status)
+        self.symbol = safe_value_fallback(order, "symbol", default_value=self.symbol)
+        self.order_type = safe_value_fallback(order, "type", default_value=self.order_type)
+        self.side = safe_value_fallback(order, "side", default_value=self.side)
+        self.price = safe_value_fallback(order, "price", default_value=self.price)
+        self.amount = safe_value_fallback(order, "amount", default_value=self.amount)
+        self.filled = safe_value_fallback(order, "filled", default_value=self.filled)
+        self.average = safe_value_fallback(order, "average", default_value=self.average)
+        self.remaining = safe_value_fallback(order, "remaining", default_value=self.remaining)
+        self.cost = safe_value_fallback(order, "cost", default_value=self.cost)
+        self.stop_price = safe_value_fallback(order, "stopPrice", default_value=self.stop_price)
+        order_date = safe_value_fallback(order, "timestamp")
+        if order_date:
+            self.order_date = dt_from_ts(order_date)
+        elif not self.order_date:
+            self.order_date = dt_now()
+
+        self.ft_is_open = True
+        if self.status in NON_OPEN_EXCHANGE_STATES:
+            self.ft_is_open = False
+            if (order.get("filled", 0.0) or 0.0) > 0 and not self.order_filled_date:
+                self.order_filled_date = dt_from_ts(
+                    safe_value_fallback(order, "lastTradeTimestamp", default_value=dt_ts())
+                )
+        self.order_update_date = datetime.now(timezone.utc)
+
+    def to_ccxt_object(self, stopPriceName: str = "stopPrice") -> dict[str, Any]:
+        order: dict[str, Any] = {
+            "id": self.order_id,
+            "symbol": self.ft_pair,
+            "price": self.price,
+            "average": self.average,
+            "amount": self.amount,
+            "cost": self.cost,
+            "type": self.order_type,
+            "side": self.ft_order_side,
+            "filled": self.filled,
+            "remaining": self.remaining,
+            "datetime": self.order_date_utc.strftime("%Y-%m-%dT%H:%M:%S.%f"),
+            "timestamp": int(self.order_date_utc.timestamp() * 1000),
+            "status": self.status,
+            "fee": None,
+            "info": {},
+        }
+        if self.ft_order_side == "stoploss":
+            order.update(
+                {
+                    stopPriceName: self.stop_price,
+                    "ft_order_type": "stoploss",
+                }
+            )
+
+        return order
+
+    def to_json(self, entry_side: str, minified: bool = False) -> dict[str, Any]:
+        """
+        :param minified: If True, only return a subset of the data is returned.
+                         Only used for backtesting.
+        """
+        resp = {
+            "amount": self.safe_amount,
+            "safe_price": self.safe_price,
+            "ft_order_side": self.ft_order_side,
+            "order_filled_timestamp": dt_ts_none(self.order_filled_utc),
+            "ft_is_entry": self.ft_order_side == entry_side,
+            "ft_order_tag": self.ft_order_tag,
+            "cost": self.cost if self.cost else 0,
+        }
+        if not minified:
+            resp.update(
+                {
+                    "pair": self.ft_pair,
+                    "order_id": self.order_id,
+                    "status": self.status,
+                    "average": round(self.average, 8) if self.average else 0,
+                    "filled": self.filled,
+                    "is_open": self.ft_is_open,
+                    "order_date": (
+                        self.order_date.strftime(DATETIME_PRINT_FORMAT) if self.order_date else None
+                    ),
+                    "order_timestamp": (
+                        int(self.order_date.replace(tzinfo=timezone.utc).timestamp() * 1000)
+                        if self.order_date
+                        else None
+                    ),
+                    "order_filled_date": (
+                        self.order_filled_date.strftime(DATETIME_PRINT_FORMAT)
+                        if self.order_filled_date
+                        else None
+                    ),
+                    "order_type": self.order_type,
+                    "price": self.price,
+                    "remaining": self.remaining,
+                    "ft_fee_base": self.ft_fee_base,
+                    "funding_fee": self.funding_fee,
+                }
+            )
+        return resp
+
+    def close_bt_order(self, close_date: datetime, trade: "LocalTrade"):
+        self.order_filled_date = close_date
+        self.filled = self.amount
+        self.remaining = 0
+        self.status = "closed"
+        self.ft_is_open = False
+        # Assign funding fees to Order.
+        # Assumes backtesting will use date_last_filled_utc to calculate future funding fees.
+        self.funding_fee = trade.funding_fee_running
+        trade.funding_fee_running = 0.0
+
+        if self.ft_order_side == trade.entry_side and self.price:
+            trade.open_rate = self.price
+            trade.recalc_trade_from_orders()
+            if trade.nr_of_successful_entries == 1:
+                trade.initial_stop_loss_pct = None
+                trade.is_stop_loss_trailing = False
+            trade.adjust_stop_loss(trade.open_rate, trade.stop_loss_pct)
+
+    @staticmethod
+    def update_orders(orders: list["Order"], order: CcxtOrder):
+        """
+        Get all non-closed orders - useful when trying to batch-update orders
+        """
+        if not isinstance(order, dict):
+            logger.warning(f"{order} is not a valid response object.")
+            return
+
+        filtered_orders = [o for o in orders if o.order_id == order.get("id")]
+        if filtered_orders:
+            oobj = filtered_orders[0]
+            oobj.update_from_ccxt_object(order)
+            Trade.commit()
+        else:
+            logger.warning(f"Did not find order for {order}.")
+
+    @classmethod
+    def parse_from_ccxt_object(
+        cls,
+        order: CcxtOrder,
+        pair: str,
+        side: str,
+        amount: float | None = None,
+        price: float | None = None,
+    ) -> Self:
+        """
+        Parse an order from a ccxt object and return a new order Object.
+        Optional support for overriding amount and price is only used for test simplification.
+        """
+        o = cls(
+            order_id=str(order["id"]),
+            ft_order_side=side,
+            ft_pair=pair,
+            ft_amount=amount or order.get("amount", None) or 0.0,
+            ft_price=price or order.get("price", None),
+        )
+
+        o.update_from_ccxt_object(order)
+        return o
+
+    @staticmethod
+    def get_open_orders() -> Sequence["Order"]:
+        """
+        Retrieve open orders from the database
+        :return: List of open orders
+        """
+        return Order.session.scalars(select(Order).filter(Order.ft_is_open.is_(True))).all()
+
+    @staticmethod
+    def order_by_id(order_id: str) -> Optional["Order"]:
+        """
+        Retrieve order based on order_id
+        :return: Order or None
+        """
+        return Order.session.scalars(select(Order).filter(Order.order_id == order_id)).first()
+
+
+class LocalTrade:
+    """
+    Trade database model.
+    Used in backtesting - must be aligned to Trade model!
+    """
+
+    use_db: bool = False
+    # Trades container for backtesting
+    bt_trades: list["LocalTrade"] = []
+    bt_trades_open: list["LocalTrade"] = []
+    # Copy of trades_open - but indexed by pair
+    bt_trades_open_pp: dict[str, list["LocalTrade"]] = defaultdict(list)
+    bt_open_open_trade_count: int = 0
+    bt_total_profit: float = 0
+    realized_profit: float = 0
+
+    id: int = 0
+
+    orders: list[Order] = []
+
+    exchange: str = ""
+    pair: str = ""
+    base_currency: str | None = ""
+    stake_currency: str | None = ""
+    is_open: bool = True
+    fee_open: float = 0.0
+    fee_open_cost: float | None = None
+    fee_open_currency: str | None = ""
+    fee_close: float | None = 0.0
+    fee_close_cost: float | None = None
+    fee_close_currency: str | None = ""
+    open_rate: float = 0.0
+    open_rate_requested: float | None = None
+    # open_trade_value - calculated via _calc_open_trade_value
+    open_trade_value: float = 0.0
+    close_rate: float | None = None
+    close_rate_requested: float | None = None
+    close_profit: float | None = None
+    close_profit_abs: float | None = None
+    stake_amount: float = 0.0
+    max_stake_amount: float | None = 0.0
+    amount: float = 0.0
+    amount_requested: float | None = None
+    open_date: datetime
+    close_date: datetime | None = None
+    # absolute value of the stop loss
+    stop_loss: float = 0.0
+    # percentage value of the stop loss
+    stop_loss_pct: float | None = 0.0
+    # absolute value of the initial stop loss
+    initial_stop_loss: float | None = 0.0
+    # percentage value of the initial stop loss
+    initial_stop_loss_pct: float | None = None
+    is_stop_loss_trailing: bool = False
+    # absolute value of the highest reached price
+    max_rate: float | None = None
+    # Lowest price reached
+    min_rate: float | None = None
+    exit_reason: str | None = ""
+    exit_order_status: str | None = ""
+    strategy: str | None = ""
+    enter_tag: str | None = None
+    timeframe: int | None = None
+
+    trading_mode: TradingMode = TradingMode.SPOT
+    amount_precision: float | None = None
+    price_precision: float | None = None
+    precision_mode: int | None = None
+    precision_mode_price: int | None = None
+    contract_size: float | None = None
+
+    # Leverage trading properties
+    liquidation_price: float | None = None
+    is_short: bool = False
+    leverage: float = 1.0
+
+    # Margin trading properties
+    interest_rate: float = 0.0
+
+    # Futures properties
+    funding_fees: float | None = None
+    # Used to keep running funding fees - between the last filled order and now
+    # Shall not be used for calculations!
+    funding_fee_running: float | None = None
+    # v 2 -> correct max_stake_amount calculation for leveraged trades
+    record_version: int = 2
+
+    @property
+    def stoploss_or_liquidation(self) -> float:
+        if self.liquidation_price:
+            if self.is_short:
+                return min(self.stop_loss, self.liquidation_price)
+            else:
+                return max(self.stop_loss, self.liquidation_price)
+
+        return self.stop_loss
+
+    @property
+    def buy_tag(self) -> str | None:
+        """
+        Compatibility between buy_tag (old) and enter_tag (new)
+        Consider buy_tag deprecated
+        """
+        return self.enter_tag
+
+    @property
+    def has_no_leverage(self) -> bool:
+        """Returns true if this is a non-leverage, non-short trade"""
+        return (self.leverage == 1.0 or self.leverage is None) and not self.is_short
+
+    @property
+    def borrowed(self) -> float:
+        """
+        The amount of currency borrowed from the exchange for leverage trades
+        If a long trade, the amount is in base currency
+        If a short trade, the amount is in the other currency being traded
+        """
+        if self.has_no_leverage:
+            return 0.0
+        elif not self.is_short:
+            return (self.amount * self.open_rate) * ((self.leverage - 1) / self.leverage)
+        else:
+            return self.amount
+
+    @property
+    def _date_last_filled_utc(self) -> datetime | None:
+        """Date of the last filled order"""
+        orders = self.select_filled_orders()
+        if orders:
+            return max(o.order_filled_utc for o in orders if o.order_filled_utc)
+        return None
+
+    @property
+    def date_last_filled_utc(self) -> datetime:
+        """Date of the last filled order - or open_date if no orders are filled"""
+        dt_last_filled = self._date_last_filled_utc
+        if not dt_last_filled:
+            return self.open_date_utc
+        return max([self.open_date_utc, dt_last_filled])
+
+    @property
+    def date_entry_fill_utc(self) -> datetime | None:
+        """Date of the first filled order"""
+        orders = self.select_filled_orders(self.entry_side)
+        if orders and len(
+            filled_date := [o.order_filled_utc for o in orders if o.order_filled_utc]
+        ):
+            return min(filled_date)
+        return None
+
+    @property
+    def open_date_utc(self):
+        return self.open_date.replace(tzinfo=timezone.utc)
+
+    @property
+    def stoploss_last_update_utc(self):
+        if self.has_open_sl_orders:
+            return max(o.order_date_utc for o in self.open_sl_orders)
+        return None
+
+    @property
+    def close_date_utc(self):
+        return self.close_date.replace(tzinfo=timezone.utc) if self.close_date else None
+
+    @property
+    def entry_side(self) -> str:
+        if self.is_short:
+            return "sell"
+        else:
+            return "buy"
+
+    @property
+    def exit_side(self) -> BuySell:
+        if self.is_short:
+            return "buy"
+        else:
+            return "sell"
+
+    @property
+    def trade_direction(self) -> LongShort:
+        if self.is_short:
+            return "short"
+        else:
+            return "long"
+
+    @property
+    def safe_base_currency(self) -> str:
+        """
+        Compatibility layer for asset - which can be empty for old trades.
+        """
+        try:
+            return self.base_currency or self.pair.split("/")[0]
+        except IndexError:
+            return ""
+
+    @property
+    def safe_quote_currency(self) -> str:
+        """
+        Compatibility layer for asset - which can be empty for old trades.
+        """
+        try:
+            return self.stake_currency or self.pair.split("/")[1].split(":")[0]
+        except IndexError:
+            return ""
+
+    @property
+    def open_orders(self) -> list[Order]:
+        """
+        All open orders for this trade excluding stoploss orders
+        """
+        return [o for o in self.orders if o.ft_is_open and o.ft_order_side != "stoploss"]
+
+    @property
+    def has_open_orders(self) -> bool:
+        """
+        True if there are open orders for this trade excluding stoploss orders
+        """
+        open_orders_wo_sl = [
+            o for o in self.orders if o.ft_order_side not in ["stoploss"] and o.ft_is_open
+        ]
+        return len(open_orders_wo_sl) > 0
+
+    @property
+    def has_open_position(self) -> bool:
+        """
+        True if there is an open position for this trade
+        """
+        return self.amount > 0
+
+    @property
+    def open_sl_orders(self) -> list[Order]:
+        """
+        All open stoploss orders for this trade
+        """
+        return [o for o in self.orders if o.ft_order_side in ["stoploss"] and o.ft_is_open]
+
+    @property
+    def has_open_sl_orders(self) -> bool:
+        """
+        True if there are open stoploss orders for this trade
+        """
+        open_sl_orders = [
+            o for o in self.orders if o.ft_order_side in ["stoploss"] and o.ft_is_open
+        ]
+        return len(open_sl_orders) > 0
+
+    @property
+    def sl_orders(self) -> list[Order]:
+        """
+        All stoploss orders for this trade
+        """
+        return [o for o in self.orders if o.ft_order_side in ["stoploss"]]
+
+    @property
+    def open_orders_ids(self) -> list[str]:
+        open_orders_ids_wo_sl = [
+            oo.order_id for oo in self.open_orders if oo.ft_order_side not in ["stoploss"]
+        ]
+        return open_orders_ids_wo_sl
+
+    def __init__(self, **kwargs):
+        for key in kwargs:
+            setattr(self, key, kwargs[key])
+        self.recalc_open_trade_value()
+        self.orders = []
+        if self.trading_mode == TradingMode.MARGIN and self.interest_rate is None:
+            raise OperationalException(
+                f"{self.trading_mode} trading requires param interest_rate on trades"
+            )
+
+    def __repr__(self):
+        open_since = (
+            self.open_date_utc.strftime(DATETIME_PRINT_FORMAT) if self.is_open else "closed"
+        )
+
+        return (
+            f"Trade(id={self.id}, pair={self.pair}, amount={self.amount:.8f}, "
+            f"is_short={self.is_short or False}, leverage={self.leverage or 1.0}, "
+            f"open_rate={self.open_rate:.8f}, open_since={open_since})"
+        )
+
+    def to_json(self, minified: bool = False) -> dict[str, Any]:
+        """
+        :param minified: If True, only return a subset of the data is returned.
+                         Only used for backtesting.
+        :return: Dictionary with trade data
+        """
+        filled_or_open_orders = self.select_filled_or_open_orders()
+        orders_json = [order.to_json(self.entry_side, minified) for order in filled_or_open_orders]
+
+        return {
+            "trade_id": self.id,
+            "pair": self.pair,
+            "base_currency": self.safe_base_currency,
+            "quote_currency": self.safe_quote_currency,
+            "is_open": self.is_open,
+            "exchange": self.exchange,
+            "amount": round(self.amount, 8),
+            "amount_requested": round(self.amount_requested, 8) if self.amount_requested else None,
+            "stake_amount": round(self.stake_amount, 8),
+            "max_stake_amount": round(self.max_stake_amount, 8) if self.max_stake_amount else None,
+            "strategy": self.strategy,
+            "enter_tag": self.enter_tag,
+            "timeframe": self.timeframe,
+            "fee_open": self.fee_open,
+            "fee_open_cost": self.fee_open_cost,
+            "fee_open_currency": self.fee_open_currency,
+            "fee_close": self.fee_close,
+            "fee_close_cost": self.fee_close_cost,
+            "fee_close_currency": self.fee_close_currency,
+            "open_date": self.open_date.strftime(DATETIME_PRINT_FORMAT),
+            "open_timestamp": dt_ts_none(self.open_date_utc),
+            "open_fill_date": (
+                self.date_entry_fill_utc.strftime(DATETIME_PRINT_FORMAT)
+                if self.date_entry_fill_utc
+                else None
+            ),
+            "open_fill_timestamp": dt_ts_none(self.date_entry_fill_utc),
+            "open_rate": self.open_rate,
+            "open_rate_requested": self.open_rate_requested,
+            "open_trade_value": round(self.open_trade_value, 8),
+            "close_date": (
+                self.close_date.strftime(DATETIME_PRINT_FORMAT) if self.close_date else None
+            ),
+            "close_timestamp": dt_ts_none(self.close_date_utc),
+            "realized_profit": self.realized_profit or 0.0,
+            # Close-profit corresponds to relative realized_profit ratio
+            "realized_profit_ratio": self.close_profit or None,
+            "close_rate": self.close_rate,
+            "close_rate_requested": self.close_rate_requested,
+            "close_profit": self.close_profit,  # Deprecated
+            "close_profit_pct": round(self.close_profit * 100, 2) if self.close_profit else None,
+            "close_profit_abs": self.close_profit_abs,  # Deprecated
+            "trade_duration_s": (
+                int((self.close_date_utc - self.open_date_utc).total_seconds())
+                if self.close_date
+                else None
+            ),
+            "trade_duration": (
+                int((self.close_date_utc - self.open_date_utc).total_seconds() // 60)
+                if self.close_date
+                else None
+            ),
+            "profit_ratio": self.close_profit,
+            "profit_pct": round(self.close_profit * 100, 2) if self.close_profit else None,
+            "profit_abs": self.close_profit_abs,
+            "exit_reason": self.exit_reason,
+            "exit_order_status": self.exit_order_status,
+            "stop_loss_abs": self.stop_loss,
+            "stop_loss_ratio": self.stop_loss_pct if self.stop_loss_pct else None,
+            "stop_loss_pct": (self.stop_loss_pct * 100) if self.stop_loss_pct else None,
+            "stoploss_last_update": (
+                self.stoploss_last_update_utc.strftime(DATETIME_PRINT_FORMAT)
+                if self.stoploss_last_update_utc
+                else None
+            ),
+            "stoploss_last_update_timestamp": dt_ts_none(self.stoploss_last_update_utc),
+            "initial_stop_loss_abs": self.initial_stop_loss,
+            "initial_stop_loss_ratio": (
+                self.initial_stop_loss_pct if self.initial_stop_loss_pct else None
+            ),
+            "initial_stop_loss_pct": (
+                self.initial_stop_loss_pct * 100 if self.initial_stop_loss_pct else None
+            ),
+            "min_rate": self.min_rate,
+            "max_rate": self.max_rate,
+            "leverage": self.leverage,
+            "interest_rate": self.interest_rate,
+            "liquidation_price": self.liquidation_price,
+            "is_short": self.is_short,
+            "trading_mode": self.trading_mode,
+            "funding_fees": self.funding_fees,
+            "amount_precision": self.amount_precision,
+            "price_precision": self.price_precision,
+            "precision_mode": self.precision_mode,
+            "precision_mode_price": self.precision_mode_price,
+            "contract_size": self.contract_size,
+            "has_open_orders": self.has_open_orders,
+            "orders": orders_json,
+        }
+
+    @staticmethod
+    def reset_trades() -> None:
+        """
+        Resets all trades. Only active for backtesting mode.
+        """
+        LocalTrade.bt_trades = []
+        LocalTrade.bt_trades_open = []
+        LocalTrade.bt_trades_open_pp = defaultdict(list)
+        LocalTrade.bt_open_open_trade_count = 0
+        LocalTrade.bt_total_profit = 0
+
+    def adjust_min_max_rates(self, current_price: float, current_price_low: float) -> None:
+        """
+        Adjust the max_rate and min_rate.
+        """
+        self.max_rate = max(current_price, self.max_rate or self.open_rate)
+        self.min_rate = min(current_price_low, self.min_rate or self.open_rate)
+
+    def set_liquidation_price(self, liquidation_price: float | None):
+        """
+        Method you should use to set self.liquidation price.
+        Assures stop_loss is not passed the liquidation price
+        """
+        if liquidation_price is None:
+            return
+        self.liquidation_price = price_to_precision(
+            liquidation_price, self.price_precision, self.precision_mode_price
+        )
+
+    def set_funding_fees(self, funding_fee: float) -> None:
+        """
+        Assign funding fees to Trade.
+        """
+        if funding_fee is None:
+            return
+        self.funding_fee_running = funding_fee
+        prior_funding_fees = sum([o.funding_fee for o in self.orders if o.funding_fee])
+        self.funding_fees = prior_funding_fees + funding_fee
+
+    def __set_stop_loss(self, stop_loss: float, percent: float):
+        """
+        Method used internally to set self.stop_loss.
+        """
+        if not self.stop_loss:
+            self.initial_stop_loss = stop_loss
+        self.stop_loss = stop_loss
+
+        self.stop_loss_pct = -1 * abs(percent)
+
+    def adjust_stop_loss(
+        self,
+        current_price: float,
+        stoploss: float | None,
+        initial: bool = False,
+        allow_refresh: bool = False,
+    ) -> None:
+        """
+        This adjusts the stop loss to it's most recently observed setting
+        :param current_price: Current rate the asset is traded
+        :param stoploss: Stoploss as factor (sample -0.05 -> -5% below current price).
+        :param initial: Called to initiate stop_loss.
+            Skips everything if self.stop_loss is already set.
+        :param refresh: Called to refresh stop_loss, allows adjustment in both directions
+        """
+        if stoploss is None or (initial and not (self.stop_loss is None or self.stop_loss == 0)):
+            # Don't modify if called with initial and nothing to do
+            return
+
+        leverage = self.leverage or 1.0
+        if self.is_short:
+            new_loss = float(current_price * (1 + abs(stoploss / leverage)))
+        else:
+            new_loss = float(current_price * (1 - abs(stoploss / leverage)))
+
+        stop_loss_norm = price_to_precision(
+            new_loss,
+            self.price_precision,
+            self.precision_mode_price,
+            rounding_mode=ROUND_DOWN if self.is_short else ROUND_UP,
+        )
+        # no stop loss assigned yet
+        if self.initial_stop_loss_pct is None:
+            self.__set_stop_loss(stop_loss_norm, stoploss)
+            self.initial_stop_loss = price_to_precision(
+                stop_loss_norm,
+                self.price_precision,
+                self.precision_mode_price,
+                rounding_mode=ROUND_DOWN if self.is_short else ROUND_UP,
+            )
+            self.initial_stop_loss_pct = -1 * abs(stoploss)
+
+        # evaluate if the stop loss needs to be updated
+        else:
+            higher_stop = stop_loss_norm > self.stop_loss
+            lower_stop = stop_loss_norm < self.stop_loss
+
+            # stop losses only walk up, never down!,
+            #   ? But adding more to a leveraged trade would create a lower liquidation price,
+            #   ? decreasing the minimum stoploss
+            if (
+                allow_refresh
+                or (higher_stop and not self.is_short)
+                or (lower_stop and self.is_short)
+            ):
+                logger.debug(f"{self.pair} - Adjusting stoploss...")
+                if not allow_refresh:
+                    self.is_stop_loss_trailing = True
+                self.__set_stop_loss(stop_loss_norm, stoploss)
+            else:
+                logger.debug(f"{self.pair} - Keeping current stoploss...")
+
+        logger.debug(
+            f"{self.pair} - Stoploss adjusted. current_price={current_price:.8f}, "
+            f"open_rate={self.open_rate:.8f}, max_rate={self.max_rate or self.open_rate:.8f}, "
+            f"initial_stop_loss={self.initial_stop_loss:.8f}, "
+            f"stop_loss={self.stop_loss:.8f}. "
+            f"Trailing stoploss saved us: "
+            f"{float(self.stop_loss) - float(self.initial_stop_loss or 0.0):.8f}."
+        )
+
+    def update_trade(self, order: Order, recalculating: bool = False) -> None:
+        """
+        Updates this entity with amount and actual open/close rates.
+        :param order: order retrieved by exchange.fetch_order()
+        :return: None
+        """
+
+        # Ignore open and cancelled orders
+        if order.status == "open" or order.safe_price is None:
+            return
+
+        logger.info(f"Updating trade (id={self.id}) ...")
+        if order.ft_order_side != "stoploss":
+            order.funding_fee = self.funding_fee_running
+            # Reset running funding fees
+            self.funding_fee_running = 0.0
+        order_type = order.order_type.upper() if order.order_type else None
+
+        if order.ft_order_side == self.entry_side:
+            # Update open rate and actual amount
+            self.open_rate = order.safe_price
+            self.amount = order.safe_amount_after_fee
+            if self.is_open:
+                payment = "SELL" if self.is_short else "BUY"
+                logger.info(f"{order_type}_{payment} has been fulfilled for {self}.")
+
+            self.recalc_trade_from_orders()
+        elif order.ft_order_side == self.exit_side:
+            if self.is_open:
+                payment = "BUY" if self.is_short else "SELL"
+                # * On margin shorts, you buy a little bit more than the amount (amount + interest)
+                logger.info(f"{order_type}_{payment} has been fulfilled for {self}.")
+
+        elif order.ft_order_side == "stoploss" and order.status not in ("open",):
+            self.close_rate_requested = self.stop_loss
+            self.exit_reason = ExitType.STOPLOSS_ON_EXCHANGE.value
+            if self.is_open and order.safe_filled > 0:
+                logger.info(f"{order_type} is hit for {self}.")
+        else:
+            raise ValueError(f"Unknown order type: {order.order_type}")
+
+        if order.ft_order_side != self.entry_side:
+            amount_tr = amount_to_contract_precision(
+                self.amount, self.amount_precision, self.precision_mode, self.contract_size
+            )
+            if isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC) or (
+                not recalculating and order.safe_amount_after_fee > amount_tr
+            ):
+                # When recalculating a trade, only coming out to 0 can force a close
+                self.close(order.safe_price)
+            else:
+                self.recalc_trade_from_orders()
+
+        Trade.commit()
+
+    def close(self, rate: float, *, show_msg: bool = True) -> None:
+        """
+        Sets close_rate to the given rate, calculates total profit
+        and marks trade as closed
+        """
+        self.close_rate = rate
+        self.close_date = self.close_date or self._date_last_filled_utc or dt_now()
+        self.is_open = False
+        self.exit_order_status = "closed"
+        self.recalc_trade_from_orders(is_closing=True)
+        if show_msg:
+            logger.info(
+                f"Marking {self} as closed as the trade is fulfilled "
+                "and found no open orders for it."
+            )
+
+    def update_fee(
+        self, fee_cost: float, fee_currency: str | None, fee_rate: float | None, side: str
+    ) -> None:
+        """
+        Update Fee parameters. Only acts once per side
+        """
+        if self.entry_side == side and self.fee_open_currency is None:
+            self.fee_open_cost = fee_cost
+            self.fee_open_currency = fee_currency
+            if fee_rate is not None:
+                self.fee_open = fee_rate
+                # Assume close-fee will fall into the same fee category and take an educated guess
+                self.fee_close = fee_rate
+        elif self.exit_side == side and self.fee_close_currency is None:
+            self.fee_close_cost = fee_cost
+            self.fee_close_currency = fee_currency
+            if fee_rate is not None:
+                self.fee_close = fee_rate
+
+    def fee_updated(self, side: str) -> bool:
+        """
+        Verify if this side (buy / sell) has already been updated
+        """
+        if self.entry_side == side:
+            return self.fee_open_currency is not None
+        elif self.exit_side == side:
+            return self.fee_close_currency is not None
+        else:
+            return False
+
+    def update_order(self, order: CcxtOrder) -> None:
+        Order.update_orders(self.orders, order)
+
+    @property
+    def fully_canceled_entry_order_count(self) -> int:
+        """
+        Get amount of failed exiting orders
+        assumes full exits.
+        """
+        return len(
+            [
+                o
+                for o in self.orders
+                if o.ft_order_side == self.entry_side
+                and o.status in CANCELED_EXCHANGE_STATES
+                and o.filled == 0
+            ]
+        )
+
+    @property
+    def canceled_exit_order_count(self) -> int:
+        """
+        Get amount of failed exiting orders
+        assumes full exits.
+        """
+        return len(
+            [
+                o
+                for o in self.orders
+                if o.ft_order_side == self.exit_side and o.status in CANCELED_EXCHANGE_STATES
+            ]
+        )
+
+    def get_canceled_exit_order_count(self) -> int:
+        """
+        Get amount of failed exiting orders
+        assumes full exits.
+        """
+        return self.canceled_exit_order_count
+
+    def _calc_open_trade_value(self, amount: float, open_rate: float) -> float:
+        """
+        Calculate the open_rate including open_fee.
+        :return: Price in of the open trade incl. Fees
+        """
+        open_trade = FtPrecise(amount) * FtPrecise(open_rate)
+        fees = open_trade * FtPrecise(self.fee_open)
+        if self.is_short:
+            return float(open_trade - fees)
+        else:
+            return float(open_trade + fees)
+
+    def recalc_open_trade_value(self) -> None:
+        """
+        Recalculate open_trade_value.
+        Must be called whenever open_rate, fee_open is changed.
+        """
+        self.open_trade_value = self._calc_open_trade_value(self.amount, self.open_rate)
+
+    def calculate_interest(self) -> FtPrecise:
+        """
+        Calculate interest for this trade. Only applicable for Margin trading.
+        """
+        zero = FtPrecise(0.0)
+        # If nothing was borrowed
+        if self.trading_mode != TradingMode.MARGIN or self.has_no_leverage:
+            return zero
+
+        open_date = self.open_date.replace(tzinfo=None)
+        now = (self.close_date or datetime.now(timezone.utc)).replace(tzinfo=None)
+        sec_per_hour = FtPrecise(3600)
+        total_seconds = FtPrecise((now - open_date).total_seconds())
+        hours = total_seconds / sec_per_hour or zero
+
+        rate = FtPrecise(self.interest_rate)
+        borrowed = FtPrecise(self.borrowed)
+
+        return interest(exchange_name=self.exchange, borrowed=borrowed, rate=rate, hours=hours)
+
+    def _calc_base_close(self, amount: FtPrecise, rate: float, fee: float | None) -> FtPrecise:
+        close_trade = amount * FtPrecise(rate)
+        fees = close_trade * FtPrecise(fee or 0.0)
+
+        if self.is_short:
+            return close_trade + fees
+        else:
+            return close_trade - fees
+
+    def calc_close_trade_value(self, rate: float, amount: float | None = None) -> float:
+        """
+        Calculate the Trade's close value including fees
+        :param rate: rate to compare with.
+        :return: value in stake currency of the open trade
+        """
+        if rate is None and not self.close_rate:
+            return 0.0
+
+        amount1 = FtPrecise(amount or self.amount)
+        trading_mode = self.trading_mode or TradingMode.SPOT
+
+        if trading_mode == TradingMode.SPOT:
+            return float(self._calc_base_close(amount1, rate, self.fee_close))
+
+        elif trading_mode == TradingMode.MARGIN:
+            total_interest = self.calculate_interest()
+
+            if self.is_short:
+                amount1 = amount1 + total_interest
+                return float(self._calc_base_close(amount1, rate, self.fee_close))
+            else:
+                # Currency already owned for longs, no need to purchase
+                return float(self._calc_base_close(amount1, rate, self.fee_close) - total_interest)
+
+        elif trading_mode == TradingMode.FUTURES:
+            funding_fees = self.funding_fees or 0.0
+            # Positive funding_fees -> Trade has gained from fees.
+            # Negative funding_fees -> Trade had to pay the fees.
+            if self.is_short:
+                return float(self._calc_base_close(amount1, rate, self.fee_close)) - funding_fees
+            else:
+                return float(self._calc_base_close(amount1, rate, self.fee_close)) + funding_fees
+        else:
+            raise OperationalException(
+                f"{self.trading_mode} trading is not yet available using freqtrade"
+            )
+
+    def calc_profit(
+        self, rate: float, amount: float | None = None, open_rate: float | None = None
+    ) -> float:
+        """
+        Calculate the absolute profit in stake currency between Close and Open trade
+        Deprecated - only available for backwards compatibility
+        :param rate: close rate to compare with.
+        :param amount: Amount to use for the calculation. Falls back to trade.amount if not set.
+        :param open_rate: open_rate to use. Defaults to self.open_rate if not provided.
+        :return: profit in stake currency as float
+        """
+        prof = self.calculate_profit(rate, amount, open_rate)
+        return prof.profit_abs
+
+    def calculate_profit(
+        self, rate: float, amount: float | None = None, open_rate: float | None = None
+    ) -> ProfitStruct:
+        """
+        Calculate profit metrics (absolute, ratio, total, total ratio).
+        All calculations include fees.
+        :param rate: close rate to compare with.
+        :param amount: Amount to use for the calculation. Falls back to trade.amount if not set.
+        :param open_rate: open_rate to use. Defaults to self.open_rate if not provided.
+        :return: Profit structure, containing absolute and relative profits.
+        """
+
+        close_trade_value = self.calc_close_trade_value(rate, amount)
+        if amount is None or open_rate is None:
+            open_trade_value = self.open_trade_value
+        else:
+            open_trade_value = self._calc_open_trade_value(amount, open_rate)
+
+        if self.is_short:
+            profit_abs = open_trade_value - close_trade_value
+        else:
+            profit_abs = close_trade_value - open_trade_value
+
+        try:
+            if self.is_short:
+                profit_ratio = (1 - (close_trade_value / open_trade_value)) * self.leverage
+            else:
+                profit_ratio = ((close_trade_value / open_trade_value) - 1) * self.leverage
+            profit_ratio = float(f"{profit_ratio:.8f}")
+        except ZeroDivisionError:
+            profit_ratio = 0.0
+
+        total_profit_abs = profit_abs + self.realized_profit
+        if self.max_stake_amount:
+            max_stake = self.max_stake_amount * (
+                (1 - self.fee_open) if self.is_short else (1 + self.fee_open)
+            )
+            total_profit_ratio = total_profit_abs / max_stake
+            total_profit_ratio = float(f"{total_profit_ratio:.8f}")
+        else:
+            total_profit_ratio = 0.0
+        profit_abs = float(f"{profit_abs:.8f}")
+        total_profit_abs = float(f"{total_profit_abs:.8f}")
+
+        return ProfitStruct(
+            profit_abs=profit_abs,
+            profit_ratio=profit_ratio,
+            total_profit=total_profit_abs,
+            total_profit_ratio=total_profit_ratio,
+        )
+
+    def calc_profit_ratio(
+        self, rate: float, amount: float | None = None, open_rate: float | None = None
+    ) -> float:
+        """
+        Calculates the profit as ratio (including fee).
+        :param rate: rate to compare with.
+        :param amount: Amount to use for the calculation. Falls back to trade.amount if not set.
+        :param open_rate: open_rate to use. Defaults to self.open_rate if not provided.
+        :return: profit ratio as float
+        """
+        close_trade_value = self.calc_close_trade_value(rate, amount)
+
+        if amount is None or open_rate is None:
+            open_trade_value = self.open_trade_value
+        else:
+            open_trade_value = self._calc_open_trade_value(amount, open_rate)
+
+        if open_trade_value == 0.0:
+            return 0.0
+        else:
+            if self.is_short:
+                profit_ratio = (1 - (close_trade_value / open_trade_value)) * self.leverage
+            else:
+                profit_ratio = ((close_trade_value / open_trade_value) - 1) * self.leverage
+
+        return float(f"{profit_ratio:.8f}")
+
+    def recalc_trade_from_orders(self, *, is_closing: bool = False):
+        ZERO = FtPrecise(0.0)
+        current_amount = FtPrecise(0.0)
+        current_stake = FtPrecise(0.0)
+        max_stake_amount = FtPrecise(0.0)
+        total_stake = 0.0  # Total stake after all buy orders (does not subtract!)
+        avg_price = FtPrecise(0.0)
+        close_profit = 0.0
+        close_profit_abs = 0.0
+        # Reset funding fees
+        self.funding_fees = 0.0
+        funding_fees = 0.0
+        ordercount = len(self.orders) - 1
+        for i, o in enumerate(self.orders):
+            if o.ft_is_open or not o.filled:
+                continue
+            funding_fees += o.funding_fee or 0.0
+            tmp_amount = FtPrecise(o.safe_amount_after_fee)
+            tmp_price = FtPrecise(o.safe_price)
+
+            is_exit = o.ft_order_side != self.entry_side
+            side = FtPrecise(-1 if is_exit else 1)
+            if tmp_amount > ZERO and tmp_price is not None:
+                current_amount += tmp_amount * side
+                price = avg_price if is_exit else tmp_price
+                current_stake += price * tmp_amount * side
+
+                if current_amount > ZERO and not is_exit:
+                    avg_price = current_stake / current_amount
+
+            if is_exit:
+                # Process exits
+                if i == ordercount and is_closing:
+                    # Apply funding fees only to the last closing order
+                    self.funding_fees = funding_fees
+
+                exit_rate = o.safe_price
+                exit_amount = o.safe_amount_after_fee
+                prof = self.calculate_profit(exit_rate, exit_amount, float(avg_price))
+                close_profit_abs += prof.profit_abs
+                if total_stake > 0:
+                    # This needs to be calculated based on the last occurring exit to be aligned
+                    # with realized_profit.
+                    close_profit = (close_profit_abs / total_stake) * self.leverage
+            else:
+                total_stake += self._calc_open_trade_value(tmp_amount, price)
+                max_stake_amount += tmp_amount * price
+        self.funding_fees = funding_fees
+        self.max_stake_amount = float(max_stake_amount) / (self.leverage or 1.0)
+
+        if close_profit:
+            self.close_profit = close_profit
+            self.realized_profit = close_profit_abs
+            self.close_profit_abs = prof.profit_abs
+
+        current_amount_tr = amount_to_contract_precision(
+            float(current_amount), self.amount_precision, self.precision_mode, self.contract_size
+        )
+        if current_amount_tr > 0.0:
+            # Trade is still open
+            # Leverage not updated, as we don't allow changing leverage through DCA at the moment.
+            self.open_rate = price_to_precision(
+                float(current_stake / current_amount),
+                self.price_precision,
+                self.precision_mode_price,
+            )
+            self.amount = current_amount_tr
+            self.stake_amount = float(current_stake) / (self.leverage or 1.0)
+            self.fee_open_cost = self.fee_open * float(self.max_stake_amount)
+            self.recalc_open_trade_value()
+            if self.stop_loss_pct is not None and self.open_rate is not None:
+                self.adjust_stop_loss(self.open_rate, self.stop_loss_pct)
+        elif is_closing and total_stake > 0:
+            # Close profit abs / maximum owned
+            # Fees are considered as they are part of close_profit_abs
+            self.close_profit = (close_profit_abs / total_stake) * self.leverage
+            self.close_profit_abs = close_profit_abs
+
+    def select_order_by_order_id(self, order_id: str) -> Order | None:
+        """
+        Finds order object by Order id.
+        :param order_id: Exchange order id
+        """
+        for o in self.orders:
+            if o.order_id == order_id:
+                return o
+        return None
+
+    def select_order(
+        self,
+        order_side: str | None = None,
+        is_open: bool | None = None,
+        only_filled: bool = False,
+    ) -> Order | None:
+        """
+        Finds latest order for this orderside and status
+        :param order_side: ft_order_side of the order (either 'buy', 'sell' or 'stoploss')
+        :param is_open: Only search for open orders?
+        :param only_filled: Only search for Filled orders (only valid with is_open=False).
+        :return: latest Order object if it exists, else None
+        """
+        orders = self.orders
+        if order_side:
+            orders = [o for o in orders if o.ft_order_side == order_side]
+        if is_open is not None:
+            orders = [o for o in orders if o.ft_is_open == is_open]
+        if is_open is False and only_filled:
+            orders = [o for o in orders if o.filled and o.status in NON_OPEN_EXCHANGE_STATES]
+        if len(orders) > 0:
+            return orders[-1]
+        else:
+            return None
+
+    def select_filled_orders(self, order_side: str | None = None) -> list["Order"]:
+        """
+        Finds filled orders for this order side.
+        Will not return open orders which already partially filled.
+        :param order_side: Side of the order (either 'buy', 'sell', or None)
+        :return: array of Order objects
+        """
+        return [
+            o
+            for o in self.orders
+            if ((o.ft_order_side == order_side) or (order_side is None))
+            and o.ft_is_open is False
+            and o.filled
+            and o.status in NON_OPEN_EXCHANGE_STATES
+        ]
+
+    def select_filled_or_open_orders(self) -> list["Order"]:
+        """
+        Finds filled or open orders
+        :param order_side: Side of the order (either 'buy', 'sell', or None)
+        :return: array of Order objects
+        """
+        return [
+            o
+            for o in self.orders
+            if (
+                o.ft_is_open is False
+                and (o.filled or 0) > 0
+                and o.status in NON_OPEN_EXCHANGE_STATES
+            )
+            or (o.ft_is_open is True and o.status is not None)
+        ]
+
+    def set_custom_data(self, key: str, value: Any) -> None:
+        """
+        Set custom data for this trade
+        :param key: key of the custom data
+        :param value: value of the custom data (must be JSON serializable)
+        """
+        CustomDataWrapper.set_custom_data(trade_id=self.id, key=key, value=value)
+
+    def get_custom_data(self, key: str, default: Any = None) -> Any:
+        """
+        Get custom data for this trade.
+
+        :param key: key of the custom data
+        :param default: value to return if no data is found
+        """
+        data = CustomDataWrapper.get_custom_data(trade_id=self.id, key=key)
+        if data:
+            return data[0].value
+        return default
+
+    def get_custom_data_entry(self, key: str) -> _CustomData | None:
+        """
+        Get custom data for this trade
+        :param key: key of the custom data
+        """
+        data = CustomDataWrapper.get_custom_data(trade_id=self.id, key=key)
+        if data:
+            return data[0]
+        return None
+
+    def get_all_custom_data(self) -> list[_CustomData]:
+        """
+        Get all custom data for this trade
+        """
+        return CustomDataWrapper.get_custom_data(trade_id=self.id)
+
+    @property
+    def nr_of_successful_entries(self) -> int:
+        """
+        Helper function to count the number of entry orders that have been filled.
+        :return: int count of entry orders that have been filled for this trade.
+        """
+
+        return len(self.select_filled_orders(self.entry_side))
+
+    @property
+    def nr_of_successful_exits(self) -> int:
+        """
+        Helper function to count the number of exit orders that have been filled.
+        :return: int count of exit orders that have been filled for this trade.
+        """
+        return len(self.select_filled_orders(self.exit_side))
+
+    @property
+    def nr_of_successful_buys(self) -> int:
+        """
+        Helper function to count the number of buy orders that have been filled.
+        WARNING: Please use nr_of_successful_entries for short support.
+        :return: int count of buy orders that have been filled for this trade.
+        """
+
+        return len(self.select_filled_orders("buy"))
+
+    @property
+    def nr_of_successful_sells(self) -> int:
+        """
+        Helper function to count the number of sell orders that have been filled.
+        WARNING: Please use nr_of_successful_exits for short support.
+        :return: int count of sell orders that have been filled for this trade.
+        """
+        return len(self.select_filled_orders("sell"))
+
+    @property
+    def sell_reason(self) -> str | None:
+        """DEPRECATED! Please use exit_reason instead."""
+        return self.exit_reason
+
+    @property
+    def safe_close_rate(self) -> float:
+        return self.close_rate or self.close_rate_requested or 0.0
+
+    @staticmethod
+    def get_trades_proxy(
+        *,
+        pair: str | None = None,
+        is_open: bool | None = None,
+        open_date: datetime | None = None,
+        close_date: datetime | None = None,
+    ) -> list["LocalTrade"]:
+        """
+        Helper function to query Trades.
+        Returns a List of trades, filtered on the parameters given.
+        In live mode, converts the filter to a database query and returns all rows
+        In Backtest mode, uses filters on Trade.bt_trades to get the result.
+
+        :param pair: Filter by pair
+        :param is_open: Filter by open/closed status
+        :param open_date: Filter by open_date (filters via trade.open_date > input)
+        :param close_date: Filter by close_date (filters via trade.close_date > input)
+                           Will implicitly only return closed trades.
+        :return: unsorted List[Trade]
+        """
+
+        # Offline mode - without database
+        if is_open is not None:
+            if is_open:
+                sel_trades = LocalTrade.bt_trades_open
+            else:
+                sel_trades = LocalTrade.bt_trades
+
+        else:
+            # Not used during backtesting, but might be used by a strategy
+            sel_trades = list(LocalTrade.bt_trades + LocalTrade.bt_trades_open)
+
+        if pair:
+            sel_trades = [trade for trade in sel_trades if trade.pair == pair]
+        if open_date:
+            sel_trades = [trade for trade in sel_trades if trade.open_date > open_date]
+        if close_date:
+            sel_trades = [
+                trade for trade in sel_trades if trade.close_date and trade.close_date > close_date
+            ]
+
+        return sel_trades
+
+    @staticmethod
+    def close_bt_trade(trade):
+        LocalTrade.bt_trades_open.remove(trade)
+        LocalTrade.bt_trades_open_pp[trade.pair].remove(trade)
+        LocalTrade.bt_open_open_trade_count -= 1
+        LocalTrade.bt_trades.append(trade)
+        LocalTrade.bt_total_profit += trade.close_profit_abs
+
+    @staticmethod
+    def add_bt_trade(trade):
+        if trade.is_open:
+            LocalTrade.bt_trades_open.append(trade)
+            LocalTrade.bt_trades_open_pp[trade.pair].append(trade)
+            LocalTrade.bt_open_open_trade_count += 1
+        else:
+            LocalTrade.bt_trades.append(trade)
+
+    @staticmethod
+    def remove_bt_trade(trade):
+        LocalTrade.bt_trades_open.remove(trade)
+        LocalTrade.bt_trades_open_pp[trade.pair].remove(trade)
+        LocalTrade.bt_open_open_trade_count -= 1
+
+    @staticmethod
+    def get_open_trades() -> list[Any]:
+        """
+        Retrieve open trades
+        """
+        return Trade.get_trades_proxy(is_open=True)
+
+    @staticmethod
+    def get_open_trade_count() -> int:
+        """
+        get open trade count
+        """
+        if Trade.use_db:
+            return Trade.session.execute(
+                select(func.count(Trade.id)).filter(Trade.is_open.is_(True))
+            ).scalar_one()
+        else:
+            return LocalTrade.bt_open_open_trade_count
+
+    @staticmethod
+    def stoploss_reinitialization(desired_stoploss: float):
+        """
+        Adjust initial Stoploss to desired stoploss for all open trades.
+        """
+        trade: Trade
+        for trade in Trade.get_open_trades():
+            logger.info(f"Found open trade: {trade}")
+
+            # skip case if trailing-stop changed the stoploss already.
+            if not trade.is_stop_loss_trailing and trade.initial_stop_loss_pct != desired_stoploss:
+                # Stoploss value got changed
+
+                logger.info(f"Stoploss for {trade} needs adjustment...")
+                # Force reset of stoploss
+                trade.stop_loss = 0.0
+                trade.initial_stop_loss_pct = None
+                trade.adjust_stop_loss(trade.open_rate, desired_stoploss)
+                logger.info(f"New stoploss: {trade.stop_loss}.")
+
+    @classmethod
+    def from_json(cls, json_str: str) -> Self:
+        """
+        Create a Trade instance from a json string.
+
+        Used for debugging purposes - please keep.
+        :param json_str: json string to parse
+        :return: Trade instance
+        """
+        from uuid import uuid4
+
+        import rapidjson
+
+        data = rapidjson.loads(json_str)
+        trade = cls(
+            __FROM_JSON=True,
+            id=data.get("trade_id"),
+            pair=data["pair"],
+            base_currency=data.get("base_currency"),
+            stake_currency=data.get("quote_currency"),
+            is_open=data["is_open"],
+            exchange=data.get("exchange", "import"),
+            amount=data["amount"],
+            amount_requested=data.get("amount_requested", data["amount"]),
+            stake_amount=data["stake_amount"],
+            strategy=data.get("strategy"),
+            enter_tag=data["enter_tag"],
+            timeframe=data.get("timeframe"),
+            fee_open=data["fee_open"],
+            fee_open_cost=data.get("fee_open_cost"),
+            fee_open_currency=data.get("fee_open_currency"),
+            fee_close=data["fee_close"],
+            fee_close_cost=data.get("fee_close_cost"),
+            fee_close_currency=data.get("fee_close_currency"),
+            open_date=datetime.fromtimestamp(data["open_timestamp"] // 1000, tz=timezone.utc),
+            open_rate=data["open_rate"],
+            open_rate_requested=data.get("open_rate_requested", data["open_rate"]),
+            open_trade_value=data.get("open_trade_value"),
+            close_date=(
+                datetime.fromtimestamp(data["close_timestamp"] // 1000, tz=timezone.utc)
+                if data["close_timestamp"]
+                else None
+            ),
+            realized_profit=data.get("realized_profit", 0),
+            close_rate=data["close_rate"],
+            close_rate_requested=data.get("close_rate_requested", data["close_rate"]),
+            close_profit=data.get("close_profit", data.get("profit_ratio")),
+            close_profit_abs=data.get("close_profit_abs", data.get("profit_abs")),
+            exit_reason=data["exit_reason"],
+            exit_order_status=data.get("exit_order_status"),
+            stop_loss=data["stop_loss_abs"],
+            stop_loss_pct=data["stop_loss_ratio"],
+            initial_stop_loss=data["initial_stop_loss_abs"],
+            initial_stop_loss_pct=data["initial_stop_loss_ratio"],
+            min_rate=data["min_rate"],
+            max_rate=data["max_rate"],
+            leverage=data["leverage"],
+            interest_rate=data.get("interest_rate"),
+            liquidation_price=data.get("liquidation_price"),
+            is_short=data["is_short"],
+            trading_mode=data.get("trading_mode"),
+            funding_fees=data.get("funding_fees"),
+            amount_precision=data.get("amount_precision", None),
+            price_precision=data.get("price_precision", None),
+            precision_mode=data.get("precision_mode", None),
+            precision_mode_price=data.get("precision_mode_price", data.get("precision_mode", None)),
+            contract_size=data.get("contract_size", None),
+        )
+        for order in data["orders"]:
+            order_obj = Order(
+                amount=order["amount"],
+                ft_amount=order["amount"],
+                ft_order_side=order["ft_order_side"],
+                ft_pair=order.get("pair", data["pair"]),
+                ft_is_open=order.get("is_open", False),
+                order_id=order.get("order_id", uuid4().hex),
+                status=order.get("status"),
+                average=order.get("average", order.get("safe_price")),
+                cost=order["cost"],
+                filled=order.get("filled", order["amount"]),
+                order_date=datetime.strptime(order["order_date"], DATETIME_PRINT_FORMAT)
+                if order.get("order_date")
+                else None,
+                order_filled_date=(
+                    datetime.fromtimestamp(order["order_filled_timestamp"] // 1000, tz=timezone.utc)
+                    if order["order_filled_timestamp"]
+                    else None
+                ),
+                order_type=order.get("order_type"),
+                price=order.get("price", order.get("safe_price")),
+                ft_price=order.get("price", order.get("safe_price")),
+                remaining=order.get("remaining", 0.0),
+                funding_fee=order.get("funding_fee", None),
+                ft_order_tag=order.get("ft_order_tag", None),
+            )
+            trade.orders.append(order_obj)
+
+        return trade
+
+
+class Trade(ModelBase, LocalTrade):
+    """
+    Trade database model.
+    Also handles updating and querying trades
+
+    Note: Fields must be aligned with LocalTrade class
+    """
+
+    __tablename__ = "trades"
+    session: ClassVar[SessionType]
+
+    use_db: bool = True
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)  # type: ignore
+
+    orders: Mapped[list[Order]] = relationship(
+        "Order", order_by="Order.id", cascade="all, delete-orphan", lazy="selectin", innerjoin=True
+    )  # type: ignore
+    custom_data: Mapped[list[_CustomData]] = relationship(
+        "_CustomData", cascade="all, delete-orphan", lazy="raise"
+    )
+
+    exchange: Mapped[str] = mapped_column(String(25), nullable=False)  # type: ignore
+    pair: Mapped[str] = mapped_column(String(25), nullable=False, index=True)  # type: ignore
+    base_currency: Mapped[str | None] = mapped_column(String(25), nullable=True)  # type: ignore
+    stake_currency: Mapped[str | None] = mapped_column(String(25), nullable=True)  # type: ignore
+    is_open: Mapped[bool] = mapped_column(nullable=False, default=True, index=True)  # type: ignore
+    fee_open: Mapped[float] = mapped_column(Float(), nullable=False, default=0.0)  # type: ignore
+    fee_open_cost: Mapped[float | None] = mapped_column(Float(), nullable=True)  # type: ignore
+    fee_open_currency: Mapped[str | None] = mapped_column(  # type: ignore
+        String(25), nullable=True
+    )
+    fee_close: Mapped[float | None] = mapped_column(  # type: ignore
+        Float(), nullable=False, default=0.0
+    )
+    fee_close_cost: Mapped[float | None] = mapped_column(Float(), nullable=True)  # type: ignore
+    fee_close_currency: Mapped[str | None] = mapped_column(  # type: ignore
+        String(25), nullable=True
+    )
+    open_rate: Mapped[float] = mapped_column(Float())  # type: ignore
+    open_rate_requested: Mapped[float | None] = mapped_column(  # type: ignore
+        Float(), nullable=True
+    )
+    # open_trade_value - calculated via _calc_open_trade_value
+    open_trade_value: Mapped[float] = mapped_column(Float(), nullable=True)  # type: ignore
+    close_rate: Mapped[float | None] = mapped_column(Float())  # type: ignore
+    close_rate_requested: Mapped[float | None] = mapped_column(Float())  # type: ignore
+    realized_profit: Mapped[float] = mapped_column(  # type: ignore
+        Float(), default=0.0, nullable=True
+    )
+    close_profit: Mapped[float | None] = mapped_column(Float())  # type: ignore
+    close_profit_abs: Mapped[float | None] = mapped_column(Float())  # type: ignore
+    stake_amount: Mapped[float] = mapped_column(Float(), nullable=False)  # type: ignore
+    max_stake_amount: Mapped[float | None] = mapped_column(Float())  # type: ignore
+    amount: Mapped[float] = mapped_column(Float())  # type: ignore
+    amount_requested: Mapped[float | None] = mapped_column(Float())  # type: ignore
+    open_date: Mapped[datetime] = mapped_column(  # type: ignore
+        nullable=False, default=datetime.now
+    )
+    close_date: Mapped[datetime | None] = mapped_column()  # type: ignore
+    # absolute value of the stop loss
+    stop_loss: Mapped[float] = mapped_column(Float(), nullable=True, default=0.0)  # type: ignore
+    # percentage value of the stop loss
+    stop_loss_pct: Mapped[float | None] = mapped_column(Float(), nullable=True)  # type: ignore
+    # absolute value of the initial stop loss
+    initial_stop_loss: Mapped[float | None] = mapped_column(  # type: ignore
+        Float(), nullable=True, default=0.0
+    )
+    # percentage value of the initial stop loss
+    initial_stop_loss_pct: Mapped[float | None] = mapped_column(  # type: ignore
+        Float(), nullable=True
+    )
+    is_stop_loss_trailing: Mapped[bool] = mapped_column(  # type: ignore
+        nullable=False, default=False
+    )
+    # absolute value of the highest reached price
+    max_rate: Mapped[float | None] = mapped_column(  # type: ignore
+        Float(), nullable=True, default=0.0
+    )
+    # Lowest price reached
+    min_rate: Mapped[float | None] = mapped_column(Float(), nullable=True)  # type: ignore
+    exit_reason: Mapped[str | None] = mapped_column(  # type: ignore
+        String(CUSTOM_TAG_MAX_LENGTH), nullable=True
+    )
+    exit_order_status: Mapped[str | None] = mapped_column(  # type: ignore
+        String(100), nullable=True
+    )
+    strategy: Mapped[str | None] = mapped_column(String(100), nullable=True)  # type: ignore
+    enter_tag: Mapped[str | None] = mapped_column(  # type: ignore
+        String(CUSTOM_TAG_MAX_LENGTH), nullable=True
+    )
+    timeframe: Mapped[int | None] = mapped_column(Integer, nullable=True)  # type: ignore
+
+    trading_mode: Mapped[TradingMode] = mapped_column(  # type: ignore
+        Enum(TradingMode), nullable=True
+    )
+    amount_precision: Mapped[float | None] = mapped_column(  # type: ignore
+        Float(), nullable=True
+    )
+    price_precision: Mapped[float | None] = mapped_column(Float(), nullable=True)  # type: ignore
+    precision_mode: Mapped[int | None] = mapped_column(Integer, nullable=True)  # type: ignore
+    precision_mode_price: Mapped[int | None] = mapped_column(  # type: ignore
+        Integer, nullable=True
+    )
+    contract_size: Mapped[float | None] = mapped_column(Float(), nullable=True)  # type: ignore
+
+    # Leverage trading properties
+    leverage: Mapped[float] = mapped_column(Float(), nullable=True, default=1.0)  # type: ignore
+    is_short: Mapped[bool] = mapped_column(nullable=False, default=False)  # type: ignore
+    liquidation_price: Mapped[float | None] = mapped_column(  # type: ignore
+        Float(), nullable=True
+    )
+
+    # Margin Trading Properties
+    interest_rate: Mapped[float] = mapped_column(  # type: ignore
+        Float(), nullable=False, default=0.0
+    )
+
+    # Futures properties
+    funding_fees: Mapped[float | None] = mapped_column(  # type: ignore
+        Float(), nullable=True, default=None
+    )
+    funding_fee_running: Mapped[float | None] = mapped_column(  # type: ignore
+        Float(), nullable=True, default=None
+    )
+
+    record_version: Mapped[int] = mapped_column(Integer, nullable=False, default=2)  # type: ignore
+
+    def __init__(self, **kwargs):
+        from_json = kwargs.pop("__FROM_JSON", None)
+        super().__init__(**kwargs)
+        if not from_json:
+            # Skip recalculation when loading from json
+            self.realized_profit = 0
+            self.recalc_open_trade_value()
+
+    @validates("enter_tag", "exit_reason")
+    def validate_string_len(self, key, value):
+        max_len = getattr(self.__class__, key).prop.columns[0].type.length
+        if value and len(value) > max_len:
+            return value[:max_len]
+        return value
+
+    def delete(self) -> None:
+        for order in self.orders:
+            Order.session.delete(order)
+
+        CustomDataWrapper.delete_custom_data(trade_id=self.id)
+
+        Trade.session.delete(self)
+        Trade.commit()
+
+    @staticmethod
+    def commit():
+        Trade.session.commit()
+
+    @staticmethod
+    def rollback():
+        Trade.session.rollback()
+
+    @staticmethod
+    def get_trades_proxy(
+        *,
+        pair: str | None = None,
+        is_open: bool | None = None,
+        open_date: datetime | None = None,
+        close_date: datetime | None = None,
+    ) -> list["LocalTrade"]:
+        """
+        Helper function to query Trades.j
+        Returns a List of trades, filtered on the parameters given.
+        In live mode, converts the filter to a database query and returns all rows
+        In Backtest mode, uses filters on Trade.bt_trades to get the result.
+
+        :return: unsorted List[Trade]
+        """
+        if Trade.use_db:
+            trade_filter = []
+            if pair:
+                trade_filter.append(Trade.pair == pair)
+            if open_date:
+                trade_filter.append(Trade.open_date > open_date)
+            if close_date:
+                trade_filter.append(Trade.close_date > close_date)
+            if is_open is not None:
+                trade_filter.append(Trade.is_open.is_(is_open))
+            return cast(list[LocalTrade], Trade.get_trades(trade_filter).all())
+        else:
+            return LocalTrade.get_trades_proxy(
+                pair=pair, is_open=is_open, open_date=open_date, close_date=close_date
+            )
+
+    @staticmethod
+    def get_trades_query(trade_filter=None, include_orders: bool = True) -> Select:
+        """
+        Helper function to query Trades using filters.
+        NOTE: Not supported in Backtesting.
+        :param trade_filter: Optional filter to apply to trades
+                             Can be either a Filter object, or a List of filters
+                             e.g. `(trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True),])`
+                             e.g. `(trade_filter=Trade.id == trade_id)`
+        :return: unsorted query object
+        """
+        if not Trade.use_db:
+            raise NotImplementedError("`Trade.get_trades()` not supported in backtesting mode.")
+        if trade_filter is not None:
+            if not isinstance(trade_filter, list):
+                trade_filter = [trade_filter]
+            this_query = select(Trade).filter(*trade_filter)
+        else:
+            this_query = select(Trade)
+        if not include_orders:
+            # Don't load order relations
+            # Consider using noload or raiseload instead of lazyload
+            this_query = this_query.options(lazyload(Trade.orders))
+        return this_query
+
+    @staticmethod
+    def get_trades(trade_filter=None, include_orders: bool = True) -> ScalarResult["Trade"]:
+        """
+        Helper function to query Trades using filters.
+        NOTE: Not supported in Backtesting.
+        :param trade_filter: Optional filter to apply to trades
+                             Can be either a Filter object, or a List of filters
+                             e.g. `(trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True),])`
+                             e.g. `(trade_filter=Trade.id == trade_id)`
+        :return: unsorted query object
+        """
+        query = Trade.get_trades_query(trade_filter, include_orders)
+        # this should remain split. if use_db is False, session is not available and the above will
+        # raise an exception.
+        return Trade.session.scalars(query)
+
+    @staticmethod
+    def get_open_trades_without_assigned_fees():
+        """
+        Returns all open trades which don't have open fees set correctly
+        NOTE: Not supported in Backtesting.
+        """
+        return Trade.get_trades(
+            [
+                Trade.fee_open_currency.is_(None),
+                Trade.orders.any(),
+                Trade.is_open.is_(True),
+            ]
+        ).all()
+
+    @staticmethod
+    def get_closed_trades_without_assigned_fees():
+        """
+        Returns all closed trades which don't have fees set correctly
+        NOTE: Not supported in Backtesting.
+        """
+        return Trade.get_trades(
+            [
+                Trade.fee_close_currency.is_(None),
+                Trade.orders.any(),
+                Trade.is_open.is_(False),
+            ]
+        ).all()
+
+    @staticmethod
+    def get_total_closed_profit() -> float:
+        """
+        Retrieves total realized profit
+        """
+        if Trade.use_db:
+            total_profit = Trade.session.execute(
+                select(func.sum(Trade.close_profit_abs)).filter(Trade.is_open.is_(False))
+            ).scalar_one()
+        else:
+            total_profit = sum(
+                t.close_profit_abs  # type: ignore
+                for t in LocalTrade.get_trades_proxy(is_open=False)
+            )
+        return total_profit or 0
+
+    @staticmethod
+    def total_open_trades_stakes() -> float:
+        """
+        Calculates total invested amount in open trades
+        in stake currency
+        """
+        if Trade.use_db:
+            total_open_stake_amount = Trade.session.scalar(
+                select(func.sum(Trade.stake_amount)).filter(Trade.is_open.is_(True))
+            )
+        else:
+            total_open_stake_amount = sum(
+                t.stake_amount for t in LocalTrade.get_trades_proxy(is_open=True)
+            )
+        return total_open_stake_amount or 0
+
+    @staticmethod
+    def _generic_performance_query(columns: list, filters: list, fallback: str = "") -> Select:
+        """
+        Retrieve a generic select object to calculate performance grouped on `columns`.
+        Returns the following columns:
+        - columns
+        - profit_ratio
+        - profit_sum_abs
+        - count
+        NOTE: Not supported in Backtesting.
+        """
+        columns_coal = [func.coalesce(c, fallback).label(c.name) for c in columns]
+        pair_costs = (
+            select(
+                *columns_coal,
+                func.sum(
+                    (
+                        func.coalesce(Order.filled, Order.amount)
+                        * func.coalesce(Order.average, Order.price, Order.ft_price)
+                    )
+                    / func.coalesce(Trade.leverage, 1)
+                ).label("cost_per_pair"),
+            )
+            .join(Order, Trade.id == Order.ft_trade_id)
+            .filter(
+                *filters,
+                Order.ft_order_side == case((Trade.is_short.is_(True), "sell"), else_="buy"),
+                Order.filled > 0,
+            )
+            .group_by(*columns)
+            .cte("pair_costs")
+        )
+        trades_grouped = (
+            select(
+                *columns_coal,
+                func.sum(Trade.close_profit_abs).label("profit_sum_abs"),
+                func.count(*columns_coal).label("count"),
+            )
+            .filter(*filters)
+            .group_by(*columns_coal)
+            .cte("trades_grouped")
+        )
+        q = (
+            select(
+                *[trades_grouped.c[x.name] for x in columns],
+                (trades_grouped.c.profit_sum_abs / pair_costs.c.cost_per_pair).label(
+                    "profit_ratio"
+                ),
+                trades_grouped.c.profit_sum_abs,
+                trades_grouped.c.count,
+            )
+            .join(pair_costs, *[trades_grouped.c[x.name] == pair_costs.c[x.name] for x in columns])
+            .order_by(desc("profit_sum_abs"))
+        )
+        return q
+
+    @staticmethod
+    def get_overall_performance(start_date: datetime | None = None) -> list[dict[str, Any]]:
+        """
+        Returns List of dicts containing all Trades, including profit and trade count
+        NOTE: Not supported in Backtesting.
+        """
+        filters: list = [Trade.is_open.is_(False)]
+        if start_date:
+            filters.append(Trade.close_date >= start_date)
+        pair_rates_query = Trade._generic_performance_query([Trade.pair], filters)
+        pair_rates = Trade.session.execute(pair_rates_query).all()
+        return [
+            {
+                "pair": pair,
+                "profit_ratio": profit,
+                "profit": round(profit * 100, 2),  # Compatibility mode
+                "profit_pct": round(profit * 100, 2),
+                "profit_abs": profit_abs,
+                "count": count,
+            }
+            for pair, profit, profit_abs, count in pair_rates
+        ]
+
+    @staticmethod
+    def get_enter_tag_performance(pair: str | None) -> list[dict[str, Any]]:
+        """
+        Returns List of dicts containing all Trades, based on buy tag performance
+        Can either be average for all pairs or a specific pair provided
+        NOTE: Not supported in Backtesting.
+        """
+
+        filters: list = [Trade.is_open.is_(False)]
+        if pair is not None:
+            filters.append(Trade.pair == pair)
+
+        pair_rates_query = Trade._generic_performance_query([Trade.enter_tag], filters, "Other")
+        enter_tag_perf = Trade.session.execute(pair_rates_query).all()
+
+        return [
+            {
+                "enter_tag": enter_tag if enter_tag is not None else "Other",
+                "profit_ratio": profit,
+                "profit_pct": round(profit * 100, 2),
+                "profit_abs": profit_abs,
+                "count": count,
+            }
+            for enter_tag, profit, profit_abs, count in enter_tag_perf
+        ]
+
+    @staticmethod
+    def get_exit_reason_performance(pair: str | None) -> list[dict[str, Any]]:
+        """
+        Returns List of dicts containing all Trades, based on exit reason performance
+        Can either be average for all pairs or a specific pair provided
+        NOTE: Not supported in Backtesting.
+        """
+
+        filters: list = [Trade.is_open.is_(False)]
+        if pair is not None:
+            filters.append(Trade.pair == pair)
+
+        pair_rates_query = Trade._generic_performance_query([Trade.exit_reason], filters, "Other")
+        sell_tag_perf = Trade.session.execute(pair_rates_query).all()
+
+        return [
+            {
+                "exit_reason": exit_reason if exit_reason is not None else "Other",
+                "profit_ratio": profit,
+                "profit_pct": round(profit * 100, 2),
+                "profit_abs": profit_abs,
+                "count": count,
+            }
+            for exit_reason, profit, profit_abs, count in sell_tag_perf
+        ]
+
+    @staticmethod
+    def get_mix_tag_performance(pair: str | None) -> list[dict[str, Any]]:
+        """
+        Returns List of dicts containing all Trades, based on entry_tag + exit_reason performance
+        Can either be average for all pairs or a specific pair provided
+        NOTE: Not supported in Backtesting.
+        """
+
+        filters: list = [Trade.is_open.is_(False)]
+        if pair is not None:
+            filters.append(Trade.pair == pair)
+        mix_tag_perf = Trade.session.execute(
+            select(
+                Trade.id,
+                Trade.enter_tag,
+                Trade.exit_reason,
+                func.sum(Trade.close_profit).label("profit_sum"),
+                func.sum(Trade.close_profit_abs).label("profit_sum_abs"),
+                func.count(Trade.pair).label("count"),
+            )
+            .filter(*filters)
+            .group_by(Trade.id)
+            .order_by(desc("profit_sum_abs"))
+        ).all()
+
+        resp: list[dict] = []
+        for _, enter_tag, exit_reason, profit, profit_abs, count in mix_tag_perf:
+            enter_tag = enter_tag if enter_tag is not None else "Other"
+            exit_reason = exit_reason if exit_reason is not None else "Other"
+
+            if exit_reason is not None and enter_tag is not None:
+                mix_tag = enter_tag + " " + exit_reason
+                i = 0
+                if not any(item["mix_tag"] == mix_tag for item in resp):
+                    resp.append(
+                        {
+                            "mix_tag": mix_tag,
+                            "profit_ratio": profit,
+                            "profit_pct": round(profit * 100, 2),
+                            "profit_abs": profit_abs,
+                            "count": count,
+                        }
+                    )
+                else:
+                    while i < len(resp):
+                        if resp[i]["mix_tag"] == mix_tag:
+                            resp[i] = {
+                                "mix_tag": mix_tag,
+                                "profit_ratio": profit + resp[i]["profit_ratio"],
+                                "profit_pct": round(profit + resp[i]["profit_ratio"] * 100, 2),
+                                "profit_abs": profit_abs + resp[i]["profit_abs"],
+                                "count": 1 + resp[i]["count"],
+                            }
+                        i += 1
+
+        return resp
+
+    @staticmethod
+    def get_best_pair(start_date: datetime | None = None):
+        """
+        Get best pair with closed trade.
+        NOTE: Not supported in Backtesting.
+        :returns: Tuple containing (pair, profit_sum)
+        """
+        filters: list = [Trade.is_open.is_(False)]
+        if start_date:
+            filters.append(Trade.close_date >= start_date)
+
+        pair_rates_query = Trade._generic_performance_query([Trade.pair], filters)
+        best_pair = Trade.session.execute(pair_rates_query).first()
+        # returns pair, profit_ratio, abs_profit, count
+        return best_pair
+
+    @staticmethod
+    def get_trading_volume(start_date: datetime | None = None) -> float:
+        """
+        Get Trade volume based on Orders
+        NOTE: Not supported in Backtesting.
+        :returns: Tuple containing (pair, profit_sum)
+        """
+        filters = [Order.status == "closed"]
+        if start_date:
+            filters.append(Order.order_filled_date >= start_date)
+        trading_volume = Trade.session.execute(
+            select(func.sum(Order.cost).label("volume")).filter(*filters)
+        ).scalar_one()
+        return trading_volume or 0.0
diff --git a/freqtrade/persistence/usedb_context.py b/freqtrade/persistence/usedb_context.py
new file mode 100644
index 000000000..3266ca157
--- /dev/null
+++ b/freqtrade/persistence/usedb_context.py
@@ -0,0 +1,35 @@
+from freqtrade.persistence.custom_data import CustomDataWrapper
+from freqtrade.persistence.pairlock_middleware import PairLocks
+from freqtrade.persistence.trade_model import Trade
+
+
+def disable_database_use(timeframe: str) -> None:
+    """
+    Disable database usage for PairLocks and Trade models.
+    Used for backtesting, and some other utility commands.
+    """
+    PairLocks.use_db = False
+    PairLocks.timeframe = timeframe
+    Trade.use_db = False
+    CustomDataWrapper.use_db = False
+
+
+def enable_database_use() -> None:
+    """
+    Cleanup function to restore database usage.
+    """
+    PairLocks.use_db = True
+    PairLocks.timeframe = ""
+    Trade.use_db = True
+    CustomDataWrapper.use_db = True
+
+
+class FtNoDBContext:
+    def __init__(self, timeframe: str = ""):
+        self.timeframe = timeframe
+
+    def __enter__(self):
+        disable_database_use(self.timeframe)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        enable_database_use()
diff --git a/freqtrade/plot/__init__.py b/freqtrade/plot/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/freqtrade/plot/plotting.py b/freqtrade/plot/plotting.py
new file mode 100644
index 000000000..6419ab0c2
--- /dev/null
+++ b/freqtrade/plot/plotting.py
@@ -0,0 +1,716 @@
+import logging
+from datetime import datetime, timezone
+from pathlib import Path
+
+import pandas as pd
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import Config
+from freqtrade.data.btanalysis import (
+    analyze_trade_parallelism,
+    extract_trades_of_period,
+    load_trades,
+)
+from freqtrade.data.converter import trim_dataframe
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.data.history import get_timerange, load_data
+from freqtrade.data.metrics import (
+    calculate_max_drawdown,
+    calculate_underwater,
+    combine_dataframes_with_mean,
+    create_cum_profit,
+)
+from freqtrade.enums import CandleType
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds
+from freqtrade.misc import pair_to_filename
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
+from freqtrade.resolvers import ExchangeResolver, StrategyResolver
+from freqtrade.strategy import IStrategy
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util import get_dry_run_wallet
+
+
+logger = logging.getLogger(__name__)
+
+
+try:
+    import plotly.graph_objects as go
+    from plotly.offline import plot
+    from plotly.subplots import make_subplots
+except ImportError:
+    logger.exception("Module plotly not found \n Please install using `pip3 install plotly`")
+    exit(1)
+
+
+def init_plotscript(config, markets: list, startup_candles: int = 0):
+    """
+    Initialize objects needed for plotting
+    :return: Dict with candle (OHLCV) data, trades and pairs
+    """
+
+    if "pairs" in config:
+        pairs = expand_pairlist(config["pairs"], markets)
+    else:
+        pairs = expand_pairlist(config["exchange"]["pair_whitelist"], markets)
+
+    # Set timerange to use
+    timerange = TimeRange.parse_timerange(config.get("timerange"))
+
+    data = load_data(
+        datadir=config.get("datadir"),
+        pairs=pairs,
+        timeframe=config["timeframe"],
+        timerange=timerange,
+        startup_candles=startup_candles,
+        data_format=config["dataformat_ohlcv"],
+        candle_type=config.get("candle_type_def", CandleType.SPOT),
+    )
+
+    if startup_candles and data:
+        min_date, max_date = get_timerange(data)
+        logger.info(f"Loading data from {min_date} to {max_date}")
+        timerange.adjust_start_if_necessary(
+            timeframe_to_seconds(config["timeframe"]), startup_candles, min_date
+        )
+
+    no_trades = False
+    filename = config.get("exportfilename")
+    if config.get("no_trades", False):
+        no_trades = True
+    elif config["trade_source"] == "file":
+        if not filename.is_dir() and not filename.is_file():
+            logger.warning("Backtest file is missing skipping trades.")
+            no_trades = True
+    try:
+        trades = load_trades(
+            config["trade_source"],
+            db_url=config.get("db_url"),
+            exportfilename=filename,
+            no_trades=no_trades,
+            strategy=config.get("strategy"),
+        )
+    except ValueError as e:
+        raise OperationalException(e) from e
+    if not trades.empty:
+        trades = trim_dataframe(trades, timerange, df_date_col="open_date")
+
+    return {
+        "ohlcv": data,
+        "trades": trades,
+        "pairs": pairs,
+        "timerange": timerange,
+    }
+
+
+def add_indicators(fig, row, indicators: dict[str, dict], data: pd.DataFrame) -> make_subplots:
+    """
+    Generate all the indicators selected by the user for a specific row, based on the configuration
+    :param fig: Plot figure to append to
+    :param row: row number for this plot
+    :param indicators: Dict of Indicators with configuration options.
+                       Dict key must correspond to dataframe column.
+    :param data: candlestick DataFrame
+    """
+    plot_kinds = {
+        "scatter": go.Scatter,
+        "bar": go.Bar,
+    }
+    for indicator, conf in indicators.items():
+        logger.debug(f"indicator {indicator} with config {conf}")
+        if indicator in data:
+            kwargs = {"x": data["date"], "y": data[indicator].values, "name": indicator}
+
+            plot_type = conf.get("type", "scatter")
+            color = conf.get("color")
+            if plot_type == "bar":
+                kwargs.update(
+                    {
+                        "marker_color": color or "DarkSlateGrey",
+                        "marker_line_color": color or "DarkSlateGrey",
+                    }
+                )
+            else:
+                if color:
+                    kwargs.update({"line": {"color": color}})
+                kwargs["mode"] = "lines"
+                if plot_type != "scatter":
+                    logger.warning(
+                        f"Indicator {indicator} has unknown plot trace kind {plot_type}"
+                        f', assuming "scatter".'
+                    )
+
+            kwargs.update(conf.get("plotly", {}))
+            trace = plot_kinds[plot_type](**kwargs)
+            fig.add_trace(trace, row, 1)
+        else:
+            logger.info(
+                'Indicator "%s" ignored. Reason: This indicator is not found in your strategy.',
+                indicator,
+            )
+
+    return fig
+
+
+def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_subplots:
+    """
+    Add profit-plot
+    :param fig: Plot figure to append to
+    :param row: row number for this plot
+    :param data: candlestick DataFrame
+    :param column: Column to use for plot
+    :param name: Name to use
+    :return: fig with added profit plot
+    """
+    profit = go.Scatter(
+        x=data.index,
+        y=data[column],
+        name=name,
+    )
+    fig.add_trace(profit, row, 1)
+
+    return fig
+
+
+def add_max_drawdown(
+    fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame, timeframe: str, starting_balance: float
+) -> make_subplots:
+    """
+    Add scatter points indicating max drawdown
+    """
+    try:
+        drawdown = calculate_max_drawdown(trades, starting_balance=starting_balance)
+
+        drawdown = go.Scatter(
+            x=[drawdown.high_date, drawdown.low_date],
+            y=[
+                df_comb.loc[timeframe_to_prev_date(timeframe, drawdown.high_date), "cum_profit"],
+                df_comb.loc[timeframe_to_prev_date(timeframe, drawdown.low_date), "cum_profit"],
+            ],
+            mode="markers",
+            name=f"Max drawdown {drawdown.relative_account_drawdown:.2%}",
+            text=f"Max drawdown {drawdown.relative_account_drawdown:.2%}",
+            marker=dict(symbol="square-open", size=9, line=dict(width=2), color="green"),
+        )
+        fig.add_trace(drawdown, row, 1)
+    except ValueError:
+        logger.warning("No trades found - not plotting max drawdown.")
+    return fig
+
+
+def add_underwater(fig, row, trades: pd.DataFrame, starting_balance: float) -> make_subplots:
+    """
+    Add underwater plots
+    """
+    try:
+        underwater = calculate_underwater(
+            trades, value_col="profit_abs", starting_balance=starting_balance
+        )
+
+        underwater_plot = go.Scatter(
+            x=underwater["date"],
+            y=underwater["drawdown"],
+            name="Underwater Plot",
+            fill="tozeroy",
+            fillcolor="#cc362b",
+            line={"color": "#cc362b"},
+        )
+
+        underwater_plot_relative = go.Scatter(
+            x=underwater["date"],
+            y=(-underwater["drawdown_relative"]),
+            name="Underwater Plot (%)",
+            fill="tozeroy",
+            fillcolor="green",
+            line={"color": "green"},
+        )
+
+        fig.add_trace(underwater_plot, row, 1)
+        fig.add_trace(underwater_plot_relative, row + 1, 1)
+    except ValueError:
+        logger.warning("No trades found - not plotting underwater plot")
+    return fig
+
+
+def add_parallelism(fig, row, trades: pd.DataFrame, timeframe: str) -> make_subplots:
+    """
+    Add Chart showing trade parallelism
+    """
+    try:
+        result = analyze_trade_parallelism(trades, timeframe)
+
+        drawdown = go.Scatter(
+            x=result.index,
+            y=result["open_trades"],
+            name="Parallel trades",
+            fill="tozeroy",
+            fillcolor="#242222",
+            line={"color": "#242222"},
+        )
+        fig.add_trace(drawdown, row, 1)
+    except ValueError:
+        logger.warning("No trades found - not plotting Parallelism.")
+    return fig
+
+
+def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
+    """
+    Add trades to "fig"
+    """
+    # Trades can be empty
+    if trades is not None and len(trades) > 0:
+        # Create description for exit summarizing the trade
+        trades["desc"] = trades.apply(
+            lambda row: f"{row['profit_ratio']:.2%}, "
+            + (f"{row['enter_tag']}, " if row["enter_tag"] is not None else "")
+            + f"{row['exit_reason']}, "
+            + f"{row['trade_duration']} min",
+            axis=1,
+        )
+        trade_entries = go.Scatter(
+            x=trades["open_date"],
+            y=trades["open_rate"],
+            mode="markers",
+            name="Trade entry",
+            text=trades["desc"],
+            marker=dict(symbol="circle-open", size=11, line=dict(width=2), color="cyan"),
+        )
+
+        trade_exits = go.Scatter(
+            x=trades.loc[trades["profit_ratio"] > 0, "close_date"],
+            y=trades.loc[trades["profit_ratio"] > 0, "close_rate"],
+            text=trades.loc[trades["profit_ratio"] > 0, "desc"],
+            mode="markers",
+            name="Exit - Profit",
+            marker=dict(symbol="square-open", size=11, line=dict(width=2), color="green"),
+        )
+        trade_exits_loss = go.Scatter(
+            x=trades.loc[trades["profit_ratio"] <= 0, "close_date"],
+            y=trades.loc[trades["profit_ratio"] <= 0, "close_rate"],
+            text=trades.loc[trades["profit_ratio"] <= 0, "desc"],
+            mode="markers",
+            name="Exit - Loss",
+            marker=dict(symbol="square-open", size=11, line=dict(width=2), color="red"),
+        )
+        fig.add_trace(trade_entries, 1, 1)
+        fig.add_trace(trade_exits, 1, 1)
+        fig.add_trace(trade_exits_loss, 1, 1)
+    else:
+        logger.warning("No trades found.")
+    return fig
+
+
+def create_plotconfig(
+    indicators1: list[str], indicators2: list[str], plot_config: dict[str, dict]
+) -> dict[str, dict]:
+    """
+    Combines indicators 1 and indicators 2 into plot_config if necessary
+    :param indicators1: List containing Main plot indicators
+    :param indicators2: List containing Sub plot indicators
+    :param plot_config: Dict of Dicts containing advanced plot configuration
+    :return: plot_config - eventually with indicators 1 and 2
+    """
+
+    if plot_config:
+        if indicators1:
+            plot_config["main_plot"] = {ind: {} for ind in indicators1}
+        if indicators2:
+            plot_config["subplots"] = {"Other": {ind: {} for ind in indicators2}}
+
+    if not plot_config:
+        # If no indicators and no plot-config given, use defaults.
+        if not indicators1:
+            indicators1 = ["sma", "ema3", "ema5"]
+        if not indicators2:
+            indicators2 = ["macd", "macdsignal"]
+
+        # Create subplot configuration if plot_config is not available.
+        plot_config = {
+            "main_plot": {ind: {} for ind in indicators1},
+            "subplots": {"Other": {ind: {} for ind in indicators2}},
+        }
+    if "main_plot" not in plot_config:
+        plot_config["main_plot"] = {}
+
+    if "subplots" not in plot_config:
+        plot_config["subplots"] = {}
+    return plot_config
+
+
+def plot_area(
+    fig,
+    row: int,
+    data: pd.DataFrame,
+    indicator_a: str,
+    indicator_b: str,
+    label: str = "",
+    fill_color: str = "rgba(0,176,246,0.2)",
+) -> make_subplots:
+    """Creates a plot for the area between two traces and adds it to fig.
+    :param fig: Plot figure to append to
+    :param row: row number for this plot
+    :param data: candlestick DataFrame
+    :param indicator_a: indicator name as populated in strategy
+    :param indicator_b: indicator name as populated in strategy
+    :param label: label for the filled area
+    :param fill_color: color to be used for the filled area
+    :return: fig with added  filled_traces plot
+    """
+    if indicator_a in data and indicator_b in data:
+        # make lines invisible to get the area plotted, only.
+        line = {"color": "rgba(255,255,255,0)"}
+        # TODO: Figure out why scattergl causes problems plotly/plotly.js#2284
+        trace_a = go.Scatter(x=data.date, y=data[indicator_a], showlegend=False, line=line)
+        trace_b = go.Scatter(
+            x=data.date,
+            y=data[indicator_b],
+            name=label,
+            fill="tonexty",
+            fillcolor=fill_color,
+            line=line,
+        )
+        fig.add_trace(trace_a, row, 1)
+        fig.add_trace(trace_b, row, 1)
+    return fig
+
+
+def add_areas(fig, row: int, data: pd.DataFrame, indicators) -> make_subplots:
+    """Adds all area plots (specified in plot_config) to fig.
+    :param fig: Plot figure to append to
+    :param row: row number for this plot
+    :param data: candlestick DataFrame
+    :param indicators: dict with indicators. ie.: plot_config['main_plot'] or
+                            plot_config['subplots'][subplot_label]
+    :return: fig with added  filled_traces plot
+    """
+    for indicator, ind_conf in indicators.items():
+        if "fill_to" in ind_conf:
+            indicator_b = ind_conf["fill_to"]
+            if indicator in data and indicator_b in data:
+                label = ind_conf.get("fill_label", f"{indicator}<>{indicator_b}")
+                fill_color = ind_conf.get("fill_color", "rgba(0,176,246,0.2)")
+                fig = plot_area(
+                    fig, row, data, indicator, indicator_b, label=label, fill_color=fill_color
+                )
+            elif indicator not in data:
+                logger.info(
+                    'Indicator "%s" ignored. Reason: This indicator is not found in your strategy.',
+                    indicator,
+                )
+            elif indicator_b not in data:
+                logger.info(
+                    'fill_to: "%s" ignored. Reason: This indicator is not in your strategy.',
+                    indicator_b,
+                )
+    return fig
+
+
+def create_scatter(data, column_name, color, direction) -> go.Scatter | None:
+    if column_name in data.columns:
+        df_short = data[data[column_name] == 1]
+        if len(df_short) > 0:
+            shorts = go.Scatter(
+                x=df_short.date,
+                y=df_short.close,
+                mode="markers",
+                name=column_name,
+                marker=dict(
+                    symbol=f"triangle-{direction}-dot",
+                    size=9,
+                    line=dict(width=1),
+                    color=color,
+                ),
+            )
+            return shorts
+        else:
+            logger.warning(f"No {column_name}-signals found.")
+
+    return None
+
+
+def generate_candlestick_graph(
+    pair: str,
+    data: pd.DataFrame,
+    trades: pd.DataFrame | None = None,
+    *,
+    indicators1: list[str] | None = None,
+    indicators2: list[str] | None = None,
+    plot_config: dict[str, dict] | None = None,
+) -> go.Figure:
+    """
+    Generate the graph from the data generated by Backtesting or from DB
+    Volume will always be plotted in row2, so Row 1 and 3 are to our disposal for custom indicators
+    :param pair: Pair to Display on the graph
+    :param data: OHLCV DataFrame containing indicators and entry/exit signals
+    :param trades: All trades created
+    :param indicators1: List containing Main plot indicators
+    :param indicators2: List containing Sub plot indicators
+    :param plot_config: Dict of Dicts containing advanced plot configuration
+    :return: Plotly figure
+    """
+    plot_config = create_plotconfig(
+        indicators1 or [],
+        indicators2 or [],
+        plot_config or {},
+    )
+    rows = 2 + len(plot_config["subplots"])
+    row_widths = [1 for _ in plot_config["subplots"]]
+    # Define the graph
+    fig = make_subplots(
+        rows=rows,
+        cols=1,
+        shared_xaxes=True,
+        row_width=[*row_widths, 1, 4],
+        vertical_spacing=0.0001,
+    )
+    fig["layout"].update(title=pair)
+    fig["layout"]["yaxis1"].update(title="Price")
+    fig["layout"]["yaxis2"].update(title="Volume")
+    for i, name in enumerate(plot_config["subplots"]):
+        fig["layout"][f"yaxis{3 + i}"].update(title=name)
+    fig["layout"]["xaxis"]["rangeslider"].update(visible=False)
+    fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
+
+    # Common information
+    candles = go.Candlestick(
+        x=data.date, open=data.open, high=data.high, low=data.low, close=data.close, name="Price"
+    )
+    fig.add_trace(candles, 1, 1)
+
+    longs = create_scatter(data, "enter_long", "green", "up")
+    exit_longs = create_scatter(data, "exit_long", "red", "down")
+    shorts = create_scatter(data, "enter_short", "blue", "down")
+    exit_shorts = create_scatter(data, "exit_short", "violet", "up")
+
+    for scatter in [longs, exit_longs, shorts, exit_shorts]:
+        if scatter:
+            fig.add_trace(scatter, 1, 1)
+
+    # Add Bollinger Bands
+    fig = plot_area(fig, 1, data, "bb_lowerband", "bb_upperband", label="Bollinger Band")
+    # prevent bb_lower and bb_upper from plotting
+    try:
+        del plot_config["main_plot"]["bb_lowerband"]
+        del plot_config["main_plot"]["bb_upperband"]
+    except KeyError:
+        pass
+    # main plot goes to row 1
+    fig = add_indicators(fig=fig, row=1, indicators=plot_config["main_plot"], data=data)
+    fig = add_areas(fig, 1, data, plot_config["main_plot"])
+    fig = plot_trades(fig, trades)
+    # sub plot: Volume goes to row 2
+    volume = go.Bar(
+        x=data["date"],
+        y=data["volume"],
+        name="Volume",
+        marker_color="DarkSlateGrey",
+        marker_line_color="DarkSlateGrey",
+    )
+    fig.add_trace(volume, 2, 1)
+    # add each sub plot to a separate row
+    for i, label in enumerate(plot_config["subplots"]):
+        sub_config = plot_config["subplots"][label]
+        row = 3 + i
+        fig = add_indicators(fig=fig, row=row, indicators=sub_config, data=data)
+        # fill area between indicators ( 'fill_to': 'other_indicator')
+        fig = add_areas(fig, row, data, sub_config)
+
+    return fig
+
+
+def generate_profit_graph(
+    pairs: str,
+    data: dict[str, pd.DataFrame],
+    trades: pd.DataFrame,
+    timeframe: str,
+    stake_currency: str,
+    starting_balance: float,
+) -> go.Figure:
+    # Combine close-values for all pairs, rename columns to "pair"
+    try:
+        df_comb = combine_dataframes_with_mean(data, "close")
+    except ValueError:
+        raise OperationalException(
+            "No data found. Please make sure that data is available for "
+            "the timerange and pairs selected."
+        )
+
+    # Trim trades to available OHLCV data
+    trades = extract_trades_of_period(df_comb, trades, date_index=True)
+    if len(trades) == 0:
+        raise OperationalException("No trades found in selected timerange.")
+
+    # Add combined cumulative profit
+    df_comb = create_cum_profit(df_comb, trades, "cum_profit", timeframe)
+
+    # Plot the pairs average close prices, and total profit growth
+    avgclose = go.Scatter(
+        x=df_comb.index,
+        y=df_comb["mean"],
+        name="Avg close price",
+    )
+
+    fig = make_subplots(
+        rows=6,
+        cols=1,
+        shared_xaxes=True,
+        row_heights=[1, 1, 1, 0.5, 0.75, 0.75],
+        vertical_spacing=0.05,
+        subplot_titles=[
+            "AVG Close Price",
+            "Combined Profit",
+            "Profit per pair",
+            "Parallelism",
+            "Underwater",
+            "Relative Drawdown",
+        ],
+    )
+    fig["layout"].update(title="Freqtrade Profit plot")
+    fig["layout"]["yaxis1"].update(title="Price")
+    fig["layout"]["yaxis2"].update(title=f"Profit {stake_currency}")
+    fig["layout"]["yaxis3"].update(title=f"Profit {stake_currency}")
+    fig["layout"]["yaxis4"].update(title="Trade count")
+    fig["layout"]["yaxis5"].update(title="Underwater Plot")
+    fig["layout"]["yaxis6"].update(title="Underwater Plot Relative (%)", tickformat=",.2%")
+    fig["layout"]["xaxis"]["rangeslider"].update(visible=False)
+    fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
+
+    fig.add_trace(avgclose, 1, 1)
+    fig = add_profit(fig, 2, df_comb, "cum_profit", "Profit")
+    fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe, starting_balance)
+    fig = add_parallelism(fig, 4, trades, timeframe)
+    # Two rows consumed
+    fig = add_underwater(fig, 5, trades, starting_balance)
+
+    for pair in pairs:
+        profit_col = f"cum_profit_{pair}"
+        try:
+            df_comb = create_cum_profit(
+                df_comb, trades[trades["pair"] == pair], profit_col, timeframe
+            )
+            fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
+        except ValueError:
+            pass
+    return fig
+
+
+def generate_plot_filename(pair: str, timeframe: str) -> str:
+    """
+    Generate filenames per pair/timeframe to be used for storing plots
+    """
+    pair_s = pair_to_filename(pair)
+    file_name = "freqtrade-plot-" + pair_s + "-" + timeframe + ".html"
+
+    logger.info("Generate plot file for %s", pair)
+
+    return file_name
+
+
+def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False) -> None:
+    """
+    Generate a plot html file from pre populated fig plotly object
+    :param fig: Plotly Figure to plot
+    :param filename: Name to store the file as
+    :param directory: Directory to store the file in
+    :param auto_open: Automatically open files saved
+    :return: None
+    """
+    directory.mkdir(parents=True, exist_ok=True)
+
+    _filename = directory.joinpath(filename)
+    plot(fig, filename=str(_filename), auto_open=auto_open)
+    logger.info(f"Stored plot as {_filename}")
+
+
+def load_and_plot_trades(config: Config):
+    """
+    From configuration provided
+    - Initializes plot-script
+    - Get candle (OHLCV) data
+    - Generate Dafaframes populated with indicators and signals based on configured strategy
+    - Load trades executed during the selected period
+    - Generate Plotly plot objects
+    - Generate plot files
+    :return: None
+    """
+    strategy = StrategyResolver.load_strategy(config)
+
+    exchange = ExchangeResolver.load_exchange(config)
+    IStrategy.dp = DataProvider(config, exchange)
+    strategy.ft_bot_start()
+    strategy_safe_wrapper(strategy.bot_loop_start)(current_time=datetime.now(timezone.utc))
+    plot_elements = init_plotscript(config, list(exchange.markets), strategy.startup_candle_count)
+    timerange = plot_elements["timerange"]
+    trades = plot_elements["trades"]
+    pair_counter = 0
+    for pair, data in plot_elements["ohlcv"].items():
+        pair_counter += 1
+        logger.info("analyse pair %s", pair)
+
+        df_analyzed = strategy.analyze_ticker(data, {"pair": pair})
+        df_analyzed = trim_dataframe(df_analyzed, timerange)
+        if not trades.empty:
+            trades_pair = trades.loc[trades["pair"] == pair]
+            trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
+        else:
+            trades_pair = trades
+
+        fig = generate_candlestick_graph(
+            pair=pair,
+            data=df_analyzed,
+            trades=trades_pair,
+            indicators1=config.get("indicators1", []),
+            indicators2=config.get("indicators2", []),
+            plot_config=strategy.plot_config if hasattr(strategy, "plot_config") else {},
+        )
+
+        store_plot_file(
+            fig,
+            filename=generate_plot_filename(pair, config["timeframe"]),
+            directory=config["user_data_dir"] / "plot",
+        )
+
+    logger.info("End of plotting process. %s plots generated", pair_counter)
+
+
+def plot_profit(config: Config) -> None:
+    """
+    Plots the total profit for all pairs.
+    Note, the profit calculation isn't realistic.
+    But should be somewhat proportional, and therefore useful
+    in helping out to find a good algorithm.
+    """
+    if "timeframe" not in config:
+        raise OperationalException("Timeframe must be set in either config or via --timeframe.")
+
+    exchange = ExchangeResolver.load_exchange(config)
+    plot_elements = init_plotscript(config, list(exchange.markets))
+    trades = plot_elements["trades"]
+    # Filter trades to relevant pairs
+    # Remove open pairs - we don't know the profit yet so can't calculate profit for these.
+    # Also, If only one open pair is left, then the profit-generation would fail.
+    trades = trades[
+        (trades["pair"].isin(plot_elements["pairs"])) & (~trades["close_date"].isnull())
+    ]
+    if len(trades) == 0:
+        raise OperationalException(
+            "No trades found, cannot generate Profit-plot without "
+            "trades from either Backtest result or database."
+        )
+
+    # Create an average close price of all the pairs that were involved.
+    # this could be useful to gauge the overall market trend
+    fig = generate_profit_graph(
+        plot_elements["pairs"],
+        plot_elements["ohlcv"],
+        trades,
+        config["timeframe"],
+        config.get("stake_currency", ""),
+        config.get("available_capital", get_dry_run_wallet(config)),
+    )
+    store_plot_file(
+        fig,
+        filename="freqtrade-profit-plot.html",
+        directory=config["user_data_dir"] / "plot",
+        auto_open=config.get("plot_auto_open", False),
+    )
diff --git a/freqtrade/plugins/__init__.py b/freqtrade/plugins/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/freqtrade/plugins/pairlist/AgeFilter.py b/freqtrade/plugins/pairlist/AgeFilter.py
new file mode 100644
index 000000000..03ba12770
--- /dev/null
+++ b/freqtrade/plugins/pairlist/AgeFilter.py
@@ -0,0 +1,166 @@
+"""
+Minimum age (days listed) pair list filter
+"""
+
+import logging
+from copy import deepcopy
+from datetime import timedelta
+
+from pandas import DataFrame
+
+from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.misc import plural
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+from freqtrade.util import PeriodicCache, dt_floor_day, dt_now, dt_ts
+
+
+logger = logging.getLogger(__name__)
+
+
+class AgeFilter(IPairList):
+    supports_backtesting = SupportsBacktesting.NO
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        # Checked symbols cache (dictionary of ticker symbol => timestamp)
+        self._symbolsChecked: dict[str, int] = {}
+        self._symbolsCheckFailed = PeriodicCache(maxsize=1000, ttl=86_400)
+
+        self._min_days_listed = self._pairlistconfig.get("min_days_listed", 10)
+        self._max_days_listed = self._pairlistconfig.get("max_days_listed")
+
+        candle_limit = self._exchange.ohlcv_candle_limit("1d", self._config["candle_type_def"])
+        if self._min_days_listed < 1:
+            raise OperationalException("AgeFilter requires min_days_listed to be >= 1")
+        if self._min_days_listed > candle_limit:
+            raise OperationalException(
+                "AgeFilter requires min_days_listed to not exceed "
+                "exchange max request size "
+                f"({candle_limit})"
+            )
+        if self._max_days_listed and self._max_days_listed <= self._min_days_listed:
+            raise OperationalException("AgeFilter max_days_listed <= min_days_listed not permitted")
+        if self._max_days_listed and self._max_days_listed > candle_limit:
+            raise OperationalException(
+                "AgeFilter requires max_days_listed to not exceed "
+                "exchange max request size "
+                f"({candle_limit})"
+            )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return (
+            f"{self.name} - Filtering pairs with age less than "
+            f"{self._min_days_listed} {plural(self._min_days_listed, 'day')}"
+        ) + (
+            (" or more than {self._max_days_listed} {plural(self._max_days_listed, 'day')}")
+            if self._max_days_listed
+            else ""
+        )
+
+    @staticmethod
+    def description() -> str:
+        return "Filter pairs by age (days listed)."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "min_days_listed": {
+                "type": "number",
+                "default": 10,
+                "description": "Minimum Days Listed",
+                "help": "Minimum number of days a pair must have been listed on the exchange.",
+            },
+            "max_days_listed": {
+                "type": "number",
+                "default": None,
+                "description": "Maximum Days Listed",
+                "help": "Maximum number of days a pair must have been listed on the exchange.",
+            },
+        }
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new allowlist
+        """
+        needed_pairs: ListPairsWithTimeframes = [
+            (p, "1d", self._config["candle_type_def"])
+            for p in pairlist
+            if p not in self._symbolsChecked and p not in self._symbolsCheckFailed
+        ]
+        if not needed_pairs:
+            # Remove pairs that have been removed before
+            return [p for p in pairlist if p not in self._symbolsCheckFailed]
+
+        since_days = (
+            -(self._max_days_listed if self._max_days_listed else self._min_days_listed) - 1
+        )
+        since_ms = dt_ts(dt_floor_day(dt_now()) + timedelta(days=since_days))
+        candles = self._exchange.refresh_latest_ohlcv(needed_pairs, since_ms=since_ms, cache=False)
+        if self._enabled:
+            for p in deepcopy(pairlist):
+                daily_candles = (
+                    candles[(p, "1d", self._config["candle_type_def"])]
+                    if (p, "1d", self._config["candle_type_def"]) in candles
+                    else None
+                )
+                if not self._validate_pair_loc(p, daily_candles):
+                    pairlist.remove(p)
+        self.log_once(f"Validated {len(pairlist)} pairs.", logger.info)
+        return pairlist
+
+    def _validate_pair_loc(self, pair: str, daily_candles: DataFrame | None) -> bool:
+        """
+        Validate age for the ticker
+        :param pair: Pair that's currently validated
+        :param daily_candles: Downloaded daily candles
+        :return: True if the pair can stay, false if it should be removed
+        """
+        # Check symbol in cache
+        if pair in self._symbolsChecked:
+            return True
+
+        if daily_candles is not None:
+            if len(daily_candles) >= self._min_days_listed and (
+                not self._max_days_listed or len(daily_candles) <= self._max_days_listed
+            ):
+                # We have fetched at least the minimum required number of daily candles
+                # Add to cache, store the time we last checked this symbol
+                self._symbolsChecked[pair] = dt_ts()
+                return True
+            else:
+                self.log_once(
+                    (
+                        f"Removed {pair} from whitelist, because age "
+                        f"{len(daily_candles)} is less than {self._min_days_listed} "
+                        f"{plural(self._min_days_listed, 'day')}"
+                    )
+                    + (
+                        (
+                            " or more than "
+                            f"{self._max_days_listed} {plural(self._max_days_listed, 'day')}"
+                        )
+                        if self._max_days_listed
+                        else ""
+                    ),
+                    logger.info,
+                )
+                self._symbolsCheckFailed[pair] = dt_ts()
+                return False
+        return False
diff --git a/freqtrade/plugins/pairlist/FullTradesFilter.py b/freqtrade/plugins/pairlist/FullTradesFilter.py
new file mode 100644
index 000000000..c9eecdb49
--- /dev/null
+++ b/freqtrade/plugins/pairlist/FullTradesFilter.py
@@ -0,0 +1,52 @@
+"""
+Full trade slots pair list filter
+"""
+
+import logging
+
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.persistence import Trade
+from freqtrade.plugins.pairlist.IPairList import IPairList, SupportsBacktesting
+
+
+logger = logging.getLogger(__name__)
+
+
+class FullTradesFilter(IPairList):
+    supports_backtesting = SupportsBacktesting.NO_ACTION
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short allowlist method description - used for startup-messages
+        """
+        return f"{self.name} - Shrink whitelist when trade slots are full."
+
+    @staticmethod
+    def description() -> str:
+        return "Shrink whitelist when trade slots are full."
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the allowlist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new allowlist
+        """
+        # Get the number of open trades and max open trades config
+        num_open = Trade.get_open_trade_count()
+        max_trades = self._config["max_open_trades"]
+
+        if (num_open >= max_trades) and (max_trades > 0):
+            return []
+
+        return pairlist
diff --git a/freqtrade/plugins/pairlist/IPairList.py b/freqtrade/plugins/pairlist/IPairList.py
new file mode 100644
index 000000000..89a9e68cd
--- /dev/null
+++ b/freqtrade/plugins/pairlist/IPairList.py
@@ -0,0 +1,281 @@
+"""
+PairList Handler base class
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from copy import deepcopy
+from enum import Enum
+from typing import Any, Literal, TypedDict
+
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import Exchange, market_is_active
+from freqtrade.exchange.exchange_types import Ticker, Tickers
+from freqtrade.mixins import LoggingMixin
+
+
+logger = logging.getLogger(__name__)
+
+
+class __PairlistParameterBase(TypedDict):
+    description: str
+    help: str
+
+
+class __NumberPairlistParameter(__PairlistParameterBase):
+    type: Literal["number"]
+    default: int | float | None
+
+
+class __StringPairlistParameter(__PairlistParameterBase):
+    type: Literal["string"]
+    default: str | None
+
+
+class __OptionPairlistParameter(__PairlistParameterBase):
+    type: Literal["option"]
+    default: str | None
+    options: list[str]
+
+
+class __ListPairListParamenter(__PairlistParameterBase):
+    type: Literal["list"]
+    default: list[str] | None
+
+
+class __BoolPairlistParameter(__PairlistParameterBase):
+    type: Literal["boolean"]
+    default: bool | None
+
+
+PairlistParameter = (
+    __NumberPairlistParameter
+    | __StringPairlistParameter
+    | __OptionPairlistParameter
+    | __BoolPairlistParameter
+    | __ListPairListParamenter
+)
+
+
+class SupportsBacktesting(str, Enum):
+    """
+    Enum to indicate if a Pairlist Handler supports backtesting.
+    """
+
+    YES = "yes"
+    NO = "no"
+    NO_ACTION = "no_action"
+    BIASED = "biased"
+
+
+class IPairList(LoggingMixin, ABC):
+    is_pairlist_generator = False
+    supports_backtesting: SupportsBacktesting = SupportsBacktesting.NO
+
+    def __init__(
+        self,
+        exchange: Exchange,
+        pairlistmanager,
+        config: Config,
+        pairlistconfig: dict[str, Any],
+        pairlist_pos: int,
+    ) -> None:
+        """
+        :param exchange: Exchange instance
+        :param pairlistmanager: Instantiated Pairlist manager
+        :param config: Global bot configuration
+        :param pairlistconfig: Configuration for this Pairlist Handler - can be empty.
+        :param pairlist_pos: Position of the Pairlist Handler in the chain
+        """
+        self._enabled = True
+
+        self._exchange: Exchange = exchange
+        self._pairlistmanager = pairlistmanager
+        self._config = config
+        self._pairlistconfig = pairlistconfig
+        self._pairlist_pos = pairlist_pos
+        self.refresh_period = self._pairlistconfig.get("refresh_period", 1800)
+        LoggingMixin.__init__(self, logger, self.refresh_period)
+
+    @property
+    def name(self) -> str:
+        """
+        Gets name of the class
+        -> no need to overwrite in subclasses
+        """
+        return self.__class__.__name__
+
+    @property
+    @abstractmethod
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    @staticmethod
+    @abstractmethod
+    def description() -> str:
+        """
+        Return description of this Pairlist Handler
+        -> Please overwrite in subclasses
+        """
+        return ""
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        """
+        Return parameters used by this Pairlist Handler, and their type
+        contains a dictionary with the parameter name as key, and a dictionary
+        with the type and default value.
+        -> Please overwrite in subclasses
+        """
+        return {}
+
+    @staticmethod
+    def refresh_period_parameter() -> dict[str, PairlistParameter]:
+        return {
+            "refresh_period": {
+                "type": "number",
+                "default": 1800,
+                "description": "Refresh period",
+                "help": "Refresh period in seconds",
+            }
+        }
+
+    @abstractmethod
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        -> Please overwrite in subclasses
+        """
+
+    def _validate_pair(self, pair: str, ticker: Ticker | None) -> bool:
+        """
+        Check one pair against Pairlist Handler's specific conditions.
+
+        Either implement it in the Pairlist Handler or override the generic
+        filter_pairlist() method.
+
+        :param pair: Pair that's currently validated
+        :param ticker: ticker dict as returned from ccxt.fetch_ticker
+        :return: True if the pair can stay, false if it should be removed
+        """
+        raise NotImplementedError()
+
+    def gen_pairlist(self, tickers: Tickers) -> list[str]:
+        """
+        Generate the pairlist.
+
+        This method is called once by the pairlistmanager in the refresh_pairlist()
+        method to supply the starting pairlist for the chain of the Pairlist Handlers.
+        Pairlist Filters (those Pairlist Handlers that cannot be used at the first
+        position in the chain) shall not override this base implementation --
+        it will raise the exception if a Pairlist Handler is used at the first
+        position in the chain.
+
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: List of pairs
+        """
+        raise OperationalException(
+            "This Pairlist Handler should not be used "
+            "at the first position in the list of Pairlist Handlers."
+        )
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+
+        Called on each bot iteration - please use internal caching if necessary
+        This generic implementation calls self._validate_pair() for each pair
+        in the pairlist.
+
+        Some Pairlist Handlers override this generic implementation and employ
+        own filtration.
+
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        if self._enabled:
+            # Copy list since we're modifying this list
+            for p in deepcopy(pairlist):
+                # Filter out assets
+                if not self._validate_pair(p, tickers[p] if p in tickers else None):
+                    pairlist.remove(p)
+
+        return pairlist
+
+    def verify_blacklist(self, pairlist: list[str], logmethod) -> list[str]:
+        """
+        Proxy method to verify_blacklist for easy access for child classes.
+        :param pairlist: Pairlist to validate
+        :param logmethod: Function that'll be called, `logger.info` or `logger.warning`.
+        :return: pairlist - blacklisted pairs
+        """
+        return self._pairlistmanager.verify_blacklist(pairlist, logmethod)
+
+    def verify_whitelist(
+        self, pairlist: list[str], logmethod, keep_invalid: bool = False
+    ) -> list[str]:
+        """
+        Proxy method to verify_whitelist for easy access for child classes.
+        :param pairlist: Pairlist to validate
+        :param logmethod: Function that'll be called, `logger.info` or `logger.warning`
+        :param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes.
+        :return: pairlist - whitelisted pairs
+        """
+        return self._pairlistmanager.verify_whitelist(pairlist, logmethod, keep_invalid)
+
+    def _whitelist_for_active_markets(self, pairlist: list[str]) -> list[str]:
+        """
+        Check available markets and remove pair from whitelist if necessary
+        :param pairlist: the sorted list of pairs the user might want to trade
+        :return: the list of pairs the user wants to trade without those unavailable or
+        black_listed
+        """
+        markets = self._exchange.markets
+        if not markets:
+            raise OperationalException(
+                "Markets not loaded. Make sure that exchange is initialized correctly."
+            )
+
+        sanitized_whitelist: list[str] = []
+        for pair in pairlist:
+            # pair is not in the generated dynamic market or has the wrong stake currency
+            if pair not in markets:
+                self.log_once(
+                    f"Pair {pair} is not compatible with exchange "
+                    f"{self._exchange.name}. Removing it from whitelist..",
+                    logger.warning,
+                )
+                continue
+
+            if not self._exchange.market_is_tradable(markets[pair]):
+                self.log_once(
+                    f"Pair {pair} is not tradable with Freqtrade. Removing it from whitelist..",
+                    logger.warning,
+                )
+                continue
+
+            if self._exchange.get_pair_quote_currency(pair) != self._config["stake_currency"]:
+                self.log_once(
+                    f"Pair {pair} is not compatible with your stake currency "
+                    f"{self._config['stake_currency']}. Removing it from whitelist..",
+                    logger.warning,
+                )
+                continue
+
+            # Check if market is active
+            market = markets[pair]
+            if not market_is_active(market):
+                self.log_once(f"Ignoring {pair} from whitelist. Market is not active.", logger.info)
+                continue
+            if pair not in sanitized_whitelist:
+                sanitized_whitelist.append(pair)
+
+        # We need to remove pairs that are unknown
+        return sanitized_whitelist
diff --git a/freqtrade/plugins/pairlist/MarketCapPairList.py b/freqtrade/plugins/pairlist/MarketCapPairList.py
new file mode 100644
index 000000000..b95cec50f
--- /dev/null
+++ b/freqtrade/plugins/pairlist/MarketCapPairList.py
@@ -0,0 +1,212 @@
+"""
+Market Cap PairList provider
+
+Provides dynamic pair list based on Market Cap
+"""
+
+import logging
+import math
+
+from cachetools import TTLCache
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+from freqtrade.util.coin_gecko import FtCoinGeckoApi
+
+
+logger = logging.getLogger(__name__)
+
+
+class MarketCapPairList(IPairList):
+    is_pairlist_generator = True
+    supports_backtesting = SupportsBacktesting.BIASED
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        if "number_assets" not in self._pairlistconfig:
+            raise OperationalException(
+                "`number_assets` not specified. Please check your configuration "
+                'for "pairlist.config.number_assets"'
+            )
+
+        self._stake_currency = self._config["stake_currency"]
+        self._number_assets = self._pairlistconfig["number_assets"]
+        self._max_rank = self._pairlistconfig.get("max_rank", 30)
+        self._refresh_period = self._pairlistconfig.get("refresh_period", 86400)
+        self._categories = self._pairlistconfig.get("categories", [])
+        self._marketcap_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
+        self._def_candletype = self._config["candle_type_def"]
+
+        _coingecko_config = self._config.get("coingecko", {})
+
+        self._coingecko: FtCoinGeckoApi = FtCoinGeckoApi(
+            api_key=_coingecko_config.get("api_key", ""),
+            is_demo=_coingecko_config.get("is_demo", True),
+        )
+
+        if self._categories:
+            categories = self._coingecko.get_coins_categories_list()
+            category_ids = [cat["category_id"] for cat in categories]
+
+            for category in self._categories:
+                if category not in category_ids:
+                    raise OperationalException(
+                        f"Category {category} not in coingecko category list. "
+                        f"You can choose from {category_ids}"
+                    )
+
+        if self._max_rank > 250:
+            self.logger.warning(
+                f"The max rank you have set ({self._max_rank}) is quite high. "
+                "This may lead to coingecko API rate limit issues. "
+                "Please ensure this value is necessary for your use case.",
+            )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        num = self._number_assets
+        rank = self._max_rank
+        msg = f"{self.name} - {num} pairs placed within top {rank} market cap."
+        return msg
+
+    @staticmethod
+    def description() -> str:
+        return "Provides pair list based on CoinGecko's market cap rank."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "number_assets": {
+                "type": "number",
+                "default": 30,
+                "description": "Number of assets",
+                "help": "Number of assets to use from the pairlist",
+            },
+            "max_rank": {
+                "type": "number",
+                "default": 30,
+                "description": "Max rank of assets",
+                "help": "Maximum rank of assets to use from the pairlist",
+            },
+            "categories": {
+                "type": "list",
+                "default": [],
+                "description": "Coin Categories",
+                "help": (
+                    "The Category of the coin e.g layer-1 default [] "
+                    "(https://www.coingecko.com/en/categories)"
+                ),
+            },
+            "refresh_period": {
+                "type": "number",
+                "default": 86400,
+                "description": "Refresh period",
+                "help": "Refresh period in seconds",
+            },
+        }
+
+    def gen_pairlist(self, tickers: Tickers) -> list[str]:
+        """
+        Generate the pairlist
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: List of pairs
+        """
+        # Generate dynamic whitelist
+        # Must always run if this pairlist is the first in the list.
+        pairlist = self._marketcap_cache.get("pairlist_mc")
+        if pairlist:
+            # Item found - no refresh necessary
+            return pairlist.copy()
+        else:
+            # Use fresh pairlist
+            # Check if pair quote currency equals to the stake currency.
+            _pairlist = [
+                k
+                for k in self._exchange.get_markets(
+                    quote_currencies=[self._stake_currency], tradable_only=True, active_only=True
+                ).keys()
+            ]
+            # No point in testing for blacklisted pairs...
+            _pairlist = self.verify_blacklist(_pairlist, logger.info)
+
+            pairlist = self.filter_pairlist(_pairlist, tickers)
+            self._marketcap_cache["pairlist_mc"] = pairlist.copy()
+
+        return pairlist
+
+    def filter_pairlist(self, pairlist: list[str], tickers: dict) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        marketcap_list = self._marketcap_cache.get("marketcap")
+
+        default_kwargs = {
+            "vs_currency": "usd",
+            "order": "market_cap_desc",
+            "per_page": "250",
+            "page": "1",
+            "sparkline": "false",
+            "locale": "en",
+        }
+
+        if marketcap_list is None:
+            data = []
+
+            if not self._categories:
+                pages_required = math.ceil(self._max_rank / 250)
+                for page in range(1, pages_required + 1):
+                    default_kwargs["page"] = str(page)
+                    page_data = self._coingecko.get_coins_markets(**default_kwargs)
+                    data.extend(page_data)
+            else:
+                for category in self._categories:
+                    category_data = self._coingecko.get_coins_markets(
+                        **default_kwargs, **({"category": category} if category else {})
+                    )
+                    data += category_data
+
+            data.sort(key=lambda d: float(d.get("market_cap") or 0.0), reverse=True)
+
+            if data:
+                marketcap_list = [row["symbol"] for row in data]
+                self._marketcap_cache["marketcap"] = marketcap_list
+
+        if marketcap_list:
+            filtered_pairlist = []
+
+            market = self._config["trading_mode"]
+            pair_format = f"{self._stake_currency.upper()}"
+            if market == "futures":
+                pair_format += f":{self._stake_currency.upper()}"
+
+            top_marketcap = marketcap_list[: self._max_rank :]
+
+            for mc_pair in top_marketcap:
+                test_pair = f"{mc_pair.upper()}/{pair_format}"
+                if test_pair in pairlist and test_pair not in filtered_pairlist:
+                    filtered_pairlist.append(test_pair)
+                    if len(filtered_pairlist) == self._number_assets:
+                        break
+
+            if len(filtered_pairlist) > 0:
+                return filtered_pairlist
+
+        # If no pairs are found, return the original pairlist
+        return []
diff --git a/freqtrade/plugins/pairlist/OffsetFilter.py b/freqtrade/plugins/pairlist/OffsetFilter.py
new file mode 100644
index 000000000..f6e5df063
--- /dev/null
+++ b/freqtrade/plugins/pairlist/OffsetFilter.py
@@ -0,0 +1,82 @@
+"""
+Offset pair list filter
+"""
+
+import logging
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+
+
+logger = logging.getLogger(__name__)
+
+
+class OffsetFilter(IPairList):
+    supports_backtesting = SupportsBacktesting.YES
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._offset = self._pairlistconfig.get("offset", 0)
+        self._number_pairs = self._pairlistconfig.get("number_assets", 0)
+
+        if self._offset < 0:
+            raise OperationalException("OffsetFilter requires offset to be >= 0")
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        if self._number_pairs:
+            return f"{self.name} - Taking {self._number_pairs} Pairs, starting from {self._offset}."
+        return f"{self.name} - Offsetting pairs by {self._offset}."
+
+    @staticmethod
+    def description() -> str:
+        return "Offset pair list filter."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "offset": {
+                "type": "number",
+                "default": 0,
+                "description": "Offset",
+                "help": "Offset of the pairlist.",
+            },
+            "number_assets": {
+                "type": "number",
+                "default": 0,
+                "description": "Number of assets",
+                "help": "Number of assets to use from the pairlist, starting from offset.",
+            },
+        }
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        if self._offset > len(pairlist):
+            self.log_once(
+                f"Offset of {self._offset} is larger than " + f"pair count of {len(pairlist)}",
+                logger.warning,
+            )
+        pairs = pairlist[self._offset :]
+        if self._number_pairs:
+            pairs = pairs[: self._number_pairs]
+
+        return pairs
diff --git a/freqtrade/plugins/pairlist/PercentChangePairList.py b/freqtrade/plugins/pairlist/PercentChangePairList.py
new file mode 100644
index 000000000..4f91a47d1
--- /dev/null
+++ b/freqtrade/plugins/pairlist/PercentChangePairList.py
@@ -0,0 +1,345 @@
+"""
+Percent Change PairList provider
+
+Provides dynamic pair list based on trade change
+sorted based on percentage change in price over a
+defined period or as coming from ticker
+"""
+
+import logging
+from datetime import timedelta
+from typing import TypedDict
+
+from cachetools import TTLCache
+from pandas import DataFrame
+
+from freqtrade.constants import ListPairsWithTimeframes, PairWithTimeframe
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
+from freqtrade.exchange.exchange_types import Ticker, Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+from freqtrade.util import dt_now, format_ms_time
+
+
+logger = logging.getLogger(__name__)
+
+
+class SymbolWithPercentage(TypedDict):
+    symbol: str
+    percentage: float | None
+
+
+class PercentChangePairList(IPairList):
+    is_pairlist_generator = True
+    supports_backtesting = SupportsBacktesting.NO
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        if "number_assets" not in self._pairlistconfig:
+            raise OperationalException(
+                "`number_assets` not specified. Please check your configuration "
+                'for "pairlist.config.number_assets"'
+            )
+
+        self._stake_currency = self._config["stake_currency"]
+        self._number_pairs = self._pairlistconfig["number_assets"]
+        self._min_value = self._pairlistconfig.get("min_value", None)
+        self._max_value = self._pairlistconfig.get("max_value", None)
+        self._refresh_period = self._pairlistconfig.get("refresh_period", 1800)
+        self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
+        self._lookback_days = self._pairlistconfig.get("lookback_days", 0)
+        self._lookback_timeframe = self._pairlistconfig.get("lookback_timeframe", "1d")
+        self._lookback_period = self._pairlistconfig.get("lookback_period", 0)
+        self._sort_direction: str | None = self._pairlistconfig.get("sort_direction", "desc")
+        self._def_candletype = self._config["candle_type_def"]
+
+        if (self._lookback_days > 0) & (self._lookback_period > 0):
+            raise OperationalException(
+                "Ambiguous configuration: lookback_days and lookback_period both set in pairlist "
+                "config. Please set lookback_days only or lookback_period and lookback_timeframe "
+                "and restart the bot."
+            )
+
+        # overwrite lookback timeframe and days when lookback_days is set
+        if self._lookback_days > 0:
+            self._lookback_timeframe = "1d"
+            self._lookback_period = self._lookback_days
+
+        # get timeframe in minutes and seconds
+        self._tf_in_min = timeframe_to_minutes(self._lookback_timeframe)
+        _tf_in_sec = self._tf_in_min * 60
+
+        # whether to use range lookback or not
+        self._use_range = (self._tf_in_min > 0) & (self._lookback_period > 0)
+
+        if self._use_range & (self._refresh_period < _tf_in_sec):
+            raise OperationalException(
+                f"Refresh period of {self._refresh_period} seconds is smaller than one "
+                f"timeframe of {self._lookback_timeframe}. Please adjust refresh_period "
+                f"to at least {_tf_in_sec} and restart the bot."
+            )
+
+        if not self._use_range and not (
+            self._exchange.exchange_has("fetchTickers")
+            and self._exchange.get_option("tickers_have_percentage")
+        ):
+            raise OperationalException(
+                "Exchange does not support dynamic whitelist in this configuration. "
+                "Please edit your config and either remove PercentChangePairList, "
+                "or switch to using candles. and restart the bot."
+            )
+
+        candle_limit = self._exchange.ohlcv_candle_limit(
+            self._lookback_timeframe, self._config["candle_type_def"]
+        )
+
+        if self._lookback_period > candle_limit:
+            raise OperationalException(
+                "ChangeFilter requires lookback_period to not "
+                f"exceed exchange max request size ({candle_limit})"
+            )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return not self._use_range
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return f"{self.name} - top {self._pairlistconfig['number_assets']} percent change pairs."
+
+    @staticmethod
+    def description() -> str:
+        return "Provides dynamic pair list based on percentage change."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "number_assets": {
+                "type": "number",
+                "default": 30,
+                "description": "Number of assets",
+                "help": "Number of assets to use from the pairlist",
+            },
+            "min_value": {
+                "type": "number",
+                "default": None,
+                "description": "Minimum value",
+                "help": "Minimum value to use for filtering the pairlist.",
+            },
+            "max_value": {
+                "type": "number",
+                "default": None,
+                "description": "Maximum value",
+                "help": "Maximum value to use for filtering the pairlist.",
+            },
+            "sort_direction": {
+                "type": "option",
+                "default": "desc",
+                "options": ["", "asc", "desc"],
+                "description": "Sort pairlist",
+                "help": "Sort Pairlist ascending or descending by rate of change.",
+            },
+            **IPairList.refresh_period_parameter(),
+            "lookback_days": {
+                "type": "number",
+                "default": 0,
+                "description": "Lookback Days",
+                "help": "Number of days to look back at.",
+            },
+            "lookback_timeframe": {
+                "type": "string",
+                "default": "1d",
+                "description": "Lookback Timeframe",
+                "help": "Timeframe to use for lookback.",
+            },
+            "lookback_period": {
+                "type": "number",
+                "default": 0,
+                "description": "Lookback Period",
+                "help": "Number of periods to look back at.",
+            },
+        }
+
+    def gen_pairlist(self, tickers: Tickers) -> list[str]:
+        """
+        Generate the pairlist
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: List of pairs
+        """
+        pairlist = self._pair_cache.get("pairlist")
+        if pairlist:
+            # Item found - no refresh necessary
+            return pairlist.copy()
+        else:
+            # Use fresh pairlist
+            # Check if pair quote currency equals to the stake currency.
+            _pairlist = [
+                k
+                for k in self._exchange.get_markets(
+                    quote_currencies=[self._stake_currency], tradable_only=True, active_only=True
+                ).keys()
+            ]
+
+            # No point in testing for blacklisted pairs...
+            _pairlist = self.verify_blacklist(_pairlist, logger.info)
+            if not self._use_range:
+                filtered_tickers = [
+                    v
+                    for k, v in tickers.items()
+                    if (
+                        self._exchange.get_pair_quote_currency(k) == self._stake_currency
+                        and v["symbol"] in _pairlist
+                    )
+                ]
+                pairlist = [s["symbol"] for s in filtered_tickers]
+            else:
+                pairlist = _pairlist
+
+            pairlist = self.filter_pairlist(pairlist, tickers)
+            self._pair_cache["pairlist"] = pairlist.copy()
+
+        return pairlist
+
+    def filter_pairlist(self, pairlist: list[str], tickers: dict) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        filtered_tickers: list[SymbolWithPercentage] = [
+            {"symbol": k, "percentage": None} for k in pairlist
+        ]
+        if self._use_range:
+            # calculating using lookback_period
+            filtered_tickers = self.fetch_percent_change_from_lookback_period(filtered_tickers)
+        else:
+            # Fetching 24h change by default from supported exchange tickers
+            filtered_tickers = self.fetch_percent_change_from_tickers(filtered_tickers, tickers)
+
+        if self._min_value is not None:
+            filtered_tickers = [v for v in filtered_tickers if v["percentage"] > self._min_value]
+        if self._max_value is not None:
+            filtered_tickers = [v for v in filtered_tickers if v["percentage"] < self._max_value]
+
+        sorted_tickers = sorted(
+            filtered_tickers,
+            reverse=self._sort_direction == "desc",
+            key=lambda t: t["percentage"],  # type: ignore
+        )
+
+        # Validate whitelist to only have active market pairs
+        pairs = self._whitelist_for_active_markets([s["symbol"] for s in sorted_tickers])
+        pairs = self.verify_blacklist(pairs, logmethod=logger.info)
+        # Limit pairlist to the requested number of pairs
+        pairs = pairs[: self._number_pairs]
+
+        return pairs
+
+    def fetch_candles_for_lookback_period(
+        self, filtered_tickers: list[SymbolWithPercentage]
+    ) -> dict[PairWithTimeframe, DataFrame]:
+        since_ms = (
+            int(
+                timeframe_to_prev_date(
+                    self._lookback_timeframe,
+                    dt_now()
+                    + timedelta(
+                        minutes=-(self._lookback_period * self._tf_in_min) - self._tf_in_min
+                    ),
+                ).timestamp()
+            )
+            * 1000
+        )
+        to_ms = (
+            int(
+                timeframe_to_prev_date(
+                    self._lookback_timeframe, dt_now() - timedelta(minutes=self._tf_in_min)
+                ).timestamp()
+            )
+            * 1000
+        )
+        self.log_once(
+            f"Using change range of {self._lookback_period} candles, timeframe: "
+            f"{self._lookback_timeframe}, starting from {format_ms_time(since_ms)} "
+            f"till {format_ms_time(to_ms)}",
+            logger.info,
+        )
+        needed_pairs: ListPairsWithTimeframes = [
+            (p, self._lookback_timeframe, self._def_candletype)
+            for p in [s["symbol"] for s in filtered_tickers]
+            if p not in self._pair_cache
+        ]
+        candles = self._exchange.refresh_ohlcv_with_cache(needed_pairs, since_ms)
+        return candles
+
+    def fetch_percent_change_from_lookback_period(
+        self, filtered_tickers: list[SymbolWithPercentage]
+    ) -> list[SymbolWithPercentage]:
+        # get lookback period in ms, for exchange ohlcv fetch
+        candles = self.fetch_candles_for_lookback_period(filtered_tickers)
+
+        for i, p in enumerate(filtered_tickers):
+            pair_candles = (
+                candles[(p["symbol"], self._lookback_timeframe, self._def_candletype)]
+                if (p["symbol"], self._lookback_timeframe, self._def_candletype) in candles
+                else None
+            )
+
+            # in case of candle data calculate typical price and change for candle
+            if pair_candles is not None and not pair_candles.empty:
+                current_close = pair_candles["close"].iloc[-1]
+                previous_close = pair_candles["close"].shift(self._lookback_period).iloc[-1]
+                pct_change = (
+                    ((current_close - previous_close) / previous_close) * 100
+                    if previous_close > 0
+                    else 0
+                )
+
+                # replace change with a range change sum calculated above
+                filtered_tickers[i]["percentage"] = pct_change
+            else:
+                filtered_tickers[i]["percentage"] = 0
+        return filtered_tickers
+
+    def fetch_percent_change_from_tickers(
+        self, filtered_tickers: list[SymbolWithPercentage], tickers
+    ) -> list[SymbolWithPercentage]:
+        valid_tickers: list[SymbolWithPercentage] = []
+        for p in filtered_tickers:
+            # Filter out assets
+            if (
+                self._validate_pair(
+                    p["symbol"], tickers[p["symbol"]] if p["symbol"] in tickers else None
+                )
+                and p["symbol"] != "UNI/USDT"
+            ):
+                p["percentage"] = tickers[p["symbol"]]["percentage"]
+                valid_tickers.append(p)
+        return valid_tickers
+
+    def _validate_pair(self, pair: str, ticker: Ticker | None) -> bool:
+        """
+        Check if one price-step (pip) is > than a certain barrier.
+        :param pair: Pair that's currently validated
+        :param ticker: ticker dict as returned from ccxt.fetch_ticker
+        :return: True if the pair can stay, false if it should be removed
+        """
+        if not ticker or "percentage" not in ticker or ticker["percentage"] is None:
+            self.log_once(
+                f"Removed {pair} from whitelist, because "
+                "ticker['percentage'] is empty (Usually no trade in the last 24h).",
+                logger.info,
+            )
+            return False
+
+        return True
diff --git a/freqtrade/plugins/pairlist/PerformanceFilter.py b/freqtrade/plugins/pairlist/PerformanceFilter.py
new file mode 100644
index 000000000..6e7dfe3c3
--- /dev/null
+++ b/freqtrade/plugins/pairlist/PerformanceFilter.py
@@ -0,0 +1,111 @@
+"""
+Performance pair list filter
+"""
+
+import logging
+from datetime import timedelta
+
+import pandas as pd
+
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.persistence import Trade
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+from freqtrade.util.datetime_helpers import dt_now
+
+
+logger = logging.getLogger(__name__)
+
+
+class PerformanceFilter(IPairList):
+    supports_backtesting = SupportsBacktesting.NO_ACTION
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._minutes = self._pairlistconfig.get("minutes", 0)
+        self._min_profit = self._pairlistconfig.get("min_profit")
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short allowlist method description - used for startup-messages
+        """
+        return f"{self.name} - Sorting pairs by performance."
+
+    @staticmethod
+    def description() -> str:
+        return "Filter pairs by performance."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "minutes": {
+                "type": "number",
+                "default": 0,
+                "description": "Minutes",
+                "help": "Consider trades from the last X minutes. 0 means all trades.",
+            },
+            "min_profit": {
+                "type": "number",
+                "default": None,
+                "description": "Minimum profit",
+                "help": "Minimum profit in percent. Pairs with less profit are removed.",
+            },
+        }
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the allowlist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new allowlist
+        """
+        # Get the trading performance for pairs from database
+        try:
+            start_date = dt_now() - timedelta(minutes=self._minutes)
+            performance = pd.DataFrame(Trade.get_overall_performance(start_date))
+        except AttributeError:
+            # Performancefilter does not work in backtesting.
+            self.log_once("PerformanceFilter is not available in this mode.", logger.warning)
+            return pairlist
+
+        # Skip performance-based sorting if no performance data is available
+        if len(performance) == 0:
+            return pairlist
+
+        # Get pairlist from performance dataframe values
+        list_df = pd.DataFrame({"pair": pairlist})
+        list_df["prior_idx"] = list_df.index
+
+        # Set initial value for pairs with no trades to 0
+        # Sort the list using:
+        #  - primarily performance (high to low)
+        #  - then count (low to high, so as to favor same performance with fewer trades)
+        #  - then by prior index, keeping original sorting order
+        sorted_df = (
+            list_df.merge(performance, on="pair", how="left")
+            .fillna(0)
+            .sort_values(by=["profit_ratio", "count", "prior_idx"], ascending=[False, True, True])
+        )
+        if self._min_profit is not None:
+            removed = sorted_df[sorted_df["profit_ratio"] < self._min_profit]
+            for _, row in removed.iterrows():
+                self.log_once(
+                    f"Removing pair {row['pair']} since {row['profit_ratio']} is "
+                    f"below {self._min_profit}",
+                    logger.info,
+                )
+            sorted_df = sorted_df[sorted_df["profit_ratio"] >= self._min_profit]
+
+        pairlist = sorted_df["pair"].tolist()
+
+        return pairlist
diff --git a/freqtrade/plugins/pairlist/PrecisionFilter.py b/freqtrade/plugins/pairlist/PrecisionFilter.py
new file mode 100644
index 000000000..d0af200cd
--- /dev/null
+++ b/freqtrade/plugins/pairlist/PrecisionFilter.py
@@ -0,0 +1,85 @@
+"""
+Precision pair list filter
+"""
+
+import logging
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import ROUND_UP
+from freqtrade.exchange.exchange_types import Ticker
+from freqtrade.plugins.pairlist.IPairList import IPairList, SupportsBacktesting
+
+
+logger = logging.getLogger(__name__)
+
+
+class PrecisionFilter(IPairList):
+    supports_backtesting = SupportsBacktesting.BIASED
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        if "stoploss" not in self._config:
+            raise OperationalException(
+                "PrecisionFilter can only work with stoploss defined. Please add the "
+                "stoploss key to your configuration (overwrites eventual strategy settings)."
+            )
+        self._stoploss = self._config["stoploss"]
+        self._enabled = self._stoploss != 0
+
+        # Precalculate sanitized stoploss value to avoid recalculation for every pair
+        self._stoploss = 1 - abs(self._stoploss)
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return True
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return f"{self.name} - Filtering untradable pairs."
+
+    @staticmethod
+    def description() -> str:
+        return "Filters low-value coins which would not allow setting stoplosses."
+
+    def _validate_pair(self, pair: str, ticker: Ticker | None) -> bool:
+        """
+        Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
+        low value pairs.
+        :param pair: Pair that's currently validated
+        :param ticker: ticker dict as returned from ccxt.fetch_ticker
+        :return: True if the pair can stay, false if it should be removed
+        """
+        if not ticker or ticker.get("last", None) is None:
+            self.log_once(
+                f"Removed {pair} from whitelist, because "
+                "ticker['last'] is empty (Usually no trade in the last 24h).",
+                logger.info,
+            )
+            return False
+        stop_price = ticker["last"] * self._stoploss
+
+        # Adjust stop-prices to precision
+        sp = self._exchange.price_to_precision(pair, stop_price, rounding_mode=ROUND_UP)
+
+        stop_gap_price = self._exchange.price_to_precision(
+            pair, stop_price * 0.99, rounding_mode=ROUND_UP
+        )
+        logger.debug(f"{pair} - {sp} : {stop_gap_price}")
+
+        if sp <= stop_gap_price:
+            self.log_once(
+                f"Removed {pair} from whitelist, because "
+                f"stop price {sp} would be <= stop limit {stop_gap_price}",
+                logger.info,
+            )
+            return False
+
+        return True
diff --git a/freqtrade/plugins/pairlist/PriceFilter.py b/freqtrade/plugins/pairlist/PriceFilter.py
new file mode 100644
index 000000000..ffdd83407
--- /dev/null
+++ b/freqtrade/plugins/pairlist/PriceFilter.py
@@ -0,0 +1,175 @@
+"""
+Price pair list filter
+"""
+
+import logging
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Ticker
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+
+
+logger = logging.getLogger(__name__)
+
+
+class PriceFilter(IPairList):
+    supports_backtesting = SupportsBacktesting.BIASED
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._low_price_ratio = self._pairlistconfig.get("low_price_ratio", 0)
+        if self._low_price_ratio < 0:
+            raise OperationalException("PriceFilter requires low_price_ratio to be >= 0")
+        self._min_price = self._pairlistconfig.get("min_price", 0)
+        if self._min_price < 0:
+            raise OperationalException("PriceFilter requires min_price to be >= 0")
+        self._max_price = self._pairlistconfig.get("max_price", 0)
+        if self._max_price < 0:
+            raise OperationalException("PriceFilter requires max_price to be >= 0")
+        self._max_value = self._pairlistconfig.get("max_value", 0)
+        if self._max_value < 0:
+            raise OperationalException("PriceFilter requires max_value to be >= 0")
+        self._enabled = (
+            (self._low_price_ratio > 0)
+            or (self._min_price > 0)
+            or (self._max_price > 0)
+            or (self._max_value > 0)
+        )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return True
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        active_price_filters = []
+        if self._low_price_ratio != 0:
+            active_price_filters.append(f"below {self._low_price_ratio:.1%}")
+        if self._min_price != 0:
+            active_price_filters.append(f"below {self._min_price:.8f}")
+        if self._max_price != 0:
+            active_price_filters.append(f"above {self._max_price:.8f}")
+        if self._max_value != 0:
+            active_price_filters.append(f"Value above {self._max_value:.8f}")
+
+        if len(active_price_filters):
+            return f"{self.name} - Filtering pairs priced {' or '.join(active_price_filters)}."
+
+        return f"{self.name} - No price filters configured."
+
+    @staticmethod
+    def description() -> str:
+        return "Filter pairs by price."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "low_price_ratio": {
+                "type": "number",
+                "default": 0,
+                "description": "Low price ratio",
+                "help": (
+                    "Remove pairs where a price move of 1 price unit (pip) is above this ratio."
+                ),
+            },
+            "min_price": {
+                "type": "number",
+                "default": 0,
+                "description": "Minimum price",
+                "help": "Remove pairs with a price below this value.",
+            },
+            "max_price": {
+                "type": "number",
+                "default": 0,
+                "description": "Maximum price",
+                "help": "Remove pairs with a price above this value.",
+            },
+            "max_value": {
+                "type": "number",
+                "default": 0,
+                "description": "Maximum value",
+                "help": "Remove pairs with a value (price * amount) above this value.",
+            },
+        }
+
+    def _validate_pair(self, pair: str, ticker: Ticker | None) -> bool:
+        """
+        Check if one price-step (pip) is > than a certain barrier.
+        :param pair: Pair that's currently validated
+        :param ticker: ticker dict as returned from ccxt.fetch_ticker
+        :return: True if the pair can stay, false if it should be removed
+        """
+        if ticker and "last" in ticker and ticker["last"] is not None and ticker.get("last") != 0:
+            price: float = ticker["last"]
+        else:
+            self.log_once(
+                f"Removed {pair} from whitelist, because "
+                "ticker['last'] is empty (Usually no trade in the last 24h).",
+                logger.info,
+            )
+            return False
+
+        # Perform low_price_ratio check.
+        if self._low_price_ratio != 0:
+            compare = self._exchange.price_get_one_pip(pair, price)
+            changeperc = compare / price
+            if changeperc > self._low_price_ratio:
+                self.log_once(
+                    f"Removed {pair} from whitelist, because 1 unit is {changeperc:.3%}",
+                    logger.info,
+                )
+                return False
+
+        # Perform low_amount check
+        if self._max_value != 0:
+            market = self._exchange.markets[pair]
+            limits = market["limits"]
+            if limits["amount"]["min"] is not None:
+                min_amount = limits["amount"]["min"]
+                min_precision = market["precision"]["amount"]
+
+                min_value = min_amount * price
+                if self._exchange.precisionMode == 4:
+                    # tick size
+                    next_value = (min_amount + min_precision) * price
+                else:
+                    # Decimal places
+                    min_precision = pow(0.1, min_precision)
+                    next_value = (min_amount + min_precision) * price
+                diff = next_value - min_value
+
+                if diff > self._max_value:
+                    self.log_once(
+                        f"Removed {pair} from whitelist, "
+                        f"because min value change of {diff} > {self._max_value}.",
+                        logger.info,
+                    )
+                    return False
+
+        # Perform min_price check.
+        if self._min_price != 0:
+            if price < self._min_price:
+                self.log_once(
+                    f"Removed {pair} from whitelist, because last price < {self._min_price:.8f}",
+                    logger.info,
+                )
+                return False
+
+        # Perform max_price check.
+        if self._max_price != 0:
+            if price > self._max_price:
+                self.log_once(
+                    f"Removed {pair} from whitelist, because last price > {self._max_price:.8f}",
+                    logger.info,
+                )
+                return False
+
+        return True
diff --git a/freqtrade/plugins/pairlist/ProducerPairList.py b/freqtrade/plugins/pairlist/ProducerPairList.py
new file mode 100644
index 000000000..89e4a0869
--- /dev/null
+++ b/freqtrade/plugins/pairlist/ProducerPairList.py
@@ -0,0 +1,118 @@
+"""
+External Pair List provider
+
+Provides pair list from Leader data
+"""
+
+import logging
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+
+
+logger = logging.getLogger(__name__)
+
+
+class ProducerPairList(IPairList):
+    """
+    PairList plugin for use with external_message_consumer.
+    Will use pairs given from leader data.
+
+    Usage:
+        "pairlists": [
+            {
+                "method": "ProducerPairList",
+                "number_assets": 5,
+                "producer_name": "default",
+            }
+        ],
+    """
+
+    is_pairlist_generator = True
+    supports_backtesting = SupportsBacktesting.NO
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._num_assets: int = self._pairlistconfig.get("number_assets", 0)
+        self._producer_name = self._pairlistconfig.get("producer_name", "default")
+        if not self._config.get("external_message_consumer", {}).get("enabled"):
+            raise OperationalException(
+                "ProducerPairList requires external_message_consumer to be enabled."
+            )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        -> Please overwrite in subclasses
+        """
+        return f"{self.name} - {self._producer_name}"
+
+    @staticmethod
+    def description() -> str:
+        return "Get a pairlist from an upstream bot."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "number_assets": {
+                "type": "number",
+                "default": 0,
+                "description": "Number of assets",
+                "help": "Number of assets to use from the pairlist",
+            },
+            "producer_name": {
+                "type": "string",
+                "default": "default",
+                "description": "Producer name",
+                "help": (
+                    "Name of the producer to use. Requires additional "
+                    "external_message_consumer configuration."
+                ),
+            },
+        }
+
+    def _filter_pairlist(self, pairlist: list[str] | None):
+        upstream_pairlist = self._pairlistmanager._dataprovider.get_producer_pairs(
+            self._producer_name
+        )
+
+        if pairlist is None:
+            pairlist = self._pairlistmanager._dataprovider.get_producer_pairs(self._producer_name)
+
+        pairs = list(dict.fromkeys(pairlist + upstream_pairlist))
+        if self._num_assets:
+            pairs = pairs[: self._num_assets]
+
+        return pairs
+
+    def gen_pairlist(self, tickers: Tickers) -> list[str]:
+        """
+        Generate the pairlist
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: List of pairs
+        """
+        pairs = self._filter_pairlist(None)
+        self.log_once(f"Received pairs: {pairs}", logger.debug)
+        pairs = self._whitelist_for_active_markets(self.verify_whitelist(pairs, logger.info))
+        return pairs
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        return self._filter_pairlist(pairlist)
diff --git a/freqtrade/plugins/pairlist/RemotePairList.py b/freqtrade/plugins/pairlist/RemotePairList.py
new file mode 100644
index 000000000..db32d5a77
--- /dev/null
+++ b/freqtrade/plugins/pairlist/RemotePairList.py
@@ -0,0 +1,318 @@
+"""
+Remote PairList provider
+
+Provides pair list fetched from a remote source
+"""
+
+import logging
+from pathlib import Path
+from typing import Any
+
+import rapidjson
+import requests
+from cachetools import TTLCache
+
+from freqtrade import __version__
+from freqtrade.configuration.load_config import CONFIG_PARSE_MODE
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
+
+
+logger = logging.getLogger(__name__)
+
+
+class RemotePairList(IPairList):
+    is_pairlist_generator = True
+    # Potential winner bias
+    supports_backtesting = SupportsBacktesting.BIASED
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        if "number_assets" not in self._pairlistconfig:
+            raise OperationalException(
+                "`number_assets` not specified. Please check your configuration "
+                'for "pairlist.config.number_assets"'
+            )
+
+        if "pairlist_url" not in self._pairlistconfig:
+            raise OperationalException(
+                "`pairlist_url` not specified. Please check your configuration "
+                'for "pairlist.config.pairlist_url"'
+            )
+
+        self._mode = self._pairlistconfig.get("mode", "whitelist")
+        self._processing_mode = self._pairlistconfig.get("processing_mode", "filter")
+        self._number_pairs = self._pairlistconfig["number_assets"]
+        self._refresh_period: int = self._pairlistconfig.get("refresh_period", 1800)
+        self._keep_pairlist_on_failure = self._pairlistconfig.get("keep_pairlist_on_failure", True)
+        self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
+        self._pairlist_url = self._pairlistconfig.get("pairlist_url", "")
+        self._read_timeout = self._pairlistconfig.get("read_timeout", 60)
+        self._bearer_token = self._pairlistconfig.get("bearer_token", "")
+        self._init_done = False
+        self._save_to_file = self._pairlistconfig.get("save_to_file", None)
+        self._last_pairlist: list[Any] = list()
+
+        if self._mode not in ["whitelist", "blacklist"]:
+            raise OperationalException(
+                '`mode` not configured correctly. Supported Modes are "whitelist","blacklist"'
+            )
+
+        if self._processing_mode not in ["filter", "append"]:
+            raise OperationalException(
+                '`processing_mode` not configured correctly. Supported Modes are "filter","append"'
+            )
+
+        if self._pairlist_pos == 0 and self._mode == "blacklist":
+            raise OperationalException(
+                "A `blacklist` mode RemotePairList can not be on the first "
+                "position of your pairlist."
+            )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return f"{self.name} - {self._pairlistconfig['number_assets']} pairs from RemotePairlist."
+
+    @staticmethod
+    def description() -> str:
+        return "Retrieve pairs from a remote API or local file."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "pairlist_url": {
+                "type": "string",
+                "default": "",
+                "description": "URL to fetch pairlist from",
+                "help": "URL to fetch pairlist from",
+            },
+            "number_assets": {
+                "type": "number",
+                "default": 30,
+                "description": "Number of assets",
+                "help": "Number of assets to use from the pairlist.",
+            },
+            "mode": {
+                "type": "option",
+                "default": "whitelist",
+                "options": ["whitelist", "blacklist"],
+                "description": "Pairlist mode",
+                "help": "Should this pairlist operate as a whitelist or blacklist?",
+            },
+            "processing_mode": {
+                "type": "option",
+                "default": "filter",
+                "options": ["filter", "append"],
+                "description": "Processing mode",
+                "help": "Append pairs to incoming pairlist or filter them?",
+            },
+            **IPairList.refresh_period_parameter(),
+            "keep_pairlist_on_failure": {
+                "type": "boolean",
+                "default": True,
+                "description": "Keep last pairlist on failure",
+                "help": "Keep last pairlist on failure",
+            },
+            "read_timeout": {
+                "type": "number",
+                "default": 60,
+                "description": "Read timeout",
+                "help": "Request timeout for remote pairlist",
+            },
+            "bearer_token": {
+                "type": "string",
+                "default": "",
+                "description": "Bearer token",
+                "help": "Bearer token - used for auth against the upstream service.",
+            },
+            "save_to_file": {
+                "type": "string",
+                "default": "",
+                "description": "Filename to save processed pairlist to.",
+                "help": "Specify a filename to save the processed pairlist in JSON format.",
+            },
+        }
+
+    def process_json(self, jsonparse) -> list[str]:
+        pairlist = jsonparse.get("pairs", [])
+        remote_refresh_period = int(jsonparse.get("refresh_period", self._refresh_period))
+
+        if self._refresh_period < remote_refresh_period:
+            self.log_once(
+                f"Refresh Period has been increased from {self._refresh_period}"
+                f" to minimum allowed: {remote_refresh_period} from Remote.",
+                logger.info,
+            )
+
+            self._refresh_period = remote_refresh_period
+            self._pair_cache = TTLCache(maxsize=1, ttl=remote_refresh_period)
+
+        self._init_done = True
+
+        return pairlist
+
+    def return_last_pairlist(self) -> list[str]:
+        if self._keep_pairlist_on_failure:
+            pairlist = self._last_pairlist
+            self.log_once("Keeping last fetched pairlist", logger.info)
+        else:
+            pairlist = []
+
+        return pairlist
+
+    def fetch_pairlist(self) -> tuple[list[str], float]:
+        headers = {"User-Agent": "Freqtrade/" + __version__ + " Remotepairlist"}
+
+        if self._bearer_token:
+            headers["Authorization"] = f"Bearer {self._bearer_token}"
+
+        try:
+            response = requests.get(self._pairlist_url, headers=headers, timeout=self._read_timeout)
+            content_type = response.headers.get("content-type")
+            time_elapsed = response.elapsed.total_seconds()
+
+            if "application/json" in str(content_type):
+                jsonparse = response.json()
+
+                try:
+                    pairlist = self.process_json(jsonparse)
+                except Exception as e:
+                    pairlist = self._handle_error(f"Failed processing JSON data: {type(e)}")
+            else:
+                pairlist = self._handle_error(
+                    f"RemotePairList is not of type JSON. {self._pairlist_url}"
+                )
+
+        except requests.exceptions.RequestException:
+            pairlist = self._handle_error(
+                f"Was not able to fetch pairlist from: {self._pairlist_url}"
+            )
+
+            time_elapsed = 0
+
+        return pairlist, time_elapsed
+
+    def _handle_error(self, error: str) -> list[str]:
+        if self._init_done:
+            self.log_once("Error: " + error, logger.info)
+            return self.return_last_pairlist()
+        else:
+            raise OperationalException(error)
+
+    def gen_pairlist(self, tickers: Tickers) -> list[str]:
+        """
+        Generate the pairlist
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: List of pairs
+        """
+
+        if self._init_done:
+            pairlist = self._pair_cache.get("pairlist")
+            if pairlist == [None]:
+                # Valid but empty pairlist.
+                return []
+        else:
+            pairlist = []
+
+        time_elapsed = 0.0
+
+        if pairlist:
+            # Item found - no refresh necessary
+            return pairlist.copy()
+        else:
+            if self._pairlist_url.startswith("file:///"):
+                filename = self._pairlist_url.split("file:///", 1)[1]
+                file_path = Path(filename)
+
+                if file_path.exists():
+                    with file_path.open() as json_file:
+                        try:
+                            # Load the JSON data into a dictionary
+                            jsonparse = rapidjson.load(json_file, parse_mode=CONFIG_PARSE_MODE)
+                            pairlist = self.process_json(jsonparse)
+                        except Exception as e:
+                            pairlist = self._handle_error(f"processing JSON data: {type(e)}")
+                else:
+                    pairlist = self._handle_error(f"{self._pairlist_url} does not exist.")
+
+            else:
+                # Fetch Pairlist from Remote URL
+                pairlist, time_elapsed = self.fetch_pairlist()
+
+        self.log_once(f"Fetched pairs: {pairlist}", logger.debug)
+
+        pairlist = expand_pairlist(pairlist, list(self._exchange.get_markets().keys()))
+        pairlist = self._whitelist_for_active_markets(pairlist)
+        pairlist = pairlist[: self._number_pairs]
+
+        if pairlist:
+            self._pair_cache["pairlist"] = pairlist.copy()
+        else:
+            # If pairlist is empty, set a dummy value to avoid fetching again
+            self._pair_cache["pairlist"] = [None]
+
+        if time_elapsed != 0.0:
+            self.log_once(f"Pairlist Fetched in {time_elapsed} seconds.", logger.info)
+        else:
+            self.log_once("Fetched Pairlist.", logger.info)
+
+        self._last_pairlist = list(pairlist)
+
+        if self._save_to_file:
+            self.save_pairlist(pairlist, self._save_to_file)
+
+        return pairlist
+
+    def save_pairlist(self, pairlist: list[str], filename: str) -> None:
+        pairlist_data = {"pairs": pairlist}
+        try:
+            file_path = Path(filename)
+            with file_path.open("w") as json_file:
+                rapidjson.dump(pairlist_data, json_file)
+                logger.info(f"Processed pairlist saved to {filename}")
+        except Exception as e:
+            logger.error(f"Error saving processed pairlist to {filename}: {e}")
+
+    def filter_pairlist(self, pairlist: list[str], tickers: dict) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        rpl_pairlist = self.gen_pairlist(tickers)
+        merged_list = []
+        filtered = []
+
+        if self._mode == "whitelist":
+            if self._processing_mode == "filter":
+                merged_list = [pair for pair in pairlist if pair in rpl_pairlist]
+            elif self._processing_mode == "append":
+                merged_list = pairlist + rpl_pairlist
+            merged_list = sorted(set(merged_list), key=merged_list.index)
+        else:
+            for pair in pairlist:
+                if pair not in rpl_pairlist:
+                    merged_list.append(pair)
+                else:
+                    filtered.append(pair)
+            if filtered:
+                self.log_once(f"Blacklist - Filtered out pairs: {filtered}", logger.info)
+
+        merged_list = merged_list[: self._number_pairs]
+        return merged_list
diff --git a/freqtrade/plugins/pairlist/ShuffleFilter.py b/freqtrade/plugins/pairlist/ShuffleFilter.py
new file mode 100644
index 000000000..be536e705
--- /dev/null
+++ b/freqtrade/plugins/pairlist/ShuffleFilter.py
@@ -0,0 +1,98 @@
+"""
+Shuffle pair list filter
+"""
+
+import logging
+import random
+from typing import Literal
+
+from freqtrade.enums import RunMode
+from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+from freqtrade.util.periodic_cache import PeriodicCache
+
+
+logger = logging.getLogger(__name__)
+
+ShuffleValues = Literal["candle", "iteration"]
+
+
+class ShuffleFilter(IPairList):
+    supports_backtesting = SupportsBacktesting.YES
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        # Apply seed in backtesting mode to get comparable results,
+        # but not in live modes to get a non-repeating order of pairs during live modes.
+        if self._config.get("runmode") in (RunMode.LIVE, RunMode.DRY_RUN):
+            self._seed = None
+            logger.info("Live mode detected, not applying seed.")
+        else:
+            self._seed = self._pairlistconfig.get("seed")
+            logger.info(f"Backtesting mode detected, applying seed value: {self._seed}")
+
+        self._random = random.Random(self._seed)  # noqa: S311
+        self._shuffle_freq: ShuffleValues = self._pairlistconfig.get("shuffle_frequency", "candle")
+        self.__pairlist_cache = PeriodicCache(
+            maxsize=1000, ttl=timeframe_to_seconds(self._config["timeframe"])
+        )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return f"{self.name} - Shuffling pairs every {self._shuffle_freq}" + (
+            f", seed = {self._seed}." if self._seed is not None else "."
+        )
+
+    @staticmethod
+    def description() -> str:
+        return "Randomize pairlist order."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "shuffle_frequency": {
+                "type": "option",
+                "default": "candle",
+                "options": ["candle", "iteration"],
+                "description": "Shuffle frequency",
+                "help": "Shuffle frequency. Can be either 'candle' or 'iteration'.",
+            },
+            "seed": {
+                "type": "number",
+                "default": None,
+                "description": "Random Seed",
+                "help": "Seed for random number generator. Not used in live mode.",
+            },
+        }
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        pairlist_bef = tuple(pairlist)
+        pairlist_new = self.__pairlist_cache.get(pairlist_bef)
+        if pairlist_new and self._shuffle_freq == "candle":
+            # Use cached pairlist.
+            return pairlist_new
+        # Shuffle is done inplace
+        self._random.shuffle(pairlist)
+        self.__pairlist_cache[pairlist_bef] = pairlist
+
+        return pairlist
diff --git a/freqtrade/plugins/pairlist/SpreadFilter.py b/freqtrade/plugins/pairlist/SpreadFilter.py
new file mode 100644
index 000000000..442de2bf2
--- /dev/null
+++ b/freqtrade/plugins/pairlist/SpreadFilter.py
@@ -0,0 +1,83 @@
+"""
+Spread pair list filter
+"""
+
+import logging
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Ticker
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+
+
+logger = logging.getLogger(__name__)
+
+
+class SpreadFilter(IPairList):
+    supports_backtesting = SupportsBacktesting.NO
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._max_spread_ratio = self._pairlistconfig.get("max_spread_ratio", 0.005)
+        self._enabled = self._max_spread_ratio != 0
+
+        if not self._exchange.get_option("tickers_have_bid_ask"):
+            raise OperationalException(
+                f"{self.name} requires exchange to have bid/ask data for tickers, "
+                "which is not available for the selected exchange / trading mode."
+            )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return True
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return (
+            f"{self.name} - Filtering pairs with ask/bid diff above {self._max_spread_ratio:.2%}."
+        )
+
+    @staticmethod
+    def description() -> str:
+        return "Filter by bid/ask difference."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "max_spread_ratio": {
+                "type": "number",
+                "default": 0.005,
+                "description": "Max spread ratio",
+                "help": "Max spread ratio for a pair to be considered.",
+            },
+        }
+
+    def _validate_pair(self, pair: str, ticker: Ticker | None) -> bool:
+        """
+        Validate spread for the ticker
+        :param pair: Pair that's currently validated
+        :param ticker: ticker dict as returned from ccxt.fetch_ticker
+        :return: True if the pair can stay, false if it should be removed
+        """
+        if ticker and "bid" in ticker and "ask" in ticker and ticker["ask"] and ticker["bid"]:
+            spread = 1 - ticker["bid"] / ticker["ask"]
+            if spread > self._max_spread_ratio:
+                self.log_once(
+                    f"Removed {pair} from whitelist, because spread "
+                    f"{spread:.3%} > {self._max_spread_ratio:.3%}",
+                    logger.info,
+                )
+                return False
+            else:
+                return True
+        self.log_once(
+            f"Removed {pair} from whitelist due to invalid ticker data: {ticker}", logger.info
+        )
+        return False
diff --git a/freqtrade/plugins/pairlist/StaticPairList.py b/freqtrade/plugins/pairlist/StaticPairList.py
new file mode 100644
index 000000000..bff2105b3
--- /dev/null
+++ b/freqtrade/plugins/pairlist/StaticPairList.py
@@ -0,0 +1,85 @@
+"""
+Static Pair List provider
+
+Provides pair white list as it configured in config
+"""
+
+import logging
+from copy import deepcopy
+
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+
+
+logger = logging.getLogger(__name__)
+
+
+class StaticPairList(IPairList):
+    is_pairlist_generator = True
+    supports_backtesting = SupportsBacktesting.YES
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._allow_inactive = self._pairlistconfig.get("allow_inactive", False)
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        -> Please overwrite in subclasses
+        """
+        return f"{self.name}"
+
+    @staticmethod
+    def description() -> str:
+        return "Use pairlist as configured in config."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "allow_inactive": {
+                "type": "boolean",
+                "default": False,
+                "description": "Allow inactive pairs",
+                "help": "Allow inactive pairs to be in the whitelist.",
+            },
+        }
+
+    def gen_pairlist(self, tickers: Tickers) -> list[str]:
+        """
+        Generate the pairlist
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: List of pairs
+        """
+        wl = self.verify_whitelist(
+            self._config["exchange"]["pair_whitelist"], logger.info, keep_invalid=True
+        )
+        if self._allow_inactive:
+            return wl
+        else:
+            # Avoid implicit filtering of "verify_whitelist" to keep
+            # proper warnings in the log
+            return self._whitelist_for_active_markets(wl)
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        pairlist_ = deepcopy(pairlist)
+        for pair in self._config["exchange"]["pair_whitelist"]:
+            if pair not in pairlist_:
+                pairlist_.append(pair)
+        return pairlist_
diff --git a/freqtrade/plugins/pairlist/VolatilityFilter.py b/freqtrade/plugins/pairlist/VolatilityFilter.py
new file mode 100644
index 000000000..87ca488c1
--- /dev/null
+++ b/freqtrade/plugins/pairlist/VolatilityFilter.py
@@ -0,0 +1,186 @@
+"""
+Volatility pairlist filter
+"""
+
+import logging
+import sys
+from datetime import timedelta
+
+import numpy as np
+from cachetools import TTLCache
+from pandas import DataFrame
+
+from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.misc import plural
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+from freqtrade.util import dt_floor_day, dt_now, dt_ts
+
+
+logger = logging.getLogger(__name__)
+
+
+class VolatilityFilter(IPairList):
+    """
+    Filters pairs by volatility
+    """
+
+    supports_backtesting = SupportsBacktesting.NO
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._days = self._pairlistconfig.get("lookback_days", 10)
+        self._min_volatility = self._pairlistconfig.get("min_volatility", 0)
+        self._max_volatility = self._pairlistconfig.get("max_volatility", sys.maxsize)
+        self._refresh_period = self._pairlistconfig.get("refresh_period", 1440)
+        self._def_candletype = self._config["candle_type_def"]
+        self._sort_direction: str | None = self._pairlistconfig.get("sort_direction", None)
+
+        self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
+
+        candle_limit = self._exchange.ohlcv_candle_limit("1d", self._config["candle_type_def"])
+        if self._days < 1:
+            raise OperationalException("VolatilityFilter requires lookback_days to be >= 1")
+        if self._days > candle_limit:
+            raise OperationalException(
+                "VolatilityFilter requires lookback_days to not "
+                f"exceed exchange max request size ({candle_limit})"
+            )
+        if self._sort_direction not in [None, "asc", "desc"]:
+            raise OperationalException(
+                "VolatilityFilter requires sort_direction to be "
+                "either None (undefined), 'asc' or 'desc'"
+            )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return (
+            f"{self.name} - Filtering pairs with volatility range "
+            f"{self._min_volatility}-{self._max_volatility} "
+            f" the last {self._days} {plural(self._days, 'day')}."
+        )
+
+    @staticmethod
+    def description() -> str:
+        return "Filter pairs by their recent volatility."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "lookback_days": {
+                "type": "number",
+                "default": 10,
+                "description": "Lookback Days",
+                "help": "Number of days to look back at.",
+            },
+            "min_volatility": {
+                "type": "number",
+                "default": 0,
+                "description": "Minimum Volatility",
+                "help": "Minimum volatility a pair must have to be considered.",
+            },
+            "max_volatility": {
+                "type": "number",
+                "default": None,
+                "description": "Maximum Volatility",
+                "help": "Maximum volatility a pair must have to be considered.",
+            },
+            "sort_direction": {
+                "type": "option",
+                "default": None,
+                "options": ["", "asc", "desc"],
+                "description": "Sort pairlist",
+                "help": "Sort Pairlist ascending or descending by volatility.",
+            },
+            **IPairList.refresh_period_parameter(),
+        }
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        Validate trading range
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new allowlist
+        """
+        needed_pairs: ListPairsWithTimeframes = [
+            (p, "1d", self._def_candletype) for p in pairlist if p not in self._pair_cache
+        ]
+
+        since_ms = dt_ts(dt_floor_day(dt_now()) - timedelta(days=self._days))
+        candles = self._exchange.refresh_ohlcv_with_cache(needed_pairs, since_ms=since_ms)
+
+        resulting_pairlist: list[str] = []
+        volatilitys: dict[str, float] = {}
+        for p in pairlist:
+            daily_candles = candles.get((p, "1d", self._def_candletype), None)
+
+            volatility_avg = self._calculate_volatility(p, daily_candles)
+
+            if volatility_avg is not None:
+                if self._validate_pair_loc(p, volatility_avg):
+                    resulting_pairlist.append(p)
+                    volatilitys[p] = (
+                        volatility_avg if volatility_avg and not np.isnan(volatility_avg) else 0
+                    )
+            else:
+                self.log_once(f"Removed {p} from whitelist, no candles found.", logger.info)
+
+        if self._sort_direction:
+            resulting_pairlist = sorted(
+                resulting_pairlist,
+                key=lambda p: volatilitys[p],
+                reverse=self._sort_direction == "desc",
+            )
+        return resulting_pairlist
+
+    def _calculate_volatility(self, pair: str, daily_candles: DataFrame) -> float | None:
+        # Check symbol in cache
+        if (volatility_avg := self._pair_cache.get(pair, None)) is not None:
+            return volatility_avg
+
+        if daily_candles is not None and not daily_candles.empty:
+            returns = np.log(daily_candles["close"].shift(1) / daily_candles["close"])
+            returns.fillna(0, inplace=True)
+
+            volatility_series = returns.rolling(window=self._days).std() * np.sqrt(self._days)
+            volatility_avg = volatility_series.mean()
+            self._pair_cache[pair] = volatility_avg
+
+            return volatility_avg
+        else:
+            return None
+
+    def _validate_pair_loc(self, pair: str, volatility_avg: float) -> bool:
+        """
+        Validate trading range
+        :param pair: Pair that's currently validated
+        :param volatility_avg: Average volatility
+        :return: True if the pair can stay, false if it should be removed
+        """
+
+        if self._min_volatility <= volatility_avg <= self._max_volatility:
+            result = True
+        else:
+            self.log_once(
+                f"Removed {pair} from whitelist, because volatility "
+                f"over {self._days} {plural(self._days, 'day')} "
+                f"is: {volatility_avg:.3f} "
+                f"which is not in the configured range of "
+                f"{self._min_volatility}-{self._max_volatility}.",
+                logger.info,
+            )
+            result = False
+        return result
diff --git a/freqtrade/plugins/pairlist/VolumePairList.py b/freqtrade/plugins/pairlist/VolumePairList.py
new file mode 100644
index 000000000..b8f806858
--- /dev/null
+++ b/freqtrade/plugins/pairlist/VolumePairList.py
@@ -0,0 +1,316 @@
+"""
+Volume PairList provider
+
+Provides dynamic pair list based on trade volumes
+"""
+
+import logging
+from datetime import timedelta
+from typing import Any, Literal
+
+from cachetools import TTLCache
+
+from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+from freqtrade.util import dt_now, format_ms_time
+
+
+logger = logging.getLogger(__name__)
+
+
+SORT_VALUES = ["quoteVolume"]
+
+
+class VolumePairList(IPairList):
+    is_pairlist_generator = True
+    supports_backtesting = SupportsBacktesting.NO
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        if "number_assets" not in self._pairlistconfig:
+            raise OperationalException(
+                "`number_assets` not specified. Please check your configuration "
+                'for "pairlist.config.number_assets"'
+            )
+
+        self._stake_currency = self._config["stake_currency"]
+        self._number_pairs = self._pairlistconfig["number_assets"]
+        self._sort_key: Literal["quoteVolume"] = self._pairlistconfig.get("sort_key", "quoteVolume")
+        self._min_value = self._pairlistconfig.get("min_value", 0)
+        self._max_value = self._pairlistconfig.get("max_value", None)
+        self._refresh_period = self._pairlistconfig.get("refresh_period", 1800)
+        self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
+        self._lookback_days = self._pairlistconfig.get("lookback_days", 0)
+        self._lookback_timeframe = self._pairlistconfig.get("lookback_timeframe", "1d")
+        self._lookback_period = self._pairlistconfig.get("lookback_period", 0)
+        self._def_candletype = self._config["candle_type_def"]
+
+        if (self._lookback_days > 0) & (self._lookback_period > 0):
+            raise OperationalException(
+                "Ambiguous configuration: lookback_days and lookback_period both set in pairlist "
+                "config. Please set lookback_days only or lookback_period and lookback_timeframe "
+                "and restart the bot."
+            )
+
+        # overwrite lookback timeframe and days when lookback_days is set
+        if self._lookback_days > 0:
+            self._lookback_timeframe = "1d"
+            self._lookback_period = self._lookback_days
+
+        # get timeframe in minutes and seconds
+        self._tf_in_min = timeframe_to_minutes(self._lookback_timeframe)
+        _tf_in_sec = self._tf_in_min * 60
+
+        # whether to use range lookback or not
+        self._use_range = (self._tf_in_min > 0) & (self._lookback_period > 0)
+
+        if self._use_range & (self._refresh_period < _tf_in_sec):
+            raise OperationalException(
+                f"Refresh period of {self._refresh_period} seconds is smaller than one "
+                f"timeframe of {self._lookback_timeframe}. Please adjust refresh_period "
+                f"to at least {_tf_in_sec} and restart the bot."
+            )
+
+        if not self._use_range and not (
+            self._exchange.exchange_has("fetchTickers")
+            and self._exchange.get_option("tickers_have_quoteVolume")
+        ):
+            raise OperationalException(
+                "Exchange does not support dynamic whitelist in this configuration. "
+                "Please edit your config and either remove Volumepairlist, "
+                "or switch to using candles. and restart the bot."
+            )
+
+        if not self._validate_keys(self._sort_key):
+            raise OperationalException(f"key {self._sort_key} not in {SORT_VALUES}")
+
+        candle_limit = self._exchange.ohlcv_candle_limit(
+            self._lookback_timeframe, self._config["candle_type_def"]
+        )
+        if self._lookback_period < 0:
+            raise OperationalException("VolumeFilter requires lookback_period to be >= 0")
+        if self._lookback_period > candle_limit:
+            raise OperationalException(
+                "VolumeFilter requires lookback_period to not "
+                f"exceed exchange max request size ({candle_limit})"
+            )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return not self._use_range
+
+    def _validate_keys(self, key):
+        return key in SORT_VALUES
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return f"{self.name} - top {self._pairlistconfig['number_assets']} volume pairs."
+
+    @staticmethod
+    def description() -> str:
+        return "Provides dynamic pair list based on trade volumes."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "number_assets": {
+                "type": "number",
+                "default": 30,
+                "description": "Number of assets",
+                "help": "Number of assets to use from the pairlist",
+            },
+            "sort_key": {
+                "type": "option",
+                "default": "quoteVolume",
+                "options": SORT_VALUES,
+                "description": "Sort key",
+                "help": "Sort key to use for sorting the pairlist.",
+            },
+            "min_value": {
+                "type": "number",
+                "default": 0,
+                "description": "Minimum value",
+                "help": "Minimum value to use for filtering the pairlist.",
+            },
+            "max_value": {
+                "type": "number",
+                "default": None,
+                "description": "Maximum value",
+                "help": "Maximum value to use for filtering the pairlist.",
+            },
+            **IPairList.refresh_period_parameter(),
+            "lookback_days": {
+                "type": "number",
+                "default": 0,
+                "description": "Lookback Days",
+                "help": "Number of days to look back at.",
+            },
+            "lookback_timeframe": {
+                "type": "string",
+                "default": "",
+                "description": "Lookback Timeframe",
+                "help": "Timeframe to use for lookback.",
+            },
+            "lookback_period": {
+                "type": "number",
+                "default": 0,
+                "description": "Lookback Period",
+                "help": "Number of periods to look back at.",
+            },
+        }
+
+    def gen_pairlist(self, tickers: Tickers) -> list[str]:
+        """
+        Generate the pairlist
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: List of pairs
+        """
+        # Generate dynamic whitelist
+        # Must always run if this pairlist is not the first in the list.
+        pairlist = self._pair_cache.get("pairlist")
+        if pairlist:
+            # Item found - no refresh necessary
+            return pairlist.copy()
+        else:
+            # Use fresh pairlist
+            # Check if pair quote currency equals to the stake currency.
+            _pairlist = [
+                k
+                for k in self._exchange.get_markets(
+                    quote_currencies=[self._stake_currency], tradable_only=True, active_only=True
+                ).keys()
+            ]
+            # No point in testing for blacklisted pairs...
+            _pairlist = self.verify_blacklist(_pairlist, logger.info)
+            if not self._use_range:
+                filtered_tickers = [
+                    v
+                    for k, v in tickers.items()
+                    if (
+                        self._exchange.get_pair_quote_currency(k) == self._stake_currency
+                        and (self._use_range or v.get(self._sort_key) is not None)
+                        and v["symbol"] in _pairlist
+                    )
+                ]
+                pairlist = [s["symbol"] for s in filtered_tickers]
+            else:
+                pairlist = _pairlist
+
+            pairlist = self.filter_pairlist(pairlist, tickers)
+            self._pair_cache["pairlist"] = pairlist.copy()
+
+        return pairlist
+
+    def filter_pairlist(self, pairlist: list[str], tickers: dict) -> list[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        if self._use_range:
+            # Create bare minimum from tickers structure.
+            filtered_tickers: list[dict[str, Any]] = [{"symbol": k} for k in pairlist]
+
+            # get lookback period in ms, for exchange ohlcv fetch
+            since_ms = (
+                int(
+                    timeframe_to_prev_date(
+                        self._lookback_timeframe,
+                        dt_now()
+                        + timedelta(
+                            minutes=-(self._lookback_period * self._tf_in_min) - self._tf_in_min
+                        ),
+                    ).timestamp()
+                )
+                * 1000
+            )
+
+            to_ms = (
+                int(
+                    timeframe_to_prev_date(
+                        self._lookback_timeframe, dt_now() - timedelta(minutes=self._tf_in_min)
+                    ).timestamp()
+                )
+                * 1000
+            )
+
+            # todo: utc date output for starting date
+            self.log_once(
+                f"Using volume range of {self._lookback_period} candles, timeframe: "
+                f"{self._lookback_timeframe}, starting from {format_ms_time(since_ms)} "
+                f"till {format_ms_time(to_ms)}",
+                logger.info,
+            )
+            needed_pairs: ListPairsWithTimeframes = [
+                (p, self._lookback_timeframe, self._def_candletype)
+                for p in [s["symbol"] for s in filtered_tickers]
+                if p not in self._pair_cache
+            ]
+
+            candles = self._exchange.refresh_ohlcv_with_cache(needed_pairs, since_ms)
+
+            for i, p in enumerate(filtered_tickers):
+                contract_size = self._exchange.markets[p["symbol"]].get("contractSize", 1.0) or 1.0
+                pair_candles = (
+                    candles[(p["symbol"], self._lookback_timeframe, self._def_candletype)]
+                    if (p["symbol"], self._lookback_timeframe, self._def_candletype) in candles
+                    else None
+                )
+                # in case of candle data calculate typical price and quoteVolume for candle
+                if pair_candles is not None and not pair_candles.empty:
+                    if self._exchange.get_option("ohlcv_volume_currency") == "base":
+                        pair_candles["typical_price"] = (
+                            pair_candles["high"] + pair_candles["low"] + pair_candles["close"]
+                        ) / 3
+
+                        pair_candles["quoteVolume"] = (
+                            pair_candles["volume"] * pair_candles["typical_price"] * contract_size
+                        )
+                    else:
+                        # Exchange ohlcv data is in quote volume already.
+                        pair_candles["quoteVolume"] = pair_candles["volume"]
+                    # ensure that a rolling sum over the lookback_period is built
+                    # if pair_candles contains more candles than lookback_period
+                    quoteVolume = (
+                        pair_candles["quoteVolume"]
+                        .rolling(self._lookback_period)
+                        .sum()
+                        .fillna(0)
+                        .iloc[-1]
+                    )
+
+                    # replace quoteVolume with range quoteVolume sum calculated above
+                    filtered_tickers[i]["quoteVolume"] = quoteVolume
+                else:
+                    filtered_tickers[i]["quoteVolume"] = 0
+        else:
+            # Tickers mode - filter based on incoming pairlist.
+            filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
+
+        if self._min_value > 0:
+            filtered_tickers = [v for v in filtered_tickers if v[self._sort_key] > self._min_value]
+        if self._max_value is not None:
+            filtered_tickers = [v for v in filtered_tickers if v[self._sort_key] < self._max_value]
+
+        sorted_tickers = sorted(filtered_tickers, reverse=True, key=lambda t: t[self._sort_key])
+
+        # Validate whitelist to only have active market pairs
+        pairs = self._whitelist_for_active_markets([s["symbol"] for s in sorted_tickers])
+        pairs = self.verify_blacklist(pairs, logmethod=logger.info)
+        # Limit pairlist to the requested number of pairs
+        pairs = pairs[: self._number_pairs]
+
+        return pairs
diff --git a/freqtrade/plugins/pairlist/__init__.py b/freqtrade/plugins/pairlist/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/freqtrade/plugins/pairlist/pairlist_helpers.py b/freqtrade/plugins/pairlist/pairlist_helpers.py
new file mode 100644
index 000000000..b3138f8f2
--- /dev/null
+++ b/freqtrade/plugins/pairlist/pairlist_helpers.py
@@ -0,0 +1,49 @@
+import re
+
+from freqtrade.constants import Config
+
+
+def expand_pairlist(
+    wildcardpl: list[str], available_pairs: list[str], keep_invalid: bool = False
+) -> list[str]:
+    """
+    Expand pairlist potentially containing wildcards based on available markets.
+    This will implicitly filter all pairs in the wildcard-list which are not in available_pairs.
+    :param wildcardpl: List of Pairlists, which may contain regex
+    :param available_pairs: List of all available pairs (`exchange.get_markets().keys()`)
+    :param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes
+    :return: expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
+    :raises: ValueError if a wildcard is invalid (like '*/BTC' - which should be `.*/BTC`)
+    """
+    result = []
+    if keep_invalid:
+        for pair_wc in wildcardpl:
+            try:
+                comp = re.compile(pair_wc, re.IGNORECASE)
+                result_partial = [pair for pair in available_pairs if re.fullmatch(comp, pair)]
+                # Add all matching pairs.
+                # If there are no matching pairs (Pair not on exchange) keep it.
+                result += result_partial or [pair_wc]
+            except re.error as err:
+                raise ValueError(f"Wildcard error in {pair_wc}, {err}")
+
+        # Remove wildcard pairs that didn't have a match.
+        result = [element for element in result if re.fullmatch(r"^[A-Za-z0-9:/-]+$", element)]
+
+    else:
+        for pair_wc in wildcardpl:
+            try:
+                comp = re.compile(pair_wc, re.IGNORECASE)
+                result += [pair for pair in available_pairs if re.fullmatch(comp, pair)]
+            except re.error as err:
+                raise ValueError(f"Wildcard error in {pair_wc}, {err}")
+    return result
+
+
+def dynamic_expand_pairlist(config: Config, markets: list[str]) -> list[str]:
+    expanded_pairs = expand_pairlist(config["pairs"], markets)
+    if config.get("freqai", {}).get("enabled", False):
+        corr_pairlist = config["freqai"]["feature_parameters"]["include_corr_pairlist"]
+        expanded_pairs += [pair for pair in corr_pairlist if pair not in config["pairs"]]
+
+    return expanded_pairs
diff --git a/freqtrade/plugins/pairlist/rangestabilityfilter.py b/freqtrade/plugins/pairlist/rangestabilityfilter.py
new file mode 100644
index 000000000..ca5543bb0
--- /dev/null
+++ b/freqtrade/plugins/pairlist/rangestabilityfilter.py
@@ -0,0 +1,184 @@
+"""
+Rate of change pairlist filter
+"""
+
+import logging
+from datetime import timedelta
+
+from cachetools import TTLCache
+from pandas import DataFrame
+
+from freqtrade.constants import ListPairsWithTimeframes
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.misc import plural
+from freqtrade.plugins.pairlist.IPairList import IPairList, PairlistParameter, SupportsBacktesting
+from freqtrade.util import dt_floor_day, dt_now, dt_ts
+
+
+logger = logging.getLogger(__name__)
+
+
+class RangeStabilityFilter(IPairList):
+    supports_backtesting = SupportsBacktesting.NO
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self._days = self._pairlistconfig.get("lookback_days", 10)
+        self._min_rate_of_change = self._pairlistconfig.get("min_rate_of_change", 0.01)
+        self._max_rate_of_change = self._pairlistconfig.get("max_rate_of_change")
+        self._refresh_period = self._pairlistconfig.get("refresh_period", 86400)
+        self._def_candletype = self._config["candle_type_def"]
+        self._sort_direction: str | None = self._pairlistconfig.get("sort_direction", None)
+
+        self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
+
+        candle_limit = self._exchange.ohlcv_candle_limit("1d", self._config["candle_type_def"])
+        if self._days < 1:
+            raise OperationalException("RangeStabilityFilter requires lookback_days to be >= 1")
+        if self._days > candle_limit:
+            raise OperationalException(
+                "RangeStabilityFilter requires lookback_days to not "
+                f"exceed exchange max request size ({candle_limit})"
+            )
+        if self._sort_direction not in [None, "asc", "desc"]:
+            raise OperationalException(
+                "RangeStabilityFilter requires sort_direction to be "
+                "either None (undefined), 'asc' or 'desc'"
+            )
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        max_rate_desc = ""
+        if self._max_rate_of_change:
+            max_rate_desc = f" and above {self._max_rate_of_change}"
+        return (
+            f"{self.name} - Filtering pairs with rate of change below "
+            f"{self._min_rate_of_change}{max_rate_desc} over the "
+            f"last {plural(self._days, 'day')}."
+        )
+
+    @staticmethod
+    def description() -> str:
+        return "Filters pairs by their rate of change."
+
+    @staticmethod
+    def available_parameters() -> dict[str, PairlistParameter]:
+        return {
+            "lookback_days": {
+                "type": "number",
+                "default": 10,
+                "description": "Lookback Days",
+                "help": "Number of days to look back at.",
+            },
+            "min_rate_of_change": {
+                "type": "number",
+                "default": 0.01,
+                "description": "Minimum Rate of Change",
+                "help": "Minimum rate of change to filter pairs.",
+            },
+            "max_rate_of_change": {
+                "type": "number",
+                "default": None,
+                "description": "Maximum Rate of Change",
+                "help": "Maximum rate of change to filter pairs.",
+            },
+            "sort_direction": {
+                "type": "option",
+                "default": None,
+                "options": ["", "asc", "desc"],
+                "description": "Sort pairlist",
+                "help": "Sort Pairlist ascending or descending by rate of change.",
+            },
+            **IPairList.refresh_period_parameter(),
+        }
+
+    def filter_pairlist(self, pairlist: list[str], tickers: Tickers) -> list[str]:
+        """
+        Validate trading range
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new allowlist
+        """
+        needed_pairs: ListPairsWithTimeframes = [
+            (p, "1d", self._def_candletype) for p in pairlist if p not in self._pair_cache
+        ]
+
+        since_ms = dt_ts(dt_floor_day(dt_now()) - timedelta(days=self._days + 1))
+        candles = self._exchange.refresh_ohlcv_with_cache(needed_pairs, since_ms=since_ms)
+
+        resulting_pairlist: list[str] = []
+        pct_changes: dict[str, float] = {}
+
+        for p in pairlist:
+            daily_candles = candles.get((p, "1d", self._def_candletype), None)
+
+            pct_change = self._calculate_rate_of_change(p, daily_candles)
+
+            if pct_change is not None:
+                if self._validate_pair_loc(p, pct_change):
+                    resulting_pairlist.append(p)
+                    pct_changes[p] = pct_change
+            else:
+                self.log_once(f"Removed {p} from whitelist, no candles found.", logger.info)
+
+        if self._sort_direction:
+            resulting_pairlist = sorted(
+                resulting_pairlist,
+                key=lambda p: pct_changes[p],
+                reverse=self._sort_direction == "desc",
+            )
+        return resulting_pairlist
+
+    def _calculate_rate_of_change(self, pair: str, daily_candles: DataFrame) -> float | None:
+        # Check symbol in cache
+        if (pct_change := self._pair_cache.get(pair, None)) is not None:
+            return pct_change
+        if daily_candles is not None and not daily_candles.empty:
+            highest_high = daily_candles["high"].max()
+            lowest_low = daily_candles["low"].min()
+            pct_change = ((highest_high - lowest_low) / lowest_low) if lowest_low > 0 else 0
+            self._pair_cache[pair] = pct_change
+            return pct_change
+        else:
+            return None
+
+    def _validate_pair_loc(self, pair: str, pct_change: float) -> bool:
+        """
+        Validate trading range
+        :param pair: Pair that's currently validated
+        :param pct_change: Rate of change
+        :return: True if the pair can stay, false if it should be removed
+        """
+
+        result = True
+        if pct_change < self._min_rate_of_change:
+            self.log_once(
+                f"Removed {pair} from whitelist, because rate of change "
+                f"over {self._days} {plural(self._days, 'day')} is {pct_change:.3f}, "
+                f"which is below the threshold of {self._min_rate_of_change}.",
+                logger.info,
+            )
+            result = False
+        if self._max_rate_of_change:
+            if pct_change > self._max_rate_of_change:
+                self.log_once(
+                    f"Removed {pair} from whitelist, because rate of change "
+                    f"over {self._days} {plural(self._days, 'day')} is {pct_change:.3f}, "
+                    f"which is above the threshold of {self._max_rate_of_change}.",
+                    logger.info,
+                )
+                result = False
+        return result
diff --git a/freqtrade/plugins/pairlistmanager.py b/freqtrade/plugins/pairlistmanager.py
new file mode 100644
index 000000000..73786cba6
--- /dev/null
+++ b/freqtrade/plugins/pairlistmanager.py
@@ -0,0 +1,205 @@
+"""
+PairList manager class
+"""
+
+import logging
+from functools import partial
+
+from cachetools import TTLCache, cached
+
+from freqtrade.constants import Config, ListPairsWithTimeframes
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import CandleType
+from freqtrade.enums.runmode import RunMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange_types import Tickers
+from freqtrade.mixins import LoggingMixin
+from freqtrade.plugins.pairlist.IPairList import IPairList, SupportsBacktesting
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
+from freqtrade.resolvers import PairListResolver
+
+
+logger = logging.getLogger(__name__)
+
+
+class PairListManager(LoggingMixin):
+    def __init__(self, exchange, config: Config, dataprovider: DataProvider | None = None) -> None:
+        self._exchange = exchange
+        self._config = config
+        self._whitelist = self._config["exchange"].get("pair_whitelist")
+        self._blacklist = self._config["exchange"].get("pair_blacklist", [])
+        self._pairlist_handlers: list[IPairList] = []
+        self._tickers_needed = False
+        self._dataprovider: DataProvider | None = dataprovider
+        for pairlist_handler_config in self._config.get("pairlists", []):
+            pairlist_handler = PairListResolver.load_pairlist(
+                pairlist_handler_config["method"],
+                exchange=exchange,
+                pairlistmanager=self,
+                config=config,
+                pairlistconfig=pairlist_handler_config,
+                pairlist_pos=len(self._pairlist_handlers),
+            )
+            self._tickers_needed |= pairlist_handler.needstickers
+            self._pairlist_handlers.append(pairlist_handler)
+
+        if not self._pairlist_handlers:
+            raise OperationalException("No Pairlist Handlers defined")
+
+        if self._tickers_needed and not self._exchange.exchange_has("fetchTickers"):
+            invalid = ". ".join([p.name for p in self._pairlist_handlers if p.needstickers])
+
+            raise OperationalException(
+                "Exchange does not support fetchTickers, therefore the following pairlists "
+                "cannot be used. Please edit your config and restart the bot.\n"
+                f"{invalid}."
+            )
+
+        self._check_backtest()
+
+        refresh_period = config.get("pairlist_refresh_period", 3600)
+        LoggingMixin.__init__(self, logger, refresh_period)
+
+    def _check_backtest(self) -> None:
+        if self._config["runmode"] not in (RunMode.BACKTEST, RunMode.EDGE, RunMode.HYPEROPT):
+            return
+
+        pairlist_errors: list[str] = []
+        noaction_pairlists: list[str] = []
+        biased_pairlists: list[str] = []
+        for pairlist_handler in self._pairlist_handlers:
+            if pairlist_handler.supports_backtesting == SupportsBacktesting.NO:
+                pairlist_errors.append(pairlist_handler.name)
+            if pairlist_handler.supports_backtesting == SupportsBacktesting.NO_ACTION:
+                noaction_pairlists.append(pairlist_handler.name)
+            if pairlist_handler.supports_backtesting == SupportsBacktesting.BIASED:
+                biased_pairlists.append(pairlist_handler.name)
+
+        if noaction_pairlists:
+            logger.warning(
+                f"Pairlist Handlers {', '.join(noaction_pairlists)} do not generate "
+                "any changes during backtesting. While it's safe to leave them enabled, they will "
+                "not behave like in dry/live modes. "
+            )
+
+        if biased_pairlists:
+            logger.warning(
+                f"Pairlist Handlers {', '.join(biased_pairlists)} will introduce a lookahead bias "
+                "to your backtest results, as they use today's data - which inheritly suffers from "
+                "'winner bias'."
+            )
+        if pairlist_errors:
+            raise OperationalException(
+                f"Pairlist Handlers {', '.join(pairlist_errors)} do not support backtesting."
+            )
+
+    @property
+    def whitelist(self) -> list[str]:
+        """The current whitelist"""
+        return self._whitelist
+
+    @property
+    def blacklist(self) -> list[str]:
+        """
+        The current blacklist
+        -> no need to overwrite in subclasses
+        """
+        return self._blacklist
+
+    @property
+    def expanded_blacklist(self) -> list[str]:
+        """The expanded blacklist (including wildcard expansion)"""
+        return expand_pairlist(self._blacklist, self._exchange.get_markets().keys())
+
+    @property
+    def name_list(self) -> list[str]:
+        """Get list of loaded Pairlist Handler names"""
+        return [p.name for p in self._pairlist_handlers]
+
+    def short_desc(self) -> list[dict]:
+        """List of short_desc for each Pairlist Handler"""
+        return [{p.name: p.short_desc()} for p in self._pairlist_handlers]
+
+    @cached(TTLCache(maxsize=1, ttl=1800))
+    def _get_cached_tickers(self) -> Tickers:
+        return self._exchange.get_tickers()
+
+    def refresh_pairlist(self) -> None:
+        """Run pairlist through all configured Pairlist Handlers."""
+        # Tickers should be cached to avoid calling the exchange on each call.
+        tickers: dict = {}
+        if self._tickers_needed:
+            tickers = self._get_cached_tickers()
+
+        # Generate the pairlist with first Pairlist Handler in the chain
+        pairlist = self._pairlist_handlers[0].gen_pairlist(tickers)
+
+        # Process all Pairlist Handlers in the chain
+        # except for the first one, which is the generator.
+        for pairlist_handler in self._pairlist_handlers[1:]:
+            pairlist = pairlist_handler.filter_pairlist(pairlist, tickers)
+
+        # Validation against blacklist happens after the chain of Pairlist Handlers
+        # to ensure blacklist is respected.
+        pairlist = self.verify_blacklist(pairlist, logger.warning)
+
+        self.log_once(f"Whitelist with {len(pairlist)} pairs: {pairlist}", logger.info)
+
+        self._whitelist = pairlist
+
+    def verify_blacklist(self, pairlist: list[str], logmethod) -> list[str]:
+        """
+        Verify and remove items from pairlist - returning a filtered pairlist.
+        Logs a warning or info depending on `aswarning`.
+        Pairlist Handlers explicitly using this method shall use
+        `logmethod=logger.info` to avoid spamming with warning messages
+        :param pairlist: Pairlist to validate
+        :param logmethod: Function that'll be called, `logger.info` or `logger.warning`.
+        :return: pairlist - blacklisted pairs
+        """
+        try:
+            blacklist = self.expanded_blacklist
+        except ValueError as err:
+            logger.error(f"Pair blacklist contains an invalid Wildcard: {err}")
+            return []
+        log_once = partial(self.log_once, logmethod=logmethod)
+        for pair in pairlist.copy():
+            if pair in blacklist:
+                log_once(f"Pair {pair} in your blacklist. Removing it from whitelist...")
+                pairlist.remove(pair)
+        return pairlist
+
+    def verify_whitelist(
+        self, pairlist: list[str], logmethod, keep_invalid: bool = False
+    ) -> list[str]:
+        """
+        Verify and remove items from pairlist - returning a filtered pairlist.
+        Logs a warning or info depending on `aswarning`.
+        Pairlist Handlers explicitly using this method shall use
+        `logmethod=logger.info` to avoid spamming with warning messages
+        :param pairlist: Pairlist to validate
+        :param logmethod: Function that'll be called, `logger.info` or `logger.warning`
+        :param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes.
+        :return: pairlist - whitelisted pairs
+        """
+        try:
+            whitelist = expand_pairlist(pairlist, self._exchange.get_markets().keys(), keep_invalid)
+        except ValueError as err:
+            logger.error(f"Pair whitelist contains an invalid Wildcard: {err}")
+            return []
+        return whitelist
+
+    def create_pair_list(
+        self, pairs: list[str], timeframe: str | None = None
+    ) -> ListPairsWithTimeframes:
+        """
+        Create list of pair tuples with (pair, timeframe)
+        """
+        return [
+            (
+                pair,
+                timeframe or self._config["timeframe"],
+                self._config.get("candle_type_def", CandleType.SPOT),
+            )
+            for pair in pairs
+        ]
diff --git a/freqtrade/plugins/protectionmanager.py b/freqtrade/plugins/protectionmanager.py
new file mode 100644
index 000000000..187b9ee63
--- /dev/null
+++ b/freqtrade/plugins/protectionmanager.py
@@ -0,0 +1,115 @@
+"""
+Protection manager class
+"""
+
+import logging
+from datetime import datetime, timezone
+from typing import Any
+
+from freqtrade.constants import Config, LongShort
+from freqtrade.exceptions import ConfigurationError
+from freqtrade.persistence import PairLocks
+from freqtrade.persistence.models import PairLock
+from freqtrade.plugins.protections import IProtection
+from freqtrade.resolvers import ProtectionResolver
+
+
+logger = logging.getLogger(__name__)
+
+
+class ProtectionManager:
+    def __init__(self, config: Config, protections: list) -> None:
+        self._config = config
+
+        self._protection_handlers: list[IProtection] = []
+        self.validate_protections(protections)
+        for protection_handler_config in protections:
+            protection_handler = ProtectionResolver.load_protection(
+                protection_handler_config["method"],
+                config=config,
+                protection_config=protection_handler_config,
+            )
+            self._protection_handlers.append(protection_handler)
+
+        if not self._protection_handlers:
+            logger.info("No protection Handlers defined.")
+
+    @property
+    def name_list(self) -> list[str]:
+        """
+        Get list of loaded Protection Handler names
+        """
+        return [p.name for p in self._protection_handlers]
+
+    def short_desc(self) -> list[dict]:
+        """
+        List of short_desc for each Pairlist Handler
+        """
+        return [{p.name: p.short_desc()} for p in self._protection_handlers]
+
+    def global_stop(self, now: datetime | None = None, side: LongShort = "long") -> PairLock | None:
+        if not now:
+            now = datetime.now(timezone.utc)
+        result = None
+        for protection_handler in self._protection_handlers:
+            if protection_handler.has_global_stop:
+                lock = protection_handler.global_stop(date_now=now, side=side)
+                if lock and lock.until:
+                    if not PairLocks.is_global_lock(lock.until, side=lock.lock_side):
+                        result = PairLocks.lock_pair(
+                            "*", lock.until, lock.reason, now=now, side=lock.lock_side
+                        )
+        return result
+
+    def stop_per_pair(
+        self, pair, now: datetime | None = None, side: LongShort = "long"
+    ) -> PairLock | None:
+        if not now:
+            now = datetime.now(timezone.utc)
+        result = None
+        for protection_handler in self._protection_handlers:
+            if protection_handler.has_local_stop:
+                lock = protection_handler.stop_per_pair(pair=pair, date_now=now, side=side)
+                if lock and lock.until:
+                    if not PairLocks.is_pair_locked(pair, lock.until, lock.lock_side):
+                        result = PairLocks.lock_pair(
+                            pair, lock.until, lock.reason, now=now, side=lock.lock_side
+                        )
+        return result
+
+    @staticmethod
+    def validate_protections(protections: list[dict[str, Any]]) -> None:
+        """
+        Validate protection setup validity
+        """
+
+        for prot in protections:
+            parsed_unlock_at = None
+            if (config_unlock_at := prot.get("unlock_at")) is not None:
+                try:
+                    parsed_unlock_at = datetime.strptime(config_unlock_at, "%H:%M")
+                except ValueError:
+                    raise ConfigurationError(
+                        f"Invalid date format for unlock_at: {config_unlock_at}."
+                    )
+
+            if "stop_duration" in prot and "stop_duration_candles" in prot:
+                raise ConfigurationError(
+                    "Protections must specify either `stop_duration` or `stop_duration_candles`.\n"
+                    f"Please fix the protection {prot.get('method')}."
+                )
+
+            if "lookback_period" in prot and "lookback_period_candles" in prot:
+                raise ConfigurationError(
+                    "Protections must specify either `lookback_period` or "
+                    f"`lookback_period_candles`.\n Please fix the protection {prot.get('method')}."
+                )
+
+            if parsed_unlock_at is not None and (
+                "stop_duration" in prot or "stop_duration_candles" in prot
+            ):
+                raise ConfigurationError(
+                    "Protections must specify either `unlock_at`, `stop_duration` or "
+                    "`stop_duration_candles`.\n"
+                    f"Please fix the protection {prot.get('method')}."
+                )
diff --git a/freqtrade/plugins/protections/__init__.py b/freqtrade/plugins/protections/__init__.py
new file mode 100644
index 000000000..936355052
--- /dev/null
+++ b/freqtrade/plugins/protections/__init__.py
@@ -0,0 +1,2 @@
+# flake8: noqa: F401
+from freqtrade.plugins.protections.iprotection import IProtection, ProtectionReturn
diff --git a/freqtrade/plugins/protections/cooldown_period.py b/freqtrade/plugins/protections/cooldown_period.py
new file mode 100644
index 000000000..0f6aaa79a
--- /dev/null
+++ b/freqtrade/plugins/protections/cooldown_period.py
@@ -0,0 +1,74 @@
+import logging
+from datetime import datetime, timedelta
+
+from freqtrade.constants import LongShort
+from freqtrade.persistence import Trade
+from freqtrade.plugins.protections import IProtection, ProtectionReturn
+
+
+logger = logging.getLogger(__name__)
+
+
+class CooldownPeriod(IProtection):
+    has_global_stop: bool = False
+    has_local_stop: bool = True
+
+    def _reason(self) -> str:
+        """
+        LockReason to use
+        """
+        return f"Cooldown period for {self.unlock_reason_time_element}."
+
+    def short_desc(self) -> str:
+        """
+        Short method description - used for startup messages
+        """
+        return f"{self.name} - Cooldown period {self.unlock_reason_time_element}."
+
+    def _cooldown_period(self, pair: str, date_now: datetime) -> ProtectionReturn | None:
+        """
+        Get last trade for this pair
+        """
+        look_back_until = date_now - timedelta(minutes=self._lookback_period)
+        # filters = [
+        #     Trade.is_open.is_(False),
+        #     Trade.close_date > look_back_until,
+        #     Trade.pair == pair,
+        # ]
+        # trade = Trade.get_trades(filters).first()
+        trades = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until)
+        if trades:
+            # Get latest trade
+            # Ignore type error as we know we only get closed trades.
+            trade = sorted(trades, key=lambda t: t.close_date)[-1]  # type: ignore
+            self.log_once(f"Cooldown for {pair} {self.unlock_reason_time_element}.", logger.info)
+            until = self.calculate_lock_end([trade])
+
+            return ProtectionReturn(
+                lock=True,
+                until=until,
+                reason=self._reason(),
+            )
+
+        return None
+
+    def global_stop(self, date_now: datetime, side: LongShort) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for all pairs
+        This must evaluate to true for the whole period of the "cooldown period".
+        :return: Tuple of [bool, until, reason].
+            If true, all pairs will be locked with <reason> until <until>
+        """
+        # Not implemented for cooldown period.
+        return None
+
+    def stop_per_pair(
+        self, pair: str, date_now: datetime, side: LongShort
+    ) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for this pair
+        This must evaluate to true for the whole period of the "cooldown period".
+        :return: Tuple of [bool, until, reason].
+            If true, this pair will be locked with <reason> until <until>
+        """
+        return self._cooldown_period(pair, date_now)
diff --git a/freqtrade/plugins/protections/iprotection.py b/freqtrade/plugins/protections/iprotection.py
new file mode 100644
index 000000000..38a726dcc
--- /dev/null
+++ b/freqtrade/plugins/protections/iprotection.py
@@ -0,0 +1,141 @@
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from datetime import datetime, timedelta, timezone
+from typing import Any
+
+from freqtrade.constants import Config, LongShort
+from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.misc import plural
+from freqtrade.mixins import LoggingMixin
+from freqtrade.persistence import LocalTrade
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ProtectionReturn:
+    lock: bool
+    until: datetime
+    reason: str | None
+    lock_side: str = "*"
+
+
+class IProtection(LoggingMixin, ABC):
+    # Can globally stop the bot
+    has_global_stop: bool = False
+    # Can stop trading for one pair
+    has_local_stop: bool = False
+
+    def __init__(self, config: Config, protection_config: dict[str, Any]) -> None:
+        self._config = config
+        self._protection_config = protection_config
+        self._stop_duration_candles: int | None = None
+        self._stop_duration: int = 0
+        self._lookback_period_candles: int | None = None
+        self._unlock_at: str | None = None
+
+        tf_in_min = timeframe_to_minutes(config["timeframe"])
+        if "stop_duration_candles" in protection_config:
+            self._stop_duration_candles = int(protection_config.get("stop_duration_candles", 1))
+            self._stop_duration = tf_in_min * self._stop_duration_candles
+        elif "unlock_at" in protection_config:
+            self._unlock_at = protection_config.get("unlock_at")
+        else:
+            self._stop_duration = int(protection_config.get("stop_duration", 60))
+
+        if "lookback_period_candles" in protection_config:
+            self._lookback_period_candles = int(protection_config.get("lookback_period_candles", 1))
+            self._lookback_period = tf_in_min * self._lookback_period_candles
+        else:
+            self._lookback_period_candles = None
+            self._lookback_period = int(protection_config.get("lookback_period", 60))
+
+        LoggingMixin.__init__(self, logger)
+
+    @property
+    def name(self) -> str:
+        return self.__class__.__name__
+
+    @property
+    def stop_duration_str(self) -> str:
+        """
+        Output configured stop duration in either candles or minutes
+        """
+        if self._stop_duration_candles:
+            return (
+                f"{self._stop_duration_candles} "
+                f"{plural(self._stop_duration_candles, 'candle', 'candles')}"
+            )
+        else:
+            return f"{self._stop_duration} {plural(self._stop_duration, 'minute', 'minutes')}"
+
+    @property
+    def lookback_period_str(self) -> str:
+        """
+        Output configured lookback period in either candles or minutes
+        """
+        if self._lookback_period_candles:
+            return (
+                f"{self._lookback_period_candles} "
+                f"{plural(self._lookback_period_candles, 'candle', 'candles')}"
+            )
+        else:
+            return f"{self._lookback_period} {plural(self._lookback_period, 'minute', 'minutes')}"
+
+    @property
+    def unlock_reason_time_element(self) -> str:
+        """
+        Output configured unlock time or stop duration
+        """
+        if self._unlock_at is not None:
+            return f"until {self._unlock_at}"
+        else:
+            return f"for {self.stop_duration_str}"
+
+    @abstractmethod
+    def short_desc(self) -> str:
+        """
+        Short method description - used for startup-messages
+        -> Please overwrite in subclasses
+        """
+
+    @abstractmethod
+    def global_stop(self, date_now: datetime, side: LongShort) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for all pairs
+        This must evaluate to true for the whole period of the "cooldown period".
+        """
+
+    @abstractmethod
+    def stop_per_pair(
+        self, pair: str, date_now: datetime, side: LongShort
+    ) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for this pair
+        This must evaluate to true for the whole period of the "cooldown period".
+        :return: Tuple of [bool, until, reason].
+            If true, this pair will be locked with <reason> until <until>
+        """
+
+    def calculate_lock_end(self, trades: list[LocalTrade]) -> datetime:
+        """
+        Get lock end time
+        Implicitly uses `self._stop_duration` or `self._unlock_at` depending on the configuration.
+        """
+        max_date: datetime = max([trade.close_date for trade in trades if trade.close_date])
+        # coming from Database, tzinfo is not set.
+        if max_date.tzinfo is None:
+            max_date = max_date.replace(tzinfo=timezone.utc)
+
+        if self._unlock_at is not None:
+            # unlock_at case with fixed hour of the day
+            hour, minutes = self._unlock_at.split(":")
+            unlock_at = max_date.replace(hour=int(hour), minute=int(minutes))
+            if unlock_at < max_date:
+                unlock_at += timedelta(days=1)
+            return unlock_at
+
+        until = max_date + timedelta(minutes=self._stop_duration)
+        return until
diff --git a/freqtrade/plugins/protections/low_profit_pairs.py b/freqtrade/plugins/protections/low_profit_pairs.py
new file mode 100644
index 000000000..84934f394
--- /dev/null
+++ b/freqtrade/plugins/protections/low_profit_pairs.py
@@ -0,0 +1,102 @@
+import logging
+from datetime import datetime, timedelta
+from typing import Any
+
+from freqtrade.constants import Config, LongShort
+from freqtrade.persistence import Trade
+from freqtrade.plugins.protections import IProtection, ProtectionReturn
+
+
+logger = logging.getLogger(__name__)
+
+
+class LowProfitPairs(IProtection):
+    has_global_stop: bool = False
+    has_local_stop: bool = True
+
+    def __init__(self, config: Config, protection_config: dict[str, Any]) -> None:
+        super().__init__(config, protection_config)
+
+        self._trade_limit = protection_config.get("trade_limit", 1)
+        self._required_profit = protection_config.get("required_profit", 0.0)
+        self._only_per_side = protection_config.get("only_per_side", False)
+
+    def short_desc(self) -> str:
+        """
+        Short method description - used for startup-messages
+        """
+        return (
+            f"{self.name} - Low Profit Protection, locks pairs with "
+            f"profit < {self._required_profit} within {self.lookback_period_str}."
+        )
+
+    def _reason(self, profit: float) -> str:
+        """
+        LockReason to use
+        """
+        return (
+            f"{profit} < {self._required_profit} in {self.lookback_period_str}, "
+            f"locking {self.unlock_reason_time_element}."
+        )
+
+    def _low_profit(
+        self, date_now: datetime, pair: str, side: LongShort
+    ) -> ProtectionReturn | None:
+        """
+        Evaluate recent trades for pair
+        """
+        look_back_until = date_now - timedelta(minutes=self._lookback_period)
+        # filters = [
+        #     Trade.is_open.is_(False),
+        #     Trade.close_date > look_back_until,
+        # ]
+        # if pair:
+        #     filters.append(Trade.pair == pair)
+
+        trades = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until)
+        # trades = Trade.get_trades(filters).all()
+        if len(trades) < self._trade_limit:
+            # Not enough trades in the relevant period
+            return None
+
+        profit = sum(
+            trade.close_profit
+            for trade in trades
+            if trade.close_profit and (not self._only_per_side or trade.trade_direction == side)
+        )
+        if profit < self._required_profit:
+            self.log_once(
+                f"Trading for {pair} stopped due to {profit:.2f} < {self._required_profit} "
+                f"within {self._lookback_period} minutes.",
+                logger.info,
+            )
+            until = self.calculate_lock_end(trades)
+
+            return ProtectionReturn(
+                lock=True,
+                until=until,
+                reason=self._reason(profit),
+                lock_side=(side if self._only_per_side else "*"),
+            )
+
+        return None
+
+    def global_stop(self, date_now: datetime, side: LongShort) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for all pairs
+        This must evaluate to true for the whole period of the "cooldown period".
+        :return: Tuple of [bool, until, reason].
+            If true, all pairs will be locked with <reason> until <until>
+        """
+        return None
+
+    def stop_per_pair(
+        self, pair: str, date_now: datetime, side: LongShort
+    ) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for this pair
+        This must evaluate to true for the whole period of the "cooldown period".
+        :return: Tuple of [bool, until, reason].
+            If true, this pair will be locked with <reason> until <until>
+        """
+        return self._low_profit(date_now, pair=pair, side=side)
diff --git a/freqtrade/plugins/protections/max_drawdown_protection.py b/freqtrade/plugins/protections/max_drawdown_protection.py
new file mode 100644
index 000000000..e8996d3a8
--- /dev/null
+++ b/freqtrade/plugins/protections/max_drawdown_protection.py
@@ -0,0 +1,102 @@
+import logging
+from datetime import datetime, timedelta
+from typing import Any
+
+import pandas as pd
+
+from freqtrade.constants import Config, LongShort
+from freqtrade.data.metrics import calculate_max_drawdown
+from freqtrade.persistence import Trade
+from freqtrade.plugins.protections import IProtection, ProtectionReturn
+
+
+logger = logging.getLogger(__name__)
+
+
+class MaxDrawdown(IProtection):
+    has_global_stop: bool = True
+    has_local_stop: bool = False
+
+    def __init__(self, config: Config, protection_config: dict[str, Any]) -> None:
+        super().__init__(config, protection_config)
+
+        self._trade_limit = protection_config.get("trade_limit", 1)
+        self._max_allowed_drawdown = protection_config.get("max_allowed_drawdown", 0.0)
+        # TODO: Implement checks to limit max_drawdown to sensible values
+
+    def short_desc(self) -> str:
+        """
+        Short method description - used for startup-messages
+        """
+        return (
+            f"{self.name} - Max drawdown protection, stop trading if drawdown is > "
+            f"{self._max_allowed_drawdown} within {self.lookback_period_str}."
+        )
+
+    def _reason(self, drawdown: float) -> str:
+        """
+        LockReason to use
+        """
+        return (
+            f"{drawdown} passed {self._max_allowed_drawdown} in {self.lookback_period_str}, "
+            f"locking {self.unlock_reason_time_element}."
+        )
+
+    def _max_drawdown(self, date_now: datetime) -> ProtectionReturn | None:
+        """
+        Evaluate recent trades for drawdown ...
+        """
+        look_back_until = date_now - timedelta(minutes=self._lookback_period)
+
+        trades = Trade.get_trades_proxy(is_open=False, close_date=look_back_until)
+
+        trades_df = pd.DataFrame([trade.to_json() for trade in trades])
+
+        if len(trades) < self._trade_limit:
+            # Not enough trades in the relevant period
+            return None
+
+        # Drawdown is always positive
+        try:
+            # TODO: This should use absolute profit calculation, considering account balance.
+            drawdown_obj = calculate_max_drawdown(trades_df, value_col="close_profit")
+            drawdown = drawdown_obj.drawdown_abs
+        except ValueError:
+            return None
+
+        if drawdown > self._max_allowed_drawdown:
+            self.log_once(
+                f"Trading stopped due to Max Drawdown {drawdown:.2f} > {self._max_allowed_drawdown}"
+                f" within {self.lookback_period_str}.",
+                logger.info,
+            )
+
+            until = self.calculate_lock_end(trades)
+
+            return ProtectionReturn(
+                lock=True,
+                until=until,
+                reason=self._reason(drawdown),
+            )
+
+        return None
+
+    def global_stop(self, date_now: datetime, side: LongShort) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for all pairs
+        This must evaluate to true for the whole period of the "cooldown period".
+        :return: Tuple of [bool, until, reason].
+            If true, all pairs will be locked with <reason> until <until>
+        """
+        return self._max_drawdown(date_now)
+
+    def stop_per_pair(
+        self, pair: str, date_now: datetime, side: LongShort
+    ) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for this pair
+        This must evaluate to true for the whole period of the "cooldown period".
+        :return: Tuple of [bool, until, reason].
+            If true, this pair will be locked with <reason> until <until>
+        """
+        return None
diff --git a/freqtrade/plugins/protections/stoploss_guard.py b/freqtrade/plugins/protections/stoploss_guard.py
new file mode 100644
index 000000000..a429a2f80
--- /dev/null
+++ b/freqtrade/plugins/protections/stoploss_guard.py
@@ -0,0 +1,109 @@
+import logging
+from datetime import datetime, timedelta
+from typing import Any
+
+from freqtrade.constants import Config, LongShort
+from freqtrade.enums import ExitType
+from freqtrade.persistence import Trade
+from freqtrade.plugins.protections import IProtection, ProtectionReturn
+
+
+logger = logging.getLogger(__name__)
+
+
+class StoplossGuard(IProtection):
+    has_global_stop: bool = True
+    has_local_stop: bool = True
+
+    def __init__(self, config: Config, protection_config: dict[str, Any]) -> None:
+        super().__init__(config, protection_config)
+
+        self._trade_limit = protection_config.get("trade_limit", 10)
+        self._disable_global_stop = protection_config.get("only_per_pair", False)
+        self._only_per_side = protection_config.get("only_per_side", False)
+        self._profit_limit = protection_config.get("required_profit", 0.0)
+
+    def short_desc(self) -> str:
+        """
+        Short method description - used for startup-messages
+        """
+        return (
+            f"{self.name} - Frequent Stoploss Guard, {self._trade_limit} stoplosses "
+            f"with profit < {self._profit_limit:.2%} within {self.lookback_period_str}."
+        )
+
+    def _reason(self) -> str:
+        """
+        LockReason to use
+        """
+        return (
+            f"{self._trade_limit} stoplosses in {self._lookback_period} min, "
+            f"locking {self.unlock_reason_time_element}."
+        )
+
+    def _stoploss_guard(
+        self, date_now: datetime, pair: str | None, side: LongShort
+    ) -> ProtectionReturn | None:
+        """
+        Evaluate recent trades
+        """
+        look_back_until = date_now - timedelta(minutes=self._lookback_period)
+
+        trades1 = Trade.get_trades_proxy(pair=pair, is_open=False, close_date=look_back_until)
+        trades = [
+            trade
+            for trade in trades1
+            if (
+                str(trade.exit_reason)
+                in (
+                    ExitType.TRAILING_STOP_LOSS.value,
+                    ExitType.STOP_LOSS.value,
+                    ExitType.STOPLOSS_ON_EXCHANGE.value,
+                    ExitType.LIQUIDATION.value,
+                )
+                and trade.close_profit
+                and trade.close_profit < self._profit_limit
+            )
+        ]
+
+        if self._only_per_side:
+            # Long or short trades only
+            trades = [trade for trade in trades if trade.trade_direction == side]
+
+        if len(trades) < self._trade_limit:
+            return None
+
+        self.log_once(
+            f"Trading stopped due to {self._trade_limit} "
+            f"stoplosses within {self._lookback_period} minutes.",
+            logger.info,
+        )
+        until = self.calculate_lock_end(trades)
+        return ProtectionReturn(
+            lock=True,
+            until=until,
+            reason=self._reason(),
+            lock_side=(side if self._only_per_side else "*"),
+        )
+
+    def global_stop(self, date_now: datetime, side: LongShort) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for all pairs
+        This must evaluate to true for the whole period of the "cooldown period".
+        :return: Tuple of [bool, until, reason].
+            If true, all pairs will be locked with <reason> until <until>
+        """
+        if self._disable_global_stop:
+            return None
+        return self._stoploss_guard(date_now, None, side)
+
+    def stop_per_pair(
+        self, pair: str, date_now: datetime, side: LongShort
+    ) -> ProtectionReturn | None:
+        """
+        Stops trading (position entering) for this pair
+        This must evaluate to true for the whole period of the "cooldown period".
+        :return: Tuple of [bool, until, reason].
+            If true, this pair will be locked with <reason> until <until>
+        """
+        return self._stoploss_guard(date_now, pair, side)
diff --git a/freqtrade/resolvers/__init__.py b/freqtrade/resolvers/__init__.py
new file mode 100644
index 000000000..4bc2e8220
--- /dev/null
+++ b/freqtrade/resolvers/__init__.py
@@ -0,0 +1,11 @@
+# flake8: noqa: F401
+# isort: off
+from freqtrade.resolvers.iresolver import IResolver
+from freqtrade.resolvers.exchange_resolver import ExchangeResolver
+
+# isort: on
+# Don't import HyperoptResolver to avoid loading the whole Optimize tree
+# from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
+from freqtrade.resolvers.pairlist_resolver import PairListResolver
+from freqtrade.resolvers.protection_resolver import ProtectionResolver
+from freqtrade.resolvers.strategy_resolver import StrategyResolver
diff --git a/freqtrade/resolvers/exchange_resolver.py b/freqtrade/resolvers/exchange_resolver.py
new file mode 100644
index 000000000..e6edfb942
--- /dev/null
+++ b/freqtrade/resolvers/exchange_resolver.py
@@ -0,0 +1,114 @@
+"""
+This module loads custom exchanges
+"""
+
+import logging
+from inspect import isclass
+from typing import Any
+
+import freqtrade.exchange as exchanges
+from freqtrade.constants import Config, ExchangeConfig
+from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, Exchange
+from freqtrade.resolvers.iresolver import IResolver
+
+
+logger = logging.getLogger(__name__)
+
+
+class ExchangeResolver(IResolver):
+    """
+    This class contains all the logic to load a custom exchange class
+    """
+
+    object_type = Exchange
+
+    @staticmethod
+    def load_exchange(
+        config: Config,
+        *,
+        exchange_config: ExchangeConfig | None = None,
+        validate: bool = True,
+        load_leverage_tiers: bool = False,
+    ) -> Exchange:
+        """
+        Load the custom class from config parameter
+        :param exchange_name: name of the Exchange to load
+        :param config: configuration dictionary
+        """
+        exchange_name: str = config["exchange"]["name"]
+        # Map exchange name to avoid duplicate classes for identical exchanges
+        exchange_name = MAP_EXCHANGE_CHILDCLASS.get(exchange_name, exchange_name)
+        exchange_name = exchange_name.title()
+        exchange = None
+        try:
+            exchange = ExchangeResolver._load_exchange(
+                exchange_name,
+                kwargs={
+                    "config": config,
+                    "validate": validate,
+                    "exchange_config": exchange_config,
+                    "load_leverage_tiers": load_leverage_tiers,
+                },
+            )
+        except ImportError:
+            logger.info(
+                f"No {exchange_name} specific subclass found. Using the generic class instead."
+            )
+        if not exchange:
+            exchange = Exchange(
+                config,
+                validate=validate,
+                exchange_config=exchange_config,
+            )
+        return exchange
+
+    @staticmethod
+    def _load_exchange(exchange_name: str, kwargs: dict) -> Exchange:
+        """
+        Loads the specified exchange.
+        Only checks for exchanges exported in freqtrade.exchanges
+        :param exchange_name: name of the module to import
+        :return: Exchange instance or None
+        """
+
+        try:
+            ex_class = getattr(exchanges, exchange_name)
+
+            exchange = ex_class(**kwargs)
+            if exchange:
+                logger.info(f"Using resolved exchange '{exchange_name}'...")
+                return exchange
+        except AttributeError:
+            # Pass and raise ImportError instead
+            pass
+
+        raise ImportError(
+            f"Impossible to load Exchange '{exchange_name}'. This class does not exist "
+            "or contains Python code errors."
+        )
+
+    @classmethod
+    def search_all_objects(
+        cls, config: Config, enum_failed: bool, recursive: bool = False
+    ) -> list[dict[str, Any]]:
+        """
+        Searches for valid objects
+        :param config: Config object
+        :param enum_failed: If True, will return None for modules which fail.
+            Otherwise, failing modules are skipped.
+        :param recursive: Recursively walk directory tree searching for strategies
+        :return: List of dicts containing 'name', 'class' and 'location' entries
+        """
+        result = []
+        for exchange_name in dir(exchanges):
+            exchange = getattr(exchanges, exchange_name)
+            if isclass(exchange) and issubclass(exchange, Exchange):
+                result.append(
+                    {
+                        "name": exchange_name,
+                        "class": exchange,
+                        "location": exchange.__module__,
+                        "location_rel: ": exchange.__module__.replace("freqtrade.", ""),
+                    }
+                )
+        return result
diff --git a/freqtrade/resolvers/freqaimodel_resolver.py b/freqtrade/resolvers/freqaimodel_resolver.py
new file mode 100644
index 000000000..1bdf473d2
--- /dev/null
+++ b/freqtrade/resolvers/freqaimodel_resolver.py
@@ -0,0 +1,57 @@
+# pragma pylint: disable=attribute-defined-outside-init
+
+"""
+This module load a custom model for freqai
+"""
+
+import logging
+from pathlib import Path
+
+from freqtrade.constants import USERPATH_FREQAIMODELS, Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+from freqtrade.resolvers import IResolver
+
+
+logger = logging.getLogger(__name__)
+
+
+class FreqaiModelResolver(IResolver):
+    """
+    This class contains all the logic to load custom hyperopt loss class
+    """
+
+    object_type = IFreqaiModel
+    object_type_str = "FreqaiModel"
+    user_subdir = USERPATH_FREQAIMODELS
+    initial_search_path = (
+        Path(__file__).parent.parent.joinpath("freqai/prediction_models").resolve()
+    )
+    extra_path = "freqaimodel_path"
+
+    @staticmethod
+    def load_freqaimodel(config: Config) -> IFreqaiModel:
+        """
+        Load the custom class from config parameter
+        :param config: configuration dictionary
+        """
+        disallowed_models = ["BaseRegressionModel"]
+
+        freqaimodel_name = config.get("freqaimodel")
+        if not freqaimodel_name:
+            raise OperationalException(
+                "No freqaimodel set. Please use `--freqaimodel` to "
+                "specify the FreqaiModel class to use.\n"
+            )
+        if freqaimodel_name in disallowed_models:
+            raise OperationalException(
+                f"{freqaimodel_name} is a baseclass and cannot be used directly. Please choose "
+                "an existing child class or inherit from this baseclass.\n"
+            )
+        freqaimodel = FreqaiModelResolver.load_object(
+            freqaimodel_name,
+            config,
+            kwargs={"config": config},
+        )
+
+        return freqaimodel
diff --git a/freqtrade/resolvers/hyperopt_resolver.py b/freqtrade/resolvers/hyperopt_resolver.py
new file mode 100644
index 000000000..80c2e7890
--- /dev/null
+++ b/freqtrade/resolvers/hyperopt_resolver.py
@@ -0,0 +1,50 @@
+# pragma pylint: disable=attribute-defined-outside-init
+
+"""
+This module load custom hyperopt
+"""
+
+import logging
+from pathlib import Path
+
+from freqtrade.constants import HYPEROPT_LOSS_BUILTIN, USERPATH_HYPEROPTS, Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.hyperopt_loss.hyperopt_loss_interface import IHyperOptLoss
+from freqtrade.resolvers import IResolver
+
+
+logger = logging.getLogger(__name__)
+
+
+class HyperOptLossResolver(IResolver):
+    """
+    This class contains all the logic to load custom hyperopt loss class
+    """
+
+    object_type = IHyperOptLoss
+    object_type_str = "HyperoptLoss"
+    user_subdir = USERPATH_HYPEROPTS
+    initial_search_path = Path(__file__).parent.parent.joinpath("optimize/hyperopt_loss").resolve()
+
+    @staticmethod
+    def load_hyperoptloss(config: Config) -> IHyperOptLoss:
+        """
+        Load the custom class from config parameter
+        :param config: configuration dictionary
+        """
+
+        hyperoptloss_name = config.get("hyperopt_loss")
+        if not hyperoptloss_name:
+            raise OperationalException(
+                "No Hyperopt loss set. Please use `--hyperopt-loss` to "
+                "specify the Hyperopt-Loss class to use.\n"
+                f"Built-in Hyperopt-loss-functions are: {', '.join(HYPEROPT_LOSS_BUILTIN)}"
+            )
+        hyperoptloss = HyperOptLossResolver.load_object(
+            hyperoptloss_name, config, kwargs={}, extra_dir=config.get("hyperopt_path")
+        )
+
+        # Assign timeframe to be used in hyperopt
+        hyperoptloss.__class__.timeframe = str(config["timeframe"])
+
+        return hyperoptloss
diff --git a/freqtrade/resolvers/iresolver.py b/freqtrade/resolvers/iresolver.py
new file mode 100644
index 000000000..cb0c3de33
--- /dev/null
+++ b/freqtrade/resolvers/iresolver.py
@@ -0,0 +1,290 @@
+# pragma pylint: disable=attribute-defined-outside-init
+
+"""
+This module load custom objects
+"""
+
+import importlib.util
+import inspect
+import logging
+import sys
+from collections.abc import Iterator
+from pathlib import Path
+from typing import Any
+
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+class PathModifier:
+    def __init__(self, path: Path):
+        self.path = path
+
+    def __enter__(self):
+        """Inject path to allow importing with relative imports."""
+        sys.path.insert(0, str(self.path))
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Undo insertion of local path."""
+        str_path = str(self.path)
+        if str_path in sys.path:
+            sys.path.remove(str_path)
+
+
+class IResolver:
+    """
+    This class contains all the logic to load custom classes
+    """
+
+    # Childclasses need to override this
+    object_type: type[Any]
+    object_type_str: str
+    user_subdir: str | None = None
+    initial_search_path: Path | None = None
+    # Optional config setting containing a path (strategy_path, freqaimodel_path)
+    extra_path: str | None = None
+
+    @classmethod
+    def build_search_paths(
+        cls,
+        config: Config,
+        user_subdir: str | None = None,
+        extra_dirs: list[str] | None = None,
+    ) -> list[Path]:
+        abs_paths: list[Path] = []
+        if cls.initial_search_path:
+            abs_paths.append(cls.initial_search_path)
+
+        if user_subdir:
+            abs_paths.insert(0, config["user_data_dir"].joinpath(user_subdir))
+
+        # Add extra directory to the top of the search paths
+        if extra_dirs:
+            for directory in extra_dirs:
+                abs_paths.insert(0, Path(directory).resolve())
+
+        if cls.extra_path and (extra := config.get(cls.extra_path)):
+            abs_paths.insert(0, Path(extra).resolve())
+
+        return abs_paths
+
+    @classmethod
+    def _get_valid_object(
+        cls, module_path: Path, object_name: str | None, enum_failed: bool = False
+    ) -> Iterator[Any]:
+        """
+        Generator returning objects with matching object_type and object_name in the path given.
+        :param module_path: absolute path to the module
+        :param object_name: Class name of the object
+        :param enum_failed: If True, will return None for modules which fail.
+            Otherwise, failing modules are skipped.
+        :return: generator containing tuple of matching objects
+             Tuple format: [Object, source]
+        """
+
+        # Generate spec based on absolute path
+        # Pass object_name as first argument to have logging print a reasonable name.
+        with PathModifier(module_path.parent):
+            module_name = module_path.stem or ""
+            spec = importlib.util.spec_from_file_location(module_name, str(module_path))
+            if not spec:
+                return iter([None])
+
+            module = importlib.util.module_from_spec(spec)
+            try:
+                spec.loader.exec_module(module)  # type: ignore # importlib does not use typehints
+            except (
+                AttributeError,
+                ModuleNotFoundError,
+                SyntaxError,
+                ImportError,
+                NameError,
+            ) as err:
+                # Catch errors in case a specific module is not installed
+                logger.warning(f"Could not import {module_path} due to '{err}'")
+                if enum_failed:
+                    return iter([None])
+
+            def is_valid_class(obj):
+                try:
+                    return (
+                        inspect.isclass(obj)
+                        and issubclass(obj, cls.object_type)
+                        and obj is not cls.object_type
+                        and obj.__module__ == module_name
+                    )
+                except TypeError:
+                    return False
+
+            valid_objects_gen = (
+                (obj, inspect.getsource(module))
+                for name, obj in inspect.getmembers(module, is_valid_class)
+                if (object_name is None or object_name == name)
+            )
+            # The __module__ check ensures we only use strategies that are defined in this folder.
+            return valid_objects_gen
+
+    @classmethod
+    def _search_object(
+        cls, directory: Path, *, object_name: str, add_source: bool = False
+    ) -> tuple[Any, Path] | tuple[None, None]:
+        """
+        Search for the objectname in the given directory
+        :param directory: relative or absolute directory path
+        :param object_name: ClassName of the object to load
+        :return: object class
+        """
+        logger.debug(f"Searching for {cls.object_type.__name__} {object_name} in '{directory}'")
+        for entry in directory.iterdir():
+            # Only consider python files
+            if entry.suffix != ".py":
+                logger.debug("Ignoring %s", entry)
+                continue
+            if entry.is_symlink() and not entry.is_file():
+                logger.debug("Ignoring broken symlink %s", entry)
+                continue
+            module_path = entry.resolve()
+
+            obj = next(cls._get_valid_object(module_path, object_name), None)
+
+            if obj:
+                obj[0].__file__ = str(entry)
+                if add_source:
+                    obj[0].__source__ = obj[1]
+                return (obj[0], module_path)
+        return (None, None)
+
+    @classmethod
+    def _load_object(
+        cls, paths: list[Path], *, object_name: str, add_source: bool = False, kwargs: dict
+    ) -> Any | None:
+        """
+        Try to load object from path list.
+        """
+
+        for _path in paths:
+            try:
+                (module, module_path) = cls._search_object(
+                    directory=_path, object_name=object_name, add_source=add_source
+                )
+                if module:
+                    logger.info(
+                        f"Using resolved {cls.object_type.__name__.lower()[1:]} {object_name} "
+                        f"from '{module_path}'..."
+                    )
+                    return module(**kwargs)
+            except FileNotFoundError:
+                logger.warning('Path "%s" does not exist.', _path.resolve())
+
+        return None
+
+    @classmethod
+    def load_object(
+        cls, object_name: str, config: Config, *, kwargs: dict, extra_dir: str | None = None
+    ) -> Any:
+        """
+        Search and loads the specified object as configured in the child class.
+        :param object_name: name of the module to import
+        :param config: configuration dictionary
+        :param extra_dir: additional directory to search for the given pairlist
+        :raises: OperationalException if the class is invalid or does not exist.
+        :return: Object instance or None
+        """
+
+        extra_dirs: list[str] = []
+        if extra_dir:
+            extra_dirs.append(extra_dir)
+
+        abs_paths = cls.build_search_paths(
+            config, user_subdir=cls.user_subdir, extra_dirs=extra_dirs
+        )
+
+        found_object = cls._load_object(paths=abs_paths, object_name=object_name, kwargs=kwargs)
+        if found_object:
+            return found_object
+        raise OperationalException(
+            f"Impossible to load {cls.object_type_str} '{object_name}'. This class does not exist "
+            "or contains Python code errors."
+        )
+
+    @classmethod
+    def search_all_objects(
+        cls, config: Config, enum_failed: bool, recursive: bool = False
+    ) -> list[dict[str, Any]]:
+        """
+        Searches for valid objects
+        :param config: Config object
+        :param enum_failed: If True, will return None for modules which fail.
+            Otherwise, failing modules are skipped.
+        :param recursive: Recursively walk directory tree searching for strategies
+        :return: List of dicts containing 'name', 'class' and 'location' entries
+        """
+        result = []
+
+        abs_paths = cls.build_search_paths(config, user_subdir=cls.user_subdir)
+        for path in abs_paths:
+            result.extend(cls._search_all_objects(path, enum_failed, recursive))
+        return result
+
+    @classmethod
+    def _build_rel_location(cls, directory: Path, entry: Path) -> str:
+        builtin = cls.initial_search_path == directory
+        return (
+            f"<builtin>/{entry.relative_to(directory)}"
+            if builtin
+            else str(entry.relative_to(directory))
+        )
+
+    @classmethod
+    def _search_all_objects(
+        cls,
+        directory: Path,
+        enum_failed: bool,
+        recursive: bool = False,
+        basedir: Path | None = None,
+    ) -> list[dict[str, Any]]:
+        """
+        Searches a directory for valid objects
+        :param directory: Path to search
+        :param enum_failed: If True, will return None for modules which fail.
+            Otherwise, failing modules are skipped.
+        :param recursive: Recursively walk directory tree searching for strategies
+        :return: List of dicts containing 'name', 'class' and 'location' entries
+        """
+        logger.debug(f"Searching for {cls.object_type.__name__} '{directory}'")
+        objects: list[dict[str, Any]] = []
+        if not directory.is_dir():
+            logger.info(f"'{directory}' is not a directory, skipping.")
+            return objects
+        for entry in directory.iterdir():
+            if (
+                recursive
+                and entry.is_dir()
+                and not entry.name.startswith("__")
+                and not entry.name.startswith(".")
+            ):
+                objects.extend(
+                    cls._search_all_objects(entry, enum_failed, recursive, basedir or directory)
+                )
+            # Only consider python files
+            if entry.suffix != ".py":
+                logger.debug("Ignoring %s", entry)
+                continue
+            module_path = entry.resolve()
+            logger.debug(f"Path {module_path}")
+            for obj in cls._get_valid_object(
+                module_path, object_name=None, enum_failed=enum_failed
+            ):
+                objects.append(
+                    {
+                        "name": obj[0].__name__ if obj is not None else "",
+                        "class": obj[0] if obj is not None else None,
+                        "location": entry,
+                        "location_rel": cls._build_rel_location(basedir or directory, entry),
+                    }
+                )
+        return objects
diff --git a/freqtrade/resolvers/pairlist_resolver.py b/freqtrade/resolvers/pairlist_resolver.py
new file mode 100644
index 000000000..e6aed5ec0
--- /dev/null
+++ b/freqtrade/resolvers/pairlist_resolver.py
@@ -0,0 +1,57 @@
+# pragma pylint: disable=attribute-defined-outside-init
+
+"""
+This module load custom pairlists
+"""
+
+import logging
+from pathlib import Path
+
+from freqtrade.constants import Config
+from freqtrade.plugins.pairlist.IPairList import IPairList
+from freqtrade.resolvers import IResolver
+
+
+logger = logging.getLogger(__name__)
+
+
+class PairListResolver(IResolver):
+    """
+    This class contains all the logic to load custom PairList class
+    """
+
+    object_type = IPairList
+    object_type_str = "Pairlist"
+    user_subdir = None
+    initial_search_path = Path(__file__).parent.parent.joinpath("plugins/pairlist").resolve()
+
+    @staticmethod
+    def load_pairlist(
+        pairlist_name: str,
+        exchange,
+        pairlistmanager,
+        config: Config,
+        pairlistconfig: dict,
+        pairlist_pos: int,
+    ) -> IPairList:
+        """
+        Load the pairlist with pairlist_name
+        :param pairlist_name: Classname of the pairlist
+        :param exchange: Initialized exchange class
+        :param pairlistmanager: Initialized pairlist manager
+        :param config: configuration dictionary
+        :param pairlistconfig: Configuration dedicated to this pairlist
+        :param pairlist_pos: Position of the pairlist in the list of pairlists
+        :return: initialized Pairlist class
+        """
+        return PairListResolver.load_object(
+            pairlist_name,
+            config,
+            kwargs={
+                "exchange": exchange,
+                "pairlistmanager": pairlistmanager,
+                "config": config,
+                "pairlistconfig": pairlistconfig,
+                "pairlist_pos": pairlist_pos,
+            },
+        )
diff --git a/freqtrade/resolvers/protection_resolver.py b/freqtrade/resolvers/protection_resolver.py
new file mode 100644
index 000000000..661791ace
--- /dev/null
+++ b/freqtrade/resolvers/protection_resolver.py
@@ -0,0 +1,44 @@
+"""
+This module load custom pairlists
+"""
+
+import logging
+from pathlib import Path
+
+from freqtrade.constants import Config
+from freqtrade.plugins.protections import IProtection
+from freqtrade.resolvers import IResolver
+
+
+logger = logging.getLogger(__name__)
+
+
+class ProtectionResolver(IResolver):
+    """
+    This class contains all the logic to load custom PairList class
+    """
+
+    object_type = IProtection
+    object_type_str = "Protection"
+    user_subdir = None
+    initial_search_path = Path(__file__).parent.parent.joinpath("plugins/protections").resolve()
+
+    @staticmethod
+    def load_protection(
+        protection_name: str, config: Config, protection_config: dict
+    ) -> IProtection:
+        """
+        Load the protection with protection_name
+        :param protection_name: Classname of the pairlist
+        :param config: configuration dictionary
+        :param protection_config: Configuration dedicated to this pairlist
+        :return: initialized Protection class
+        """
+        return ProtectionResolver.load_object(
+            protection_name,
+            config,
+            kwargs={
+                "config": config,
+                "protection_config": protection_config,
+            },
+        )
diff --git a/freqtrade/resolvers/strategy_resolver.py b/freqtrade/resolvers/strategy_resolver.py
new file mode 100644
index 000000000..b56ed8e11
--- /dev/null
+++ b/freqtrade/resolvers/strategy_resolver.py
@@ -0,0 +1,326 @@
+# pragma pylint: disable=attribute-defined-outside-init
+
+"""
+This module load custom strategies
+"""
+
+import logging
+import tempfile
+from base64 import urlsafe_b64decode
+from inspect import getfullargspec
+from os import walk
+from pathlib import Path
+from typing import Any
+
+from freqtrade.configuration.config_validation import validate_migrated_strategy_settings
+from freqtrade.constants import REQUIRED_ORDERTIF, REQUIRED_ORDERTYPES, USERPATH_STRATEGIES, Config
+from freqtrade.enums import TradingMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.resolvers.iresolver import IResolver
+from freqtrade.strategy.interface import IStrategy
+
+
+logger = logging.getLogger(__name__)
+
+
+class StrategyResolver(IResolver):
+    """
+    This class contains the logic to load custom strategy class
+    """
+
+    object_type = IStrategy
+    object_type_str = "Strategy"
+    user_subdir = USERPATH_STRATEGIES
+    initial_search_path = None
+    extra_path = "strategy_path"
+
+    @staticmethod
+    def load_strategy(config: Config | None = None) -> IStrategy:
+        """
+        Load the custom class from config parameter
+        :param config: configuration dictionary or None
+        """
+        config = config or {}
+
+        if not config.get("strategy"):
+            raise OperationalException(
+                "No strategy set. Please use `--strategy` to specify the strategy class to use."
+            )
+
+        strategy_name = config["strategy"]
+        strategy: IStrategy = StrategyResolver._load_strategy(
+            strategy_name, config=config, extra_dir=config.get("strategy_path")
+        )
+        strategy.ft_load_params_from_file()
+        # Set attributes
+        # Check if we need to override configuration
+        #             (Attribute name,                    default,     subkey)
+        attributes = [
+            ("minimal_roi", {"0": 10.0}),
+            ("timeframe", None),
+            ("stoploss", None),
+            ("trailing_stop", None),
+            ("trailing_stop_positive", None),
+            ("trailing_stop_positive_offset", 0.0),
+            ("trailing_only_offset_is_reached", None),
+            ("use_custom_stoploss", None),
+            ("process_only_new_candles", None),
+            ("order_types", None),
+            ("order_time_in_force", None),
+            ("stake_currency", None),
+            ("stake_amount", None),
+            ("startup_candle_count", None),
+            ("unfilledtimeout", None),
+            ("use_exit_signal", True),
+            ("exit_profit_only", False),
+            ("ignore_roi_if_entry_signal", False),
+            ("exit_profit_offset", 0.0),
+            ("disable_dataframe_checks", False),
+            ("ignore_buying_expired_candle_after", 0),
+            ("position_adjustment_enable", False),
+            ("max_entry_position_adjustment", -1),
+            ("max_open_trades", -1),
+        ]
+        for attribute, default in attributes:
+            StrategyResolver._override_attribute_helper(strategy, config, attribute, default)
+
+        # Loop this list again to have output combined
+        for attribute, _ in attributes:
+            if attribute in config:
+                logger.info("Strategy using %s: %s", attribute, config[attribute])
+
+        StrategyResolver._normalize_attributes(strategy)
+
+        StrategyResolver._strategy_sanity_validations(strategy)
+        return strategy
+
+    @staticmethod
+    def _override_attribute_helper(strategy, config: Config, attribute: str, default: Any):
+        """
+        Override attributes in the strategy.
+        Prevalence:
+        - Configuration
+        - Strategy
+        - default (if not None)
+        """
+        if attribute in config and not isinstance(
+            getattr(type(strategy), attribute, None), property
+        ):
+            # Ensure Properties are not overwritten
+            setattr(strategy, attribute, config[attribute])
+            logger.info(
+                "Override strategy '%s' with value in config file: %s.",
+                attribute,
+                config[attribute],
+            )
+        elif hasattr(strategy, attribute):
+            val = getattr(strategy, attribute)
+            # None's cannot exist in the config, so do not copy them
+            if val is not None:
+                # max_open_trades set to -1 in the strategy will be copied as infinity in the config
+                if attribute == "max_open_trades" and val == -1:
+                    config[attribute] = float("inf")
+                else:
+                    config[attribute] = val
+        # Explicitly check for None here as other "falsy" values are possible
+        elif default is not None:
+            setattr(strategy, attribute, default)
+            config[attribute] = default
+
+    @staticmethod
+    def _normalize_attributes(strategy: IStrategy) -> IStrategy:
+        """
+        Normalize attributes to have the correct type.
+        """
+        # Sort and apply type conversions
+        if hasattr(strategy, "minimal_roi"):
+            strategy.minimal_roi = dict(
+                sorted(
+                    {int(key): value for (key, value) in strategy.minimal_roi.items()}.items(),
+                    key=lambda t: t[0],
+                )
+            )
+        if hasattr(strategy, "stoploss"):
+            strategy.stoploss = float(strategy.stoploss)
+        if hasattr(strategy, "max_open_trades") and strategy.max_open_trades < 0:
+            strategy.max_open_trades = float("inf")
+        return strategy
+
+    @staticmethod
+    def _strategy_sanity_validations(strategy: IStrategy):
+        # Ensure necessary migrations are performed first.
+        validate_migrated_strategy_settings(strategy.config)
+
+        if not all(k in strategy.order_types for k in REQUIRED_ORDERTYPES):
+            raise ImportError(
+                f"Impossible to load Strategy '{strategy.__class__.__name__}'. "
+                f"Order-types mapping is incomplete."
+            )
+        if not all(k in strategy.order_time_in_force for k in REQUIRED_ORDERTIF):
+            raise ImportError(
+                f"Impossible to load Strategy '{strategy.__class__.__name__}'. "
+                f"Order-time-in-force mapping is incomplete."
+            )
+        trading_mode = strategy.config.get("trading_mode", TradingMode.SPOT)
+
+        if strategy.can_short and trading_mode == TradingMode.SPOT:
+            raise ImportError(
+                "Short strategies cannot run in spot markets. Please make sure that this "
+                "is the correct strategy and that your trading mode configuration is correct. "
+                "You can run this strategy in spot markets by setting `can_short=False`"
+                " in your strategy. Please note that short signals will be ignored in that case."
+            )
+
+    @staticmethod
+    def validate_strategy(strategy: IStrategy) -> IStrategy:
+        if strategy.config.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT:
+            # Require new method
+            warn_deprecated_setting(strategy, "sell_profit_only", "exit_profit_only", True)
+            warn_deprecated_setting(strategy, "sell_profit_offset", "exit_profit_offset", True)
+            warn_deprecated_setting(strategy, "use_sell_signal", "use_exit_signal", True)
+            warn_deprecated_setting(
+                strategy, "ignore_roi_if_buy_signal", "ignore_roi_if_entry_signal", True
+            )
+
+            if not check_override(strategy, IStrategy, "populate_entry_trend"):
+                raise OperationalException("`populate_entry_trend` must be implemented.")
+            if not check_override(strategy, IStrategy, "populate_exit_trend"):
+                raise OperationalException("`populate_exit_trend` must be implemented.")
+            if check_override(strategy, IStrategy, "check_buy_timeout"):
+                raise OperationalException(
+                    "Please migrate your implementation "
+                    "of `check_buy_timeout` to `check_entry_timeout`."
+                )
+            if check_override(strategy, IStrategy, "check_sell_timeout"):
+                raise OperationalException(
+                    "Please migrate your implementation "
+                    "of `check_sell_timeout` to `check_exit_timeout`."
+                )
+
+            if check_override(strategy, IStrategy, "custom_sell"):
+                raise OperationalException(
+                    "Please migrate your implementation of `custom_sell` to `custom_exit`."
+                )
+
+        else:
+            # TODO: Implementing one of the following methods should show a deprecation warning
+            #  buy_trend and sell_trend, custom_sell
+            warn_deprecated_setting(strategy, "sell_profit_only", "exit_profit_only")
+            warn_deprecated_setting(strategy, "sell_profit_offset", "exit_profit_offset")
+            warn_deprecated_setting(strategy, "use_sell_signal", "use_exit_signal")
+            warn_deprecated_setting(
+                strategy, "ignore_roi_if_buy_signal", "ignore_roi_if_entry_signal"
+            )
+
+            if not check_override(strategy, IStrategy, "populate_buy_trend") and not check_override(
+                strategy, IStrategy, "populate_entry_trend"
+            ):
+                raise OperationalException(
+                    "`populate_entry_trend` or `populate_buy_trend` must be implemented."
+                )
+            if not check_override(
+                strategy, IStrategy, "populate_sell_trend"
+            ) and not check_override(strategy, IStrategy, "populate_exit_trend"):
+                raise OperationalException(
+                    "`populate_exit_trend` or `populate_sell_trend` must be implemented."
+                )
+
+            _populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
+            _buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
+            _sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
+            if any(x == 2 for x in [_populate_fun_len, _buy_fun_len, _sell_fun_len]):
+                raise OperationalException(
+                    "Strategy Interface v1 is no longer supported. "
+                    "Please update your strategy to implement "
+                    "`populate_indicators`, `populate_entry_trend` and `populate_exit_trend` "
+                    "with the metadata argument. "
+                )
+
+        has_after_fill = "after_fill" in getfullargspec(
+            strategy.custom_stoploss
+        ).args and check_override(strategy, IStrategy, "custom_stoploss")
+        if has_after_fill:
+            strategy._ft_stop_uses_after_fill = True
+
+        if check_override(strategy, IStrategy, "adjust_order_price") and (
+            check_override(strategy, IStrategy, "adjust_entry_price")
+            or check_override(strategy, IStrategy, "adjust_exit_price")
+        ):
+            raise OperationalException(
+                "If you implement `adjust_order_price`, `adjust_entry_price` and "
+                "`adjust_exit_price` will not be used. Please pick one approach for your strategy."
+            )
+        return strategy
+
+    @staticmethod
+    def _load_strategy(
+        strategy_name: str, config: Config, extra_dir: str | None = None
+    ) -> IStrategy:
+        """
+        Search and loads the specified strategy.
+        :param strategy_name: name of the module to import
+        :param config: configuration for the strategy
+        :param extra_dir: additional directory to search for the given strategy
+        :return: Strategy instance or None
+        """
+        if config.get("recursive_strategy_search", False):
+            extra_dirs: list[str] = [
+                path[0] for path in walk(f"{config['user_data_dir']}/{USERPATH_STRATEGIES}")
+            ]  # sub-directories
+        else:
+            extra_dirs = []
+
+        if extra_dir:
+            extra_dirs.append(extra_dir)
+
+        abs_paths = StrategyResolver.build_search_paths(
+            config, user_subdir=USERPATH_STRATEGIES, extra_dirs=extra_dirs
+        )
+
+        if ":" in strategy_name:
+            logger.info("loading base64 encoded strategy")
+            strat = strategy_name.split(":")
+
+            if len(strat) == 2:
+                temp = Path(tempfile.mkdtemp("freq", "strategy"))
+                name = strat[0] + ".py"
+
+                temp.joinpath(name).write_text(urlsafe_b64decode(strat[1]).decode("utf-8"))
+                temp.joinpath("__init__.py").touch()
+
+                strategy_name = strat[0]
+
+                # register temp path with the bot
+                abs_paths.insert(0, temp.resolve())
+
+        strategy = StrategyResolver._load_object(
+            paths=abs_paths,
+            object_name=strategy_name,
+            add_source=True,
+            kwargs={"config": config},
+        )
+
+        if strategy:
+            return StrategyResolver.validate_strategy(strategy)
+
+        raise OperationalException(
+            f"Impossible to load Strategy '{strategy_name}'. This class does not exist "
+            "or contains Python code errors."
+        )
+
+
+def warn_deprecated_setting(strategy: IStrategy, old: str, new: str, error=False):
+    if hasattr(strategy, old):
+        errormsg = f"DEPRECATED: Using '{old}' moved to '{new}'."
+        if error:
+            raise OperationalException(errormsg)
+        logger.warning(errormsg)
+        setattr(strategy, new, getattr(strategy, f"{old}"))
+
+
+def check_override(obj, parentclass, attribute: str):
+    """
+    Checks if a object overrides the parent class attribute.
+    :returns: True if the object is overridden.
+    """
+    return getattr(type(obj), attribute) != getattr(parentclass, attribute)
diff --git a/freqtrade/rpc/__init__.py b/freqtrade/rpc/__init__.py
index 163e0a8aa..07f83abc0 100644
--- a/freqtrade/rpc/__init__.py
+++ b/freqtrade/rpc/__init__.py
@@ -1,415 +1,2 @@
-import logging
-import re
-import arrow
-from decimal import Decimal
-from datetime import datetime, timedelta
-from pandas import DataFrame
-import sqlalchemy as sql
-# from sqlalchemy import and_, func, text
-
-from freqtrade.persistence import Trade
-from freqtrade.misc import State, get_state, update_state
-from freqtrade import exchange
-from freqtrade.fiat_convert import CryptoToFiatConverter
-from . import telegram
-
-logger = logging.getLogger(__name__)
-
-_FIAT_CONVERT = CryptoToFiatConverter()
-REGISTERED_MODULES = []
-
-
-def init(config: dict) -> None:
-    """
-    Initializes all enabled rpc modules
-    :param config: config to use
-    :return: None
-    """
-
-    if config['telegram'].get('enabled', False):
-        logger.info('Enabling rpc.telegram ...')
-        REGISTERED_MODULES.append('telegram')
-        telegram.init(config)
-
-
-def cleanup() -> None:
-    """
-    Stops all enabled rpc modules
-    :return: None
-    """
-    if 'telegram' in REGISTERED_MODULES:
-        logger.debug('Cleaning up rpc.telegram ...')
-        telegram.cleanup()
-
-
-def send_msg(msg: str) -> None:
-    """
-    Send given markdown message to all registered rpc modules
-    :param msg: message
-    :return: None
-    """
-    logger.info(msg)
-    if 'telegram' in REGISTERED_MODULES:
-        telegram.send_msg(msg)
-
-
-def shorten_date(_date):
-    """
-    Trim the date so it fits on small screens
-    """
-    new_date = re.sub('seconds?', 'sec', _date)
-    new_date = re.sub('minutes?', 'min', new_date)
-    new_date = re.sub('hours?', 'h', new_date)
-    new_date = re.sub('days?', 'd', new_date)
-    new_date = re.sub('^an?', '1', new_date)
-    return new_date
-
-
-#
-# Below follows the RPC backend
-# it is prefixed with rpc_
-# to raise awareness that it is
-# a remotely exposed function
-
-
-def rpc_trade_status():
-    # Fetch open trade
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    if get_state() != State.RUNNING:
-        return (True, '*Status:* `trader is not running`')
-    elif not trades:
-        return (True, '*Status:* `no active trade`')
-    else:
-        result = []
-        for trade in trades:
-            order = None
-            if trade.open_order_id:
-                order = exchange.get_order(trade.open_order_id)
-            # calculate profit and send message to user
-            current_rate = exchange.get_ticker(trade.pair, False)['bid']
-            current_profit = trade.calc_profit_percent(current_rate)
-            fmt_close_profit = '{:.2f}%'.format(
-                round(trade.close_profit * 100, 2)
-            ) if trade.close_profit else None
-            message = """
-*Trade ID:* `{trade_id}`
-*Current Pair:* [{pair}]({market_url})
-*Open Since:* `{date}`
-*Amount:* `{amount}`
-*Open Rate:* `{open_rate:.8f}`
-*Close Rate:* `{close_rate}`
-*Current Rate:* `{current_rate:.8f}`
-*Close Profit:* `{close_profit}`
-*Current Profit:* `{current_profit:.2f}%`
-*Open Order:* `{open_order}`
-            """.format(
-                trade_id=trade.id,
-                pair=trade.pair,
-                market_url=exchange.get_pair_detail_url(trade.pair),
-                date=arrow.get(trade.open_date).humanize(),
-                open_rate=trade.open_rate,
-                close_rate=trade.close_rate,
-                current_rate=current_rate,
-                amount=round(trade.amount, 8),
-                close_profit=fmt_close_profit,
-                current_profit=round(current_profit * 100, 2),
-                open_order='({} rem={:.8f})'.format(
-                    order['type'], order['remaining']
-                ) if order else None,
-            )
-            result.append(message)
-        return (False, result)
-
-
-def rpc_status_table():
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    if get_state() != State.RUNNING:
-        return (True, '*Status:* `trader is not running`')
-    elif not trades:
-        return (True, '*Status:* `no active order`')
-    else:
-        trades_list = []
-        for trade in trades:
-            # calculate profit and send message to user
-            current_rate = exchange.get_ticker(trade.pair, False)['bid']
-            trades_list.append([
-                trade.id,
-                trade.pair,
-                shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
-                '{:.2f}%'.format(100 * trade.calc_profit_percent(current_rate))
-            ])
-
-        columns = ['ID', 'Pair', 'Since', 'Profit']
-        df_statuses = DataFrame.from_records(trades_list, columns=columns)
-        df_statuses = df_statuses.set_index(columns[0])
-        # The style used throughout is to return a tuple
-        # consisting of (error_occured?, result)
-        # Another approach would be to just return the
-        # result, or raise error
-        return (False, df_statuses)
-
-
-def rpc_daily_profit(timescale, stake_currency, fiat_display_currency):
-    today = datetime.utcnow().date()
-    profit_days = {}
-
-    if not (isinstance(timescale, int) and timescale > 0):
-        return (True, '*Daily [n]:* `must be an integer greater than 0`')
-
-    fiat = _FIAT_CONVERT
-    for day in range(0, timescale):
-        profitday = today - timedelta(days=day)
-        trades = Trade.query \
-            .filter(Trade.is_open.is_(False)) \
-            .filter(Trade.close_date >= profitday)\
-            .filter(Trade.close_date < (profitday + timedelta(days=1)))\
-            .order_by(Trade.close_date)\
-            .all()
-        curdayprofit = sum(trade.calc_profit() for trade in trades)
-        profit_days[profitday] = {
-            'amount': format(curdayprofit, '.8f'),
-            'trades': len(trades)
-        }
-
-    stats = [
-        [
-            key,
-            '{value:.8f} {symbol}'.format(
-                value=float(value['amount']),
-                symbol=stake_currency
-            ),
-            '{value:.3f} {symbol}'.format(
-                value=fiat.convert_amount(
-                    value['amount'],
-                    stake_currency,
-                    fiat_display_currency
-                ),
-                symbol=fiat_display_currency
-            ),
-            '{value} trade{s}'.format(value=value['trades'], s='' if value['trades'] < 2 else 's'),
-        ]
-        for key, value in profit_days.items()
-    ]
-    return (False, stats)
-
-
-def rpc_trade_statistics(stake_currency, fiat_display_currency) -> None:
-    """
-    :return: cumulative profit statistics.
-    """
-    trades = Trade.query.order_by(Trade.id).all()
-
-    profit_all_coin = []
-    profit_all_percent = []
-    profit_closed_coin = []
-    profit_closed_percent = []
-    durations = []
-
-    for trade in trades:
-        current_rate = None
-
-        if not trade.open_rate:
-            continue
-        if trade.close_date:
-            durations.append((trade.close_date - trade.open_date).total_seconds())
-
-        if not trade.is_open:
-            profit_percent = trade.calc_profit_percent()
-            profit_closed_coin.append(trade.calc_profit())
-            profit_closed_percent.append(profit_percent)
-        else:
-            # Get current rate
-            current_rate = exchange.get_ticker(trade.pair, False)['bid']
-            profit_percent = trade.calc_profit_percent(rate=current_rate)
-
-        profit_all_coin.append(trade.calc_profit(rate=Decimal(trade.close_rate or current_rate)))
-        profit_all_percent.append(profit_percent)
-
-    best_pair = Trade.session.query(Trade.pair,
-                                    sql.func.sum(Trade.close_profit).label('profit_sum')) \
-        .filter(Trade.is_open.is_(False)) \
-        .group_by(Trade.pair) \
-        .order_by(sql.text('profit_sum DESC')) \
-        .first()
-
-    if not best_pair:
-        return (True, '*Status:* `no closed trade`')
-
-    bp_pair, bp_rate = best_pair
-
-    # FIX: we want to keep fiatconverter in a state/environment,
-    #      doing this will utilize its caching functionallity, instead we reinitialize it here
-    fiat = _FIAT_CONVERT
-    # Prepare data to display
-    profit_closed_coin = round(sum(profit_closed_coin), 8)
-    profit_closed_percent = round(sum(profit_closed_percent) * 100, 2)
-    profit_closed_fiat = fiat.convert_amount(
-        profit_closed_coin,
-        stake_currency,
-        fiat_display_currency
-    )
-    profit_all_coin = round(sum(profit_all_coin), 8)
-    profit_all_percent = round(sum(profit_all_percent) * 100, 2)
-    profit_all_fiat = fiat.convert_amount(
-        profit_all_coin,
-        stake_currency,
-        fiat_display_currency
-    )
-    num = float(len(durations) or 1)
-    return (False,
-            {'profit_closed_coin': profit_closed_coin,
-             'profit_closed_percent': profit_closed_percent,
-             'profit_closed_fiat': profit_closed_fiat,
-             'profit_all_coin': profit_all_coin,
-             'profit_all_percent': profit_all_percent,
-             'profit_all_fiat': profit_all_fiat,
-             'trade_count': len(trades),
-             'first_trade_date': arrow.get(trades[0].open_date).humanize(),
-             'latest_trade_date': arrow.get(trades[-1].open_date).humanize(),
-             'avg_duration': str(timedelta(seconds=sum(durations) /
-                                           num)).split('.')[0],
-             'best_pair': bp_pair,
-             'best_rate': round(bp_rate * 100, 2)
-             })
-
-
-def rpc_balance(fiat_display_currency):
-    """
-    :return: current account balance per crypto
-    """
-    balances = [
-        c for c in exchange.get_balances()
-        if c['Balance'] or c['Available'] or c['Pending']
-    ]
-    if not balances:
-        return (True, '`All balances are zero.`')
-
-    output = []
-    total = 0.0
-    for currency in balances:
-        coin = currency['Currency']
-        if coin == 'BTC':
-            currency["Rate"] = 1.0
-        else:
-            if coin == 'USDT':
-                currency["Rate"] = 1.0 / exchange.get_ticker('USDT_BTC', False)['bid']
-            else:
-                currency["Rate"] = exchange.get_ticker('BTC_' + coin, False)['bid']
-        currency['BTC'] = currency["Rate"] * currency["Balance"]
-        total = total + currency['BTC']
-        output.append({'currency': currency['Currency'],
-                       'available': currency['Available'],
-                       'balance': currency['Balance'],
-                       'pending': currency['Pending'],
-                       'est_btc': currency['BTC']
-                       })
-    fiat = _FIAT_CONVERT
-    symbol = fiat_display_currency
-    value = fiat.convert_amount(total, 'BTC', symbol)
-    return (False, (output, total, symbol, value))
-
-
-def rpc_start():
-    """
-    Handler for start.
-    """
-    if get_state() == State.RUNNING:
-        return (True, '*Status:* `already running`')
-    else:
-        update_state(State.RUNNING)
-
-
-def rpc_stop():
-    """
-    Handler for stop.
-    """
-    if get_state() == State.RUNNING:
-        update_state(State.STOPPED)
-        return (False, '`Stopping trader ...`')
-    else:
-        return (True, '*Status:* `already stopped`')
-
-
-# FIX: no test for this!!!!
-def rpc_forcesell(trade_id) -> None:
-    """
-    Handler for forcesell <id>.
-    Sells the given trade at current price
-    :return: error or None
-    """
-    def _exec_forcesell(trade: Trade) -> str:
-        # Check if there is there is an open order
-        if trade.open_order_id:
-            order = exchange.get_order(trade.open_order_id)
-
-            # Cancel open LIMIT_BUY orders and close trade
-            if order and not order['closed'] and order['type'] == 'LIMIT_BUY':
-                exchange.cancel_order(trade.open_order_id)
-                trade.close(order.get('rate') or trade.open_rate)
-                # TODO: sell amount which has been bought already
-                return
-
-            # Ignore trades with an attached LIMIT_SELL order
-            if order and not order['closed'] and order['type'] == 'LIMIT_SELL':
-                return
-
-        # Get current rate and execute sell
-        current_rate = exchange.get_ticker(trade.pair, False)['bid']
-        from freqtrade.main import execute_sell
-        execute_sell(trade, current_rate)
-    # ---- EOF def _exec_forcesell ----
-
-    if get_state() != State.RUNNING:
-        return (True, '`trader is not running`')
-
-    if trade_id == 'all':
-        # Execute sell for all open orders
-        for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
-            _exec_forcesell(trade)
-        return (False, '')
-
-    # Query for trade
-    trade = Trade.query.filter(sql.and_(
-        Trade.id == trade_id,
-        Trade.is_open.is_(True)
-    )).first()
-    if not trade:
-        logger.warning('forcesell: Invalid argument received')
-        return (True, 'Invalid argument.')
-
-    _exec_forcesell(trade)
-    return (False, '')
-
-
-def rpc_performance() -> None:
-    """
-    Handler for performance.
-    Shows a performance statistic from finished trades
-    """
-    if get_state() != State.RUNNING:
-        return (True, '`trader is not running`')
-
-    pair_rates = Trade.session.query(Trade.pair,
-                                     sql.func.sum(Trade.close_profit).label('profit_sum'),
-                                     sql.func.count(Trade.pair).label('count')) \
-        .filter(Trade.is_open.is_(False)) \
-        .group_by(Trade.pair) \
-        .order_by(sql.text('profit_sum DESC')) \
-        .all()
-    trades = []
-    for (pair, rate, count) in pair_rates:
-        trades.append({'pair': pair, 'profit': round(rate * 100, 2), 'count': count})
-
-    return (False, trades)
-
-
-def rpc_count() -> None:
-    """
-    Returns the number of trades running
-    :return: None
-    """
-    if get_state() != State.RUNNING:
-        return (True, '`trader is not running`')
-
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    return (False, trades)
+from .rpc import RPC, RPCException, RPCHandler  # noqa: F401
+from .rpc_manager import RPCManager  # noqa: F401
diff --git a/freqtrade/rpc/api_server/__init__.py b/freqtrade/rpc/api_server/__init__.py
new file mode 100644
index 000000000..b2ed3e6e0
--- /dev/null
+++ b/freqtrade/rpc/api_server/__init__.py
@@ -0,0 +1 @@
+from .webserver import ApiServer  # noqa: F401
diff --git a/freqtrade/rpc/api_server/api_auth.py b/freqtrade/rpc/api_server/api_auth.py
new file mode 100644
index 000000000..5f63c0aa5
--- /dev/null
+++ b/freqtrade/rpc/api_server/api_auth.py
@@ -0,0 +1,161 @@
+import logging
+import secrets
+from datetime import datetime, timedelta, timezone
+from typing import Any
+
+import jwt
+from fastapi import APIRouter, Depends, HTTPException, Query, WebSocket, status
+from fastapi.security import OAuth2PasswordBearer
+from fastapi.security.http import HTTPBasic, HTTPBasicCredentials
+
+from freqtrade.rpc.api_server.api_schemas import AccessAndRefreshToken, AccessToken
+from freqtrade.rpc.api_server.deps import get_api_config
+
+
+logger = logging.getLogger(__name__)
+
+ALGORITHM = "HS256"
+
+router_login = APIRouter()
+
+
+def verify_auth(api_config, username: str, password: str):
+    """Verify username/password"""
+    return secrets.compare_digest(username, api_config.get("username")) and secrets.compare_digest(
+        password, api_config.get("password")
+    )
+
+
+httpbasic = HTTPBasic(auto_error=False)
+security = HTTPBasic()
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
+
+
+def get_user_from_token(token, secret_key: str, token_type: str = "access") -> str:  # noqa: S107
+    credentials_exception = HTTPException(
+        status_code=status.HTTP_401_UNAUTHORIZED,
+        detail="Could not validate credentials",
+        headers={"WWW-Authenticate": "Bearer"},
+    )
+    try:
+        payload = jwt.decode(token, secret_key, algorithms=[ALGORITHM])
+        username: str = payload.get("identity", {}).get("u")
+        if username is None:
+            raise credentials_exception
+        if payload.get("type") != token_type:
+            raise credentials_exception
+
+    except jwt.PyJWTError:
+        raise credentials_exception
+    return username
+
+
+# This should be reimplemented to better realign with the existing tools provided
+# by FastAPI regarding API Tokens
+# https://github.com/tiangolo/fastapi/blob/master/fastapi/security/api_key.py
+async def validate_ws_token(
+    ws: WebSocket,
+    ws_token: str | None = Query(default=None, alias="token"),
+    api_config: dict[str, Any] = Depends(get_api_config),
+):
+    secret_ws_token = api_config.get("ws_token", None)
+    secret_jwt_key = api_config.get("jwt_secret_key", "super-secret")
+
+    # Check if ws_token is/in secret_ws_token
+    if ws_token and secret_ws_token:
+        is_valid_ws_token = False
+        if isinstance(secret_ws_token, str):
+            is_valid_ws_token = secrets.compare_digest(secret_ws_token, ws_token)
+        elif isinstance(secret_ws_token, list):
+            is_valid_ws_token = any(
+                [secrets.compare_digest(potential, ws_token) for potential in secret_ws_token]
+            )
+
+        if is_valid_ws_token:
+            return ws_token
+
+    # Check if ws_token is a JWT
+    try:
+        user = get_user_from_token(ws_token, secret_jwt_key)
+        return user
+    # If the token is a jwt, and it's valid return the user
+    except HTTPException:
+        pass
+
+    # If it doesn't match, close the websocket connection
+    await ws.close(code=status.WS_1008_POLICY_VIOLATION)
+
+
+def create_token(data: dict, secret_key: str, token_type: str = "access") -> str:  # noqa: S107
+    to_encode = data.copy()
+    if token_type == "access":  # noqa: S105
+        expire = datetime.now(timezone.utc) + timedelta(minutes=15)
+    elif token_type == "refresh":  # noqa: S105
+        expire = datetime.now(timezone.utc) + timedelta(days=30)
+    else:
+        raise ValueError()
+    to_encode.update(
+        {
+            "exp": expire,
+            "iat": datetime.now(timezone.utc),
+            "type": token_type,
+        }
+    )
+    encoded_jwt = jwt.encode(to_encode, secret_key, algorithm=ALGORITHM)
+    return encoded_jwt
+
+
+def http_basic_or_jwt_token(
+    form_data: HTTPBasicCredentials = Depends(httpbasic),
+    token: str = Depends(oauth2_scheme),
+    api_config=Depends(get_api_config),
+):
+    if token:
+        return get_user_from_token(token, api_config.get("jwt_secret_key", "super-secret"))
+    elif form_data and verify_auth(api_config, form_data.username, form_data.password):
+        return form_data.username
+
+    raise HTTPException(
+        status_code=status.HTTP_401_UNAUTHORIZED,
+        detail="Unauthorized",
+    )
+
+
+@router_login.post("/token/login", response_model=AccessAndRefreshToken)
+def token_login(
+    form_data: HTTPBasicCredentials = Depends(security), api_config=Depends(get_api_config)
+):
+    if verify_auth(api_config, form_data.username, form_data.password):
+        token_data = {"identity": {"u": form_data.username}}
+        access_token = create_token(
+            token_data,
+            api_config.get("jwt_secret_key", "super-secret"),
+            token_type="access",  # noqa: S106
+        )
+        refresh_token = create_token(
+            token_data,
+            api_config.get("jwt_secret_key", "super-secret"),
+            token_type="refresh",  # noqa: S106
+        )
+        return {
+            "access_token": access_token,
+            "refresh_token": refresh_token,
+        }
+    else:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Incorrect username or password",
+        )
+
+
+@router_login.post("/token/refresh", response_model=AccessToken)
+def token_refresh(token: str = Depends(oauth2_scheme), api_config=Depends(get_api_config)):
+    # Refresh token
+    u = get_user_from_token(token, api_config.get("jwt_secret_key", "super-secret"), "refresh")
+    token_data = {"identity": {"u": u}}
+    access_token = create_token(
+        token_data,
+        api_config.get("jwt_secret_key", "super-secret"),
+        token_type="access",  # noqa: S106
+    )
+    return {"access_token": access_token}
diff --git a/freqtrade/rpc/api_server/api_background_tasks.py b/freqtrade/rpc/api_server/api_background_tasks.py
new file mode 100644
index 000000000..b41828d6f
--- /dev/null
+++ b/freqtrade/rpc/api_server/api_background_tasks.py
@@ -0,0 +1,45 @@
+import logging
+
+from fastapi import APIRouter
+from fastapi.exceptions import HTTPException
+
+from freqtrade.rpc.api_server.api_schemas import BackgroundTaskStatus
+from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
+
+
+logger = logging.getLogger(__name__)
+
+# Private API, protected by authentication and webserver_mode dependency
+router = APIRouter()
+
+
+@router.get("/background", response_model=list[BackgroundTaskStatus], tags=["webserver"])
+def background_job_list():
+    return [
+        {
+            "job_id": jobid,
+            "job_category": job["category"],
+            "status": job["status"],
+            "running": job["is_running"],
+            "progress": job.get("progress"),
+            "progress_tasks": job.get("progress_tasks"),
+            "error": job.get("error", None),
+        }
+        for jobid, job in ApiBG.jobs.items()
+    ]
+
+
+@router.get("/background/{jobid}", response_model=BackgroundTaskStatus, tags=["webserver"])
+def background_job(jobid: str):
+    if not (job := ApiBG.jobs.get(jobid)):
+        raise HTTPException(status_code=404, detail="Job not found.")
+
+    return {
+        "job_id": jobid,
+        "job_category": job["category"],
+        "status": job["status"],
+        "running": job["is_running"],
+        "progress": job.get("progress"),
+        "progress_tasks": job.get("progress_tasks"),
+        "error": job.get("error", None),
+    }
diff --git a/freqtrade/rpc/api_server/api_backtest.py b/freqtrade/rpc/api_server/api_backtest.py
new file mode 100644
index 000000000..5d06e8a48
--- /dev/null
+++ b/freqtrade/rpc/api_server/api_backtest.py
@@ -0,0 +1,375 @@
+import asyncio
+import logging
+from copy import deepcopy
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+from fastapi import APIRouter, BackgroundTasks, Depends
+from fastapi.exceptions import HTTPException
+
+from freqtrade.configuration.config_validation import validate_config_consistency
+from freqtrade.constants import Config
+from freqtrade.data.btanalysis import (
+    delete_backtest_result,
+    get_backtest_market_change,
+    get_backtest_result,
+    get_backtest_resultlist,
+    load_and_merge_backtest_result,
+    update_backtest_metadata,
+)
+from freqtrade.enums import BacktestState
+from freqtrade.exceptions import ConfigurationError, DependencyException, OperationalException
+from freqtrade.exchange.common import remove_exchange_credentials
+from freqtrade.ft_types import get_BacktestResultType_default
+from freqtrade.misc import deep_merge_dicts, is_file_in_dir
+from freqtrade.rpc.api_server.api_schemas import (
+    BacktestHistoryEntry,
+    BacktestMarketChange,
+    BacktestMetadataUpdate,
+    BacktestRequest,
+    BacktestResponse,
+)
+from freqtrade.rpc.api_server.deps import get_config
+from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
+from freqtrade.rpc.rpc import RPCException
+
+
+logger = logging.getLogger(__name__)
+
+# Private API, protected by authentication and webserver_mode dependency
+router = APIRouter()
+
+
+def __run_backtest_bg(btconfig: Config):
+    from freqtrade.data.metrics import combined_dataframes_with_rel_mean
+    from freqtrade.optimize.optimize_reports import generate_backtest_stats, store_backtest_results
+    from freqtrade.resolvers import StrategyResolver
+
+    asyncio.set_event_loop(asyncio.new_event_loop())
+    try:
+        # Reload strategy
+        lastconfig = ApiBG.bt["last_config"]
+        strat = StrategyResolver.load_strategy(btconfig)
+        validate_config_consistency(btconfig)
+
+        if (
+            not ApiBG.bt["bt"]
+            or lastconfig.get("timeframe") != strat.timeframe
+            or lastconfig.get("timeframe_detail") != btconfig.get("timeframe_detail")
+            or lastconfig.get("timerange") != btconfig["timerange"]
+        ):
+            from freqtrade.optimize.backtesting import Backtesting
+
+            ApiBG.bt["bt"] = Backtesting(btconfig)
+            ApiBG.bt["bt"].load_bt_data_detail()
+        else:
+            ApiBG.bt["bt"].config = btconfig
+            ApiBG.bt["bt"].init_backtest()
+        # Only reload data if timeframe changed.
+        if (
+            not ApiBG.bt["data"]
+            or not ApiBG.bt["timerange"]
+            or lastconfig.get("timeframe") != strat.timeframe
+            or lastconfig.get("timerange") != btconfig["timerange"]
+        ):
+            ApiBG.bt["data"], ApiBG.bt["timerange"] = ApiBG.bt["bt"].load_bt_data()
+
+        lastconfig["timerange"] = btconfig["timerange"]
+        lastconfig["timeframe"] = strat.timeframe
+        lastconfig["enable_protections"] = btconfig.get("enable_protections")
+        lastconfig["dry_run_wallet"] = btconfig.get("dry_run_wallet")
+
+        ApiBG.bt["bt"].enable_protections = btconfig.get("enable_protections", False)
+        ApiBG.bt["bt"].strategylist = [strat]
+        ApiBG.bt["bt"].results = get_BacktestResultType_default()
+        ApiBG.bt["bt"].load_prior_backtest()
+
+        ApiBG.bt["bt"].abort = False
+        strategy_name = strat.get_strategy_name()
+        if ApiBG.bt["bt"].results and strategy_name in ApiBG.bt["bt"].results["strategy"]:
+            # When previous result hash matches - reuse that result and skip backtesting.
+            logger.info(f"Reusing result of previous backtest for {strategy_name}")
+        else:
+            min_date, max_date = ApiBG.bt["bt"].backtest_one_strategy(
+                strat, ApiBG.bt["data"], ApiBG.bt["timerange"]
+            )
+
+            ApiBG.bt["bt"].results = generate_backtest_stats(
+                ApiBG.bt["data"], ApiBG.bt["bt"].all_results, min_date=min_date, max_date=max_date
+            )
+
+            if btconfig.get("export", "none") == "trades":
+                combined_res = combined_dataframes_with_rel_mean(
+                    ApiBG.bt["data"], min_date, max_date
+                )
+                fn = store_backtest_results(
+                    btconfig,
+                    ApiBG.bt["bt"].results,
+                    datetime.now().strftime("%Y-%m-%d_%H-%M-%S"),
+                    market_change_data=combined_res,
+                    strategy_files={
+                        s.get_strategy_name(): s.__file__ for s in ApiBG.bt["bt"].strategylist
+                    },
+                )
+                ApiBG.bt["bt"].results["metadata"][strategy_name]["filename"] = str(fn.stem)
+                ApiBG.bt["bt"].results["metadata"][strategy_name]["strategy"] = strategy_name
+
+        logger.info("Backtest finished.")
+
+    except ConfigurationError as e:
+        logger.error(f"Backtesting encountered a configuration Error: {e}")
+
+    except (Exception, OperationalException, DependencyException) as e:
+        logger.exception(f"Backtesting caused an error: {e}")
+        ApiBG.bt["bt_error"] = str(e)
+    finally:
+        ApiBG.bgtask_running = False
+
+
+@router.post("/backtest", response_model=BacktestResponse, tags=["webserver", "backtest"])
+async def api_start_backtest(
+    bt_settings: BacktestRequest, background_tasks: BackgroundTasks, config=Depends(get_config)
+):
+    ApiBG.bt["bt_error"] = None
+    """Start backtesting if not done so already"""
+    if ApiBG.bgtask_running:
+        raise RPCException("Bot Background task already running")
+
+    if ":" in bt_settings.strategy:
+        raise HTTPException(status_code=500, detail="base64 encoded strategies are not allowed.")
+
+    btconfig = deepcopy(config)
+    remove_exchange_credentials(btconfig["exchange"], True)
+    settings = dict(bt_settings)
+    if settings.get("freqai", None) is not None:
+        settings["freqai"] = dict(settings["freqai"])
+    # Pydantic models will contain all keys, but non-provided ones are None
+
+    btconfig = deep_merge_dicts(settings, btconfig, allow_null_overrides=False)
+    try:
+        btconfig["stake_amount"] = float(btconfig["stake_amount"])
+    except ValueError:
+        pass
+
+    # Force dry-run for backtesting
+    btconfig["dry_run"] = True
+
+    # Start backtesting
+    # Initialize backtesting object
+
+    background_tasks.add_task(__run_backtest_bg, btconfig=btconfig)
+    ApiBG.bgtask_running = True
+
+    return {
+        "status": "running",
+        "running": True,
+        "progress": 0,
+        "step": str(BacktestState.STARTUP),
+        "status_msg": "Backtest started",
+    }
+
+
+@router.get("/backtest", response_model=BacktestResponse, tags=["webserver", "backtest"])
+def api_get_backtest():
+    """
+    Get backtesting result.
+    Returns Result after backtesting has been ran.
+    """
+    from freqtrade.persistence import LocalTrade
+
+    if ApiBG.bgtask_running:
+        return {
+            "status": "running",
+            "running": True,
+            "step": (
+                ApiBG.bt["bt"].progress.action if ApiBG.bt["bt"] else str(BacktestState.STARTUP)
+            ),
+            "progress": ApiBG.bt["bt"].progress.progress if ApiBG.bt["bt"] else 0,
+            "trade_count": len(LocalTrade.bt_trades),
+            "status_msg": "Backtest running",
+        }
+
+    if not ApiBG.bt["bt"]:
+        return {
+            "status": "not_started",
+            "running": False,
+            "step": "",
+            "progress": 0,
+            "status_msg": "Backtest not yet executed",
+        }
+    if ApiBG.bt["bt_error"]:
+        return {
+            "status": "error",
+            "running": False,
+            "step": "",
+            "progress": 0,
+            "status_msg": f"Backtest failed with {ApiBG.bt['bt_error']}",
+        }
+
+    return {
+        "status": "ended",
+        "running": False,
+        "status_msg": "Backtest ended",
+        "step": "finished",
+        "progress": 1,
+        "backtest_result": ApiBG.bt["bt"].results,
+    }
+
+
+@router.delete("/backtest", response_model=BacktestResponse, tags=["webserver", "backtest"])
+def api_delete_backtest():
+    """Reset backtesting"""
+    if ApiBG.bgtask_running:
+        return {
+            "status": "running",
+            "running": True,
+            "step": "",
+            "progress": 0,
+            "status_msg": "Backtest running",
+        }
+    if ApiBG.bt["bt"]:
+        ApiBG.bt["bt"].cleanup()
+        del ApiBG.bt["bt"]
+        ApiBG.bt["bt"] = None
+        del ApiBG.bt["data"]
+        ApiBG.bt["data"] = None
+        logger.info("Backtesting reset")
+    return {
+        "status": "reset",
+        "running": False,
+        "step": "",
+        "progress": 0,
+        "status_msg": "Backtest reset",
+    }
+
+
+@router.get("/backtest/abort", response_model=BacktestResponse, tags=["webserver", "backtest"])
+def api_backtest_abort():
+    if not ApiBG.bgtask_running:
+        return {
+            "status": "not_running",
+            "running": False,
+            "step": "",
+            "progress": 0,
+            "status_msg": "Backtest ended",
+        }
+    ApiBG.bt["bt"].abort = True
+    return {
+        "status": "stopping",
+        "running": False,
+        "step": "",
+        "progress": 0,
+        "status_msg": "Backtest ended",
+    }
+
+
+@router.get(
+    "/backtest/history", response_model=list[BacktestHistoryEntry], tags=["webserver", "backtest"]
+)
+def api_backtest_history(config=Depends(get_config)):
+    # Get backtest result history, read from metadata files
+    return get_backtest_resultlist(config["user_data_dir"] / "backtest_results")
+
+
+@router.get(
+    "/backtest/history/result", response_model=BacktestResponse, tags=["webserver", "backtest"]
+)
+def api_backtest_history_result(filename: str, strategy: str, config=Depends(get_config)):
+    # Get backtest result history, read from metadata files
+    bt_results_base: Path = config["user_data_dir"] / "backtest_results"
+    for ext in [".zip", ".json"]:
+        fn = (bt_results_base / filename).with_suffix(ext)
+        if is_file_in_dir(fn, bt_results_base):
+            break
+    else:
+        raise HTTPException(status_code=404, detail="File not found.")
+
+    results: dict[str, Any] = {
+        "metadata": {},
+        "strategy": {},
+        "strategy_comparison": [],
+    }
+    load_and_merge_backtest_result(strategy, fn, results)
+    return {
+        "status": "ended",
+        "running": False,
+        "step": "",
+        "progress": 1,
+        "status_msg": "Historic result",
+        "backtest_result": results,
+    }
+
+
+@router.delete(
+    "/backtest/history/{file}",
+    response_model=list[BacktestHistoryEntry],
+    tags=["webserver", "backtest"],
+)
+def api_delete_backtest_history_entry(file: str, config=Depends(get_config)):
+    # Get backtest result history, read from metadata files
+    bt_results_base: Path = config["user_data_dir"] / "backtest_results"
+    for ext in [".zip", ".json"]:
+        file_abs = (bt_results_base / file).with_suffix(ext)
+        # Ensure file is in backtest_results directory
+        if is_file_in_dir(file_abs, bt_results_base):
+            break
+    else:
+        raise HTTPException(status_code=404, detail="File not found.")
+
+    delete_backtest_result(file_abs)
+    return get_backtest_resultlist(config["user_data_dir"] / "backtest_results")
+
+
+@router.patch(
+    "/backtest/history/{file}",
+    response_model=list[BacktestHistoryEntry],
+    tags=["webserver", "backtest"],
+)
+def api_update_backtest_history_entry(
+    file: str, body: BacktestMetadataUpdate, config=Depends(get_config)
+):
+    # Get backtest result history, read from metadata files
+    bt_results_base: Path = config["user_data_dir"] / "backtest_results"
+    for ext in [".zip", ".json"]:
+        file_abs = (bt_results_base / file).with_suffix(ext)
+        # Ensure file is in backtest_results directory
+        if is_file_in_dir(file_abs, bt_results_base):
+            break
+    else:
+        raise HTTPException(status_code=404, detail="File not found.")
+
+    content = {"notes": body.notes}
+    try:
+        update_backtest_metadata(file_abs, body.strategy, content)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+    return get_backtest_result(file_abs)
+
+
+@router.get(
+    "/backtest/history/{file}/market_change",
+    response_model=BacktestMarketChange,
+    tags=["webserver", "backtest"],
+)
+def api_get_backtest_market_change(file: str, config=Depends(get_config)):
+    bt_results_base: Path = config["user_data_dir"] / "backtest_results"
+    for fn in (
+        Path(file).with_suffix(".zip"),
+        Path(f"{file}_market_change").with_suffix(".feather"),
+    ):
+        file_abs = bt_results_base / fn
+        # Ensure file is in backtest_results directory
+        if is_file_in_dir(file_abs, bt_results_base):
+            break
+    else:
+        raise HTTPException(status_code=404, detail="File not found.")
+
+    df = get_backtest_market_change(file_abs)
+
+    return {
+        "columns": df.columns.tolist(),
+        "data": df.values.tolist(),
+        "length": len(df),
+    }
diff --git a/freqtrade/rpc/api_server/api_download_data.py b/freqtrade/rpc/api_server/api_download_data.py
new file mode 100644
index 000000000..b8c14ea13
--- /dev/null
+++ b/freqtrade/rpc/api_server/api_download_data.py
@@ -0,0 +1,85 @@
+import logging
+from copy import deepcopy
+
+from fastapi import APIRouter, BackgroundTasks, Depends
+from fastapi.exceptions import HTTPException
+
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence import FtNoDBContext
+from freqtrade.rpc.api_server.api_pairlists import handleExchangePayload
+from freqtrade.rpc.api_server.api_schemas import BgJobStarted, DownloadDataPayload
+from freqtrade.rpc.api_server.deps import get_config, get_exchange
+from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
+from freqtrade.util.progress_tracker import get_progress_tracker
+
+
+logger = logging.getLogger(__name__)
+
+# Private API, protected by authentication and webserver_mode dependency
+router = APIRouter(tags=["download-data", "webserver"])
+
+
+def __run_download(job_id: str, config_loc: Config):
+    try:
+        ApiBG.jobs[job_id]["is_running"] = True
+        from freqtrade.data.history.history_utils import download_data
+
+        with FtNoDBContext():
+            exchange = get_exchange(config_loc)
+
+            def ft_callback(task) -> None:
+                ApiBG.jobs[job_id]["progress_tasks"][str(task.id)] = {
+                    "progress": task.completed,
+                    "total": task.total,
+                    "description": task.description,
+                }
+
+            pt = get_progress_tracker(ft_callback=ft_callback)
+
+            download_data(config_loc, exchange, progress_tracker=pt)
+            ApiBG.jobs[job_id]["status"] = "success"
+    except (OperationalException, Exception) as e:
+        logger.exception(e)
+        ApiBG.jobs[job_id]["error"] = str(e)
+        ApiBG.jobs[job_id]["status"] = "failed"
+    finally:
+        ApiBG.jobs[job_id]["is_running"] = False
+        ApiBG.download_data_running = False
+
+
+@router.post("/download_data", response_model=BgJobStarted)
+def pairlists_evaluate(
+    payload: DownloadDataPayload, background_tasks: BackgroundTasks, config=Depends(get_config)
+):
+    if ApiBG.download_data_running:
+        raise HTTPException(status_code=400, detail="Data Download is already running.")
+    config_loc = deepcopy(config)
+    config_loc["stake_currency"] = ""
+    config_loc["pairs"] = payload.pairs
+    config_loc["timerange"] = payload.timerange
+    config_loc["days"] = payload.days
+    config_loc["timeframes"] = payload.timeframes
+    config_loc["erase"] = payload.erase
+    config_loc["download_trades"] = payload.download_trades
+
+    handleExchangePayload(payload, config_loc)
+
+    job_id = ApiBG.get_job_id()
+
+    ApiBG.jobs[job_id] = {
+        "category": "download_data",
+        "status": "pending",
+        "progress": None,
+        "progress_tasks": {},
+        "is_running": False,
+        "result": {},
+        "error": None,
+    }
+    background_tasks.add_task(__run_download, job_id, config_loc)
+    ApiBG.download_data_running = True
+
+    return {
+        "status": "Data Download started in background.",
+        "job_id": job_id,
+    }
diff --git a/freqtrade/rpc/api_server/api_pair_history.py b/freqtrade/rpc/api_server/api_pair_history.py
new file mode 100644
index 000000000..31fd86f46
--- /dev/null
+++ b/freqtrade/rpc/api_server/api_pair_history.py
@@ -0,0 +1,77 @@
+import logging
+from copy import deepcopy
+
+from fastapi import APIRouter, Depends, HTTPException
+
+from freqtrade.configuration import validate_config_consistency
+from freqtrade.rpc.api_server.api_pairlists import handleExchangePayload
+from freqtrade.rpc.api_server.api_schemas import PairHistory, PairHistoryRequest
+from freqtrade.rpc.api_server.deps import get_config, get_exchange
+from freqtrade.rpc.rpc import RPC
+
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+
+@router.get("/pair_history", response_model=PairHistory, tags=["candle data"])
+def pair_history(
+    pair: str,
+    timeframe: str,
+    timerange: str,
+    strategy: str,
+    freqaimodel: str | None = None,
+    config=Depends(get_config),
+    exchange=Depends(get_exchange),
+):
+    # The initial call to this endpoint can be slow, as it may need to initialize
+    # the exchange class.
+    config_loc = deepcopy(config)
+    config_loc.update(
+        {
+            "timeframe": timeframe,
+            "strategy": strategy,
+            "timerange": timerange,
+            "freqaimodel": freqaimodel if freqaimodel else config_loc.get("freqaimodel"),
+        }
+    )
+    validate_config_consistency(config_loc)
+    try:
+        return RPC._rpc_analysed_history_full(config_loc, pair, timeframe, exchange, None, False)
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=str(e))
+
+
+@router.post("/pair_history", response_model=PairHistory, tags=["candle data"])
+def pair_history_filtered(payload: PairHistoryRequest, config=Depends(get_config)):
+    # The initial call to this endpoint can be slow, as it may need to initialize
+    # the exchange class.
+    config_loc = deepcopy(config)
+    config_loc.update(
+        {
+            "timeframe": payload.timeframe,
+            "strategy": payload.strategy,
+            "timerange": payload.timerange,
+            "freqaimodel": (
+                payload.freqaimodel if payload.freqaimodel else config_loc.get("freqaimodel")
+            ),
+        }
+    )
+    handleExchangePayload(payload, config_loc)
+    exchange = get_exchange(config_loc)
+
+    validate_config_consistency(config_loc)
+
+    try:
+        return RPC._rpc_analysed_history_full(
+            config_loc,
+            payload.pair,
+            payload.timeframe,
+            exchange,
+            payload.columns,
+            payload.live_mode,
+        )
+    except Exception as e:
+        logger.exception("Error in pair_history_filtered")
+        raise HTTPException(status_code=502, detail=str(e))
diff --git a/freqtrade/rpc/api_server/api_pairlists.py b/freqtrade/rpc/api_server/api_pairlists.py
new file mode 100644
index 000000000..727798566
--- /dev/null
+++ b/freqtrade/rpc/api_server/api_pairlists.py
@@ -0,0 +1,149 @@
+import logging
+from copy import deepcopy
+
+from fastapi import APIRouter, BackgroundTasks, Depends
+from fastapi.exceptions import HTTPException
+
+from freqtrade.constants import Config
+from freqtrade.enums import CandleType
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence import FtNoDBContext
+from freqtrade.rpc.api_server.api_schemas import (
+    BgJobStarted,
+    ExchangeModePayloadMixin,
+    PairListsPayload,
+    PairListsResponse,
+    WhitelistEvaluateResponse,
+)
+from freqtrade.rpc.api_server.deps import get_config, get_exchange
+from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
+
+
+logger = logging.getLogger(__name__)
+
+# Private API, protected by authentication and webserver_mode dependency
+router = APIRouter()
+
+
+@router.get(
+    "/pairlists/available", response_model=PairListsResponse, tags=["pairlists", "webserver"]
+)
+def list_pairlists(config=Depends(get_config)):
+    from freqtrade.resolvers import PairListResolver
+
+    pairlists = PairListResolver.search_all_objects(config, False)
+    pairlists = sorted(pairlists, key=lambda x: x["name"])
+
+    return {
+        "pairlists": [
+            {
+                "name": x["name"],
+                "is_pairlist_generator": x["class"].is_pairlist_generator,
+                "params": x["class"].available_parameters(),
+                "description": x["class"].description(),
+            }
+            for x in pairlists
+        ]
+    }
+
+
+def __run_pairlist(job_id: str, config_loc: Config):
+    try:
+        ApiBG.jobs[job_id]["is_running"] = True
+        from freqtrade.plugins.pairlistmanager import PairListManager
+
+        with FtNoDBContext():
+            exchange = get_exchange(config_loc)
+            pairlists = PairListManager(exchange, config_loc)
+            pairlists.refresh_pairlist()
+            ApiBG.jobs[job_id]["result"] = {
+                "method": pairlists.name_list,
+                "length": len(pairlists.whitelist),
+                "whitelist": pairlists.whitelist,
+            }
+            ApiBG.jobs[job_id]["status"] = "success"
+    except (OperationalException, Exception) as e:
+        logger.exception(e)
+        ApiBG.jobs[job_id]["error"] = str(e)
+        ApiBG.jobs[job_id]["status"] = "failed"
+    finally:
+        ApiBG.jobs[job_id]["is_running"] = False
+        ApiBG.pairlist_running = False
+
+
+@router.post("/pairlists/evaluate", response_model=BgJobStarted, tags=["pairlists", "webserver"])
+def pairlists_evaluate(
+    payload: PairListsPayload, background_tasks: BackgroundTasks, config=Depends(get_config)
+):
+    if ApiBG.pairlist_running:
+        raise HTTPException(status_code=400, detail="Pairlist evaluation is already running.")
+
+    config_loc = deepcopy(config)
+    config_loc["stake_currency"] = payload.stake_currency
+    config_loc["pairlists"] = payload.pairlists
+    handleExchangePayload(payload, config_loc)
+    # TODO: overwrite blacklist? make it optional and fall back to the one in config?
+    # Outcome depends on the UI approach.
+    config_loc["exchange"]["pair_blacklist"] = payload.blacklist
+    # Random job id
+    job_id = ApiBG.get_job_id()
+
+    ApiBG.jobs[job_id] = {
+        "category": "pairlist",
+        "status": "pending",
+        "progress": None,
+        "is_running": False,
+        "result": {},
+        "error": None,
+    }
+    background_tasks.add_task(__run_pairlist, job_id, config_loc)
+    ApiBG.pairlist_running = True
+
+    return {
+        "status": "Pairlist evaluation started in background.",
+        "job_id": job_id,
+    }
+
+
+def handleExchangePayload(payload: ExchangeModePayloadMixin, config_loc: Config):
+    """
+    Handle exchange and trading mode payload.
+    Updates the configuration with the payload values.
+    """
+    from freqtrade.configuration.directory_operations import create_datadir
+
+    if payload.exchange:
+        config_loc["exchange"]["name"] = payload.exchange
+        config_loc.update({"datadir": create_datadir(config_loc, None)})
+    if payload.trading_mode:
+        config_loc["trading_mode"] = payload.trading_mode
+        config_loc["candle_type_def"] = CandleType.get_default(
+            config_loc.get("trading_mode", "spot") or "spot"
+        )
+
+    if payload.margin_mode:
+        config_loc["margin_mode"] = payload.margin_mode
+
+
+@router.get(
+    "/pairlists/evaluate/{jobid}",
+    response_model=WhitelistEvaluateResponse,
+    tags=["pairlists", "webserver"],
+)
+def pairlists_evaluate_get(jobid: str):
+    if not (job := ApiBG.jobs.get(jobid)):
+        raise HTTPException(status_code=404, detail="Job not found.")
+
+    if job["is_running"]:
+        raise HTTPException(status_code=400, detail="Job not finished yet.")
+
+    if error := job["error"]:
+        return {
+            "status": "failed",
+            "error": error,
+        }
+
+    return {
+        "status": "success",
+        "result": job["result"],
+    }
diff --git a/freqtrade/rpc/api_server/api_schemas.py b/freqtrade/rpc/api_server/api_schemas.py
new file mode 100644
index 000000000..975166458
--- /dev/null
+++ b/freqtrade/rpc/api_server/api_schemas.py
@@ -0,0 +1,652 @@
+from datetime import date, datetime
+from typing import Any
+
+from pydantic import AwareDatetime, BaseModel, RootModel, SerializeAsAny, model_validator
+
+from freqtrade.constants import DL_DATA_TIMEFRAMES, IntOrInf
+from freqtrade.enums import MarginMode, OrderTypeValues, SignalDirection, TradingMode
+from freqtrade.ft_types import ValidExchangesType
+from freqtrade.rpc.api_server.webserver_bgwork import ProgressTask
+
+
+class ExchangeModePayloadMixin(BaseModel):
+    trading_mode: TradingMode | None = None
+    margin_mode: MarginMode | None = None
+    exchange: str | None = None
+
+
+class Ping(BaseModel):
+    status: str
+
+
+class AccessToken(BaseModel):
+    access_token: str
+
+
+class AccessAndRefreshToken(AccessToken):
+    refresh_token: str
+
+
+class Version(BaseModel):
+    version: str
+
+
+class StatusMsg(BaseModel):
+    status: str
+
+
+class BgJobStarted(StatusMsg):
+    job_id: str
+
+
+class BackgroundTaskStatus(BaseModel):
+    job_id: str
+    job_category: str
+    status: str
+    running: bool
+    progress: float | None = None
+    progress_tasks: dict[str, ProgressTask] | None = None
+    error: str | None = None
+
+
+class BackgroundTaskResult(BaseModel):
+    error: str | None = None
+    status: str
+
+
+class ResultMsg(BaseModel):
+    result: str
+
+
+class Balance(BaseModel):
+    currency: str
+    free: float
+    balance: float
+    used: float
+    bot_owned: float | None = None
+    est_stake: float
+    est_stake_bot: float | None = None
+    stake: str
+    # Starting with 2.x
+    side: str
+    is_position: bool
+    position: float
+    is_bot_managed: bool
+
+
+class Balances(BaseModel):
+    currencies: list[Balance]
+    total: float
+    total_bot: float
+    symbol: str
+    value: float
+    value_bot: float
+    stake: str
+    note: str
+    starting_capital: float
+    starting_capital_ratio: float
+    starting_capital_pct: float
+    starting_capital_fiat: float
+    starting_capital_fiat_ratio: float
+    starting_capital_fiat_pct: float
+
+
+class Count(BaseModel):
+    current: int
+    max: int
+    total_stake: float
+
+
+class __BaseStatsModel(BaseModel):
+    profit_ratio: float
+    profit_pct: float
+    profit_abs: float
+    count: int
+
+
+class Entry(__BaseStatsModel):
+    enter_tag: str
+
+
+class Exit(__BaseStatsModel):
+    exit_reason: str
+
+
+class MixTag(__BaseStatsModel):
+    mix_tag: str
+
+
+class PerformanceEntry(__BaseStatsModel):
+    pair: str
+    profit: float
+
+
+class Profit(BaseModel):
+    profit_closed_coin: float
+    profit_closed_percent_mean: float
+    profit_closed_ratio_mean: float
+    profit_closed_percent_sum: float
+    profit_closed_ratio_sum: float
+    profit_closed_percent: float
+    profit_closed_ratio: float
+    profit_closed_fiat: float
+    profit_all_coin: float
+    profit_all_percent_mean: float
+    profit_all_ratio_mean: float
+    profit_all_percent_sum: float
+    profit_all_ratio_sum: float
+    profit_all_percent: float
+    profit_all_ratio: float
+    profit_all_fiat: float
+    trade_count: int
+    closed_trade_count: int
+    first_trade_date: str
+    first_trade_humanized: str
+    first_trade_timestamp: int
+    latest_trade_date: str
+    latest_trade_humanized: str
+    latest_trade_timestamp: int
+    avg_duration: str
+    best_pair: str
+    best_rate: float
+    best_pair_profit_ratio: float
+    best_pair_profit_abs: float
+    winning_trades: int
+    losing_trades: int
+    profit_factor: float
+    winrate: float
+    expectancy: float
+    expectancy_ratio: float
+    max_drawdown: float
+    max_drawdown_abs: float
+    max_drawdown_start: str
+    max_drawdown_start_timestamp: int
+    max_drawdown_end: str
+    max_drawdown_end_timestamp: int
+    trading_volume: float | None = None
+    bot_start_timestamp: int
+    bot_start_date: str
+
+
+class SellReason(BaseModel):
+    wins: int
+    losses: int
+    draws: int
+
+
+class Stats(BaseModel):
+    exit_reasons: dict[str, SellReason]
+    durations: dict[str, float | None]
+
+
+class DailyWeeklyMonthlyRecord(BaseModel):
+    date: date
+    abs_profit: float
+    rel_profit: float
+    starting_balance: float
+    fiat_value: float
+    trade_count: int
+
+
+class DailyWeeklyMonthly(BaseModel):
+    data: list[DailyWeeklyMonthlyRecord]
+    fiat_display_currency: str
+    stake_currency: str
+
+
+class UnfilledTimeout(BaseModel):
+    entry: int | None = None
+    exit: int | None = None
+    unit: str | None = None
+    exit_timeout_count: int | None = None
+
+
+class OrderTypes(BaseModel):
+    entry: OrderTypeValues
+    exit: OrderTypeValues
+    emergency_exit: OrderTypeValues | None = None
+    force_exit: OrderTypeValues | None = None
+    force_entry: OrderTypeValues | None = None
+    stoploss: OrderTypeValues
+    stoploss_on_exchange: bool
+    stoploss_on_exchange_interval: int | None = None
+
+
+class ShowConfig(BaseModel):
+    version: str
+    strategy_version: str | None = None
+    api_version: float
+    dry_run: bool
+    trading_mode: str
+    short_allowed: bool
+    stake_currency: str
+    stake_amount: str
+    available_capital: float | None = None
+    stake_currency_decimals: int
+    max_open_trades: IntOrInf
+    minimal_roi: dict[str, Any]
+    stoploss: float | None = None
+    stoploss_on_exchange: bool
+    trailing_stop: bool | None = None
+    trailing_stop_positive: float | None = None
+    trailing_stop_positive_offset: float | None = None
+    trailing_only_offset_is_reached: bool | None = None
+    unfilledtimeout: UnfilledTimeout | None = None  # Empty in webserver mode
+    order_types: OrderTypes | None = None
+    use_custom_stoploss: bool | None = None
+    timeframe: str | None = None
+    timeframe_ms: int
+    timeframe_min: int
+    exchange: str
+    strategy: str | None = None
+    force_entry_enable: bool
+    exit_pricing: dict[str, Any]
+    entry_pricing: dict[str, Any]
+    bot_name: str
+    state: str
+    runmode: str
+    position_adjustment_enable: bool
+    max_entry_position_adjustment: int
+
+
+class OrderSchema(BaseModel):
+    pair: str
+    order_id: str
+    status: str
+    remaining: float | None = None
+    amount: float
+    safe_price: float
+    cost: float
+    filled: float | None = None
+    ft_order_side: str
+    order_type: str
+    is_open: bool
+    order_timestamp: int | None = None
+    order_filled_timestamp: int | None = None
+    ft_fee_base: float | None = None
+    ft_order_tag: str | None = None
+
+
+class TradeSchema(BaseModel):
+    trade_id: int
+    pair: str
+    base_currency: str
+    quote_currency: str
+    is_open: bool
+    is_short: bool
+    exchange: str
+    amount: float
+    amount_requested: float
+    stake_amount: float
+    max_stake_amount: float | None = None
+    strategy: str
+    enter_tag: str | None = None
+    timeframe: int
+    fee_open: float | None = None
+    fee_open_cost: float | None = None
+    fee_open_currency: str | None = None
+    fee_close: float | None = None
+    fee_close_cost: float | None = None
+    fee_close_currency: str | None = None
+
+    open_date: str
+    open_timestamp: int
+    open_fill_date: str | None
+    open_fill_timestamp: int | None
+    open_rate: float
+    open_rate_requested: float | None = None
+    open_trade_value: float
+
+    close_date: str | None = None
+    close_timestamp: int | None = None
+    close_rate: float | None = None
+    close_rate_requested: float | None = None
+
+    close_profit: float | None = None
+    close_profit_pct: float | None = None
+    close_profit_abs: float | None = None
+
+    profit_ratio: float | None = None
+    profit_pct: float | None = None
+    profit_abs: float | None = None
+    profit_fiat: float | None = None
+
+    realized_profit: float
+    realized_profit_ratio: float | None = None
+
+    exit_reason: str | None = None
+    exit_order_status: str | None = None
+
+    stop_loss_abs: float | None = None
+    stop_loss_ratio: float | None = None
+    stop_loss_pct: float | None = None
+    stoploss_last_update: str | None = None
+    stoploss_last_update_timestamp: int | None = None
+    initial_stop_loss_abs: float | None = None
+    initial_stop_loss_ratio: float | None = None
+    initial_stop_loss_pct: float | None = None
+
+    min_rate: float | None = None
+    max_rate: float | None = None
+    has_open_orders: bool
+    orders: list[OrderSchema]
+
+    leverage: float | None = None
+    interest_rate: float | None = None
+    liquidation_price: float | None = None
+    funding_fees: float | None = None
+    trading_mode: TradingMode | None = None
+
+    amount_precision: float | None = None
+    price_precision: float | None = None
+    precision_mode: int | None = None
+
+
+class OpenTradeSchema(TradeSchema):
+    stoploss_current_dist: float | None = None
+    stoploss_current_dist_pct: float | None = None
+    stoploss_current_dist_ratio: float | None = None
+    stoploss_entry_dist: float | None = None
+    stoploss_entry_dist_ratio: float | None = None
+    current_rate: float
+    total_profit_abs: float
+    total_profit_fiat: float | None = None
+    total_profit_ratio: float | None = None
+
+
+class TradeResponse(BaseModel):
+    trades: list[TradeSchema]
+    trades_count: int
+    offset: int
+    total_trades: int
+
+
+ForceEnterResponse = RootModel[TradeSchema | StatusMsg]
+
+
+class LockModel(BaseModel):
+    id: int
+    active: bool
+    lock_end_time: str
+    lock_end_timestamp: int
+    lock_time: str
+    lock_timestamp: int
+    pair: str
+    side: str
+    reason: str | None = None
+
+
+class Locks(BaseModel):
+    lock_count: int
+    locks: list[LockModel]
+
+
+class LocksPayload(BaseModel):
+    pair: str
+    side: str = "*"  # Default to both sides
+    until: AwareDatetime
+    reason: str | None = None
+
+
+class DeleteLockRequest(BaseModel):
+    pair: str | None = None
+    lockid: int | None = None
+
+
+class Logs(BaseModel):
+    log_count: int
+    logs: list[list]
+
+
+class ForceEnterPayload(BaseModel):
+    pair: str
+    side: SignalDirection = SignalDirection.LONG
+    price: float | None = None
+    ordertype: OrderTypeValues | None = None
+    stakeamount: float | None = None
+    entry_tag: str | None = None
+    leverage: float | None = None
+
+
+class ForceExitPayload(BaseModel):
+    tradeid: str | int
+    ordertype: OrderTypeValues | None = None
+    amount: float | None = None
+
+
+class BlacklistPayload(BaseModel):
+    blacklist: list[str]
+
+
+class BlacklistResponse(BaseModel):
+    blacklist: list[str]
+    blacklist_expanded: list[str]
+    errors: dict
+    length: int
+    method: list[str]
+
+
+class WhitelistResponse(BaseModel):
+    whitelist: list[str]
+    length: int
+    method: list[str]
+
+
+class WhitelistEvaluateResponse(BackgroundTaskResult):
+    result: WhitelistResponse | None = None
+
+
+class DeleteTrade(BaseModel):
+    cancel_order_count: int
+    result: str
+    result_msg: str
+    trade_id: int
+
+
+class PlotConfig_(BaseModel):
+    main_plot: dict[str, Any]
+    subplots: dict[str, Any]
+
+
+PlotConfig = RootModel[PlotConfig_ | dict]
+
+
+class StrategyListResponse(BaseModel):
+    strategies: list[str]
+
+
+class ExchangeListResponse(BaseModel):
+    exchanges: list[ValidExchangesType]
+
+
+class HyperoptLoss(BaseModel):
+    name: str
+    description: str
+
+
+class HyperoptLossListResponse(BaseModel):
+    loss_functions: list[HyperoptLoss]
+
+
+class PairListResponse(BaseModel):
+    name: str
+    description: str
+    is_pairlist_generator: bool
+    params: dict[str, Any]
+
+
+class PairListsResponse(BaseModel):
+    pairlists: list[PairListResponse]
+
+
+class PairListsPayload(ExchangeModePayloadMixin, BaseModel):
+    pairlists: list[dict[str, Any]]
+    blacklist: list[str]
+    stake_currency: str
+
+
+class DownloadDataPayload(ExchangeModePayloadMixin, BaseModel):
+    pairs: list[str]
+    timeframes: list[str] | None = DL_DATA_TIMEFRAMES
+    days: int | None = None
+    timerange: str | None = None
+    erase: bool = False
+    download_trades: bool = False
+
+    @model_validator(mode="before")
+    def check_mutually_exclusive(cls, values):
+        timeframes, days = values.get("timerange"), values.get("days")
+        if timeframes and days:
+            raise ValueError("Only one of timeframes or days can be provided, not both.")
+        return values
+
+
+class FreqAIModelListResponse(BaseModel):
+    freqaimodels: list[str]
+
+
+class StrategyResponse(BaseModel):
+    strategy: str
+    code: str
+    timeframe: str | None
+
+
+class AvailablePairs(BaseModel):
+    length: int
+    pairs: list[str]
+    pair_interval: list[list[str]]
+
+
+class PairCandlesRequest(BaseModel):
+    pair: str
+    timeframe: str
+    limit: int | None = None
+    columns: list[str] | None = None
+
+
+class PairHistoryRequest(PairCandlesRequest, ExchangeModePayloadMixin):
+    timerange: str
+    strategy: str | None = None
+    freqaimodel: str | None = None
+    live_mode: bool = False
+
+
+class PairHistory(BaseModel):
+    strategy: str
+    pair: str
+    timeframe: str
+    timeframe_ms: int
+    columns: list[str]
+    all_columns: list[str] = []
+    data: SerializeAsAny[list[Any]]
+    length: int
+    buy_signals: int
+    sell_signals: int
+    enter_long_signals: int
+    exit_long_signals: int
+    enter_short_signals: int
+    exit_short_signals: int
+    last_analyzed: datetime
+    last_analyzed_ts: int
+    data_start_ts: int
+    data_start: str
+    data_stop: str
+    data_stop_ts: int
+
+
+class BacktestFreqAIInputs(BaseModel):
+    identifier: str
+
+
+class BacktestRequest(BaseModel):
+    strategy: str
+    timeframe: str | None = None
+    timeframe_detail: str | None = None
+    timerange: str | None = None
+    max_open_trades: IntOrInf | None = None
+    stake_amount: str | float | None = None
+    enable_protections: bool
+    dry_run_wallet: float | None = None
+    backtest_cache: str | None = None
+    freqaimodel: str | None = None
+    freqai: BacktestFreqAIInputs | None = None
+
+
+class BacktestResponse(BaseModel):
+    status: str
+    running: bool
+    status_msg: str
+    step: str
+    progress: float
+    trade_count: float | None = None
+    # TODO: Properly type backtestresult...
+    backtest_result: dict[str, Any] | None = None
+
+
+# TODO: This is a copy of BacktestHistoryEntryType
+class BacktestHistoryEntry(BaseModel):
+    filename: str
+    strategy: str
+    run_id: str
+    backtest_start_time: int
+    notes: str | None = ""
+    backtest_start_ts: int | None = None
+    backtest_end_ts: int | None = None
+    timeframe: str | None = None
+    timeframe_detail: str | None = None
+
+
+class BacktestMetadataUpdate(BaseModel):
+    strategy: str
+    notes: str = ""
+
+
+class BacktestMarketChange(BaseModel):
+    columns: list[str]
+    length: int
+    data: list[list[Any]]
+
+
+class MarketRequest(ExchangeModePayloadMixin, BaseModel):
+    base: str | None = None
+    quote: str | None = None
+
+
+class MarketModel(BaseModel):
+    symbol: str
+    base: str
+    quote: str
+    spot: bool
+    swap: bool
+
+
+class MarketResponse(BaseModel):
+    markets: dict[str, MarketModel]
+    exchange_id: str
+
+
+class SysInfo(BaseModel):
+    cpu_pct: list[float]
+    ram_pct: float
+
+
+class Health(BaseModel):
+    last_process: datetime | None = None
+    last_process_ts: int | None = None
+    bot_start: datetime | None = None
+    bot_start_ts: int | None = None
+    bot_startup: datetime | None = None
+    bot_startup_ts: int | None = None
+
+
+class CustomDataEntry(BaseModel):
+    key: str
+    type: str
+    value: Any
+    created_at: datetime
+    updated_at: datetime | None = None
+
+
+class ListCustomData(BaseModel):
+    trade_id: int
+    custom_data: list[CustomDataEntry]
diff --git a/freqtrade/rpc/api_server/api_v1.py b/freqtrade/rpc/api_server/api_v1.py
new file mode 100644
index 000000000..0081871f0
--- /dev/null
+++ b/freqtrade/rpc/api_server/api_v1.py
@@ -0,0 +1,558 @@
+import logging
+from copy import deepcopy
+from typing import Annotated
+
+from fastapi import APIRouter, Depends, Query
+from fastapi.exceptions import HTTPException
+
+from freqtrade import __version__
+from freqtrade.data.history import get_datahandler
+from freqtrade.enums import CandleType, RunMode, State, TradingMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.rpc import RPC
+from freqtrade.rpc.api_server.api_pairlists import handleExchangePayload
+from freqtrade.rpc.api_server.api_schemas import (
+    AvailablePairs,
+    Balances,
+    BlacklistPayload,
+    BlacklistResponse,
+    Count,
+    DailyWeeklyMonthly,
+    DeleteLockRequest,
+    DeleteTrade,
+    Entry,
+    ExchangeListResponse,
+    Exit,
+    ForceEnterPayload,
+    ForceEnterResponse,
+    ForceExitPayload,
+    FreqAIModelListResponse,
+    Health,
+    HyperoptLossListResponse,
+    ListCustomData,
+    Locks,
+    LocksPayload,
+    Logs,
+    MarketRequest,
+    MarketResponse,
+    MixTag,
+    OpenTradeSchema,
+    PairCandlesRequest,
+    PairHistory,
+    PerformanceEntry,
+    Ping,
+    PlotConfig,
+    Profit,
+    ResultMsg,
+    ShowConfig,
+    Stats,
+    StatusMsg,
+    StrategyListResponse,
+    StrategyResponse,
+    SysInfo,
+    Version,
+    WhitelistResponse,
+)
+from freqtrade.rpc.api_server.deps import get_config, get_exchange, get_rpc, get_rpc_optional
+from freqtrade.rpc.rpc import RPCException
+
+
+logger = logging.getLogger(__name__)
+
+# API version
+# Pre-1.1, no version was provided
+# Version increments should happen in "small" steps (1.1, 1.12, ...) unless big changes happen.
+# 1.11: forcebuy and forcesell accept ordertype
+# 1.12: add blacklist delete endpoint
+# 1.13: forcebuy supports stake_amount
+# versions 2.xx -> futures/short branch
+# 2.14: Add entry/exit orders to trade response
+# 2.15: Add backtest history endpoints
+# 2.16: Additional daily metrics
+# 2.17: Forceentry - leverage, partial force_exit
+# 2.20: Add websocket endpoints
+# 2.21: Add new_candle messagetype
+# 2.22: Add FreqAI to backtesting
+# 2.23: Allow plot config request in webserver mode
+# 2.24: Add cancel_open_order endpoint
+# 2.25: Add several profit values to /status endpoint
+# 2.26: increase /balance output
+# 2.27: Add /trades/<id>/reload endpoint
+# 2.28: Switch reload endpoint to Post
+# 2.29: Add /exchanges endpoint
+# 2.30: new /pairlists endpoint
+# 2.31: new /backtest/history/ delete endpoint
+# 2.32: new /backtest/history/ patch endpoint
+# 2.33: Additional weekly/monthly metrics
+# 2.34: new entries/exits/mix_tags endpoints
+# 2.35: pair_candles and pair_history endpoints as Post variant
+# 2.40: Add hyperopt-loss endpoint
+# 2.41: Add download-data endpoint
+# 2.42: Add /pair_history endpoint with live data
+API_VERSION = 2.42
+
+# Public API, requires no auth.
+router_public = APIRouter()
+# Private API, protected by authentication
+router = APIRouter()
+
+
+@router_public.get("/ping", response_model=Ping)
+def ping():
+    """simple ping"""
+    return {"status": "pong"}
+
+
+@router.get("/version", response_model=Version, tags=["info"])
+def version():
+    """Bot Version info"""
+    return {"version": __version__}
+
+
+@router.get("/balance", response_model=Balances, tags=["info"])
+def balance(rpc: RPC = Depends(get_rpc), config=Depends(get_config)):
+    """Account Balances"""
+    return rpc._rpc_balance(
+        config["stake_currency"],
+        config.get("fiat_display_currency", ""),
+    )
+
+
+@router.get("/count", response_model=Count, tags=["info"])
+def count(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_count()
+
+
+@router.get("/entries", response_model=list[Entry], tags=["info"])
+def entries(pair: str | None = None, rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_enter_tag_performance(pair)
+
+
+@router.get("/exits", response_model=list[Exit], tags=["info"])
+def exits(pair: str | None = None, rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_exit_reason_performance(pair)
+
+
+@router.get("/mix_tags", response_model=list[MixTag], tags=["info"])
+def mix_tags(pair: str | None = None, rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_mix_tag_performance(pair)
+
+
+@router.get("/performance", response_model=list[PerformanceEntry], tags=["info"])
+def performance(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_performance()
+
+
+@router.get("/profit", response_model=Profit, tags=["info"])
+def profit(rpc: RPC = Depends(get_rpc), config=Depends(get_config)):
+    return rpc._rpc_trade_statistics(config["stake_currency"], config.get("fiat_display_currency"))
+
+
+@router.get("/stats", response_model=Stats, tags=["info"])
+def stats(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_stats()
+
+
+@router.get("/daily", response_model=DailyWeeklyMonthly, tags=["info"])
+def daily(
+    timescale: int = Query(7, ge=1, description="Number of days to fetch data for"),
+    rpc: RPC = Depends(get_rpc),
+    config=Depends(get_config),
+):
+    return rpc._rpc_timeunit_profit(
+        timescale, config["stake_currency"], config.get("fiat_display_currency", "")
+    )
+
+
+@router.get("/weekly", response_model=DailyWeeklyMonthly, tags=["info"])
+def weekly(
+    timescale: int = Query(4, ge=1, description="Number of weeks to fetch data for"),
+    rpc: RPC = Depends(get_rpc),
+    config=Depends(get_config),
+):
+    return rpc._rpc_timeunit_profit(
+        timescale, config["stake_currency"], config.get("fiat_display_currency", ""), "weeks"
+    )
+
+
+@router.get("/monthly", response_model=DailyWeeklyMonthly, tags=["info"])
+def monthly(
+    timescale: int = Query(3, ge=1, description="Number of months to fetch data for"),
+    rpc: RPC = Depends(get_rpc),
+    config=Depends(get_config),
+):
+    return rpc._rpc_timeunit_profit(
+        timescale, config["stake_currency"], config.get("fiat_display_currency", ""), "months"
+    )
+
+
+@router.get("/status", response_model=list[OpenTradeSchema], tags=["info"])
+def status(rpc: RPC = Depends(get_rpc)):
+    try:
+        return rpc._rpc_trade_status()
+    except RPCException:
+        return []
+
+
+# Using the responsemodel here will cause a ~100% increase in response time (from 1s to 2s)
+# on big databases. Correct response model: response_model=TradeResponse,
+@router.get("/trades", tags=["info", "trading"])
+def trades(
+    limit: int = Query(500, ge=1, description="Maximum number of different trades to return data"),
+    offset: int = Query(0, ge=0, description="Number of trades to skip for pagination"),
+    rpc: RPC = Depends(get_rpc),
+):
+    return rpc._rpc_trade_history(limit, offset=offset, order_by_id=True)
+
+
+@router.get("/trade/{tradeid}", response_model=OpenTradeSchema, tags=["info", "trading"])
+def trade(tradeid: int = 0, rpc: RPC = Depends(get_rpc)):
+    try:
+        return rpc._rpc_trade_status([tradeid])[0]
+    except (RPCException, KeyError):
+        raise HTTPException(status_code=404, detail="Trade not found.")
+
+
+@router.delete("/trades/{tradeid}", response_model=DeleteTrade, tags=["info", "trading"])
+def trades_delete(tradeid: int, rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_delete(tradeid)
+
+
+@router.delete("/trades/{tradeid}/open-order", response_model=OpenTradeSchema, tags=["trading"])
+def trade_cancel_open_order(tradeid: int, rpc: RPC = Depends(get_rpc)):
+    rpc._rpc_cancel_open_order(tradeid)
+    return rpc._rpc_trade_status([tradeid])[0]
+
+
+@router.post("/trades/{tradeid}/reload", response_model=OpenTradeSchema, tags=["trading"])
+def trade_reload(tradeid: int, rpc: RPC = Depends(get_rpc)):
+    rpc._rpc_reload_trade_from_exchange(tradeid)
+    return rpc._rpc_trade_status([tradeid])[0]
+
+
+@router.get("/trades/open/custom-data", response_model=list[ListCustomData], tags=["trading"])
+def list_open_trades_custom_data(
+    key: str | None = Query(None, description="Optional key to filter data"),
+    limit: int = Query(100, ge=1, description="Maximum number of different trades to return data"),
+    offset: int = Query(0, ge=0, description="Number of trades to skip for pagination"),
+    rpc: RPC = Depends(get_rpc),
+):
+    """
+    Fetch custom data for all open trades.
+    If a key is provided, it will be used to filter data accordingly.
+    Pagination is implemented via the `limit` and `offset` parameters.
+    """
+    try:
+        return rpc._rpc_list_custom_data(key=key, limit=limit, offset=offset)
+    except RPCException as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@router.get("/trades/{trade_id}/custom-data", response_model=list[ListCustomData], tags=["trading"])
+def list_custom_data(trade_id: int, key: str | None = Query(None), rpc: RPC = Depends(get_rpc)):
+    """
+    Fetch custom data for a specific trade.
+    If a key is provided, it will be used to filter data accordingly.
+    """
+    try:
+        return rpc._rpc_list_custom_data(trade_id, key=key)
+    except RPCException as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+# TODO: Missing response model
+@router.get("/edge", tags=["info"])
+def edge(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_edge()
+
+
+@router.get("/show_config", response_model=ShowConfig, tags=["info"])
+def show_config(rpc: RPC | None = Depends(get_rpc_optional), config=Depends(get_config)):
+    state: State | str = ""
+    strategy_version = None
+    if rpc:
+        state = rpc._freqtrade.state
+        strategy_version = rpc._freqtrade.strategy.version()
+    resp = RPC._rpc_show_config(config, state, strategy_version)
+    resp["api_version"] = API_VERSION
+    return resp
+
+
+# /forcebuy is deprecated with short addition. use /forceentry instead
+@router.post("/forceenter", response_model=ForceEnterResponse, tags=["trading"])
+@router.post("/forcebuy", response_model=ForceEnterResponse, tags=["trading"])
+def force_entry(payload: ForceEnterPayload, rpc: RPC = Depends(get_rpc)):
+    ordertype = payload.ordertype.value if payload.ordertype else None
+
+    trade = rpc._rpc_force_entry(
+        payload.pair,
+        payload.price,
+        order_side=payload.side,
+        order_type=ordertype,
+        stake_amount=payload.stakeamount,
+        enter_tag=payload.entry_tag or "force_entry",
+        leverage=payload.leverage,
+    )
+
+    if trade:
+        return ForceEnterResponse.model_validate(trade.to_json())
+    else:
+        return ForceEnterResponse.model_validate(
+            {"status": f"Error entering {payload.side} trade for pair {payload.pair}."}
+        )
+
+
+# /forcesell is deprecated with short addition. use /forceexit instead
+@router.post("/forceexit", response_model=ResultMsg, tags=["trading"])
+@router.post("/forcesell", response_model=ResultMsg, tags=["trading"])
+def forceexit(payload: ForceExitPayload, rpc: RPC = Depends(get_rpc)):
+    ordertype = payload.ordertype.value if payload.ordertype else None
+    return rpc._rpc_force_exit(str(payload.tradeid), ordertype, amount=payload.amount)
+
+
+@router.get("/blacklist", response_model=BlacklistResponse, tags=["info", "pairlist"])
+def blacklist(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_blacklist()
+
+
+@router.post("/blacklist", response_model=BlacklistResponse, tags=["info", "pairlist"])
+def blacklist_post(payload: BlacklistPayload, rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_blacklist(payload.blacklist)
+
+
+@router.delete("/blacklist", response_model=BlacklistResponse, tags=["info", "pairlist"])
+def blacklist_delete(pairs_to_delete: list[str] = Query([]), rpc: RPC = Depends(get_rpc)):
+    """Provide a list of pairs to delete from the blacklist"""
+
+    return rpc._rpc_blacklist_delete(pairs_to_delete)
+
+
+@router.get("/whitelist", response_model=WhitelistResponse, tags=["info", "pairlist"])
+def whitelist(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_whitelist()
+
+
+@router.get("/locks", response_model=Locks, tags=["info", "locks"])
+def locks(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_locks()
+
+
+@router.delete("/locks/{lockid}", response_model=Locks, tags=["info", "locks"])
+def delete_lock(lockid: int, rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_delete_lock(lockid=lockid)
+
+
+@router.post("/locks/delete", response_model=Locks, tags=["info", "locks"])
+def delete_lock_pair(payload: DeleteLockRequest, rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_delete_lock(lockid=payload.lockid, pair=payload.pair)
+
+
+@router.post("/locks", response_model=Locks, tags=["info", "locks"])
+def add_locks(payload: list[LocksPayload], rpc: RPC = Depends(get_rpc)):
+    for lock in payload:
+        rpc._rpc_add_lock(lock.pair, lock.until, lock.reason, lock.side)
+    return rpc._rpc_locks()
+
+
+@router.get("/logs", response_model=Logs, tags=["info"])
+def logs(limit: int | None = None):
+    return RPC._rpc_get_logs(limit)
+
+
+@router.post("/start", response_model=StatusMsg, tags=["botcontrol"])
+def start(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_start()
+
+
+@router.post("/stop", response_model=StatusMsg, tags=["botcontrol"])
+def stop(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_stop()
+
+
+@router.post("/pause", response_model=StatusMsg, tags=["botcontrol"])
+@router.post("/stopentry", response_model=StatusMsg, tags=["botcontrol"])
+@router.post("/stopbuy", response_model=StatusMsg, tags=["botcontrol"])
+def pause(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_pause()
+
+
+@router.post("/reload_config", response_model=StatusMsg, tags=["botcontrol"])
+def reload_config(rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_reload_config()
+
+
+@router.get("/pair_candles", response_model=PairHistory, tags=["candle data"])
+def pair_candles(pair: str, timeframe: str, limit: int | None = None, rpc: RPC = Depends(get_rpc)):
+    return rpc._rpc_analysed_dataframe(pair, timeframe, limit, None)
+
+
+@router.post("/pair_candles", response_model=PairHistory, tags=["candle data"])
+def pair_candles_filtered(payload: PairCandlesRequest, rpc: RPC = Depends(get_rpc)):
+    # Advanced pair_candles endpoint with column filtering
+    return rpc._rpc_analysed_dataframe(
+        payload.pair, payload.timeframe, payload.limit, payload.columns
+    )
+
+
+@router.get("/plot_config", response_model=PlotConfig, tags=["candle data"])
+def plot_config(
+    strategy: str | None = None,
+    config=Depends(get_config),
+    rpc: RPC | None = Depends(get_rpc_optional),
+):
+    if not strategy:
+        if not rpc:
+            raise RPCException("Strategy is mandatory in webserver mode.")
+        return PlotConfig.model_validate(rpc._rpc_plot_config())
+    else:
+        config1 = deepcopy(config)
+        config1.update({"strategy": strategy})
+    try:
+        return PlotConfig.model_validate(RPC._rpc_plot_config_with_strategy(config1))
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=str(e))
+
+
+@router.get("/strategies", response_model=StrategyListResponse, tags=["strategy"])
+def list_strategies(config=Depends(get_config)):
+    from freqtrade.resolvers.strategy_resolver import StrategyResolver
+
+    strategies = StrategyResolver.search_all_objects(
+        config, False, config.get("recursive_strategy_search", False)
+    )
+    strategies = sorted(strategies, key=lambda x: x["name"])
+
+    return {"strategies": [x["name"] for x in strategies]}
+
+
+@router.get("/strategy/{strategy}", response_model=StrategyResponse, tags=["strategy"])
+def get_strategy(strategy: str, config=Depends(get_config)):
+    if ":" in strategy:
+        raise HTTPException(status_code=500, detail="base64 encoded strategies are not allowed.")
+
+    config_ = deepcopy(config)
+    from freqtrade.resolvers.strategy_resolver import StrategyResolver
+
+    try:
+        strategy_obj = StrategyResolver._load_strategy(
+            strategy, config_, extra_dir=config_.get("strategy_path")
+        )
+    except OperationalException:
+        raise HTTPException(status_code=404, detail="Strategy not found")
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=str(e))
+    return {
+        "strategy": strategy_obj.get_strategy_name(),
+        "code": strategy_obj.__source__,
+        "timeframe": getattr(strategy_obj, "timeframe", None),
+    }
+
+
+@router.get("/exchanges", response_model=ExchangeListResponse, tags=[])
+def list_exchanges(config=Depends(get_config)):
+    from freqtrade.exchange import list_available_exchanges
+
+    exchanges = list_available_exchanges(config)
+    return {
+        "exchanges": exchanges,
+    }
+
+
+@router.get(
+    "/hyperoptloss", response_model=HyperoptLossListResponse, tags=["hyperopt", "webserver"]
+)
+def list_hyperoptloss(
+    config=Depends(get_config),
+):
+    import textwrap
+
+    from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
+
+    loss_functions = HyperOptLossResolver.search_all_objects(config, False)
+    loss_functions = sorted(loss_functions, key=lambda x: x["name"])
+
+    return {
+        "loss_functions": [
+            {
+                "name": x["name"],
+                "description": textwrap.dedent((x["class"].__doc__ or "").strip()),
+            }
+            for x in loss_functions
+        ]
+    }
+
+
+@router.get("/freqaimodels", response_model=FreqAIModelListResponse, tags=["freqai"])
+def list_freqaimodels(config=Depends(get_config)):
+    from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
+
+    models = FreqaiModelResolver.search_all_objects(config, False)
+    models = sorted(models, key=lambda x: x["name"])
+
+    return {"freqaimodels": [x["name"] for x in models]}
+
+
+@router.get("/available_pairs", response_model=AvailablePairs, tags=["candle data"])
+def list_available_pairs(
+    timeframe: str | None = None,
+    stake_currency: str | None = None,
+    candletype: CandleType | None = None,
+    config=Depends(get_config),
+):
+    dh = get_datahandler(config["datadir"], config.get("dataformat_ohlcv"))
+    trading_mode: TradingMode = config.get("trading_mode", TradingMode.SPOT)
+    pair_interval = dh.ohlcv_get_available_data(config["datadir"], trading_mode)
+
+    if timeframe:
+        pair_interval = [pair for pair in pair_interval if pair[1] == timeframe]
+    if stake_currency:
+        pair_interval = [pair for pair in pair_interval if pair[0].endswith(stake_currency)]
+    if candletype:
+        pair_interval = [pair for pair in pair_interval if pair[2] == candletype]
+    else:
+        candle_type = CandleType.get_default(trading_mode)
+        pair_interval = [pair for pair in pair_interval if pair[2] == candle_type]
+
+    pair_interval = sorted(pair_interval, key=lambda x: x[0])
+
+    pairs = list({x[0] for x in pair_interval})
+    pairs.sort()
+    result = {
+        "length": len(pairs),
+        "pairs": pairs,
+        "pair_interval": pair_interval,
+    }
+    return result
+
+
+@router.get("/markets", response_model=MarketResponse, tags=["candle data", "webserver"])
+def markets(
+    query: Annotated[MarketRequest, Query()],
+    config=Depends(get_config),
+    rpc: RPC | None = Depends(get_rpc_optional),
+):
+    if not rpc or config["runmode"] == RunMode.WEBSERVER:
+        # webserver mode
+        config_loc = deepcopy(config)
+        handleExchangePayload(query, config_loc)
+        exchange = get_exchange(config_loc)
+    else:
+        exchange = rpc._freqtrade.exchange
+
+    return {
+        "markets": exchange.get_markets(
+            base_currencies=[query.base] if query.base else None,
+            quote_currencies=[query.quote] if query.quote else None,
+        ),
+        "exchange_id": exchange.id,
+    }
+
+
+@router.get("/sysinfo", response_model=SysInfo, tags=["info"])
+def sysinfo():
+    return RPC._rpc_sysinfo()
+
+
+@router.get("/health", response_model=Health, tags=["info"])
+def health(rpc: RPC = Depends(get_rpc)):
+    return rpc.health()
diff --git a/freqtrade/rpc/api_server/api_ws.py b/freqtrade/rpc/api_server/api_ws.py
new file mode 100644
index 000000000..b0e197b19
--- /dev/null
+++ b/freqtrade/rpc/api_server/api_ws.py
@@ -0,0 +1,124 @@
+import logging
+import time
+from typing import Any
+
+from fastapi import APIRouter, Depends
+from fastapi.websockets import WebSocket
+from pydantic import ValidationError
+
+from freqtrade.enums import RPCMessageType, RPCRequestType
+from freqtrade.exceptions import FreqtradeException
+from freqtrade.rpc.api_server.api_auth import validate_ws_token
+from freqtrade.rpc.api_server.deps import get_message_stream, get_rpc
+from freqtrade.rpc.api_server.ws.channel import WebSocketChannel, create_channel
+from freqtrade.rpc.api_server.ws.message_stream import MessageStream
+from freqtrade.rpc.api_server.ws_schemas import (
+    WSAnalyzedDFMessage,
+    WSErrorMessage,
+    WSMessageSchema,
+    WSRequestSchema,
+    WSWhitelistMessage,
+)
+from freqtrade.rpc.rpc import RPC
+
+
+logger = logging.getLogger(__name__)
+
+# Private router, protected by API Key authentication
+router = APIRouter()
+
+
+async def channel_reader(channel: WebSocketChannel, rpc: RPC):
+    """
+    Iterate over the messages from the channel and process the request
+    """
+    async for message in channel:
+        try:
+            await _process_consumer_request(message, channel, rpc)
+        except FreqtradeException:
+            logger.exception(f"Error processing request from {channel}")
+            response = WSErrorMessage(data="Error processing request")
+
+            await channel.send(response.dict(exclude_none=True))
+
+
+async def channel_broadcaster(channel: WebSocketChannel, message_stream: MessageStream):
+    """
+    Iterate over messages in the message stream and send them
+    """
+    async for message, ts in message_stream:
+        if channel.subscribed_to(message.get("type")):
+            # Log a warning if this channel is behind
+            # on the message stream by a lot
+            if (time.time() - ts) > 60:
+                logger.warning(
+                    f"Channel {channel} is behind MessageStream by 1 minute,"
+                    " this can cause a memory leak if you see this message"
+                    " often, consider reducing pair list size or amount of"
+                    " consumers."
+                )
+
+            await channel.send(message, use_timeout=True)
+
+
+async def _process_consumer_request(request: dict[str, Any], channel: WebSocketChannel, rpc: RPC):
+    """
+    Validate and handle a request from a websocket consumer
+    """
+    # Validate the request, makes sure it matches the schema
+    try:
+        websocket_request = WSRequestSchema.model_validate(request)
+    except ValidationError as e:
+        logger.error(f"Invalid request from {channel}: {e}")
+        return
+
+    type_, data = websocket_request.type, websocket_request.data
+    response: WSMessageSchema
+
+    logger.debug(f"Request of type {type_} from {channel}")
+
+    # If we have a request of type SUBSCRIBE, set the topics in this channel
+    if type_ == RPCRequestType.SUBSCRIBE:
+        # If the request is empty, do nothing
+        if not data:
+            return
+
+        # If all topics passed are a valid RPCMessageType, set subscriptions on channel
+        if all([any(x.value == topic for x in RPCMessageType) for topic in data]):
+            channel.set_subscriptions(data)
+
+        # We don't send a response for subscriptions
+        return
+
+    elif type_ == RPCRequestType.WHITELIST:
+        # Get whitelist
+        whitelist = rpc._ws_request_whitelist()
+
+        # Format response
+        response = WSWhitelistMessage(data=whitelist)
+        await channel.send(response.model_dump(exclude_none=True))
+
+    elif type_ == RPCRequestType.ANALYZED_DF:
+        # Limit the amount of candles per dataframe to 'limit' or 1500
+        limit = int(min(data.get("limit", 1500), 1500)) if data else None
+        pair = data.get("pair", None) if data else None
+
+        # For every pair in the generator, send a separate message
+        for message in rpc._ws_request_analyzed_df(limit, pair):
+            # Format response
+            response = WSAnalyzedDFMessage(data=message)
+            await channel.send(response.model_dump(exclude_none=True))
+
+
+@router.websocket("/message/ws")
+async def message_endpoint(
+    websocket: WebSocket,
+    token: str = Depends(validate_ws_token),
+    rpc: RPC = Depends(get_rpc),
+    message_stream: MessageStream = Depends(get_message_stream),
+):
+    if token:
+        async with create_channel(websocket) as channel:
+            await channel.run_channel_tasks(
+                channel_reader(channel, rpc), channel_broadcaster(channel, message_stream)
+            )
diff --git a/freqtrade/rpc/api_server/deps.py b/freqtrade/rpc/api_server/deps.py
new file mode 100644
index 000000000..74ee82a8c
--- /dev/null
+++ b/freqtrade/rpc/api_server/deps.py
@@ -0,0 +1,71 @@
+from collections.abc import AsyncIterator
+from typing import Any
+from uuid import uuid4
+
+from fastapi import Depends, HTTPException
+
+from freqtrade.constants import Config
+from freqtrade.enums import RunMode
+from freqtrade.persistence import Trade
+from freqtrade.persistence.models import _request_id_ctx_var
+from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
+from freqtrade.rpc.rpc import RPC, RPCException
+
+from .webserver import ApiServer
+
+
+def get_rpc_optional() -> RPC | None:
+    if ApiServer._has_rpc:
+        return ApiServer._rpc
+    return None
+
+
+async def get_rpc() -> AsyncIterator[RPC] | None:
+    _rpc = get_rpc_optional()
+    if _rpc:
+        request_id = str(uuid4())
+        ctx_token = _request_id_ctx_var.set(request_id)
+        Trade.rollback()
+        try:
+            yield _rpc
+        finally:
+            Trade.session.remove()
+            _request_id_ctx_var.reset(ctx_token)
+
+    else:
+        raise RPCException("Bot is not in the correct state")
+
+
+def get_config() -> dict[str, Any]:
+    return ApiServer._config
+
+
+def get_api_config() -> dict[str, Any]:
+    return ApiServer._config["api_server"]
+
+
+def _generate_exchange_key(config: Config) -> str:
+    """
+    Exchange key - used for caching the exchange object.
+    """
+    return f"{config['exchange']['name']}_{config.get('trading_mode', 'spot')}"
+
+
+def get_exchange(config=Depends(get_config)):
+    exchange_key = _generate_exchange_key(config)
+    if not (exchange := ApiBG.exchanges.get(exchange_key)):
+        from freqtrade.resolvers import ExchangeResolver
+
+        exchange = ExchangeResolver.load_exchange(config, validate=False, load_leverage_tiers=False)
+        ApiBG.exchanges[exchange_key] = exchange
+    return exchange
+
+
+def get_message_stream():
+    return ApiServer._message_stream
+
+
+def is_webserver_mode(config=Depends(get_config)):
+    if config["runmode"] != RunMode.WEBSERVER:
+        raise HTTPException(status_code=503, detail="Bot is not in the correct state.")
+    return None
diff --git a/freqtrade/rpc/api_server/ui/fallback_file.html b/freqtrade/rpc/api_server/ui/fallback_file.html
new file mode 100644
index 000000000..7943530af
--- /dev/null
+++ b/freqtrade/rpc/api_server/ui/fallback_file.html
@@ -0,0 +1,31 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Freqtrade UI</title>
+  <style>
+    body {
+      background-color: #3c3c3c;
+      color: #dedede;
+      text-align: center;
+    }
+    .main-container {
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      flex-direction: column;
+    }
+    h1 {
+      margin-top: 10rem;
+    }
+  </style>
+</head>
+<body>
+  <div class="main-container">
+    <h1>Freqtrade UI not installed.</h1>
+    <p>Please run `freqtrade install-ui` in your terminal to install the UI files and restart your bot.</p>
+    <p>You can then refresh this page and you should see the UI.</p>
+  </div>
+</body>
+</html>
diff --git a/freqtrade/rpc/api_server/ui/favicon.ico b/freqtrade/rpc/api_server/ui/favicon.ico
new file mode 100644
index 000000000..78c7e43b1
Binary files /dev/null and b/freqtrade/rpc/api_server/ui/favicon.ico differ
diff --git a/freqtrade/rpc/api_server/ui/installed/.gitkeep b/freqtrade/rpc/api_server/ui/installed/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/freqtrade/rpc/api_server/uvicorn_threaded.py b/freqtrade/rpc/api_server/uvicorn_threaded.py
new file mode 100644
index 000000000..d765472f6
--- /dev/null
+++ b/freqtrade/rpc/api_server/uvicorn_threaded.py
@@ -0,0 +1,64 @@
+import threading
+import time
+
+import uvicorn
+
+
+def asyncio_setup() -> None:  # pragma: no cover
+    # Set eventloop for win32 setups
+    # Reverts a change done in uvicorn 0.15.0 - which now sets the eventloop
+    # via policy.
+    # TODO: is this workaround actually needed?
+    import sys
+
+    if sys.version_info >= (3, 8) and sys.platform == "win32":
+        import asyncio
+        import selectors
+
+        selector = selectors.SelectSelector()
+        loop = asyncio.SelectorEventLoop(selector)
+        asyncio.set_event_loop(loop)
+
+
+class UvicornServer(uvicorn.Server):
+    """
+    Multithreaded server - as found in https://github.com/encode/uvicorn/issues/742
+
+    Removed install_signal_handlers() override based on changes from this commit:
+        https://github.com/encode/uvicorn/commit/ce2ef45a9109df8eae038c0ec323eb63d644cbc6
+
+    Cannot rely on asyncio.get_event_loop() to create new event loop because of this check:
+        https://github.com/python/cpython/blob/4d7f11e05731f67fd2c07ec2972c6cb9861d52be/Lib/asyncio/events.py#L638
+
+    Fix by overriding run() and forcing creation of new event loop if uvloop is available
+    """
+
+    def run(self, sockets=None):
+        import asyncio
+
+        """
+        Parent implementation calls self.config.setup_event_loop(),
+            but we need to create uvloop event loop manually
+        """
+        try:
+            import uvloop
+        except ImportError:  # pragma: no cover
+            asyncio_setup()
+        else:
+            asyncio.set_event_loop(uvloop.new_event_loop())
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            # When running in a thread, we'll not have an eventloop yet.
+            loop = asyncio.new_event_loop()
+        loop.run_until_complete(self.serve(sockets=sockets))
+
+    def run_in_thread(self):
+        self.thread = threading.Thread(target=self.run, name="FTUvicorn")
+        self.thread.start()
+        while not self.started:
+            time.sleep(1e-3)
+
+    def cleanup(self):
+        self.should_exit = True
+        self.thread.join()
diff --git a/freqtrade/rpc/api_server/web_ui.py b/freqtrade/rpc/api_server/web_ui.py
new file mode 100644
index 000000000..0a8a73011
--- /dev/null
+++ b/freqtrade/rpc/api_server/web_ui.py
@@ -0,0 +1,55 @@
+from pathlib import Path
+
+from fastapi import APIRouter
+from fastapi.exceptions import HTTPException
+from starlette.responses import FileResponse
+
+
+router_ui = APIRouter()
+
+
+@router_ui.get("/favicon.ico", include_in_schema=False)
+async def favicon():
+    return FileResponse(str(Path(__file__).parent / "ui/favicon.ico"))
+
+
+@router_ui.get("/fallback_file.html", include_in_schema=False)
+async def fallback():
+    return FileResponse(str(Path(__file__).parent / "ui/fallback_file.html"))
+
+
+@router_ui.get("/ui_version", include_in_schema=False)
+async def ui_version():
+    from freqtrade.commands.deploy_ui import read_ui_version
+
+    uibase = Path(__file__).parent / "ui/installed/"
+    version = read_ui_version(uibase)
+
+    return {
+        "version": version if version else "not_installed",
+    }
+
+
+@router_ui.get("/{rest_of_path:path}", include_in_schema=False)
+async def index_html(rest_of_path: str):
+    """
+    Emulate path fallback to index.html.
+    """
+    if rest_of_path.startswith("api") or rest_of_path.startswith("."):
+        raise HTTPException(status_code=404, detail="Not Found")
+    uibase = Path(__file__).parent / "ui/installed/"
+    filename = uibase / rest_of_path
+    # It's security relevant to check "relative_to".
+    # Without this, Directory-traversal is possible.
+    media_type: str | None = None
+    if filename.suffix == ".js":
+        # Force text/javascript for .js files - Circumvent faulty system configuration
+        media_type = "application/javascript"
+    if filename.is_file() and filename.is_relative_to(uibase):
+        return FileResponse(str(filename), media_type=media_type)
+
+    index_file = uibase / "index.html"
+    if not index_file.is_file():
+        return FileResponse(str(uibase.parent / "fallback_file.html"))
+    # Fall back to index.html, as indicated by vue router docs
+    return FileResponse(str(index_file))
diff --git a/freqtrade/rpc/api_server/webserver.py b/freqtrade/rpc/api_server/webserver.py
new file mode 100644
index 000000000..aa81684fc
--- /dev/null
+++ b/freqtrade/rpc/api_server/webserver.py
@@ -0,0 +1,244 @@
+import logging
+from ipaddress import ip_address
+from typing import Any
+
+import orjson
+import uvicorn
+from fastapi import Depends, FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from starlette.responses import JSONResponse
+
+from freqtrade.configuration import running_in_docker
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
+from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
+from freqtrade.rpc.api_server.ws.message_stream import MessageStream
+from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
+from freqtrade.rpc.rpc_types import RPCSendMsg
+
+
+logger = logging.getLogger(__name__)
+
+
+class FTJSONResponse(JSONResponse):
+    media_type = "application/json"
+
+    def render(self, content: Any) -> bytes:
+        """
+        Use rapidjson for responses
+        Handles NaN and Inf / -Inf in a javascript way by default.
+        """
+        return orjson.dumps(content, option=orjson.OPT_SERIALIZE_NUMPY)
+
+
+class ApiServer(RPCHandler):
+    __instance = None
+    __initialized = False
+
+    _rpc: RPC
+    _has_rpc: bool = False
+    _config: Config = {}
+    # websocket message stuff
+    _message_stream: MessageStream | None = None
+
+    def __new__(cls, *args, **kwargs):
+        """
+        This class is a singleton.
+        We'll only have one instance of it around.
+        """
+        if ApiServer.__instance is None:
+            ApiServer.__instance = object.__new__(cls)
+            ApiServer.__initialized = False
+        return ApiServer.__instance
+
+    def __init__(self, config: Config, standalone: bool = False) -> None:
+        ApiServer._config = config
+        if self.__initialized and (standalone or self._standalone):
+            return
+        self._standalone: bool = standalone
+        self._server = None
+
+        ApiServer.__initialized = True
+
+        api_config = self._config["api_server"]
+
+        self.app = FastAPI(
+            title="Freqtrade API",
+            docs_url="/docs" if api_config.get("enable_openapi", False) else None,
+            redoc_url=None,
+            default_response_class=FTJSONResponse,
+        )
+        self.configure_app(self.app, self._config)
+        self.start_api()
+
+    def add_rpc_handler(self, rpc: RPC):
+        """
+        Attach rpc handler
+        """
+        if not ApiServer._has_rpc:
+            ApiServer._rpc = rpc
+            ApiServer._has_rpc = True
+        else:
+            # This should not happen assuming we didn't mess up.
+            raise OperationalException("RPC Handler already attached.")
+
+    def cleanup(self) -> None:
+        """Cleanup pending module resources"""
+        ApiServer._has_rpc = False
+        del ApiServer._rpc
+        ApiBG.exchanges = {}
+        ApiBG.jobs = {}
+        if self._server and not self._standalone:
+            logger.info("Stopping API Server")
+            # self._server.force_exit, self._server.should_exit = True, True
+            self._server.cleanup()
+
+    @classmethod
+    def shutdown(cls):
+        cls.__initialized = False
+        del cls.__instance
+        cls.__instance = None
+        cls._has_rpc = False
+        cls._rpc = None
+
+    def send_msg(self, msg: RPCSendMsg) -> None:
+        """
+        Publish the message to the message stream
+        """
+        if ApiServer._message_stream:
+            ApiServer._message_stream.publish(msg)
+
+    def handle_rpc_exception(self, request, exc):
+        logger.error(f"API Error calling: {exc}")
+        return JSONResponse(
+            status_code=502, content={"error": f"Error querying {request.url.path}: {exc.message}"}
+        )
+
+    def configure_app(self, app: FastAPI, config):
+        from freqtrade.rpc.api_server.api_auth import http_basic_or_jwt_token, router_login
+        from freqtrade.rpc.api_server.api_background_tasks import router as api_bg_tasks
+        from freqtrade.rpc.api_server.api_backtest import router as api_backtest
+        from freqtrade.rpc.api_server.api_download_data import router as api_download_data
+        from freqtrade.rpc.api_server.api_pair_history import router as api_pair_history
+        from freqtrade.rpc.api_server.api_pairlists import router as api_pairlists
+        from freqtrade.rpc.api_server.api_v1 import router as api_v1
+        from freqtrade.rpc.api_server.api_v1 import router_public as api_v1_public
+        from freqtrade.rpc.api_server.api_ws import router as ws_router
+        from freqtrade.rpc.api_server.deps import is_webserver_mode
+        from freqtrade.rpc.api_server.web_ui import router_ui
+
+        app.include_router(api_v1_public, prefix="/api/v1")
+
+        app.include_router(router_login, prefix="/api/v1", tags=["auth"])
+        app.include_router(
+            api_v1,
+            prefix="/api/v1",
+            dependencies=[Depends(http_basic_or_jwt_token)],
+        )
+        app.include_router(
+            api_backtest,
+            prefix="/api/v1",
+            dependencies=[Depends(http_basic_or_jwt_token), Depends(is_webserver_mode)],
+        )
+        app.include_router(
+            api_bg_tasks,
+            prefix="/api/v1",
+            dependencies=[Depends(http_basic_or_jwt_token), Depends(is_webserver_mode)],
+        )
+        app.include_router(
+            api_pair_history,
+            prefix="/api/v1",
+            dependencies=[Depends(http_basic_or_jwt_token), Depends(is_webserver_mode)],
+        )
+        app.include_router(
+            api_pairlists,
+            prefix="/api/v1",
+            dependencies=[Depends(http_basic_or_jwt_token), Depends(is_webserver_mode)],
+        )
+        app.include_router(
+            api_download_data,
+            prefix="/api/v1",
+            dependencies=[Depends(http_basic_or_jwt_token), Depends(is_webserver_mode)],
+        )
+        app.include_router(ws_router, prefix="/api/v1")
+        # UI Router MUST be last!
+        app.include_router(router_ui, prefix="")
+
+        app.add_middleware(
+            CORSMiddleware,
+            allow_origins=config["api_server"].get("CORS_origins", []),
+            allow_credentials=True,
+            allow_methods=["*"],
+            allow_headers=["*"],
+        )
+
+        app.add_exception_handler(RPCException, self.handle_rpc_exception)
+        app.add_event_handler(event_type="startup", func=self._api_startup_event)
+        app.add_event_handler(event_type="shutdown", func=self._api_shutdown_event)
+
+    async def _api_startup_event(self):
+        """
+        Creates the MessageStream class on startup
+        so it has access to the same event loop
+        as uvicorn
+        """
+        if not ApiServer._message_stream:
+            ApiServer._message_stream = MessageStream()
+
+    async def _api_shutdown_event(self):
+        """
+        Removes the MessageStream class on shutdown
+        """
+        if ApiServer._message_stream:
+            ApiServer._message_stream = None
+
+    def start_api(self):
+        """
+        Start API ... should be run in thread.
+        """
+        rest_ip = self._config["api_server"]["listen_ip_address"]
+        rest_port = self._config["api_server"]["listen_port"]
+
+        logger.info(f"Starting HTTP Server at {rest_ip}:{rest_port}")
+        if not ip_address(rest_ip).is_loopback and not running_in_docker():
+            logger.warning("SECURITY WARNING - Local Rest Server listening to external connections")
+            logger.warning(
+                "SECURITY WARNING - This is insecure please set to your loopback,"
+                "e.g 127.0.0.1 in config.json"
+            )
+
+        if not self._config["api_server"].get("password"):
+            logger.warning(
+                "SECURITY WARNING - No password for local REST Server defined. "
+                "Please make sure that this is intentional!"
+            )
+
+        if self._config["api_server"].get("jwt_secret_key", "super-secret") in (
+            "super-secret, somethingrandom"
+        ):
+            logger.warning(
+                "SECURITY WARNING - `jwt_secret_key` seems to be default."
+                "Others may be able to log into your bot."
+            )
+
+        logger.info("Starting Local Rest Server.")
+        verbosity = self._config["api_server"].get("verbosity", "error")
+
+        uvconfig = uvicorn.Config(
+            self.app,
+            port=rest_port,
+            host=rest_ip,
+            use_colors=False,
+            log_config=None,
+            access_log=True if verbosity != "error" else False,
+            ws_ping_interval=None,  # We do this explicitly ourselves
+        )
+        try:
+            self._server = UvicornServer(uvconfig)
+            if self._standalone:
+                self._server.run()
+            else:
+                self._server.run_in_thread()
+        except Exception:
+            logger.exception("Api server failed to start.")
diff --git a/freqtrade/rpc/api_server/webserver_bgwork.py b/freqtrade/rpc/api_server/webserver_bgwork.py
new file mode 100644
index 000000000..4c335a2f2
--- /dev/null
+++ b/freqtrade/rpc/api_server/webserver_bgwork.py
@@ -0,0 +1,48 @@
+from typing import Any, Literal
+from uuid import uuid4
+
+from typing_extensions import NotRequired, TypedDict
+
+from freqtrade.exchange.exchange import Exchange
+
+
+class ProgressTask(TypedDict):
+    progress: float
+    total: float
+    description: str
+
+
+class JobsContainer(TypedDict):
+    category: Literal["pairlist", "download_data"]
+    is_running: bool
+    status: str
+    progress: float | None
+    progress_tasks: NotRequired[dict[str, ProgressTask]]
+    result: Any
+    error: str | None
+
+
+class ApiBG:
+    # Backtesting type: Backtesting
+    bt: dict[str, Any] = {
+        "bt": None,
+        "data": None,
+        "timerange": None,
+        "last_config": {},
+        "bt_error": None,
+    }
+    bgtask_running: bool = False
+    # Exchange - only available in webserver mode.
+    exchanges: dict[str, Exchange] = {}
+
+    # Generic background jobs
+
+    # TODO: Change this to TTLCache
+    jobs: dict[str, JobsContainer] = {}
+    # Pairlist evaluate things
+    pairlist_running: bool = False
+    download_data_running: bool = False
+
+    @staticmethod
+    def get_job_id() -> str:
+        return str(uuid4())
diff --git a/freqtrade/rpc/api_server/ws/__init__.py b/freqtrade/rpc/api_server/ws/__init__.py
new file mode 100644
index 000000000..035ad6795
--- /dev/null
+++ b/freqtrade/rpc/api_server/ws/__init__.py
@@ -0,0 +1,6 @@
+# isort: off
+from freqtrade.rpc.api_server.ws.ws_types import WebSocketType  # noqa: F401
+from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy  # noqa: F401
+from freqtrade.rpc.api_server.ws.serializer import HybridJSONWebSocketSerializer  # noqa: F401
+from freqtrade.rpc.api_server.ws.channel import WebSocketChannel  # noqa: F401
+from freqtrade.rpc.api_server.ws.message_stream import MessageStream  # noqa: F401
diff --git a/freqtrade/rpc/api_server/ws/channel.py b/freqtrade/rpc/api_server/ws/channel.py
new file mode 100644
index 000000000..bf5d64b6c
--- /dev/null
+++ b/freqtrade/rpc/api_server/ws/channel.py
@@ -0,0 +1,240 @@
+import asyncio
+import logging
+import time
+from collections import deque
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+from typing import Any
+from uuid import uuid4
+
+from fastapi import WebSocketDisconnect
+from websockets.exceptions import ConnectionClosed
+
+from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
+from freqtrade.rpc.api_server.ws.serializer import (
+    HybridJSONWebSocketSerializer,
+    WebSocketSerializer,
+)
+from freqtrade.rpc.api_server.ws.ws_types import WebSocketType
+from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
+
+
+logger = logging.getLogger(__name__)
+
+
+class WebSocketChannel:
+    """
+    Object to help facilitate managing a websocket connection
+    """
+
+    def __init__(
+        self,
+        websocket: WebSocketType,
+        channel_id: str | None = None,
+        serializer_cls: type[WebSocketSerializer] = HybridJSONWebSocketSerializer,
+        send_throttle: float = 0.01,
+    ):
+        self.channel_id = channel_id if channel_id else uuid4().hex[:8]
+        self._websocket = WebSocketProxy(websocket)
+
+        # Internal event to signify a closed websocket
+        self._closed = asyncio.Event()
+        # The async tasks created for the channel
+        self._channel_tasks: list[asyncio.Task] = []
+
+        # Deque for average send times
+        self._send_times: deque[float] = deque([], maxlen=10)
+        # High limit defaults to 3 to start
+        self._send_high_limit = 3
+        self._send_throttle = send_throttle
+
+        # The subscribed message types
+        self._subscriptions: list[str] = []
+
+        # Wrap the WebSocket in the Serializing class
+        self._wrapped_ws = serializer_cls(self._websocket)
+
+    def __repr__(self):
+        return f"WebSocketChannel({self.channel_id}, {self.remote_addr})"
+
+    @property
+    def raw_websocket(self):
+        return self._websocket.raw_websocket
+
+    @property
+    def remote_addr(self):
+        return self._websocket.remote_addr
+
+    @property
+    def avg_send_time(self):
+        return sum(self._send_times) / len(self._send_times)
+
+    def _calc_send_limit(self):
+        """
+        Calculate the send high limit for this channel
+        """
+
+        # Only update if we have enough data
+        if len(self._send_times) == self._send_times.maxlen:
+            # At least 1s or twice the average of send times, with a
+            # maximum of 3 seconds per message
+            self._send_high_limit = min(max(self.avg_send_time * 2, 1), 3)
+
+    async def send(self, message: WSMessageSchemaType | dict[str, Any], use_timeout: bool = False):
+        """
+        Send a message on the wrapped websocket. If the sending
+        takes too long, it will raise a TimeoutError and
+        disconnect the connection.
+
+        :param message: The message to send
+        :param use_timeout: Enforce send high limit, defaults to False
+        """
+        try:
+            _ = time.time()
+            # If the send times out, it will raise
+            # a TimeoutError and bubble up to the
+            # message_endpoint to close the connection
+            await asyncio.wait_for(
+                self._wrapped_ws.send(message),
+                timeout=self._send_high_limit if use_timeout else None,
+            )
+            total_time = time.time() - _
+            self._send_times.append(total_time)
+
+            self._calc_send_limit()
+        except asyncio.TimeoutError:
+            logger.info(f"Connection for {self} timed out, disconnecting")
+            raise
+
+        # Explicitly give control back to event loop as
+        # websockets.send does not
+        # Also throttles how fast we send
+        await asyncio.sleep(self._send_throttle)
+
+    async def recv(self):
+        """
+        Receive a message on the wrapped websocket
+        """
+        return await self._wrapped_ws.recv()
+
+    async def ping(self):
+        """
+        Ping the websocket
+        """
+        return await self._websocket.ping()
+
+    async def accept(self):
+        """
+        Accept the underlying websocket connection,
+        if the connection has been closed before we can
+        accept, just close the channel.
+        """
+        try:
+            return await self._websocket.accept()
+        except RuntimeError:
+            await self.close()
+
+    async def close(self):
+        """
+        Close the WebSocketChannel
+        """
+
+        self._closed.set()
+
+        try:
+            await self._websocket.close()
+        except RuntimeError:
+            pass
+
+    def is_closed(self) -> bool:
+        """
+        Closed flag
+        """
+        return self._closed.is_set()
+
+    def set_subscriptions(self, subscriptions: list[str]) -> None:
+        """
+        Set which subscriptions this channel is subscribed to
+
+        :param subscriptions: List of subscriptions, List[str]
+        """
+        self._subscriptions = subscriptions
+
+    def subscribed_to(self, message_type: str) -> bool:
+        """
+        Check if this channel is subscribed to the message_type
+
+        :param message_type: The message type to check
+        """
+        return message_type in self._subscriptions
+
+    async def run_channel_tasks(self, *tasks, **kwargs):
+        """
+        Create and await on the channel tasks unless an exception
+        was raised, then cancel them all.
+
+        :params *tasks: All coros or tasks to be run concurrently
+        :param **kwargs: Any extra kwargs to pass to gather
+        """
+
+        if not self.is_closed():
+            # Wrap the coros into tasks if they aren't already
+            self._channel_tasks = [
+                task if isinstance(task, asyncio.Task) else asyncio.create_task(task)
+                for task in tasks
+            ]
+
+            try:
+                return await asyncio.gather(*self._channel_tasks, **kwargs)
+            except Exception:
+                # If an exception occurred, cancel the rest of the tasks
+                await self.cancel_channel_tasks()
+
+    async def cancel_channel_tasks(self):
+        """
+        Cancel and wait on all channel tasks
+        """
+        for task in self._channel_tasks:
+            task.cancel()
+
+            # Wait for tasks to finish cancelling
+            try:
+                await task
+            except (
+                asyncio.CancelledError,
+                asyncio.TimeoutError,
+                WebSocketDisconnect,
+                ConnectionClosed,
+                RuntimeError,
+            ):
+                pass
+            except Exception as e:
+                logger.info(f"Encountered unknown exception: {e}", exc_info=e)
+
+        self._channel_tasks = []
+
+    async def __aiter__(self):
+        """
+        Generator for received messages
+        """
+        # We can not catch any errors here as websocket.recv is
+        # the first to catch any disconnects and bubble it up
+        # so the connection is garbage collected right away
+        while not self.is_closed():
+            yield await self.recv()
+
+
+@asynccontextmanager
+async def create_channel(websocket: WebSocketType, **kwargs) -> AsyncIterator[WebSocketChannel]:
+    """
+    Context manager for safely opening and closing a WebSocketChannel
+    """
+    channel = WebSocketChannel(websocket, **kwargs)
+    try:
+        await channel.accept()
+        logger.info(f"Connected to channel - {channel}")
+
+        yield channel
+    finally:
+        await channel.close()
+        logger.info(f"Disconnected from channel - {channel}")
diff --git a/freqtrade/rpc/api_server/ws/message_stream.py b/freqtrade/rpc/api_server/ws/message_stream.py
new file mode 100644
index 000000000..f33bd7aef
--- /dev/null
+++ b/freqtrade/rpc/api_server/ws/message_stream.py
@@ -0,0 +1,32 @@
+import asyncio
+import time
+
+
+class MessageStream:
+    """
+    A message stream for consumers to subscribe to,
+    and for producers to publish to.
+    """
+
+    def __init__(self):
+        self._loop = asyncio.get_running_loop()
+        self._waiter = self._loop.create_future()
+
+    def publish(self, message):
+        """
+        Publish a message to this MessageStream
+
+        :param message: The message to publish
+        """
+        waiter, self._waiter = self._waiter, self._loop.create_future()
+        waiter.set_result((message, time.time(), self._waiter))
+
+    async def __aiter__(self):
+        """
+        Iterate over the messages in the message stream
+        """
+        waiter = self._waiter
+        while True:
+            # Shield the future from being cancelled by a task waiting on it
+            message, ts, waiter = await asyncio.shield(waiter)
+            yield message, ts
diff --git a/freqtrade/rpc/api_server/ws/proxy.py b/freqtrade/rpc/api_server/ws/proxy.py
new file mode 100644
index 000000000..ed5f085b9
--- /dev/null
+++ b/freqtrade/rpc/api_server/ws/proxy.py
@@ -0,0 +1,73 @@
+from typing import Any
+
+from fastapi import WebSocket as FastAPIWebSocket
+from websockets.asyncio.client import ClientConnection as WebSocket
+
+from freqtrade.rpc.api_server.ws.ws_types import WebSocketType
+
+
+class WebSocketProxy:
+    """
+    WebSocketProxy object to bring the FastAPIWebSocket and websockets.WebSocketClientProtocol
+    under the same API
+    """
+
+    def __init__(self, websocket: WebSocketType):
+        self._websocket: FastAPIWebSocket | WebSocket = websocket
+
+    @property
+    def raw_websocket(self):
+        return self._websocket
+
+    @property
+    def remote_addr(self) -> tuple[Any, ...]:
+        if isinstance(self._websocket, WebSocket):
+            return self._websocket.remote_address
+        elif isinstance(self._websocket, FastAPIWebSocket):
+            if self._websocket.client:
+                client, port = self._websocket.client.host, self._websocket.client.port
+                return (client, port)
+        return ("unknown", 0)
+
+    async def send(self, data):
+        """
+        Send data on the wrapped websocket
+        """
+        if hasattr(self._websocket, "send_text"):
+            await self._websocket.send_text(data)
+        else:
+            await self._websocket.send(data)
+
+    async def recv(self):
+        """
+        Receive data on the wrapped websocket
+        """
+        if hasattr(self._websocket, "receive_text"):
+            return await self._websocket.receive_text()
+        else:
+            return await self._websocket.recv()
+
+    async def ping(self):
+        """
+        Ping the websocket, not supported by FastAPI WebSockets
+        """
+        if hasattr(self._websocket, "ping"):
+            return await self._websocket.ping()
+        return False
+
+    async def close(self, code: int = 1000):
+        """
+        Close the websocket connection, only supported by FastAPI WebSockets
+        """
+        if hasattr(self._websocket, "close"):
+            try:
+                return await self._websocket.close(code)
+            except RuntimeError:
+                pass
+
+    async def accept(self):
+        """
+        Accept the WebSocket connection, only support by FastAPI WebSockets
+        """
+        if hasattr(self._websocket, "accept"):
+            return await self._websocket.accept()
diff --git a/freqtrade/rpc/api_server/ws/serializer.py b/freqtrade/rpc/api_server/ws/serializer.py
new file mode 100644
index 000000000..cee7da124
--- /dev/null
+++ b/freqtrade/rpc/api_server/ws/serializer.py
@@ -0,0 +1,57 @@
+import logging
+from abc import ABC, abstractmethod
+from typing import Any
+
+import orjson
+import rapidjson
+from pandas import DataFrame
+
+from freqtrade.misc import dataframe_to_json, json_to_dataframe
+from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
+from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
+
+
+logger = logging.getLogger(__name__)
+
+
+class WebSocketSerializer(ABC):
+    def __init__(self, websocket: WebSocketProxy):
+        self._websocket: WebSocketProxy = websocket
+
+    @abstractmethod
+    def _serialize(self, data):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def _deserialize(self, data):
+        raise NotImplementedError()
+
+    async def send(self, data: WSMessageSchemaType | dict[str, Any]):
+        await self._websocket.send(self._serialize(data))
+
+    async def recv(self) -> bytes:
+        data = await self._websocket.recv()
+        return self._deserialize(data)
+
+
+class HybridJSONWebSocketSerializer(WebSocketSerializer):
+    def _serialize(self, data) -> str:
+        return str(orjson.dumps(data, default=_json_default), "utf-8")
+
+    def _deserialize(self, data: str):
+        # RapidJSON expects strings
+        return rapidjson.loads(data, object_hook=_json_object_hook)
+
+
+# Support serializing pandas DataFrames
+def _json_default(z):
+    if isinstance(z, DataFrame):
+        return {"__type__": "dataframe", "__value__": dataframe_to_json(z)}
+    raise TypeError
+
+
+# Support deserializing JSON to pandas DataFrames
+def _json_object_hook(z):
+    if z.get("__type__") == "dataframe":
+        return json_to_dataframe(z.get("__value__"))
+    return z
diff --git a/freqtrade/rpc/api_server/ws/ws_types.py b/freqtrade/rpc/api_server/ws/ws_types.py
new file mode 100644
index 000000000..7ce2eb4bf
--- /dev/null
+++ b/freqtrade/rpc/api_server/ws/ws_types.py
@@ -0,0 +1,8 @@
+from typing import Any, TypeVar
+
+from fastapi import WebSocket as FastAPIWebSocket
+from websockets.asyncio.client import ClientConnection as WebSocket
+
+
+WebSocketType = TypeVar("WebSocketType", FastAPIWebSocket, WebSocket)
+MessageType = dict[str, Any]
diff --git a/freqtrade/rpc/api_server/ws_schemas.py b/freqtrade/rpc/api_server/ws_schemas.py
new file mode 100644
index 000000000..3f9e80eea
--- /dev/null
+++ b/freqtrade/rpc/api_server/ws_schemas.py
@@ -0,0 +1,73 @@
+from datetime import datetime
+from typing import Any, TypedDict
+
+from pandas import DataFrame
+from pydantic import BaseModel, ConfigDict
+
+from freqtrade.constants import PairWithTimeframe
+from freqtrade.enums import RPCMessageType, RPCRequestType
+
+
+class BaseArbitraryModel(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+
+class WSRequestSchema(BaseArbitraryModel):
+    type: RPCRequestType
+    data: Any | None = None
+
+
+class WSMessageSchemaType(TypedDict):
+    # Type for typing to avoid doing pydantic typechecks.
+    type: RPCMessageType
+    data: dict[str, Any] | None
+
+
+class WSMessageSchema(BaseArbitraryModel):
+    type: RPCMessageType
+    data: Any | None = None
+    model_config = ConfigDict(extra="allow")
+
+
+# ------------------------------ REQUEST SCHEMAS ----------------------------
+
+
+class WSSubscribeRequest(WSRequestSchema):
+    type: RPCRequestType = RPCRequestType.SUBSCRIBE
+    data: list[RPCMessageType]
+
+
+class WSWhitelistRequest(WSRequestSchema):
+    type: RPCRequestType = RPCRequestType.WHITELIST
+    data: None = None
+
+
+class WSAnalyzedDFRequest(WSRequestSchema):
+    type: RPCRequestType = RPCRequestType.ANALYZED_DF
+    data: dict[str, Any] = {"limit": 1500, "pair": None}
+
+
+# ------------------------------ MESSAGE SCHEMAS ----------------------------
+
+
+class WSWhitelistMessage(WSMessageSchema):
+    type: RPCMessageType = RPCMessageType.WHITELIST
+    data: list[str]
+
+
+class WSAnalyzedDFMessage(WSMessageSchema):
+    class AnalyzedDFData(BaseArbitraryModel):
+        key: PairWithTimeframe
+        df: DataFrame
+        la: datetime
+
+    type: RPCMessageType = RPCMessageType.ANALYZED_DF
+    data: AnalyzedDFData
+
+
+class WSErrorMessage(WSMessageSchema):
+    type: RPCMessageType = RPCMessageType.EXCEPTION
+    data: str
+
+
+# --------------------------------------------------------------------------
diff --git a/freqtrade/rpc/discord.py b/freqtrade/rpc/discord.py
new file mode 100644
index 000000000..03f5fb2f8
--- /dev/null
+++ b/freqtrade/rpc/discord.py
@@ -0,0 +1,61 @@
+import logging
+
+from freqtrade.constants import Config
+from freqtrade.enums import RPCMessageType
+from freqtrade.rpc import RPC
+from freqtrade.rpc.webhook import Webhook
+
+
+logger = logging.getLogger(__name__)
+
+
+class Discord(Webhook):
+    def __init__(self, rpc: "RPC", config: Config):
+        self._config = config
+        self.rpc = rpc
+        self.strategy = config.get("strategy", "")
+        self.timeframe = config.get("timeframe", "")
+        self.bot_name = config.get("bot_name", "")
+
+        self._url = config["discord"]["webhook_url"]
+        self._format = "json"
+        self._retries = 1
+        self._retry_delay = 0.1
+        self._timeout = self._config["discord"].get("timeout", 10)
+
+    def cleanup(self) -> None:
+        """
+        Cleanup pending module resources.
+        This will do nothing for webhooks, they will simply not be called anymore
+        """
+        pass
+
+    def send_msg(self, msg) -> None:
+        if fields := self._config["discord"].get(msg["type"].value):
+            logger.info(f"Sending discord message: {msg}")
+
+            msg["strategy"] = self.strategy
+            msg["timeframe"] = self.timeframe
+            msg["bot_name"] = self.bot_name
+            color = 0x0000FF
+            if msg["type"] in (RPCMessageType.EXIT, RPCMessageType.EXIT_FILL):
+                profit_ratio = msg.get("profit_ratio")
+                color = 0x00FF00 if profit_ratio > 0 else 0xFF0000
+            title = msg["type"].value
+            if "pair" in msg:
+                title = f"Trade: {msg['pair']} {msg['type'].value}"
+            embeds = [
+                {
+                    "title": title,
+                    "color": color,
+                    "fields": [],
+                }
+            ]
+            for f in fields:
+                for k, v in f.items():
+                    v = v.format(**msg)
+                    embeds[0]["fields"].append({"name": k, "value": v, "inline": True})
+
+            # Send the message to discord channel
+            payload = {"embeds": embeds}
+            self._send_msg(payload)
diff --git a/freqtrade/rpc/external_message_consumer.py b/freqtrade/rpc/external_message_consumer.py
new file mode 100644
index 000000000..fe22613bd
--- /dev/null
+++ b/freqtrade/rpc/external_message_consumer.py
@@ -0,0 +1,389 @@
+"""
+ExternalMessageConsumer module
+
+Main purpose is to connect to external bot's message websocket to consume data
+from it
+"""
+
+import asyncio
+import logging
+import socket
+from collections.abc import Callable
+from threading import Thread
+from typing import Any, TypedDict
+
+import websockets
+from pydantic import ValidationError
+
+from freqtrade.constants import FULL_DATAFRAME_THRESHOLD
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import RPCMessageType
+from freqtrade.misc import remove_entry_exit_signals
+from freqtrade.rpc.api_server.ws.channel import WebSocketChannel, create_channel
+from freqtrade.rpc.api_server.ws.message_stream import MessageStream
+from freqtrade.rpc.api_server.ws_schemas import (
+    WSAnalyzedDFMessage,
+    WSAnalyzedDFRequest,
+    WSMessageSchema,
+    WSRequestSchema,
+    WSSubscribeRequest,
+    WSWhitelistMessage,
+    WSWhitelistRequest,
+)
+
+
+class Producer(TypedDict):
+    name: str
+    host: str
+    port: int
+    secure: bool
+    ws_token: str
+
+
+logger = logging.getLogger(__name__)
+
+
+def schema_to_dict(schema: WSMessageSchema | WSRequestSchema):
+    return schema.model_dump(exclude_none=True)
+
+
+class ExternalMessageConsumer:
+    """
+    The main controller class for consuming external messages from
+    other freqtrade bot's
+    """
+
+    def __init__(self, config: dict[str, Any], dataprovider: DataProvider):
+        self._config = config
+        self._dp = dataprovider
+
+        self._running = False
+        self._thread = None
+        self._loop = None
+        self._main_task = None
+        self._sub_tasks = None
+
+        self._emc_config = self._config.get("external_message_consumer", {})
+
+        self.enabled = self._emc_config.get("enabled", False)
+        self.producers: list[Producer] = self._emc_config.get("producers", [])
+
+        self.wait_timeout = self._emc_config.get("wait_timeout", 30)  # in seconds
+        self.ping_timeout = self._emc_config.get("ping_timeout", 10)  # in seconds
+        self.sleep_time = self._emc_config.get("sleep_time", 10)  # in seconds
+
+        # The amount of candles per dataframe on the initial request
+        self.initial_candle_limit = self._emc_config.get("initial_candle_limit", 1500)
+
+        # Message size limit, in megabytes. Default 8mb, Use bitwise operator << 20 to convert
+        # as the websockets client expects bytes.
+        self.message_size_limit = self._emc_config.get("message_size_limit", 8) << 20
+
+        # Setting these explicitly as they probably shouldn't be changed by a user
+        # Unless we somehow integrate this with the strategy to allow creating
+        # callbacks for the messages
+        self.topics = [RPCMessageType.WHITELIST, RPCMessageType.ANALYZED_DF]
+
+        # Allow setting data for each initial request
+        self._initial_requests: list[WSRequestSchema] = [
+            WSSubscribeRequest(data=self.topics),
+            WSWhitelistRequest(),
+            WSAnalyzedDFRequest(),
+        ]
+
+        # Specify which function to use for which RPCMessageType
+        self._message_handlers: dict[str, Callable[[str, WSMessageSchema], None]] = {
+            RPCMessageType.WHITELIST: self._consume_whitelist_message,
+            RPCMessageType.ANALYZED_DF: self._consume_analyzed_df_message,
+        }
+
+        self._channel_streams: dict[str, MessageStream] = {}
+
+        self.start()
+
+    def start(self):
+        """
+        Start the main internal loop in another thread to run coroutines
+        """
+        if self._thread and self._loop:
+            return
+
+        logger.info("Starting ExternalMessageConsumer")
+
+        self._loop = asyncio.new_event_loop()
+        self._thread = Thread(target=self._loop.run_forever)
+        self._running = True
+        self._thread.start()
+
+        self._main_task = asyncio.run_coroutine_threadsafe(self._main(), loop=self._loop)
+
+    def shutdown(self):
+        """
+        Shutdown the loop, thread, and tasks
+        """
+        if self._thread and self._loop:
+            logger.info("Stopping ExternalMessageConsumer")
+            self._running = False
+
+            self._channel_streams = {}
+
+            if self._sub_tasks:
+                # Cancel sub tasks
+                for task in self._sub_tasks:
+                    task.cancel()
+
+            if self._main_task:
+                # Cancel the main task
+                self._main_task.cancel()
+
+            self._thread.join()
+
+            self._thread = None
+            self._loop = None
+            self._sub_tasks = None
+            self._main_task = None
+
+    async def _main(self):
+        """
+        The main task coroutine
+        """
+        lock = asyncio.Lock()
+
+        try:
+            # Create a connection to each producer
+            self._sub_tasks = [
+                self._loop.create_task(self._handle_producer_connection(producer, lock))
+                for producer in self.producers
+            ]
+
+            await asyncio.gather(*self._sub_tasks)
+        except asyncio.CancelledError:
+            pass
+        finally:
+            # Stop the loop once we are done
+            self._loop.stop()
+
+    async def _handle_producer_connection(self, producer: Producer, lock: asyncio.Lock):
+        """
+        Main connection loop for the consumer
+
+        :param producer: Dictionary containing producer info
+        :param lock: An asyncio Lock
+        """
+        try:
+            await self._create_connection(producer, lock)
+        except asyncio.CancelledError:
+            # Exit silently
+            pass
+
+    async def _create_connection(self, producer: Producer, lock: asyncio.Lock):
+        """
+        Actually creates and handles the websocket connection, pinging on timeout
+        and handling connection errors.
+
+        :param producer: Dictionary containing producer info
+        :param lock: An asyncio Lock
+        """
+        while self._running:
+            try:
+                host, port = producer["host"], producer["port"]
+                token = producer["ws_token"]
+                name = producer["name"]
+                scheme = "wss" if producer.get("secure", False) else "ws"
+                ws_url = f"{scheme}://{host}:{port}/api/v1/message/ws?token={token}"
+
+                # This will raise InvalidURI if the url is bad
+                async with websockets.connect(
+                    ws_url, max_size=self.message_size_limit, ping_interval=None
+                ) as ws:
+                    async with create_channel(ws, channel_id=name, send_throttle=0.5) as channel:
+                        # Create the message stream for this channel
+                        self._channel_streams[name] = MessageStream()
+
+                        # Run the channel tasks while connected
+                        await channel.run_channel_tasks(
+                            self._receive_messages(channel, producer, lock),
+                            self._send_requests(channel, self._channel_streams[name]),
+                        )
+
+            except (websockets.exceptions.InvalidURI, ValueError) as e:
+                logger.error(f"{ws_url} is an invalid WebSocket URL - {e}")
+                break
+
+            except (
+                socket.gaierror,
+                ConnectionRefusedError,
+                websockets.exceptions.InvalidHandshake,
+            ) as e:
+                logger.error(f"Connection Refused - {e} retrying in {self.sleep_time}s")
+                await asyncio.sleep(self.sleep_time)
+                continue
+
+            except (
+                websockets.exceptions.ConnectionClosedError,
+                websockets.exceptions.ConnectionClosedOK,
+            ):
+                # Just keep trying to connect again indefinitely
+                await asyncio.sleep(self.sleep_time)
+                continue
+
+            except Exception as e:
+                # An unforeseen error has occurred, log and continue
+                logger.error("Unexpected error has occurred:")
+                logger.exception(e)
+                await asyncio.sleep(self.sleep_time)
+                continue
+
+    async def _send_requests(self, channel: WebSocketChannel, channel_stream: MessageStream):
+        # Send the initial requests
+        for init_request in self._initial_requests:
+            await channel.send(schema_to_dict(init_request))
+
+        # Now send any subsequent requests published to
+        # this channel's stream
+        async for request, _ in channel_stream:
+            logger.debug(f"Sending request to channel - {channel} - {request}")
+            await channel.send(request)
+
+    async def _receive_messages(
+        self, channel: WebSocketChannel, producer: Producer, lock: asyncio.Lock
+    ):
+        """
+        Loop to handle receiving messages from a Producer
+
+        :param channel: The WebSocketChannel object for the WebSocket
+        :param producer: Dictionary containing producer info
+        :param lock: An asyncio Lock
+        """
+        while self._running:
+            try:
+                message = await asyncio.wait_for(channel.recv(), timeout=self.wait_timeout)
+
+                try:
+                    async with lock:
+                        # Handle the message
+                        self.handle_producer_message(producer, message)
+                except Exception as e:
+                    logger.exception(f"Error handling producer message: {e}")
+
+            except (asyncio.TimeoutError, websockets.exceptions.ConnectionClosed):
+                # We haven't received data yet. Check the connection and continue.
+                try:
+                    # ping
+                    pong = await channel.ping()
+                    latency = await asyncio.wait_for(pong, timeout=self.ping_timeout) * 1000
+
+                    logger.info(f"Connection to {channel} still alive, latency: {latency}ms")
+                    continue
+
+                except Exception as e:
+                    # Just eat the error and continue reconnecting
+                    logger.warning(f"Ping error {channel} - {e} - retrying in {self.sleep_time}s")
+                    logger.debug(e, exc_info=e)
+                    raise
+
+    def send_producer_request(self, producer_name: str, request: WSRequestSchema | dict[str, Any]):
+        """
+        Publish a message to the producer's message stream to be
+        sent by the channel task.
+
+        :param producer_name: The name of the producer to publish the message to
+        :param request: The request to send to the producer
+        """
+        if isinstance(request, WSRequestSchema):
+            request = schema_to_dict(request)
+
+        if channel_stream := self._channel_streams.get(producer_name):
+            channel_stream.publish(request)
+
+    def handle_producer_message(self, producer: Producer, message: dict[str, Any]):
+        """
+        Handles external messages from a Producer
+        """
+        producer_name = producer.get("name", "default")
+
+        try:
+            producer_message = WSMessageSchema.model_validate(message)
+        except ValidationError as e:
+            logger.error(f"Invalid message from `{producer_name}`: {e}")
+            return
+
+        if not producer_message.data:
+            logger.error(f"Empty message received from `{producer_name}`")
+            return
+
+        logger.debug(f"Received message of type `{producer_message.type}` from `{producer_name}`")
+
+        message_handler = self._message_handlers.get(producer_message.type)
+
+        if not message_handler:
+            logger.info(f"Received unhandled message: `{producer_message.data}`, ignoring...")
+            return
+
+        message_handler(producer_name, producer_message)
+
+    def _consume_whitelist_message(self, producer_name: str, message: WSMessageSchema):
+        try:
+            # Validate the message
+            whitelist_message = WSWhitelistMessage.model_validate(message.model_dump())
+        except ValidationError as e:
+            logger.error(f"Invalid message from `{producer_name}`: {e}")
+            return
+
+        # Add the pairlist data to the DataProvider
+        self._dp._set_producer_pairs(whitelist_message.data, producer_name=producer_name)
+
+        logger.debug(f"Consumed message from `{producer_name}` of type `RPCMessageType.WHITELIST`")
+
+    def _consume_analyzed_df_message(self, producer_name: str, message: WSMessageSchema):
+        try:
+            df_message = WSAnalyzedDFMessage.model_validate(message.model_dump())
+        except ValidationError as e:
+            logger.error(f"Invalid message from `{producer_name}`: {e}")
+            return
+
+        key = df_message.data.key
+        df = df_message.data.df
+        la = df_message.data.la
+
+        pair, timeframe, candle_type = key
+
+        if df.empty:
+            logger.debug(f"Received Empty Dataframe for {key}")
+            return
+
+        # If set, remove the Entry and Exit signals from the Producer
+        if self._emc_config.get("remove_entry_exit_signals", False):
+            df = remove_entry_exit_signals(df)
+
+        logger.debug(f"Received {len(df)} candle(s) for {key}")
+
+        did_append, n_missing = self._dp._add_external_df(
+            pair,
+            df,
+            last_analyzed=la,
+            timeframe=timeframe,
+            candle_type=candle_type,
+            producer_name=producer_name,
+        )
+
+        if not did_append:
+            # We want an overlap in candles in case some data has changed
+            n_missing += 1
+            # Set to None for all candles if we missed a full df's worth of candles
+            n_missing = n_missing if n_missing < FULL_DATAFRAME_THRESHOLD else 1500
+
+            logger.warning(
+                f"Holes in data or no existing df, requesting {n_missing} candles "
+                f"for {key} from `{producer_name}`"
+            )
+
+            self.send_producer_request(
+                producer_name, WSAnalyzedDFRequest(data={"limit": n_missing, "pair": pair})
+            )
+            return
+
+        logger.debug(
+            f"Consumed message from `{producer_name}` "
+            f"of type `RPCMessageType.ANALYZED_DF` for {key}"
+        )
diff --git a/freqtrade/rpc/fiat_convert.py b/freqtrade/rpc/fiat_convert.py
new file mode 100644
index 000000000..f4589a7da
--- /dev/null
+++ b/freqtrade/rpc/fiat_convert.py
@@ -0,0 +1,200 @@
+"""
+Module that define classes to convert Crypto-currency to FIAT
+e.g BTC to USD
+"""
+
+import logging
+from datetime import datetime
+from typing import Any
+
+from cachetools import TTLCache
+from requests.exceptions import RequestException
+
+from freqtrade.constants import SUPPORTED_FIAT, Config
+from freqtrade.mixins.logging_mixin import LoggingMixin
+from freqtrade.util.coin_gecko import FtCoinGeckoApi
+
+
+logger = logging.getLogger(__name__)
+
+
+# Manually map symbol to ID for some common coins
+# with duplicate coingecko entries
+coingecko_mapping = {
+    "eth": "ethereum",
+    "bnb": "binancecoin",
+    "sol": "solana",
+    "usdt": "tether",
+    "busd": "binance-usd",
+    "tusd": "true-usd",
+    "usdc": "usd-coin",
+    "btc": "bitcoin",
+}
+
+
+class CryptoToFiatConverter(LoggingMixin):
+    """
+    Main class to initiate Crypto to FIAT.
+    This object contains a list of pair Crypto, FIAT
+    This object is also a Singleton
+    """
+
+    __instance = None
+
+    _coinlistings: list[dict] = []
+    _backoff: float = 0.0
+
+    def __new__(cls, *args: Any, **kwargs: Any) -> Any:
+        """
+        Singleton pattern to ensure only one instance is created.
+        """
+        if not cls.__instance:
+            cls.__instance = super().__new__(cls)
+        return cls.__instance
+
+    def __init__(self, config: Config) -> None:
+        # Timeout: 6h
+        self._pair_price: TTLCache = TTLCache(maxsize=500, ttl=6 * 60 * 60)
+
+        _coingecko_config = config.get("coingecko", {})
+        self._coingecko = FtCoinGeckoApi(
+            api_key=_coingecko_config.get("api_key", ""),
+            is_demo=_coingecko_config.get("is_demo", True),
+            retries=1,
+        )
+        LoggingMixin.__init__(self, logger, 3600)
+        self._load_cryptomap()
+
+    def _load_cryptomap(self) -> None:
+        try:
+            # Use list-comprehension to ensure we get a list.
+            self._coinlistings = [x for x in self._coingecko.get_coins_list()]
+        except RequestException as request_exception:
+            if "429" in str(request_exception):
+                logger.warning(
+                    "Too many requests for CoinGecko API, backing off and trying again later."
+                )
+                # Set backoff timestamp to 60 seconds in the future
+                self._backoff = datetime.now().timestamp() + 60
+                return
+            # If the request is not a 429 error we want to raise the normal error
+            logger.error(
+                "Could not load FIAT Cryptocurrency map for the following problem: "
+                f"{request_exception}"
+            )
+        except Exception as exception:
+            logger.error(
+                f"Could not load FIAT Cryptocurrency map for the following problem: {exception}"
+            )
+
+    def _get_gecko_id(self, crypto_symbol):
+        if not self._coinlistings:
+            if self._backoff <= datetime.now().timestamp():
+                self._load_cryptomap()
+                # Still not loaded.
+                if not self._coinlistings:
+                    return None
+            else:
+                return None
+        found = [x for x in self._coinlistings if x["symbol"].lower() == crypto_symbol]
+
+        if crypto_symbol in coingecko_mapping.keys():
+            found = [x for x in self._coinlistings if x["id"] == coingecko_mapping[crypto_symbol]]
+
+        if len(found) == 1:
+            return found[0]["id"]
+
+        if len(found) > 0:
+            # Wrong!
+            logger.warning(f"Found multiple mappings in CoinGecko for {crypto_symbol}.")
+            return None
+
+    def convert_amount(self, crypto_amount: float, crypto_symbol: str, fiat_symbol: str) -> float:
+        """
+        Convert an amount of crypto-currency to fiat
+        :param crypto_amount: amount of crypto-currency to convert
+        :param crypto_symbol: crypto-currency used
+        :param fiat_symbol: fiat to convert to
+        :return: float, value in fiat of the crypto-currency amount
+        """
+        if crypto_symbol == fiat_symbol:
+            return float(crypto_amount)
+        price = self.get_price(crypto_symbol=crypto_symbol, fiat_symbol=fiat_symbol)
+        return float(crypto_amount) * float(price)
+
+    def get_price(self, crypto_symbol: str, fiat_symbol: str) -> float:
+        """
+        Return the price of the Crypto-currency in Fiat
+        :param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
+        :param fiat_symbol: FIAT currency you want to convert to (e.g USD)
+        :return: Price in FIAT
+        """
+        crypto_symbol = crypto_symbol.lower()
+        fiat_symbol = fiat_symbol.lower()
+        inverse = False
+
+        if crypto_symbol == "usd":
+            # usd corresponds to "uniswap-state-dollar" for coingecko.
+            # We'll therefore need to "swap" the currencies
+            logger.info(f"reversing Rates {crypto_symbol}, {fiat_symbol}")
+            crypto_symbol = fiat_symbol
+            fiat_symbol = "usd"
+            inverse = True
+
+        symbol = f"{crypto_symbol}/{fiat_symbol}"
+        # Check if the fiat conversion you want is supported
+        if not self._is_supported_fiat(fiat=fiat_symbol):
+            raise ValueError(f"The fiat {fiat_symbol} is not supported.")
+
+        price = self._pair_price.get(symbol, None)
+
+        if not price:
+            price = self._find_price(crypto_symbol=crypto_symbol, fiat_symbol=fiat_symbol)
+            if inverse and price != 0.0:
+                price = 1 / price
+            self._pair_price[symbol] = price
+
+        return price
+
+    def _is_supported_fiat(self, fiat: str) -> bool:
+        """
+        Check if the FIAT your want to convert to is supported
+        :param fiat: FIAT to check (e.g USD)
+        :return: bool, True supported, False not supported
+        """
+
+        return fiat.upper() in SUPPORTED_FIAT
+
+    def _find_price(self, crypto_symbol: str, fiat_symbol: str) -> float:
+        """
+        Call CoinGecko API to retrieve the price in the FIAT
+        :param crypto_symbol: Crypto-currency you want to convert (e.g btc)
+        :param fiat_symbol: FIAT currency you want to convert to (e.g usd)
+        :return: float, price of the crypto-currency in Fiat
+        """
+        # Check if the fiat conversion you want is supported
+        if not self._is_supported_fiat(fiat=fiat_symbol):
+            raise ValueError(f"The fiat {fiat_symbol} is not supported.")
+
+        # No need to convert if both crypto and fiat are the same
+        if crypto_symbol == fiat_symbol:
+            return 1.0
+
+        _gecko_id = self._get_gecko_id(crypto_symbol)
+
+        if not _gecko_id:
+            # return 0 for unsupported stake currencies (fiat-convert should not break the bot)
+            self.log_once(
+                f"unsupported crypto-symbol {crypto_symbol.upper()} - returning 0.0", logger.warning
+            )
+            return 0.0
+
+        try:
+            return float(
+                self._coingecko.get_price(ids=_gecko_id, vs_currencies=fiat_symbol)[_gecko_id][
+                    fiat_symbol
+                ]
+            )
+        except Exception as exception:
+            logger.error("Error in _find_price: %s", exception)
+            return 0.0
diff --git a/freqtrade/rpc/rpc.py b/freqtrade/rpc/rpc.py
new file mode 100644
index 000000000..8903c7f2c
--- /dev/null
+++ b/freqtrade/rpc/rpc.py
@@ -0,0 +1,1627 @@
+"""
+This module contains class to define a RPC communications
+"""
+
+import logging
+from abc import abstractmethod
+from collections.abc import Generator, Sequence
+from datetime import date, datetime, timedelta, timezone
+from typing import TYPE_CHECKING, Any
+
+import psutil
+from dateutil.relativedelta import relativedelta
+from dateutil.tz import tzlocal
+from numpy import inf, int64, isnan, mean, nan
+from pandas import DataFrame, NaT
+from sqlalchemy import func, select
+
+from freqtrade import __version__
+from freqtrade.configuration.timerange import TimeRange
+from freqtrade.constants import CANCEL_REASON, DEFAULT_DATAFRAME_COLUMNS, Config
+from freqtrade.data.history import load_data
+from freqtrade.data.metrics import DrawDownResult, calculate_expectancy, calculate_max_drawdown
+from freqtrade.enums import (
+    CandleType,
+    ExitCheckTuple,
+    ExitType,
+    MarketDirection,
+    SignalDirection,
+    State,
+    TradingMode,
+)
+from freqtrade.exceptions import ExchangeError, PricingError
+from freqtrade.exchange import Exchange, timeframe_to_minutes, timeframe_to_msecs
+from freqtrade.exchange.exchange_utils import price_to_precision
+from freqtrade.loggers import bufferHandler
+from freqtrade.persistence import CustomDataWrapper, KeyStoreKeys, KeyValueStore, PairLocks, Trade
+from freqtrade.persistence.models import PairLock
+from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
+from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
+from freqtrade.rpc.rpc_types import RPCSendMsg
+from freqtrade.util import (
+    decimals_per_coin,
+    dt_from_ts,
+    dt_humanize_delta,
+    dt_now,
+    dt_ts,
+    dt_ts_def,
+    format_date,
+    shorten_date,
+)
+from freqtrade.wallets import PositionWallet, Wallet
+
+
+logger = logging.getLogger(__name__)
+
+
+class RPCException(Exception):
+    """
+    Should be raised with a rpc-formatted message in an _rpc_* method
+    if the required state is wrong, i.e.:
+
+    raise RPCException('*Status:* `no active trade`')
+    """
+
+    def __init__(self, message: str) -> None:
+        super().__init__(self)
+        self.message = message
+
+    def __str__(self):
+        return self.message
+
+    def __json__(self):
+        return {"msg": self.message}
+
+
+class RPCHandler:
+    def __init__(self, rpc: "RPC", config: Config) -> None:
+        """
+        Initializes RPCHandlers
+        :param rpc: instance of RPC Helper class
+        :param config: Configuration object
+        :return: None
+        """
+        self._rpc = rpc
+        self._config: Config = config
+
+    @property
+    def name(self) -> str:
+        """Returns the lowercase name of the implementation"""
+        return self.__class__.__name__.lower()
+
+    @abstractmethod
+    def cleanup(self) -> None:
+        """Cleanup pending module resources"""
+
+    @abstractmethod
+    def send_msg(self, msg: RPCSendMsg) -> None:
+        """Sends a message to all registered rpc modules"""
+
+
+class RPC:
+    """
+    RPC class can be used to have extra feature, like bot data, and access to DB data
+    """
+
+    # Bind _fiat_converter if needed
+    _fiat_converter: CryptoToFiatConverter | None = None
+    if TYPE_CHECKING:
+        from freqtrade.freqtradebot import FreqtradeBot
+
+        _freqtrade: FreqtradeBot
+
+    def __init__(self, freqtrade) -> None:
+        """
+        Initializes all enabled rpc modules
+        :param freqtrade: Instance of a freqtrade bot
+        :return: None
+        """
+        self._freqtrade = freqtrade
+        self._config: Config = freqtrade.config
+        if self._config.get("fiat_display_currency"):
+            self._fiat_converter = CryptoToFiatConverter(self._config)
+
+    @staticmethod
+    def _rpc_show_config(
+        config, botstate: State | str, strategy_version: str | None = None
+    ) -> dict[str, Any]:
+        """
+        Return a dict of config options.
+        Explicitly does NOT return the full config to avoid leakage of sensitive
+        information via rpc.
+        """
+        val = {
+            "version": __version__,
+            "strategy_version": strategy_version,
+            "dry_run": config["dry_run"],
+            "trading_mode": config.get("trading_mode", "spot"),
+            "short_allowed": config.get("trading_mode", "spot") != "spot",
+            "stake_currency": config["stake_currency"],
+            "stake_currency_decimals": decimals_per_coin(config["stake_currency"]),
+            "stake_amount": str(config["stake_amount"]),
+            "available_capital": config.get("available_capital"),
+            "max_open_trades": (
+                config.get("max_open_trades", 0)
+                if config.get("max_open_trades", 0) != float("inf")
+                else -1
+            ),
+            "minimal_roi": config["minimal_roi"].copy() if "minimal_roi" in config else {},
+            "stoploss": config.get("stoploss"),
+            "stoploss_on_exchange": config.get("order_types", {}).get(
+                "stoploss_on_exchange", False
+            ),
+            "trailing_stop": config.get("trailing_stop"),
+            "trailing_stop_positive": config.get("trailing_stop_positive"),
+            "trailing_stop_positive_offset": config.get("trailing_stop_positive_offset"),
+            "trailing_only_offset_is_reached": config.get("trailing_only_offset_is_reached"),
+            "unfilledtimeout": config.get("unfilledtimeout"),
+            "use_custom_stoploss": config.get("use_custom_stoploss"),
+            "order_types": config.get("order_types"),
+            "bot_name": config.get("bot_name", "freqtrade"),
+            "timeframe": config.get("timeframe"),
+            "timeframe_ms": timeframe_to_msecs(config["timeframe"]) if "timeframe" in config else 0,
+            "timeframe_min": (
+                timeframe_to_minutes(config["timeframe"]) if "timeframe" in config else 0
+            ),
+            "exchange": config["exchange"]["name"],
+            "strategy": config["strategy"],
+            "force_entry_enable": config.get("force_entry_enable", False),
+            "exit_pricing": config.get("exit_pricing", {}),
+            "entry_pricing": config.get("entry_pricing", {}),
+            "state": str(botstate),
+            "runmode": config["runmode"].value,
+            "position_adjustment_enable": config.get("position_adjustment_enable", False),
+            "max_entry_position_adjustment": (
+                config.get("max_entry_position_adjustment", -1)
+                if config.get("max_entry_position_adjustment") != float("inf")
+                else -1
+            ),
+        }
+        return val
+
+    def _rpc_trade_status(self, trade_ids: list[int] | None = None) -> list[dict[str, Any]]:
+        """
+        Below follows the RPC backend it is prefixed with rpc_ to raise awareness that it is
+        a remotely exposed function
+        """
+        # Fetch open trades
+        if trade_ids:
+            trades: Sequence[Trade] = Trade.get_trades(trade_filter=Trade.id.in_(trade_ids)).all()
+        else:
+            trades = Trade.get_open_trades()
+
+        if not trades:
+            raise RPCException("no active trade")
+        else:
+            results = []
+            for trade in trades:
+                current_profit_fiat: float | None = None
+                total_profit_fiat: float | None = None
+
+                # prepare open orders details
+                oo_details: str | None = ""
+                oo_details_lst = [
+                    f"({oo.order_type} {oo.side} rem={oo.safe_remaining:.8f})"
+                    for oo in trade.open_orders
+                    if oo.ft_order_side not in ["stoploss"]
+                ]
+                oo_details = ", ".join(oo_details_lst)
+
+                total_profit_abs = 0.0
+                total_profit_ratio: float | None = None
+                # calculate profit and send message to user
+                if trade.is_open:
+                    try:
+                        current_rate: float = self._freqtrade.exchange.get_rate(
+                            trade.pair, side="exit", is_short=trade.is_short, refresh=False
+                        )
+                    except (ExchangeError, PricingError):
+                        current_rate = nan
+                    if len(trade.select_filled_orders(trade.entry_side)) > 0:
+                        current_profit = current_profit_abs = current_profit_fiat = nan
+                        if not isnan(current_rate):
+                            prof = trade.calculate_profit(current_rate)
+                            current_profit = prof.profit_ratio
+                            current_profit_abs = prof.profit_abs
+                            total_profit_abs = prof.total_profit
+                            total_profit_ratio = prof.total_profit_ratio
+                    else:
+                        current_profit = current_profit_abs = current_profit_fiat = 0.0
+
+                else:
+                    # Closed trade ...
+                    current_rate = trade.close_rate or 0.0
+                    current_profit = trade.close_profit or 0.0
+                    current_profit_abs = trade.close_profit_abs or 0.0
+
+                # Calculate fiat profit
+                if not isnan(current_profit_abs) and self._fiat_converter:
+                    current_profit_fiat = self._fiat_converter.convert_amount(
+                        current_profit_abs,
+                        self._freqtrade.config["stake_currency"],
+                        self._freqtrade.config["fiat_display_currency"],
+                    )
+                    total_profit_fiat = self._fiat_converter.convert_amount(
+                        total_profit_abs,
+                        self._freqtrade.config["stake_currency"],
+                        self._freqtrade.config["fiat_display_currency"],
+                    )
+
+                # Calculate guaranteed profit (in case of trailing stop)
+                stop_entry = trade.calculate_profit(trade.stop_loss)
+
+                stoploss_entry_dist = stop_entry.profit_abs
+                stoploss_entry_dist_ratio = stop_entry.profit_ratio
+
+                # calculate distance to stoploss
+                stoploss_current_dist = price_to_precision(
+                    trade.stop_loss - current_rate,
+                    trade.price_precision,
+                    trade.precision_mode_price,
+                )
+                stoploss_current_dist_ratio = stoploss_current_dist / current_rate
+
+                trade_dict = trade.to_json()
+                trade_dict.update(
+                    dict(
+                        close_profit=trade.close_profit if not trade.is_open else None,
+                        current_rate=current_rate,
+                        profit_ratio=current_profit,
+                        profit_pct=round(current_profit * 100, 2),
+                        profit_abs=current_profit_abs,
+                        profit_fiat=current_profit_fiat,
+                        total_profit_abs=total_profit_abs,
+                        total_profit_fiat=total_profit_fiat,
+                        total_profit_ratio=total_profit_ratio,
+                        stoploss_current_dist=stoploss_current_dist,
+                        stoploss_current_dist_ratio=round(stoploss_current_dist_ratio, 8),
+                        stoploss_current_dist_pct=round(stoploss_current_dist_ratio * 100, 2),
+                        stoploss_entry_dist=stoploss_entry_dist,
+                        stoploss_entry_dist_ratio=round(stoploss_entry_dist_ratio, 8),
+                        open_orders=oo_details,
+                        nr_of_successful_entries=trade.nr_of_successful_entries,
+                    )
+                )
+                results.append(trade_dict)
+            return results
+
+    def _rpc_status_table(
+        self, stake_currency: str, fiat_display_currency: str
+    ) -> tuple[list, list, float, float]:
+        """
+        :return: list of trades, list of columns, sum of fiat profit
+        """
+        nonspot = self._config.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT
+        if not Trade.get_open_trades():
+            raise RPCException("no active trade")
+
+        trades_list = []
+        fiat_profit_sum = nan
+        fiat_total_profit_sum = nan
+        for trade in self._rpc_trade_status():
+            # Format profit as a string with the right sign
+            profit = f"{trade['profit_ratio']:.2%}"
+            fiat_profit = trade.get("profit_fiat", None)
+            if fiat_profit is None or isnan(fiat_profit):
+                fiat_profit = trade.get("profit_abs", 0.0)
+            if not isnan(fiat_profit):
+                profit += f" ({fiat_profit:.2f})"
+                fiat_profit_sum = (
+                    fiat_profit if isnan(fiat_profit_sum) else fiat_profit_sum + fiat_profit
+                )
+            total_profit = trade.get("total_profit_fiat", None)
+            if total_profit is None or isnan(total_profit):
+                total_profit = trade.get("total_profit_abs", 0.0)
+            if not isnan(total_profit):
+                fiat_total_profit_sum = (
+                    total_profit
+                    if isnan(fiat_total_profit_sum)
+                    else fiat_total_profit_sum + total_profit
+                )
+
+            # Format the active order side symbols
+            active_order_side = ""
+            orders = trade.get("orders", [])
+            if orders:
+                active_order_side = ".".join(
+                    "*" if (o.get("is_open") and o.get("ft_is_entry")) else "**"
+                    for o in orders
+                    if o.get("is_open") and o.get("ft_order_side") != "stoploss"
+                )
+
+            # Direction string for non-spot
+            direction_str = ""
+            if nonspot:
+                leverage = trade.get("leverage", 1.0)
+                direction_str = f"{'S' if trade.get('is_short') else 'L'} {leverage:.3g}x"
+
+            detail_trade = [
+                f"{trade['trade_id']} {direction_str}",
+                f"{trade['pair']}{active_order_side}",
+                shorten_date(dt_humanize_delta(dt_from_ts(trade["open_timestamp"]))),
+                profit,
+            ]
+
+            # Add number of entries if position adjustment is enabled
+            if self._config.get("position_adjustment_enable", False):
+                max_entry_str = ""
+                if self._config.get("max_entry_position_adjustment", -1) > 0:
+                    max_entry_str = f"/{self._config['max_entry_position_adjustment'] + 1}"
+                filled_entries = trade.get("nr_of_successful_entries", 0)
+                detail_trade.append(f"{filled_entries}{max_entry_str}")
+
+            trades_list.append(detail_trade)
+
+        columns = [
+            "ID L/S" if nonspot else "ID",
+            "Pair",
+            "Since",
+            f"Profit ({fiat_display_currency if self._fiat_converter else stake_currency})",
+        ]
+
+        if self._config.get("position_adjustment_enable", False):
+            columns.append("# Entries")
+
+        return trades_list, columns, fiat_profit_sum, fiat_total_profit_sum
+
+    def _rpc_timeunit_profit(
+        self,
+        timescale: int,
+        stake_currency: str,
+        fiat_display_currency: str,
+        timeunit: str = "days",
+    ) -> dict[str, Any]:
+        """
+        :param timeunit: Valid entries are 'days', 'weeks', 'months'
+        """
+        start_date = datetime.now(timezone.utc).date()
+        if timeunit == "weeks":
+            # weekly
+            start_date = start_date - timedelta(days=start_date.weekday())  # Monday
+        if timeunit == "months":
+            start_date = start_date.replace(day=1)
+
+        def time_offset(step: int):
+            if timeunit == "months":
+                return relativedelta(months=step)
+            return timedelta(**{timeunit: step})
+
+        if not (isinstance(timescale, int) and timescale > 0):
+            raise RPCException("timescale must be an integer greater than 0")
+
+        profit_units: dict[date, dict] = {}
+        daily_stake = self._freqtrade.wallets.get_total_stake_amount()
+
+        for day in range(0, timescale):
+            profitday = start_date - time_offset(day)
+            # Only query for necessary columns for performance reasons.
+            trades = Trade.session.execute(
+                select(Trade.close_profit_abs)
+                .filter(
+                    Trade.is_open.is_(False),
+                    Trade.close_date >= profitday,
+                    Trade.close_date < (profitday + time_offset(1)),
+                )
+                .order_by(Trade.close_date)
+            ).all()
+
+            curdayprofit = sum(
+                trade.close_profit_abs for trade in trades if trade.close_profit_abs is not None
+            )
+            # Calculate this periods starting balance
+            daily_stake = daily_stake - curdayprofit
+            profit_units[profitday] = {
+                "amount": curdayprofit,
+                "daily_stake": daily_stake,
+                "rel_profit": round(curdayprofit / daily_stake, 8) if daily_stake > 0 else 0,
+                "trades": len(trades),
+            }
+
+        data = [
+            {
+                "date": key,
+                "abs_profit": value["amount"],
+                "starting_balance": value["daily_stake"],
+                "rel_profit": value["rel_profit"],
+                "fiat_value": (
+                    self._fiat_converter.convert_amount(
+                        value["amount"], stake_currency, fiat_display_currency
+                    )
+                    if self._fiat_converter
+                    else 0
+                ),
+                "trade_count": value["trades"],
+            }
+            for key, value in profit_units.items()
+        ]
+        return {
+            "stake_currency": stake_currency,
+            "fiat_display_currency": fiat_display_currency,
+            "data": data,
+        }
+
+    def _rpc_trade_history(self, limit: int, offset: int = 0, order_by_id: bool = False) -> dict:
+        """Returns the X last trades"""
+        order_by: Any = Trade.id if order_by_id else Trade.close_date.desc()
+        if limit:
+            trades = Trade.session.scalars(
+                Trade.get_trades_query([Trade.is_open.is_(False)])
+                .order_by(order_by)
+                .limit(limit)
+                .offset(offset)
+            )
+        else:
+            trades = Trade.session.scalars(
+                Trade.get_trades_query([Trade.is_open.is_(False)]).order_by(Trade.close_date.desc())
+            )
+
+        output = [trade.to_json() for trade in trades]
+        total_trades = Trade.session.scalar(
+            select(func.count(Trade.id)).filter(Trade.is_open.is_(False))
+        )
+
+        return {
+            "trades": output,
+            "trades_count": len(output),
+            "offset": offset,
+            "total_trades": total_trades,
+        }
+
+    def _rpc_stats(self) -> dict[str, Any]:
+        """
+        Generate generic stats for trades in database
+        """
+
+        def trade_win_loss(trade):
+            if trade.close_profit > 0:
+                return "wins"
+            elif trade.close_profit < 0:
+                return "losses"
+            else:
+                return "draws"
+
+        trades = Trade.get_trades([Trade.is_open.is_(False)], include_orders=False)
+        # Duration
+        dur: dict[str, list[float]] = {"wins": [], "draws": [], "losses": []}
+        # Exit reason
+        exit_reasons = {}
+        for trade in trades:
+            if trade.exit_reason not in exit_reasons:
+                exit_reasons[trade.exit_reason] = {"wins": 0, "losses": 0, "draws": 0}
+            exit_reasons[trade.exit_reason][trade_win_loss(trade)] += 1
+
+            if trade.close_date is not None and trade.open_date is not None:
+                trade_dur = (trade.close_date - trade.open_date).total_seconds()
+                dur[trade_win_loss(trade)].append(trade_dur)
+
+        wins_dur = sum(dur["wins"]) / len(dur["wins"]) if len(dur["wins"]) > 0 else None
+        draws_dur = sum(dur["draws"]) / len(dur["draws"]) if len(dur["draws"]) > 0 else None
+        losses_dur = sum(dur["losses"]) / len(dur["losses"]) if len(dur["losses"]) > 0 else None
+
+        durations = {"wins": wins_dur, "draws": draws_dur, "losses": losses_dur}
+        return {"exit_reasons": exit_reasons, "durations": durations}
+
+    def _rpc_trade_statistics(
+        self, stake_currency: str, fiat_display_currency: str, start_date: datetime | None = None
+    ) -> dict[str, Any]:
+        """Returns cumulative profit statistics"""
+
+        start_date = datetime.fromtimestamp(0) if start_date is None else start_date
+
+        trade_filter = (
+            Trade.is_open.is_(False) & (Trade.close_date >= start_date)
+        ) | Trade.is_open.is_(True)
+        trades: Sequence[Trade] = Trade.session.scalars(
+            Trade.get_trades_query(trade_filter, include_orders=False).order_by(Trade.id)
+        ).all()
+
+        profit_all_coin = []
+        profit_all_ratio = []
+        profit_closed_coin = []
+        profit_closed_ratio = []
+        durations = []
+        winning_trades = 0
+        losing_trades = 0
+        winning_profit = 0.0
+        losing_profit = 0.0
+
+        for trade in trades:
+            current_rate: float = 0.0
+
+            if trade.close_date:
+                durations.append((trade.close_date - trade.open_date).total_seconds())
+
+            if not trade.is_open:
+                profit_ratio = trade.close_profit or 0.0
+                profit_abs = trade.close_profit_abs or 0.0
+                profit_closed_coin.append(profit_abs)
+                profit_closed_ratio.append(profit_ratio)
+                if profit_ratio >= 0:
+                    winning_trades += 1
+                    winning_profit += profit_abs
+                else:
+                    losing_trades += 1
+                    losing_profit += profit_abs
+            else:
+                # Get current rate
+                if len(trade.select_filled_orders(trade.entry_side)) == 0:
+                    # Skip trades with no filled orders
+                    continue
+                try:
+                    current_rate = self._freqtrade.exchange.get_rate(
+                        trade.pair, side="exit", is_short=trade.is_short, refresh=False
+                    )
+                except (PricingError, ExchangeError):
+                    current_rate = nan
+                    profit_ratio = nan
+                    profit_abs = nan
+                else:
+                    _profit = trade.calculate_profit(trade.close_rate or current_rate)
+
+                    profit_ratio = _profit.profit_ratio
+                    profit_abs = _profit.total_profit
+
+            profit_all_coin.append(profit_abs)
+            profit_all_ratio.append(profit_ratio)
+
+        closed_trade_count = len([t for t in trades if not t.is_open])
+
+        best_pair = Trade.get_best_pair(start_date)
+        trading_volume = Trade.get_trading_volume(start_date)
+
+        # Prepare data to display
+        profit_closed_coin_sum = round(sum(profit_closed_coin), 8)
+        profit_closed_ratio_mean = float(mean(profit_closed_ratio) if profit_closed_ratio else 0.0)
+        profit_closed_ratio_sum = sum(profit_closed_ratio) if profit_closed_ratio else 0.0
+
+        profit_closed_fiat = (
+            self._fiat_converter.convert_amount(
+                profit_closed_coin_sum, stake_currency, fiat_display_currency
+            )
+            if self._fiat_converter
+            else 0
+        )
+
+        profit_all_coin_sum = round(sum(profit_all_coin), 8)
+        profit_all_ratio_mean = float(mean(profit_all_ratio) if profit_all_ratio else 0.0)
+        # Doing the sum is not right - overall profit needs to be based on initial capital
+        profit_all_ratio_sum = sum(profit_all_ratio) if profit_all_ratio else 0.0
+        starting_balance = self._freqtrade.wallets.get_starting_balance()
+        profit_closed_ratio_fromstart = 0.0
+        profit_all_ratio_fromstart = 0.0
+        if starting_balance:
+            profit_closed_ratio_fromstart = profit_closed_coin_sum / starting_balance
+            profit_all_ratio_fromstart = profit_all_coin_sum / starting_balance
+
+        profit_factor = winning_profit / abs(losing_profit) if losing_profit else float("inf")
+
+        winrate = (winning_trades / closed_trade_count) if closed_trade_count > 0 else 0
+
+        trades_df = DataFrame(
+            [
+                {
+                    "close_date": format_date(trade.close_date),
+                    "close_date_dt": trade.close_date,
+                    "profit_abs": trade.close_profit_abs,
+                }
+                for trade in trades
+                if not trade.is_open and trade.close_date
+            ]
+        )
+
+        expectancy, expectancy_ratio = calculate_expectancy(trades_df)
+
+        drawdown = DrawDownResult()
+        if len(trades_df) > 0:
+            try:
+                drawdown = calculate_max_drawdown(
+                    trades_df,
+                    value_col="profit_abs",
+                    date_col="close_date_dt",
+                    starting_balance=starting_balance,
+                )
+            except ValueError:
+                # ValueError if no losing trade.
+                pass
+
+        profit_all_fiat = (
+            self._fiat_converter.convert_amount(
+                profit_all_coin_sum, stake_currency, fiat_display_currency
+            )
+            if self._fiat_converter
+            else 0
+        )
+
+        first_date = trades[0].open_date_utc if trades else None
+        last_date = trades[-1].open_date_utc if trades else None
+        num = float(len(durations) or 1)
+        bot_start = KeyValueStore.get_datetime_value(KeyStoreKeys.BOT_START_TIME)
+        return {
+            "profit_closed_coin": profit_closed_coin_sum,
+            "profit_closed_percent_mean": round(profit_closed_ratio_mean * 100, 2),
+            "profit_closed_ratio_mean": profit_closed_ratio_mean,
+            "profit_closed_percent_sum": round(profit_closed_ratio_sum * 100, 2),
+            "profit_closed_ratio_sum": profit_closed_ratio_sum,
+            "profit_closed_ratio": profit_closed_ratio_fromstart,
+            "profit_closed_percent": round(profit_closed_ratio_fromstart * 100, 2),
+            "profit_closed_fiat": profit_closed_fiat,
+            "profit_all_coin": profit_all_coin_sum,
+            "profit_all_percent_mean": round(profit_all_ratio_mean * 100, 2),
+            "profit_all_ratio_mean": profit_all_ratio_mean,
+            "profit_all_percent_sum": round(profit_all_ratio_sum * 100, 2),
+            "profit_all_ratio_sum": profit_all_ratio_sum,
+            "profit_all_ratio": profit_all_ratio_fromstart,
+            "profit_all_percent": round(profit_all_ratio_fromstart * 100, 2),
+            "profit_all_fiat": profit_all_fiat,
+            "trade_count": len(trades),
+            "closed_trade_count": closed_trade_count,
+            "first_trade_date": format_date(first_date),
+            "first_trade_humanized": dt_humanize_delta(first_date) if first_date else "",
+            "first_trade_timestamp": dt_ts_def(first_date, 0),
+            "latest_trade_date": format_date(last_date),
+            "latest_trade_humanized": dt_humanize_delta(last_date) if last_date else "",
+            "latest_trade_timestamp": dt_ts_def(last_date, 0),
+            "avg_duration": str(timedelta(seconds=sum(durations) / num)).split(".")[0],
+            "best_pair": best_pair[0] if best_pair else "",
+            "best_rate": round(best_pair[1] * 100, 2) if best_pair else 0,  # Deprecated
+            "best_pair_profit_ratio": best_pair[1] if best_pair else 0,
+            "best_pair_profit_abs": best_pair[2] if best_pair else 0,
+            "winning_trades": winning_trades,
+            "losing_trades": losing_trades,
+            "profit_factor": profit_factor,
+            "winrate": winrate,
+            "expectancy": expectancy,
+            "expectancy_ratio": expectancy_ratio,
+            "max_drawdown": drawdown.relative_account_drawdown,
+            "max_drawdown_abs": drawdown.drawdown_abs,
+            "max_drawdown_start": format_date(drawdown.high_date),
+            "max_drawdown_start_timestamp": dt_ts_def(drawdown.high_date),
+            "max_drawdown_end": format_date(drawdown.low_date),
+            "max_drawdown_end_timestamp": dt_ts_def(drawdown.low_date),
+            "drawdown_high": drawdown.high_value,
+            "drawdown_low": drawdown.low_value,
+            "trading_volume": trading_volume,
+            "bot_start_timestamp": dt_ts_def(bot_start, 0),
+            "bot_start_date": format_date(bot_start),
+        }
+
+    def __balance_get_est_stake(
+        self, coin: str, stake_currency: str, amount: float, balance: Wallet
+    ) -> tuple[float, float]:
+        est_stake = 0.0
+        est_bot_stake = 0.0
+        is_futures = self._config.get("trading_mode", TradingMode.SPOT) == TradingMode.FUTURES
+        if coin == self._freqtrade.exchange.get_proxy_coin():
+            est_stake = balance.total
+            if is_futures:
+                # in Futures, "total" includes the locked stake, and therefore all positions
+                est_stake = balance.free
+            est_bot_stake = amount
+        else:
+            try:
+                rate: float | None = self._freqtrade.exchange.get_conversion_rate(
+                    coin, stake_currency
+                )
+                if rate:
+                    est_stake = rate * (balance.free if is_futures else balance.total)
+                    est_bot_stake = rate * amount
+
+                return est_stake, est_bot_stake
+            except (ExchangeError, PricingError) as e:
+                logger.warning(f"Error {e} getting rate for {coin}")
+                pass
+        return est_stake, est_bot_stake
+
+    def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> dict:
+        """Returns current account balance per crypto"""
+        currencies: list[dict] = []
+        total = 0.0
+        total_bot = 0.0
+
+        open_trades: list[Trade] = Trade.get_open_trades()
+        open_assets: dict[str, Trade] = {t.safe_base_currency: t for t in open_trades}
+        self._freqtrade.wallets.update(require_update=False)
+        starting_capital = self._freqtrade.wallets.get_starting_balance()
+        starting_cap_fiat = (
+            self._fiat_converter.convert_amount(
+                starting_capital, stake_currency, fiat_display_currency
+            )
+            if self._fiat_converter
+            else 0
+        )
+        coin: str
+        balance: Wallet
+        for coin, balance in self._freqtrade.wallets.get_all_balances().items():
+            if not balance.total and not balance.free:
+                continue
+
+            trade = (
+                open_assets.get(coin, None)
+                if self._freqtrade.trading_mode != TradingMode.FUTURES
+                else None
+            )
+            is_stake_currency = coin == self._freqtrade.exchange.get_proxy_coin()
+            is_bot_managed = is_stake_currency or trade is not None
+            trade_amount = trade.amount if trade else 0
+            if is_stake_currency:
+                trade_amount = self._freqtrade.wallets.get_available_stake_amount()
+
+            try:
+                est_stake, est_stake_bot = self.__balance_get_est_stake(
+                    coin, stake_currency, trade_amount, balance
+                )
+            except ValueError:
+                continue
+
+            total += est_stake
+
+            if is_bot_managed:
+                total_bot += est_stake_bot
+            currencies.append(
+                {
+                    "currency": coin,
+                    "free": balance.free,
+                    "balance": balance.total,
+                    "used": balance.used,
+                    "bot_owned": trade_amount,
+                    "est_stake": est_stake or 0,
+                    "est_stake_bot": est_stake_bot if is_bot_managed else 0,
+                    "stake": stake_currency,
+                    "side": "long",
+                    "position": 0,
+                    "is_bot_managed": is_bot_managed,
+                    "is_position": False,
+                }
+            )
+        symbol: str
+        position: PositionWallet
+        for symbol, position in self._freqtrade.wallets.get_all_positions().items():
+            total += position.collateral
+            total_bot += position.collateral
+
+            currencies.append(
+                {
+                    "currency": symbol,
+                    "free": 0,
+                    "balance": 0,
+                    "used": 0,
+                    "position": position.position,
+                    "est_stake": position.collateral,
+                    "est_stake_bot": position.collateral,
+                    "stake": stake_currency,
+                    "side": position.side,
+                    "is_bot_managed": True,
+                    "is_position": True,
+                }
+            )
+
+        value = (
+            self._fiat_converter.convert_amount(total, stake_currency, fiat_display_currency)
+            if self._fiat_converter
+            else 0
+        )
+        value_bot = (
+            self._fiat_converter.convert_amount(total_bot, stake_currency, fiat_display_currency)
+            if self._fiat_converter
+            else 0
+        )
+
+        trade_count = len(Trade.get_trades_proxy())
+        starting_capital_ratio = (total_bot / starting_capital) - 1 if starting_capital else 0.0
+        starting_cap_fiat_ratio = (value_bot / starting_cap_fiat) - 1 if starting_cap_fiat else 0.0
+
+        return {
+            "currencies": currencies,
+            "total": total,
+            "total_bot": total_bot,
+            "symbol": fiat_display_currency,
+            "value": value,
+            "value_bot": value_bot,
+            "stake": stake_currency,
+            "starting_capital": starting_capital,
+            "starting_capital_ratio": starting_capital_ratio,
+            "starting_capital_pct": round(starting_capital_ratio * 100, 2),
+            "starting_capital_fiat": starting_cap_fiat,
+            "starting_capital_fiat_ratio": starting_cap_fiat_ratio,
+            "starting_capital_fiat_pct": round(starting_cap_fiat_ratio * 100, 2),
+            "trade_count": trade_count,
+            "note": "Simulated balances" if self._freqtrade.config["dry_run"] else "",
+        }
+
+    def _rpc_start(self) -> dict[str, str]:
+        """Handler for start"""
+        if self._freqtrade.state == State.RUNNING:
+            return {"status": "already running"}
+
+        self._freqtrade.state = State.RUNNING
+        return {"status": "starting trader ..."}
+
+    def _rpc_stop(self) -> dict[str, str]:
+        """Handler for stop"""
+        if self._freqtrade.state != State.STOPPED:
+            self._freqtrade.state = State.STOPPED
+            return {"status": "stopping trader ..."}
+
+        return {"status": "already stopped"}
+
+    def _rpc_reload_config(self) -> dict[str, str]:
+        """Handler for reload_config."""
+        self._freqtrade.state = State.RELOAD_CONFIG
+        return {"status": "Reloading config ..."}
+
+    def _rpc_pause(self) -> dict[str, str]:
+        """
+        Handler to pause trading (stop entering new trades), but handle open trades gracefully.
+        """
+        if self._freqtrade.state == State.RUNNING:
+            self._freqtrade.state = State.PAUSED
+
+        if self._freqtrade.state == State.STOPPED:
+            self._freqtrade.state = State.PAUSED
+            return {
+                "status": (
+                    "starting bot with trader in paused state, no entries will occur. "
+                    "Run /start to enable entries."
+                )
+            }
+
+        return {
+            "status": "paused, no more entries will occur from now. Run /start to enable entries."
+        }
+
+    def _rpc_reload_trade_from_exchange(self, trade_id: int) -> dict[str, str]:
+        """
+        Handler for reload_trade_from_exchange.
+        Reloads a trade from it's orders, should manual interaction have happened.
+        """
+        trade = Trade.get_trades(trade_filter=[Trade.id == trade_id]).first()
+        if not trade:
+            raise RPCException(f"Could not find trade with id {trade_id}.")
+
+        self._freqtrade.handle_onexchange_order(trade)
+        return {"status": "Reloaded from orders from exchange"}
+
+    def __exec_force_exit(
+        self, trade: Trade, ordertype: str | None, amount: float | None = None
+    ) -> bool:
+        # Check if there is there are open orders
+        trade_entry_cancelation_registry = []
+        for oo in trade.open_orders:
+            trade_entry_cancelation_res = {"order_id": oo.order_id, "cancel_state": False}
+            order = self._freqtrade.exchange.fetch_order(oo.order_id, trade.pair)
+
+            if order["side"] == trade.entry_side:
+                fully_canceled = self._freqtrade.handle_cancel_enter(
+                    trade, order, oo, CANCEL_REASON["FORCE_EXIT"]
+                )
+                trade_entry_cancelation_res["cancel_state"] = fully_canceled
+                trade_entry_cancelation_registry.append(trade_entry_cancelation_res)
+
+            if order["side"] == trade.exit_side:
+                # Cancel order - so it is placed anew with a fresh price.
+                self._freqtrade.handle_cancel_exit(trade, order, oo, CANCEL_REASON["FORCE_EXIT"])
+
+        if all(tocr["cancel_state"] is False for tocr in trade_entry_cancelation_registry):
+            if trade.has_open_orders:
+                # Order cancellation failed, so we can't exit.
+                return False
+            # Get current rate and execute sell
+            current_rate = self._freqtrade.exchange.get_rate(
+                trade.pair, side="exit", is_short=trade.is_short, refresh=True
+            )
+            exit_check = ExitCheckTuple(exit_type=ExitType.FORCE_EXIT)
+            order_type = ordertype or self._freqtrade.strategy.order_types.get(
+                "force_exit", self._freqtrade.strategy.order_types["exit"]
+            )
+            sub_amount: float | None = None
+            if amount and amount < trade.amount:
+                # Partial exit ...
+                min_exit_stake = self._freqtrade.exchange.get_min_pair_stake_amount(
+                    trade.pair, current_rate, trade.stop_loss_pct or 0.0
+                )
+                remaining = (trade.amount - amount) * current_rate
+                if min_exit_stake and remaining < min_exit_stake:
+                    raise RPCException(f"Remaining amount of {remaining} would be too small.")
+                sub_amount = amount
+
+            self._freqtrade.execute_trade_exit(
+                trade, current_rate, exit_check, ordertype=order_type, sub_trade_amt=sub_amount
+            )
+
+            return True
+        return False
+
+    def _rpc_force_exit(
+        self, trade_id: str, ordertype: str | None = None, *, amount: float | None = None
+    ) -> dict[str, str]:
+        """
+        Handler for forceexit <id>.
+        Sells the given trade at current price
+        """
+
+        if self._freqtrade.state == State.STOPPED:
+            raise RPCException("trader is not running")
+
+        with self._freqtrade._exit_lock:
+            if trade_id == "all":
+                # Execute exit for all open orders
+                for trade in Trade.get_open_trades():
+                    self.__exec_force_exit(trade, ordertype)
+                Trade.commit()
+                self._freqtrade.wallets.update()
+                return {"result": "Created exit orders for all open trades."}
+
+            # Query for trade
+            trade = Trade.get_trades(
+                trade_filter=[
+                    Trade.id == trade_id,
+                    Trade.is_open.is_(True),
+                ]
+            ).first()
+            if not trade:
+                logger.warning("force_exit: Invalid argument received")
+                raise RPCException("invalid argument")
+
+            result = self.__exec_force_exit(trade, ordertype, amount)
+            Trade.commit()
+            self._freqtrade.wallets.update()
+            if not result:
+                raise RPCException("Failed to exit trade.")
+            return {"result": f"Created exit order for trade {trade_id}."}
+
+    def _force_entry_validations(self, pair: str, order_side: SignalDirection):
+        if not self._freqtrade.config.get("force_entry_enable", False):
+            raise RPCException("Force_entry not enabled.")
+
+        if self._freqtrade.state != State.RUNNING:
+            raise RPCException("trader is not running")
+
+        if order_side == SignalDirection.SHORT and self._freqtrade.trading_mode == TradingMode.SPOT:
+            raise RPCException("Can't go short on Spot markets.")
+
+        if pair not in self._freqtrade.exchange.get_markets(tradable_only=True):
+            raise RPCException("Symbol does not exist or market is not active.")
+        # Check if pair quote currency equals to the stake currency.
+        stake_currency = self._freqtrade.config.get("stake_currency")
+        if not self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
+            raise RPCException(
+                f"Wrong pair selected. Only pairs with stake-currency {stake_currency} allowed."
+            )
+
+    def _rpc_force_entry(
+        self,
+        pair: str,
+        price: float | None,
+        *,
+        order_type: str | None = None,
+        order_side: SignalDirection = SignalDirection.LONG,
+        stake_amount: float | None = None,
+        enter_tag: str | None = "force_entry",
+        leverage: float | None = None,
+    ) -> Trade | None:
+        """
+        Handler for forcebuy <asset> <price>
+        Buys a pair trade at the given or current price
+        """
+        self._force_entry_validations(pair, order_side)
+
+        # check if valid pair
+
+        # check if pair already has an open pair
+        trade: Trade | None = Trade.get_trades(
+            [Trade.is_open.is_(True), Trade.pair == pair]
+        ).first()
+        is_short = order_side == SignalDirection.SHORT
+        if trade:
+            is_short = trade.is_short
+            if not self._freqtrade.strategy.position_adjustment_enable:
+                raise RPCException(f"position for {pair} already open - id: {trade.id}")
+            if trade.has_open_orders:
+                raise RPCException(
+                    f"position for {pair} already open - id: {trade.id} "
+                    f"and has open order {','.join(trade.open_orders_ids)}"
+                )
+        else:
+            if Trade.get_open_trade_count() >= self._config["max_open_trades"]:
+                raise RPCException("Maximum number of trades is reached.")
+
+        if not stake_amount:
+            # gen stake amount
+            stake_amount = self._freqtrade.wallets.get_trade_stake_amount(
+                pair, self._config["max_open_trades"]
+            )
+
+        # execute buy
+        if not order_type:
+            order_type = self._freqtrade.strategy.order_types.get(
+                "force_entry", self._freqtrade.strategy.order_types["entry"]
+            )
+        with self._freqtrade._exit_lock:
+            if self._freqtrade.execute_entry(
+                pair,
+                stake_amount,
+                price,
+                ordertype=order_type,
+                trade=trade,
+                is_short=is_short,
+                enter_tag=enter_tag,
+                leverage_=leverage,
+                mode="pos_adjust" if trade else "initial",
+            ):
+                Trade.commit()
+                trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
+                return trade
+            else:
+                raise RPCException(f"Failed to enter position for {pair}.")
+
+    def _rpc_cancel_open_order(self, trade_id: int):
+        if self._freqtrade.state == State.STOPPED:
+            raise RPCException("trader is not running")
+        with self._freqtrade._exit_lock:
+            # Query for trade
+            trade = Trade.get_trades(
+                trade_filter=[
+                    Trade.id == trade_id,
+                    Trade.is_open.is_(True),
+                ]
+            ).first()
+            if not trade:
+                logger.warning("cancel_open_order: Invalid trade_id received.")
+                raise RPCException("Invalid trade_id.")
+            if not trade.has_open_orders:
+                logger.warning("cancel_open_order: No open order for trade_id.")
+                raise RPCException("No open order for trade_id.")
+
+            for open_order in trade.open_orders:
+                try:
+                    order = self._freqtrade.exchange.fetch_order(open_order.order_id, trade.pair)
+                except ExchangeError as e:
+                    logger.info(f"Cannot query order for {trade} due to {e}.", exc_info=True)
+                    raise RPCException("Order not found.")
+                self._freqtrade.handle_cancel_order(
+                    order, open_order, trade, CANCEL_REASON["USER_CANCEL"]
+                )
+            Trade.commit()
+
+    def _rpc_delete(self, trade_id: int) -> dict[str, str | int]:
+        """
+        Handler for delete <id>.
+        Delete the given trade and close eventually existing open orders.
+        """
+        with self._freqtrade._exit_lock:
+            c_count = 0
+            trade = Trade.get_trades(trade_filter=[Trade.id == trade_id]).first()
+            if not trade:
+                logger.warning("delete trade: Invalid argument received")
+                raise RPCException("invalid argument")
+
+            # Try cancelling regular order if that exists
+            for open_order in trade.open_orders:
+                try:
+                    self._freqtrade.exchange.cancel_order(open_order.order_id, trade.pair)
+                    c_count += 1
+                except ExchangeError:
+                    pass
+
+            # cancel stoploss on exchange orders ...
+            if (
+                self._freqtrade.strategy.order_types.get("stoploss_on_exchange")
+                and trade.has_open_sl_orders
+            ):
+                for oslo in trade.open_sl_orders:
+                    try:
+                        self._freqtrade.exchange.cancel_stoploss_order(oslo.order_id, trade.pair)
+                        c_count += 1
+                    except ExchangeError:
+                        pass
+
+            trade.delete()
+            self._freqtrade.wallets.update()
+            return {
+                "result": "success",
+                "trade_id": trade_id,
+                "result_msg": f"Deleted trade {trade_id}. Closed {c_count} open orders.",
+                "cancel_order_count": c_count,
+            }
+
+    def _rpc_list_custom_data(
+        self, trade_id: int | None = None, key: str | None = None, limit: int = 100, offset: int = 0
+    ) -> list[dict[str, Any]]:
+        """
+        Fetch custom data for a specific trade, or all open trades if `trade_id` is not provided.
+        Pagination is applied via `limit` and `offset`.
+
+        Returns an array of dictionaries, each containing:
+        - "trade_id": the ID of the trade (int)
+        - "custom_data": a list of custom data dicts, each with the fields:
+                "id", "key", "type", "value", "created_at", "updated_at"
+        """
+        trades: Sequence[Trade]
+        if trade_id is None:
+            # Get all open trades
+            trades = Trade.session.scalars(
+                Trade.get_trades_query([Trade.is_open.is_(True)])
+                .order_by(Trade.id)
+                .limit(limit)
+                .offset(offset)
+            ).all()
+        else:
+            trades = Trade.get_trades(trade_filter=[Trade.id == trade_id]).all()
+
+        if not trades:
+            raise RPCException(
+                f"No trade found for trade_id: {trade_id}" if trade_id else "No open trades found."
+            )
+
+        results = []
+        for trade in trades:
+            # Depending on whether a specific key is provided, retrieve custom data accordingly.
+            if key:
+                data = trade.get_custom_data_entry(key=key)
+                # If data exists, wrap it in a list so the output remains consistent.
+                custom_data = [data] if data else []
+            else:
+                custom_data = trade.get_all_custom_data()
+
+            # Format and Append result for the trade if any custom data was found.
+            if custom_data:
+                formatted_custom_data = [
+                    {
+                        "key": data_entry.cd_key,
+                        "type": data_entry.cd_type,
+                        "value": CustomDataWrapper._convert_custom_data(data_entry).value,
+                        "created_at": data_entry.created_at,
+                        "updated_at": data_entry.updated_at,
+                    }
+                    for data_entry in custom_data
+                ]
+                results.append({"trade_id": trade.id, "custom_data": formatted_custom_data})
+
+            # Handle case when there is no custom data found across trades.
+            if not results:
+                message_details = ""
+                if key:
+                    message_details += f"with key '{key}' "
+                message_details += (
+                    f"found for Trade ID: {trade_id}." if trade_id else "found for any open trades."
+                )
+                raise RPCException(f"No custom-data {message_details}")
+
+        return results
+
+    def _rpc_performance(self) -> list[dict[str, Any]]:
+        """
+        Handler for performance.
+        Shows a performance statistic from finished trades
+        """
+        pair_rates = Trade.get_overall_performance()
+
+        return pair_rates
+
+    def _rpc_enter_tag_performance(self, pair: str | None) -> list[dict[str, Any]]:
+        """
+        Handler for buy tag performance.
+        Shows a performance statistic from finished trades
+        """
+        return Trade.get_enter_tag_performance(pair)
+
+    def _rpc_exit_reason_performance(self, pair: str | None) -> list[dict[str, Any]]:
+        """
+        Handler for exit reason performance.
+        Shows a performance statistic from finished trades
+        """
+        return Trade.get_exit_reason_performance(pair)
+
+    def _rpc_mix_tag_performance(self, pair: str | None) -> list[dict[str, Any]]:
+        """
+        Handler for mix tag (enter_tag + exit_reason) performance.
+        Shows a performance statistic from finished trades
+        """
+        mix_tags = Trade.get_mix_tag_performance(pair)
+
+        return mix_tags
+
+    def _rpc_count(self) -> dict[str, float]:
+        """Returns the number of trades running"""
+        if self._freqtrade.state == State.STOPPED:
+            raise RPCException("trader is not running")
+
+        trades = Trade.get_open_trades()
+        return {
+            "current": len(trades),
+            "max": (
+                int(self._freqtrade.config["max_open_trades"])
+                if self._freqtrade.config["max_open_trades"] != float("inf")
+                else -1
+            ),
+            "total_stake": sum((trade.open_rate * trade.amount) for trade in trades),
+        }
+
+    def _rpc_locks(self) -> dict[str, Any]:
+        """Returns the  current locks"""
+
+        locks = PairLocks.get_pair_locks(None)
+        return {"lock_count": len(locks), "locks": [lock.to_json() for lock in locks]}
+
+    def _rpc_delete_lock(
+        self, lockid: int | None = None, pair: str | None = None
+    ) -> dict[str, Any]:
+        """Delete specific lock(s)"""
+        locks: Sequence[PairLock] = []
+
+        if pair:
+            locks = PairLocks.get_pair_locks(pair)
+        if lockid:
+            locks = PairLock.session.scalars(select(PairLock).filter(PairLock.id == lockid)).all()
+
+        for lock in locks:
+            lock.active = False
+            lock.lock_end_time = datetime.now(timezone.utc)
+
+        Trade.commit()
+
+        return self._rpc_locks()
+
+    def _rpc_add_lock(self, pair: str, until: datetime, reason: str | None, side: str) -> PairLock:
+        lock = PairLocks.lock_pair(
+            pair=pair,
+            until=until,
+            reason=reason,
+            side=side,
+        )
+        return lock
+
+    def _rpc_whitelist(self) -> dict:
+        """Returns the currently active whitelist"""
+        res = {
+            "method": self._freqtrade.pairlists.name_list,
+            "length": len(self._freqtrade.active_pair_whitelist),
+            "whitelist": self._freqtrade.active_pair_whitelist,
+        }
+        return res
+
+    def _rpc_blacklist_delete(self, delete: list[str]) -> dict:
+        """Removes pairs from currently active blacklist"""
+        errors = {}
+        for pair in delete:
+            if pair in self._freqtrade.pairlists.blacklist:
+                self._freqtrade.pairlists.blacklist.remove(pair)
+            else:
+                errors[pair] = {"error_msg": f"Pair {pair} is not in the current blacklist."}
+        resp = self._rpc_blacklist()
+        resp["errors"] = errors
+        return resp
+
+    def _rpc_blacklist(self, add: list[str] | None = None) -> dict:
+        """Returns the currently active blacklist"""
+        errors = {}
+        if add:
+            for pair in add:
+                if pair not in self._freqtrade.pairlists.blacklist:
+                    try:
+                        expand_pairlist([pair], list(self._freqtrade.exchange.get_markets().keys()))
+                        self._freqtrade.pairlists.blacklist.append(pair)
+
+                    except ValueError:
+                        errors[pair] = {"error_msg": f"Pair {pair} is not a valid wildcard."}
+                else:
+                    errors[pair] = {"error_msg": f"Pair {pair} already in pairlist."}
+
+        res = {
+            "method": self._freqtrade.pairlists.name_list,
+            "length": len(self._freqtrade.pairlists.blacklist),
+            "blacklist": self._freqtrade.pairlists.blacklist,
+            "blacklist_expanded": self._freqtrade.pairlists.expanded_blacklist,
+            "errors": errors,
+        }
+        return res
+
+    @staticmethod
+    def _rpc_get_logs(limit: int | None) -> dict[str, Any]:
+        """Returns the last X logs"""
+        if limit:
+            buffer = bufferHandler.buffer[-limit:]
+        else:
+            buffer = bufferHandler.buffer
+        records = [
+            [
+                format_date(datetime.fromtimestamp(r.created)),
+                r.created * 1000,
+                r.name,
+                r.levelname,
+                r.message + ("\n" + r.exc_text if r.exc_text else ""),
+            ]
+            for r in buffer
+            if hasattr(r, "message")
+        ]
+
+        # Log format:
+        # [logtime-formatted, logepoch, logger-name, loglevel, message \n + exception]
+        # e.g. ["2020-08-27 11:35:01", 1598520901097.9397,
+        #       "freqtrade.worker", "INFO", "Starting worker develop"]
+
+        return {"log_count": len(records), "logs": records}
+
+    def _rpc_edge(self) -> list[dict[str, Any]]:
+        """Returns information related to Edge"""
+        if not self._freqtrade.edge:
+            raise RPCException("Edge is not enabled.")
+        return self._freqtrade.edge.accepted_pairs()
+
+    @staticmethod
+    def _convert_dataframe_to_dict(
+        strategy: str,
+        pair: str,
+        timeframe: str,
+        dataframe: DataFrame,
+        last_analyzed: datetime,
+        selected_cols: list[str] | None,
+    ) -> dict[str, Any]:
+        has_content = len(dataframe) != 0
+        dataframe_columns = list(dataframe.columns)
+        signals = {
+            "enter_long": 0,
+            "exit_long": 0,
+            "enter_short": 0,
+            "exit_short": 0,
+        }
+        if has_content:
+            if selected_cols is not None:
+                # Ensure OHLCV columns are always present
+                cols_set = set(DEFAULT_DATAFRAME_COLUMNS + list(signals.keys()) + selected_cols)
+                df_cols = [col for col in dataframe_columns if col in cols_set]
+                dataframe = dataframe.loc[:, df_cols]
+
+            dataframe.loc[:, "__date_ts"] = dataframe.loc[:, "date"].astype(int64) // 1000 // 1000
+            # Move signal close to separate column when signal for easy plotting
+            for sig_type in signals.keys():
+                if sig_type in dataframe.columns:
+                    mask = dataframe[sig_type] == 1
+                    signals[sig_type] = int(mask.sum())
+                    dataframe.loc[mask, f"_{sig_type}_signal_close"] = dataframe.loc[mask, "close"]
+
+            # band-aid until this is fixed:
+            # https://github.com/pandas-dev/pandas/issues/45836
+            datetime_types = ["datetime", "datetime64", "datetime64[ns, UTC]"]
+            date_columns = dataframe.select_dtypes(include=datetime_types)
+            for date_column in date_columns:
+                # replace NaT with `None`
+                dataframe[date_column] = dataframe[date_column].astype(object).replace({NaT: None})
+
+            dataframe = dataframe.replace({inf: None, -inf: None, nan: None})
+
+        res = {
+            "pair": pair,
+            "timeframe": timeframe,
+            "timeframe_ms": timeframe_to_msecs(timeframe),
+            "strategy": strategy,
+            "all_columns": dataframe_columns,
+            "columns": list(dataframe.columns),
+            "data": dataframe.values.tolist(),
+            "length": len(dataframe),
+            "buy_signals": signals["enter_long"],  # Deprecated
+            "sell_signals": signals["exit_long"],  # Deprecated
+            "enter_long_signals": signals["enter_long"],
+            "exit_long_signals": signals["exit_long"],
+            "enter_short_signals": signals["enter_short"],
+            "exit_short_signals": signals["exit_short"],
+            "last_analyzed": last_analyzed,
+            "last_analyzed_ts": int(last_analyzed.timestamp()),
+            "data_start": "",
+            "data_start_ts": 0,
+            "data_stop": "",
+            "data_stop_ts": 0,
+        }
+        if has_content:
+            res.update(
+                {
+                    "data_start": str(dataframe.iloc[0]["date"]),
+                    "data_start_ts": int(dataframe.iloc[0]["__date_ts"]),
+                    "data_stop": str(dataframe.iloc[-1]["date"]),
+                    "data_stop_ts": int(dataframe.iloc[-1]["__date_ts"]),
+                }
+            )
+        return res
+
+    def _rpc_analysed_dataframe(
+        self, pair: str, timeframe: str, limit: int | None, selected_cols: list[str] | None
+    ) -> dict[str, Any]:
+        """Analyzed dataframe in Dict form"""
+
+        _data, last_analyzed = self.__rpc_analysed_dataframe_raw(pair, timeframe, limit)
+        return RPC._convert_dataframe_to_dict(
+            self._freqtrade.config["strategy"], pair, timeframe, _data, last_analyzed, selected_cols
+        )
+
+    def __rpc_analysed_dataframe_raw(
+        self, pair: str, timeframe: str, limit: int | None
+    ) -> tuple[DataFrame, datetime]:
+        """
+        Get the dataframe and last analyze from the dataprovider
+
+        :param pair: The pair to get
+        :param timeframe: The timeframe of data to get
+        :param limit: The amount of candles in the dataframe
+        """
+        _data, last_analyzed = self._freqtrade.dataprovider.get_analyzed_dataframe(pair, timeframe)
+        _data = _data.copy()
+
+        if limit:
+            _data = _data.iloc[-limit:]
+
+        return _data, last_analyzed
+
+    def _ws_all_analysed_dataframes(
+        self, pairlist: list[str], limit: int | None
+    ) -> Generator[dict[str, Any], None, None]:
+        """
+        Get the analysed dataframes of each pair in the pairlist.
+        If specified, only return the most recent `limit` candles for
+        each dataframe.
+
+        :param pairlist: A list of pairs to get
+        :param limit: If an integer, limits the size of dataframe
+                      If a list of string date times, only returns those candles
+        :returns: A generator of dictionaries with the key, dataframe, and last analyzed timestamp
+        """
+        timeframe = self._freqtrade.config["timeframe"]
+        candle_type = self._freqtrade.config.get("candle_type_def", CandleType.SPOT)
+
+        for pair in pairlist:
+            dataframe, last_analyzed = self.__rpc_analysed_dataframe_raw(pair, timeframe, limit)
+
+            yield {"key": (pair, timeframe, candle_type), "df": dataframe, "la": last_analyzed}
+
+    def _ws_request_analyzed_df(self, limit: int | None = None, pair: str | None = None):
+        """Historical Analyzed Dataframes for WebSocket"""
+        pairlist = [pair] if pair else self._freqtrade.active_pair_whitelist
+
+        return self._ws_all_analysed_dataframes(pairlist, limit)
+
+    def _ws_request_whitelist(self):
+        """Whitelist data for WebSocket"""
+        return self._freqtrade.active_pair_whitelist
+
+    @staticmethod
+    def _rpc_analysed_history_full(
+        config: Config,
+        pair: str,
+        timeframe: str,
+        exchange: Exchange,
+        selected_cols: list[str] | None,
+        live: bool,
+    ) -> dict[str, Any]:
+        timerange_parsed = TimeRange.parse_timerange(config.get("timerange"))
+
+        from freqtrade.data.converter import trim_dataframe
+        from freqtrade.data.dataprovider import DataProvider
+        from freqtrade.resolvers.strategy_resolver import StrategyResolver
+
+        strategy_name = ""
+        startup_candles = 0
+        if config.get("strategy"):
+            strategy = StrategyResolver.load_strategy(config)
+            startup_candles = strategy.startup_candle_count
+            strategy_name = strategy.get_strategy_name()
+
+        if live:
+            data = exchange.get_historic_ohlcv(
+                pair=pair,
+                timeframe=timeframe,
+                since_ms=timerange_parsed.startts * 1000
+                if timerange_parsed.startts
+                else dt_ts(dt_now() - timedelta(days=30)),
+                is_new_pair=True,  # history is never available - so always treat as new pair
+                candle_type=config.get("candle_type_def", CandleType.SPOT),
+                until_ms=timerange_parsed.stopts,
+            )
+        else:
+            _data = load_data(
+                datadir=config["datadir"],
+                pairs=[pair],
+                timeframe=timeframe,
+                timerange=timerange_parsed,
+                data_format=config["dataformat_ohlcv"],
+                candle_type=config.get("candle_type_def", CandleType.SPOT),
+                startup_candles=startup_candles,
+            )
+            if pair not in _data:
+                raise RPCException(
+                    f"No data for {pair}, {timeframe} in {config.get('timerange')} found."
+                )
+            data = _data[pair]
+
+        if config.get("strategy"):
+            strategy.dp = DataProvider(config, exchange=exchange, pairlists=None)
+            strategy.ft_bot_start()
+
+            df_analyzed = strategy.analyze_ticker(data, {"pair": pair})
+            df_analyzed = trim_dataframe(
+                df_analyzed, timerange_parsed, startup_candles=startup_candles
+            )
+        else:
+            df_analyzed = data
+
+        return RPC._convert_dataframe_to_dict(
+            strategy_name,
+            pair,
+            timeframe,
+            df_analyzed.copy(),
+            dt_now(),
+            selected_cols,
+        )
+
+    def _rpc_plot_config(self) -> dict[str, Any]:
+        if (
+            self._freqtrade.strategy.plot_config
+            and "subplots" not in self._freqtrade.strategy.plot_config
+        ):
+            self._freqtrade.strategy.plot_config["subplots"] = {}
+        return self._freqtrade.strategy.plot_config
+
+    @staticmethod
+    def _rpc_plot_config_with_strategy(config: Config) -> dict[str, Any]:
+        from freqtrade.resolvers.strategy_resolver import StrategyResolver
+
+        strategy = StrategyResolver.load_strategy(config)
+        # Manually load hyperparameters, as we don't call the bot-start callback.
+        strategy.ft_load_hyper_params(False)
+
+        if strategy.plot_config and "subplots" not in strategy.plot_config:
+            strategy.plot_config["subplots"] = {}
+        return strategy.plot_config
+
+    @staticmethod
+    def _rpc_sysinfo() -> dict[str, Any]:
+        return {
+            "cpu_pct": psutil.cpu_percent(interval=1, percpu=True),
+            "ram_pct": psutil.virtual_memory().percent,
+        }
+
+    def health(self) -> dict[str, str | int | None]:
+        last_p = self._freqtrade.last_process
+        res: dict[str, None | str | int] = {
+            "last_process": None,
+            "last_process_loc": None,
+            "last_process_ts": None,
+            "bot_start": None,
+            "bot_start_loc": None,
+            "bot_start_ts": None,
+            "bot_startup": None,
+            "bot_startup_loc": None,
+            "bot_startup_ts": None,
+        }
+
+        if last_p is not None:
+            res.update(
+                {
+                    "last_process": str(last_p),
+                    "last_process_loc": format_date(last_p.astimezone(tzlocal())),
+                    "last_process_ts": int(last_p.timestamp()),
+                }
+            )
+
+        if bot_start := KeyValueStore.get_datetime_value(KeyStoreKeys.BOT_START_TIME):
+            res.update(
+                {
+                    "bot_start": str(bot_start),
+                    "bot_start_loc": format_date(bot_start.astimezone(tzlocal())),
+                    "bot_start_ts": int(bot_start.timestamp()),
+                }
+            )
+        if bot_startup := KeyValueStore.get_datetime_value(KeyStoreKeys.STARTUP_TIME):
+            res.update(
+                {
+                    "bot_startup": str(bot_startup),
+                    "bot_startup_loc": format_date(bot_startup.astimezone(tzlocal())),
+                    "bot_startup_ts": int(bot_startup.timestamp()),
+                }
+            )
+
+        return res
+
+    def _update_market_direction(self, direction: MarketDirection) -> None:
+        self._freqtrade.strategy.market_direction = direction
+
+    def _get_market_direction(self) -> MarketDirection:
+        return self._freqtrade.strategy.market_direction
diff --git a/freqtrade/rpc/rpc_manager.py b/freqtrade/rpc/rpc_manager.py
new file mode 100644
index 000000000..0a4f48a35
--- /dev/null
+++ b/freqtrade/rpc/rpc_manager.py
@@ -0,0 +1,142 @@
+"""
+This module contains class to manage RPC communications (Telegram, API, ...)
+"""
+
+import logging
+from collections import deque
+
+from freqtrade.constants import Config
+from freqtrade.enums import NO_ECHO_MESSAGES, RPCMessageType
+from freqtrade.rpc import RPC, RPCHandler
+from freqtrade.rpc.rpc_types import RPCSendMsg
+
+
+logger = logging.getLogger(__name__)
+
+
+class RPCManager:
+    """
+    Class to manage RPC objects (Telegram, API, ...)
+    """
+
+    def __init__(self, freqtrade) -> None:
+        """Initializes all enabled rpc modules"""
+        self.registered_modules: list[RPCHandler] = []
+        self._rpc = RPC(freqtrade)
+        config = freqtrade.config
+        # Enable telegram
+        if config.get("telegram", {}).get("enabled", False):
+            logger.info("Enabling rpc.telegram ...")
+            from freqtrade.rpc.telegram import Telegram
+
+            self.registered_modules.append(Telegram(self._rpc, config))
+
+        # Enable discord
+        if config.get("discord", {}).get("enabled", False):
+            logger.info("Enabling rpc.discord ...")
+            from freqtrade.rpc.discord import Discord
+
+            self.registered_modules.append(Discord(self._rpc, config))
+
+        # Enable Webhook
+        if config.get("webhook", {}).get("enabled", False):
+            logger.info("Enabling rpc.webhook ...")
+            from freqtrade.rpc.webhook import Webhook
+
+            self.registered_modules.append(Webhook(self._rpc, config))
+
+        # Enable local rest api server for cmd line control
+        if config.get("api_server", {}).get("enabled", False):
+            logger.info("Enabling rpc.api_server")
+            from freqtrade.rpc.api_server import ApiServer
+
+            apiserver = ApiServer(config)
+            apiserver.add_rpc_handler(self._rpc)
+            self.registered_modules.append(apiserver)
+
+    def cleanup(self) -> None:
+        """Stops all enabled rpc modules"""
+        logger.info("Cleaning up rpc modules ...")
+        while self.registered_modules:
+            mod = self.registered_modules.pop()
+            logger.info("Cleaning up rpc.%s ...", mod.name)
+            mod.cleanup()
+            del mod
+
+    def send_msg(self, msg: RPCSendMsg) -> None:
+        """
+        Send given message to all registered rpc modules.
+        A message consists of one or more key value pairs of strings.
+        e.g.:
+        {
+            'status': 'stopping bot'
+        }
+        """
+        if msg.get("type") not in NO_ECHO_MESSAGES:
+            logger.info("Sending rpc message: %s", msg)
+        for mod in self.registered_modules:
+            logger.debug("Forwarding message to rpc.%s", mod.name)
+            try:
+                mod.send_msg(msg)
+            except NotImplementedError:
+                logger.error(f"Message type '{msg['type']}' not implemented by handler {mod.name}.")
+            except Exception:
+                logger.exception("Exception occurred within RPC module %s", mod.name)
+
+    def process_msg_queue(self, queue: deque) -> None:
+        """
+        Process all messages in the queue.
+        """
+        while queue:
+            msg = queue.popleft()
+            logger.info("Sending rpc strategy_msg: %s", msg)
+            for mod in self.registered_modules:
+                if mod._config.get(mod.name, {}).get("allow_custom_messages", False):
+                    mod.send_msg(
+                        {
+                            "type": RPCMessageType.STRATEGY_MSG,
+                            "msg": msg,
+                        }
+                    )
+
+    def startup_messages(self, config: Config, pairlist, protections) -> None:
+        if config["dry_run"]:
+            self.send_msg(
+                {
+                    "type": RPCMessageType.WARNING,
+                    "status": "Dry run is enabled. All trades are simulated.",
+                }
+            )
+        stake_currency = config["stake_currency"]
+        stake_amount = config["stake_amount"]
+        minimal_roi = config["minimal_roi"]
+        stoploss = config["stoploss"]
+        trailing_stop = config["trailing_stop"]
+        timeframe = config["timeframe"]
+        exchange_name = config["exchange"]["name"]
+        strategy_name = config.get("strategy", "")
+        pos_adjust_enabled = "On" if config["position_adjustment_enable"] else "Off"
+        self.send_msg(
+            {
+                "type": RPCMessageType.STARTUP,
+                "status": f"*Exchange:* `{exchange_name}`\n"
+                f"*Stake per trade:* `{stake_amount} {stake_currency}`\n"
+                f"*Minimum ROI:* `{minimal_roi}`\n"
+                f"*{'Trailing ' if trailing_stop else ''}Stoploss:* `{stoploss}`\n"
+                f"*Position adjustment:* `{pos_adjust_enabled}`\n"
+                f"*Timeframe:* `{timeframe}`\n"
+                f"*Strategy:* `{strategy_name}`",
+            }
+        )
+        self.send_msg(
+            {
+                "type": RPCMessageType.STARTUP,
+                "status": f"Searching for {stake_currency} pairs to buy and sell "
+                f"based on {pairlist.short_desc()}",
+            }
+        )
+        if len(protections.name_list) > 0:
+            prots = "\n".join([p for prot in protections.short_desc() for k, p in prot.items()])
+            self.send_msg(
+                {"type": RPCMessageType.STARTUP, "status": f"Using Protections: \n{prots}"}
+            )
diff --git a/freqtrade/rpc/rpc_types.py b/freqtrade/rpc/rpc_types.py
new file mode 100644
index 000000000..ad6138a91
--- /dev/null
+++ b/freqtrade/rpc/rpc_types.py
@@ -0,0 +1,139 @@
+from datetime import datetime
+from typing import Any, Literal, TypedDict
+
+from freqtrade.constants import PairWithTimeframe
+from freqtrade.enums import RPCMessageType
+
+
+ProfitLossStr = Literal["profit", "loss"]
+
+
+class RPCSendMsgBase(TypedDict):
+    pass
+    # ty1pe: Literal[RPCMessageType]
+
+
+class RPCStatusMsg(RPCSendMsgBase):
+    """Used for Status, Startup and Warning messages"""
+
+    type: Literal[RPCMessageType.STATUS, RPCMessageType.STARTUP, RPCMessageType.WARNING]
+    status: str
+
+
+class RPCStrategyMsg(RPCSendMsgBase):
+    """Used for Status, Startup and Warning messages"""
+
+    type: Literal[RPCMessageType.STRATEGY_MSG]
+    msg: str
+
+
+class RPCProtectionMsg(RPCSendMsgBase):
+    type: Literal[RPCMessageType.PROTECTION_TRIGGER, RPCMessageType.PROTECTION_TRIGGER_GLOBAL]
+    id: int
+    pair: str
+    base_currency: str | None
+    lock_time: str
+    lock_timestamp: int
+    lock_end_time: str
+    lock_end_timestamp: int
+    reason: str
+    side: str
+    active: bool
+
+
+class RPCWhitelistMsg(RPCSendMsgBase):
+    type: Literal[RPCMessageType.WHITELIST]
+    data: list[str]
+
+
+class __RPCEntryExitMsgBase(RPCSendMsgBase):
+    trade_id: int
+    buy_tag: str | None
+    enter_tag: str | None
+    exchange: str
+    pair: str
+    base_currency: str
+    quote_currency: str
+    leverage: float | None
+    direction: str
+    limit: float
+    open_rate: float
+    order_type: str
+    stake_amount: float
+    stake_currency: str
+    fiat_currency: str | None
+    amount: float
+    open_date: datetime
+    current_rate: float | None
+    sub_trade: bool
+
+
+class RPCEntryMsg(__RPCEntryExitMsgBase):
+    type: Literal[RPCMessageType.ENTRY, RPCMessageType.ENTRY_FILL]
+
+
+class RPCCancelMsg(__RPCEntryExitMsgBase):
+    type: Literal[RPCMessageType.ENTRY_CANCEL]
+    reason: str
+
+
+class RPCExitMsg(__RPCEntryExitMsgBase):
+    type: Literal[RPCMessageType.EXIT, RPCMessageType.EXIT_FILL]
+    cumulative_profit: float
+    gain: ProfitLossStr
+    close_rate: float
+    profit_amount: float
+    profit_ratio: float
+    exit_reason: str | None
+    close_date: datetime
+    # current_rate: float | None
+    order_rate: float | None
+    final_profit_ratio: float | None
+    is_final_exit: bool
+
+
+class RPCExitCancelMsg(__RPCEntryExitMsgBase):
+    type: Literal[RPCMessageType.EXIT_CANCEL]
+    reason: str
+    gain: ProfitLossStr
+    profit_amount: float
+    profit_ratio: float
+    exit_reason: str | None
+    close_date: datetime
+
+
+class _AnalyzedDFData(TypedDict):
+    key: PairWithTimeframe
+    df: Any
+    la: datetime
+
+
+class RPCAnalyzedDFMsg(RPCSendMsgBase):
+    """New Analyzed dataframe message"""
+
+    type: Literal[RPCMessageType.ANALYZED_DF]
+    data: _AnalyzedDFData
+
+
+class RPCNewCandleMsg(RPCSendMsgBase):
+    """New candle ping message, issued once per new candle/pair"""
+
+    type: Literal[RPCMessageType.NEW_CANDLE]
+    data: PairWithTimeframe
+
+
+RPCOrderMsg = RPCEntryMsg | RPCExitMsg | RPCExitCancelMsg | RPCCancelMsg
+
+
+RPCSendMsg = (
+    RPCStatusMsg
+    | RPCStrategyMsg
+    | RPCProtectionMsg
+    | RPCWhitelistMsg
+    | RPCEntryMsg
+    | RPCCancelMsg
+    | RPCExitMsg
+    | RPCExitCancelMsg
+    | RPCAnalyzedDFMsg
+    | RPCNewCandleMsg
+)
diff --git a/freqtrade/rpc/telegram.py b/freqtrade/rpc/telegram.py
index 676eba8d7..f3f4a4196 100644
--- a/freqtrade/rpc/telegram.py
+++ b/freqtrade/rpc/telegram.py
@@ -1,436 +1,2187 @@
+# pragma pylint: disable=unused-argument, unused-variable, protected-access, invalid-name
+
+"""
+This module manage Telegram communication
+"""
+
+import asyncio
+import json
 import logging
-from typing import Any, Callable
+import re
+from collections.abc import Callable, Coroutine
+from copy import deepcopy
+from dataclasses import dataclass
+from datetime import date, datetime, timedelta
+from functools import partial, wraps
+from html import escape
+from itertools import chain
+from math import isnan
+from threading import Thread
+from typing import Any, Literal
 
 from tabulate import tabulate
-from telegram import Bot, ParseMode, ReplyKeyboardMarkup, Update
-from telegram.error import NetworkError, TelegramError
-from telegram.ext import CommandHandler, Updater
+from telegram import (
+    CallbackQuery,
+    InlineKeyboardButton,
+    InlineKeyboardMarkup,
+    KeyboardButton,
+    Message,
+    ReplyKeyboardMarkup,
+    Update,
+)
+from telegram.constants import MessageLimit, ParseMode
+from telegram.error import BadRequest, NetworkError, TelegramError
+from telegram.ext import Application, CallbackContext, CallbackQueryHandler, CommandHandler
+from telegram.helpers import escape_markdown
 
-from freqtrade.rpc.__init__ import (rpc_status_table,
-                                    rpc_trade_status,
-                                    rpc_daily_profit,
-                                    rpc_trade_statistics,
-                                    rpc_balance,
-                                    rpc_start,
-                                    rpc_stop,
-                                    rpc_forcesell,
-                                    rpc_performance,
-                                    rpc_count,
-                                    )
-
-from freqtrade import __version__
+from freqtrade.__init__ import __version__
+from freqtrade.constants import DUST_PER_COIN, Config
+from freqtrade.enums import MarketDirection, RPCMessageType, SignalDirection, TradingMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.misc import chunks, plural
+from freqtrade.persistence import Trade
+from freqtrade.rpc import RPC, RPCException, RPCHandler
+from freqtrade.rpc.rpc_types import RPCEntryMsg, RPCExitMsg, RPCOrderMsg, RPCSendMsg
+from freqtrade.util import (
+    dt_from_ts,
+    dt_humanize_delta,
+    fmt_coin,
+    fmt_coin2,
+    format_date,
+    round_value,
+)
+
+
+MAX_MESSAGE_LENGTH = MessageLimit.MAX_TEXT_LENGTH
 
 
-# Remove noisy log messages
-logging.getLogger('requests.packages.urllib3').setLevel(logging.INFO)
-logging.getLogger('telegram').setLevel(logging.INFO)
 logger = logging.getLogger(__name__)
 
-_UPDATER: Updater = None
-_CONF = {}
+logger.debug("Included module rpc.telegram ...")
 
 
-def init(config: dict) -> None:
+def safe_async_db(func: Callable[..., Any]):
     """
-    Initializes this module with the given config,
-    registers all known command handlers
-    and starts polling for message updates
-    :param config: config to use
-    :return: None
-    """
-    global _UPDATER
-
-    _CONF.update(config)
-    if not is_enabled():
-        return
-
-    _UPDATER = Updater(token=config['telegram']['token'], workers=0)
-
-    # Register command handler and start telegram message polling
-    handles = [
-        CommandHandler('status', _status),
-        CommandHandler('profit', _profit),
-        CommandHandler('balance', _balance),
-        CommandHandler('start', _start),
-        CommandHandler('stop', _stop),
-        CommandHandler('forcesell', _forcesell),
-        CommandHandler('performance', _performance),
-        CommandHandler('daily', _daily),
-        CommandHandler('count', _count),
-        CommandHandler('help', _help),
-        CommandHandler('version', _version),
-    ]
-    for handle in handles:
-        _UPDATER.dispatcher.add_handler(handle)
-    _UPDATER.start_polling(
-        clean=True,
-        bootstrap_retries=-1,
-        timeout=30,
-        read_latency=60,
-    )
-    logger.info(
-        'rpc.telegram is listening for following commands: %s',
-        [h.command for h in handles]
-    )
-
-
-def cleanup() -> None:
-    """
-    Stops all running telegram threads.
-    :return: None
-    """
-    if not is_enabled():
-        return
-    _UPDATER.stop()
-
-
-def is_enabled() -> bool:
-    """
-    Returns True if the telegram module is activated, False otherwise
-    """
-    return bool(_CONF['telegram'].get('enabled', False))
-
-
-def authorized_only(command_handler: Callable[[Bot, Update], None]) -> Callable[..., Any]:
-    """
-    Decorator to check if the message comes from the correct chat_id
-    :param command_handler: Telegram CommandHandler
+    Decorator to safely handle sessions when switching async context
+    :param func: function to decorate
     :return: decorated function
     """
+
+    @wraps(func)
     def wrapper(*args, **kwargs):
-        update = kwargs.get('update') or args[1]
-
-        # Reject unauthorized messages
-        chat_id = int(_CONF['telegram']['chat_id'])
-        if int(update.message.chat_id) != chat_id:
-            logger.info('Rejected unauthorized message from: %s', update.message.chat_id)
-            return wrapper
-
-        logger.info('Executing handler: %s for chat_id: %s', command_handler.__name__, chat_id)
+        """Decorator logic"""
         try:
-            return command_handler(*args, **kwargs)
-        except BaseException:
-            logger.exception('Exception occurred within Telegram module')
+            return func(*args, **kwargs)
+        finally:
+            Trade.session.remove()
+
     return wrapper
 
 
-@authorized_only
-def _status(bot: Bot, update: Update) -> None:
+@dataclass
+class TimeunitMappings:
+    header: str
+    message: str
+    message2: str
+    callback: str
+    default: int
+    dateformat: str
+
+
+def authorized_only(command_handler: Callable[..., Coroutine[Any, Any, None]]):
     """
-    Handler for /status.
-    Returns the current TradeThread status
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
+    Decorator to check if the message comes from the correct chat_id
+    can only be used with Telegram Class to decorate instance methods.
+    :param command_handler: Telegram CommandHandler
+    :return: decorated function
     """
 
-    # Check if additional parameters are passed
-    params = update.message.text.replace('/status', '').split(' ') \
-        if update.message.text else []
-    if 'table' in params:
-        _status_table(bot, update)
-        return
+    @wraps(command_handler)
+    async def wrapper(self, *args, **kwargs) -> None:
+        """Decorator logic"""
+        update = kwargs.get("update") or args[0]
 
-    # Fetch open trade
-    (error, trades) = rpc_trade_status()
-    if error:
-        send_msg(trades, bot=bot)
-    else:
-        for trademsg in trades:
-            send_msg(trademsg, bot=bot)
+        # Reject unauthorized messages
+        message: Message = (
+            update.message if update.callback_query is None else update.callback_query.message
+        )
+        cchat_id: int = int(message.chat_id)
+        ctopic_id: int | None = message.message_thread_id
+        from_user_id: str = str(update.effective_user.id if update.effective_user else "")
 
+        chat_id = int(self._config["telegram"]["chat_id"])
+        if cchat_id != chat_id:
+            logger.info(f"Rejected unauthorized message from: {cchat_id}")
+            return None
+        if (topic_id := self._config["telegram"].get("topic_id")) is not None:
+            if str(ctopic_id) != topic_id:
+                # This can be quite common in multi-topic environments.
+                logger.debug(f"Rejected message from wrong channel: {cchat_id}, {ctopic_id}")
+                return None
 
-@authorized_only
-def _status_table(bot: Bot, update: Update) -> None:
-    """
-    Handler for /status table.
-    Returns the current TradeThread status in table format
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-    # Fetch open trade
-    (err, df_statuses) = rpc_status_table()
-    if err:
-        send_msg(df_statuses, bot=bot)
-    else:
-        message = tabulate(df_statuses, headers='keys', tablefmt='simple')
-        message = "<pre>{}</pre>".format(message)
-
-        send_msg(message, parse_mode=ParseMode.HTML)
-
-
-@authorized_only
-def _daily(bot: Bot, update: Update) -> None:
-    """
-    Handler for /daily <n>
-    Returns a daily profit (in BTC) over the last n days.
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-    try:
-        timescale = int(update.message.text.replace('/daily', '').strip())
-    except (TypeError, ValueError):
-        timescale = 7
-    (error, stats) = rpc_daily_profit(timescale,
-                                      _CONF['stake_currency'],
-                                      _CONF['fiat_display_currency'])
-    if error:
-        send_msg(stats, bot=bot)
-    else:
-        stats = tabulate(stats,
-                         headers=[
-                             'Day',
-                             'Profit {}'.format(_CONF['stake_currency']),
-                             'Profit {}'.format(_CONF['fiat_display_currency'])
-                         ],
-                         tablefmt='simple')
-        message = '<b>Daily Profit over the last {} days</b>:\n<pre>{}</pre>'.format(
-                  timescale, stats)
-        send_msg(message, bot=bot, parse_mode=ParseMode.HTML)
-
-
-@authorized_only
-def _profit(bot: Bot, update: Update) -> None:
-    """
-    Handler for /profit.
-    Returns a cumulative profit statistics.
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-    (error, stats) = rpc_trade_statistics(_CONF['stake_currency'],
-                                          _CONF['fiat_display_currency'])
-    if error:
-        send_msg(stats, bot=bot)
-        return
-
-    # Message to display
-    markdown_msg = """
-*ROI:* Close trades
-  ∙ `{profit_closed_coin:.8f} {coin} ({profit_closed_percent:.2f}%)`
-  ∙ `{profit_closed_fiat:.3f} {fiat}`
-*ROI:* All trades
-  ∙ `{profit_all_coin:.8f} {coin} ({profit_all_percent:.2f}%)`
-  ∙ `{profit_all_fiat:.3f} {fiat}`
-
-*Total Trade Count:* `{trade_count}`
-*First Trade opened:* `{first_trade_date}`
-*Latest Trade opened:* `{latest_trade_date}`
-*Avg. Duration:* `{avg_duration}`
-*Best Performing:* `{best_pair}: {best_rate:.2f}%`
-    """.format(
-        coin=_CONF['stake_currency'],
-        fiat=_CONF['fiat_display_currency'],
-        profit_closed_coin=stats['profit_closed_coin'],
-        profit_closed_percent=stats['profit_closed_percent'],
-        profit_closed_fiat=stats['profit_closed_fiat'],
-        profit_all_coin=stats['profit_all_coin'],
-        profit_all_percent=stats['profit_all_percent'],
-        profit_all_fiat=stats['profit_all_fiat'],
-        trade_count=stats['trade_count'],
-        first_trade_date=stats['first_trade_date'],
-        latest_trade_date=stats['latest_trade_date'],
-        avg_duration=stats['avg_duration'],
-        best_pair=stats['best_pair'],
-        best_rate=stats['best_rate']
-    )
-    send_msg(markdown_msg, bot=bot)
-
-
-@authorized_only
-def _balance(bot: Bot, update: Update) -> None:
-    """
-    Handler for /balance
-    """
-    (error, result) = rpc_balance(_CONF['fiat_display_currency'])
-    if error:
-        send_msg('`All balances are zero.`')
-        return
-
-    (currencys, total, symbol, value) = result
-    output = ''
-    for currency in currencys:
-        output += """*Currency*: {currency}
-*Available*: {available}
-*Balance*: {balance}
-*Pending*: {pending}
-*Est. BTC*: {est_btc: .8f}
-""".format(**currency)
-
-    output += """*Estimated Value*:
-*BTC*: {0: .8f}
-*{1}*: {2: .2f}
-""".format(total, symbol, value)
-    send_msg(output)
-
-
-@authorized_only
-def _start(bot: Bot, update: Update) -> None:
-    """
-    Handler for /start.
-    Starts TradeThread
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-    (error, msg) = rpc_start()
-    if error:
-        send_msg(msg, bot=bot)
-
-
-@authorized_only
-def _stop(bot: Bot, update: Update) -> None:
-    """
-    Handler for /stop.
-    Stops TradeThread
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-    (error, msg) = rpc_stop()
-    send_msg(msg, bot=bot)
-
-
-# FIX: no test for this!!!!
-@authorized_only
-def _forcesell(bot: Bot, update: Update) -> None:
-    """
-    Handler for /forcesell <id>.
-    Sells the given trade at current price
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-
-    trade_id = update.message.text.replace('/forcesell', '').strip()
-    (error, message) = rpc_forcesell(trade_id)
-    if error:
-        send_msg(message, bot=bot)
-        return
-
-
-@authorized_only
-def _performance(bot: Bot, update: Update) -> None:
-    """
-    Handler for /performance.
-    Shows a performance statistic from finished trades
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-    (error, trades) = rpc_performance()
-    if error:
-        send_msg(trades, bot=bot)
-        return
-
-    stats = '\n'.join('{index}.\t<code>{pair}\t{profit:.2f}% ({count})</code>'.format(
-        index=i + 1,
-        pair=trade['pair'],
-        profit=trade['profit'],
-        count=trade['count']
-    ) for i, trade in enumerate(trades))
-    message = '<b>Performance:</b>\n{}'.format(stats)
-    send_msg(message, parse_mode=ParseMode.HTML)
-
-
-@authorized_only
-def _count(bot: Bot, update: Update) -> None:
-    """
-    Handler for /count.
-    Returns the number of trades running
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-    (error, trades) = rpc_count()
-    if error:
-        send_msg(trades, bot=bot)
-        return
-
-    message = tabulate({
-        'current': [len(trades)],
-        'max': [_CONF['max_open_trades']]
-    }, headers=['current', 'max'], tablefmt='simple')
-    message = "<pre>{}</pre>".format(message)
-    logger.debug(message)
-    send_msg(message, parse_mode=ParseMode.HTML)
-
-
-@authorized_only
-def _help(bot: Bot, update: Update) -> None:
-    """
-    Handler for /help.
-    Show commands of the bot
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-    message = """
-*/start:* `Starts the trader`
-*/stop:* `Stops the trader`
-*/status [table]:* `Lists all open trades`
-            *table :* `will display trades in a table`
-*/profit:* `Lists cumulative profit from all finished trades`
-*/forcesell <trade_id>|all:* `Instantly sells the given trade or all trades, regardless of profit`
-*/performance:* `Show performance of each finished trade grouped by pair`
-*/daily <n>:* `Shows profit or loss per day, over the last n days`
-*/count:* `Show number of trades running compared to allowed number of trades`
-*/balance:* `Show account balance per currency`
-*/help:* `This help message`
-*/version:* `Show version`
-    """
-    send_msg(message, bot=bot)
-
-
-@authorized_only
-def _version(bot: Bot, update: Update) -> None:
-    """
-    Handler for /version.
-    Show version information
-    :param bot: telegram bot
-    :param update: message update
-    :return: None
-    """
-    send_msg('*Version:* `{}`'.format(__version__), bot=bot)
-
-
-def send_msg(msg: str, bot: Bot = None, parse_mode: ParseMode = ParseMode.MARKDOWN) -> None:
-    """
-    Send given markdown message
-    :param msg: message
-    :param bot: alternative bot
-    :param parse_mode: telegram parse mode
-    :return: None
-    """
-    if not is_enabled():
-        return
-
-    bot = bot or _UPDATER.bot
-
-    keyboard = [['/daily', '/profit', '/balance'],
-                ['/status', '/status table', '/performance'],
-                ['/count', '/start', '/stop', '/help']]
-
-    reply_markup = ReplyKeyboardMarkup(keyboard)
-
-    try:
+        authorized = self._config["telegram"].get("authorized_users", None)
+        if authorized is not None and from_user_id not in authorized:
+            logger.info(f"Unauthorized user tried to control the bot: {from_user_id}")
+            return None
+        # Rollback session to avoid getting data stored in a transaction.
+        Trade.rollback()
+        logger.debug("Executing handler: %s for chat_id: %s", command_handler.__name__, chat_id)
         try:
-            bot.send_message(
-                _CONF['telegram']['chat_id'], msg,
-                parse_mode=parse_mode, reply_markup=reply_markup
+            return await command_handler(self, *args, **kwargs)
+        except RPCException as e:
+            await self._send_msg(str(e))
+        except BaseException:
+            logger.exception("Exception occurred within Telegram module")
+        finally:
+            Trade.session.remove()
+
+    return wrapper
+
+
+class Telegram(RPCHandler):
+    """This class handles all telegram communication"""
+
+    def __init__(self, rpc: RPC, config: Config) -> None:
+        """
+        Init the Telegram call, and init the super class RPCHandler
+        :param rpc: instance of RPC Helper class
+        :param config: Configuration object
+        :return: None
+        """
+        super().__init__(rpc, config)
+
+        self._app: Application
+        self._loop: asyncio.AbstractEventLoop
+        self._init_keyboard()
+        self._start_thread()
+
+    def _start_thread(self):
+        """
+        Creates and starts the polling thread
+        """
+        self._thread = Thread(target=self._init, name="FTTelegram")
+        self._thread.start()
+
+    def _init_keyboard(self) -> None:
+        """
+        Validates the keyboard configuration from telegram config
+        section.
+        """
+        self._keyboard: list[list[str | KeyboardButton]] = [
+            ["/daily", "/profit", "/balance"],
+            ["/status", "/status table", "/performance"],
+            ["/count", "/start", "/stop", "/help"],
+        ]
+        # do not allow commands with mandatory arguments and critical cmds
+        # TODO: DRY! - its not good to list all valid cmds here. But otherwise
+        #       this needs refactoring of the whole telegram module (same
+        #       problem in _help()).
+        valid_keys: list[str] = [
+            r"/start$",
+            r"/pause$",
+            r"/stop$",
+            r"/status$",
+            r"/status table$",
+            r"/trades$",
+            r"/performance$",
+            r"/buys",
+            r"/entries",
+            r"/sells",
+            r"/exits",
+            r"/mix_tags",
+            r"/daily$",
+            r"/daily \d+$",
+            r"/profit$",
+            r"/profit \d+",
+            r"/stats$",
+            r"/count$",
+            r"/locks$",
+            r"/balance$",
+            r"/stopbuy$",
+            r"/stopentry$",
+            r"/reload_config$",
+            r"/show_config$",
+            r"/logs$",
+            r"/whitelist$",
+            r"/whitelist(\ssorted|\sbaseonly)+$",
+            r"/blacklist$",
+            r"/bl_delete$",
+            r"/weekly$",
+            r"/weekly \d+$",
+            r"/monthly$",
+            r"/monthly \d+$",
+            r"/forcebuy$",
+            r"/forcelong$",
+            r"/forceshort$",
+            r"/forcesell$",
+            r"/forceexit$",
+            r"/edge$",
+            r"/health$",
+            r"/help$",
+            r"/version$",
+            r"/marketdir (long|short|even|none)$",
+            r"/marketdir$",
+        ]
+        # Create keys for generation
+        valid_keys_print = [k.replace("$", "") for k in valid_keys]
+
+        # custom keyboard specified in config.json
+        cust_keyboard = self._config["telegram"].get("keyboard", [])
+        if cust_keyboard:
+            combined = "(" + ")|(".join(valid_keys) + ")"
+            # check for valid shortcuts
+            invalid_keys = [
+                b for b in chain.from_iterable(cust_keyboard) if not re.match(combined, b)
+            ]
+            if len(invalid_keys):
+                err_msg = (
+                    "config.telegram.keyboard: Invalid commands for "
+                    f"custom Telegram keyboard: {invalid_keys}"
+                    f"\nvalid commands are: {valid_keys_print}"
+                )
+                raise OperationalException(err_msg)
+            else:
+                self._keyboard = cust_keyboard
+                logger.info(f"using custom keyboard from config.json: {self._keyboard}")
+
+    def _init_telegram_app(self):
+        return Application.builder().token(self._config["telegram"]["token"]).build()
+
+    def _init(self) -> None:
+        """
+        Initializes this module with the given config,
+        registers all known command handlers
+        and starts polling for message updates
+        Runs in a separate thread.
+        """
+        try:
+            self._loop = asyncio.get_running_loop()
+        except RuntimeError:
+            self._loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(self._loop)
+
+        self._app = self._init_telegram_app()
+
+        # Register command handler and start telegram message polling
+        handles = [
+            CommandHandler("status", self._status),
+            CommandHandler("profit", self._profit),
+            CommandHandler("balance", self._balance),
+            CommandHandler("start", self._start),
+            CommandHandler("stop", self._stop),
+            CommandHandler(["forcesell", "forceexit", "fx"], self._force_exit),
+            CommandHandler(
+                ["forcebuy", "forcelong"],
+                partial(self._force_enter, order_side=SignalDirection.LONG),
+            ),
+            CommandHandler(
+                "forceshort", partial(self._force_enter, order_side=SignalDirection.SHORT)
+            ),
+            CommandHandler("reload_trade", self._reload_trade_from_exchange),
+            CommandHandler("trades", self._trades),
+            CommandHandler("delete", self._delete_trade),
+            CommandHandler(["coo", "cancel_open_order"], self._cancel_open_order),
+            CommandHandler("performance", self._performance),
+            CommandHandler(["buys", "entries"], self._enter_tag_performance),
+            CommandHandler(["sells", "exits"], self._exit_reason_performance),
+            CommandHandler("mix_tags", self._mix_tag_performance),
+            CommandHandler("stats", self._stats),
+            CommandHandler("daily", self._daily),
+            CommandHandler("weekly", self._weekly),
+            CommandHandler("monthly", self._monthly),
+            CommandHandler("count", self._count),
+            CommandHandler("locks", self._locks),
+            CommandHandler(["unlock", "delete_locks"], self._delete_locks),
+            CommandHandler(["reload_config", "reload_conf"], self._reload_config),
+            CommandHandler(["show_config", "show_conf"], self._show_config),
+            CommandHandler(["stopbuy", "stopentry", "pause"], self._pause),
+            CommandHandler("whitelist", self._whitelist),
+            CommandHandler("blacklist", self._blacklist),
+            CommandHandler(["blacklist_delete", "bl_delete"], self._blacklist_delete),
+            CommandHandler("logs", self._logs),
+            CommandHandler("edge", self._edge),
+            CommandHandler("health", self._health),
+            CommandHandler("help", self._help),
+            CommandHandler("version", self._version),
+            CommandHandler("marketdir", self._changemarketdir),
+            CommandHandler("order", self._order),
+            CommandHandler("list_custom_data", self._list_custom_data),
+            CommandHandler("tg_info", self._tg_info),
+        ]
+        callbacks = [
+            CallbackQueryHandler(self._status_table, pattern="update_status_table"),
+            CallbackQueryHandler(self._daily, pattern="update_daily"),
+            CallbackQueryHandler(self._weekly, pattern="update_weekly"),
+            CallbackQueryHandler(self._monthly, pattern="update_monthly"),
+            CallbackQueryHandler(self._profit, pattern="update_profit"),
+            CallbackQueryHandler(self._balance, pattern="update_balance"),
+            CallbackQueryHandler(self._performance, pattern="update_performance"),
+            CallbackQueryHandler(
+                self._enter_tag_performance, pattern="update_enter_tag_performance"
+            ),
+            CallbackQueryHandler(
+                self._exit_reason_performance, pattern="update_exit_reason_performance"
+            ),
+            CallbackQueryHandler(self._mix_tag_performance, pattern="update_mix_tag_performance"),
+            CallbackQueryHandler(self._count, pattern="update_count"),
+            CallbackQueryHandler(self._force_exit_inline, pattern=r"force_exit__\S+"),
+            CallbackQueryHandler(self._force_enter_inline, pattern=r"force_enter__\S+"),
+        ]
+        for handle in handles:
+            self._app.add_handler(handle)
+
+        for callback in callbacks:
+            self._app.add_handler(callback)
+
+        logger.info(
+            "rpc.telegram is listening for following commands: %s",
+            [[x for x in sorted(h.commands)] for h in handles],
+        )
+        self._loop.run_until_complete(self._startup_telegram())
+
+    async def _startup_telegram(self) -> None:
+        await self._app.initialize()
+        await self._app.start()
+        if self._app.updater:
+            await self._app.updater.start_polling(
+                bootstrap_retries=-1,
+                timeout=20,
+                # read_latency=60,  # Assumed transmission latency
+                drop_pending_updates=True,
+                # stop_signals=[],  # Necessary as we don't run on the main thread
             )
-        except NetworkError as network_err:
-            # Sometimes the telegram server resets the current connection,
-            # if this is the case we send the message again.
-            logger.warning(
-                'Telegram NetworkError: %s! Trying one more time.',
-                network_err.message
+            while True:
+                await asyncio.sleep(10)
+                if not self._app.updater.running:
+                    break
+
+    async def _cleanup_telegram(self) -> None:
+        if self._app.updater:
+            await self._app.updater.stop()
+        await self._app.stop()
+        await self._app.shutdown()
+
+    def cleanup(self) -> None:
+        """
+        Stops all running telegram threads.
+        :return: None
+        """
+        # This can take up to `timeout` from the call to `start_polling`.
+        asyncio.run_coroutine_threadsafe(self._cleanup_telegram(), self._loop)
+        self._thread.join()
+
+    def _exchange_from_msg(self, msg: RPCOrderMsg) -> str:
+        """
+        Extracts the exchange name from the given message.
+        :param msg: The message to extract the exchange name from.
+        :return: The exchange name.
+        """
+        return f"{msg['exchange']}{' (dry)' if self._config['dry_run'] else ''}"
+
+    def _add_analyzed_candle(self, pair: str) -> str:
+        candle_val = (
+            self._config["telegram"].get("notification_settings", {}).get("show_candle", "off")
+        )
+        if candle_val != "off":
+            if candle_val == "ohlc":
+                analyzed_df, _ = self._rpc._freqtrade.dataprovider.get_analyzed_dataframe(
+                    pair, self._config["timeframe"]
+                )
+                candle = analyzed_df.iloc[-1].squeeze() if len(analyzed_df) > 0 else None
+                if candle is not None:
+                    return (
+                        f"*Candle OHLC*: `{candle['open']}, {candle['high']}, "
+                        f"{candle['low']}, {candle['close']}`\n"
+                    )
+
+        return ""
+
+    def _format_entry_msg(self, msg: RPCEntryMsg) -> str:
+        is_fill = msg["type"] in [RPCMessageType.ENTRY_FILL]
+        emoji = "\N{CHECK MARK}" if is_fill else "\N{LARGE BLUE CIRCLE}"
+
+        terminology = {
+            "1_enter": "New Trade",
+            "1_entered": "New Trade filled",
+            "x_enter": "Increasing position",
+            "x_entered": "Position increase filled",
+        }
+
+        key = f"{'x' if msg['sub_trade'] else '1'}_{'entered' if is_fill else 'enter'}"
+        wording = terminology[key]
+
+        message = (
+            f"{emoji} *{self._exchange_from_msg(msg)}:*"
+            f" {wording} (#{msg['trade_id']})\n"
+            f"*Pair:* `{msg['pair']}`\n"
+        )
+        message += self._add_analyzed_candle(msg["pair"])
+        message += f"*Enter Tag:* `{msg['enter_tag']}`\n" if msg.get("enter_tag") else ""
+        message += f"*Amount:* `{round_value(msg['amount'], 8)}`\n"
+        message += f"*Direction:* `{msg['direction']}"
+        if msg.get("leverage") and msg.get("leverage", 1.0) != 1.0:
+            message += f" ({msg['leverage']:.3g}x)"
+        message += "`\n"
+        message += f"*Open Rate:* `{fmt_coin2(msg['open_rate'], msg['quote_currency'])}`\n"
+        if msg["type"] == RPCMessageType.ENTRY and msg["current_rate"]:
+            message += (
+                f"*Current Rate:* `{fmt_coin2(msg['current_rate'], msg['quote_currency'])}`\n"
             )
-            bot.send_message(
-                _CONF['telegram']['chat_id'], msg,
-                parse_mode=parse_mode, reply_markup=reply_markup
+
+        profit_fiat_extra = self.__format_profit_fiat(msg, "stake_amount")  # type: ignore
+        total = fmt_coin(msg["stake_amount"], msg["quote_currency"])
+
+        message += f"*{'New ' if msg['sub_trade'] else ''}Total:* `{total}{profit_fiat_extra}`"
+
+        return message
+
+    def _format_exit_msg(self, msg: RPCExitMsg) -> str:
+        duration = msg["close_date"].replace(microsecond=0) - msg["open_date"].replace(
+            microsecond=0
+        )
+        duration_min = duration.total_seconds() / 60
+
+        leverage_text = (
+            f" ({msg['leverage']:.3g}x)"
+            if msg.get("leverage") and msg.get("leverage", 1.0) != 1.0
+            else ""
+        )
+
+        profit_fiat_extra = self.__format_profit_fiat(msg, "profit_amount")
+
+        profit_extra = (
+            f" ({msg['gain']}: {fmt_coin(msg['profit_amount'], msg['quote_currency'])}"
+            f"{profit_fiat_extra})"
+        )
+
+        is_fill = msg["type"] == RPCMessageType.EXIT_FILL
+        is_sub_trade = msg.get("sub_trade")
+        is_sub_profit = msg["profit_amount"] != msg.get("cumulative_profit")
+        is_final_exit = msg.get("is_final_exit", False) and is_sub_profit
+        profit_prefix = "Sub " if is_sub_trade else ""
+        cp_extra = ""
+        exit_wording = "Exited" if is_fill else "Exiting"
+        if is_sub_trade or is_final_exit:
+            cp_fiat = self.__format_profit_fiat(msg, "cumulative_profit")
+
+            if is_final_exit:
+                profit_prefix = "Sub "
+                cp_extra = (
+                    f"*Final Profit:* `{msg['final_profit_ratio']:.2%} "
+                    f"({msg['cumulative_profit']:.8f} {msg['quote_currency']}{cp_fiat})`\n"
+                )
+            else:
+                exit_wording = f"Partially {exit_wording.lower()}"
+                if msg["cumulative_profit"]:
+                    cp_extra = (
+                        f"*Cumulative Profit:* `"
+                        f"{fmt_coin(msg['cumulative_profit'], msg['stake_currency'])}{cp_fiat}`\n"
+                    )
+        enter_tag = f"*Enter Tag:* `{msg['enter_tag']}`\n" if msg.get("enter_tag") else ""
+        message = (
+            f"{self._get_exit_emoji(msg)} *{self._exchange_from_msg(msg)}:* "
+            f"{exit_wording} {msg['pair']} (#{msg['trade_id']})\n"
+            f"{self._add_analyzed_candle(msg['pair'])}"
+            f"*{f'{profit_prefix}Profit' if is_fill else f'Unrealized {profit_prefix}Profit'}:* "
+            f"`{msg['profit_ratio']:.2%}{profit_extra}`\n"
+            f"{cp_extra}"
+            f"{enter_tag}"
+            f"*Exit Reason:* `{msg['exit_reason']}`\n"
+            f"*Direction:* `{msg['direction']}"
+            f"{leverage_text}`\n"
+            f"*Amount:* `{round_value(msg['amount'], 8)}`\n"
+            f"*Open Rate:* `{fmt_coin2(msg['open_rate'], msg['quote_currency'])}`\n"
+        )
+        if msg["type"] == RPCMessageType.EXIT and msg["current_rate"]:
+            message += (
+                f"*Current Rate:* `{fmt_coin2(msg['current_rate'], msg['quote_currency'])}`\n"
             )
-    except TelegramError as telegram_err:
-        logger.warning('TelegramError: %s! Giving up on that message.', telegram_err.message)
+            if msg["order_rate"]:
+                message += f"*Exit Rate:* `{fmt_coin2(msg['order_rate'], msg['quote_currency'])}`"
+        elif msg["type"] == RPCMessageType.EXIT_FILL:
+            message += f"*Exit Rate:* `{fmt_coin2(msg['close_rate'], msg['quote_currency'])}`"
+
+        if is_sub_trade:
+            stake_amount_fiat = self.__format_profit_fiat(msg, "stake_amount")
+
+            rem = fmt_coin(msg["stake_amount"], msg["quote_currency"])
+            message += f"\n*Remaining:* `{rem}{stake_amount_fiat}`"
+        else:
+            message += f"\n*Duration:* `{duration} ({duration_min:.1f} min)`"
+        return message
+
+    def __format_profit_fiat(
+        self, msg: RPCExitMsg, key: Literal["stake_amount", "profit_amount", "cumulative_profit"]
+    ) -> str:
+        """
+        Format Fiat currency to append to regular profit output
+        """
+        profit_fiat_extra = ""
+        if self._rpc._fiat_converter and (fiat_currency := msg.get("fiat_currency")):
+            profit_fiat = self._rpc._fiat_converter.convert_amount(
+                msg[key], msg["stake_currency"], fiat_currency
+            )
+            profit_fiat_extra = f" / {profit_fiat:.3f} {fiat_currency}"
+        return profit_fiat_extra
+
+    def compose_message(self, msg: RPCSendMsg) -> str | None:
+        if msg["type"] == RPCMessageType.ENTRY or msg["type"] == RPCMessageType.ENTRY_FILL:
+            message = self._format_entry_msg(msg)
+
+        elif msg["type"] == RPCMessageType.EXIT or msg["type"] == RPCMessageType.EXIT_FILL:
+            message = self._format_exit_msg(msg)
+
+        elif (
+            msg["type"] == RPCMessageType.ENTRY_CANCEL or msg["type"] == RPCMessageType.EXIT_CANCEL
+        ):
+            message_side = "enter" if msg["type"] == RPCMessageType.ENTRY_CANCEL else "exit"
+            message = (
+                f"\N{WARNING SIGN} *{self._exchange_from_msg(msg)}:* "
+                f"Cancelling {'partial ' if msg.get('sub_trade') else ''}"
+                f"{message_side} Order for {msg['pair']} "
+                f"(#{msg['trade_id']}). Reason: {msg['reason']}."
+            )
+
+        elif msg["type"] == RPCMessageType.PROTECTION_TRIGGER:
+            message = (
+                f"*Protection* triggered due to {msg['reason']}. "
+                f"`{msg['pair']}` will be locked until `{msg['lock_end_time']}`."
+            )
+
+        elif msg["type"] == RPCMessageType.PROTECTION_TRIGGER_GLOBAL:
+            message = (
+                f"*Protection* triggered due to {msg['reason']}. "
+                f"*All pairs* will be locked until `{msg['lock_end_time']}`."
+            )
+
+        elif msg["type"] == RPCMessageType.STATUS:
+            message = f"*Status:* `{msg['status']}`"
+
+        elif msg["type"] == RPCMessageType.WARNING:
+            message = f"\N{WARNING SIGN} *Warning:* `{msg['status']}`"
+        elif msg["type"] == RPCMessageType.EXCEPTION:
+            # Errors will contain exceptions, which are wrapped in triple ticks.
+            message = f"\N{WARNING SIGN} *ERROR:* \n {msg['status']}"
+
+        elif msg["type"] == RPCMessageType.STARTUP:
+            message = f"{msg['status']}"
+        elif msg["type"] == RPCMessageType.STRATEGY_MSG:
+            message = f"{msg['msg']}"
+        else:
+            logger.debug("Unknown message type: %s", msg["type"])
+            return None
+        return message
+
+    def _message_loudness(self, msg: RPCSendMsg) -> str:
+        """Determine the loudness of the message - on, off or silent"""
+        default_noti = "on"
+
+        msg_type = msg["type"]
+        noti = ""
+        if msg["type"] == RPCMessageType.EXIT or msg["type"] == RPCMessageType.EXIT_FILL:
+            sell_noti = (
+                self._config["telegram"].get("notification_settings", {}).get(str(msg_type), {})
+            )
+
+            # For backward compatibility sell still can be string
+            if isinstance(sell_noti, str):
+                noti = sell_noti
+            else:
+                default_noti = sell_noti.get("*", default_noti)
+                noti = sell_noti.get(str(msg["exit_reason"]), default_noti)
+        else:
+            noti = (
+                self._config["telegram"]
+                .get("notification_settings", {})
+                .get(str(msg_type), default_noti)
+            )
+
+        return noti
+
+    def send_msg(self, msg: RPCSendMsg) -> None:
+        """Send a message to telegram channel"""
+        noti = self._message_loudness(msg)
+
+        if noti == "off":
+            logger.info(f"Notification '{msg['type']}' not sent.")
+            # Notification disabled
+            return
+
+        message = self.compose_message(deepcopy(msg))
+        if message:
+            asyncio.run_coroutine_threadsafe(
+                self._send_msg(message, disable_notification=(noti == "silent")), self._loop
+            )
+
+    def _get_exit_emoji(self, msg):
+        """
+        Get emoji for exit-messages
+        """
+
+        if float(msg["profit_ratio"]) >= 0.05:
+            return "\N{ROCKET}"
+        elif float(msg["profit_ratio"]) >= 0.0:
+            return "\N{EIGHT SPOKED ASTERISK}"
+        elif msg["exit_reason"] == "stop_loss":
+            return "\N{WARNING SIGN}"
+        else:
+            return "\N{CROSS MARK}"
+
+    def _prepare_order_details(self, filled_orders: list, quote_currency: str, is_open: bool):
+        """
+        Prepare details of trade with entry adjustment enabled
+        """
+        lines_detail: list[str] = []
+        if len(filled_orders) > 0:
+            first_avg = filled_orders[0]["safe_price"]
+        order_nr = 0
+        for order in filled_orders:
+            lines: list[str] = []
+            if order["is_open"] is True:
+                continue
+            order_nr += 1
+            wording = "Entry" if order["ft_is_entry"] else "Exit"
+
+            cur_entry_amount = order["filled"] or order["amount"]
+            cur_entry_average = order["safe_price"]
+            lines.append("  ")
+            lines.append(f"*{wording} #{order_nr}:*")
+            if order_nr == 1:
+                lines.append(
+                    f"*Amount:* {round_value(cur_entry_amount, 8)} "
+                    f"({fmt_coin(order['cost'], quote_currency)})"
+                )
+                lines.append(f"*Average Price:* {round_value(cur_entry_average, 8)}")
+            else:
+                # TODO: This calculation ignores fees.
+                price_to_1st_entry = (cur_entry_average - first_avg) / first_avg
+                if is_open:
+                    lines.append("({})".format(dt_humanize_delta(order["order_filled_date"])))
+                lines.append(
+                    f"*Amount:* {round_value(cur_entry_amount, 8)} "
+                    f"({fmt_coin(order['cost'], quote_currency)})"
+                )
+                lines.append(
+                    f"*Average {wording} Price:* {round_value(cur_entry_average, 8)} "
+                    f"({price_to_1st_entry:.2%} from 1st entry rate)"
+                )
+                lines.append(f"*Order Filled:* {order['order_filled_date']}")
+
+            lines_detail.append("\n".join(lines))
+
+        return lines_detail
+
+    @authorized_only
+    async def _order(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /order.
+        Returns the orders of the trade
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+
+        trade_ids = []
+        if context.args and len(context.args) > 0:
+            trade_ids = [int(i) for i in context.args if i.isnumeric()]
+
+        results = self._rpc._rpc_trade_status(trade_ids=trade_ids)
+        for r in results:
+            lines = ["*Order List for Trade #*`{trade_id}`"]
+
+            lines_detail = self._prepare_order_details(
+                r["orders"], r["quote_currency"], r["is_open"]
+            )
+            lines.extend(lines_detail if lines_detail else "")
+            await self.__send_order_msg(lines, r)
+
+    async def __send_order_msg(self, lines: list[str], r: dict[str, Any]) -> None:
+        """
+        Send status message.
+        """
+        msg = ""
+
+        for line in lines:
+            if line:
+                if (len(msg) + len(line) + 1) < MAX_MESSAGE_LENGTH:
+                    msg += line + "\n"
+                else:
+                    await self._send_msg(msg.format(**r))
+                    msg = "*Order List for Trade #*`{trade_id}` - continued\n" + line + "\n"
+
+        await self._send_msg(msg.format(**r))
+
+    @authorized_only
+    async def _status(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /status.
+        Returns the current TradeThread status
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+
+        if context.args and "table" in context.args:
+            await self._status_table(update, context)
+            return
+        else:
+            await self._status_msg(update, context)
+
+    async def _status_msg(self, update: Update, context: CallbackContext) -> None:
+        """
+        handler for `/status` and `/status <id>`.
+
+        """
+        # Check if there's at least one numerical ID provided.
+        # If so, try to get only these trades.
+        trade_ids = []
+        if context.args and len(context.args) > 0:
+            trade_ids = [int(i) for i in context.args if i.isnumeric()]
+
+        results = self._rpc._rpc_trade_status(trade_ids=trade_ids)
+        position_adjust = self._config.get("position_adjustment_enable", False)
+        max_entries = self._config.get("max_entry_position_adjustment", -1)
+        for r in results:
+            r["open_date_hum"] = dt_humanize_delta(r["open_date"])
+            r["num_entries"] = len([o for o in r["orders"] if o["ft_is_entry"]])
+            r["num_exits"] = len(
+                [
+                    o
+                    for o in r["orders"]
+                    if not o["ft_is_entry"] and not o["ft_order_side"] == "stoploss"
+                ]
+            )
+            r["exit_reason"] = r.get("exit_reason", "")
+            r["stake_amount_r"] = fmt_coin(r["stake_amount"], r["quote_currency"])
+            r["max_stake_amount_r"] = fmt_coin(
+                r["max_stake_amount"] or r["stake_amount"], r["quote_currency"]
+            )
+            r["profit_abs_r"] = fmt_coin(r["profit_abs"], r["quote_currency"])
+            r["realized_profit_r"] = fmt_coin(r["realized_profit"], r["quote_currency"])
+            r["total_profit_abs_r"] = fmt_coin(r["total_profit_abs"], r["quote_currency"])
+            lines = [
+                "*Trade ID:* `{trade_id}`" + (" `(since {open_date_hum})`" if r["is_open"] else ""),
+                "*Current Pair:* {pair}",
+                (
+                    f"*Direction:* {'`Short`' if r.get('is_short') else '`Long`'}"
+                    + " ` ({leverage}x)`"
+                    if r.get("leverage")
+                    else ""
+                ),
+                "*Amount:* `{amount} ({stake_amount_r})`",
+                "*Total invested:* `{max_stake_amount_r}`" if position_adjust else "",
+                "*Enter Tag:* `{enter_tag}`" if r["enter_tag"] else "",
+                "*Exit Reason:* `{exit_reason}`" if r["exit_reason"] else "",
+            ]
+
+            if position_adjust:
+                max_buy_str = f"/{max_entries + 1}" if (max_entries > 0) else ""
+                lines.extend(
+                    [
+                        "*Number of Entries:* `{num_entries}" + max_buy_str + "`",
+                        "*Number of Exits:* `{num_exits}`",
+                    ]
+                )
+
+            lines.extend(
+                [
+                    f"*Open Rate:* `{round_value(r['open_rate'], 8)}`",
+                    f"*Close Rate:* `{round_value(r['close_rate'], 8)}`" if r["close_rate"] else "",
+                    "*Open Date:* `{open_date}`",
+                    "*Close Date:* `{close_date}`" if r["close_date"] else "",
+                    (
+                        f" \n*Current Rate:* `{round_value(r['current_rate'], 8)}`"
+                        if r["is_open"]
+                        else ""
+                    ),
+                    ("*Unrealized Profit:* " if r["is_open"] else "*Close Profit: *")
+                    + "`{profit_ratio:.2%}` `({profit_abs_r})`",
+                ]
+            )
+
+            if r["is_open"]:
+                if r.get("realized_profit"):
+                    lines.extend(
+                        [
+                            "*Realized Profit:* `{realized_profit_ratio:.2%} "
+                            "({realized_profit_r})`",
+                            "*Total Profit:* `{total_profit_ratio:.2%} ({total_profit_abs_r})`",
+                        ]
+                    )
+
+                # Append empty line to improve readability
+                lines.append(" ")
+                if (
+                    r["stop_loss_abs"] != r["initial_stop_loss_abs"]
+                    and r["initial_stop_loss_ratio"] is not None
+                ):
+                    # Adding initial stoploss only if it is different from stoploss
+                    lines.append(
+                        "*Initial Stoploss:* `{initial_stop_loss_abs:.8f}` "
+                        "`({initial_stop_loss_ratio:.2%})`"
+                    )
+
+                # Adding stoploss and stoploss percentage only if it is not None
+                lines.append(
+                    f"*Stoploss:* `{round_value(r['stop_loss_abs'], 8)}` "
+                    + ("`({stop_loss_ratio:.2%})`" if r["stop_loss_ratio"] else "")
+                )
+                lines.append(
+                    f"*Stoploss distance:* `{round_value(r['stoploss_current_dist'], 8)}` "
+                    "`({stoploss_current_dist_ratio:.2%})`"
+                )
+                if r.get("open_orders"):
+                    lines.append(
+                        "*Open Order:* `{open_orders}`"
+                        + ("- `{exit_order_status}`" if r["exit_order_status"] else "")
+                    )
+
+            await self.__send_status_msg(lines, r)
+
+    async def __send_status_msg(self, lines: list[str], r: dict[str, Any]) -> None:
+        """
+        Send status message.
+        """
+        msg = ""
+
+        for line in lines:
+            if line:
+                if (len(msg) + len(line) + 1) < MAX_MESSAGE_LENGTH:
+                    msg += line + "\n"
+                else:
+                    await self._send_msg(msg.format(**r))
+                    msg = "*Trade ID:* `{trade_id}` - continued\n" + line + "\n"
+
+        await self._send_msg(msg.format(**r))
+
+    @authorized_only
+    async def _status_table(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /status table.
+        Returns the current TradeThread status in table format
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        fiat_currency = self._config.get("fiat_display_currency", "")
+        statlist, head, fiat_profit_sum, fiat_total_profit_sum = self._rpc._rpc_status_table(
+            self._config["stake_currency"], fiat_currency
+        )
+
+        show_total = not isnan(fiat_profit_sum) and len(statlist) > 1
+        show_total_realized = (
+            not isnan(fiat_total_profit_sum) and len(statlist) > 1 and fiat_profit_sum
+        ) != fiat_total_profit_sum
+        max_trades_per_msg = 50
+        """
+        Calculate the number of messages of 50 trades per message
+        0.99 is used to make sure that there are no extra (empty) messages
+        As an example with 50 trades, there will be int(50/50 + 0.99) = 1 message
+        """
+        messages_count = max(int(len(statlist) / max_trades_per_msg + 0.99), 1)
+        for i in range(0, messages_count):
+            trades = statlist[i * max_trades_per_msg : (i + 1) * max_trades_per_msg]
+            if show_total and i == messages_count - 1:
+                # append total line
+                trades.append(["Total", "", "", f"{fiat_profit_sum:.2f} {fiat_currency}"])
+                if show_total_realized:
+                    trades.append(
+                        [
+                            "Total",
+                            "(incl. realized Profits)",
+                            "",
+                            f"{fiat_total_profit_sum:.2f} {fiat_currency}",
+                        ]
+                    )
+
+            message = tabulate(trades, headers=head, tablefmt="simple")
+            if show_total and i == messages_count - 1:
+                # insert separators line between Total
+                lines = message.split("\n")
+                offset = 2 if show_total_realized else 1
+                message = "\n".join(lines[:-offset] + [lines[1]] + lines[-offset:])
+            await self._send_msg(
+                f"<pre>{message}</pre>",
+                parse_mode=ParseMode.HTML,
+                reload_able=True,
+                callback_path="update_status_table",
+                query=update.callback_query,
+            )
+
+    async def _timeunit_stats(self, update: Update, context: CallbackContext, unit: str) -> None:
+        """
+        Handler for /daily <n>
+        Returns a daily profit (in BTC) over the last n days.
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+
+        vals = {
+            "days": TimeunitMappings("Day", "Daily", "days", "update_daily", 7, "%Y-%m-%d"),
+            "weeks": TimeunitMappings(
+                "Monday", "Weekly", "weeks (starting from Monday)", "update_weekly", 8, "%Y-%m-%d"
+            ),
+            "months": TimeunitMappings("Month", "Monthly", "months", "update_monthly", 6, "%Y-%m"),
+        }
+        val = vals[unit]
+
+        stake_cur = self._config["stake_currency"]
+        fiat_disp_cur = self._config.get("fiat_display_currency", "")
+        try:
+            timescale = int(context.args[0]) if context.args else val.default
+        except (TypeError, ValueError, IndexError):
+            timescale = val.default
+        stats = self._rpc._rpc_timeunit_profit(timescale, stake_cur, fiat_disp_cur, unit)
+        stats_tab = tabulate(
+            [
+                [
+                    f"{period['date']:{val.dateformat}} ({period['trade_count']})",
+                    f"{fmt_coin(period['abs_profit'], stats['stake_currency'])}",
+                    f"{period['fiat_value']:.2f} {stats['fiat_display_currency']}",
+                    f"{period['rel_profit']:.2%}",
+                ]
+                for period in stats["data"]
+            ],
+            headers=[
+                f"{val.header} (count)",
+                f"{stake_cur}",
+                f"{fiat_disp_cur}",
+                "Profit %",
+                "Trades",
+            ],
+            tablefmt="simple",
+        )
+        message = (
+            f"<b>{val.message} Profit over the last {timescale} {val.message2}</b>:\n"
+            f"<pre>{stats_tab}</pre>"
+        )
+        await self._send_msg(
+            message,
+            parse_mode=ParseMode.HTML,
+            reload_able=True,
+            callback_path=val.callback,
+            query=update.callback_query,
+        )
+
+    @authorized_only
+    async def _daily(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /daily <n>
+        Returns a daily profit (in BTC) over the last n days.
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        await self._timeunit_stats(update, context, "days")
+
+    @authorized_only
+    async def _weekly(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /weekly <n>
+        Returns a weekly profit (in BTC) over the last n weeks.
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        await self._timeunit_stats(update, context, "weeks")
+
+    @authorized_only
+    async def _monthly(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /monthly <n>
+        Returns a monthly profit (in BTC) over the last n months.
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        await self._timeunit_stats(update, context, "months")
+
+    @authorized_only
+    async def _profit(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /profit.
+        Returns a cumulative profit statistics.
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        stake_cur = self._config["stake_currency"]
+        fiat_disp_cur = self._config.get("fiat_display_currency", "")
+
+        start_date = datetime.fromtimestamp(0)
+        timescale = None
+        try:
+            if context.args:
+                timescale = int(context.args[0]) - 1
+                today_start = datetime.combine(date.today(), datetime.min.time())
+                start_date = today_start - timedelta(days=timescale)
+        except (TypeError, ValueError, IndexError):
+            pass
+
+        stats = self._rpc._rpc_trade_statistics(stake_cur, fiat_disp_cur, start_date)
+        profit_closed_coin = stats["profit_closed_coin"]
+        profit_closed_ratio_mean = stats["profit_closed_ratio_mean"]
+        profit_closed_percent = stats["profit_closed_percent"]
+        profit_closed_fiat = stats["profit_closed_fiat"]
+        profit_all_coin = stats["profit_all_coin"]
+        profit_all_ratio_mean = stats["profit_all_ratio_mean"]
+        profit_all_percent = stats["profit_all_percent"]
+        profit_all_fiat = stats["profit_all_fiat"]
+        trade_count = stats["trade_count"]
+        first_trade_date = f"{stats['first_trade_humanized']} ({stats['first_trade_date']})"
+        latest_trade_date = f"{stats['latest_trade_humanized']} ({stats['latest_trade_date']})"
+        avg_duration = stats["avg_duration"]
+        best_pair = stats["best_pair"]
+        best_pair_profit_ratio = stats["best_pair_profit_ratio"]
+        best_pair_profit_abs = fmt_coin(stats["best_pair_profit_abs"], stake_cur)
+        winrate = stats["winrate"]
+        expectancy = stats["expectancy"]
+        expectancy_ratio = stats["expectancy_ratio"]
+
+        if stats["trade_count"] == 0:
+            markdown_msg = f"No trades yet.\n*Bot started:* `{stats['bot_start_date']}`"
+        else:
+            # Message to display
+            if stats["closed_trade_count"] > 0:
+                markdown_msg = (
+                    "*ROI:* Closed trades\n"
+                    f"∙ `{fmt_coin(profit_closed_coin, stake_cur)} "
+                    f"({profit_closed_ratio_mean:.2%}) "
+                    f"({profit_closed_percent} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
+                    f"∙ `{fmt_coin(profit_closed_fiat, fiat_disp_cur)}`\n"
+                )
+            else:
+                markdown_msg = "`No closed trade` \n"
+            fiat_all_trades = (
+                f"∙ `{fmt_coin(profit_all_fiat, fiat_disp_cur)}`\n" if fiat_disp_cur else ""
+            )
+            markdown_msg += (
+                f"*ROI:* All trades\n"
+                f"∙ `{fmt_coin(profit_all_coin, stake_cur)} "
+                f"({profit_all_ratio_mean:.2%}) "
+                f"({profit_all_percent} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
+                f"{fiat_all_trades}"
+                f"*Total Trade Count:* `{trade_count}`\n"
+                f"*Bot started:* `{stats['bot_start_date']}`\n"
+                f"*{'First Trade opened' if not timescale else 'Showing Profit since'}:* "
+                f"`{first_trade_date}`\n"
+                f"*Latest Trade opened:* `{latest_trade_date}`\n"
+                f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`\n"
+                f"*Winrate:* `{winrate:.2%}`\n"
+                f"*Expectancy (Ratio):* `{expectancy:.2f} ({expectancy_ratio:.2f})`"
+            )
+            if stats["closed_trade_count"] > 0:
+                markdown_msg += (
+                    f"\n*Avg. Duration:* `{avg_duration}`\n"
+                    f"*Best Performing:* `{best_pair}: {best_pair_profit_abs} "
+                    f"({best_pair_profit_ratio:.2%})`\n"
+                    f"*Trading volume:* `{fmt_coin(stats['trading_volume'], stake_cur)}`\n"
+                    f"*Profit factor:* `{stats['profit_factor']:.2f}`\n"
+                    f"*Max Drawdown:* `{stats['max_drawdown']:.2%} "
+                    f"({fmt_coin(stats['max_drawdown_abs'], stake_cur)})`\n"
+                    f"    from `{stats['max_drawdown_start']} "
+                    f"({fmt_coin(stats['drawdown_high'], stake_cur)})`\n"
+                    f"    to `{stats['max_drawdown_end']} "
+                    f"({fmt_coin(stats['drawdown_low'], stake_cur)})`\n"
+                )
+        await self._send_msg(
+            markdown_msg,
+            reload_able=True,
+            callback_path="update_profit",
+            query=update.callback_query,
+        )
+
+    @authorized_only
+    async def _stats(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /stats
+        Show stats of recent trades
+        """
+        stats = self._rpc._rpc_stats()
+
+        reason_map = {
+            "roi": "ROI",
+            "stop_loss": "Stoploss",
+            "trailing_stop_loss": "Trail. Stop",
+            "stoploss_on_exchange": "Stoploss",
+            "exit_signal": "Exit Signal",
+            "force_exit": "Force Exit",
+            "emergency_exit": "Emergency Exit",
+        }
+        exit_reasons_tabulate = [
+            [reason_map.get(reason, reason), sum(count.values()), count["wins"], count["losses"]]
+            for reason, count in stats["exit_reasons"].items()
+        ]
+        exit_reasons_msg = "No trades yet."
+        for reason in chunks(exit_reasons_tabulate, 25):
+            exit_reasons_msg = tabulate(reason, headers=["Exit Reason", "Exits", "Wins", "Losses"])
+            if len(exit_reasons_tabulate) > 25:
+                await self._send_msg(f"```\n{exit_reasons_msg}```", ParseMode.MARKDOWN)
+                exit_reasons_msg = ""
+
+        durations = stats["durations"]
+        duration_msg = tabulate(
+            [
+                [
+                    "Wins",
+                    (
+                        str(timedelta(seconds=durations["wins"]))
+                        if durations["wins"] is not None
+                        else "N/A"
+                    ),
+                ],
+                [
+                    "Losses",
+                    (
+                        str(timedelta(seconds=durations["losses"]))
+                        if durations["losses"] is not None
+                        else "N/A"
+                    ),
+                ],
+            ],
+            headers=["", "Avg. Duration"],
+        )
+        msg = f"""```\n{exit_reasons_msg}```\n```\n{duration_msg}```"""
+
+        await self._send_msg(msg, ParseMode.MARKDOWN)
+
+    @authorized_only
+    async def _balance(self, update: Update, context: CallbackContext) -> None:
+        """Handler for /balance"""
+        full_result = context.args and "full" in context.args
+        result = self._rpc._rpc_balance(
+            self._config["stake_currency"], self._config.get("fiat_display_currency", "")
+        )
+
+        balance_dust_level = self._config["telegram"].get("balance_dust_level", 0.0)
+        if not balance_dust_level:
+            balance_dust_level = DUST_PER_COIN.get(self._config["stake_currency"], 1.0)
+
+        output = ""
+        if self._config["dry_run"]:
+            output += "*Warning:* Simulated balances in Dry Mode.\n"
+        starting_cap = fmt_coin(result["starting_capital"], self._config["stake_currency"])
+        output += f"Starting capital: `{starting_cap}`"
+        starting_cap_fiat = (
+            fmt_coin(result["starting_capital_fiat"], self._config["fiat_display_currency"])
+            if result["starting_capital_fiat"] > 0
+            else ""
+        )
+        output += (f" `, {starting_cap_fiat}`.\n") if result["starting_capital_fiat"] > 0 else ".\n"
+
+        total_dust_balance = 0
+        total_dust_currencies = 0
+        for curr in result["currencies"]:
+            curr_output = ""
+            if (curr["is_position"] or curr["est_stake"] > balance_dust_level) and (
+                full_result or curr["is_bot_managed"]
+            ):
+                if curr["is_position"]:
+                    curr_output = (
+                        f"*{curr['currency']}:*\n"
+                        f"\t`{curr['side']}: {curr['position']:.8f}`\n"
+                        f"\t`Est. {curr['stake']}: "
+                        f"{fmt_coin(curr['est_stake'], curr['stake'], False)}`\n"
+                    )
+                else:
+                    est_stake = fmt_coin(
+                        curr["est_stake" if full_result else "est_stake_bot"], curr["stake"], False
+                    )
+
+                    curr_output = (
+                        f"*{curr['currency']}:*\n"
+                        f"\t`Available: {curr['free']:.8f}`\n"
+                        f"\t`Balance: {curr['balance']:.8f}`\n"
+                        f"\t`Pending: {curr['used']:.8f}`\n"
+                        f"\t`Bot Owned: {curr['bot_owned']:.8f}`\n"
+                        f"\t`Est. {curr['stake']}: {est_stake}`\n"
+                    )
+
+            elif curr["est_stake"] <= balance_dust_level:
+                total_dust_balance += curr["est_stake"]
+                total_dust_currencies += 1
+
+            # Handle overflowing message length
+            if len(output + curr_output) >= MAX_MESSAGE_LENGTH:
+                await self._send_msg(output)
+                output = curr_output
+            else:
+                output += curr_output
+
+        if total_dust_balance > 0:
+            output += (
+                f"*{total_dust_currencies} Other "
+                f"{plural(total_dust_currencies, 'Currency', 'Currencies')} "
+                f"(< {balance_dust_level} {result['stake']}):*\n"
+                f"\t`Est. {result['stake']}: "
+                f"{fmt_coin(total_dust_balance, result['stake'], False)}`\n"
+            )
+        tc = result["trade_count"] > 0
+        stake_improve = f" `({result['starting_capital_ratio']:.2%})`" if tc else ""
+        fiat_val = f" `({result['starting_capital_fiat_ratio']:.2%})`" if tc else ""
+        value = fmt_coin(result["value" if full_result else "value_bot"], result["symbol"], False)
+        total_stake = fmt_coin(
+            result["total" if full_result else "total_bot"], result["stake"], False
+        )
+        output += (
+            f"\n*Estimated Value{' (Bot managed assets only)' if not full_result else ''}*:\n"
+            f"\t`{result['stake']}: {total_stake}`{stake_improve}\n"
+            f"\t`{result['symbol']}: {value}`{fiat_val}\n"
+        )
+        await self._send_msg(
+            output, reload_able=True, callback_path="update_balance", query=update.callback_query
+        )
+
+    @authorized_only
+    async def _start(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /start.
+        Starts TradeThread
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        msg = self._rpc._rpc_start()
+        await self._send_msg(f"Status: `{msg['status']}`")
+
+    @authorized_only
+    async def _stop(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /stop.
+        Stops TradeThread
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        msg = self._rpc._rpc_stop()
+        await self._send_msg(f"Status: `{msg['status']}`")
+
+    @authorized_only
+    async def _reload_config(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /reload_config.
+        Triggers a config file reload
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        msg = self._rpc._rpc_reload_config()
+        await self._send_msg(f"Status: `{msg['status']}`")
+
+    @authorized_only
+    async def _pause(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /stop_buy /stop_entry and /pause.
+        Sets bot state to paused
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        msg = self._rpc._rpc_pause()
+        await self._send_msg(f"Status: `{msg['status']}`")
+
+    @authorized_only
+    async def _reload_trade_from_exchange(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /reload_trade <tradeid>.
+        """
+        if not context.args or len(context.args) == 0:
+            raise RPCException("Trade-id not set.")
+        trade_id = int(context.args[0])
+        msg = self._rpc._rpc_reload_trade_from_exchange(trade_id)
+        await self._send_msg(f"Status: `{msg['status']}`")
+
+    @authorized_only
+    async def _force_exit(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /forceexit <id>.
+        Sells the given trade at current price
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+
+        if context.args:
+            trade_id = context.args[0]
+            await self._force_exit_action(trade_id)
+        else:
+            fiat_currency = self._config.get("fiat_display_currency", "")
+            try:
+                statlist, _, _, _ = self._rpc._rpc_status_table(
+                    self._config["stake_currency"], fiat_currency
+                )
+            except RPCException:
+                await self._send_msg(msg="No open trade found.")
+                return
+            trades = []
+            for trade in statlist:
+                trades.append((trade[0], f"{trade[0]} {trade[1]} {trade[2]} {trade[3]}"))
+
+            trade_buttons = [
+                InlineKeyboardButton(text=trade[1], callback_data=f"force_exit__{trade[0]}")
+                for trade in trades
+            ]
+            buttons_aligned = self._layout_inline_keyboard(trade_buttons, cols=1)
+
+            buttons_aligned.append(
+                [InlineKeyboardButton(text="Cancel", callback_data="force_exit__cancel")]
+            )
+            await self._send_msg(msg="Which trade?", keyboard=buttons_aligned)
+
+    async def _force_exit_action(self, trade_id: str):
+        if trade_id != "cancel":
+            try:
+                loop = asyncio.get_running_loop()
+                # Workaround to avoid nested loops
+                await loop.run_in_executor(None, safe_async_db(self._rpc._rpc_force_exit), trade_id)
+            except RPCException as e:
+                await self._send_msg(str(e))
+
+    async def _force_exit_inline(self, update: Update, _: CallbackContext) -> None:
+        if update.callback_query:
+            query = update.callback_query
+            if query.data and "__" in query.data:
+                # Input data is "force_exit__<tradid|cancel>"
+                trade_id = query.data.split("__")[1].split(" ")[0]
+                if trade_id == "cancel":
+                    await query.answer()
+                    await query.edit_message_text(text="Force exit canceled.")
+                    return
+                trade: Trade | None = Trade.get_trades(trade_filter=Trade.id == trade_id).first()
+                await query.answer()
+                if trade:
+                    await query.edit_message_text(
+                        text=f"Manually exiting Trade #{trade_id}, {trade.pair}"
+                    )
+                    await self._force_exit_action(trade_id)
+                else:
+                    await query.edit_message_text(text=f"Trade {trade_id} not found.")
+
+    async def _force_enter_action(self, pair, price: float | None, order_side: SignalDirection):
+        if pair != "cancel":
+            try:
+
+                @safe_async_db
+                def _force_enter():
+                    self._rpc._rpc_force_entry(pair, price, order_side=order_side)
+
+                loop = asyncio.get_running_loop()
+                # Workaround to avoid nested loops
+                await loop.run_in_executor(None, _force_enter)
+            except RPCException as e:
+                logger.exception("Forcebuy error!")
+                await self._send_msg(str(e), ParseMode.HTML)
+
+    async def _force_enter_inline(self, update: Update, _: CallbackContext) -> None:
+        if update.callback_query:
+            query = update.callback_query
+            if query.data and "__" in query.data:
+                # Input data is "force_enter__<pair|cancel>_<side>"
+                payload = query.data.split("__")[1]
+                if payload == "cancel":
+                    await query.answer()
+                    await query.edit_message_text(text="Force enter canceled.")
+                    return
+                if payload and "_||_" in payload:
+                    pair, side = payload.split("_||_")
+                    order_side = SignalDirection(side)
+                    await query.answer()
+                    await query.edit_message_text(text=f"Manually entering {order_side} for {pair}")
+                    await self._force_enter_action(pair, None, order_side)
+
+    @staticmethod
+    def _layout_inline_keyboard(
+        buttons: list[InlineKeyboardButton], cols=3
+    ) -> list[list[InlineKeyboardButton]]:
+        return [buttons[i : i + cols] for i in range(0, len(buttons), cols)]
+
+    @authorized_only
+    async def _force_enter(
+        self, update: Update, context: CallbackContext, order_side: SignalDirection
+    ) -> None:
+        """
+        Handler for /forcelong <asset> <price> and `/forceshort <asset> <price>
+        Buys a pair trade at the given or current price
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        if context.args:
+            pair = context.args[0]
+            price = float(context.args[1]) if len(context.args) > 1 else None
+            await self._force_enter_action(pair, price, order_side)
+        else:
+            whitelist = self._rpc._rpc_whitelist()["whitelist"]
+            pair_buttons = [
+                InlineKeyboardButton(
+                    text=pair, callback_data=f"force_enter__{pair}_||_{order_side}"
+                )
+                for pair in sorted(whitelist)
+            ]
+            buttons_aligned = self._layout_inline_keyboard(pair_buttons)
+
+            buttons_aligned.append(
+                [InlineKeyboardButton(text="Cancel", callback_data="force_enter__cancel")]
+            )
+            await self._send_msg(
+                msg="Which pair?", keyboard=buttons_aligned, query=update.callback_query
+            )
+
+    @authorized_only
+    async def _trades(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /trades <n>
+        Returns last n recent trades.
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        stake_cur = self._config["stake_currency"]
+        try:
+            nrecent = int(context.args[0]) if context.args else 10
+        except (TypeError, ValueError, IndexError):
+            nrecent = 10
+        nonspot = self._config.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT
+        trades = self._rpc._rpc_trade_history(nrecent)
+        trades_tab = tabulate(
+            [
+                [
+                    dt_humanize_delta(dt_from_ts(trade["close_timestamp"])),
+                    f"{trade['pair']} (#{trade['trade_id']}"
+                    f"{(' ' + ('S' if trade['is_short'] else 'L')) if nonspot else ''})",
+                    f"{(trade['close_profit']):.2%} ({trade['close_profit_abs']})",
+                ]
+                for trade in trades["trades"]
+            ],
+            headers=[
+                "Close Date",
+                "Pair (ID L/S)" if nonspot else "Pair (ID)",
+                f"Profit ({stake_cur})",
+            ],
+            tablefmt="simple",
+        )
+        message = f"<b>{min(trades['trades_count'], nrecent)} recent trades</b>:\n" + (
+            f"<pre>{trades_tab}</pre>" if trades["trades_count"] > 0 else ""
+        )
+        await self._send_msg(message, parse_mode=ParseMode.HTML)
+
+    @authorized_only
+    async def _delete_trade(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /delete <id>.
+        Delete the given trade
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        if not context.args or len(context.args) == 0:
+            raise RPCException("Trade-id not set.")
+        trade_id = int(context.args[0])
+        msg = self._rpc._rpc_delete(trade_id)
+        await self._send_msg(
+            f"`{msg['result_msg']}`\n"
+            "Please make sure to take care of this asset on the exchange manually."
+        )
+
+    @authorized_only
+    async def _cancel_open_order(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /cancel_open_order <id>.
+        Cancel open order for tradeid
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        if not context.args or len(context.args) == 0:
+            raise RPCException("Trade-id not set.")
+        trade_id = int(context.args[0])
+        self._rpc._rpc_cancel_open_order(trade_id)
+        await self._send_msg("Open order canceled.")
+
+    @authorized_only
+    async def _performance(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /performance.
+        Shows a performance statistic from finished trades
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        trades = self._rpc._rpc_performance()
+        output = "<b>Performance:</b>\n"
+        for i, trade in enumerate(trades):
+            stat_line = (
+                f"{i + 1}.\t <code>{trade['pair']}\t"
+                f"{fmt_coin(trade['profit_abs'], self._config['stake_currency'])} "
+                f"({trade['profit_ratio']:.2%}) "
+                f"({trade['count']})</code>\n"
+            )
+
+            if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
+                await self._send_msg(output, parse_mode=ParseMode.HTML)
+                output = stat_line
+            else:
+                output += stat_line
+
+        await self._send_msg(
+            output,
+            parse_mode=ParseMode.HTML,
+            reload_able=True,
+            callback_path="update_performance",
+            query=update.callback_query,
+        )
+
+    @authorized_only
+    async def _enter_tag_performance(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /entries PAIR .
+        Shows a performance statistic from finished trades
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        pair = None
+        if context.args and isinstance(context.args[0], str):
+            pair = context.args[0]
+
+        trades = self._rpc._rpc_enter_tag_performance(pair)
+        output = "*Entry Tag Performance:*\n"
+        for i, trade in enumerate(trades):
+            stat_line = (
+                f"{i + 1}.\t `{trade['enter_tag']}\t"
+                f"{fmt_coin(trade['profit_abs'], self._config['stake_currency'])} "
+                f"({trade['profit_ratio']:.2%}) "
+                f"({trade['count']})`\n"
+            )
+
+            if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
+                await self._send_msg(output, parse_mode=ParseMode.MARKDOWN)
+                output = stat_line
+            else:
+                output += stat_line
+
+        await self._send_msg(
+            output,
+            parse_mode=ParseMode.MARKDOWN,
+            reload_able=True,
+            callback_path="update_enter_tag_performance",
+            query=update.callback_query,
+        )
+
+    @authorized_only
+    async def _exit_reason_performance(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /exits.
+        Shows a performance statistic from finished trades
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        pair = None
+        if context.args and isinstance(context.args[0], str):
+            pair = context.args[0]
+
+        trades = self._rpc._rpc_exit_reason_performance(pair)
+        output = "*Exit Reason Performance:*\n"
+        for i, trade in enumerate(trades):
+            stat_line = (
+                f"{i + 1}.\t `{trade['exit_reason']}\t"
+                f"{fmt_coin(trade['profit_abs'], self._config['stake_currency'])} "
+                f"({trade['profit_ratio']:.2%}) "
+                f"({trade['count']})`\n"
+            )
+
+            if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
+                await self._send_msg(output, parse_mode=ParseMode.MARKDOWN)
+                output = stat_line
+            else:
+                output += stat_line
+
+        await self._send_msg(
+            output,
+            parse_mode=ParseMode.MARKDOWN,
+            reload_able=True,
+            callback_path="update_exit_reason_performance",
+            query=update.callback_query,
+        )
+
+    @authorized_only
+    async def _mix_tag_performance(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /mix_tags.
+        Shows a performance statistic from finished trades
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        pair = None
+        if context.args and isinstance(context.args[0], str):
+            pair = context.args[0]
+
+        trades = self._rpc._rpc_mix_tag_performance(pair)
+        output = "*Mix Tag Performance:*\n"
+        for i, trade in enumerate(trades):
+            stat_line = (
+                f"{i + 1}.\t `{trade['mix_tag']}\t"
+                f"{fmt_coin(trade['profit_abs'], self._config['stake_currency'])} "
+                f"({trade['profit_ratio']:.2%}) "
+                f"({trade['count']})`\n"
+            )
+
+            if len(output + stat_line) >= MAX_MESSAGE_LENGTH:
+                await self._send_msg(output, parse_mode=ParseMode.MARKDOWN)
+                output = stat_line
+            else:
+                output += stat_line
+
+        await self._send_msg(
+            output,
+            parse_mode=ParseMode.MARKDOWN,
+            reload_able=True,
+            callback_path="update_mix_tag_performance",
+            query=update.callback_query,
+        )
+
+    @authorized_only
+    async def _count(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /count.
+        Returns the number of trades running
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        counts = self._rpc._rpc_count()
+        message = tabulate(
+            {k: [v] for k, v in counts.items()},
+            headers=["current", "max", "total stake"],
+            tablefmt="simple",
+        )
+        message = f"<pre>{message}</pre>"
+        logger.debug(message)
+        await self._send_msg(
+            message,
+            parse_mode=ParseMode.HTML,
+            reload_able=True,
+            callback_path="update_count",
+            query=update.callback_query,
+        )
+
+    @authorized_only
+    async def _locks(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /locks.
+        Returns the currently active locks
+        """
+        rpc_locks = self._rpc._rpc_locks()
+        if not rpc_locks["locks"]:
+            await self._send_msg("No active locks.", parse_mode=ParseMode.HTML)
+
+        for locks in chunks(rpc_locks["locks"], 25):
+            message = tabulate(
+                [
+                    [lock["id"], lock["pair"], lock["lock_end_time"], lock["reason"]]
+                    for lock in locks
+                ],
+                headers=["ID", "Pair", "Until", "Reason"],
+                tablefmt="simple",
+            )
+            message = f"<pre>{escape(message)}</pre>"
+            logger.debug(message)
+            await self._send_msg(message, parse_mode=ParseMode.HTML)
+
+    @authorized_only
+    async def _delete_locks(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /delete_locks.
+        Returns the currently active locks
+        """
+        arg = context.args[0] if context.args and len(context.args) > 0 else None
+        lockid = None
+        pair = None
+        if arg:
+            try:
+                lockid = int(arg)
+            except ValueError:
+                pair = arg
+
+        self._rpc._rpc_delete_lock(lockid=lockid, pair=pair)
+        await self._locks(update, context)
+
+    @authorized_only
+    async def _whitelist(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /whitelist
+        Shows the currently active whitelist
+        """
+        whitelist = self._rpc._rpc_whitelist()
+
+        if context.args:
+            if "sorted" in context.args:
+                whitelist["whitelist"] = sorted(whitelist["whitelist"])
+            if "baseonly" in context.args:
+                whitelist["whitelist"] = [pair.split("/")[0] for pair in whitelist["whitelist"]]
+
+        message = f"Using whitelist `{whitelist['method']}` with {whitelist['length']} pairs\n"
+        message += f"`{', '.join(whitelist['whitelist'])}`"
+
+        logger.debug(message)
+        await self._send_msg(message)
+
+    @authorized_only
+    async def _blacklist(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /blacklist
+        Shows the currently active blacklist
+        """
+        await self.send_blacklist_msg(self._rpc._rpc_blacklist(context.args))
+
+    async def send_blacklist_msg(self, blacklist: dict):
+        errmsgs = []
+        for _, error in blacklist["errors"].items():
+            errmsgs.append(f"Error: {error['error_msg']}")
+        if errmsgs:
+            await self._send_msg("\n".join(errmsgs))
+
+        message = f"Blacklist contains {blacklist['length']} pairs\n"
+        message += f"`{', '.join(blacklist['blacklist'])}`"
+
+        logger.debug(message)
+        await self._send_msg(message)
+
+    @authorized_only
+    async def _blacklist_delete(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /bl_delete
+        Deletes pair(s) from current blacklist
+        """
+        await self.send_blacklist_msg(self._rpc._rpc_blacklist_delete(context.args or []))
+
+    @authorized_only
+    async def _logs(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /logs
+        Shows the latest logs
+        """
+        try:
+            limit = int(context.args[0]) if context.args else 10
+        except (TypeError, ValueError, IndexError):
+            limit = 10
+        logs = RPC._rpc_get_logs(limit)["logs"]
+        msgs = ""
+        msg_template = "*{}* {}: {} \\- `{}`"
+        for logrec in logs:
+            msg = msg_template.format(
+                escape_markdown(logrec[0], version=2),
+                escape_markdown(logrec[2], version=2),
+                escape_markdown(logrec[3], version=2),
+                escape_markdown(logrec[4], version=2),
+            )
+            if len(msgs + msg) + 10 >= MAX_MESSAGE_LENGTH:
+                # Send message immediately if it would become too long
+                await self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2)
+                msgs = msg + "\n"
+            else:
+                # Append message to messages to send
+                msgs += msg + "\n"
+
+        if msgs:
+            await self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2)
+
+    @authorized_only
+    async def _edge(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /edge
+        Shows information related to Edge
+        """
+        edge_pairs = self._rpc._rpc_edge()
+        if not edge_pairs:
+            message = "<b>Edge only validated following pairs:</b>"
+            await self._send_msg(message, parse_mode=ParseMode.HTML)
+
+        for chunk in chunks(edge_pairs, 25):
+            edge_pairs_tab = tabulate(chunk, headers="keys", tablefmt="simple")
+            message = f"<b>Edge only validated following pairs:</b>\n<pre>{edge_pairs_tab}</pre>"
+
+            await self._send_msg(message, parse_mode=ParseMode.HTML)
+
+    @authorized_only
+    async def _help(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /help.
+        Show commands of the bot
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        force_enter_text = (
+            "*/forcelong <pair> [<rate>]:* `Instantly buys the given pair. "
+            "Optionally takes a rate at which to buy "
+            "(only applies to limit orders).` \n"
+        )
+        if self._rpc._freqtrade.trading_mode != TradingMode.SPOT:
+            force_enter_text += (
+                "*/forceshort <pair> [<rate>]:* `Instantly shorts the given pair. "
+                "Optionally takes a rate at which to sell "
+                "(only applies to limit orders).` \n"
+            )
+        message = (
+            "_Bot Control_\n"
+            "------------\n"
+            "*/start:* `Starts the trader`\n"
+            "*/pause:* `Pause the new entries for trader, but handles open trades gracefully`\n"
+            "*/stop:* `Stops the trader`\n"
+            "*/stopentry:* `Stops entering, but handles open trades gracefully` \n"
+            "*/forceexit <trade_id>|all:* `Instantly exits the given trade or all trades, "
+            "regardless of profit`\n"
+            "*/fx <trade_id>|all:* `Alias to /forceexit`\n"
+            f"{force_enter_text if self._config.get('force_entry_enable', False) else ''}"
+            "*/delete <trade_id>:* `Instantly delete the given trade in the database`\n"
+            "*/reload_trade <trade_id>:* `Reload trade from exchange Orders`\n"
+            "*/cancel_open_order <trade_id>:* `Cancels open orders for trade. "
+            "Only valid when the trade has open orders.`\n"
+            "*/coo <trade_id>|all:* `Alias to /cancel_open_order`\n"
+            "*/whitelist [sorted] [baseonly]:* `Show current whitelist. Optionally in "
+            "order and/or only displaying the base currency of each pairing.`\n"
+            "*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs "
+            "to the blacklist.` \n"
+            "*/blacklist_delete [pairs]| /bl_delete [pairs]:* "
+            "`Delete pair / pattern from blacklist. Will reset on reload_conf.` \n"
+            "*/reload_config:* `Reload configuration file` \n"
+            "*/unlock <pair|id>:* `Unlock this Pair (or this lock id if it's numeric)`\n"
+            "_Current state_\n"
+            "------------\n"
+            "*/show_config:* `Show running configuration` \n"
+            "*/locks:* `Show currently locked pairs`\n"
+            "*/balance:* `Show bot managed balance per currency`\n"
+            "*/balance total:* `Show account balance per currency`\n"
+            "*/logs [limit]:* `Show latest logs - defaults to 10` \n"
+            "*/count:* `Show number of active trades compared to allowed number of trades`\n"
+            "*/edge:* `Shows validated pairs by Edge if it is enabled` \n"
+            "*/health* `Show latest process timestamp - defaults to 1970-01-01 00:00:00` \n"
+            "*/marketdir [long | short | even | none]:* `Updates the user managed variable "
+            "that represents the current market direction. If no direction is provided `"
+            "`the currently set market direction will be output.` \n"
+            "*/list_custom_data <trade_id> <key>:* `List custom_data for Trade ID & Key combo.`\n"
+            "`If no Key is supplied it will list all key-value pairs found for that Trade ID.`\n"
+            "_Statistics_\n"
+            "------------\n"
+            "*/status <trade_id>|[table]:* `Lists all open trades`\n"
+            "         *<trade_id> :* `Lists one or more specific trades.`\n"
+            "                        `Separate multiple <trade_id> with a blank space.`\n"
+            "         *table :* `will display trades in a table`\n"
+            "                `pending buy orders are marked with an asterisk (*)`\n"
+            "                `pending sell orders are marked with a double asterisk (**)`\n"
+            "*/entries <pair|none>:* `Shows the enter_tag performance`\n"
+            "*/exits <pair|none>:* `Shows the exit reason performance`\n"
+            "*/mix_tags <pair|none>:* `Shows combined entry tag + exit reason performance`\n"
+            "*/trades [limit]:* `Lists last closed trades (limited to 10 by default)`\n"
+            "*/profit [<n>]:* `Lists cumulative profit from all finished trades, "
+            "over the last n days`\n"
+            "*/performance:* `Show performance of each finished trade grouped by pair`\n"
+            "*/daily <n>:* `Shows profit or loss per day, over the last n days`\n"
+            "*/weekly <n>:* `Shows statistics per week, over the last n weeks`\n"
+            "*/monthly <n>:* `Shows statistics per month, over the last n months`\n"
+            "*/stats:* `Shows Wins / losses by Sell reason as well as "
+            "Avg. holding durations for buys and sells.`\n"
+            "*/help:* `This help message`\n"
+            "*/version:* `Show version`\n"
+        )
+
+        await self._send_msg(message, parse_mode=ParseMode.MARKDOWN)
+
+    @authorized_only
+    async def _health(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /health
+        Shows the last process timestamp
+        """
+        health = self._rpc.health()
+        message = f"Last process: `{health['last_process_loc']}`\n"
+        message += f"Initial bot start: `{health['bot_start_loc']}`\n"
+        message += f"Last bot restart: `{health['bot_startup_loc']}`"
+        await self._send_msg(message)
+
+    @authorized_only
+    async def _version(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /version.
+        Show version information
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        strategy_version = self._rpc._freqtrade.strategy.version()
+        version_string = f"*Version:* `{__version__}`"
+        if strategy_version is not None:
+            version_string += f"\n*Strategy version: * `{strategy_version}`"
+
+        await self._send_msg(version_string)
+
+    @authorized_only
+    async def _show_config(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /show_config.
+        Show config information information
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        val = RPC._rpc_show_config(self._config, self._rpc._freqtrade.state)
+
+        if val["trailing_stop"]:
+            sl_info = (
+                f"*Initial Stoploss:* `{val['stoploss']}`\n"
+                f"*Trailing stop positive:* `{val['trailing_stop_positive']}`\n"
+                f"*Trailing stop offset:* `{val['trailing_stop_positive_offset']}`\n"
+                f"*Only trail above offset:* `{val['trailing_only_offset_is_reached']}`\n"
+            )
+
+        else:
+            sl_info = f"*Stoploss:* `{val['stoploss']}`\n"
+
+        if val["position_adjustment_enable"]:
+            pa_info = (
+                f"*Position adjustment:* On\n"
+                f"*Max enter position adjustment:* `{val['max_entry_position_adjustment']}`\n"
+            )
+        else:
+            pa_info = "*Position adjustment:* Off\n"
+
+        await self._send_msg(
+            f"*Mode:* `{'Dry-run' if val['dry_run'] else 'Live'}`\n"
+            f"*Exchange:* `{val['exchange']}`\n"
+            f"*Market: * `{val['trading_mode']}`\n"
+            f"*Stake per trade:* `{val['stake_amount']} {val['stake_currency']}`\n"
+            f"*Max open Trades:* `{val['max_open_trades']}`\n"
+            f"*Minimum ROI:* `{val['minimal_roi']}`\n"
+            f"*Entry strategy:* ```\n{json.dumps(val['entry_pricing'])}```\n"
+            f"*Exit strategy:* ```\n{json.dumps(val['exit_pricing'])}```\n"
+            f"{sl_info}"
+            f"{pa_info}"
+            f"*Timeframe:* `{val['timeframe']}`\n"
+            f"*Strategy:* `{val['strategy']}`\n"
+            f"*Current state:* `{val['state']}`"
+        )
+
+    @authorized_only
+    async def _list_custom_data(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /list_custom_data <id> <key>.
+        List custom_data for specified trade (and key if supplied).
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        try:
+            if not context.args or len(context.args) == 0:
+                raise RPCException("Trade-id not set.")
+            trade_id = int(context.args[0])
+            key = None if len(context.args) < 2 else str(context.args[1])
+
+            results = self._rpc._rpc_list_custom_data(trade_id, key)
+            messages = []
+            if len(results) > 0:
+                trade_custom_data = results[0]["custom_data"]
+                messages.append(
+                    "Found custom-data entr" + ("ies: " if len(trade_custom_data) > 1 else "y: ")
+                )
+                for custom_data in trade_custom_data:
+                    lines = [
+                        f"*Key:* `{custom_data['key']}`",
+                        f"*Type:* `{custom_data['type']}`",
+                        f"*Value:* `{custom_data['value']}`",
+                        f"*Create Date:* `{format_date(custom_data['created_at'])}`",
+                        f"*Update Date:* `{format_date(custom_data['updated_at'])}`",
+                    ]
+                    # Filter empty lines using list-comprehension
+                    messages.append("\n".join([line for line in lines if line]))
+                for msg in messages:
+                    if len(msg) > MAX_MESSAGE_LENGTH:
+                        msg = "Message dropped because length exceeds "
+                        msg += f"maximum allowed characters: {MAX_MESSAGE_LENGTH}"
+                        logger.warning(msg)
+                    await self._send_msg(msg)
+            else:
+                message = f"Didn't find any custom-data entries for Trade ID: `{trade_id}`"
+                message += f" and Key: `{key}`." if key is not None else ""
+                await self._send_msg(message)
+
+        except RPCException as e:
+            await self._send_msg(str(e))
+
+    async def _update_msg(
+        self,
+        query: CallbackQuery,
+        msg: str,
+        callback_path: str = "",
+        reload_able: bool = False,
+        parse_mode: str = ParseMode.MARKDOWN,
+    ) -> None:
+        if reload_able:
+            reply_markup = InlineKeyboardMarkup(
+                [
+                    [InlineKeyboardButton("Refresh", callback_data=callback_path)],
+                ]
+            )
+        else:
+            reply_markup = InlineKeyboardMarkup([[]])
+        msg += f"\nUpdated: {datetime.now().ctime()}"
+        if not query.message:
+            return
+
+        try:
+            await query.edit_message_text(
+                text=msg, parse_mode=parse_mode, reply_markup=reply_markup
+            )
+        except BadRequest as e:
+            if "not modified" in e.message.lower():
+                pass
+            else:
+                logger.warning("TelegramError: %s", e.message)
+        except TelegramError as telegram_err:
+            logger.warning("TelegramError: %s! Giving up on that message.", telegram_err.message)
+
+    async def _send_msg(
+        self,
+        msg: str,
+        parse_mode: str = ParseMode.MARKDOWN,
+        disable_notification: bool = False,
+        keyboard: list[list[InlineKeyboardButton]] | None = None,
+        callback_path: str = "",
+        reload_able: bool = False,
+        query: CallbackQuery | None = None,
+    ) -> None:
+        """
+        Send given markdown message
+        :param msg: message
+        :param bot: alternative bot
+        :param parse_mode: telegram parse mode
+        :return: None
+        """
+        reply_markup: InlineKeyboardMarkup | ReplyKeyboardMarkup
+        if query:
+            await self._update_msg(
+                query=query,
+                msg=msg,
+                parse_mode=parse_mode,
+                callback_path=callback_path,
+                reload_able=reload_able,
+            )
+            return
+        if reload_able and self._config["telegram"].get("reload", True):
+            reply_markup = InlineKeyboardMarkup(
+                [[InlineKeyboardButton("Refresh", callback_data=callback_path)]]
+            )
+        else:
+            if keyboard is not None:
+                reply_markup = InlineKeyboardMarkup(keyboard)
+            else:
+                reply_markup = ReplyKeyboardMarkup(self._keyboard, resize_keyboard=True)
+        try:
+            try:
+                await self._app.bot.send_message(
+                    self._config["telegram"]["chat_id"],
+                    text=msg,
+                    parse_mode=parse_mode,
+                    reply_markup=reply_markup,
+                    disable_notification=disable_notification,
+                    message_thread_id=self._config["telegram"].get("topic_id"),
+                )
+            except NetworkError as network_err:
+                # Sometimes the telegram server resets the current connection,
+                # if this is the case we send the message again.
+                logger.warning(
+                    "Telegram NetworkError: %s! Trying one more time.", network_err.message
+                )
+                await self._app.bot.send_message(
+                    self._config["telegram"]["chat_id"],
+                    text=msg,
+                    parse_mode=parse_mode,
+                    reply_markup=reply_markup,
+                    disable_notification=disable_notification,
+                    message_thread_id=self._config["telegram"].get("topic_id"),
+                )
+        except TelegramError as telegram_err:
+            logger.warning("TelegramError: %s! Giving up on that message.", telegram_err.message)
+
+    @authorized_only
+    async def _changemarketdir(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /marketdir.
+        Updates the bot's market_direction
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        if context.args and len(context.args) == 1:
+            new_market_dir_arg = context.args[0]
+            old_market_dir = self._rpc._get_market_direction()
+            new_market_dir = None
+            if new_market_dir_arg == "long":
+                new_market_dir = MarketDirection.LONG
+            elif new_market_dir_arg == "short":
+                new_market_dir = MarketDirection.SHORT
+            elif new_market_dir_arg == "even":
+                new_market_dir = MarketDirection.EVEN
+            elif new_market_dir_arg == "none":
+                new_market_dir = MarketDirection.NONE
+
+            if new_market_dir is not None:
+                self._rpc._update_market_direction(new_market_dir)
+                await self._send_msg(
+                    "Successfully updated market direction"
+                    f" from *{old_market_dir}* to *{new_market_dir}*."
+                )
+            else:
+                raise RPCException(
+                    "Invalid market direction provided. \n"
+                    "Valid market directions: *long, short, even, none*"
+                )
+        elif context.args is not None and len(context.args) == 0:
+            old_market_dir = self._rpc._get_market_direction()
+            await self._send_msg(f"Currently set market direction: *{old_market_dir}*")
+        else:
+            raise RPCException(
+                "Invalid usage of command /marketdir. \n"
+                "Usage: */marketdir [short |  long | even | none]*"
+            )
+
+    async def _tg_info(self, update: Update, context: CallbackContext) -> None:
+        """
+        Intentionally unauthenticated Handler for /tg_info.
+        Returns information about the current telegram chat - even if chat_id does not
+        correspond to this chat.
+
+        :param update: message update
+        :return: None
+        """
+        if not update.message:
+            return
+        chat_id = update.message.chat_id
+        topic_id = update.message.message_thread_id
+        user_id = (
+            update.effective_user.id if topic_id is not None and update.effective_user else None
+        )
+
+        msg = f"""Freqtrade Bot Info:
+        ```json
+            {{
+                "enabled": true,
+                "token": "********",
+                "chat_id": "{chat_id}",
+                {f'"topic_id": "{topic_id}",' if topic_id else ""}
+                {f'//"authorized_users": ["{user_id}"]' if topic_id and user_id else ""}
+            }}
+        ```
+        """
+        try:
+            await context.bot.send_message(
+                chat_id=chat_id,
+                text=msg,
+                parse_mode=ParseMode.MARKDOWN_V2,
+                message_thread_id=topic_id,
+            )
+        except TelegramError as telegram_err:
+            logger.warning("TelegramError: %s! Giving up on that message.", telegram_err.message)
diff --git a/freqtrade/rpc/webhook.py b/freqtrade/rpc/webhook.py
new file mode 100644
index 000000000..eb9aa37c8
--- /dev/null
+++ b/freqtrade/rpc/webhook.py
@@ -0,0 +1,134 @@
+"""
+This module manages webhook communication
+"""
+
+import logging
+import time
+from typing import Any
+
+from requests import RequestException, post
+
+from freqtrade.constants import Config
+from freqtrade.enums import RPCMessageType
+from freqtrade.rpc import RPC, RPCHandler
+from freqtrade.rpc.rpc_types import RPCSendMsg
+
+
+logger = logging.getLogger(__name__)
+
+logger.debug("Included module rpc.webhook ...")
+
+
+class Webhook(RPCHandler):
+    """This class handles all webhook communication"""
+
+    def __init__(self, rpc: RPC, config: Config) -> None:
+        """
+        Init the Webhook class, and init the super class RPCHandler
+        :param rpc: instance of RPC Helper class
+        :param config: Configuration object
+        :return: None
+        """
+        super().__init__(rpc, config)
+
+        self._url = self._config["webhook"]["url"]
+        self._format = self._config["webhook"].get("format", "form")
+        self._retries = self._config["webhook"].get("retries", 0)
+        self._retry_delay = self._config["webhook"].get("retry_delay", 0.1)
+        self._timeout = self._config["webhook"].get("timeout", 10)
+
+    def cleanup(self) -> None:
+        """
+        Cleanup pending module resources.
+        This will do nothing for webhooks, they will simply not be called anymore
+        """
+        pass
+
+    def _get_value_dict(self, msg: RPCSendMsg) -> dict[str, Any] | None:
+        whconfig = self._config["webhook"]
+        if msg["type"].value in whconfig:
+            # Explicit types should have priority
+            valuedict = whconfig.get(msg["type"].value)
+        # Deprecated 2022.10 - only keep generic method.
+        elif msg["type"] in [RPCMessageType.ENTRY]:
+            valuedict = whconfig.get("webhookentry")
+        elif msg["type"] in [RPCMessageType.ENTRY_CANCEL]:
+            valuedict = whconfig.get("webhookentrycancel")
+        elif msg["type"] in [RPCMessageType.ENTRY_FILL]:
+            valuedict = whconfig.get("webhookentryfill")
+        elif msg["type"] == RPCMessageType.EXIT:
+            valuedict = whconfig.get("webhookexit")
+        elif msg["type"] == RPCMessageType.EXIT_FILL:
+            valuedict = whconfig.get("webhookexitfill")
+        elif msg["type"] == RPCMessageType.EXIT_CANCEL:
+            valuedict = whconfig.get("webhookexitcancel")
+        elif msg["type"] in (
+            RPCMessageType.STATUS,
+            RPCMessageType.STARTUP,
+            RPCMessageType.EXCEPTION,
+            RPCMessageType.WARNING,
+        ):
+            valuedict = whconfig.get("webhookstatus")
+        elif msg["type"] in (
+            RPCMessageType.PROTECTION_TRIGGER,
+            RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
+            RPCMessageType.WHITELIST,
+            RPCMessageType.ANALYZED_DF,
+            RPCMessageType.NEW_CANDLE,
+            RPCMessageType.STRATEGY_MSG,
+        ):
+            # Don't fail for non-implemented types
+            return None
+        return valuedict
+
+    def send_msg(self, msg: RPCSendMsg) -> None:
+        """Send a message to telegram channel"""
+        try:
+            valuedict = self._get_value_dict(msg)
+
+            if not valuedict:
+                logger.debug("Message type '%s' not configured for webhooks", msg["type"])
+                return
+
+            payload = {key: value.format(**msg) for (key, value) in valuedict.items()}
+            self._send_msg(payload)
+        except KeyError as exc:
+            logger.exception(
+                "Problem calling Webhook. Please check your webhook configuration. Exception: %s",
+                exc,
+            )
+
+    def _send_msg(self, payload: dict) -> None:
+        """do the actual call to the webhook"""
+
+        success = False
+        attempts = 0
+        while not success and attempts <= self._retries:
+            if attempts:
+                if self._retry_delay:
+                    time.sleep(self._retry_delay)
+                logger.info("Retrying webhook...")
+
+            attempts += 1
+
+            try:
+                if self._format == "form":
+                    response = post(self._url, data=payload, timeout=self._timeout)
+                elif self._format == "json":
+                    response = post(self._url, json=payload, timeout=self._timeout)
+                elif self._format == "raw":
+                    response = post(
+                        self._url,
+                        data=payload["data"],
+                        headers={"Content-Type": "text/plain"},
+                        timeout=self._timeout,
+                    )
+                else:
+                    raise NotImplementedError(f"Unknown format: {self._format}")
+
+                # Throw a RequestException if the post was not successful
+                response.raise_for_status()
+                success = True
+
+            except RequestException as exc:
+                logger.warning("Could not call webhook url. Exception: %s", exc)
diff --git a/freqtrade/strategy/__init__.py b/freqtrade/strategy/__init__.py
index e69de29bb..e99473b4e 100644
--- a/freqtrade/strategy/__init__.py
+++ b/freqtrade/strategy/__init__.py
@@ -0,0 +1,47 @@
+# flake8: noqa: F401
+from freqtrade.exchange import (
+    timeframe_to_minutes,
+    timeframe_to_msecs,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    timeframe_to_seconds,
+)
+from freqtrade.persistence import Order, PairLocks, Trade
+from freqtrade.strategy.informative_decorator import informative
+from freqtrade.strategy.interface import IStrategy
+from freqtrade.strategy.parameters import (
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+)
+from freqtrade.strategy.strategy_helper import (
+    merge_informative_pair,
+    stoploss_from_absolute,
+    stoploss_from_open,
+)
+
+
+# Imports to be used for `from freqtrade.strategy import *`
+__all__ = [
+    "IStrategy",
+    "Trade",
+    "Order",
+    "PairLocks",
+    "informative",
+    # Parameters
+    "BooleanParameter",
+    "CategoricalParameter",
+    "DecimalParameter",
+    "IntParameter",
+    "RealParameter",
+    # timeframe helpers
+    "timeframe_to_minutes",
+    "timeframe_to_next_date",
+    "timeframe_to_prev_date",
+    # Strategy helper functions
+    "merge_informative_pair",
+    "stoploss_from_absolute",
+    "stoploss_from_open",
+]
diff --git a/freqtrade/strategy/default_strategy.py b/freqtrade/strategy/default_strategy.py
deleted file mode 100644
index 2247ecf27..000000000
--- a/freqtrade/strategy/default_strategy.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
-
-import talib.abstract as ta
-from pandas import DataFrame
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-from freqtrade.strategy.interface import IStrategy
-from freqtrade.indicator_helpers import fishers_inverse
-
-class_name = 'DefaultStrategy'
-
-
-class DefaultStrategy(IStrategy):
-    """
-    Default Strategy provided by freqtrade bot.
-    You can override it with your own strategy
-    """
-
-    # Minimal ROI designed for the strategy
-    minimal_roi = {
-        "40":  0.0,
-        "30":  0.01,
-        "20":  0.02,
-        "0":  0.04
-    }
-
-    # Optimal stoploss designed for the strategy
-    stoploss = -0.10
-
-    # Optimal ticker interval for the strategy
-    ticker_interval = 5
-
-    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Adds several different TA indicators to the given DataFrame
-
-        Performance Note: For the best performance be frugal on the number of indicators
-        you are using. Let uncomment only the indicator you are using in your strategies
-        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
-        """
-
-        # Momentum Indicator
-        # ------------------------------------
-
-        # ADX
-        dataframe['adx'] = ta.ADX(dataframe)
-
-        # Awesome oscillator
-        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
-        """
-        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
-        dataframe['cci'] = ta.CCI(dataframe)
-        """
-        # MACD
-        macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['macdsignal'] = macd['macdsignal']
-        dataframe['macdhist'] = macd['macdhist']
-
-        # MFI
-        dataframe['mfi'] = ta.MFI(dataframe)
-
-        # Minus Directional Indicator / Movement
-        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
-        # Plus Directional Indicator / Movement
-        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
-        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
-        """
-        # ROC
-        dataframe['roc'] = ta.ROC(dataframe)
-        """
-        # RSI
-        dataframe['rsi'] = ta.RSI(dataframe)
-
-        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
-        dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi'])
-
-        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
-        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
-
-        # Stoch
-        stoch = ta.STOCH(dataframe)
-        dataframe['slowd'] = stoch['slowd']
-        dataframe['slowk'] = stoch['slowk']
-
-        # Stoch fast
-        stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        dataframe['fastk'] = stoch_fast['fastk']
-        """
-        # Stoch RSI
-        stoch_rsi = ta.STOCHRSI(dataframe)
-        dataframe['fastd_rsi'] = stoch_rsi['fastd']
-        dataframe['fastk_rsi'] = stoch_rsi['fastk']
-        """
-
-        # Overlap Studies
-        # ------------------------------------
-
-        # Previous Bollinger bands
-        # Because ta.BBANDS implementation is broken with small numbers, it actually
-        # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
-        # and use middle band instead.
-        dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
-
-        # Bollinger bands
-        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-        dataframe['bb_lowerband'] = bollinger['lower']
-        dataframe['bb_middleband'] = bollinger['mid']
-        dataframe['bb_upperband'] = bollinger['upper']
-
-        # EMA - Exponential Moving Average
-        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
-        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
-        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
-        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
-        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
-
-        # SAR Parabol
-        dataframe['sar'] = ta.SAR(dataframe)
-
-        # SMA - Simple Moving Average
-        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-
-        # TEMA - Triple Exponential Moving Average
-        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
-
-        # Cycle Indicator
-        # ------------------------------------
-        # Hilbert Transform Indicator - SineWave
-        hilbert = ta.HT_SINE(dataframe)
-        dataframe['htsine'] = hilbert['sine']
-        dataframe['htleadsine'] = hilbert['leadsine']
-
-        # Pattern Recognition - Bullish candlestick patterns
-        # ------------------------------------
-        """
-        # Hammer: values [0, 100]
-        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
-        # Inverted Hammer: values [0, 100]
-        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
-        # Dragonfly Doji: values [0, 100]
-        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
-        # Piercing Line: values [0, 100]
-        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
-        # Morningstar: values [0, 100]
-        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
-        # Three White Soldiers: values [0, 100]
-        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
-        """
-
-        # Pattern Recognition - Bearish candlestick patterns
-        # ------------------------------------
-        """
-        # Hanging Man: values [0, 100]
-        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
-        # Shooting Star: values [0, 100]
-        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
-        # Gravestone Doji: values [0, 100]
-        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
-        # Dark Cloud Cover: values [0, 100]
-        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
-        # Evening Doji Star: values [0, 100]
-        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
-        # Evening Star: values [0, 100]
-        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
-        """
-
-        # Pattern Recognition - Bullish/Bearish candlestick patterns
-        # ------------------------------------
-        """
-        # Three Line Strike: values [0, -100, 100]
-        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
-        # Spinning Top: values [0, -100, 100]
-        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
-        # Engulfing: values [0, -100, 100]
-        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
-        # Harami: values [0, -100, 100]
-        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
-        # Three Outside Up/Down: values [0, -100, 100]
-        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
-        # Three Inside Up/Down: values [0, -100, 100]
-        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
-        """
-
-        # Chart type
-        # ------------------------------------
-        # Heikinashi stategy
-        heikinashi = qtpylib.heikinashi(dataframe)
-        dataframe['ha_open'] = heikinashi['open']
-        dataframe['ha_close'] = heikinashi['close']
-        dataframe['ha_high'] = heikinashi['high']
-        dataframe['ha_low'] = heikinashi['low']
-
-        return dataframe
-
-    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Based on TA indicators, populates the buy signal for the given dataframe
-        :param dataframe: DataFrame
-        :return: DataFrame with buy column
-        """
-        dataframe.loc[
-            (
-                (dataframe['rsi'] < 35) &
-                (dataframe['fastd'] < 35) &
-                (dataframe['adx'] > 30) &
-                (dataframe['plus_di'] > 0.5)
-            ) |
-            (
-                (dataframe['adx'] > 65) &
-                (dataframe['plus_di'] > 0.5)
-            ),
-            'buy'] = 1
-
-        return dataframe
-
-    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Based on TA indicators, populates the sell signal for the given dataframe
-        :param dataframe: DataFrame
-        :return: DataFrame with buy column
-        """
-        dataframe.loc[
-            (
-                (
-                    (qtpylib.crossed_above(dataframe['rsi'], 70)) |
-                    (qtpylib.crossed_above(dataframe['fastd'], 70))
-                ) &
-                (dataframe['adx'] > 10) &
-                (dataframe['minus_di'] > 0)
-            ) |
-            (
-                (dataframe['adx'] > 70) &
-                (dataframe['minus_di'] > 0.5)
-            ),
-            'sell'] = 1
-        return dataframe
diff --git a/freqtrade/strategy/hyper.py b/freqtrade/strategy/hyper.py
new file mode 100644
index 000000000..6281ab754
--- /dev/null
+++ b/freqtrade/strategy/hyper.py
@@ -0,0 +1,216 @@
+"""
+IHyperStrategy interface, hyperoptable Parameter class.
+This module defines a base class for auto-hyperoptable strategies.
+"""
+
+import logging
+from collections.abc import Iterator
+from pathlib import Path
+from typing import Any
+
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.misc import deep_merge_dicts
+from freqtrade.optimize.hyperopt_tools import HyperoptTools
+from freqtrade.strategy.parameters import BaseParameter
+
+
+logger = logging.getLogger(__name__)
+
+
+class HyperStrategyMixin:
+    """
+    A helper base class which allows HyperOptAuto class to reuse implementations of buy/sell
+     strategy logic.
+    """
+
+    def __init__(self, config: Config, *args, **kwargs):
+        """
+        Initialize hyperoptable strategy mixin.
+        """
+        self.config = config
+        self.ft_buy_params: list[BaseParameter] = []
+        self.ft_sell_params: list[BaseParameter] = []
+        self.ft_protection_params: list[BaseParameter] = []
+
+        params = self.load_params_from_file()
+        params = params.get("params", {})
+        self._ft_params_from_file = params
+        # Init/loading of parameters is done as part of ft_bot_start().
+
+    def enumerate_parameters(
+        self, category: str | None = None
+    ) -> Iterator[tuple[str, BaseParameter]]:
+        """
+        Find all optimizable parameters and return (name, attr) iterator.
+        :param category:
+        :return:
+        """
+        if category not in ("buy", "sell", "protection", None):
+            raise OperationalException(
+                'Category must be one of: "buy", "sell", "protection", None.'
+            )
+
+        if category is None:
+            params = self.ft_buy_params + self.ft_sell_params + self.ft_protection_params
+        else:
+            params = getattr(self, f"ft_{category}_params")
+
+        for par in params:
+            yield par.name, par
+
+    @classmethod
+    def detect_all_parameters(cls) -> dict:
+        """Detect all parameters and return them as a list"""
+        params: dict[str, Any] = {
+            "buy": list(detect_parameters(cls, "buy")),
+            "sell": list(detect_parameters(cls, "sell")),
+            "protection": list(detect_parameters(cls, "protection")),
+        }
+        params.update({"count": len(params["buy"] + params["sell"] + params["protection"])})
+
+        return params
+
+    def ft_load_params_from_file(self) -> None:
+        """
+        Load Parameters from parameter file
+        Should/must run before config values are loaded in strategy_resolver.
+        """
+        if self._ft_params_from_file:
+            # Set parameters from Hyperopt results file
+            params = self._ft_params_from_file
+            self.minimal_roi = params.get("roi", getattr(self, "minimal_roi", {}))
+
+            self.stoploss = params.get("stoploss", {}).get(
+                "stoploss", getattr(self, "stoploss", -0.1)
+            )
+            self.max_open_trades = params.get("max_open_trades", {}).get(
+                "max_open_trades", getattr(self, "max_open_trades", -1)
+            )
+            trailing = params.get("trailing", {})
+            self.trailing_stop = trailing.get(
+                "trailing_stop", getattr(self, "trailing_stop", False)
+            )
+            self.trailing_stop_positive = trailing.get(
+                "trailing_stop_positive", getattr(self, "trailing_stop_positive", None)
+            )
+            self.trailing_stop_positive_offset = trailing.get(
+                "trailing_stop_positive_offset", getattr(self, "trailing_stop_positive_offset", 0)
+            )
+            self.trailing_only_offset_is_reached = trailing.get(
+                "trailing_only_offset_is_reached",
+                getattr(self, "trailing_only_offset_is_reached", 0.0),
+            )
+
+    def ft_load_hyper_params(self, hyperopt: bool = False) -> None:
+        """
+        Load Hyperoptable parameters
+        Prevalence:
+        * Parameters from parameter file
+        * Parameters defined in parameters objects (buy_params, sell_params, ...)
+        * Parameter defaults
+        """
+
+        buy_params = deep_merge_dicts(
+            self._ft_params_from_file.get("buy", {}), getattr(self, "buy_params", {})
+        )
+        sell_params = deep_merge_dicts(
+            self._ft_params_from_file.get("sell", {}), getattr(self, "sell_params", {})
+        )
+        protection_params = deep_merge_dicts(
+            self._ft_params_from_file.get("protection", {}), getattr(self, "protection_params", {})
+        )
+
+        self._ft_load_params(buy_params, "buy", hyperopt)
+        self._ft_load_params(sell_params, "sell", hyperopt)
+        self._ft_load_params(protection_params, "protection", hyperopt)
+
+    def load_params_from_file(self) -> dict:
+        filename_str = getattr(self, "__file__", "")
+        if not filename_str:
+            return {}
+        filename = Path(filename_str).with_suffix(".json")
+
+        if filename.is_file():
+            logger.info(f"Loading parameters from file {filename}")
+            try:
+                params = HyperoptTools.load_params(filename)
+                if params.get("strategy_name") != self.__class__.__name__:
+                    raise OperationalException("Invalid parameter file provided.")
+                return params
+            except ValueError:
+                logger.warning("Invalid parameter file format.")
+                return {}
+        logger.info("Found no parameter file.")
+
+        return {}
+
+    def _ft_load_params(self, params: dict, space: str, hyperopt: bool = False) -> None:
+        """
+        Set optimizable parameter values.
+        :param params: Dictionary with new parameter values.
+        """
+        if not params:
+            logger.info(f"No params for {space} found, using default values.")
+        param_container: list[BaseParameter] = getattr(self, f"ft_{space}_params")
+
+        for attr_name, attr in detect_parameters(self, space):
+            attr.name = attr_name
+            attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
+            if not attr.category:
+                attr.category = space
+
+            param_container.append(attr)
+
+            if params and attr_name in params:
+                if attr.load:
+                    attr.value = params[attr_name]
+                    logger.info(f"Strategy Parameter: {attr_name} = {attr.value}")
+                else:
+                    logger.warning(
+                        f'Parameter "{attr_name}" exists, but is disabled. '
+                        f'Default value "{attr.value}" used.'
+                    )
+            else:
+                logger.info(f"Strategy Parameter(default): {attr_name} = {attr.value}")
+
+    def get_no_optimize_params(self) -> dict[str, dict]:
+        """
+        Returns list of Parameters that are not part of the current optimize job
+        """
+        params: dict[str, dict] = {
+            "buy": {},
+            "sell": {},
+            "protection": {},
+        }
+        for name, p in self.enumerate_parameters():
+            if p.category and (not p.optimize or not p.in_space):
+                params[p.category][name] = p.value
+        return params
+
+
+def detect_parameters(
+    obj: HyperStrategyMixin | type[HyperStrategyMixin], category: str
+) -> Iterator[tuple[str, BaseParameter]]:
+    """
+    Detect all parameters for 'category' for "obj"
+    :param obj: Strategy object or class
+    :param category: category - usually `'buy', 'sell', 'protection',...
+    """
+    for attr_name in dir(obj):
+        if not attr_name.startswith("__"):  # Ignore internals, not strictly necessary.
+            attr = getattr(obj, attr_name)
+            if issubclass(attr.__class__, BaseParameter):
+                if (
+                    attr_name.startswith(category + "_")
+                    and attr.category is not None
+                    and attr.category != category
+                ):
+                    raise OperationalException(
+                        f"Inconclusive parameter name {attr_name}, category: {attr.category}."
+                    )
+
+                if category == attr.category or (
+                    attr_name.startswith(category + "_") and attr.category is None
+                ):
+                    yield attr_name, attr
diff --git a/freqtrade/strategy/informative_decorator.py b/freqtrade/strategy/informative_decorator.py
new file mode 100644
index 000000000..69cf6f0ce
--- /dev/null
+++ b/freqtrade/strategy/informative_decorator.py
@@ -0,0 +1,166 @@
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any
+
+from pandas import DataFrame
+
+from freqtrade.enums import CandleType
+from freqtrade.exceptions import OperationalException
+from freqtrade.strategy.strategy_helper import merge_informative_pair
+
+
+PopulateIndicators = Callable[[Any, DataFrame, dict], DataFrame]
+
+
+@dataclass
+class InformativeData:
+    asset: str | None
+    timeframe: str
+    fmt: str | Callable[[Any], str] | None
+    ffill: bool
+    candle_type: CandleType | None
+
+
+def informative(
+    timeframe: str,
+    asset: str = "",
+    fmt: str | Callable[[Any], str] | None = None,
+    *,
+    candle_type: CandleType | str | None = None,
+    ffill: bool = True,
+) -> Callable[[PopulateIndicators], PopulateIndicators]:
+    """
+    A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
+    define informative indicators.
+
+    Example usage:
+
+        @informative('1h')
+        def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+            dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+            return dataframe
+
+    :param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe.
+    :param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use
+                  current pair. Also supports limited pair format strings (see below)
+    :param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not
+    specified, defaults to:
+    * {base}_{quote}_{column}_{timeframe} if asset is specified.
+    * {column}_{timeframe} if asset is not specified.
+    Pair format supports these format variables:
+    * {base} - base currency in lower case, for example 'eth'.
+    * {BASE} - same as {base}, except in upper case.
+    * {quote} - quote currency in lower case, for example 'usdt'.
+    * {QUOTE} - same as {quote}, except in upper case.
+    Format string additionally supports this variables.
+    * {asset} - full name of the asset, for example 'BTC/USDT'.
+    * {column} - name of dataframe column.
+    * {timeframe} - timeframe of informative dataframe.
+    :param ffill: ffill dataframe after merging informative pair.
+    :param candle_type: '', mark, index, premiumIndex, or funding_rate
+    """
+    _asset = asset
+    _timeframe = timeframe
+    _fmt = fmt
+    _ffill = ffill
+    _candle_type = CandleType.from_string(candle_type) if candle_type else None
+
+    def decorator(fn: PopulateIndicators):
+        informative_pairs = getattr(fn, "_ft_informative", [])
+        informative_pairs.append(InformativeData(_asset, _timeframe, _fmt, _ffill, _candle_type))
+        setattr(fn, "_ft_informative", informative_pairs)  # noqa: B010
+        return fn
+
+    return decorator
+
+
+def __get_pair_formats(market: dict[str, Any] | None) -> dict[str, str]:
+    if not market:
+        return {}
+    base = market["base"]
+    quote = market["quote"]
+    return {
+        "base": base.lower(),
+        "BASE": base.upper(),
+        "quote": quote.lower(),
+        "QUOTE": quote.upper(),
+    }
+
+
+def _format_pair_name(config, pair: str, market: dict[str, Any] | None = None) -> str:
+    return pair.format(
+        stake_currency=config["stake_currency"],
+        stake=config["stake_currency"],
+        **__get_pair_formats(market),
+    ).upper()
+
+
+def _create_and_merge_informative_pair(
+    strategy,
+    dataframe: DataFrame,
+    metadata: dict,
+    inf_data: InformativeData,
+    populate_indicators: PopulateIndicators,
+):
+    asset = inf_data.asset or ""
+    timeframe = inf_data.timeframe
+    fmt = inf_data.fmt
+    candle_type = inf_data.candle_type
+
+    config = strategy.config
+
+    if asset:
+        # Insert stake currency if needed.
+        market1 = strategy.dp.market(metadata["pair"])
+        asset = _format_pair_name(config, asset, market1)
+    else:
+        # Not specifying an asset will define informative dataframe for current pair.
+        asset = metadata["pair"]
+
+    market = strategy.dp.market(asset)
+    if market is None:
+        raise OperationalException(f"Market {asset} is not available.")
+
+    # Default format. This optimizes for the common case: informative pairs using same stake
+    # currency. When quote currency matches stake currency, column name will omit base currency.
+    # This allows easily reconfiguring strategy to use different base currency. In a rare case
+    # where it is desired to keep quote currency in column name at all times user should specify
+    # fmt='{base}_{quote}_{column}_{timeframe}' format or similar.
+    if not fmt:
+        fmt = "{column}_{timeframe}"  # Informatives of current pair
+        if inf_data.asset:
+            fmt = "{base}_{quote}_" + fmt  # Informatives of other pairs
+
+    inf_metadata = {"pair": asset, "timeframe": timeframe}
+    inf_dataframe = strategy.dp.get_pair_dataframe(asset, timeframe, candle_type)
+    inf_dataframe = populate_indicators(strategy, inf_dataframe, inf_metadata)
+
+    formatter: Any = None
+    if callable(fmt):
+        formatter = fmt  # A custom user-specified formatter function.
+    else:
+        formatter = fmt.format  # A default string formatter.
+
+    fmt_args = {
+        **__get_pair_formats(market),
+        "asset": asset,
+        "timeframe": timeframe,
+    }
+    inf_dataframe.rename(columns=lambda column: formatter(column=column, **fmt_args), inplace=True)
+
+    date_column = formatter(column="date", **fmt_args)
+    if date_column in dataframe.columns:
+        raise OperationalException(
+            f"Duplicate column name {date_column} exists in "
+            f"dataframe! Ensure column names are unique!"
+        )
+    dataframe = merge_informative_pair(
+        dataframe,
+        inf_dataframe,
+        strategy.timeframe,
+        timeframe,
+        ffill=inf_data.ffill,
+        append_timeframe=False,
+        date_column=date_column,
+    )
+    return dataframe
diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py
index dc9f33244..28a6e2c78 100644
--- a/freqtrade/strategy/interface.py
+++ b/freqtrade/strategy/interface.py
@@ -3,11 +3,48 @@ IStrategy interface
 This module defines the interface to apply for strategies
 """
 
+import logging
 from abc import ABC, abstractmethod
+from datetime import datetime, timedelta, timezone
+from math import isinf, isnan
+
 from pandas import DataFrame
 
+from freqtrade.constants import CUSTOM_TAG_MAX_LENGTH, Config, IntOrInf, ListPairsWithTimeframes
+from freqtrade.data.converter import populate_dataframe_with_trades
+from freqtrade.data.converter.converter import reduce_dataframe_footprint
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import (
+    CandleType,
+    ExitCheckTuple,
+    ExitType,
+    MarketDirection,
+    RunMode,
+    SignalDirection,
+    SignalTagType,
+    SignalType,
+    TradingMode,
+)
+from freqtrade.exceptions import OperationalException, StrategyError
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
+from freqtrade.misc import remove_entry_exit_signals
+from freqtrade.persistence import Order, PairLocks, Trade
+from freqtrade.strategy.hyper import HyperStrategyMixin
+from freqtrade.strategy.informative_decorator import (
+    InformativeData,
+    PopulateIndicators,
+    _create_and_merge_informative_pair,
+    _format_pair_name,
+)
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util import dt_now
+from freqtrade.wallets import Wallets
 
-class IStrategy(ABC):
+
+logger = logging.getLogger(__name__)
+
+
+class IStrategy(ABC, HyperStrategyMixin):
     """
     Interface for freqtrade strategies
     Defines the mandatory structure must follow any custom strategies
@@ -15,30 +52,1731 @@ class IStrategy(ABC):
     Attributes you can use:
         minimal_roi -> Dict: Minimal ROI designed for the strategy
         stoploss -> float: optimal stoploss designed for the strategy
-        ticker_interval -> int: value of the ticker interval to use for the strategy
+        timeframe -> str: value of the timeframe to use with the strategy
     """
 
-    @abstractmethod
-    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+    # Strategy interface version
+    # Default to version 2
+    # Version 1 is the initial interface without metadata dict - deprecated and no longer supported.
+    # Version 2 populate_* include metadata dict
+    # Version 3 - First version with short and leverage support
+    INTERFACE_VERSION: int = 3
+
+    _ft_params_from_file: dict
+    # associated minimal roi
+    minimal_roi: dict = {}
+
+    # associated stoploss
+    stoploss: float
+
+    # max open trades for the strategy
+    max_open_trades: IntOrInf
+
+    # trailing stoploss
+    trailing_stop: bool = False
+    trailing_stop_positive: float | None = None
+    trailing_stop_positive_offset: float = 0.0
+    trailing_only_offset_is_reached = False
+    use_custom_stoploss: bool = False
+
+    # Can this strategy go short?
+    can_short: bool = False
+
+    # associated timeframe
+    timeframe: str
+
+    # Optional order types
+    order_types: dict = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": False,
+        "stoploss_on_exchange_interval": 60,
+    }
+
+    # Optional time in force
+    order_time_in_force: dict = {
+        "entry": "GTC",
+        "exit": "GTC",
+    }
+
+    # run "populate_indicators" only for new candle
+    process_only_new_candles: bool = True
+
+    use_exit_signal: bool
+    exit_profit_only: bool
+    exit_profit_offset: float
+    ignore_roi_if_entry_signal: bool
+
+    # Position adjustment is disabled by default
+    position_adjustment_enable: bool = False
+    max_entry_position_adjustment: int = -1
+
+    # Number of seconds after which the candle will no longer result in a buy on expired candles
+    ignore_buying_expired_candle_after: int = 0
+
+    # Disable checking the dataframe (converts the error into a warning message)
+    disable_dataframe_checks: bool = False
+
+    # Count of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 0
+
+    # Protections
+    protections: list = []
+
+    # Class level variables (intentional) containing
+    # the dataprovider (dp) (access to other candles, historic data, ...)
+    # and wallets - access to the current balance.
+    dp: DataProvider
+    wallets: Wallets | None = None
+    # Filled from configuration
+    stake_currency: str
+    # container variable for strategy source code
+    __source__: str = ""
+    __file__: str = ""
+
+    # Definition of plot_config. See plotting documentation for more details.
+    plot_config: dict = {}
+
+    # A self set parameter that represents the market direction. filled from configuration
+    market_direction: MarketDirection = MarketDirection.NONE
+
+    # Global cache dictionary
+    _cached_grouped_trades_per_pair: dict[str, DataFrame] = {}
+
+    def __init__(self, config: Config) -> None:
+        self.config = config
+        # Dict to determine if analysis is necessary
+        self._last_candle_seen_per_pair: dict[str, datetime] = {}
+        super().__init__(config)
+
+        # Gather informative pairs from @informative-decorated methods.
+        self._ft_informative: list[tuple[InformativeData, PopulateIndicators]] = []
+        for attr_name in dir(self.__class__):
+            cls_method = getattr(self.__class__, attr_name)
+            if not callable(cls_method):
+                continue
+            informative_data_list = getattr(cls_method, "_ft_informative", None)
+            if not isinstance(informative_data_list, list):
+                # Type check is required because mocker would return a mock object that evaluates to
+                # True, confusing this code.
+                continue
+            strategy_timeframe_minutes = timeframe_to_minutes(self.timeframe)
+            for informative_data in informative_data_list:
+                if timeframe_to_minutes(informative_data.timeframe) < strategy_timeframe_minutes:
+                    raise OperationalException(
+                        "Informative timeframe must be equal or higher than strategy timeframe!"
+                    )
+                if not informative_data.candle_type:
+                    informative_data.candle_type = config["candle_type_def"]
+                self._ft_informative.append((informative_data, cls_method))
+
+    def load_freqAI_model(self) -> None:
+        if self.config.get("freqai", {}).get("enabled", False):
+            # Import here to avoid importing this if freqAI is disabled
+            from freqtrade.freqai.utils import download_all_data_for_training
+            from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
+
+            self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
+            self.freqai_info = self.config["freqai"]
+
+            # download the desired data in dry/live
+            if self.config.get("runmode") in (RunMode.DRY_RUN, RunMode.LIVE):
+                logger.info(
+                    "Downloading all training data for all pairs in whitelist and "
+                    "corr_pairlist, this may take a while if the data is not "
+                    "already on disk."
+                )
+                download_all_data_for_training(self.dp, self.config)
+        else:
+            # Gracious failures if freqAI is disabled but "start" is called.
+            class DummyClass:
+                def start(self, *args, **kwargs):
+                    raise OperationalException(
+                        "freqAI is not enabled. "
+                        "Please enable it in your config to use this strategy."
+                    )
+
+                def shutdown(self, *args, **kwargs):
+                    pass
+
+            self.freqai = DummyClass()  # type: ignore
+
+    def ft_bot_start(self, **kwargs) -> None:
         """
-        Populate indicators that will be used in the Buy and Sell strategy
-        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+        Strategy init - runs after dataprovider has been added.
+        Must call bot_start()
+        """
+        self.load_freqAI_model()
+
+        strategy_safe_wrapper(self.bot_start)()
+
+        self.ft_load_hyper_params(self.config.get("runmode") == RunMode.HYPEROPT)
+
+    def ft_bot_cleanup(self) -> None:
+        """
+        Clean up FreqAI and child threads
+        """
+        self.freqai.shutdown()
+
+    @abstractmethod
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populate indicators that will be used in the Buy, Sell, Short, Exit_short strategy
+        :param dataframe: DataFrame with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
         :return: a Dataframe with all mandatory indicators for the strategies
         """
+        return dataframe
 
-    @abstractmethod
-    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Based on TA indicators, populates the buy signal for the given dataframe
+        DEPRECATED - please migrate to populate_entry_trend
         :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
         :return: DataFrame with buy column
-        :return:
         """
+        return dataframe
 
-    @abstractmethod
-    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
+        Based on TA indicators, populates the entry signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with entry columns populated
+        """
+        return self.populate_buy_trend(dataframe, metadata)
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        DEPRECATED - please migrate to populate_exit_trend
         Based on TA indicators, populates the sell signal for the given dataframe
         :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with sell column
+        """
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the exit signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with exit columns populated
+        """
+        return self.populate_sell_trend(dataframe, metadata)
+
+    def bot_start(self, **kwargs) -> None:
+        """
+        Called only once after bot instantiation.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        pass
+
+    def bot_loop_start(self, current_time: datetime, **kwargs) -> None:
+        """
+        Called at the start of the bot iteration (one loop).
+        Might be used to perform pair-independent tasks
+        (e.g. gather some remote resource for comparison)
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        pass
+
+    def check_buy_timeout(
+        self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs
+    ) -> bool:
+        """
+        DEPRECATED: Please use `check_entry_timeout` instead.
+        """
+        return False
+
+    def check_entry_timeout(
+        self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs
+    ) -> bool:
+        """
+        Check entry timeout function callback.
+        This method can be used to override the entry-timeout.
+        It is called whenever a limit entry order has been created,
+        and is not yet fully filled.
+        Configuration options in `unfilledtimeout` will be verified before this,
+        so ensure to set these timeouts high enough.
+
+        When not implemented by a strategy, this simply returns False.
+        :param pair: Pair the trade is for
+        :param trade: Trade object.
+        :param order: Order object.
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the entry order is cancelled.
+        """
+        return self.check_buy_timeout(
+            pair=pair, trade=trade, order=order, current_time=current_time
+        )
+
+    def check_sell_timeout(
+        self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs
+    ) -> bool:
+        """
+        DEPRECATED: Please use `check_exit_timeout` instead.
+        """
+        return False
+
+    def check_exit_timeout(
+        self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs
+    ) -> bool:
+        """
+        Check exit timeout function callback.
+        This method can be used to override the exit-timeout.
+        It is called whenever a limit exit order has been created,
+        and is not yet fully filled.
+        Configuration options in `unfilledtimeout` will be verified before this,
+        so ensure to set these timeouts high enough.
+
+        When not implemented by a strategy, this simply returns False.
+        :param pair: Pair the trade is for
+        :param trade: Trade object.
+        :param order: Order object
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the exit-order is cancelled.
+        """
+        return self.check_sell_timeout(
+            pair=pair, trade=trade, order=order, current_time=current_time
+        )
+
+    def confirm_trade_entry(
+        self,
+        pair: str,
+        order_type: str,
+        amount: float,
+        rate: float,
+        time_in_force: str,
+        current_time: datetime,
+        entry_tag: str | None,
+        side: str,
+        **kwargs,
+    ) -> bool:
+        """
+        Called right before placing a entry order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair that's about to be bought/shorted.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in target (base) currency that's going to be traded.
+        :param rate: Rate that's going to be used when using limit orders
+                     or current rate for market orders.
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param current_time: datetime object, containing the current datetime
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the buy-order is placed on the exchange.
+            False aborts the process
+        """
+        return True
+
+    def confirm_trade_exit(
+        self,
+        pair: str,
+        trade: Trade,
+        order_type: str,
+        amount: float,
+        rate: float,
+        time_in_force: str,
+        exit_reason: str,
+        current_time: datetime,
+        **kwargs,
+    ) -> bool:
+        """
+        Called right before placing a regular exit order.
+        Timing for this function is critical, so avoid doing heavy computations or
+        network requests in this method.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns True (always confirming).
+
+        :param pair: Pair for trade that's about to be exited.
+        :param trade: trade object.
+        :param order_type: Order type (as configured in order_types). usually limit or market.
+        :param amount: Amount in base currency.
+        :param rate: Rate that's going to be used when using limit orders
+                     or current rate for market orders.
+        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param exit_reason: Exit reason.
+            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
+                           'exit_signal', 'force_exit', 'emergency_exit']
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True, then the exit-order is placed on the exchange.
+            False aborts the process
+        """
+        return True
+
+    def order_filled(
+        self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs
+    ) -> None:
+        """
+        Called right after an order fills.
+        Will be called for all order types (entry, exit, stoploss, position adjustment).
+        :param pair: Pair for trade
+        :param trade: trade object.
+        :param order: Order object.
+        :param current_time: datetime object, containing the current datetime
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        pass
+
+    def custom_stoploss(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        after_fill: bool,
+        **kwargs,
+    ) -> float | None:
+        """
+        Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
+        e.g. returning -0.05 would create a stoploss 5% below current_rate.
+        The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns the initial stoploss value.
+        Only called when use_custom_stoploss is set to True.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param after_fill: True if the stoploss is called after the order was filled.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: New stoploss value, relative to the current_rate
+        """
+        return self.stoploss
+
+    def custom_entry_price(
+        self,
+        pair: str,
+        trade: Trade | None,
+        current_time: datetime,
+        proposed_rate: float,
+        entry_tag: str | None,
+        side: str,
+        **kwargs,
+    ) -> float:
+        """
+        Custom entry price logic, returning the new entry price.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns None, orderbook is used to set entry price
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object (None for initial entries).
+        :param current_time: datetime object, containing the current datetime
+        :param proposed_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: New entry price value if provided
+        """
+        return proposed_rate
+
+    def custom_exit_price(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        proposed_rate: float,
+        current_profit: float,
+        exit_tag: str | None,
+        **kwargs,
+    ) -> float:
+        """
+        Custom exit price logic, returning the new exit price.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns None, orderbook is used to set exit price
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param proposed_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param exit_tag: Exit reason.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: New exit price value if provided
+        """
+        return proposed_rate
+
+    def custom_sell(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        **kwargs,
+    ) -> str | bool | None:
+        """
+        DEPRECATED - please use custom_exit instead.
+        Custom exit signal logic indicating that specified position should be sold. Returning a
+        string or True from this method is equal to setting exit signal on a candle at specified
+        time. This method is not called when exit signal is set.
+
+        This method should be overridden to create exit signals that depend on trade parameters. For
+        example you could implement an exit relative to the candle when the trade was opened,
+        or a custom 1:2 risk-reward ROI.
+
+        Custom exit reason max length is 64. Exceeding characters will be removed.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return: To execute exit, return a string with custom exit reason or True. Otherwise return
+        None or False.
+        """
+        return None
+
+    def custom_exit(
+        self,
+        pair: str,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        **kwargs,
+    ) -> str | bool | None:
+        """
+        Custom exit signal logic indicating that specified position should be sold. Returning a
+        string or True from this method is equal to setting exit signal on a candle at specified
+        time. This method is not called when exit signal is set.
+
+        This method should be overridden to create exit signals that depend on trade parameters. For
+        example you could implement an exit relative to the candle when the trade was opened,
+        or a custom 1:2 risk-reward ROI.
+
+        Custom exit reason max length is 64. Exceeding characters will be removed.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return: To execute exit, return a string with custom exit reason or True. Otherwise return
+        None or False.
+        """
+        return self.custom_sell(pair, trade, current_time, current_rate, current_profit, **kwargs)
+
+    def custom_stake_amount(
+        self,
+        pair: str,
+        current_time: datetime,
+        current_rate: float,
+        proposed_stake: float,
+        min_stake: float | None,
+        max_stake: float,
+        leverage: float,
+        entry_tag: str | None,
+        side: str,
+        **kwargs,
+    ) -> float:
+        """
+        Customize stake size for each new trade.
+
+        :param pair: Pair that's currently analyzed
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param proposed_stake: A stake amount proposed by the bot.
+        :param min_stake: Minimal stake size allowed by exchange.
+        :param max_stake: Balance available for trading.
+        :param leverage: Leverage selected for this trade.
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :return: A stake size, which is between min_stake and max_stake.
+        """
+        return proposed_stake
+
+    def adjust_trade_position(
+        self,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        min_stake: float | None,
+        max_stake: float,
+        current_entry_rate: float,
+        current_exit_rate: float,
+        current_entry_profit: float,
+        current_exit_profit: float,
+        **kwargs,
+    ) -> float | None | tuple[float | None, str | None]:
+        """
+        Custom trade adjustment logic, returning the stake amount that a trade should be
+        increased or decreased.
+        This means extra entry or exit orders with additional fees.
+        Only called when `position_adjustment_enable` is set to True.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns None
+
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Current entry rate (same as current_entry_profit)
+        :param current_profit: Current profit (as ratio), calculated based on current_rate
+                               (same as current_entry_profit).
+        :param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
+        :param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
+        :param current_entry_rate: Current rate using entry pricing.
+        :param current_exit_rate: Current rate using exit pricing.
+        :param current_entry_profit: Current profit using entry pricing.
+        :param current_exit_profit: Current profit using exit pricing.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: Stake amount to adjust your trade,
+                       Positive values to increase position, Negative values to decrease position.
+                       Return None for no action.
+                       Optionally, return a tuple with a 2nd element with an order reason
+        """
+        return None
+
+    def adjust_entry_price(
+        self,
+        trade: Trade,
+        order: Order | None,
+        pair: str,
+        current_time: datetime,
+        proposed_rate: float,
+        current_order_rate: float,
+        entry_tag: str | None,
+        side: str,
+        **kwargs,
+    ) -> float | None:
+        """
+        Entry price re-adjustment logic, returning the user desired limit price.
+        This only executes when a order was already placed, still open (unfilled fully or partially)
+        and not timed out on subsequent candles after entry trigger.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-callbacks/
+
+        When not implemented by a strategy, returns current_order_rate as default.
+        If current_order_rate is returned then the existing order is maintained.
+        If None is returned then order gets canceled but not replaced by a new one.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: Trade object.
+        :param order: Order object
+        :param current_time: datetime object, containing the current datetime
+        :param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
+        :param current_order_rate: Rate of the existing order in place.
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float or None: New entry price value if provided
+
+        """
+        return current_order_rate
+
+    def adjust_exit_price(
+        self,
+        trade: Trade,
+        order: Order | None,
+        pair: str,
+        current_time: datetime,
+        proposed_rate: float,
+        current_order_rate: float,
+        entry_tag: str | None,
+        side: str,
+        **kwargs,
+    ) -> float | None:
+        """
+        Exit price re-adjustment logic, returning the user desired limit price.
+        This only executes when a order was already placed, still open (unfilled fully or partially)
+        and not timed out on subsequent candles after entry trigger.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-callbacks/
+
+        When not implemented by a strategy, returns current_order_rate as default.
+        If current_order_rate is returned then the existing order is maintained.
+        If None is returned then order gets canceled but not replaced by a new one.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: Trade object.
+        :param order: Order object
+        :param current_time: datetime object, containing the current datetime
+        :param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
+        :param current_order_rate: Rate of the existing order in place.
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float or None: New exit price value if provided
+
+        """
+        return current_order_rate
+
+    def adjust_order_price(
+        self,
+        trade: Trade,
+        order: Order | None,
+        pair: str,
+        current_time: datetime,
+        proposed_rate: float,
+        current_order_rate: float,
+        entry_tag: str | None,
+        side: str,
+        is_entry: bool,
+        **kwargs,
+    ) -> float | None:
+        """
+        Exit and entry order price re-adjustment logic, returning the user desired limit price.
+        This only executes when a order was already placed, still open (unfilled fully or partially)
+        and not timed out on subsequent candles after entry trigger.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-callbacks/
+
+        When not implemented by a strategy, returns current_order_rate as default.
+        If current_order_rate is returned then the existing order is maintained.
+        If None is returned then order gets canceled but not replaced by a new one.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: Trade object.
+        :param order: Order object
+        :param current_time: datetime object, containing the current datetime
+        :param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
+        :param current_order_rate: Rate of the existing order in place.
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :param is_entry: True if the order is an entry order, False if it's an exit order.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float or None: New entry price value if provided
+        """
+        if is_entry:
+            return self.adjust_entry_price(
+                trade=trade,
+                order=order,
+                pair=pair,
+                current_time=current_time,
+                proposed_rate=proposed_rate,
+                current_order_rate=current_order_rate,
+                entry_tag=entry_tag,
+                side=side,
+                **kwargs,
+            )
+        else:
+            return self.adjust_exit_price(
+                trade=trade,
+                order=order,
+                pair=pair,
+                current_time=current_time,
+                proposed_rate=proposed_rate,
+                current_order_rate=current_order_rate,
+                entry_tag=entry_tag,
+                side=side,
+                **kwargs,
+            )
+
+    def leverage(
+        self,
+        pair: str,
+        current_time: datetime,
+        current_rate: float,
+        proposed_leverage: float,
+        max_leverage: float,
+        entry_tag: str | None,
+        side: str,
+        **kwargs,
+    ) -> float:
+        """
+        Customize leverage for each new trade. This method is only called in futures mode.
+
+        :param pair: Pair that's currently analyzed
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+        :param proposed_leverage: A leverage proposed by the bot.
+        :param max_leverage: Max leverage allowed on this pair
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :return: A leverage amount, which is between 1.0 and max_leverage.
+        """
+        return 1.0
+
+    def informative_pairs(self) -> ListPairsWithTimeframes:
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def version(self) -> str | None:
+        """
+        Returns version of the strategy.
+        """
+        return None
+
+    def populate_any_indicators(
+        self,
+        pair: str,
+        df: DataFrame,
+        tf: str,
+        informative: DataFrame | None = None,
+        set_generalized_indicators: bool = False,
+    ) -> DataFrame:
+        """
+        DEPRECATED - USE FEATURE ENGINEERING FUNCTIONS INSTEAD
+        Function designed to automatically generate, name and merge features
+        from user indicated timeframes in the configuration file. User can add
+        additional features here, but must follow the naming convention.
+        This method is *only* used in FreqaiDataKitchen class and therefore
+        it is only called if FreqAI is active.
+        :param pair: pair to be used as informative
+        :param df: strategy dataframe which will receive merges from informatives
+        :param tf: timeframe of the dataframe which will modify the feature names
+        :param informative: the dataframe associated with the informative pair
+        """
+        return df
+
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
+    ) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param dataframe: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        :param metadata: metadata of current pair
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+        return dataframe
+
+    def feature_engineering_expand_basic(
+        self, dataframe: DataFrame, metadata: dict, **kwargs
+    ) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param dataframe: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        return dataframe
+
+    def feature_engineering_standard(
+        self, dataframe: DataFrame, metadata: dict, **kwargs
+    ) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param dataframe: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param dataframe: strategy dataframe which will receive the targets
+        :param metadata: metadata of current pair
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        return dataframe
+
+    ###
+    # END - Intended to be overridden by strategy
+    ###
+
+    _ft_stop_uses_after_fill = False
+
+    def _adjust_trade_position_internal(
+        self,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        min_stake: float | None,
+        max_stake: float,
+        current_entry_rate: float,
+        current_exit_rate: float,
+        current_entry_profit: float,
+        current_exit_profit: float,
+        **kwargs,
+    ) -> tuple[float | None, str]:
+        """
+        wrapper around adjust_trade_position to handle the return value
+        """
+        resp = strategy_safe_wrapper(
+            self.adjust_trade_position, default_retval=(None, ""), supress_error=True
+        )(
+            trade=trade,
+            current_time=current_time,
+            current_rate=current_rate,
+            current_profit=current_profit,
+            min_stake=min_stake,
+            max_stake=max_stake,
+            current_entry_rate=current_entry_rate,
+            current_exit_rate=current_exit_rate,
+            current_entry_profit=current_entry_profit,
+            current_exit_profit=current_exit_profit,
+            **kwargs,
+        )
+        order_tag = ""
+        if isinstance(resp, tuple):
+            if len(resp) >= 1:
+                stake_amount = resp[0]
+            if len(resp) > 1:
+                order_tag = resp[1] or ""
+        else:
+            stake_amount = resp
+        return stake_amount, order_tag
+
+    def __informative_pairs_freqai(self) -> ListPairsWithTimeframes:
+        """
+        Create informative-pairs needed for FreqAI
+        """
+        if self.config.get("freqai", {}).get("enabled", False):
+            whitelist_pairs = self.dp.current_whitelist()
+            candle_type = self.config.get("candle_type_def", CandleType.SPOT)
+            corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
+            informative_pairs = []
+            for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
+                for pair in set(whitelist_pairs + corr_pairs):
+                    informative_pairs.append((pair, tf, candle_type))
+            return informative_pairs
+
+        return []
+
+    def gather_informative_pairs(self) -> ListPairsWithTimeframes:
+        """
+        Internal method which gathers all informative pairs (user or automatically defined).
+        """
+        informative_pairs = self.informative_pairs()
+        # Compatibility code for 2 tuple informative pairs
+        informative_pairs = [
+            (
+                p[0],
+                p[1],
+                (
+                    CandleType.from_string(p[2])
+                    if len(p) > 2 and p[2] != ""
+                    else self.config.get("candle_type_def", CandleType.SPOT)
+                ),
+            )
+            for p in informative_pairs
+        ]
+        for inf_data, _ in self._ft_informative:
+            # Get default candle type if not provided explicitly.
+            candle_type = (
+                inf_data.candle_type
+                if inf_data.candle_type
+                else self.config.get("candle_type_def", CandleType.SPOT)
+            )
+            if inf_data.asset:
+                if any(s in inf_data.asset for s in ("{BASE}", "{base}")):
+                    for pair in self.dp.current_whitelist():
+                        pair_tf = (
+                            _format_pair_name(self.config, inf_data.asset, self.dp.market(pair)),
+                            inf_data.timeframe,
+                            candle_type,
+                        )
+                        informative_pairs.append(pair_tf)
+
+                else:
+                    pair_tf = (
+                        _format_pair_name(self.config, inf_data.asset),
+                        inf_data.timeframe,
+                        candle_type,
+                    )
+                    informative_pairs.append(pair_tf)
+            else:
+                for pair in self.dp.current_whitelist():
+                    informative_pairs.append((pair, inf_data.timeframe, candle_type))
+        informative_pairs.extend(self.__informative_pairs_freqai())
+        return list(set(informative_pairs))
+
+    def get_strategy_name(self) -> str:
+        """
+        Returns strategy class name
+        """
+        return self.__class__.__name__
+
+    def lock_pair(
+        self, pair: str, until: datetime, reason: str | None = None, side: str = "*"
+    ) -> None:
+        """
+        Locks pair until a given timestamp happens.
+        Locked pairs are not analyzed, and are prevented from opening new trades.
+        Locks can only count up (allowing users to lock pairs for a longer period of time).
+        To remove a lock from a pair, use `unlock_pair()`
+        :param pair: Pair to lock
+        :param until: datetime in UTC until the pair should be blocked from opening new trades.
+                Needs to be timezone aware `datetime.now(timezone.utc)`
+        :param reason: Optional string explaining why the pair was locked.
+        :param side: Side to check, can be long, short or '*'
+        """
+        PairLocks.lock_pair(pair, until, reason, side=side)
+
+    def unlock_pair(self, pair: str) -> None:
+        """
+        Unlocks a pair previously locked using lock_pair.
+        Not used by freqtrade itself, but intended to be used if users lock pairs
+        manually from within the strategy, to allow an easy way to unlock pairs.
+        :param pair: Unlock pair to allow trading again
+        """
+        PairLocks.unlock_pair(pair, datetime.now(timezone.utc))
+
+    def unlock_reason(self, reason: str) -> None:
+        """
+        Unlocks all pairs previously locked using lock_pair with specified reason.
+        Not used by freqtrade itself, but intended to be used if users lock pairs
+        manually from within the strategy, to allow an easy way to unlock pairs.
+        :param reason: Unlock pairs to allow trading again
+        """
+        PairLocks.unlock_reason(reason, datetime.now(timezone.utc))
+
+    def is_pair_locked(
+        self, pair: str, *, candle_date: datetime | None = None, side: str = "*"
+    ) -> bool:
+        """
+        Checks if a pair is currently locked
+        The 2nd, optional parameter ensures that locks are applied until the new candle arrives,
+        and not stop at 14:00:00 - while the next candle arrives at 14:00:02 leaving a gap
+        of 2 seconds for an entry order to happen on an old signal.
+        :param pair: "Pair to check"
+        :param candle_date: Date of the last candle. Optional, defaults to current date
+        :param side: Side to check, can be long, short or '*'
+        :returns: locking state of the pair in question.
+        """
+
+        if not candle_date:
+            # Simple call ...
+            return PairLocks.is_pair_locked(pair, side=side)
+        else:
+            lock_time = timeframe_to_next_date(self.timeframe, candle_date)
+            return PairLocks.is_pair_locked(pair, lock_time, side=side)
+
+    def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Parses the given candle (OHLCV) data and returns a populated DataFrame
+        add several TA indicators and entry order signal to it
+        Should only be used in live.
+        :param dataframe: Dataframe containing data from exchange
+        :param metadata: Metadata dictionary with additional data (e.g. 'pair')
+        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
+        """
+        logger.debug("TA Analysis Launched")
+        dataframe = self.advise_indicators(dataframe, metadata)
+        dataframe = self.advise_entry(dataframe, metadata)
+        dataframe = self.advise_exit(dataframe, metadata)
+        logger.debug("TA Analysis Ended")
+        return dataframe
+
+    def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Parses the given candle (OHLCV) data and returns a populated DataFrame
+        add several TA indicators and buy signal to it
+        WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
+        :param dataframe: Dataframe containing data from exchange
+        :param metadata: Metadata dictionary with additional data (e.g. 'pair')
+        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
+        """
+        pair = str(metadata.get("pair"))
+
+        new_candle = self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]["date"]
+        # Test if seen this pair and last candle before.
+        # always run if process_only_new_candles is set to false
+        if not self.process_only_new_candles or new_candle:
+            # Defs that only make change on new candle data.
+            dataframe = self.analyze_ticker(dataframe, metadata)
+
+            self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]["date"]
+
+            candle_type = self.config.get("candle_type_def", CandleType.SPOT)
+            self.dp._set_cached_df(pair, self.timeframe, dataframe, candle_type=candle_type)
+            self.dp._emit_df((pair, self.timeframe, candle_type), dataframe, new_candle)
+
+        else:
+            logger.debug("Skipping TA Analysis for already analyzed candle")
+            dataframe = remove_entry_exit_signals(dataframe)
+
+        logger.debug("Loop Analysis Launched")
+
+        return dataframe
+
+    def analyze_pair(self, pair: str) -> None:
+        """
+        Fetch data for this pair from dataprovider and analyze.
+        Stores the dataframe into the dataprovider.
+        The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`.
+        :param pair: Pair to analyze.
+        """
+        dataframe = self.dp.ohlcv(
+            pair, self.timeframe, candle_type=self.config.get("candle_type_def", CandleType.SPOT)
+        )
+        if not isinstance(dataframe, DataFrame) or dataframe.empty:
+            logger.warning("Empty candle (OHLCV) data for pair %s", pair)
+            return
+
+        try:
+            df_len, df_close, df_date = self.preserve_df(dataframe)
+
+            dataframe = strategy_safe_wrapper(self._analyze_ticker_internal, message="")(
+                dataframe, {"pair": pair}
+            )
+
+            self.assert_df(dataframe, df_len, df_close, df_date)
+        except StrategyError as error:
+            logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
+            return
+
+        if dataframe.empty:
+            logger.warning("Empty dataframe for pair %s", pair)
+            return
+
+    def analyze(self, pairs: list[str]) -> None:
+        """
+        Analyze all pairs using analyze_pair().
+        :param pairs: List of pairs to analyze
+        """
+        for pair in pairs:
+            self.analyze_pair(pair)
+
+    @staticmethod
+    def preserve_df(dataframe: DataFrame) -> tuple[int, float, datetime]:
+        """keep some data for dataframes"""
+        return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
+
+    def assert_df(self, dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
+        """
+        Ensure dataframe (length, last candle) was not modified, and has all elements we need.
+        """
+        message_template = "Dataframe returned from strategy has mismatching {}."
+        message = ""
+        if dataframe is None:
+            message = "No dataframe returned (return statement missing?)."
+        elif df_len != len(dataframe):
+            message = message_template.format("length")
+        elif df_close != dataframe["close"].iloc[-1]:
+            message = message_template.format("last close price")
+        elif df_date != dataframe["date"].iloc[-1]:
+            message = message_template.format("last date")
+        if message:
+            if self.disable_dataframe_checks:
+                logger.warning(message)
+            else:
+                raise StrategyError(message)
+
+    def get_latest_candle(
+        self,
+        pair: str,
+        timeframe: str,
+        dataframe: DataFrame,
+    ) -> tuple[DataFrame | None, datetime | None]:
+        """
+        Calculates current signal based based on the entry order or exit order
+        columns of the dataframe.
+        Used by Bot to get the signal to enter, or exit
+        :param pair: pair in format ANT/BTC
+        :param timeframe: timeframe to use
+        :param dataframe: Analyzed dataframe to get signal from.
+        :return: (None, None) or (Dataframe, latest_date) - corresponding to the last candle
+        """
+        if not isinstance(dataframe, DataFrame) or dataframe.empty:
+            logger.warning(f"Empty candle (OHLCV) data for pair {pair}")
+            return None, None
+
+        try:
+            latest_date_pd = dataframe["date"].max()
+            latest = dataframe.loc[dataframe["date"] == latest_date_pd].iloc[-1]
+        except Exception as e:
+            logger.warning(f"Unable to get latest candle (OHLCV) data for pair {pair} - {e}")
+            return None, None
+        # Explicitly convert to datetime object to ensure the below comparison does not fail
+        latest_date: datetime = latest_date_pd.to_pydatetime()
+
+        # Check if dataframe is out of date
+        timeframe_minutes = timeframe_to_minutes(timeframe)
+        offset = self.config.get("exchange", {}).get("outdated_offset", 5)
+        if latest_date < (dt_now() - timedelta(minutes=timeframe_minutes * 2 + offset)):
+            logger.warning(
+                "Outdated history for pair %s. Last tick is %s minutes old",
+                pair,
+                int((dt_now() - latest_date).total_seconds() // 60),
+            )
+            return None, None
+        return latest, latest_date
+
+    def get_exit_signal(
+        self, pair: str, timeframe: str, dataframe: DataFrame, is_short: bool | None = None
+    ) -> tuple[bool, bool, str | None]:
+        """
+        Calculates current exit signal based based on the dataframe
+        columns of the dataframe.
+        Used by Bot to get the signal to exit.
+        depending on is_short, looks at "short" or "long" columns.
+        :param pair: pair in format ANT/BTC
+        :param timeframe: timeframe to use
+        :param dataframe: Analyzed dataframe to get signal from.
+        :param is_short: Indicating existing trade direction.
+        :return: (enter, exit) A bool-tuple with enter / exit values.
+        """
+        latest, _latest_date = self.get_latest_candle(pair, timeframe, dataframe)
+        if latest is None:
+            return False, False, None
+
+        if is_short:
+            enter = latest.get(SignalType.ENTER_SHORT.value, 0) == 1
+            exit_ = latest.get(SignalType.EXIT_SHORT.value, 0) == 1
+
+        else:
+            enter = latest.get(SignalType.ENTER_LONG.value, 0) == 1
+            exit_ = latest.get(SignalType.EXIT_LONG.value, 0) == 1
+        exit_tag = latest.get(SignalTagType.EXIT_TAG.value, None)
+        # Tags can be None, which does not resolve to False.
+        exit_tag = exit_tag if isinstance(exit_tag, str) and exit_tag != "nan" else None
+
+        logger.debug(f"exit-trigger: {latest['date']} (pair={pair}) enter={enter} exit={exit_}")
+
+        return enter, exit_, exit_tag
+
+    def get_entry_signal(
+        self,
+        pair: str,
+        timeframe: str,
+        dataframe: DataFrame,
+    ) -> tuple[SignalDirection | None, str | None]:
+        """
+        Calculates current entry signal based based on the dataframe signals
+        columns of the dataframe.
+        Used by Bot to get the signal to enter trades.
+        :param pair: pair in format ANT/BTC
+        :param timeframe: timeframe to use
+        :param dataframe: Analyzed dataframe to get signal from.
+        :return: (SignalDirection, entry_tag)
+        """
+        latest, latest_date = self.get_latest_candle(pair, timeframe, dataframe)
+        if latest is None or latest_date is None:
+            return None, None
+
+        enter_long = latest.get(SignalType.ENTER_LONG.value, 0) == 1
+        exit_long = latest.get(SignalType.EXIT_LONG.value, 0) == 1
+        enter_short = latest.get(SignalType.ENTER_SHORT.value, 0) == 1
+        exit_short = latest.get(SignalType.EXIT_SHORT.value, 0) == 1
+
+        enter_signal: SignalDirection | None = None
+        enter_tag: str | None = None
+        if enter_long == 1 and not any([exit_long, enter_short]):
+            enter_signal = SignalDirection.LONG
+            enter_tag = latest.get(SignalTagType.ENTER_TAG.value, None)
+        if (
+            self.config.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT
+            and self.can_short
+            and enter_short == 1
+            and not any([exit_short, enter_long])
+        ):
+            enter_signal = SignalDirection.SHORT
+            enter_tag = latest.get(SignalTagType.ENTER_TAG.value, None)
+
+        enter_tag = enter_tag if isinstance(enter_tag, str) and enter_tag != "nan" else None
+
+        timeframe_seconds = timeframe_to_seconds(timeframe)
+
+        if self.ignore_expired_candle(
+            latest_date=latest_date,
+            current_time=dt_now(),
+            timeframe_seconds=timeframe_seconds,
+            enter=bool(enter_signal),
+        ):
+            return None, enter_tag
+
+        logger.debug(
+            f"entry trigger: {latest['date']} (pair={pair}) "
+            f"enter={enter_long} enter_tag_value={enter_tag}"
+        )
+        return enter_signal, enter_tag
+
+    def ignore_expired_candle(
+        self, latest_date: datetime, current_time: datetime, timeframe_seconds: int, enter: bool
+    ):
+        if self.ignore_buying_expired_candle_after and enter:
+            time_delta = current_time - (latest_date + timedelta(seconds=timeframe_seconds))
+            return time_delta.total_seconds() > self.ignore_buying_expired_candle_after
+        else:
+            return False
+
+    def should_exit(
+        self,
+        trade: Trade,
+        rate: float,
+        current_time: datetime,
+        *,
+        enter: bool,
+        exit_: bool,
+        low: float | None = None,
+        high: float | None = None,
+        force_stoploss: float = 0,
+    ) -> list[ExitCheckTuple]:
+        """
+        This function evaluates if one of the conditions required to trigger an exit order
+        has been reached, which can either be a stop-loss, ROI or exit-signal.
+        :param low: Only used during backtesting to simulate (long)stoploss/(short)ROI
+        :param high: Only used during backtesting, to simulate (short)stoploss/(long)ROI
+        :param force_stoploss: Externally provided stoploss
+        :return: List of exit reasons - or empty list.
+        """
+        exits: list[ExitCheckTuple] = []
+        current_rate = rate
+        current_profit = trade.calc_profit_ratio(current_rate)
+        current_profit_best = current_profit
+        if low is not None or high is not None:
+            # Set current rate to high for backtesting ROI exits
+            current_rate_best = (low if trade.is_short else high) or rate
+            current_profit_best = trade.calc_profit_ratio(current_rate_best)
+
+        trade.adjust_min_max_rates(high or current_rate, low or current_rate)
+
+        stoplossflag = self.ft_stoploss_reached(
+            current_rate=current_rate,
+            trade=trade,
+            current_time=current_time,
+            current_profit=current_profit,
+            force_stoploss=force_stoploss,
+            low=low,
+            high=high,
+        )
+
+        # if enter signal and ignore_roi is set, we don't need to evaluate min_roi.
+        roi_reached = not (enter and self.ignore_roi_if_entry_signal) and self.min_roi_reached(
+            trade=trade, current_profit=current_profit_best, current_time=current_time
+        )
+
+        exit_signal = ExitType.NONE
+        custom_reason = ""
+
+        if self.use_exit_signal:
+            if exit_ and not enter:
+                exit_signal = ExitType.EXIT_SIGNAL
+            else:
+                reason_cust = strategy_safe_wrapper(self.custom_exit, default_retval=False)(
+                    pair=trade.pair,
+                    trade=trade,
+                    current_time=current_time,
+                    current_rate=current_rate,
+                    current_profit=current_profit,
+                )
+                if reason_cust:
+                    exit_signal = ExitType.CUSTOM_EXIT
+                    if isinstance(reason_cust, str):
+                        custom_reason = reason_cust
+                        if len(reason_cust) > CUSTOM_TAG_MAX_LENGTH:
+                            logger.warning(
+                                f"Custom exit reason returned from "
+                                f"custom_exit is too long and was trimmed"
+                                f"to {CUSTOM_TAG_MAX_LENGTH} characters."
+                            )
+                            custom_reason = reason_cust[:CUSTOM_TAG_MAX_LENGTH]
+                    else:
+                        custom_reason = ""
+            if exit_signal == ExitType.CUSTOM_EXIT or (
+                exit_signal == ExitType.EXIT_SIGNAL
+                and (not self.exit_profit_only or current_profit > self.exit_profit_offset)
+            ):
+                logger.debug(
+                    f"{trade.pair} - Sell signal received. "
+                    f"exit_type=ExitType.{exit_signal.name}"
+                    + (f", custom_reason={custom_reason}" if custom_reason else "")
+                )
+                exits.append(ExitCheckTuple(exit_type=exit_signal, exit_reason=custom_reason))
+
+        # Sequence:
+        # Exit-signal
+        # Stoploss
+        # ROI
+        # Trailing stoploss
+
+        if stoplossflag.exit_type in (ExitType.STOP_LOSS, ExitType.LIQUIDATION):
+            logger.debug(f"{trade.pair} - Stoploss hit. exit_type={stoplossflag.exit_type}")
+            exits.append(stoplossflag)
+
+        if roi_reached:
+            logger.debug(f"{trade.pair} - Required profit reached. exit_type=ExitType.ROI")
+            exits.append(ExitCheckTuple(exit_type=ExitType.ROI))
+
+        if stoplossflag.exit_type == ExitType.TRAILING_STOP_LOSS:
+            logger.debug(f"{trade.pair} - Trailing stoploss hit.")
+            exits.append(stoplossflag)
+
+        return exits
+
+    def ft_stoploss_adjust(
+        self,
+        current_rate: float,
+        trade: Trade,
+        current_time: datetime,
+        current_profit: float,
+        force_stoploss: float,
+        low: float | None = None,
+        high: float | None = None,
+        after_fill: bool = False,
+    ) -> None:
+        """
+        Adjust stop-loss dynamically if configured to do so.
+        :param current_profit: current profit as ratio
+        :param low: Low value of this candle, only set in backtesting
+        :param high: High value of this candle, only set in backtesting
+        """
+        if after_fill and not self._ft_stop_uses_after_fill:
+            # Skip if the strategy doesn't support after fill.
+            return
+
+        stop_loss_value = force_stoploss if force_stoploss else self.stoploss
+
+        # Initiate stoploss with open_rate. Does nothing if stoploss is already set.
+        trade.adjust_stop_loss(trade.open_rate, stop_loss_value, initial=True)
+
+        dir_correct = (
+            trade.stop_loss < (low or current_rate)
+            if not trade.is_short
+            else trade.stop_loss > (high or current_rate)
+        )
+
+        # Make sure current_profit is calculated using high for backtesting.
+        bound = low if trade.is_short else high
+        bound_profit = current_profit if not bound else trade.calc_profit_ratio(bound)
+        if self.use_custom_stoploss and dir_correct:
+            stop_loss_value_custom = strategy_safe_wrapper(
+                self.custom_stoploss, default_retval=None, supress_error=True
+            )(
+                pair=trade.pair,
+                trade=trade,
+                current_time=current_time,
+                current_rate=(bound or current_rate),
+                current_profit=bound_profit,
+                after_fill=after_fill,
+            )
+            # Sanity check - error cases will return None
+            if stop_loss_value_custom and not (
+                isnan(stop_loss_value_custom) or isinf(stop_loss_value_custom)
+            ):
+                stop_loss_value = stop_loss_value_custom
+                trade.adjust_stop_loss(
+                    bound or current_rate, stop_loss_value, allow_refresh=after_fill
+                )
+            else:
+                logger.debug("CustomStoploss function did not return valid stoploss")
+
+        if self.trailing_stop and dir_correct:
+            # trailing stoploss handling
+            sl_offset = self.trailing_stop_positive_offset
+            # Make sure current_profit is calculated using high for backtesting.
+
+            # Don't update stoploss if trailing_only_offset_is_reached is true.
+            if not (self.trailing_only_offset_is_reached and bound_profit < sl_offset):
+                # Specific handling for trailing_stop_positive
+                if self.trailing_stop_positive is not None and bound_profit > sl_offset:
+                    stop_loss_value = self.trailing_stop_positive
+                    logger.debug(
+                        f"{trade.pair} - Using positive stoploss: {stop_loss_value} "
+                        f"offset: {sl_offset:.4g} profit: {bound_profit:.2%}"
+                    )
+
+                trade.adjust_stop_loss(bound or current_rate, stop_loss_value)
+
+    def ft_stoploss_reached(
+        self,
+        current_rate: float,
+        trade: Trade,
+        current_time: datetime,
+        current_profit: float,
+        force_stoploss: float,
+        low: float | None = None,
+        high: float | None = None,
+    ) -> ExitCheckTuple:
+        """
+        Based on current profit of the trade and configured (trailing) stoploss,
+        decides to exit or not
+        :param current_profit: current profit as ratio
+        :param low: Low value of this candle, only set in backtesting
+        :param high: High value of this candle, only set in backtesting
+        """
+        self.ft_stoploss_adjust(
+            current_rate, trade, current_time, current_profit, force_stoploss, low, high
+        )
+
+        sl_higher_long = trade.stop_loss >= (low or current_rate) and not trade.is_short
+        sl_lower_short = trade.stop_loss <= (high or current_rate) and trade.is_short
+        liq_higher_long = (
+            trade.liquidation_price
+            and trade.liquidation_price >= (low or current_rate)
+            and not trade.is_short
+        )
+        liq_lower_short = (
+            trade.liquidation_price
+            and trade.liquidation_price <= (high or current_rate)
+            and trade.is_short
+        )
+
+        # evaluate if the stoploss was hit if stoploss is not on exchange
+        # in Dry-Run, this handles stoploss logic as well, as the logic will not be different to
+        # regular stoploss handling.
+        if (sl_higher_long or sl_lower_short) and (
+            not self.order_types.get("stoploss_on_exchange") or self.config["dry_run"]
+        ):
+            exit_type = ExitType.STOP_LOSS
+
+            # If initial stoploss is not the same as current one then it is trailing.
+            if trade.is_stop_loss_trailing:
+                exit_type = ExitType.TRAILING_STOP_LOSS
+                logger.debug(
+                    f"{trade.pair} - HIT STOP: current price at "
+                    f"{((high if trade.is_short else low) or current_rate):.6f}, "
+                    f"stoploss is {trade.stop_loss:.6f}, "
+                    f"initial stoploss was at {trade.initial_stop_loss:.6f}, "
+                    f"trade opened at {trade.open_rate:.6f}"
+                )
+
+            return ExitCheckTuple(exit_type=exit_type)
+
+        if liq_higher_long or liq_lower_short:
+            logger.debug(f"{trade.pair} - Liquidation price hit. exit_type=ExitType.LIQUIDATION")
+            return ExitCheckTuple(exit_type=ExitType.LIQUIDATION)
+
+        return ExitCheckTuple(exit_type=ExitType.NONE)
+
+    def min_roi_reached_entry(self, trade_dur: int) -> tuple[int | None, float | None]:
+        """
+        Based on trade duration defines the ROI entry that may have been reached.
+        :param trade_dur: trade duration in minutes
+        :return: minimal ROI entry value or None if none proper ROI entry was found.
+        """
+        # Get highest entry in ROI dict where key <= trade-duration
+        roi_list = [x for x in self.minimal_roi.keys() if x <= trade_dur]
+        if not roi_list:
+            return None, None
+        roi_entry = max(roi_list)
+        return roi_entry, self.minimal_roi[roi_entry]
+
+    def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
+        """
+        Based on trade duration, current profit of the trade and ROI configuration,
+        decides whether bot should exit.
+        :param current_profit: current profit as ratio
+        :return: True if bot should exit at current rate
+        """
+        # Check if time matches and current rate is above threshold
+        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
+        _, roi = self.min_roi_reached_entry(trade_dur)
+        if roi is None:
+            return False
+        else:
+            return current_profit > roi
+
+    def ft_check_timed_out(self, trade: Trade, order: Order, current_time: datetime) -> bool:
+        """
+        FT Internal method.
+        Check if timeout is active, and if the order is still open and timed out
+        """
+        side = "entry" if order.ft_order_side == trade.entry_side else "exit"
+
+        timeout = self.config.get("unfilledtimeout", {}).get(side)
+        if timeout is not None:
+            timeout_unit = self.config.get("unfilledtimeout", {}).get("unit", "minutes")
+            timeout_kwargs = {timeout_unit: -timeout}
+            timeout_threshold = current_time + timedelta(**timeout_kwargs)
+            timedout = order.status == "open" and order.order_date_utc < timeout_threshold
+            if timedout:
+                return True
+        time_method = (
+            self.check_exit_timeout
+            if order.ft_order_side == trade.exit_side
+            else self.check_entry_timeout
+        )
+
+        return strategy_safe_wrapper(time_method, default_retval=False)(
+            pair=trade.pair, trade=trade, order=order, current_time=current_time
+        )
+
+    def advise_all_indicators(self, data: dict[str, DataFrame]) -> dict[str, DataFrame]:
+        """
+        Populates indicators for given candle (OHLCV) data (for multiple pairs)
+        Does not run advise_entry or advise_exit!
+        Used by optimize operations only, not during dry / live runs.
+        Using .copy() to get a fresh copy of the dataframe for every strategy run.
+        Also copy on output to avoid PerformanceWarnings pandas 1.3.0 started to show.
+        Has positive effects on memory usage for whatever reason - also when
+        using only one strategy.
+        """
+        return {
+            pair: self.advise_indicators(pair_data.copy(), {"pair": pair}).copy()
+            for pair, pair_data in data.items()
+        }
+
+    def ft_advise_signals(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Call advise_entry and advise_exit and return the resulting dataframe.
+        :param dataframe: Dataframe containing data from exchange, as well as pre-calculated
+                          indicators
+        :param metadata: Metadata dictionary with additional data (e.g. 'pair')
+        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
+
+        """
+
+        dataframe = self.advise_entry(dataframe, metadata)
+        dataframe = self.advise_exit(dataframe, metadata)
+        return dataframe
+
+    def _if_enabled_populate_trades(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        use_public_trades = self.config.get("exchange", {}).get("use_public_trades", False)
+        if use_public_trades:
+            pair = metadata["pair"]
+            trades = self.dp.trades(pair=pair, copy=False)
+
+            # TODO: slice trades to size of dataframe for faster backtesting
+            cached_grouped_trades: DataFrame | None = self._cached_grouped_trades_per_pair.get(pair)
+            dataframe, cached_grouped_trades = populate_dataframe_with_trades(
+                cached_grouped_trades, self.config, dataframe, trades
+            )
+
+            # dereference old cache
+            if pair in self._cached_grouped_trades_per_pair:
+                del self._cached_grouped_trades_per_pair[pair]
+            self._cached_grouped_trades_per_pair[pair] = cached_grouped_trades
+
+            logger.debug("Populated dataframe with trades.")
+        return dataframe
+
+    def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Populate indicators that will be used in the Buy, Sell, short, exit_short strategy
+        This method should not be overridden.
+        :param dataframe: Dataframe with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+        logger.debug(f"Populating indicators for pair {metadata.get('pair')}.")
+
+        # call populate_indicators_Nm() which were tagged with @informative decorator.
+        for inf_data, populate_fn in self._ft_informative:
+            dataframe = _create_and_merge_informative_pair(
+                self, dataframe, metadata, inf_data, populate_fn
+            )
+
+        dataframe = self._if_enabled_populate_trades(dataframe, metadata)
+        dataframe = self.populate_indicators(dataframe, metadata)
+        if self.config.get("reduce_df_footprint", False) and self.config.get("runmode") not in [
+            RunMode.DRY_RUN,
+            RunMode.LIVE,
+        ]:
+            dataframe = reduce_dataframe_footprint(dataframe)
+        return dataframe
+
+    def advise_entry(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the entry order signal for the given dataframe
+        This method should not be overridden.
+        :param dataframe: DataFrame
+        :param metadata: Additional information dictionary, with details like the
+            currently traded pair
         :return: DataFrame with buy column
         """
+
+        logger.debug(f"Populating enter signals for pair {metadata.get('pair')}.")
+        # Initialize column to work around Pandas bug #56503.
+        dataframe.loc[:, "enter_tag"] = ""
+        df = self.populate_entry_trend(dataframe, metadata)
+        if "enter_long" not in df.columns:
+            df = df.rename({"buy": "enter_long", "buy_tag": "enter_tag"}, axis="columns")
+
+        return df
+
+    def advise_exit(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the exit order signal for the given dataframe
+        This method should not be overridden.
+        :param dataframe: DataFrame
+        :param metadata: Additional information dictionary, with details like the
+            currently traded pair
+        :return: DataFrame with exit column
+        """
+        # Initialize column to work around Pandas bug #56503.
+        dataframe.loc[:, "exit_tag"] = ""
+        logger.debug(f"Populating exit signals for pair {metadata.get('pair')}.")
+        df = self.populate_exit_trend(dataframe, metadata)
+        if "exit_long" not in df.columns:
+            df = df.rename({"sell": "exit_long"}, axis="columns")
+        return df
diff --git a/freqtrade/strategy/parameters.py b/freqtrade/strategy/parameters.py
new file mode 100644
index 000000000..282e630d0
--- /dev/null
+++ b/freqtrade/strategy/parameters.py
@@ -0,0 +1,369 @@
+"""
+IHyperStrategy interface, hyperoptable Parameter class.
+This module defines a base class for auto-hyperoptable strategies.
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from collections.abc import Sequence
+from contextlib import suppress
+from typing import Any, Union
+
+from freqtrade.enums import HyperoptState
+from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer
+
+
+with suppress(ImportError):
+    from skopt.space import Categorical, Integer, Real
+
+    from freqtrade.optimize.space import SKDecimal
+
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseParameter(ABC):
+    """
+    Defines a parameter that can be optimized by hyperopt.
+    """
+
+    category: str | None
+    default: Any
+    value: Any
+    in_space: bool = False
+    name: str
+
+    def __init__(
+        self,
+        *,
+        default: Any,
+        space: str | None = None,
+        optimize: bool = True,
+        load: bool = True,
+        **kwargs,
+    ):
+        """
+        Initialize hyperopt-optimizable parameter.
+        :param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
+         parameter field
+         name is prefixed with 'buy_' or 'sell_'.
+        :param optimize: Include parameter in hyperopt optimizations.
+        :param load: Load parameter value from {space}_params.
+        :param kwargs: Extra parameters to skopt.space.(Integer|Real|Categorical).
+        """
+        if "name" in kwargs:
+            raise OperationalException(
+                "Name is determined by parameter field name and can not be specified manually."
+            )
+        self.category = space
+        self._space_params = kwargs
+        self.value = default
+        self.optimize = optimize
+        self.load = load
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self.value})"
+
+    @abstractmethod
+    def get_space(self, name: str) -> Union["Integer", "Real", "SKDecimal", "Categorical"]:
+        """
+        Get-space - will be used by Hyperopt to get the hyperopt Space
+        """
+
+    def can_optimize(self):
+        return (
+            self.in_space
+            and self.optimize
+            and HyperoptStateContainer.state != HyperoptState.OPTIMIZE
+        )
+
+
+class NumericParameter(BaseParameter):
+    """Internal parameter used for Numeric purposes"""
+
+    float_or_int = int | float
+    default: float_or_int
+    value: float_or_int
+
+    def __init__(
+        self,
+        low: float_or_int | Sequence[float_or_int],
+        high: float_or_int | None = None,
+        *,
+        default: float_or_int,
+        space: str | None = None,
+        optimize: bool = True,
+        load: bool = True,
+        **kwargs,
+    ):
+        """
+        Initialize hyperopt-optimizable numeric parameter.
+        Cannot be instantiated, but provides the validation for other numeric parameters
+        :param low: Lower end (inclusive) of optimization space or [low, high].
+        :param high: Upper end (inclusive) of optimization space.
+                     Must be none of entire range is passed first parameter.
+        :param default: A default value.
+        :param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
+                      parameter fieldname is prefixed with 'buy_' or 'sell_'.
+        :param optimize: Include parameter in hyperopt optimizations.
+        :param load: Load parameter value from {space}_params.
+        :param kwargs: Extra parameters to skopt.space.*.
+        """
+        if high is not None and isinstance(low, Sequence):
+            raise OperationalException(f"{self.__class__.__name__} space invalid.")
+        if high is None or isinstance(low, Sequence):
+            if not isinstance(low, Sequence) or len(low) != 2:
+                raise OperationalException(f"{self.__class__.__name__} space must be [low, high]")
+            self.low, self.high = low
+        else:
+            self.low = low
+            self.high = high
+
+        super().__init__(default=default, space=space, optimize=optimize, load=load, **kwargs)
+
+
+class IntParameter(NumericParameter):
+    default: int
+    value: int
+    low: int
+    high: int
+
+    def __init__(
+        self,
+        low: int | Sequence[int],
+        high: int | None = None,
+        *,
+        default: int,
+        space: str | None = None,
+        optimize: bool = True,
+        load: bool = True,
+        **kwargs,
+    ):
+        """
+        Initialize hyperopt-optimizable integer parameter.
+        :param low: Lower end (inclusive) of optimization space or [low, high].
+        :param high: Upper end (inclusive) of optimization space.
+                     Must be none of entire range is passed first parameter.
+        :param default: A default value.
+        :param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
+                      parameter fieldname is prefixed with 'buy_' or 'sell_'.
+        :param optimize: Include parameter in hyperopt optimizations.
+        :param load: Load parameter value from {space}_params.
+        :param kwargs: Extra parameters to skopt.space.Integer.
+        """
+
+        super().__init__(
+            low=low, high=high, default=default, space=space, optimize=optimize, load=load, **kwargs
+        )
+
+    def get_space(self, name: str) -> "Integer":
+        """
+        Create skopt optimization space.
+        :param name: A name of parameter field.
+        """
+        return Integer(low=self.low, high=self.high, name=name, **self._space_params)
+
+    @property
+    def range(self):
+        """
+        Get each value in this space as list.
+        Returns a List from low to high (inclusive) in Hyperopt mode.
+        Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
+        calculating 100ds of indicators.
+        """
+        if self.can_optimize():
+            # Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
+            return range(self.low, self.high + 1)
+        else:
+            return range(self.value, self.value + 1)
+
+
+class RealParameter(NumericParameter):
+    default: float
+    value: float
+
+    def __init__(
+        self,
+        low: float | Sequence[float],
+        high: float | None = None,
+        *,
+        default: float,
+        space: str | None = None,
+        optimize: bool = True,
+        load: bool = True,
+        **kwargs,
+    ):
+        """
+        Initialize hyperopt-optimizable floating point parameter with unlimited precision.
+        :param low: Lower end (inclusive) of optimization space or [low, high].
+        :param high: Upper end (inclusive) of optimization space.
+                     Must be none if entire range is passed first parameter.
+        :param default: A default value.
+        :param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
+                      parameter fieldname is prefixed with 'buy_' or 'sell_'.
+        :param optimize: Include parameter in hyperopt optimizations.
+        :param load: Load parameter value from {space}_params.
+        :param kwargs: Extra parameters to skopt.space.Real.
+        """
+        super().__init__(
+            low=low, high=high, default=default, space=space, optimize=optimize, load=load, **kwargs
+        )
+
+    def get_space(self, name: str) -> "Real":
+        """
+        Create skopt optimization space.
+        :param name: A name of parameter field.
+        """
+        return Real(low=self.low, high=self.high, name=name, **self._space_params)
+
+
+class DecimalParameter(NumericParameter):
+    default: float
+    value: float
+
+    def __init__(
+        self,
+        low: float | Sequence[float],
+        high: float | None = None,
+        *,
+        default: float,
+        decimals: int = 3,
+        space: str | None = None,
+        optimize: bool = True,
+        load: bool = True,
+        **kwargs,
+    ):
+        """
+        Initialize hyperopt-optimizable decimal parameter with a limited precision.
+        :param low: Lower end (inclusive) of optimization space or [low, high].
+        :param high: Upper end (inclusive) of optimization space.
+                     Must be none if entire range is passed first parameter.
+        :param default: A default value.
+        :param decimals: A number of decimals after floating point to be included in testing.
+        :param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
+                      parameter fieldname is prefixed with 'buy_' or 'sell_'.
+        :param optimize: Include parameter in hyperopt optimizations.
+        :param load: Load parameter value from {space}_params.
+        :param kwargs: Extra parameters to skopt.space.Integer.
+        """
+        self._decimals = decimals
+        default = round(default, self._decimals)
+
+        super().__init__(
+            low=low, high=high, default=default, space=space, optimize=optimize, load=load, **kwargs
+        )
+
+    def get_space(self, name: str) -> "SKDecimal":
+        """
+        Create skopt optimization space.
+        :param name: A name of parameter field.
+        """
+        return SKDecimal(
+            low=self.low, high=self.high, decimals=self._decimals, name=name, **self._space_params
+        )
+
+    @property
+    def range(self):
+        """
+        Get each value in this space as list.
+        Returns a List from low to high (inclusive) in Hyperopt mode.
+        Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
+        calculating 100ds of indicators.
+        """
+        if self.can_optimize():
+            low = int(self.low * pow(10, self._decimals))
+            high = int(self.high * pow(10, self._decimals)) + 1
+            return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)]
+        else:
+            return [self.value]
+
+
+class CategoricalParameter(BaseParameter):
+    default: Any
+    value: Any
+    opt_range: Sequence[Any]
+
+    def __init__(
+        self,
+        categories: Sequence[Any],
+        *,
+        default: Any | None = None,
+        space: str | None = None,
+        optimize: bool = True,
+        load: bool = True,
+        **kwargs,
+    ):
+        """
+        Initialize hyperopt-optimizable parameter.
+        :param categories: Optimization space, [a, b, ...].
+        :param default: A default value. If not specified, first item from specified space will be
+         used.
+        :param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
+         parameter field
+         name is prefixed with 'buy_' or 'sell_'.
+        :param optimize: Include parameter in hyperopt optimizations.
+        :param load: Load parameter value from {space}_params.
+        :param kwargs: Extra parameters to skopt.space.Categorical.
+        """
+        if len(categories) < 2:
+            raise OperationalException(
+                "CategoricalParameter space must be [a, b, ...] (at least two parameters)"
+            )
+        self.opt_range = categories
+        super().__init__(default=default, space=space, optimize=optimize, load=load, **kwargs)
+
+    def get_space(self, name: str) -> "Categorical":
+        """
+        Create skopt optimization space.
+        :param name: A name of parameter field.
+        """
+        return Categorical(self.opt_range, name=name, **self._space_params)
+
+    @property
+    def range(self):
+        """
+        Get each value in this space as list.
+        Returns a List of categories in Hyperopt mode.
+        Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
+        calculating 100ds of indicators.
+        """
+        if self.can_optimize():
+            return self.opt_range
+        else:
+            return [self.value]
+
+
+class BooleanParameter(CategoricalParameter):
+    def __init__(
+        self,
+        *,
+        default: Any | None = None,
+        space: str | None = None,
+        optimize: bool = True,
+        load: bool = True,
+        **kwargs,
+    ):
+        """
+        Initialize hyperopt-optimizable Boolean Parameter.
+        It's a shortcut to `CategoricalParameter([True, False])`.
+        :param default: A default value. If not specified, first item from specified space will be
+         used.
+        :param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
+         parameter field
+         name is prefixed with 'buy_' or 'sell_'.
+        :param optimize: Include parameter in hyperopt optimizations.
+        :param load: Load parameter value from {space}_params.
+        :param kwargs: Extra parameters to skopt.space.Categorical.
+        """
+
+        categories = [True, False]
+        super().__init__(
+            categories=categories,
+            default=default,
+            space=space,
+            optimize=optimize,
+            load=load,
+            **kwargs,
+        )
diff --git a/freqtrade/strategy/strategy.py b/freqtrade/strategy/strategy.py
deleted file mode 100644
index 27f334d5c..000000000
--- a/freqtrade/strategy/strategy.py
+++ /dev/null
@@ -1,180 +0,0 @@
-# pragma pylint: disable=attribute-defined-outside-init
-
-"""
-This module load custom strategies
-"""
-import os
-import sys
-import logging
-import importlib
-from collections import OrderedDict
-
-from pandas import DataFrame
-from freqtrade.strategy.interface import IStrategy
-
-
-sys.path.insert(0, r'../../user_data/strategies')
-
-
-class Strategy(object):
-    """
-    This class contains all the logic to load custom strategy class
-    """
-    __instance = None
-
-    DEFAULT_STRATEGY = 'default_strategy'
-
-    def __new__(cls) -> object:
-        """
-        Used to create the Singleton
-        :return: Strategy object
-        """
-        if Strategy.__instance is None:
-            Strategy.__instance = object.__new__(cls)
-        return Strategy.__instance
-
-    def init(self, config: dict) -> None:
-        """
-        Load the custom class from config parameter
-        :param config:
-        :return:
-        """
-        self.logger = logging.getLogger(__name__)
-
-        # Verify the strategy is in the configuration, otherwise fallback to the default strategy
-        if 'strategy' in config:
-            strategy = config['strategy']
-        else:
-            strategy = self.DEFAULT_STRATEGY
-
-        # Load the strategy
-        self._load_strategy(strategy)
-
-        # Set attributes
-        # Check if we need to override configuration
-        if 'minimal_roi' in config:
-            self.custom_strategy.minimal_roi = config['minimal_roi']
-            self.logger.info("Override strategy \'minimal_roi\' with value in config file.")
-
-        if 'stoploss' in config:
-            self.custom_strategy.stoploss = config['stoploss']
-            self.logger.info(
-                "Override strategy \'stoploss\' with value in config file: %s.", config['stoploss']
-            )
-
-        if 'ticker_interval' in config:
-            self.custom_strategy.ticker_interval = config['ticker_interval']
-            self.logger.info(
-                "Override strategy \'ticker_interval\' with value in config file: %s.",
-                config['ticker_interval']
-            )
-
-        # Minimal ROI designed for the strategy
-        self.minimal_roi = OrderedDict(sorted(
-            {int(key): value for (key, value) in self.custom_strategy.minimal_roi.items()}.items(),
-            key=lambda tuple: tuple[0]))  # sort after converting to number
-
-        # Optimal stoploss designed for the strategy
-        self.stoploss = float(self.custom_strategy.stoploss)
-
-        self.ticker_interval = self.custom_strategy.ticker_interval
-
-    def _load_strategy(self, strategy_name: str) -> None:
-        """
-        Search and load the custom strategy. If no strategy found, fallback on the default strategy
-        Set the object into self.custom_strategy
-        :param strategy_name: name of the module to import
-        :return: None
-        """
-
-        try:
-            # Start by sanitizing the file name (remove any extensions)
-            strategy_name = self._sanitize_module_name(filename=strategy_name)
-
-            # Search where can be the strategy file
-            path = self._search_strategy(filename=strategy_name)
-
-            # Load the strategy
-            self.custom_strategy = self._load_class(path + strategy_name)
-
-        # Fallback to the default strategy
-        except (ImportError, TypeError) as error:
-            self.logger.error(
-                "Impossible to load Strategy 'user_data/strategies/%s.py'. This file does not exist"
-                " or contains Python code errors",
-                strategy_name
-            )
-            self.logger.error(
-                "The error is:\n%s.",
-                error
-            )
-
-    def _load_class(self, filename: str) -> IStrategy:
-        """
-        Import a strategy as a module
-        :param filename: path to the strategy (path from freqtrade/strategy/)
-        :return: return the strategy class
-        """
-        module = importlib.import_module(filename, __package__)
-        custom_strategy = getattr(module, module.class_name)
-
-        self.logger.info("Load strategy class: %s (%s.py)", module.class_name, filename)
-        return custom_strategy()
-
-    @staticmethod
-    def _sanitize_module_name(filename: str) -> str:
-        """
-        Remove any extension from filename
-        :param filename: filename to sanatize
-        :return: return the filename without extensions
-        """
-        filename = os.path.basename(filename)
-        filename = os.path.splitext(filename)[0]
-        return filename
-
-    @staticmethod
-    def _search_strategy(filename: str) -> str:
-        """
-        Search for the Strategy file in different folder
-        1. search into the user_data/strategies folder
-        2. search into the freqtrade/strategy folder
-        3. if nothing found, return None
-        :param strategy_name: module name to search
-        :return: module path where is the strategy
-        """
-        pwd = os.path.dirname(os.path.realpath(__file__)) + '/'
-        user_data = os.path.join(pwd, '..', '..', 'user_data', 'strategies', filename + '.py')
-        strategy_folder = os.path.join(pwd, filename + '.py')
-
-        path = None
-        if os.path.isfile(user_data):
-            path = 'user_data.strategies.'
-        elif os.path.isfile(strategy_folder):
-            path = '.'
-
-        return path
-
-    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Populate indicators that will be used in the Buy and Sell strategy
-        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
-        :return: a Dataframe with all mandatory indicators for the strategies
-        """
-        return self.custom_strategy.populate_indicators(dataframe)
-
-    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Based on TA indicators, populates the buy signal for the given dataframe
-        :param dataframe: DataFrame
-        :return: DataFrame with buy column
-        :return:
-        """
-        return self.custom_strategy.populate_buy_trend(dataframe)
-
-    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Based on TA indicators, populates the sell signal for the given dataframe
-        :param dataframe: DataFrame
-        :return: DataFrame with buy column
-        """
-        return self.custom_strategy.populate_sell_trend(dataframe)
diff --git a/freqtrade/strategy/strategy_helper.py b/freqtrade/strategy/strategy_helper.py
new file mode 100644
index 000000000..71dcc3cbf
--- /dev/null
+++ b/freqtrade/strategy/strategy_helper.py
@@ -0,0 +1,171 @@
+import pandas as pd
+
+from freqtrade.exchange import timeframe_to_minutes
+
+
+def merge_informative_pair(
+    dataframe: pd.DataFrame,
+    informative: pd.DataFrame,
+    timeframe: str,
+    timeframe_inf: str,
+    ffill: bool = True,
+    append_timeframe: bool = True,
+    date_column: str = "date",
+    suffix: str | None = None,
+) -> pd.DataFrame:
+    """
+    Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
+
+    Since dates are candle open dates, merging a 15m candle that starts at 15:00, and a
+    1h candle that starts at 15:00 will result in all candles to know the close at 16:00
+    which they should not know.
+
+    Moves the date of the informative pair by 1 time interval forward.
+    This way, the 14:00 1h candle is merged to 15:00 15m candle, since the 14:00 1h candle is the
+    last candle that's closed at 15:00, 15:15, 15:30 or 15:45.
+
+    Assuming inf_tf = '1d' - then the resulting columns will be:
+    date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
+
+    :param dataframe: Original dataframe
+    :param informative: Informative pair, most likely loaded via dp.get_pair_dataframe
+    :param timeframe: Timeframe of the original pair sample.
+    :param timeframe_inf: Timeframe of the informative pair sample.
+    :param ffill: Forwardfill missing values - optional but usually required
+    :param append_timeframe: Rename columns by appending timeframe.
+    :param date_column: A custom date column name.
+    :param suffix: A string suffix to add at the end of the informative columns. If specified,
+                   append_timeframe must be false.
+    :return: Merged dataframe
+    :raise: ValueError if the secondary timeframe is shorter than the dataframe timeframe
+    """
+    informative = informative.copy()
+    minutes_inf = timeframe_to_minutes(timeframe_inf)
+    minutes = timeframe_to_minutes(timeframe)
+    if minutes == minutes_inf:
+        # No need to forwardshift if the timeframes are identical
+        informative["date_merge"] = informative[date_column]
+    elif minutes < minutes_inf:
+        # Subtract "small" timeframe so merging is not delayed by 1 small candle
+        # Detailed explanation in https://github.com/freqtrade/freqtrade/issues/4073
+        if not informative.empty:
+            if timeframe_inf == "1M":
+                informative["date_merge"] = (
+                    informative[date_column] + pd.offsets.MonthBegin(1)
+                ) - pd.to_timedelta(minutes, "m")
+            else:
+                informative["date_merge"] = (
+                    informative[date_column]
+                    + pd.to_timedelta(minutes_inf, "m")
+                    - pd.to_timedelta(minutes, "m")
+                )
+        else:
+            informative["date_merge"] = informative[date_column]
+    else:
+        raise ValueError(
+            "Tried to merge a faster timeframe to a slower timeframe."
+            "This would create new rows, and can throw off your regular indicators."
+        )
+
+    # Rename columns to be unique
+    date_merge = "date_merge"
+    if suffix and append_timeframe:
+        raise ValueError("You can not specify `append_timeframe` as True and a `suffix`.")
+    elif append_timeframe:
+        date_merge = f"date_merge_{timeframe_inf}"
+        informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
+
+    elif suffix:
+        date_merge = f"date_merge_{suffix}"
+        informative.columns = [f"{col}_{suffix}" for col in informative.columns]
+
+    # Combine the 2 dataframes
+    # all indicators on the informative sample MUST be calculated before this point
+    if ffill:
+        # https://pandas.pydata.org/docs/user_guide/merging.html#timeseries-friendly-merging
+        # merge_ordered - ffill method is 2.5x faster than separate ffill()
+        dataframe = pd.merge_ordered(
+            dataframe,
+            informative,
+            fill_method="ffill",
+            left_on="date",
+            right_on=date_merge,
+            how="left",
+        )
+    else:
+        dataframe = pd.merge(
+            dataframe, informative, left_on="date", right_on=date_merge, how="left"
+        )
+    dataframe = dataframe.drop(date_merge, axis=1)
+
+    return dataframe
+
+
+def stoploss_from_open(
+    open_relative_stop: float, current_profit: float, is_short: bool = False, leverage: float = 1.0
+) -> float:
+    """
+    Given the current profit, and a desired stop loss value relative to the trade entry price,
+    return a stop loss value that is relative to the current price, and which can be
+    returned from `custom_stoploss`.
+
+    The requested stop can be positive for a stop above the open price, or negative for
+    a stop below the open price. The return value is always >= 0.
+    `open_relative_stop` will be considered as adjusted for leverage if leverage is provided..
+
+    Returns 0 if the resulting stop price would be above/below (longs/shorts) the current price
+
+    :param open_relative_stop: Desired stop loss percentage, relative to the open price,
+                               adjusted for leverage
+    :param current_profit: The current profit percentage
+    :param is_short: When true, perform the calculation for short instead of long
+    :param leverage: Leverage to use for the calculation
+    :return: Stop loss value relative to current price
+    """
+
+    # formula is undefined for current_profit -1 (longs) or 1 (shorts), return maximum value
+    _current_profit = current_profit / leverage
+    if (_current_profit == -1 and not is_short) or (is_short and _current_profit == 1):
+        return 1
+
+    if is_short is True:
+        stoploss = -1 + ((1 - open_relative_stop / leverage) / (1 - _current_profit))
+    else:
+        stoploss = 1 - ((1 + open_relative_stop / leverage) / (1 + _current_profit))
+
+    # negative stoploss values indicate the requested stop price is higher/lower
+    # (long/short) than the current price
+    return max(stoploss * leverage, 0.0)
+
+
+def stoploss_from_absolute(
+    stop_rate: float, current_rate: float, is_short: bool = False, leverage: float = 1.0
+) -> float:
+    """
+    Given current price and desired stop price, return a stop loss value that is relative to current
+    price.
+
+    The requested stop can be positive for a stop above the open price, or negative for
+    a stop below the open price. The return value is always >= 0.
+
+    Returns 0 if the resulting stop price would be above the current price.
+
+    :param stop_rate: Stop loss price.
+    :param current_rate: Current asset price.
+    :param is_short: When true, perform the calculation for short instead of long
+    :param leverage: Leverage to use for the calculation
+    :return: Positive stop loss value relative to current price
+    """
+
+    # formula is undefined for current_rate 0, return maximum value
+    if current_rate == 0:
+        return 1
+
+    stoploss = 1 - (stop_rate / current_rate)
+    if is_short:
+        stoploss = -stoploss
+
+    # negative stoploss values indicate the requested stop price is higher/lower
+    # (long/short) than the current price
+    # shorts can yield stoploss values higher than 1, so limit that as well
+    return max(min(stoploss, 1.0), 0.0) * leverage
diff --git a/freqtrade/strategy/strategy_wrapper.py b/freqtrade/strategy/strategy_wrapper.py
new file mode 100644
index 000000000..25d44fae7
--- /dev/null
+++ b/freqtrade/strategy/strategy_wrapper.py
@@ -0,0 +1,43 @@
+import logging
+from collections.abc import Callable
+from copy import deepcopy
+from functools import wraps
+from typing import Any, TypeVar, cast
+
+from freqtrade.exceptions import StrategyError
+
+
+logger = logging.getLogger(__name__)
+
+
+F = TypeVar("F", bound=Callable[..., Any])
+
+
+def strategy_safe_wrapper(f: F, message: str = "", default_retval=None, supress_error=False) -> F:
+    """
+    Wrapper around user-provided methods and functions.
+    Caches all exceptions and returns either the default_retval (if it's not None) or raises
+    a StrategyError exception, which then needs to be handled by the calling method.
+    """
+
+    @wraps(f)
+    def wrapper(*args, **kwargs):
+        try:
+            if not (getattr(f, "__qualname__", "")).startswith("IStrategy."):
+                # Don't deep-copy if the function is not implemented in the user strategy.``
+                if "trade" in kwargs:
+                    # Protect accidental modifications from within the strategy
+                    kwargs["trade"] = deepcopy(kwargs["trade"])
+            return f(*args, **kwargs)
+        except ValueError as error:
+            logger.warning(f"{message}Strategy caused the following exception: {error}{f}")
+            if default_retval is None and not supress_error:
+                raise StrategyError(str(error)) from error
+            return default_retval
+        except Exception as error:
+            logger.exception(f"{message}Unexpected error {error} calling {f}")
+            if default_retval is None and not supress_error:
+                raise StrategyError(str(error)) from error
+            return default_retval
+
+    return cast(F, wrapper)
diff --git a/freqtrade/strategy/strategyupdater.py b/freqtrade/strategy/strategyupdater.py
new file mode 100644
index 000000000..998a3aac3
--- /dev/null
+++ b/freqtrade/strategy/strategyupdater.py
@@ -0,0 +1,253 @@
+import shutil
+from pathlib import Path
+
+import ast_comments
+
+from freqtrade.constants import Config
+
+
+class StrategyUpdater:
+    name_mapping = {
+        "ticker_interval": "timeframe",
+        "buy": "enter_long",
+        "sell": "exit_long",
+        "buy_tag": "enter_tag",
+        "sell_reason": "exit_reason",
+        "sell_signal": "exit_signal",
+        "custom_sell": "custom_exit",
+        "force_sell": "force_exit",
+        "emergency_sell": "emergency_exit",
+        # Strategy/config settings:
+        "use_sell_signal": "use_exit_signal",
+        "sell_profit_only": "exit_profit_only",
+        "sell_profit_offset": "exit_profit_offset",
+        "ignore_roi_if_buy_signal": "ignore_roi_if_entry_signal",
+        "forcebuy_enable": "force_entry_enable",
+    }
+
+    function_mapping = {
+        "populate_buy_trend": "populate_entry_trend",
+        "populate_sell_trend": "populate_exit_trend",
+        "custom_sell": "custom_exit",
+        "check_buy_timeout": "check_entry_timeout",
+        "check_sell_timeout": "check_exit_timeout",
+        # '': '',
+    }
+    # order_time_in_force, order_types, unfilledtimeout
+    otif_ot_unfilledtimeout = {
+        "buy": "entry",
+        "sell": "exit",
+    }
+
+    # create a dictionary that maps the old column names to the new ones
+    rename_dict = {"buy": "enter_long", "sell": "exit_long", "buy_tag": "enter_tag"}
+
+    def start(self, config: Config, strategy_obj: dict) -> None:
+        """
+        Run strategy updater
+        It updates a strategy to v3 with the help of the ast-module
+        :return: None
+        """
+
+        source_file = strategy_obj["location"]
+        strategies_backup_folder = Path.joinpath(config["user_data_dir"], "strategies_orig_updater")
+        target_file = Path.joinpath(strategies_backup_folder, strategy_obj["location_rel"])
+
+        # read the file
+        with Path(source_file).open("r") as f:
+            old_code = f.read()
+        if not strategies_backup_folder.is_dir():
+            Path(strategies_backup_folder).mkdir(parents=True, exist_ok=True)
+
+        # backup original
+        # => currently no date after the filename,
+        # could get overridden pretty fast if this is fired twice!
+        # The folder is always the same and the file name too (currently).
+        shutil.copy(source_file, target_file)
+
+        # update the code
+        new_code = self.update_code(old_code)
+        # write the modified code to the destination folder
+        with Path(source_file).open("w") as f:
+            f.write(new_code)
+
+    # define the function to update the code
+    def update_code(self, code):
+        # parse the code into an AST
+        tree = ast_comments.parse(code)
+
+        # use the AST to update the code
+        updated_code = self.modify_ast(tree)
+
+        # return the modified code without executing it
+        return updated_code
+
+    # function that uses the ast module to update the code
+    def modify_ast(self, tree):
+        # use the visitor to update the names and functions in the AST
+        NameUpdater().visit(tree)
+
+        # first fix the comments, so it understands "\n" properly inside multi line comments.
+        ast_comments.fix_missing_locations(tree)
+        ast_comments.increment_lineno(tree, n=1)
+
+        # generate the new code from the updated AST
+        # without indent {} parameters would just be written straight one after the other.
+
+        # ast_comments would be amazing since this is the only solution that carries over comments,
+        # but it does currently not have an unparse function, hopefully in the future ... !
+        # return ast_comments.unparse(tree)
+
+        return ast_comments.unparse(tree)
+
+
+# Here we go through each respective node, slice, elt, key ... to replace outdated entries.
+class NameUpdater(ast_comments.NodeTransformer):
+    def generic_visit(self, node):
+        # space is not yet transferred from buy/sell to entry/exit and thereby has to be skipped.
+        if isinstance(node, ast_comments.keyword):
+            if node.arg == "space":
+                return node
+
+        # from here on this is the original function.
+        for field, old_value in ast_comments.iter_fields(node):
+            if isinstance(old_value, list):
+                new_values = []
+                for value in old_value:
+                    if isinstance(value, ast_comments.AST):
+                        value = self.visit(value)
+                        if value is None:
+                            continue
+                        elif not isinstance(value, ast_comments.AST):
+                            new_values.extend(value)
+                            continue
+                    new_values.append(value)
+                old_value[:] = new_values
+            elif isinstance(old_value, ast_comments.AST):
+                new_node = self.visit(old_value)
+                if new_node is None:
+                    delattr(node, field)
+                else:
+                    setattr(node, field, new_node)
+        return node
+
+    def visit_Expr(self, node):
+        if hasattr(node.value, "left") and hasattr(node.value.left, "id"):
+            node.value.left.id = self.check_dict(StrategyUpdater.name_mapping, node.value.left.id)
+            self.visit(node.value)
+        return node
+
+    # Renames an element if contained inside a dictionary.
+    @staticmethod
+    def check_dict(current_dict: dict, element: str):
+        if element in current_dict:
+            element = current_dict[element]
+        return element
+
+    def visit_arguments(self, node):
+        if isinstance(node.args, list):
+            for arg in node.args:
+                arg.arg = self.check_dict(StrategyUpdater.name_mapping, arg.arg)
+        return node
+
+    def visit_Name(self, node):
+        # if the name is in the mapping, update it
+        node.id = self.check_dict(StrategyUpdater.name_mapping, node.id)
+        return node
+
+    def visit_Import(self, node):
+        # do not update the names in import statements
+        return node
+
+    def visit_ImportFrom(self, node):
+        # if hasattr(node, "module"):
+        #    if node.module == "freqtrade.strategy.hyper":
+        #        node.module = "freqtrade.strategy"
+        return node
+
+    def visit_If(self, node: ast_comments.If):
+        for child in ast_comments.iter_child_nodes(node):
+            self.visit(child)
+        return node
+
+    def visit_FunctionDef(self, node):
+        node.name = self.check_dict(StrategyUpdater.function_mapping, node.name)
+        self.generic_visit(node)
+        return node
+
+    def visit_Attribute(self, node):
+        if (
+            isinstance(node.value, ast_comments.Name)
+            and node.value.id == "trade"
+            and node.attr == "nr_of_successful_buys"
+        ):
+            node.attr = "nr_of_successful_entries"
+        return node
+
+    def visit_ClassDef(self, node):
+        # check if the class is derived from IStrategy
+        if any(
+            isinstance(base, ast_comments.Name) and base.id == "IStrategy" for base in node.bases
+        ):
+            # check if the INTERFACE_VERSION variable exists
+            has_interface_version = any(
+                isinstance(child, ast_comments.Assign)
+                and isinstance(child.targets[0], ast_comments.Name)
+                and child.targets[0].id == "INTERFACE_VERSION"
+                for child in node.body
+            )
+
+            # if the INTERFACE_VERSION variable does not exist, add it as the first child
+            if not has_interface_version:
+                node.body.insert(0, ast_comments.parse("INTERFACE_VERSION = 3").body[0])
+            # otherwise, update its value to 3
+            else:
+                for child in node.body:
+                    if (
+                        isinstance(child, ast_comments.Assign)
+                        and isinstance(child.targets[0], ast_comments.Name)
+                        and child.targets[0].id == "INTERFACE_VERSION"
+                    ):
+                        child.value = ast_comments.parse("3").body[0].value
+        self.generic_visit(node)
+        return node
+
+    def visit_Subscript(self, node):
+        if isinstance(node.slice, ast_comments.Constant):
+            if node.slice.value in StrategyUpdater.rename_dict:
+                # Replace the slice attributes with the values from rename_dict
+                node.slice.value = StrategyUpdater.rename_dict[node.slice.value]
+        if hasattr(node.slice, "elts"):
+            self.visit_elts(node.slice.elts)
+        if hasattr(node.slice, "value"):
+            if hasattr(node.slice.value, "elts"):
+                self.visit_elts(node.slice.value.elts)
+        return node
+
+    # elts can have elts (technically recursively)
+    def visit_elts(self, elts):
+        if isinstance(elts, list):
+            for elt in elts:
+                self.visit_elt(elt)
+        else:
+            self.visit_elt(elts)
+        return elts
+
+    # sub function again needed since the structure itself is highly flexible ...
+    def visit_elt(self, elt):
+        if isinstance(elt, ast_comments.Constant) and elt.value in StrategyUpdater.rename_dict:
+            elt.value = StrategyUpdater.rename_dict[elt.value]
+        if hasattr(elt, "elts"):
+            self.visit_elts(elt.elts)
+        if hasattr(elt, "args"):
+            if isinstance(elt.args, ast_comments.arguments):
+                self.visit_elts(elt.args)
+            else:
+                for arg in elt.args:
+                    self.visit_elts(arg)
+        return elt
+
+    def visit_Constant(self, node):
+        node.value = self.check_dict(StrategyUpdater.otif_ot_unfilledtimeout, node.value)
+        node.value = self.check_dict(StrategyUpdater.name_mapping, node.value)
+        return node
diff --git a/freqtrade/system/__init__.py b/freqtrade/system/__init__.py
new file mode 100644
index 000000000..e69e90de8
--- /dev/null
+++ b/freqtrade/system/__init__.py
@@ -0,0 +1,8 @@
+"""system specific and performance tuning"""
+
+from freqtrade.system.asyncio_config import asyncio_setup
+from freqtrade.system.gc_setup import gc_set_threshold
+from freqtrade.system.version_info import print_version_info
+
+
+__all__ = ["asyncio_setup", "gc_set_threshold", "print_version_info"]
diff --git a/freqtrade/system/asyncio_config.py b/freqtrade/system/asyncio_config.py
new file mode 100644
index 000000000..c670cce79
--- /dev/null
+++ b/freqtrade/system/asyncio_config.py
@@ -0,0 +1,10 @@
+import sys
+
+
+def asyncio_setup() -> None:  # pragma: no cover
+    # Set eventloop for win32 setups
+
+    if sys.platform == "win32":
+        import asyncio
+
+        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
diff --git a/freqtrade/system/gc_setup.py b/freqtrade/system/gc_setup.py
new file mode 100644
index 000000000..a3532cbab
--- /dev/null
+++ b/freqtrade/system/gc_setup.py
@@ -0,0 +1,18 @@
+import gc
+import logging
+import platform
+
+
+logger = logging.getLogger(__name__)
+
+
+def gc_set_threshold():
+    """
+    Reduce number of GC runs to improve performance (explanation video)
+    https://www.youtube.com/watch?v=p4Sn6UcFTOU
+
+    """
+    if platform.python_implementation() == "CPython":
+        # allocs, g1, g2 = gc.get_threshold()
+        gc.set_threshold(50_000, 500, 1000)
+        logger.debug("Adjusting python allocations to reduce GC runs")
diff --git a/freqtrade/system/version_info.py b/freqtrade/system/version_info.py
new file mode 100644
index 000000000..505992a7f
--- /dev/null
+++ b/freqtrade/system/version_info.py
@@ -0,0 +1,15 @@
+from freqtrade import __version__
+
+
+def print_version_info():
+    """Print version information for freqtrade and its key dependencies."""
+    import platform
+    import sys
+
+    import ccxt
+
+    print(f"Operating System:\t{platform.platform()}")
+    print(f"Python Version:\t\tPython {sys.version.split(' ')[0]}")
+    print(f"CCXT Version:\t\t{ccxt.__version__}")
+    print()
+    print(f"Freqtrade Version:\tfreqtrade {__version__}")
diff --git a/freqtrade/templates/FreqaiExampleHybridStrategy.py b/freqtrade/templates/FreqaiExampleHybridStrategy.py
new file mode 100644
index 000000000..908c4b174
--- /dev/null
+++ b/freqtrade/templates/FreqaiExampleHybridStrategy.py
@@ -0,0 +1,320 @@
+import logging
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+import talib.abstract as ta
+from pandas import DataFrame
+from technical import qtpylib
+
+from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair  # noqa
+
+
+logger = logging.getLogger(__name__)
+
+
+class FreqaiExampleHybridStrategy(IStrategy):
+    """
+    Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
+    FreqAI to bolster a typical Freqtrade strategy.
+
+    Launching this strategy would be:
+
+    freqtrade trade --strategy FreqaiExampleHybridStrategy --strategy-path freqtrade/templates
+    --freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
+
+    or the user simply adds this to their config:
+
+    "freqai": {
+        "enabled": true,
+        "purge_old_models": 2,
+        "train_period_days": 15,
+        "identifier": "unique-id",
+        "feature_parameters": {
+            "include_timeframes": [
+                "3m",
+                "15m",
+                "1h"
+            ],
+            "include_corr_pairlist": [
+                "BTC/USDT",
+                "ETH/USDT"
+            ],
+            "label_period_candles": 20,
+            "include_shifted_candles": 2,
+            "DI_threshold": 0.9,
+            "weight_factor": 0.9,
+            "principal_component_analysis": false,
+            "use_SVM_to_remove_outliers": true,
+            "indicator_periods_candles": [10, 20]
+        },
+        "data_split_parameters": {
+            "test_size": 0,
+            "random_state": 1
+        },
+        "model_training_parameters": {
+            "n_estimators": 800
+        }
+    },
+
+    Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
+    """
+
+    minimal_roi = {
+        # "120": 0.0,  # exit after 120 minutes at break even
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04,
+    }
+
+    plot_config = {
+        "main_plot": {
+            "tema": {},
+        },
+        "subplots": {
+            "MACD": {
+                "macd": {"color": "blue"},
+                "macdsignal": {"color": "orange"},
+            },
+            "RSI": {
+                "rsi": {"color": "red"},
+            },
+            "Up_or_down": {
+                "&s-up_or_down": {"color": "green"},
+            },
+        },
+    }
+
+    process_only_new_candles = True
+    stoploss = -0.05
+    use_exit_signal = True
+    startup_candle_count: int = 30
+    can_short = True
+
+    # Hyperoptable parameters
+    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
+    short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
+    exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
+    ) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param dataframe: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        :param metadata: metadata of current pair
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
+
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
+
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+    def feature_engineering_expand_basic(
+        self, dataframe: DataFrame, metadata: dict, **kwargs
+    ) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param dataframe: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(
+        self, dataframe: DataFrame, metadata: dict, **kwargs
+    ) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param dataframe: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param dataframe: strategy dataframe which will receive the targets
+        :param metadata: metadata of current pair
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        self.freqai.class_names = ["down", "up"]
+        dataframe["&s-up_or_down"] = np.where(
+            dataframe["close"].shift(-50) > dataframe["close"], "up", "down"
+        )
+
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # noqa: C901
+        # User creates their own custom strat here. Present example is a supertrend
+        # based strategy.
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        # TA indicators to combine with the Freqai targets
+        # RSI
+        dataframe["rsi"] = ta.RSI(dataframe)
+
+        # Bollinger Bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe["bb_lowerband"] = bollinger["lower"]
+        dataframe["bb_middleband"] = bollinger["mid"]
+        dataframe["bb_upperband"] = bollinger["upper"]
+        dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
+            dataframe["bb_upperband"] - dataframe["bb_lowerband"]
+        )
+        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
+            "bb_middleband"
+        ]
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
+
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        df.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(df["rsi"], self.buy_rsi.value))
+                & (df["tema"] <= df["bb_middleband"])  # Guard: tema below BB middle
+                & (df["tema"] > df["tema"].shift(1))  # Guard: tema is raising
+                & (df["volume"] > 0)  # Make sure Volume is not 0
+                & (df["do_predict"] == 1)  # Make sure Freqai is confident in the prediction
+                &
+                # Only enter trade if Freqai thinks the trend is in this direction
+                (df["&s-up_or_down"] == "up")
+            ),
+            "enter_long",
+        ] = 1
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(df["rsi"], self.short_rsi.value))
+                & (df["tema"] > df["bb_middleband"])  # Guard: tema above BB middle
+                & (df["tema"] < df["tema"].shift(1))  # Guard: tema is falling
+                & (df["volume"] > 0)  # Make sure Volume is not 0
+                & (df["do_predict"] == 1)  # Make sure Freqai is confident in the prediction
+                &
+                # Only enter trade if Freqai thinks the trend is in this direction
+                (df["&s-up_or_down"] == "down")
+            ),
+            "enter_short",
+        ] = 1
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        df.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(df["rsi"], self.sell_rsi.value))
+                & (df["tema"] > df["bb_middleband"])  # Guard: tema above BB middle
+                & (df["tema"] < df["tema"].shift(1))  # Guard: tema is falling
+                & (df["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "exit_long",
+        ] = 1
+
+        df.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(df["rsi"], self.exit_short_rsi.value))
+                &
+                # Guard: tema below BB middle
+                (df["tema"] <= df["bb_middleband"])
+                & (df["tema"] > df["tema"].shift(1))  # Guard: tema is raising
+                & (df["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "exit_short",
+        ] = 1
+
+        return df
diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
new file mode 100644
index 000000000..b07487496
--- /dev/null
+++ b/freqtrade/templates/FreqaiExampleStrategy.py
@@ -0,0 +1,293 @@
+import logging
+from functools import reduce
+
+import talib.abstract as ta
+from pandas import DataFrame
+from technical import qtpylib
+
+from freqtrade.strategy import IStrategy
+
+
+logger = logging.getLogger(__name__)
+
+
+class FreqaiExampleStrategy(IStrategy):
+    """
+    Example strategy showing how the user connects their own
+    IFreqaiModel to the strategy.
+
+    Warning! This is a showcase of functionality,
+    which means that it is designed to show various functions of FreqAI
+    and it runs on all computers. We use this showcase to help users
+    understand how to build a strategy, and we use it as a benchmark
+    to help debug possible problems.
+
+    This means this is *not* meant to be run live in production.
+    """
+
+    minimal_roi = {"0": 0.1, "240": -1}
+
+    plot_config = {
+        "main_plot": {},
+        "subplots": {
+            "&-s_close": {"&-s_close": {"color": "blue"}},
+            "do_predict": {
+                "do_predict": {"color": "brown"},
+            },
+        },
+    }
+
+    process_only_new_candles = True
+    stoploss = -0.05
+    use_exit_signal = True
+    # this is the maximum period fed to talib (timeframe independent)
+    startup_candle_count: int = 40
+    can_short = True
+
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
+    ) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        Access metadata such as the current pair/timeframe with:
+
+        `metadata["pair"]` `metadata["tf"]`
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param dataframe: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        :param metadata: metadata of current pair
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
+
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
+
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+    def feature_engineering_expand_basic(
+        self, dataframe: DataFrame, metadata: dict, **kwargs
+    ) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        Access metadata such as the current pair/timeframe with:
+
+        `metadata["pair"]` `metadata["tf"]`
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param dataframe: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(
+        self, dataframe: DataFrame, metadata: dict, **kwargs
+    ) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        Access metadata such as the current pair with:
+
+        `metadata["pair"]`
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param dataframe: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        Access metadata such as the current pair with:
+
+        `metadata["pair"]`
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param dataframe: strategy dataframe which will receive the targets
+        :param metadata: metadata of current pair
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
+        )
+
+        # Classifiers are typically set up with strings as targets:
+        # df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
+        #                                 df["close"], 'up', 'down')
+
+        # If user wishes to use multiple targets, they can add more by
+        # appending more columns with '&'. User should keep in mind that multi targets
+        # requires a multioutput prediction model such as
+        # freqai/prediction_models/CatboostRegressorMultiTarget.py,
+        # freqtrade trade --freqaimodel CatboostRegressorMultiTarget
+
+        # df["&-s_range"] = (
+        #     df["close"]
+        #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .max()
+        #     -
+        #     df["close"]
+        #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .min()
+        # )
+
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # All indicators must be populated by feature_engineering_*() functions
+
+        # the model will return all labels created by user in `set_freqai_targets()`
+        # (& appended targets), an indication of whether or not the prediction should be accepted,
+        # the target mean/std values for each of the labels created by user in
+        # `set_freqai_targets()` for each training period.
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        enter_long_conditions = [
+            df["do_predict"] == 1,
+            df["&-s_close"] > 0.01,
+        ]
+
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
+            ] = (1, "long")
+
+        enter_short_conditions = [
+            df["do_predict"] == 1,
+            df["&-s_close"] < -0.01,
+        ]
+
+        if enter_short_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
+            ] = (1, "short")
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < 0]
+        if exit_long_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
+
+        exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > 0]
+        if exit_short_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
+
+        return df
+
+    def confirm_trade_entry(
+        self,
+        pair: str,
+        order_type: str,
+        amount: float,
+        rate: float,
+        time_in_force: str,
+        current_time,
+        entry_tag,
+        side: str,
+        **kwargs,
+    ) -> bool:
+        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = df.iloc[-1].squeeze()
+
+        if side == "long":
+            if rate > (last_candle["close"] * (1 + 0.0025)):
+                return False
+        else:
+            if rate < (last_candle["close"] * (1 - 0.0025)):
+                return False
+
+        return True
diff --git a/freqtrade/templates/base_config.json.j2 b/freqtrade/templates/base_config.json.j2
new file mode 100644
index 000000000..86a717a40
--- /dev/null
+++ b/freqtrade/templates/base_config.json.j2
@@ -0,0 +1,69 @@
+{%set volume_pairlist = '{
+            "method": "VolumePairList",
+            "number_assets": 20,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }' %}
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": {{ max_open_trades }},
+    "stake_currency": "{{ stake_currency }}",
+    "stake_amount": {{ stake_amount }},
+    "tradable_balance_ratio": 0.99,
+{{- ('\n    "fiat_display_currency": "' + fiat_display_currency + '",') if fiat_display_currency else ''}}
+{{- ('\n    "timeframe": "' + timeframe + '",') if timeframe else '' }}
+    "dry_run": {{ dry_run | lower }},
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": false,
+    "trading_mode": "{{ trading_mode }}",
+    "margin_mode": "{{ margin_mode }}",
+    "unfilledtimeout": {
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    {{ exchange | indent(4) }},
+    "pairlists": [
+        {{ volume_pairlist }}
+    ],
+    "telegram": {
+        "enabled": {{ telegram | lower }},
+        "token": "{{ telegram_token }}",
+        "chat_id": "{{ telegram_chat_id }}"
+    },
+    "api_server": {
+        "enabled": {{ api_server | lower }},
+        "listen_ip_address": "{{ api_server_listen_addr | default("127.0.0.1", true) }}",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "{{ api_server_jwt_key }}",
+        "ws_token": "{{ api_server_ws_token }}",
+        "CORS_origins": [],
+        "username": "{{ api_server_username }}",
+        "password": "{{ api_server_password }}"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}
diff --git a/freqtrade/templates/base_strategy.py.j2 b/freqtrade/templates/base_strategy.py.j2
new file mode 100644
index 000000000..fe577826a
--- /dev/null
+++ b/freqtrade/templates/base_strategy.py.j2
@@ -0,0 +1,179 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these imports ---
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta, timezone
+from pandas import DataFrame
+from typing import Dict, Optional, Union, Tuple
+
+from freqtrade.strategy import (
+    IStrategy,
+    Trade,
+    Order,
+    PairLocks,
+    informative,  # @informative decorator
+    # Hyperopt Parameters
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+    # timeframe helpers
+    timeframe_to_minutes,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    # Strategy helper functions
+    merge_informative_pair,
+    stoploss_from_absolute,
+    stoploss_from_open,
+)
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import pandas_ta as pta
+from technical import qtpylib
+
+
+class {{ strategy }}(IStrategy):
+    """
+    This is a strategy template to get you started.
+    More information in https://www.freqtrade.io/en/latest/strategy-customization/
+
+    You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
+    You should keep:
+    - timeframe, minimal_roi, stoploss, trailing_*
+    """
+    # Strategy interface version - allow new iterations of the strategy interface.
+    # Check the documentation or the Sample strategy to get the latest version.
+    INTERFACE_VERSION = 3
+
+    # Optimal timeframe for the strategy.
+    timeframe = "5m"
+
+    # Can this strategy go short?
+    can_short: bool = False
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi".
+    minimal_roi = {
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy.
+    # This attribute will be overridden if the config file contains "stoploss".
+    stoploss = -0.10
+
+    # Trailing stoploss
+    trailing_stop = False
+    # trailing_only_offset_is_reached = False
+    # trailing_stop_positive = 0.01
+    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = True
+
+    # These values can be overridden in the config.
+    use_exit_signal = True
+    exit_profit_only = False
+    ignore_roi_if_entry_signal = False
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 30
+
+    # Strategy parameters
+    buy_rsi = IntParameter(10, 40, default=30, space="buy")
+    sell_rsi = IntParameter(60, 90, default=70, space="sell")
+
+    {{- attributes | indent(4) }}
+    {{- plot_config | indent(4) }}
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Dataframe with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+        {{- indicators | indent(8) }}
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the entry signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with entry columns populated
+        """
+        dataframe.loc[
+            (
+                {{ buy_trend | indent(16) }}
+                (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "enter_long"] = 1
+        # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
+        """
+        dataframe.loc[
+            (
+                {{ sell_trend | indent(16) }}
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'enter_short'] = 1
+        """
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the exit signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with exit columns populated
+        """
+        dataframe.loc[
+            (
+                {{ sell_trend | indent(16) }}
+                (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "exit_long"] = 1
+        # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
+        """
+        dataframe.loc[
+            (
+                {{ buy_trend | indent(16) }}
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'exit_short'] = 1
+        """
+        return dataframe
+    {{- additional_methods | indent(4) }}
diff --git a/freqtrade/templates/sample_hyperopt_loss.py b/freqtrade/templates/sample_hyperopt_loss.py
new file mode 100644
index 000000000..20a348f2b
--- /dev/null
+++ b/freqtrade/templates/sample_hyperopt_loss.py
@@ -0,0 +1,57 @@
+from datetime import datetime
+from math import exp
+
+from pandas import DataFrame
+
+from freqtrade.constants import Config
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+
+# Define some constants:
+
+# set TARGET_TRADES to suit your number concurrent trades so its realistic
+# to the number of days
+TARGET_TRADES = 600
+# This is assumed to be expected avg profit * expected trade count.
+# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
+# self.expected_max_profit = 3.85
+# Check that the reported Σ% values do not exceed this!
+# Note, this is ratio. 3.85 stated above means 385Σ%.
+EXPECTED_MAX_PROFIT = 3.0
+
+# max average trade duration in minutes
+# if eval ends with higher value, we consider it a failed eval
+MAX_ACCEPTED_TRADE_DURATION = 300
+
+
+class SampleHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the default loss function for hyperopt
+    This is intended to give you some inspiration for your own loss function.
+
+    The Function needs to return a number (float) - which becomes smaller for better backtest
+    results.
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(
+        results: DataFrame,
+        trade_count: int,
+        min_date: datetime,
+        max_date: datetime,
+        config: Config,
+        processed: dict[str, DataFrame],
+        *args,
+        **kwargs,
+    ) -> float:
+        """
+        Objective function, returns smaller number for better results
+        """
+        total_profit = results["profit_ratio"].sum()
+        trade_duration = results["trade_duration"].mean()
+
+        trade_loss = 1 - 0.25 * exp(-((trade_count - TARGET_TRADES) ** 2) / 10**5.8)
+        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
+        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
+        result = trade_loss + profit_loss + duration_loss
+        return result
diff --git a/freqtrade/templates/sample_strategy.py b/freqtrade/templates/sample_strategy.py
new file mode 100644
index 000000000..75a0fe385
--- /dev/null
+++ b/freqtrade/templates/sample_strategy.py
@@ -0,0 +1,426 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these imports ---
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta, timezone
+from pandas import DataFrame
+from typing import Optional, Union
+
+from freqtrade.strategy import (
+    IStrategy,
+    Trade,
+    Order,
+    PairLocks,
+    informative,  # @informative decorator
+    # Hyperopt Parameters
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+    # timeframe helpers
+    timeframe_to_minutes,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    # Strategy helper functions
+    merge_informative_pair,
+    stoploss_from_absolute,
+    stoploss_from_open,
+)
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+from technical import qtpylib
+
+
+# This class is a sample. Feel free to customize it.
+class SampleStrategy(IStrategy):
+    """
+    This is a sample strategy to inspire you.
+    More information in https://www.freqtrade.io/en/latest/strategy-customization/
+
+    You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
+    You should keep:
+    - timeframe, minimal_roi, stoploss, trailing_*
+    """
+
+    # Strategy interface version - allow new iterations of the strategy interface.
+    # Check the documentation or the Sample strategy to get the latest version.
+    INTERFACE_VERSION = 3
+
+    # Can this strategy go short?
+    can_short: bool = False
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi".
+    minimal_roi = {
+        # "120": 0.0,  # exit after 120 minutes at break even
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04,
+    }
+
+    # Optimal stoploss designed for the strategy.
+    # This attribute will be overridden if the config file contains "stoploss".
+    stoploss = -0.10
+
+    # Trailing stoploss
+    trailing_stop = False
+    # trailing_only_offset_is_reached = False
+    # trailing_stop_positive = 0.01
+    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
+
+    # Optimal timeframe for the strategy.
+    timeframe = "5m"
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = True
+
+    # These values can be overridden in the config.
+    use_exit_signal = True
+    exit_profit_only = False
+    ignore_roi_if_entry_signal = False
+
+    # Hyperoptable parameters
+    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
+    short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
+    exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 200
+
+    # Optional order type mapping.
+    order_types = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+
+    # Optional order time in force.
+    order_time_in_force = {"entry": "GTC", "exit": "GTC"}
+
+    plot_config = {
+        "main_plot": {
+            "tema": {},
+            "sar": {"color": "white"},
+        },
+        "subplots": {
+            "MACD": {
+                "macd": {"color": "blue"},
+                "macdsignal": {"color": "orange"},
+            },
+            "RSI": {
+                "rsi": {"color": "red"},
+            },
+        },
+    }
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Dataframe with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+        # Momentum Indicators
+        # ------------------------------------
+
+        # ADX
+        dataframe["adx"] = ta.ADX(dataframe)
+
+        # # Plus Directional Indicator / Movement
+        # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        # dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+
+        # # Minus Directional Indicator / Movement
+        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # # Aroon, Aroon Oscillator
+        # aroon = ta.AROON(dataframe)
+        # dataframe['aroonup'] = aroon['aroonup']
+        # dataframe['aroondown'] = aroon['aroondown']
+        # dataframe['aroonosc'] = ta.AROONOSC(dataframe)
+
+        # # Awesome Oscillator
+        # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # # Keltner Channel
+        # keltner = qtpylib.keltner_channel(dataframe)
+        # dataframe["kc_upperband"] = keltner["upper"]
+        # dataframe["kc_lowerband"] = keltner["lower"]
+        # dataframe["kc_middleband"] = keltner["mid"]
+        # dataframe["kc_percent"] = (
+        #     (dataframe["close"] - dataframe["kc_lowerband"]) /
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
+        # )
+        # dataframe["kc_width"] = (
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
+        # )
+
+        # # Ultimate Oscillator
+        # dataframe['uo'] = ta.ULTOSC(dataframe)
+
+        # # Commodity Channel Index: values [Oversold:-100, Overbought:100]
+        # dataframe['cci'] = ta.CCI(dataframe)
+
+        # RSI
+        dataframe["rsi"] = ta.RSI(dataframe)
+
+        # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        # rsi = 0.1 * (dataframe['rsi'] - 50)
+        # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
+
+        # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
+        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # # Stochastic Slow
+        # stoch = ta.STOCH(dataframe)
+        # dataframe['slowd'] = stoch['slowd']
+        # dataframe['slowk'] = stoch['slowk']
+
+        # Stochastic Fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe["fastd"] = stoch_fast["fastd"]
+        dataframe["fastk"] = stoch_fast["fastk"]
+
+        # # Stochastic RSI
+        # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
+        # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
+        # stoch_rsi = ta.STOCHRSI(dataframe)
+        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        # dataframe['fastk_rsi'] = stoch_rsi['fastk']
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
+
+        # MFI
+        dataframe["mfi"] = ta.MFI(dataframe)
+
+        # # ROC
+        # dataframe['roc'] = ta.ROC(dataframe)
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # Bollinger Bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe["bb_lowerband"] = bollinger["lower"]
+        dataframe["bb_middleband"] = bollinger["mid"]
+        dataframe["bb_upperband"] = bollinger["upper"]
+        dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
+            dataframe["bb_upperband"] - dataframe["bb_lowerband"]
+        )
+        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
+            "bb_middleband"
+        ]
+
+        # Bollinger Bands - Weighted (EMA based instead of SMA)
+        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
+        #     qtpylib.typical_price(dataframe), window=20, stds=2
+        # )
+        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
+        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
+        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
+        # dataframe["wbb_percent"] = (
+        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
+        # )
+        # dataframe["wbb_width"] = (
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
+        #     dataframe["wbb_middleband"]
+        # )
+
+        # # EMA - Exponential Moving Average
+        # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
+        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # # SMA - Simple Moving Average
+        # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
+        # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
+        # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
+        # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
+        # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
+        # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
+
+        # Parabolic SAR
+        dataframe["sar"] = ta.SAR(dataframe)
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe["htsine"] = hilbert["sine"]
+        dataframe["htleadsine"] = hilbert["leadsine"]
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        # # Hammer: values [0, 100]
+        # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # # Inverted Hammer: values [0, 100]
+        # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # # Dragonfly Doji: values [0, 100]
+        # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # # Piercing Line: values [0, 100]
+        # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # # Morningstar: values [0, 100]
+        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # # Three White Soldiers: values [0, 100]
+        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        # # Hanging Man: values [0, 100]
+        # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # # Shooting Star: values [0, 100]
+        # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # # Gravestone Doji: values [0, 100]
+        # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # # Dark Cloud Cover: values [0, 100]
+        # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # # Evening Doji Star: values [0, 100]
+        # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # # Evening Star: values [0, 100]
+        # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        # # Three Line Strike: values [0, -100, 100]
+        # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # # Spinning Top: values [0, -100, 100]
+        # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # # Engulfing: values [0, -100, 100]
+        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # # Harami: values [0, -100, 100]
+        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # # Three Outside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # # Three Inside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+
+        # # Chart type
+        # # ------------------------------------
+        # # Heikin Ashi Strategy
+        # heikinashi = qtpylib.heikinashi(dataframe)
+        # dataframe['ha_open'] = heikinashi['open']
+        # dataframe['ha_close'] = heikinashi['close']
+        # dataframe['ha_high'] = heikinashi['high']
+        # dataframe['ha_low'] = heikinashi['low']
+
+        # Retrieve best bid and best ask from the orderbook
+        # ------------------------------------
+        """
+        # first check if dataprovider is available
+        if self.dp:
+            if self.dp.runmode.value in ('live', 'dry_run'):
+                ob = self.dp.orderbook(metadata['pair'], 1)
+                dataframe['best_bid'] = ob['bids'][0][0]
+                dataframe['best_ask'] = ob['asks'][0][0]
+        """
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the entry signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with entry columns populated
+        """
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value))
+                & (dataframe["tema"] <= dataframe["bb_middleband"])  # Guard: tema below BB middle
+                & (dataframe["tema"] > dataframe["tema"].shift(1))  # Guard: tema is raising
+                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "enter_long",
+        ] = 1
+
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value))
+                & (dataframe["tema"] > dataframe["bb_middleband"])  # Guard: tema above BB middle
+                & (dataframe["tema"] < dataframe["tema"].shift(1))  # Guard: tema is falling
+                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "enter_short",
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the exit signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with exit columns populated
+        """
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
+                & (dataframe["tema"] > dataframe["bb_middleband"])  # Guard: tema above BB middle
+                & (dataframe["tema"] < dataframe["tema"].shift(1))  # Guard: tema is falling
+                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "exit_long",
+        ] = 1
+
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value))
+                &
+                # Guard: tema below BB middle
+                (dataframe["tema"] <= dataframe["bb_middleband"])
+                & (dataframe["tema"] > dataframe["tema"].shift(1))  # Guard: tema is raising
+                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "exit_short",
+        ] = 1
+
+        return dataframe
diff --git a/freqtrade/templates/strategy_analysis_example.ipynb b/freqtrade/templates/strategy_analysis_example.ipynb
new file mode 100644
index 000000000..c81a76f72
--- /dev/null
+++ b/freqtrade/templates/strategy_analysis_example.ipynb
@@ -0,0 +1,480 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Strategy analysis example\n",
+    "\n",
+    "Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.\n",
+    "The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.\n",
+    "Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "### Change Working directory to repository root"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "# Change directory\n",
+    "# Modify this cell to insure that the output shows the correct path.\n",
+    "# Define all paths relative to the project root shown in the cell output\n",
+    "project_root = \"somedir/freqtrade\"\n",
+    "i = 0\n",
+    "try:\n",
+    "    os.chdir(project_root)\n",
+    "    if not Path(\"LICENSE\").is_file():\n",
+    "        i = 0\n",
+    "        while i < 4 and (not Path(\"LICENSE\").is_file()):\n",
+    "            os.chdir(Path(Path.cwd(), \"../\"))\n",
+    "            i += 1\n",
+    "        project_root = Path.cwd()\n",
+    "except FileNotFoundError:\n",
+    "    print(\"Please define the project root relative to the current directory\")\n",
+    "print(Path.cwd())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure Freqtrade environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.configuration import Configuration\n",
+    "\n",
+    "\n",
+    "# Customize these according to your needs.\n",
+    "\n",
+    "# Initialize empty configuration object\n",
+    "config = Configuration.from_files([])\n",
+    "# Optionally (recommended), use existing configuration file\n",
+    "# config = Configuration.from_files([\"user_data/config.json\"])\n",
+    "\n",
+    "# Define some constants\n",
+    "config[\"timeframe\"] = \"5m\"\n",
+    "# Name of the strategy class\n",
+    "config[\"strategy\"] = \"SampleStrategy\"\n",
+    "# Location of the data\n",
+    "data_location = config[\"datadir\"]\n",
+    "# Pair to analyze - Only use one pair here\n",
+    "pair = \"BTC/USDT\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data using values set above\n",
+    "from freqtrade.data.history import load_pair_history\n",
+    "from freqtrade.enums import CandleType\n",
+    "\n",
+    "\n",
+    "candles = load_pair_history(\n",
+    "    datadir=data_location,\n",
+    "    timeframe=config[\"timeframe\"],\n",
+    "    pair=pair,\n",
+    "    data_format=\"json\",  # Make sure to update this to your data\n",
+    "    candle_type=CandleType.SPOT,\n",
+    ")\n",
+    "\n",
+    "# Confirm success\n",
+    "print(f\"Loaded {len(candles)} rows of data for {pair} from {data_location}\")\n",
+    "candles.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load and run strategy\n",
+    "* Rerun each time the strategy file is changed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load strategy using values set above\n",
+    "from freqtrade.data.dataprovider import DataProvider\n",
+    "from freqtrade.resolvers import StrategyResolver\n",
+    "\n",
+    "\n",
+    "strategy = StrategyResolver.load_strategy(config)\n",
+    "strategy.dp = DataProvider(config, None, None)\n",
+    "strategy.ft_bot_start()\n",
+    "\n",
+    "# Generate buy/sell signals using strategy\n",
+    "df = strategy.analyze_ticker(candles, {\"pair\": pair})\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display the trade details\n",
+    "\n",
+    "* Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
+    "* Some possible problems\n",
+    "    * Columns with NaN values at the end of the dataframe\n",
+    "    * Columns used in `crossed*()` functions with completely different units\n",
+    "* Comparison with full backtest\n",
+    "    * having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.\n",
+    "    * Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple \"buy\" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot (\"max_open_trades\") is still available), or on one of the middle signals, as soon as a \"slot\" becomes available.  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Report results\n",
+    "print(f\"Generated {df['enter_long'].sum()} entry signals\")\n",
+    "data = df.set_index(\"date\", drop=False)\n",
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load existing objects into a Jupyter notebook\n",
+    "\n",
+    "The following cells assume that you have already generated data using the cli.  \n",
+    "They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load backtest results to pandas dataframe\n",
+    "\n",
+    "Analyze a trades dataframe (also used below for plotting)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats\n",
+    "\n",
+    "\n",
+    "# if backtest_dir points to a directory, it'll automatically load the last backtest file.\n",
+    "backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
+    "# backtest_dir can also point to a specific file\n",
+    "# backtest_dir = (\n",
+    "#   config[\"user_data_dir\"] / \"backtest_results/backtest-result-2020-07-01_20-04-22.json\"\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can get the full backtest statistics by using the following command.\n",
+    "# This contains all information used to generate the backtest result.\n",
+    "stats = load_backtest_stats(backtest_dir)\n",
+    "\n",
+    "strategy = \"SampleStrategy\"\n",
+    "# All statistics are available per strategy, so if `--strategy-list` was used during backtest,\n",
+    "# this will be reflected here as well.\n",
+    "# Example usages:\n",
+    "print(stats[\"strategy\"][strategy][\"results_per_pair\"])\n",
+    "# Get pairlist used for this backtest\n",
+    "print(stats[\"strategy\"][strategy][\"pairlist\"])\n",
+    "# Get market change (average change of all pairs from start to end of the backtest period)\n",
+    "print(stats[\"strategy\"][strategy][\"market_change\"])\n",
+    "# Maximum drawdown ()\n",
+    "print(stats[\"strategy\"][strategy][\"max_drawdown_abs\"])\n",
+    "# Maximum drawdown start and end\n",
+    "print(stats[\"strategy\"][strategy][\"drawdown_start\"])\n",
+    "print(stats[\"strategy\"][strategy][\"drawdown_end\"])\n",
+    "\n",
+    "\n",
+    "# Get strategy comparison (only relevant if multiple strategies were compared)\n",
+    "print(stats[\"strategy_comparison\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load backtested trades as dataframe\n",
+    "trades = load_backtest_data(backtest_dir)\n",
+    "\n",
+    "# Show value-counts per pair\n",
+    "trades.groupby(\"pair\")[\"exit_reason\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting daily profit / equity line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)\n",
+    "\n",
+    "import pandas as pd\n",
+    "import plotly.express as px\n",
+    "\n",
+    "from freqtrade.configuration import Configuration\n",
+    "from freqtrade.data.btanalysis import load_backtest_stats\n",
+    "\n",
+    "\n",
+    "# strategy = 'SampleStrategy'\n",
+    "# config = Configuration.from_files([\"user_data/config.json\"])\n",
+    "# backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
+    "\n",
+    "stats = load_backtest_stats(backtest_dir)\n",
+    "strategy_stats = stats[\"strategy\"][strategy]\n",
+    "\n",
+    "df = pd.DataFrame(columns=[\"dates\", \"equity\"], data=strategy_stats[\"daily_profit\"])\n",
+    "df[\"equity_daily\"] = df[\"equity\"].cumsum()\n",
+    "\n",
+    "fig = px.line(df, x=\"dates\", y=\"equity_daily\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load live trading results into a pandas dataframe\n",
+    "\n",
+    "In case you did already some trading and want to analyze your performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.data.btanalysis import load_trades_from_db\n",
+    "\n",
+    "\n",
+    "# Fetch trades from database\n",
+    "trades = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
+    "\n",
+    "# Display results\n",
+    "trades.groupby(\"pair\")[\"exit_reason\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyze the loaded trades for trade parallelism\n",
+    "This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with a very high `max_open_trades` setting.\n",
+    "\n",
+    "`analyze_trade_parallelism()` returns a timeseries dataframe with an \"open_trades\" column, specifying the number of open trades for each candle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.data.btanalysis import analyze_trade_parallelism\n",
+    "\n",
+    "\n",
+    "# Analyze the above\n",
+    "parallel_trades = analyze_trade_parallelism(trades, \"5m\")\n",
+    "\n",
+    "parallel_trades.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot results\n",
+    "\n",
+    "Freqtrade offers interactive plotting capabilities based on plotly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.plot.plotting import generate_candlestick_graph\n",
+    "\n",
+    "\n",
+    "# Limit graph period to keep plotly quick and reactive\n",
+    "\n",
+    "# Filter trades to one pair\n",
+    "trades_red = trades.loc[trades[\"pair\"] == pair]\n",
+    "\n",
+    "data_red = data[\"2019-06-01\":\"2019-06-10\"]\n",
+    "# Generate candlestick graph\n",
+    "graph = generate_candlestick_graph(\n",
+    "    pair=pair,\n",
+    "    data=data_red,\n",
+    "    trades=trades_red,\n",
+    "    indicators1=[\"sma20\", \"ema50\", \"ema55\"],\n",
+    "    indicators2=[\"rsi\", \"macd\", \"macdsignal\", \"macdhist\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show graph inline\n",
+    "# graph.show()\n",
+    "\n",
+    "# Render graph in a separate window\n",
+    "graph.show(renderer=\"browser\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot average profit per trade as distribution graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.figure_factory as ff\n",
+    "\n",
+    "\n",
+    "hist_data = [trades.profit_ratio]\n",
+    "group_labels = [\"profit_ratio\"]  # name of the dataset\n",
+    "\n",
+    "fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data."
+   ]
+  }
+ ],
+ "metadata": {
+  "file_extension": ".py",
+  "kernelspec": {
+   "display_name": "Python 3.9.7 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "mimetype": "text/x-python",
+  "name": "python",
+  "npconvert_exporter": "python",
+  "pygments_lexer": "ipython3",
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  },
+  "version": 3,
+  "vscode": {
+   "interpreter": {
+    "hash": "675f32a300d6d26767470181ad0b11dd4676bcce7ed1dd2ffe2fbc370c95fc7c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/freqtrade/templates/strategy_subtemplates/buy_trend_full.j2 b/freqtrade/templates/strategy_subtemplates/buy_trend_full.j2
new file mode 100644
index 000000000..7a50fd4dc
--- /dev/null
+++ b/freqtrade/templates/strategy_subtemplates/buy_trend_full.j2
@@ -0,0 +1,3 @@
+(qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)) &  # Signal: RSI crosses above buy_rsi
+(dataframe["tema"] <= dataframe["bb_middleband"]) &  # Guard: tema below BB middle
+(dataframe["tema"] > dataframe["tema"].shift(1)) &  # Guard: tema is raising
diff --git a/freqtrade/templates/strategy_subtemplates/buy_trend_minimal.j2 b/freqtrade/templates/strategy_subtemplates/buy_trend_minimal.j2
new file mode 100644
index 000000000..bcecacc3c
--- /dev/null
+++ b/freqtrade/templates/strategy_subtemplates/buy_trend_minimal.j2
@@ -0,0 +1 @@
+(qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)) &  # Signal: RSI crosses above buy_rsi
diff --git a/freqtrade/templates/strategy_subtemplates/indicators_full.j2 b/freqtrade/templates/strategy_subtemplates/indicators_full.j2
new file mode 100644
index 000000000..e4c4daac4
--- /dev/null
+++ b/freqtrade/templates/strategy_subtemplates/indicators_full.j2
@@ -0,0 +1,206 @@
+
+# Momentum Indicators
+# ------------------------------------
+
+# ADX
+dataframe["adx"] = ta.ADX(dataframe)
+
+# # Plus Directional Indicator / Movement
+# dataframe["plus_dm"] = ta.PLUS_DM(dataframe)
+# dataframe["plus_di"] = ta.PLUS_DI(dataframe)
+
+# # Minus Directional Indicator / Movement
+# dataframe["minus_dm"] = ta.MINUS_DM(dataframe)
+# dataframe["minus_di"] = ta.MINUS_DI(dataframe)
+
+# # Aroon, Aroon Oscillator
+# aroon = ta.AROON(dataframe)
+# dataframe["aroonup"] = aroon["aroonup"]
+# dataframe["aroondown"] = aroon["aroondown"]
+# dataframe["aroonosc"] = ta.AROONOSC(dataframe)
+
+# # Awesome Oscillator
+# dataframe["ao"] = qtpylib.awesome_oscillator(dataframe)
+
+# # Keltner Channel
+# keltner = qtpylib.keltner_channel(dataframe)
+# dataframe["kc_upperband"] = keltner["upper"]
+# dataframe["kc_lowerband"] = keltner["lower"]
+# dataframe["kc_middleband"] = keltner["mid"]
+# dataframe["kc_percent"] = (
+#     (dataframe["close"] - dataframe["kc_lowerband"]) /
+#     (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
+# )
+# dataframe["kc_width"] = (
+#     (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
+# )
+
+# # Ultimate Oscillator
+# dataframe["uo"] = ta.ULTOSC(dataframe)
+
+# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
+# dataframe["cci"] = ta.CCI(dataframe)
+
+# RSI
+dataframe["rsi"] = ta.RSI(dataframe)
+
+# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+# rsi = 0.1 * (dataframe["rsi"] - 50)
+# dataframe["fisher_rsi"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
+
+# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
+# dataframe["fisher_rsi_norma"] = 50 * (dataframe["fisher_rsi"] + 1)
+
+# # Stochastic Slow
+# stoch = ta.STOCH(dataframe)
+# dataframe["slowd"] = stoch["slowd"]
+# dataframe["slowk"] = stoch["slowk"]
+
+# Stochastic Fast
+stoch_fast = ta.STOCHF(dataframe)
+dataframe["fastd"] = stoch_fast["fastd"]
+dataframe["fastk"] = stoch_fast["fastk"]
+
+# # Stochastic RSI
+# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
+# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
+# stoch_rsi = ta.STOCHRSI(dataframe)
+# dataframe["fastd_rsi"] = stoch_rsi["fastd"]
+# dataframe["fastk_rsi"] = stoch_rsi["fastk"]
+
+# MACD
+macd = ta.MACD(dataframe)
+dataframe["macd"] = macd["macd"]
+dataframe["macdsignal"] = macd["macdsignal"]
+dataframe["macdhist"] = macd["macdhist"]
+
+# MFI
+dataframe["mfi"] = ta.MFI(dataframe)
+
+# # ROC
+# dataframe["roc"] = ta.ROC(dataframe)
+
+# Overlap Studies
+# ------------------------------------
+
+# Bollinger Bands
+bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+dataframe["bb_lowerband"] = bollinger["lower"]
+dataframe["bb_middleband"] = bollinger["mid"]
+dataframe["bb_upperband"] = bollinger["upper"]
+dataframe["bb_percent"] = (
+    (dataframe["close"] - dataframe["bb_lowerband"]) /
+    (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
+)
+dataframe["bb_width"] = (
+    (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
+)
+
+# Bollinger Bands - Weighted (EMA based instead of SMA)
+# weighted_bollinger = qtpylib.weighted_bollinger_bands(
+#     qtpylib.typical_price(dataframe), window=20, stds=2
+# )
+# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
+# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
+# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
+# dataframe["wbb_percent"] = (
+#     (dataframe["close"] - dataframe["wbb_lowerband"]) /
+#     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
+# )
+# dataframe["wbb_width"] = (
+#     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
+# )
+
+# # EMA - Exponential Moving Average
+# dataframe["ema3"] = ta.EMA(dataframe, timeperiod=3)
+# dataframe["ema5"] = ta.EMA(dataframe, timeperiod=5)
+# dataframe["ema10"] = ta.EMA(dataframe, timeperiod=10)
+# dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21)
+# dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
+# dataframe["ema100"] = ta.EMA(dataframe, timeperiod=100)
+
+# # SMA - Simple Moving Average
+# dataframe["sma3"] = ta.SMA(dataframe, timeperiod=3)
+# dataframe["sma5"] = ta.SMA(dataframe, timeperiod=5)
+# dataframe["sma10"] = ta.SMA(dataframe, timeperiod=10)
+# dataframe["sma21"] = ta.SMA(dataframe, timeperiod=21)
+# dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50)
+# dataframe["sma100"] = ta.SMA(dataframe, timeperiod=100)
+
+# Parabolic SAR
+dataframe["sar"] = ta.SAR(dataframe)
+
+# TEMA - Triple Exponential Moving Average
+dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
+
+# Cycle Indicator
+# ------------------------------------
+# Hilbert Transform Indicator - SineWave
+hilbert = ta.HT_SINE(dataframe)
+dataframe["htsine"] = hilbert["sine"]
+dataframe["htleadsine"] = hilbert["leadsine"]
+
+# Pattern Recognition - Bullish candlestick patterns
+# ------------------------------------
+# # Hammer: values [0, 100]
+# dataframe["CDLHAMMER"] = ta.CDLHAMMER(dataframe)
+# # Inverted Hammer: values [0, 100]
+# dataframe["CDLINVERTEDHAMMER"] = ta.CDLINVERTEDHAMMER(dataframe)
+# # Dragonfly Doji: values [0, 100]
+# dataframe["CDLDRAGONFLYDOJI"] = ta.CDLDRAGONFLYDOJI(dataframe)
+# # Piercing Line: values [0, 100]
+# dataframe["CDLPIERCING"] = ta.CDLPIERCING(dataframe) # values [0, 100]
+# # Morningstar: values [0, 100]
+# dataframe["CDLMORNINGSTAR"] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+# # Three White Soldiers: values [0, 100]
+# dataframe["CDL3WHITESOLDIERS"] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+
+# Pattern Recognition - Bearish candlestick patterns
+# ------------------------------------
+# # Hanging Man: values [0, 100]
+# dataframe["CDLHANGINGMAN"] = ta.CDLHANGINGMAN(dataframe)
+# # Shooting Star: values [0, 100]
+# dataframe["CDLSHOOTINGSTAR"] = ta.CDLSHOOTINGSTAR(dataframe)
+# # Gravestone Doji: values [0, 100]
+# dataframe["CDLGRAVESTONEDOJI"] = ta.CDLGRAVESTONEDOJI(dataframe)
+# # Dark Cloud Cover: values [0, 100]
+# dataframe["CDLDARKCLOUDCOVER"] = ta.CDLDARKCLOUDCOVER(dataframe)
+# # Evening Doji Star: values [0, 100]
+# dataframe["CDLEVENINGDOJISTAR"] = ta.CDLEVENINGDOJISTAR(dataframe)
+# # Evening Star: values [0, 100]
+# dataframe["CDLEVENINGSTAR"] = ta.CDLEVENINGSTAR(dataframe)
+
+# Pattern Recognition - Bullish/Bearish candlestick patterns
+# ------------------------------------
+# # Three Line Strike: values [0, -100, 100]
+# dataframe["CDL3LINESTRIKE"] = ta.CDL3LINESTRIKE(dataframe)
+# # Spinning Top: values [0, -100, 100]
+# dataframe["CDLSPINNINGTOP"] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+# # Engulfing: values [0, -100, 100]
+# dataframe["CDLENGULFING"] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+# # Harami: values [0, -100, 100]
+# dataframe["CDLHARAMI"] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+# # Three Outside Up/Down: values [0, -100, 100]
+# dataframe["CDL3OUTSIDE"] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+# # Three Inside Up/Down: values [0, -100, 100]
+# dataframe["CDL3INSIDE"] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+
+# # Chart type
+# # ------------------------------------
+# # Heikin Ashi Strategy
+# heikinashi = qtpylib.heikinashi(dataframe)
+# dataframe["ha_open"] = heikinashi["open"]
+# dataframe["ha_close"] = heikinashi["close"]
+# dataframe["ha_high"] = heikinashi["high"]
+# dataframe["ha_low"] = heikinashi["low"]
+
+# Retrieve best bid and best ask from the orderbook
+# ------------------------------------
+"""
+# first check if dataprovider is available
+if self.dp:
+    if self.dp.runmode.value in ("live", "dry_run"):
+        ob = self.dp.orderbook(metadata["pair"], 1)
+        dataframe["best_bid"] = ob["bids"][0][0]
+        dataframe["best_ask"] = ob["asks"][0][0]
+"""
diff --git a/freqtrade/templates/strategy_subtemplates/indicators_minimal.j2 b/freqtrade/templates/strategy_subtemplates/indicators_minimal.j2
new file mode 100644
index 000000000..1594a8988
--- /dev/null
+++ b/freqtrade/templates/strategy_subtemplates/indicators_minimal.j2
@@ -0,0 +1,17 @@
+
+# Momentum Indicators
+# ------------------------------------
+
+# RSI
+dataframe["rsi"] = ta.RSI(dataframe)
+
+# Retrieve best bid and best ask from the orderbook
+# ------------------------------------
+"""
+# first check if dataprovider is available
+if self.dp:
+    if self.dp.runmode.value in ("live", "dry_run"):
+        ob = self.dp.orderbook(metadata["pair"], 1)
+        dataframe["best_bid"] = ob["bids"][0][0]
+        dataframe["best_ask"] = ob["asks"][0][0]
+"""
diff --git a/freqtrade/templates/strategy_subtemplates/plot_config_full.j2 b/freqtrade/templates/strategy_subtemplates/plot_config_full.j2
new file mode 100644
index 000000000..08eb3c29f
--- /dev/null
+++ b/freqtrade/templates/strategy_subtemplates/plot_config_full.j2
@@ -0,0 +1,20 @@
+
+@property
+def plot_config(self):
+    return {
+        # Main plot indicators (Moving averages, ...)
+        "main_plot": {
+            "tema": {},
+            "sar": {"color": "white"},
+        },
+        "subplots": {
+            # Subplots - each dict defines one additional plot
+            "MACD": {
+                "macd": {"color": "blue"},
+                "macdsignal": {"color": "orange"},
+            },
+            "RSI": {
+                "rsi": {"color": "red"},
+            }
+        }
+    }
diff --git a/freqtrade/templates/strategy_subtemplates/plot_config_minimal.j2 b/freqtrade/templates/strategy_subtemplates/plot_config_minimal.j2
new file mode 100644
index 000000000..e69de29bb
diff --git a/freqtrade/templates/strategy_subtemplates/sell_trend_full.j2 b/freqtrade/templates/strategy_subtemplates/sell_trend_full.j2
new file mode 100644
index 000000000..08cb68cd1
--- /dev/null
+++ b/freqtrade/templates/strategy_subtemplates/sell_trend_full.j2
@@ -0,0 +1,3 @@
+(qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
+(dataframe["tema"] > dataframe["bb_middleband"]) &  # Guard: tema above BB middle
+(dataframe["tema"] < dataframe["tema"].shift(1)) &  # Guard: tema is falling
diff --git a/freqtrade/templates/strategy_subtemplates/sell_trend_minimal.j2 b/freqtrade/templates/strategy_subtemplates/sell_trend_minimal.j2
new file mode 100644
index 000000000..821b547c3
--- /dev/null
+++ b/freqtrade/templates/strategy_subtemplates/sell_trend_minimal.j2
@@ -0,0 +1 @@
+(qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
diff --git a/freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2 b/freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2
new file mode 100644
index 000000000..5ae361996
--- /dev/null
+++ b/freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2
@@ -0,0 +1,13 @@
+# Optional order type mapping.
+order_types = {
+    "entry": "limit",
+    "exit": "limit",
+    "stoploss": "market",
+    "stoploss_on_exchange": False
+}
+
+# Optional order time in force.
+order_time_in_force = {
+    "entry": "GTC",
+    "exit": "GTC"
+}
diff --git a/freqtrade/templates/strategy_subtemplates/strategy_attributes_minimal.j2 b/freqtrade/templates/strategy_subtemplates/strategy_attributes_minimal.j2
new file mode 100644
index 000000000..e69de29bb
diff --git a/freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2 b/freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2
new file mode 100644
index 000000000..dc133c75a
--- /dev/null
+++ b/freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2
@@ -0,0 +1,401 @@
+
+def bot_loop_start(self, current_time: datetime, **kwargs) -> None:
+    """
+    Called at the start of the bot iteration (one loop).
+    Might be used to perform pair-independent tasks
+    (e.g. gather some remote resource for comparison)
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, this simply does nothing.
+    :param current_time: datetime object, containing the current datetime
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    """
+    pass
+
+def custom_entry_price(
+    self,
+    pair: str,
+    trade: Trade | None,
+    current_time: datetime,
+    proposed_rate: float,
+    entry_tag: str | None,
+    side: str,
+    **kwargs,
+) -> float:
+    """
+    Custom entry price logic, returning the new entry price.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, returns None, orderbook is used to set entry price
+
+    :param pair: Pair that's currently analyzed
+    :param trade: trade object (None for initial entries).
+    :param current_time: datetime object, containing the current datetime
+    :param proposed_rate: Rate, calculated based on pricing settings in exit_pricing.
+    :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return float: New entry price value if provided
+    """
+    return proposed_rate
+
+def adjust_order_price(
+    self,
+    trade: Trade,
+    order: Order | None,
+    pair: str,
+    current_time: datetime,
+    proposed_rate: float,
+    current_order_rate: float,
+    entry_tag: str | None,
+    side: str,
+    is_entry: bool,
+    **kwargs,
+) -> float | None:
+    """
+    Exit and entry order price re-adjustment logic, returning the user desired limit price.
+    This only executes when a order was already placed, still open (unfilled fully or partially)
+    and not timed out on subsequent candles after entry trigger.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-callbacks/
+
+    When not implemented by a strategy, returns current_order_rate as default.
+    If current_order_rate is returned then the existing order is maintained.
+    If None is returned then order gets canceled but not replaced by a new one.
+
+    :param pair: Pair that's currently analyzed
+    :param trade: Trade object.
+    :param order: Order object
+    :param current_time: datetime object, containing the current datetime
+    :param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
+    :param current_order_rate: Rate of the existing order in place.
+    :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+    :param side: 'long' or 'short' - indicating the direction of the proposed trade
+    :param is_entry: True if the order is an entry order, False if it's an exit order.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return float or None: New entry price value if provided
+    """
+    return current_order_rate
+
+def custom_exit_price(
+    self,
+    pair: str,
+    trade: Trade,
+    current_time: datetime,
+    proposed_rate: float,
+    current_profit: float,
+    exit_tag: str | None,
+    **kwargs,
+) -> float:
+    """
+    Custom exit price logic, returning the new exit price.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, returns None, orderbook is used to set exit price
+
+    :param pair: Pair that's currently analyzed
+    :param trade: trade object.
+    :param current_time: datetime object, containing the current datetime
+    :param proposed_rate: Rate, calculated based on pricing settings in exit_pricing.
+    :param current_profit: Current profit (as ratio), calculated based on current_rate.
+    :param exit_tag: Exit reason.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return float: New exit price value if provided
+    """
+    return proposed_rate
+
+def custom_stake_amount(
+    self,
+    pair: str,
+    current_time: datetime,
+    current_rate: float,
+    proposed_stake: float,
+    min_stake: float | None,
+    max_stake: float,
+    leverage: float,
+    entry_tag: str | None,
+    side: str,
+    **kwargs,
+) -> float:
+    """
+    Customize stake size for each new trade.
+
+    :param pair: Pair that's currently analyzed
+    :param current_time: datetime object, containing the current datetime
+    :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+    :param proposed_stake: A stake amount proposed by the bot.
+    :param min_stake: Minimal stake size allowed by exchange.
+    :param max_stake: Balance available for trading.
+    :param leverage: Leverage selected for this trade.
+    :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+    :param side: 'long' or 'short' - indicating the direction of the proposed trade
+    :return: A stake size, which is between min_stake and max_stake.
+    """
+    return proposed_stake
+
+use_custom_stoploss = True
+
+def custom_stoploss(
+    self,
+    pair: str,
+    trade: Trade,
+    current_time: datetime,
+    current_rate: float,
+    current_profit: float,
+    after_fill: bool,
+    **kwargs,
+) -> float | None:
+    """
+    Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
+    e.g. returning -0.05 would create a stoploss 5% below current_rate.
+    The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, returns the initial stoploss value.
+    Only called when use_custom_stoploss is set to True.
+
+    :param pair: Pair that's currently analyzed
+    :param trade: trade object.
+    :param current_time: datetime object, containing the current datetime
+    :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+    :param current_profit: Current profit (as ratio), calculated based on current_rate.
+    :param after_fill: True if the stoploss is called after the order was filled.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return float: New stoploss value, relative to the current_rate
+    """
+
+def custom_exit(
+    self,
+    pair: str,
+    trade: Trade,
+    current_time: datetime,
+    current_rate: float,
+    current_profit: float,
+    **kwargs,
+) -> str | bool | None:
+    """
+    Custom exit signal logic indicating that specified position should be sold. Returning a
+    string or True from this method is equal to setting sell signal on a candle at specified
+    time. This method is not called when sell signal is set.
+
+    This method should be overridden to create sell signals that depend on trade parameters. For
+    example you could implement a sell relative to the candle when the trade was opened,
+    or a custom 1:2 risk-reward ROI.
+
+    Custom exit reason max length is 64. Exceeding characters will be removed.
+
+    :param pair: Pair that's currently analyzed
+    :param trade: trade object.
+    :param current_time: datetime object, containing the current datetime
+    :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+    :param current_profit: Current profit (as ratio), calculated based on current_rate.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return: To execute sell, return a string with custom exit reason or True. Otherwise return
+    None or False.
+    """
+    return None
+
+def confirm_trade_entry(
+    self,
+    pair: str,
+    order_type: str,
+    amount: float,
+    rate: float,
+    time_in_force: str,
+    current_time: datetime,
+    entry_tag: str | None,
+    side: str,
+    **kwargs,
+) -> bool:
+    """
+    Called right before placing a entry order.
+    Timing for this function is critical, so avoid doing heavy computations or
+    network requests in this method.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, returns True (always confirming).
+
+    :param pair: Pair that's about to be bought/shorted.
+    :param order_type: Order type (as configured in order_types). usually limit or market.
+    :param amount: Amount in target (base) currency that's going to be traded.
+    :param rate: Rate that's going to be used when using limit orders
+                 or current rate for market orders.
+    :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+    :param current_time: datetime object, containing the current datetime
+    :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+    :param side: 'long' or 'short' - indicating the direction of the proposed trade
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return bool: When True is returned, then the buy-order is placed on the exchange.
+        False aborts the process
+    """
+    return True
+
+def confirm_trade_exit(
+    self,
+    pair: str,
+    trade: Trade,
+    order_type: str,
+    amount: float,
+    rate: float,
+    time_in_force: str,
+    exit_reason: str,
+    current_time: datetime,
+    **kwargs,
+) -> bool:
+    """
+    Called right before placing a regular exit order.
+    Timing for this function is critical, so avoid doing heavy computations or
+    network requests in this method.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, returns True (always confirming).
+
+    :param pair: Pair for trade that's about to be exited.
+    :param trade: trade object.
+    :param order_type: Order type (as configured in order_types). usually limit or market.
+    :param amount: Amount in base currency.
+    :param rate: Rate that's going to be used when using limit orders
+                 or current rate for market orders.
+    :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+    :param exit_reason: Exit reason.
+        Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
+                        'exit_signal', 'force_exit', 'emergency_exit']
+    :param current_time: datetime object, containing the current datetime
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return bool: When True, then the exit-order is placed on the exchange.
+        False aborts the process
+    """
+    return True
+
+def check_entry_timeout(
+    self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs
+) -> bool:
+    """
+    Check entry timeout function callback.
+    This method can be used to override the entry-timeout.
+    It is called whenever a limit entry order has been created,
+    and is not yet fully filled.
+    Configuration options in `unfilledtimeout` will be verified before this,
+    so ensure to set these timeouts high enough.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, this simply returns False.
+    :param pair: Pair the trade is for
+    :param trade: Trade object.
+    :param order: Order object.
+    :param current_time: datetime object, containing the current datetime
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return bool: When True is returned, then the entry order is cancelled.
+    """
+    return False
+
+def check_exit_timeout(
+    self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs
+) -> bool:
+    """
+    Check exit timeout function callback.
+    This method can be used to override the exit-timeout.
+    It is called whenever a limit exit order has been created,
+    and is not yet fully filled.
+    Configuration options in `unfilledtimeout` will be verified before this,
+    so ensure to set these timeouts high enough.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, this simply returns False.
+    :param pair: Pair the trade is for
+    :param trade: Trade object.
+    :param order: Order object.
+    :param current_time: datetime object, containing the current datetime
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return bool: When True is returned, then the exit-order is cancelled.
+    """
+    return False
+
+def adjust_trade_position(
+    self,
+    trade: Trade,
+    current_time: datetime,
+    current_rate: float,
+    current_profit: float,
+    min_stake: float | None,
+    max_stake: float,
+    current_entry_rate: float,
+    current_exit_rate: float,
+    current_entry_profit: float,
+    current_exit_profit: float,
+    **kwargs,
+) -> float | None | tuple[float | None, str | None]:
+    """
+    Custom trade adjustment logic, returning the stake amount that a trade should be
+    increased or decreased.
+    This means extra entry or exit orders with additional fees.
+    Only called when `position_adjustment_enable` is set to True.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, returns None
+
+    :param trade: trade object.
+    :param current_time: datetime object, containing the current datetime
+    :param current_rate: Current entry rate (same as current_entry_profit)
+    :param current_profit: Current profit (as ratio), calculated based on current_rate
+                            (same as current_entry_profit).
+    :param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
+    :param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
+    :param current_entry_rate: Current rate using entry pricing.
+    :param current_exit_rate: Current rate using exit pricing.
+    :param current_entry_profit: Current profit using entry pricing.
+    :param current_exit_profit: Current profit using exit pricing.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return float: Stake amount to adjust your trade,
+                   Positive values to increase position, Negative values to decrease position.
+                   Return None for no action.
+    """
+    return None
+
+def leverage(
+    self,
+    pair: str,
+    current_time: datetime,
+    current_rate: float,
+    proposed_leverage: float,
+    max_leverage: float,
+    entry_tag: str | None,
+    side: str,
+    **kwargs,
+) -> float:
+    """
+    Customize leverage for each new trade. This method is only called in futures mode.
+
+    :param pair: Pair that's currently analyzed
+    :param current_time: datetime object, containing the current datetime
+    :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
+    :param proposed_leverage: A leverage proposed by the bot.
+    :param max_leverage: Max leverage allowed on this pair
+    :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+    :param side: 'long' or 'short' - indicating the direction of the proposed trade
+    :return: A leverage amount, which is between 1.0 and max_leverage.
+    """
+    return 1.0
+
+
+def order_filled(
+    self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs
+) -> None:
+    """
+    Called right after an order fills.
+    Will be called for all order types (entry, exit, stoploss, position adjustment).
+    :param pair: Pair for trade
+    :param trade: trade object.
+    :param order: Order object.
+    :param current_time: datetime object, containing the current datetime
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    """
+    pass
diff --git a/freqtrade/templates/strategy_subtemplates/strategy_methods_empty.j2 b/freqtrade/templates/strategy_subtemplates/strategy_methods_empty.j2
new file mode 100644
index 000000000..e69de29bb
diff --git a/freqtrade/templates/subtemplates/exchange_binance.j2 b/freqtrade/templates/subtemplates/exchange_binance.j2
new file mode 100644
index 000000000..dc2272119
--- /dev/null
+++ b/freqtrade/templates/subtemplates/exchange_binance.j2
@@ -0,0 +1,12 @@
+"exchange": {
+    "name": "{{ exchange_name | lower }}",
+    "key": "{{ exchange_key }}",
+    "secret": "{{ exchange_secret }}",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    "pair_whitelist": [
+    ],
+    "pair_blacklist": [
+        "BNB/.*"
+    ]
+}
diff --git a/freqtrade/templates/subtemplates/exchange_bittrex.j2 b/freqtrade/templates/subtemplates/exchange_bittrex.j2
new file mode 100644
index 000000000..118e8104e
--- /dev/null
+++ b/freqtrade/templates/subtemplates/exchange_bittrex.j2
@@ -0,0 +1,18 @@
+"order_types": {
+    "entry": "limit",
+    "exit": "limit",
+    "emergency_exit": "limit",
+    "stoploss": "limit",
+    "stoploss_on_exchange": false
+},
+"exchange": {
+    "name": "{{ exchange_name | lower }}",
+    "key": "{{ exchange_key }}",
+    "secret": "{{ exchange_secret }}",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    "pair_whitelist": [
+    ],
+    "pair_blacklist": [
+    ]
+}
diff --git a/freqtrade/templates/subtemplates/exchange_gateio.j2 b/freqtrade/templates/subtemplates/exchange_gateio.j2
new file mode 100644
index 000000000..63dff052f
--- /dev/null
+++ b/freqtrade/templates/subtemplates/exchange_gateio.j2
@@ -0,0 +1,12 @@
+"exchange": {
+    "name": "{{ exchange_name | lower }}",
+    "key": "{{ exchange_key }}",
+    "secret": "{{ exchange_secret }}",
+    "unknown_fee_rate": 1,
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    "pair_whitelist": [
+    ],
+    "pair_blacklist": [
+    ]
+}
diff --git a/freqtrade/templates/subtemplates/exchange_generic.j2 b/freqtrade/templates/subtemplates/exchange_generic.j2
new file mode 100644
index 000000000..01f637638
--- /dev/null
+++ b/freqtrade/templates/subtemplates/exchange_generic.j2
@@ -0,0 +1,11 @@
+"exchange": {
+    "name": "{{ exchange_name | lower }}",
+    "key": "{{ exchange_key }}",
+    "secret": "{{ exchange_secret }}",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    "pair_whitelist": [
+    ],
+    "pair_blacklist": [
+    ]
+}
diff --git a/freqtrade/templates/subtemplates/exchange_huobi.j2 b/freqtrade/templates/subtemplates/exchange_huobi.j2
new file mode 100644
index 000000000..3cb521785
--- /dev/null
+++ b/freqtrade/templates/subtemplates/exchange_huobi.j2
@@ -0,0 +1,12 @@
+"exchange": {
+    "name": "{{ exchange_name | lower }}",
+    "key": "{{ exchange_key }}",
+    "secret": "{{ exchange_secret }}",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    "pair_whitelist": [
+    ],
+    "pair_blacklist": [
+        "HT/.*"
+    ]
+}
diff --git a/freqtrade/templates/subtemplates/exchange_kraken.j2 b/freqtrade/templates/subtemplates/exchange_kraken.j2
new file mode 100644
index 000000000..7dd646720
--- /dev/null
+++ b/freqtrade/templates/subtemplates/exchange_kraken.j2
@@ -0,0 +1,13 @@
+"download_trades": true,
+"exchange": {
+    "name": "kraken",
+    "key": "{{ exchange_key }}",
+    "secret": "{{ exchange_secret }}",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    "pair_whitelist": [
+    ],
+    "pair_blacklist": [
+
+    ]
+}
diff --git a/freqtrade/templates/subtemplates/exchange_kucoin.j2 b/freqtrade/templates/subtemplates/exchange_kucoin.j2
new file mode 100644
index 000000000..b797dda41
--- /dev/null
+++ b/freqtrade/templates/subtemplates/exchange_kucoin.j2
@@ -0,0 +1,12 @@
+"exchange": {
+    "name": "{{ exchange_name | lower }}",
+    "key": "{{ exchange_key }}",
+    "secret": "{{ exchange_secret }}",
+    "password": "{{ exchange_key_password }}",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    "pair_whitelist": [
+    ],
+    "pair_blacklist": [
+    ]
+}
diff --git a/freqtrade/templates/subtemplates/exchange_okex.j2 b/freqtrade/templates/subtemplates/exchange_okex.j2
new file mode 100644
index 000000000..b797dda41
--- /dev/null
+++ b/freqtrade/templates/subtemplates/exchange_okex.j2
@@ -0,0 +1,12 @@
+"exchange": {
+    "name": "{{ exchange_name | lower }}",
+    "key": "{{ exchange_key }}",
+    "secret": "{{ exchange_secret }}",
+    "password": "{{ exchange_key_password }}",
+    "ccxt_config": {},
+    "ccxt_async_config": {},
+    "pair_whitelist": [
+    ],
+    "pair_blacklist": [
+    ]
+}
diff --git a/freqtrade/tests/conftest.py b/freqtrade/tests/conftest.py
deleted file mode 100644
index edeb89a59..000000000
--- a/freqtrade/tests/conftest.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# pragma pylint: disable=missing-docstring
-from datetime import datetime
-from unittest.mock import MagicMock
-from functools import reduce
-
-import json
-import arrow
-import pytest
-from jsonschema import validate
-from telegram import Chat, Message, Update
-from freqtrade.analyze import parse_ticker_dataframe
-from freqtrade.strategy.strategy import Strategy
-
-from freqtrade.misc import CONF_SCHEMA
-
-
-def log_has(line, logs):
-    # caplog mocker returns log as a tuple: ('freqtrade.analyze', logging.WARNING, 'foobar')
-    # and we want to match line against foobar in the tuple
-    return reduce(lambda a, b: a or b,
-                  filter(lambda x: x[2] == line, logs),
-                  False)
-
-
-@pytest.fixture(scope="module")
-def default_conf():
-    """ Returns validated configuration suitable for most tests """
-    configuration = {
-        "max_open_trades": 1,
-        "stake_currency": "BTC",
-        "stake_amount": 0.001,
-        "fiat_display_currency": "USD",
-        "ticker_interval": 5,
-        "dry_run": True,
-        "minimal_roi": {
-            "40": 0.0,
-            "30": 0.01,
-            "20": 0.02,
-            "0": 0.04
-        },
-        "stoploss": -0.10,
-        "unfilledtimeout": 600,
-        "bid_strategy": {
-            "ask_last_balance": 0.0
-        },
-        "exchange": {
-            "name": "bittrex",
-            "enabled": True,
-            "key": "key",
-            "secret": "secret",
-            "pair_whitelist": [
-                "BTC_ETH",
-                "BTC_TKN",
-                "BTC_TRST",
-                "BTC_SWT",
-                "BTC_BCC"
-            ]
-        },
-        "telegram": {
-            "enabled": True,
-            "token": "token",
-            "chat_id": "0"
-        },
-        "initial_state": "running"
-    }
-    validate(configuration, CONF_SCHEMA)
-    return configuration
-
-
-@pytest.fixture
-def update():
-    _update = Update(0)
-    _update.message = Message(0, 0, datetime.utcnow(), Chat(0, 0))
-    return _update
-
-
-@pytest.fixture
-def ticker():
-    return MagicMock(return_value={
-        'bid': 0.00001098,
-        'ask': 0.00001099,
-        'last': 0.00001098,
-    })
-
-
-@pytest.fixture
-def ticker_sell_up():
-    return MagicMock(return_value={
-        'bid': 0.00001172,
-        'ask': 0.00001173,
-        'last': 0.00001172,
-    })
-
-
-@pytest.fixture
-def ticker_sell_down():
-    return MagicMock(return_value={
-        'bid': 0.00001044,
-        'ask': 0.00001043,
-        'last': 0.00001044,
-    })
-
-
-@pytest.fixture
-def health():
-    return MagicMock(return_value=[{
-        'Currency': 'BTC',
-        'IsActive': True,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }, {
-        'Currency': 'ETH',
-        'IsActive': True,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }, {
-        'Currency': 'TRST',
-        'IsActive': True,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }, {
-        'Currency': 'SWT',
-        'IsActive': True,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }, {
-        'Currency': 'BCC',
-        'IsActive': False,
-        'LastChecked': '2017-11-13T20:15:00.00',
-        'Notice': None
-    }])
-
-
-@pytest.fixture
-def limit_buy_order():
-    return {
-        'id': 'mocked_limit_buy',
-        'type': 'LIMIT_BUY',
-        'pair': 'mocked',
-        'opened': str(arrow.utcnow().datetime),
-        'rate': 0.00001099,
-        'amount': 90.99181073,
-        'remaining': 0.0,
-        'closed': str(arrow.utcnow().datetime),
-    }
-
-
-@pytest.fixture
-def limit_buy_order_old():
-    return {
-        'id': 'mocked_limit_buy_old',
-        'type': 'LIMIT_BUY',
-        'pair': 'BTC_ETH',
-        'opened': str(arrow.utcnow().shift(minutes=-601).datetime),
-        'rate': 0.00001099,
-        'amount': 90.99181073,
-        'remaining': 90.99181073,
-    }
-
-
-@pytest.fixture
-def limit_sell_order_old():
-    return {
-        'id': 'mocked_limit_sell_old',
-        'type': 'LIMIT_SELL',
-        'pair': 'BTC_ETH',
-        'opened': str(arrow.utcnow().shift(minutes=-601).datetime),
-        'rate': 0.00001099,
-        'amount': 90.99181073,
-        'remaining': 90.99181073,
-    }
-
-
-@pytest.fixture
-def limit_buy_order_old_partial():
-    return {
-        'id': 'mocked_limit_buy_old_partial',
-        'type': 'LIMIT_BUY',
-        'pair': 'BTC_ETH',
-        'opened': str(arrow.utcnow().shift(minutes=-601).datetime),
-        'rate': 0.00001099,
-        'amount': 90.99181073,
-        'remaining': 67.99181073,
-    }
-
-
-@pytest.fixture
-def limit_sell_order():
-    return {
-        'id': 'mocked_limit_sell',
-        'type': 'LIMIT_SELL',
-        'pair': 'mocked',
-        'opened': str(arrow.utcnow().datetime),
-        'rate': 0.00001173,
-        'amount': 90.99181073,
-        'remaining': 0.0,
-        'closed': str(arrow.utcnow().datetime),
-    }
-
-
-@pytest.fixture
-def ticker_history():
-    return [
-        {
-            "O": 8.794e-05,
-            "H": 8.948e-05,
-            "L": 8.794e-05,
-            "C": 8.88e-05,
-            "V": 991.09056638,
-            "T": "2017-11-26T08:50:00",
-            "BV": 0.0877869
-        },
-        {
-            "O": 8.88e-05,
-            "H": 8.942e-05,
-            "L": 8.88e-05,
-            "C": 8.893e-05,
-            "V": 658.77935965,
-            "T": "2017-11-26T08:55:00",
-            "BV": 0.05874751
-        },
-        {
-            "O": 8.891e-05,
-            "H": 8.893e-05,
-            "L": 8.875e-05,
-            "C": 8.877e-05,
-            "V": 7920.73570705,
-            "T": "2017-11-26T09:00:00",
-            "BV": 0.7039405
-        }
-    ]
-
-
-@pytest.fixture
-def ticker_history_without_bv():
-    return [
-        {
-            "O": 8.794e-05,
-            "H": 8.948e-05,
-            "L": 8.794e-05,
-            "C": 8.88e-05,
-            "V": 991.09056638,
-            "T": "2017-11-26T08:50:00"
-        },
-        {
-            "O": 8.88e-05,
-            "H": 8.942e-05,
-            "L": 8.88e-05,
-            "C": 8.893e-05,
-            "V": 658.77935965,
-            "T": "2017-11-26T08:55:00"
-        },
-        {
-            "O": 8.891e-05,
-            "H": 8.893e-05,
-            "L": 8.875e-05,
-            "C": 8.877e-05,
-            "V": 7920.73570705,
-            "T": "2017-11-26T09:00:00"
-        }
-    ]
-
-
-# FIX: Perhaps change result fixture to use BTC_UNITEST instead?
-@pytest.fixture
-def result():
-    with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
-        return parse_ticker_dataframe(json.load(data_file))
-
-
-@pytest.fixture
-def default_strategy():
-    strategy = Strategy()
-    strategy.init({'strategy': 'default_strategy'})
-    return strategy
-
-
-# FIX:
-# Create an fixture/function
-# that inserts a trade of some type and open-status
-# return the open-order-id
-# See tests in rpc/main that could use this
diff --git a/freqtrade/tests/exchange/test_exchange.py b/freqtrade/tests/exchange/test_exchange.py
deleted file mode 100644
index 4f6eab083..000000000
--- a/freqtrade/tests/exchange/test_exchange.py
+++ /dev/null
@@ -1,285 +0,0 @@
-# pragma pylint: disable=missing-docstring, C0103, bad-continuation, global-statement
-# pragma pylint: disable=protected-access
-from unittest.mock import MagicMock
-from random import randint
-import logging
-from requests.exceptions import RequestException
-import pytest
-
-from freqtrade import OperationalException
-from freqtrade.exchange import init, validate_pairs, buy, sell, get_balance, get_balances, \
-    get_ticker, get_ticker_history, cancel_order, get_name, get_fee
-import freqtrade.exchange as exchange
-from freqtrade.tests.conftest import log_has
-
-API_INIT = False
-
-
-def maybe_init_api(conf, mocker):
-    global API_INIT
-    if not API_INIT:
-        mocker.patch('freqtrade.exchange.validate_pairs',
-                     side_effect=lambda s: True)
-        init(config=conf)
-        API_INIT = True
-
-
-def test_init(default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    maybe_init_api(default_conf, mocker)
-    assert log_has('Instance is running with dry_run enabled', caplog.record_tuples)
-
-
-def test_init_exception(default_conf):
-    default_conf['exchange']['name'] = 'wrong_exchange_name'
-
-    with pytest.raises(
-            OperationalException,
-            match='Exchange {} is not supported'.format(default_conf['exchange']['name'])):
-        init(config=default_conf)
-
-
-def test_validate_pairs(default_conf, mocker):
-    api_mock = MagicMock()
-    api_mock.get_markets = MagicMock(return_value=[
-        'BTC_ETH', 'BTC_TKN', 'BTC_TRST', 'BTC_SWT', 'BTC_BCC',
-    ])
-    mocker.patch('freqtrade.exchange._API', api_mock)
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-    validate_pairs(default_conf['exchange']['pair_whitelist'])
-
-
-def test_validate_pairs_not_available(default_conf, mocker):
-    api_mock = MagicMock()
-    api_mock.get_markets = MagicMock(return_value=[])
-    mocker.patch('freqtrade.exchange._API', api_mock)
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-    with pytest.raises(OperationalException, match=r'not available'):
-        validate_pairs(default_conf['exchange']['pair_whitelist'])
-
-
-def test_validate_pairs_not_compatible(default_conf, mocker):
-    api_mock = MagicMock()
-    api_mock.get_markets = MagicMock(
-        return_value=['BTC_ETH', 'BTC_TKN', 'BTC_TRST', 'BTC_SWT'])
-    default_conf['stake_currency'] = 'ETH'
-    mocker.patch('freqtrade.exchange._API', api_mock)
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-    with pytest.raises(OperationalException, match=r'not compatible'):
-        validate_pairs(default_conf['exchange']['pair_whitelist'])
-
-
-def test_validate_pairs_exception(default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    api_mock = MagicMock()
-    api_mock.get_markets = MagicMock(side_effect=RequestException())
-    mocker.patch('freqtrade.exchange._API', api_mock)
-
-    # with pytest.raises(RequestException, match=r'Unable to validate pairs'):
-    validate_pairs(default_conf['exchange']['pair_whitelist'])
-    assert log_has('Unable to validate pairs (assuming they are correct). Reason: ',
-                   caplog.record_tuples)
-
-
-def test_buy_dry_run(default_conf, mocker):
-    default_conf['dry_run'] = True
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-
-    assert 'dry_run_buy_' in buy(pair='BTC_ETH', rate=200, amount=1)
-
-
-def test_buy_prod(default_conf, mocker):
-    api_mock = MagicMock()
-    api_mock.buy = MagicMock(
-        return_value='dry_run_buy_{}'.format(randint(0, 10**6)))
-    mocker.patch('freqtrade.exchange._API', api_mock)
-
-    default_conf['dry_run'] = False
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-
-    assert 'dry_run_buy_' in buy(pair='BTC_ETH', rate=200, amount=1)
-
-
-def test_sell_dry_run(default_conf, mocker):
-    default_conf['dry_run'] = True
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-
-    assert 'dry_run_sell_' in sell(pair='BTC_ETH', rate=200, amount=1)
-
-
-def test_sell_prod(default_conf, mocker):
-    api_mock = MagicMock()
-    api_mock.sell = MagicMock(
-        return_value='dry_run_sell_{}'.format(randint(0, 10**6)))
-    mocker.patch('freqtrade.exchange._API', api_mock)
-
-    default_conf['dry_run'] = False
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-
-    assert 'dry_run_sell_' in sell(pair='BTC_ETH', rate=200, amount=1)
-
-
-def test_get_balance_dry_run(default_conf, mocker):
-    default_conf['dry_run'] = True
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-
-    assert get_balance(currency='BTC') == 999.9
-
-
-def test_get_balance_prod(default_conf, mocker):
-    api_mock = MagicMock()
-    api_mock.get_balance = MagicMock(return_value=123.4)
-    mocker.patch('freqtrade.exchange._API', api_mock)
-
-    default_conf['dry_run'] = False
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-
-    assert get_balance(currency='BTC') == 123.4
-
-
-def test_get_balances_dry_run(default_conf, mocker):
-    default_conf['dry_run'] = True
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-
-    assert get_balances() == []
-
-
-def test_get_balances_prod(default_conf, mocker):
-    balance_item = {
-        'Currency': '1ST',
-        'Balance': 10.0,
-        'Available': 10.0,
-        'Pending': 0.0,
-        'CryptoAddress': None
-    }
-
-    api_mock = MagicMock()
-    api_mock.get_balances = MagicMock(
-        return_value=[balance_item, balance_item, balance_item])
-    mocker.patch('freqtrade.exchange._API', api_mock)
-
-    default_conf['dry_run'] = False
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-
-    assert len(get_balances()) == 3
-    assert get_balances()[0]['Currency'] == '1ST'
-    assert get_balances()[0]['Balance'] == 10.0
-    assert get_balances()[0]['Available'] == 10.0
-    assert get_balances()[0]['Pending'] == 0.0
-
-
-# This test is somewhat redundant with
-# test_exchange_bittrex.py::test_exchange_bittrex_get_ticker
-def test_get_ticker(default_conf, mocker):
-    maybe_init_api(default_conf, mocker)
-    api_mock = MagicMock()
-    tick = {"success": True, 'result': {'Bid': 0.00001098, 'Ask': 0.00001099, 'Last': 0.0001}}
-    api_mock.get_ticker = MagicMock(return_value=tick)
-    mocker.patch('freqtrade.exchange.bittrex._API', api_mock)
-
-    # retrieve original ticker
-    ticker = get_ticker(pair='BTC_ETH')
-    assert ticker['bid'] == 0.00001098
-    assert ticker['ask'] == 0.00001099
-
-    # change the ticker
-    tick = {"success": True, 'result': {"Bid": 0.5, "Ask": 1, "Last": 42}}
-    api_mock.get_ticker = MagicMock(return_value=tick)
-    mocker.patch('freqtrade.exchange.bittrex._API', api_mock)
-
-    # if not caching the result we should get the same ticker
-    # if not fetching a new result we should get the cached ticker
-    ticker = get_ticker(pair='BTC_ETH', refresh=False)
-    assert ticker['bid'] == 0.00001098
-    assert ticker['ask'] == 0.00001099
-
-    # force ticker refresh
-    ticker = get_ticker(pair='BTC_ETH', refresh=True)
-    assert ticker['bid'] == 0.5
-    assert ticker['ask'] == 1
-
-
-def test_get_ticker_history(default_conf, mocker):
-    api_mock = MagicMock()
-    tick = 123
-    api_mock.get_ticker_history = MagicMock(return_value=tick)
-    mocker.patch('freqtrade.exchange._API', api_mock)
-
-    # retrieve original ticker
-    ticks = get_ticker_history('BTC_ETH', int(default_conf['ticker_interval']))
-    assert ticks == 123
-
-    # change the ticker
-    tick = 999
-    api_mock.get_ticker_history = MagicMock(return_value=tick)
-    mocker.patch('freqtrade.exchange._API', api_mock)
-
-    # ensure caching will still return the original ticker
-    ticks = get_ticker_history('BTC_ETH', int(default_conf['ticker_interval']))
-    assert ticks == 123
-
-
-def test_cancel_order_dry_run(default_conf, mocker):
-    default_conf['dry_run'] = True
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-
-    assert cancel_order(order_id='123') is None
-
-
-# Ensure that if not dry_run, we should call API
-def test_cancel_order(default_conf, mocker):
-    default_conf['dry_run'] = False
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-    api_mock = MagicMock()
-    api_mock.cancel_order = MagicMock(return_value=123)
-    mocker.patch('freqtrade.exchange._API', api_mock)
-    assert cancel_order(order_id='_') == 123
-
-
-def test_get_order(default_conf, mocker):
-    default_conf['dry_run'] = True
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-    order = MagicMock()
-    order.myid = 123
-    exchange._DRY_RUN_OPEN_ORDERS['X'] = order
-    print(exchange.get_order('X'))
-    assert exchange.get_order('X').myid == 123
-
-    default_conf['dry_run'] = False
-    mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
-    api_mock = MagicMock()
-    api_mock.get_order = MagicMock(return_value=456)
-    mocker.patch('freqtrade.exchange._API', api_mock)
-    assert exchange.get_order('X') == 456
-
-
-def test_get_name(default_conf, mocker):
-    mocker.patch('freqtrade.exchange.validate_pairs',
-                 side_effect=lambda s: True)
-    default_conf['exchange']['name'] = 'bittrex'
-    init(default_conf)
-
-    assert get_name() == 'Bittrex'
-
-
-def test_get_fee(default_conf, mocker):
-    mocker.patch('freqtrade.exchange.validate_pairs',
-                 side_effect=lambda s: True)
-    init(default_conf)
-
-    assert get_fee() == 0.0025
-
-
-def test_exchange_misc(mocker):
-    api_mock = MagicMock()
-    mocker.patch('freqtrade.exchange._API', api_mock)
-    exchange.get_markets()
-    assert api_mock.get_markets.call_count == 1
-    exchange.get_market_summaries()
-    assert api_mock.get_market_summaries.call_count == 1
-    api_mock.name = 123
-    assert exchange.get_name() == 123
-    api_mock.fee = 456
-    assert exchange.get_fee() == 456
-    exchange.get_wallet_health()
-    assert api_mock.get_wallet_health.call_count == 1
diff --git a/freqtrade/tests/exchange/test_exchange_bittrex.py b/freqtrade/tests/exchange/test_exchange_bittrex.py
deleted file mode 100644
index 058c25de1..000000000
--- a/freqtrade/tests/exchange/test_exchange_bittrex.py
+++ /dev/null
@@ -1,347 +0,0 @@
-# pragma pylint: disable=missing-docstring, C0103, protected-access, unused-argument
-
-from unittest.mock import MagicMock
-import pytest
-from requests.exceptions import ContentDecodingError
-from freqtrade.exchange.bittrex import Bittrex
-import freqtrade.exchange.bittrex as btx
-
-
-# Eat this flake8
-# +------------------+
-# |  bittrex.Bittrex |
-# +------------------+
-#         |
-#       (mock Fake_bittrex)
-#         |
-# +-----------------------------+
-# |  freqtrade.exchange.Bittrex |
-# +-----------------------------+
-# Call into Bittrex will flow up to the
-# external package bittrex.Bittrex.
-# By inserting a mock, we redirect those
-# calls.
-# The faked bittrex API is called just 'fb'
-# The freqtrade.exchange.Bittrex is a
-# wrapper, and is called 'wb'
-
-
-def _stub_config():
-    return {'key': '',
-            'secret': ''}
-
-
-class FakeBittrex():
-    def __init__(self, success=True):
-        self.success = True  # Believe in yourself
-        self.result = None
-        self.get_ticker_call_count = 0
-        # This is really ugly, doing side-effect during instance creation
-        # But we're allowed to in testing-code
-        btx._API = MagicMock()
-        btx._API.buy_limit = self.fake_buysell_limit
-        btx._API.sell_limit = self.fake_buysell_limit
-        btx._API.get_balance = self.fake_get_balance
-        btx._API.get_balances = self.fake_get_balances
-        btx._API.get_ticker = self.fake_get_ticker
-        btx._API.get_order = self.fake_get_order
-        btx._API.cancel = self.fake_cancel_order
-        btx._API.get_markets = self.fake_get_markets
-        btx._API.get_market_summaries = self.fake_get_market_summaries
-        btx._API_V2 = MagicMock()
-        btx._API_V2.get_candles = self.fake_get_candles
-        btx._API_V2.get_wallet_health = self.fake_get_wallet_health
-
-    def fake_buysell_limit(self, pair, amount, limit):
-        return {'success': self.success,
-                'result': {'uuid': '1234'},
-                'message': 'barter'}
-
-    def fake_get_balance(self, cur):
-        return {'success': self.success,
-                'result': {'Balance': 1234},
-                'message': 'unbalanced'}
-
-    def fake_get_balances(self):
-        return {'success': self.success,
-                'result': [{'BTC_ETH': 1234}],
-                'message': 'no balances'}
-
-    def fake_get_ticker(self, pair):
-        self.get_ticker_call_count += 1
-        return self.result or {'success': self.success,
-                               'result': {'Bid': 1, 'Ask': 1, 'Last': 1},
-                               'message': 'NO_API_RESPONSE'}
-
-    def fake_get_candles(self, pair, interval):
-        return self.result or {'success': self.success,
-                               'result': [{'C': 0, 'V': 0, 'O': 0, 'H': 0, 'L': 0, 'T': 0}],
-                               'message': 'candles lit'}
-
-    def fake_get_order(self, uuid):
-        return {'success': self.success,
-                'result': {'OrderUuid': 'ABC123',
-                           'Type': 'Type',
-                           'Exchange': 'BTC_ETH',
-                           'Opened': True,
-                           'PricePerUnit': 1,
-                           'Quantity': 1,
-                           'QuantityRemaining': 1,
-                           'Closed': True},
-                'message': 'lost'}
-
-    def fake_cancel_order(self, uuid):
-        return self.result or {'success': self.success,
-                               'message': 'no such order'}
-
-    def fake_get_markets(self):
-        return self.result or {'success': self.success,
-                               'message': 'market gone',
-                               'result': [{'MarketName': '-_'}]}
-
-    def fake_get_market_summaries(self):
-        return self.result or {'success': self.success,
-                               'message': 'no summary',
-                               'result': ['sum']}
-
-    def fake_get_wallet_health(self):
-        return self.result or {'success': self.success,
-                               'message': 'bad health',
-                               'result': [{'Health': {'Currency': 'BTC_ETH',
-                                                      'IsActive': True,
-                                                      'LastChecked': 0},
-                                           'Currency': {'Notice': True}}]}
-
-
-# The freqtrade.exchange.bittrex is called wrap_bittrex
-# to not confuse naming with bittrex.bittrex
-def make_wrap_bittrex():
-    conf = _stub_config()
-    wb = btx.Bittrex(conf)
-    return wb
-
-
-def test_exchange_bittrex_class():
-    conf = _stub_config()
-    b = Bittrex(conf)
-    assert isinstance(b, Bittrex)
-    slots = dir(b)
-    for name in ['fee', 'buy', 'sell', 'get_balance', 'get_balances',
-                 'get_ticker', 'get_ticker_history', 'get_order',
-                 'cancel_order', 'get_pair_detail_url', 'get_markets',
-                 'get_market_summaries', 'get_wallet_health']:
-        assert name in slots
-        # FIX: ensure that the slot is also a method in the class
-        # getattr(b, name) => bound method Bittrex.buy
-        # type(getattr(b, name)) => class 'method'
-
-
-def test_exchange_bittrex_fee():
-    fee = Bittrex.fee.__get__(Bittrex)
-    assert fee >= 0 and fee < 0.1  # Fee is 0-10 %
-
-
-def test_exchange_bittrex_buy_good():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    uuid = wb.buy('BTC_ETH', 1, 1)
-    assert uuid == fb.fake_buysell_limit(1, 2, 3)['result']['uuid']
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'barter.*'):
-        wb.buy('BAD', 1, 1)
-
-
-def test_exchange_bittrex_sell_good():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    uuid = wb.sell('BTC_ETH', 1, 1)
-    assert uuid == fb.fake_buysell_limit(1, 2, 3)['result']['uuid']
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'barter.*'):
-        uuid = wb.sell('BAD', 1, 1)
-
-
-def test_exchange_bittrex_get_balance():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    bal = wb.get_balance('BTC_ETH')
-    assert bal == fb.fake_get_balance(1)['result']['Balance']
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'unbalanced'):
-        wb.get_balance('BTC_ETH')
-
-
-def test_exchange_bittrex_get_balances():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    bals = wb.get_balances()
-    assert bals == fb.fake_get_balances()['result']
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'no balances'):
-        wb.get_balances()
-
-
-def test_exchange_bittrex_get_ticker():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-
-    # Poll ticker, which updates the cache
-    tick = wb.get_ticker('BTC_ETH')
-    for x in ['bid', 'ask', 'last']:
-        assert x in tick
-    # Ensure the side-effect was made (update the ticker cache)
-    assert 'BTC_ETH' in wb.cached_ticker.keys()
-
-    # taint the cache, so we can recognize the cache wall utilized
-    wb.cached_ticker['BTC_ETH']['bid'] = 1234
-    # Poll again, getting the cached result
-    fb.get_ticker_call_count = 0
-    tick = wb.get_ticker('BTC_ETH', False)
-    # Ensure the result was from the cache, and that we didn't call exchange
-    assert wb.cached_ticker['BTC_ETH']['bid'] == 1234
-    assert fb.get_ticker_call_count == 0
-
-
-def test_exchange_bittrex_get_ticker_bad():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    fb.result = {'success': True, 'result': {'Bid': 1, 'Ask': 0}}  # incomplete result
-
-    with pytest.raises(ContentDecodingError, match=r'.*Invalid response from Bittrex params.*'):
-        wb.get_ticker('BTC_ETH')
-    fb.result = {'success': False, 'message': 'gone bad'}
-    with pytest.raises(btx.OperationalException, match=r'.*gone bad.*'):
-        wb.get_ticker('BTC_ETH')
-
-    fb.result = {'success': True, 'result': {}}  # incomplete result
-    with pytest.raises(ContentDecodingError, match=r'.*Invalid response from Bittrex params.*'):
-        wb.get_ticker('BTC_ETH')
-    fb.result = {'success': False, 'message': 'gone bad'}
-    with pytest.raises(btx.OperationalException, match=r'.*gone bad.*'):
-        wb.get_ticker('BTC_ETH')
-
-    fb.result = {'success': True,
-                 'result': {'Bid': 1, 'Ask': 0, 'Last': None}}  # incomplete result
-    with pytest.raises(ContentDecodingError, match=r'.*Invalid response from Bittrex params.*'):
-        wb.get_ticker('BTC_ETH')
-
-
-def test_exchange_bittrex_get_ticker_history_intervals():
-    wb = make_wrap_bittrex()
-    FakeBittrex()
-    for tick_interval in [1, 5, 30, 60, 1440]:
-        assert ([{'C': 0, 'V': 0, 'O': 0, 'H': 0, 'L': 0, 'T': 0}] ==
-                wb.get_ticker_history('BTC_ETH', tick_interval))
-
-
-def test_exchange_bittrex_get_ticker_history():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    assert wb.get_ticker_history('BTC_ETH', 5)
-    with pytest.raises(ValueError, match=r'.*Unknown tick_interval.*'):
-        wb.get_ticker_history('BTC_ETH', 2)
-
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'candles lit.*'):
-        wb.get_ticker_history('BTC_ETH', 5)
-
-    fb.success = True
-    with pytest.raises(ContentDecodingError, match=r'.*Invalid response from Bittrex.*'):
-        fb.result = {'bad': 0}
-        wb.get_ticker_history('BTC_ETH', 5)
-
-    with pytest.raises(ContentDecodingError, match=r'.*Required property C not present.*'):
-        fb.result = {'success': True,
-                     'result': [{'V': 0, 'O': 0, 'H': 0, 'L': 0, 'T': 0}],  # close is missing
-                     'message': 'candles lit'}
-        wb.get_ticker_history('BTC_ETH', 5)
-
-
-def test_exchange_bittrex_get_order():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    order = wb.get_order('someUUID')
-    assert order['id'] == 'ABC123'
-    fb.success = False
-    with pytest.raises(btx.OperationalException, match=r'lost'):
-        wb.get_order('someUUID')
-
-
-def test_exchange_bittrex_cancel_order():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    wb.cancel_order('someUUID')
-    with pytest.raises(btx.OperationalException, match=r'no such order'):
-        fb.success = False
-        wb.cancel_order('someUUID')
-    # Note: this can be a bug in exchange.bittrex._validate_response
-    with pytest.raises(KeyError):
-        fb.result = {'success': False}  # message is missing!
-        wb.cancel_order('someUUID')
-    with pytest.raises(btx.OperationalException, match=r'foo'):
-        fb.result = {'success': False, 'message': 'foo'}
-        wb.cancel_order('someUUID')
-
-
-def test_exchange_get_pair_detail_url():
-    wb = make_wrap_bittrex()
-    assert wb.get_pair_detail_url('BTC_ETH')
-
-
-def test_exchange_get_markets():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    x = wb.get_markets()
-    assert x == ['__']
-    with pytest.raises(btx.OperationalException, match=r'market gone'):
-        fb.success = False
-        wb.get_markets()
-
-
-def test_exchange_get_market_summaries():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    assert ['sum'] == wb.get_market_summaries()
-    with pytest.raises(btx.OperationalException, match=r'no summary'):
-        fb.success = False
-        wb.get_market_summaries()
-
-
-def test_exchange_get_wallet_health():
-    wb = make_wrap_bittrex()
-    fb = FakeBittrex()
-    x = wb.get_wallet_health()
-    assert x[0]['Currency'] == 'BTC_ETH'
-    with pytest.raises(btx.OperationalException, match=r'bad health'):
-        fb.success = False
-        wb.get_wallet_health()
-
-
-def test_validate_response_success():
-    response = {
-        'message': '',
-        'result': [],
-    }
-    Bittrex._validate_response(response)
-
-
-def test_validate_response_no_api_response():
-    response = {
-        'message': 'NO_API_RESPONSE',
-        'result': None,
-    }
-    with pytest.raises(ContentDecodingError, match=r'.*NO_API_RESPONSE.*'):
-        Bittrex._validate_response(response)
-
-
-def test_validate_response_min_trade_requirement_not_met():
-    response = {
-        'message': 'MIN_TRADE_REQUIREMENT_NOT_MET',
-        'result': None,
-    }
-    with pytest.raises(ContentDecodingError, match=r'.*MIN_TRADE_REQUIREMENT_NOT_MET.*'):
-        Bittrex._validate_response(response)
diff --git a/freqtrade/tests/optimize/test_backtesting.py b/freqtrade/tests/optimize/test_backtesting.py
deleted file mode 100644
index 36e14f00e..000000000
--- a/freqtrade/tests/optimize/test_backtesting.py
+++ /dev/null
@@ -1,350 +0,0 @@
-# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103
-import random
-import logging
-import math
-from unittest.mock import MagicMock
-import pandas as pd
-import numpy as np
-from freqtrade import exchange, optimize
-from freqtrade.exchange import Bittrex
-from freqtrade.optimize import preprocess
-from freqtrade.optimize.backtesting import backtest, generate_text_table, get_timeframe
-import freqtrade.optimize.backtesting as backtesting
-from freqtrade.tests.conftest import log_has
-
-
-def trim_dictlist(dict_list, num):
-    new = {}
-    for pair, pair_data in dict_list.items():
-        new[pair] = pair_data[num:]
-    return new
-
-
-# use for mock freqtrade.exchange.get_ticker_history'
-def _load_pair_as_ticks(pair, tickfreq):
-    ticks = optimize.load_data(None, ticker_interval=8, pairs=[pair])
-    ticks = trim_dictlist(ticks, -200)
-    return ticks[pair]
-
-
-# FIX: fixturize this?
-def _make_backtest_conf(conf=None,
-                        pair='BTC_UNITEST',
-                        record=None):
-    data = optimize.load_data(None, ticker_interval=8, pairs=[pair])
-    data = trim_dictlist(data, -200)
-    return {'stake_amount': conf['stake_amount'],
-            'processed': optimize.preprocess(data),
-            'max_open_trades': 10,
-            'realistic': True,
-            'record': record}
-
-
-def _trend(signals, buy_value, sell_value):
-    n = len(signals['low'])
-    buy = np.zeros(n)
-    sell = np.zeros(n)
-    for i in range(0, len(signals['buy'])):
-        if random.random() > 0.5:  # Both buy and sell signals at same timeframe
-            buy[i] = buy_value
-            sell[i] = sell_value
-    signals['buy'] = buy
-    signals['sell'] = sell
-    return signals
-
-
-def _trend_alternate(dataframe=None):
-    signals = dataframe
-    low = signals['low']
-    n = len(low)
-    buy = np.zeros(n)
-    sell = np.zeros(n)
-    for i in range(0, len(buy)):
-        if i % 2 == 0:
-            buy[i] = 1
-        else:
-            sell[i] = 1
-    signals['buy'] = buy
-    signals['sell'] = sell
-    return dataframe
-
-
-def _run_backtest_1(strategy, fun, backtest_conf):
-    # strategy is a global (hidden as a singleton), so we
-    # emulate strategy being pure, by override/restore here
-    # if we dont do this, the override in strategy will carry over
-    # to other tests
-    old_buy = strategy.populate_buy_trend
-    old_sell = strategy.populate_sell_trend
-    strategy.populate_buy_trend = fun  # Override
-    strategy.populate_sell_trend = fun  # Override
-    results = backtest(backtest_conf)
-    strategy.populate_buy_trend = old_buy  # restore override
-    strategy.populate_sell_trend = old_sell  # restore override
-    return results
-
-
-def test_generate_text_table():
-    results = pd.DataFrame(
-        {
-            'currency': ['BTC_ETH', 'BTC_ETH'],
-            'profit_percent': [0.1, 0.2],
-            'profit_BTC': [0.2, 0.4],
-            'duration': [10, 30],
-            'profit': [2, 0],
-            'loss': [0, 0]
-        }
-    )
-    print(generate_text_table({'BTC_ETH': {}}, results, 'BTC'))
-    assert generate_text_table({'BTC_ETH': {}}, results, 'BTC') == (
-        'pair       buy count    avg profit %    total profit BTC    avg duration    profit    loss\n'  # noqa
-        '-------  -----------  --------------  ------------------  --------------  --------  ------\n'  # noqa
-        'BTC_ETH            2           15.00          0.60000000            20.0         2       0\n'  # noqa
-        'TOTAL              2           15.00          0.60000000            20.0         2       0')  # noqa
-
-
-def test_get_timeframe(default_strategy):
-    data = preprocess(optimize.load_data(
-        None, ticker_interval=1, pairs=['BTC_UNITEST']))
-    min_date, max_date = get_timeframe(data)
-    assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
-    assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
-
-
-def test_backtest(default_strategy, default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
-    data = trim_dictlist(data, -200)
-    results = backtest({'stake_amount': default_conf['stake_amount'],
-                        'processed': optimize.preprocess(data),
-                        'max_open_trades': 10,
-                        'realistic': True})
-    assert not results.empty
-
-
-def test_backtest_1min_ticker_interval(default_strategy, default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    # Run a backtesting for an exiting 5min ticker_interval
-    data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
-    data = trim_dictlist(data, -200)
-    results = backtest({'stake_amount': default_conf['stake_amount'],
-                        'processed': optimize.preprocess(data),
-                        'max_open_trades': 1,
-                        'realistic': True})
-    assert not results.empty
-
-
-def load_data_test(what):
-    timerange = ((None, 'line'), None, -100)
-    data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'], timerange=timerange)
-    pair = data['BTC_UNITEST']
-    datalen = len(pair)
-    # Depending on the what parameter we now adjust the
-    # loaded data looks:
-    # pair :: [{'O': 0.123, 'H': 0.123, 'L': 0.123,
-    #           'C': 0.123, 'V': 123.123,
-    #           'T': '2017-11-04T23:02:00', 'BV': 0.123}]
-    base = 0.001
-    if what == 'raise':
-        return {'BTC_UNITEST':
-                [{'T': pair[x]['T'],  # Keep old dates
-                  'V': pair[x]['V'],  # Keep old volume
-                  'BV': pair[x]['BV'],  # keep too
-                  'O': x * base,        # But replace O,H,L,C
-                  'H': x * base + 0.0001,
-                  'L': x * base - 0.0001,
-                  'C': x * base} for x in range(0, datalen)]}
-    if what == 'lower':
-        return {'BTC_UNITEST':
-                [{'T': pair[x]['T'],  # Keep old dates
-                  'V': pair[x]['V'],  # Keep old volume
-                  'BV': pair[x]['BV'],  # keep too
-                  'O': 1 - x * base,        # But replace O,H,L,C
-                  'H': 1 - x * base + 0.0001,
-                  'L': 1 - x * base - 0.0001,
-                  'C': 1 - x * base} for x in range(0, datalen)]}
-    if what == 'sine':
-        hz = 0.1  # frequency
-        return {'BTC_UNITEST':
-                [{'T': pair[x]['T'],  # Keep old dates
-                  'V': pair[x]['V'],  # Keep old volume
-                  'BV': pair[x]['BV'],  # keep too
-                  # But replace O,H,L,C
-                  'O': math.sin(x * hz) / 1000 + base,
-                  'H': math.sin(x * hz) / 1000 + base + 0.0001,
-                  'L': math.sin(x * hz) / 1000 + base - 0.0001,
-                  'C': math.sin(x * hz) / 1000 + base} for x in range(0, datalen)]}
-    return data
-
-
-def simple_backtest(config, contour, num_results):
-    data = load_data_test(contour)
-    processed = optimize.preprocess(data)
-    assert isinstance(processed, dict)
-    results = backtest({'stake_amount': config['stake_amount'],
-                        'processed': processed,
-                        'max_open_trades': 1,
-                        'realistic': True})
-    # results :: <class 'pandas.core.frame.DataFrame'>
-    assert len(results) == num_results
-
-
-# Test backtest using offline data (testdata directory)
-
-
-def test_backtest_ticks(default_conf, mocker, default_strategy):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    ticks = [1, 5]
-    fun = default_strategy.populate_buy_trend
-    for tick in ticks:
-        backtest_conf = _make_backtest_conf(conf=default_conf)
-        results = _run_backtest_1(default_strategy, fun, backtest_conf)
-        assert not results.empty
-
-
-def test_backtest_clash_buy_sell(default_conf, mocker, default_strategy):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    # Override the default buy trend function in our default_strategy
-    def fun(dataframe=None):
-        buy_value = 1
-        sell_value = 1
-        return _trend(dataframe, buy_value, sell_value)
-
-    backtest_conf = _make_backtest_conf(conf=default_conf)
-    results = _run_backtest_1(default_strategy, fun, backtest_conf)
-    assert results.empty
-
-
-def test_backtest_only_sell(default_conf, mocker, default_strategy):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    # Override the default buy trend function in our default_strategy
-    def fun(dataframe=None):
-        buy_value = 0
-        sell_value = 1
-        return _trend(dataframe, buy_value, sell_value)
-
-    backtest_conf = _make_backtest_conf(conf=default_conf)
-    results = _run_backtest_1(default_strategy, fun, backtest_conf)
-    assert results.empty
-
-
-def test_backtest_alternate_buy_sell(default_conf, mocker, default_strategy):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    backtest_conf = _make_backtest_conf(conf=default_conf, pair='BTC_UNITEST')
-    results = _run_backtest_1(default_strategy, _trend_alternate,
-                              backtest_conf)
-    assert len(results) == 3
-
-
-def test_backtest_record(default_conf, mocker, default_strategy):
-    names = []
-    records = []
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.misc.file_dump_json',
-                 new=lambda n, r: (names.append(n), records.append(r)))
-    backtest_conf = _make_backtest_conf(
-        conf=default_conf,
-        pair='BTC_UNITEST',
-        record="trades"
-    )
-    results = _run_backtest_1(default_strategy, _trend_alternate,
-                              backtest_conf)
-    assert len(results) == 3
-    # Assert file_dump_json was only called once
-    assert names == ['backtest-result.json']
-    records = records[0]
-    # Ensure records are of correct type
-    assert len(records) == 3
-    # ('BTC_UNITEST', 0.00331158, '1510684320', '1510691700', 0, 117)
-    # Below follows just a typecheck of the schema/type of trade-records
-    oix = None
-    for (pair, profit, date_buy, date_sell, buy_index, dur) in records:
-        assert pair == 'BTC_UNITEST'
-        isinstance(profit, float)
-        # FIX: buy/sell should be converted to ints
-        isinstance(date_buy, str)
-        isinstance(date_sell, str)
-        isinstance(buy_index, pd._libs.tslib.Timestamp)
-        if oix:
-            assert buy_index > oix
-        oix = buy_index
-        assert dur > 0
-
-
-def test_processed(default_conf, mocker, default_strategy):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    dict_of_tickerrows = load_data_test('raise')
-    dataframes = optimize.preprocess(dict_of_tickerrows)
-    dataframe = dataframes['BTC_UNITEST']
-    cols = dataframe.columns
-    # assert the dataframe got some of the indicator columns
-    for col in ['close', 'high', 'low', 'open', 'date',
-                'ema50', 'ao', 'macd', 'plus_dm']:
-        assert col in cols
-
-
-def test_backtest_pricecontours(default_conf, mocker, default_strategy):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    tests = [['raise', 17], ['lower', 0], ['sine', 17]]
-    for [contour, numres] in tests:
-        simple_backtest(default_conf, contour, numres)
-
-
-def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False, timerange=None):
-    tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1, timerange=timerange)
-    pairdata = {'BTC_UNITEST': tickerdata}
-    return pairdata
-
-
-def test_backtest_start(default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.misc.load_config', new=lambda s: default_conf)
-    mocker.patch.multiple('freqtrade.optimize',
-                          load_data=mocked_load_data)
-    args = MagicMock()
-    args.ticker_interval = 1
-    args.level = 10
-    args.live = False
-    args.datadir = None
-    args.export = None
-    args.timerange = '-100'  # needed due to MagicMock malleability
-    backtesting.start(args)
-    # check the logs, that will contain the backtest result
-    exists = ['Using max_open_trades: 1 ...',
-              'Using stake_amount: 0.001 ...',
-              'Measuring data from 2017-11-14T21:17:00+00:00 '
-              'up to 2017-11-14T22:59:00+00:00 (0 days)..']
-    for line in exists:
-        assert log_has(line, caplog.record_tuples)
-
-
-def test_backtest_start_live(default_strategy, default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
-    mocker.patch('freqtrade.exchange.get_ticker_history',
-                 new=lambda n, i: _load_pair_as_ticks(n, i))
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.misc.load_config', new=lambda s: default_conf)
-    args = MagicMock()
-    args.ticker_interval = 1
-    args.level = 10
-    args.live = True
-    args.datadir = None
-    args.export = None
-    args.timerange = '-100'  # needed due to MagicMock malleability
-    backtesting.start(args)
-    # check the logs, that will contain the backtest result
-    exists = ['Using max_open_trades: 1 ...',
-              'Using stake_amount: 0.001 ...',
-              'Measuring data from 2017-11-14T19:32:00+00:00 '
-              'up to 2017-11-14T22:59:00+00:00 (0 days)..']
-    for line in exists:
-        assert log_has(line, caplog.record_tuples)
diff --git a/freqtrade/tests/optimize/test_hyperopt.py b/freqtrade/tests/optimize/test_hyperopt.py
deleted file mode 100644
index 13b924f1d..000000000
--- a/freqtrade/tests/optimize/test_hyperopt.py
+++ /dev/null
@@ -1,294 +0,0 @@
-# pragma pylint: disable=missing-docstring,W0212,C0103
-import logging
-
-from unittest.mock import MagicMock
-
-import pandas as pd
-
-from freqtrade.optimize.hyperopt import calculate_loss, TARGET_TRADES, EXPECTED_MAX_PROFIT, start, \
-    log_results, save_trials, read_trials, generate_roi_table, has_space
-from freqtrade.strategy.strategy import Strategy
-import freqtrade.optimize.hyperopt as hyperopt
-
-
-def test_loss_calculation_prefer_correct_trade_count():
-    correct = calculate_loss(1, TARGET_TRADES, 20)
-    over = calculate_loss(1, TARGET_TRADES + 100, 20)
-    under = calculate_loss(1, TARGET_TRADES - 100, 20)
-    assert over > correct
-    assert under > correct
-
-
-def test_loss_calculation_prefer_shorter_trades():
-    shorter = calculate_loss(1, 100, 20)
-    longer = calculate_loss(1, 100, 30)
-    assert shorter < longer
-
-
-def test_loss_calculation_has_limited_profit():
-    correct = calculate_loss(EXPECTED_MAX_PROFIT, TARGET_TRADES, 20)
-    over = calculate_loss(EXPECTED_MAX_PROFIT * 2, TARGET_TRADES, 20)
-    under = calculate_loss(EXPECTED_MAX_PROFIT / 2, TARGET_TRADES, 20)
-    assert over == correct
-    assert under > correct
-
-
-def create_trials(mocker):
-    """
-    When creating trials, mock the hyperopt Trials so that *by default*
-      - we don't create any pickle'd files in the filesystem
-      - we might have a pickle'd file so make sure that we return
-        false when looking for it
-    """
-    mocker.patch('freqtrade.optimize.hyperopt.TRIALS_FILE',
-                 return_value='freqtrade/tests/optimize/ut_trials.pickle')
-    mocker.patch('freqtrade.optimize.hyperopt.os.path.exists',
-                 return_value=False)
-    mocker.patch('freqtrade.optimize.hyperopt.save_trials',
-                 return_value=None)
-    mocker.patch('freqtrade.optimize.hyperopt.read_trials',
-                 return_value=None)
-    mocker.patch('freqtrade.optimize.hyperopt.os.remove',
-                 return_value=True)
-    return mocker.Mock(
-        results=[{
-            'loss': 1,
-            'result': 'foo',
-            'status': 'ok'
-        }],
-        best_trial={'misc': {'vals': {'adx': 999}}}
-    )
-
-
-def test_start_calls_fmin(mocker):
-    trials = create_trials(mocker)
-    mocker.patch('freqtrade.optimize.tickerdata_to_dataframe')
-    mocker.patch('freqtrade.optimize.hyperopt.TRIALS', return_value=trials)
-    mocker.patch('freqtrade.optimize.hyperopt.sorted',
-                 return_value=trials.results)
-    mocker.patch('freqtrade.optimize.preprocess')
-    mocker.patch('freqtrade.optimize.load_data')
-    mock_fmin = mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
-
-    args = mocker.Mock(epochs=1, config='config.json.example', mongodb=False,
-                       timerange=None, spaces='all')
-    Strategy().init({'strategy': 'default_strategy'})
-    start(args)
-
-    mock_fmin.assert_called_once()
-
-
-def test_start_uses_mongotrials(mocker):
-    mock_mongotrials = mocker.patch('freqtrade.optimize.hyperopt.MongoTrials',
-                                    return_value=create_trials(mocker))
-    mocker.patch('freqtrade.optimize.tickerdata_to_dataframe')
-    mocker.patch('freqtrade.optimize.load_data')
-    mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
-
-    args = mocker.Mock(epochs=1, config='config.json.example', mongodb=True,
-                       timerange=None, spaces='all')
-    Strategy().init({'strategy': 'default_strategy'})
-    start(args)
-
-    mock_mongotrials.assert_called_once()
-
-
-def test_log_results_if_loss_improves(mocker):
-    logger = mocker.patch('freqtrade.optimize.hyperopt.logger.info')
-    global CURRENT_BEST_LOSS
-    CURRENT_BEST_LOSS = 2
-    log_results({
-        'loss': 1,
-        'current_tries': 1,
-        'total_tries': 2,
-        'result': 'foo'
-    })
-
-    logger.assert_called_once()
-
-
-def test_no_log_if_loss_does_not_improve(mocker):
-    logger = mocker.patch('freqtrade.optimize.hyperopt.logger.info')
-    global CURRENT_BEST_LOSS
-    CURRENT_BEST_LOSS = 2
-    log_results({
-        'loss': 3,
-    })
-
-    assert not logger.called
-
-
-def test_fmin_best_results(mocker, caplog):
-    caplog.set_level(logging.INFO)
-    fmin_result = {
-        "macd_below_zero": 0,
-        "adx": 1,
-        "adx-value": 15.0,
-        "fastd": 1,
-        "fastd-value": 40.0,
-        "green_candle": 1,
-        "mfi": 0,
-        "over_sar": 0,
-        "rsi": 1,
-        "rsi-value": 37.0,
-        "trigger": 2,
-        "uptrend_long_ema": 1,
-        "uptrend_short_ema": 0,
-        "uptrend_sma": 0,
-        "stoploss": -0.1,
-        "roi_t1": 1,
-        "roi_t2": 2,
-        "roi_t3": 3,
-        "roi_p1": 1,
-        "roi_p2": 2,
-        "roi_p3": 3,
-    }
-
-    mocker.patch('freqtrade.optimize.hyperopt.MongoTrials', return_value=create_trials(mocker))
-    mocker.patch('freqtrade.optimize.tickerdata_to_dataframe')
-    mocker.patch('freqtrade.optimize.load_data')
-    mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value=fmin_result)
-
-    args = mocker.Mock(epochs=1, config='config.json.example',
-                       timerange=None, spaces='all')
-    Strategy().init({'strategy': 'default_strategy'})
-    start(args)
-
-    exists = [
-        'Best parameters',
-        '"adx": {\n        "enabled": true,\n        "value": 15.0\n    },',
-        '"green_candle": {\n        "enabled": true\n    },',
-        '"mfi": {\n        "enabled": false\n    },',
-        '"trigger": {\n        "type": "faststoch10"\n    },',
-        '"stoploss": -0.1',
-    ]
-
-    for line in exists:
-        assert line in caplog.text
-
-
-def test_fmin_throw_value_error(mocker, caplog):
-    caplog.set_level(logging.INFO)
-    Strategy().init({'strategy': 'default_strategy'})
-    mocker.patch('freqtrade.optimize.hyperopt.MongoTrials', return_value=create_trials(mocker))
-    mocker.patch('freqtrade.optimize.tickerdata_to_dataframe')
-    mocker.patch('freqtrade.optimize.load_data')
-    mocker.patch('freqtrade.optimize.hyperopt.fmin', side_effect=ValueError())
-
-    args = mocker.Mock(epochs=1, config='config.json.example',
-                       timerange=None, spaces='all')
-    Strategy().init({'strategy': 'default_strategy'})
-    start(args)
-
-    exists = [
-        'Best Result:',
-        'Sorry, Hyperopt was not able to find good parameters. Please try with more epochs '
-        '(param: -e).',
-    ]
-
-    for line in exists:
-        assert line in caplog.text
-
-
-def test_resuming_previous_hyperopt_results_succeeds(mocker):
-    import freqtrade.optimize.hyperopt as hyperopt
-    trials = create_trials(mocker)
-    mocker.patch('freqtrade.optimize.hyperopt.TRIALS',
-                 return_value=trials)
-    mocker.patch('freqtrade.optimize.hyperopt.os.path.exists',
-                 return_value=True)
-    mocker.patch('freqtrade.optimize.hyperopt.len',
-                 return_value=len(trials.results))
-    mock_read = mocker.patch('freqtrade.optimize.hyperopt.read_trials',
-                             return_value=trials)
-    mock_save = mocker.patch('freqtrade.optimize.hyperopt.save_trials',
-                             return_value=None)
-    mocker.patch('freqtrade.optimize.hyperopt.sorted',
-                 return_value=trials.results)
-    mocker.patch('freqtrade.optimize.preprocess')
-    mocker.patch('freqtrade.optimize.load_data')
-    mocker.patch('freqtrade.optimize.hyperopt.fmin',
-                 return_value={})
-    args = mocker.Mock(epochs=1,
-                       config='config.json.example',
-                       mongodb=False,
-                       timerange=None,
-                       spaces='all')
-    Strategy().init({'strategy': 'default_strategy'})
-    start(args)
-
-    mock_read.assert_called_once()
-    mock_save.assert_called_once()
-
-    current_tries = hyperopt._CURRENT_TRIES
-    total_tries = hyperopt.TOTAL_TRIES
-
-    assert current_tries == len(trials.results)
-    assert total_tries == (current_tries + len(trials.results))
-
-
-def test_save_trials_saves_trials(mocker):
-    trials = create_trials(mocker)
-    mock_dump = mocker.patch('freqtrade.optimize.hyperopt.pickle.dump',
-                             return_value=None)
-    trials_path = mocker.patch('freqtrade.optimize.hyperopt.TRIALS_FILE',
-                               return_value='ut_trials.pickle')
-    mocker.patch('freqtrade.optimize.hyperopt.open',
-                 return_value=trials_path)
-    save_trials(trials, trials_path)
-
-    mock_dump.assert_called_once_with(trials, trials_path)
-
-
-def test_read_trials_returns_trials_file(mocker):
-    trials = create_trials(mocker)
-    mock_load = mocker.patch('freqtrade.optimize.hyperopt.pickle.load',
-                             return_value=trials)
-    mock_open = mocker.patch('freqtrade.optimize.hyperopt.open',
-                             return_value=mock_load)
-
-    assert read_trials() == trials
-    mock_open.assert_called_once()
-    mock_load.assert_called_once()
-
-
-def test_roi_table_generation():
-    params = {
-        'roi_t1': 5,
-        'roi_t2': 10,
-        'roi_t3': 15,
-        'roi_p1': 1,
-        'roi_p2': 2,
-        'roi_p3': 3,
-    }
-    assert generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
-
-
-# test log_trials_result
-# test buy_strategy_generator def populate_buy_trend
-# test optimizer if 'ro_t1' in params
-
-def test_format_results():
-    trades = [('BTC_ETH', 2, 2, 123),
-              ('BTC_LTC', 1, 1, 123),
-              ('BTC_XRP', -1, -2, -246)]
-    labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
-    df = pd.DataFrame.from_records(trades, columns=labels)
-    x = hyperopt.format_results(df)
-    assert x.find(' 66.67%')
-
-
-def test_signal_handler(mocker):
-    m = MagicMock()
-    mocker.patch('sys.exit', m)
-    mocker.patch('freqtrade.optimize.hyperopt.save_trials', m)
-    mocker.patch('freqtrade.optimize.hyperopt.log_trials_result', m)
-    hyperopt.signal_handler(9, None)
-    assert m.call_count == 3
-
-
-def test_has_space():
-    assert has_space(['buy', 'roi'], 'roi')
-    assert has_space(['buy', 'roi'], 'buy')
-    assert not has_space(['buy', 'roi'], 'stoploss')
-    assert has_space(['all'], 'buy')
diff --git a/freqtrade/tests/optimize/test_hyperopt_config.py b/freqtrade/tests/optimize/test_hyperopt_config.py
deleted file mode 100644
index aa9424826..000000000
--- a/freqtrade/tests/optimize/test_hyperopt_config.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# pragma pylint: disable=missing-docstring,W0212
-
-from user_data.hyperopt_conf import hyperopt_optimize_conf
-
-
-def test_hyperopt_optimize_conf():
-    hyperopt_conf = hyperopt_optimize_conf()
-
-    assert "max_open_trades" in hyperopt_conf
-    assert "stake_currency" in hyperopt_conf
-    assert "stake_amount" in hyperopt_conf
-    assert "minimal_roi" in hyperopt_conf
-    assert "stoploss" in hyperopt_conf
-    assert "bid_strategy" in hyperopt_conf
-    assert "exchange" in hyperopt_conf
-    assert "pair_whitelist" in hyperopt_conf['exchange']
diff --git a/freqtrade/tests/optimize/test_optimize.py b/freqtrade/tests/optimize/test_optimize.py
deleted file mode 100644
index ed43f0f4e..000000000
--- a/freqtrade/tests/optimize/test_optimize.py
+++ /dev/null
@@ -1,296 +0,0 @@
-# pragma pylint: disable=missing-docstring, protected-access, C0103
-
-import os
-import json
-import logging
-import uuid
-from shutil import copyfile
-from freqtrade import exchange, optimize
-from freqtrade.exchange import Bittrex
-from freqtrade.optimize.__init__ import make_testdata_path, download_pairs,\
-    download_backtesting_testdata, load_tickerdata_file, trim_tickerlist, file_dump_json
-from freqtrade.tests.conftest import log_has
-from freqtrade.strategy.strategy import Strategy
-
-# Change this if modifying BTC_UNITEST testdatafile
-_BTC_UNITTEST_LENGTH = 13681
-
-
-def _backup_file(file: str, copy_file: bool = False) -> None:
-    """
-    Backup existing file to avoid deleting the user file
-    :param file: complete path to the file
-    :param touch_file: create an empty file in replacement
-    :return: None
-    """
-    file_swp = file + '.swp'
-    if os.path.isfile(file):
-        os.rename(file, file_swp)
-
-        if copy_file:
-            copyfile(file_swp, file)
-
-
-def _clean_test_file(file: str) -> None:
-    """
-    Backup existing file to avoid deleting the user file
-    :param file: complete path to the file
-    :return: None
-    """
-    file_swp = file + '.swp'
-    # 1. Delete file from the test
-    if os.path.isfile(file):
-        os.remove(file)
-
-    # 2. Rollback to the initial file
-    if os.path.isfile(file_swp):
-        os.rename(file_swp, file)
-
-
-def test_load_data_30min_ticker(default_conf, ticker_history, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    file = 'freqtrade/tests/testdata/BTC_UNITTEST-30.json'
-    _backup_file(file, copy_file=True)
-    optimize.load_data(None, pairs=['BTC_UNITTEST'], ticker_interval=30)
-    assert os.path.isfile(file) is True
-    assert not log_has('Download the pair: "BTC_ETH", Interval: 30 min', caplog.record_tuples)
-    _clean_test_file(file)
-
-
-def test_load_data_5min_ticker(default_conf, ticker_history, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    file = 'freqtrade/tests/testdata/BTC_ETH-5.json'
-    _backup_file(file, copy_file=True)
-    optimize.load_data(None, pairs=['BTC_ETH'], ticker_interval=5)
-    assert os.path.isfile(file) is True
-    assert not log_has('Download the pair: "BTC_ETH", Interval: 5 min', caplog.record_tuples)
-    _clean_test_file(file)
-
-
-def test_load_data_1min_ticker(default_conf, ticker_history, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    file = 'freqtrade/tests/testdata/BTC_ETH-1.json'
-    _backup_file(file, copy_file=True)
-    optimize.load_data(None, ticker_interval=1, pairs=['BTC_ETH'])
-    assert os.path.isfile(file) is True
-    assert not log_has('Download the pair: "BTC_ETH", Interval: 1 min', caplog.record_tuples)
-    _clean_test_file(file)
-
-
-def test_load_data_with_new_pair_1min(default_conf, ticker_history, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    file = 'freqtrade/tests/testdata/BTC_MEME-1.json'
-    _backup_file(file)
-    optimize.load_data(None, ticker_interval=1, pairs=['BTC_MEME'])
-    assert os.path.isfile(file) is True
-    assert log_has('Download the pair: "BTC_MEME", Interval: 1 min', caplog.record_tuples)
-    _clean_test_file(file)
-
-
-def test_testdata_path():
-    assert os.path.join('freqtrade', 'tests', 'testdata') in make_testdata_path(None)
-
-
-def test_download_pairs(default_conf, ticker_history, mocker):
-    mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    file1_1 = 'freqtrade/tests/testdata/BTC_MEME-1.json'
-    file1_5 = 'freqtrade/tests/testdata/BTC_MEME-5.json'
-    file2_1 = 'freqtrade/tests/testdata/BTC_CFI-1.json'
-    file2_5 = 'freqtrade/tests/testdata/BTC_CFI-5.json'
-
-    _backup_file(file1_1)
-    _backup_file(file1_5)
-    _backup_file(file2_1)
-    _backup_file(file2_5)
-
-    assert os.path.isfile(file1_1) is False
-    assert os.path.isfile(file2_1) is False
-
-    assert download_pairs(None, pairs=['BTC-MEME', 'BTC-CFI'], ticker_interval=1) is True
-
-    assert os.path.isfile(file1_1) is True
-    assert os.path.isfile(file2_1) is True
-
-    # clean files freshly downloaded
-    _clean_test_file(file1_1)
-    _clean_test_file(file2_1)
-
-    assert os.path.isfile(file1_5) is False
-    assert os.path.isfile(file2_5) is False
-
-    assert download_pairs(None, pairs=['BTC-MEME', 'BTC-CFI'], ticker_interval=5) is True
-
-    assert os.path.isfile(file1_5) is True
-    assert os.path.isfile(file2_5) is True
-
-    # clean files freshly downloaded
-    _clean_test_file(file1_5)
-    _clean_test_file(file2_5)
-
-
-def test_download_pairs_exception(default_conf, ticker_history, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=ticker_history)
-    mocker.patch('freqtrade.optimize.__init__.download_backtesting_testdata',
-                 side_effect=BaseException('File Error'))
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    file1_1 = 'freqtrade/tests/testdata/BTC_MEME-1.json'
-    file1_5 = 'freqtrade/tests/testdata/BTC_MEME-5.json'
-    _backup_file(file1_1)
-    _backup_file(file1_5)
-
-    download_pairs(None, pairs=['BTC-MEME'], ticker_interval=1)
-    # clean files freshly downloaded
-    _clean_test_file(file1_1)
-    _clean_test_file(file1_5)
-    assert log_has('Failed to download the pair: "BTC-MEME", Interval: 1 min', caplog.record_tuples)
-
-
-def test_download_backtesting_testdata(default_conf, ticker_history, mocker):
-    mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=ticker_history)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    exchange._API = Bittrex({'key': '', 'secret': ''})
-
-    # Download a 1 min ticker file
-    file1 = 'freqtrade/tests/testdata/BTC_XEL-1.json'
-    _backup_file(file1)
-    download_backtesting_testdata(None, pair="BTC-XEL", interval=1)
-    assert os.path.isfile(file1) is True
-    _clean_test_file(file1)
-
-    # Download a 5 min ticker file
-    file2 = 'freqtrade/tests/testdata/BTC_STORJ-5.json'
-    _backup_file(file2)
-
-    download_backtesting_testdata(None, pair="BTC-STORJ", interval=5)
-    assert os.path.isfile(file2) is True
-    _clean_test_file(file2)
-
-
-def test_download_backtesting_testdata2(mocker):
-    tick = [{'T': 'bar'}, {'T': 'foo'}]
-    mocker.patch('freqtrade.misc.file_dump_json', return_value=None)
-    mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=tick)
-    assert download_backtesting_testdata(None, pair="BTC-UNITEST", interval=1)
-    assert download_backtesting_testdata(None, pair="BTC-UNITEST", interval=3)
-
-
-def test_load_tickerdata_file():
-    # 7 does not exist in either format.
-    assert not load_tickerdata_file(None, 'BTC_UNITEST', 7)
-    # 1 exists only as a .json
-    tickerdata = load_tickerdata_file(None, 'BTC_UNITEST', 1)
-    assert _BTC_UNITTEST_LENGTH == len(tickerdata)
-    # 8 .json is empty and will fail if it's loaded. .json.gz is a copy of 1.json
-    tickerdata = load_tickerdata_file(None, 'BTC_UNITEST', 8)
-    assert _BTC_UNITTEST_LENGTH == len(tickerdata)
-
-
-def test_init(default_conf, mocker):
-    conf = {'exchange': {'pair_whitelist': []}}
-    mocker.patch('freqtrade.optimize.hyperopt_optimize_conf', return_value=conf)
-    assert {} == optimize.load_data('', pairs=[], refresh_pairs=True,
-                                    ticker_interval=int(default_conf['ticker_interval']))
-
-
-def test_tickerdata_to_dataframe():
-    Strategy().init({'strategy': 'default_strategy'})
-    timerange = ((None, 'line'), None, -100)
-    tick = load_tickerdata_file(None, 'BTC_UNITEST', 1, timerange=timerange)
-    tickerlist = {'BTC_UNITEST': tick}
-    data = optimize.tickerdata_to_dataframe(tickerlist)
-    assert len(data['BTC_UNITEST']) == 100
-
-
-def test_trim_tickerlist():
-    with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
-        ticker_list = json.load(data_file)
-    ticker_list_len = len(ticker_list)
-
-    # Test the pattern ^(-\d+)$
-    # This pattern remove X element from the beginning
-    timerange = ((None, 'line'), None, 5)
-    ticker = trim_tickerlist(ticker_list, timerange)
-    ticker_len = len(ticker)
-
-    assert ticker_list_len == ticker_len + 5
-    assert ticker_list[0] is not ticker[0]  # The first element should be different
-    assert ticker_list[-1] is ticker[-1]  # The last element must be the same
-
-    # Test the pattern ^(\d+)-$
-    # This pattern keep X element from the end
-    timerange = (('line', None), 5, None)
-    ticker = trim_tickerlist(ticker_list, timerange)
-    ticker_len = len(ticker)
-
-    assert ticker_len == 5
-    assert ticker_list[0] is ticker[0]  # The first element must be the same
-    assert ticker_list[-1] is not ticker[-1]  # The last element should be different
-
-    # Test the pattern ^(\d+)-(\d+)$
-    # This pattern extract a window
-    timerange = (('index', 'index'), 5, 10)
-    ticker = trim_tickerlist(ticker_list, timerange)
-    ticker_len = len(ticker)
-
-    assert ticker_len == 5
-    assert ticker_list[0] is not ticker[0]  # The first element should be different
-    assert ticker_list[5] is ticker[0]  # The list starts at the index 5
-    assert ticker_list[9] is ticker[-1]  # The list ends at the index 9 (5 elements)
-
-    # Test a wrong pattern
-    # This pattern must return the list unchanged
-    timerange = ((None, None), None, 5)
-    ticker = trim_tickerlist(ticker_list, timerange)
-    ticker_len = len(ticker)
-
-    assert ticker_list_len == ticker_len
-
-
-def test_file_dump_json():
-    file = 'freqtrade/tests/testdata/test_{id}.json'.format(id=str(uuid.uuid4()))
-    data = {'bar': 'foo'}
-
-    # check the file we will create does not exist
-    assert os.path.isfile(file) is False
-
-    # Create the Json file
-    file_dump_json(file, data)
-
-    # Check the file was create
-    assert os.path.isfile(file) is True
-
-    # Open the Json file created and test the data is in it
-    with open(file) as data_file:
-        json_from_file = json.load(data_file)
-
-    assert 'bar' in json_from_file
-    assert json_from_file['bar'] == 'foo'
-
-    # Remove the file
-    _clean_test_file(file)
diff --git a/freqtrade/tests/rpc/test_rpc.py b/freqtrade/tests/rpc/test_rpc.py
deleted file mode 100644
index 8102e8885..000000000
--- a/freqtrade/tests/rpc/test_rpc.py
+++ /dev/null
@@ -1,406 +0,0 @@
-# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors, C0103
-from datetime import datetime
-from copy import deepcopy
-from unittest.mock import MagicMock
-from sqlalchemy import create_engine
-
-from freqtrade.rpc import init, cleanup, send_msg
-from freqtrade.persistence import Trade
-import freqtrade.main as main
-import freqtrade.misc as misc
-import freqtrade.rpc as rpc
-
-
-def prec_satoshi(a, b):
-    """
-    :return: True if A and B differs less than one satoshi.
-    """
-    return abs(a - b) < 0.00000001
-
-
-def test_init_telegram_enabled(default_conf, mocker):
-    module_list = []
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', module_list)
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.init', MagicMock())
-
-    init(default_conf)
-
-    assert telegram_mock.call_count == 1
-    assert 'telegram' in module_list
-
-
-def test_init_telegram_disabled(default_conf, mocker):
-    module_list = []
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', module_list)
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.init', MagicMock())
-
-    conf = deepcopy(default_conf)
-    conf['telegram']['enabled'] = False
-    init(conf)
-
-    assert telegram_mock.call_count == 0
-    assert 'telegram' not in module_list
-
-
-def test_cleanup_telegram_enabled(mocker):
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', ['telegram'])
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.cleanup', MagicMock())
-    cleanup()
-    assert telegram_mock.call_count == 1
-
-
-def test_cleanup_telegram_disabled(mocker):
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', [])
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.cleanup', MagicMock())
-    cleanup()
-    assert telegram_mock.call_count == 0
-
-
-def test_send_msg_telegram_enabled(mocker):
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', ['telegram'])
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.send_msg', MagicMock())
-    send_msg('test')
-    assert telegram_mock.call_count == 1
-
-
-def test_send_msg_telegram_disabled(mocker):
-    mocker.patch('freqtrade.rpc.REGISTERED_MODULES', [])
-    telegram_mock = mocker.patch('freqtrade.rpc.telegram.send_msg', MagicMock())
-    send_msg('test')
-    assert telegram_mock.call_count == 0
-
-
-def test_rpc_forcesell(default_conf, update, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    cancel_order_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          cancel_order=cancel_order_mock,
-                          get_order=MagicMock(return_value={
-                              'closed': True,
-                              'type': 'LIMIT_BUY',
-                          }))
-    main.init(default_conf, create_engine('sqlite://'))
-
-    misc.update_state(misc.State.STOPPED)
-    (error, res) = rpc.rpc_forcesell(None)
-    assert error
-    assert res == '`trader is not running`'
-    misc.update_state(misc.State.RUNNING)
-    (error, res) = rpc.rpc_forcesell(None)
-    assert error
-    assert res == 'Invalid argument.'
-
-    (error, res) = rpc.rpc_forcesell('all')
-    assert not error
-    assert res == ''
-
-    main.create_trade(0.001, 5)
-    (error, res) = rpc.rpc_forcesell('all')
-    assert not error
-    assert res == ''
-
-    (error, res) = rpc.rpc_forcesell('1')
-    assert not error
-    assert res == ''
-
-    misc.update_state(misc.State.STOPPED)
-
-    (error, res) = rpc.rpc_forcesell(None)
-    assert error
-    assert res == '`trader is not running`'
-
-    (error, res) = rpc.rpc_forcesell('all')
-    assert error
-    assert res == '`trader is not running`'
-
-    misc.update_state(misc.State.RUNNING)
-
-    assert cancel_order_mock.call_count == 0
-    # make an limit-buy open trade
-    mocker.patch.multiple('freqtrade.exchange',
-                          get_order=MagicMock(return_value={
-                              'closed': None,
-                              'type': 'LIMIT_BUY'
-                          }))
-    # check that the trade is called, which is done
-    # by ensuring exchange.cancel_order is called
-    (error, res) = rpc.rpc_forcesell('1')
-    assert not error
-    assert res == ''
-    assert cancel_order_mock.call_count == 1
-
-    main.create_trade(0.001, 5)
-    # make an limit-sell open trade
-    mocker.patch.multiple('freqtrade.exchange',
-                          get_order=MagicMock(return_value={
-                              'closed': None,
-                              'type': 'LIMIT_SELL'
-                          }))
-    (error, res) = rpc.rpc_forcesell('2')
-    assert not error
-    assert res == ''
-    # status quo, no exchange calls
-    assert cancel_order_mock.call_count == 1
-
-
-def test_rpc_trade_status(default_conf, update, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    main.init(default_conf, create_engine('sqlite://'))
-
-    misc.update_state(misc.State.STOPPED)
-    (error, result) = rpc.rpc_trade_status()
-    assert error
-    assert result.find('trader is not running') >= 0
-
-    misc.update_state(misc.State.RUNNING)
-    (error, result) = rpc.rpc_trade_status()
-    assert error
-    assert result.find('no active trade') >= 0
-
-    main.create_trade(0.001, 5)
-    (error, result) = rpc.rpc_trade_status()
-    assert not error
-    trade = result[0]
-    assert trade.find('[BTC_ETH]') >= 0
-
-
-def test_rpc_daily_profit(default_conf, update, ticker, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch.multiple('freqtrade.fiat_convert.Market',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}))
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    main.init(default_conf, create_engine('sqlite://'))
-    stake_currency = default_conf['stake_currency']
-    fiat_display_currency = default_conf['fiat_display_currency']
-
-    # Create some test data
-    main.create_trade(0.001, 5)
-    trade = Trade.query.first()
-    assert trade
-
-    # Simulate buy & sell
-    trade.update(limit_buy_order)
-    trade.update(limit_sell_order)
-    trade.close_date = datetime.utcnow()
-    trade.is_open = False
-
-    # Try valid data
-    update.message.text = '/daily 2'
-    (error, days) = rpc.rpc_daily_profit(7, stake_currency,
-                                         fiat_display_currency)
-    assert not error
-    assert len(days) == 7
-    for day in days:
-        # [datetime.date(2018, 1, 11), '0.00000000 BTC', '0.000 USD']
-        assert (day[1] == '0.00000000 BTC' or
-                day[1] == '0.00006217 BTC')
-
-        assert (day[2] == '0.000 USD' or
-                day[2] == '0.933 USD')
-    # ensure first day is current date
-    assert str(days[0][0]) == str(datetime.utcnow().date())
-
-    # Try invalid data
-    (error, days) = rpc.rpc_daily_profit(0, stake_currency,
-                                         fiat_display_currency)
-    assert error
-    assert days.find('must be an integer greater than 0') >= 0
-
-
-def test_rpc_trade_statistics(
-        default_conf, update, ticker, ticker_sell_up, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch.multiple('freqtrade.fiat_convert.Market',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}))
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    main.init(default_conf, create_engine('sqlite://'))
-    stake_currency = default_conf['stake_currency']
-    fiat_display_currency = default_conf['fiat_display_currency']
-
-    (error, stats) = rpc.rpc_trade_statistics(stake_currency,
-                                              fiat_display_currency)
-    assert error
-    assert stats.find('no closed trade') >= 0
-
-    # Create some test data
-    main.create_trade(0.001, 5)
-    trade = Trade.query.first()
-    # Simulate fulfilled LIMIT_BUY order for trade
-    trade.update(limit_buy_order)
-    # Update the ticker with a market going up
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_up)
-    trade.update(limit_sell_order)
-    trade.close_date = datetime.utcnow()
-    trade.is_open = False
-
-    (error, stats) = rpc.rpc_trade_statistics(stake_currency,
-                                              fiat_display_currency)
-    assert not error
-    assert prec_satoshi(stats['profit_closed_coin'], 6.217e-05)
-    assert prec_satoshi(stats['profit_closed_percent'], 6.2)
-    assert prec_satoshi(stats['profit_closed_fiat'], 0.93255)
-    assert prec_satoshi(stats['profit_all_coin'], 6.217e-05)
-    assert prec_satoshi(stats['profit_all_percent'], 6.2)
-    assert prec_satoshi(stats['profit_all_fiat'], 0.93255)
-    assert stats['trade_count'] == 1
-    assert stats['first_trade_date'] == 'just now'
-    assert stats['latest_trade_date'] == 'just now'
-    assert stats['avg_duration'] == '0:00:00'
-    assert stats['best_pair'] == 'BTC_ETH'
-    assert prec_satoshi(stats['best_rate'], 6.2)
-
-
-# Test that rpc_trade_statistics can handle trades that lacks
-# trade.open_rate (it is set to None)
-def test_rpc_trade_statistics_closed(
-        default_conf, update, ticker, ticker_sell_up, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch.multiple('freqtrade.fiat_convert.Market',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}))
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    main.init(default_conf, create_engine('sqlite://'))
-    stake_currency = default_conf['stake_currency']
-    fiat_display_currency = default_conf['fiat_display_currency']
-
-    # Create some test data
-    main.create_trade(0.001, 5)
-    trade = Trade.query.first()
-    # Simulate fulfilled LIMIT_BUY order for trade
-    trade.update(limit_buy_order)
-    # Update the ticker with a market going up
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_up)
-    trade.update(limit_sell_order)
-    trade.close_date = datetime.utcnow()
-    trade.is_open = False
-
-    for trade in Trade.query.order_by(Trade.id).all():
-        trade.open_rate = None
-
-    (error, stats) = rpc.rpc_trade_statistics(stake_currency,
-                                              fiat_display_currency)
-    assert not error
-    assert prec_satoshi(stats['profit_closed_coin'], 0)
-    assert prec_satoshi(stats['profit_closed_percent'], 0)
-    assert prec_satoshi(stats['profit_closed_fiat'], 0)
-    assert prec_satoshi(stats['profit_all_coin'], 0)
-    assert prec_satoshi(stats['profit_all_percent'], 0)
-    assert prec_satoshi(stats['profit_all_fiat'], 0)
-    assert stats['trade_count'] == 1
-    assert stats['first_trade_date'] == 'just now'
-    assert stats['latest_trade_date'] == 'just now'
-    assert stats['avg_duration'] == '0:00:00'
-    assert stats['best_pair'] == 'BTC_ETH'
-    assert prec_satoshi(stats['best_rate'], 6.2)
-
-
-def test_rpc_balance_handle(default_conf, update, mocker):
-    mock_balance = [{
-        'Currency': 'BTC',
-        'Balance': 10.0,
-        'Available': 12.0,
-        'Pending': 0.0,
-        'CryptoAddress': 'XXXX',
-    }, {
-        'Currency': 'ETH',
-        'Balance': 0.0,
-        'Available': 0.0,
-        'Pending': 0.0,
-        'CryptoAddress': 'XXXX',
-    }]
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_balances=MagicMock(return_value=mock_balance))
-    mocker.patch.multiple('freqtrade.fiat_convert.Market',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}))
-
-    (error, res) = rpc.rpc_balance(default_conf['fiat_display_currency'])
-    assert not error
-    (trade, x, y, z) = res
-    assert prec_satoshi(x, 10)
-    assert prec_satoshi(z, 150000)
-    assert y == 'USD'
-    assert len(trade) == 1
-    assert trade[0]['currency'] == 'BTC'
-    assert prec_satoshi(trade[0]['available'], 12)
-    assert prec_satoshi(trade[0]['balance'], 10)
-    assert prec_satoshi(trade[0]['pending'], 0)
-    assert prec_satoshi(trade[0]['est_btc'], 10)
-
-
-def test_performance_handle(
-        default_conf, update, ticker, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    msg_mock = MagicMock()
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    main.init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    main.create_trade(0.001, int(default_conf['ticker_interval']))
-    trade = Trade.query.first()
-    assert trade
-
-    # Simulate fulfilled LIMIT_BUY order for trade
-    trade.update(limit_buy_order)
-
-    # Simulate fulfilled LIMIT_SELL order for trade
-    trade.update(limit_sell_order)
-
-    trade.close_date = datetime.utcnow()
-    trade.is_open = False
-    (error, res) = rpc.rpc_performance()
-    assert not error
-    assert len(res) == 1
-    assert res[0]['pair'] == 'BTC_ETH'
-    assert res[0]['count'] == 1
-    assert prec_satoshi(res[0]['profit'], 6.2)
diff --git a/freqtrade/tests/rpc/test_rpc_telegram.py b/freqtrade/tests/rpc/test_rpc_telegram.py
deleted file mode 100644
index 82d6dd865..000000000
--- a/freqtrade/tests/rpc/test_rpc_telegram.py
+++ /dev/null
@@ -1,802 +0,0 @@
-# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors, C0103
-# pragma pylint: disable=unused-argument
-import re
-from datetime import datetime
-from random import randint
-from unittest.mock import MagicMock
-
-from sqlalchemy import create_engine
-from telegram import Update, Message, Chat
-from telegram.error import NetworkError
-
-from freqtrade import __version__
-from freqtrade.main import init, create_trade
-from freqtrade.misc import update_state, State, get_state
-from freqtrade.persistence import Trade
-from freqtrade.rpc import telegram
-from freqtrade.rpc.telegram import authorized_only, is_enabled, send_msg, _status, _status_table, \
-    _profit, _forcesell, _performance, _daily, _count, _start, _stop, _balance, _version, _help
-
-import freqtrade.rpc.telegram as tg
-
-
-def test_is_enabled(default_conf, mocker):
-    mocker.patch.dict('freqtrade.rpc.telegram._CONF', default_conf)
-    default_conf['telegram']['enabled'] = False
-    assert is_enabled() is False
-
-
-def test_init_disabled(default_conf, mocker):
-    mocker.patch.dict('freqtrade.rpc.telegram._CONF', default_conf)
-    default_conf['telegram']['enabled'] = False
-    telegram.init(default_conf)
-
-
-def test_authorized_only(default_conf, mocker):
-    mocker.patch.dict('freqtrade.rpc.telegram._CONF', default_conf)
-
-    chat = Chat(0, 0)
-    update = Update(randint(1, 100))
-    update.message = Message(randint(1, 100), 0, datetime.utcnow(), chat)
-    state = {'called': False}
-
-    @authorized_only
-    def dummy_handler(*args, **kwargs) -> None:
-        state['called'] = True
-
-    dummy_handler(MagicMock(), update)
-    assert state['called'] is True
-
-
-def test_authorized_only_unauthorized(default_conf, mocker):
-    mocker.patch.dict('freqtrade.rpc.telegram._CONF', default_conf)
-
-    chat = Chat(0xdeadbeef, 0)
-    update = Update(randint(1, 100))
-    update.message = Message(randint(1, 100), 0, datetime.utcnow(), chat)
-    state = {'called': False}
-
-    @authorized_only
-    def dummy_handler(*args, **kwargs) -> None:
-        state['called'] = True
-
-    dummy_handler(MagicMock(), update)
-    assert state['called'] is False
-
-
-def test_authorized_only_exception(default_conf, mocker):
-    mocker.patch.dict('freqtrade.rpc.telegram._CONF', default_conf)
-
-    update = Update(randint(1, 100))
-    update.message = Message(randint(1, 100), 0, datetime.utcnow(), Chat(0, 0))
-
-    @authorized_only
-    def dummy_handler(*args, **kwargs) -> None:
-        raise Exception('test')
-
-    dummy_handler(MagicMock(), update)
-
-
-def test_status_handle(default_conf, update, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    msg_mock = MagicMock()
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    init(default_conf, create_engine('sqlite://'))
-
-    update_state(State.STOPPED)
-    _status(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'trader is not running' in msg_mock.call_args_list[0][0][0]
-    msg_mock.reset_mock()
-
-    update_state(State.RUNNING)
-    _status(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'no active trade' in msg_mock.call_args_list[0][0][0]
-    msg_mock.reset_mock()
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-    # Trigger status while we have a fulfilled order for the open trade
-    _status(bot=MagicMock(), update=update)
-
-    assert msg_mock.call_count == 1
-    assert '[BTC_ETH]' in msg_mock.call_args_list[0][0][0]
-
-
-def test_status_table_handle(default_conf, update, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    msg_mock = MagicMock()
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple(
-        'freqtrade.rpc.telegram',
-        _CONF=default_conf,
-        init=MagicMock(),
-        send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_order_id'))
-    init(default_conf, create_engine('sqlite://'))
-    update_state(State.STOPPED)
-    _status_table(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'trader is not running' in msg_mock.call_args_list[0][0][0]
-    msg_mock.reset_mock()
-
-    update_state(State.RUNNING)
-    _status_table(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'no active order' in msg_mock.call_args_list[0][0][0]
-    msg_mock.reset_mock()
-
-    # Create some test data
-    create_trade(15.0, int(default_conf['ticker_interval']))
-
-    _status_table(bot=MagicMock(), update=update)
-
-    text = re.sub('</?pre>', '', msg_mock.call_args_list[-1][0][0])
-    line = text.split("\n")
-    fields = re.sub('[ ]+', ' ', line[2].strip()).split(' ')
-
-    assert int(fields[0]) == 1
-    assert fields[1] == 'BTC_ETH'
-    assert msg_mock.call_count == 1
-
-
-def test_profit_handle(
-        default_conf, update, ticker, ticker_sell_up, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    msg_mock = MagicMock()
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch.multiple('freqtrade.fiat_convert.Market',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}))
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    init(default_conf, create_engine('sqlite://'))
-
-    _profit(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'no closed trade' in msg_mock.call_args_list[0][0][0]
-    msg_mock.reset_mock()
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-    trade = Trade.query.first()
-
-    # Simulate fulfilled LIMIT_BUY order for trade
-    trade.update(limit_buy_order)
-
-    _profit(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'no closed trade' in msg_mock.call_args_list[-1][0][0]
-    msg_mock.reset_mock()
-
-    # Update the ticker with a market going up
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_up)
-    trade.update(limit_sell_order)
-
-    trade.close_date = datetime.utcnow()
-    trade.is_open = False
-
-    _profit(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert '*ROI:* Close trades' in msg_mock.call_args_list[-1][0][0]
-    assert '∙ `0.00006217 BTC (6.20%)`' in msg_mock.call_args_list[-1][0][0]
-    assert '∙ `0.933 USD`' in msg_mock.call_args_list[-1][0][0]
-    assert '*ROI:* All trades' in msg_mock.call_args_list[-1][0][0]
-    assert '∙ `0.00006217 BTC (6.20%)`' in msg_mock.call_args_list[-1][0][0]
-    assert '∙ `0.933 USD`' in msg_mock.call_args_list[-1][0][0]
-
-    assert '*Best Performing:* `BTC_ETH: 6.20%`' in msg_mock.call_args_list[-1][0][0]
-
-
-def test_forcesell_handle(default_conf, update, ticker, ticker_sell_up, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    assert trade
-
-    # Increase the price and sell it
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_up)
-
-    update.message.text = '/forcesell 1'
-    _forcesell(bot=MagicMock(), update=update)
-
-    assert rpc_mock.call_count == 2
-    assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
-    assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
-    assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
-    assert 'profit: 6.11%, 0.00006126' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.919 USD' in rpc_mock.call_args_list[-1][0][0]
-
-
-def test_forcesell_down_handle(default_conf, update, ticker, ticker_sell_down, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    # Decrease the price and sell it
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_down)
-
-    trade = Trade.query.first()
-    assert trade
-
-    update.message.text = '/forcesell 1'
-    _forcesell(bot=MagicMock(), update=update)
-
-    assert rpc_mock.call_count == 2
-    assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
-    assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
-    assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.00001044' in rpc_mock.call_args_list[-1][0][0]
-    assert 'loss: -5.48%, -0.00005492' in rpc_mock.call_args_list[-1][0][0]
-    assert '-0.824 USD' in rpc_mock.call_args_list[-1][0][0]
-
-
-def test_forcesell_all_handle(default_conf, update, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    for _ in range(4):
-        create_trade(0.001, int(default_conf['ticker_interval']))
-    rpc_mock.reset_mock()
-
-    update.message.text = '/forcesell all'
-    _forcesell(bot=MagicMock(), update=update)
-
-    assert rpc_mock.call_count == 4
-    for args in rpc_mock.call_args_list:
-        assert '0.00001098' in args[0][0]
-        assert 'loss: -0.59%, -0.00000591 BTC' in args[0][0]
-        assert '-0.089 USD' in args[0][0]
-
-
-def test_forcesell_handle_invalid(default_conf, update, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, True))
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock())
-    init(default_conf, create_engine('sqlite://'))
-
-    # Trader is not running
-    update_state(State.STOPPED)
-    update.message.text = '/forcesell 1'
-    _forcesell(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'not running' in msg_mock.call_args_list[0][0][0]
-
-    # No argument
-    msg_mock.reset_mock()
-    update_state(State.RUNNING)
-    update.message.text = '/forcesell'
-    _forcesell(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'Invalid argument' in msg_mock.call_args_list[0][0][0]
-
-    # Invalid argument
-    msg_mock.reset_mock()
-    update_state(State.RUNNING)
-    update.message.text = '/forcesell 123456'
-    _forcesell(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'Invalid argument.' in msg_mock.call_args_list[0][0][0]
-
-
-def test_performance_handle(
-        default_conf, update, ticker, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    msg_mock = MagicMock()
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-    trade = Trade.query.first()
-    assert trade
-
-    # Simulate fulfilled LIMIT_BUY order for trade
-    trade.update(limit_buy_order)
-
-    # Simulate fulfilled LIMIT_SELL order for trade
-    trade.update(limit_sell_order)
-
-    trade.close_date = datetime.utcnow()
-    trade.is_open = False
-    _performance(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'Performance' in msg_mock.call_args_list[0][0][0]
-    assert '<code>BTC_ETH\t6.20% (1)</code>' in msg_mock.call_args_list[0][0][0]
-
-
-def test_daily_handle(default_conf, update, ticker, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    msg_mock = MagicMock()
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch.multiple('freqtrade.fiat_convert.Market',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}))
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-    trade = Trade.query.first()
-    assert trade
-
-    # Simulate fulfilled LIMIT_BUY order for trade
-    trade.update(limit_buy_order)
-
-    # Simulate fulfilled LIMIT_SELL order for trade
-    trade.update(limit_sell_order)
-
-    trade.close_date = datetime.utcnow()
-    trade.is_open = False
-
-    # Try valid data
-    update.message.text = '/daily 2'
-    _daily(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'Daily' in msg_mock.call_args_list[0][0][0]
-    assert str(datetime.utcnow().date()) in msg_mock.call_args_list[0][0][0]
-    assert str('  0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
-    assert str('  0.933 USD') in msg_mock.call_args_list[0][0][0]
-    assert str('  1 trade') in msg_mock.call_args_list[0][0][0]
-    assert str('  0 trade') in msg_mock.call_args_list[0][0][0]
-
-    # Reset msg_mock
-    msg_mock.reset_mock()
-    # Add two other trades
-    create_trade(0.001, int(default_conf['ticker_interval']))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trades = Trade.query.all()
-    for trade in trades:
-        trade.update(limit_buy_order)
-        trade.update(limit_sell_order)
-        trade.close_date = datetime.utcnow()
-        trade.is_open = False
-
-    update.message.text = '/daily 1'
-
-    _daily(bot=MagicMock(), update=update)
-    assert str('  0.00018651 BTC') in msg_mock.call_args_list[0][0][0]
-    assert str('  2.798 USD') in msg_mock.call_args_list[0][0][0]
-    assert str('  3 trades') in msg_mock.call_args_list[0][0][0]
-
-
-def test_daily_wrong_input(default_conf, update, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    msg_mock = MagicMock()
-    mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch.multiple('freqtrade.fiat_convert.Market',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}))
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Try invalid data
-    msg_mock.reset_mock()
-    update_state(State.RUNNING)
-    update.message.text = '/daily -2'
-    _daily(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'must be an integer greater than 0' in msg_mock.call_args_list[0][0][0]
-
-    # Try invalid data
-    msg_mock.reset_mock()
-    update_state(State.RUNNING)
-    update.message.text = '/daily today'
-    _daily(bot=MagicMock(), update=update)
-    assert str('Daily Profit over the last 7 days') in msg_mock.call_args_list[0][0][0]
-
-
-def test_count_handle(default_conf, update, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    msg_mock = MagicMock()
-    mocker.patch.multiple(
-        'freqtrade.rpc.telegram',
-        _CONF=default_conf,
-        init=MagicMock(),
-        send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_order_id'))
-    init(default_conf, create_engine('sqlite://'))
-    update_state(State.STOPPED)
-    _count(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'not running' in msg_mock.call_args_list[0][0][0]
-    msg_mock.reset_mock()
-    update_state(State.RUNNING)
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-    msg_mock.reset_mock()
-    _count(bot=MagicMock(), update=update)
-
-    msg = '<pre>  current    max\n---------  -----\n        1      {}</pre>'.format(
-        default_conf['max_open_trades']
-    )
-    assert msg in msg_mock.call_args_list[0][0][0]
-
-
-def test_performance_handle_invalid(default_conf, update, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, True))
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock())
-    init(default_conf, create_engine('sqlite://'))
-
-    # Trader is not running
-    update_state(State.STOPPED)
-    _performance(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert 'not running' in msg_mock.call_args_list[0][0][0]
-
-
-def test_start_handle(default_conf, update, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    init(default_conf, create_engine('sqlite://'))
-    update_state(State.STOPPED)
-    assert get_state() == State.STOPPED
-    _start(bot=MagicMock(), update=update)
-    assert get_state() == State.RUNNING
-    assert msg_mock.call_count == 0
-
-
-def test_start_handle_already_running(default_conf, update, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    init(default_conf, create_engine('sqlite://'))
-    update_state(State.RUNNING)
-    assert get_state() == State.RUNNING
-    _start(bot=MagicMock(), update=update)
-    assert get_state() == State.RUNNING
-    assert msg_mock.call_count == 1
-    assert 'already running' in msg_mock.call_args_list[0][0][0]
-
-
-def test_stop_handle(default_conf, update, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    init(default_conf, create_engine('sqlite://'))
-    update_state(State.RUNNING)
-    assert get_state() == State.RUNNING
-    _stop(bot=MagicMock(), update=update)
-    assert get_state() == State.STOPPED
-    assert msg_mock.call_count == 1
-    assert 'Stopping trader' in msg_mock.call_args_list[0][0][0]
-
-
-def test_stop_handle_already_stopped(default_conf, update, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    init(default_conf, create_engine('sqlite://'))
-    update_state(State.STOPPED)
-    assert get_state() == State.STOPPED
-    _stop(bot=MagicMock(), update=update)
-    assert get_state() == State.STOPPED
-    assert msg_mock.call_count == 1
-    assert 'already stopped' in msg_mock.call_args_list[0][0][0]
-
-
-def test_telegram_balance_handle(default_conf, update, mocker):
-    mock_balance = [{
-        'Currency': 'BTC',
-        'Balance': 10.0,
-        'Available': 12.0,
-        'Pending': 0.0,
-        'CryptoAddress': 'XXXX',
-    }, {
-        'Currency': 'ETH',
-        'Balance': 0.0,
-        'Available': 0.0,
-        'Pending': 0.0,
-        'CryptoAddress': 'XXXX',
-    }, {
-        'Currency': 'USDT',
-        'Balance': 10000.0,
-        'Available': 0.0,
-        'Pending': 0.0,
-        'CryptoAddress': 'XXXX',
-    }, {
-        'Currency': 'LTC',
-        'Balance': 10.0,
-        'Available': 10.0,
-        'Pending': 0.0,
-        'CryptoAddress': 'XXXX',
-    }]
-
-    def mock_ticker(symbol, refresh):
-        if symbol == 'USDT_BTC':
-            return {
-                'bid': 10000.00,
-                'ask': 10000.00,
-                'last': 10000.00,
-            }
-        else:
-            return {
-                'bid': 0.1,
-                'ask': 0.1,
-                'last': 0.1,
-            }
-
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_balances=MagicMock(return_value=mock_balance))
-    mocker.patch.multiple('freqtrade.fiat_convert.Market',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}))
-    mocker.patch('freqtrade.main.exchange.get_ticker', side_effect=mock_ticker)
-
-    _balance(bot=MagicMock(), update=update)
-    result = msg_mock.call_args_list[0][0][0]
-    assert msg_mock.call_count == 1
-    assert '*Currency*: BTC' in result
-    assert '*Currency*: ETH' not in result
-    assert '*Currency*: USDT' in result
-    assert 'Balance' in result
-    assert 'Est. BTC' in result
-    assert '*BTC*:  12.00000000' in result
-
-
-def test_zero_balance_handle(default_conf, update, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_balances=MagicMock(return_value=[]))
-    _balance(bot=MagicMock(), update=update)
-    result = msg_mock.call_args_list[0][0][0]
-    assert msg_mock.call_count == 1
-    assert '`All balances are zero.`' in result
-
-
-def test_help_handle(default_conf, update, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-
-    _help(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert '*/help:* `This help message`' in msg_mock.call_args_list[0][0][0]
-
-
-def test_version_handle(default_conf, update, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    msg_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=msg_mock)
-
-    _version(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 1
-    assert '*Version:* `{}`'.format(__version__) in msg_mock.call_args_list[0][0][0]
-
-
-def test_send_msg(default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    bot = MagicMock()
-    send_msg('test', bot)
-    assert not bot.method_calls
-    bot.reset_mock()
-
-    default_conf['telegram']['enabled'] = True
-    send_msg('test', bot)
-    assert len(bot.method_calls) == 1
-
-
-def test_send_msg_network_error(default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    default_conf['telegram']['enabled'] = True
-    bot = MagicMock()
-    bot.send_message = MagicMock(side_effect=NetworkError('Oh snap'))
-    send_msg('test', bot)
-
-    # Bot should've tried to send it twice
-    assert len(bot.method_calls) == 2
-
-
-def test_init(default_conf, mocker):
-    start_polling = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          # mock telegram.ext.Updater
-                          Updater=MagicMock(return_value=start_polling))
-    # not enabled
-    tg.init(default_conf)
-    assert start_polling.call_count == 0
-    # number of handles registered
-    assert start_polling.dispatcher.add_handler.call_count == 11
-    assert start_polling.start_polling.call_count == 1
-
-    # enabled
-    default_conf['telegram'] = {}
-    default_conf['telegram']['enabled'] = True
-    default_conf['telegram']['token'] = ''
-    tg.init(default_conf)
-
-
-def test_cleanup(default_conf, mocker):
-    default_conf['telegram'] = {}
-    default_conf['telegram']['enabled'] = False
-    updater_mock = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          _UPDATER=updater_mock)
-    # not enabled
-    tg.cleanup()
-    assert updater_mock.stop.call_count == 0
-
-    # enabled
-    default_conf['telegram']['enabled'] = True
-    tg.cleanup()
-    assert updater_mock.stop.call_count == 1
-
-
-def test_status(default_conf, update, mocker):
-    update.message.chat.id = 123
-    default_conf['telegram'] = {}
-    default_conf['telegram']['chat_id'] = 123
-    mocker.patch('telegram.update', MagicMock())
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock())
-    msg_mock = MagicMock()
-    status_table = MagicMock()
-    mocker.patch.multiple('freqtrade.rpc',
-                          send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          rpc_trade_status=MagicMock(return_value=(False, [1, 2, 3])),
-                          _status_table=status_table,
-                          send_msg=msg_mock)
-    _status(bot=MagicMock(), update=update)
-    assert msg_mock.call_count == 3
-    update.message.text = MagicMock()
-    update.message.text.replace = MagicMock(return_value='table 2 3')
-    _status(bot=MagicMock(), update=update)
-    assert status_table.call_count == 1
diff --git a/freqtrade/tests/strategy/test_default_strategy.py b/freqtrade/tests/strategy/test_default_strategy.py
deleted file mode 100644
index f23c1fa48..000000000
--- a/freqtrade/tests/strategy/test_default_strategy.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import json
-import pytest
-from pandas import DataFrame
-from freqtrade.strategy.default_strategy import DefaultStrategy, class_name
-from freqtrade.analyze import parse_ticker_dataframe
-
-
-@pytest.fixture
-def result():
-    with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
-        return parse_ticker_dataframe(json.load(data_file))
-
-
-def test_default_strategy_class_name():
-    assert class_name == DefaultStrategy.__name__
-
-
-def test_default_strategy_structure():
-    assert hasattr(DefaultStrategy, 'minimal_roi')
-    assert hasattr(DefaultStrategy, 'stoploss')
-    assert hasattr(DefaultStrategy, 'ticker_interval')
-    assert hasattr(DefaultStrategy, 'populate_indicators')
-    assert hasattr(DefaultStrategy, 'populate_buy_trend')
-    assert hasattr(DefaultStrategy, 'populate_sell_trend')
-
-
-def test_default_strategy(result):
-    strategy = DefaultStrategy()
-
-    assert type(strategy.minimal_roi) is dict
-    assert type(strategy.stoploss) is float
-    assert type(strategy.ticker_interval) is int
-    indicators = strategy.populate_indicators(result)
-    assert type(indicators) is DataFrame
-    assert type(strategy.populate_buy_trend(indicators)) is DataFrame
-    assert type(strategy.populate_sell_trend(indicators)) is DataFrame
diff --git a/freqtrade/tests/strategy/test_strategy.py b/freqtrade/tests/strategy/test_strategy.py
deleted file mode 100644
index b0ce88e98..000000000
--- a/freqtrade/tests/strategy/test_strategy.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# pragma pylint: disable=missing-docstring, protected-access, C0103
-
-import logging
-from freqtrade.strategy.strategy import Strategy
-
-
-def test_sanitize_module_name():
-    assert Strategy._sanitize_module_name('default_strategy') == 'default_strategy'
-    assert Strategy._sanitize_module_name('default_strategy.py') == 'default_strategy'
-    assert Strategy._sanitize_module_name('../default_strategy.py') == 'default_strategy'
-    assert Strategy._sanitize_module_name('../default_strategy') == 'default_strategy'
-    assert Strategy._sanitize_module_name('.default_strategy') == '.default_strategy'
-    assert Strategy._sanitize_module_name('foo-bar') == 'foo-bar'
-    assert Strategy._sanitize_module_name('foo/bar') == 'bar'
-
-
-def test_search_strategy():
-    assert Strategy._search_strategy('default_strategy') == '.'
-    assert Strategy._search_strategy('test_strategy') == 'user_data.strategies.'
-    assert Strategy._search_strategy('super_duper') is None
-
-
-def test_strategy_structure():
-    assert hasattr(Strategy, 'init')
-    assert hasattr(Strategy, 'populate_indicators')
-    assert hasattr(Strategy, 'populate_buy_trend')
-    assert hasattr(Strategy, 'populate_sell_trend')
-
-
-def test_load_strategy(result):
-    strategy = Strategy()
-    strategy.logger = logging.getLogger(__name__)
-
-    assert not hasattr(Strategy, 'custom_strategy')
-    strategy._load_strategy('test_strategy')
-
-    assert not hasattr(Strategy, 'custom_strategy')
-
-    assert hasattr(strategy.custom_strategy, 'populate_indicators')
-    assert 'adx' in strategy.populate_indicators(result)
-
-
-def test_load_not_found_strategy(caplog):
-    strategy = Strategy()
-    strategy.logger = logging.getLogger(__name__)
-
-    assert not hasattr(Strategy, 'custom_strategy')
-    strategy._load_strategy('NotFoundStrategy')
-
-    error_msg = "Impossible to load Strategy 'user_data/strategies/{}.py'. This file does not " \
-                "exist or contains Python code errors".format('NotFoundStrategy')
-    assert ('test_strategy', logging.ERROR, error_msg) in caplog.record_tuples
-
-
-def test_strategy(result):
-    strategy = Strategy()
-    strategy.init({'strategy': 'default_strategy'})
-
-    assert hasattr(strategy.custom_strategy, 'minimal_roi')
-    assert strategy.minimal_roi[0] == 0.04
-
-    assert hasattr(strategy.custom_strategy, 'stoploss')
-    assert strategy.stoploss == -0.10
-
-    assert hasattr(strategy.custom_strategy, 'populate_indicators')
-    assert 'adx' in strategy.populate_indicators(result)
-
-    assert hasattr(strategy.custom_strategy, 'populate_buy_trend')
-    dataframe = strategy.populate_buy_trend(strategy.populate_indicators(result))
-    assert 'buy' in dataframe.columns
-
-    assert hasattr(strategy.custom_strategy, 'populate_sell_trend')
-    dataframe = strategy.populate_sell_trend(strategy.populate_indicators(result))
-    assert 'sell' in dataframe.columns
-
-
-def test_strategy_override_minimal_roi(caplog):
-    caplog.set_level(logging.INFO)
-    config = {
-        'strategy': 'default_strategy',
-        'minimal_roi': {
-            "0": 0.5
-        }
-    }
-    strategy = Strategy()
-    strategy.init(config)
-
-    assert hasattr(strategy.custom_strategy, 'minimal_roi')
-    assert strategy.minimal_roi[0] == 0.5
-    assert ('freqtrade.strategy.strategy',
-            logging.INFO,
-            'Override strategy \'minimal_roi\' with value in config file.'
-            ) in caplog.record_tuples
-
-
-def test_strategy_override_stoploss(caplog):
-    caplog.set_level(logging.INFO)
-    config = {
-        'strategy': 'default_strategy',
-        'stoploss': -0.5
-    }
-    strategy = Strategy()
-    strategy.init(config)
-
-    assert hasattr(strategy.custom_strategy, 'stoploss')
-    assert strategy.stoploss == -0.5
-    assert ('freqtrade.strategy.strategy',
-            logging.INFO,
-            'Override strategy \'stoploss\' with value in config file: -0.5.'
-            ) in caplog.record_tuples
-
-
-def test_strategy_override_ticker_interval(caplog):
-    caplog.set_level(logging.INFO)
-
-    config = {
-        'strategy': 'default_strategy',
-        'ticker_interval': 60
-    }
-    strategy = Strategy()
-    strategy.init(config)
-
-    assert hasattr(strategy.custom_strategy, 'ticker_interval')
-    assert strategy.ticker_interval == 60
-    assert ('freqtrade.strategy.strategy',
-            logging.INFO,
-            'Override strategy \'ticker_interval\' with value in config file: 60.'
-            ) in caplog.record_tuples
-
-
-def test_strategy_fallback_default_strategy():
-    strategy = Strategy()
-    strategy.logger = logging.getLogger(__name__)
-
-    assert not hasattr(Strategy, 'custom_strategy')
-    strategy._load_strategy('../../super_duper')
-    assert not hasattr(Strategy, 'custom_strategy')
-
-
-def test_strategy_singleton():
-    strategy1 = Strategy()
-    strategy1.init({'strategy': 'default_strategy'})
-
-    assert hasattr(strategy1.custom_strategy, 'minimal_roi')
-    assert strategy1.minimal_roi[0] == 0.04
-
-    strategy2 = Strategy()
-    assert hasattr(strategy2.custom_strategy, 'minimal_roi')
-    assert strategy2.minimal_roi[0] == 0.04
diff --git a/freqtrade/tests/test_acl_pair.py b/freqtrade/tests/test_acl_pair.py
deleted file mode 100644
index b70596091..000000000
--- a/freqtrade/tests/test_acl_pair.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# pragma pylint: disable=missing-docstring,C0103
-
-from freqtrade.main import refresh_whitelist, gen_pair_whitelist
-
-# whitelist, blacklist, filtering, all of that will
-# eventually become some rules to run on a generic ACL engine
-# perhaps try to anticipate that by using some python package
-
-
-def whitelist_conf():
-    return {
-        'stake_currency': 'BTC',
-        'exchange': {
-            'pair_whitelist': [
-                'BTC_ETH',
-                'BTC_TKN',
-                'BTC_TRST',
-                'BTC_SWT',
-                'BTC_BCC'
-            ],
-            'pair_blacklist': [
-                'BTC_BLK'
-            ],
-        },
-    }
-
-
-def get_market_summaries():
-    return [{
-        'MarketName': 'BTC-TKN',
-        'High': 0.00000919,
-        'Low': 0.00000820,
-        'Volume': 74339.61396015,
-        'Last': 0.00000820,
-        'BaseVolume': 1664,
-        'TimeStamp': '2014-07-09T07:19:30.15',
-        'Bid': 0.00000820,
-        'Ask': 0.00000831,
-        'OpenBuyOrders': 15,
-        'OpenSellOrders': 15,
-        'PrevDay': 0.00000821,
-        'Created': '2014-03-20T06:00:00',
-        'DisplayMarketName': ''
-    }, {
-        'MarketName': 'BTC-ETH',
-        'High': 0.00000072,
-        'Low': 0.00000001,
-        'Volume': 166340678.42280999,
-        'Last': 0.00000005,
-        'BaseVolume': 42,
-        'TimeStamp': '2014-07-09T07:21:40.51',
-        'Bid': 0.00000004,
-        'Ask': 0.00000005,
-        'OpenBuyOrders': 18,
-        'OpenSellOrders': 18,
-        'PrevDay': 0.00000002,
-        'Created': '2014-05-30T07:57:49.637',
-        'DisplayMarketName': ''
-    }, {
-        'MarketName': 'BTC-BLK',
-        'High': 0.00000072,
-        'Low': 0.00000001,
-        'Volume': 166340678.42280999,
-        'Last': 0.00000005,
-        'BaseVolume': 3,
-        'TimeStamp': '2014-07-09T07:21:40.51',
-        'Bid': 0.00000004,
-        'Ask': 0.00000005,
-        'OpenBuyOrders': 18,
-        'OpenSellOrders': 18,
-        'PrevDay': 0.00000002,
-        'Created': '2014-05-30T07:57:49.637',
-        'DisplayMarketName': ''
-    }]
-
-
-def get_health():
-    return [{'Currency': 'ETH', 'IsActive': True},
-            {'Currency': 'TKN', 'IsActive': True},
-            {'Currency': 'BLK', 'IsActive': True}]
-
-
-def get_health_empty():
-    return []
-
-
-def test_refresh_market_pair_not_in_whitelist(mocker):
-    conf = whitelist_conf()
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_wallet_health=get_health)
-    refreshedwhitelist = refresh_whitelist(
-        conf['exchange']['pair_whitelist'] + ['BTC_XXX'])
-    # List ordered by BaseVolume
-    whitelist = ['BTC_ETH', 'BTC_TKN']
-    # Ensure all except those in whitelist are removed
-    assert whitelist == refreshedwhitelist
-
-
-def test_refresh_whitelist(mocker):
-    conf = whitelist_conf()
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_wallet_health=get_health)
-    refreshedwhitelist = refresh_whitelist(conf['exchange']['pair_whitelist'])
-    # List ordered by BaseVolume
-    whitelist = ['BTC_ETH', 'BTC_TKN']
-    # Ensure all except those in whitelist are removed
-    assert whitelist == refreshedwhitelist
-
-
-def test_refresh_whitelist_dynamic(mocker):
-    conf = whitelist_conf()
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_wallet_health=get_health)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_market_summaries=get_market_summaries)
-    # argument: use the whitelist dynamically by exchange-volume
-    whitelist = ['BTC_TKN', 'BTC_ETH']
-    refreshedwhitelist = refresh_whitelist(
-        gen_pair_whitelist(conf['stake_currency']))
-    assert whitelist == refreshedwhitelist
-
-
-def test_refresh_whitelist_dynamic_empty(mocker):
-    conf = whitelist_conf()
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_wallet_health=get_health_empty)
-    # argument: use the whitelist dynamically by exchange-volume
-    whitelist = []
-    conf['exchange']['pair_whitelist'] = []
-    refresh_whitelist(whitelist)
-    pairslist = conf['exchange']['pair_whitelist']
-    assert set(whitelist) == set(pairslist)
diff --git a/freqtrade/tests/test_analyze.py b/freqtrade/tests/test_analyze.py
deleted file mode 100644
index 7e8f74b7e..000000000
--- a/freqtrade/tests/test_analyze.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# pragma pylint: disable=missing-docstring, C0103
-import datetime
-from unittest.mock import MagicMock
-
-import arrow
-import logging
-from pandas import DataFrame
-
-from freqtrade.tests.conftest import log_has
-from freqtrade.analyze import (get_signal, parse_ticker_dataframe,
-                               populate_buy_trend, populate_indicators,
-                               populate_sell_trend)
-from freqtrade.strategy.strategy import Strategy
-
-
-def test_dataframe_correct_columns(result):
-    assert result.columns.tolist() == \
-        ['close', 'high', 'low', 'open', 'date', 'volume']
-
-
-def test_dataframe_correct_length(result):
-    dataframe = parse_ticker_dataframe(result)
-    assert len(result.index) == len(dataframe.index)
-
-
-def test_populates_buy_trend(result):
-    # Load the default strategy for the unit test, because this logic is done in main.py
-    Strategy().init({'strategy': 'default_strategy'})
-
-    dataframe = populate_buy_trend(populate_indicators(result))
-    assert 'buy' in dataframe.columns
-
-
-def test_populates_sell_trend(result):
-    # Load the default strategy for the unit test, because this logic is done in main.py
-    Strategy().init({'strategy': 'default_strategy'})
-
-    dataframe = populate_sell_trend(populate_indicators(result))
-    assert 'sell' in dataframe.columns
-
-
-def test_returns_latest_buy_signal(mocker):
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
-    mocker.patch(
-        'freqtrade.analyze.analyze_ticker',
-        return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
-    )
-    assert get_signal('BTC-ETH', 5) == (True, False)
-
-    mocker.patch(
-        'freqtrade.analyze.analyze_ticker',
-        return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
-    )
-    assert get_signal('BTC-ETH', 5) == (False, True)
-
-
-def test_returns_latest_sell_signal(mocker):
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
-    mocker.patch(
-        'freqtrade.analyze.analyze_ticker',
-        return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
-    )
-    assert get_signal('BTC-ETH', 5) == (False, True)
-
-    mocker.patch(
-        'freqtrade.analyze.analyze_ticker',
-        return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
-    )
-    assert get_signal('BTC-ETH', 5) == (True, False)
-
-
-def test_get_signal_empty(default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=None)
-    assert (False, False) == get_signal('foo', int(default_conf['ticker_interval']))
-    assert log_has('Empty ticker history for pair foo', caplog.record_tuples)
-
-
-def test_get_signal_exception_valueerror(default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
-    mocker.patch('freqtrade.analyze.analyze_ticker',
-                 side_effect=ValueError('xyz'))
-    assert (False, False) == get_signal('foo', int(default_conf['ticker_interval']))
-    assert log_has('Unable to analyze ticker for pair foo: xyz', caplog.record_tuples)
-
-
-def test_get_signal_empty_dataframe(default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
-    mocker.patch('freqtrade.analyze.analyze_ticker', return_value=DataFrame([]))
-    assert (False, False) == get_signal('xyz', int(default_conf['ticker_interval']))
-    assert log_has('Empty dataframe for pair xyz', caplog.record_tuples)
-
-
-def test_get_signal_old_dataframe(default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
-    # FIX: The get_signal function has hardcoded 10, which we must inturn hardcode
-    oldtime = arrow.utcnow() - datetime.timedelta(minutes=11)
-    ticks = DataFrame([{'buy': 1, 'date': oldtime}])
-    mocker.patch('freqtrade.analyze.analyze_ticker', return_value=DataFrame(ticks))
-    assert (False, False) == get_signal('xyz', int(default_conf['ticker_interval']))
-    assert log_has('Outdated history for pair xyz. Last tick is 11 minutes old',
-                   caplog.record_tuples)
-
-
-def test_get_signal_handles_exceptions(mocker):
-    mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
-    mocker.patch('freqtrade.analyze.analyze_ticker',
-                 side_effect=Exception('invalid ticker history '))
-
-    assert get_signal('BTC-ETH', 5) == (False, False)
-
-
-def test_parse_ticker_dataframe(ticker_history, ticker_history_without_bv):
-    columns = ['close', 'high', 'low', 'open', 'date', 'volume']
-
-    # Test file with BV data
-    dataframe = parse_ticker_dataframe(ticker_history)
-    assert dataframe.columns.tolist() == columns
-
-    # Test file without BV data
-    dataframe = parse_ticker_dataframe(ticker_history_without_bv)
-    assert dataframe.columns.tolist() == columns
diff --git a/freqtrade/tests/test_dataframe.py b/freqtrade/tests/test_dataframe.py
deleted file mode 100644
index 44b2a7e8c..000000000
--- a/freqtrade/tests/test_dataframe.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# pragma pylint: disable=missing-docstring, C0103
-
-import pandas
-import freqtrade.optimize
-from freqtrade import analyze
-from freqtrade.strategy.strategy import Strategy
-
-_pairs = ['BTC_ETH']
-
-
-def load_dataframe_pair(pairs):
-    ld = freqtrade.optimize.load_data(None, ticker_interval=5, pairs=pairs)
-    assert isinstance(ld, dict)
-    assert isinstance(pairs[0], str)
-    dataframe = ld[pairs[0]]
-    dataframe = analyze.analyze_ticker(dataframe)
-    return dataframe
-
-
-def test_dataframe_load():
-    Strategy().init({'strategy': 'default_strategy'})
-    dataframe = load_dataframe_pair(_pairs)
-    assert isinstance(dataframe, pandas.core.frame.DataFrame)
-
-
-def test_dataframe_columns_exists():
-    Strategy().init({'strategy': 'default_strategy'})
-    dataframe = load_dataframe_pair(_pairs)
-    assert 'high' in dataframe.columns
-    assert 'low' in dataframe.columns
-    assert 'close' in dataframe.columns
diff --git a/freqtrade/tests/test_fiat_convert.py b/freqtrade/tests/test_fiat_convert.py
deleted file mode 100644
index 49a2adc05..000000000
--- a/freqtrade/tests/test_fiat_convert.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors,
-# pragma pylint: disable=protected-access, C0103
-
-import time
-from unittest.mock import MagicMock
-
-import pytest
-
-from freqtrade.fiat_convert import CryptoFiat, CryptoToFiatConverter
-
-
-def test_pair_convertion_object():
-    pair_convertion = CryptoFiat(
-        crypto_symbol='btc',
-        fiat_symbol='usd',
-        price=12345.0
-    )
-
-    # Check the cache duration is 6 hours
-    assert pair_convertion.CACHE_DURATION == 6 * 60 * 60
-
-    # Check a regular usage
-    assert pair_convertion.crypto_symbol == 'BTC'
-    assert pair_convertion.fiat_symbol == 'USD'
-    assert pair_convertion.price == 12345.0
-    assert pair_convertion.is_expired() is False
-
-    # Update the expiration time (- 2 hours) and check the behavior
-    pair_convertion._expiration = time.time() - 2 * 60 * 60
-    assert pair_convertion.is_expired() is True
-
-    # Check set price behaviour
-    time_reference = time.time() + pair_convertion.CACHE_DURATION
-    pair_convertion.set_price(price=30000.123)
-    assert pair_convertion.is_expired() is False
-    assert pair_convertion._expiration >= time_reference
-    assert pair_convertion.price == 30000.123
-
-
-def test_fiat_convert_is_supported():
-    fiat_convert = CryptoToFiatConverter()
-    assert fiat_convert._is_supported_fiat(fiat='USD') is True
-    assert fiat_convert._is_supported_fiat(fiat='usd') is True
-    assert fiat_convert._is_supported_fiat(fiat='abc') is False
-    assert fiat_convert._is_supported_fiat(fiat='ABC') is False
-
-
-def test_fiat_convert_add_pair():
-    fiat_convert = CryptoToFiatConverter()
-
-    pair_len = len(fiat_convert._pairs)
-    assert pair_len == 0
-
-    fiat_convert._add_pair(crypto_symbol='btc', fiat_symbol='usd', price=12345.0)
-    pair_len = len(fiat_convert._pairs)
-    assert pair_len == 1
-    assert fiat_convert._pairs[0].crypto_symbol == 'BTC'
-    assert fiat_convert._pairs[0].fiat_symbol == 'USD'
-    assert fiat_convert._pairs[0].price == 12345.0
-
-    fiat_convert._add_pair(crypto_symbol='btc', fiat_symbol='Eur', price=13000.2)
-    pair_len = len(fiat_convert._pairs)
-    assert pair_len == 2
-    assert fiat_convert._pairs[1].crypto_symbol == 'BTC'
-    assert fiat_convert._pairs[1].fiat_symbol == 'EUR'
-    assert fiat_convert._pairs[1].price == 13000.2
-
-
-def test_fiat_convert_find_price(mocker):
-    api_mock = MagicMock(return_value={
-        'price_usd': 12345.0,
-        'price_eur': 13000.2
-    })
-    mocker.patch('freqtrade.fiat_convert.Market.ticker', api_mock)
-    fiat_convert = CryptoToFiatConverter()
-
-    with pytest.raises(ValueError, match=r'The fiat ABC is not supported.'):
-        fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='ABC')
-
-    with pytest.raises(ValueError, match=r'The crypto symbol XRP is not supported.'):
-        fiat_convert.get_price(crypto_symbol='XRP', fiat_symbol='USD')
-
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=12345.0)
-    assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 12345.0
-    assert fiat_convert.get_price(crypto_symbol='btc', fiat_symbol='usd') == 12345.0
-
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=13000.2)
-    assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='EUR') == 13000.2
-
-
-def test_fiat_convert_get_price(mocker):
-    api_mock = MagicMock(return_value={
-        'price_usd': 28000.0,
-        'price_eur': 15000.0
-    })
-    mocker.patch('freqtrade.fiat_convert.Market.ticker', api_mock)
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=28000.0)
-
-    fiat_convert = CryptoToFiatConverter()
-
-    with pytest.raises(ValueError, match=r'The fiat US DOLLAR is not supported.'):
-        fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='US Dollar')
-
-    # Check the value return by the method
-    pair_len = len(fiat_convert._pairs)
-    assert pair_len == 0
-    assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 28000.0
-    assert fiat_convert._pairs[0].crypto_symbol == 'BTC'
-    assert fiat_convert._pairs[0].fiat_symbol == 'USD'
-    assert fiat_convert._pairs[0].price == 28000.0
-    assert fiat_convert._pairs[0]._expiration is not 0
-    assert len(fiat_convert._pairs) == 1
-
-    # Verify the cached is used
-    fiat_convert._pairs[0].price = 9867.543
-    expiration = fiat_convert._pairs[0]._expiration
-    assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 9867.543
-    assert fiat_convert._pairs[0]._expiration == expiration
-
-    # Verify the cache expiration
-    expiration = time.time() - 2 * 60 * 60
-    fiat_convert._pairs[0]._expiration = expiration
-    assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 28000.0
-    assert fiat_convert._pairs[0]._expiration is not expiration
-
-
-def test_fiat_convert_without_network():
-    # Because CryptoToFiatConverter is a Singleton we reset the value of _coinmarketcap
-    CryptoToFiatConverter._coinmarketcap = None
-
-    fiat_convert = CryptoToFiatConverter()
-    assert fiat_convert._coinmarketcap is None
-    assert fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='USD') == 0.0
diff --git a/freqtrade/tests/test_indicator_helpers.py b/freqtrade/tests/test_indicator_helpers.py
deleted file mode 100644
index 90330a6ef..000000000
--- a/freqtrade/tests/test_indicator_helpers.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import pandas as pd
-from freqtrade.indicator_helpers import went_up, went_down
-
-
-def test_went_up():
-    series = pd.Series([1, 2, 3, 1])
-    assert went_up(series).equals(pd.Series([False, True, True, False]))
-
-
-def test_went_down():
-    series = pd.Series([1, 2, 3, 1])
-    assert went_down(series).equals(pd.Series([False, False, False, True]))
diff --git a/freqtrade/tests/test_main.py b/freqtrade/tests/test_main.py
deleted file mode 100644
index 5f490a183..000000000
--- a/freqtrade/tests/test_main.py
+++ /dev/null
@@ -1,839 +0,0 @@
-# pragma pylint: disable=missing-docstring, C0103
-import copy
-import logging
-from unittest.mock import MagicMock
-
-import arrow
-import pytest
-import requests
-from sqlalchemy import create_engine
-
-import freqtrade.main as main
-from freqtrade import DependencyException, OperationalException
-from freqtrade.exchange import Exchanges
-from freqtrade.main import (_process, check_handle_timedout, create_trade,
-                            execute_sell, get_target_bid, handle_trade, init)
-from freqtrade.misc import State, get_state
-from freqtrade.persistence import Trade
-from freqtrade.tests.conftest import log_has
-
-
-def test_parse_args_backtesting(mocker):
-    """ Test that main() can start backtesting or hyperopt.
-        and also ensure we can pass some specific arguments
-        further argument parsing is done in test_misc.py """
-    backtesting_mock = mocker.patch(
-        'freqtrade.optimize.backtesting.start', MagicMock())
-    main.main(['backtesting'])
-    assert backtesting_mock.call_count == 1
-    call_args = backtesting_mock.call_args[0][0]
-    assert call_args.config == 'config.json'
-    assert call_args.live is False
-    assert call_args.loglevel == 20
-    assert call_args.subparser == 'backtesting'
-    assert call_args.func is not None
-    assert call_args.ticker_interval is None
-
-
-def test_main_start_hyperopt(mocker):
-    hyperopt_mock = mocker.patch(
-        'freqtrade.optimize.hyperopt.start', MagicMock())
-    main.main(['hyperopt'])
-    assert hyperopt_mock.call_count == 1
-    call_args = hyperopt_mock.call_args[0][0]
-    assert call_args.config == 'config.json'
-    assert call_args.loglevel == 20
-    assert call_args.subparser == 'hyperopt'
-    assert call_args.func is not None
-
-
-def test_process_maybe_execute_buy(default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.create_trade', return_value=True)
-    assert main.process_maybe_execute_buy(int(default_conf['ticker_interval']))
-    mocker.patch('freqtrade.main.create_trade', return_value=False)
-    assert not main.process_maybe_execute_buy(int(default_conf['ticker_interval']))
-
-
-def test_process_maybe_execute_sell(default_conf, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.handle_trade', return_value=True)
-    mocker.patch('freqtrade.exchange.get_order', return_value=1)
-    trade = MagicMock()
-    trade.open_order_id = '123'
-    assert not main.process_maybe_execute_sell(trade, int(default_conf['ticker_interval']))
-    trade.is_open = True
-    trade.open_order_id = None
-    # Assert we call handle_trade() if trade is feasible for execution
-    assert main.process_maybe_execute_sell(trade, int(default_conf['ticker_interval']))
-
-
-def test_process_maybe_execute_buy_exception(default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.create_trade', MagicMock(side_effect=DependencyException))
-    main.process_maybe_execute_buy(int(default_conf['ticker_interval']))
-    assert log_has('Unable to create trade: ', caplog.record_tuples)
-
-
-def test_process_trade_creation(default_conf, ticker, limit_buy_order, health, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_wallet_health=health,
-                          buy=MagicMock(return_value='mocked_limit_buy'),
-                          get_order=MagicMock(return_value=limit_buy_order))
-    init(default_conf, create_engine('sqlite://'))
-
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    assert not trades
-
-    result = _process(interval=int(default_conf['ticker_interval']))
-    assert result is True
-
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    assert len(trades) == 1
-    trade = trades[0]
-    assert trade is not None
-    assert trade.stake_amount == default_conf['stake_amount']
-    assert trade.is_open
-    assert trade.open_date is not None
-    assert trade.exchange == Exchanges.BITTREX.name
-    assert trade.open_rate == 0.00001099
-    assert trade.amount == 90.99181073703367
-
-
-def test_process_exchange_failures(default_conf, ticker, health, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    sleep_mock = mocker.patch('time.sleep', side_effect=lambda _: None)
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_wallet_health=health,
-                          buy=MagicMock(side_effect=requests.exceptions.RequestException))
-    init(default_conf, create_engine('sqlite://'))
-    result = _process(interval=int(default_conf['ticker_interval']))
-    assert result is False
-    assert sleep_mock.has_calls()
-
-
-def test_process_operational_exception(default_conf, ticker, health, mocker):
-    msg_mock = MagicMock()
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=msg_mock)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_wallet_health=health,
-                          buy=MagicMock(side_effect=OperationalException))
-    init(default_conf, create_engine('sqlite://'))
-    assert get_state() == State.RUNNING
-
-    result = _process(interval=int(default_conf['ticker_interval']))
-    assert result is False
-    assert get_state() == State.STOPPED
-    assert 'OperationalException' in msg_mock.call_args_list[-1][0][0]
-
-
-def test_process_trade_handling(default_conf, ticker, limit_buy_order, health, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_wallet_health=health,
-                          buy=MagicMock(return_value='mocked_limit_buy'),
-                          get_order=MagicMock(return_value=limit_buy_order))
-    init(default_conf, create_engine('sqlite://'))
-
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    assert not trades
-    result = _process(interval=int(default_conf['ticker_interval']))
-    assert result is True
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    assert len(trades) == 1
-
-    result = _process(interval=int(default_conf['ticker_interval']))
-    assert result is False
-
-
-def test_create_trade(default_conf, ticker, limit_buy_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-    # Save state of current whitelist
-    whitelist = copy.deepcopy(default_conf['exchange']['pair_whitelist'])
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    assert trade is not None
-    assert trade.stake_amount == 0.001
-    assert trade.is_open
-    assert trade.open_date is not None
-    assert trade.exchange == Exchanges.BITTREX.name
-
-    # Simulate fulfilled LIMIT_BUY order for trade
-    trade.update(limit_buy_order)
-
-    assert trade.open_rate == 0.00001099
-    assert trade.amount == 90.99181073
-
-    assert whitelist == default_conf['exchange']['pair_whitelist']
-
-
-def test_create_trade_minimal_amount(default_conf, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    buy_mock = mocker.patch(
-        'freqtrade.main.exchange.buy', MagicMock(return_value='mocked_limit_buy')
-    )
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    init(default_conf, create_engine('sqlite://'))
-    min_stake_amount = 0.0005
-    create_trade(min_stake_amount, int(default_conf['ticker_interval']))
-    rate, amount = buy_mock.call_args[0][1], buy_mock.call_args[0][2]
-    assert rate * amount >= min_stake_amount
-
-
-def test_create_trade_no_stake_amount(default_conf, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'),
-                          get_balance=MagicMock(return_value=default_conf['stake_amount'] * 0.5))
-    with pytest.raises(DependencyException, match=r'.*stake amount.*'):
-        create_trade(default_conf['stake_amount'], int(default_conf['ticker_interval']))
-
-
-def test_create_trade_no_pairs(default_conf, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    with pytest.raises(DependencyException, match=r'.*No currency pairs in whitelist.*'):
-        conf = copy.deepcopy(default_conf)
-        conf['exchange']['pair_whitelist'] = []
-        mocker.patch.dict('freqtrade.main._CONF', conf)
-        create_trade(default_conf['stake_amount'], int(default_conf['ticker_interval']))
-
-
-def test_create_trade_no_pairs_after_blacklist(default_conf, ticker, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    with pytest.raises(DependencyException, match=r'.*No currency pairs in whitelist.*'):
-        conf = copy.deepcopy(default_conf)
-        conf['exchange']['pair_whitelist'] = ["BTC_ETH"]
-        conf['exchange']['pair_blacklist'] = ["BTC_ETH"]
-        mocker.patch.dict('freqtrade.main._CONF', conf)
-        create_trade(default_conf['stake_amount'], int(default_conf['ticker_interval']))
-
-
-def test_create_trade_no_signal(default_conf, mocker):
-    default_conf['dry_run'] = True
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', MagicMock(return_value=(False, False)))
-    mocker.patch.multiple('freqtrade.exchange',
-                          get_ticker_history=MagicMock(return_value=20))
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          get_balance=MagicMock(return_value=20))
-    stake_amount = 10
-    Trade.query = MagicMock()
-    Trade.query.filter = MagicMock()
-    assert not create_trade(stake_amount, int(default_conf['ticker_interval']))
-
-
-def test_handle_trade(default_conf, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00001172,
-                              'ask': 0.00001173,
-                              'last': 0.00001172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'),
-                          sell=MagicMock(return_value='mocked_limit_sell'))
-    mocker.patch.multiple('freqtrade.fiat_convert.Market',
-                          ticker=MagicMock(return_value={'price_usd': 15000.0}))
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    assert trade
-
-    trade.update(limit_buy_order)
-    assert trade.is_open is True
-
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
-    assert handle_trade(trade, int(default_conf['ticker_interval'])) is True
-    assert trade.open_order_id == 'mocked_limit_sell'
-
-    # Simulate fulfilled LIMIT_SELL order for trade
-    trade.update(limit_sell_order)
-
-    assert trade.close_rate == 0.00001173
-    assert trade.close_profit == 0.06201057
-    assert trade.calc_profit() == 0.00006217
-    assert trade.close_date is not None
-
-
-def test_handle_overlpapping_signals(default_conf, ticker, mocker):
-    default_conf.update({'experimental': {'use_sell_signal': True}})
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, True))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    # Buy and Sell triggering, so doing nothing ...
-    trades = Trade.query.all()
-    nb_trades = len(trades)
-    assert nb_trades == 0
-
-    # Buy is triggering, so buying ...
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-    trades = Trade.query.all()
-    nb_trades = len(trades)
-    assert nb_trades == 1
-    assert trades[0].is_open is True
-
-    # Buy and Sell are not triggering, so doing nothing ...
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, False))
-    assert handle_trade(trades[0], int(default_conf['ticker_interval'])) is False
-    trades = Trade.query.all()
-    nb_trades = len(trades)
-    assert nb_trades == 1
-    assert trades[0].is_open is True
-
-    # Buy and Sell are triggering, so doing nothing ...
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, True))
-    assert handle_trade(trades[0], int(default_conf['ticker_interval'])) is False
-    trades = Trade.query.all()
-    nb_trades = len(trades)
-    assert nb_trades == 1
-    assert trades[0].is_open is True
-
-    # Sell is triggering, guess what : we are Selling!
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
-    trades = Trade.query.all()
-    assert handle_trade(trades[0], int(default_conf['ticker_interval'])) is True
-
-
-def test_handle_trade_roi(default_conf, ticker, mocker, caplog):
-    caplog.set_level(logging.DEBUG)
-    default_conf.update({'experimental': {'use_sell_signal': True}})
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=True)
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    trade.is_open = True
-
-    # FIX: sniffing logs, suggest handle_trade should not execute_sell
-    #      instead that responsibility should be moved out of handle_trade(),
-    #      we might just want to check if we are in a sell condition without
-    #      executing
-    # if ROI is reached we must sell
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
-    assert handle_trade(trade, interval=int(default_conf['ticker_interval']))
-    assert log_has('Required profit reached. Selling..', caplog.record_tuples)
-    # if ROI is reached we must sell even if sell-signal is not signalled
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
-    assert handle_trade(trade, interval=int(default_conf['ticker_interval']))
-    assert log_has('Required profit reached. Selling..', caplog.record_tuples)
-
-
-def test_handle_trade_experimental(default_conf, ticker, mocker, caplog):
-    caplog.set_level(logging.DEBUG)
-    default_conf.update({'experimental': {'use_sell_signal': True}})
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    trade.is_open = True
-
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, False))
-    value_returned = handle_trade(trade, int(default_conf['ticker_interval']))
-    assert value_returned is False
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
-    assert handle_trade(trade, int(default_conf['ticker_interval']))
-    assert log_has('Sell signal received. Selling..', caplog.record_tuples)
-
-
-def test_close_trade(default_conf, ticker, limit_buy_order, limit_sell_order, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    # Create trade and sell it
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    assert trade
-
-    trade.update(limit_buy_order)
-    trade.update(limit_sell_order)
-    assert trade.is_open is False
-
-    with pytest.raises(ValueError, match=r'.*closed trade.*'):
-        handle_trade(trade, int(default_conf['ticker_interval']))
-
-
-def test_check_handle_timedout_buy(default_conf, ticker, limit_buy_order_old, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    cancel_order_mock = MagicMock()
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_order=MagicMock(return_value=limit_buy_order_old),
-                          cancel_order=cancel_order_mock)
-    init(default_conf, create_engine('sqlite://'))
-
-    trade_buy = Trade(
-        pair='BTC_ETH',
-        open_rate=0.00001099,
-        exchange='BITTREX',
-        open_order_id='123456789',
-        amount=90.99181073,
-        fee=0.0,
-        stake_amount=1,
-        open_date=arrow.utcnow().shift(minutes=-601).datetime,
-        is_open=True
-    )
-
-    Trade.session.add(trade_buy)
-
-    # check it does cancel buy orders over the time limit
-    check_handle_timedout(600)
-    assert cancel_order_mock.call_count == 1
-    assert rpc_mock.call_count == 1
-    trades = Trade.query.filter(Trade.open_order_id.is_(trade_buy.open_order_id)).all()
-    nb_trades = len(trades)
-    assert nb_trades == 0
-
-
-def test_handle_timedout_limit_buy(mocker):
-    cancel_order = MagicMock()
-    mocker.patch('freqtrade.exchange.cancel_order', cancel_order)
-    Trade.session = MagicMock()
-    trade = MagicMock()
-    order = {'remaining': 1,
-             'amount': 1}
-    assert main.handle_timedout_limit_buy(trade, order)
-    assert cancel_order.call_count == 1
-    order['amount'] = 2
-    assert not main.handle_timedout_limit_buy(trade, order)
-    assert cancel_order.call_count == 2
-
-
-def test_check_handle_timedout_sell(default_conf, ticker, limit_sell_order_old, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    cancel_order_mock = MagicMock()
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_order=MagicMock(return_value=limit_sell_order_old),
-                          cancel_order=cancel_order_mock)
-    init(default_conf, create_engine('sqlite://'))
-
-    trade_sell = Trade(
-        pair='BTC_ETH',
-        open_rate=0.00001099,
-        exchange='BITTREX',
-        open_order_id='123456789',
-        amount=90.99181073,
-        fee=0.0,
-        stake_amount=1,
-        open_date=arrow.utcnow().shift(hours=-5).datetime,
-        close_date=arrow.utcnow().shift(minutes=-601).datetime,
-        is_open=False
-    )
-
-    Trade.session.add(trade_sell)
-
-    # check it does cancel sell orders over the time limit
-    check_handle_timedout(600)
-    assert cancel_order_mock.call_count == 1
-    assert rpc_mock.call_count == 1
-    assert trade_sell.is_open is True
-
-
-def test_handle_timedout_limit_sell(mocker):
-    cancel_order = MagicMock()
-    mocker.patch('freqtrade.exchange.cancel_order', cancel_order)
-    trade = MagicMock()
-    order = {'remaining': 1,
-             'amount': 1}
-    assert main.handle_timedout_limit_sell(trade, order)
-    assert cancel_order.call_count == 1
-    order['amount'] = 2
-    assert not main.handle_timedout_limit_sell(trade, order)
-    # Assert cancel_order was not called (callcount remains unchanged)
-    assert cancel_order.call_count == 1
-
-
-def test_check_handle_timedout_partial(default_conf, ticker, limit_buy_order_old_partial,
-                                       mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    cancel_order_mock = MagicMock()
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker,
-                          get_order=MagicMock(return_value=limit_buy_order_old_partial),
-                          cancel_order=cancel_order_mock)
-    init(default_conf, create_engine('sqlite://'))
-
-    trade_buy = Trade(
-        pair='BTC_ETH',
-        open_rate=0.00001099,
-        exchange='BITTREX',
-        open_order_id='123456789',
-        amount=90.99181073,
-        fee=0.0,
-        stake_amount=1,
-        open_date=arrow.utcnow().shift(minutes=-601).datetime,
-        is_open=True
-    )
-
-    Trade.session.add(trade_buy)
-
-    # check it does cancel buy orders over the time limit
-    # note this is for a partially-complete buy order
-    check_handle_timedout(600)
-    assert cancel_order_mock.call_count == 1
-    assert rpc_mock.call_count == 1
-    trades = Trade.query.filter(Trade.open_order_id.is_(trade_buy.open_order_id)).all()
-    assert len(trades) == 1
-    assert trades[0].amount == 23.0
-    assert trades[0].stake_amount == trade_buy.open_rate * trades[0].amount
-
-
-def test_balance_fully_ask_side(mocker):
-    mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 0.0}})
-    assert get_target_bid({'ask': 20, 'last': 10}) == 20
-
-
-def test_balance_fully_last_side(mocker):
-    mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
-    assert get_target_bid({'ask': 20, 'last': 10}) == 10
-
-
-def test_balance_bigger_last_ask(mocker):
-    mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
-    assert get_target_bid({'ask': 5, 'last': 10}) == 5
-
-
-def test_execute_sell_up(default_conf, ticker, ticker_sell_up, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    assert trade
-
-    # Increase the price and sell it
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_up)
-
-    execute_sell(trade=trade, limit=ticker_sell_up()['bid'])
-
-    assert rpc_mock.call_count == 2
-    assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
-    assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
-    assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
-    assert 'Profit' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
-    assert 'profit: 6.11%, 0.00006126' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.919 USD' in rpc_mock.call_args_list[-1][0][0]
-
-
-def test_execute_sell_down(default_conf, ticker, ticker_sell_down, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.rpc.telegram',
-                          _CONF=default_conf,
-                          init=MagicMock(),
-                          send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    assert trade
-
-    # Decrease the price and sell it
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_down)
-
-    execute_sell(trade=trade, limit=ticker_sell_down()['bid'])
-
-    assert rpc_mock.call_count == 2
-    assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
-    assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
-    assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.00001044' in rpc_mock.call_args_list[-1][0][0]
-    assert 'loss: -5.48%, -0.00005492' in rpc_mock.call_args_list[-1][0][0]
-    assert '-0.824 USD' in rpc_mock.call_args_list[-1][0][0]
-
-
-def test_execute_sell_without_conf_sell_down(default_conf, ticker, ticker_sell_down, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    assert trade
-
-    # Decrease the price and sell it
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_down)
-    mocker.patch('freqtrade.main._CONF', {})
-
-    execute_sell(trade=trade, limit=ticker_sell_down()['bid'])
-
-    assert rpc_mock.call_count == 2
-    assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
-    assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.00001044' in rpc_mock.call_args_list[-1][0][0]
-    assert 'loss: -5.48%, -0.00005492' in rpc_mock.call_args_list[-1][0][0]
-
-
-def test_execute_sell_without_conf_sell_up(default_conf, ticker, ticker_sell_up, mocker):
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch('freqtrade.rpc.init', MagicMock())
-    rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker)
-    init(default_conf, create_engine('sqlite://'))
-
-    # Create some test data
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    assert trade
-
-    # Increase the price and sell it
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=ticker_sell_up)
-    mocker.patch('freqtrade.main._CONF', {})
-
-    execute_sell(trade=trade, limit=ticker_sell_up()['bid'])
-
-    assert rpc_mock.call_count == 2
-    assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
-    assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
-    assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
-    assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
-    assert '(profit: 6.11%, 0.00006126)' in rpc_mock.call_args_list[-1][0][0]
-    assert 'USD' not in rpc_mock.call_args_list[-1][0][0]
-
-
-def test_sell_profit_only_enable_profit(default_conf, limit_buy_order, mocker):
-    default_conf['experimental'] = {
-        'use_sell_signal': True,
-        'sell_profit_only': True,
-    }
-
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00002172,
-                              'ask': 0.00002173,
-                              'last': 0.00002172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    trade.update(limit_buy_order)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
-    assert handle_trade(trade, int(default_conf['ticker_interval'])) is True
-
-
-def test_sell_profit_only_disable_profit(default_conf, limit_buy_order, mocker):
-    default_conf['experimental'] = {
-        'use_sell_signal': True,
-        'sell_profit_only': False,
-    }
-
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00002172,
-                              'ask': 0.00002173,
-                              'last': 0.00002172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    trade.update(limit_buy_order)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
-    assert handle_trade(trade, int(default_conf['ticker_interval'])) is True
-
-
-def test_sell_profit_only_enable_loss(default_conf, limit_buy_order, mocker):
-    default_conf['experimental'] = {
-        'use_sell_signal': True,
-        'sell_profit_only': True,
-    }
-
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00000172,
-                              'ask': 0.00000173,
-                              'last': 0.00000172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    trade.update(limit_buy_order)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
-    assert handle_trade(trade, int(default_conf['ticker_interval'])) is False
-
-
-def test_sell_profit_only_disable_loss(default_conf, limit_buy_order, mocker):
-    default_conf['experimental'] = {
-        'use_sell_signal': True,
-        'sell_profit_only': False,
-    }
-
-    mocker.patch.dict('freqtrade.main._CONF', default_conf)
-    mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
-    mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
-    mocker.patch.multiple('freqtrade.main.exchange',
-                          validate_pairs=MagicMock(),
-                          get_ticker=MagicMock(return_value={
-                              'bid': 0.00000172,
-                              'ask': 0.00000173,
-                              'last': 0.00000172
-                          }),
-                          buy=MagicMock(return_value='mocked_limit_buy'))
-
-    init(default_conf, create_engine('sqlite://'))
-    create_trade(0.001, int(default_conf['ticker_interval']))
-
-    trade = Trade.query.first()
-    trade.update(limit_buy_order)
-    mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
-    assert handle_trade(trade, int(default_conf['ticker_interval'])) is True
diff --git a/freqtrade/tests/test_misc.py b/freqtrade/tests/test_misc.py
deleted file mode 100644
index c0d4db7a1..000000000
--- a/freqtrade/tests/test_misc.py
+++ /dev/null
@@ -1,196 +0,0 @@
-# pragma pylint: disable=missing-docstring,C0103
-import argparse
-import json
-import time
-from copy import deepcopy
-from unittest.mock import MagicMock
-
-import datetime
-import pytest
-from jsonschema import ValidationError
-from freqtrade.analyze import parse_ticker_dataframe
-from freqtrade.misc import (common_args_parser, file_dump_json, load_config,
-                            parse_args, parse_timerange, throttle, datesarray_to_datetimearray)
-
-
-def test_throttle():
-
-    def func():
-        return 42
-
-    start = time.time()
-    result = throttle(func, min_secs=0.1)
-    end = time.time()
-
-    assert result == 42
-    assert end - start > 0.1
-
-    result = throttle(func, min_secs=-1)
-    assert result == 42
-
-
-def test_throttle_with_assets():
-
-    def func(nb_assets=-1):
-        return nb_assets
-
-    result = throttle(func, min_secs=0.1, nb_assets=666)
-    assert result == 666
-
-    result = throttle(func, min_secs=0.1)
-    assert result == -1
-
-
-# Parse common command-line-arguments. Used for all tools
-
-def test_parse_args_none():
-    args = common_args_parser('')
-    assert isinstance(args, argparse.ArgumentParser)
-
-
-def test_parse_args_defaults():
-    args = parse_args([], '')
-    assert args.config == 'config.json'
-    assert args.dynamic_whitelist is None
-    assert args.loglevel == 20
-
-
-def test_parse_args_config():
-    args = parse_args(['-c', '/dev/null'], '')
-    assert args.config == '/dev/null'
-
-    args = parse_args(['--config', '/dev/null'], '')
-    assert args.config == '/dev/null'
-
-
-def test_parse_args_verbose():
-    args = parse_args(['-v'], '')
-    assert args.loglevel == 10
-
-    args = parse_args(['--verbose'], '')
-    assert args.loglevel == 10
-
-
-def test_parse_args_version():
-    with pytest.raises(SystemExit, match=r'0'):
-        parse_args(['--version'], '')
-
-
-def test_parse_args_invalid():
-    with pytest.raises(SystemExit, match=r'2'):
-        parse_args(['-c'], '')
-
-
-# Parse command-line-arguments
-# used for main, backtesting and hyperopt
-
-
-def test_parse_args_dynamic_whitelist():
-    args = parse_args(['--dynamic-whitelist'], '')
-    assert args.dynamic_whitelist == 20
-
-
-def test_parse_args_dynamic_whitelist_10():
-    args = parse_args(['--dynamic-whitelist', '10'], '')
-    assert args.dynamic_whitelist == 10
-
-
-def test_parse_args_dynamic_whitelist_invalid_values():
-    with pytest.raises(SystemExit, match=r'2'):
-        parse_args(['--dynamic-whitelist', 'abc'], '')
-
-
-def test_parse_args_backtesting_invalid():
-    with pytest.raises(SystemExit, match=r'2'):
-        parse_args(['backtesting --ticker-interval'], '')
-
-    with pytest.raises(SystemExit, match=r'2'):
-        parse_args(['backtesting --ticker-interval', 'abc'], '')
-
-
-def test_parse_args_backtesting_custom():
-    args = [
-        '-c', 'test_conf.json',
-        'backtesting',
-        '--live',
-        '--ticker-interval', '1',
-        '--refresh-pairs-cached']
-    call_args = parse_args(args, '')
-    assert call_args.config == 'test_conf.json'
-    assert call_args.live is True
-    assert call_args.loglevel == 20
-    assert call_args.subparser == 'backtesting'
-    assert call_args.func is not None
-    assert call_args.ticker_interval == 1
-    assert call_args.refresh_pairs is True
-
-
-def test_parse_args_hyperopt_custom():
-    args = ['-c', 'test_conf.json', 'hyperopt', '--epochs', '20']
-    call_args = parse_args(args, '')
-    assert call_args.config == 'test_conf.json'
-    assert call_args.epochs == 20
-    assert call_args.loglevel == 20
-    assert call_args.subparser == 'hyperopt'
-    assert call_args.func is not None
-
-
-def test_file_dump_json(mocker):
-    file_open = mocker.patch('freqtrade.misc.open', MagicMock())
-    json_dump = mocker.patch('json.dump', MagicMock())
-    file_dump_json('somefile', [1, 2, 3])
-    assert file_open.call_count == 1
-    assert json_dump.call_count == 1
-
-
-def test_parse_timerange_incorrect():
-    assert ((None, 'line'), None, -200) == parse_timerange('-200')
-    assert (('line', None), 200, None) == parse_timerange('200-')
-    with pytest.raises(Exception, match=r'Incorrect syntax.*'):
-        parse_timerange('-')
-
-
-def test_load_config(default_conf, mocker):
-    file_mock = mocker.patch('freqtrade.misc.open', mocker.mock_open(
-        read_data=json.dumps(default_conf)
-    ))
-    validated_conf = load_config('somefile')
-    assert file_mock.call_count == 1
-    assert validated_conf.items() >= default_conf.items()
-
-
-def test_load_config_invalid_pair(default_conf, mocker):
-    conf = deepcopy(default_conf)
-    conf['exchange']['pair_whitelist'].append('BTC-ETH')
-    mocker.patch(
-        'freqtrade.misc.open',
-        mocker.mock_open(
-            read_data=json.dumps(conf)))
-    with pytest.raises(ValidationError, match=r'.*does not match.*'):
-        load_config('somefile')
-
-
-def test_load_config_missing_attributes(default_conf, mocker):
-    conf = deepcopy(default_conf)
-    conf.pop('exchange')
-    mocker.patch(
-        'freqtrade.misc.open',
-        mocker.mock_open(
-            read_data=json.dumps(conf)))
-    with pytest.raises(ValidationError, match=r'.*\'exchange\' is a required property.*'):
-        load_config('somefile')
-
-
-def test_datesarray_to_datetimearray(ticker_history):
-    dataframes = parse_ticker_dataframe(ticker_history)
-    dates = datesarray_to_datetimearray(dataframes['date'])
-
-    assert isinstance(dates[0], datetime.datetime)
-    assert dates[0].year == 2017
-    assert dates[0].month == 11
-    assert dates[0].day == 26
-    assert dates[0].hour == 8
-    assert dates[0].minute == 50
-
-    date_len = len(dates)
-    assert date_len == 3
diff --git a/freqtrade/tests/test_persistence.py b/freqtrade/tests/test_persistence.py
deleted file mode 100644
index f6c5318ce..000000000
--- a/freqtrade/tests/test_persistence.py
+++ /dev/null
@@ -1,350 +0,0 @@
-# pragma pylint: disable=missing-docstring, C0103
-import os
-import pytest
-from sqlalchemy import create_engine
-from freqtrade.exchange import Exchanges
-from freqtrade.persistence import Trade, init, clean_dry_run_db
-
-
-def test_init_create_session(default_conf, mocker):
-    mocker.patch.dict('freqtrade.persistence._CONF', default_conf)
-
-    # Check if init create a session
-    init(default_conf)
-    assert hasattr(Trade, 'session')
-    assert 'Session' in type(Trade.session).__name__
-
-
-def test_init_dry_run_db(default_conf, mocker):
-    default_conf.update({'dry_run_db': True})
-    mocker.patch.dict('freqtrade.persistence._CONF', default_conf)
-
-    # First, protect the existing 'tradesv3.dry_run.sqlite' (Do not delete user data)
-    dry_run_db = 'tradesv3.dry_run.sqlite'
-    dry_run_db_swp = dry_run_db + '.swp'
-
-    if os.path.isfile(dry_run_db):
-        os.rename(dry_run_db, dry_run_db_swp)
-
-    # Check if the new tradesv3.dry_run.sqlite was created
-    init(default_conf)
-    assert os.path.isfile(dry_run_db) is True
-
-    # Delete the file made for this unitest and rollback to the previous
-    # tradesv3.dry_run.sqlite file
-
-    # 1. Delete file from the test
-    if os.path.isfile(dry_run_db):
-        os.remove(dry_run_db)
-
-    # 2. Rollback to the initial file
-    if os.path.isfile(dry_run_db_swp):
-        os.rename(dry_run_db_swp, dry_run_db)
-
-
-def test_init_dry_run_without_db(default_conf, mocker):
-    default_conf.update({'dry_run_db': False})
-    mocker.patch.dict('freqtrade.persistence._CONF', default_conf)
-
-    # First, protect the existing 'tradesv3.dry_run.sqlite' (Do not delete user data)
-    dry_run_db = 'tradesv3.dry_run.sqlite'
-    dry_run_db_swp = dry_run_db + '.swp'
-
-    if os.path.isfile(dry_run_db):
-        os.rename(dry_run_db, dry_run_db_swp)
-
-    # Check if the new tradesv3.dry_run.sqlite was created
-    init(default_conf)
-    assert os.path.isfile(dry_run_db) is False
-
-    # Rollback to the initial 'tradesv3.dry_run.sqlite' file
-    if os.path.isfile(dry_run_db_swp):
-        os.rename(dry_run_db_swp, dry_run_db)
-
-
-def test_init_prod_db(default_conf, mocker):
-    default_conf.update({'dry_run': False})
-    mocker.patch.dict('freqtrade.persistence._CONF', default_conf)
-
-    # First, protect the existing 'tradesv3.sqlite' (Do not delete user data)
-    prod_db = 'tradesv3.sqlite'
-    prod_db_swp = prod_db + '.swp'
-
-    if os.path.isfile(prod_db):
-        os.rename(prod_db, prod_db_swp)
-
-    # Check if the new tradesv3.sqlite was created
-    init(default_conf)
-    assert os.path.isfile(prod_db) is True
-
-    # Delete the file made for this unitest and rollback to the previous tradesv3.sqlite file
-
-    # 1. Delete file from the test
-    if os.path.isfile(prod_db):
-        os.remove(prod_db)
-
-    # Rollback to the initial 'tradesv3.sqlite' file
-    if os.path.isfile(prod_db_swp):
-        os.rename(prod_db_swp, prod_db)
-
-
-def test_update_with_bittrex(limit_buy_order, limit_sell_order):
-    """
-    On this test we will buy and sell a crypto currency.
-
-    Buy
-    - Buy: 90.99181073 Crypto at 0.00001099 BTC
-        (90.99181073*0.00001099 = 0.0009999 BTC)
-    - Buying fee: 0.25%
-    - Total cost of buy trade: 0.001002500 BTC
-        ((90.99181073*0.00001099) + ((90.99181073*0.00001099)*0.0025))
-
-    Sell
-    - Sell: 90.99181073 Crypto at 0.00001173 BTC
-        (90.99181073*0.00001173 = 0,00106733394 BTC)
-    - Selling fee: 0.25%
-    - Total cost of sell trade: 0.001064666 BTC
-        ((90.99181073*0.00001173) - ((90.99181073*0.00001173)*0.0025))
-
-    Profit/Loss: +0.000062166 BTC
-        (Sell:0.001064666 - Buy:0.001002500)
-    Profit/Loss percentage: 0.0620
-        ((0.001064666/0.001002500)-1 = 6.20%)
-
-    :param limit_buy_order:
-    :param limit_sell_order:
-    :return:
-    """
-
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
-    )
-    assert trade.open_order_id is None
-    assert trade.open_rate is None
-    assert trade.close_profit is None
-    assert trade.close_date is None
-
-    trade.open_order_id = 'something'
-    trade.update(limit_buy_order)
-    assert trade.open_order_id is None
-    assert trade.open_rate == 0.00001099
-    assert trade.close_profit is None
-    assert trade.close_date is None
-
-    trade.open_order_id = 'something'
-    trade.update(limit_sell_order)
-    assert trade.open_order_id is None
-    assert trade.close_rate == 0.00001173
-    assert trade.close_profit == 0.06201057
-    assert trade.close_date is not None
-
-
-def test_calc_open_close_trade_price(limit_buy_order, limit_sell_order):
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
-    )
-
-    trade.open_order_id = 'something'
-    trade.update(limit_buy_order)
-    assert trade.calc_open_trade_price() == 0.001002500
-
-    trade.update(limit_sell_order)
-    assert trade.calc_close_trade_price() == 0.0010646656
-
-    # Profit in BTC
-    assert trade.calc_profit() == 0.00006217
-
-    # Profit in percent
-    assert trade.calc_profit_percent() == 0.06201057
-
-
-def test_calc_close_trade_price_exception(limit_buy_order):
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
-    )
-
-    trade.open_order_id = 'something'
-    trade.update(limit_buy_order)
-    assert trade.calc_close_trade_price() == 0.0
-
-
-def test_update_open_order(limit_buy_order):
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=1.00,
-        fee=0.1,
-        exchange=Exchanges.BITTREX,
-    )
-
-    assert trade.open_order_id is None
-    assert trade.open_rate is None
-    assert trade.close_profit is None
-    assert trade.close_date is None
-
-    limit_buy_order['closed'] = False
-    trade.update(limit_buy_order)
-
-    assert trade.open_order_id is None
-    assert trade.open_rate is None
-    assert trade.close_profit is None
-    assert trade.close_date is None
-
-
-def test_update_invalid_order(limit_buy_order):
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=1.00,
-        fee=0.1,
-        exchange=Exchanges.BITTREX,
-    )
-    limit_buy_order['type'] = 'invalid'
-    with pytest.raises(ValueError, match=r'Unknown order type'):
-        trade.update(limit_buy_order)
-
-
-def test_calc_open_trade_price(limit_buy_order):
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
-    )
-    trade.open_order_id = 'open_trade'
-    trade.update(limit_buy_order)  # Buy @ 0.00001099
-
-    # Get the open rate price with the standard fee rate
-    assert trade.calc_open_trade_price() == 0.001002500
-
-    # Get the open rate price with a custom fee rate
-    assert trade.calc_open_trade_price(fee=0.003) == 0.001003000
-
-
-def test_calc_close_trade_price(limit_buy_order, limit_sell_order):
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
-    )
-    trade.open_order_id = 'close_trade'
-    trade.update(limit_buy_order)  # Buy @ 0.00001099
-
-    # Get the close rate price with a custom close rate and a regular fee rate
-    assert trade.calc_close_trade_price(rate=0.00001234) == 0.0011200318
-
-    # Get the close rate price with a custom close rate and a custom fee rate
-    assert trade.calc_close_trade_price(rate=0.00001234, fee=0.003) == 0.0011194704
-
-    # Test when we apply a Sell order, and ask price with a custom fee rate
-    trade.update(limit_sell_order)
-    assert trade.calc_close_trade_price(fee=0.005) == 0.0010619972
-
-
-def test_calc_profit(limit_buy_order, limit_sell_order):
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
-    )
-    trade.open_order_id = 'profit_percent'
-    trade.update(limit_buy_order)  # Buy @ 0.00001099
-
-    # Custom closing rate and regular fee rate
-    # Higher than open rate
-    assert trade.calc_profit(rate=0.00001234) == 0.00011753
-    # Lower than open rate
-    assert trade.calc_profit(rate=0.00000123) == -0.00089086
-
-    # Custom closing rate and custom fee rate
-    # Higher than open rate
-    assert trade.calc_profit(rate=0.00001234, fee=0.003) == 0.00011697
-    # Lower than open rate
-    assert trade.calc_profit(rate=0.00000123, fee=0.003) == -0.00089092
-
-    # Test when we apply a Sell order. Sell higher than open rate @ 0.00001173
-    trade.update(limit_sell_order)
-    assert trade.calc_profit() == 0.00006217
-
-    # Test with a custom fee rate on the close trade
-    assert trade.calc_profit(fee=0.003) == 0.00006163
-
-
-def test_calc_profit_percent(limit_buy_order, limit_sell_order):
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=0.001,
-        fee=0.0025,
-        exchange=Exchanges.BITTREX,
-    )
-    trade.open_order_id = 'profit_percent'
-    trade.update(limit_buy_order)  # Buy @ 0.00001099
-
-    # Get percent of profit with a custom rate (Higher than open rate)
-    assert trade.calc_profit_percent(rate=0.00001234) == 0.1172387
-
-    # Get percent of profit with a custom rate (Lower than open rate)
-    assert trade.calc_profit_percent(rate=0.00000123) == -0.88863827
-
-    # Test when we apply a Sell order. Sell higher than open rate @ 0.00001173
-    trade.update(limit_sell_order)
-    assert trade.calc_profit_percent() == 0.06201057
-
-    # Test with a custom fee rate on the close trade
-    assert trade.calc_profit_percent(fee=0.003) == 0.0614782
-
-
-def test_clean_dry_run_db(default_conf):
-    init(default_conf, create_engine('sqlite://'))
-
-    # Simulate dry_run entries
-    trade = Trade(
-        pair='BTC_ETH',
-        stake_amount=0.001,
-        amount=123.0,
-        fee=0.0025,
-        open_rate=0.123,
-        exchange='BITTREX',
-        open_order_id='dry_run_buy_12345'
-    )
-    Trade.session.add(trade)
-
-    trade = Trade(
-        pair='BTC_ETC',
-        stake_amount=0.001,
-        amount=123.0,
-        fee=0.0025,
-        open_rate=0.123,
-        exchange='BITTREX',
-        open_order_id='dry_run_sell_12345'
-    )
-    Trade.session.add(trade)
-
-    # Simulate prod entry
-    trade = Trade(
-        pair='BTC_ETC',
-        stake_amount=0.001,
-        amount=123.0,
-        fee=0.0025,
-        open_rate=0.123,
-        exchange='BITTREX',
-        open_order_id='prod_buy_12345'
-    )
-    Trade.session.add(trade)
-
-    # We have 3 entries: 2 dry_run, 1 prod
-    assert len(Trade.query.filter(Trade.open_order_id.isnot(None)).all()) == 3
-
-    clean_dry_run_db()
-
-    # We have now only the prod
-    assert len(Trade.query.filter(Trade.open_order_id.isnot(None)).all()) == 1
diff --git a/freqtrade/tests/testdata/BTC_ADA-1.json b/freqtrade/tests/testdata/BTC_ADA-1.json
deleted file mode 100644
index dd31cac0f..000000000
--- a/freqtrade/tests/testdata/BTC_ADA-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_ADA-5.json b/freqtrade/tests/testdata/BTC_ADA-5.json
deleted file mode 100644
index b8b183f69..000000000
--- a/freqtrade/tests/testdata/BTC_ADA-5.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_DASH-1.json b/freqtrade/tests/testdata/BTC_DASH-1.json
deleted file mode 100644
index 34995a535..000000000
--- a/freqtrade/tests/testdata/BTC_DASH-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_DASH-5.json b/freqtrade/tests/testdata/BTC_DASH-5.json
deleted file mode 100644
index 901e312d0..000000000
--- a/freqtrade/tests/testdata/BTC_DASH-5.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_ETC-1.json b/freqtrade/tests/testdata/BTC_ETC-1.json
deleted file mode 100644
index 280717db8..000000000
--- a/freqtrade/tests/testdata/BTC_ETC-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_ETC-5.json b/freqtrade/tests/testdata/BTC_ETC-5.json
deleted file mode 100644
index 076f898e8..000000000
--- a/freqtrade/tests/testdata/BTC_ETC-5.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_ETH-1.json b/freqtrade/tests/testdata/BTC_ETH-1.json
deleted file mode 100644
index 7458fca76..000000000
--- a/freqtrade/tests/testdata/BTC_ETH-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_ETH-5.json b/freqtrade/tests/testdata/BTC_ETH-5.json
deleted file mode 100644
index 8f72a86da..000000000
--- a/freqtrade/tests/testdata/BTC_ETH-5.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_LTC-1.json b/freqtrade/tests/testdata/BTC_LTC-1.json
deleted file mode 100644
index fba8573ba..000000000
--- a/freqtrade/tests/testdata/BTC_LTC-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_LTC-5.json b/freqtrade/tests/testdata/BTC_LTC-5.json
deleted file mode 100644
index d8f73aa96..000000000
--- a/freqtrade/tests/testdata/BTC_LTC-5.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_NXT-1.json b/freqtrade/tests/testdata/BTC_NXT-1.json
deleted file mode 100644
index b34bf609f..000000000
--- a/freqtrade/tests/testdata/BTC_NXT-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_NXT-5.json b/freqtrade/tests/testdata/BTC_NXT-5.json
deleted file mode 100644
index 70a1a0418..000000000
--- a/freqtrade/tests/testdata/BTC_NXT-5.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_POWR-1.json b/freqtrade/tests/testdata/BTC_POWR-1.json
deleted file mode 100644
index f49799818..000000000
--- a/freqtrade/tests/testdata/BTC_POWR-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_POWR-5.json b/freqtrade/tests/testdata/BTC_POWR-5.json
deleted file mode 100644
index c07acaca4..000000000
--- a/freqtrade/tests/testdata/BTC_POWR-5.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_UNITEST-1.json b/freqtrade/tests/testdata/BTC_UNITEST-1.json
deleted file mode 100644
index ea7c3aaac..000000000
--- a/freqtrade/tests/testdata/BTC_UNITEST-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_UNITEST-30.json b/freqtrade/tests/testdata/BTC_UNITEST-30.json
deleted file mode 100644
index 62ef409ab..000000000
--- a/freqtrade/tests/testdata/BTC_UNITEST-30.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_UNITEST-8.json b/freqtrade/tests/testdata/BTC_UNITEST-8.json
deleted file mode 100644
index e4766ad4f..000000000
--- a/freqtrade/tests/testdata/BTC_UNITEST-8.json
+++ /dev/null
@@ -1,3 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_UNITEST-8.json.gz b/freqtrade/tests/testdata/BTC_UNITEST-8.json.gz
deleted file mode 100644
index 38194582c..000000000
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diff --git a/freqtrade/tests/testdata/BTC_XLM-1.json b/freqtrade/tests/testdata/BTC_XLM-1.json
deleted file mode 100644
index a99022b05..000000000
--- a/freqtrade/tests/testdata/BTC_XLM-1.json
+++ /dev/null
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_XLM-5.json b/freqtrade/tests/testdata/BTC_XLM-5.json
deleted file mode 100644
index 5ad201ec7..000000000
--- a/freqtrade/tests/testdata/BTC_XLM-5.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_XMR-1.json b/freqtrade/tests/testdata/BTC_XMR-1.json
deleted file mode 100644
index b2b3dd043..000000000
--- a/freqtrade/tests/testdata/BTC_XMR-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_XMR-5.json b/freqtrade/tests/testdata/BTC_XMR-5.json
deleted file mode 100644
index bb165d20d..000000000
--- a/freqtrade/tests/testdata/BTC_XMR-5.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_ZEC-1.json b/freqtrade/tests/testdata/BTC_ZEC-1.json
deleted file mode 100644
index 00a725f51..000000000
--- a/freqtrade/tests/testdata/BTC_ZEC-1.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/freqtrade/tests/testdata/BTC_ZEC-5.json b/freqtrade/tests/testdata/BTC_ZEC-5.json
deleted file mode 100644
index 3da387e11..000000000
--- a/freqtrade/tests/testdata/BTC_ZEC-5.json
+++ /dev/null
@@ -1 +0,0 @@
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0.03919, "C": 0.03928789, "V": 14.09480345, "T": "2018-01-30T04:45:00", "BV": 0.55266744}]
\ No newline at end of file
diff --git a/freqtrade/tests/testdata/download_backtest_data.py b/freqtrade/tests/testdata/download_backtest_data.py
deleted file mode 100755
index 0cb545b3a..000000000
--- a/freqtrade/tests/testdata/download_backtest_data.py
+++ /dev/null
@@ -1,38 +0,0 @@
-#!/usr/bin/env python3
-
-"""This script generate json data from bittrex"""
-import sys
-import json
-
-from freqtrade import exchange
-from freqtrade.exchange import Bittrex
-from freqtrade import misc
-
-parser = misc.common_args_parser('download utility')
-parser.add_argument(
-        '-p', '--pair',
-        help='JSON file containing pairs to download',
-        dest='pair',
-        default=None
-)
-args = parser.parse_args(sys.argv[1:])
-
-TICKER_INTERVALS = [1, 5]  # ticker interval in minutes (currently implemented: 1 and 5)
-PAIRS = []
-
-if args.pair:
-    with open(args.pair) as file:
-        PAIRS = json.load(file)
-PAIRS = list(set(PAIRS))
-
-print('About to download pairs:', PAIRS)
-
-# Init Bittrex exchange
-exchange._API = Bittrex({'key': '', 'secret': ''})
-
-for pair in PAIRS:
-    for tick_interval in TICKER_INTERVALS:
-        print('downloading pair %s, interval %s' % (pair, tick_interval))
-        data = exchange.get_ticker_history(pair, tick_interval)
-        filename = '{}-{}.json'.format(pair, tick_interval)
-        misc.file_dump_json(filename, data)
diff --git a/freqtrade/tests/testdata/pairs.json b/freqtrade/tests/testdata/pairs.json
deleted file mode 100644
index 5eb7b0205..000000000
--- a/freqtrade/tests/testdata/pairs.json
+++ /dev/null
@@ -1,26 +0,0 @@
-[
-    "BTC_ADA",
-    "BTC_BAT",
-    "BTC_DASH",
-    "BTC_ETC",
-    "BTC_ETH",
-    "BTC_GBYTE",
-    "BTC_LSK",
-    "BTC_LTC",
-    "BTC_NEO",
-    "BTC_NXT",
-    "BTC_POWR",
-    "BTC_STORJ",
-    "BTC_QTUM",
-    "BTC_WAVES",
-    "BTC_VTC",
-    "BTC_XLM",
-    "BTC_XMR",
-    "BTC_XVG",
-    "BTC_XRP",
-    "BTC_ZEC",
-    "USDT_BTC",
-    "USDT_LTC",
-    "USDT_ETH"
-]
-
diff --git a/freqtrade/util/__init__.py b/freqtrade/util/__init__.py
new file mode 100644
index 000000000..5a4857eea
--- /dev/null
+++ b/freqtrade/util/__init__.py
@@ -0,0 +1,53 @@
+from freqtrade.util.datetime_helpers import (
+    dt_floor_day,
+    dt_from_ts,
+    dt_humanize_delta,
+    dt_now,
+    dt_ts,
+    dt_ts_def,
+    dt_ts_none,
+    dt_utc,
+    format_date,
+    format_ms_time,
+    format_ms_time_det,
+    shorten_date,
+)
+from freqtrade.util.dry_run_wallet import get_dry_run_wallet
+from freqtrade.util.formatters import decimals_per_coin, fmt_coin, fmt_coin2, round_value
+from freqtrade.util.ft_precise import FtPrecise
+from freqtrade.util.measure_time import MeasureTime
+from freqtrade.util.periodic_cache import PeriodicCache
+from freqtrade.util.progress_tracker import (  # noqa F401
+    get_progress_tracker,
+    retrieve_progress_tracker,
+)
+from freqtrade.util.rich_progress import CustomProgress
+from freqtrade.util.rich_tables import print_df_rich_table, print_rich_table
+from freqtrade.util.template_renderer import render_template, render_template_with_fallback  # noqa
+
+
+__all__ = [
+    "dt_floor_day",
+    "dt_from_ts",
+    "dt_humanize_delta",
+    "dt_now",
+    "dt_ts",
+    "dt_ts_def",
+    "dt_ts_none",
+    "dt_utc",
+    "format_date",
+    "format_ms_time",
+    "format_ms_time_det",
+    "get_dry_run_wallet",
+    "FtPrecise",
+    "PeriodicCache",
+    "shorten_date",
+    "decimals_per_coin",
+    "round_value",
+    "fmt_coin",
+    "fmt_coin2",
+    "MeasureTime",
+    "print_rich_table",
+    "print_df_rich_table",
+    "CustomProgress",
+]
diff --git a/freqtrade/util/coin_gecko.py b/freqtrade/util/coin_gecko.py
new file mode 100644
index 000000000..515e9654f
--- /dev/null
+++ b/freqtrade/util/coin_gecko.py
@@ -0,0 +1,13 @@
+from pycoingecko import CoinGeckoAPI
+
+
+class FtCoinGeckoApi(CoinGeckoAPI):
+    """
+    Simple wrapper around pycoingecko's api to support Demo API keys.
+    """
+
+    def __init__(self, api_key: str = "", *, is_demo=True, retries=5):
+        if api_key and is_demo:
+            super().__init__(retries=retries, demo_api_key=api_key)
+        else:
+            super().__init__(api_key=api_key, retries=retries)
diff --git a/freqtrade/util/datetime_helpers.py b/freqtrade/util/datetime_helpers.py
new file mode 100644
index 000000000..03ee3b6d9
--- /dev/null
+++ b/freqtrade/util/datetime_helpers.py
@@ -0,0 +1,118 @@
+import re
+from datetime import datetime, timezone
+from time import time
+
+import humanize
+
+from freqtrade.constants import DATETIME_PRINT_FORMAT
+
+
+def dt_now() -> datetime:
+    """Return the current datetime in UTC."""
+    return datetime.now(timezone.utc)
+
+
+def dt_utc(
+    year: int,
+    month: int,
+    day: int,
+    hour: int = 0,
+    minute: int = 0,
+    second: int = 0,
+    microsecond: int = 0,
+) -> datetime:
+    """Return a datetime in UTC."""
+    return datetime(year, month, day, hour, minute, second, microsecond, tzinfo=timezone.utc)
+
+
+def dt_ts(dt: datetime | None = None) -> int:
+    """
+    Return dt in ms as a timestamp in UTC.
+    If dt is None, return the current datetime in UTC.
+    """
+    if dt:
+        return int(dt.timestamp() * 1000)
+    return int(time() * 1000)
+
+
+def dt_ts_def(dt: datetime | None, default: int = 0) -> int:
+    """
+    Return dt in ms as a timestamp in UTC.
+    If dt is None, return the given default.
+    """
+    if dt:
+        return int(dt.timestamp() * 1000)
+    return default
+
+
+def dt_ts_none(dt: datetime | None) -> int | None:
+    """
+    Return dt in ms as a timestamp in UTC.
+    If dt is None, return the given default.
+    """
+    if dt:
+        return int(dt.timestamp() * 1000)
+    return None
+
+
+def dt_floor_day(dt: datetime) -> datetime:
+    """Return the floor of the day for the given datetime."""
+    return dt.replace(hour=0, minute=0, second=0, microsecond=0)
+
+
+def dt_from_ts(timestamp: float) -> datetime:
+    """
+    Return a datetime from a timestamp.
+    :param timestamp: timestamp in seconds or milliseconds
+    """
+    if timestamp > 1e10:
+        # Timezone in ms - convert to seconds
+        timestamp /= 1000
+    return datetime.fromtimestamp(timestamp, tz=timezone.utc)
+
+
+def shorten_date(_date: str) -> str:
+    """
+    Trim the date so it fits on small screens
+    """
+    new_date = re.sub("seconds?", "sec", _date)
+    new_date = re.sub("minutes?", "min", new_date)
+    new_date = re.sub("hours?", "h", new_date)
+    new_date = re.sub("days?", "d", new_date)
+    new_date = re.sub("^an?", "1", new_date)
+    return new_date
+
+
+def dt_humanize_delta(dt: datetime):
+    """
+    Return a humanized string for the given timedelta.
+    """
+    return humanize.naturaltime(dt)
+
+
+def format_date(date: datetime | None) -> str:
+    """
+    Return a formatted date string.
+    Returns an empty string if date is None.
+    :param date: datetime to format
+    """
+    if date:
+        return date.strftime(DATETIME_PRINT_FORMAT)
+    return ""
+
+
+def format_ms_time(date: int | float) -> str:
+    """
+    convert MS date to readable format.
+    : epoch-string in ms
+    """
+    return dt_from_ts(date).strftime("%Y-%m-%dT%H:%M:%S")
+
+
+def format_ms_time_det(date: int | float) -> str:
+    """
+    convert MS date to readable format - detailed.
+    : epoch-string in ms
+    """
+    # return dt_from_ts(date).isoformat(timespec="milliseconds")
+    return dt_from_ts(date).strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3]
diff --git a/freqtrade/util/dry_run_wallet.py b/freqtrade/util/dry_run_wallet.py
new file mode 100644
index 000000000..c904db6b3
--- /dev/null
+++ b/freqtrade/util/dry_run_wallet.py
@@ -0,0 +1,12 @@
+from freqtrade.constants import Config
+
+
+def get_dry_run_wallet(config: Config) -> int | float:
+    """
+    Return dry-run wallet balance in stake currency from configuration.
+    This setup also supports dictionary mode for dry-run-wallet.
+    """
+    if isinstance(_start_cap := config["dry_run_wallet"], float | int):
+        return _start_cap
+    else:
+        return _start_cap.get("stake_currency")
diff --git a/freqtrade/util/formatters.py b/freqtrade/util/formatters.py
new file mode 100644
index 000000000..82ac1cf66
--- /dev/null
+++ b/freqtrade/util/formatters.py
@@ -0,0 +1,68 @@
+from freqtrade.constants import DECIMAL_PER_COIN_FALLBACK, DECIMALS_PER_COIN
+
+
+def decimals_per_coin(coin: str):
+    """
+    Helper method getting decimal amount for this coin
+    example usage: f".{decimals_per_coin('USD')}f"
+    :param coin: Which coin are we printing the price / value for
+    """
+    return DECIMALS_PER_COIN.get(coin, DECIMAL_PER_COIN_FALLBACK)
+
+
+def strip_trailing_zeros(value: str) -> str:
+    """
+    Strip trailing zeros from a string
+    :param value: Value to be stripped
+    :return: Stripped value
+    """
+    return value.rstrip("0").rstrip(".")
+
+
+def round_value(value: float, decimals: int, keep_trailing_zeros=False) -> str:
+    """
+    Round value to given decimals
+    :param value: Value to be rounded
+    :param decimals: Number of decimals to round to
+    :param keep_trailing_zeros: Keep trailing zeros "222.200" vs. "222.2"
+    :return: Rounded value as string
+    """
+    val = f"{value:.{decimals}f}"
+    if not keep_trailing_zeros:
+        val = strip_trailing_zeros(val)
+    return val
+
+
+def fmt_coin(value: float, coin: str, show_coin_name=True, keep_trailing_zeros=False) -> str:
+    """
+    Format price value for this coin
+    :param value: Value to be printed
+    :param coin: Which coin are we printing the price / value for
+    :param show_coin_name: Return string in format: "222.22 USDT" or "222.22"
+    :param keep_trailing_zeros: Keep trailing zeros "222.200" vs. "222.2"
+    :return: Formatted / rounded value (with or without coin name)
+    """
+    val = round_value(value, decimals_per_coin(coin), keep_trailing_zeros)
+    if show_coin_name:
+        val = f"{val} {coin}"
+
+    return val
+
+
+def fmt_coin2(
+    value: float, coin: str, decimals: int = 8, *, show_coin_name=True, keep_trailing_zeros=False
+) -> str:
+    """
+    Format price value for this coin. Should be preferred for rate formatting
+    :param value: Value to be printed
+    :param coin: Which coin are we printing the price / value for
+    :param decimals: Number of decimals to round to
+    :param show_coin_name: Return string in format: "222.22 USDT" or "222.22"
+    :param keep_trailing_zeros: Keep trailing zeros "222.200" vs. "222.2"
+    :return: Formatted / rounded value (with or without coin name)
+    """
+    val = round_value(value, decimals, keep_trailing_zeros)
+    if show_coin_name:
+        val = f"{val} {coin}"
+
+    return val
diff --git a/freqtrade/util/ft_precise.py b/freqtrade/util/ft_precise.py
new file mode 100644
index 000000000..9487e3d6e
--- /dev/null
+++ b/freqtrade/util/ft_precise.py
@@ -0,0 +1,13 @@
+"""
+Slim wrapper around ccxt's Precise (string math)
+To have imports from freqtrade - and support float initializers
+"""
+
+from ccxt import Precise
+
+
+class FtPrecise(Precise):
+    def __init__(self, number, decimals=None):
+        if not isinstance(number, str):
+            number = str(number)
+        super().__init__(number, decimals)
diff --git a/freqtrade/util/measure_time.py b/freqtrade/util/measure_time.py
new file mode 100644
index 000000000..baa94f062
--- /dev/null
+++ b/freqtrade/util/measure_time.py
@@ -0,0 +1,45 @@
+import logging
+import time
+from collections.abc import Callable
+
+from cachetools import TTLCache
+
+
+logger = logging.getLogger(__name__)
+
+
+class MeasureTime:
+    """
+    Measure the time of a block of code and call a callback if the time limit is exceeded.
+    """
+
+    def __init__(
+        self, callback: Callable[[float, float], None], time_limit: float, ttl: int = 3600 * 4
+    ):
+        """
+        :param callback: The callback to call if the time limit is exceeded.
+            This callback will be called once every "ttl" seconds,
+            with the parameters "duration" (in seconds) and
+            "time limit" - representing the passed in time limit.
+        :param time_limit: The time limit in seconds.
+        :param ttl: The time to live of the cache in seconds.
+            defaults to 4 hours.
+        """
+        self._callback = callback
+        self._time_limit = time_limit
+        self.__cache: TTLCache = TTLCache(maxsize=1, ttl=ttl)
+
+    def __enter__(self):
+        self._start = time.time()
+
+    def __exit__(self, *args):
+        end = time.time()
+        if self.__cache.get("value"):
+            return
+        duration = end - self._start
+
+        if duration < self._time_limit:
+            return
+        self._callback(duration, self._time_limit)
+
+        self.__cache["value"] = True
diff --git a/freqtrade/util/migrations/__init__.py b/freqtrade/util/migrations/__init__.py
new file mode 100644
index 000000000..4e81b1c57
--- /dev/null
+++ b/freqtrade/util/migrations/__init__.py
@@ -0,0 +1,9 @@
+from freqtrade.exchange import Exchange
+from freqtrade.util.migrations.binance_mig import migrate_binance_futures_data
+from freqtrade.util.migrations.funding_rate_mig import migrate_funding_fee_timeframe
+
+
+def migrate_data(config, exchange: Exchange | None = None):
+    migrate_binance_futures_data(config)
+
+    migrate_funding_fee_timeframe(config, exchange)
diff --git a/freqtrade/util/migrations/binance_mig.py b/freqtrade/util/migrations/binance_mig.py
new file mode 100644
index 000000000..e5255fa6d
--- /dev/null
+++ b/freqtrade/util/migrations/binance_mig.py
@@ -0,0 +1,79 @@
+import logging
+
+from packaging import version
+from sqlalchemy import select
+
+from freqtrade.constants import DOCS_LINK, Config
+from freqtrade.enums import TradingMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence.pairlock import PairLock
+from freqtrade.persistence.trade_model import Trade
+
+
+logger = logging.getLogger(__name__)
+
+
+def migrate_binance_futures_names(config: Config):
+    if not (
+        config.get("trading_mode", TradingMode.SPOT) == TradingMode.FUTURES
+        and config["exchange"]["name"] == "binance"
+    ):
+        # only act on new futures
+        return
+    import ccxt
+
+    if version.parse("2.6.26") > version.parse(ccxt.__version__):
+        raise OperationalException(
+            "Please follow the update instructions in the docs "
+            f"({DOCS_LINK}/updating/) to install a compatible ccxt version."
+        )
+    _migrate_binance_futures_db(config)
+    migrate_binance_futures_data(config)
+
+
+def _migrate_binance_futures_db(config: Config):
+    logger.warning("Migrating binance futures pairs in database.")
+    trades = Trade.get_trades([Trade.exchange == "binance", Trade.trading_mode == "FUTURES"]).all()
+    for trade in trades:
+        if ":" in trade.pair:
+            # already migrated
+            continue
+        new_pair = f"{trade.pair}:{trade.stake_currency}"
+        trade.pair = new_pair
+
+        for order in trade.orders:
+            order.ft_pair = new_pair
+            # Should symbol be migrated too?
+            # order.symbol = new_pair
+    Trade.commit()
+    pls = PairLock.session.scalars(select(PairLock).filter(PairLock.pair.notlike("%:%"))).all()
+    for pl in pls:
+        pl.pair = f"{pl.pair}:{config['stake_currency']}"
+    # print(pls)
+    # pls.update({'pair': concat(PairLock.pair,':USDT')})
+    Trade.commit()
+    logger.warning("Done migrating binance futures pairs in database.")
+
+
+def migrate_binance_futures_data(config: Config):
+    if not (
+        config.get("trading_mode", TradingMode.SPOT) == TradingMode.FUTURES
+        and config["exchange"]["name"] == "binance"
+    ):
+        # only act on new futures
+        return
+
+    from freqtrade.data.history import get_datahandler
+
+    dhc = get_datahandler(config["datadir"], config["dataformat_ohlcv"])
+
+    paircombs = dhc.ohlcv_get_available_data(
+        config["datadir"], config.get("trading_mode", TradingMode.SPOT)
+    )
+
+    for pair, timeframe, candle_type in paircombs:
+        if ":" in pair:
+            # already migrated
+            continue
+        new_pair = f"{pair}:{config['stake_currency']}"
+        dhc.rename_futures_data(pair, new_pair, timeframe, candle_type)
diff --git a/freqtrade/util/migrations/funding_rate_mig.py b/freqtrade/util/migrations/funding_rate_mig.py
new file mode 100644
index 000000000..060a5a1b6
--- /dev/null
+++ b/freqtrade/util/migrations/funding_rate_mig.py
@@ -0,0 +1,26 @@
+import logging
+
+from freqtrade.constants import Config
+from freqtrade.enums import TradingMode
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+def migrate_funding_fee_timeframe(config: Config, exchange: Exchange | None):
+    from freqtrade.data.history import get_datahandler
+
+    if config.get("trading_mode", TradingMode.SPOT) != TradingMode.FUTURES:
+        # only act on futures
+        return
+
+    if not exchange:
+        from freqtrade.resolvers import ExchangeResolver
+
+        exchange = ExchangeResolver.load_exchange(config, validate=False)
+
+    ff_timeframe = exchange.get_option("funding_fee_timeframe")
+
+    dhc = get_datahandler(config["datadir"], config["dataformat_ohlcv"])
+    dhc.fix_funding_fee_timeframe(ff_timeframe)
diff --git a/freqtrade/util/periodic_cache.py b/freqtrade/util/periodic_cache.py
new file mode 100644
index 000000000..4f7405d2b
--- /dev/null
+++ b/freqtrade/util/periodic_cache.py
@@ -0,0 +1,19 @@
+from datetime import datetime, timezone
+
+from cachetools import TTLCache
+
+
+class PeriodicCache(TTLCache):
+    """
+    Special cache that expires at "straight" times
+    A timer with ttl of 3600 (1h) will expire at every full hour (:00).
+    """
+
+    def __init__(self, maxsize, ttl, getsizeof=None):
+        def local_timer():
+            ts = datetime.now(timezone.utc).timestamp()
+            offset = ts % ttl
+            return ts - offset
+
+        # Init with smlight offset
+        super().__init__(maxsize=maxsize, ttl=ttl - 1e-5, timer=local_timer, getsizeof=getsizeof)
diff --git a/freqtrade/util/progress_tracker.py b/freqtrade/util/progress_tracker.py
new file mode 100644
index 000000000..aac1418bc
--- /dev/null
+++ b/freqtrade/util/progress_tracker.py
@@ -0,0 +1,37 @@
+from rich.progress import (
+    BarColumn,
+    MofNCompleteColumn,
+    TaskProgressColumn,
+    TextColumn,
+    TimeElapsedColumn,
+    TimeRemainingColumn,
+)
+
+from freqtrade.util.rich_progress import CustomProgress
+
+
+def retrieve_progress_tracker(pt: CustomProgress | None) -> CustomProgress:
+    if pt is None:
+        return get_progress_tracker()
+    return pt
+
+
+def get_progress_tracker(**kwargs) -> CustomProgress:
+    """
+    Get progress Bar with custom columns.
+    """
+    from freqtrade.loggers import error_console
+
+    return CustomProgress(
+        TextColumn("[progress.description]{task.description}"),
+        BarColumn(bar_width=None),
+        MofNCompleteColumn(),
+        TaskProgressColumn(),
+        "•",
+        TimeElapsedColumn(),
+        "•",
+        TimeRemainingColumn(),
+        expand=True,
+        console=error_console,
+        **kwargs,
+    )
diff --git a/freqtrade/util/rich_progress.py b/freqtrade/util/rich_progress.py
new file mode 100644
index 000000000..96d629819
--- /dev/null
+++ b/freqtrade/util/rich_progress.py
@@ -0,0 +1,58 @@
+from collections.abc import Callable
+from typing import Any
+
+from rich.console import ConsoleRenderable, Group, RichCast
+from rich.progress import Progress, Task, TaskID
+
+
+class CustomProgress(Progress):
+    def __init__(
+        self,
+        *args,
+        cust_objs: list[ConsoleRenderable] | None = None,
+        cust_callables: list[Callable[[], ConsoleRenderable]] | None = None,
+        ft_callback: Callable[[Task], None] | None = None,
+        **kwargs,
+    ) -> None:
+        self._cust_objs = cust_objs or []
+        self._cust_callables = cust_callables or []
+        self._ft_callback = ft_callback
+        if self._ft_callback:
+            kwargs["disable"] = True
+
+        super().__init__(*args, **kwargs)
+
+    def update(
+        self,
+        task_id: TaskID,
+        *,
+        total: float | None = None,
+        completed: float | None = None,
+        advance: float | None = None,
+        description: str | None = None,
+        visible: bool | None = None,
+        refresh: bool = False,
+        **fields: Any,
+    ) -> None:
+        t = super().update(
+            task_id,
+            total=total,
+            completed=completed,
+            advance=advance,
+            description=description,
+            visible=visible,
+            refresh=refresh,
+            **fields,
+        )
+        if self._ft_callback:
+            self._ft_callback(
+                self.tasks[task_id],
+            )
+        return t
+
+    def get_renderable(self) -> ConsoleRenderable | RichCast | str:
+        objs = [obj for obj in self._cust_objs]
+        for cust_call in self._cust_callables:
+            objs.append(cust_call())
+        renderable = Group(*objs, *self.get_renderables())
+        return renderable
diff --git a/freqtrade/util/rich_tables.py b/freqtrade/util/rich_tables.py
new file mode 100644
index 000000000..093c73584
--- /dev/null
+++ b/freqtrade/util/rich_tables.py
@@ -0,0 +1,74 @@
+from collections.abc import Sequence
+from typing import Any, TypeAlias
+
+from pandas import DataFrame
+from rich.table import Column, Table
+from rich.text import Text
+
+from freqtrade.loggers.rich_console import get_rich_console
+
+
+TextOrString: TypeAlias = str | Text
+
+
+def print_rich_table(
+    tabular_data: Sequence[dict[str, Any] | Sequence[TextOrString]],
+    headers: Sequence[str],
+    summary: str | None = None,
+    *,
+    justify="right",
+    table_kwargs: dict[str, Any] | None = None,
+) -> None:
+    table = Table(
+        *[c if isinstance(c, Column) else Column(c, justify=justify) for c in headers],
+        title=summary,
+        **(table_kwargs or {}),
+    )
+
+    for row in tabular_data:
+        if isinstance(row, dict):
+            table.add_row(
+                *[
+                    row[header] if isinstance(row[header], Text) else str(row[header])
+                    for header in headers
+                ]
+            )
+
+        else:
+            row_to_add: list[str | Text] = [r if isinstance(r, Text) else str(r) for r in row]
+            table.add_row(*row_to_add)
+
+    console = get_rich_console()
+    console.print(table)
+
+
+def _format_value(value: Any, *, floatfmt: str) -> str:
+    if isinstance(value, float):
+        return f"{value:{floatfmt}}"
+    return str(value)
+
+
+def print_df_rich_table(
+    tabular_data: DataFrame,
+    headers: Sequence[str],
+    summary: str | None = None,
+    *,
+    show_index=False,
+    index_name: str | None = None,
+    table_kwargs: dict[str, Any] | None = None,
+) -> None:
+    table = Table(title=summary, **(table_kwargs or {}))
+
+    if show_index:
+        index_name = str(index_name) if index_name else tabular_data.index.name
+        table.add_column(index_name)
+
+    for header in headers:
+        table.add_column(header, justify="right")
+
+    for value_list in tabular_data.itertuples(index=show_index):
+        row = [_format_value(x, floatfmt=".3f") for x in value_list]
+        table.add_row(*row)
+
+    console = get_rich_console()
+    console.print(table)
diff --git a/freqtrade/util/template_renderer.py b/freqtrade/util/template_renderer.py
new file mode 100644
index 000000000..c9dcfce52
--- /dev/null
+++ b/freqtrade/util/template_renderer.py
@@ -0,0 +1,30 @@
+"""
+Jinja2 rendering utils, used to generate new strategy and configurations.
+"""
+
+
+def render_template(templatefile: str, arguments: dict) -> str:
+    from jinja2 import Environment, PackageLoader, select_autoescape
+
+    env = Environment(
+        loader=PackageLoader("freqtrade", "templates"),
+        autoescape=select_autoescape(["html", "xml"]),
+    )
+    template = env.get_template(templatefile)
+    return template.render(**arguments)
+
+
+def render_template_with_fallback(
+    templatefile: str, templatefallbackfile: str, arguments: dict | None = None
+) -> str:
+    """
+    Use templatefile if possible, otherwise fall back to templatefallbackfile
+    """
+    from jinja2.exceptions import TemplateNotFound
+
+    if arguments is None:
+        arguments = {}
+    try:
+        return render_template(templatefile, arguments)
+    except TemplateNotFound:
+        return render_template(templatefallbackfile, arguments)
diff --git a/freqtrade/vendor/qtpylib/indicators.py b/freqtrade/vendor/qtpylib/indicators.py
index ee1f14e1f..7c1aec5a4 100644
--- a/freqtrade/vendor/qtpylib/indicators.py
+++ b/freqtrade/vendor/qtpylib/indicators.py
@@ -1,16 +1,13 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-#
 # QTPyLib: Quantitative Trading Python Library
 # https://github.com/ranaroussi/qtpylib
 #
-# Copyright 2016 Ran Aroussi
+# Copyright 2016-2018 Ran Aroussi
 #
-# Licensed under the GNU Lesser General Public License, v3.0 (the "License");
+# Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
-#     https://www.gnu.org/licenses/lgpl-3.0.en.html
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
@@ -19,7 +16,6 @@
 # limitations under the License.
 #
 
-import sys
 import warnings
 from datetime import datetime, timedelta
 
@@ -27,10 +23,6 @@ import numpy as np
 import pandas as pd
 from pandas.core.base import PandasObject
 
-# =============================================
-# check min, python version
-if sys.version_info < (3, 4):
-    raise SystemError("QTPyLib requires Python version >= 3.4")
 
 # =============================================
 warnings.simplefilter(action="ignore", category=RuntimeWarning)
@@ -40,7 +32,7 @@ warnings.simplefilter(action="ignore", category=RuntimeWarning)
 
 def numpy_rolling_window(data, window):
     shape = data.shape[:-1] + (data.shape[-1] - window + 1, window)
-    strides = data.strides + (data.strides[-1],)
+    strides = (*data.strides, data.strides[-1])
     return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
 
 
@@ -50,7 +42,7 @@ def numpy_rolling_series(func):
 
         new_series = np.empty(len(series)) * np.nan
         calculated = func(series, window)
-        new_series[-len(calculated):] = calculated
+        new_series[-len(calculated) :] = calculated
 
         if as_source and isinstance(data, pd.Series):
             return pd.Series(index=data.index, data=new_series)
@@ -62,145 +54,161 @@ def numpy_rolling_series(func):
 
 @numpy_rolling_series
 def numpy_rolling_mean(data, window, as_source=False):
-    return np.mean(numpy_rolling_window(data, window), -1)
+    return np.mean(numpy_rolling_window(data, window), axis=-1)
 
 
 @numpy_rolling_series
 def numpy_rolling_std(data, window, as_source=False):
-    return np.std(numpy_rolling_window(data, window), -1)
+    return np.std(numpy_rolling_window(data, window), axis=-1, ddof=1)
+
 
 # ---------------------------------------------
 
 
-def session(df, start='17:00', end='16:00'):
-    """ remove previous globex day from df """
-    if len(df) == 0:
+def session(df, start="17:00", end="16:00"):
+    """remove previous globex day from df"""
+    if df.empty:
         return df
 
     # get start/end/now as decimals
-    int_start = list(map(int, start.split(':')))
+    int_start = list(map(int, start.split(":")))
     int_start = (int_start[0] + int_start[1] - 1 / 100) - 0.0001
-    int_end = list(map(int, end.split(':')))
+    int_end = list(map(int, end.split(":")))
     int_end = int_end[0] + int_end[1] / 100
-    int_now = (df[-1:].index.hour[0] + (df[:1].index.minute[0]) / 100)
+    int_now = df[-1:].index.hour[0] + (df[:1].index.minute[0]) / 100
 
     # same-dat session?
     is_same_day = int_end > int_start
 
     # set pointers
-    curr = prev = df[-1:].index[0].strftime('%Y-%m-%d')
+    curr = prev = df[-1:].index[0].strftime("%Y-%m-%d")
 
     # globex/forex session
     if not is_same_day:
-        prev = (datetime.strptime(curr, '%Y-%m-%d') -
-                timedelta(1)).strftime('%Y-%m-%d')
+        prev = (datetime.strptime(curr, "%Y-%m-%d") - timedelta(1)).strftime("%Y-%m-%d")
 
     # slice
     if int_now >= int_start:
-        df = df[df.index >= curr + ' ' + start]
+        df = df[df.index >= curr + " " + start]
     else:
-        df = df[df.index >= prev + ' ' + start]
+        df = df[df.index >= prev + " " + start]
 
     return df.copy()
 
 
 # ---------------------------------------------
 
+
 def heikinashi(bars):
     bars = bars.copy()
-    bars['ha_close'] = (bars['open'] + bars['high'] +
-                        bars['low'] + bars['close']) / 4
-    bars['ha_open'] = (bars['open'].shift(1) + bars['close'].shift(1)) / 2
-    bars.loc[:1, 'ha_open'] = bars['open'].values[0]
-    bars.loc[1:, 'ha_open'] = (
-        (bars['ha_open'].shift(1) + bars['ha_close'].shift(1)) / 2)[1:]
-    bars['ha_high'] = bars.loc[:, ['high', 'ha_open', 'ha_close']].max(axis=1)
-    bars['ha_low'] = bars.loc[:, ['low', 'ha_open', 'ha_close']].min(axis=1)
+    bars["ha_close"] = (bars["open"] + bars["high"] + bars["low"] + bars["close"]) / 4
+
+    # ha open
+    bars.at[0, "ha_open"] = (bars.at[0, "open"] + bars.at[0, "close"]) / 2
+    for i in range(1, len(bars)):
+        bars.at[i, "ha_open"] = (bars.at[i - 1, "ha_open"] + bars.at[i - 1, "ha_close"]) / 2
+
+    bars["ha_high"] = bars.loc[:, ["high", "ha_open", "ha_close"]].max(axis=1)
+    bars["ha_low"] = bars.loc[:, ["low", "ha_open", "ha_close"]].min(axis=1)
 
     return pd.DataFrame(
         index=bars.index,
         data={
-            'open': bars['ha_open'],
-            'high': bars['ha_high'],
-            'low': bars['ha_low'],
-            'close': bars['ha_close']})
+            "open": bars["ha_open"],
+            "high": bars["ha_high"],
+            "low": bars["ha_low"],
+            "close": bars["ha_close"],
+        },
+    )
 
 
 # ---------------------------------------------
 
-def tdi(series, rsi_len=13, bollinger_len=34, rsi_smoothing=2,
-        rsi_signal_len=7, bollinger_std=1.6185):
-    rsi_series = rsi(series, rsi_len)
-    bb_series = bollinger_bands(rsi_series, bollinger_len, bollinger_std)
-    signal = sma(rsi_series, rsi_signal_len)
-    rsi_series = sma(rsi_series, rsi_smoothing)
 
-    return pd.DataFrame(index=series.index, data={
-        "rsi": rsi_series,
-        "signal": signal,
-        "bbupper": bb_series['upper'],
-        "bblower": bb_series['lower'],
-        "bbmid": bb_series['mid']
-    })
+def tdi(series, rsi_lookback=13, rsi_smooth_len=2, rsi_signal_len=7, bb_lookback=34, bb_std=1.6185):
+    rsi_data = rsi(series, rsi_lookback)
+    rsi_smooth = sma(rsi_data, rsi_smooth_len)
+    rsi_signal = sma(rsi_data, rsi_signal_len)
+
+    bb_series = bollinger_bands(rsi_data, bb_lookback, bb_std)
+
+    return pd.DataFrame(
+        index=series.index,
+        data={
+            "rsi": rsi_data,
+            "rsi_signal": rsi_signal,
+            "rsi_smooth": rsi_smooth,
+            "rsi_bb_upper": bb_series["upper"],
+            "rsi_bb_lower": bb_series["lower"],
+            "rsi_bb_mid": bb_series["mid"],
+        },
+    )
+
 
 # ---------------------------------------------
 
 
 def awesome_oscillator(df, weighted=False, fast=5, slow=34):
-    midprice = (df['high'] + df['low']) / 2
+    midprice = (df["high"] + df["low"]) / 2
 
     if weighted:
         ao = (midprice.ewm(fast).mean() - midprice.ewm(slow).mean()).values
     else:
-        ao = numpy_rolling_mean(midprice, fast) - \
-            numpy_rolling_mean(midprice, slow)
+        ao = numpy_rolling_mean(midprice, fast) - numpy_rolling_mean(midprice, slow)
 
     return pd.Series(index=df.index, data=ao)
 
 
 # ---------------------------------------------
 
-def nans(len=1):
-    mtx = np.empty(len)
+
+def nans(length=1):
+    mtx = np.empty(length)
     mtx[:] = np.nan
     return mtx
 
 
 # ---------------------------------------------
 
+
 def typical_price(bars):
-    res = (bars['high'] + bars['low'] + bars['close']) / 3.
+    res = (bars["high"] + bars["low"] + bars["close"]) / 3.0
     return pd.Series(index=bars.index, data=res)
 
 
 # ---------------------------------------------
 
+
 def mid_price(bars):
-    res = (bars['high'] + bars['low']) / 2.
+    res = (bars["high"] + bars["low"]) / 2.0
     return pd.Series(index=bars.index, data=res)
 
 
 # ---------------------------------------------
 
+
 def ibs(bars):
-    """ Internal bar strength """
-    res = np.round((bars['close'] - bars['low']) /
-                   (bars['high'] - bars['low']), 2)
+    """Internal bar strength"""
+    res = np.round((bars["close"] - bars["low"]) / (bars["high"] - bars["low"]), 2)
     return pd.Series(index=bars.index, data=res)
 
 
 # ---------------------------------------------
 
+
 def true_range(bars):
-    return pd.DataFrame({
-        "hl": bars['high'] - bars['low'],
-        "hc": abs(bars['high'] - bars['close'].shift(1)),
-        "lc": abs(bars['low'] - bars['close'].shift(1))
-    }).max(axis=1)
+    return pd.DataFrame(
+        {
+            "hl": bars["high"] - bars["low"],
+            "hc": abs(bars["high"] - bars["close"].shift(1)),
+            "lc": abs(bars["low"] - bars["close"].shift(1)),
+        }
+    ).max(axis=1)
 
 
 # ---------------------------------------------
 
+
 def atr(bars, window=14, exp=False):
     tr = true_range(bars)
 
@@ -209,31 +217,29 @@ def atr(bars, window=14, exp=False):
     else:
         res = rolling_mean(tr, window)
 
-    res = pd.Series(res)
-    return (res.shift(1) * (window - 1) + res) / window
+    return pd.Series(res)
 
 
 # ---------------------------------------------
 
+
 def crossed(series1, series2, direction=None):
     if isinstance(series1, np.ndarray):
         series1 = pd.Series(series1)
 
-    if isinstance(series2, int) or isinstance(series2, float) or isinstance(series2, np.ndarray):
+    if isinstance(series2, float | int | np.ndarray | np.integer | np.floating):
         series2 = pd.Series(index=series1.index, data=series2)
 
     if direction is None or direction == "above":
-        above = pd.Series((series1 > series2) & (
-            series1.shift(1) <= series2.shift(1)))
+        above = pd.Series((series1 > series2) & (series1.shift(1) <= series2.shift(1)))
 
     if direction is None or direction == "below":
-        below = pd.Series((series1 < series2) & (
-            series1.shift(1) >= series2.shift(1)))
+        below = pd.Series((series1 < series2) & (series1.shift(1) >= series2.shift(1)))
 
     if direction is None:
-        return above or below
+        return above | below
 
-    return above if direction is "above" else below
+    return above if direction == "above" else below
 
 
 def crossed_above(series1, series2):
@@ -243,118 +249,123 @@ def crossed_above(series1, series2):
 def crossed_below(series1, series2):
     return crossed(series1, series2, "below")
 
+
 # ---------------------------------------------
 
 
 def rolling_std(series, window=200, min_periods=None):
     min_periods = window if min_periods is None else min_periods
-    try:
-        if min_periods == window:
-            return numpy_rolling_std(series, window, True)
-        else:
-            try:
-                return series.rolling(window=window, min_periods=min_periods).std()
-            except BaseException:
-                return pd.Series(series).rolling(window=window, min_periods=min_periods).std()
-    except BaseException:
-        return pd.rolling_std(series, window=window, min_periods=min_periods)
+    if min_periods == window and len(series) > window:
+        return numpy_rolling_std(series, window, True)
+    else:
+        try:
+            return series.rolling(window=window, min_periods=min_periods).std()
+        except Exception as e:  # noqa: F841
+            return pd.Series(series).rolling(window=window, min_periods=min_periods).std()
 
 
 # ---------------------------------------------
 
+
 def rolling_mean(series, window=200, min_periods=None):
     min_periods = window if min_periods is None else min_periods
-    try:
-        if min_periods == window:
-            return numpy_rolling_mean(series, window, True)
-        else:
-            try:
-                return series.rolling(window=window, min_periods=min_periods).mean()
-            except BaseException:
-                return pd.Series(series).rolling(window=window, min_periods=min_periods).mean()
-    except BaseException:
-        return pd.rolling_mean(series, window=window, min_periods=min_periods)
+    if min_periods == window and len(series) > window:
+        return numpy_rolling_mean(series, window, True)
+    else:
+        try:
+            return series.rolling(window=window, min_periods=min_periods).mean()
+        except Exception as e:  # noqa: F841
+            return pd.Series(series).rolling(window=window, min_periods=min_periods).mean()
 
 
 # ---------------------------------------------
 
+
 def rolling_min(series, window=14, min_periods=None):
     min_periods = window if min_periods is None else min_periods
     try:
-        try:
-            return series.rolling(window=window, min_periods=min_periods).min()
-        except BaseException:
-            return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
-    except BaseException:
-        return pd.rolling_min(series, window=window, min_periods=min_periods)
+        return series.rolling(window=window, min_periods=min_periods).min()
+    except Exception as e:  # noqa: F841
+        return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
 
 
 # ---------------------------------------------
 
+
 def rolling_max(series, window=14, min_periods=None):
     min_periods = window if min_periods is None else min_periods
     try:
-        try:
-            return series.rolling(window=window, min_periods=min_periods).min()
-        except BaseException:
-            return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
-    except BaseException:
-        return pd.rolling_min(series, window=window, min_periods=min_periods)
+        return series.rolling(window=window, min_periods=min_periods).max()
+    except Exception as e:  # noqa: F841
+        return pd.Series(series).rolling(window=window, min_periods=min_periods).max()
 
 
 # ---------------------------------------------
 
+
 def rolling_weighted_mean(series, window=200, min_periods=None):
     min_periods = window if min_periods is None else min_periods
     try:
         return series.ewm(span=window, min_periods=min_periods).mean()
-    except BaseException:
+    except Exception as e:  # noqa: F841
         return pd.ewma(series, span=window, min_periods=min_periods)
 
 
 # ---------------------------------------------
 
-def hull_moving_average(series, window=200):
-    wma = (2 * rolling_weighted_mean(series, window=window / 2)) - \
-        rolling_weighted_mean(series, window=window)
-    return rolling_weighted_mean(wma, window=np.sqrt(window))
+
+def hull_moving_average(series, window=200, min_periods=None):
+    min_periods = window if min_periods is None else min_periods
+    ma = (2 * rolling_weighted_mean(series, window / 2, min_periods)) - rolling_weighted_mean(
+        series, window, min_periods
+    )
+    return rolling_weighted_mean(ma, np.sqrt(window), min_periods)
 
 
 # ---------------------------------------------
 
+
 def sma(series, window=200, min_periods=None):
     return rolling_mean(series, window=window, min_periods=min_periods)
 
 
 # ---------------------------------------------
 
+
 def wma(series, window=200, min_periods=None):
     return rolling_weighted_mean(series, window=window, min_periods=min_periods)
 
 
 # ---------------------------------------------
 
-def hma(series, window=200):
-    return hull_moving_average(series, window=window)
+
+def hma(series, window=200, min_periods=None):
+    return hull_moving_average(series, window=window, min_periods=min_periods)
 
 
 # ---------------------------------------------
 
+
 def vwap(bars):
     """
     calculate vwap of entire time series
     (input can be pandas series or numpy array)
     bars are usually mid [ (h+l)/2 ] or typical [ (h+l+c)/3 ]
     """
-    typical = ((bars['high'] + bars['low'] + bars['close']) / 3).values
-    volume = bars['volume'].values
+    raise ValueError(
+        "using `qtpylib.vwap` facilitates lookahead bias. Please use "
+        "`qtpylib.rolling_vwap` instead, which calculates vwap in a rolling manner."
+    )
+    # typical = ((bars['high'] + bars['low'] + bars['close']) / 3).values
+    # volume = bars['volume'].values
 
-    return pd.Series(index=bars.index,
-                     data=np.cumsum(volume * typical) / np.cumsum(volume))
+    # return pd.Series(index=bars.index,
+    #                  data=np.cumsum(volume * typical) / np.cumsum(volume))
 
 
 # ---------------------------------------------
 
+
 def rolling_vwap(bars, window=200, min_periods=None):
     """
     calculate vwap using moving window
@@ -363,31 +374,36 @@ def rolling_vwap(bars, window=200, min_periods=None):
     """
     min_periods = window if min_periods is None else min_periods
 
-    typical = ((bars['high'] + bars['low'] + bars['close']) / 3)
-    volume = bars['volume']
+    typical = (bars["high"] + bars["low"] + bars["close"]) / 3
+    volume = bars["volume"]
 
-    left = (volume * typical).rolling(window=window,
-                                      min_periods=min_periods).sum()
+    left = (volume * typical).rolling(window=window, min_periods=min_periods).sum()
     right = volume.rolling(window=window, min_periods=min_periods).sum()
 
-    return pd.Series(index=bars.index, data=(left / right))
+    return (
+        pd.Series(index=bars.index, data=(left / right))
+        .replace([np.inf, -np.inf], float("NaN"))
+        .ffill()
+    )
 
 
 # ---------------------------------------------
 
+
 def rsi(series, window=14):
     """
     compute the n period relative strength indicator
     """
+
     # 100-(100/relative_strength)
     deltas = np.diff(series)
-    seed = deltas[:window + 1]
+    seed = deltas[: window + 1]
 
     # default values
     ups = seed[seed > 0].sum() / window
     downs = -seed[seed < 0].sum() / window
     rsival = np.zeros_like(series)
-    rsival[:window] = 100. - 100. / (1. + ups / downs)
+    rsival[:window] = 100.0 - 100.0 / (1.0 + ups / downs)
 
     # period values
     for i in range(window, len(series)):
@@ -400,8 +416,8 @@ def rsi(series, window=14):
             downval = -delta
 
         ups = (ups * (window - 1) + upval) / window
-        downs = (downs * (window - 1.) + downval) / window
-        rsival[i] = 100. - 100. / (1. + ups / downs)
+        downs = (downs * (window - 1.0) + downval) / window
+        rsival[i] = 100.0 - 100.0 / (1.0 + ups / downs)
 
     # return rsival
     return pd.Series(index=series.index, data=rsival)
@@ -409,61 +425,58 @@ def rsi(series, window=14):
 
 # ---------------------------------------------
 
+
 def macd(series, fast=3, slow=10, smooth=16):
     """
     compute the MACD (Moving Average Convergence/Divergence)
     using a fast and slow exponential moving avg'
     return value is emaslow, emafast, macd which are len(x) arrays
     """
-    macd = rolling_weighted_mean(series, window=fast) - \
-        rolling_weighted_mean(series, window=slow)
-    signal = rolling_weighted_mean(macd, window=smooth)
-    histogram = macd - signal
-    # return macd, signal, histogram
-    return pd.DataFrame(index=series.index, data={
-        'macd': macd.values,
-        'signal': signal.values,
-        'histogram': histogram.values
-    })
+    macd_line = rolling_weighted_mean(series, window=fast) - rolling_weighted_mean(
+        series, window=slow
+    )
+    signal = rolling_weighted_mean(macd_line, window=smooth)
+    histogram = macd_line - signal
+    # return macd_line, signal, histogram
+    return pd.DataFrame(
+        index=series.index,
+        data={"macd": macd_line.values, "signal": signal.values, "histogram": histogram.values},
+    )
 
 
 # ---------------------------------------------
 
+
 def bollinger_bands(series, window=20, stds=2):
-    sma = rolling_mean(series, window=window)
-    std = rolling_std(series, window=window)
-    upper = sma + std * stds
-    lower = sma - std * stds
+    ma = rolling_mean(series, window=window, min_periods=1)
+    std = rolling_std(series, window=window, min_periods=1)
+    upper = ma + std * stds
+    lower = ma - std * stds
 
-    return pd.DataFrame(index=series.index, data={
-        'upper': upper,
-        'mid': sma,
-        'lower': lower
-    })
+    return pd.DataFrame(index=series.index, data={"upper": upper, "mid": ma, "lower": lower})
 
 
 # ---------------------------------------------
 
+
 def weighted_bollinger_bands(series, window=20, stds=2):
     ema = rolling_weighted_mean(series, window=window)
     std = rolling_std(series, window=window)
     upper = ema + std * stds
     lower = ema - std * stds
 
-    return pd.DataFrame(index=series.index, data={
-        'upper': upper.values,
-        'mid': ema.values,
-        'lower': lower.values
-    })
+    return pd.DataFrame(
+        index=series.index, data={"upper": upper.values, "mid": ema.values, "lower": lower.values}
+    )
 
 
 # ---------------------------------------------
 
+
 def returns(series):
     try:
-        res = (series / series.shift(1) -
-               1).replace([np.inf, -np.inf], float('NaN'))
-    except BaseException:
+        res = (series / series.shift(1) - 1).replace([np.inf, -np.inf], float("NaN"))
+    except Exception as e:  # noqa: F841
         res = nans(len(series))
 
     return pd.Series(index=series.index, data=res)
@@ -471,11 +484,11 @@ def returns(series):
 
 # ---------------------------------------------
 
+
 def log_returns(series):
     try:
-        res = np.log(series / series.shift(1)
-                     ).replace([np.inf, -np.inf], float('NaN'))
-    except BaseException:
+        res = np.log(series / series.shift(1)).replace([np.inf, -np.inf], float("NaN"))
+    except Exception as e:  # noqa: F841
         res = nans(len(series))
 
     return pd.Series(index=series.index, data=res)
@@ -483,12 +496,12 @@ def log_returns(series):
 
 # ---------------------------------------------
 
+
 def implied_volatility(series, window=252):
     try:
-        logret = np.log(series / series.shift(1)
-                        ).replace([np.inf, -np.inf], float('NaN'))
+        logret = np.log(series / series.shift(1)).replace([np.inf, -np.inf], float("NaN"))
         res = numpy_rolling_std(logret, window) * np.sqrt(window)
-    except BaseException:
+    except Exception as e:  # noqa: F841
         res = nans(len(series))
 
     return pd.Series(index=series.index, data=res)
@@ -496,6 +509,7 @@ def implied_volatility(series, window=252):
 
 # ---------------------------------------------
 
+
 def keltner_channel(bars, window=14, atrs=2):
     typical_mean = rolling_mean(typical_price(bars), window)
     atrval = atr(bars, window) * atrs
@@ -503,15 +517,15 @@ def keltner_channel(bars, window=14, atrs=2):
     upper = typical_mean + atrval
     lower = typical_mean - atrval
 
-    return pd.DataFrame(index=bars.index, data={
-        'upper': upper.values,
-        'mid': typical_mean.values,
-        'lower': lower.values
-    })
+    return pd.DataFrame(
+        index=bars.index,
+        data={"upper": upper.values, "mid": typical_mean.values, "lower": lower.values},
+    )
 
 
 # ---------------------------------------------
 
+
 def roc(series, window=14):
     """
     compute rate of change
@@ -522,71 +536,105 @@ def roc(series, window=14):
 
 # ---------------------------------------------
 
+
 def cci(series, window=14):
     """
     compute commodity channel index
     """
     price = typical_price(series)
     typical_mean = rolling_mean(price, window)
-    res = (price - typical_mean) / (.015 * np.std(typical_mean))
+    res = (price - typical_mean) / (0.015 * np.std(typical_mean))
     return pd.Series(index=series.index, data=res)
 
 
 # ---------------------------------------------
 
+
 def stoch(df, window=14, d=3, k=3, fast=False):
     """
     compute the n period relative strength indicator
     http://excelta.blogspot.co.il/2013/09/stochastic-oscillator-technical.html
     """
-    highs_ma = pd.concat([df['high'].shift(i)
-                          for i in np.arange(window)], 1).apply(list, 1)
-    highs_ma = highs_ma.T.max().T
 
-    lows_ma = pd.concat([df['low'].shift(i)
-                         for i in np.arange(window)], 1).apply(list, 1)
-    lows_ma = lows_ma.T.min().T
+    my_df = pd.DataFrame(index=df.index)
 
-    fast_k = ((df['close'] - lows_ma) / (highs_ma - lows_ma)) * 100
-    fast_d = numpy_rolling_mean(fast_k, d)
+    my_df["rolling_max"] = df["high"].rolling(window).max()
+    my_df["rolling_min"] = df["low"].rolling(window).min()
+
+    my_df["fast_k"] = (
+        100 * (df["close"] - my_df["rolling_min"]) / (my_df["rolling_max"] - my_df["rolling_min"])
+    )
+    my_df["fast_d"] = my_df["fast_k"].rolling(d).mean()
 
     if fast:
-        data = {
-            'k': fast_k,
-            'd': fast_d
-        }
+        return my_df.loc[:, ["fast_k", "fast_d"]]
 
-    else:
-        slow_k = numpy_rolling_mean(fast_k, k)
-        slow_d = numpy_rolling_mean(slow_k, d)
-        data = {
-            'k': slow_k,
-            'd': slow_d
-        }
+    my_df["slow_k"] = my_df["fast_k"].rolling(k).mean()
+    my_df["slow_d"] = my_df["slow_k"].rolling(d).mean()
 
-    return pd.DataFrame(index=df.index, data=data)
+    return my_df.loc[:, ["slow_k", "slow_d"]]
 
 
 # ---------------------------------------------
 
-def zscore(bars, window=20, stds=1, col='close'):
-    """ get zscore of price """
+
+def zlma(series, window=20, min_periods=None, kind="ema"):
+    """
+    John Ehlers' Zero lag (exponential) moving average
+    https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average
+    """
+    min_periods = window if min_periods is None else min_periods
+
+    lag = (window - 1) // 2
+    series = 2 * series - series.shift(lag)
+    if kind in ["ewm", "ema"]:
+        return wma(series, lag, min_periods)
+    elif kind == "hma":
+        return hma(series, lag, min_periods)
+    return sma(series, lag, min_periods)
+
+
+def zlema(series, window, min_periods=None):
+    return zlma(series, window, min_periods, kind="ema")
+
+
+def zlsma(series, window, min_periods=None):
+    return zlma(series, window, min_periods, kind="sma")
+
+
+def zlhma(series, window, min_periods=None):
+    return zlma(series, window, min_periods, kind="hma")
+
+
+# ---------------------------------------------
+
+
+def zscore(bars, window=20, stds=1, col="close"):
+    """get zscore of price"""
     std = numpy_rolling_std(bars[col], window)
     mean = numpy_rolling_mean(bars[col], window)
     return (bars[col] - mean) / (std * stds)
 
+
 # ---------------------------------------------
 
 
 def pvt(bars):
-    """ Price Volume Trend """
-    pvt = ((bars['close'] - bars['close'].shift(1)) /
-           bars['close'].shift(1)) * bars['volume']
-    return pvt.cumsum()
+    """Price Volume Trend"""
+    trend = ((bars["close"] - bars["close"].shift(1)) / bars["close"].shift(1)) * bars["volume"]
+    return trend.cumsum()
+
+
+def chopiness(bars, window=14):
+    atrsum = true_range(bars).rolling(window).sum()
+    highs = bars["high"].rolling(window).max()
+    lows = bars["low"].rolling(window).min()
+    return 100 * np.log10(atrsum / (highs - lows)) / np.log10(window)
 
 
 # =============================================
 
+
 PandasObject.session = session
 PandasObject.atr = atr
 PandasObject.bollinger_bands = bollinger_bands
@@ -611,6 +659,7 @@ PandasObject.rsi = rsi
 PandasObject.stoch = stoch
 PandasObject.zscore = zscore
 PandasObject.pvt = pvt
+PandasObject.chopiness = chopiness
 PandasObject.tdi = tdi
 PandasObject.true_range = true_range
 PandasObject.mid_price = mid_price
@@ -622,4 +671,11 @@ PandasObject.rolling_weighted_mean = rolling_weighted_mean
 
 PandasObject.sma = sma
 PandasObject.wma = wma
+PandasObject.ema = wma
 PandasObject.hma = hma
+
+PandasObject.zlsma = zlsma
+PandasObject.zlwma = zlema
+PandasObject.zlema = zlema
+PandasObject.zlhma = zlhma
+PandasObject.zlma = zlma
diff --git a/freqtrade/wallets.py b/freqtrade/wallets.py
new file mode 100644
index 000000000..cb0887415
--- /dev/null
+++ b/freqtrade/wallets.py
@@ -0,0 +1,446 @@
+# pragma pylint: disable=W0603
+"""Wallet"""
+
+import logging
+from datetime import datetime, timedelta
+from typing import Literal, NamedTuple
+
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config, IntOrInf
+from freqtrade.enums import RunMode, TradingMode
+from freqtrade.exceptions import DependencyException
+from freqtrade.exchange import Exchange
+from freqtrade.misc import safe_value_fallback
+from freqtrade.persistence import LocalTrade, Trade
+from freqtrade.util.datetime_helpers import dt_now
+
+
+logger = logging.getLogger(__name__)
+
+
+# wallet data structure
+class Wallet(NamedTuple):
+    currency: str
+    free: float = 0
+    used: float = 0
+    total: float = 0
+
+
+class PositionWallet(NamedTuple):
+    symbol: str
+    position: float = 0
+    leverage: float | None = 0  # Don't use this - it's not guaranteed to be set
+    collateral: float = 0
+    side: str = "long"
+
+
+class Wallets:
+    def __init__(self, config: Config, exchange: Exchange, is_backtest: bool = False) -> None:
+        self._config = config
+        self._is_backtest = is_backtest
+        self._exchange = exchange
+        self._wallets: dict[str, Wallet] = {}
+        self._positions: dict[str, PositionWallet] = {}
+        self._start_cap: dict[str, float] = {}
+
+        self._stake_currency = self._exchange.get_proxy_coin()
+
+        if isinstance(_start_cap := config["dry_run_wallet"], float | int):
+            self._start_cap[self._stake_currency] = _start_cap
+        else:
+            self._start_cap = _start_cap
+
+        self._last_wallet_refresh: datetime | None = None
+        self.update()
+
+    def get_free(self, currency: str) -> float:
+        balance = self._wallets.get(currency)
+        if balance and balance.free:
+            return balance.free
+        else:
+            return 0
+
+    def get_used(self, currency: str) -> float:
+        balance = self._wallets.get(currency)
+        if balance and balance.used:
+            return balance.used
+        else:
+            return 0
+
+    def get_total(self, currency: str) -> float:
+        balance = self._wallets.get(currency)
+        if balance and balance.total:
+            return balance.total
+        else:
+            return 0
+
+    def get_collateral(self) -> float:
+        """
+        Get total collateral for liquidation price calculation.
+        """
+        if self._config.get("margin_mode") == "cross":
+            # free includes all balances and, combined with position collateral,
+            # is used as "wallet balance".
+            return self.get_free(self._stake_currency) + sum(
+                pos.collateral for pos in self._positions.values()
+            )
+        return self.get_total(self._stake_currency)
+
+    def get_owned(self, pair: str, base_currency: str) -> float:
+        """
+        Get currently owned value.
+        Designed to work across both spot and futures.
+        """
+        if self._config.get("trading_mode", "spot") != TradingMode.FUTURES:
+            return self.get_total(base_currency) or 0
+        if pos := self._positions.get(pair):
+            return pos.position
+        return 0
+
+    def _update_dry(self) -> None:
+        """
+        Update from database in dry-run mode
+        - Apply profits of closed trades on top of stake amount
+        - Subtract currently tied up stake_amount in open trades
+        - update balances for currencies currently in trades
+        """
+        # Recreate _wallets to reset closed trade balances
+        _wallets = {}
+        _positions = {}
+        open_trades = Trade.get_trades_proxy(is_open=True)
+        if not self._is_backtest:
+            # Live / Dry-run mode
+            tot_profit = Trade.get_total_closed_profit()
+        else:
+            # Backtest mode
+            tot_profit = LocalTrade.bt_total_profit
+        tot_profit += sum(trade.realized_profit for trade in open_trades)
+        tot_in_trades = sum(trade.stake_amount for trade in open_trades)
+        used_stake = 0.0
+
+        if self._config.get("trading_mode", "spot") != TradingMode.FUTURES:
+            for trade in open_trades:
+                curr = self._exchange.get_pair_base_currency(trade.pair)
+                used_stake += sum(
+                    o.stake_amount for o in trade.open_orders if o.ft_order_side == trade.entry_side
+                )
+                pending = sum(
+                    o.amount
+                    for o in trade.open_orders
+                    if o.amount and o.ft_order_side == trade.exit_side
+                )
+                curr_wallet_bal = self._start_cap.get(curr, 0)
+
+                _wallets[curr] = Wallet(
+                    curr,
+                    curr_wallet_bal + trade.amount - pending,
+                    pending,
+                    trade.amount + curr_wallet_bal,
+                )
+        else:
+            for position in open_trades:
+                _positions[position.pair] = PositionWallet(
+                    position.pair,
+                    position=position.amount,
+                    leverage=position.leverage,
+                    collateral=position.stake_amount,
+                    side=position.trade_direction,
+                )
+
+            used_stake = tot_in_trades
+
+        cross_margin = 0.0
+        if self._config.get("margin_mode") == "cross":
+            # In cross-margin mode, the total balance is used as collateral.
+            # This is moved as "free" into the stake currency balance.
+            # strongly tied to the get_collateral() implementation.
+            for curr, bal in self._start_cap.items():
+                if curr == self._stake_currency:
+                    continue
+                rate = self._exchange.get_conversion_rate(curr, self._stake_currency)
+                if rate:
+                    cross_margin += bal * rate
+
+        current_stake = self._start_cap.get(self._stake_currency, 0) + tot_profit - tot_in_trades
+        total_stake = current_stake + used_stake
+
+        _wallets[self._stake_currency] = Wallet(
+            currency=self._stake_currency,
+            free=current_stake + cross_margin,
+            used=used_stake,
+            total=total_stake,
+        )
+        for currency, bal in self._start_cap.items():
+            if currency not in _wallets:
+                _wallets[currency] = Wallet(currency, bal, 0, bal)
+
+        self._wallets = _wallets
+        self._positions = _positions
+
+    def _update_live(self) -> None:
+        balances = self._exchange.get_balances()
+        _wallets = {}
+
+        for currency in balances:
+            if isinstance(balances[currency], dict):
+                _wallets[currency] = Wallet(
+                    currency,
+                    balances[currency].get("free", 0),
+                    balances[currency].get("used", 0),
+                    balances[currency].get("total", 0),
+                )
+
+        positions = self._exchange.fetch_positions()
+        _parsed_positions = {}
+        for position in positions:
+            symbol = position["symbol"]
+            if position["side"] is None or position["collateral"] == 0.0:
+                # Position is not open ...
+                continue
+            size = self._exchange._contracts_to_amount(symbol, position["contracts"])
+            collateral = safe_value_fallback(position, "initialMargin", "collateral", 0.0)
+            leverage = position.get("leverage")
+            _parsed_positions[symbol] = PositionWallet(
+                symbol,
+                position=size,
+                leverage=leverage,
+                collateral=collateral,
+                side=position["side"],
+            )
+        self._positions = _parsed_positions
+        self._wallets = _wallets
+
+    def update(self, require_update: bool = True) -> None:
+        """
+        Updates wallets from the configured version.
+        By default, updates from the exchange.
+        Update-skipping should only be used for user-invoked /balance calls, since
+        for trading operations, the latest balance is needed.
+        :param require_update: Allow skipping an update if balances were recently refreshed
+        """
+        now = dt_now()
+        if (
+            require_update
+            or self._last_wallet_refresh is None
+            or (self._last_wallet_refresh + timedelta(seconds=3600) < now)
+        ):
+            if not self._config["dry_run"] or self._config.get("runmode") == RunMode.LIVE:
+                self._update_live()
+            else:
+                self._update_dry()
+            self._local_log("Wallets synced.")
+            self._last_wallet_refresh = dt_now()
+
+    def get_all_balances(self) -> dict[str, Wallet]:
+        return self._wallets
+
+    def get_all_positions(self) -> dict[str, PositionWallet]:
+        return self._positions
+
+    def _check_exit_amount(self, trade: Trade) -> bool:
+        if trade.trading_mode != TradingMode.FUTURES:
+            # Slightly higher offset than in safe_exit_amount.
+            wallet_amount: float = self.get_total(trade.safe_base_currency) * (2 - 0.981)
+        else:
+            # wallet_amount: float = self.wallets.get_free(trade.safe_base_currency)
+            position = self._positions.get(trade.pair)
+            if position is None:
+                # We don't own anything :O
+                return False
+            wallet_amount = position.position
+
+        if wallet_amount >= trade.amount:
+            return True
+        return False
+
+    def check_exit_amount(self, trade: Trade) -> bool:
+        """
+        Checks if the exit amount is available in the wallet.
+        :param trade: Trade to check
+        :return: True if the exit amount is available, False otherwise
+        """
+        if not self._check_exit_amount(trade):
+            # Update wallets just to make sure
+            self.update()
+            return self._check_exit_amount(trade)
+
+        return True
+
+    def get_starting_balance(self) -> float:
+        """
+        Retrieves starting balance - based on either available capital,
+        or by using current balance subtracting
+        """
+        if "available_capital" in self._config:
+            return self._config["available_capital"]
+        else:
+            tot_profit = Trade.get_total_closed_profit()
+            open_stakes = Trade.total_open_trades_stakes()
+            available_balance = self.get_free(self._stake_currency)
+            return (available_balance - tot_profit + open_stakes) * self._config[
+                "tradable_balance_ratio"
+            ]
+
+    def get_total_stake_amount(self):
+        """
+        Return the total currently available balance in stake currency, including tied up stake and
+        respecting tradable_balance_ratio.
+        Calculated as
+        (<open_trade stakes> + free amount) * tradable_balance_ratio
+        """
+        val_tied_up = Trade.total_open_trades_stakes()
+        if "available_capital" in self._config:
+            starting_balance = self._config["available_capital"]
+            tot_profit = Trade.get_total_closed_profit()
+            available_amount = starting_balance + tot_profit
+
+        else:
+            # Ensure <tradable_balance_ratio>% is used from the overall balance
+            # Otherwise we'd risk lowering stakes with each open trade.
+            # (tied up + current free) * ratio) - tied up
+            available_amount = (val_tied_up + self.get_free(self._stake_currency)) * self._config[
+                "tradable_balance_ratio"
+            ]
+        return available_amount
+
+    def get_available_stake_amount(self) -> float:
+        """
+        Return the total currently available balance in stake currency,
+        respecting tradable_balance_ratio.
+        Calculated as
+        (<open_trade stakes> + free amount) * tradable_balance_ratio - <open_trade stakes>
+        """
+
+        free = self.get_free(self._stake_currency)
+        return min(self.get_total_stake_amount() - Trade.total_open_trades_stakes(), free)
+
+    def _calculate_unlimited_stake_amount(
+        self, available_amount: float, val_tied_up: float, max_open_trades: IntOrInf
+    ) -> float:
+        """
+        Calculate stake amount for "unlimited" stake amount
+        :return: 0 if max number of trades reached, else stake_amount to use.
+        """
+        if max_open_trades == 0:
+            return 0
+
+        possible_stake = (available_amount + val_tied_up) / max_open_trades
+        # Theoretical amount can be above available amount - therefore limit to available amount!
+        return min(possible_stake, available_amount)
+
+    def _check_available_stake_amount(self, stake_amount: float, available_amount: float) -> float:
+        """
+        Check if stake amount can be fulfilled with the available balance
+        for the stake currency
+        :return: float: Stake amount
+        :raise: DependencyException if balance is lower than stake-amount
+        """
+
+        if self._config["amend_last_stake_amount"]:
+            # Remaining amount needs to be at least stake_amount * last_stake_amount_min_ratio
+            # Otherwise the remaining amount is too low to trade.
+            if available_amount > (stake_amount * self._config["last_stake_amount_min_ratio"]):
+                stake_amount = min(stake_amount, available_amount)
+            else:
+                stake_amount = 0
+
+        if available_amount < stake_amount:
+            raise DependencyException(
+                f"Available balance ({available_amount} {self._config['stake_currency']}) is "
+                f"lower than stake amount ({stake_amount} {self._config['stake_currency']})"
+            )
+
+        return max(stake_amount, 0)
+
+    def get_trade_stake_amount(
+        self, pair: str, max_open_trades: IntOrInf, edge=None, update: bool = True
+    ) -> float:
+        """
+        Calculate stake amount for the trade
+        :return: float: Stake amount
+        :raise: DependencyException if the available stake amount is too low
+        """
+        stake_amount: float
+        # Ensure wallets are up-to-date.
+        if update:
+            self.update()
+        val_tied_up = Trade.total_open_trades_stakes()
+        available_amount = self.get_available_stake_amount()
+
+        if edge:
+            stake_amount = edge.stake_amount(
+                pair,
+                self.get_free(self._stake_currency),
+                self.get_total(self._stake_currency),
+                val_tied_up,
+            )
+        else:
+            stake_amount = self._config["stake_amount"]
+            if stake_amount == UNLIMITED_STAKE_AMOUNT:
+                stake_amount = self._calculate_unlimited_stake_amount(
+                    available_amount, val_tied_up, max_open_trades
+                )
+
+        return self._check_available_stake_amount(stake_amount, available_amount)
+
+    def validate_stake_amount(
+        self,
+        pair: str,
+        stake_amount: float | None,
+        min_stake_amount: float | None,
+        max_stake_amount: float,
+        trade_amount: float | None,
+    ):
+        if not stake_amount:
+            self._local_log(
+                f"Stake amount is {stake_amount}, ignoring possible trade for {pair}.",
+                level="debug",
+            )
+            return 0
+
+        max_allowed_stake = min(max_stake_amount, self.get_available_stake_amount())
+        if trade_amount:
+            # if in a trade, then the resulting trade size cannot go beyond the max stake
+            # Otherwise we could no longer exit.
+            max_allowed_stake = min(max_allowed_stake, max_stake_amount - trade_amount)
+
+        if min_stake_amount is not None and min_stake_amount > max_allowed_stake:
+            self._local_log(
+                "Minimum stake amount > available balance. "
+                f"{min_stake_amount} > {max_allowed_stake}",
+                level="warning",
+            )
+            return 0
+        if min_stake_amount is not None and stake_amount < min_stake_amount:
+            self._local_log(
+                f"Stake amount for pair {pair} is too small "
+                f"({stake_amount} < {min_stake_amount}), adjusting to {min_stake_amount}."
+            )
+            if stake_amount * 1.3 < min_stake_amount:
+                # Top-cap stake-amount adjustments to +30%.
+                self._local_log(
+                    f"Adjusted stake amount for pair {pair} is more than 30% bigger than "
+                    f"the desired stake amount of ({stake_amount:.8f} * 1.3 = "
+                    f"{stake_amount * 1.3:.8f}) < {min_stake_amount}), ignoring trade."
+                )
+                return 0
+            stake_amount = min_stake_amount
+
+        if stake_amount > max_allowed_stake:
+            self._local_log(
+                f"Stake amount for pair {pair} is too big "
+                f"({stake_amount} > {max_allowed_stake}), adjusting to {max_allowed_stake}."
+            )
+            stake_amount = max_allowed_stake
+        return stake_amount
+
+    def _local_log(self, msg: str, level: Literal["info", "warning", "debug"] = "info") -> None:
+        """
+        Log a message to the local log.
+        """
+        if not self._is_backtest:
+            if level == "warning":
+                logger.warning(msg)
+            elif level == "debug":
+                logger.debug(msg)
+            else:
+                logger.info(msg)
diff --git a/freqtrade/worker.py b/freqtrade/worker.py
new file mode 100644
index 000000000..8b754ddc3
--- /dev/null
+++ b/freqtrade/worker.py
@@ -0,0 +1,239 @@
+"""
+Main Freqtrade worker class.
+"""
+
+import logging
+import time
+import traceback
+from collections.abc import Callable
+from os import getpid
+from typing import Any
+
+import sdnotify
+
+from freqtrade import __version__
+from freqtrade.configuration import Configuration
+from freqtrade.constants import PROCESS_THROTTLE_SECS, RETRY_TIMEOUT, Config
+from freqtrade.enums import RPCMessageType, State
+from freqtrade.exceptions import OperationalException, TemporaryError
+from freqtrade.exchange import timeframe_to_next_date
+from freqtrade.freqtradebot import FreqtradeBot
+
+
+logger = logging.getLogger(__name__)
+
+
+class Worker:
+    """
+    Freqtradebot worker class
+    """
+
+    def __init__(self, args: dict[str, Any], config: Config | None = None) -> None:
+        """
+        Init all variables and objects the bot needs to work
+        """
+        logger.info(f"Starting worker {__version__}")
+
+        self._args = args
+        self._config = config
+        self._init(False)
+
+        self._heartbeat_msg: float = 0
+
+        # Tell systemd that we completed initialization phase
+        self._notify("READY=1")
+
+    def _init(self, reconfig: bool) -> None:
+        """
+        Also called from the _reconfigure() method (with reconfig=True).
+        """
+        if reconfig or self._config is None:
+            # Load configuration
+            self._config = Configuration(self._args, None).get_config()
+
+        # Init the instance of the bot
+        self.freqtrade = FreqtradeBot(self._config)
+
+        internals_config = self._config.get("internals", {})
+        self._throttle_secs = internals_config.get("process_throttle_secs", PROCESS_THROTTLE_SECS)
+        self._heartbeat_interval = internals_config.get("heartbeat_interval", 60)
+
+        self._sd_notify = (
+            sdnotify.SystemdNotifier()
+            if self._config.get("internals", {}).get("sd_notify", False)
+            else None
+        )
+
+    def _notify(self, message: str) -> None:
+        """
+        Removes the need to verify in all occurrences if sd_notify is enabled
+        :param message: Message to send to systemd if it's enabled.
+        """
+        if self._sd_notify:
+            logger.debug(f"sd_notify: {message}")
+            self._sd_notify.notify(message)
+
+    def run(self) -> None:
+        state = None
+        while True:
+            state = self._worker(old_state=state)
+            if state == State.RELOAD_CONFIG:
+                self._reconfigure()
+
+    def _worker(self, old_state: State | None) -> State:
+        """
+        The main routine that runs each throttling iteration and handles the states.
+        :param old_state: the previous service state from the previous call
+        :return: current service state
+        """
+        state = self.freqtrade.state
+
+        # Log state transition
+        if state != old_state:
+            if old_state != State.RELOAD_CONFIG:
+                self.freqtrade.notify_status(f"{state.name.lower()}")
+
+            logger.info(
+                f"Changing state{f' from {old_state.name}' if old_state else ''} to: {state.name}"
+            )
+            if state in (State.RUNNING, State.PAUSED) and old_state not in (
+                State.RUNNING,
+                State.PAUSED,
+            ):
+                self.freqtrade.startup()
+
+            if state == State.STOPPED:
+                self.freqtrade.check_for_open_trades()
+
+            # Reset heartbeat timestamp to log the heartbeat message at
+            # first throttling iteration when the state changes
+            self._heartbeat_msg = 0
+
+        if state == State.STOPPED:
+            # Ping systemd watchdog before sleeping in the stopped state
+            self._notify("WATCHDOG=1\nSTATUS=State: STOPPED.")
+
+            self._throttle(func=self._process_stopped, throttle_secs=self._throttle_secs)
+
+        elif state in (State.RUNNING, State.PAUSED):
+            state_str = "RUNNING" if state == State.RUNNING else "PAUSED"
+            # Ping systemd watchdog before throttling
+            self._notify(f"WATCHDOG=1\nSTATUS=State: {state_str}.")
+
+            # Use an offset of 1s to ensure a new candle has been issued
+            self._throttle(
+                func=self._process_running,
+                throttle_secs=self._throttle_secs,
+                timeframe=self._config["timeframe"] if self._config else None,
+                timeframe_offset=1,
+            )
+
+        if self._heartbeat_interval:
+            now = time.time()
+            if (now - self._heartbeat_msg) > self._heartbeat_interval:
+                version = __version__
+                strategy_version = self.freqtrade.strategy.version()
+                if strategy_version is not None:
+                    version += ", strategy_version: " + strategy_version
+                logger.info(
+                    f"Bot heartbeat. PID={getpid()}, version='{version}', state='{state.name}'"
+                )
+                self._heartbeat_msg = now
+
+        return state
+
+    def _throttle(
+        self,
+        func: Callable[..., Any],
+        throttle_secs: float,
+        timeframe: str | None = None,
+        timeframe_offset: float = 1.0,
+        *args,
+        **kwargs,
+    ) -> Any:
+        """
+        Throttles the given callable that it
+        takes at least `min_secs` to finish execution.
+        :param func: Any callable
+        :param throttle_secs: throttling iteration execution time limit in seconds
+        :param timeframe: ensure iteration is executed at the beginning of the next candle.
+        :param timeframe_offset: offset in seconds to apply to the next candle time.
+        :return: Any (result of execution of func)
+        """
+        last_throttle_start_time = time.time()
+        logger.debug("========================================")
+        result = func(*args, **kwargs)
+        time_passed = time.time() - last_throttle_start_time
+        sleep_duration = throttle_secs - time_passed
+        if timeframe:
+            next_tf = timeframe_to_next_date(timeframe)
+            # Maximum throttling should be until new candle arrives
+            # Offset is added to ensure a new candle has been issued.
+            next_tft = next_tf.timestamp() - time.time()
+            next_tf_with_offset = next_tft + timeframe_offset
+            if next_tft < sleep_duration and sleep_duration < next_tf_with_offset:
+                # Avoid hitting a new loop between the new candle and the candle with offset
+                sleep_duration = next_tf_with_offset
+            sleep_duration = min(sleep_duration, next_tf_with_offset)
+        sleep_duration = max(sleep_duration, 0.0)
+        # next_iter = datetime.now(timezone.utc) + timedelta(seconds=sleep_duration)
+
+        logger.debug(
+            f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
+            f"last iteration took {time_passed:.2f} s."
+            #  f"next: {next_iter}"
+        )
+        self._sleep(sleep_duration)
+        return result
+
+    @staticmethod
+    def _sleep(sleep_duration: float) -> None:
+        """Local sleep method - to improve testability"""
+        time.sleep(sleep_duration)
+
+    def _process_stopped(self) -> None:
+        self.freqtrade.process_stopped()
+
+    def _process_running(self) -> None:
+        try:
+            self.freqtrade.process()
+        except TemporaryError as error:
+            logger.warning(f"Error: {error}, retrying in {RETRY_TIMEOUT} seconds...")
+            time.sleep(RETRY_TIMEOUT)
+        except OperationalException:
+            tb = traceback.format_exc()
+            hint = "Issue `/start` if you think it is safe to restart."
+
+            self.freqtrade.notify_status(
+                f"*OperationalException:*\n```\n{tb}```\n {hint}", msg_type=RPCMessageType.EXCEPTION
+            )
+
+            logger.exception("OperationalException. Stopping trader ...")
+            self.freqtrade.state = State.STOPPED
+
+    def _reconfigure(self) -> None:
+        """
+        Cleans up current freqtradebot instance, reloads the configuration and
+        replaces it with the new instance
+        """
+        # Tell systemd that we initiated reconfiguration
+        self._notify("RELOADING=1")
+
+        # Clean up current freqtrade modules
+        self.freqtrade.cleanup()
+
+        # Load and validate config and create new instance of the bot
+        self._init(True)
+
+        self.freqtrade.notify_status(f"{State(self.freqtrade.state)} after config reloaded")
+
+        # Tell systemd that we completed reconfiguration
+        self._notify("READY=1")
+
+    def exit(self) -> None:
+        # Tell systemd that we are exiting now
+        self._notify("STOPPING=1")
+
+        if self.freqtrade:
+            self.freqtrade.notify_status("process died")
+            self.freqtrade.cleanup()
diff --git a/ft_client/LICENSE b/ft_client/LICENSE
new file mode 120000
index 000000000..ea5b60640
--- /dev/null
+++ b/ft_client/LICENSE
@@ -0,0 +1 @@
+../LICENSE
\ No newline at end of file
diff --git a/ft_client/MANIFEST.in b/ft_client/MANIFEST.in
new file mode 100644
index 000000000..cee1ed220
--- /dev/null
+++ b/ft_client/MANIFEST.in
@@ -0,0 +1,4 @@
+include LICENSE
+include README.md
+
+prune tests
diff --git a/ft_client/README.md b/ft_client/README.md
new file mode 100644
index 000000000..a1921aa24
--- /dev/null
+++ b/ft_client/README.md
@@ -0,0 +1,7 @@
+# Freqtrade Client
+
+# ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)
+
+Provides a minimal rest client for the freqtrade rest api.
+
+Please check out the [main project](https://github.com/freqtrade/freqtrade) for more information or details.
diff --git a/ft_client/freqtrade_client/__init__.py b/ft_client/freqtrade_client/__init__.py
new file mode 100644
index 000000000..8c7e9919c
--- /dev/null
+++ b/ft_client/freqtrade_client/__init__.py
@@ -0,0 +1,37 @@
+from freqtrade_client.ft_rest_client import FtRestClient
+
+
+__version__ = "2025.4-dev"
+
+if "dev" in __version__:
+    from pathlib import Path
+
+    try:
+        import subprocess  # noqa: S404, RUF100
+
+        freqtrade_basedir = Path(__file__).parent
+
+        __version__ = (
+            __version__
+            + "-"
+            + subprocess.check_output(
+                ["git", "log", '--format="%h"', "-n 1"],
+                stderr=subprocess.DEVNULL,
+                cwd=freqtrade_basedir,
+            )
+            .decode("utf-8")
+            .rstrip()
+            .strip('"')
+        )
+
+    except Exception:  # pragma: no cover
+        # git not available, ignore
+        try:
+            # Try Fallback to freqtrade_commit file (created by CI while building docker image)
+            versionfile = Path("./freqtrade_commit")
+            if versionfile.is_file():
+                __version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"
+        except Exception:  # noqa: S110
+            pass
+
+__all__ = ["FtRestClient"]
diff --git a/ft_client/freqtrade_client/ft_client.py b/ft_client/freqtrade_client/ft_client.py
new file mode 100644
index 000000000..13bcbc2b9
--- /dev/null
+++ b/ft_client/freqtrade_client/ft_client.py
@@ -0,0 +1,124 @@
+import argparse
+import inspect
+import json
+import logging
+import re
+import sys
+from pathlib import Path
+from typing import Any
+
+import rapidjson
+
+from freqtrade_client import __version__
+from freqtrade_client.ft_rest_client import FtRestClient
+
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+)
+logger = logging.getLogger("ft_rest_client")
+
+
+def add_arguments(args: Any = None):
+    parser = argparse.ArgumentParser(
+        prog="freqtrade-client",
+        description="Client for the freqtrade REST API",
+    )
+    parser.add_argument(
+        "command", help="Positional argument defining the command to execute.", nargs="?"
+    )
+    parser.add_argument("-V", "--version", action="version", version=f"%(prog)s {__version__}")
+    parser.add_argument(
+        "--show",
+        help="Show possible methods with this client",
+        dest="show",
+        action="store_true",
+        default=False,
+    )
+
+    parser.add_argument(
+        "-c",
+        "--config",
+        help="Specify configuration file (default: %(default)s). ",
+        dest="config",
+        type=str,
+        metavar="PATH",
+        default="config.json",
+    )
+
+    parser.add_argument(
+        "command_arguments",
+        help="Positional arguments for the parameters for [command]",
+        nargs="*",
+        default=[],
+    )
+
+    pargs = parser.parse_args(args)
+    return vars(pargs)
+
+
+def load_config(configfile):
+    file = Path(configfile)
+    if file.is_file():
+        with file.open("r") as f:
+            config = rapidjson.load(
+                f, parse_mode=rapidjson.PM_COMMENTS | rapidjson.PM_TRAILING_COMMAS
+            )
+        return config
+    else:
+        logger.warning(f"Could not load config file {file}.")
+        sys.exit(1)
+
+
+def print_commands():
+    # Print dynamic help for the different commands using the commands doc-strings
+    client = FtRestClient(None)
+    print("Possible commands:\n")
+    for x, _ in inspect.getmembers(client):
+        if not x.startswith("_"):
+            doc = re.sub(":return:.*", "", getattr(client, x).__doc__, flags=re.MULTILINE).rstrip()
+            print(f"{x}\n\t{doc}\n")
+
+
+def main_exec(parsed: dict[str, Any]):
+    if parsed.get("show"):
+        print_commands()
+        sys.exit()
+
+    config = load_config(parsed["config"])
+    url = config.get("api_server", {}).get("listen_ip_address", "127.0.0.1")
+    port = config.get("api_server", {}).get("listen_port", "8080")
+    username = config.get("api_server", {}).get("username")
+    password = config.get("api_server", {}).get("password")
+
+    server_url = f"http://{url}:{port}"
+    client = FtRestClient(server_url, username, password)
+
+    m = [x for x, y in inspect.getmembers(client) if not x.startswith("_")]
+    command = parsed["command"]
+    if command not in m:
+        logger.error(f"Command {command} not defined")
+        print_commands()
+        return
+
+    # Split arguments with = into key/value pairs
+    kwargs = {x.split("=")[0]: x.split("=")[1] for x in parsed["command_arguments"] if "=" in x}
+    args = [x for x in parsed["command_arguments"] if "=" not in x]
+    try:
+        res = getattr(client, command)(*args, **kwargs)
+        print(json.dumps(res))
+    except TypeError as e:
+        logger.error(f"Error executing command {command}: {e}")
+        sys.exit(1)
+    except Exception as e:
+        logger.error(f"Fatal Error executing command {command}: {e}")
+        sys.exit(1)
+
+
+def main():
+    """
+    Main entry point for the client
+    """
+    args = add_arguments()
+    main_exec(args)
diff --git a/ft_client/freqtrade_client/ft_rest_client.py b/ft_client/freqtrade_client/ft_rest_client.py
new file mode 100755
index 000000000..5e15bc185
--- /dev/null
+++ b/ft_client/freqtrade_client/ft_rest_client.py
@@ -0,0 +1,516 @@
+"""
+A Rest Client for Freqtrade bot
+
+Should not import anything from freqtrade,
+so it can be used as a standalone script, and can be installed independently.
+"""
+
+import json
+import logging
+from typing import Any
+from urllib.parse import urlencode, urlparse, urlunparse
+
+import requests
+from requests.adapters import HTTPAdapter
+from requests.exceptions import ConnectionError as RequestConnectionError
+
+
+logger = logging.getLogger("ft_rest_client")
+
+ParamsT = dict[str, Any] | None
+PostDataT = dict[str, Any] | list[dict[str, Any]] | None
+
+
+class FtRestClient:
+    def __init__(
+        self,
+        serverurl,
+        username=None,
+        password=None,
+        *,
+        pool_connections=10,
+        pool_maxsize=10,
+        timeout=10,
+    ):
+        self._serverurl = serverurl
+        self._session = requests.Session()
+        self._timeout = timeout
+
+        # allow configuration of pool
+        adapter = HTTPAdapter(pool_connections=pool_connections, pool_maxsize=pool_maxsize)
+        self._session.mount("http://", adapter)
+
+        if username and password:
+            self._session.auth = (username, password)
+
+    def _call(self, method, apipath, params: dict | None = None, data=None, files=None):
+        if str(method).upper() not in ("GET", "POST", "PUT", "DELETE"):
+            raise ValueError(f"invalid method <{method}>")
+        basepath = f"{self._serverurl}/api/v1/{apipath}"
+
+        hd = {"Accept": "application/json", "Content-Type": "application/json"}
+
+        # Split url
+        schema, netloc, path, par, query, fragment = urlparse(basepath)
+        # URLEncode query string
+        query = urlencode(params) if params else ""
+        # recombine url
+        url = urlunparse((schema, netloc, path, par, query, fragment))
+
+        try:
+            resp = self._session.request(
+                method, url, headers=hd, timeout=self._timeout, data=json.dumps(data)
+            )
+            # return resp.text
+            return resp.json()
+        except RequestConnectionError:
+            logger.warning(f"Connection error - could not connect to {netloc}.")
+
+    def _get(self, apipath, params: ParamsT = None):
+        return self._call("GET", apipath, params=params)
+
+    def _delete(self, apipath, params: ParamsT = None):
+        return self._call("DELETE", apipath, params=params)
+
+    def _post(self, apipath, params: ParamsT = None, data: PostDataT = None):
+        return self._call("POST", apipath, params=params, data=data)
+
+    def start(self):
+        """Start the bot if it's in the stopped state.
+
+        :return: json object
+        """
+        return self._post("start")
+
+    def stop(self):
+        """Stop the bot. Use `start` to restart.
+
+        :return: json object
+        """
+        return self._post("stop")
+
+    def stopbuy(self):
+        """Stop buying (but handle sells gracefully). Use `reload_config` to reset.
+
+        :return: json object
+        """
+        return self._post("stopbuy")
+
+    def reload_config(self):
+        """Reload configuration.
+
+        :return: json object
+        """
+        return self._post("reload_config")
+
+    def balance(self):
+        """Get the account balance.
+
+        :return: json object
+        """
+        return self._get("balance")
+
+    def count(self):
+        """Return the amount of open trades.
+
+        :return: json object
+        """
+        return self._get("count")
+
+    def entries(self, pair=None):
+        """Returns List of dicts containing all Trades, based on buy tag performance
+        Can either be average for all pairs or a specific pair provided
+
+        :return: json object
+        """
+        return self._get("entries", params={"pair": pair} if pair else None)
+
+    def exits(self, pair=None):
+        """Returns List of dicts containing all Trades, based on exit reason performance
+        Can either be average for all pairs or a specific pair provided
+
+        :return: json object
+        """
+        return self._get("exits", params={"pair": pair} if pair else None)
+
+    def mix_tags(self, pair=None):
+        """Returns List of dicts containing all Trades, based on entry_tag + exit_reason performance
+        Can either be average for all pairs or a specific pair provided
+
+        :return: json object
+        """
+        return self._get("mix_tags", params={"pair": pair} if pair else None)
+
+    def locks(self):
+        """Return current locks
+
+        :return: json object
+        """
+        return self._get("locks")
+
+    def delete_lock(self, lock_id):
+        """Delete (disable) lock from the database.
+
+        :param lock_id: ID for the lock to delete
+        :return: json object
+        """
+        return self._delete(f"locks/{lock_id}")
+
+    def lock_add(self, pair: str, until: str, side: str = "*", reason: str = ""):
+        """Lock pair
+
+        :param pair: Pair to lock
+        :param until: Lock until this date (format "2024-03-30 16:00:00Z")
+        :param side: Side to lock (long, short, *)
+        :param reason: Reason for the lock
+        :return: json object
+        """
+        data = [{"pair": pair, "until": until, "side": side, "reason": reason}]
+        return self._post("locks", data=data)
+
+    def daily(self, days=None):
+        """Return the profits for each day, and amount of trades.
+
+        :return: json object
+        """
+        return self._get("daily", params={"timescale": days} if days else None)
+
+    def weekly(self, weeks=None):
+        """Return the profits for each week, and amount of trades.
+
+        :return: json object
+        """
+        return self._get("weekly", params={"timescale": weeks} if weeks else None)
+
+    def monthly(self, months=None):
+        """Return the profits for each month, and amount of trades.
+
+        :return: json object
+        """
+        return self._get("monthly", params={"timescale": months} if months else None)
+
+    def edge(self):
+        """Return information about edge.
+
+        :return: json object
+        """
+        return self._get("edge")
+
+    def profit(self):
+        """Return the profit summary.
+
+        :return: json object
+        """
+        return self._get("profit")
+
+    def stats(self):
+        """Return the stats report (durations, sell-reasons).
+
+        :return: json object
+        """
+        return self._get("stats")
+
+    def performance(self):
+        """Return the performance of the different coins.
+
+        :return: json object
+        """
+        return self._get("performance")
+
+    def status(self):
+        """Get the status of open trades.
+
+        :return: json object
+        """
+        return self._get("status")
+
+    def version(self):
+        """Return the version of the bot.
+
+        :return: json object containing the version
+        """
+        return self._get("version")
+
+    def show_config(self):
+        """Returns part of the configuration, relevant for trading operations.
+        :return: json object containing the version
+        """
+        return self._get("show_config")
+
+    def ping(self):
+        """simple ping"""
+        configstatus = self.show_config()
+        if not configstatus:
+            return {"status": "not_running"}
+        elif configstatus["state"] == "running":
+            return {"status": "pong"}
+        else:
+            return {"status": "not_running"}
+
+    def logs(self, limit=None):
+        """Show latest logs.
+
+        :param limit: Limits log messages to the last <limit> logs. No limit to get the entire log.
+        :return: json object
+        """
+        return self._get("logs", params={"limit": limit} if limit else {})
+
+    def trades(self, limit=None, offset=None):
+        """Return trades history, sorted by id
+
+        :param limit: Limits trades to the X last trades. Max 500 trades.
+        :param offset: Offset by this amount of trades.
+        :return: json object
+        """
+        params = {}
+        if limit:
+            params["limit"] = limit
+        if offset:
+            params["offset"] = offset
+        return self._get("trades", params)
+
+    def list_open_trades_custom_data(self, key=None, limit=100, offset=0):
+        """List open trades custom-data of the running bot.
+
+        :param key: str, optional - Key of the custom-data
+        :param limit: limit of trades
+        :param offset: trades offset for pagination
+        :return: json object
+        """
+        params = {}
+        params["limit"] = limit
+        params["offset"] = offset
+        if key is not None:
+            params["key"] = key
+
+        return self._get("trades/open/custom-data", params=params)
+
+    def list_custom_data(self, trade_id, key=None):
+        """List custom-data of the running bot for a specific trade.
+
+        :param trade_id: ID of the trade
+        :param key: str, optional - Key of the custom-data
+        :return: JSON object
+        """
+        params = {}
+        params["trade_id"] = trade_id
+        if key is not None:
+            params["key"] = key
+
+        return self._get(f"trades/{trade_id}/custom-data", params=params)
+
+    def trade(self, trade_id):
+        """Return specific trade
+
+        :param trade_id: Specify which trade to get.
+        :return: json object
+        """
+        return self._get(f"trade/{trade_id}")
+
+    def delete_trade(self, trade_id):
+        """Delete trade from the database.
+        Tries to close open orders. Requires manual handling of this asset on the exchange.
+
+        :param trade_id: Deletes the trade with this ID from the database.
+        :return: json object
+        """
+        return self._delete(f"trades/{trade_id}")
+
+    def cancel_open_order(self, trade_id):
+        """Cancel open order for trade.
+
+        :param trade_id: Cancels open orders for this trade.
+        :return: json object
+        """
+        return self._delete(f"trades/{trade_id}/open-order")
+
+    def whitelist(self):
+        """Show the current whitelist.
+
+        :return: json object
+        """
+        return self._get("whitelist")
+
+    def blacklist(self, *args):
+        """Show the current blacklist.
+
+        :param add: List of coins to add (example: "BNB/BTC")
+        :return: json object
+        """
+        if not args:
+            return self._get("blacklist")
+        else:
+            return self._post("blacklist", data={"blacklist": args})
+
+    def forcebuy(self, pair, price=None):
+        """Buy an asset.
+
+        :param pair: Pair to buy (ETH/BTC)
+        :param price: Optional - price to buy
+        :return: json object of the trade
+        """
+        data = {"pair": pair, "price": price}
+        return self._post("forcebuy", data=data)
+
+    def forceenter(
+        self,
+        pair,
+        side,
+        price=None,
+        *,
+        order_type=None,
+        stake_amount=None,
+        leverage=None,
+        enter_tag=None,
+    ):
+        """Force entering a trade
+
+        :param pair: Pair to buy (ETH/BTC)
+        :param side: 'long' or 'short'
+        :param price: Optional - price to buy
+        :param order_type: Optional keyword argument - 'limit' or 'market'
+        :param stake_amount: Optional keyword argument - stake amount (as float)
+        :param leverage: Optional keyword argument - leverage (as float)
+        :param enter_tag: Optional keyword argument - entry tag (as string, default: 'force_enter')
+        :return: json object of the trade
+        """
+        data = {
+            "pair": pair,
+            "side": side,
+        }
+
+        if price:
+            data["price"] = price
+
+        if order_type:
+            data["ordertype"] = order_type
+
+        if stake_amount:
+            data["stakeamount"] = stake_amount
+
+        if leverage:
+            data["leverage"] = leverage
+
+        if enter_tag:
+            data["entry_tag"] = enter_tag
+
+        return self._post("forceenter", data=data)
+
+    def forceexit(self, tradeid, ordertype=None, amount=None):
+        """Force-exit a trade.
+
+        :param tradeid: Id of the trade (can be received via status command)
+        :param ordertype: Order type to use (must be market or limit)
+        :param amount: Amount to sell. Full sell if not given
+        :return: json object
+        """
+
+        return self._post(
+            "forceexit",
+            data={
+                "tradeid": tradeid,
+                "ordertype": ordertype,
+                "amount": amount,
+            },
+        )
+
+    def strategies(self):
+        """Lists available strategies
+
+        :return: json object
+        """
+        return self._get("strategies")
+
+    def strategy(self, strategy):
+        """Get strategy details
+
+        :param strategy: Strategy class name
+        :return: json object
+        """
+        return self._get(f"strategy/{strategy}")
+
+    def pairlists_available(self):
+        """Lists available pairlist providers
+
+        :return: json object
+        """
+        return self._get("pairlists/available")
+
+    def plot_config(self):
+        """Return plot configuration if the strategy defines one.
+
+        :return: json object
+        """
+        return self._get("plot_config")
+
+    def available_pairs(self, timeframe=None, stake_currency=None):
+        """Return available pair (backtest data) based on timeframe / stake_currency selection
+
+        :param timeframe: Only pairs with this timeframe available.
+        :param stake_currency: Only pairs that include this timeframe
+        :return: json object
+        """
+        return self._get(
+            "available_pairs",
+            params={
+                "stake_currency": stake_currency if timeframe else "",
+                "timeframe": timeframe if timeframe else "",
+            },
+        )
+
+    def pair_candles(self, pair, timeframe, limit=None, columns=None):
+        """Return live dataframe for <pair><timeframe>.
+
+        :param pair: Pair to get data for
+        :param timeframe: Only pairs with this timeframe available.
+        :param limit: Limit result to the last n candles.
+        :param columns: List of dataframe columns to return. Empty list will return OHLCV.
+        :return: json object
+        """
+        params = {
+            "pair": pair,
+            "timeframe": timeframe,
+        }
+        if limit:
+            params["limit"] = limit
+
+        if columns is not None:
+            params["columns"] = columns
+            return self._post("pair_candles", data=params)
+
+        return self._get("pair_candles", params=params)
+
+    def pair_history(self, pair, timeframe, strategy, timerange=None, freqaimodel=None):
+        """Return historic, analyzed dataframe
+
+        :param pair: Pair to get data for
+        :param timeframe: Only pairs with this timeframe available.
+        :param strategy: Strategy to analyze and get values for
+        :param freqaimodel: FreqAI model to use for analysis
+        :param timerange: Timerange to get data for (same format than --timerange endpoints)
+        :return: json object
+        """
+        return self._get(
+            "pair_history",
+            params={
+                "pair": pair,
+                "timeframe": timeframe,
+                "strategy": strategy,
+                "freqaimodel": freqaimodel,
+                "timerange": timerange if timerange else "",
+            },
+        )
+
+    def sysinfo(self):
+        """Provides system information (CPU, RAM usage)
+
+        :return: json object
+        """
+        return self._get("sysinfo")
+
+    def health(self):
+        """Provides a quick health check of the running bot.
+
+        :return: json object
+        """
+        return self._get("health")
diff --git a/ft_client/pyproject.toml b/ft_client/pyproject.toml
new file mode 100644
index 000000000..a29b3d08c
--- /dev/null
+++ b/ft_client/pyproject.toml
@@ -0,0 +1,53 @@
+[build-system]
+requires = ["setuptools >= 64.0.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "freqtrade-client"
+dynamic = ["version"]
+
+authors = [
+  {name = "Freqtrade Team"},
+  {name = "Freqtrade Team", email = "freqtrade@protonmail.com"},
+]
+
+description = "Freqtrade - Client scripts"
+readme = "README.md"
+requires-python = ">=3.10"
+license = {text = "GPLv3"}
+# license = "GPLv3"
+classifiers = [
+    "Environment :: Console",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Operating System :: MacOS",
+    "Operating System :: Unix",
+    "Topic :: Office/Business :: Financial :: Investment",
+]
+
+dependencies = [
+  'requests >= 2.26.0',
+  'python-rapidjson >= 1.0',
+]
+
+
+[project.urls]
+Homepage = "https://github.com/freqtrade/freqtrade"
+Documentation = "https://freqtrade.io"
+"Bug Tracker" = "https://github.com/freqtrade/freqtrade/issues"
+
+
+[project.scripts]
+freqtrade-client = "freqtrade_client.ft_client:main"
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["freqtrade_client*"]
+exclude = ["tests", "tests.*"]
+namespaces = true
+
+[tool.setuptools.dynamic]
+version = {attr = "freqtrade_client.__version__"}
diff --git a/ft_client/requirements.txt b/ft_client/requirements.txt
new file mode 100644
index 000000000..4e2983ba8
--- /dev/null
+++ b/ft_client/requirements.txt
@@ -0,0 +1,3 @@
+# Requirements for freqtrade client library
+requests==2.32.3
+python-rapidjson==1.20
diff --git a/ft_client/test_client/__init__.py b/ft_client/test_client/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/ft_client/test_client/test_rest_client.py b/ft_client/test_client/test_rest_client.py
new file mode 100644
index 000000000..c1af6ded2
--- /dev/null
+++ b/ft_client/test_client/test_rest_client.py
@@ -0,0 +1,206 @@
+import re
+from unittest.mock import ANY, MagicMock
+
+import pytest
+from requests.exceptions import ConnectionError as RequestsConnectionError
+
+from freqtrade_client import FtRestClient
+from freqtrade_client.ft_client import add_arguments, main_exec
+
+
+def log_has_re(line, logs):
+    """Check if line matches some caplog's message."""
+    return any(re.match(line, message) for message in logs.messages)
+
+
+def get_rest_client():
+    client = FtRestClient("http://localhost:8080", "freqtrader", "password")
+    client._session = MagicMock()
+    request_mock = MagicMock()
+    client._session.request = request_mock
+    return client, request_mock
+
+
+def test_FtRestClient_init():
+    client = FtRestClient("http://localhost:8080", "freqtrader", "password")
+    assert client is not None
+    assert client._serverurl == "http://localhost:8080"
+    assert client._session is not None
+    assert client._session.auth is not None
+    assert client._session.auth == ("freqtrader", "password")
+
+
+@pytest.mark.parametrize("method", ["GET", "POST", "DELETE"])
+def test_FtRestClient_call(method):
+    client, mock = get_rest_client()
+    client._call(method, "/dummytest")
+    assert mock.call_count == 1
+
+    getattr(client, f"_{method.lower()}")("/dummytest")
+    assert mock.call_count == 2
+
+
+def test_FtRestClient_call_invalid(caplog):
+    client, _ = get_rest_client()
+    with pytest.raises(ValueError):
+        client._call("PUTTY", "/dummytest")
+
+    client._session.request = MagicMock(side_effect=RequestsConnectionError())
+    client._call("GET", "/dummytest")
+
+    assert log_has_re("Connection error", caplog)
+
+
+@pytest.mark.parametrize(
+    "method,args,kwargs",
+    [
+        ("start", [], {}),
+        ("stop", [], {}),
+        ("stopbuy", [], {}),
+        ("reload_config", [], {}),
+        ("balance", [], {}),
+        ("count", [], {}),
+        ("entries", [], {}),
+        ("exits", [], {}),
+        ("mix_tags", [], {}),
+        ("locks", [], {}),
+        ("lock_add", ["XRP/USDT", "2024-01-01 20:00:00Z", "*", "rand"], {}),
+        ("delete_lock", [2], {}),
+        ("daily", [], {}),
+        ("daily", [15], {}),
+        ("weekly", [], {}),
+        ("weekly", [15], {}),
+        ("monthly", [], {}),
+        ("monthly", [12], {}),
+        ("edge", [], {}),
+        ("profit", [], {}),
+        ("stats", [], {}),
+        ("performance", [], {}),
+        ("status", [], {}),
+        ("version", [], {}),
+        ("show_config", [], {}),
+        ("ping", [], {}),
+        ("logs", [], {}),
+        ("logs", [55], {}),
+        ("trades", [], {}),
+        ("trades", [5], {}),
+        ("trades", [5, 5], {}),  # With offset
+        ("trade", [1], {}),
+        ("delete_trade", [1], {}),
+        ("cancel_open_order", [1], {}),
+        ("whitelist", [], {}),
+        ("blacklist", [], {}),
+        ("blacklist", ["XRP/USDT"], {}),
+        ("blacklist", ["XRP/USDT", "BTC/USDT"], {}),
+        ("forcebuy", ["XRP/USDT"], {}),
+        ("forcebuy", ["XRP/USDT", 1.5], {}),
+        ("forceenter", ["XRP/USDT", "short"], {}),
+        ("forceenter", ["XRP/USDT", "short", 1.5], {}),
+        ("forceenter", ["XRP/USDT", "short", 1.5], {"order_type": "market"}),
+        ("forceenter", ["XRP/USDT", "short", 1.5], {"order_type": "market", "stake_amount": 100}),
+        (
+            "forceenter",
+            ["XRP/USDT", "short", 1.5],
+            {"order_type": "market", "stake_amount": 100, "leverage": 10.0},
+        ),
+        (
+            "forceenter",
+            ["XRP/USDT", "short", 1.5],
+            {
+                "order_type": "market",
+                "stake_amount": 100,
+                "leverage": 10.0,
+                "enter_tag": "test_force_enter",
+            },
+        ),
+        ("forceexit", [1], {}),
+        ("forceexit", [1, "limit"], {}),
+        ("forceexit", [1, "limit", 100], {}),
+        ("strategies", [], {}),
+        ("strategy", ["sampleStrategy"], {}),
+        ("pairlists_available", [], {}),
+        ("plot_config", [], {}),
+        ("available_pairs", [], {}),
+        ("available_pairs", ["5m"], {}),
+        ("pair_candles", ["XRP/USDT", "5m"], {}),
+        ("pair_candles", ["XRP/USDT", "5m", 500], {}),
+        ("pair_candles", ["XRP/USDT", "5m", 500], {"columns": ["close_time,close"]}),
+        ("pair_history", ["XRP/USDT", "5m", "SampleStrategy"], {}),
+        ("pair_history", ["XRP/USDT", "5m"], {"strategy": "SampleStrategy"}),
+        ("sysinfo", [], {}),
+        ("health", [], {}),
+    ],
+)
+def test_FtRestClient_call_explicit_methods(method, args, kwargs):
+    client, mock = get_rest_client()
+    executor = getattr(client, method)
+    executor(*args, **kwargs)
+    assert mock.call_count == 1
+
+
+def test_ft_client(mocker, capsys, caplog):
+    with pytest.raises(SystemExit):
+        args = add_arguments(["-V"])
+
+    args = add_arguments(["--show"])
+    assert isinstance(args, dict)
+    assert args["show"] is True
+    with pytest.raises(SystemExit):
+        main_exec(args)
+    captured = capsys.readouterr()
+    assert "Possible commands" in captured.out
+
+    mock = mocker.patch("freqtrade_client.ft_client.FtRestClient._call")
+    args = add_arguments(["--config", "tests/testdata/testconfigs/main_test_config.json", "ping"])
+    main_exec(args)
+    captured = capsys.readouterr()
+    assert mock.call_count == 1
+
+    with pytest.raises(SystemExit):
+        args = add_arguments(["--config", "tests/testdata/testconfigs/nonexisting.json"])
+        main_exec(args)
+
+    assert log_has_re(r"Could not load config file .*nonexisting\.json\.", caplog)
+
+    args = add_arguments(
+        ["--config", "tests/testdata/testconfigs/main_test_config.json", "whatever"]
+    )
+    main_exec(args)
+    assert log_has_re("Command whatever not defined", caplog)
+
+
+@pytest.mark.parametrize(
+    "params, expected_args, expected_kwargs",
+    [
+        ("forceenter BTC/USDT long", ["BTC/USDT", "long"], {}),
+        ("forceenter BTC/USDT long limit", ["BTC/USDT", "long", "limit"], {}),
+        (
+            # Skip most parameters, only providing enter_tag
+            "forceenter BTC/USDT long enter_tag=deadBeef",
+            ["BTC/USDT", "long"],
+            {"enter_tag": "deadBeef"},
+        ),
+        (
+            "forceenter BTC/USDT long invalid_key=123",
+            [],
+            SystemExit,
+            # {"invalid_key": "deadBeef"},
+        ),
+    ],
+)
+def test_ft_client_argparsing(mocker, params, expected_args, expected_kwargs, caplog):
+    mocked_method = params.split(" ")[0]
+    mocker.patch("freqtrade_client.ft_client.load_config", return_value={}, autospec=True)
+    mm = mocker.patch(
+        f"freqtrade_client.ft_client.FtRestClient.{mocked_method}", return_value={}, autospec=True
+    )
+    args = add_arguments(params.split(" "))
+    if isinstance(expected_kwargs, dict):
+        main_exec(args)
+        mm.assert_called_once_with(ANY, *expected_args, **expected_kwargs)
+    else:
+        with pytest.raises(expected_kwargs):
+            main_exec(args)
+
+        assert log_has_re(f"Error executing command {mocked_method}: got an unexpected .*", caplog)
+        mm.assert_not_called()
diff --git a/install_ta-lib.sh b/install_ta-lib.sh
deleted file mode 100755
index 15ab985a3..000000000
--- a/install_ta-lib.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-if [ ! -f "ta-lib/CHANGELOG.TXT" ]; then
-  curl -O -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
-  tar zxvf ta-lib-0.4.0-src.tar.gz
-  cd ta-lib && ./configure && make && sudo make install && cd ..
-else
-  echo "TA-lib already installed, skipping download and build."
-  cd ta-lib && sudo make install && cd ..
-fi
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 000000000..550d3e7d5
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,128 @@
+site_name: Freqtrade
+site_url: !ENV [READTHEDOCS_CANONICAL_URL, 'https://www.freqtrade.io/en/']
+site_description: Freqtrade is a free and open source crypto trading bot written in Python, designed to support all major exchanges and be controlled via Telegram or builtin Web UI
+repo_url: https://github.com/freqtrade/freqtrade
+edit_uri: edit/develop/docs/
+use_directory_urls: True
+nav:
+    - Home: index.md
+    - Quickstart with Docker: docker_quickstart.md
+    - Installation:
+        - Linux/MacOS/Raspberry: installation.md
+        - Windows: windows_installation.md
+    - Freqtrade Basics: bot-basics.md
+    - Configuration: configuration.md
+    - Strategy Quickstart: strategy-101.md
+    - Strategy Customization: strategy-customization.md
+    - Strategy Callbacks: strategy-callbacks.md
+    - Stoploss: stoploss.md
+    - Plugins: plugins.md
+    - Start the bot: bot-usage.md
+    - Control the bot:
+        - Telegram: telegram-usage.md
+        - freqUI: freq-ui.md
+        - REST API: rest-api.md
+        - Web Hook: webhook-config.md
+    - Data Downloading: data-download.md
+    - Backtesting: backtesting.md
+    - Hyperopt: hyperopt.md
+    - FreqAI:
+        - Introduction: freqai.md
+        - Configuration: freqai-configuration.md
+        - Parameter table: freqai-parameter-table.md
+        - Feature engineering: freqai-feature-engineering.md
+        - Running FreqAI: freqai-running.md
+        - Reinforcement Learning: freqai-reinforcement-learning.md
+        - Developer guide: freqai-developers.md
+    - Short / Leverage: leverage.md
+    - Utility Sub-commands: utils.md
+    - Plotting: plotting.md
+    - Exchange-specific Notes: exchanges.md
+    - Data Analysis:
+        - Jupyter Notebooks: data-analysis.md
+        - Strategy analysis: strategy_analysis_example.md
+        - Backtest analysis: advanced-backtesting.md
+    - Advanced Topics:
+        - Advanced Post-installation Tasks: advanced-setup.md
+        - Trade Object: trade-object.md
+        - Lookahead analysis: lookahead-analysis.md
+        - Recursive analysis: recursive-analysis.md
+        - Advanced Strategy: strategy-advanced.md
+        - Advanced Hyperopt: advanced-hyperopt.md
+        - Orderflow: advanced-orderflow.md
+        - Producer/Consumer mode: producer-consumer.md
+        - SQL Cheat-sheet: sql_cheatsheet.md
+        - Edge Positioning: edge.md
+    - FAQ: faq.md
+    - Strategy migration: strategy_migration.md
+    - Updating Freqtrade: updating.md
+    - Deprecated Features: deprecated.md
+    - Contributors Guide: developer.md
+theme:
+  name: material
+  logo: "images/logo.png"
+  favicon: "images/logo.png"
+  custom_dir: "docs/overrides"
+  features:
+    - content.code.annotate
+    - search.share
+    - content.code.copy
+    - navigation.top
+    - navigation.footer
+  palette:
+    - scheme: default
+      primary: "blue grey"
+      accent: "tear"
+      toggle:
+        icon: material/toggle-switch-off-outline
+        name: Switch to dark mode
+    - scheme: slate
+      primary: "blue grey"
+      accent: "tear"
+      toggle:
+        icon: material/toggle-switch
+        name: Switch to light mode
+extra_css:
+  - "stylesheets/ft.extra.css"
+extra_javascript:
+  - javascripts/config.js
+  - https://polyfill.io/v3/polyfill.min.js?features=es6
+  - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
+markdown_extensions:
+  - attr_list
+  - admonition
+  - footnotes
+  - codehilite:
+      guess_lang: false
+  - toc:
+      permalink: true
+  - pymdownx.arithmatex:
+      generic: true
+  - pymdownx.details
+  - pymdownx.inlinehilite
+  - pymdownx.magiclink
+  - pymdownx.pathconverter
+  - pymdownx.smartsymbols
+  - pymdownx.snippets:
+      base_path: docs
+      check_paths: true
+  - pymdownx.superfences
+  - pymdownx.tabbed:
+      alternate_style: true
+  - pymdownx.tasklist:
+      custom_checkbox: true
+  - pymdownx.tilde
+  - mdx_truly_sane_lists
+extra:
+  version:
+    provider: mike
+    alias: true
+  analytics:
+    provider: google
+    property: G-VH170LG9M5
+plugins:
+  - search:
+      enabled: true
+  - mike:
+      deploy_prefix: 'en'
+      canonical_version: 'stable'
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000..98d2a6cbd
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,336 @@
+[build-system]
+requires = ["setuptools >= 64.0.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "freqtrade"
+dynamic = ["version"]
+
+authors = [
+  {name = "Freqtrade Team"},
+  {name = "Freqtrade Team", email = "freqtrade@protonmail.com"},
+]
+
+description = "Freqtrade - Crypto Trading Bot"
+readme = "README.md"
+requires-python = ">=3.10"
+license = {text = "GPLv3"}
+classifiers = [
+    "Environment :: Console",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Operating System :: MacOS",
+    "Operating System :: Unix",
+    "Topic :: Office/Business :: Financial :: Investment",
+]
+
+dependencies = [
+  # from requirements.txt
+  "ccxt>=4.4.60",
+  "SQLAlchemy>=2.0.6",
+  "python-telegram-bot>=20.1",
+  "humanize>=4.0.0",
+  "cachetools",
+  "requests",
+  "httpx>=0.24.1",
+  "urllib3",
+  "jsonschema",
+  "numpy<2.0",
+  "pandas>=2.2.0,<3.0",
+  "TA-Lib",
+  "pandas-ta",
+  "technical",
+  "tabulate",
+  "pycoingecko>=3.2.0",
+  "py_find_1st",
+  "python-rapidjson",
+  "orjson",
+  "jinja2",
+  "questionary",
+  "prompt-toolkit",
+  "joblib>=1.2.0",
+  "rich",
+  'pyarrow; platform_machine != "armv7l"',
+  "fastapi",
+  "pydantic>=2.2.0",
+  "pyjwt",
+  "websockets",
+  "uvicorn",
+  "psutil",
+  "schedule",
+  "janus",
+  "ast-comments",
+  "aiofiles",
+  "aiohttp",
+  "cryptography",
+  "sdnotify",
+  "python-dateutil",
+  "pytz",
+  "packaging",
+  "freqtrade-client",
+]
+
+[project.optional-dependencies]
+# Requirements used for submodules
+plot = ["plotly>=4.0"]
+hyperopt = [
+  "scipy",
+  "scikit-learn",
+  "ft-scikit-optimize>=0.9.2",
+  "filelock",
+]
+freqai = [
+  "scikit-learn",
+  "joblib",
+  'catboost; platform_machine != "aarch64"',
+  "lightgbm",
+  "xgboost",
+  "tensorboard",
+  "datasieve>=0.1.5",
+]
+freqai_rl = [
+  "torch",
+  "gymnasium",
+  "stable-baselines3",
+  "sb3-contrib",
+  "tqdm",
+]
+develop = [
+  "coveralls",
+  "isort",
+  "mypy",
+  "pre-commit",
+  "pytest-asyncio",
+  "pytest-cov",
+  "pytest-mock",
+  "pytest-random-order",
+  "pytest-timeout",
+  "pytest-xdist",
+  "pytest",
+  "ruff",
+  "time-machine",
+  "types-cachetools",
+  "types-filelock",
+  "types-python-dateutil",
+  "types-requests",
+  "types-tabulate",
+]
+jupyter = [
+  "jupyter",
+  "nbstripout",
+  "ipykernel",
+  "nbconvert",
+]
+all = [
+  "freqtrade[plot,hyperopt,freqai,freqai_rl,jupyter]",
+]
+dev = [
+  "freqtrade[all,develop]",
+]
+
+[project.urls]
+Homepage = "https://github.com/freqtrade/freqtrade"
+Documentation = "https://freqtrade.io"
+"Bug Tracker" = "https://github.com/freqtrade/freqtrade/issues"
+
+
+[project.scripts]
+freqtrade = "freqtrade.main:main"
+
+[tool.setuptools]
+include-package-data = true
+zip-safe = false
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["freqtrade*"]
+exclude = ["tests", "tests.*", "user_data", "user_data*"]
+namespaces = true
+
+[tool.setuptools.dynamic]
+version = {attr = "freqtrade.__version__"}
+
+[tool.black]
+line-length = 100
+exclude = '''
+(
+  /(
+      \.eggs         # exclude a few common directories in the
+    | \.git          # root of the project
+    | \.hg
+    | \.mypy_cache
+    | \.tox
+    | \.venv
+    | _build
+    | buck-out
+    | build
+    | dist
+  )/
+  # Exclude vendor directory
+  | vendor
+)
+'''
+
+[tool.isort]
+line_length = 100
+profile = "black"
+# multi_line_output=3
+lines_after_imports=2
+skip_glob = ["**/.env*", "**/env/*", "**/.venv/*", "**/docs/*", "**/user_data/*"]
+known_first_party = ["freqtrade_client"]
+
+[tool.pytest.ini_options]
+log_format = "%(asctime)s %(levelname)s %(message)s"
+log_date_format = "%Y-%m-%d %H:%M:%S"
+
+asyncio_mode = "auto"
+asyncio_default_fixture_loop_scope = "function"
+addopts = "--dist loadscope"
+
+[tool.mypy]
+ignore_missing_imports = true
+namespace_packages = false
+warn_unused_ignores = true
+exclude = [
+    '^build_helpers\.py$',
+    '^ft_client/build/.*$',
+]
+plugins = [
+  "sqlalchemy.ext.mypy.plugin"
+]
+
+[[tool.mypy.overrides]]
+module = "tests.*"
+ignore_errors = true
+
+[tool.pyright]
+include = ["freqtrade", "ft_client"]
+exclude = [
+  "**/__pycache__",
+  "build_helpers/*.py",
+  "ft_client/build/*",
+  "build/*",
+]
+ignore = ["freqtrade/vendor/**"]
+pythonPlatform = "All"
+pythonVersion = "3.9"
+
+typeCheckingMode = "off"
+# analyzeUnannotatedFunctions = false
+
+reportArgumentType = false  # 155
+reportAssignmentType = false  # 12
+reportAttributeAccessIssue = false  # 255
+reportCallIssue = false  # 23
+reportGeneralTypeIssues = false  # 48
+reportIncompatibleMethodOverride = false  # 15
+reportIncompatibleVariableOverride = false  # 5
+reportIndexIssue = false  # 22
+reportMissingImports = false  # 5
+reportOperatorIssue = false  # 7
+reportOptionalMemberAccess = false  # 35
+reportOptionalOperand = false  # 7
+reportPossiblyUnboundVariable = false  # 36
+reportPrivateImportUsage = false  # 5
+reportRedeclaration = false  # 1
+reportReturnType = false  # 28
+reportTypedDictNotRequiredAccess = false  # 27
+
+
+[tool.ruff]
+line-length = 100
+extend-exclude = [".env", ".venv"]
+
+[tool.ruff.lint]
+extend-select = [
+  "C90",    # mccabe
+  "B",      # bugbear
+  # "N",    # pep8-naming
+  "F",      # pyflakes
+  "E",      # pycodestyle
+  "W",      # pycodestyle
+  "UP",     # pyupgrade
+  "I",      # isort
+  "A",      # flake8-builtins
+  "TID",    # flake8-tidy-imports
+  # "EXE",  # flake8-executable
+  # "C4",     # flake8-comprehensions
+  "YTT",    # flake8-2020
+  "S",      # flake8-bandit
+  # "DTZ",  # flake8-datetimez
+  # "RSE",  # flake8-raise
+  # "TCH",  # flake8-type-checking
+  "PTH",    # flake8-use-pathlib
+  "RUF",    # ruff
+  "ASYNC",  # flake8-async
+  "NPY",    # numpy
+]
+
+extend-ignore = [
+  "E241",  # Multiple spaces after comma
+  "E272",  # Multiple spaces before keyword
+  "E221",  # Multiple spaces before operator
+  "B007",  # Loop control variable not used
+  "B904",  # BugBear - except raise from
+  "S603",  # `subprocess` call: check for execution of untrusted input
+  "S607",  # Starting a process with a partial executable path
+  "S608",  # Possible SQL injection vector through string-based query construction
+  "NPY002",  # Numpy legacy random generator
+  "RUF010",  # Use explicit conversion flag
+  "RUF012",  # mutable-class-default
+  "RUF022",  # unsorted-dunder-all
+]
+
+[tool.ruff.lint.mccabe]
+max-complexity = 12
+
+[tool.ruff.lint.per-file-ignores]
+"freqtrade/freqai/**/*.py" = [
+  "S311",  # Standard pseudo-random generators are not suitable for cryptographic purposes
+]
+"tests/**.py" = [
+  "S101",  # allow assert in tests
+  "S104",  #  Possible binding to all interfaces
+  "S311",  # Standard pseudo-random generators are not suitable for cryptographic purposes
+  "S105",  # Possible hardcoded password assigned to: "secret"
+  "S106",  # Possible hardcoded password assigned to argument: "token_type"
+  "S110",  # `try`-`except`-`pass` detected, consider logging the exception
+  ]
+
+"freqtrade/templates/**.py" = [
+  "RUF100",  # Allow unused noqa
+]
+
+"ft_client/test_client/**.py" = [
+  "S101",  # allow assert in tests
+]
+
+[tool.ruff.lint.flake8-bugbear]
+# Allow default arguments like, e.g., `data: List[str] = fastapi.Query(None)`.
+extend-immutable-calls = ["fastapi.Depends", "fastapi.Query"]
+
+[tool.ruff.lint.isort]
+lines-after-imports = 2
+known-first-party = ["freqtrade_client"]
+
+[tool.flake8]
+# Default from https://flake8.pycqa.org/en/latest/user/options.html#cmdoption-flake8-ignore
+# minus E226
+ignore = ["E121","E123","E126","E24", "E203","E704","W503","W504"]
+max-line-length = 100
+max-complexity = 12
+exclude = [
+    ".git",
+    "__pycache__",
+    ".eggs",
+    "user_data",
+    ".venv",
+    ".env",
+]
+
+[tool.codespell]
+ignore-words-list = "coo,fo,strat,zar,selectin"
+skip="*.svg,./user_data,freqtrade/rpc/api_server/ui/installed,freqtrade/exchange/*.json"
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100644
index 000000000..ea513914b
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,32 @@
+# Include all requirements to run the bot.
+-r requirements.txt
+-r requirements-plot.txt
+-r requirements-hyperopt.txt
+-r requirements-freqai.txt
+-r requirements-freqai-rl.txt
+-r docs/requirements-docs.txt
+
+coveralls==4.0.1
+ruff==0.11.4
+mypy==1.15.0
+pre-commit==4.2.0
+pytest==8.3.5
+pytest-asyncio==0.26.0
+pytest-cov==6.1.1
+pytest-mock==3.14.0
+pytest-random-order==1.1.1
+pytest-timeout==2.3.1
+pytest-xdist==3.6.1
+isort==6.0.1
+# For datetime mocking
+time-machine==2.16.0
+
+# Convert jupyter notebooks to markdown documents
+nbconvert==7.16.6
+
+# mypy types
+types-cachetools==5.5.0.20240820
+types-filelock==3.2.7
+types-requests==2.32.0.20250328
+types-tabulate==0.9.0.20241207
+types-python-dateutil==2.9.0.20241206
diff --git a/requirements-freqai-rl.txt b/requirements-freqai-rl.txt
new file mode 100644
index 000000000..b170da518
--- /dev/null
+++ b/requirements-freqai-rl.txt
@@ -0,0 +1,12 @@
+# Include all requirements to run the bot.
+-r requirements-freqai.txt
+
+# Required for freqai-rl
+torch==2.6.0; sys_platform != 'darwin' or platform_machine != 'x86_64'
+gymnasium==0.29.1
+# SB3 >=2.5.0 depends on torch 2.3.0 - which implies it dropped support x86 macos
+stable_baselines3==2.4.1; sys_platform == 'darwin' and platform_machine == 'x86_64'
+stable_baselines3==2.5.0; sys_platform != 'darwin' or platform_machine != 'x86_64'
+sb3_contrib>=2.2.1
+# Progress bar for stable-baselines3 and sb3-contrib
+tqdm==4.67.1
diff --git a/requirements-freqai.txt b/requirements-freqai.txt
new file mode 100644
index 000000000..57c012a8b
--- /dev/null
+++ b/requirements-freqai.txt
@@ -0,0 +1,12 @@
+# Include all requirements to run the bot.
+-r requirements.txt
+-r requirements-plot.txt
+
+# Required for freqai
+scikit-learn==1.6.1
+joblib==1.4.2
+catboost==1.2.7; 'arm' not in platform_machine
+lightgbm==4.6.0
+xgboost==2.1.4
+tensorboard==2.19.0
+datasieve==0.1.7
diff --git a/requirements-hyperopt.txt b/requirements-hyperopt.txt
new file mode 100644
index 000000000..2a326d654
--- /dev/null
+++ b/requirements-hyperopt.txt
@@ -0,0 +1,8 @@
+# Include all requirements to run the bot.
+-r requirements.txt
+
+# Required for hyperopt
+scipy==1.15.2
+scikit-learn==1.6.1
+ft-scikit-optimize==0.9.2
+filelock==3.18.0
diff --git a/requirements-plot.txt b/requirements-plot.txt
new file mode 100644
index 000000000..f3d27df41
--- /dev/null
+++ b/requirements-plot.txt
@@ -0,0 +1,4 @@
+# Include all requirements to run the bot.
+-r requirements.txt
+
+plotly==6.0.1
diff --git a/requirements.txt b/requirements.txt
index 29361ecdb..56b1a6bde 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,25 +1,62 @@
-python-bittrex==0.3.0
-SQLAlchemy==1.2.5
-python-telegram-bot==10.0.1
-arrow==0.12.1
-cachetools==2.0.1
-requests==2.18.4
-urllib3==1.22
-wrapt==1.10.11
-pandas==0.22.0
-scikit-learn==0.19.1
-scipy==1.0.0
-jsonschema==2.6.0
-numpy==1.14.2
-TA-Lib==0.4.17
-pytest==3.4.2
-pytest-mock==1.7.1
-pytest-cov==2.5.1
-hyperopt==0.1
-# do not upgrade networkx before this is fixed https://github.com/hyperopt/hyperopt/issues/325
-networkx==1.11
-tabulate==0.8.2
-coinmarketcap==4.2.1
+numpy==1.26.4
+pandas==2.2.3
+bottleneck==1.4.2
+numexpr==2.10.2
+pandas-ta==0.3.14b
 
-# Required for plotting data
-#plotly==2.3.0
+ccxt==4.4.73
+cryptography==44.0.2
+aiohttp==3.9.5
+SQLAlchemy==2.0.40
+python-telegram-bot==22.0
+# can't be hard-pinned due to telegram-bot pinning httpx with ~
+httpx>=0.24.1
+humanize==4.12.2
+cachetools==5.5.2
+requests==2.32.3
+urllib3==2.3.0
+jsonschema==4.23.0
+TA-Lib==0.4.38
+technical==1.5.0
+tabulate==0.9.0
+pycoingecko==3.2.0
+jinja2==3.1.6
+joblib==1.4.2
+rich==14.0.0
+pyarrow==19.0.1; platform_machine != 'armv7l'
+
+# find first, C search in arrays
+py_find_1st==1.1.7
+
+# Load ticker files 30% faster
+python-rapidjson==1.20
+# Properly format api responses
+orjson==3.10.16
+
+# Notify systemd
+sdnotify==0.3.2
+
+# API Server
+fastapi==0.115.12
+pydantic==2.11.2
+uvicorn==0.34.0
+pyjwt==2.10.1
+aiofiles==24.1.0
+psutil==7.0.0
+
+# Building config files interactively
+questionary==2.1.0
+prompt-toolkit==3.0.50
+# Extensions to datetime library
+python-dateutil==2.9.0.post0
+pytz==2025.2
+
+#Futures
+schedule==1.2.2
+
+#WS Messages
+websockets==15.0.1
+janus==2.0.0
+
+ast-comments==1.2.2
+packaging==24.2
diff --git a/scripts/plot_dataframe.py b/scripts/plot_dataframe.py
deleted file mode 100755
index e62465bf7..000000000
--- a/scripts/plot_dataframe.py
+++ /dev/null
@@ -1,143 +0,0 @@
-#!/usr/bin/env python3
-
-import sys
-import logging
-
-from plotly import tools
-from plotly.offline import plot
-import plotly.graph_objs as go
-
-from freqtrade import exchange, analyze
-from freqtrade.strategy.strategy import Strategy
-import freqtrade.misc as misc
-import freqtrade.optimize as optimize
-
-
-logger = logging.getLogger(__name__)
-
-
-def plot_parse_args(args):
-    parser = misc.common_args_parser('Graph dataframe')
-    misc.backtesting_options(parser)
-    misc.scripts_options(parser)
-    return parser.parse_args(args)
-
-
-def plot_analyzed_dataframe(args) -> None:
-    """
-    Calls analyze() and plots the returned dataframe
-    :param pair: pair as str
-    :return: None
-    """
-    pair = args.pair.replace('-', '_')
-    timerange = misc.parse_timerange(args.timerange)
-
-    # Init strategy
-    strategy = Strategy()
-    strategy.init({'strategy': args.strategy})
-    tick_interval = strategy.ticker_interval
-
-    tickers = {}
-    if args.live:
-        logger.info('Downloading pair.')
-        # Init Bittrex to use public API
-        exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
-        tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
-    else:
-        tickers = optimize.load_data(args.datadir, pairs=[pair],
-                                     ticker_interval=tick_interval,
-                                     refresh_pairs=False,
-                                     timerange=timerange)
-    dataframes = optimize.tickerdata_to_dataframe(tickers)
-    dataframe = dataframes[pair]
-    dataframe = analyze.populate_buy_trend(dataframe)
-    dataframe = analyze.populate_sell_trend(dataframe)
-
-    if (len(dataframe.index) > 750):
-        logger.warn('Ticker contained more than 750 candles, clipping.')
-    df = dataframe.tail(750)
-
-    candles = go.Candlestick(x=df.date,
-                             open=df.open,
-                             high=df.high,
-                             low=df.low,
-                             close=df.close,
-                             name='Price')
-
-    df_buy = df[df['buy'] == 1]
-    buys = go.Scattergl(
-        x=df_buy.date,
-        y=df_buy.close,
-        mode='markers',
-        name='buy',
-        marker=dict(
-            symbol='triangle-up-dot',
-            size=9,
-            line=dict(
-                width=1,
-            ),
-            color='green',
-        )
-    )
-    df_sell = df[df['sell'] == 1]
-    sells = go.Scattergl(
-        x=df_sell.date,
-        y=df_sell.close,
-        mode='markers',
-        name='sell',
-        marker=dict(
-            symbol='triangle-down-dot',
-            size=9,
-            line=dict(
-                width=1,
-            ),
-            color='red',
-        )
-    )
-
-    bb_lower = go.Scatter(
-        x=df.date,
-        y=df.bb_lowerband,
-        name='BB lower',
-        line={'color': "transparent"},
-    )
-    bb_upper = go.Scatter(
-        x=df.date,
-        y=df.bb_upperband,
-        name='BB upper',
-        fill="tonexty",
-        fillcolor="rgba(0,176,246,0.2)",
-        line={'color': "transparent"},
-    )
-    macd = go.Scattergl(x=df['date'], y=df['macd'], name='MACD')
-    macdsignal = go.Scattergl(x=df['date'], y=df['macdsignal'], name='MACD signal')
-    volume = go.Bar(x=df['date'], y=df['volume'], name='Volume')
-
-    fig = tools.make_subplots(
-        rows=3,
-        cols=1,
-        shared_xaxes=True,
-        row_width=[1, 1, 4],
-        vertical_spacing=0.0001,
-    )
-
-    fig.append_trace(candles, 1, 1)
-    fig.append_trace(bb_lower, 1, 1)
-    fig.append_trace(bb_upper, 1, 1)
-    fig.append_trace(buys, 1, 1)
-    fig.append_trace(sells, 1, 1)
-    fig.append_trace(volume, 2, 1)
-    fig.append_trace(macd, 3, 1)
-    fig.append_trace(macdsignal, 3, 1)
-
-    fig['layout'].update(title=args.pair)
-    fig['layout']['yaxis1'].update(title='Price')
-    fig['layout']['yaxis2'].update(title='Volume')
-    fig['layout']['yaxis3'].update(title='MACD')
-
-    plot(fig, filename='freqtrade-plot.html')
-
-
-if __name__ == '__main__':
-    args = plot_parse_args(sys.argv[1:])
-    plot_analyzed_dataframe(args)
diff --git a/scripts/plot_profit.py b/scripts/plot_profit.py
deleted file mode 100755
index c51b29309..000000000
--- a/scripts/plot_profit.py
+++ /dev/null
@@ -1,156 +0,0 @@
-#!/usr/bin/env python3
-
-import sys
-import json
-import numpy as np
-
-from plotly import tools
-from plotly.offline import plot
-import plotly.graph_objs as go
-
-import freqtrade.optimize as optimize
-import freqtrade.misc as misc
-from freqtrade.strategy.strategy import Strategy
-
-
-def plot_parse_args(args):
-    parser = misc.common_args_parser('Graph profits')
-    # FIX: perhaps delete those backtesting options that are not feasible (shows up in -h)
-    misc.backtesting_options(parser)
-    misc.scripts_options(parser)
-    return parser.parse_args(args)
-
-
-# data:: [ pair,      profit-%,  enter,         exit,        time, duration]
-# data:: ['BTC_XMR', 0.00537847, '1511176800', '1511178000', 5057, 1]
-# FIX: make use of the enter/exit dates to insert the
-# profit more precisely into the pg array
-def make_profit_array(data, px, filter_pairs=[]):
-    pg = np.zeros(px)
-    # Go through the trades
-    # and make an total profit
-    # array
-    for trade in data:
-        pair = trade[0]
-        if filter_pairs and pair not in filter_pairs:
-            continue
-        profit = trade[1]
-        tim = trade[4]
-        dur = trade[5]
-        ix = tim + dur - 1
-        if ix < px:
-            pg[ix] += profit
-
-    # rewrite the pg array to go from
-    # total profits at each timeframe
-    # to accumulated profits
-    pa = 0
-    for x in range(0, len(pg)):
-        p = pg[x]  # Get current total percent
-        pa += p  # Add to the accumulated percent
-        pg[x] = pa  # write back to save memory
-
-    return pg
-
-
-def plot_profit(args) -> None:
-    """
-    Plots the total profit for all pairs.
-    Note, the profit calculation isn't realistic.
-    But should be somewhat proportional, and therefor useful
-    in helping out to find a good algorithm.
-    """
-
-    # We need to use the same pairs, same tick_interval
-    # and same timeperiod as used in backtesting
-    # to match the tickerdata against the profits-results
-
-    filter_pairs = args.pair
-
-    config = misc.load_config(args.config)
-    config.update({'strategy': args.strategy})
-
-    # Init strategy
-    strategy = Strategy()
-    strategy.init(config)
-
-    pairs = config['exchange']['pair_whitelist']
-
-    if filter_pairs:
-        filter_pairs = filter_pairs.split(',')
-        pairs = list(set(pairs) & set(filter_pairs))
-        print('Filter, keep pairs %s' % pairs)
-
-    timerange = misc.parse_timerange(args.timerange)
-    tickers = optimize.load_data(args.datadir, pairs=pairs,
-                                 ticker_interval=strategy.ticker_interval,
-                                 refresh_pairs=False,
-                                 timerange=timerange)
-    dataframes = optimize.preprocess(tickers)
-
-    # NOTE: the dataframes are of unequal length,
-    # 'dates' is an merged date array of them all.
-
-    dates = misc.common_datearray(dataframes)
-    max_x = dates.size
-
-    # Make an average close price of all the pairs that was involved.
-    # this could be useful to gauge the overall market trend
-    # We are essentially saying:
-    #  array <- sum dataframes[*]['close'] / num_items dataframes
-    #  FIX: there should be some onliner numpy/panda for this
-    avgclose = np.zeros(max_x)
-    num = 0
-    for pair, pair_data in dataframes.items():
-        close = pair_data['close']
-        maxprice = max(close)  # Normalize price to [0,1]
-        print('Pair %s has length %s' % (pair, len(close)))
-        for x in range(0, len(close)):
-            avgclose[x] += close[x] / maxprice
-        # avgclose += close
-        num += 1
-    avgclose /= num
-
-    # Load the profits results
-    # And make an profits-growth array
-
-    filename = 'backtest-result.json'
-    with open(filename) as file:
-        data = json.load(file)
-    pg = make_profit_array(data, max_x, filter_pairs)
-
-    #
-    # Plot the pairs average close prices, and total profit growth
-    #
-
-    avgclose = go.Scattergl(
-        x=dates,
-        y=avgclose,
-        name='Avg close price',
-    )
-    profit = go.Scattergl(
-        x=dates,
-        y=pg,
-        name='Profit',
-    )
-
-    fig = tools.make_subplots(rows=3, cols=1, shared_xaxes=True, row_width=[1, 1, 1])
-
-    fig.append_trace(avgclose, 1, 1)
-    fig.append_trace(profit, 2, 1)
-
-    for pair in pairs:
-        pg = make_profit_array(data, max_x, pair)
-        pair_profit = go.Scattergl(
-            x=dates,
-            y=pg,
-            name=pair,
-        )
-        fig.append_trace(pair_profit, 3, 1)
-
-    plot(fig, filename='freqtrade-profit-plot.html')
-
-
-if __name__ == '__main__':
-    args = plot_parse_args(sys.argv[1:])
-    plot_profit(args)
diff --git a/scripts/rest_client.py b/scripts/rest_client.py
new file mode 100755
index 000000000..88862b044
--- /dev/null
+++ b/scripts/rest_client.py
@@ -0,0 +1,14 @@
+#!/usr/bin/env python3
+"""
+Simple command line client into RPC commands
+Can be used as an alternate to Telegram
+
+Should not import anything from freqtrade,
+so it can be used as a standalone script.
+"""
+
+from freqtrade_client.ft_client import main
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/start-hyperopt-worker.py b/scripts/start-hyperopt-worker.py
deleted file mode 100755
index 8b0ae6326..000000000
--- a/scripts/start-hyperopt-worker.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/usr/bin/env python3
-import multiprocessing
-import os
-import subprocess
-
-PROC_COUNT = multiprocessing.cpu_count() - 1
-DB_NAME = 'freqtrade_hyperopt'
-WORK_DIR = os.path.join(
-    os.path.sep,
-    os.path.abspath(os.path.dirname(__file__)),
-    '..', '.hyperopt', 'worker'
-)
-if not os.path.exists(WORK_DIR):
-    os.makedirs(WORK_DIR)
-
-# Spawn workers
-command = [
-    'hyperopt-mongo-worker',
-    '--mongo=127.0.0.1:1234/{}'.format(DB_NAME),
-    '--poll-interval=0.1',
-    '--workdir={}'.format(WORK_DIR),
-]
-processes = [subprocess.Popen(command) for i in range(PROC_COUNT)]
-
-# Join all workers
-for proc in processes:
-    proc.wait()
diff --git a/scripts/start-mongodb.py b/scripts/start-mongodb.py
deleted file mode 100755
index 910ee9233..000000000
--- a/scripts/start-mongodb.py
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import subprocess
-
-
-DB_PATH = os.path.join(
-    os.path.sep,
-    os.path.abspath(os.path.dirname(__file__)),
-    '..', '.hyperopt', 'mongodb'
-)
-if not os.path.exists(DB_PATH):
-    os.makedirs(DB_PATH)
-
-subprocess.Popen([
-    'mongod',
-    '--bind_ip=127.0.0.1',
-    '--port=1234',
-    '--nohttpinterface',
-    '--dbpath={}'.format(DB_PATH),
-]).wait()
diff --git a/scripts/ws_client.py b/scripts/ws_client.py
new file mode 100755
index 000000000..46dd2da10
--- /dev/null
+++ b/scripts/ws_client.py
@@ -0,0 +1,322 @@
+#!/usr/bin/env python3
+"""
+Simple command line client for Testing/debugging
+a Freqtrade bot's message websocket
+
+Should not import anything from freqtrade,
+so it can be used as a standalone script.
+"""
+
+import argparse
+import asyncio
+import logging
+import socket
+import sys
+import time
+from pathlib import Path
+
+import orjson
+import pandas
+import rapidjson
+import websockets
+
+
+logger = logging.getLogger("WebSocketClient")
+
+
+# ---------------------------------------------------------------------------
+
+
+def setup_logging(filename: str):
+    logging.basicConfig(
+        level=logging.DEBUG,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        handlers=[logging.FileHandler(filename), logging.StreamHandler()],
+    )
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c",
+        "--config",
+        help="Specify configuration file (default: %(default)s). ",
+        dest="config",
+        type=str,
+        metavar="PATH",
+        default="config.json",
+    )
+    parser.add_argument(
+        "-l",
+        "--logfile",
+        help="The filename to log to.",
+        dest="logfile",
+        type=str,
+        default="ws_client.log",
+    )
+
+    args = parser.parse_args()
+    return vars(args)
+
+
+def load_config(configfile):
+    file = Path(configfile)
+    if file.is_file():
+        with file.open("r") as f:
+            config = rapidjson.load(
+                f, parse_mode=rapidjson.PM_COMMENTS | rapidjson.PM_TRAILING_COMMAS
+            )
+        return config
+    else:
+        logger.warning(f"Could not load config file {file}.")
+        sys.exit(1)
+
+
+def readable_timedelta(delta):
+    """
+    Convert a millisecond delta to a readable format
+
+    :param delta: A delta between two timestamps in milliseconds
+    :returns: The readable time difference string
+    """
+    seconds, milliseconds = divmod(delta, 1000)
+    minutes, seconds = divmod(seconds, 60)
+
+    return f"{int(minutes)}:{int(seconds)}.{int(milliseconds)}"
+
+
+# ----------------------------------------------------------------------------
+
+
+def json_serialize(message):
+    """
+    Serialize a message to JSON using orjson
+    :param message: The message to serialize
+    """
+    return str(orjson.dumps(message), "utf-8")
+
+
+def json_deserialize(message):
+    """
+    Deserialize JSON to a dict
+    :param message: The message to deserialize
+    """
+
+    def json_to_dataframe(data: str) -> pandas.DataFrame:
+        dataframe = pandas.read_json(data, orient="split")
+        if "date" in dataframe.columns:
+            dataframe["date"] = pandas.to_datetime(dataframe["date"], unit="ms", utc=True)
+
+        return dataframe
+
+    def _json_object_hook(z):
+        if z.get("__type__") == "dataframe":
+            return json_to_dataframe(z.get("__value__"))
+        return z
+
+    return rapidjson.loads(message, object_hook=_json_object_hook)
+
+
+# ---------------------------------------------------------------------------
+
+
+class ClientProtocol:
+    logger = logging.getLogger("WebSocketClient.Protocol")
+    _MESSAGE_COUNT = 0
+    _LAST_RECEIVED_AT = 0  # The epoch we received a message most recently
+
+    async def on_connect(self, websocket):
+        # On connection we have to send our initial requests
+        initial_requests = [
+            {
+                "type": "subscribe",  # The subscribe request should always be first
+                "data": ["analyzed_df", "whitelist"],  # The message types we want
+            },
+            {
+                "type": "whitelist",
+                "data": None,
+            },
+            {"type": "analyzed_df", "data": {"limit": 1500}},
+        ]
+
+        for request in initial_requests:
+            await websocket.send(json_serialize(request))
+
+    async def on_message(self, websocket, name, message):
+        deserialized = json_deserialize(message)
+
+        message_size = sys.getsizeof(message)
+        message_type = deserialized.get("type")
+        message_data = deserialized.get("data")
+
+        self.logger.info(
+            f"Received message of type {message_type} [{message_size} bytes] @ [{name}]"
+        )
+
+        time_difference = self._calculate_time_difference()
+
+        if self._MESSAGE_COUNT > 0:
+            self.logger.info(f"Time since last message: {time_difference}")
+
+        message_handler = getattr(self, f"_handle_{message_type}", None) or self._handle_default
+        await message_handler(name, message_type, message_data)
+
+        self._MESSAGE_COUNT += 1
+        self.logger.info(f"[{self._MESSAGE_COUNT}] total messages..")
+        self.logger.info("-" * 80)
+
+    def _calculate_time_difference(self):
+        old_last_received_at = self._LAST_RECEIVED_AT
+        self._LAST_RECEIVED_AT = time.time() * 1e3
+        time_delta = self._LAST_RECEIVED_AT - old_last_received_at
+
+        return readable_timedelta(time_delta)
+
+    async def _handle_whitelist(self, name, msgtype, data):
+        self.logger.info(data)
+
+    async def _handle_analyzed_df(self, name, msgtype, data):
+        key, la, df = data["key"], data["la"], data["df"]
+
+        if not df.empty:
+            columns = ", ".join([str(column) for column in df.columns])
+
+            self.logger.info(key)
+            self.logger.info(f"Last analyzed datetime: {la}")
+            self.logger.info(f"Latest candle datetime: {df.iloc[-1]['date']}")
+            self.logger.info(f"DataFrame length: {len(df)}")
+            self.logger.info(f"DataFrame columns: {columns}")
+        else:
+            self.logger.info("Empty DataFrame")
+
+    async def _handle_default(self, name, msgtype, data):
+        self.logger.info("Unknown message of type {msgtype} received...")
+        self.logger.info(data)
+
+
+async def create_client(
+    host,
+    port,
+    token,
+    scheme="ws",
+    name="default",
+    protocol=None,
+    sleep_time=10,
+    ping_timeout=10,
+    wait_timeout=30,
+    **kwargs,
+):
+    """
+    Create a websocket client and listen for messages
+    :param host: The host
+    :param port: The port
+    :param token: The websocket auth token
+    :param scheme: `ws` for most connections, `wss` for ssl
+    :param name: The name of the producer
+    :param **kwargs: Any extra kwargs passed to websockets.connect
+    """
+    if not protocol:
+        protocol = ClientProtocol()
+
+    while 1:
+        try:
+            websocket_url = f"{scheme}://{host}:{port}/api/v1/message/ws?token={token}"
+            logger.info(f"Attempting to connect to {name} @ {host}:{port}")
+
+            async with websockets.connect(websocket_url, **kwargs) as ws:
+                logger.info("Connection successful...")
+                await protocol.on_connect(ws)
+
+                # Now listen for messages
+                while 1:
+                    try:
+                        message = await asyncio.wait_for(ws.recv(), timeout=wait_timeout)
+
+                        await protocol.on_message(ws, name, message)
+
+                    except (asyncio.TimeoutError, websockets.exceptions.WebSocketException):
+                        # Try pinging
+                        try:
+                            pong = await ws.ping()
+                            latency = await asyncio.wait_for(pong, timeout=ping_timeout) * 1000
+
+                            logger.info(f"Connection still alive, latency: {latency}ms")
+
+                            continue
+
+                        except asyncio.TimeoutError:
+                            logger.error(f"Ping timed out, retrying in {sleep_time}s")
+                            await asyncio.sleep(sleep_time)
+
+                            break
+
+        except (
+            socket.gaierror,
+            ConnectionRefusedError,
+            websockets.exceptions.InvalidHandshake,
+        ) as e:
+            logger.error(f"Connection Refused - {e} retrying in {sleep_time}s")
+            await asyncio.sleep(sleep_time)
+
+            continue
+
+        except (
+            websockets.exceptions.ConnectionClosedError,
+            websockets.exceptions.ConnectionClosedOK,
+        ):
+            logger.info("Connection was closed")
+            # Just keep trying to connect again indefinitely
+            await asyncio.sleep(sleep_time)
+
+            continue
+
+        except Exception as e:
+            # An unforeseen error has occurred, log and try reconnecting again
+            logger.error("Unexpected error has occurred:")
+            logger.exception(e)
+
+            await asyncio.sleep(sleep_time)
+            continue
+
+
+# ---------------------------------------------------------------------------
+
+
+async def _main(args):
+    setup_logging(args["logfile"])
+    config = load_config(args["config"])
+
+    emc_config = config.get("external_message_consumer", {})
+
+    producers = emc_config.get("producers", [])
+    producer = producers[0]
+
+    wait_timeout = emc_config.get("wait_timeout", 30)
+    ping_timeout = emc_config.get("ping_timeout", 10)
+    sleep_time = emc_config.get("sleep_time", 10)
+    message_size_limit = emc_config.get("message_size_limit", 8) << 20
+
+    await create_client(
+        producer["host"],
+        producer["port"],
+        producer["ws_token"],
+        "wss" if producer.get("secure", False) else "ws",
+        producer["name"],
+        sleep_time=sleep_time,
+        ping_timeout=ping_timeout,
+        wait_timeout=wait_timeout,
+        max_size=message_size_limit,
+        ping_interval=None,
+    )
+
+
+def main():
+    args = parse_args()
+    try:
+        asyncio.run(_main(args))
+    except KeyboardInterrupt:
+        logger.info("Exiting...")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index ba065a7c2..000000000
--- a/setup.cfg
+++ /dev/null
@@ -1,4 +0,0 @@
-[flake8]
-#ignore =
-max-line-length = 100
-max-complexity = 12
diff --git a/setup.ps1 b/setup.ps1
new file mode 100644
index 000000000..116ed0a2b
--- /dev/null
+++ b/setup.ps1
@@ -0,0 +1,282 @@
+Clear-Host
+
+$Timestamp = Get-Date -Format "yyyyMMdd_HHmmss"
+$Global:LogFilePath = Join-Path $env:TEMP "script_log_$Timestamp.txt"
+
+$RequirementFiles = @("requirements.txt", "requirements-dev.txt", "requirements-hyperopt.txt", "requirements-freqai.txt", "requirements-freqai-rl.txt", "requirements-plot.txt")
+$VenvName = ".venv"
+$VenvDir = Join-Path $PSScriptRoot $VenvName
+
+function Write-Log {
+  param (
+    [string]$Message,
+    [string]$Level = 'INFO'
+  )
+
+  if (-not (Test-Path -Path $LogFilePath)) {
+    New-Item -ItemType File -Path $LogFilePath -Force | Out-Null
+  }
+
+  switch ($Level) {
+    'INFO' { Write-Host $Message -ForegroundColor Green }
+    'WARNING' { Write-Host $Message -ForegroundColor Yellow }
+    'ERROR' { Write-Host $Message -ForegroundColor Red }
+    'PROMPT' { Write-Host $Message -ForegroundColor Cyan }
+  }
+
+  "${Level}: $Message" | Out-File $LogFilePath -Append
+}
+
+function Get-UserSelection {
+  param (
+    [string]$Prompt,
+    [string[]]$Options,
+    [string]$DefaultChoice = 'A',
+    [bool]$AllowMultipleSelections = $true
+  )
+
+  Write-Log "$Prompt`n" -Level 'PROMPT'
+  for ($I = 0; $I -lt $Options.Length; $I++) {
+    Write-Log "$([char](65 + $I)). $($Options[$I])" -Level 'PROMPT'
+  }
+
+  if ($AllowMultipleSelections) {
+    Write-Log "`nSelect one or more options by typing the corresponding letters, separated by commas." -Level 'PROMPT'
+  }
+  else {
+    Write-Log "`nSelect an option by typing the corresponding letter." -Level 'PROMPT'
+  }
+
+  [string]$UserInput = Read-Host
+  if ([string]::IsNullOrEmpty($UserInput)) {
+    $UserInput = $DefaultChoice
+  }
+  $UserInput = $UserInput.ToUpper()
+
+  if ($AllowMultipleSelections) {
+    $Selections = $UserInput.Split(',') | ForEach-Object { $_.Trim() }
+    $SelectedIndices = @()
+    foreach ($Selection in $Selections) {
+      if ($Selection -match '^[A-Z]$') {
+        $Index = [int][char]$Selection - [int][char]'A'
+        if ($Index -ge 0 -and $Index -lt $Options.Length) {
+          $SelectedIndices += $Index
+        }
+        else {
+          Write-Log "Invalid input: $Selection. Please enter letters within the valid range of options." -Level 'ERROR'
+          return -1
+        }
+      }
+      else {
+        Write-Log "Invalid input: $Selection. Please enter a letter between A and Z." -Level 'ERROR'
+        return -1
+      }
+    }
+    return $SelectedIndices
+  }
+  else {
+    if ($UserInput -match '^[A-Z]$') {
+      $SelectedIndex = [int][char]$UserInput - [int][char]'A'
+      if ($SelectedIndex -ge 0 -and $SelectedIndex -lt $Options.Length) {
+        return $SelectedIndex
+      }
+      else {
+        Write-Log "Invalid input: $UserInput. Please enter a letter within the valid range of options." -Level 'ERROR'
+        return -1
+      }
+    }
+    else {
+      Write-Log "Invalid input: $UserInput. Please enter a letter between A and Z." -Level 'ERROR'
+      return -1
+    }
+  }
+}
+
+function Exit-Script {
+  param (
+    [int]$ExitCode,
+    [bool]$WaitForKeypress = $true
+  )
+
+  if ($ExitCode -ne 0) {
+    Write-Log "Script failed. Would you like to open the log file? (Y/N)" -Level 'PROMPT'
+    $openLog = Read-Host
+    if ($openLog -eq 'Y' -or $openLog -eq 'y') {
+      Start-Process notepad.exe -ArgumentList $LogFilePath
+    }
+  }
+  elseif ($WaitForKeypress) {
+    Write-Log "Press any key to exit..."
+    $host.UI.RawUI.ReadKey("NoEcho,IncludeKeyDown") | Out-Null
+  }
+
+  return $ExitCode
+}
+
+function Test-PythonExecutable {
+  param(
+    [string]$PythonExecutable
+  )
+
+  $DeactivateVenv = Join-Path $VenvDir "Scripts\Deactivate.bat"
+  if (Test-Path $DeactivateVenv) {
+    Write-Host "Deactivating virtual environment..." 2>&1 | Out-File $LogFilePath -Append
+    & $DeactivateVenv
+    Write-Host "Virtual environment deactivated." 2>&1 | Out-File $LogFilePath -Append
+  }
+  else {
+    Write-Host "Deactivation script not found: $DeactivateVenv" 2>&1 | Out-File $LogFilePath -Append
+  }
+
+  $PythonCmd = Get-Command $PythonExecutable -ErrorAction SilentlyContinue
+  if ($PythonCmd) {
+    $VersionOutput = & $PythonCmd.Source --version 2>&1
+    if ($LASTEXITCODE -eq 0) {
+      $Version = $VersionOutput | Select-String -Pattern "Python (\d+\.\d+\.\d+)" | ForEach-Object { $_.Matches.Groups[1].Value }
+      Write-Log "Python version $Version found using executable '$PythonExecutable'."
+      return $true
+    }
+    else {
+      Write-Log "Python executable '$PythonExecutable' not working correctly." -Level 'ERROR'
+      return $false
+    }
+  }
+  else {
+    Write-Log "Python executable '$PythonExecutable' not found." -Level 'ERROR'
+    return $false
+  }
+}
+
+function Find-PythonExecutable {
+  $PythonExecutables = @(
+    "python",
+    "python3.12",
+    "python3.11",
+    "python3.10",
+    "python3",
+    "C:\Users\$env:USERNAME\AppData\Local\Programs\Python\Python312\python.exe",
+    "C:\Users\$env:USERNAME\AppData\Local\Programs\Python\Python311\python.exe",
+    "C:\Users\$env:USERNAME\AppData\Local\Programs\Python\Python310\python.exe",
+    "C:\Python312\python.exe",
+    "C:\Python311\python.exe",
+    "C:\Python310\python.exe"
+  )
+
+
+  foreach ($Executable in $PythonExecutables) {
+    if (Test-PythonExecutable -PythonExecutable $Executable) {
+      return $Executable
+    }
+  }
+
+  return $null
+}
+function Main {
+  "Starting the operations..." | Out-File $LogFilePath -Append
+  "Current directory: $(Get-Location)" | Out-File $LogFilePath -Append
+
+  # Exit on lower versions than Python 3.10 or when Python executable not found
+  $PythonExecutable = Find-PythonExecutable
+  if ($null -eq $PythonExecutable) {
+    Write-Log "No suitable Python executable found. Please ensure that Python 3.10 or higher is installed and available in the system PATH." -Level 'ERROR'
+    Exit 1
+  }
+
+  # Define the path to the Python executable in the virtual environment
+  $ActivateVenv = "$VenvDir\Scripts\Activate.ps1"
+
+  # Check if the virtual environment exists, if not, create it
+  if (-Not (Test-Path $ActivateVenv)) {
+    Write-Log "Virtual environment not found. Creating virtual environment..." -Level 'ERROR'
+    & $PythonExecutable -m venv $VenvName 2>&1 | Out-File $LogFilePath -Append
+    if ($LASTEXITCODE -ne 0) {
+      Write-Log "Failed to create virtual environment." -Level 'ERROR'
+      Exit-Script -exitCode 1
+    }
+    else {
+      Write-Log "Virtual environment created."
+    }
+  }
+
+  # Activate the virtual environment and check if it was successful
+  Write-Log "Virtual environment found. Activating virtual environment..."
+  & $ActivateVenv 2>&1 | Out-File $LogFilePath -Append
+  # Check if virtual environment is activated
+  if ($env:VIRTUAL_ENV) {
+    Write-Log "Virtual environment is activated at: $($env:VIRTUAL_ENV)"
+  }
+  else {
+    Write-Log "Failed to activate virtual environment." -Level 'ERROR'
+    Exit-Script -exitCode 1
+  }
+
+  # Ensure pip
+  python -m ensurepip --default-pip 2>&1 | Out-File $LogFilePath -Append
+
+  # Pull latest updates only if the repository state is not dirty
+  Write-Log "Checking if the repository is clean..."
+  $Status = & "git" status --porcelain
+  if ($Status) {
+    Write-Log "Changes in local git repository. Skipping git pull."
+  }
+  else {
+    Write-Log "Pulling latest updates..."
+    & "git" pull 2>&1 | Out-File $LogFilePath -Append
+    if ($LASTEXITCODE -ne 0) {
+      Write-Log "Failed to pull updates from Git." -Level 'ERROR'
+      Exit-Script -exitCode 1
+    }
+  }
+
+  if (-not (Test-Path "$VenvDir\Lib\site-packages\talib")) {
+    # Install TA-Lib using the virtual environment's pip
+    Write-Log "Installing TA-Lib using virtual environment's pip..."
+    python -m pip install --find-links=build_helpers\ --prefer-binary TA-Lib 2>&1 | Out-File $LogFilePath -Append
+    if ($LASTEXITCODE -ne 0) {
+      Write-Log "Failed to install TA-Lib." -Level 'ERROR'
+      Exit-Script -exitCode 1
+    }
+  }
+
+  # Present options for requirement files
+  $SelectedIndices = Get-UserSelection -prompt "Select which requirement files to install:" -options $RequirementFiles -defaultChoice 'A'
+
+  # Cache the selected requirement files
+  $SelectedRequirementFiles = @()
+  $PipInstallArguments = @()
+  foreach ($Index in $SelectedIndices) {
+    $RelativePath = $RequirementFiles[$Index]
+    if (Test-Path $RelativePath) {
+      $SelectedRequirementFiles += $RelativePath
+      $PipInstallArguments += "-r", $RelativePath  # Add each flag and path as separate elements
+    }
+    else {
+      Write-Log "Requirement file not found: $RelativePath" -Level 'ERROR'
+      Exit-Script -exitCode 1
+    }
+  }
+  if ($PipInstallArguments.Count -ne 0) {
+    & pip install @PipInstallArguments # Use array splatting to pass arguments correctly
+  }
+
+  # Install freqtrade from setup using the virtual environment's Python
+  Write-Log "Installing freqtrade from setup..."
+  pip install -e . 2>&1 | Out-File $LogFilePath -Append
+  if ($LASTEXITCODE -ne 0) {
+    Write-Log "Failed to install freqtrade." -Level 'ERROR'
+    Exit-Script -exitCode 1
+  }
+
+  Write-Log "Installing freqUI..."
+  python freqtrade install-ui 2>&1 | Out-File $LogFilePath -Append
+  if ($LASTEXITCODE -ne 0) {
+    Write-Log "Failed to install freqUI." -Level 'ERROR'
+    Exit-Script -exitCode 1
+  }
+
+  Write-Log "Installation/Update complete!"
+  Exit-Script -exitCode 0
+}
+
+# Call the Main function
+Main
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 4b0635efa..000000000
--- a/setup.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from sys import version_info
-from setuptools import setup
-
-if version_info.major == 3 and version_info.minor < 6 or \
-        version_info.major < 3:
-    print('Your Python interpreter must be 3.6 or greater!')
-    exit(1)
-
-from freqtrade import __version__
-
-
-setup(name='freqtrade',
-      version=__version__,
-      description='Simple High Frequency Trading Bot for crypto currencies',
-      url='https://github.com/gcarq/freqtrade',
-      author='gcarq and contributors',
-      author_email='michael.egger@tsn.at',
-      license='GPLv3',
-      packages=['freqtrade'],
-      scripts=['bin/freqtrade'],
-      setup_requires=['pytest-runner'],
-      tests_require=['pytest', 'pytest-mock', 'pytest-cov'],
-      install_requires=[
-          'python-bittrex',
-          'SQLAlchemy',
-          'python-telegram-bot',
-          'arrow',
-          'requests',
-          'urllib3',
-          'wrapt',
-          'pandas',
-          'scikit-learn',
-          'scipy',
-          'jsonschema',
-          'TA-Lib',
-          'tabulate',
-          'cachetools',
-          'coinmarketcap',
-      ],
-      include_package_data=True,
-      zip_safe=False,
-      classifiers=[
-          'Programming Language :: Python :: 3.6',
-          'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
-          'Topic :: Office/Business :: Financial :: Investment',
-          'Intended Audience :: Science/Research',
-      ])
diff --git a/setup.sh b/setup.sh
index b38f2c645..0d490a134 100755
--- a/setup.sh
+++ b/setup.sh
@@ -1,190 +1,292 @@
 #!/usr/bin/env bash
 #encoding=utf8
 
-function updateenv () {
-    echo "
-    -------------------------
-    Update your virtual env
-    -------------------------
-    "
-    source .env/bin/activate
-    pip3.6 install --upgrade pip
-    pip3 install -r requirements.txt --upgrade
-    pip3 install -r requirements.txt
-    pip3 install -e .
+function echo_block() {
+    echo "----------------------------"
+    echo $1
+    echo "----------------------------"
+}
+
+function check_installed_pip() {
+   ${PYTHON} -m pip > /dev/null
+   if [ $? -ne 0 ]; then
+        echo_block "Installing Pip for ${PYTHON}"
+        curl https://bootstrap.pypa.io/get-pip.py -s -o get-pip.py
+        ${PYTHON} get-pip.py
+        rm get-pip.py
+   fi
+}
+
+# Check which python version is installed
+function check_installed_python() {
+    if [ -n "${VIRTUAL_ENV}" ]; then
+        echo "Please deactivate your virtual environment before running setup.sh."
+        echo "You can do this by running 'deactivate'."
+        exit 2
+    fi
+
+    for v in 12 11 10
+    do
+        PYTHON="python3.${v}"
+        which $PYTHON
+        if [ $? -eq 0 ]; then
+            echo "using ${PYTHON}"
+            check_installed_pip
+            return
+        fi
+    done
+
+    echo "No usable python found. Please make sure to have python3.10 or newer installed."
+    exit 1
+}
+
+function updateenv() {
+    echo_block "Updating your virtual environment"
+    if [ ! -f .venv/bin/activate ]; then
+        echo "Something went wrong, no virtual environment found."
+        exit 1
+    fi
+    source .venv/bin/activate
+    SYS_ARCH=$(uname -m)
+    echo "pip install in-progress. Please wait..."
+    ${PYTHON} -m pip install --upgrade pip wheel setuptools
+    REQUIREMENTS_HYPEROPT=""
+    REQUIREMENTS_PLOT=""
+    REQUIREMENTS_FREQAI=""
+    REQUIREMENTS_FREQAI_RL=""
+    REQUIREMENTS=requirements.txt
+
+    read -p "Do you want to install dependencies for development (Performs a full install with all dependencies) [y/N]? "
+    dev=$REPLY
+    if [[ $REPLY =~ ^[Yy]$ ]]
+    then
+        REQUIREMENTS=requirements-dev.txt
+    else
+        # requirements-dev.txt includes all the below requirements already, so further questions are pointless.
+        read -p "Do you want to install plotting dependencies (plotly) [y/N]? "
+        if [[ $REPLY =~ ^[Yy]$ ]]
+        then
+            REQUIREMENTS_PLOT="-r requirements-plot.txt"
+        fi
+        if [ "${SYS_ARCH}" == "armv7l" ] || [ "${SYS_ARCH}" == "armv6l" ]; then
+            echo "Detected Raspberry, installing cython, skipping hyperopt installation."
+            ${PYTHON} -m pip install --upgrade cython
+        else
+            # Is not Raspberry
+            read -p "Do you want to install hyperopt dependencies [y/N]? "
+            if [[ $REPLY =~ ^[Yy]$ ]]
+            then
+                REQUIREMENTS_HYPEROPT="-r requirements-hyperopt.txt"
+            fi
+        fi
+
+        read -p "Do you want to install dependencies for freqai [y/N]? "
+        if [[ $REPLY =~ ^[Yy]$ ]]
+        then
+            REQUIREMENTS_FREQAI="-r requirements-freqai.txt --use-pep517"
+            read -p "Do you also want dependencies for freqai-rl or PyTorch (~700mb additional space required) [y/N]? "
+            if [[ $REPLY =~ ^[Yy]$ ]]
+            then
+                REQUIREMENTS_FREQAI="-r requirements-freqai-rl.txt"
+            fi
+        fi
+    fi
+    install_talib
+
+    ${PYTHON} -m pip install --upgrade -r ${REQUIREMENTS} ${REQUIREMENTS_HYPEROPT} ${REQUIREMENTS_PLOT} ${REQUIREMENTS_FREQAI} ${REQUIREMENTS_FREQAI_RL}
+    if [ $? -ne 0 ]; then
+        echo "Failed installing dependencies"
+        exit 1
+    fi
+    ${PYTHON} -m pip install -e .
+    if [ $? -ne 0 ]; then
+        echo "Failed installing Freqtrade"
+        exit 1
+    fi
+
+    echo "Installing freqUI"
+    freqtrade install-ui
+
+    echo "pip install completed"
+    echo
+    if [[ $dev =~ ^[Yy]$ ]]; then
+        ${PYTHON} -m pre_commit install
+        if [ $? -ne 0 ]; then
+            echo "Failed installing pre-commit"
+            exit 1
+        fi
+    fi
 }
 
 # Install tab lib
-function install_talib () {
-    curl -O -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
-    tar zxvf ta-lib-0.4.0-src.tar.gz
-    cd ta-lib && ./configure --prefix=/usr && make && sudo make install
-    cd .. && rm -rf ./ta-lib*
+function install_talib() {
+    if [ -f /usr/local/lib/libta_lib.a ] || [ -f /usr/local/lib/libta_lib.so ] || [ -f /usr/lib/libta_lib.so ]; then
+        echo "ta-lib already installed, skipping"
+        return
+    fi
+
+    cd build_helpers && ./install_ta-lib.sh
+
+    if [ $? -ne 0 ]; then
+        echo "Quitting. Please fix the above error before continuing."
+        cd ..
+        exit 1
+    fi;
+
+    cd ..
 }
 
+
 # Install bot MacOS
-function install_macos () {
+function install_macos() {
     if [ ! -x "$(command -v brew)" ]
     then
-        echo "-------------------------"
-        echo "Install Brew"
-        echo "-------------------------"
-        echo
+        echo_block "Installing Brew"
         /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
     fi
-    brew install python3 wget ta-lib
+
+    brew install gettext libomp
+
+    #Gets number after decimal in python version
+    version=$(egrep -o 3.\[0-9\]+ <<< $PYTHON | sed 's/3.//g')
 }
 
 # Install bot Debian_ubuntu
-function install_debian () {
-    sudo add-apt-repository ppa:jonathonf/python-3.6
+function install_debian() {
     sudo apt-get update
-    sudo apt-get install python3.6 python3.6-venv python3.6-dev build-essential autoconf libtool pkg-config make wget git
-    install_talib
+    sudo apt-get install -y gcc build-essential autoconf libtool pkg-config make wget git curl $(echo lib${PYTHON}-dev ${PYTHON}-venv)
+}
+
+# Install bot RedHat_CentOS
+function install_redhat() {
+    sudo yum update
+    sudo yum install -y gcc gcc-c++ make autoconf libtool pkg-config wget git $(echo ${PYTHON}-devel | sed 's/\.//g')
 }
 
 # Upgrade the bot
-function update () {
+function update() {
     git pull
+    if [ -f .env/bin/activate  ]; then
+        # Old environment found - updating to new environment.
+        recreate_environments
+    fi
+    updateenv
+    echo "Update completed."
+    echo_block "Don't forget to activate your virtual environment with 'source .venv/bin/activate'!"
+
+}
+
+function check_git_changes() {
+    if [ -z "$(git status --porcelain)" ]; then
+        echo "No changes in git directory"
+        return 1
+    else
+        echo "Changes in git directory"
+        return 0
+    fi
+}
+
+function recreate_environments() {
+    if [ -d ".env" ]; then
+        # Remove old virtual env
+        echo "- Deleting your previous virtual env"
+        echo "Warning: Your new environment will be at .venv!"
+        rm -rf .env
+    fi
+    if [ -d ".venv" ]; then
+        echo "- Deleting your previous virtual env"
+        rm -rf .venv
+    fi
+
+    echo
+    ${PYTHON} -m venv .venv
+    if [ $? -ne 0 ]; then
+        echo "Could not create virtual environment. Leaving now"
+        exit 1
+    fi
+
+}
+
+# Reset Develop or Stable branch
+function reset() {
+    echo_block "Resetting branch and virtual env"
+
+    if [ "1" == $(git branch -vv |grep -cE "\* develop|\* stable") ]
+    then
+        if check_git_changes; then
+            read -p "Keep your local changes? (Otherwise will remove all changes you made!) [Y/n]? "
+            if [[ $REPLY =~ ^[Nn]$ ]]; then
+
+                git fetch -a
+
+                if [ "1" == $(git branch -vv | grep -c "* develop") ]
+                then
+                    echo "- Hard resetting of 'develop' branch."
+                    git reset --hard origin/develop
+                elif [ "1" == $(git branch -vv | grep -c "* stable") ]
+                then
+                    echo "- Hard resetting of 'stable' branch."
+                    git reset --hard origin/stable
+                fi
+            fi
+        fi
+    else
+        echo "Reset ignored because you are not on 'stable' or 'develop'."
+    fi
+    recreate_environments
+
     updateenv
 }
 
-# Reset Develop or Master branch
-function reset () {
-    echo "----------------------------"
-    echo "Reset branch and virtual env"
-    echo "----------------------------"
-    echo
-    if [ "1" == $(git branch -vv |grep -cE "\* develop|\* master") ]
-    then
-        if [ -d ".env" ]; then
-          echo "- Delete your previous virtual env"
-          rm -rf .env
-        fi
-
-        git fetch -a
-
-        if [ "1" == $(git branch -vv |grep -c "* develop") ]
-        then
-          echo "- Hard resetting of 'develop' branch."
-          git reset --hard origin/develop
-        elif [ "1" == $(git branch -vv |grep -c "* master") ]
-        then
-          echo "- Hard resetting of 'master' branch."
-          git reset --hard origin/master
-        fi
-    else
-        echo "Reset ignored because you are not on 'master' or 'develop'."
-    fi
-
-    python3.6 -m venv .env
-    updateenv
+function config() {
+    echo_block "Please use 'freqtrade new-config -c user_data/config.json' to generate a new configuration file."
 }
 
-function config_generator () {
+function install() {
 
-    echo "Starting to generate config.json"
+    echo_block "Installing mandatory dependencies"
 
-    echo "-------------------------"
-    echo "General configuration"
-    echo "-------------------------"
-    echo
-    read -p "Max open trades: (Default: 3) " max_trades
-
-    read -p "Stake amount: (Default: 0.05) " stake_amount
-
-    read -p "Stake currency: (Default: BTC) " stake_currency
-
-    read -p "Fiat currency: (Default: USD) " fiat_currency
-
-    echo "------------------------"
-    echo "Bittrex config generator"
-    echo "------------------------"
-    echo
-    read -p "Exchange API key: " api_key
-    read -p "Exchange API Secret: " api_secret
-
-    echo "-------------------------"
-    echo "Telegram config generator"
-    echo "-------------------------"
-    read -p "Telegram Token: " token
-    read -p "Telegram Chat_id: " chat_id
-
-    sed -e "s/\"max_open_trades\": 3,/\"max_open_trades\": $max_trades,/g" \
-        -e "s/\"stake_amount\": 0.05,/\"stake_amount\": $stake_amount,/g" \
-        -e "s/\"stake_currency\": \"BTC\",/\"stake_currency\": \"$stake_currency\",/g" \
-        -e "s/\"fiat_display_currency\": \"USD\",/\"fiat_display_currency\": \"$fiat_currency\",/g" \
-        -e "s/\"your_exchange_key\"/\"$api_key\"/g" \
-        -e "s/\"your_exchange_secret\"/\"$api_secret\"/g" \
-        -e "s/\"your_telegram_token\"/\"$token\"/g" \
-        -e "s/\"your_telegram_chat_id\"/\"$chat_id\"/g"
-        -e "s/\"dry_run\": false,/\"dry_run\": true,/g" config.json.example > config.json
-
-}
-
-function config () {
-    if [ -f config.json ]
-    then
-    read -p "A config file already exist, do you want to override it [Y/N]? "
-    if [[ $REPLY =~ ^[Yy]$ ]]
-    then
-        config_generator
-    else
-        echo "Configuration of config.json ignored."
-    fi
-    else
-        config_generator
-    fi
-
-    echo "Edit ./config.json to modify Pair and other configurations."
-}
-
-function install () {
-    echo "-------------------------"
-    echo "Install mandatory dependencies"
-    echo "-------------------------"
-    echo
-
-    if [ "$(uname -s)" == "Darwin" ]
-    then
-        echo "- You are on macOS"
+    if [ "$(uname -s)" == "Darwin" ]; then
+        echo "macOS detected. Setup for this system in-progress"
         install_macos
-    elif [ -x "$(command -v apt-get)" ]
-    then
-        echo "- You are on Debian/Ubuntu"
+    elif [ -x "$(command -v apt-get)" ]; then
+        echo "Debian/Ubuntu detected. Setup for this system in-progress"
         install_debian
+    elif [ -x "$(command -v yum)" ]; then
+        echo "Red Hat/CentOS detected. Setup for this system in-progress"
+        install_redhat
     else
         echo "This script does not support your OS."
-        echo "If you have Python3.6, pip, virtualenv, ta-lib you can continue."
+        echo "If you have Python version 3.10 - 3.12, pip, virtualenv, ta-lib you can continue."
         echo "Wait 10 seconds to continue the next install steps or use ctrl+c to interrupt this shell."
         sleep 10
     fi
+    echo
     reset
-    echo "
-    - Install complete.
-    "
     config
-    echo "You can now use the bot by executing 'source .env/bin/activate; python3 freqtrade/main.py'."
+    echo_block "Run the bot !"
+    echo "You can now use the bot by executing 'source .venv/bin/activate; freqtrade <subcommand>'."
+    echo "You can see the list of available bot sub-commands by executing 'source .venv/bin/activate; freqtrade --help'."
+    echo "You verify that freqtrade is installed successfully by running 'source .venv/bin/activate; freqtrade --version'."
 }
 
-function plot () {
-echo "
------------------------------------------
-Install dependencies for Plotting scripts
------------------------------------------
-"
-pip install plotly --upgrade
+function plot() {
+    echo_block "Installing dependencies for Plotting scripts"
+    ${PYTHON} -m pip install plotly --upgrade
 }
 
-function help () {
+function help() {
     echo "usage:"
     echo "	-i,--install    Install freqtrade from scratch"
     echo "	-u,--update     Command git pull to update."
-    echo "	-r,--reset      Hard reset your develop/master branch."
+    echo "	-r,--reset      Hard reset your develop/stable branch."
     echo "	-c,--config     Easy config generator (Will override your existing file)."
     echo "	-p,--plot       Install dependencies for Plotting scripts."
 }
 
+# Verify if 3.10+ is installed
+check_installed_python
+
 case $* in
 --install|-i)
 install
@@ -205,4 +307,4 @@ plot
 help
 ;;
 esac
-exit 0
\ No newline at end of file
+exit 0
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/commands/__init__.py b/tests/commands/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/commands/test_build_config.py b/tests/commands/test_build_config.py
new file mode 100644
index 000000000..9d4a57db1
--- /dev/null
+++ b/tests/commands/test_build_config.py
@@ -0,0 +1,121 @@
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+import rapidjson
+
+from freqtrade.commands.build_config_commands import start_new_config
+from freqtrade.configuration.deploy_config import (
+    ask_user_config,
+    ask_user_overwrite,
+    validate_is_float,
+    validate_is_int,
+)
+from freqtrade.exceptions import OperationalException
+from tests.conftest import get_args, log_has_re
+
+
+def test_validate_is_float():
+    assert validate_is_float("2.0")
+    assert validate_is_float("2.1")
+    assert validate_is_float("0.1")
+    assert validate_is_float("-0.5")
+    assert not validate_is_float("-0.5e")
+
+
+def test_validate_is_int():
+    assert validate_is_int("2")
+    assert validate_is_int("6")
+    assert validate_is_int("-1")
+    assert validate_is_int("500")
+    assert not validate_is_int("2.0")
+    assert not validate_is_int("2.1")
+    assert not validate_is_int("-2.1")
+    assert not validate_is_int("-ee")
+
+
+@pytest.mark.parametrize("exchange", ["bybit", "binance", "kraken"])
+def test_start_new_config(mocker, caplog, exchange):
+    wt_mock = mocker.patch.object(Path, "write_text", MagicMock())
+    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
+    unlink_mock = mocker.patch.object(Path, "unlink", MagicMock())
+    mocker.patch("freqtrade.configuration.deploy_config.ask_user_overwrite", return_value=True)
+
+    sample_selections = {
+        "max_open_trades": 3,
+        "stake_currency": "USDT",
+        "stake_amount": 100,
+        "fiat_display_currency": "EUR",
+        "timeframe": "15m",
+        "dry_run": True,
+        "trading_mode": "spot",
+        "margin_mode": "",
+        "exchange_name": exchange,
+        "exchange_key": "sampleKey",
+        "exchange_secret": "Samplesecret",
+        "telegram": False,
+        "telegram_token": "asdf1244",
+        "telegram_chat_id": "1144444",
+        "api_server": False,
+        "api_server_listen_addr": "127.0.0.1",
+        "api_server_username": "freqtrader",
+        "api_server_password": "MoneyMachine",
+    }
+    mocker.patch(
+        "freqtrade.configuration.deploy_config.ask_user_config", return_value=sample_selections
+    )
+    args = ["new-config", "--config", "coolconfig.json"]
+    start_new_config(get_args(args))
+
+    assert log_has_re("Writing config to .*", caplog)
+    assert wt_mock.call_count == 1
+    assert unlink_mock.call_count == 1
+    result = rapidjson.loads(
+        wt_mock.call_args_list[0][0][0],
+        parse_mode=rapidjson.PM_COMMENTS | rapidjson.PM_TRAILING_COMMAS,
+    )
+    assert result["exchange"]["name"] == exchange
+    assert result["timeframe"] == "15m"
+
+
+def test_start_new_config_exists(mocker, caplog):
+    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
+    mocker.patch("freqtrade.configuration.deploy_config.ask_user_overwrite", return_value=False)
+    args = ["new-config", "--config", "coolconfig.json"]
+    with pytest.raises(OperationalException, match=r"Configuration .* already exists\."):
+        start_new_config(get_args(args))
+
+
+def test_ask_user_overwrite(mocker):
+    """
+    Once https://github.com/tmbo/questionary/issues/35 is implemented, improve this test.
+    """
+    prompt_mock = mocker.patch(
+        "freqtrade.configuration.deploy_config.prompt", return_value={"overwrite": False}
+    )
+    assert not ask_user_overwrite(Path("test.json"))
+    assert prompt_mock.call_count == 1
+
+    prompt_mock.reset_mock()
+    prompt_mock = mocker.patch(
+        "freqtrade.configuration.deploy_config.prompt", return_value={"overwrite": True}
+    )
+    assert ask_user_overwrite(Path("test.json"))
+    assert prompt_mock.call_count == 1
+
+
+def test_ask_user_config(mocker):
+    """
+    Once https://github.com/tmbo/questionary/issues/35 is implemented, improve this test.
+    """
+    prompt_mock = mocker.patch(
+        "freqtrade.configuration.deploy_config.prompt", return_value={"overwrite": False}
+    )
+    answers = ask_user_config()
+    assert isinstance(answers, dict)
+    assert prompt_mock.call_count == 1
+
+    prompt_mock = mocker.patch("freqtrade.configuration.deploy_config.prompt", return_value={})
+
+    with pytest.raises(OperationalException, match=r"User interrupted interactive questions\."):
+        ask_user_config()
diff --git a/tests/commands/test_commands.py b/tests/commands/test_commands.py
new file mode 100644
index 000000000..9dd5ffebc
--- /dev/null
+++ b/tests/commands/test_commands.py
@@ -0,0 +1,1939 @@
+import json
+import re
+import shutil
+from datetime import datetime, timedelta
+from io import BytesIO
+from pathlib import Path
+from unittest.mock import MagicMock, PropertyMock
+from zipfile import ZipFile
+
+import pytest
+
+from freqtrade.commands import (
+    start_backtesting_show,
+    start_convert_data,
+    start_convert_db,
+    start_convert_trades,
+    start_create_userdir,
+    start_download_data,
+    start_hyperopt_list,
+    start_hyperopt_show,
+    start_install_ui,
+    start_list_data,
+    start_list_exchanges,
+    start_list_freqAI_models,
+    start_list_hyperopt_loss_functions,
+    start_list_markets,
+    start_list_strategies,
+    start_list_timeframes,
+    start_new_strategy,
+    start_show_config,
+    start_show_trades,
+    start_strategy_update,
+    start_test_pairlist,
+    start_trading,
+    start_webserver,
+)
+from freqtrade.commands.deploy_ui import (
+    clean_ui_subdir,
+    download_and_install_ui,
+    get_ui_download_url,
+    read_ui_version,
+)
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence.models import init_db
+from freqtrade.persistence.pairlock_middleware import PairLocks
+from freqtrade.util import dt_utc
+from tests.conftest import (
+    CURRENT_TEST_STRATEGY,
+    EXMS,
+    create_mock_trades,
+    get_args,
+    log_has,
+    log_has_re,
+    patch_exchange,
+    patched_configuration_load_config_file,
+)
+from tests.conftest_hyperopt import hyperopt_test_result
+from tests.conftest_trades import MOCK_TRADE_COUNT
+
+
+def test_setup_utils_configuration():
+    args = [
+        "list-exchanges",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+    ]
+
+    config = setup_utils_configuration(get_args(args), RunMode.OTHER)
+    assert "exchange" in config
+    assert config["dry_run"] is True
+
+    args = [
+        "list-exchanges",
+        "--config",
+        "tests/testdata/testconfigs/testconfig.json",
+    ]
+
+    config = setup_utils_configuration(get_args(args), RunMode.OTHER, set_dry=False)
+    assert "exchange" in config
+    assert config["dry_run"] is False
+
+
+def test_start_trading_fail(mocker, caplog):
+    mocker.patch("freqtrade.worker.Worker.run", MagicMock(side_effect=OperationalException))
+
+    mocker.patch("freqtrade.worker.Worker.__init__", MagicMock(return_value=None))
+
+    exitmock = mocker.patch("freqtrade.worker.Worker.exit", MagicMock())
+    args = ["trade", "-c", "tests/testdata/testconfigs/main_test_config.json"]
+    with pytest.raises(OperationalException):
+        start_trading(get_args(args))
+    assert exitmock.call_count == 1
+
+    exitmock.reset_mock()
+    caplog.clear()
+    mocker.patch("freqtrade.worker.Worker.__init__", MagicMock(side_effect=OperationalException))
+    with pytest.raises(OperationalException):
+        start_trading(get_args(args))
+    assert exitmock.call_count == 0
+
+
+def test_start_webserver(mocker, caplog):
+    api_server_mock = mocker.patch(
+        "freqtrade.rpc.api_server.ApiServer",
+    )
+
+    args = ["webserver", "-c", "tests/testdata/testconfigs/main_test_config.json"]
+    start_webserver(get_args(args))
+    assert api_server_mock.call_count == 1
+
+
+def test_list_exchanges(capsys):
+    args = [
+        "list-exchanges",
+    ]
+
+    start_list_exchanges(get_args(args))
+    captured = capsys.readouterr()
+    assert re.search(r".*Exchanges available for Freqtrade.*", captured.out)
+    assert re.search(r".*binance.*", captured.out)
+    assert re.search(r".*bybit.*", captured.out)
+
+    # Test with --one-column
+    args = [
+        "list-exchanges",
+        "--one-column",
+    ]
+
+    start_list_exchanges(get_args(args))
+    captured = capsys.readouterr()
+    assert re.search(r"^binance$", captured.out, re.MULTILINE)
+    assert re.search(r"^bybit$", captured.out, re.MULTILINE)
+
+    # Test with --all
+    args = [
+        "list-exchanges",
+        "--all",
+    ]
+
+    start_list_exchanges(get_args(args))
+    captured = capsys.readouterr()
+    assert re.search(r"All exchanges supported by the ccxt library.*", captured.out)
+    assert re.search(r".*binance.*", captured.out)
+    assert re.search(r".*bingx.*", captured.out)
+    assert re.search(r".*bitmex.*", captured.out)
+
+    # Test with --one-column --all
+    args = [
+        "list-exchanges",
+        "--one-column",
+        "--all",
+    ]
+
+    start_list_exchanges(get_args(args))
+    captured = capsys.readouterr()
+    assert re.search(r"^binance$", captured.out, re.MULTILINE)
+    assert re.search(r"^bingx$", captured.out, re.MULTILINE)
+    assert re.search(r"^bitmex$", captured.out, re.MULTILINE)
+
+
+def test_list_timeframes(mocker, capsys):
+    api_mock = MagicMock()
+    api_mock.timeframes = {
+        "1m": "oneMin",
+        "5m": "fiveMin",
+        "30m": "thirtyMin",
+        "1h": "hour",
+        "1d": "day",
+    }
+    patch_exchange(mocker, api_mock=api_mock, exchange="bybit")
+    args = [
+        "list-timeframes",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(
+        OperationalException, match=r"This command requires a configured exchange.*"
+    ):
+        start_list_timeframes(pargs)
+
+    # Test with --config tests/testdata/testconfigs/main_test_config.json
+    args = [
+        "list-timeframes",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+    ]
+    start_list_timeframes(get_args(args))
+    captured = capsys.readouterr()
+    assert re.match(
+        "Timeframes available for the exchange `Bybit`: 1m, 5m, 30m, 1h, 1d", captured.out
+    )
+
+    # Test with --exchange bybit
+    args = [
+        "list-timeframes",
+        "--exchange",
+        "bybit",
+    ]
+    start_list_timeframes(get_args(args))
+    captured = capsys.readouterr()
+    assert re.match(
+        "Timeframes available for the exchange `Bybit`: 1m, 5m, 30m, 1h, 1d", captured.out
+    )
+
+    api_mock.timeframes = {
+        "1m": "1m",
+        "5m": "5m",
+        "15m": "15m",
+        "30m": "30m",
+        "1h": "1h",
+        "6h": "6h",
+        "12h": "12h",
+        "1d": "1d",
+        "3d": "3d",
+    }
+    patch_exchange(mocker, api_mock=api_mock, exchange="binance")
+    # Test with --exchange binance
+    args = [
+        "list-timeframes",
+        "--exchange",
+        "binance",
+    ]
+    start_list_timeframes(get_args(args))
+    captured = capsys.readouterr()
+    assert re.match(
+        "Timeframes available for the exchange `Binance`: 1m, 5m, 15m, 30m, 1h, 6h, 12h, 1d, 3d",
+        captured.out,
+    )
+
+    # Test with --one-column
+    args = [
+        "list-timeframes",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--one-column",
+    ]
+    start_list_timeframes(get_args(args))
+    captured = capsys.readouterr()
+    assert re.search(r"^1m$", captured.out, re.MULTILINE)
+    assert re.search(r"^5m$", captured.out, re.MULTILINE)
+    assert re.search(r"^1h$", captured.out, re.MULTILINE)
+    assert re.search(r"^1d$", captured.out, re.MULTILINE)
+
+    # Test with --exchange binance --one-column
+    args = [
+        "list-timeframes",
+        "--exchange",
+        "binance",
+        "--one-column",
+    ]
+    start_list_timeframes(get_args(args))
+    captured = capsys.readouterr()
+    assert re.search(r"^1m$", captured.out, re.MULTILINE)
+    assert re.search(r"^5m$", captured.out, re.MULTILINE)
+    assert re.search(r"^1h$", captured.out, re.MULTILINE)
+    assert re.search(r"^1d$", captured.out, re.MULTILINE)
+
+
+def test_list_markets(mocker, markets_static, capsys):
+    api_mock = MagicMock()
+    patch_exchange(mocker, api_mock=api_mock, exchange="binance", mock_markets=markets_static)
+
+    # Test with no --config
+    args = [
+        "list-markets",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(
+        OperationalException, match=r"This command requires a configured exchange.*"
+    ):
+        start_list_markets(pargs, False)
+
+    # Test with --config tests/testdata/testconfigs/main_test_config.json
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 12 active markets: "
+        "ADA/USDT:USDT, BLK/BTC, ETH/BTC, ETH/USDT, ETH/USDT:USDT, LTC/BTC, "
+        "LTC/ETH, LTC/USD, NEO/BTC, TKN/BTC, XLTCUSDT, XRP/BTC.\n" in captured.out
+    )
+
+    patch_exchange(mocker, api_mock=api_mock, exchange="binance", mock_markets=markets_static)
+    # Test with --exchange
+    args = ["list-markets", "--exchange", "binance"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_markets(pargs, False)
+    captured = capsys.readouterr()
+    assert re.search(r".*Exchange Binance has 12 active markets.*", captured.out)
+
+    patch_exchange(mocker, api_mock=api_mock, exchange="binance", mock_markets=markets_static)
+    # Test with --all: all markets
+    args = [
+        "list-markets",
+        "--all",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 14 markets: "
+        "ADA/USDT:USDT, BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, ETH/USDT:USDT, "
+        "LTC/BTC, LTC/ETH, LTC/USD, LTC/USDT, NEO/BTC, TKN/BTC, XLTCUSDT, XRP/BTC.\n"
+        in captured.out
+    )
+
+    # Test list-pairs subcommand: active pairs
+    args = [
+        "list-pairs",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), True)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 9 active pairs: "
+        "BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, NEO/BTC, TKN/BTC, XRP/BTC.\n"
+        in captured.out
+    )
+
+    # Test list-pairs subcommand with --all: all pairs
+    args = [
+        "list-pairs",
+        "--all",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), True)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 11 pairs: "
+        "BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, LTC/USDT, NEO/BTC, "
+        "TKN/BTC, XRP/BTC.\n" in captured.out
+    )
+
+    # active markets, base=ETH, LTC
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--base",
+        "ETH",
+        "LTC",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 7 active markets with ETH, LTC as base currencies: "
+        "ETH/BTC, ETH/USDT, ETH/USDT:USDT, LTC/BTC, LTC/ETH, LTC/USD, XLTCUSDT.\n" in captured.out
+    )
+
+    # active markets, base=LTC
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--base",
+        "LTC",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 4 active markets with LTC as base currency: "
+        "LTC/BTC, LTC/ETH, LTC/USD, XLTCUSDT.\n" in captured.out
+    )
+
+    # active markets, quote=USDT, USD
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--quote",
+        "USDT",
+        "USD",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 5 active markets with USDT, USD as quote currencies: "
+        "ADA/USDT:USDT, ETH/USDT, ETH/USDT:USDT, LTC/USD, XLTCUSDT.\n" in captured.out
+    )
+
+    # active markets, quote=USDT
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--quote",
+        "USDT",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 4 active markets with USDT as quote currency: "
+        "ADA/USDT:USDT, ETH/USDT, ETH/USDT:USDT, XLTCUSDT.\n" in captured.out
+    )
+
+    # active markets, base=LTC, quote=USDT
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--base",
+        "LTC",
+        "--quote",
+        "USDT",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 1 active market with LTC as base currency and "
+        "with USDT as quote currency: XLTCUSDT.\n" in captured.out
+    )
+
+    # active pairs, base=LTC, quote=USDT
+    args = [
+        "list-pairs",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--base",
+        "LTC",
+        "--quote",
+        "USD",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), True)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 1 active pair with LTC as base currency and "
+        "with USD as quote currency: LTC/USD.\n" in captured.out
+    )
+
+    # active markets, base=LTC, quote=USDT, NONEXISTENT
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--base",
+        "LTC",
+        "--quote",
+        "USDT",
+        "NONEXISTENT",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 1 active market with LTC as base currency and "
+        "with USDT, NONEXISTENT as quote currencies: XLTCUSDT.\n" in captured.out
+    )
+
+    # active markets, base=LTC, quote=NONEXISTENT
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--base",
+        "LTC",
+        "--quote",
+        "NONEXISTENT",
+        "--print-list",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 0 active markets with LTC as base currency and "
+        "with NONEXISTENT as quote currency.\n" in captured.out
+    )
+
+    # Test tabular output
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert "Exchange Binance has 12 active markets" in captured.out
+
+    # Test tabular output, no markets found
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--base",
+        "LTC",
+        "--quote",
+        "NONEXISTENT",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        "Exchange Binance has 0 active markets with LTC as base currency and "
+        "with NONEXISTENT as quote currency.\n" in captured.out
+    )
+
+    # Test --print-json
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--print-json",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert (
+        '["ADA/USDT:USDT","BLK/BTC","ETH/BTC","ETH/USDT","ETH/USDT:USDT",'
+        '"LTC/BTC","LTC/ETH","LTC/USD","NEO/BTC","TKN/BTC","XLTCUSDT","XRP/BTC"]' in captured.out
+    )
+
+    # Test --print-csv
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--print-csv",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert "Id,Symbol,Base,Quote,Active,Spot,Margin,Future,Leverage" in captured.out
+    assert "blkbtc,BLK/BTC,BLK,BTC,True,Spot" in captured.out
+    assert "USD-LTC,LTC/USD,LTC,USD,True,Spot" in captured.out
+
+    # Test --one-column
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--one-column",
+    ]
+    start_list_markets(get_args(args), False)
+    captured = capsys.readouterr()
+    assert re.search(r"^BLK/BTC$", captured.out, re.MULTILINE)
+    assert re.search(r"^LTC/USD$", captured.out, re.MULTILINE)
+
+    mocker.patch(f"{EXMS}.markets", PropertyMock(side_effect=ValueError))
+    # Test --one-column
+    args = [
+        "list-markets",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--one-column",
+    ]
+    with pytest.raises(OperationalException, match=r"Cannot get markets.*"):
+        start_list_markets(get_args(args), False)
+
+
+def test_create_datadir_failed(caplog):
+    args = [
+        "create-userdir",
+    ]
+    with pytest.raises(SystemExit):
+        start_create_userdir(get_args(args))
+    assert log_has("`create-userdir` requires --userdir to be set.", caplog)
+
+
+def test_create_datadir(mocker):
+    cud = mocker.patch(
+        "freqtrade.configuration.directory_operations.create_userdata_dir", MagicMock()
+    )
+    csf = mocker.patch(
+        "freqtrade.configuration.directory_operations.copy_sample_files", MagicMock()
+    )
+    args = ["create-userdir", "--userdir", "/temp/freqtrade/test"]
+    start_create_userdir(get_args(args))
+
+    assert cud.call_count == 1
+    assert csf.call_count == 1
+
+
+def test_start_new_strategy(caplog, user_dir):
+    strategy_dir = user_dir / "strategies"
+    strategy_dir.mkdir(parents=True, exist_ok=True)
+
+    assert strategy_dir.is_dir()
+    args = ["new-strategy", "--strategy", "CoolNewStrategy"]
+    start_new_strategy(get_args(args))
+    assert strategy_dir.exists()
+    assert (strategy_dir / "CoolNewStrategy.py").exists()
+
+    assert log_has_re("Writing strategy to .*", caplog)
+
+    with pytest.raises(
+        OperationalException, match=r".* already exists. Please choose another Strategy Name\."
+    ):
+        start_new_strategy(get_args(args))
+
+    args = ["new-strategy", "--strategy", "CoolNewStrategy", "--strategy-path", str(user_dir)]
+    start_new_strategy(get_args(args))
+    assert (user_dir / "CoolNewStrategy.py").exists()
+
+    # strategy-path that doesn't exist
+    args = [
+        "new-strategy",
+        "--strategy",
+        "CoolNewStrategy",
+        "--strategy-path",
+        str(user_dir / "nonexistent"),
+    ]
+    start_new_strategy(get_args(args))
+    assert (user_dir / "CoolNewStrategy.py").exists()
+
+    assert log_has_re("Creating strategy directory .*", caplog)
+    assert (user_dir / "nonexistent").is_dir()
+    assert (user_dir / "nonexistent" / "CoolNewStrategy.py").exists()
+
+    shutil.rmtree(str(user_dir))
+
+
+def test_start_new_strategy_no_arg():
+    args = [
+        "new-strategy",
+    ]
+    with pytest.raises(OperationalException, match="`new-strategy` requires --strategy to be set."):
+        start_new_strategy(get_args(args))
+
+
+def test_start_install_ui(mocker):
+    clean_mock = mocker.patch("freqtrade.commands.deploy_ui.clean_ui_subdir")
+    get_url_mock = mocker.patch(
+        "freqtrade.commands.deploy_ui.get_ui_download_url",
+        return_value=("https://example.com/whatever", "0.0.1"),
+    )
+    download_mock = mocker.patch("freqtrade.commands.deploy_ui.download_and_install_ui")
+    mocker.patch("freqtrade.commands.deploy_ui.read_ui_version", return_value=None)
+    args = [
+        "install-ui",
+    ]
+    start_install_ui(get_args(args))
+    assert clean_mock.call_count == 1
+    assert get_url_mock.call_count == 1
+    assert download_mock.call_count == 1
+
+    clean_mock.reset_mock()
+    get_url_mock.reset_mock()
+    download_mock.reset_mock()
+
+    args = [
+        "install-ui",
+        "--erase",
+    ]
+    start_install_ui(get_args(args))
+    assert clean_mock.call_count == 1
+    assert get_url_mock.call_count == 1
+    assert download_mock.call_count == 0
+
+
+def test_clean_ui_subdir(mocker, tmp_path, caplog):
+    mocker.patch("freqtrade.commands.deploy_ui.Path.is_dir", side_effect=[True, True])
+    mocker.patch("freqtrade.commands.deploy_ui.Path.is_file", side_effect=[False, True])
+    rd_mock = mocker.patch("freqtrade.commands.deploy_ui.Path.rmdir")
+    ul_mock = mocker.patch("freqtrade.commands.deploy_ui.Path.unlink")
+
+    mocker.patch(
+        "freqtrade.commands.deploy_ui.Path.glob",
+        return_value=[Path("test1"), Path("test2"), Path(".gitkeep")],
+    )
+    folder = tmp_path / "uitests"
+    clean_ui_subdir(folder)
+    assert log_has("Removing UI directory content.", caplog)
+    assert rd_mock.call_count == 1
+    assert ul_mock.call_count == 1
+
+
+def test_download_and_install_ui(mocker, tmp_path):
+    # Create zipfile
+    requests_mock = MagicMock()
+    file_like_object = BytesIO()
+    with ZipFile(file_like_object, mode="w") as zipfile:
+        for file in ("test1.txt", "hello/", "test2.txt"):
+            zipfile.writestr(file, file)
+    file_like_object.seek(0)
+    requests_mock.content = file_like_object.read()
+
+    mocker.patch("freqtrade.commands.deploy_ui.requests.get", return_value=requests_mock)
+
+    mocker.patch("freqtrade.commands.deploy_ui.Path.is_dir", side_effect=[True, False])
+    wb_mock = mocker.patch("freqtrade.commands.deploy_ui.Path.write_bytes")
+
+    folder = tmp_path / "uitests_dl"
+    folder.mkdir(exist_ok=True)
+
+    assert read_ui_version(folder) is None
+
+    download_and_install_ui(folder, "http://whatever.xxx/download/file.zip", "22")
+
+    assert wb_mock.call_count == 2
+
+    assert read_ui_version(folder) == "22"
+
+
+def test_get_ui_download_url(mocker):
+    response = MagicMock()
+    responses = [
+        [
+            {
+                # Pre-release is ignored
+                "assets_url": "http://whatever.json",
+                "name": "0.0.2",
+                "created_at": "2024-02-01T00:00:00Z",
+                "prerelease": True,
+            },
+            {
+                "assets_url": "http://whatever.json",
+                "name": "0.0.1",
+                "created_at": "2024-01-01T00:00:00Z",
+                "prerelease": False,
+            },
+        ],
+        [{"browser_download_url": "http://download.zip"}],
+    ]
+    response.json = MagicMock(side_effect=responses)
+    get_mock = mocker.patch("freqtrade.commands.deploy_ui.requests.get", return_value=response)
+    x, last_version = get_ui_download_url(None, False)
+    assert get_mock.call_count == 2
+    assert last_version == "0.0.1"
+    assert x == "http://download.zip"
+
+    response.json = MagicMock(side_effect=responses)
+    get_mock.reset_mock()
+    x, last_version = get_ui_download_url(None, True)
+    assert get_mock.call_count == 2
+    assert last_version == "0.0.2"
+    assert x == "http://download.zip"
+
+
+def test_get_ui_download_url_direct(mocker):
+    response = MagicMock()
+    response.json = MagicMock(
+        return_value=[
+            {
+                "assets_url": "http://whatever.json",
+                "name": "0.0.2",
+                "created_at": "2024-02-01T00:00:00Z",
+                "prerelease": False,
+                "assets": [{"browser_download_url": "http://download22.zip"}],
+            },
+            {
+                "assets_url": "http://whatever.json",
+                "name": "0.0.1",
+                "created_at": "2024-01-01T00:00:00Z",
+                "prerelease": False,
+                "assets": [{"browser_download_url": "http://download1.zip"}],
+            },
+        ]
+    )
+    get_mock = mocker.patch("freqtrade.commands.deploy_ui.requests.get", return_value=response)
+    x, last_version = get_ui_download_url(None, False)
+    assert get_mock.call_count == 1
+    assert last_version == "0.0.2"
+    assert x == "http://download22.zip"
+    get_mock.reset_mock()
+    response.json.reset_mock()
+
+    x, last_version = get_ui_download_url("0.0.1", False)
+    assert last_version == "0.0.1"
+    assert x == "http://download1.zip"
+
+    with pytest.raises(ValueError, match="UI-Version not found."):
+        x, last_version = get_ui_download_url("0.0.3", False)
+
+
+def test_download_data_keyboardInterrupt(mocker, markets):
+    dl_mock = mocker.patch(
+        "freqtrade.data.history.download_data_main",
+        MagicMock(side_effect=KeyboardInterrupt),
+    )
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    args = [
+        "download-data",
+        "--exchange",
+        "binance",
+        "--pairs",
+        "ETH/BTC",
+        "XRP/BTC",
+    ]
+    with pytest.raises(SystemExit):
+        pargs = get_args(args)
+        pargs["config"] = None
+
+        start_download_data(pargs)
+
+    assert dl_mock.call_count == 1
+
+
+@pytest.mark.parametrize("time", ["00:00", "00:03", "00:30", "23:56"])
+@pytest.mark.parametrize(
+    "tzoffset",
+    ["00:00", "+01:00", "-01:00", "+05:00", "-05:00"],
+)
+def test_download_data_timerange(mocker, markets, time_machine, time, tzoffset):
+    time_machine.move_to(f"2024-11-01 {time}:00 {tzoffset}")
+    dl_mock = mocker.patch(
+        "freqtrade.data.history.history_utils.refresh_backtest_ohlcv_data",
+        MagicMock(return_value=["ETH/BTC", "XRP/BTC"]),
+    )
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    args = [
+        "download-data",
+        "--exchange",
+        "binance",
+        "--pairs",
+        "ETH/BTC",
+        "XRP/BTC",
+        "--days",
+        "20",
+        "--timerange",
+        "20200101-",
+    ]
+    with pytest.raises(OperationalException, match=r"--days and --timerange are mutually.*"):
+        pargs = get_args(args)
+        pargs["config"] = None
+        start_download_data(pargs)
+    assert dl_mock.call_count == 0
+
+    args = [
+        "download-data",
+        "--exchange",
+        "binance",
+        "--pairs",
+        "ETH/BTC",
+        "XRP/BTC",
+        "--days",
+        "20",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_download_data(pargs)
+    assert dl_mock.call_count == 1
+    # 20days ago
+    days_ago = datetime.now() - timedelta(days=20)
+    days_ago = dt_utc(days_ago.year, days_ago.month, days_ago.day)
+    assert dl_mock.call_args_list[0][1]["timerange"].startts == days_ago.timestamp()
+
+    dl_mock.reset_mock()
+    args = [
+        "download-data",
+        "--exchange",
+        "binance",
+        "--pairs",
+        "ETH/BTC",
+        "XRP/BTC",
+        "--timerange",
+        "20200101-",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_download_data(pargs)
+    assert dl_mock.call_count == 1
+
+    assert dl_mock.call_args_list[0][1]["timerange"].startts == int(dt_utc(2020, 1, 1).timestamp())
+
+
+def test_download_data_no_exchange(mocker):
+    mocker.patch(
+        "freqtrade.data.history.history_utils.refresh_backtest_ohlcv_data",
+        MagicMock(return_value=["ETH/BTC", "XRP/BTC"]),
+    )
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_markets", return_value={})
+    args = [
+        "download-data",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(
+        OperationalException, match=r"This command requires a configured exchange.*"
+    ):
+        start_download_data(pargs)
+
+
+def test_download_data_no_pairs(mocker):
+    mocker.patch(
+        "freqtrade.data.history.history_utils.refresh_backtest_ohlcv_data",
+        MagicMock(return_value=["ETH/BTC", "XRP/BTC"]),
+    )
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value={}))
+    args = [
+        "download-data",
+        "--exchange",
+        "binance",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(
+        OperationalException, match=r"Downloading data requires a list of pairs\..*"
+    ):
+        start_download_data(pargs)
+
+
+def test_download_data_all_pairs(mocker, markets):
+    dl_mock = mocker.patch(
+        "freqtrade.data.history.history_utils.refresh_backtest_ohlcv_data",
+        MagicMock(return_value=["ETH/BTC", "XRP/BTC"]),
+    )
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    args = ["download-data", "--exchange", "binance", "--pairs", ".*/USDT"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_download_data(pargs)
+    expected = set(["BTC/USDT", "ETH/USDT", "XRP/USDT", "NEO/USDT", "TKN/USDT"])
+    assert set(dl_mock.call_args_list[0][1]["pairs"]) == expected
+    assert dl_mock.call_count == 1
+
+    dl_mock.reset_mock()
+    args = [
+        "download-data",
+        "--exchange",
+        "binance",
+        "--pairs",
+        ".*/USDT",
+        "--include-inactive-pairs",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_download_data(pargs)
+    expected = set(["BTC/USDT", "ETH/USDT", "LTC/USDT", "XRP/USDT", "NEO/USDT", "TKN/USDT"])
+    assert set(dl_mock.call_args_list[0][1]["pairs"]) == expected
+
+
+def test_download_data_trades(mocker):
+    dl_mock = mocker.patch(
+        "freqtrade.data.history.history_utils.refresh_backtest_trades_data",
+        MagicMock(return_value=[]),
+    )
+    convert_mock = mocker.patch(
+        "freqtrade.data.history.history_utils.convert_trades_to_ohlcv", MagicMock(return_value=[])
+    )
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_markets", return_value={"ETH/BTC": {}, "XRP/BTC": {}})
+    args = [
+        "download-data",
+        "--exchange",
+        "kraken",
+        "--pairs",
+        "ETH/BTC",
+        "XRP/BTC",
+        "--days",
+        "20",
+        "--dl-trades",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_download_data(pargs)
+    assert dl_mock.call_args[1]["timerange"].starttype == "date"
+    assert dl_mock.call_count == 1
+    assert convert_mock.call_count == 1
+    args = [
+        "download-data",
+        "--exchange",
+        "kraken",
+        "--pairs",
+        "ETH/BTC",
+        "XRP/BTC",
+        "--days",
+        "20",
+        "--trading-mode",
+        "futures",
+        "--dl-trades",
+    ]
+
+
+def test_download_data_data_invalid(mocker):
+    patch_exchange(mocker, exchange="kraken")
+    mocker.patch(f"{EXMS}.get_markets", return_value={"ETH/BTC": {}, "XRP/BTC": {}})
+    args = [
+        "download-data",
+        "--exchange",
+        "kraken",
+        "--pairs",
+        "ETH/BTC",
+        "XRP/BTC",
+        "--days",
+        "20",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(OperationalException, match=r"Historic klines not available for .*"):
+        start_download_data(pargs)
+
+
+def test_start_convert_trades(mocker):
+    convert_mock = mocker.patch(
+        "freqtrade.data.converter.convert_trades_to_ohlcv", MagicMock(return_value=[])
+    )
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_markets")
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value={}))
+    args = [
+        "trades-to-ohlcv",
+        "--exchange",
+        "kraken",
+        "--pairs",
+        "ETH/BTC",
+        "XRP/BTC",
+    ]
+    start_convert_trades(get_args(args))
+    assert convert_mock.call_count == 1
+
+
+def test_start_list_strategies(capsys):
+    args = [
+        "list-strategies",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy" / "strats"),
+        "-1",
+    ]
+    pargs = get_args(args)
+    # pargs['config'] = None
+    start_list_strategies(pargs)
+    captured = capsys.readouterr()
+    assert "StrategyTestV2" in captured.out
+    assert "strategy_test_v2.py" not in captured.out
+    assert CURRENT_TEST_STRATEGY in captured.out
+
+    # Test regular output
+    args = [
+        "list-strategies",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy" / "strats"),
+        "--no-color",
+    ]
+    pargs = get_args(args)
+    # pargs['config'] = None
+    start_list_strategies(pargs)
+    captured = capsys.readouterr()
+    assert "StrategyTestV2" in captured.out
+    assert "strategy_test_v2.py" in captured.out
+    assert CURRENT_TEST_STRATEGY in captured.out
+
+    # Test color output
+    args = [
+        "list-strategies",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy" / "strats"),
+    ]
+    pargs = get_args(args)
+    # pargs['config'] = None
+    start_list_strategies(pargs)
+    captured = capsys.readouterr()
+    assert "StrategyTestV2" in captured.out
+    assert "strategy_test_v2.py" in captured.out
+    assert CURRENT_TEST_STRATEGY in captured.out
+    assert "LOAD FAILED" in captured.out
+    # Recursive
+    assert "TestStrategyNoImplements" not in captured.out
+
+    # Test recursive
+    args = [
+        "list-strategies",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy" / "strats"),
+        "--no-color",
+        "--recursive-strategy-search",
+    ]
+    pargs = get_args(args)
+    # pargs['config'] = None
+    start_list_strategies(pargs)
+    captured = capsys.readouterr()
+    assert "StrategyTestV2" in captured.out
+    assert "strategy_test_v2.py" in captured.out
+    assert "StrategyTestV2" in captured.out
+    assert "TestStrategyNoImplements" in captured.out
+    assert str(Path("broken_strats/broken_futures_strategies.py")) in captured.out
+
+
+def test_start_list_hyperopt_loss_functions(capsys):
+    args = ["list-hyperoptloss", "-1"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_hyperopt_loss_functions(pargs)
+    captured = capsys.readouterr()
+    assert "CalmarHyperOptLoss" in captured.out
+    assert "MaxDrawDownHyperOptLoss" in captured.out
+    assert "SortinoHyperOptLossDaily" in captured.out
+    assert "<builtin>/hyperopt_loss_sortino_daily.py" not in captured.out
+
+    args = ["list-hyperoptloss"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_hyperopt_loss_functions(pargs)
+    captured = capsys.readouterr()
+    assert "CalmarHyperOptLoss" in captured.out
+    assert "MaxDrawDownHyperOptLoss" in captured.out
+    assert "SortinoHyperOptLossDaily" in captured.out
+    assert "<builtin>/hyperopt_loss_sortino_daily.py" in captured.out
+
+
+def test_start_list_freqAI_models(capsys):
+    args = ["list-freqaimodels", "-1"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_freqAI_models(pargs)
+    captured = capsys.readouterr()
+    assert "LightGBMClassifier" in captured.out
+    assert "LightGBMRegressor" in captured.out
+    assert "XGBoostRegressor" in captured.out
+    assert "<builtin>/LightGBMRegressor.py" not in captured.out
+
+    args = [
+        "list-freqaimodels",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_freqAI_models(pargs)
+    captured = capsys.readouterr()
+    assert "LightGBMClassifier" in captured.out
+    assert "LightGBMRegressor" in captured.out
+    assert "XGBoostRegressor" in captured.out
+    assert "<builtin>/LightGBMRegressor.py" in captured.out
+
+
+def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
+    patch_exchange(mocker, mock_markets=True)
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+
+    default_conf["pairlists"] = [
+        {
+            "method": "VolumePairList",
+            "number_assets": 5,
+            "sort_key": "quoteVolume",
+        },
+        {"method": "PrecisionFilter"},
+        {"method": "PriceFilter", "low_price_ratio": 0.02},
+    ]
+
+    patched_configuration_load_config_file(mocker, default_conf)
+    args = ["test-pairlist", "-c", "tests/testdata/testconfigs/main_test_config.json"]
+
+    start_test_pairlist(get_args(args))
+
+    assert log_has_re(r"^Using resolved pairlist VolumePairList.*", caplog)
+    assert log_has_re(r"^Using resolved pairlist PrecisionFilter.*", caplog)
+    assert log_has_re(r"^Using resolved pairlist PriceFilter.*", caplog)
+    captured = capsys.readouterr()
+    assert re.match(r"Pairs for .*", captured.out)
+    assert re.match("['ETH/BTC', 'TKN/BTC', 'BLK/BTC', 'LTC/BTC', 'XRP/BTC']", captured.out)
+
+    args = [
+        "test-pairlist",
+        "-c",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--one-column",
+    ]
+    start_test_pairlist(get_args(args))
+    captured = capsys.readouterr()
+    assert re.match(r"ETH/BTC\nTKN/BTC\nBLK/BTC\nLTC/BTC\nXRP/BTC\n", captured.out)
+
+    args = [
+        "test-pairlist",
+        "-c",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--print-json",
+    ]
+    start_test_pairlist(get_args(args))
+    captured = capsys.readouterr()
+    try:
+        json_pairs = json.loads(captured.out)
+        assert "ETH/BTC" in json_pairs
+        assert "TKN/BTC" in json_pairs
+        assert "BLK/BTC" in json_pairs
+        assert "LTC/BTC" in json_pairs
+        assert "XRP/BTC" in json_pairs
+    except json.decoder.JSONDecodeError:
+        pytest.fail(f"Expected well formed JSON, but failed to parse: {captured.out}")
+
+
+def test_hyperopt_list(mocker, capsys, caplog, tmp_path):
+    saved_hyperopt_results = hyperopt_test_result()
+    csv_file = tmp_path / "test.csv"
+    mocker.patch(
+        "freqtrade.optimize.hyperopt_tools.HyperoptTools._test_hyperopt_results_exist",
+        return_value=True,
+    )
+
+    def fake_iterator(*args, **kwargs):
+        yield from [saved_hyperopt_results]
+
+    mocker.patch(
+        "freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results", side_effect=fake_iterator
+    )
+
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(
+        x in captured.out
+        for x in [
+            " 1/12",
+            " 2/12",
+            " 3/12",
+            " 4/12",
+            " 5/12",
+            " 6/12",
+            " 7/12",
+            " 8/12",
+            " 9/12",
+            " 10/12",
+            " 11/12",
+            " 12/12",
+        ]
+    )
+    args = [
+        "hyperopt-list",
+        "--best",
+        "--no-details",
+        "--no-color",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out for x in [" 1/12", " 5/12", " 10/12"])
+    assert all(
+        x not in captured.out
+        for x in [" 2/12", " 3/12", " 4/12", " 6/12", " 7/12", " 8/12", " 9/12", " 11/12", " 12/12"]
+    )
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-details",
+        "--no-color",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out for x in [" 2/12", " 10/12"])
+    assert all(
+        x not in captured.out
+        for x in [
+            " 1/12",
+            " 3/12",
+            " 4/12",
+            " 5/12",
+            " 6/12",
+            " 7/12",
+            " 8/12",
+            " 9/12",
+            " 11/12",
+            " 12/12",
+        ]
+    )
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-color",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(
+        x in captured.out
+        for x in [
+            " 2/12",
+            " 10/12",
+            "Best result:",
+            "Buy hyperspace params",
+            "Sell hyperspace params",
+            "ROI table",
+            "Stoploss",
+        ]
+    )
+    assert all(
+        x not in captured.out
+        for x in [
+            " 1/12",
+            " 3/12",
+            " 4/12",
+            " 5/12",
+            " 6/12",
+            " 7/12",
+            " 8/12",
+            " 9/12",
+            " 11/12",
+            " 12/12",
+        ]
+    )
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--min-trades",
+        "20",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out for x in [" 3/12", " 6/12", " 7/12", " 9/12", " 11/12"])
+    assert all(
+        x not in captured.out
+        for x in [" 1/12", " 2/12", " 4/12", " 5/12", " 8/12", " 10/12", " 12/12"]
+    )
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-details",
+        "--no-color",
+        "--max-trades",
+        "20",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out for x in [" 2/12", " 10/12"])
+    assert all(
+        x not in captured.out
+        for x in [
+            " 1/12",
+            " 3/12",
+            " 4/12",
+            " 5/12",
+            " 6/12",
+            " 7/12",
+            " 8/12",
+            " 9/12",
+            " 11/12",
+            " 12/12",
+        ]
+    )
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-details",
+        "--no-color",
+        "--min-avg-profit",
+        "0.11",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out for x in [" 2/12"])
+    assert all(
+        x not in captured.out
+        for x in [
+            " 1/12",
+            " 3/12",
+            " 4/12",
+            " 5/12",
+            " 6/12",
+            " 7/12",
+            " 8/12",
+            " 9/12",
+            " 10/12",
+            " 11/12",
+            " 12/12",
+        ]
+    )
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--max-avg-profit",
+        "0.10",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(
+        x in captured.out
+        for x in [" 1/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12", " 11/12"]
+    )
+    assert all(x not in captured.out for x in [" 2/12", " 4/12", " 10/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--min-total-profit",
+        "0.4",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out for x in [" 10/12"])
+    assert all(
+        x not in captured.out
+        for x in [
+            " 1/12",
+            " 2/12",
+            " 3/12",
+            " 4/12",
+            " 5/12",
+            " 6/12",
+            " 7/12",
+            " 8/12",
+            " 9/12",
+            " 11/12",
+            " 12/12",
+        ]
+    )
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--max-total-profit",
+        "0.4",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(
+        x in captured.out
+        for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12", " 11/12"]
+    )
+    assert all(x not in captured.out for x in [" 4/12", " 10/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--min-objective",
+        "0.1",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out for x in [" 10/12"])
+    assert all(
+        x not in captured.out
+        for x in [
+            " 1/12",
+            " 2/12",
+            " 3/12",
+            " 4/12",
+            " 5/12",
+            " 6/12",
+            " 7/12",
+            " 8/12",
+            " 9/12",
+            " 11/12",
+            " 12/12",
+        ]
+    )
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--max-objective",
+        "0.1",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(
+        x in captured.out
+        for x in [" 1/12", " 2/12", " 3/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12", " 11/12"]
+    )
+    assert all(x not in captured.out for x in [" 4/12", " 10/12", " 12/12"])
+    args = [
+        "hyperopt-list",
+        "--profitable",
+        "--no-details",
+        "--no-color",
+        "--min-avg-time",
+        "2000",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out for x in [" 10/12"])
+    assert all(
+        x not in captured.out
+        for x in [
+            " 1/12",
+            " 2/12",
+            " 3/12",
+            " 4/12",
+            " 5/12",
+            " 6/12",
+            " 7/12",
+            " 8/12",
+            " 9/12",
+            " 11/12",
+            " 12/12",
+        ]
+    )
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--max-avg-time",
+        "1500",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    assert all(x in captured.out for x in [" 2/12", " 6/12"])
+    assert all(
+        x not in captured.out
+        for x in [
+            " 1/12",
+            " 3/12",
+            " 4/12",
+            " 5/12",
+            " 7/12",
+            " 8/12",
+            " 9/12",
+            " 10/12",
+            " 11/12",
+            " 12/12",
+        ]
+    )
+    args = [
+        "hyperopt-list",
+        "--no-details",
+        "--no-color",
+        "--export-csv",
+        str(csv_file),
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_list(pargs)
+    captured = capsys.readouterr()
+    log_has("CSV file created: test_file.csv", caplog)
+    assert csv_file.is_file()
+    line = csv_file.read_text()
+    assert (
+        'Best,1,2,-1.25%,-1.2222,-0.00125625,BTC,-2.51,"3,930.0 m",-0.00125625,23.00%,0.43662'
+        in line
+        or "Best,1,2,-1.25%,-1.2222,-0.00125625,BTC,-2.51,2 days 17:30:00,2,0,-0.00125625,23.00%,"
+        "0.43662"
+        in line
+    )
+    csv_file.unlink()
+
+
+def test_hyperopt_show(mocker, capsys):
+    saved_hyperopt_results = hyperopt_test_result()
+    mocker.patch(
+        "freqtrade.optimize.hyperopt_tools.HyperoptTools._test_hyperopt_results_exist",
+        return_value=True,
+    )
+
+    def fake_iterator(*args, **kwargs):
+        yield from [saved_hyperopt_results]
+
+    mocker.patch(
+        "freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results", side_effect=fake_iterator
+    )
+    mocker.patch("freqtrade.optimize.optimize_reports.show_backtest_result")
+
+    args = [
+        "hyperopt-show",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 12/12" in captured.out
+
+    args = ["hyperopt-show", "--best"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 10/12" in captured.out
+
+    args = ["hyperopt-show", "-n", "1"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 1/12" in captured.out
+
+    args = ["hyperopt-show", "--best", "-n", "2"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 5/12" in captured.out
+
+    args = ["hyperopt-show", "--best", "-n", "-1"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_hyperopt_show(pargs)
+    captured = capsys.readouterr()
+    assert " 10/12" in captured.out
+
+    args = ["hyperopt-show", "--best", "-n", "-4"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(
+        OperationalException, match="The index of the epoch to show should be greater than -4."
+    ):
+        start_hyperopt_show(pargs)
+
+    args = ["hyperopt-show", "--best", "-n", "4"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(
+        OperationalException, match="The index of the epoch to show should be less than 4."
+    ):
+        start_hyperopt_show(pargs)
+
+
+def test_convert_data(mocker, testdatadir):
+    ohlcv_mock = mocker.patch("freqtrade.data.converter.convert_ohlcv_format")
+    trades_mock = mocker.patch("freqtrade.data.converter.convert_trades_format")
+    args = [
+        "convert-data",
+        "--format-from",
+        "json",
+        "--format-to",
+        "jsongz",
+        "--datadir",
+        str(testdatadir),
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_convert_data(pargs, True)
+    assert trades_mock.call_count == 0
+    assert ohlcv_mock.call_count == 1
+    assert ohlcv_mock.call_args[1]["convert_from"] == "json"
+    assert ohlcv_mock.call_args[1]["convert_to"] == "jsongz"
+    assert ohlcv_mock.call_args[1]["erase"] is False
+
+
+def test_convert_data_trades(mocker, testdatadir):
+    ohlcv_mock = mocker.patch("freqtrade.data.converter.convert_ohlcv_format")
+    trades_mock = mocker.patch("freqtrade.data.converter.convert_trades_format")
+    args = [
+        "convert-trade-data",
+        "--format-from",
+        "jsongz",
+        "--format-to",
+        "json",
+        "--datadir",
+        str(testdatadir),
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_convert_data(pargs, False)
+    assert ohlcv_mock.call_count == 0
+    assert trades_mock.call_count == 1
+    assert trades_mock.call_args[1]["convert_from"] == "jsongz"
+    assert trades_mock.call_args[1]["convert_to"] == "json"
+    assert trades_mock.call_args[1]["erase"] is False
+
+
+def test_start_list_data(testdatadir, capsys):
+    args = [
+        "list-data",
+        "--datadir",
+        str(testdatadir),
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_data(pargs)
+    captured = capsys.readouterr()
+    assert "Found 16 pair / timeframe combinations." in captured.out
+    assert re.search(r".*Pair.*Timeframe.*Type.*\n", captured.out)
+    assert re.search(r"\n.* UNITTEST/BTC .* 1m, 5m, 8m, 30m .* spot |\n", captured.out)
+
+    args = [
+        "list-data",
+        "--data-format-ohlcv",
+        "feather",
+        "--pairs",
+        "XRP/ETH",
+        "--datadir",
+        str(testdatadir),
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_data(pargs)
+    captured = capsys.readouterr()
+    assert "Found 2 pair / timeframe combinations." in captured.out
+    assert re.search(r".*Pair.*Timeframe.*Type.*\n", captured.out)
+    assert "UNITTEST/BTC" not in captured.out
+    assert re.search(r"\n.* XRP/ETH .* 1m, 5m .* spot |\n", captured.out)
+
+    args = [
+        "list-data",
+        "--trading-mode",
+        "futures",
+        "--datadir",
+        str(testdatadir),
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_data(pargs)
+    captured = capsys.readouterr()
+
+    assert "Found 6 pair / timeframe combinations." in captured.out
+    assert re.search(r".*Pair.*Timeframe.*Type.*\n", captured.out)
+    assert re.search(r"\n.* XRP/USDT:USDT .* 5m, 1h .* futures |\n", captured.out)
+    assert re.search(r"\n.* XRP/USDT:USDT .* 1h, 8h .* mark |\n", captured.out)
+
+    args = [
+        "list-data",
+        "--pairs",
+        "XRP/ETH",
+        "--datadir",
+        str(testdatadir),
+        "--show-timerange",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_data(pargs)
+    captured = capsys.readouterr()
+    assert "Found 2 pair / timeframe combinations." in captured.out
+    assert re.search(r".*Pair.*Timeframe.*Type.*From .* To .* Candles .*\n", captured.out)
+    assert "UNITTEST/BTC" not in captured.out
+    assert re.search(
+        r"\n.* XRP/USDT .* 1m .* spot .* 2019-10-11 00:00:00 .* 2019-10-13 11:19:00 .* 2469 |\n",
+        captured.out,
+    )
+
+
+def test_start_list_trades_data(testdatadir, capsys):
+    args = [
+        "list-data",
+        "--datadir",
+        str(testdatadir),
+        "--trades",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_data(pargs)
+    captured = capsys.readouterr()
+    assert "Found trades data for 1 pair." in captured.out
+    assert re.search(r".*Pair.*Type.*\n", captured.out)
+    assert re.search(r"\n.* XRP/ETH .* spot |\n", captured.out)
+
+    args = [
+        "list-data",
+        "--datadir",
+        str(testdatadir),
+        "--trades",
+        "--show-timerange",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_data(pargs)
+    captured = capsys.readouterr()
+    assert "Found trades data for 1 pair." in captured.out
+    assert re.search(r".*Pair.*Type.*From.*To.*Trades.*\n", captured.out)
+    assert re.search(
+        r"\n.* XRP/ETH .* spot .* 2019-10-11 00:00:01 .* 2019-10-13 11:19:28 .* 12477 .*|\n",
+        captured.out,
+    )
+
+    args = [
+        "list-data",
+        "--datadir",
+        str(testdatadir),
+        "--trades",
+        "--trading-mode",
+        "futures",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_list_data(pargs)
+    captured = capsys.readouterr()
+    assert "Found trades data for 0 pairs." in captured.out
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_show_trades(mocker, fee, capsys, caplog):
+    mocker.patch("freqtrade.persistence.init_db")
+    create_mock_trades(fee, False)
+    args = ["show-trades", "--db-url", "sqlite:///"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_show_trades(pargs)
+    assert log_has(f"Printing {MOCK_TRADE_COUNT} Trades: ", caplog)
+    captured = capsys.readouterr()
+    assert "Trade(id=1" in captured.out
+    assert "Trade(id=2" in captured.out
+    assert "Trade(id=3" in captured.out
+    args = ["show-trades", "--db-url", "sqlite:///", "--print-json", "--trade-ids", "1", "2"]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_show_trades(pargs)
+
+    captured = capsys.readouterr()
+    assert log_has("Printing 2 Trades: ", caplog)
+    assert '"trade_id": 1' in captured.out
+    assert '"trade_id": 2' in captured.out
+    assert '"trade_id": 3' not in captured.out
+    args = [
+        "show-trades",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+
+    with pytest.raises(OperationalException, match=r"--db-url is required for this command."):
+        start_show_trades(pargs)
+
+
+def test_backtesting_show(mocker, testdatadir, capsys):
+    sbr = mocker.patch("freqtrade.optimize.optimize_reports.show_backtest_results")
+    args = [
+        "backtesting-show",
+        "--export-filename",
+        f"{testdatadir / 'backtest_results/backtest-result.json'}",
+        "--show-pair-list",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_backtesting_show(pargs)
+    assert sbr.call_count == 1
+    out, _err = capsys.readouterr()
+    assert "Pairs for Strategy" in out
+
+
+def test_start_convert_db(fee, tmp_path):
+    db_src_file = tmp_path / "db.sqlite"
+    db_from = f"sqlite:///{db_src_file}"
+    db_target_file = tmp_path / "db_target.sqlite"
+    db_to = f"sqlite:///{db_target_file}"
+    args = [
+        "convert-db",
+        "--db-url-from",
+        db_from,
+        "--db-url",
+        db_to,
+    ]
+
+    assert not db_src_file.is_file()
+    init_db(db_from)
+
+    create_mock_trades(fee)
+
+    PairLocks.timeframe = "5m"
+    PairLocks.lock_pair("XRP/USDT", datetime.now(), "Random reason 125", side="long")
+    assert db_src_file.is_file()
+    assert not db_target_file.is_file()
+
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_convert_db(pargs)
+
+    assert db_target_file.is_file()
+
+
+def test_start_strategy_updater(mocker, tmp_path):
+    sc_mock = mocker.patch("freqtrade.commands.strategy_utils_commands.start_conversion")
+    teststrats = Path(__file__).parent.parent / "strategy/strats"
+    args = [
+        "strategy-updater",
+        "--userdir",
+        str(tmp_path),
+        "--strategy-path",
+        str(teststrats),
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_strategy_update(pargs)
+    # Number of strategies in the test directory
+    assert sc_mock.call_count == 12
+
+    sc_mock.reset_mock()
+    args = [
+        "strategy-updater",
+        "--userdir",
+        str(tmp_path),
+        "--strategy-path",
+        str(teststrats),
+        "--strategy-list",
+        "StrategyTestV3",
+        "StrategyTestV2",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    start_strategy_update(pargs)
+    # Number of strategies in the test directory
+    assert sc_mock.call_count == 2
+
+
+def test_start_show_config(capsys, caplog):
+    args = [
+        "show-config",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+    ]
+    pargs = get_args(args)
+    start_show_config(pargs)
+
+    captured = capsys.readouterr()
+    assert "Your combined configuration is:" in captured.out
+    assert '"max_open_trades":' in captured.out
+    assert '"secret": "REDACTED"' in captured.out
+
+    args = [
+        "show-config",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--show-sensitive",
+    ]
+    pargs = get_args(args)
+    start_show_config(pargs)
+
+    captured = capsys.readouterr()
+    assert "Your combined configuration is:" in captured.out
+    assert '"max_open_trades":' in captured.out
+    assert '"secret": "REDACTED"' not in captured.out
+    assert log_has_re(r"Sensitive information will be shown in the upcoming output.*", caplog)
diff --git a/tests/commands/test_startup_time.py b/tests/commands/test_startup_time.py
new file mode 100644
index 000000000..62baab20a
--- /dev/null
+++ b/tests/commands/test_startup_time.py
@@ -0,0 +1,20 @@
+import subprocess  # noqa: S404, RUF100
+import time
+
+from tests.conftest import is_arm, is_mac
+
+
+MAXIMUM_STARTUP_TIME = 0.7 if is_mac() and not is_arm() else 0.5
+
+
+def test_startup_time():
+    # warm up to generate pyc
+    subprocess.run(["freqtrade", "-h"])
+
+    start = time.time()
+    subprocess.run(["freqtrade", "-h"])
+    elapsed = time.time() - start
+    assert elapsed < MAXIMUM_STARTUP_TIME, (
+        "The startup time is too long, try to use lazy import in the command entry function"
+        f" (maximum {MAXIMUM_STARTUP_TIME}s, got {elapsed}s)"
+    )
diff --git a/tests/config_test_comments.json b/tests/config_test_comments.json
new file mode 100644
index 000000000..19d82c454
--- /dev/null
+++ b/tests/config_test_comments.json
@@ -0,0 +1,129 @@
+{
+    /* Single-line C-style comment */
+    "max_open_trades": 3,
+    /*
+     * Multi-line C-style comment
+     */
+    "stake_currency": "BTC",
+    "stake_amount": 0.05,
+    "fiat_display_currency": "USD", // C++-style comment
+    "amount_reserve_percent": 0.05, // And more, tabs before this comment
+    "dry_run": false,
+    "timeframe": "5m",
+    "trailing_stop": false,
+    "trailing_stop_positive": 0.005,
+    "trailing_stop_positive_offset": 0.0051,
+    "trailing_only_offset_is_reached": false,
+    "minimal_roi": {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
+    },
+    "stoploss": -0.10,
+    "unfilledtimeout": {
+        "buy": 10,
+        "sell": 30, // Trailing comma should also be accepted now
+    },
+    "bid_strategy": {
+        "use_order_book": false,
+        "ask_last_balance": 0.0,
+        "order_book_top": 1,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "ask_strategy": {
+        "use_order_book": false,
+        "order_book_min": 1,
+        "order_book_max": 9
+    },
+    "order_types": {
+        "buy": "limit",
+        "sell": "limit",
+        "stoploss": "market",
+        "stoploss_on_exchange": false,
+        "stoploss_on_exchange_interval": 60
+    },
+    "order_time_in_force": {
+        "buy": "gtc",
+        "sell": "gtc"
+    },
+    "pairlist": {
+        "method": "VolumePairList",
+        "config": {
+            "number_assets": 20,
+            "sort_key": "quoteVolume",
+            "precision_filter": false
+        }
+    },
+    "exchange": {
+        "name": "binance",
+        "sandbox": false,
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "password": "",
+        "ccxt_config": {
+            "enableRateLimit": true
+        },
+        "ccxt_async_config": {
+            "enableRateLimit": false,
+            "rateLimit": 500,
+            "aiohttp_trust_env": false
+        },
+        "pair_whitelist": [
+            "ETH/BTC",
+            "LTC/BTC",
+            "ETC/BTC",
+            "DASH/BTC",
+            "ZEC/BTC",
+            "XLM/BTC",
+            "NXT/BTC",
+            "TRX/BTC",
+            "ADA/BTC",
+            "XMR/BTC"
+        ],
+        "pair_blacklist": [
+            "DOGE/BTC"
+        ],
+        "outdated_offset": 5,
+        "markets_refresh_interval": 60
+    },
+    "edge": {
+        "enabled": false,
+        "process_throttle_secs": 3600,
+        "calculate_since_number_of_days": 7,
+        "allowed_risk": 0.01,
+        "stoploss_range_min": -0.01,
+        "stoploss_range_max": -0.1,
+        "stoploss_range_step": -0.01,
+        "minimum_winrate": 0.60,
+        "minimum_expectancy": 0.20,
+        "min_trade_number": 10,
+        "max_trade_duration_minute": 1440,
+        "remove_pumps": false
+    },
+    "telegram": {
+        // We can now comment out some settings
+        //        "enabled": true,
+        "enabled": false,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id"
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
+    },
+    "db_url": "sqlite:///tradesv3.sqlite",
+    "initial_state": "running",
+    "forcebuy_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    },
+    "strategy": "DefaultStrategy",
+    "strategy_path": "user_data/strategies/"
+}
\ No newline at end of file
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 000000000..892b49d8c
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,3452 @@
+# pragma pylint: disable=missing-docstring
+import json
+import logging
+import platform
+import re
+from copy import deepcopy
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from unittest.mock import MagicMock, Mock, PropertyMock
+
+import numpy as np
+import pandas as pd
+import pytest
+from xdist.scheduler.loadscope import LoadScopeScheduling
+
+from freqtrade import constants
+from freqtrade.commands import Arguments
+from freqtrade.data.converter import ohlcv_to_dataframe, trades_list_to_df
+from freqtrade.edge import PairInfo
+from freqtrade.enums import CandleType, MarginMode, RunMode, SignalDirection, TradingMode
+from freqtrade.exchange import Exchange, timeframe_to_minutes, timeframe_to_seconds
+from freqtrade.freqtradebot import FreqtradeBot
+from freqtrade.persistence import LocalTrade, Order, Trade, init_db
+from freqtrade.resolvers import ExchangeResolver
+from freqtrade.util import dt_now, dt_ts
+from freqtrade.worker import Worker
+from tests.conftest_trades import (
+    leverage_trade,
+    mock_trade_1,
+    mock_trade_2,
+    mock_trade_3,
+    mock_trade_4,
+    mock_trade_5,
+    mock_trade_6,
+    short_trade,
+)
+from tests.conftest_trades_usdt import (
+    mock_trade_usdt_1,
+    mock_trade_usdt_2,
+    mock_trade_usdt_3,
+    mock_trade_usdt_4,
+    mock_trade_usdt_5,
+    mock_trade_usdt_6,
+    mock_trade_usdt_7,
+)
+
+
+logging.getLogger("").setLevel(logging.INFO)
+
+
+# Do not mask numpy errors as warnings that no one read, raise the exception
+np.seterr(all="raise")
+
+CURRENT_TEST_STRATEGY = "StrategyTestV3"
+TRADE_SIDES = ("long", "short")
+EXMS = "freqtrade.exchange.exchange.Exchange"
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--longrun",
+        action="store_true",
+        dest="longrun",
+        default=False,
+        help="Enable long-run tests (ccxt compat)",
+    )
+
+
+def pytest_configure(config):
+    config.addinivalue_line(
+        "markers", "longrun: mark test that is running slowly and should not be run regularly"
+    )
+    if not config.option.longrun:
+        config.option.markexpr = "not longrun"
+
+
+class FixtureScheduler(LoadScopeScheduling):
+    # Based on the suggestion in
+    # https://github.com/pytest-dev/pytest-xdist/issues/18
+
+    def _split_scope(self, nodeid):
+        if "exchange_online" in nodeid:
+            try:
+                # Extract exchange ID from nodeid
+                exchange_id = nodeid.split("[")[1].split("-")[0].rstrip("]")
+                return exchange_id
+            except Exception as e:
+                print(e)
+                pass
+
+        return nodeid
+
+
+def pytest_xdist_make_scheduler(config, log):
+    return FixtureScheduler(config, log)
+
+
+def log_has(line, logs):
+    """Check if line is found on some caplog's message."""
+    return any(line == message for message in logs.messages)
+
+
+def log_has_when(line, logs, when):
+    """Check if line is found in caplog's messages during a specified stage"""
+    return any(line == message.message for message in logs.get_records(when))
+
+
+def log_has_re(line, logs):
+    """Check if line matches some caplog's message."""
+    return any(re.match(line, message) for message in logs.messages)
+
+
+def num_log_has(line, logs):
+    """Check how many times line is found in caplog's messages."""
+    return sum(line == message for message in logs.messages)
+
+
+def num_log_has_re(line, logs):
+    """Check how many times line matches caplog's messages."""
+    return sum(bool(re.match(line, message)) for message in logs.messages)
+
+
+def get_args(args):
+    return Arguments(args).get_parsed_arg()
+
+
+def generate_trades_history(n_rows, start_date: datetime | None = None, days=5):
+    np.random.seed(42)
+    if not start_date:
+        start_date = datetime(2020, 1, 1, tzinfo=timezone.utc)
+
+        # Generate random data
+    end_date = start_date + timedelta(days=days)
+    _start_timestamp = start_date.timestamp()
+    _end_timestamp = pd.to_datetime(end_date).timestamp()
+
+    random_timestamps_in_seconds = np.random.uniform(_start_timestamp, _end_timestamp, n_rows)
+    timestamp = pd.to_datetime(random_timestamps_in_seconds, unit="s")
+
+    trade_id = [
+        f"a{np.random.randint(1e6, 1e7 - 1)}cd{np.random.randint(100, 999)}" for _ in range(n_rows)
+    ]
+
+    side = np.random.choice(["buy", "sell"], n_rows)
+
+    # Initial price and subsequent changes
+    initial_price = 0.019626
+    price_changes = np.random.normal(0, initial_price * 0.05, n_rows)
+    price = np.cumsum(np.concatenate(([initial_price], price_changes)))[:n_rows]
+
+    amount = np.random.uniform(0.011, 20, n_rows)
+    cost = price * amount
+
+    # Create DataFrame
+    df = pd.DataFrame(
+        {
+            "timestamp": timestamp,
+            "id": trade_id,
+            "type": None,
+            "side": side,
+            "price": price,
+            "amount": amount,
+            "cost": cost,
+        }
+    )
+    df["date"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    df = df.sort_values("timestamp").reset_index(drop=True)
+    assert list(df.columns) == [*constants.DEFAULT_TRADES_COLUMNS, "date"]
+    return df
+
+
+def generate_test_data(timeframe: str, size: int, start: str = "2020-07-05", random_seed=42):
+    np.random.seed(random_seed)
+
+    base = np.random.normal(20, 2, size=size)
+    if timeframe == "1y":
+        date = pd.date_range(start, periods=size, freq="1YS", tz="UTC")
+    elif timeframe == "1M":
+        date = pd.date_range(start, periods=size, freq="1MS", tz="UTC")
+    elif timeframe == "3M":
+        date = pd.date_range(start, periods=size, freq="3MS", tz="UTC")
+    elif timeframe == "1w" or timeframe == "7d":
+        date = pd.date_range(start, periods=size, freq="1W-MON", tz="UTC")
+    else:
+        tf_mins = timeframe_to_minutes(timeframe)
+        if tf_mins >= 1:
+            date = pd.date_range(start, periods=size, freq=f"{tf_mins}min", tz="UTC")
+        else:
+            tf_secs = timeframe_to_seconds(timeframe)
+            date = pd.date_range(start, periods=size, freq=f"{tf_secs}s", tz="UTC")
+    df = pd.DataFrame(
+        {
+            "date": date,
+            "open": base,
+            "high": base + np.random.normal(2, 1, size=size),
+            "low": base - np.random.normal(2, 1, size=size),
+            "close": base + np.random.normal(0, 1, size=size),
+            "volume": np.random.normal(200, size=size),
+        }
+    )
+    df = df.dropna()
+    return df
+
+
+def generate_test_data_raw(timeframe: str, size: int, start: str = "2020-07-05", random_seed=42):
+    """Generates data in the ohlcv format used by ccxt"""
+    df = generate_test_data(timeframe, size, start, random_seed)
+    df["date"] = df.loc[:, "date"].astype(np.int64) // 1000 // 1000
+    return list(list(x) for x in zip(*(df[x].values.tolist() for x in df.columns), strict=False))
+
+
+# Source: https://stackoverflow.com/questions/29881236/how-to-mock-asyncio-coroutines
+# TODO: This should be replaced with AsyncMock once support for python 3.7 is dropped.
+def get_mock_coro(return_value=None, side_effect=None):
+    async def mock_coro(*args, **kwargs):
+        if side_effect:
+            if isinstance(side_effect, list):
+                effect = side_effect.pop(0)
+            else:
+                effect = side_effect
+            if isinstance(effect, Exception):
+                raise effect
+            if callable(effect):
+                return effect(*args, **kwargs)
+            return effect
+        else:
+            return return_value
+
+    return Mock(wraps=mock_coro)
+
+
+def patched_configuration_load_config_file(mocker, config) -> None:
+    mocker.patch(
+        "freqtrade.configuration.load_config.load_config_file", lambda *args, **kwargs: config
+    )
+
+
+def patch_exchange(
+    mocker, api_mock=None, exchange="binance", mock_markets=True, mock_supported_modes=True
+) -> None:
+    mocker.patch(f"{EXMS}.validate_config", MagicMock())
+    mocker.patch(f"{EXMS}.validate_timeframes", MagicMock())
+    mocker.patch(f"{EXMS}.id", PropertyMock(return_value=exchange))
+    mocker.patch(f"{EXMS}.name", PropertyMock(return_value=exchange.title()))
+    mocker.patch(f"{EXMS}.precisionMode", PropertyMock(return_value=2))
+    mocker.patch(f"{EXMS}.precision_mode_price", PropertyMock(return_value=2))
+    # Temporary patch ...
+    mocker.patch("freqtrade.exchange.bybit.Bybit.cache_leverage_tiers")
+
+    if mock_markets:
+        mocker.patch(f"{EXMS}._load_async_markets", return_value={})
+        if isinstance(mock_markets, bool):
+            mock_markets = get_markets()
+        mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=mock_markets))
+
+    if mock_supported_modes:
+        mocker.patch(
+            f"freqtrade.exchange.{exchange}.{exchange.capitalize()}"
+            "._supported_trading_mode_margin_pairs",
+            PropertyMock(
+                return_value=[
+                    (TradingMode.MARGIN, MarginMode.CROSS),
+                    (TradingMode.MARGIN, MarginMode.ISOLATED),
+                    (TradingMode.FUTURES, MarginMode.CROSS),
+                    (TradingMode.FUTURES, MarginMode.ISOLATED),
+                ]
+            ),
+        )
+
+    if api_mock:
+        mocker.patch(f"{EXMS}._init_ccxt", return_value=api_mock)
+    else:
+        mocker.patch(f"{EXMS}.get_fee", return_value=0.0025)
+        mocker.patch(f"{EXMS}._init_ccxt", MagicMock())
+        mocker.patch(f"{EXMS}.timeframes", PropertyMock(return_value=["5m", "15m", "1h", "1d"]))
+
+
+def get_patched_exchange(
+    mocker, config, api_mock=None, exchange="binance", mock_markets=True, mock_supported_modes=True
+) -> Exchange:
+    patch_exchange(mocker, api_mock, exchange, mock_markets, mock_supported_modes)
+    config["exchange"]["name"] = exchange
+    try:
+        exchange = ExchangeResolver.load_exchange(config, load_leverage_tiers=True)
+    except ImportError:
+        exchange = Exchange(config)
+    return exchange
+
+
+def patch_wallet(mocker, free=999.9) -> None:
+    mocker.patch("freqtrade.wallets.Wallets.get_free", MagicMock(return_value=free))
+
+
+def patch_whitelist(mocker, conf) -> None:
+    mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot._refresh_active_whitelist",
+        MagicMock(return_value=conf["exchange"]["pair_whitelist"]),
+    )
+
+
+def patch_edge(mocker) -> None:
+    # "ETH/BTC",
+    # "LTC/BTC",
+    # "XRP/BTC",
+    # "NEO/BTC"
+
+    mocker.patch(
+        "freqtrade.edge.Edge._cached_pairs",
+        mocker.PropertyMock(
+            return_value={
+                "NEO/BTC": PairInfo(-0.20, 0.66, 3.71, 0.50, 1.71, 10, 25),
+                "LTC/BTC": PairInfo(-0.21, 0.66, 3.71, 0.50, 1.71, 11, 20),
+            }
+        ),
+    )
+    mocker.patch("freqtrade.edge.Edge.calculate", MagicMock(return_value=True))
+
+
+# Functions for recurrent object patching
+
+
+def patch_freqtradebot(mocker, config) -> None:
+    """
+    This function patch _init_modules() to not call dependencies
+    :param mocker: a Mocker object to apply patches
+    :param config: Config to pass to the bot
+    :return: None
+    """
+    mocker.patch("freqtrade.freqtradebot.RPCManager", MagicMock())
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.freqtradebot.RPCManager._init", MagicMock())
+    mocker.patch("freqtrade.freqtradebot.RPCManager.send_msg", MagicMock())
+    patch_whitelist(mocker, config)
+    mocker.patch("freqtrade.freqtradebot.ExternalMessageConsumer")
+    mocker.patch("freqtrade.configuration.config_validation._validate_consumers")
+
+
+def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
+    """
+    This function patches _init_modules() to not call dependencies
+    :param mocker: a Mocker object to apply patches
+    :param config: Config to pass to the bot
+    :return: FreqtradeBot
+    """
+    patch_freqtradebot(mocker, config)
+    return FreqtradeBot(config)
+
+
+def get_patched_worker(mocker, config) -> Worker:
+    """
+    This function patches _init_modules() to not call dependencies
+    :param mocker: a Mocker object to apply patches
+    :param config: Config to pass to the bot
+    :return: Worker
+    """
+    patch_freqtradebot(mocker, config)
+    return Worker(args=None, config=config)
+
+
+def patch_get_signal(
+    freqtrade: FreqtradeBot,
+    enter_long=True,
+    exit_long=False,
+    enter_short=False,
+    exit_short=False,
+    enter_tag: str | None = None,
+    exit_tag: str | None = None,
+) -> None:
+    """
+    :param mocker: mocker to patch IStrategy class
+    :return: None
+    """
+
+    # returns (Signal-direction, signaname)
+    def patched_get_entry_signal(*args, **kwargs):
+        direction = None
+        if enter_long and not any([exit_long, enter_short]):
+            direction = SignalDirection.LONG
+        if enter_short and not any([exit_short, enter_long]):
+            direction = SignalDirection.SHORT
+
+        return direction, enter_tag
+
+    freqtrade.strategy.get_entry_signal = patched_get_entry_signal
+
+    def patched_get_exit_signal(pair, timeframe, dataframe, is_short):
+        if is_short:
+            return enter_short, exit_short, exit_tag
+        else:
+            return enter_long, exit_long, exit_tag
+
+    # returns (enter, exit)
+    freqtrade.strategy.get_exit_signal = patched_get_exit_signal
+
+    freqtrade.exchange.refresh_latest_ohlcv = lambda p: None
+
+
+def create_mock_trades(fee, is_short: bool | None = False, use_db: bool = True):
+    """
+    Create some fake trades ...
+    :param is_short: Optional bool, None creates a mix of long and short trades.
+    """
+
+    def add_trade(trade):
+        if use_db:
+            Trade.session.add(trade)
+        else:
+            LocalTrade.add_bt_trade(trade)
+
+    is_short1 = is_short if is_short is not None else True
+    is_short2 = is_short if is_short is not None else False
+    # Simulate dry_run entries
+    trade = mock_trade_1(fee, is_short1)
+    add_trade(trade)
+
+    trade = mock_trade_2(fee, is_short1)
+    add_trade(trade)
+
+    trade = mock_trade_3(fee, is_short2)
+    add_trade(trade)
+
+    trade = mock_trade_4(fee, is_short2)
+    add_trade(trade)
+
+    trade = mock_trade_5(fee, is_short2)
+    add_trade(trade)
+
+    trade = mock_trade_6(fee, is_short1)
+    add_trade(trade)
+
+    if use_db:
+        Trade.commit()
+
+
+def create_mock_trades_with_leverage(fee, use_db: bool = True):
+    """
+    Create some fake trades ...
+    """
+    if use_db:
+        Trade.session.rollback()
+
+    def add_trade(trade):
+        if use_db:
+            Trade.session.add(trade)
+        else:
+            LocalTrade.add_bt_trade(trade)
+
+    # Simulate dry_run entries
+    trade = mock_trade_1(fee, False)
+    add_trade(trade)
+
+    trade = mock_trade_2(fee, False)
+    add_trade(trade)
+
+    trade = mock_trade_3(fee, False)
+    add_trade(trade)
+
+    trade = mock_trade_4(fee, False)
+    add_trade(trade)
+
+    trade = mock_trade_5(fee, False)
+    add_trade(trade)
+
+    trade = mock_trade_6(fee, False)
+    add_trade(trade)
+
+    trade = short_trade(fee)
+    add_trade(trade)
+
+    trade = leverage_trade(fee)
+    add_trade(trade)
+
+    if use_db:
+        Trade.session.flush()
+
+
+def create_mock_trades_usdt(fee, is_short: bool | None = False, use_db: bool = True):
+    """
+    Create some fake trades ...
+    """
+
+    def add_trade(trade):
+        if use_db:
+            Trade.session.add(trade)
+        else:
+            LocalTrade.add_bt_trade(trade)
+
+    is_short1 = is_short if is_short is not None else True
+    is_short2 = is_short if is_short is not None else False
+
+    # Simulate dry_run entries
+    trade = mock_trade_usdt_1(fee, is_short1)
+    add_trade(trade)
+
+    trade = mock_trade_usdt_2(fee, is_short1)
+    add_trade(trade)
+
+    trade = mock_trade_usdt_3(fee, is_short1)
+    add_trade(trade)
+
+    trade = mock_trade_usdt_4(fee, is_short2)
+    add_trade(trade)
+
+    trade = mock_trade_usdt_5(fee, is_short2)
+    add_trade(trade)
+
+    trade = mock_trade_usdt_6(fee, is_short1)
+    add_trade(trade)
+
+    trade = mock_trade_usdt_7(fee, is_short1)
+    add_trade(trade)
+    if use_db:
+        Trade.commit()
+
+
+@pytest.fixture(autouse=True)
+def patch_gc(mocker) -> None:
+    mocker.patch("freqtrade.main.gc_set_threshold")
+
+
+def is_arm() -> bool:
+    machine = platform.machine()
+    return "arm" in machine or "aarch64" in machine
+
+
+def is_mac() -> bool:
+    machine = platform.system()
+    return "Darwin" in machine
+
+
+@pytest.fixture(autouse=True)
+def patch_torch_initlogs(mocker) -> None:
+    if is_mac():
+        # Mock torch import completely
+        import sys
+        import types
+
+        module_name = "torch"
+        mocked_module = types.ModuleType(module_name)
+        sys.modules[module_name] = mocked_module
+    else:
+        mocker.patch("torch._logging._init_logs")
+
+
+@pytest.fixture(autouse=True)
+def user_dir(mocker, tmp_path) -> Path:
+    user_dir = tmp_path / "user_data"
+    mocker.patch("freqtrade.configuration.configuration.create_userdata_dir", return_value=user_dir)
+    return user_dir
+
+
+@pytest.fixture()
+def keep_log_config_loggers(mocker):
+    # Mock the _handle_existing_loggers function to prevent it from disabling all loggers.
+    # This is necessary to keep all loggers active, and avoid random failures if
+    # this file is ran before the test_rest_client file.
+    mocker.patch("logging.config._handle_existing_loggers")
+
+
+@pytest.fixture(autouse=True)
+def patch_coingecko(mocker) -> None:
+    """
+    Mocker to coingecko to speed up tests
+    :param mocker: mocker to patch coingecko class
+    :return: None
+    """
+
+    tickermock = MagicMock(return_value={"bitcoin": {"usd": 12345.0}, "ethereum": {"usd": 12345.0}})
+    listmock = MagicMock(
+        return_value=[
+            {"id": "bitcoin", "name": "Bitcoin", "symbol": "btc", "website_slug": "bitcoin"},
+            {"id": "ethereum", "name": "Ethereum", "symbol": "eth", "website_slug": "ethereum"},
+        ]
+    )
+    mocker.patch.multiple(
+        "freqtrade.rpc.fiat_convert.FtCoinGeckoApi",
+        get_price=tickermock,
+        get_coins_list=listmock,
+    )
+
+
+@pytest.fixture(scope="function")
+def init_persistence(default_conf):
+    init_db(default_conf["db_url"])
+
+
+@pytest.fixture(scope="function")
+def default_conf(testdatadir):
+    return get_default_conf(testdatadir)
+
+
+@pytest.fixture(scope="function")
+def default_conf_usdt(testdatadir):
+    return get_default_conf_usdt(testdatadir)
+
+
+def get_default_conf(testdatadir):
+    """Returns validated configuration suitable for most tests"""
+    configuration = {
+        "max_open_trades": 1,
+        "stake_currency": "BTC",
+        "stake_amount": 0.001,
+        "fiat_display_currency": "USD",
+        "timeframe": "5m",
+        "dry_run": True,
+        "cancel_open_orders_on_exit": False,
+        "minimal_roi": {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04},
+        "dry_run_wallet": 1000,
+        "stoploss": -0.10,
+        "unfilledtimeout": {"entry": 10, "exit": 30},
+        "entry_pricing": {
+            "price_last_balance": 0.0,
+            "use_order_book": False,
+            "order_book_top": 1,
+            "check_depth_of_market": {"enabled": False, "bids_to_ask_delta": 1},
+        },
+        "exit_pricing": {
+            "use_order_book": False,
+            "order_book_top": 1,
+        },
+        "exchange": {
+            "name": "binance",
+            "key": "key",
+            "enable_ws": False,
+            "secret": "secret",
+            "pair_whitelist": ["ETH/BTC", "LTC/BTC", "XRP/BTC", "NEO/BTC"],
+            "pair_blacklist": [
+                "DOGE/BTC",
+                "HOT/BTC",
+            ],
+        },
+        "pairlists": [{"method": "StaticPairList"}],
+        "telegram": {
+            "enabled": False,
+            "token": "token",
+            "chat_id": "1235",
+            "notification_settings": {},
+        },
+        "datadir": Path(testdatadir),
+        "initial_state": "running",
+        "db_url": "sqlite://",
+        "user_data_dir": Path("user_data"),
+        "verbosity": 3,
+        "strategy_path": str(Path(__file__).parent / "strategy" / "strats"),
+        "strategy": CURRENT_TEST_STRATEGY,
+        "disableparamexport": True,
+        "internals": {},
+        "export": "none",
+        "dataformat_ohlcv": "feather",
+        "dataformat_trades": "feather",
+        "runmode": "dry_run",
+        "trading_mode": "spot",
+        "margin_mode": "",
+        "candle_type_def": CandleType.SPOT,
+        "original_config": {},
+    }
+    return configuration
+
+
+def get_default_conf_usdt(testdatadir):
+    configuration = get_default_conf(testdatadir)
+    configuration.update(
+        {
+            "stake_amount": 60.0,
+            "stake_currency": "USDT",
+            "exchange": {
+                "name": "binance",
+                "enabled": True,
+                "key": "key",
+                "enable_ws": False,
+                "secret": "secret",
+                "pair_whitelist": [
+                    "ETH/USDT",
+                    "LTC/USDT",
+                    "XRP/USDT",
+                    "NEO/USDT",
+                    "TKN/USDT",
+                ],
+                "pair_blacklist": [
+                    "DOGE/USDT",
+                    "HOT/USDT",
+                ],
+            },
+        }
+    )
+    return configuration
+
+
+@pytest.fixture
+def fee():
+    return MagicMock(return_value=0.0025)
+
+
+@pytest.fixture
+def ticker():
+    return MagicMock(
+        return_value={
+            "bid": 0.00001098,
+            "ask": 0.00001099,
+            "last": 0.00001098,
+        }
+    )
+
+
+@pytest.fixture
+def ticker_sell_up():
+    return MagicMock(
+        return_value={
+            "bid": 0.00001172,
+            "ask": 0.00001173,
+            "last": 0.00001172,
+        }
+    )
+
+
+@pytest.fixture
+def ticker_sell_down():
+    return MagicMock(
+        return_value={
+            "bid": 0.00001044,
+            "ask": 0.00001043,
+            "last": 0.00001044,
+        }
+    )
+
+
+@pytest.fixture
+def ticker_usdt():
+    return MagicMock(
+        return_value={
+            "bid": 2.0,
+            "ask": 2.02,
+            "last": 2.0,
+        }
+    )
+
+
+@pytest.fixture
+def ticker_usdt_sell_up():
+    return MagicMock(
+        return_value={
+            "bid": 2.2,
+            "ask": 2.3,
+            "last": 2.2,
+        }
+    )
+
+
+@pytest.fixture
+def ticker_usdt_sell_down():
+    return MagicMock(
+        return_value={
+            "bid": 2.01,
+            "ask": 2.0,
+            "last": 2.01,
+        }
+    )
+
+
+@pytest.fixture
+def markets():
+    return get_markets()
+
+
+def get_markets():
+    # See get_markets_static() for immutable markets and do not modify them unless absolutely
+    # necessary!
+    return {
+        "ETH/BTC": {
+            "id": "ethbtc",
+            "symbol": "ETH/BTC",
+            "base": "ETH",
+            "quote": "BTC",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "precision": {
+                "price": 8,
+                "amount": 8,
+                "cost": 8,
+            },
+            "lot": 0.00000001,
+            "contractSize": None,
+            "limits": {
+                "amount": {
+                    "min": 0.01,
+                    "max": 100000000,
+                },
+                "price": {
+                    "min": None,
+                    "max": 500000,
+                },
+                "cost": {
+                    "min": 0.0001,
+                    "max": 500000,
+                },
+                "leverage": {"min": 1.0, "max": 2.0},
+            },
+        },
+        "TKN/BTC": {
+            "id": "tknbtc",
+            "symbol": "TKN/BTC",
+            "base": "TKN",
+            "quote": "BTC",
+            # According to ccxt, markets without active item set are also active
+            # 'active': True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "precision": {
+                "price": 8,
+                "amount": 8,
+                "cost": 8,
+            },
+            "lot": 0.00000001,
+            "contractSize": None,
+            "limits": {
+                "amount": {
+                    "min": 0.01,
+                    "max": 100000000,
+                },
+                "price": {
+                    "min": None,
+                    "max": 500000,
+                },
+                "cost": {
+                    "min": 0.0001,
+                    "max": 500000,
+                },
+                "leverage": {"min": 1.0, "max": 5.0},
+            },
+        },
+        "BLK/BTC": {
+            "id": "blkbtc",
+            "symbol": "BLK/BTC",
+            "base": "BLK",
+            "quote": "BTC",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "precision": {
+                "price": 8,
+                "amount": 8,
+                "cost": 8,
+            },
+            "lot": 0.00000001,
+            "contractSize": None,
+            "limits": {
+                "amount": {
+                    "min": 0.01,
+                    "max": 1000,
+                },
+                "price": {
+                    "min": None,
+                    "max": 500000,
+                },
+                "cost": {
+                    "min": 0.0001,
+                    "max": 500000,
+                },
+                "leverage": {"min": 1.0, "max": 3.0},
+            },
+        },
+        "LTC/BTC": {
+            "id": "ltcbtc",
+            "symbol": "LTC/BTC",
+            "base": "LTC",
+            "quote": "BTC",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "precision": {
+                "price": 8,
+                "amount": 8,
+                "cost": 8,
+            },
+            "lot": 0.00000001,
+            "contractSize": None,
+            "limits": {
+                "amount": {
+                    "min": 0.01,
+                    "max": 100000000,
+                },
+                "price": {
+                    "min": None,
+                    "max": 500000,
+                },
+                "cost": {
+                    "min": 0.0001,
+                    "max": 500000,
+                },
+                "leverage": {"min": None, "max": None},
+            },
+            "info": {},
+        },
+        "XRP/BTC": {
+            "id": "xrpbtc",
+            "symbol": "XRP/BTC",
+            "base": "XRP",
+            "quote": "BTC",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "precision": {
+                "price": 8,
+                "amount": 8,
+                "cost": 8,
+            },
+            "lot": 0.00000001,
+            "contractSize": None,
+            "limits": {
+                "amount": {
+                    "min": 0.01,
+                    "max": 100000000,
+                },
+                "price": {
+                    "min": None,
+                    "max": 500000,
+                },
+                "cost": {
+                    "min": 0.0001,
+                    "max": 500000,
+                },
+                "leverage": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {},
+        },
+        "NEO/BTC": {
+            "id": "neobtc",
+            "symbol": "NEO/BTC",
+            "base": "NEO",
+            "quote": "BTC",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "precision": {
+                "price": 8,
+                "amount": 8,
+                "cost": 8,
+            },
+            "lot": 0.00000001,
+            "contractSize": None,
+            "limits": {
+                "amount": {
+                    "min": 0.01,
+                    "max": 100000000,
+                },
+                "price": {
+                    "min": None,
+                    "max": 500000,
+                },
+                "cost": {
+                    "min": 0.0001,
+                    "max": 500000,
+                },
+                "leverage": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {},
+        },
+        "BTT/BTC": {
+            "id": "BTTBTC",
+            "symbol": "BTT/BTC",
+            "base": "BTT",
+            "quote": "BTC",
+            "active": False,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "contractSize": None,
+            "precision": {"base": 8, "quote": 8, "amount": 0, "price": 8},
+            "limits": {
+                "amount": {"min": 1.0, "max": 90000000.0},
+                "price": {"min": None, "max": None},
+                "cost": {"min": 0.0001, "max": None},
+                "leverage": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {},
+        },
+        "ETC/BTC": {
+            "id": "ETCBTC",
+            "symbol": "ETC/BTC",
+            "base": "ETC",
+            "quote": "BTC",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "contractSize": None,
+            "precision": {"base": 8, "quote": 8, "amount": 2, "price": 7},
+            "limits": {
+                "amount": {"min": 0.01, "max": 90000000.0},
+                "price": {"min": 1e-07, "max": 1000.0},
+                "cost": {"min": 0.0001, "max": 9000000.0},
+                "leverage": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {},
+        },
+        "ETH/USDT": {
+            "id": "USDT-ETH",
+            "symbol": "ETH/USDT",
+            "base": "ETH",
+            "quote": "USDT",
+            "settle": None,
+            "baseId": "ETH",
+            "quoteId": "USDT",
+            "settleId": None,
+            "type": "spot",
+            "spot": True,
+            "margin": True,
+            "swap": True,
+            "future": True,
+            "option": False,
+            "active": True,
+            "contract": None,
+            "linear": None,
+            "inverse": None,
+            "taker": 0.0006,
+            "maker": 0.0002,
+            "contractSize": None,
+            "expiry": None,
+            "expiryDateTime": None,
+            "strike": None,
+            "optionType": None,
+            "precision": {
+                "amount": 8,
+                "price": 8,
+            },
+            "limits": {
+                "leverage": {
+                    "min": 1,
+                    "max": 100,
+                },
+                "amount": {
+                    "min": 0.02214286,
+                    "max": None,
+                },
+                "price": {
+                    "min": 1e-08,
+                    "max": None,
+                },
+                "cost": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {
+                "maintenance_rate": "0.005",
+            },
+        },
+        "BTC/USDT": {
+            "id": "USDT-BTC",
+            "symbol": "BTC/USDT",
+            "base": "BTC",
+            "quote": "USDT",
+            "settle": None,
+            "baseId": "BTC",
+            "quoteId": "USDT",
+            "settleId": None,
+            "type": "spot",
+            "spot": True,
+            "margin": True,
+            "swap": False,
+            "future": False,
+            "option": False,
+            "active": True,
+            "contract": None,
+            "linear": None,
+            "inverse": None,
+            "taker": 0.0006,
+            "maker": 0.0002,
+            "contractSize": None,
+            "expiry": None,
+            "expiryDateTime": None,
+            "strike": None,
+            "optionType": None,
+            "precision": {
+                "amount": 4,
+                "price": 4,
+            },
+            "limits": {
+                "leverage": {
+                    "min": 1,
+                    "max": 100,
+                },
+                "amount": {
+                    "min": 0.000221,
+                    "max": None,
+                },
+                "price": {
+                    "min": 1e-02,
+                    "max": None,
+                },
+                "cost": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {
+                "maintenance_rate": "0.005",
+            },
+        },
+        "LTC/USDT": {
+            "id": "USDT-LTC",
+            "symbol": "LTC/USDT",
+            "base": "LTC",
+            "quote": "USDT",
+            "active": False,
+            "spot": True,
+            "future": True,
+            "swap": True,
+            "margin": True,
+            "linear": None,
+            "inverse": False,
+            "type": "spot",
+            "contractSize": None,
+            "taker": 0.0006,
+            "maker": 0.0002,
+            "precision": {"amount": 8, "price": 8},
+            "limits": {
+                "amount": {"min": 0.06646786, "max": None},
+                "price": {"min": 1e-08, "max": None},
+                "leverage": {
+                    "min": None,
+                    "max": None,
+                },
+                "cost": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {},
+        },
+        "XRP/USDT": {
+            "id": "xrpusdt",
+            "symbol": "XRP/USDT",
+            "base": "XRP",
+            "quote": "USDT",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "taker": 0.0006,
+            "maker": 0.0002,
+            "precision": {
+                "price": 8,
+                "amount": 8,
+                "cost": 8,
+            },
+            "lot": 0.00000001,
+            "contractSize": None,
+            "limits": {
+                "amount": {
+                    "min": 0.01,
+                    "max": 1000,
+                },
+                "price": {
+                    "min": None,
+                    "max": 500000,
+                },
+                "cost": {
+                    "min": 0.0001,
+                    "max": 500000,
+                },
+            },
+            "info": {},
+        },
+        "NEO/USDT": {
+            "id": "neousdt",
+            "symbol": "NEO/USDT",
+            "base": "NEO",
+            "quote": "USDT",
+            "settle": "",
+            "baseId": "NEO",
+            "quoteId": "USDT",
+            "settleId": "",
+            "type": "spot",
+            "spot": True,
+            "margin": True,
+            "swap": False,
+            "futures": False,
+            "option": False,
+            "active": True,
+            "contract": False,
+            "linear": None,
+            "inverse": None,
+            "taker": 0.0006,
+            "maker": 0.0002,
+            "contractSize": None,
+            "expiry": None,
+            "expiryDatetime": None,
+            "strike": None,
+            "optionType": None,
+            "tierBased": None,
+            "percentage": None,
+            "lot": 0.00000001,
+            "precision": {
+                "price": 8,
+                "amount": 8,
+                "cost": 8,
+            },
+            "limits": {
+                "leverage": {"min": 1, "max": 10},
+                "amount": {
+                    "min": 0.01,
+                    "max": 1000,
+                },
+                "price": {
+                    "min": None,
+                    "max": 500000,
+                },
+                "cost": {
+                    "min": 0.0001,
+                    "max": 500000,
+                },
+            },
+            "info": {},
+        },
+        "TKN/USDT": {
+            "id": "tknusdt",
+            "symbol": "TKN/USDT",
+            "base": "TKN",
+            "quote": "USDT",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "contractSize": None,
+            "taker": 0.0006,
+            "maker": 0.0002,
+            "precision": {
+                "price": 8,
+                "amount": 8,
+                "cost": 8,
+            },
+            "lot": 0.00000001,
+            "limits": {
+                "amount": {
+                    "min": 0.01,
+                    "max": 100000000000,
+                },
+                "price": {"min": None, "max": 500000},
+                "cost": {
+                    "min": 0.0001,
+                    "max": 500000,
+                },
+                "leverage": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {},
+        },
+        "LTC/USD": {
+            "id": "USD-LTC",
+            "symbol": "LTC/USD",
+            "base": "LTC",
+            "quote": "USD",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "contractSize": None,
+            "precision": {"amount": 8, "price": 8},
+            "limits": {
+                "amount": {"min": 0.06646786, "max": None},
+                "price": {"min": 1e-08, "max": None},
+                "leverage": {
+                    "min": None,
+                    "max": None,
+                },
+                "cost": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {},
+        },
+        "XLTCUSDT": {
+            "id": "xLTCUSDT",
+            "symbol": "XLTCUSDT",
+            "base": "LTC",
+            "quote": "USDT",
+            "active": True,
+            "spot": False,
+            "type": "swap",
+            "contractSize": 0.01,
+            "swap": False,
+            "linear": False,
+            "taker": 0.0006,
+            "maker": 0.0002,
+            "precision": {"amount": 8, "price": 8},
+            "limits": {
+                "leverage": {
+                    "min": None,
+                    "max": None,
+                },
+                "amount": {"min": 0.06646786, "max": None},
+                "price": {"min": 1e-08, "max": None},
+                "cost": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "info": {},
+        },
+        "LTC/ETH": {
+            "id": "LTCETH",
+            "symbol": "LTC/ETH",
+            "base": "LTC",
+            "quote": "ETH",
+            "active": True,
+            "spot": True,
+            "swap": False,
+            "linear": None,
+            "type": "spot",
+            "contractSize": None,
+            "precision": {"base": 8, "quote": 8, "amount": 3, "price": 5},
+            "limits": {
+                "leverage": {
+                    "min": None,
+                    "max": None,
+                },
+                "amount": {"min": 0.001, "max": 10000000.0},
+                "price": {"min": 1e-05, "max": 1000.0},
+                "cost": {"min": 0.01, "max": None},
+            },
+            "info": {},
+        },
+        "ETH/USDT:USDT": {
+            "id": "ETH_USDT",
+            "symbol": "ETH/USDT:USDT",
+            "base": "ETH",
+            "quote": "USDT",
+            "settle": "USDT",
+            "baseId": "ETH",
+            "quoteId": "USDT",
+            "settleId": "USDT",
+            "type": "swap",
+            "spot": False,
+            "margin": False,
+            "swap": True,
+            "future": True,  # Binance mode ...
+            "option": False,
+            "contract": True,
+            "linear": True,
+            "inverse": False,
+            "tierBased": False,
+            "percentage": True,
+            "taker": 0.0006,
+            "maker": 0.0002,
+            "contractSize": 10,
+            "active": True,
+            "expiry": None,
+            "expiryDatetime": None,
+            "strike": None,
+            "optionType": None,
+            "limits": {
+                "leverage": {"min": 1, "max": 100},
+                "amount": {"min": 1, "max": 300000},
+                "price": {
+                    "min": None,
+                    "max": None,
+                },
+                "cost": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "precision": {"price": 0.05, "amount": 1},
+            "info": {},
+        },
+        "ADA/USDT:USDT": {
+            "limits": {
+                "leverage": {
+                    "min": 1,
+                    "max": 20,
+                },
+                "amount": {
+                    "min": 1,
+                    "max": 1000000,
+                },
+                "price": {
+                    "min": 0.52981,
+                    "max": 1.58943,
+                },
+                "cost": {
+                    "min": None,
+                    "max": None,
+                },
+            },
+            "precision": {"amount": 1, "price": 0.00001},
+            "tierBased": True,
+            "percentage": True,
+            "taker": 0.0000075,
+            "maker": -0.0000025,
+            "feeSide": "get",
+            "tiers": {
+                "maker": [
+                    [0, 0.002],
+                    [1.5, 0.00185],
+                    [3, 0.00175],
+                    [6, 0.00165],
+                    [12.5, 0.00155],
+                    [25, 0.00145],
+                    [75, 0.00135],
+                    [200, 0.00125],
+                    [500, 0.00115],
+                    [1250, 0.00105],
+                    [2500, 0.00095],
+                    [3000, 0.00085],
+                    [6000, 0.00075],
+                    [11000, 0.00065],
+                    [20000, 0.00055],
+                    [40000, 0.00055],
+                    [75000, 0.00055],
+                ],
+                "taker": [
+                    [0, 0.002],
+                    [1.5, 0.00195],
+                    [3, 0.00185],
+                    [6, 0.00175],
+                    [12.5, 0.00165],
+                    [25, 0.00155],
+                    [75, 0.00145],
+                    [200, 0.00135],
+                    [500, 0.00125],
+                    [1250, 0.00115],
+                    [2500, 0.00105],
+                    [3000, 0.00095],
+                    [6000, 0.00085],
+                    [11000, 0.00075],
+                    [20000, 0.00065],
+                    [40000, 0.00065],
+                    [75000, 0.00065],
+                ],
+            },
+            "id": "ADA_USDT",
+            "symbol": "ADA/USDT:USDT",
+            "base": "ADA",
+            "quote": "USDT",
+            "settle": "USDT",
+            "baseId": "ADA",
+            "quoteId": "USDT",
+            "settleId": "usdt",
+            "type": "swap",
+            "spot": False,
+            "margin": False,
+            "swap": True,
+            "future": True,  # Binance mode ...
+            "option": False,
+            "active": True,
+            "contract": True,
+            "linear": True,
+            "inverse": False,
+            "contractSize": 0.01,
+            "expiry": None,
+            "expiryDatetime": None,
+            "strike": None,
+            "optionType": None,
+            "info": {},
+        },
+        "SOL/BUSD:BUSD": {
+            "limits": {
+                "leverage": {"min": None, "max": None},
+                "amount": {"min": 1, "max": 1000000},
+                "price": {"min": 0.04, "max": 100000},
+                "cost": {"min": 5, "max": None},
+                "market": {"min": 1, "max": 1500},
+            },
+            "precision": {"amount": 0, "price": 2, "base": 8, "quote": 8},
+            "tierBased": False,
+            "percentage": True,
+            "taker": 0.0004,
+            "maker": 0.0002,
+            "feeSide": "get",
+            "id": "SOLBUSD",
+            "lowercaseId": "solbusd",
+            "symbol": "SOL/BUSD",
+            "base": "SOL",
+            "quote": "BUSD",
+            "settle": "BUSD",
+            "baseId": "SOL",
+            "quoteId": "BUSD",
+            "settleId": "BUSD",
+            "type": "future",
+            "spot": False,
+            "margin": False,
+            "future": True,
+            "delivery": False,
+            "option": False,
+            "active": True,
+            "contract": True,
+            "linear": True,
+            "inverse": False,
+            "contractSize": 1,
+            "expiry": None,
+            "expiryDatetime": None,
+            "strike": None,
+            "optionType": None,
+            "info": {
+                "symbol": "SOLBUSD",
+                "pair": "SOLBUSD",
+                "contractType": "PERPETUAL",
+                "deliveryDate": "4133404800000",
+                "onboardDate": "1630566000000",
+                "status": "TRADING",
+                "maintMarginPercent": "2.5000",
+                "requiredMarginPercent": "5.0000",
+                "baseAsset": "SOL",
+                "quoteAsset": "BUSD",
+                "marginAsset": "BUSD",
+                "pricePrecision": "4",
+                "quantityPrecision": "0",
+                "baseAssetPrecision": "8",
+                "quotePrecision": "8",
+                "underlyingType": "COIN",
+                "underlyingSubType": [],
+                "settlePlan": "0",
+                "triggerProtect": "0.0500",
+                "liquidationFee": "0.005000",
+                "marketTakeBound": "0.05",
+                "filters": [
+                    {
+                        "minPrice": "0.0400",
+                        "maxPrice": "100000",
+                        "filterType": "PRICE_FILTER",
+                        "tickSize": "0.0100",
+                    },
+                    {"stepSize": "1", "filterType": "LOT_SIZE", "maxQty": "1000000", "minQty": "1"},
+                    {
+                        "stepSize": "1",
+                        "filterType": "MARKET_LOT_SIZE",
+                        "maxQty": "1500",
+                        "minQty": "1",
+                    },
+                    {"limit": "200", "filterType": "MAX_NUM_ORDERS"},
+                    {"limit": "10", "filterType": "MAX_NUM_ALGO_ORDERS"},
+                    {"notional": "5", "filterType": "MIN_NOTIONAL"},
+                    {
+                        "multiplierDown": "0.9500",
+                        "multiplierUp": "1.0500",
+                        "multiplierDecimal": "4",
+                        "filterType": "PERCENT_PRICE",
+                    },
+                ],
+                "orderTypes": [
+                    "LIMIT",
+                    "MARKET",
+                    "STOP",
+                    "STOP_MARKET",
+                    "TAKE_PROFIT",
+                    "TAKE_PROFIT_MARKET",
+                    "TRAILING_STOP_MARKET",
+                ],
+                "timeInForce": ["GTC", "IOC", "FOK", "GTX"],
+            },
+        },
+    }
+
+
+@pytest.fixture
+def markets_static():
+    # These markets are used in some tests that would need adaptation should anything change in
+    # market list. Do not modify this list without a good reason! Do not modify market parameters
+    # of listed pairs in get_markets() without a good reason either!
+    static_markets = [
+        "BLK/BTC",
+        "BTT/BTC",
+        "ETH/BTC",
+        "ETH/USDT",
+        "LTC/BTC",
+        "LTC/ETH",
+        "LTC/USD",
+        "LTC/USDT",
+        "NEO/BTC",
+        "TKN/BTC",
+        "XLTCUSDT",
+        "XRP/BTC",
+        "ADA/USDT:USDT",
+        "ETH/USDT:USDT",
+    ]
+    all_markets = get_markets()
+    return {m: all_markets[m] for m in static_markets}
+
+
+@pytest.fixture
+def shitcoinmarkets(markets_static):
+    """
+    Fixture with shitcoin markets - used to test filters in pairlists
+    """
+    shitmarkets = deepcopy(markets_static)
+    shitmarkets.update(
+        {
+            "HOT/BTC": {
+                "id": "HOTBTC",
+                "symbol": "HOT/BTC",
+                "base": "HOT",
+                "quote": "BTC",
+                "active": True,
+                "spot": True,
+                "type": "spot",
+                "precision": {"base": 8, "quote": 8, "amount": 0, "price": 8},
+                "limits": {
+                    "amount": {"min": 1.0, "max": 90000000.0},
+                    "price": {"min": None, "max": None},
+                    "cost": {"min": 0.001, "max": None},
+                },
+                "info": {},
+            },
+            "FUEL/BTC": {
+                "id": "FUELBTC",
+                "symbol": "FUEL/BTC",
+                "base": "FUEL",
+                "quote": "BTC",
+                "active": True,
+                "spot": True,
+                "type": "spot",
+                "precision": {"base": 8, "quote": 8, "amount": 0, "price": 8},
+                "limits": {
+                    "amount": {"min": 1.0, "max": 90000000.0},
+                    "price": {"min": 1e-08, "max": 1000.0},
+                    "cost": {"min": 0.001, "max": None},
+                },
+                "info": {},
+            },
+            "NANO/USDT": {
+                "percentage": True,
+                "tierBased": False,
+                "taker": 0.001,
+                "maker": 0.001,
+                "precision": {"base": 8, "quote": 8, "amount": 2, "price": 4},
+                "limits": {
+                    "leverage": {
+                        "min": None,
+                        "max": None,
+                    },
+                    "amount": {
+                        "min": None,
+                        "max": None,
+                    },
+                    "price": {
+                        "min": None,
+                        "max": None,
+                    },
+                    "cost": {
+                        "min": None,
+                        "max": None,
+                    },
+                },
+                "id": "NANOUSDT",
+                "symbol": "NANO/USDT",
+                "base": "NANO",
+                "quote": "USDT",
+                "baseId": "NANO",
+                "quoteId": "USDT",
+                "info": {},
+                "type": "spot",
+                "spot": True,
+                "future": False,
+                "active": True,
+            },
+            "ADAHALF/USDT": {
+                "percentage": True,
+                "tierBased": False,
+                "taker": 0.001,
+                "maker": 0.001,
+                "precision": {"base": 8, "quote": 8, "amount": 2, "price": 4},
+                "limits": {
+                    "leverage": {
+                        "min": None,
+                        "max": None,
+                    },
+                    "amount": {
+                        "min": None,
+                        "max": None,
+                    },
+                    "price": {
+                        "min": None,
+                        "max": None,
+                    },
+                    "cost": {
+                        "min": None,
+                        "max": None,
+                    },
+                },
+                "id": "ADAHALFUSDT",
+                "symbol": "ADAHALF/USDT",
+                "base": "ADAHALF",
+                "quote": "USDT",
+                "baseId": "ADAHALF",
+                "quoteId": "USDT",
+                "info": {},
+                "type": "spot",
+                "spot": True,
+                "future": False,
+                "active": True,
+            },
+            "ADADOUBLE/USDT": {
+                "percentage": True,
+                "tierBased": False,
+                "taker": 0.001,
+                "maker": 0.001,
+                "precision": {"base": 8, "quote": 8, "amount": 2, "price": 4},
+                "limits": {
+                    "leverage": {
+                        "min": None,
+                        "max": None,
+                    },
+                    "amount": {
+                        "min": None,
+                        "max": None,
+                    },
+                    "price": {
+                        "min": None,
+                        "max": None,
+                    },
+                    "cost": {
+                        "min": None,
+                        "max": None,
+                    },
+                },
+                "id": "ADADOUBLEUSDT",
+                "symbol": "ADADOUBLE/USDT",
+                "base": "ADADOUBLE",
+                "quote": "USDT",
+                "baseId": "ADADOUBLE",
+                "quoteId": "USDT",
+                "info": {},
+                "type": "spot",
+                "spot": True,
+                "future": False,
+                "active": True,
+            },
+        }
+    )
+    return shitmarkets
+
+
+@pytest.fixture
+def markets_empty():
+    return MagicMock(return_value=[])
+
+
+@pytest.fixture(scope="function")
+def limit_buy_order_open():
+    return {
+        "id": "mocked_limit_buy",
+        "type": "limit",
+        "side": "buy",
+        "symbol": "mocked",
+        "timestamp": dt_ts(),
+        "datetime": dt_now().isoformat(),
+        "price": 0.00001099,
+        "average": 0.00001099,
+        "amount": 90.99181073,
+        "filled": 0.0,
+        "cost": 0.0009999,
+        "remaining": 90.99181073,
+        "status": "open",
+    }
+
+
+@pytest.fixture
+def limit_buy_order_old():
+    return {
+        "id": "mocked_limit_buy_old",
+        "type": "limit",
+        "side": "buy",
+        "symbol": "mocked",
+        "datetime": (dt_now() - timedelta(minutes=601)).isoformat(),
+        "timestamp": dt_ts(dt_now() - timedelta(minutes=601)),
+        "price": 0.00001099,
+        "amount": 90.99181073,
+        "filled": 0.0,
+        "remaining": 90.99181073,
+        "status": "open",
+    }
+
+
+@pytest.fixture
+def limit_sell_order_old():
+    return {
+        "id": "mocked_limit_sell_old",
+        "type": "limit",
+        "side": "sell",
+        "symbol": "ETH/BTC",
+        "timestamp": dt_ts(dt_now() - timedelta(minutes=601)),
+        "datetime": (dt_now() - timedelta(minutes=601)).isoformat(),
+        "price": 0.00001099,
+        "amount": 90.99181073,
+        "filled": 0.0,
+        "remaining": 90.99181073,
+        "status": "open",
+    }
+
+
+@pytest.fixture
+def limit_buy_order_old_partial():
+    return {
+        "id": "mocked_limit_buy_old_partial",
+        "type": "limit",
+        "side": "buy",
+        "symbol": "ETH/BTC",
+        "timestamp": dt_ts(dt_now() - timedelta(minutes=601)),
+        "datetime": (dt_now() - timedelta(minutes=601)).isoformat(),
+        "price": 0.00001099,
+        "amount": 90.99181073,
+        "filled": 23.0,
+        "cost": 90.99181073 * 23.0,
+        "remaining": 67.99181073,
+        "status": "open",
+    }
+
+
+@pytest.fixture
+def limit_buy_order_old_partial_canceled(limit_buy_order_old_partial):
+    res = deepcopy(limit_buy_order_old_partial)
+    res["status"] = "canceled"
+    res["fee"] = {"cost": 0.023, "currency": "ETH"}
+    return res
+
+
+@pytest.fixture(scope="function")
+def limit_buy_order_canceled_empty(request):
+    # Indirect fixture
+    # Documentation:
+    # https://docs.pytest.org/en/latest/example/parametrize.html#apply-indirect-on-particular-arguments
+
+    exchange_name = request.param
+    if exchange_name == "kraken":
+        return {
+            "info": {},
+            "id": "AZNPFF-4AC4N-7MKTAT",
+            "clientOrderId": None,
+            "timestamp": dt_ts(dt_now() - timedelta(minutes=601)),
+            "datetime": (dt_now() - timedelta(minutes=601)).isoformat(),
+            "lastTradeTimestamp": None,
+            "status": "canceled",
+            "symbol": "LTC/USDT",
+            "type": "limit",
+            "side": "buy",
+            "price": 34.3225,
+            "cost": 0.0,
+            "amount": 0.55,
+            "filled": 0.0,
+            "average": 0.0,
+            "remaining": 0.55,
+            "fee": {"cost": 0.0, "rate": None, "currency": "USDT"},
+            "trades": [],
+        }
+    elif exchange_name == "binance":
+        return {
+            "info": {},
+            "id": "1234512345",
+            "clientOrderId": "alb1234123",
+            "timestamp": dt_ts(dt_now() - timedelta(minutes=601)),
+            "datetime": (dt_now() - timedelta(minutes=601)).isoformat(),
+            "lastTradeTimestamp": None,
+            "symbol": "LTC/USDT",
+            "type": "limit",
+            "side": "buy",
+            "price": 0.016804,
+            "amount": 0.55,
+            "cost": 0.0,
+            "average": None,
+            "filled": 0.0,
+            "remaining": 0.55,
+            "status": "canceled",
+            "fee": None,
+            "trades": None,
+        }
+    else:
+        return {
+            "info": {},
+            "id": "1234512345",
+            "clientOrderId": "alb1234123",
+            "timestamp": dt_ts(dt_now() - timedelta(minutes=601)),
+            "datetime": (dt_now() - timedelta(minutes=601)).isoformat(),
+            "lastTradeTimestamp": None,
+            "symbol": "LTC/USDT",
+            "type": "limit",
+            "side": "buy",
+            "price": 0.016804,
+            "amount": 0.55,
+            "cost": 0.0,
+            "average": None,
+            "filled": 0.0,
+            "remaining": 0.55,
+            "status": "canceled",
+            "fee": None,
+            "trades": None,
+        }
+
+
+@pytest.fixture
+def limit_sell_order_open():
+    return {
+        "id": "mocked_limit_sell",
+        "type": "limit",
+        "side": "sell",
+        "symbol": "mocked",
+        "datetime": dt_now().isoformat(),
+        "timestamp": dt_ts(),
+        "price": 0.00001173,
+        "amount": 90.99181073,
+        "filled": 0.0,
+        "remaining": 90.99181073,
+        "status": "open",
+    }
+
+
+@pytest.fixture
+def limit_sell_order(limit_sell_order_open):
+    order = deepcopy(limit_sell_order_open)
+    order["remaining"] = 0.0
+    order["filled"] = order["amount"]
+    order["status"] = "closed"
+    return order
+
+
+@pytest.fixture
+def order_book_l2():
+    return MagicMock(
+        return_value={
+            "bids": [
+                [0.043936, 10.442],
+                [0.043935, 31.865],
+                [0.043933, 11.212],
+                [0.043928, 0.088],
+                [0.043925, 10.0],
+                [0.043921, 10.0],
+                [0.04392, 37.64],
+                [0.043899, 0.066],
+                [0.043885, 0.676],
+                [0.04387, 22.758],
+            ],
+            "asks": [
+                [0.043949, 0.346],
+                [0.04395, 0.608],
+                [0.043951, 3.948],
+                [0.043954, 0.288],
+                [0.043958, 9.277],
+                [0.043995, 1.566],
+                [0.044, 0.588],
+                [0.044002, 0.992],
+                [0.044003, 0.095],
+                [0.04402, 37.64],
+            ],
+            "timestamp": None,
+            "datetime": None,
+            "nonce": 288004540,
+        }
+    )
+
+
+@pytest.fixture
+def order_book_l2_usd():
+    return MagicMock(
+        return_value={
+            "symbol": "LTC/USDT",
+            "bids": [
+                [25.563, 49.269],
+                [25.562, 83.0],
+                [25.56, 106.0],
+                [25.559, 15.381],
+                [25.558, 29.299],
+                [25.557, 34.624],
+                [25.556, 10.0],
+                [25.555, 14.684],
+                [25.554, 45.91],
+                [25.553, 50.0],
+            ],
+            "asks": [
+                [25.566, 14.27],
+                [25.567, 48.484],
+                [25.568, 92.349],
+                [25.572, 31.48],
+                [25.573, 23.0],
+                [25.574, 20.0],
+                [25.575, 89.606],
+                [25.576, 262.016],
+                [25.577, 178.557],
+                [25.578, 78.614],
+            ],
+            "timestamp": None,
+            "datetime": None,
+            "nonce": 2372149736,
+        }
+    )
+
+
+@pytest.fixture
+def ohlcv_history_list():
+    return [
+        [
+            1511686200000,  # unix timestamp ms
+            8.794e-05,  # open
+            8.948e-05,  # high
+            8.794e-05,  # low
+            8.88e-05,  # close
+            0.0877869,  # volume (in quote currency)
+        ],
+        [
+            1511686500000,
+            8.88e-05,
+            8.942e-05,
+            8.88e-05,
+            8.893e-05,
+            0.05874751,
+        ],
+        [1511686800000, 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05, 0.7039405],
+    ]
+
+
+@pytest.fixture
+def ohlcv_history(ohlcv_history_list):
+    return ohlcv_to_dataframe(
+        ohlcv_history_list, "5m", pair="UNITTEST/BTC", fill_missing=True, drop_incomplete=False
+    )
+
+
+@pytest.fixture
+def tickers():
+    return MagicMock(
+        return_value={
+            "ETH/BTC": {
+                "symbol": "ETH/BTC",
+                "timestamp": 1522014806207,
+                "datetime": "2018-03-25T21:53:26.207Z",
+                "high": 0.061697,
+                "low": 0.060531,
+                "bid": 0.061588,
+                "bidVolume": 3.321,
+                "ask": 0.061655,
+                "askVolume": 0.212,
+                "vwap": 0.06105296,
+                "open": 0.060809,
+                "close": 0.060761,
+                "first": None,
+                "last": 0.061588,
+                "change": 1.281,
+                "percentage": None,
+                "average": None,
+                "baseVolume": 111649.001,
+                "quoteVolume": 6816.50176926,
+                "info": {},
+            },
+            "TKN/BTC": {
+                "symbol": "TKN/BTC",
+                "timestamp": 1522014806169,
+                "datetime": "2018-03-25T21:53:26.169Z",
+                "high": 0.01885,
+                "low": 0.018497,
+                "bid": 0.018799,
+                "bidVolume": 8.38,
+                "ask": 0.018802,
+                "askVolume": 15.0,
+                "vwap": 0.01869197,
+                "open": 0.018585,
+                "close": 0.018573,
+                "last": 0.018799,
+                "baseVolume": 81058.66,
+                "quoteVolume": 2247.48374509,
+            },
+            "BLK/BTC": {
+                "symbol": "BLK/BTC",
+                "timestamp": 1522014806072,
+                "datetime": "2018-03-25T21:53:26.072Z",
+                "high": 0.007745,
+                "low": 0.007512,
+                "bid": 0.007729,
+                "bidVolume": 0.01,
+                "ask": 0.007743,
+                "askVolume": 21.37,
+                "vwap": 0.00761466,
+                "open": 0.007653,
+                "close": 0.007652,
+                "first": None,
+                "last": 0.007743,
+                "change": 1.176,
+                "percentage": None,
+                "average": None,
+                "baseVolume": 295152.26,
+                "quoteVolume": 1515.14631229,
+                "info": {},
+            },
+            "LTC/BTC": {
+                "symbol": "LTC/BTC",
+                "timestamp": 1523787258992,
+                "datetime": "2018-04-15T10:14:19.992Z",
+                "high": 0.015978,
+                "low": 0.0157,
+                "bid": 0.015954,
+                "bidVolume": 12.83,
+                "ask": 0.015957,
+                "askVolume": 0.49,
+                "vwap": 0.01581636,
+                "open": 0.015823,
+                "close": 0.01582,
+                "first": None,
+                "last": 0.015951,
+                "change": 0.809,
+                "percentage": None,
+                "average": None,
+                "baseVolume": 88620.68,
+                "quoteVolume": 1401.65697943,
+                "info": {},
+            },
+            "BTT/BTC": {
+                "symbol": "BTT/BTC",
+                "timestamp": 1550936557206,
+                "datetime": "2019-02-23T15:42:37.206Z",
+                "high": 0.00000026,
+                "low": 0.00000024,
+                "bid": 0.00000024,
+                "bidVolume": 2446894197.0,
+                "ask": 0.00000025,
+                "askVolume": 2447913837.0,
+                "vwap": 0.00000025,
+                "open": 0.00000026,
+                "close": 0.00000024,
+                "last": 0.00000024,
+                "previousClose": 0.00000026,
+                "change": -0.00000002,
+                "percentage": -7.692,
+                "average": None,
+                "baseVolume": 4886464537.0,
+                "quoteVolume": 1215.14489611,
+                "info": {},
+            },
+            "HOT/BTC": {
+                "symbol": "HOT/BTC",
+                "timestamp": 1572273518661,
+                "datetime": "2019-10-28T14:38:38.661Z",
+                "high": 0.00000011,
+                "low": 0.00000009,
+                "bid": 0.0000001,
+                "bidVolume": 1476027288.0,
+                "ask": 0.00000011,
+                "askVolume": 820153831.0,
+                "vwap": 0.0000001,
+                "open": 0.00000009,
+                "close": 0.00000011,
+                "last": 0.00000011,
+                "previousClose": 0.00000009,
+                "change": 0.00000002,
+                "percentage": 22.222,
+                "average": None,
+                "baseVolume": 1442290324.0,
+                "quoteVolume": 143.78311994,
+                "info": {},
+            },
+            "FUEL/BTC": {
+                "symbol": "FUEL/BTC",
+                "timestamp": 1572340250771,
+                "datetime": "2019-10-29T09:10:50.771Z",
+                "high": 0.00000040,
+                "low": 0.00000035,
+                "bid": 0.00000036,
+                "bidVolume": 8932318.0,
+                "ask": 0.00000037,
+                "askVolume": 10140774.0,
+                "vwap": 0.00000037,
+                "open": 0.00000039,
+                "close": 0.00000037,
+                "last": 0.00000037,
+                "previousClose": 0.00000038,
+                "change": -0.00000002,
+                "percentage": -5.128,
+                "average": None,
+                "baseVolume": 168927742.0,
+                "quoteVolume": 62.68220262,
+                "info": {},
+            },
+            "BTC/USDT": {
+                "symbol": "BTC/USDT",
+                "timestamp": 1573758371399,
+                "datetime": "2019-11-14T19:06:11.399Z",
+                "high": 8800.0,
+                "low": 8582.6,
+                "bid": 8648.16,
+                "bidVolume": 0.238771,
+                "ask": 8648.72,
+                "askVolume": 0.016253,
+                "vwap": 8683.13647806,
+                "open": 8759.7,
+                "close": 8648.72,
+                "last": 8648.72,
+                "previousClose": 8759.67,
+                "change": -110.98,
+                "percentage": -1.267,
+                "average": None,
+                "baseVolume": 35025.943355,
+                "quoteVolume": 304135046.4242901,
+                "info": {},
+            },
+            "ETH/USDT": {
+                "symbol": "ETH/USDT",
+                "timestamp": 1522014804118,
+                "datetime": "2018-03-25T21:53:24.118Z",
+                "high": 530.88,
+                "low": 512.0,
+                "bid": 529.73,
+                "bidVolume": 0.2,
+                "ask": 530.21,
+                "askVolume": 0.2464,
+                "vwap": 521.02438405,
+                "open": 527.27,
+                "close": 528.42,
+                "first": None,
+                "last": 530.21,
+                "change": 0.558,
+                "percentage": 2.349,
+                "average": None,
+                "baseVolume": 72300.0659,
+                "quoteVolume": 37670097.3022171,
+                "info": {},
+            },
+            "TKN/USDT": {
+                "symbol": "TKN/USDT",
+                "timestamp": 1522014806198,
+                "datetime": "2018-03-25T21:53:26.198Z",
+                "high": 8718.0,
+                "low": 8365.77,
+                "bid": 8603.64,
+                "bidVolume": 0.15846,
+                "ask": 8603.67,
+                "askVolume": 0.069147,
+                "vwap": 8536.35621697,
+                "open": 8680.0,
+                "close": 8680.0,
+                "first": None,
+                "last": 8603.67,
+                "change": -0.879,
+                "percentage": -8.95,
+                "average": None,
+                "baseVolume": 30414.604298,
+                "quoteVolume": 259629896.48584127,
+                "info": {},
+            },
+            "BLK/USDT": {
+                "symbol": "BLK/USDT",
+                "timestamp": 1522014806145,
+                "datetime": "2018-03-25T21:53:26.145Z",
+                "high": 66.95,
+                "low": 63.38,
+                "bid": 66.473,
+                "bidVolume": 4.968,
+                "ask": 66.54,
+                "askVolume": 2.704,
+                "vwap": 65.0526901,
+                "open": 66.43,
+                "close": 66.383,
+                "first": None,
+                "last": 66.5,
+                "change": 0.105,
+                "percentage": None,
+                "average": None,
+                "baseVolume": 294106.204,
+                "quoteVolume": 19132399.743954,
+                "info": {},
+            },
+            "LTC/USDT": {
+                "symbol": "LTC/USDT",
+                "timestamp": 1523787257812,
+                "datetime": "2018-04-15T10:14:18.812Z",
+                "high": 129.94,
+                "low": 124.0,
+                "bid": 129.28,
+                "bidVolume": 0.03201,
+                "ask": 129.52,
+                "askVolume": 0.14529,
+                "vwap": 126.92838682,
+                "open": 127.0,
+                "close": 127.1,
+                "first": None,
+                "last": 129.28,
+                "change": 1.795,
+                "percentage": -2.5,
+                "average": None,
+                "baseVolume": 59698.79897,
+                "quoteVolume": 29132399.743954,
+                "info": {},
+            },
+            "XRP/BTC": {
+                "symbol": "XRP/BTC",
+                "timestamp": 1573758257534,
+                "datetime": "2019-11-14T19:04:17.534Z",
+                "high": 3.126e-05,
+                "low": 3.061e-05,
+                "bid": 3.093e-05,
+                "bidVolume": 27901.0,
+                "ask": 3.095e-05,
+                "askVolume": 10551.0,
+                "vwap": 3.091e-05,
+                "open": 3.119e-05,
+                "close": 3.094e-05,
+                "last": 3.094e-05,
+                "previousClose": 3.117e-05,
+                "change": -2.5e-07,
+                "percentage": -0.802,
+                "average": None,
+                "baseVolume": 37334921.0,
+                "quoteVolume": 1154.19266394,
+                "info": {},
+            },
+            "NANO/USDT": {
+                "symbol": "NANO/USDT",
+                "timestamp": 1580469388244,
+                "datetime": "2020-01-31T11:16:28.244Z",
+                "high": 0.7519,
+                "low": 0.7154,
+                "bid": 0.7305,
+                "bidVolume": 300.3,
+                "ask": 0.7342,
+                "askVolume": 15.14,
+                "vwap": 0.73645591,
+                "open": 0.7154,
+                "close": 0.7342,
+                "last": 0.7342,
+                "previousClose": 0.7189,
+                "change": 0.0188,
+                "percentage": 2.628,
+                "average": None,
+                "baseVolume": 439472.44,
+                "quoteVolume": 323652.075405,
+                "info": {},
+            },
+            # Example of leveraged pair with incomplete info
+            "ADAHALF/USDT": {
+                "symbol": "ADAHALF/USDT",
+                "timestamp": 1580469388244,
+                "datetime": "2020-01-31T11:16:28.244Z",
+                "high": None,
+                "low": None,
+                "bid": 0.7305,
+                "bidVolume": None,
+                "ask": 0.7342,
+                "askVolume": None,
+                "vwap": None,
+                "open": None,
+                "close": None,
+                "last": None,
+                "previousClose": None,
+                "change": None,
+                "percentage": 2.628,
+                "average": None,
+                "baseVolume": 0.0,
+                "quoteVolume": 0.0,
+                "info": {},
+            },
+            "ADADOUBLE/USDT": {
+                "symbol": "ADADOUBLE/USDT",
+                "timestamp": 1580469388244,
+                "datetime": "2020-01-31T11:16:28.244Z",
+                "high": None,
+                "low": None,
+                "bid": 0.7305,
+                "bidVolume": None,
+                "ask": 0.7342,
+                "askVolume": None,
+                "vwap": None,
+                "open": None,
+                "close": None,
+                "last": 0,
+                "previousClose": None,
+                "change": None,
+                "percentage": 2.628,
+                "average": None,
+                "baseVolume": 0.0,
+                "quoteVolume": 0.0,
+                "info": {},
+            },
+        }
+    )
+
+
+@pytest.fixture
+def dataframe_1m(testdatadir):
+    with (testdatadir / "UNITTEST_BTC-1m.json").open("r") as data_file:
+        return ohlcv_to_dataframe(
+            json.load(data_file), "1m", pair="UNITTEST/BTC", fill_missing=True
+        )
+
+
+@pytest.fixture(scope="function")
+def trades_for_order():
+    return [
+        {
+            "info": {
+                "id": 34567,
+                "orderId": 123456,
+                "price": "2.0",
+                "qty": "8.00000000",
+                "commission": "0.00800000",
+                "commissionAsset": "LTC",
+                "time": 1521663363189,
+                "isBuyer": True,
+                "isMaker": False,
+                "isBestMatch": True,
+            },
+            "timestamp": 1521663363189,
+            "datetime": "2018-03-21T20:16:03.189Z",
+            "symbol": "LTC/USDT",
+            "id": "34567",
+            "order": "123456",
+            "type": None,
+            "side": "buy",
+            "price": 2.0,
+            "cost": 16.0,
+            "amount": 8.0,
+            "fee": {"cost": 0.008, "currency": "LTC"},
+        }
+    ]
+
+
+@pytest.fixture(scope="function")
+def trades_history():
+    return [
+        [1565798389463, "12618132aa9", None, "buy", 0.019627, 0.04, 0.00078508],
+        [1565798399629, "1261813bb30", None, "buy", 0.019627, 0.244, 0.004788987999999999],
+        [1565798399752, "1261813cc31", None, "sell", 0.019626, 0.011, 0.00021588599999999999],
+        [1565798399862, "126181cc332", None, "sell", 0.019626, 0.011, 0.00021588599999999999],
+        [1565798399862, "126181cc333", None, "sell", 0.019626, 0.012, 0.00021588599999999999],
+        [1565798399872, "1261aa81334", None, "sell", 0.019626, 0.011, 0.00021588599999999999],
+    ]
+
+
+@pytest.fixture(scope="function")
+def trades_history_df(trades_history):
+    trades = trades_list_to_df(trades_history)
+    trades["date"] = pd.to_datetime(trades["timestamp"], unit="ms", utc=True)
+    return trades
+
+
+@pytest.fixture(scope="function")
+def fetch_trades_result():
+    return [
+        {
+            "info": ["0.01962700", "0.04000000", "1565798399.4631551", "b", "m", "", "126181329"],
+            "timestamp": 1565798399463,
+            "datetime": "2019-08-14T15:59:59.463Z",
+            "symbol": "ETH/BTC",
+            "id": "126181329",
+            "order": None,
+            "type": None,
+            "takerOrMaker": None,
+            "side": "buy",
+            "price": 0.019627,
+            "amount": 0.04,
+            "cost": 0.00078508,
+            "fee": None,
+        },
+        {
+            "info": ["0.01962700", "0.24400000", "1565798399.6291551", "b", "m", "", "126181330"],
+            "timestamp": 1565798399629,
+            "datetime": "2019-08-14T15:59:59.629Z",
+            "symbol": "ETH/BTC",
+            "id": "126181330",
+            "order": None,
+            "type": None,
+            "takerOrMaker": None,
+            "side": "buy",
+            "price": 0.019627,
+            "amount": 0.244,
+            "cost": 0.004788987999999999,
+            "fee": None,
+        },
+        {
+            "info": ["0.01962600", "0.01100000", "1565798399.7521551", "s", "m", "", "126181331"],
+            "timestamp": 1565798399752,
+            "datetime": "2019-08-14T15:59:59.752Z",
+            "symbol": "ETH/BTC",
+            "id": "126181331",
+            "order": None,
+            "type": None,
+            "takerOrMaker": None,
+            "side": "sell",
+            "price": 0.019626,
+            "amount": 0.011,
+            "cost": 0.00021588599999999999,
+            "fee": None,
+        },
+        {
+            "info": ["0.01962600", "0.01100000", "1565798399.8621551", "s", "m", "", "126181332"],
+            "timestamp": 1565798399862,
+            "datetime": "2019-08-14T15:59:59.862Z",
+            "symbol": "ETH/BTC",
+            "id": "126181332",
+            "order": None,
+            "type": None,
+            "takerOrMaker": None,
+            "side": "sell",
+            "price": 0.019626,
+            "amount": 0.011,
+            "cost": 0.00021588599999999999,
+            "fee": None,
+        },
+        {
+            "info": [
+                "0.01952600",
+                "0.01200000",
+                "1565798399.8721551",
+                "s",
+                "m",
+                "",
+                "126181333",
+                1565798399872512133,
+            ],
+            "timestamp": 1565798399872,
+            "datetime": "2019-08-14T15:59:59.872Z",
+            "symbol": "ETH/BTC",
+            "id": "126181333",
+            "order": None,
+            "type": None,
+            "takerOrMaker": None,
+            "side": "sell",
+            "price": 0.019626,
+            "amount": 0.011,
+            "cost": 0.00021588599999999999,
+            "fee": None,
+        },
+    ]
+
+
+@pytest.fixture(scope="function")
+def trades_for_order2():
+    return [
+        {
+            "info": {},
+            "timestamp": 1521663363189,
+            "datetime": "2018-03-21T20:16:03.189Z",
+            "symbol": "LTC/ETH",
+            "id": "34567",
+            "order": "123456",
+            "type": None,
+            "side": "buy",
+            "price": 0.245441,
+            "cost": 1.963528,
+            "amount": 4.0,
+            "fee": {"cost": 0.004, "currency": "LTC"},
+        },
+        {
+            "info": {},
+            "timestamp": 1521663363189,
+            "datetime": "2018-03-21T20:16:03.189Z",
+            "symbol": "LTC/ETH",
+            "id": "34567",
+            "order": "123456",
+            "type": None,
+            "side": "buy",
+            "price": 0.245441,
+            "cost": 1.963528,
+            "amount": 4.0,
+            "fee": {"cost": 0.004, "currency": "LTC"},
+        },
+    ]
+
+
+@pytest.fixture
+def buy_order_fee():
+    return {
+        "id": "mocked_limit_buy_old",
+        "type": "limit",
+        "side": "buy",
+        "symbol": "mocked",
+        "timestamp": dt_ts(dt_now() - timedelta(minutes=601)),
+        "datetime": (dt_now() - timedelta(minutes=601)).isoformat(),
+        "price": 0.245441,
+        "amount": 8.0,
+        "cost": 1.963528,
+        "remaining": 90.99181073,
+        "status": "closed",
+        "fee": None,
+    }
+
+
+@pytest.fixture(scope="function")
+def edge_conf(default_conf):
+    conf = deepcopy(default_conf)
+    conf["runmode"] = RunMode.DRY_RUN
+    conf["max_open_trades"] = -1
+    conf["tradable_balance_ratio"] = 0.5
+    conf["stake_amount"] = constants.UNLIMITED_STAKE_AMOUNT
+    conf["edge"] = {
+        "enabled": True,
+        "process_throttle_secs": 1800,
+        "calculate_since_number_of_days": 14,
+        "allowed_risk": 0.01,
+        "stoploss_range_min": -0.01,
+        "stoploss_range_max": -0.1,
+        "stoploss_range_step": -0.01,
+        "maximum_winrate": 0.80,
+        "minimum_expectancy": 0.20,
+        "min_trade_number": 15,
+        "max_trade_duration_minute": 1440,
+        "remove_pumps": False,
+    }
+
+    return conf
+
+
+@pytest.fixture
+def rpc_balance():
+    return {
+        "BTC": {"total": 12.0, "free": 12.0, "used": 0.0},
+        "ETH": {"total": 0.0, "free": 0.0, "used": 0.0},
+        "USDT": {"total": 10000.0, "free": 10000.0, "used": 0.0},
+        "LTC": {"total": 10.0, "free": 10.0, "used": 0.0},
+        "XRP": {"total": 0.1, "free": 0.01, "used": 0.0},
+        "EUR": {"total": 10.0, "free": 10.0, "used": 0.0},
+    }
+
+
+@pytest.fixture
+def testdatadir() -> Path:
+    """Return the path where testdata files are stored"""
+    return (Path(__file__).parent / "testdata").resolve()
+
+
+@pytest.fixture(scope="function")
+def import_fails() -> None:
+    # Source of this test-method:
+    # https://stackoverflow.com/questions/2481511/mocking-importerror-in-python
+    import builtins
+
+    realimport = builtins.__import__
+
+    def mockedimport(name, *args, **kwargs):
+        if name in ["filelock", "cysystemd.journal", "uvloop"]:
+            raise ImportError(f"No module named '{name}'")
+        return realimport(name, *args, **kwargs)
+
+    builtins.__import__ = mockedimport
+
+    # Run test - then cleanup
+    yield
+
+    # restore previous importfunction
+    builtins.__import__ = realimport
+
+
+@pytest.fixture(scope="function")
+def open_trade():
+    trade = Trade(
+        pair="ETH/BTC",
+        open_rate=0.00001099,
+        exchange="binance",
+        amount=90.99181073,
+        fee_open=0.0,
+        fee_close=0.0,
+        stake_amount=1,
+        open_date=dt_now() - timedelta(minutes=601),
+        is_open=True,
+    )
+    trade.orders = [
+        Order(
+            ft_order_side="buy",
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.open_rate,
+            order_id="123456789",
+            status="closed",
+            symbol=trade.pair,
+            order_type="market",
+            side="buy",
+            price=trade.open_rate,
+            average=trade.open_rate,
+            filled=trade.amount,
+            remaining=0,
+            cost=trade.open_rate * trade.amount,
+            order_date=trade.open_date,
+            order_filled_date=trade.open_date,
+        )
+    ]
+    return trade
+
+
+@pytest.fixture(scope="function")
+def open_trade_usdt():
+    trade = Trade(
+        pair="ADA/USDT",
+        open_rate=2.0,
+        exchange="binance",
+        amount=30.0,
+        fee_open=0.0,
+        fee_close=0.0,
+        stake_amount=60.0,
+        open_date=dt_now() - timedelta(minutes=601),
+        is_open=True,
+    )
+    trade.orders = [
+        Order(
+            ft_order_side="buy",
+            ft_pair=trade.pair,
+            ft_is_open=False,
+            ft_amount=trade.amount,
+            ft_price=trade.open_rate,
+            order_id="123456789",
+            status="closed",
+            symbol=trade.pair,
+            order_type="market",
+            side="buy",
+            price=trade.open_rate,
+            average=trade.open_rate,
+            filled=trade.amount,
+            remaining=0,
+            cost=trade.open_rate * trade.amount,
+            order_date=trade.open_date,
+            order_filled_date=trade.open_date,
+        ),
+        Order(
+            ft_order_side="exit",
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.open_rate,
+            order_id="123456789_exit",
+            status="open",
+            symbol=trade.pair,
+            order_type="limit",
+            side="sell",
+            price=trade.open_rate,
+            average=trade.open_rate,
+            filled=trade.amount,
+            remaining=0,
+            cost=trade.open_rate * trade.amount,
+            order_date=trade.open_date,
+            order_filled_date=trade.open_date,
+        ),
+    ]
+    return trade
+
+
+@pytest.fixture(scope="function")
+def limit_buy_order_usdt_open():
+    return {
+        "id": "mocked_limit_buy_usdt",
+        "type": "limit",
+        "side": "buy",
+        "symbol": "mocked",
+        "datetime": dt_now().isoformat(),
+        "timestamp": dt_ts(),
+        "price": 2.00,
+        "average": 2.00,
+        "amount": 30.0,
+        "filled": 0.0,
+        "cost": 60.0,
+        "remaining": 30.0,
+        "status": "open",
+    }
+
+
+@pytest.fixture(scope="function")
+def limit_buy_order_usdt(limit_buy_order_usdt_open):
+    order = deepcopy(limit_buy_order_usdt_open)
+    order["status"] = "closed"
+    order["filled"] = order["amount"]
+    order["remaining"] = 0.0
+    return order
+
+
+@pytest.fixture
+def limit_sell_order_usdt_open():
+    return {
+        "id": "mocked_limit_sell_usdt",
+        "type": "limit",
+        "side": "sell",
+        "symbol": "mocked",
+        "datetime": dt_now().isoformat(),
+        "timestamp": dt_ts(),
+        "price": 2.20,
+        "amount": 30.0,
+        "cost": 66.0,
+        "filled": 0.0,
+        "remaining": 30.0,
+        "status": "open",
+    }
+
+
+@pytest.fixture
+def limit_sell_order_usdt(limit_sell_order_usdt_open):
+    order = deepcopy(limit_sell_order_usdt_open)
+    order["remaining"] = 0.0
+    order["filled"] = order["amount"]
+    order["status"] = "closed"
+    return order
+
+
+@pytest.fixture(scope="function")
+def market_buy_order_usdt():
+    return {
+        "id": "mocked_market_buy",
+        "type": "market",
+        "side": "buy",
+        "symbol": "mocked",
+        "timestamp": dt_ts(),
+        "datetime": dt_now().isoformat(),
+        "price": 2.00,
+        "amount": 30.0,
+        "filled": 30.0,
+        "remaining": 0.0,
+        "status": "closed",
+    }
+
+
+@pytest.fixture
+def market_buy_order_usdt_doublefee(market_buy_order_usdt):
+    order = deepcopy(market_buy_order_usdt)
+    order["fee"] = None
+    # Market orders filled with 2 trades can have fees in different currencies
+    # assuming the account runs out of BNB.
+    order["fees"] = [
+        {"cost": 0.00025125, "currency": "BNB"},
+        {"cost": 0.05030681, "currency": "USDT"},
+    ]
+    order["trades"] = [
+        {
+            "timestamp": None,
+            "datetime": None,
+            "symbol": "ETH/USDT",
+            "id": None,
+            "order": "123",
+            "type": "market",
+            "side": "sell",
+            "takerOrMaker": None,
+            "price": 2.01,
+            "amount": 25.0,
+            "cost": 50.25,
+            "fee": {"cost": 0.00025125, "currency": "BNB"},
+        },
+        {
+            "timestamp": None,
+            "datetime": None,
+            "symbol": "ETH/USDT",
+            "id": None,
+            "order": "123",
+            "type": "market",
+            "side": "sell",
+            "takerOrMaker": None,
+            "price": 2.0,
+            "amount": 5,
+            "cost": 10,
+            "fee": {"cost": 0.0100306, "currency": "USDT"},
+        },
+    ]
+    return order
+
+
+@pytest.fixture
+def market_sell_order_usdt():
+    return {
+        "id": "mocked_limit_sell",
+        "type": "market",
+        "side": "sell",
+        "symbol": "mocked",
+        "timestamp": dt_ts(),
+        "datetime": dt_now().isoformat(),
+        "price": 2.20,
+        "amount": 30.0,
+        "filled": 30.0,
+        "remaining": 0.0,
+        "status": "closed",
+    }
+
+
+@pytest.fixture(scope="function")
+def limit_order(limit_buy_order_usdt, limit_sell_order_usdt):
+    return {"buy": limit_buy_order_usdt, "sell": limit_sell_order_usdt}
+
+
+@pytest.fixture(scope="function")
+def limit_order_open(limit_buy_order_usdt_open, limit_sell_order_usdt_open):
+    return {"buy": limit_buy_order_usdt_open, "sell": limit_sell_order_usdt_open}
+
+
+@pytest.fixture(scope="function")
+def mark_ohlcv():
+    return [
+        [1630454400000, 2.77, 2.77, 2.73, 2.73, 0],
+        [1630458000000, 2.73, 2.76, 2.72, 2.74, 0],
+        [1630461600000, 2.74, 2.76, 2.74, 2.76, 0],
+        [1630465200000, 2.76, 2.76, 2.74, 2.76, 0],
+        [1630468800000, 2.76, 2.77, 2.75, 2.77, 0],
+        [1630472400000, 2.77, 2.79, 2.75, 2.78, 0],
+        [1630476000000, 2.78, 2.80, 2.77, 2.77, 0],
+        [1630479600000, 2.78, 2.79, 2.77, 2.77, 0],
+        [1630483200000, 2.77, 2.79, 2.77, 2.78, 0],
+        [1630486800000, 2.77, 2.84, 2.77, 2.84, 0],
+        [1630490400000, 2.84, 2.85, 2.81, 2.81, 0],
+        [1630494000000, 2.81, 2.83, 2.81, 2.81, 0],
+        [1630497600000, 2.81, 2.84, 2.81, 2.82, 0],
+        [1630501200000, 2.82, 2.83, 2.81, 2.81, 0],
+    ]
+
+
+@pytest.fixture(scope="function")
+def funding_rate_history_hourly():
+    return [
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": -0.000008,
+            "timestamp": 1630454400000,
+            "datetime": "2021-09-01T00:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": -0.000004,
+            "timestamp": 1630458000000,
+            "datetime": "2021-09-01T01:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": 0.000012,
+            "timestamp": 1630461600000,
+            "datetime": "2021-09-01T02:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": -0.000003,
+            "timestamp": 1630465200000,
+            "datetime": "2021-09-01T03:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": -0.000007,
+            "timestamp": 1630468800000,
+            "datetime": "2021-09-01T04:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": 0.000003,
+            "timestamp": 1630472400000,
+            "datetime": "2021-09-01T05:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": 0.000019,
+            "timestamp": 1630476000000,
+            "datetime": "2021-09-01T06:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": 0.000003,
+            "timestamp": 1630479600000,
+            "datetime": "2021-09-01T07:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": -0.000003,
+            "timestamp": 1630483200000,
+            "datetime": "2021-09-01T08:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": 0,
+            "timestamp": 1630486800000,
+            "datetime": "2021-09-01T09:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": 0.000013,
+            "timestamp": 1630490400000,
+            "datetime": "2021-09-01T10:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": 0.000077,
+            "timestamp": 1630494000000,
+            "datetime": "2021-09-01T11:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": 0.000072,
+            "timestamp": 1630497600000,
+            "datetime": "2021-09-01T12:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": 0.000097,
+            "timestamp": 1630501200000,
+            "datetime": "2021-09-01T13:00:00.000Z",
+        },
+    ]
+
+
+@pytest.fixture(scope="function")
+def funding_rate_history_octohourly():
+    return [
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": -0.000008,
+            "timestamp": 1630454400000,
+            "datetime": "2021-09-01T00:00:00.000Z",
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "fundingRate": -0.000003,
+            "timestamp": 1630483200000,
+            "datetime": "2021-09-01T08:00:00.000Z",
+        },
+    ]
+
+
+@pytest.fixture(scope="function")
+def leverage_tiers():
+    return {
+        "1000SHIB/USDT:USDT": [
+            {
+                "minNotional": 0,
+                "maxNotional": 50000,
+                "maintenanceMarginRate": 0.01,
+                "maxLeverage": 50,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 50000,
+                "maxNotional": 150000,
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
+                "maintAmt": 750.0,
+            },
+            {
+                "minNotional": 150000,
+                "maxNotional": 250000,
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 4500.0,
+            },
+            {
+                "minNotional": 250000,
+                "maxNotional": 500000,
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 17000.0,
+            },
+            {
+                "minNotional": 500000,
+                "maxNotional": 1000000,
+                "maintenanceMarginRate": 0.125,
+                "maxLeverage": 4,
+                "maintAmt": 29500.0,
+            },
+            {
+                "minNotional": 1000000,
+                "maxNotional": 2000000,
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
+                "maintAmt": 154500.0,
+            },
+            {
+                "minNotional": 2000000,
+                "maxNotional": 30000000,
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 654500.0,
+            },
+        ],
+        "1INCH/USDT:USDT": [
+            {
+                "minNotional": 0,
+                "maxNotional": 5000,
+                "maintenanceMarginRate": 0.012,
+                "maxLeverage": 50,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 5000,
+                "maxNotional": 25000,
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
+                "maintAmt": 65.0,
+            },
+            {
+                "minNotional": 25000,
+                "maxNotional": 100000,
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 690.0,
+            },
+            {
+                "minNotional": 100000,
+                "maxNotional": 250000,
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 5690.0,
+            },
+            {
+                "minNotional": 250000,
+                "maxNotional": 1000000,
+                "maintenanceMarginRate": 0.125,
+                "maxLeverage": 2,
+                "maintAmt": 11940.0,
+            },
+            {
+                "minNotional": 1000000,
+                "maxNotional": 100000000,
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 386940.0,
+            },
+        ],
+        "AAVE/USDT:USDT": [
+            {
+                "minNotional": 0,
+                "maxNotional": 5000,
+                "maintenanceMarginRate": 0.01,
+                "maxLeverage": 50,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 5000,
+                "maxNotional": 25000,
+                "maintenanceMarginRate": 0.02,
+                "maxLeverage": 25,
+                "maintAmt": 75.0,
+            },
+            {
+                "minNotional": 25000,
+                "maxNotional": 100000,
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 700.0,
+            },
+            {
+                "minNotional": 100000,
+                "maxNotional": 250000,
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 5700.0,
+            },
+            {
+                "minNotional": 250000,
+                "maxNotional": 1000000,
+                "maintenanceMarginRate": 0.125,
+                "maxLeverage": 2,
+                "maintAmt": 11950.0,
+            },
+            {
+                "minNotional": 10000000,
+                "maxNotional": 50000000,
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 386950.0,
+            },
+        ],
+        "ADA/USDT:USDT": [
+            {
+                "minNotional": 0,
+                "maxNotional": 100000,
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 100000,
+                "maxNotional": 500000,
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 2500.0,
+            },
+            {
+                "minNotional": 500000,
+                "maxNotional": 1000000,
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 27500.0,
+            },
+            {
+                "minNotional": 1000000,
+                "maxNotional": 2000000,
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
+                "maintAmt": 77500.0,
+            },
+            {
+                "minNotional": 2000000,
+                "maxNotional": 5000000,
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
+                "maintAmt": 277500.0,
+            },
+            {
+                "minNotional": 5000000,
+                "maxNotional": 30000000,
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 1527500.0,
+            },
+        ],
+        "XRP/USDT:USDT": [
+            {
+                "minNotional": 0,  # stake(before leverage) = 0
+                "maxNotional": 100000,  # max stake(before leverage) = 5000
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 100000,  # stake = 10000.0
+                "maxNotional": 500000,  # max_stake = 50000.0
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 2500.0,
+            },
+            {
+                "minNotional": 500000,  # stake = 100000.0
+                "maxNotional": 1000000,  # max_stake = 200000.0
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 27500.0,
+            },
+            {
+                "minNotional": 1000000,  # stake = 333333.3333333333
+                "maxNotional": 2000000,  # max_stake = 666666.6666666666
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
+                "maintAmt": 77500.0,
+            },
+            {
+                "minNotional": 2000000,  # stake = 1000000.0
+                "maxNotional": 5000000,  # max_stake = 2500000.0
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
+                "maintAmt": 277500.0,
+            },
+            {
+                "minNotional": 5000000,  # stake = 5000000.0
+                "maxNotional": 30000000,  # max_stake = 30000000.0
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 1527500.0,
+            },
+        ],
+        "BNB/USDT:USDT": [
+            {
+                "minNotional": 0,  # stake = 0.0
+                "maxNotional": 10000,  # max_stake = 133.33333333333334
+                "maintenanceMarginRate": 0.0065,
+                "maxLeverage": 75,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 10000,  # stake = 200.0
+                "maxNotional": 50000,  # max_stake = 1000.0
+                "maintenanceMarginRate": 0.01,
+                "maxLeverage": 50,
+                "maintAmt": 35.0,
+            },
+            {
+                "minNotional": 50000,  # stake = 2000.0
+                "maxNotional": 250000,  # max_stake = 10000.0
+                "maintenanceMarginRate": 0.02,
+                "maxLeverage": 25,
+                "maintAmt": 535.0,
+            },
+            {
+                "minNotional": 250000,  # stake = 25000.0
+                "maxNotional": 1000000,  # max_stake = 100000.0
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 8035.0,
+            },
+            {
+                "minNotional": 1000000,  # stake = 200000.0
+                "maxNotional": 2000000,  # max_stake = 400000.0
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 58035.0,
+            },
+            {
+                "minNotional": 2000000,  # stake = 500000.0
+                "maxNotional": 5000000,  # max_stake = 1250000.0
+                "maintenanceMarginRate": 0.125,
+                "maxLeverage": 4,
+                "maintAmt": 108035.0,
+            },
+            {
+                "minNotional": 5000000,  # stake = 1666666.6666666667
+                "maxNotional": 10000000,  # max_stake = 3333333.3333333335
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
+                "maintAmt": 233035.0,
+            },
+            {
+                "minNotional": 10000000,  # stake = 5000000.0
+                "maxNotional": 20000000,  # max_stake = 10000000.0
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
+                "maintAmt": 1233035.0,
+            },
+            {
+                "minNotional": 20000000,  # stake = 20000000.0
+                "maxNotional": 50000000,  # max_stake = 50000000.0
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 6233035.0,
+            },
+        ],
+        "BTC/USDT:USDT": [
+            {
+                "minNotional": 0,  # stake = 0.0
+                "maxNotional": 50000,  # max_stake = 400.0
+                "maintenanceMarginRate": 0.004,
+                "maxLeverage": 125,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 50000,  # stake = 500.0
+                "maxNotional": 250000,  # max_stake = 2500.0
+                "maintenanceMarginRate": 0.005,
+                "maxLeverage": 100,
+                "maintAmt": 50.0,
+            },
+            {
+                "minNotional": 250000,  # stake = 5000.0
+                "maxNotional": 1000000,  # max_stake = 20000.0
+                "maintenanceMarginRate": 0.01,
+                "maxLeverage": 50,
+                "maintAmt": 1300.0,
+            },
+            {
+                "minNotional": 1000000,  # stake = 50000.0
+                "maxNotional": 7500000,  # max_stake = 375000.0
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
+                "maintAmt": 16300.0,
+            },
+            {
+                "minNotional": 7500000,  # stake = 750000.0
+                "maxNotional": 40000000,  # max_stake = 4000000.0
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 203800.0,
+            },
+            {
+                "minNotional": 40000000,  # stake = 8000000.0
+                "maxNotional": 100000000,  # max_stake = 20000000.0
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 2203800.0,
+            },
+            {
+                "minNotional": 100000000,  # stake = 25000000.0
+                "maxNotional": 200000000,  # max_stake = 50000000.0
+                "maintenanceMarginRate": 0.125,
+                "maxLeverage": 4,
+                "maintAmt": 4703800.0,
+            },
+            {
+                "minNotional": 200000000,  # stake = 66666666.666666664
+                "maxNotional": 400000000,  # max_stake = 133333333.33333333
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
+                "maintAmt": 9703800.0,
+            },
+            {
+                "minNotional": 400000000,  # stake = 200000000.0
+                "maxNotional": 600000000,  # max_stake = 300000000.0
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
+                "maintAmt": 4.97038e7,
+            },
+            {
+                "minNotional": 600000000,  # stake = 600000000.0
+                "maxNotional": 1000000000,  # max_stake = 1000000000.0
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 1.997038e8,
+            },
+        ],
+        "ZEC/USDT:USDT": [
+            {
+                "minNotional": 0,
+                "maxNotional": 50000,
+                "maintenanceMarginRate": 0.01,
+                "maxLeverage": 50,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 50000,
+                "maxNotional": 150000,
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
+                "maintAmt": 750.0,
+            },
+            {
+                "minNotional": 150000,
+                "maxNotional": 250000,
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 4500.0,
+            },
+            {
+                "minNotional": 250000,
+                "maxNotional": 500000,
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 17000.0,
+            },
+            {
+                "minNotional": 500000,
+                "maxNotional": 1000000,
+                "maintenanceMarginRate": 0.125,
+                "maxLeverage": 4,
+                "maintAmt": 29500.0,
+            },
+            {
+                "minNotional": 1000000,
+                "maxNotional": 2000000,
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
+                "maintAmt": 154500.0,
+            },
+            {
+                "minNotional": 2000000,
+                "maxNotional": 30000000,
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 654500.0,
+            },
+        ],
+    }
diff --git a/tests/conftest_hyperopt.py b/tests/conftest_hyperopt.py
new file mode 100644
index 000000000..3b9638bad
--- /dev/null
+++ b/tests/conftest_hyperopt.py
@@ -0,0 +1,1001 @@
+from datetime import timedelta
+
+
+def hyperopt_test_result():
+    """
+    Sample hyperopt test result, used for some tests.
+    """
+    hyperopt_res = [
+        {
+            "loss": 0.4366182531161519,
+            "params_dict": {
+                "mfi-value": 15,
+                "fastd-value": 20,
+                "adx-value": 25,
+                "rsi-value": 28,
+                "mfi-enabled": False,
+                "fastd-enabled": True,
+                "adx-enabled": True,
+                "rsi-enabled": True,
+                "trigger": "macd_cross_signal",
+                "sell-mfi-value": 88,
+                "sell-fastd-value": 97,
+                "sell-adx-value": 51,
+                "sell-rsi-value": 67,
+                "sell-mfi-enabled": False,
+                "sell-fastd-enabled": False,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-bb_upper",
+                "roi_t1": 1190,
+                "roi_t2": 541,
+                "roi_t3": 408,
+                "roi_p1": 0.026035863879169705,
+                "roi_p2": 0.12508730043628782,
+                "roi_p3": 0.27766427921605896,
+                "stoploss": -0.2562930402099556,
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+            "params_details": {
+                "buy": {
+                    "mfi-value": 15,
+                    "fastd-value": 20,
+                    "adx-value": 25,
+                    "rsi-value": 28,
+                    "mfi-enabled": False,
+                    "fastd-enabled": True,
+                    "adx-enabled": True,
+                    "rsi-enabled": True,
+                    "trigger": "macd_cross_signal",
+                },
+                "sell": {
+                    "sell-mfi-value": 88,
+                    "sell-fastd-value": 97,
+                    "sell-adx-value": 51,
+                    "sell-rsi-value": 67,
+                    "sell-mfi-enabled": False,
+                    "sell-fastd-enabled": False,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-bb_upper",
+                },
+                "roi": {
+                    0: 0.4287874435315165,
+                    408: 0.15112316431545753,
+                    949: 0.026035863879169705,
+                    2139: 0,
+                },
+                "stoploss": {"stoploss": -0.2562930402099556},
+            },
+            "results_metrics": {
+                "total_trades": 2,
+                "trade_count_long": 2,
+                "trade_count_short": 0,
+                "wins": 0,
+                "draws": 0,
+                "losses": 2,
+                "profit_mean": -0.01254995,
+                "profit_median": -0.012222,
+                "profit_total": -0.00125625,
+                "profit_total_abs": -2.50999,
+                "max_drawdown_account": 0.23,
+                "max_drawdown_abs": -0.00125625,
+                "holding_avg": timedelta(minutes=3930.0),
+                "stake_currency": "BTC",
+                "strategy_name": "SampleStrategy",
+            },
+            "results_explanation": "     2 trades. Avg profit  -1.25%. Total profit -0.00125625 BTC (  -2.51Σ%). Avg duration 3930.0 min.",  # noqa: E501
+            "total_profit": -0.00125625,
+            "current_epoch": 1,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": True,
+        },
+        {
+            "loss": 20.0,
+            "params_dict": {
+                "mfi-value": 17,
+                "fastd-value": 38,
+                "adx-value": 48,
+                "rsi-value": 22,
+                "mfi-enabled": True,
+                "fastd-enabled": False,
+                "adx-enabled": True,
+                "rsi-enabled": True,
+                "trigger": "macd_cross_signal",
+                "sell-mfi-value": 96,
+                "sell-fastd-value": 68,
+                "sell-adx-value": 63,
+                "sell-rsi-value": 81,
+                "sell-mfi-enabled": False,
+                "sell-fastd-enabled": True,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-sar_reversal",
+                "roi_t1": 334,
+                "roi_t2": 683,
+                "roi_t3": 140,
+                "roi_p1": 0.06403981740598495,
+                "roi_p2": 0.055519840060645045,
+                "roi_p3": 0.3253712811342459,
+                "stoploss": -0.338070047333259,
+            },
+            "params_details": {
+                "buy": {
+                    "mfi-value": 17,
+                    "fastd-value": 38,
+                    "adx-value": 48,
+                    "rsi-value": 22,
+                    "mfi-enabled": True,
+                    "fastd-enabled": False,
+                    "adx-enabled": True,
+                    "rsi-enabled": True,
+                    "trigger": "macd_cross_signal",
+                },
+                "sell": {
+                    "sell-mfi-value": 96,
+                    "sell-fastd-value": 68,
+                    "sell-adx-value": 63,
+                    "sell-rsi-value": 81,
+                    "sell-mfi-enabled": False,
+                    "sell-fastd-enabled": True,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-sar_reversal",
+                },
+                "roi": {
+                    0: 0.4449309386008759,
+                    140: 0.11955965746663,
+                    823: 0.06403981740598495,
+                    1157: 0,
+                },
+                "stoploss": {"stoploss": -0.338070047333259},
+            },
+            "results_metrics": {
+                "total_trades": 1,
+                "trade_count_long": 1,
+                "trade_count_short": 0,
+                "wins": 0,
+                "draws": 0,
+                "losses": 1,
+                "profit_mean": 0.012357,
+                "profit_median": -0.012222,
+                "profit_total": 6.185e-05,
+                "profit_total_abs": 0.12357,
+                "max_drawdown_account": 0.23,
+                "max_drawdown_abs": -0.00125625,
+                "holding_avg": timedelta(minutes=1200.0),
+            },
+            "results_explanation": "     1 trades. Avg profit   0.12%. Total profit  0.00006185 BTC (   0.12Σ%). Avg duration 1200.0 min.",  # noqa: E501
+            "total_profit": 6.185e-05,
+            "current_epoch": 2,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": False,
+        },
+        {
+            "loss": 14.241196856510731,
+            "params_dict": {
+                "mfi-value": 25,
+                "fastd-value": 16,
+                "adx-value": 29,
+                "rsi-value": 20,
+                "mfi-enabled": False,
+                "fastd-enabled": False,
+                "adx-enabled": False,
+                "rsi-enabled": False,
+                "trigger": "macd_cross_signal",
+                "sell-mfi-value": 98,
+                "sell-fastd-value": 72,
+                "sell-adx-value": 51,
+                "sell-rsi-value": 82,
+                "sell-mfi-enabled": True,
+                "sell-fastd-enabled": True,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-macd_cross_signal",
+                "roi_t1": 889,
+                "roi_t2": 533,
+                "roi_t3": 263,
+                "roi_p1": 0.04759065393663096,
+                "roi_p2": 0.1488819964638463,
+                "roi_p3": 0.4102801822104605,
+                "stoploss": -0.05394588767607611,
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+            "params_details": {
+                "buy": {
+                    "mfi-value": 25,
+                    "fastd-value": 16,
+                    "adx-value": 29,
+                    "rsi-value": 20,
+                    "mfi-enabled": False,
+                    "fastd-enabled": False,
+                    "adx-enabled": False,
+                    "rsi-enabled": False,
+                    "trigger": "macd_cross_signal",
+                },
+                "sell": {
+                    "sell-mfi-value": 98,
+                    "sell-fastd-value": 72,
+                    "sell-adx-value": 51,
+                    "sell-rsi-value": 82,
+                    "sell-mfi-enabled": True,
+                    "sell-fastd-enabled": True,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-macd_cross_signal",
+                },
+                "roi": {
+                    0: 0.6067528326109377,
+                    263: 0.19647265040047726,
+                    796: 0.04759065393663096,
+                    1685: 0,
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+                "stoploss": {"stoploss": -0.05394588767607611},
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+            "results_metrics": {
+                "total_trades": 621,
+                "trade_count_long": 621,
+                "trade_count_short": 0,
+                "wins": 320,
+                "draws": 0,
+                "losses": 301,
+                "profit_mean": -0.043883302093397747,
+                "profit_median": -0.012222,
+                "profit_total": -0.13639474,
+                "profit_total_abs": -272.515306,
+                "max_drawdown_account": 0.25,
+                "max_drawdown_abs": -272.515306,
+                "holding_avg": timedelta(minutes=1691.207729468599),
+            },
+            "results_explanation": "   621 trades. Avg profit  -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.",  # noqa: E501
+            "total_profit": -0.13639474,
+            "current_epoch": 3,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": False,
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+        {
+            "loss": 100000,
+            "params_dict": {
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+                "fastd-value": 35,
+                "adx-value": 39,
+                "rsi-value": 29,
+                "mfi-enabled": True,
+                "fastd-enabled": False,
+                "adx-enabled": False,
+                "rsi-enabled": True,
+                "trigger": "macd_cross_signal",
+                "sell-mfi-value": 87,
+                "sell-fastd-value": 54,
+                "sell-adx-value": 63,
+                "sell-rsi-value": 93,
+                "sell-mfi-enabled": False,
+                "sell-fastd-enabled": True,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-bb_upper",
+                "roi_t1": 1402,
+                "roi_t2": 676,
+                "roi_t3": 215,
+                "roi_p1": 0.06264755784937427,
+                "roi_p2": 0.14258587851894644,
+                "roi_p3": 0.20671291201040828,
+                "stoploss": -0.11818343570194478,
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+                "buy": {
+                    "mfi-value": 13,
+                    "fastd-value": 35,
+                    "adx-value": 39,
+                    "rsi-value": 29,
+                    "mfi-enabled": True,
+                    "fastd-enabled": False,
+                    "adx-enabled": False,
+                    "rsi-enabled": True,
+                    "trigger": "macd_cross_signal",
+                },
+                "sell": {
+                    "sell-mfi-value": 87,
+                    "sell-fastd-value": 54,
+                    "sell-adx-value": 63,
+                    "sell-rsi-value": 93,
+                    "sell-mfi-enabled": False,
+                    "sell-fastd-enabled": True,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-bb_upper",
+                },
+                "roi": {
+                    0: 0.411946348378729,
+                    215: 0.2052334363683207,
+                    891: 0.06264755784937427,
+                    2293: 0,
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+                "stoploss": {"stoploss": -0.11818343570194478},
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+            "results_metrics": {
+                "total_trades": 0,
+                "trade_count_long": 0,
+                "trade_count_short": 0,
+                "wins": 0,
+                "draws": 0,
+                "losses": 0,
+                "profit_mean": None,
+                "profit_median": None,
+                "profit_total": 0,
+                "max_drawdown_account": 0.0,
+                "max_drawdown_abs": 0.0,
+                "holding_avg": timedelta(),
+            },
+            "results_explanation": "     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.",  # noqa: E501
+            "total_profit": 0,
+            "current_epoch": 4,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": False,
+        },
+        {
+            "loss": 0.22195522184191518,
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+                "mfi-value": 17,
+                "fastd-value": 21,
+                "adx-value": 38,
+                "rsi-value": 33,
+                "mfi-enabled": True,
+                "fastd-enabled": False,
+                "adx-enabled": True,
+                "rsi-enabled": False,
+                "trigger": "macd_cross_signal",
+                "sell-mfi-value": 87,
+                "sell-fastd-value": 82,
+                "sell-adx-value": 78,
+                "sell-rsi-value": 69,
+                "sell-mfi-enabled": True,
+                "sell-fastd-enabled": False,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": False,
+                "sell-trigger": "sell-macd_cross_signal",
+                "roi_t1": 1269,
+                "roi_t2": 601,
+                "roi_t3": 444,
+                "roi_p1": 0.07280999507931168,
+                "roi_p2": 0.08946698095898986,
+                "roi_p3": 0.1454876733325284,
+                "stoploss": -0.18181041180901014,
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+                "buy": {
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+                    "fastd-value": 21,
+                    "adx-value": 38,
+                    "rsi-value": 33,
+                    "mfi-enabled": True,
+                    "fastd-enabled": False,
+                    "adx-enabled": True,
+                    "rsi-enabled": False,
+                    "trigger": "macd_cross_signal",
+                },
+                "sell": {
+                    "sell-mfi-value": 87,
+                    "sell-fastd-value": 82,
+                    "sell-adx-value": 78,
+                    "sell-rsi-value": 69,
+                    "sell-mfi-enabled": True,
+                    "sell-fastd-enabled": False,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": False,
+                    "sell-trigger": "sell-macd_cross_signal",
+                },
+                "roi": {
+                    0: 0.3077646493708299,
+                    444: 0.16227697603830155,
+                    1045: 0.07280999507931168,
+                    2314: 0,
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+                "stoploss": {"stoploss": -0.18181041180901014},
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+                "total_trades": 14,
+                "trade_count_long": 14,
+                "trade_count_short": 0,
+                "wins": 6,
+                "draws": 0,
+                "losses": 8,
+                "profit_mean": -0.003539515,
+                "profit_median": -0.012222,
+                "profit_total": -0.002480140000000001,
+                "profit_total_abs": -4.955321,
+                "max_drawdown_account": 0.34,
+                "max_drawdown_abs": -4.955321,
+                "holding_avg": timedelta(minutes=3402.8571428571427),
+            },
+            "results_explanation": "    14 trades. Avg profit  -0.35%. Total profit -0.00248014 BTC (  -4.96Σ%). Avg duration 3402.9 min.",  # noqa: E501
+            "total_profit": -0.002480140000000001,
+            "current_epoch": 5,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": True,
+        },
+        {
+            "loss": 0.545315889154162,
+            "params_dict": {
+                "mfi-value": 22,
+                "fastd-value": 43,
+                "adx-value": 46,
+                "rsi-value": 20,
+                "mfi-enabled": False,
+                "fastd-enabled": False,
+                "adx-enabled": True,
+                "rsi-enabled": True,
+                "trigger": "bb_lower",
+                "sell-mfi-value": 87,
+                "sell-fastd-value": 65,
+                "sell-adx-value": 94,
+                "sell-rsi-value": 63,
+                "sell-mfi-enabled": False,
+                "sell-fastd-enabled": True,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-macd_cross_signal",
+                "roi_t1": 319,
+                "roi_t2": 556,
+                "roi_t3": 216,
+                "roi_p1": 0.06251955472249589,
+                "roi_p2": 0.11659519602202795,
+                "roi_p3": 0.0953744132197762,
+                "stoploss": -0.024551752215582423,
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+                "buy": {
+                    "mfi-value": 22,
+                    "fastd-value": 43,
+                    "adx-value": 46,
+                    "rsi-value": 20,
+                    "mfi-enabled": False,
+                    "fastd-enabled": False,
+                    "adx-enabled": True,
+                    "rsi-enabled": True,
+                    "trigger": "bb_lower",
+                },
+                "sell": {
+                    "sell-mfi-value": 87,
+                    "sell-fastd-value": 65,
+                    "sell-adx-value": 94,
+                    "sell-rsi-value": 63,
+                    "sell-mfi-enabled": False,
+                    "sell-fastd-enabled": True,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-macd_cross_signal",
+                },
+                "roi": {
+                    0: 0.2744891639643,
+                    216: 0.17911475074452382,
+                    772: 0.06251955472249589,
+                    1091: 0,
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+                "stoploss": {"stoploss": -0.024551752215582423},
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+            "results_metrics": {
+                "total_trades": 39,
+                "trade_count_long": 39,
+                "trade_count_short": 0,
+                "wins": 20,
+                "draws": 0,
+                "losses": 19,
+                "profit_mean": -0.0021400679487179478,
+                "profit_median": -0.012222,
+                "profit_total": -0.0041773,
+                "profit_total_abs": -8.346264999999997,
+                "max_drawdown_account": 0.45,
+                "max_drawdown_abs": -4.955321,
+                "holding_avg": timedelta(minutes=636.9230769230769),
+            },
+            "results_explanation": "    39 trades. Avg profit  -0.21%. Total profit -0.00417730 BTC (  -8.35Σ%). Avg duration 636.9 min.",  # noqa: E501
+            "total_profit": -0.0041773,
+            "current_epoch": 6,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": False,
+        },
+        {
+            "loss": 4.713497421432944,
+            "params_dict": {
+                "mfi-value": 13,
+                "fastd-value": 41,
+                "adx-value": 21,
+                "rsi-value": 29,
+                "mfi-enabled": False,
+                "fastd-enabled": True,
+                "adx-enabled": False,
+                "rsi-enabled": False,
+                "trigger": "bb_lower",
+                "sell-mfi-value": 99,
+                "sell-fastd-value": 60,
+                "sell-adx-value": 81,
+                "sell-rsi-value": 69,
+                "sell-mfi-enabled": True,
+                "sell-fastd-enabled": True,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": False,
+                "sell-trigger": "sell-macd_cross_signal",
+                "roi_t1": 771,
+                "roi_t2": 620,
+                "roi_t3": 145,
+                "roi_p1": 0.0586919200378493,
+                "roi_p2": 0.04984118697312542,
+                "roi_p3": 0.37521058680247044,
+                "stoploss": -0.14613268022709905,
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+            "params_details": {
+                "buy": {
+                    "mfi-value": 13,
+                    "fastd-value": 41,
+                    "adx-value": 21,
+                    "rsi-value": 29,
+                    "mfi-enabled": False,
+                    "fastd-enabled": True,
+                    "adx-enabled": False,
+                    "rsi-enabled": False,
+                    "trigger": "bb_lower",
+                },
+                "sell": {
+                    "sell-mfi-value": 99,
+                    "sell-fastd-value": 60,
+                    "sell-adx-value": 81,
+                    "sell-rsi-value": 69,
+                    "sell-mfi-enabled": True,
+                    "sell-fastd-enabled": True,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": False,
+                    "sell-trigger": "sell-macd_cross_signal",
+                },
+                "roi": {
+                    0: 0.4837436938134452,
+                    145: 0.10853310701097472,
+                    765: 0.0586919200378493,
+                    1536: 0,
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+                "stoploss": {"stoploss": -0.14613268022709905},
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+            "results_metrics": {
+                "total_trades": 318,
+                "trade_count_long": 318,
+                "trade_count_short": 0,
+                "wins": 100,
+                "draws": 0,
+                "losses": 218,
+                "profit_mean": -0.0039833954716981146,
+                "profit_median": -0.012222,
+                "profit_total": -0.06339929,
+                "profit_total_abs": -126.67197600000004,
+                "max_drawdown_account": 0.50,
+                "max_drawdown_abs": -200.955321,
+                "holding_avg": timedelta(minutes=3140.377358490566),
+            },
+            "results_explanation": "   318 trades. Avg profit  -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.",  # noqa: E501
+            "total_profit": -0.06339929,
+            "current_epoch": 7,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": False,
+        },
+        {
+            "loss": 20.0,
+            "params_dict": {
+                "mfi-value": 24,
+                "fastd-value": 43,
+                "adx-value": 33,
+                "rsi-value": 20,
+                "mfi-enabled": False,
+                "fastd-enabled": True,
+                "adx-enabled": True,
+                "rsi-enabled": True,
+                "trigger": "sar_reversal",
+                "sell-mfi-value": 89,
+                "sell-fastd-value": 74,
+                "sell-adx-value": 70,
+                "sell-rsi-value": 70,
+                "sell-mfi-enabled": False,
+                "sell-fastd-enabled": False,
+                "sell-adx-enabled": False,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-sar_reversal",
+                "roi_t1": 1149,
+                "roi_t2": 375,
+                "roi_t3": 289,
+                "roi_p1": 0.05571820757172588,
+                "roi_p2": 0.0606240398618907,
+                "roi_p3": 0.1729012220156157,
+                "stoploss": -0.1588514289110401,
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+            "params_details": {
+                "buy": {
+                    "mfi-value": 24,
+                    "fastd-value": 43,
+                    "adx-value": 33,
+                    "rsi-value": 20,
+                    "mfi-enabled": False,
+                    "fastd-enabled": True,
+                    "adx-enabled": True,
+                    "rsi-enabled": True,
+                    "trigger": "sar_reversal",
+                },
+                "sell": {
+                    "sell-mfi-value": 89,
+                    "sell-fastd-value": 74,
+                    "sell-adx-value": 70,
+                    "sell-rsi-value": 70,
+                    "sell-mfi-enabled": False,
+                    "sell-fastd-enabled": False,
+                    "sell-adx-enabled": False,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-sar_reversal",
+                },
+                "roi": {
+                    0: 0.2892434694492323,
+                    289: 0.11634224743361658,
+                    664: 0.05571820757172588,
+                    1813: 0,
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+                "stoploss": {"stoploss": -0.1588514289110401},
+            },
+            "results_metrics": {
+                "total_trades": 1,
+                "trade_count_long": 1,
+                "trade_count_short": 0,
+                "wins": 0,
+                "draws": 1,
+                "losses": 0,
+                "profit_mean": 0.0,
+                "profit_median": 0.0,
+                "profit_total": 0.0,
+                "profit_total_abs": 0.0,
+                "max_drawdown_account": 0.0,
+                "max_drawdown_abs": 0.52,
+                "holding_avg": timedelta(minutes=5340.0),
+            },
+            "results_explanation": "     1 trades. Avg profit   0.00%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration 5340.0 min.",  # noqa: E501
+            "total_profit": 0.0,
+            "current_epoch": 8,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": False,
+        },
+        {
+            "loss": 2.4731817780991223,
+            "params_dict": {
+                "mfi-value": 22,
+                "fastd-value": 20,
+                "adx-value": 29,
+                "rsi-value": 40,
+                "mfi-enabled": False,
+                "fastd-enabled": False,
+                "adx-enabled": False,
+                "rsi-enabled": False,
+                "trigger": "sar_reversal",
+                "sell-mfi-value": 97,
+                "sell-fastd-value": 65,
+                "sell-adx-value": 81,
+                "sell-rsi-value": 64,
+                "sell-mfi-enabled": True,
+                "sell-fastd-enabled": True,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-bb_upper",
+                "roi_t1": 1012,
+                "roi_t2": 584,
+                "roi_t3": 422,
+                "roi_p1": 0.036764323603472565,
+                "roi_p2": 0.10335480573205287,
+                "roi_p3": 0.10322347377503042,
+                "stoploss": -0.2780610808108503,
+            },
+            "params_details": {
+                "buy": {
+                    "mfi-value": 22,
+                    "fastd-value": 20,
+                    "adx-value": 29,
+                    "rsi-value": 40,
+                    "mfi-enabled": False,
+                    "fastd-enabled": False,
+                    "adx-enabled": False,
+                    "rsi-enabled": False,
+                    "trigger": "sar_reversal",
+                },
+                "sell": {
+                    "sell-mfi-value": 97,
+                    "sell-fastd-value": 65,
+                    "sell-adx-value": 81,
+                    "sell-rsi-value": 64,
+                    "sell-mfi-enabled": True,
+                    "sell-fastd-enabled": True,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-bb_upper",
+                },
+                "roi": {
+                    0: 0.2433426031105559,
+                    422: 0.14011912933552545,
+                    1006: 0.036764323603472565,
+                    2018: 0,
+                },
+                "stoploss": {"stoploss": -0.2780610808108503},
+            },
+            "results_metrics": {
+                "total_trades": 229,
+                "trade_count_long": 229,
+                "trade_count_short": 0,
+                "wins": 150,
+                "draws": 0,
+                "losses": 79,
+                "profit_mean": -0.0038433433624454144,
+                "profit_median": -0.012222,
+                "profit_total": -0.044050070000000004,
+                "profit_total_abs": -88.01256299999999,
+                "max_drawdown_account": 0.41,
+                "max_drawdown_abs": -150.955321,
+                "holding_avg": timedelta(minutes=6505.676855895196),
+            },
+            "results_explanation": "   229 trades. Avg profit  -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.",  # noqa: E501
+            "total_profit": -0.044050070000000004,
+            "current_epoch": 9,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": False,
+        },
+        {
+            "loss": -0.2604606005845212,
+            "params_dict": {
+                "mfi-value": 23,
+                "fastd-value": 24,
+                "adx-value": 22,
+                "rsi-value": 24,
+                "mfi-enabled": False,
+                "fastd-enabled": False,
+                "adx-enabled": False,
+                "rsi-enabled": True,
+                "trigger": "macd_cross_signal",
+                "sell-mfi-value": 97,
+                "sell-fastd-value": 70,
+                "sell-adx-value": 64,
+                "sell-rsi-value": 80,
+                "sell-mfi-enabled": False,
+                "sell-fastd-enabled": True,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-sar_reversal",
+                "roi_t1": 792,
+                "roi_t2": 464,
+                "roi_t3": 215,
+                "roi_p1": 0.04594053535385903,
+                "roi_p2": 0.09623192684243963,
+                "roi_p3": 0.04428219070850663,
+                "stoploss": -0.16992287161634415,
+            },
+            "params_details": {
+                "buy": {
+                    "mfi-value": 23,
+                    "fastd-value": 24,
+                    "adx-value": 22,
+                    "rsi-value": 24,
+                    "mfi-enabled": False,
+                    "fastd-enabled": False,
+                    "adx-enabled": False,
+                    "rsi-enabled": True,
+                    "trigger": "macd_cross_signal",
+                },
+                "sell": {
+                    "sell-mfi-value": 97,
+                    "sell-fastd-value": 70,
+                    "sell-adx-value": 64,
+                    "sell-rsi-value": 80,
+                    "sell-mfi-enabled": False,
+                    "sell-fastd-enabled": True,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-sar_reversal",
+                },
+                "roi": {
+                    0: 0.18645465290480528,
+                    215: 0.14217246219629864,
+                    679: 0.04594053535385903,
+                    1471: 0,
+                },
+                "stoploss": {"stoploss": -0.16992287161634415},
+            },
+            "results_metrics": {
+                "total_trades": 4,
+                "trade_count_long": 4,
+                "trade_count_short": 0,
+                "wins": 0,
+                "draws": 0,
+                "losses": 4,
+                "profit_mean": 0.001080385,
+                "profit_median": -0.012222,
+                "profit_total": 0.00021629,
+                "profit_total_abs": 0.432154,
+                "max_drawdown_account": 0.13,
+                "max_drawdown_abs": -4.955321,
+                "holding_avg": timedelta(minutes=2850.0),
+            },
+            "results_explanation": "     4 trades. Avg profit   0.11%. Total profit  0.00021629 BTC (   0.43Σ%). Avg duration 2850.0 min.",  # noqa: E501
+            "total_profit": 0.00021629,
+            "current_epoch": 10,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": True,
+        },
+        {
+            "loss": 4.876465945994304,
+            "params_dict": {
+                "mfi-value": 20,
+                "fastd-value": 32,
+                "adx-value": 49,
+                "rsi-value": 23,
+                "mfi-enabled": True,
+                "fastd-enabled": True,
+                "adx-enabled": False,
+                "rsi-enabled": False,
+                "trigger": "bb_lower",
+                "sell-mfi-value": 75,
+                "sell-fastd-value": 56,
+                "sell-adx-value": 61,
+                "sell-rsi-value": 62,
+                "sell-mfi-enabled": False,
+                "sell-fastd-enabled": False,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-macd_cross_signal",
+                "roi_t1": 579,
+                "roi_t2": 614,
+                "roi_t3": 273,
+                "roi_p1": 0.05307643172744114,
+                "roi_p2": 0.1352282078262871,
+                "roi_p3": 0.1913307406325751,
+                "stoploss": -0.25728526022513887,
+            },
+            "params_details": {
+                "buy": {
+                    "mfi-value": 20,
+                    "fastd-value": 32,
+                    "adx-value": 49,
+                    "rsi-value": 23,
+                    "mfi-enabled": True,
+                    "fastd-enabled": True,
+                    "adx-enabled": False,
+                    "rsi-enabled": False,
+                    "trigger": "bb_lower",
+                },
+                "sell": {
+                    "sell-mfi-value": 75,
+                    "sell-fastd-value": 56,
+                    "sell-adx-value": 61,
+                    "sell-rsi-value": 62,
+                    "sell-mfi-enabled": False,
+                    "sell-fastd-enabled": False,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-macd_cross_signal",
+                },
+                "roi": {
+                    0: 0.3796353801863034,
+                    273: 0.18830463955372825,
+                    887: 0.05307643172744114,
+                    1466: 0,
+                },
+                "stoploss": {"stoploss": -0.25728526022513887},
+            },
+            # New Hyperopt mode!
+            "results_metrics": {
+                "total_trades": 117,
+                "trade_count_long": 117,
+                "trade_count_short": 0,
+                "wins": 67,
+                "draws": 0,
+                "losses": 50,
+                "profit_mean": -0.012698609145299145,
+                "profit_median": -0.012222,
+                "profit_total": -0.07436117,
+                "profit_total_abs": -148.573727,
+                "max_drawdown_account": 0.52,
+                "max_drawdown_abs": -224.955321,
+                "holding_avg": timedelta(minutes=4282.5641025641025),
+            },
+            "results_explanation": "   117 trades. Avg profit  -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.",  # noqa: E501
+            "total_profit": -0.07436117,
+            "current_epoch": 11,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": False,
+        },
+        {
+            "loss": 100000,
+            "params_dict": {
+                "mfi-value": 10,
+                "fastd-value": 36,
+                "adx-value": 31,
+                "rsi-value": 22,
+                "mfi-enabled": True,
+                "fastd-enabled": True,
+                "adx-enabled": True,
+                "rsi-enabled": False,
+                "trigger": "sar_reversal",
+                "sell-mfi-value": 80,
+                "sell-fastd-value": 71,
+                "sell-adx-value": 60,
+                "sell-rsi-value": 85,
+                "sell-mfi-enabled": False,
+                "sell-fastd-enabled": False,
+                "sell-adx-enabled": True,
+                "sell-rsi-enabled": True,
+                "sell-trigger": "sell-bb_upper",
+                "roi_t1": 1156,
+                "roi_t2": 581,
+                "roi_t3": 408,
+                "roi_p1": 0.06860454019988212,
+                "roi_p2": 0.12473718444931989,
+                "roi_p3": 0.2896360635226823,
+                "stoploss": -0.30889015124682806,
+            },
+            "params_details": {
+                "buy": {
+                    "mfi-value": 10,
+                    "fastd-value": 36,
+                    "adx-value": 31,
+                    "rsi-value": 22,
+                    "mfi-enabled": True,
+                    "fastd-enabled": True,
+                    "adx-enabled": True,
+                    "rsi-enabled": False,
+                    "trigger": "sar_reversal",
+                },
+                "sell": {
+                    "sell-mfi-value": 80,
+                    "sell-fastd-value": 71,
+                    "sell-adx-value": 60,
+                    "sell-rsi-value": 85,
+                    "sell-mfi-enabled": False,
+                    "sell-fastd-enabled": False,
+                    "sell-adx-enabled": True,
+                    "sell-rsi-enabled": True,
+                    "sell-trigger": "sell-bb_upper",
+                },
+                "roi": {
+                    0: 0.4829777881718843,
+                    408: 0.19334172464920202,
+                    989: 0.06860454019988212,
+                    2145: 0,
+                },
+                "stoploss": {"stoploss": -0.30889015124682806},
+            },
+            "results_metrics": {
+                "total_trades": 0,
+                "trade_count_long": 0,
+                "trade_count_short": 0,
+                "wins": 0,
+                "draws": 0,
+                "losses": 0,
+                "profit_mean": None,
+                "profit_median": None,
+                "profit_total": 0,
+                "profit_total_abs": 0.0,
+                "max_drawdown_account": 0.0,
+                "max_drawdown_abs": 0.0,
+                "holding_avg": timedelta(),
+            },
+            "results_explanation": "     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.",  # noqa: E501
+            "total_profit": 0,
+            "current_epoch": 12,
+            "is_initial_point": True,
+            "is_random": False,
+            "is_best": False,
+        },
+    ]
+
+    for res in hyperopt_res:
+        res["results_metrics"]["holding_avg_s"] = res["results_metrics"][
+            "holding_avg"
+        ].total_seconds()
+    return hyperopt_res
diff --git a/tests/conftest_trades.py b/tests/conftest_trades.py
new file mode 100644
index 000000000..f493a9302
--- /dev/null
+++ b/tests/conftest_trades.py
@@ -0,0 +1,511 @@
+from datetime import datetime, timedelta, timezone
+
+from freqtrade.persistence.models import Order, Trade
+
+
+MOCK_TRADE_COUNT = 6
+
+
+def entry_side(is_short: bool):
+    return "sell" if is_short else "buy"
+
+
+def exit_side(is_short: bool):
+    return "buy" if is_short else "sell"
+
+
+def direc(is_short: bool):
+    return "short" if is_short else "long"
+
+
+def mock_order_1(is_short: bool):
+    return {
+        "id": f"1234_{direc(is_short)}",
+        "symbol": "ETH/BTC",
+        "status": "open",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 0.123,
+        "average": 0.123,
+        "amount": 123.0,
+        "filled": 50.0,
+        "cost": 15.129,
+        "remaining": 123.0 - 50.0,
+    }
+
+
+def mock_trade_1(fee, is_short: bool):
+    trade = Trade(
+        pair="ETH/BTC",
+        stake_amount=0.001,
+        amount=50.0,
+        amount_requested=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        is_open=True,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=17),
+        open_rate=0.123,
+        exchange="binance",
+        strategy="StrategyTestV3",
+        timeframe=5,
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_1(is_short), "ETH/BTC", entry_side(is_short))
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_2(is_short: bool):
+    return {
+        "id": f"1235_{direc(is_short)}",
+        "symbol": "ETC/BTC",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 0.123,
+        "amount": 123.0,
+        "filled": 123.0,
+        "cost": 15.129,
+        "remaining": 0.0,
+    }
+
+
+def mock_order_2_sell(is_short: bool):
+    return {
+        "id": f"12366_{direc(is_short)}",
+        "symbol": "ETC/BTC",
+        "status": "closed",
+        "side": exit_side(is_short),
+        "type": "limit",
+        "price": 0.128,
+        "amount": 123.0,
+        "filled": 123.0,
+        "cost": 15.129,
+        "remaining": 0.0,
+    }
+
+
+def mock_trade_2(fee, is_short: bool):
+    """
+    Closed trade...
+    """
+    trade = Trade(
+        pair="ETC/BTC",
+        stake_amount=0.001,
+        amount=123.0,
+        amount_requested=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.123,
+        close_rate=0.128,
+        close_profit=-0.005 if is_short else 0.005,
+        close_profit_abs=-0.005584127 if is_short else 0.000584127,
+        exchange="binance",
+        is_open=False,
+        strategy="StrategyTestV3",
+        timeframe=5,
+        enter_tag="TEST1",
+        exit_reason="sell_signal",
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=20),
+        close_date=datetime.now(tz=timezone.utc) - timedelta(minutes=2),
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_2(is_short), "ETC/BTC", entry_side(is_short))
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(mock_order_2_sell(is_short), "ETC/BTC", exit_side(is_short))
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_3(is_short: bool):
+    return {
+        "id": f"41231a12a_{direc(is_short)}",
+        "symbol": "XRP/BTC",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 0.05,
+        "amount": 123.0,
+        "filled": 123.0,
+        "cost": 15.129,
+        "remaining": 0.0,
+    }
+
+
+def mock_order_3_sell(is_short: bool):
+    return {
+        "id": f"41231a666a_{direc(is_short)}",
+        "symbol": "XRP/BTC",
+        "status": "closed",
+        "side": exit_side(is_short),
+        "type": "stop_loss_limit",
+        "price": 0.06,
+        "average": 0.06,
+        "amount": 123.0,
+        "filled": 123.0,
+        "cost": 15.129,
+        "remaining": 0.0,
+    }
+
+
+def mock_trade_3(fee, is_short: bool):
+    """
+    Closed trade
+    """
+    trade = Trade(
+        pair="XRP/BTC",
+        stake_amount=0.001,
+        amount=123.0,
+        amount_requested=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.05,
+        close_rate=0.06,
+        close_profit=-0.01 if is_short else 0.01,
+        close_profit_abs=-0.001155 if is_short else 0.000155,
+        exchange="binance",
+        is_open=False,
+        strategy="StrategyTestV3",
+        timeframe=5,
+        exit_reason="roi",
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=20),
+        close_date=datetime.now(tz=timezone.utc),
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_3(is_short), "XRP/BTC", entry_side(is_short))
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(mock_order_3_sell(is_short), "XRP/BTC", exit_side(is_short))
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_4(is_short: bool):
+    return {
+        "id": f"prod_buy_{direc(is_short)}_12345",
+        "symbol": "ETC/BTC",
+        "status": "open",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 0.123,
+        "amount": 123.0,
+        "filled": 0.0,
+        "cost": 15.129,
+        "remaining": 123.0,
+    }
+
+
+def mock_trade_4(fee, is_short: bool):
+    """
+    Simulate prod entry
+    """
+    trade = Trade(
+        pair="ETC/BTC",
+        stake_amount=0.001,
+        amount=0.0,
+        amount_requested=124.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=14),
+        is_open=True,
+        open_rate=0.123,
+        exchange="binance",
+        strategy="StrategyTestV3",
+        timeframe=5,
+        is_short=is_short,
+        stop_loss_pct=0.10,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_4(is_short), "ETC/BTC", entry_side(is_short))
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_5(is_short: bool):
+    return {
+        "id": f"prod_buy_{direc(is_short)}_3455",
+        "symbol": "XRP/BTC",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 0.123,
+        "amount": 123.0,
+        "filled": 123.0,
+        "cost": 15.129,
+        "remaining": 0.0,
+    }
+
+
+def mock_order_5_stoploss(is_short: bool):
+    return {
+        "id": f"prod_stoploss_{direc(is_short)}_3455",
+        "symbol": "XRP/BTC",
+        "status": "open",
+        "side": exit_side(is_short),
+        "type": "stop_loss_limit",
+        "price": 0.123,
+        "amount": 123.0,
+        "filled": 0.0,
+        "cost": 0.0,
+        "remaining": 123.0,
+    }
+
+
+def mock_trade_5(fee, is_short: bool):
+    """
+    Simulate prod entry with stoploss
+    """
+    trade = Trade(
+        pair="XRP/BTC",
+        stake_amount=0.001,
+        amount=123.0,
+        amount_requested=124.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=12),
+        is_open=True,
+        open_rate=0.123,
+        exchange="binance",
+        strategy="SampleStrategy",
+        enter_tag="TEST1",
+        timeframe=5,
+        is_short=is_short,
+        stop_loss_pct=0.10,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_5(is_short), "XRP/BTC", entry_side(is_short))
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(mock_order_5_stoploss(is_short), "XRP/BTC", "stoploss")
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_6(is_short: bool):
+    return {
+        "id": f"prod_buy_{direc(is_short)}_6",
+        "symbol": "LTC/BTC",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 0.15,
+        "amount": 2.0,
+        "filled": 2.0,
+        "cost": 0.3,
+        "remaining": 0.0,
+    }
+
+
+def mock_order_6_sell(is_short: bool):
+    return {
+        "id": f"prod_sell_{direc(is_short)}_6",
+        "symbol": "LTC/BTC",
+        "status": "open",
+        "side": exit_side(is_short),
+        "type": "limit",
+        "price": 0.15 if is_short else 0.20,
+        "amount": 2.0,
+        "filled": 0.0,
+        "cost": 0.0,
+        "remaining": 2.0,
+    }
+
+
+def mock_trade_6(fee, is_short: bool):
+    """
+    Simulate prod entry with open exit order
+    """
+    trade = Trade(
+        pair="LTC/BTC",
+        stake_amount=0.001,
+        amount=2.0,
+        amount_requested=2.0,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=5),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        is_open=True,
+        open_rate=0.15,
+        exchange="binance",
+        strategy="SampleStrategy",
+        enter_tag="TEST2",
+        timeframe=5,
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_6(is_short), "LTC/BTC", entry_side(is_short))
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(mock_order_6_sell(is_short), "LTC/BTC", exit_side(is_short))
+    trade.orders.append(o)
+    return trade
+
+
+def short_order():
+    return {
+        "id": "1236",
+        "symbol": "ETC/BTC",
+        "status": "closed",
+        "side": "sell",
+        "type": "limit",
+        "price": 0.123,
+        "amount": 123.0,
+        "filled": 123.0,
+        "cost": 15.129,
+        "remaining": 0.0,
+    }
+
+
+def exit_short_order():
+    return {
+        "id": "12367",
+        "symbol": "ETC/BTC",
+        "status": "closed",
+        "side": "buy",
+        "type": "limit",
+        "price": 0.128,
+        "amount": 123.0,
+        "filled": 123.0,
+        "cost": 15.744,
+        "remaining": 0.0,
+    }
+
+
+def short_trade(fee):
+    """
+    10 minute short limit trade on binance
+
+    Short trade
+    fee: 0.25% base
+    interest_rate: 0.05% per day
+    open_rate: 0.123 base
+    close_rate: 0.128 base
+    amount: 123.0 crypto
+    stake_amount: 15.129 base
+    borrowed: 123.0  crypto
+    time-periods: 10 minutes(rounds up to 1/24 time-period of 1 day)
+    interest: borrowed * interest_rate * time-periods
+                = 123.0 * 0.0005 * 1/24 = 0.0025625 crypto
+    open_value: (amount * open_rate) - (amount * open_rate * fee)
+        = (123 * 0.123) - (123 * 0.123 * 0.0025)
+        = 15.091177499999999
+    amount_closed: amount + interest = 123 + 0.0025625 = 123.0025625
+    close_value: (amount_closed * close_rate) + (amount_closed * close_rate * fee)
+        = (123.0025625 * 0.128) + (123.0025625 * 0.128 * 0.0025)
+        = 15.78368882
+    total_profit = open_value - close_value
+        = 15.091177499999999 - 15.78368882
+        = -0.6925113200000013
+    total_profit_percentage = total_profit / stake_amount
+        = -0.6925113200000013 / 15.129
+        = -0.04577376693766946
+
+    """
+    trade = Trade(
+        pair="ETC/BTC",
+        stake_amount=15.129,
+        amount=123.0,
+        amount_requested=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.123,
+        # close_rate=0.128,
+        # close_profit=-0.04577376693766946,
+        # close_profit_abs=-0.6925113200000013,
+        exchange="binance",
+        is_open=True,
+        strategy="DefaultStrategy",
+        timeframe=5,
+        exit_reason="sell_signal",
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=20),
+        # close_date=datetime.now(tz=timezone.utc) - timedelta(minutes=2),
+        is_short=True,
+    )
+    o = Order.parse_from_ccxt_object(short_order(), "ETC/BTC", "sell")
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(exit_short_order(), "ETC/BTC", "sell")
+    trade.orders.append(o)
+    return trade
+
+
+def leverage_order():
+    return {
+        "id": "1237",
+        "symbol": "DOGE/BTC",
+        "status": "closed",
+        "side": "buy",
+        "type": "limit",
+        "price": 0.123,
+        "amount": 123.0,
+        "filled": 123.0,
+        "remaining": 0.0,
+        "cost": 15.129,
+        "leverage": 5.0,
+    }
+
+
+def leverage_order_sell():
+    return {
+        "id": "12368",
+        "symbol": "DOGE/BTC",
+        "status": "closed",
+        "side": "sell",
+        "type": "limit",
+        "price": 0.128,
+        "amount": 123.0,
+        "filled": 123.0,
+        "remaining": 0.0,
+        "cost": 15.744,
+        "leverage": 5.0,
+    }
+
+
+def leverage_trade(fee):
+    """
+    5 hour short limit trade on kraken
+
+    Short trade
+    fee: 0.25% base
+    interest_rate: 0.05% per day
+    open_rate: 0.123 base
+    close_rate: 0.128 base
+    amount: 615 crypto
+    stake_amount: 15.129 base
+    borrowed: 60.516  base
+    leverage: 5
+    hours: 5
+    interest: borrowed * interest_rate * ceil(1 + hours/4)
+                = 60.516 * 0.0005 * ceil(1 + 5/4) = 0.090774 base
+    open_value: (amount * open_rate) + (amount * open_rate * fee)
+        = (615.0 * 0.123) + (615.0 * 0.123 * 0.0025)
+        = 75.83411249999999
+
+    close_value: (amount_closed * close_rate) - (amount_closed * close_rate * fee) - interest
+        = (615.0 * 0.128) - (615.0 * 0.128 * 0.0025) - 0.090774
+        = 78.432426
+    total_profit = close_value - open_value
+        = 78.432426 - 75.83411249999999
+        = 2.5983135000000175
+    total_profit_percentage = ((close_value/open_value)-1) * leverage
+        = ((78.432426/75.83411249999999)-1) * 5
+        = 0.1713156134055116
+    """
+    trade = Trade(
+        pair="DOGE/BTC",
+        stake_amount=15.129,
+        amount=615.0,
+        leverage=5.0,
+        amount_requested=615.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.123,
+        close_rate=0.128,
+        close_profit=0.1713156134055116,
+        close_profit_abs=2.5983135000000175,
+        exchange="kraken",
+        is_open=False,
+        strategy="DefaultStrategy",
+        timeframe=5,
+        exit_reason="sell_signal",
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=300),
+        close_date=datetime.now(tz=timezone.utc),
+        interest_rate=0.0005,
+    )
+    o = Order.parse_from_ccxt_object(leverage_order(), "DOGE/BTC", "sell")
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(leverage_order_sell(), "DOGE/BTC", "sell")
+    trade.orders.append(o)
+    return trade
diff --git a/tests/conftest_trades_usdt.py b/tests/conftest_trades_usdt.py
new file mode 100644
index 000000000..3a547cce2
--- /dev/null
+++ b/tests/conftest_trades_usdt.py
@@ -0,0 +1,386 @@
+from datetime import datetime, timedelta, timezone
+
+from freqtrade.persistence.models import Order, Trade
+
+
+MOCK_TRADE_COUNT = 6
+
+
+def entry_side(is_short: bool):
+    return "sell" if is_short else "buy"
+
+
+def exit_side(is_short: bool):
+    return "buy" if is_short else "sell"
+
+
+def direc(is_short: bool):
+    return "short" if is_short else "long"
+
+
+def mock_order_usdt_1(is_short: bool):
+    return {
+        "id": f"prod_entry_1_{direc(is_short)}",
+        "symbol": "LTC/USDT",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 10.0,
+        "amount": 2.0,
+        "filled": 2.0,
+        "remaining": 0.0,
+    }
+
+
+def mock_order_usdt_1_exit(is_short: bool):
+    return {
+        "id": f"prod_exit_1_{direc(is_short)}",
+        "symbol": "LTC/USDT",
+        "status": "open",
+        "side": exit_side(is_short),
+        "type": "limit",
+        "price": 8.0,
+        "amount": 2.0,
+        "filled": 0.0,
+        "remaining": 2.0,
+    }
+
+
+def mock_trade_usdt_1(fee, is_short: bool):
+    """
+    Simulate prod entry with open sell order
+    """
+    trade = Trade(
+        pair="LTC/USDT",
+        stake_amount=20.0,
+        amount=2.0,
+        amount_requested=2.0,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(days=2, minutes=20),
+        close_date=datetime.now(tz=timezone.utc) - timedelta(days=2, minutes=5),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        is_open=False,
+        open_rate=10.0,
+        close_rate=8.0,
+        close_profit=-0.2,
+        close_profit_abs=-4.09,
+        exchange="binance",
+        strategy="SampleStrategy",
+        timeframe=5,
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_usdt_1(is_short), "LTC/USDT", entry_side(is_short))
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(
+        mock_order_usdt_1_exit(is_short), "LTC/USDT", exit_side(is_short)
+    )
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_usdt_2(is_short: bool):
+    return {
+        "id": f"1235_{direc(is_short)}",
+        "symbol": "NEO/USDT",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 2.0,
+        "amount": 100.0,
+        "filled": 100.0,
+        "remaining": 0.0,
+    }
+
+
+def mock_order_usdt_2_exit(is_short: bool):
+    return {
+        "id": f"12366_{direc(is_short)}",
+        "symbol": "NEO/USDT",
+        "status": "open",
+        "side": exit_side(is_short),
+        "type": "limit",
+        "price": 2.05,
+        "amount": 100.0,
+        "filled": 0.0,
+        "remaining": 100.0,
+    }
+
+
+def mock_trade_usdt_2(fee, is_short: bool):
+    """
+    Closed trade...
+    """
+    trade = Trade(
+        pair="NEO/USDT",
+        stake_amount=200.0,
+        amount=100.0,
+        amount_requested=100.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=2.0,
+        close_rate=2.05,
+        close_profit=0.05,
+        close_profit_abs=3.9875,
+        exchange="binance",
+        is_open=False,
+        strategy="StrategyTestV2",
+        timeframe=5,
+        enter_tag="TEST1",
+        exit_reason="exit_signal",
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=20),
+        close_date=datetime.now(tz=timezone.utc) - timedelta(minutes=2),
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_usdt_2(is_short), "NEO/USDT", entry_side(is_short))
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(
+        mock_order_usdt_2_exit(is_short), "NEO/USDT", exit_side(is_short)
+    )
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_usdt_3(is_short: bool):
+    return {
+        "id": f"41231a12a_{direc(is_short)}",
+        "symbol": "XRP/USDT",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 1.0,
+        "amount": 30.0,
+        "filled": 30.0,
+        "remaining": 0.0,
+    }
+
+
+def mock_order_usdt_3_exit(is_short: bool):
+    return {
+        "id": f"41231a666a_{direc(is_short)}",
+        "symbol": "XRP/USDT",
+        "status": "closed",
+        "side": exit_side(is_short),
+        "type": "stop_loss_limit",
+        "price": 1.1,
+        "average": 1.1,
+        "amount": 30.0,
+        "filled": 30.0,
+        "remaining": 0.0,
+    }
+
+
+def mock_trade_usdt_3(fee, is_short: bool):
+    """
+    Closed trade
+    """
+    trade = Trade(
+        pair="XRP/USDT",
+        stake_amount=30.0,
+        amount=30.0,
+        amount_requested=30.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=1.0,
+        close_rate=1.1,
+        close_profit=0.1,
+        close_profit_abs=2.8425,
+        exchange="binance",
+        is_open=False,
+        strategy="StrategyTestV2",
+        timeframe=5,
+        enter_tag="TEST3",
+        exit_reason="roi",
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=20),
+        close_date=datetime.now(tz=timezone.utc),
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_usdt_3(is_short), "XRP/USDT", entry_side(is_short))
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(
+        mock_order_usdt_3_exit(is_short), "XRP/USDT", exit_side(is_short)
+    )
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_usdt_4(is_short: bool):
+    return {
+        "id": f"prod_buy_12345_{direc(is_short)}",
+        "symbol": "NEO/USDT",
+        "status": "open",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 2.0,
+        "amount": 10.0,
+        "filled": 0.0,
+        "remaining": 30.0,
+    }
+
+
+def mock_trade_usdt_4(fee, is_short: bool):
+    """
+    Simulate prod entry
+    """
+    trade = Trade(
+        pair="NEO/USDT",
+        stake_amount=20.0,
+        amount=0.0,
+        amount_requested=10.01,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=14),
+        is_open=True,
+        open_rate=2.0,
+        exchange="binance",
+        strategy="StrategyTestV2",
+        timeframe=5,
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_usdt_4(is_short), "NEO/USDT", entry_side(is_short))
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_usdt_5(is_short: bool):
+    return {
+        "id": f"prod_buy_3455_{direc(is_short)}",
+        "symbol": "XRP/USDT",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 2.0,
+        "amount": 10.0,
+        "filled": 10.0,
+        "remaining": 0.0,
+    }
+
+
+def mock_order_usdt_5_stoploss(is_short: bool):
+    return {
+        "id": f"prod_stoploss_3455_{direc(is_short)}",
+        "symbol": "XRP/USDT",
+        "status": "open",
+        "side": exit_side(is_short),
+        "type": "stop_loss_limit",
+        "price": 2.0,
+        "amount": 10.0,
+        "filled": 0.0,
+        "remaining": 30.0,
+    }
+
+
+def mock_trade_usdt_5(fee, is_short: bool):
+    """
+    Simulate prod entry with stoploss
+    """
+    trade = Trade(
+        pair="XRP/USDT",
+        stake_amount=20.0,
+        amount=10.0,
+        amount_requested=10.01,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=12),
+        is_open=True,
+        open_rate=2.0,
+        exchange="binance",
+        strategy="SampleStrategy",
+        timeframe=5,
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_usdt_5(is_short), "XRP/USDT", entry_side(is_short))
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(mock_order_usdt_5_stoploss(is_short), "XRP/USDT", "stoploss")
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_usdt_6(is_short: bool):
+    return {
+        "id": f"prod_entry_6_{direc(is_short)}",
+        "symbol": "LTC/USDT",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 10.0,
+        "amount": 2.0,
+        "filled": 2.0,
+        "remaining": 0.0,
+    }
+
+
+def mock_order_usdt_6_exit(is_short: bool):
+    return {
+        "id": f"prod_exit_6_{direc(is_short)}",
+        "symbol": "LTC/USDT",
+        "status": "open",
+        "side": exit_side(is_short),
+        "type": "limit",
+        "price": 12.0,
+        "amount": 2.0,
+        "filled": 0.0,
+        "remaining": 2.0,
+    }
+
+
+def mock_trade_usdt_6(fee, is_short: bool):
+    """
+    Simulate prod entry with open sell order
+    """
+    trade = Trade(
+        pair="LTC/USDT",
+        stake_amount=20.0,
+        amount=2.0,
+        amount_requested=2.0,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=5),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        is_open=True,
+        open_rate=10.0,
+        exchange="binance",
+        strategy="SampleStrategy",
+        timeframe=5,
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_usdt_6(is_short), "LTC/USDT", entry_side(is_short))
+    trade.orders.append(o)
+    o = Order.parse_from_ccxt_object(
+        mock_order_usdt_6_exit(is_short), "LTC/USDT", exit_side(is_short)
+    )
+    trade.orders.append(o)
+    return trade
+
+
+def mock_order_usdt_7(is_short: bool):
+    return {
+        "id": f"1234_{direc(is_short)}",
+        "symbol": "ADA/USDT",
+        "status": "closed",
+        "side": entry_side(is_short),
+        "type": "limit",
+        "price": 2.0,
+        "amount": 10.0,
+        "filled": 10.0,
+        "remaining": 0.0,
+    }
+
+
+def mock_trade_usdt_7(fee, is_short: bool):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=20.0,
+        amount=10.0,
+        amount_requested=10.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        is_open=True,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=17),
+        open_rate=2.0,
+        exchange="binance",
+        strategy="StrategyTestV2",
+        timeframe=5,
+        is_short=is_short,
+    )
+    o = Order.parse_from_ccxt_object(mock_order_usdt_7(is_short), "ADA/USDT", entry_side(is_short))
+    trade.orders.append(o)
+    return trade
diff --git a/tests/data/__init__.py b/tests/data/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/data/test_btanalysis.py b/tests/data/test_btanalysis.py
new file mode 100644
index 000000000..7adba9c19
--- /dev/null
+++ b/tests/data/test_btanalysis.py
@@ -0,0 +1,630 @@
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from unittest.mock import MagicMock
+from zipfile import ZipFile
+
+import pytest
+from pandas import DataFrame, DateOffset, Timestamp, to_datetime
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import LAST_BT_RESULT_FN
+from freqtrade.data.btanalysis import (
+    BT_DATA_COLUMNS,
+    analyze_trade_parallelism,
+    extract_trades_of_period,
+    get_latest_backtest_filename,
+    get_latest_hyperopt_file,
+    load_backtest_data,
+    load_backtest_metadata,
+    load_file_from_zip,
+    load_trades,
+    load_trades_from_db,
+)
+from freqtrade.data.history import load_data, load_pair_history
+from freqtrade.data.metrics import (
+    calculate_cagr,
+    calculate_calmar,
+    calculate_csum,
+    calculate_expectancy,
+    calculate_market_change,
+    calculate_max_drawdown,
+    calculate_sharpe,
+    calculate_sortino,
+    calculate_sqn,
+    calculate_underwater,
+    combine_dataframes_with_mean,
+    combined_dataframes_with_rel_mean,
+    create_cum_profit,
+)
+from freqtrade.exceptions import OperationalException
+from freqtrade.util import dt_utc
+from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
+from tests.conftest_trades import MOCK_TRADE_COUNT
+
+
+def test_get_latest_backtest_filename(testdatadir, mocker):
+    with pytest.raises(ValueError, match=r"Directory .* does not exist\."):
+        get_latest_backtest_filename(testdatadir / "does_not_exist")
+
+    with pytest.raises(ValueError, match=r"Directory .* does not seem to contain .*"):
+        get_latest_backtest_filename(testdatadir)
+
+    testdir_bt = testdatadir / "backtest_results"
+    res = get_latest_backtest_filename(testdir_bt)
+    assert res == "backtest-result.json"
+
+    res = get_latest_backtest_filename(str(testdir_bt))
+    assert res == "backtest-result.json"
+
+    mocker.patch("freqtrade.data.btanalysis.json_load", return_value={})
+
+    with pytest.raises(ValueError, match=r"Invalid '.last_result.json' format."):
+        get_latest_backtest_filename(testdir_bt)
+
+
+def test_get_latest_hyperopt_file(testdatadir):
+    res = get_latest_hyperopt_file(testdatadir / "does_not_exist", "testfile.pickle")
+    assert res == testdatadir / "does_not_exist/testfile.pickle"
+
+    res = get_latest_hyperopt_file(testdatadir.parent)
+    assert res == testdatadir.parent / "hyperopt_results.pickle"
+
+    res = get_latest_hyperopt_file(str(testdatadir.parent))
+    assert res == testdatadir.parent / "hyperopt_results.pickle"
+
+    # Test with absolute path
+    with pytest.raises(
+        OperationalException,
+        match="--hyperopt-filename expects only the filename, not an absolute path.",
+    ):
+        get_latest_hyperopt_file(str(testdatadir.parent), str(testdatadir.parent))
+
+
+def test_load_backtest_metadata(mocker, testdatadir):
+    res = load_backtest_metadata(testdatadir / "nonexistent.file.json")
+    assert res == {}
+
+    mocker.patch("freqtrade.data.btanalysis.get_backtest_metadata_filename")
+    mocker.patch("freqtrade.data.btanalysis.json_load", side_effect=Exception())
+    with pytest.raises(
+        OperationalException, match=r"Unexpected error.*loading backtest metadata\."
+    ):
+        load_backtest_metadata(testdatadir / "nonexistent.file.json")
+
+
+def test_load_backtest_data_old_format(testdatadir, mocker):
+    filename = testdatadir / "backtest-result_test222.json"
+    mocker.patch("freqtrade.data.btanalysis.load_backtest_stats", return_value=[])
+
+    with pytest.raises(
+        OperationalException,
+        match=r"Backtest-results with only trades data are no longer supported.",
+    ):
+        load_backtest_data(filename)
+
+
+def test_load_backtest_data_new_format(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    assert isinstance(bt_data, DataFrame)
+    assert set(bt_data.columns) == set(BT_DATA_COLUMNS)
+    assert len(bt_data) == 179
+
+    # Test loading from string (must yield same result)
+    bt_data2 = load_backtest_data(str(filename))
+    assert bt_data.equals(bt_data2)
+
+    # Test loading from folder (must yield same result)
+    bt_data3 = load_backtest_data(testdatadir / "backtest_results")
+    assert bt_data.equals(bt_data3)
+
+    with pytest.raises(ValueError, match=r"File .* does not exist\."):
+        load_backtest_data("filename" + "nofile")
+
+    with pytest.raises(ValueError, match=r"Unknown dataformat."):
+        load_backtest_data(testdatadir / "backtest_results" / LAST_BT_RESULT_FN)
+
+
+def test_load_backtest_data_multi(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result_multistrat.json"
+    for strategy in ("StrategyTestV2", "TestStrategy"):
+        bt_data = load_backtest_data(filename, strategy=strategy)
+        assert isinstance(bt_data, DataFrame)
+        assert set(bt_data.columns) == set(BT_DATA_COLUMNS)
+        assert len(bt_data) == 179
+
+        # Test loading from string (must yield same result)
+        bt_data2 = load_backtest_data(str(filename), strategy=strategy)
+        assert bt_data.equals(bt_data2)
+
+    with pytest.raises(ValueError, match=r"Strategy XYZ not available in the backtest result\."):
+        load_backtest_data(filename, strategy="XYZ")
+
+    with pytest.raises(ValueError, match=r"Detected backtest result with more than one strategy.*"):
+        load_backtest_data(filename)
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_load_trades_from_db(default_conf, fee, is_short, mocker):
+    create_mock_trades(fee, is_short)
+    # remove init so it does not init again
+    init_mock = mocker.patch("freqtrade.data.btanalysis.init_db", MagicMock())
+
+    trades = load_trades_from_db(db_url=default_conf["db_url"])
+    assert init_mock.call_count == 1
+    assert len(trades) == MOCK_TRADE_COUNT
+    assert isinstance(trades, DataFrame)
+    assert "pair" in trades.columns
+    assert "open_date" in trades.columns
+    assert "profit_ratio" in trades.columns
+
+    for col in BT_DATA_COLUMNS:
+        if col not in ["index", "open_at_end"]:
+            assert col in trades.columns
+    trades = load_trades_from_db(db_url=default_conf["db_url"], strategy=CURRENT_TEST_STRATEGY)
+    assert len(trades) == 4
+    trades = load_trades_from_db(db_url=default_conf["db_url"], strategy="NoneStrategy")
+    assert len(trades) == 0
+
+
+def test_extract_trades_of_period(testdatadir):
+    pair = "UNITTEST/BTC"
+    # 2018-11-14 06:07:00
+    timerange = TimeRange("date", None, 1510639620, 0)
+
+    data = load_pair_history(pair=pair, timeframe="1m", datadir=testdatadir, timerange=timerange)
+
+    trades = DataFrame(
+        {
+            "pair": [pair, pair, pair, pair],
+            "profit_ratio": [0.0, 0.1, -0.2, -0.5],
+            "profit_abs": [0.0, 1, -2, -5],
+            "open_date": to_datetime(
+                [
+                    datetime(2017, 11, 13, 15, 40, 0, tzinfo=timezone.utc),
+                    datetime(2017, 11, 14, 9, 41, 0, tzinfo=timezone.utc),
+                    datetime(2017, 11, 14, 14, 20, 0, tzinfo=timezone.utc),
+                    datetime(2017, 11, 15, 3, 40, 0, tzinfo=timezone.utc),
+                ],
+                utc=True,
+            ),
+            "close_date": to_datetime(
+                [
+                    datetime(2017, 11, 13, 16, 40, 0, tzinfo=timezone.utc),
+                    datetime(2017, 11, 14, 10, 41, 0, tzinfo=timezone.utc),
+                    datetime(2017, 11, 14, 15, 25, 0, tzinfo=timezone.utc),
+                    datetime(2017, 11, 15, 3, 55, 0, tzinfo=timezone.utc),
+                ],
+                utc=True,
+            ),
+        }
+    )
+    trades1 = extract_trades_of_period(data, trades)
+    # First and last trade are dropped as they are out of range
+    assert len(trades1) == 2
+    assert trades1.iloc[0].open_date == datetime(2017, 11, 14, 9, 41, 0, tzinfo=timezone.utc)
+    assert trades1.iloc[0].close_date == datetime(2017, 11, 14, 10, 41, 0, tzinfo=timezone.utc)
+    assert trades1.iloc[-1].open_date == datetime(2017, 11, 14, 14, 20, 0, tzinfo=timezone.utc)
+    assert trades1.iloc[-1].close_date == datetime(2017, 11, 14, 15, 25, 0, tzinfo=timezone.utc)
+
+
+def test_analyze_trade_parallelism(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    res = analyze_trade_parallelism(bt_data, "5m")
+    assert isinstance(res, DataFrame)
+    assert "open_trades" in res.columns
+    assert res["open_trades"].max() == 3
+    assert res["open_trades"].min() == 0
+
+
+def test_load_trades(default_conf, mocker):
+    db_mock = mocker.patch("freqtrade.data.btanalysis.load_trades_from_db", MagicMock())
+    bt_mock = mocker.patch("freqtrade.data.btanalysis.load_backtest_data", MagicMock())
+
+    load_trades(
+        "DB",
+        db_url=default_conf.get("db_url"),
+        exportfilename=default_conf.get("exportfilename"),
+        no_trades=False,
+        strategy=CURRENT_TEST_STRATEGY,
+    )
+
+    assert db_mock.call_count == 1
+    assert bt_mock.call_count == 0
+
+    db_mock.reset_mock()
+    bt_mock.reset_mock()
+    default_conf["exportfilename"] = Path("testfile.json")
+    load_trades(
+        "file",
+        db_url=default_conf.get("db_url"),
+        exportfilename=default_conf.get("exportfilename"),
+    )
+
+    assert db_mock.call_count == 0
+    assert bt_mock.call_count == 1
+
+    db_mock.reset_mock()
+    bt_mock.reset_mock()
+    default_conf["exportfilename"] = "testfile.json"
+    load_trades(
+        "file",
+        db_url=default_conf.get("db_url"),
+        exportfilename=default_conf.get("exportfilename"),
+        no_trades=True,
+    )
+
+    assert db_mock.call_count == 0
+    assert bt_mock.call_count == 0
+
+
+def test_calculate_market_change(testdatadir):
+    pairs = ["ETH/BTC", "ADA/BTC"]
+    data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
+    result = calculate_market_change(data)
+    assert isinstance(result, float)
+    assert pytest.approx(result) == 0.01100002
+
+
+def test_combine_dataframes_with_mean(testdatadir):
+    pairs = ["ETH/BTC", "ADA/BTC"]
+    data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
+    df = combine_dataframes_with_mean(data)
+    assert isinstance(df, DataFrame)
+    assert "ETH/BTC" in df.columns
+    assert "ADA/BTC" in df.columns
+    assert "mean" in df.columns
+
+
+def test_combined_dataframes_with_rel_mean(testdatadir):
+    pairs = ["ETH/BTC", "ADA/BTC"]
+    data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
+    df = combined_dataframes_with_rel_mean(
+        data, datetime(2018, 1, 12, tzinfo=timezone.utc), datetime(2018, 1, 28, tzinfo=timezone.utc)
+    )
+    assert isinstance(df, DataFrame)
+    assert "ETH/BTC" not in df.columns
+    assert "ADA/BTC" not in df.columns
+    assert "mean" in df.columns
+    assert "rel_mean" in df.columns
+    assert "count" in df.columns
+    assert df.iloc[0]["count"] == 2
+    assert df.iloc[-1]["count"] == 2
+    assert len(df) < len(data["ETH/BTC"])
+
+
+def test_combine_dataframes_with_mean_no_data(testdatadir):
+    pairs = ["ETH/BTC", "ADA/BTC"]
+    data = load_data(datadir=testdatadir, pairs=pairs, timeframe="6m")
+    with pytest.raises(ValueError, match=r"No data provided\."):
+        combine_dataframes_with_mean(data)
+
+
+def test_create_cum_profit(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    timerange = TimeRange.parse_timerange("20180110-20180112")
+
+    df = load_pair_history(pair="TRX/BTC", timeframe="5m", datadir=testdatadir, timerange=timerange)
+
+    cum_profits = create_cum_profit(
+        df.set_index("date"), bt_data[bt_data["pair"] == "TRX/BTC"], "cum_profits", timeframe="5m"
+    )
+    assert "cum_profits" in cum_profits.columns
+    assert cum_profits.iloc[0]["cum_profits"] == 0
+    assert pytest.approx(cum_profits.iloc[-1]["cum_profits"]) == 9.0225563e-05
+
+
+def test_create_cum_profit1(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    # Move close-time to "off" the candle, to make sure the logic still works
+    bt_data["close_date"] = bt_data.loc[:, "close_date"] + DateOffset(seconds=20)
+    timerange = TimeRange.parse_timerange("20180110-20180112")
+
+    df = load_pair_history(pair="TRX/BTC", timeframe="5m", datadir=testdatadir, timerange=timerange)
+
+    cum_profits = create_cum_profit(
+        df.set_index("date"), bt_data[bt_data["pair"] == "TRX/BTC"], "cum_profits", timeframe="5m"
+    )
+    assert "cum_profits" in cum_profits.columns
+    assert cum_profits.iloc[0]["cum_profits"] == 0
+    assert pytest.approx(cum_profits.iloc[-1]["cum_profits"]) == 9.0225563e-05
+
+    with pytest.raises(ValueError, match="Trade dataframe empty."):
+        create_cum_profit(
+            df.set_index("date"),
+            bt_data[bt_data["pair"] == "NOTAPAIR"],
+            "cum_profits",
+            timeframe="5m",
+        )
+
+
+def test_calculate_max_drawdown(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    drawdown = calculate_max_drawdown(bt_data, value_col="profit_abs")
+    assert isinstance(drawdown.relative_account_drawdown, float)
+    assert pytest.approx(drawdown.relative_account_drawdown) == 0.29753914
+    assert isinstance(drawdown.high_date, Timestamp)
+    assert isinstance(drawdown.low_date, Timestamp)
+    assert isinstance(drawdown.high_value, float)
+    assert isinstance(drawdown.low_value, float)
+    assert drawdown.high_date == Timestamp("2018-01-16 19:30:00", tz="UTC")
+    assert drawdown.low_date == Timestamp("2018-01-16 22:25:00", tz="UTC")
+
+    underwater = calculate_underwater(bt_data)
+    assert isinstance(underwater, DataFrame)
+
+    with pytest.raises(ValueError, match="Trade dataframe empty."):
+        calculate_max_drawdown(DataFrame())
+
+    with pytest.raises(ValueError, match="Trade dataframe empty."):
+        calculate_underwater(DataFrame())
+
+
+def test_calculate_csum(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    csum_min, csum_max = calculate_csum(bt_data)
+
+    assert isinstance(csum_min, float)
+    assert isinstance(csum_max, float)
+    assert csum_min < csum_max
+    assert csum_min < 0.0001
+    assert csum_max > 0.0002
+    csum_min1, csum_max1 = calculate_csum(bt_data, 5)
+
+    assert csum_min1 == csum_min + 5
+    assert csum_max1 == csum_max + 5
+
+    with pytest.raises(ValueError, match="Trade dataframe empty."):
+        csum_min, csum_max = calculate_csum(DataFrame())
+
+
+def test_calculate_expectancy(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    expectancy, expectancy_ratio = calculate_expectancy(DataFrame())
+    assert expectancy == 0.0
+    assert expectancy_ratio == 100
+
+    expectancy, expectancy_ratio = calculate_expectancy(bt_data)
+    assert isinstance(expectancy, float)
+    assert isinstance(expectancy_ratio, float)
+    assert pytest.approx(expectancy) == 5.820687070932315e-06
+    assert pytest.approx(expectancy_ratio) == 0.07151374226574791
+
+    data = {"profit_abs": [100, 200, 50, -150, 300, -100, 80, -30]}
+    df = DataFrame(data)
+    expectancy, expectancy_ratio = calculate_expectancy(df)
+
+    assert pytest.approx(expectancy) == 56.25
+    assert pytest.approx(expectancy_ratio) == 0.60267857
+
+
+def test_calculate_sortino(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    sortino = calculate_sortino(DataFrame(), None, None, 0)
+    assert sortino == 0.0
+
+    sortino = calculate_sortino(
+        bt_data,
+        bt_data["open_date"].min(),
+        bt_data["close_date"].max(),
+        0.01,
+    )
+    assert isinstance(sortino, float)
+    assert pytest.approx(sortino) == 35.17722
+
+
+def test_calculate_sharpe(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    sharpe = calculate_sharpe(DataFrame(), None, None, 0)
+    assert sharpe == 0.0
+
+    sharpe = calculate_sharpe(
+        bt_data,
+        bt_data["open_date"].min(),
+        bt_data["close_date"].max(),
+        0.01,
+    )
+    assert isinstance(sharpe, float)
+    assert pytest.approx(sharpe) == 44.5078669
+
+
+def test_calculate_calmar(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    calmar = calculate_calmar(DataFrame(), None, None, 0)
+    assert calmar == 0.0
+
+    calmar = calculate_calmar(
+        bt_data,
+        bt_data["open_date"].min(),
+        bt_data["close_date"].max(),
+        0.01,
+    )
+    assert isinstance(calmar, float)
+    assert pytest.approx(calmar) == 559.040508
+
+
+def test_calculate_sqn(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+
+    sqn = calculate_sqn(DataFrame(), 0)
+    assert sqn == 0.0
+
+    sqn = calculate_sqn(
+        bt_data,
+        0.01,
+    )
+    assert isinstance(sqn, float)
+    assert pytest.approx(sqn) == 3.2991
+
+
+@pytest.mark.parametrize(
+    "profits,starting_balance,expected_sqn,description",
+    [
+        ([1.0, -0.5, 2.0, -1.0, 0.5, 1.5, -0.5, 1.0], 100, 1.3229, "Mixed profits/losses"),
+        ([], 100, 0.0, "Empty dataframe"),
+        ([1.0, 0.5, 2.0, 1.5, 0.8], 100, 4.3657, "All winning trades"),
+        ([-1.0, -0.5, -2.0, -1.5, -0.8], 100, -4.3657, "All losing trades"),
+        ([1.0], 100, -100, "Single trade"),
+    ],
+)
+def test_calculate_sqn_cases(profits, starting_balance, expected_sqn, description):
+    """
+    Test SQN calculation with various scenarios:
+    """
+    trades = DataFrame({"profit_abs": profits})
+    sqn = calculate_sqn(trades, starting_balance=starting_balance)
+
+    assert isinstance(sqn, float)
+    assert pytest.approx(sqn, rel=1e-4) == expected_sqn
+
+
+@pytest.mark.parametrize(
+    "start,end,days, expected",
+    [
+        (64900, 176000, 3 * 365, 0.3945),
+        (64900, 176000, 365, 1.7119),
+        (1000, 1000, 365, 0.0),
+        (1000, 1500, 365, 0.5),
+        (1000, 1500, 100, 3.3927),  # sub year
+        (0.01000000, 0.01762792, 120, 4.6087),  # sub year BTC values
+    ],
+)
+def test_calculate_cagr(start, end, days, expected):
+    assert round(calculate_cagr(days, start, end), 4) == expected
+
+
+def test_calculate_max_drawdown2():
+    values = [
+        0.011580,
+        0.010048,
+        0.011340,
+        0.012161,
+        0.010416,
+        0.010009,
+        0.020024,
+        -0.024662,
+        -0.022350,
+        0.020496,
+        -0.029859,
+        -0.030511,
+        0.010041,
+        0.010872,
+        -0.025782,
+        0.010400,
+        0.012374,
+        0.012467,
+        0.114741,
+        0.010303,
+        0.010088,
+        -0.033961,
+        0.010680,
+        0.010886,
+        -0.029274,
+        0.011178,
+        0.010693,
+        0.010711,
+    ]
+
+    dates = [dt_utc(2020, 1, 1) + timedelta(days=i) for i in range(len(values))]
+    df = DataFrame(zip(values, dates, strict=False), columns=["profit", "open_date"])
+    # sort by profit and reset index
+    df = df.sort_values("profit").reset_index(drop=True)
+    df1 = df.copy()
+    drawdown = calculate_max_drawdown(
+        df, date_col="open_date", starting_balance=0.2, value_col="profit"
+    )
+    # Ensure df has not been altered.
+    assert df.equals(df1)
+
+    assert isinstance(drawdown.drawdown_abs, float)
+    assert isinstance(drawdown.relative_account_drawdown, float)
+    # High must be before low
+    assert drawdown.high_date < drawdown.low_date
+    # High value must be higher than low value
+    assert drawdown.high_value > drawdown.low_value
+    assert drawdown.drawdown_abs == 0.091755
+    assert pytest.approx(drawdown.relative_account_drawdown) == 0.32129575
+
+    df = DataFrame(zip(values[:5], dates[:5], strict=False), columns=["profit", "open_date"])
+    # No losing trade ...
+    drawdown = calculate_max_drawdown(df, date_col="open_date", value_col="profit")
+    assert drawdown.drawdown_abs == 0.0
+
+    df1 = DataFrame(zip(values[:5], dates[:5], strict=False), columns=["profit", "open_date"])
+    df1.loc[:, "profit"] = df1["profit"] * -1
+    # No winning trade ...
+    drawdown = calculate_max_drawdown(df1, date_col="open_date", value_col="profit")
+    assert drawdown.drawdown_abs == 0.055545
+
+
+@pytest.mark.parametrize(
+    "profits,relative,highd,lowdays,result,result_rel",
+    [
+        ([0.0, -500.0, 500.0, 10000.0, -1000.0], False, 3, 4, 1000.0, 0.090909),
+        ([0.0, -500.0, 500.0, 10000.0, -1000.0], True, 0, 1, 500.0, 0.5),
+    ],
+)
+def test_calculate_max_drawdown_abs(profits, relative, highd, lowdays, result, result_rel):
+    """
+    Test case from issue https://github.com/freqtrade/freqtrade/issues/6655
+    [1000, 500,  1000, 11000, 10000] # absolute results
+    [1000, 50%,  0%,   0%,       ~9%]   # Relative drawdowns
+    """
+    init_date = datetime(2020, 1, 1, tzinfo=timezone.utc)
+    dates = [init_date + timedelta(days=i) for i in range(len(profits))]
+    df = DataFrame(zip(profits, dates, strict=False), columns=["profit_abs", "open_date"])
+    # sort by profit and reset index
+    df = df.sort_values("profit_abs").reset_index(drop=True)
+    df1 = df.copy()
+    drawdown = calculate_max_drawdown(
+        df, date_col="open_date", starting_balance=1000, relative=relative
+    )
+    # Ensure df has not been altered.
+    assert df.equals(df1)
+
+    assert isinstance(drawdown.drawdown_abs, float)
+    assert isinstance(drawdown.relative_account_drawdown, float)
+    assert drawdown.high_date == init_date + timedelta(days=highd)
+    assert drawdown.low_date == init_date + timedelta(days=lowdays)
+
+    # High must be before low
+    assert drawdown.high_date < drawdown.low_date
+    # High value must be higher than low value
+    assert drawdown.high_value > drawdown.low_value
+    assert drawdown.drawdown_abs == result
+    assert pytest.approx(drawdown.relative_account_drawdown) == result_rel
+
+
+def test_load_file_from_zip(tmp_path):
+    with pytest.raises(ValueError, match=r"Zip file .* not found\."):
+        load_file_from_zip(tmp_path / "test.zip", "testfile.txt")
+
+    (tmp_path / "testfile.zip").touch()
+    with pytest.raises(ValueError, match=r"Bad zip file.*"):
+        load_file_from_zip(tmp_path / "testfile.zip", "testfile.txt")
+
+    zip_file = tmp_path / "testfile2.zip"
+    with ZipFile(zip_file, "w") as zipf:
+        zipf.writestr("testfile.txt", "testfile content")
+
+    content = load_file_from_zip(zip_file, "testfile.txt")
+    assert content.decode("utf-8") == "testfile content"
+
+    with pytest.raises(ValueError, match=r"File .* not found in zip.*"):
+        load_file_from_zip(zip_file, "testfile55.txt")
diff --git a/tests/data/test_converter.py b/tests/data/test_converter.py
new file mode 100644
index 000000000..3d6c9adc2
--- /dev/null
+++ b/tests/data/test_converter.py
@@ -0,0 +1,590 @@
+# pragma pylint: disable=missing-docstring, C0103
+import logging
+from shutil import copyfile
+
+import numpy as np
+import pandas as pd
+import pytest
+from pandas.testing import assert_frame_equal
+
+from freqtrade.configuration.timerange import TimeRange
+from freqtrade.data.converter import (
+    convert_ohlcv_format,
+    convert_trades_format,
+    convert_trades_to_ohlcv,
+    ohlcv_fill_up_missing_data,
+    ohlcv_to_dataframe,
+    reduce_dataframe_footprint,
+    trades_df_remove_duplicates,
+    trades_dict_to_list,
+    trades_to_ohlcv,
+    trim_dataframe,
+)
+from freqtrade.data.history import (
+    get_timerange,
+    load_data,
+    load_pair_history,
+    validate_backtest_data,
+)
+from freqtrade.data.history.datahandlers import IDataHandler
+from freqtrade.enums import CandleType
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
+from tests.conftest import generate_test_data, generate_trades_history, log_has, log_has_re
+from tests.data.test_history import _clean_test_file
+
+
+def test_dataframe_correct_columns(dataframe_1m):
+    assert dataframe_1m.columns.tolist() == ["date", "open", "high", "low", "close", "volume"]
+
+
+def test_ohlcv_to_dataframe(ohlcv_history_list, caplog):
+    columns = ["date", "open", "high", "low", "close", "volume"]
+
+    caplog.set_level(logging.DEBUG)
+    # Test file with BV data
+    dataframe = ohlcv_to_dataframe(ohlcv_history_list, "5m", pair="UNITTEST/BTC", fill_missing=True)
+    assert dataframe.columns.tolist() == columns
+    assert log_has("Converting candle (OHLCV) data to dataframe for pair UNITTEST/BTC.", caplog)
+
+
+def test_trades_to_ohlcv(trades_history_df, caplog):
+    caplog.set_level(logging.DEBUG)
+    with pytest.raises(ValueError, match="Trade-list empty."):
+        trades_to_ohlcv(pd.DataFrame(columns=trades_history_df.columns), "1m")
+
+    df = trades_to_ohlcv(trades_history_df, "1m")
+    assert not df.empty
+    assert len(df) == 1
+    assert "open" in df.columns
+    assert "high" in df.columns
+    assert "low" in df.columns
+    assert "close" in df.columns
+    assert df.iloc[0, :]["high"] == 0.019627
+    assert df.iloc[0, :]["low"] == 0.019626
+    assert df.iloc[0, :]["date"] == pd.Timestamp("2019-08-14 15:59:00+0000")
+
+    df_1h = trades_to_ohlcv(trades_history_df, "1h")
+    assert len(df_1h) == 1
+    assert df_1h.iloc[0, :]["high"] == 0.019627
+    assert df_1h.iloc[0, :]["low"] == 0.019626
+    assert df_1h.iloc[0, :]["date"] == pd.Timestamp("2019-08-14 15:00:00+0000")
+
+    df_1s = trades_to_ohlcv(trades_history_df, "1s")
+    assert len(df_1s) == 2
+    assert df_1s.iloc[0, :]["high"] == 0.019627
+    assert df_1s.iloc[0, :]["low"] == 0.019627
+    assert df_1s.iloc[0, :]["date"] == pd.Timestamp("2019-08-14 15:59:49+0000")
+    assert df_1s.iloc[-1, :]["date"] == pd.Timestamp("2019-08-14 15:59:59+0000")
+
+
+@pytest.mark.parametrize(
+    "timeframe,rows,days,candles,start,end,weekday",
+    [
+        ("1s", 20_000, 5, 19522, "2020-01-01 00:00:05", "2020-01-05 23:59:27", None),
+        ("1m", 20_000, 5, 6745, "2020-01-01 00:00:00", "2020-01-05 23:59:00", None),
+        ("5m", 20_000, 5, 1440, "2020-01-01 00:00:00", "2020-01-05 23:55:00", None),
+        ("15m", 20_000, 5, 480, "2020-01-01 00:00:00", "2020-01-05 23:45:00", None),
+        ("1h", 20_000, 5, 120, "2020-01-01 00:00:00", "2020-01-05 23:00:00", None),
+        ("2h", 20_000, 5, 60, "2020-01-01 00:00:00", "2020-01-05 22:00:00", None),
+        ("4h", 20_000, 5, 30, "2020-01-01 00:00:00", "2020-01-05 20:00:00", None),
+        ("8h", 20_000, 5, 15, "2020-01-01 00:00:00", "2020-01-05 16:00:00", None),
+        ("12h", 20_000, 5, 10, "2020-01-01 00:00:00", "2020-01-05 12:00:00", None),
+        ("1d", 20_000, 5, 5, "2020-01-01 00:00:00", "2020-01-05 00:00:00", "Sunday"),
+        ("7d", 20_000, 37, 6, "2020-01-06 00:00:00", "2020-02-10 00:00:00", "Monday"),
+        ("1w", 20_000, 37, 6, "2020-01-06 00:00:00", "2020-02-10 00:00:00", "Monday"),
+        ("1M", 20_000, 74, 3, "2020-01-01 00:00:00", "2020-03-01 00:00:00", None),
+        ("3M", 20_000, 100, 2, "2020-01-01 00:00:00", "2020-04-01 00:00:00", None),
+        ("1y", 20_000, 1000, 3, "2020-01-01 00:00:00", "2022-01-01 00:00:00", None),
+    ],
+)
+def test_trades_to_ohlcv_multi(timeframe, rows, days, candles, start, end, weekday):
+    trades_history = generate_trades_history(n_rows=rows, days=days)
+    df = trades_to_ohlcv(trades_history, timeframe)
+    assert not df.empty
+    assert len(df) == candles
+    assert df.iloc[0, :]["date"] == pd.Timestamp(f"{start}+0000")
+    assert df.iloc[-1, :]["date"] == pd.Timestamp(f"{end}+0000")
+    if weekday:
+        # Weekday is only relevant for daily and weekly candles.
+        assert df.iloc[-1, :]["date"].day_name() == weekday
+
+
+def test_ohlcv_fill_up_missing_data(testdatadir, caplog):
+    data = load_pair_history(
+        datadir=testdatadir, timeframe="1m", pair="UNITTEST/BTC", fill_up_missing=False
+    )
+    caplog.set_level(logging.DEBUG)
+    data2 = ohlcv_fill_up_missing_data(data, "1m", "UNITTEST/BTC")
+    assert len(data2) > len(data)
+    # Column names should not change
+    assert (data.columns == data2.columns).all()
+
+    assert log_has_re(
+        f"Missing data fillup for UNITTEST/BTC, 1m: before: {len(data)} - after: {len(data2)}.*",
+        caplog,
+    )
+
+    # Test fillup actually fixes invalid backtest data
+    min_date, max_date = get_timerange({"UNITTEST/BTC": data})
+    assert validate_backtest_data(data, "UNITTEST/BTC", min_date, max_date, 1)
+    assert not validate_backtest_data(data2, "UNITTEST/BTC", min_date, max_date, 1)
+
+
+def test_ohlcv_fill_up_missing_data2(caplog):
+    timeframe = "5m"
+    ticks = [
+        [
+            1511686200000,  # 8:50:00
+            8.794e-05,  # open
+            8.948e-05,  # high
+            8.794e-05,  # low
+            8.88e-05,  # close
+            2255,  # volume (in quote currency)
+        ],
+        [
+            1511686500000,  # 8:55:00
+            8.88e-05,
+            8.942e-05,
+            8.88e-05,
+            8.893e-05,
+            9911,
+        ],
+        [
+            1511687100000,  # 9:05:00
+            8.891e-05,
+            8.893e-05,
+            8.875e-05,
+            8.877e-05,
+            2251,
+        ],
+        [
+            1511687400000,  # 9:10:00
+            8.877e-05,
+            8.883e-05,
+            8.895e-05,
+            8.817e-05,
+            123551,
+        ],
+    ]
+
+    # Generate test-data without filling missing
+    data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC", fill_missing=False)
+    assert len(data) == 3
+    caplog.set_level(logging.DEBUG)
+    data2 = ohlcv_fill_up_missing_data(data, timeframe, "UNITTEST/BTC")
+    assert len(data2) == 4
+    # 3rd candle has been filled
+    row = data2.loc[2, :]
+    assert row["volume"] == 0
+    # close should match close of previous candle
+    assert row["close"] == data.loc[1, "close"]
+    assert row["open"] == row["close"]
+    assert row["high"] == row["close"]
+    assert row["low"] == row["close"]
+    # Column names should not change
+    assert (data.columns == data2.columns).all()
+
+    assert log_has_re(
+        f"Missing data fillup for UNITTEST/BTC, {timeframe}: before: "
+        f"{len(data)} - after: {len(data2)}.*",
+        caplog,
+    )
+
+
+@pytest.mark.parametrize(
+    "timeframe",
+    ["1s", "1m", "5m", "15m", "1h", "2h", "4h", "8h", "12h", "1d", "7d", "1w", "1M", "3M", "1y"],
+)
+def test_ohlcv_to_dataframe_multi(timeframe):
+    data = generate_test_data(timeframe, 180)
+    assert len(data) == 180
+    df = ohlcv_to_dataframe(data, timeframe, "UNITTEST/USDT")
+    assert len(df) == len(data) - 1
+    df1 = ohlcv_to_dataframe(data, timeframe, "UNITTEST/USDT", drop_incomplete=False)
+    assert len(df1) == len(data)
+    assert data.equals(df1)
+
+    data1 = data.copy()
+    if timeframe in ("1M", "3M", "1y"):
+        data1.loc[:, "date"] = data1.loc[:, "date"] + pd.to_timedelta("1w")
+    else:
+        # Shift by half a timeframe
+        data1.loc[:, "date"] = data1.loc[:, "date"] + (pd.to_timedelta(timeframe) / 2)
+    df2 = ohlcv_to_dataframe(data1, timeframe, "UNITTEST/USDT")
+
+    assert len(df2) == len(data) - 1
+    tfs = timeframe_to_seconds(timeframe)
+    tfm = timeframe_to_minutes(timeframe)
+    if 1 <= tfm < 10000:
+        # minute based resampling does not work on timeframes >= 1 week
+        ohlcv_dict = {
+            "open": "first",
+            "high": "max",
+            "low": "min",
+            "close": "last",
+            "volume": "sum",
+        }
+        dfs = data1.resample(f"{tfs}s", on="date").agg(ohlcv_dict).reset_index(drop=False)
+        dfm = data1.resample(f"{tfm}min", on="date").agg(ohlcv_dict).reset_index(drop=False)
+
+        assert dfs.equals(dfm)
+        assert dfs.equals(df1)
+
+
+def test_ohlcv_to_dataframe_1M():
+    # Monthly ticks from 2019-09-01 to 2023-07-01
+    ticks = [
+        [1567296000000, 8042.08, 10475.54, 7700.67, 8041.96, 608742.1109999999],
+        [1569888000000, 8285.31, 10408.48, 7172.76, 9150.0, 2439561.887],
+        [1572566400000, 9149.88, 9550.0, 6510.19, 7542.93, 4042674.725],
+        [1575158400000, 7541.08, 7800.0, 6427.0, 7189.0, 4063882.296],
+        [1577836800000, 7189.43, 9599.0, 6863.44, 9364.51, 5165281.358],
+        [1580515200000, 9364.5, 10540.0, 8450.0, 8531.98, 4581788.124],
+        [1583020800000, 8532.5, 9204.0, 3621.81, 6407.1, 10859497.479],
+        [1585699200000, 6407.1, 9479.77, 6140.0, 8624.76, 11276526.968],
+        [1588291200000, 8623.61, 10080.0, 7940.0, 9446.43, 12469561.02],
+        [1590969600000, 9446.49, 10497.25, 8816.4, 9138.87, 6684044.201],
+        [1593561600000, 9138.88, 11488.0, 8900.0, 11343.68, 5709327.926],
+        [1596240000000, 11343.67, 12499.42, 10490.0, 11658.11, 6746487.129],
+        [1598918400000, 11658.11, 12061.07, 9808.58, 10773.0, 6442697.051],
+        [1601510400000, 10773.0, 14140.0, 10371.03, 13783.73, 7404103.004],
+        [1604188800000, 13783.73, 19944.0, 13195.0, 19720.0, 12328272.549],
+        [1606780800000, 19722.09, 29376.7, 17555.0, 28951.68, 10067314.24],
+        [1609459200000, 28948.19, 42125.51, 27800.0, 33126.21, 12408873.079],
+        [1612137600000, 33125.11, 58472.14, 32322.47, 45163.36, 8784474.482],
+        [1614556800000, 45162.64, 61950.0, 44972.49, 58807.24, 9459821.267],
+        [1617235200000, 58810.99, 64986.11, 46930.43, 57684.16, 7895051.389],
+        [1619827200000, 57688.29, 59654.0, 28688.0, 37243.38, 16790964.443],
+        [1622505600000, 37244.36, 41413.0, 28780.01, 35031.39, 23474519.886],
+        [1625097600000, 35031.39, 48168.6, 29242.24, 41448.11, 16932491.175],
+        [1627776000000, 41448.1, 50600.0, 37291.0, 47150.32, 13645800.254],
+        [1630454400000, 47150.32, 52950.0, 39503.58, 43796.57, 10734742.869],
+        [1633046400000, 43799.49, 67150.0, 43260.01, 61348.61, 9111112.847],
+        [1635724800000, 61347.14, 69198.7, 53245.0, 56975.0, 7111424.463],
+        [1638316800000, 56978.06, 59100.0, 40888.89, 46210.56, 8404449.024],
+        [1640995200000, 46210.57, 48000.0, 32853.83, 38439.04, 11047479.277],
+        [1643673600000, 38439.04, 45847.5, 34303.7, 43155.0, 10910339.91],
+        [1646092800000, 43155.0, 48200.0, 37134.0, 45506.0, 10459721.586],
+        [1648771200000, 45505.9, 47448.0, 37550.0, 37614.5, 8463568.862],
+        [1651363200000, 37614.4, 40071.7, 26631.0, 31797.8, 14463715.774],
+        [1654041600000, 31797.9, 31986.1, 17593.2, 19923.5, 20710810.306],
+        [1656633600000, 19923.3, 24700.0, 18780.1, 23290.1, 20582518.513],
+        [1659312000000, 23290.1, 25200.0, 19508.0, 20041.5, 17221921.557],
+        [1661990400000, 20041.4, 22850.0, 18084.3, 19411.7, 21935261.414],
+        [1664582400000, 19411.6, 21088.0, 17917.8, 20482.0, 16625843.584],
+        [1667260800000, 20482.1, 21473.7, 15443.2, 17153.3, 18460614.013],
+        [1669852800000, 17153.4, 18400.0, 16210.0, 16537.6, 9702408.711],
+        [1672531200000, 16537.5, 23962.7, 16488.0, 23119.4, 14732180.645],
+        [1675209600000, 23119.5, 25347.6, 21338.0, 23129.6, 15025197.415],
+        [1677628800000, 23129.7, 29184.8, 19521.6, 28454.9, 23317458.541],
+        [1680307200000, 28454.8, 31059.0, 26919.3, 29223.0, 14654208.219],
+        [1682899200000, 29223.0, 29840.0, 25751.0, 27201.1, 13328157.284],
+        [1685577600000, 27201.1, 31500.0, 24777.0, 30460.2, 14099299.273],
+        [1688169600000, 30460.2, 31850.0, 28830.0, 29338.8, 8760361.377],
+    ]
+
+    data = ohlcv_to_dataframe(
+        ticks, "1M", pair="UNITTEST/USDT", fill_missing=False, drop_incomplete=False
+    )
+    assert len(data) == len(ticks)
+    assert data.iloc[0]["date"].strftime("%Y-%m-%d") == "2019-09-01"
+    assert data.iloc[-1]["date"].strftime("%Y-%m-%d") == "2023-07-01"
+
+    # Test with filling missing data
+    data = ohlcv_to_dataframe(
+        ticks, "1M", pair="UNITTEST/USDT", fill_missing=True, drop_incomplete=False
+    )
+    assert len(data) == len(ticks)
+    assert data.iloc[0]["date"].strftime("%Y-%m-%d") == "2019-09-01"
+    assert data.iloc[-1]["date"].strftime("%Y-%m-%d") == "2023-07-01"
+
+
+def test_ohlcv_drop_incomplete(caplog):
+    timeframe = "1d"
+    ticks = [
+        [
+            1559750400000,  # 2019-06-04
+            8.794e-05,  # open
+            8.948e-05,  # high
+            8.794e-05,  # low
+            8.88e-05,  # close
+            2255,  # volume (in quote currency)
+        ],
+        [
+            1559836800000,  # 2019-06-05
+            8.88e-05,
+            8.942e-05,
+            8.88e-05,
+            8.893e-05,
+            9911,
+        ],
+        [
+            1559923200000,  # 2019-06-06
+            8.891e-05,
+            8.893e-05,
+            8.875e-05,
+            8.877e-05,
+            2251,
+        ],
+        [
+            1560009600000,  # 2019-06-07
+            8.877e-05,
+            8.883e-05,
+            8.895e-05,
+            8.817e-05,
+            123551,
+        ],
+    ]
+    caplog.set_level(logging.DEBUG)
+    data = ohlcv_to_dataframe(
+        ticks, timeframe, pair="UNITTEST/BTC", fill_missing=False, drop_incomplete=False
+    )
+    assert len(data) == 4
+    assert not log_has("Dropping last candle", caplog)
+
+    # Drop last candle
+    data = ohlcv_to_dataframe(
+        ticks, timeframe, pair="UNITTEST/BTC", fill_missing=False, drop_incomplete=True
+    )
+    assert len(data) == 3
+
+    assert log_has("Dropping last candle", caplog)
+
+
+def test_trim_dataframe(testdatadir) -> None:
+    data = load_data(datadir=testdatadir, timeframe="1m", pairs=["UNITTEST/BTC"])["UNITTEST/BTC"]
+    min_date = int(data.iloc[0]["date"].timestamp())
+    max_date = int(data.iloc[-1]["date"].timestamp())
+    data_modify = data.copy()
+
+    # Remove first 30 minutes (1800 s)
+    tr = TimeRange("date", None, min_date + 1800, 0)
+    data_modify = trim_dataframe(data_modify, tr)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 30
+    assert all(data_modify.iloc[-1] == data.iloc[-1])
+    assert all(data_modify.iloc[0] == data.iloc[30])
+
+    data_modify = data.copy()
+    tr = TimeRange("date", None, min_date + 1800, 0)
+    # Remove first 20 candles - ignores min date
+    data_modify = trim_dataframe(data_modify, tr, startup_candles=20)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 20
+    assert all(data_modify.iloc[-1] == data.iloc[-1])
+    assert all(data_modify.iloc[0] == data.iloc[20])
+
+    data_modify = data.copy()
+    # Remove last 30 minutes (1800 s)
+    tr = TimeRange(None, "date", 0, max_date - 1800)
+    data_modify = trim_dataframe(data_modify, tr)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 30
+    assert all(data_modify.iloc[0] == data.iloc[0])
+    assert all(data_modify.iloc[-1] == data.iloc[-31])
+
+    data_modify = data.copy()
+    # Remove first 25 and last 30 minutes (1800 s)
+    tr = TimeRange("date", "date", min_date + 1500, max_date - 1800)
+    data_modify = trim_dataframe(data_modify, tr)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 55
+    # first row matches 25th original row
+    assert all(data_modify.iloc[0] == data.iloc[25])
+
+
+def test_trades_df_remove_duplicates(trades_history_df):
+    trades_history1 = pd.concat(
+        [trades_history_df, trades_history_df, trades_history_df]
+    ).reset_index(drop=True)
+    assert len(trades_history1) == len(trades_history_df) * 3
+    res = trades_df_remove_duplicates(trades_history1)
+    assert len(res) == len(trades_history_df)
+    assert res.equals(trades_history_df)
+
+
+def test_trades_dict_to_list(fetch_trades_result):
+    res = trades_dict_to_list(fetch_trades_result)
+    assert isinstance(res, list)
+    assert isinstance(res[0], list)
+    for i, t in enumerate(res):
+        assert t[0] == fetch_trades_result[i]["timestamp"]
+        assert t[1] == fetch_trades_result[i]["id"]
+        assert t[2] == fetch_trades_result[i]["type"]
+        assert t[3] == fetch_trades_result[i]["side"]
+        assert t[4] == fetch_trades_result[i]["price"]
+        assert t[5] == fetch_trades_result[i]["amount"]
+        assert t[6] == fetch_trades_result[i]["cost"]
+
+
+def test_convert_trades_format(default_conf, testdatadir, tmp_path):
+    files = [
+        {"old": tmp_path / "XRP_ETH-trades.json.gz", "new": tmp_path / "XRP_ETH-trades.json"},
+        {"old": tmp_path / "XRP_OLD-trades.json.gz", "new": tmp_path / "XRP_OLD-trades.json"},
+    ]
+    for file in files:
+        copyfile(testdatadir / file["old"].name, file["old"])
+        assert not file["new"].exists()
+
+    default_conf["datadir"] = tmp_path
+
+    convert_trades_format(default_conf, convert_from="jsongz", convert_to="json", erase=False)
+
+    for file in files:
+        assert file["new"].exists()
+        assert file["old"].exists()
+
+        # Remove original file
+        file["old"].unlink()
+    # Convert back
+    convert_trades_format(default_conf, convert_from="json", convert_to="jsongz", erase=True)
+    for file in files:
+        assert file["old"].exists()
+        assert not file["new"].exists()
+
+        _clean_test_file(file["old"])
+        if file["new"].exists():
+            file["new"].unlink()
+
+
+@pytest.mark.parametrize(
+    "file_base,candletype",
+    [
+        (["XRP_ETH-5m", "XRP_ETH-1m"], CandleType.SPOT),
+        (["UNITTEST_USDT_USDT-1h-mark", "XRP_USDT_USDT-1h-mark"], CandleType.MARK),
+        (["XRP_USDT_USDT-1h-futures"], CandleType.FUTURES),
+    ],
+)
+def test_convert_ohlcv_format(default_conf, testdatadir, tmp_path, file_base, candletype):
+    prependix = "" if candletype == CandleType.SPOT else "futures/"
+    files_orig = []
+    files_temp = []
+    files_new = []
+    for file in file_base:
+        file_orig = testdatadir / f"{prependix}{file}.feather"
+        file_temp = tmp_path / f"{prependix}{file}.feather"
+        file_new = tmp_path / f"{prependix}{file}.json.gz"
+        IDataHandler.create_dir_if_needed(file_temp)
+        copyfile(file_orig, file_temp)
+
+        files_orig.append(file_orig)
+        files_temp.append(file_temp)
+        files_new.append(file_new)
+
+    default_conf["datadir"] = tmp_path
+    default_conf["candle_types"] = [candletype]
+
+    if candletype == CandleType.SPOT:
+        default_conf["pairs"] = ["XRP/ETH", "XRP/USDT", "UNITTEST/USDT"]
+    else:
+        default_conf["pairs"] = ["XRP/ETH:ETH", "XRP/USDT:USDT", "UNITTEST/USDT:USDT"]
+    default_conf["timeframes"] = ["1m", "5m", "1h"]
+
+    assert not file_new.exists()
+
+    convert_ohlcv_format(
+        default_conf,
+        convert_from="feather",
+        convert_to="jsongz",
+        erase=False,
+    )
+    for file in files_temp + files_new:
+        assert file.exists()
+
+    # Remove original files
+    for file in files_temp:
+        file.unlink()
+    # Convert back
+    convert_ohlcv_format(
+        default_conf,
+        convert_from="jsongz",
+        convert_to="feather",
+        erase=True,
+    )
+    for file in files_temp:
+        assert file.exists()
+    for file in files_new:
+        assert not file.exists()
+
+
+def test_reduce_dataframe_footprint():
+    data = generate_test_data("15m", 40)
+
+    data["open_copy"] = data["open"]
+    data["close_copy"] = data["close"]
+    data["close_copy"] = data["close"]
+
+    assert data["open"].dtype == np.float64
+    assert data["open_copy"].dtype == np.float64
+    assert data["close_copy"].dtype == np.float64
+
+    df2 = reduce_dataframe_footprint(data)
+
+    # Does not modify original dataframe
+    assert data["open"].dtype == np.float64
+    assert data["open_copy"].dtype == np.float64
+    assert data["close_copy"].dtype == np.float64
+
+    # skips ohlcv columns
+    assert df2["open"].dtype == np.float64
+    assert df2["high"].dtype == np.float64
+    assert df2["low"].dtype == np.float64
+    assert df2["close"].dtype == np.float64
+    assert df2["volume"].dtype == np.float64
+
+    # Changes dtype of returned dataframe
+    assert df2["open_copy"].dtype == np.float32
+    assert df2["close_copy"].dtype == np.float32
+
+
+def test_convert_trades_to_ohlcv(testdatadir, tmp_path, caplog):
+    pair = "XRP/ETH"
+    file1 = tmp_path / "XRP_ETH-1m.feather"
+    file5 = tmp_path / "XRP_ETH-5m.feather"
+    filetrades = tmp_path / "XRP_ETH-trades.json.gz"
+    copyfile(testdatadir / file1.name, file1)
+    copyfile(testdatadir / file5.name, file5)
+    copyfile(testdatadir / filetrades.name, filetrades)
+
+    # Compare downloaded dataset with converted dataset
+    dfbak_1m = load_pair_history(datadir=tmp_path, timeframe="1m", pair=pair)
+    dfbak_5m = load_pair_history(datadir=tmp_path, timeframe="5m", pair=pair)
+
+    tr = TimeRange.parse_timerange("20191011-20191012")
+
+    convert_trades_to_ohlcv(
+        [pair],
+        timeframes=["1m", "5m"],
+        data_format_trades="jsongz",
+        datadir=tmp_path,
+        timerange=tr,
+        erase=True,
+        data_format_ohlcv="feather",
+        candle_type=CandleType.SPOT,
+    )
+
+    assert log_has("Deleting existing data for pair XRP/ETH, interval 1m.", caplog)
+    # Load new data
+    df_1m = load_pair_history(datadir=tmp_path, timeframe="1m", pair=pair)
+    df_5m = load_pair_history(datadir=tmp_path, timeframe="5m", pair=pair)
+
+    assert_frame_equal(dfbak_1m, df_1m, check_exact=True)
+    assert_frame_equal(dfbak_5m, df_5m, check_exact=True)
+    msg = "Could not convert NoDatapair to OHLCV."
+    assert not log_has(msg, caplog)
+
+    convert_trades_to_ohlcv(
+        ["NoDatapair"],
+        timeframes=["1m", "5m"],
+        data_format_trades="jsongz",
+        datadir=tmp_path,
+        timerange=tr,
+        erase=True,
+        data_format_ohlcv="feather",
+        candle_type=CandleType.SPOT,
+    )
+    assert log_has(msg, caplog)
diff --git a/tests/data/test_converter_orderflow.py b/tests/data/test_converter_orderflow.py
new file mode 100644
index 000000000..d5b2e8f83
--- /dev/null
+++ b/tests/data/test_converter_orderflow.py
@@ -0,0 +1,613 @@
+import pandas as pd
+import pytest
+
+from freqtrade.constants import DEFAULT_TRADES_COLUMNS
+from freqtrade.data.converter import populate_dataframe_with_trades
+from freqtrade.data.converter.orderflow import (
+    ORDERFLOW_ADDED_COLUMNS,
+    stacked_imbalance,
+    timeframe_to_DateOffset,
+    trades_to_volumeprofile_with_total_delta_bid_ask,
+)
+from freqtrade.data.converter.trade_converter import trades_list_to_df
+from freqtrade.data.dataprovider import DataProvider
+from tests.strategy.strats.strategy_test_v3 import StrategyTestV3
+
+
+BIN_SIZE_SCALE = 0.5
+
+
+def read_csv(filename):
+    converter_columns: list = ["side", "type"]
+    return pd.read_csv(
+        filename,
+        skipinitialspace=True,
+        index_col=0,
+        parse_dates=True,
+        date_format="ISO8601",
+        converters={col: str.strip for col in converter_columns},
+    )
+
+
+@pytest.fixture
+def populate_dataframe_with_trades_dataframe(testdatadir):
+    return pd.read_feather(testdatadir / "orderflow/populate_dataframe_with_trades_DF.feather")
+
+
+@pytest.fixture
+def populate_dataframe_with_trades_trades(testdatadir):
+    return pd.read_feather(testdatadir / "orderflow/populate_dataframe_with_trades_TRADES.feather")
+
+
+@pytest.fixture
+def candles(testdatadir):
+    # TODO: this fixture isn't really necessary and could be removed
+    return pd.read_json(testdatadir / "orderflow/candles.json").copy()
+
+
+@pytest.fixture
+def public_trades_list(testdatadir):
+    return read_csv(testdatadir / "orderflow/public_trades_list.csv").copy()
+
+
+@pytest.fixture
+def public_trades_list_simple(testdatadir):
+    return read_csv(testdatadir / "orderflow/public_trades_list_simple_example.csv").copy()
+
+
+def test_public_trades_columns_before_change(
+    populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
+):
+    assert populate_dataframe_with_trades_dataframe.columns.tolist() == [
+        "date",
+        "open",
+        "high",
+        "low",
+        "close",
+        "volume",
+    ]
+    assert populate_dataframe_with_trades_trades.columns.tolist() == [
+        "timestamp",
+        "id",
+        "type",
+        "side",
+        "price",
+        "amount",
+        "cost",
+        "date",
+    ]
+
+
+def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
+    populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
+):
+    """
+    Tests the `populate_dataframe_with_trades` function's order flow calculation.
+
+    This test checks the generated data frame and order flow for specific properties
+    based on the provided configuration and sample data.
+    """
+    # Create copies of the input data to avoid modifying the originals
+    dataframe = populate_dataframe_with_trades_dataframe.copy()
+    trades = populate_dataframe_with_trades_trades.copy()
+    # Convert the 'date' column to datetime format with milliseconds
+    dataframe["date"] = pd.to_datetime(dataframe["date"], unit="ms")
+    # Select the last rows and reset the index (optional, depends on usage)
+    dataframe = dataframe.copy().tail().reset_index(drop=True)
+    # Define the configuration for order flow calculation
+    config = {
+        "timeframe": "5m",
+        "orderflow": {
+            "cache_size": 1000,
+            "max_candles": 1500,
+            "scale": 0.005,
+            "imbalance_volume": 0,
+            "imbalance_ratio": 3,
+            "stacked_imbalance_range": 3,
+        },
+    }
+    # Apply the function to populate the data frame with order flow data
+    df, _ = populate_dataframe_with_trades(None, config, dataframe, trades)
+    # Extract results from the first row of the DataFrame
+    results = df.iloc[0]
+    t = results["trades"]
+    of = results["orderflow"]
+
+    # Assert basic properties of the results
+    assert 0 != len(results)
+    assert 151 == len(t)
+
+    # --- Order Flow Analysis ---
+    # Assert number of order flow data points
+    assert 23 == len(of)  # Assert expected number of data points
+
+    assert isinstance(of, dict)
+
+    of_values = list(of.values())
+
+    # Assert specific order flow values at the beginning of the DataFrame
+    assert of_values[0] == {
+        "bid": 0.0,
+        "ask": 1.0,
+        "delta": 4.999,
+        "bid_amount": 0.0,
+        "ask_amount": 4.999,
+        "total_volume": 4.999,
+        "total_trades": 1,
+    }
+
+    # Assert specific order flow values at the end of the DataFrame (excluding last row)
+    assert of_values[-1] == {
+        "bid": 0.0,
+        "ask": 1.0,
+        "delta": 0.103,
+        "bid_amount": 0.0,
+        "ask_amount": 0.103,
+        "total_volume": 0.103,
+        "total_trades": 1,
+    }
+
+    # Extract order flow from the last row of the DataFrame
+    of = df.iloc[-1]["orderflow"]
+
+    # Assert number of order flow data points in the last row
+    assert 19 == len(of)  # Assert expected number of data points
+
+    of_values1 = list(of.values())
+    # Assert specific order flow values at the beginning of the last row
+    assert of_values1[0] == {
+        "bid": 1.0,
+        "ask": 0.0,
+        "delta": -12.536,
+        "bid_amount": 12.536,
+        "ask_amount": 0.0,
+        "total_volume": 12.536,
+        "total_trades": 1,
+    }
+
+    # Assert specific order flow values at the end of the last row
+    assert pytest.approx(of_values1[-1]) == {
+        "bid": 4.0,
+        "ask": 3.0,
+        "delta": -40.948,
+        "bid_amount": 59.182,
+        "ask_amount": 18.23399,
+        "total_volume": 77.416,
+        "total_trades": 7,
+    }
+
+    # --- Delta and Other Results ---
+
+    # Assert delta value from the first row
+    assert pytest.approx(results["delta"]) == -50.519
+    # Assert min and max delta values from the first row
+    assert results["min_delta"] == -79.469
+    assert results["max_delta"] == 17.298
+
+    # Assert that stacked imbalances are NaN (not applicable in this test)
+    assert results["stacked_imbalances_bid"] == []
+    assert results["stacked_imbalances_ask"] == []
+
+    # Repeat assertions for the third from last row
+    results = df.iloc[-2]
+    assert pytest.approx(results["delta"]) == -20.862
+    assert pytest.approx(results["min_delta"]) == -54.559999
+    assert 82.842 == results["max_delta"]
+    assert results["stacked_imbalances_bid"] == [234.97]
+    assert results["stacked_imbalances_ask"] == [234.94]
+
+    # Repeat assertions for the last row
+    results = df.iloc[-1]
+    assert pytest.approx(results["delta"]) == -49.302
+    assert results["min_delta"] == -70.222
+    assert pytest.approx(results["max_delta"]) == 11.213
+    assert results["stacked_imbalances_bid"] == []
+    assert results["stacked_imbalances_ask"] == []
+
+
+def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
+    populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
+):
+    """
+    Tests the `populate_dataframe_with_trades` function's handling of trades,
+    ensuring correct integration of trades data into the generated DataFrame.
+    """
+
+    # Create copies of the input data to avoid modifying the originals
+    dataframe = populate_dataframe_with_trades_dataframe.copy()
+    trades = populate_dataframe_with_trades_trades.copy()
+
+    # --- Data Preparation ---
+
+    # Convert the 'date' column to datetime format with milliseconds
+    dataframe["date"] = pd.to_datetime(dataframe["date"], unit="ms")
+
+    # Select the final row of the DataFrame
+    dataframe = dataframe.tail().reset_index(drop=True)
+
+    # Filter trades to those occurring after or at the same time as the first DataFrame date
+    trades = trades.loc[trades.date >= dataframe.date[0]]
+    trades.reset_index(inplace=True, drop=True)  # Reset index for clarity
+
+    # Assert the first trade ID to ensure filtering worked correctly
+    assert trades["id"][0] == "313881442"
+
+    # --- Configuration and Function Call ---
+
+    # Define configuration for order flow calculation (used for context)
+    config = {
+        "timeframe": "5m",
+        "orderflow": {
+            "cache_size": 1000,
+            "max_candles": 1500,
+            "scale": 0.5,
+            "imbalance_volume": 0,
+            "imbalance_ratio": 3,
+            "stacked_imbalance_range": 3,
+        },
+    }
+
+    # Populate the DataFrame with trades and order flow data
+    df, _ = populate_dataframe_with_trades(None, config, dataframe, trades)
+
+    # --- DataFrame and Trade Data Validation ---
+
+    row = df.iloc[0]  # Extract the first row for assertions
+
+    # Assert DataFrame structure
+    assert list(df.columns) == [
+        # ... (list of expected column names)
+        "date",
+        "open",
+        "high",
+        "low",
+        "close",
+        "volume",
+        "trades",
+        "orderflow",
+        "imbalances",
+        "stacked_imbalances_bid",
+        "stacked_imbalances_ask",
+        "max_delta",
+        "min_delta",
+        "bid",
+        "ask",
+        "delta",
+        "total_trades",
+    ]
+    # Assert delta, bid, and ask values
+    assert pytest.approx(row["delta"]) == -50.519
+    assert row["bid"] == 219.961
+    assert row["ask"] == 169.442
+
+    # Assert the number of trades
+    assert len(row["trades"]) == 151
+
+    # Assert specific details of the first trade
+    t = row["trades"][0]
+    assert list(t.keys()) == ["timestamp", "id", "type", "side", "price", "amount", "cost", "date"]
+    assert trades["id"][0] == t["id"]
+    assert int(trades["timestamp"][0]) == int(t["timestamp"])
+    assert t["side"] == "sell"
+    assert t["id"] == "313881442"
+    assert t["price"] == 234.72
+
+
+def test_public_trades_put_volume_profile_into_ohlcv_candles(public_trades_list_simple, candles):
+    """
+    Tests the integration of volume profile data into OHLCV candles.
+
+    This test verifies that
+    the `trades_to_volumeprofile_with_total_delta_bid_ask`
+    function correctly calculates the volume profile and that
+    it correctly assigns the delta value from the volume profile to the
+    corresponding candle in the `candles` DataFrame.
+    """
+
+    # Convert the trade list to a DataFrame
+    trades_df = trades_list_to_df(public_trades_list_simple[DEFAULT_TRADES_COLUMNS].values.tolist())
+
+    # Generate the volume profile with the specified bin size
+    df = trades_to_volumeprofile_with_total_delta_bid_ask(trades_df, scale=BIN_SIZE_SCALE)
+
+    # Assert the delta value in the total-bid/delta response of the second candle
+    assert 0.14 == df.values.tolist()[1][2]
+
+    # Alternative assertion using `.iat` accessor (assuming correct assignment logic)
+    assert 0.14 == df["delta"].iat[1]
+
+
+def test_public_trades_binned_big_sample_list(public_trades_list):
+    """
+    Tests the `trades_to_volumeprofile_with_total_delta_bid_ask` function
+    with different bin sizes and verifies the generated DataFrame's structure and values.
+    """
+
+    # Define the bin size for the first test
+    BIN_SIZE_SCALE = 0.05
+
+    # Convert the trade list to a DataFrame
+    trades = trades_list_to_df(public_trades_list[DEFAULT_TRADES_COLUMNS].values.tolist())
+
+    # Generate the volume profile with the specified bin size
+    df = trades_to_volumeprofile_with_total_delta_bid_ask(trades, scale=BIN_SIZE_SCALE)
+
+    # Assert that the DataFrame has the expected columns
+    assert df.columns.tolist() == [
+        "bid",
+        "ask",
+        "delta",
+        "bid_amount",
+        "ask_amount",
+        "total_volume",
+        "total_trades",
+    ]
+
+    # Assert the number of rows in the DataFrame (expected 23 for this bin size)
+    assert len(df) == 23
+
+    # Assert that the index values are in ascending order and spaced correctly
+    assert all(df.index[i] < df.index[i + 1] for i in range(len(df) - 1))
+    assert df.index[0] + BIN_SIZE_SCALE == df.index[1]
+    assert (trades["price"].min() - BIN_SIZE_SCALE) < df.index[0] < trades["price"].max()
+    assert (df.index[0] + BIN_SIZE_SCALE) >= df.index[1]
+    assert (trades["price"].max() - BIN_SIZE_SCALE) < df.index[-1] < trades["price"].max()
+
+    # Assert specific values in the first and last rows of the DataFrame
+    assert 32 == df["bid"].iloc[0]  # bid price
+    assert 197.512 == df["bid_amount"].iloc[0]  # total bid amount
+    assert 88.98 == df["ask_amount"].iloc[0]  # total ask amount
+    assert 26 == df["ask"].iloc[0]  # ask price
+    # delta (bid amount - ask amount)
+    assert -108.532 == pytest.approx(df["delta"].iloc[0])
+
+    assert 3 == df["bid"].iloc[-1]  # bid price
+    assert 50.659 == df["bid_amount"].iloc[-1]  # total bid amount
+    assert 108.21 == df["ask_amount"].iloc[-1]  # total ask amount
+    assert 44 == df["ask"].iloc[-1]  # ask price
+    assert 57.551 == df["delta"].iloc[-1]  # delta (bid amount - ask amount)
+
+    # Repeat the process with a larger bin size
+    BIN_SIZE_SCALE = 1
+
+    # Generate the volume profile with the larger bin size
+    df = trades_to_volumeprofile_with_total_delta_bid_ask(trades, scale=BIN_SIZE_SCALE)
+
+    # Assert the number of rows in the DataFrame (expected 2 for this bin size)
+    assert len(df) == 2
+
+    # Repeat similar assertions for index ordering and spacing
+    assert all(df.index[i] < df.index[i + 1] for i in range(len(df) - 1))
+    assert (trades["price"].min() - BIN_SIZE_SCALE) < df.index[0] < trades["price"].max()
+    assert (df.index[0] + BIN_SIZE_SCALE) >= df.index[1]
+    assert (trades["price"].max() - BIN_SIZE_SCALE) < df.index[-1] < trades["price"].max()
+
+    # Assert the value in the last row of the DataFrame with the larger bin size
+    assert 1667.0 == df.index[-1]
+    assert 710.98 == df["bid_amount"].iat[0]
+    assert 111 == df["bid"].iat[0]
+    assert 52.7199999 == pytest.approx(df["delta"].iat[0])  # delta
+
+
+def test_public_trades_config_max_trades(
+    default_conf, populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
+):
+    dataframe = populate_dataframe_with_trades_dataframe.copy()
+    trades = populate_dataframe_with_trades_trades.copy()
+    default_conf["exchange"]["use_public_trades"] = True
+    orderflow_config = {
+        "timeframe": "5m",
+        "orderflow": {
+            "cache_size": 1000,
+            "max_candles": 1,
+            "scale": 0.005,
+            "imbalance_volume": 0,
+            "imbalance_ratio": 3,
+            "stacked_imbalance_range": 3,
+        },
+    }
+
+    df, _ = populate_dataframe_with_trades(None, default_conf | orderflow_config, dataframe, trades)
+    assert df.delta.count() == 1
+
+
+def test_public_trades_testdata_sanity(
+    candles,
+    public_trades_list,
+    public_trades_list_simple,
+    populate_dataframe_with_trades_dataframe,
+    populate_dataframe_with_trades_trades,
+):
+    assert 10999 == len(candles)
+    assert 1000 == len(public_trades_list)
+    assert 999 == len(populate_dataframe_with_trades_dataframe)
+    assert 293532 == len(populate_dataframe_with_trades_trades)
+
+    assert 7 == len(public_trades_list_simple)
+    assert (
+        5
+        == public_trades_list_simple.loc[
+            public_trades_list_simple["side"].str.contains("sell"), "id"
+        ].count()
+    )
+    assert (
+        2
+        == public_trades_list_simple.loc[
+            public_trades_list_simple["side"].str.contains("buy"), "id"
+        ].count()
+    )
+
+    assert public_trades_list.columns.tolist() == [
+        "timestamp",
+        "id",
+        "type",
+        "side",
+        "price",
+        "amount",
+        "cost",
+        "date",
+    ]
+
+    assert public_trades_list.columns.tolist() == [
+        "timestamp",
+        "id",
+        "type",
+        "side",
+        "price",
+        "amount",
+        "cost",
+        "date",
+    ]
+    assert public_trades_list_simple.columns.tolist() == [
+        "timestamp",
+        "id",
+        "type",
+        "side",
+        "price",
+        "amount",
+        "cost",
+        "date",
+    ]
+    assert populate_dataframe_with_trades_dataframe.columns.tolist() == [
+        "date",
+        "open",
+        "high",
+        "low",
+        "close",
+        "volume",
+    ]
+    assert populate_dataframe_with_trades_trades.columns.tolist() == [
+        "timestamp",
+        "id",
+        "type",
+        "side",
+        "price",
+        "amount",
+        "cost",
+        "date",
+    ]
+
+
+def test_analyze_with_orderflow(
+    default_conf_usdt,
+    mocker,
+    populate_dataframe_with_trades_dataframe,
+    populate_dataframe_with_trades_trades,
+):
+    ohlcv_history = populate_dataframe_with_trades_dataframe
+    # call without orderflow
+    strategy = StrategyTestV3(config=default_conf_usdt)
+    strategy.dp = DataProvider(default_conf_usdt, None, None)
+
+    mocker.patch.object(strategy.dp, "trades", return_value=populate_dataframe_with_trades_trades)
+    import freqtrade.data.converter.orderflow as orderflow_module
+
+    spy = mocker.spy(orderflow_module, "trades_to_volumeprofile_with_total_delta_bid_ask")
+
+    pair = "ETH/BTC"
+    df = strategy.advise_indicators(ohlcv_history, {"pair:": pair})
+    assert len(df) == len(ohlcv_history)
+    assert "open" in df.columns
+    assert spy.call_count == 0
+
+    # Not expected to run - shouldn't have added orderflow columns
+    for col in ORDERFLOW_ADDED_COLUMNS:
+        assert col not in df.columns, f"Column {col} found in df.columns"
+
+    default_conf_usdt["exchange"]["use_public_trades"] = True
+    default_conf_usdt["orderflow"] = {
+        "cache_size": 5,
+        "max_candles": 5,
+        "scale": 0.005,
+        "imbalance_volume": 0,
+        "imbalance_ratio": 3,
+        "stacked_imbalance_range": 3,
+    }
+
+    strategy.config = default_conf_usdt
+    # First round - builds cache
+    df1 = strategy.advise_indicators(ohlcv_history, {"pair": pair})
+    assert len(df1) == len(ohlcv_history)
+    assert "open" in df1.columns
+    assert spy.call_count == 5
+
+    for col in ORDERFLOW_ADDED_COLUMNS:
+        assert col in df1.columns, f"Column {col} not found in df.columns"
+
+        if col not in ("stacked_imbalances_bid", "stacked_imbalances_ask"):
+            assert df1[col].count() == 5, f"Column {col} has {df1[col].count()} non-NaN values"
+
+    assert len(strategy._cached_grouped_trades_per_pair[pair]) == 5
+
+    lastval_trades = df1.at[len(df1) - 1, "trades"]
+    assert isinstance(lastval_trades, list)
+    assert len(lastval_trades) == 122
+
+    lastval_of = df1.at[len(df1) - 1, "orderflow"]
+    assert isinstance(lastval_of, dict)
+
+    spy.reset_mock()
+    # Ensure caching works - call the same logic again.
+    df2 = strategy.advise_indicators(ohlcv_history, {"pair": pair})
+    assert len(df2) == len(ohlcv_history)
+    assert "open" in df2.columns
+    assert spy.call_count == 0
+    for col in ORDERFLOW_ADDED_COLUMNS:
+        assert col in df2.columns, f"Round2: Column {col} not found in df.columns"
+
+        if col not in ("stacked_imbalances_bid", "stacked_imbalances_ask"):
+            assert df2[col].count() == 5, (
+                f"Round2: Column {col} has {df2[col].count()} non-NaN values"
+            )
+
+    lastval_trade2 = df2.at[len(df2) - 1, "trades"]
+    assert isinstance(lastval_trade2, list)
+    assert len(lastval_trade2) == 122
+
+    lastval_of2 = df2.at[len(df2) - 1, "orderflow"]
+    assert isinstance(lastval_of2, dict)
+
+
+def test_stacked_imbalances_multiple_prices():
+    """Test that stacked imbalances correctly returns multiple price levels when present"""
+    # Test with empty result
+    df_no_stacks = pd.DataFrame(
+        {
+            "bid_imbalance": [False, False, True, False],
+            "ask_imbalance": [False, True, False, False],
+        },
+        index=[234.95, 234.96, 234.97, 234.98],
+    )
+    no_stacks = stacked_imbalance(df_no_stacks, "bid", stacked_imbalance_range=2)
+    assert no_stacks == []
+
+    # Create a sample DataFrame with known imbalances
+    df = pd.DataFrame(
+        {
+            "bid_imbalance": [True, True, True, False, False, True, True, False, True],
+            "ask_imbalance": [False, False, True, True, True, False, False, True, True],
+        },
+        index=[234.95, 234.96, 234.97, 234.98, 234.99, 235.00, 235.01, 235.02, 235.03],
+    )
+    # Test bid imbalances (should return prices in ascending order)
+    bid_prices = stacked_imbalance(df, "bid", stacked_imbalance_range=2)
+    assert bid_prices == [234.95, 234.96, 235.00]
+
+    # Test ask imbalances (should return prices in descending order)
+    ask_prices = stacked_imbalance(df, "ask", stacked_imbalance_range=2)
+    assert ask_prices == [234.97, 234.98, 235.02]
+
+    # Test with higher stacked_imbalance_range
+    bid_prices_higher = stacked_imbalance(df, "bid", stacked_imbalance_range=3)
+    assert bid_prices_higher == [234.95]
+
+
+def test_timeframe_to_DateOffset():
+    assert timeframe_to_DateOffset("1s") == pd.DateOffset(seconds=1)
+    assert timeframe_to_DateOffset("1m") == pd.DateOffset(minutes=1)
+    assert timeframe_to_DateOffset("5m") == pd.DateOffset(minutes=5)
+    assert timeframe_to_DateOffset("1h") == pd.DateOffset(hours=1)
+    assert timeframe_to_DateOffset("1d") == pd.DateOffset(days=1)
+    assert timeframe_to_DateOffset("1w") == pd.DateOffset(weeks=1)
+    assert timeframe_to_DateOffset("1M") == pd.DateOffset(months=1)
+    assert timeframe_to_DateOffset("1y") == pd.DateOffset(years=1)
diff --git a/tests/data/test_datahandler.py b/tests/data/test_datahandler.py
new file mode 100644
index 000000000..1cff8b54a
--- /dev/null
+++ b/tests/data/test_datahandler.py
@@ -0,0 +1,508 @@
+# pragma pylint: disable=missing-docstring, protected-access, C0103
+
+import re
+from datetime import datetime, timezone
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+from pandas import DataFrame, Timestamp
+from pandas.testing import assert_frame_equal
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import AVAILABLE_DATAHANDLERS
+from freqtrade.data.history.datahandlers.featherdatahandler import FeatherDataHandler
+from freqtrade.data.history.datahandlers.idatahandler import (
+    IDataHandler,
+    get_datahandler,
+    get_datahandlerclass,
+)
+from freqtrade.data.history.datahandlers.jsondatahandler import JsonDataHandler, JsonGzDataHandler
+from freqtrade.data.history.datahandlers.parquetdatahandler import ParquetDataHandler
+from freqtrade.enums import CandleType, TradingMode
+from freqtrade.exceptions import OperationalException
+from tests.conftest import log_has, log_has_re
+
+
+def test_datahandler_ohlcv_get_pairs(testdatadir):
+    pairs = FeatherDataHandler.ohlcv_get_pairs(testdatadir, "5m", candle_type=CandleType.SPOT)
+    # Convert to set to avoid failures due to sorting
+    assert set(pairs) == {
+        "UNITTEST/BTC",
+        "XLM/BTC",
+        "ETH/BTC",
+        "TRX/BTC",
+        "LTC/BTC",
+        "XMR/BTC",
+        "ZEC/BTC",
+        "ADA/BTC",
+        "ETC/BTC",
+        "NXT/BTC",
+        "DASH/BTC",
+        "XRP/ETH",
+    }
+
+    pairs = JsonGzDataHandler.ohlcv_get_pairs(testdatadir, "8m", candle_type=CandleType.SPOT)
+    assert set(pairs) == {"UNITTEST/BTC"}
+
+    pairs = FeatherDataHandler.ohlcv_get_pairs(testdatadir, "1h", candle_type=CandleType.MARK)
+    assert set(pairs) == {"UNITTEST/USDT:USDT", "XRP/USDT:USDT"}
+
+    pairs = JsonGzDataHandler.ohlcv_get_pairs(testdatadir, "1h", candle_type=CandleType.FUTURES)
+    assert set(pairs) == {"XRP/USDT:USDT"}
+
+
+@pytest.mark.parametrize(
+    "filename,pair,timeframe,candletype",
+    [
+        ("XMR_BTC-5m.json", "XMR_BTC", "5m", ""),
+        ("XMR_USDT-1h.h5", "XMR_USDT", "1h", ""),
+        ("BTC-PERP-1h.h5", "BTC-PERP", "1h", ""),
+        ("BTC_USDT-2h.jsongz", "BTC_USDT", "2h", ""),
+        ("BTC_USDT-2h-mark.jsongz", "BTC_USDT", "2h", "mark"),
+        ("XMR_USDT-1h-mark.h5", "XMR_USDT", "1h", "mark"),
+        ("XMR_USDT-1h-random.h5", "XMR_USDT", "1h", "random"),
+        ("BTC-PERP-1h-index.h5", "BTC-PERP", "1h", "index"),
+        ("XMR_USDT_USDT-1h-mark.h5", "XMR_USDT_USDT", "1h", "mark"),
+    ],
+)
+def test_datahandler_ohlcv_regex(filename, pair, timeframe, candletype):
+    regex = JsonDataHandler._OHLCV_REGEX
+
+    match = re.search(regex, filename)
+    assert len(match.groups()) > 1
+    assert match[1] == pair
+    assert match[2] == timeframe
+    assert match[3] == candletype
+
+
+@pytest.mark.parametrize(
+    "pair,expected",
+    [
+        ("XMR_USDT", "XMR/USDT"),
+        ("BTC_USDT", "BTC/USDT"),
+        ("USDT_BUSD", "USDT/BUSD"),
+        ("BTC_USDT_USDT", "BTC/USDT:USDT"),  # Futures
+        ("XRP_USDT_USDT", "XRP/USDT:USDT"),  # futures
+        ("BTC-PERP", "BTC-PERP"),
+        ("BTC-PERP_USDT", "BTC-PERP:USDT"),
+        ("UNITTEST_USDT", "UNITTEST/USDT"),
+    ],
+)
+def test_rebuild_pair_from_filename(pair, expected):
+    assert IDataHandler.rebuild_pair_from_filename(pair) == expected
+
+
+def test_datahandler_ohlcv_get_available_data(testdatadir):
+    paircombs = FeatherDataHandler.ohlcv_get_available_data(testdatadir, TradingMode.SPOT)
+    # Convert to set to avoid failures due to sorting
+    assert set(paircombs) == {
+        ("UNITTEST/BTC", "5m", CandleType.SPOT),
+        ("ETH/BTC", "5m", CandleType.SPOT),
+        ("XLM/BTC", "5m", CandleType.SPOT),
+        ("TRX/BTC", "5m", CandleType.SPOT),
+        ("LTC/BTC", "5m", CandleType.SPOT),
+        ("XMR/BTC", "5m", CandleType.SPOT),
+        ("ZEC/BTC", "5m", CandleType.SPOT),
+        ("UNITTEST/BTC", "1m", CandleType.SPOT),
+        ("ADA/BTC", "5m", CandleType.SPOT),
+        ("ETC/BTC", "5m", CandleType.SPOT),
+        ("NXT/BTC", "5m", CandleType.SPOT),
+        ("DASH/BTC", "5m", CandleType.SPOT),
+        ("XRP/ETH", "1m", CandleType.SPOT),
+        ("XRP/ETH", "5m", CandleType.SPOT),
+        ("UNITTEST/BTC", "30m", CandleType.SPOT),
+        ("UNITTEST/BTC", "8m", CandleType.SPOT),
+    }
+
+    paircombs = FeatherDataHandler.ohlcv_get_available_data(testdatadir, TradingMode.FUTURES)
+    # Convert to set to avoid failures due to sorting
+    assert set(paircombs) == {
+        ("UNITTEST/USDT:USDT", "1h", "mark"),
+        ("XRP/USDT:USDT", "5m", "futures"),
+        ("XRP/USDT:USDT", "1h", "futures"),
+        ("XRP/USDT:USDT", "1h", "mark"),
+        ("XRP/USDT:USDT", "8h", "mark"),
+        ("XRP/USDT:USDT", "8h", "funding_rate"),
+    }
+
+    paircombs = JsonGzDataHandler.ohlcv_get_available_data(testdatadir, TradingMode.SPOT)
+    assert set(paircombs) == {("UNITTEST/BTC", "8m", CandleType.SPOT)}
+
+
+def test_jsondatahandler_ohlcv_purge(mocker, testdatadir):
+    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
+    unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
+    dh = JsonGzDataHandler(testdatadir)
+    assert not dh.ohlcv_purge("UNITTEST/NONEXIST", "5m", "")
+    assert not dh.ohlcv_purge("UNITTEST/NONEXIST", "5m", candle_type="mark")
+    assert unlinkmock.call_count == 0
+
+    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
+    assert dh.ohlcv_purge("UNITTEST/NONEXIST", "5m", "")
+    assert dh.ohlcv_purge("UNITTEST/NONEXIST", "5m", candle_type="mark")
+    assert unlinkmock.call_count == 2
+
+
+def test_jsondatahandler_ohlcv_load(testdatadir, caplog):
+    dh = JsonDataHandler(testdatadir)
+
+    df = dh.ohlcv_load("UNITTEST/BTC", "1m", "spot")
+    assert len(df) > 0
+
+    #     # Failure case (empty array)
+    df1 = dh.ohlcv_load("NOPAIR/XXX", "4m", "spot")
+    assert len(df1) == 0
+    assert log_has("Could not load data for NOPAIR/XXX.", caplog)
+    assert df.columns.equals(df1.columns)
+
+
+def test_datahandler_ohlcv_data_min_max(testdatadir):
+    dh = JsonDataHandler(testdatadir)
+    min_max = dh.ohlcv_data_min_max("UNITTEST/BTC", "5m", "spot")
+    assert len(min_max) == 3
+
+    # Empty pair
+    min_max = dh.ohlcv_data_min_max("UNITTEST/BTC", "8m", "spot")
+    assert len(min_max) == 3
+    assert min_max[0] == datetime.fromtimestamp(0, tz=timezone.utc)
+    assert min_max[0] == min_max[1]
+    # Empty pair2
+    min_max = dh.ohlcv_data_min_max("NOPAIR/XXX", "41m", "spot")
+    assert len(min_max) == 3
+    assert min_max[0] == datetime.fromtimestamp(0, tz=timezone.utc)
+    assert min_max[0] == min_max[1]
+
+    # Existing pair ...
+    min_max = dh.ohlcv_data_min_max("UNITTEST/BTC", "1m", "spot")
+    assert len(min_max) == 3
+    assert min_max[0] == datetime(2017, 11, 4, 23, 2, tzinfo=timezone.utc)
+    assert min_max[1] == datetime(2017, 11, 14, 22, 59, tzinfo=timezone.utc)
+
+
+def test_datahandler__check_empty_df(testdatadir, caplog):
+    dh = JsonDataHandler(testdatadir)
+    expected_text = r"Price jump in UNITTEST/USDT, 1h, spot between"
+    df = DataFrame(
+        [
+            [
+                1511686200000,  # 8:50:00
+                8.794,  # open
+                8.948,  # high
+                8.794,  # low
+                8.88,  # close
+                2255,  # volume (in quote currency)
+            ],
+            [
+                1511686500000,  # 8:55:00
+                8.88,
+                8.942,
+                8.88,
+                8.893,
+                9911,
+            ],
+            [
+                1511687100000,  # 9:05:00
+                8.891,
+                8.893,
+                8.875,
+                8.877,
+                2251,
+            ],
+            [
+                1511687400000,  # 9:10:00
+                8.877,
+                8.883,
+                8.895,
+                8.817,
+                123551,
+            ],
+        ],
+        columns=["date", "open", "high", "low", "close", "volume"],
+    )
+
+    dh._check_empty_df(df, "UNITTEST/USDT", "1h", CandleType.SPOT, True, True)
+    assert not log_has_re(expected_text, caplog)
+    df = DataFrame(
+        [
+            [
+                1511686200000,  # 8:50:00
+                8.794,  # open
+                8.948,  # high
+                8.794,  # low
+                8.88,  # close
+                2255,  # volume (in quote currency)
+            ],
+            [
+                1511686500000,  # 8:55:00
+                8.88,
+                8.942,
+                8.88,
+                8.893,
+                9911,
+            ],
+            [
+                1511687100000,  # 9:05:00
+                889.1,  # Price jump by several decimals
+                889.3,
+                887.5,
+                887.7,
+                2251,
+            ],
+            [
+                1511687400000,  # 9:10:00
+                8.877,
+                8.883,
+                8.895,
+                8.817,
+                123551,
+            ],
+        ],
+        columns=["date", "open", "high", "low", "close", "volume"],
+    )
+
+    dh._check_empty_df(df, "UNITTEST/USDT", "1h", CandleType.SPOT, True, True)
+    assert log_has_re(expected_text, caplog)
+
+
+# @pytest.mark.parametrize('datahandler', [])
+@pytest.mark.skip("All datahandlers currently support trades data.")
+def test_datahandler_trades_not_supported(
+    datahandler,
+    testdatadir,
+):
+    # Currently disabled. Re-enable should a new provider not support trades data.
+    dh = get_datahandler(testdatadir, datahandler)
+    with pytest.raises(NotImplementedError):
+        dh.trades_load("UNITTEST/ETH")
+    with pytest.raises(NotImplementedError):
+        dh.trades_store("UNITTEST/ETH", MagicMock())
+
+
+def test_jsondatahandler_trades_load(testdatadir, caplog):
+    dh = JsonGzDataHandler(testdatadir)
+    logmsg = "Old trades format detected - converting"
+    dh.trades_load("XRP/ETH", TradingMode.SPOT)
+    assert not log_has(logmsg, caplog)
+
+    # Test conversation is happening
+    dh.trades_load("XRP/OLD", TradingMode.SPOT)
+    assert log_has(logmsg, caplog)
+
+
+@pytest.mark.parametrize("datahandler", AVAILABLE_DATAHANDLERS)
+def test_datahandler_ohlcv_append(
+    datahandler,
+    testdatadir,
+):
+    dh = get_datahandler(testdatadir, datahandler)
+    with pytest.raises(NotImplementedError):
+        dh.ohlcv_append("UNITTEST/ETH", "5m", DataFrame(), CandleType.SPOT)
+    with pytest.raises(NotImplementedError):
+        dh.ohlcv_append("UNITTEST/ETH", "5m", DataFrame(), CandleType.MARK)
+
+
+@pytest.mark.parametrize("datahandler", AVAILABLE_DATAHANDLERS)
+def test_datahandler_trades_append(datahandler, testdatadir):
+    dh = get_datahandler(testdatadir, datahandler)
+    with pytest.raises(NotImplementedError):
+        dh.trades_append("UNITTEST/ETH", DataFrame())
+
+
+@pytest.mark.parametrize(
+    "datahandler,expected",
+    [
+        ("jsongz", {"XRP/ETH", "XRP/OLD"}),
+        ("feather", {"XRP/ETH"}),
+        ("parquet", {"XRP/ETH"}),
+    ],
+)
+def test_datahandler_trades_get_pairs(testdatadir, datahandler, expected):
+    pairs = get_datahandlerclass(datahandler).trades_get_pairs(testdatadir)
+    # Convert to set to avoid failures due to sorting
+    assert set(pairs) == expected
+
+
+def test_hdf5datahandler_deprecated(testdatadir):
+    with pytest.raises(
+        OperationalException, match=r"DEPRECATED: The hdf5 dataformat is deprecated and has.*"
+    ):
+        get_datahandler(testdatadir, "hdf5")
+
+
+@pytest.mark.parametrize(
+    "pair,timeframe,candle_type,candle_append,startdt,enddt",
+    [
+        # Data goes from 2018-01-10 - 2018-01-30
+        ("UNITTEST/BTC", "5m", "spot", "", "2018-01-15", "2018-01-19"),
+        # Mark data goes from to 2021-11-15 2021-11-19
+        ("UNITTEST/USDT:USDT", "1h", "mark", "-mark", "2021-11-16", "2021-11-18"),
+    ],
+)
+@pytest.mark.parametrize("datahandler", ["feather", "parquet"])
+def test_generic_datahandler_ohlcv_load_and_resave(
+    datahandler,
+    mocker,
+    testdatadir,
+    tmp_path,
+    pair,
+    timeframe,
+    candle_type,
+    candle_append,
+    startdt,
+    enddt,
+    caplog,
+):
+    tmpdir2 = tmp_path
+    if candle_type not in ("", "spot"):
+        tmpdir2 = tmp_path / "futures"
+        tmpdir2.mkdir()
+    # Load data from one common file
+    dhbase = get_datahandler(testdatadir, "feather")
+    ohlcv = dhbase._ohlcv_load(pair, timeframe, None, candle_type=candle_type)
+    assert isinstance(ohlcv, DataFrame)
+    assert len(ohlcv) > 0
+
+    # Get data to test
+    dh = get_datahandler(testdatadir, datahandler)
+
+    file = tmpdir2 / f"UNITTEST_NEW-{timeframe}{candle_append}.{dh._get_file_extension()}"
+    assert not file.is_file()
+
+    dh1 = get_datahandler(tmp_path, datahandler)
+    dh1.ohlcv_store("UNITTEST/NEW", timeframe, ohlcv, candle_type=candle_type)
+    assert file.is_file()
+
+    assert not ohlcv[ohlcv["date"] < startdt].empty
+
+    timerange = TimeRange.parse_timerange(f"{startdt.replace('-', '')}-{enddt.replace('-', '')}")
+
+    ohlcv = dhbase.ohlcv_load(pair, timeframe, timerange=timerange, candle_type=candle_type)
+    ohlcv1 = dh1.ohlcv_load("UNITTEST/NEW", timeframe, timerange=timerange, candle_type=candle_type)
+
+    assert len(ohlcv) == len(ohlcv1)
+    assert ohlcv.equals(ohlcv1)
+    assert ohlcv[ohlcv["date"] < startdt].empty
+    assert ohlcv[ohlcv["date"] > enddt].empty
+
+    # Try loading inexisting file
+    ohlcv = dh1.ohlcv_load("UNITTEST/NONEXIST", timeframe, candle_type=candle_type)
+    assert ohlcv.empty
+
+    # Try loading a file that exists but errors
+    mocker.patch(
+        "freqtrade.data.history.datahandlers.featherdatahandler.read_feather",
+        side_effect=Exception("Test"),
+    )
+    mocker.patch(
+        "freqtrade.data.history.datahandlers.parquetdatahandler.read_parquet",
+        side_effect=Exception("Test"),
+    )
+    ohlcv_e = dh1.ohlcv_load("UNITTEST/NEW", timeframe, candle_type=candle_type)
+    assert ohlcv_e.empty
+    assert log_has_re("Error loading data from", caplog)
+
+
+@pytest.mark.parametrize("datahandler", ["jsongz", "feather", "parquet"])
+def test_datahandler_trades_load(testdatadir, datahandler):
+    dh = get_datahandler(testdatadir, datahandler)
+    trades = dh.trades_load("XRP/ETH", TradingMode.SPOT)
+    assert isinstance(trades, DataFrame)
+    assert trades.iloc[0]["timestamp"] == 1570752011620
+    assert trades.iloc[0]["date"] == Timestamp("2019-10-11 00:00:11.620000+0000")
+    assert trades.iloc[-1]["cost"] == 0.1986231
+
+    trades1 = dh.trades_load("UNITTEST/NONEXIST", TradingMode.SPOT)
+    assert isinstance(trades, DataFrame)
+    assert trades1.empty
+
+
+@pytest.mark.parametrize("datahandler", ["jsongz", "feather", "parquet"])
+def test_datahandler_trades_store(testdatadir, tmp_path, datahandler):
+    dh = get_datahandler(testdatadir, datahandler)
+    trades = dh.trades_load("XRP/ETH", TradingMode.SPOT)
+
+    dh1 = get_datahandler(tmp_path, datahandler)
+    dh1.trades_store("XRP/NEW", trades, TradingMode.SPOT)
+
+    file = tmp_path / f"XRP_NEW-trades.{dh1._get_file_extension()}"
+    assert file.is_file()
+    # Load trades back
+    trades_new = dh1.trades_load("XRP/NEW", TradingMode.SPOT)
+    assert_frame_equal(trades, trades_new, check_exact=True)
+    assert len(trades_new) == len(trades)
+
+
+@pytest.mark.parametrize("datahandler", ["jsongz", "feather", "parquet"])
+def test_datahandler_trades_purge(mocker, testdatadir, datahandler):
+    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
+    unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
+    dh = get_datahandler(testdatadir, datahandler)
+    assert not dh.trades_purge("UNITTEST/NONEXIST", TradingMode.SPOT)
+    assert unlinkmock.call_count == 0
+
+    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
+    assert dh.trades_purge("UNITTEST/NONEXIST", TradingMode.SPOT)
+    assert unlinkmock.call_count == 1
+
+
+def test_datahandler_trades_get_available_data(testdatadir):
+    paircombs = FeatherDataHandler.trades_get_available_data(testdatadir, TradingMode.SPOT)
+    # Convert to set to avoid failures due to sorting
+    assert set(paircombs) == {"XRP/ETH"}
+
+    paircombs = FeatherDataHandler.trades_get_available_data(testdatadir, TradingMode.FUTURES)
+    # Convert to set to avoid failures due to sorting
+    assert set(paircombs) == set()
+
+    paircombs = JsonGzDataHandler.trades_get_available_data(testdatadir, TradingMode.SPOT)
+    assert set(paircombs) == {"XRP/ETH", "XRP/OLD"}
+
+
+def test_datahandler_trades_data_min_max(testdatadir):
+    dh = FeatherDataHandler(testdatadir)
+    min_max = dh.trades_data_min_max("XRP/ETH", TradingMode.SPOT)
+    assert len(min_max) == 3
+
+    # Empty pair
+    min_max = dh.trades_data_min_max("NADA/ETH", TradingMode.SPOT)
+    assert len(min_max) == 3
+    assert min_max[0] == datetime.fromtimestamp(0, tz=timezone.utc)
+    assert min_max[0] == min_max[1]
+
+    # Existing pair ...
+    min_max = dh.trades_data_min_max("XRP/ETH", TradingMode.SPOT)
+    assert len(min_max) == 3
+    assert min_max[0] == datetime(2019, 10, 11, 0, 0, 11, 620000, tzinfo=timezone.utc)
+    assert min_max[1] == datetime(2019, 10, 13, 11, 19, 28, 844000, tzinfo=timezone.utc)
+
+
+def test_gethandlerclass():
+    cl = get_datahandlerclass("json")
+    assert cl == JsonDataHandler
+    assert issubclass(cl, IDataHandler)
+
+    cl = get_datahandlerclass("jsongz")
+    assert cl == JsonGzDataHandler
+    assert issubclass(cl, IDataHandler)
+    assert issubclass(cl, JsonDataHandler)
+
+    cl = get_datahandlerclass("feather")
+    assert cl == FeatherDataHandler
+    assert issubclass(cl, IDataHandler)
+
+    cl = get_datahandlerclass("parquet")
+    assert cl == ParquetDataHandler
+    assert issubclass(cl, IDataHandler)
+
+    with pytest.raises(ValueError, match=r"No datahandler for .*"):
+        get_datahandlerclass("DeadBeef")
+
+
+def test_get_datahandler(testdatadir):
+    dh = get_datahandler(testdatadir, "json")
+    assert isinstance(dh, JsonDataHandler)
+    dh = get_datahandler(testdatadir, "jsongz")
+    assert isinstance(dh, JsonGzDataHandler)
+    dh1 = get_datahandler(testdatadir, "jsongz", dh)
+    assert id(dh1) == id(dh)
diff --git a/tests/data/test_dataprovider.py b/tests/data/test_dataprovider.py
new file mode 100644
index 000000000..c2adb84bb
--- /dev/null
+++ b/tests/data/test_dataprovider.py
@@ -0,0 +1,614 @@
+from datetime import datetime, timezone
+from unittest.mock import MagicMock
+
+import pytest
+from pandas import DataFrame, Timestamp
+
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import CandleType, RunMode
+from freqtrade.exceptions import ExchangeError, OperationalException
+from freqtrade.plugins.pairlistmanager import PairListManager
+from tests.conftest import EXMS, generate_test_data, get_patched_exchange
+
+
+@pytest.mark.parametrize(
+    "candle_type",
+    [
+        "mark",
+        "",
+    ],
+)
+def test_dp_ohlcv(mocker, default_conf, ohlcv_history, candle_type):
+    default_conf["runmode"] = RunMode.DRY_RUN
+    timeframe = default_conf["timeframe"]
+    exchange = get_patched_exchange(mocker, default_conf)
+    candletype = CandleType.from_string(candle_type)
+    exchange._klines[("XRP/BTC", timeframe, candletype)] = ohlcv_history
+    exchange._klines[("UNITTEST/BTC", timeframe, candletype)] = ohlcv_history
+
+    dp = DataProvider(default_conf, exchange)
+    assert dp.runmode == RunMode.DRY_RUN
+    assert ohlcv_history.equals(dp.ohlcv("UNITTEST/BTC", timeframe, candle_type=candletype))
+    assert isinstance(dp.ohlcv("UNITTEST/BTC", timeframe, candle_type=candletype), DataFrame)
+    assert dp.ohlcv("UNITTEST/BTC", timeframe, candle_type=candletype) is not ohlcv_history
+    assert dp.ohlcv("UNITTEST/BTC", timeframe, copy=False, candle_type=candletype) is ohlcv_history
+    assert not dp.ohlcv("UNITTEST/BTC", timeframe, candle_type=candletype).empty
+    assert dp.ohlcv("NONSENSE/AAA", timeframe, candle_type=candletype).empty
+
+    # Test with and without parameter
+    assert dp.ohlcv("UNITTEST/BTC", timeframe, candle_type=candletype).equals(
+        dp.ohlcv("UNITTEST/BTC", candle_type=candle_type)
+    )
+
+    default_conf["runmode"] = RunMode.LIVE
+    dp = DataProvider(default_conf, exchange)
+    assert dp.runmode == RunMode.LIVE
+    assert isinstance(dp.ohlcv("UNITTEST/BTC", timeframe, candle_type=candle_type), DataFrame)
+
+    default_conf["runmode"] = RunMode.BACKTEST
+    dp = DataProvider(default_conf, exchange)
+    assert dp.runmode == RunMode.BACKTEST
+    assert dp.ohlcv("UNITTEST/BTC", timeframe, candle_type=candle_type).empty
+
+
+def test_historic_ohlcv(mocker, default_conf, ohlcv_history):
+    historymock = MagicMock(return_value=ohlcv_history)
+    mocker.patch("freqtrade.data.dataprovider.load_pair_history", historymock)
+
+    dp = DataProvider(default_conf, None)
+    data = dp.historic_ohlcv("UNITTEST/BTC", "5m")
+    assert isinstance(data, DataFrame)
+    assert historymock.call_count == 1
+    assert historymock.call_args_list[0][1]["timeframe"] == "5m"
+
+
+def test_historic_trades(mocker, default_conf, trades_history_df):
+    historymock = MagicMock(return_value=trades_history_df)
+    mocker.patch(
+        "freqtrade.data.history.datahandlers.featherdatahandler.FeatherDataHandler._trades_load",
+        historymock,
+    )
+
+    dp = DataProvider(default_conf, None)
+    # Live mode..
+    with pytest.raises(OperationalException, match=r"Exchange is not available to DataProvider\."):
+        dp.trades("UNITTEST/BTC", "5m")
+
+    exchange = get_patched_exchange(mocker, default_conf)
+    dp = DataProvider(default_conf, exchange)
+    data = dp.trades("UNITTEST/BTC", "5m")
+
+    assert isinstance(data, DataFrame)
+    assert len(data) == 0
+
+    # Switch to backtest mode
+    default_conf["runmode"] = RunMode.BACKTEST
+    default_conf["dataformat_trades"] = "feather"
+    exchange = get_patched_exchange(mocker, default_conf)
+    dp = DataProvider(default_conf, exchange)
+    data = dp.trades("UNITTEST/BTC", "5m")
+    assert isinstance(data, DataFrame)
+    assert len(data) == len(trades_history_df)
+
+
+def test_historic_ohlcv_dataformat(mocker, default_conf, ohlcv_history):
+    parquetloadmock = MagicMock(return_value=ohlcv_history)
+    featherloadmock = MagicMock(return_value=ohlcv_history)
+    mocker.patch(
+        "freqtrade.data.history.datahandlers.parquetdatahandler.ParquetDataHandler._ohlcv_load",
+        parquetloadmock,
+    )
+    mocker.patch(
+        "freqtrade.data.history.datahandlers.featherdatahandler.FeatherDataHandler._ohlcv_load",
+        featherloadmock,
+    )
+
+    default_conf["runmode"] = RunMode.BACKTEST
+    exchange = get_patched_exchange(mocker, default_conf)
+    dp = DataProvider(default_conf, exchange)
+    data = dp.historic_ohlcv("UNITTEST/BTC", "5m")
+    assert isinstance(data, DataFrame)
+    parquetloadmock.assert_not_called()
+    featherloadmock.assert_called_once()
+
+    # Switching to dataformat parquet
+    parquetloadmock.reset_mock()
+    featherloadmock.reset_mock()
+    default_conf["dataformat_ohlcv"] = "parquet"
+    dp = DataProvider(default_conf, exchange)
+    data = dp.historic_ohlcv("UNITTEST/BTC", "5m")
+    assert isinstance(data, DataFrame)
+    parquetloadmock.assert_called_once()
+    featherloadmock.assert_not_called()
+
+
+@pytest.mark.parametrize(
+    "candle_type",
+    [
+        "mark",
+        "futures",
+        "",
+    ],
+)
+def test_get_pair_dataframe(mocker, default_conf, ohlcv_history, candle_type):
+    default_conf["runmode"] = RunMode.DRY_RUN
+    timeframe = default_conf["timeframe"]
+    exchange = get_patched_exchange(mocker, default_conf)
+    candletype = CandleType.from_string(candle_type)
+    exchange._klines[("XRP/BTC", timeframe, candletype)] = ohlcv_history
+    exchange._klines[("UNITTEST/BTC", timeframe, candletype)] = ohlcv_history
+
+    dp = DataProvider(default_conf, exchange)
+    assert dp.runmode == RunMode.DRY_RUN
+    assert ohlcv_history.equals(
+        dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type)
+    )
+    assert ohlcv_history.equals(
+        dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candletype)
+    )
+    assert isinstance(
+        dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type), DataFrame
+    )
+    assert (
+        dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type)
+        is not ohlcv_history
+    )
+    assert not dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type).empty
+    assert dp.get_pair_dataframe("NONSENSE/AAA", timeframe, candle_type=candle_type).empty
+
+    # Test with and without parameter
+    assert dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type).equals(
+        dp.get_pair_dataframe("UNITTEST/BTC", candle_type=candle_type)
+    )
+
+    default_conf["runmode"] = RunMode.LIVE
+    dp = DataProvider(default_conf, exchange)
+    assert dp.runmode == RunMode.LIVE
+    assert isinstance(
+        dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type), DataFrame
+    )
+    assert dp.get_pair_dataframe("NONSENSE/AAA", timeframe, candle_type=candle_type).empty
+
+    historymock = MagicMock(return_value=ohlcv_history)
+    mocker.patch("freqtrade.data.dataprovider.load_pair_history", historymock)
+    default_conf["runmode"] = RunMode.BACKTEST
+    dp = DataProvider(default_conf, exchange)
+    assert dp.runmode == RunMode.BACKTEST
+    df = dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type)
+    assert isinstance(df, DataFrame)
+    assert len(df) == 3  # ohlcv_history mock has just 3 rows
+
+    dp._set_dataframe_max_date(ohlcv_history.iloc[-1]["date"])
+    df = dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type)
+    assert isinstance(df, DataFrame)
+    assert len(df) == 2  # ohlcv_history is limited to 2 rows now
+
+
+def test_available_pairs(mocker, default_conf, ohlcv_history):
+    exchange = get_patched_exchange(mocker, default_conf)
+    timeframe = default_conf["timeframe"]
+    exchange._klines[("XRP/BTC", timeframe)] = ohlcv_history
+    exchange._klines[("UNITTEST/BTC", timeframe)] = ohlcv_history
+
+    dp = DataProvider(default_conf, exchange)
+    assert len(dp.available_pairs) == 2
+    assert dp.available_pairs == [
+        ("XRP/BTC", timeframe),
+        ("UNITTEST/BTC", timeframe),
+    ]
+
+
+def test_producer_pairs(default_conf):
+    dataprovider = DataProvider(default_conf, None)
+
+    producer = "default"
+    whitelist = ["XRP/BTC", "ETH/BTC"]
+    assert len(dataprovider.get_producer_pairs(producer)) == 0
+
+    dataprovider._set_producer_pairs(whitelist, producer)
+    assert len(dataprovider.get_producer_pairs(producer)) == 2
+
+    new_whitelist = ["BTC/USDT"]
+    dataprovider._set_producer_pairs(new_whitelist, producer)
+    assert dataprovider.get_producer_pairs(producer) == new_whitelist
+
+    assert dataprovider.get_producer_pairs("bad") == []
+
+
+def test_get_producer_df(default_conf):
+    dataprovider = DataProvider(default_conf, None)
+    ohlcv_history = generate_test_data("5m", 150)
+    pair = "BTC/USDT"
+    timeframe = default_conf["timeframe"]
+    candle_type = CandleType.SPOT
+
+    empty_la = datetime.fromtimestamp(0, tz=timezone.utc)
+    now = datetime.now(timezone.utc)
+
+    # no data has been added, any request should return an empty dataframe
+    dataframe, la = dataprovider.get_producer_df(pair, timeframe, candle_type)
+    assert dataframe.empty
+    assert la == empty_la
+
+    # the data is added, should return that added dataframe
+    dataprovider._add_external_df(pair, ohlcv_history, now, timeframe, candle_type)
+    dataframe, la = dataprovider.get_producer_df(pair, timeframe, candle_type)
+    assert len(dataframe) > 0
+    assert la > empty_la
+
+    # no data on this producer, should return empty dataframe
+    dataframe, la = dataprovider.get_producer_df(pair, producer_name="bad")
+    assert dataframe.empty
+    assert la == empty_la
+
+    # non existent timeframe, empty dataframe
+    _dataframe, la = dataprovider.get_producer_df(pair, timeframe="1h")
+    assert dataframe.empty
+    assert la == empty_la
+
+
+def test_emit_df(mocker, default_conf, ohlcv_history):
+    mocker.patch("freqtrade.rpc.rpc_manager.RPCManager.__init__", MagicMock())
+    rpc_mock = mocker.patch("freqtrade.rpc.rpc_manager.RPCManager", MagicMock())
+    send_mock = mocker.patch("freqtrade.rpc.rpc_manager.RPCManager.send_msg", MagicMock())
+
+    dataprovider = DataProvider(default_conf, exchange=None, rpc=rpc_mock)
+    dataprovider_no_rpc = DataProvider(default_conf, exchange=None)
+
+    pair = "BTC/USDT"
+
+    # No emit yet
+    assert send_mock.call_count == 0
+
+    # Rpc is added, we call emit, should call send_msg
+    dataprovider._emit_df(pair, ohlcv_history, False)
+    assert send_mock.call_count == 1
+
+    send_mock.reset_mock()
+    dataprovider._emit_df(pair, ohlcv_history, True)
+    assert send_mock.call_count == 2
+
+    send_mock.reset_mock()
+
+    # No rpc added, emit called, should not call send_msg
+    dataprovider_no_rpc._emit_df(pair, ohlcv_history, False)
+    assert send_mock.call_count == 0
+
+
+def test_refresh(mocker, default_conf):
+    refresh_mock = mocker.patch(f"{EXMS}.refresh_latest_ohlcv")
+    mock_refresh_trades = mocker.patch(f"{EXMS}.refresh_latest_trades")
+
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    timeframe = default_conf["timeframe"]
+    pairs = [("XRP/BTC", timeframe), ("UNITTEST/BTC", timeframe)]
+
+    pairs_non_trad = [("ETH/USDT", timeframe), ("BTC/TUSD", "1h")]
+
+    dp = DataProvider(default_conf, exchange)
+    dp.refresh(pairs)
+    assert mock_refresh_trades.call_count == 0
+    assert refresh_mock.call_count == 1
+    assert len(refresh_mock.call_args[0]) == 1
+    assert len(refresh_mock.call_args[0][0]) == len(pairs)
+    assert refresh_mock.call_args[0][0] == pairs
+
+    refresh_mock.reset_mock()
+    dp.refresh(pairs, pairs_non_trad)
+    assert mock_refresh_trades.call_count == 0
+    assert refresh_mock.call_count == 1
+    assert len(refresh_mock.call_args[0]) == 1
+    assert len(refresh_mock.call_args[0][0]) == len(pairs) + len(pairs_non_trad)
+    assert refresh_mock.call_args[0][0] == pairs + pairs_non_trad
+
+    # Test with public trades
+    refresh_mock.reset_mock()
+    refresh_mock.reset_mock()
+    default_conf["exchange"]["use_public_trades"] = True
+    dp.refresh(pairs, pairs_non_trad)
+    assert mock_refresh_trades.call_count == 1
+    assert refresh_mock.call_count == 1
+
+
+def test_orderbook(mocker, default_conf, order_book_l2):
+    api_mock = MagicMock()
+    api_mock.fetch_l2_order_book = order_book_l2
+    exchange = get_patched_exchange(mocker, default_conf, api_mock=api_mock)
+
+    dp = DataProvider(default_conf, exchange)
+    res = dp.orderbook("ETH/BTC", 5)
+    assert order_book_l2.call_count == 1
+    assert order_book_l2.call_args_list[0][0][0] == "ETH/BTC"
+    assert order_book_l2.call_args_list[0][0][1] >= 5
+
+    assert isinstance(res, dict)
+    assert "bids" in res
+    assert "asks" in res
+
+
+def test_market(mocker, default_conf, markets):
+    api_mock = MagicMock()
+    api_mock.markets = markets
+    exchange = get_patched_exchange(mocker, default_conf, api_mock=api_mock)
+
+    dp = DataProvider(default_conf, exchange)
+    res = dp.market("ETH/BTC")
+
+    assert isinstance(res, dict)
+    assert "symbol" in res
+    assert res["symbol"] == "ETH/BTC"
+
+    res = dp.market("UNITTEST/BTC")
+    assert res is None
+
+
+def test_ticker(mocker, default_conf, tickers):
+    ticker_mock = MagicMock(return_value=tickers()["ETH/BTC"])
+    mocker.patch(f"{EXMS}.fetch_ticker", ticker_mock)
+    exchange = get_patched_exchange(mocker, default_conf)
+    dp = DataProvider(default_conf, exchange)
+    res = dp.ticker("ETH/BTC")
+    assert isinstance(res, dict)
+    assert "symbol" in res
+    assert res["symbol"] == "ETH/BTC"
+
+    ticker_mock = MagicMock(side_effect=ExchangeError("Pair not found"))
+    mocker.patch(f"{EXMS}.fetch_ticker", ticker_mock)
+    exchange = get_patched_exchange(mocker, default_conf)
+    dp = DataProvider(default_conf, exchange)
+    res = dp.ticker("UNITTEST/BTC")
+    assert res == {}
+
+
+def test_current_whitelist(mocker, default_conf, tickers):
+    # patch default conf to volumepairlist
+    default_conf["pairlists"][0] = {"method": "VolumePairList", "number_assets": 5}
+
+    mocker.patch.multiple(EXMS, exchange_has=MagicMock(return_value=True), get_tickers=tickers)
+    exchange = get_patched_exchange(mocker, default_conf)
+
+    pairlist = PairListManager(exchange, default_conf)
+    dp = DataProvider(default_conf, exchange, pairlist)
+
+    # Simulate volumepairs from exchange.
+    pairlist.refresh_pairlist()
+
+    assert dp.current_whitelist() == pairlist._whitelist
+    # The identity of the 2 lists should not be identical, but a copy
+    assert dp.current_whitelist() is not pairlist._whitelist
+
+    with pytest.raises(OperationalException):
+        dp = DataProvider(default_conf, exchange)
+        dp.current_whitelist()
+
+
+def test_get_analyzed_dataframe(mocker, default_conf, ohlcv_history):
+    default_conf["runmode"] = RunMode.DRY_RUN
+
+    timeframe = default_conf["timeframe"]
+    exchange = get_patched_exchange(mocker, default_conf)
+
+    dp = DataProvider(default_conf, exchange)
+    dp._set_cached_df("XRP/BTC", timeframe, ohlcv_history, CandleType.SPOT)
+    dp._set_cached_df("UNITTEST/BTC", timeframe, ohlcv_history, CandleType.SPOT)
+
+    assert dp.runmode == RunMode.DRY_RUN
+    dataframe, time = dp.get_analyzed_dataframe("UNITTEST/BTC", timeframe)
+    assert ohlcv_history.equals(dataframe)
+    assert isinstance(time, datetime)
+
+    dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
+    assert ohlcv_history.equals(dataframe)
+    assert isinstance(time, datetime)
+
+    dataframe, time = dp.get_analyzed_dataframe("NOTHING/BTC", timeframe)
+    assert dataframe.empty
+    assert isinstance(time, datetime)
+    assert time == datetime(1970, 1, 1, tzinfo=timezone.utc)
+
+    # Test backtest mode
+    default_conf["runmode"] = RunMode.BACKTEST
+    dp._set_dataframe_max_index("XRP/BTC", 1)
+    dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
+
+    assert len(dataframe) == 1
+
+    dp._set_dataframe_max_index("XRP/BTC", 2)
+    dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
+    assert len(dataframe) == 2
+
+    dp._set_dataframe_max_index("XRP/BTC", 3)
+    dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
+    assert len(dataframe) == 3
+
+    dp._set_dataframe_max_index("XRP/BTC", 500)
+    dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
+    assert len(dataframe) == len(ohlcv_history)
+
+
+def test_no_exchange_mode(default_conf):
+    dp = DataProvider(default_conf, None)
+
+    message = "Exchange is not available to DataProvider."
+
+    with pytest.raises(OperationalException, match=message):
+        dp.refresh([()])
+
+    with pytest.raises(OperationalException, match=message):
+        dp.ohlcv("XRP/USDT", "5m", "")
+
+    with pytest.raises(OperationalException, match=message):
+        dp.market("XRP/USDT")
+
+    with pytest.raises(OperationalException, match=message):
+        dp.ticker("XRP/USDT")
+
+    with pytest.raises(OperationalException, match=message):
+        dp.orderbook("XRP/USDT", 20)
+
+    with pytest.raises(OperationalException, match=message):
+        dp.available_pairs()
+
+
+def test_dp_send_msg(default_conf):
+    default_conf["runmode"] = RunMode.DRY_RUN
+
+    default_conf["timeframe"] = "1h"
+    dp = DataProvider(default_conf, None)
+    msg = "Test message"
+    dp.send_msg(msg)
+
+    assert msg in dp._msg_queue
+    dp._msg_queue.pop()
+    assert msg not in dp._msg_queue
+    # Message is not resent due to caching
+    dp.send_msg(msg)
+    assert msg not in dp._msg_queue
+    dp.send_msg(msg, always_send=True)
+    assert msg in dp._msg_queue
+
+    default_conf["runmode"] = RunMode.BACKTEST
+    dp = DataProvider(default_conf, None)
+    dp.send_msg(msg, always_send=True)
+    assert msg not in dp._msg_queue
+
+
+def test_dp__add_external_df(default_conf_usdt):
+    timeframe = "1h"
+    default_conf_usdt["timeframe"] = timeframe
+    dp = DataProvider(default_conf_usdt, None)
+    df = generate_test_data(timeframe, 24, "2022-01-01 00:00:00+00:00")
+    last_analyzed = datetime.now(timezone.utc)
+
+    res = dp._add_external_df("ETH/USDT", df, last_analyzed, timeframe, CandleType.SPOT)
+    assert res[0] is False
+    # Why 1000 ??
+    assert res[1] == 1000
+
+    # Hard add dataframe
+    dp._replace_external_df("ETH/USDT", df, last_analyzed, timeframe, CandleType.SPOT)
+    # BTC is not stored yet
+    res = dp._add_external_df("BTC/USDT", df, last_analyzed, timeframe, CandleType.SPOT)
+    assert res[0] is False
+    df_res, _ = dp.get_producer_df("ETH/USDT", timeframe, CandleType.SPOT)
+    assert len(df_res) == 24
+
+    # Add the same dataframe again - dataframe size shall not change.
+    res = dp._add_external_df("ETH/USDT", df, last_analyzed, timeframe, CandleType.SPOT)
+    assert res[0] is True
+    assert isinstance(res[1], int)
+    assert res[1] == 0
+    df, _ = dp.get_producer_df("ETH/USDT", timeframe, CandleType.SPOT)
+    assert len(df) == 24
+
+    # Add a new day.
+    df2 = generate_test_data(timeframe, 24, "2022-01-02 00:00:00+00:00")
+
+    res = dp._add_external_df("ETH/USDT", df2, last_analyzed, timeframe, CandleType.SPOT)
+    assert res[0] is True
+    assert isinstance(res[1], int)
+    assert res[1] == 0
+    df, _ = dp.get_producer_df("ETH/USDT", timeframe, CandleType.SPOT)
+    assert len(df) == 48
+
+    # Add a dataframe with a 12 hour offset - so 12 candles are overlapping, and 12 valid.
+    df3 = generate_test_data(timeframe, 24, "2022-01-02 12:00:00+00:00")
+
+    res = dp._add_external_df("ETH/USDT", df3, last_analyzed, timeframe, CandleType.SPOT)
+    assert res[0] is True
+    assert isinstance(res[1], int)
+    assert res[1] == 0
+    df, _ = dp.get_producer_df("ETH/USDT", timeframe, CandleType.SPOT)
+    # New length = 48 + 12 (since we have a 12 hour offset).
+    assert len(df) == 60
+    assert df.iloc[-1]["date"] == df3.iloc[-1]["date"]
+    assert df.iloc[-1]["date"] == Timestamp("2022-01-03 11:00:00+00:00")
+
+    # Generate 1 new candle
+    df4 = generate_test_data(timeframe, 1, "2022-01-03 12:00:00+00:00")
+    res = dp._add_external_df("ETH/USDT", df4, last_analyzed, timeframe, CandleType.SPOT)
+    # assert res[0] is True
+    # assert res[1] == 0
+    df, _ = dp.get_producer_df("ETH/USDT", timeframe, CandleType.SPOT)
+    # New length = 61 + 1
+    assert len(df) == 61
+    assert df.iloc[-2]["date"] == Timestamp("2022-01-03 11:00:00+00:00")
+    assert df.iloc[-1]["date"] == Timestamp("2022-01-03 12:00:00+00:00")
+
+    # Gap in the data ...
+    df4 = generate_test_data(timeframe, 1, "2022-01-05 00:00:00+00:00")
+    res = dp._add_external_df("ETH/USDT", df4, last_analyzed, timeframe, CandleType.SPOT)
+    assert res[0] is False
+    # 36 hours - from 2022-01-03 12:00:00+00:00 to 2022-01-05 00:00:00+00:00
+    assert isinstance(res[1], int)
+    assert res[1] == 36
+    df, _ = dp.get_producer_df("ETH/USDT", timeframe, CandleType.SPOT)
+    # New length = 61 + 1
+    assert len(df) == 61
+
+    # Empty dataframe
+    df4 = generate_test_data(timeframe, 0, "2022-01-05 00:00:00+00:00")
+    res = dp._add_external_df("ETH/USDT", df4, last_analyzed, timeframe, CandleType.SPOT)
+    assert res[0] is False
+    # 36 hours - from 2022-01-03 12:00:00+00:00 to 2022-01-05 00:00:00+00:00
+    assert isinstance(res[1], int)
+    assert res[1] == 0
+
+
+def test_dp_get_required_startup(default_conf_usdt):
+    timeframe = "1h"
+    default_conf_usdt["timeframe"] = timeframe
+    dp = DataProvider(default_conf_usdt, None)
+
+    # No FreqAI config
+    assert dp.get_required_startup("5m") == 0
+    assert dp.get_required_startup("1h") == 0
+    assert dp.get_required_startup("1d") == 0
+
+    dp._config["startup_candle_count"] = 20
+    assert dp.get_required_startup("5m") == 20
+    assert dp.get_required_startup("1h") == 20
+    assert dp.get_required_startup("1h") == 20
+
+    # With freqAI config
+
+    dp._config["freqai"] = {
+        "enabled": True,
+        "train_period_days": 20,
+        "feature_parameters": {
+            "indicator_periods_candles": [
+                5,
+                20,
+            ]
+        },
+    }
+    assert dp.get_required_startup("5m") == 5780
+    assert dp.get_required_startup("1h") == 500
+    assert dp.get_required_startup("1d") == 40
+
+    # FreqAI kindof ignores startup_candle_count if it's below indicator_periods_candles
+    dp._config["startup_candle_count"] = 0
+    assert dp.get_required_startup("5m") == 5780
+    assert dp.get_required_startup("1h") == 500
+    assert dp.get_required_startup("1d") == 40
+
+    dp._config["freqai"]["feature_parameters"]["indicator_periods_candles"][1] = 50
+    assert dp.get_required_startup("5m") == 5810
+    assert dp.get_required_startup("1h") == 530
+    assert dp.get_required_startup("1d") == 70
+
+    # scenario from issue https://github.com/freqtrade/freqtrade/issues/9432
+    dp._config["freqai"] = {
+        "enabled": True,
+        "train_period_days": 180,
+        "feature_parameters": {
+            "indicator_periods_candles": [
+                10,
+                20,
+            ]
+        },
+    }
+    dp._config["startup_candle_count"] = 40
+    assert dp.get_required_startup("5m") == 51880
+    assert dp.get_required_startup("1h") == 4360
+    assert dp.get_required_startup("1d") == 220
diff --git a/tests/data/test_download_data.py b/tests/data/test_download_data.py
new file mode 100644
index 000000000..16a3f5559
--- /dev/null
+++ b/tests/data/test_download_data.py
@@ -0,0 +1,117 @@
+from unittest.mock import MagicMock, PropertyMock
+
+import pytest
+
+from freqtrade.configuration.config_setup import setup_utils_configuration
+from freqtrade.data.history.history_utils import download_data_main
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import OperationalException
+from tests.conftest import EXMS, log_has_re, patch_exchange
+
+
+def test_download_data_main_no_markets(mocker, caplog):
+    dl_mock = mocker.patch(
+        "freqtrade.data.history.history_utils.refresh_backtest_ohlcv_data",
+        MagicMock(return_value=["ETH/BTC", "XRP/BTC"]),
+    )
+    patch_exchange(mocker, exchange="binance")
+    mocker.patch(f"{EXMS}.get_markets", return_value={})
+    config = setup_utils_configuration({"exchange": "binance"}, RunMode.UTIL_EXCHANGE)
+    config.update({"days": 20, "pairs": ["ETH/BTC", "XRP/BTC"], "timeframes": ["5m", "1h"]})
+    download_data_main(config)
+    assert dl_mock.call_count == 0
+    assert log_has_re("No pairs available for download..*", caplog)
+
+
+def test_download_data_main_all_pairs(mocker, markets):
+    dl_mock = mocker.patch(
+        "freqtrade.data.history.history_utils.refresh_backtest_ohlcv_data",
+        MagicMock(return_value=["ETH/BTC", "XRP/BTC"]),
+    )
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+
+    config = setup_utils_configuration({"exchange": "binance"}, RunMode.UTIL_EXCHANGE)
+    config.update({"pairs": [".*/USDT"], "timeframes": ["5m", "1h"]})
+    download_data_main(config)
+    expected = set(["BTC/USDT", "ETH/USDT", "XRP/USDT", "NEO/USDT", "TKN/USDT"])
+    assert set(dl_mock.call_args_list[0][1]["pairs"]) == expected
+    assert dl_mock.call_count == 1
+
+    dl_mock.reset_mock()
+
+    config.update({"pairs": [".*/USDT"], "timeframes": ["5m", "1h"], "include_inactive": True})
+    download_data_main(config)
+    expected = set(["BTC/USDT", "ETH/USDT", "LTC/USDT", "XRP/USDT", "NEO/USDT", "TKN/USDT"])
+    assert set(dl_mock.call_args_list[0][1]["pairs"]) == expected
+
+
+def test_download_data_main_trades(mocker):
+    dl_mock = mocker.patch(
+        "freqtrade.data.history.history_utils.refresh_backtest_trades_data",
+        MagicMock(return_value=[]),
+    )
+    convert_mock = mocker.patch(
+        "freqtrade.data.history.history_utils.convert_trades_to_ohlcv", MagicMock(return_value=[])
+    )
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_markets", return_value={"ETH/BTC": {}, "XRP/BTC": {}})
+    config = setup_utils_configuration({"exchange": "binance"}, RunMode.UTIL_EXCHANGE)
+    config.update(
+        {
+            "days": 20,
+            "pairs": ["ETH/BTC", "XRP/BTC"],
+            "timeframes": ["5m", "1h"],
+            "download_trades": True,
+        }
+    )
+    download_data_main(config)
+
+    assert dl_mock.call_args[1]["timerange"].starttype == "date"
+    assert dl_mock.call_count == 1
+    assert convert_mock.call_count == 0
+    dl_mock.reset_mock()
+
+    config.update(
+        {
+            "convert_trades": True,
+        }
+    )
+    download_data_main(config)
+
+    assert dl_mock.call_args[1]["timerange"].starttype == "date"
+    assert dl_mock.call_count == 1
+    assert convert_mock.call_count == 1
+
+    # Exchange that doesn't support historic downloads
+    config["exchange"]["name"] = "bybit"
+    with pytest.raises(OperationalException, match=r"Trade history not available for .*"):
+        download_data_main(config)
+
+
+def test_download_data_main_data_invalid(mocker):
+    patch_exchange(mocker, exchange="kraken")
+    mocker.patch(f"{EXMS}.get_markets", return_value={"ETH/BTC": {}})
+    config = setup_utils_configuration({"exchange": "kraken"}, RunMode.UTIL_EXCHANGE)
+    config.update(
+        {
+            "days": 20,
+            "pairs": ["ETH/BTC", "XRP/BTC"],
+            "timeframes": ["5m", "1h"],
+        }
+    )
+    with pytest.raises(OperationalException, match=r"Historic klines not available for .*"):
+        download_data_main(config)
+
+    patch_exchange(mocker, exchange="hyperliquid")
+    mocker.patch(f"{EXMS}.get_markets", return_value={"ETH/USDC": {}})
+    config2 = setup_utils_configuration({"exchange": "hyperliquid"}, RunMode.UTIL_EXCHANGE)
+    config2.update(
+        {
+            "days": 20,
+            "pairs": ["ETH/USDC", "XRP/USDC"],
+            "timeframes": ["5m", "1h"],
+        }
+    )
+    with pytest.raises(OperationalException, match=r"Historic data not available for .*"):
+        download_data_main(config2)
diff --git a/tests/data/test_entryexitanalysis.py b/tests/data/test_entryexitanalysis.py
new file mode 100644
index 000000000..63ac3c1a3
--- /dev/null
+++ b/tests/data/test_entryexitanalysis.py
@@ -0,0 +1,562 @@
+import logging
+from unittest.mock import MagicMock, PropertyMock
+
+import pandas as pd
+import pytest
+
+from freqtrade.commands.analyze_commands import start_analysis_entries_exits
+from freqtrade.commands.optimize_commands import start_backtesting
+from freqtrade.enums import ExitType
+from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.backtesting import Backtesting
+from tests.conftest import get_args, patch_exchange, patched_configuration_load_config_file
+
+
+@pytest.fixture(autouse=True)
+def entryexitanalysis_cleanup() -> None:
+    yield None
+
+    Backtesting.cleanup()
+
+
+def test_backtest_analysis_on_entry_and_rejected_signals_nomock(
+    default_conf, mocker, caplog, testdatadir, user_dir, capsys
+):
+    caplog.set_level(logging.INFO)
+    (user_dir / "backtest_results").mkdir(parents=True, exist_ok=True)
+
+    default_conf.update(
+        {
+            "use_exit_signal": True,
+            "exit_profit_only": False,
+            "exit_profit_offset": 0.0,
+            "ignore_roi_if_entry_signal": False,
+        }
+    )
+    patch_exchange(mocker)
+    result1 = pd.DataFrame(
+        {
+            "pair": ["ETH/BTC", "LTC/BTC", "ETH/BTC", "LTC/BTC"],
+            "profit_ratio": [0.025, 0.05, -0.1, -0.05],
+            "profit_abs": [0.5, 2.0, -4.0, -2.0],
+            "open_date": pd.to_datetime(
+                [
+                    "2018-01-29 18:40:00",
+                    "2018-01-30 03:30:00",
+                    "2018-01-30 08:10:00",
+                    "2018-01-31 13:30:00",
+                ],
+                utc=True,
+            ),
+            "close_date": pd.to_datetime(
+                [
+                    "2018-01-29 20:45:00",
+                    "2018-01-30 05:35:00",
+                    "2018-01-30 09:10:00",
+                    "2018-01-31 15:00:00",
+                ],
+                utc=True,
+            ),
+            "trade_duration": [235, 40, 60, 90],
+            "is_open": [False, False, False, False],
+            "stake_amount": [0.01, 0.01, 0.01, 0.01],
+            "open_rate": [0.104445, 0.10302485, 0.10302485, 0.10302485],
+            "close_rate": [0.104969, 0.103541, 0.102041, 0.102541],
+            "is_short": [False, False, False, False],
+            "enter_tag": [
+                "enter_tag_long_a",
+                "enter_tag_long_b",
+                "enter_tag_long_a",
+                "enter_tag_long_b",
+            ],
+            "exit_reason": [
+                ExitType.ROI.value,
+                ExitType.EXIT_SIGNAL.value,
+                ExitType.STOP_LOSS.value,
+                ExitType.TRAILING_STOP_LOSS.value,
+            ],
+        }
+    )
+
+    backtestmock = MagicMock(
+        side_effect=[
+            {
+                "results": result1,
+                "config": default_conf,
+                "locks": [],
+                "rejected_signals": 20,
+                "timedout_entry_orders": 0,
+                "timedout_exit_orders": 0,
+                "canceled_trade_entries": 0,
+                "canceled_entry_orders": 0,
+                "replaced_entry_orders": 0,
+                "final_balance": 1000,
+            }
+        ]
+    )
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["ETH/BTC", "LTC/BTC", "DASH/BTC"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest", backtestmock)
+
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--user-data-dir",
+        str(user_dir),
+        "--timeframe",
+        "5m",
+        "--timerange",
+        "1515560100-1517287800",
+        "--export",
+        "signals",
+        "--cache",
+        "none",
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+
+    captured = capsys.readouterr()
+    assert "BACKTESTING REPORT" in captured.out
+    assert "EXIT REASON STATS" in captured.out
+    assert "LEFT OPEN TRADES REPORT" in captured.out
+
+    base_args = [
+        "backtesting-analysis",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--user-data-dir",
+        str(user_dir),
+    ]
+
+    # test group 0 and indicator list
+    args = get_args(
+        [*base_args, "--analysis-groups", "0", "--indicator-list", "close", "rsi", "profit_abs"]
+    )
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert "LTC/BTC" in captured.out
+    assert "ETH/BTC" in captured.out
+    assert "enter_tag_long_a" in captured.out
+    assert "enter_tag_long_b" in captured.out
+    assert "exit_signal" in captured.out
+    assert "roi" in captured.out
+    assert "stop_loss" in captured.out
+    assert "trailing_stop_loss" in captured.out
+    assert "0.5" in captured.out
+    assert "-4" in captured.out
+    assert "-2" in captured.out
+    assert "-3.5" in captured.out
+    assert "50" in captured.out
+    assert "0" in captured.out
+    assert "0.016" in captured.out
+    assert "34.049" in captured.out
+    assert "0.104" in captured.out
+    assert "52.829" in captured.out
+    # assert indicator list
+    assert "close (entry)" in captured.out
+    assert "0.016" in captured.out
+    assert "rsi (entry)" in captured.out
+    assert "54.320" in captured.out
+    assert "close (exit)" in captured.out
+    assert "rsi (exit)" in captured.out
+    assert "52.829" in captured.out
+    assert "profit_abs" in captured.out
+
+    # test group 1
+    args = get_args([*base_args, "--analysis-groups", "1"])
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert "enter_tag_long_a" in captured.out
+    assert "enter_tag_long_b" in captured.out
+    assert "total_profit_pct" in captured.out
+    assert "-3.5" in captured.out
+    assert "-1.75" in captured.out
+    assert "-7.5" in captured.out
+    assert "-3.75" in captured.out
+    assert "0" in captured.out
+
+    # test group 2
+    args = get_args([*base_args, "--analysis-groups", "2"])
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert "enter_tag_long_a" in captured.out
+    assert "enter_tag_long_b" in captured.out
+    assert "exit_signal" in captured.out
+    assert "roi" in captured.out
+    assert "stop_loss" in captured.out
+    assert "trailing_stop_loss" in captured.out
+    assert "total_profit_pct" in captured.out
+    assert "-10" in captured.out
+    assert "-5" in captured.out
+    assert "2.5" in captured.out
+
+    # test group 3
+    args = get_args([*base_args, "--analysis-groups", "3"])
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert "LTC/BTC" in captured.out
+    assert "ETH/BTC" in captured.out
+    assert "enter_tag_long_a" in captured.out
+    assert "enter_tag_long_b" in captured.out
+    assert "total_profit_pct" in captured.out
+    assert "-7.5" in captured.out
+    assert "-3.75" in captured.out
+    assert "-1.75" in captured.out
+    assert "0" in captured.out
+    assert "2" in captured.out
+
+    # test group 4
+    args = get_args([*base_args, "--analysis-groups", "4"])
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert "LTC/BTC" in captured.out
+    assert "ETH/BTC" in captured.out
+    assert "enter_tag_long_a" in captured.out
+    assert "enter_tag_long_b" in captured.out
+    assert "exit_signal" in captured.out
+    assert "roi" in captured.out
+    assert "stop_loss" in captured.out
+    assert "trailing_stop_loss" in captured.out
+    assert "total_profit_pct" in captured.out
+    assert "-10" in captured.out
+    assert "-5" in captured.out
+    assert "-4" in captured.out
+    assert "0.5" in captured.out
+    assert "1" in captured.out
+    assert "2.5" in captured.out
+
+    # test group 5
+    args = get_args([*base_args, "--analysis-groups", "5"])
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert "exit_signal" in captured.out
+    assert "roi" in captured.out
+    assert "stop_loss" in captured.out
+    assert "trailing_stop_loss" in captured.out
+
+    # test date filtering
+    args = get_args(
+        [*base_args, "--analysis-groups", "0", "1", "2", "--timerange", "20180129-20180130"]
+    )
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert "enter_tag_long_a" in captured.out
+    assert "enter_tag_long_b" not in captured.out
+
+    # Due to the backtest mock, there's no rejected signals generated.
+    args = get_args([*base_args, "--rejected-signals"])
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert "no rejected signals" in captured.out
+
+
+def test_backtest_analysis_with_invalid_config(
+    default_conf, mocker, caplog, testdatadir, user_dir, capsys
+):
+    caplog.set_level(logging.INFO)
+    (user_dir / "backtest_results").mkdir(parents=True, exist_ok=True)
+
+    default_conf.update(
+        {
+            "use_exit_signal": True,
+            "exit_profit_only": False,
+            "exit_profit_offset": 0.0,
+            "ignore_roi_if_entry_signal": False,
+        }
+    )
+    patch_exchange(mocker)
+    result1 = pd.DataFrame(
+        {
+            "pair": ["ETH/BTC", "LTC/BTC", "ETH/BTC", "LTC/BTC"],
+            "profit_ratio": [0.025, 0.05, -0.1, -0.05],
+            "profit_abs": [0.5, 2.0, -4.0, -2.0],
+            "open_date": pd.to_datetime(
+                [
+                    "2018-01-29 18:40:00",
+                    "2018-01-30 03:30:00",
+                    "2018-01-30 08:10:00",
+                    "2018-01-31 13:30:00",
+                ],
+                utc=True,
+            ),
+            "close_date": pd.to_datetime(
+                [
+                    "2018-01-29 20:45:00",
+                    "2018-01-30 05:35:00",
+                    "2018-01-30 09:10:00",
+                    "2018-01-31 15:00:00",
+                ],
+                utc=True,
+            ),
+            "trade_duration": [235, 40, 60, 90],
+            "is_open": [False, False, False, False],
+            "stake_amount": [0.01, 0.01, 0.01, 0.01],
+            "open_rate": [0.104445, 0.10302485, 0.10302485, 0.10302485],
+            "close_rate": [0.104969, 0.103541, 0.102041, 0.102541],
+            "is_short": [False, False, False, False],
+            "enter_tag": [
+                "enter_tag_long_a",
+                "enter_tag_long_b",
+                "enter_tag_long_a",
+                "enter_tag_long_b",
+            ],
+            "exit_reason": [
+                ExitType.ROI.value,
+                ExitType.EXIT_SIGNAL.value,
+                ExitType.STOP_LOSS.value,
+                ExitType.TRAILING_STOP_LOSS.value,
+            ],
+        }
+    )
+
+    backtestmock = MagicMock(
+        side_effect=[
+            {
+                "results": result1,
+                "config": default_conf,
+                "locks": [],
+                "rejected_signals": 20,
+                "timedout_entry_orders": 0,
+                "timedout_exit_orders": 0,
+                "canceled_trade_entries": 0,
+                "canceled_entry_orders": 0,
+                "replaced_entry_orders": 0,
+                "final_balance": 1000,
+            }
+        ]
+    )
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["ETH/BTC", "LTC/BTC", "DASH/BTC"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest", backtestmock)
+
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--user-data-dir",
+        str(user_dir),
+        "--timeframe",
+        "5m",
+        "--timerange",
+        "1515560100-1517287800",
+        "--export",
+        "signals",
+        "--cache",
+        "none",
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+
+    captured = capsys.readouterr()
+    assert "BACKTESTING REPORT" in captured.out
+    assert "EXIT REASON STATS" in captured.out
+    assert "LEFT OPEN TRADES REPORT" in captured.out
+
+    base_args = [
+        "backtesting-analysis",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--user-data-dir",
+        str(user_dir),
+    ]
+
+    # test with both entry and exit only arguments
+    args = get_args(
+        [
+            *base_args,
+            "--analysis-groups",
+            "0",
+            "--indicator-list",
+            "close",
+            "rsi",
+            "profit_abs",
+            "--entry-only",
+            "--exit-only",
+        ]
+    )
+    with pytest.raises(
+        OperationalException,
+        match=r"Cannot use --entry-only and --exit-only at the same time. Please choose one.",
+    ):
+        start_analysis_entries_exits(args)
+
+
+def test_backtest_analysis_on_entry_and_rejected_signals_only_entry_signals(
+    default_conf, mocker, caplog, testdatadir, user_dir, capsys
+):
+    caplog.set_level(logging.INFO)
+    (user_dir / "backtest_results").mkdir(parents=True, exist_ok=True)
+
+    default_conf.update(
+        {
+            "use_exit_signal": True,
+            "exit_profit_only": False,
+            "exit_profit_offset": 0.0,
+            "ignore_roi_if_entry_signal": False,
+        }
+    )
+    patch_exchange(mocker)
+    result1 = pd.DataFrame(
+        {
+            "pair": ["ETH/BTC", "LTC/BTC", "ETH/BTC", "LTC/BTC"],
+            "profit_ratio": [0.025, 0.05, -0.1, -0.05],
+            "profit_abs": [0.5, 2.0, -4.0, -2.0],
+            "open_date": pd.to_datetime(
+                [
+                    "2018-01-29 18:40:00",
+                    "2018-01-30 03:30:00",
+                    "2018-01-30 08:10:00",
+                    "2018-01-31 13:30:00",
+                ],
+                utc=True,
+            ),
+            "close_date": pd.to_datetime(
+                [
+                    "2018-01-29 20:45:00",
+                    "2018-01-30 05:35:00",
+                    "2018-01-30 09:10:00",
+                    "2018-01-31 15:00:00",
+                ],
+                utc=True,
+            ),
+            "trade_duration": [235, 40, 60, 90],
+            "is_open": [False, False, False, False],
+            "stake_amount": [0.01, 0.01, 0.01, 0.01],
+            "open_rate": [0.104445, 0.10302485, 0.10302485, 0.10302485],
+            "close_rate": [0.104969, 0.103541, 0.102041, 0.102541],
+            "is_short": [False, False, False, False],
+            "enter_tag": [
+                "enter_tag_long_a",
+                "enter_tag_long_b",
+                "enter_tag_long_a",
+                "enter_tag_long_b",
+            ],
+            "exit_reason": [
+                ExitType.ROI.value,
+                ExitType.EXIT_SIGNAL.value,
+                ExitType.STOP_LOSS.value,
+                ExitType.TRAILING_STOP_LOSS.value,
+            ],
+        }
+    )
+
+    backtestmock = MagicMock(
+        side_effect=[
+            {
+                "results": result1,
+                "config": default_conf,
+                "locks": [],
+                "rejected_signals": 20,
+                "timedout_entry_orders": 0,
+                "timedout_exit_orders": 0,
+                "canceled_trade_entries": 0,
+                "canceled_entry_orders": 0,
+                "replaced_entry_orders": 0,
+                "final_balance": 1000,
+            }
+        ]
+    )
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["ETH/BTC", "LTC/BTC", "DASH/BTC"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest", backtestmock)
+
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--user-data-dir",
+        str(user_dir),
+        "--timeframe",
+        "5m",
+        "--timerange",
+        "1515560100-1517287800",
+        "--export",
+        "signals",
+        "--cache",
+        "none",
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+
+    captured = capsys.readouterr()
+    assert "BACKTESTING REPORT" in captured.out
+    assert "EXIT REASON STATS" in captured.out
+    assert "LEFT OPEN TRADES REPORT" in captured.out
+
+    base_args = [
+        "backtesting-analysis",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--user-data-dir",
+        str(user_dir),
+    ]
+
+    # test group 0 and indicator list
+    args = get_args(
+        [
+            *base_args,
+            "--analysis-groups",
+            "0",
+            "--indicator-list",
+            "close",
+            "rsi",
+            "profit_abs",
+            "--entry-only",
+        ]
+    )
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert "LTC/BTC" in captured.out
+    assert "ETH/BTC" in captured.out
+    assert "enter_tag_long_a" in captured.out
+    assert "enter_tag_long_b" in captured.out
+    assert "exit_signal" in captured.out
+    assert "roi" in captured.out
+    assert "stop_loss" in captured.out
+    assert "trailing_stop_loss" in captured.out
+    assert "0.5" in captured.out
+    assert "-4" in captured.out
+    assert "-2" in captured.out
+    assert "-3.5" in captured.out
+    assert "50" in captured.out
+    assert "0" in captured.out
+    assert "0.016" in captured.out
+    assert "34.049" in captured.out
+    assert "0.104" in captured.out
+    assert "52.829" in captured.out
+    # assert indicator list
+    assert "close" in captured.out
+    assert "close (entry)" not in captured.out
+    assert "0.016" in captured.out
+    assert "rsi (entry)" not in captured.out
+    assert "rsi" in captured.out
+    assert "54.320" in captured.out
+    assert "close (exit)" not in captured.out
+    assert "rsi (exit)" not in captured.out
+    assert "52.829" in captured.out
+    assert "profit_abs" in captured.out
diff --git a/tests/data/test_history.py b/tests/data/test_history.py
new file mode 100644
index 000000000..e9af7b226
--- /dev/null
+++ b/tests/data/test_history.py
@@ -0,0 +1,700 @@
+# pragma pylint: disable=missing-docstring, protected-access, C0103
+
+import json
+import logging
+import uuid
+from datetime import timedelta
+from pathlib import Path
+from shutil import copyfile
+from unittest.mock import MagicMock, PropertyMock
+
+import pytest
+from pandas import DataFrame
+from pandas.testing import assert_frame_equal
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DATETIME_PRINT_FORMAT
+from freqtrade.data.converter import ohlcv_to_dataframe
+from freqtrade.data.history import get_datahandler
+from freqtrade.data.history.datahandlers.jsondatahandler import JsonDataHandler, JsonGzDataHandler
+from freqtrade.data.history.history_utils import (
+    _download_pair_history,
+    _download_trades_history,
+    _load_cached_data_for_updating,
+    get_timerange,
+    load_data,
+    load_pair_history,
+    refresh_backtest_ohlcv_data,
+    refresh_backtest_trades_data,
+    refresh_data,
+    validate_backtest_data,
+)
+from freqtrade.enums import CandleType, TradingMode
+from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.misc import file_dump_json
+from freqtrade.resolvers import StrategyResolver
+from freqtrade.util import dt_ts, dt_utc
+from tests.conftest import (
+    CURRENT_TEST_STRATEGY,
+    EXMS,
+    get_patched_exchange,
+    log_has,
+    log_has_re,
+    patch_exchange,
+)
+
+
+def _clean_test_file(file: Path) -> None:
+    """
+    Backup existing file to avoid deleting the user file
+    :param file: complete path to the file
+    :return: None
+    """
+    file_swp = Path(str(file) + ".swp")
+    # 1. Delete file from the test
+    if file.is_file():
+        file.unlink()
+
+    # 2. Rollback to the initial file
+    if file_swp.is_file():
+        file_swp.rename(file)
+
+
+def test_load_data_30min_timeframe(caplog, testdatadir) -> None:
+    ld = load_pair_history(pair="UNITTEST/BTC", timeframe="30m", datadir=testdatadir)
+    assert isinstance(ld, DataFrame)
+    assert not log_has(
+        'Download history data for pair: "UNITTEST/BTC", timeframe: 30m and store in None.',
+        caplog,
+    )
+
+
+def test_load_data_7min_timeframe(caplog, testdatadir) -> None:
+    ld = load_pair_history(pair="UNITTEST/BTC", timeframe="7m", datadir=testdatadir)
+    assert isinstance(ld, DataFrame)
+    assert ld.empty
+    assert log_has(
+        "No history for UNITTEST/BTC, spot, 7m found. "
+        "Use `freqtrade download-data` to download the data",
+        caplog,
+    )
+
+
+def test_load_data_1min_timeframe(ohlcv_history, mocker, caplog, testdatadir) -> None:
+    mocker.patch(f"{EXMS}.get_historic_ohlcv", return_value=ohlcv_history)
+    file = testdatadir / "UNITTEST_BTC-1m.feather"
+    load_data(datadir=testdatadir, timeframe="1m", pairs=["UNITTEST/BTC"])
+    assert file.is_file()
+    assert not log_has(
+        'Download history data for pair: "UNITTEST/BTC", interval: 1m and store in None.', caplog
+    )
+
+
+def test_load_data_mark(ohlcv_history, mocker, caplog, testdatadir) -> None:
+    mocker.patch(f"{EXMS}.get_historic_ohlcv", return_value=ohlcv_history)
+    file = testdatadir / "futures/UNITTEST_USDT_USDT-1h-mark.feather"
+    load_data(datadir=testdatadir, timeframe="1h", pairs=["UNITTEST/BTC"], candle_type="mark")
+    assert file.is_file()
+    assert not log_has(
+        'Download history data for pair: "UNITTEST/USDT:USDT", interval: 1m and store in None.',
+        caplog,
+    )
+
+
+def test_load_data_startup_candles(mocker, testdatadir) -> None:
+    ltfmock = mocker.patch(
+        "freqtrade.data.history.datahandlers.featherdatahandler.FeatherDataHandler._ohlcv_load",
+        MagicMock(return_value=DataFrame()),
+    )
+    timerange = TimeRange("date", None, 1510639620, 0)
+    load_pair_history(
+        pair="UNITTEST/BTC",
+        timeframe="1m",
+        datadir=testdatadir,
+        timerange=timerange,
+        startup_candles=20,
+    )
+
+    assert ltfmock.call_count == 1
+    assert ltfmock.call_args_list[0][1]["timerange"] != timerange
+    # startts is 20 minutes earlier
+    assert ltfmock.call_args_list[0][1]["timerange"].startts == timerange.startts - 20 * 60
+
+
+@pytest.mark.parametrize("candle_type", ["mark", ""])
+def test_load_data_with_new_pair_1min(
+    ohlcv_history, mocker, caplog, default_conf, tmp_path, candle_type
+) -> None:
+    """
+    Test load_pair_history() with 1 min timeframe
+    """
+    exchange = get_patched_exchange(mocker, default_conf)
+    mocker.patch.object(exchange, "get_historic_ohlcv", return_value=ohlcv_history)
+    file = tmp_path / "MEME_BTC-1m.feather"
+
+    # do not download a new pair if refresh_pairs isn't set
+    load_pair_history(datadir=tmp_path, timeframe="1m", pair="MEME/BTC", candle_type=candle_type)
+    assert not file.is_file()
+    assert log_has(
+        f"No history for MEME/BTC, {candle_type}, 1m found. "
+        "Use `freqtrade download-data` to download the data",
+        caplog,
+    )
+
+    # download a new pair if refresh_pairs is set
+    refresh_data(
+        datadir=tmp_path,
+        timeframe="1m",
+        pairs=["MEME/BTC"],
+        exchange=exchange,
+        candle_type=CandleType.SPOT,
+    )
+    load_pair_history(datadir=tmp_path, timeframe="1m", pair="MEME/BTC", candle_type=candle_type)
+    assert file.is_file()
+    assert log_has_re(r'Download history data for "MEME/BTC", 1m, ' r"spot and store in .*", caplog)
+
+
+def test_testdata_path(testdatadir) -> None:
+    assert str(Path("tests") / "testdata") in str(testdatadir)
+
+
+@pytest.mark.parametrize(
+    "pair,timeframe,expected_result,candle_type",
+    [
+        ("ETH/BTC", "5m", "freqtrade/hello/world/ETH_BTC-5m.json", ""),
+        ("ETH/USDT", "1M", "freqtrade/hello/world/ETH_USDT-1Mo.json", ""),
+        ("Fabric Token/ETH", "5m", "freqtrade/hello/world/Fabric_Token_ETH-5m.json", ""),
+        ("ETHH20", "5m", "freqtrade/hello/world/ETHH20-5m.json", ""),
+        (".XBTBON2H", "5m", "freqtrade/hello/world/_XBTBON2H-5m.json", ""),
+        ("ETHUSD.d", "5m", "freqtrade/hello/world/ETHUSD_d-5m.json", ""),
+        ("ACC_OLD/BTC", "5m", "freqtrade/hello/world/ACC_OLD_BTC-5m.json", ""),
+        ("ETH/BTC", "5m", "freqtrade/hello/world/futures/ETH_BTC-5m-mark.json", "mark"),
+        ("ACC_OLD/BTC", "5m", "freqtrade/hello/world/futures/ACC_OLD_BTC-5m-index.json", "index"),
+    ],
+)
+def test_json_pair_data_filename(pair, timeframe, expected_result, candle_type):
+    fn = JsonDataHandler._pair_data_filename(
+        Path("freqtrade/hello/world"), pair, timeframe, CandleType.from_string(candle_type)
+    )
+    assert isinstance(fn, Path)
+    assert fn == Path(expected_result)
+    fn = JsonGzDataHandler._pair_data_filename(
+        Path("freqtrade/hello/world"),
+        pair,
+        timeframe,
+        candle_type=CandleType.from_string(candle_type),
+    )
+    assert isinstance(fn, Path)
+    assert fn == Path(expected_result + ".gz")
+
+
+@pytest.mark.parametrize(
+    "pair,trading_mode,expected_result",
+    [
+        ("ETH/BTC", "", "freqtrade/hello/world/ETH_BTC-trades.json"),
+        ("ETH/USDT:USDT", "futures", "freqtrade/hello/world/futures/ETH_USDT_USDT-trades.json"),
+        ("Fabric Token/ETH", "", "freqtrade/hello/world/Fabric_Token_ETH-trades.json"),
+        ("ETHH20", "", "freqtrade/hello/world/ETHH20-trades.json"),
+        (".XBTBON2H", "", "freqtrade/hello/world/_XBTBON2H-trades.json"),
+        ("ETHUSD.d", "", "freqtrade/hello/world/ETHUSD_d-trades.json"),
+        ("ACC_OLD_BTC", "", "freqtrade/hello/world/ACC_OLD_BTC-trades.json"),
+    ],
+)
+def test_json_pair_trades_filename(pair, trading_mode, expected_result):
+    fn = JsonDataHandler._pair_trades_filename(Path("freqtrade/hello/world"), pair, trading_mode)
+    assert isinstance(fn, Path)
+    assert fn == Path(expected_result)
+
+    fn = JsonGzDataHandler._pair_trades_filename(Path("freqtrade/hello/world"), pair, trading_mode)
+    assert isinstance(fn, Path)
+    assert fn == Path(expected_result + ".gz")
+
+
+def test_load_cached_data_for_updating(testdatadir) -> None:
+    data_handler = get_datahandler(testdatadir, "json")
+
+    test_data = None
+    test_filename = testdatadir.joinpath("UNITTEST_BTC-1m.json")
+    with test_filename.open("rt") as file:
+        test_data = json.load(file)
+
+    test_data_df = ohlcv_to_dataframe(
+        test_data, "1m", "UNITTEST/BTC", fill_missing=False, drop_incomplete=False
+    )
+    # now = last cached item + 1 hour
+    now_ts = test_data[-1][0] / 1000 + 60 * 60
+
+    # timeframe starts earlier than the cached data
+    # Update timestamp to candle end date
+    timerange = TimeRange("date", None, test_data[0][0] / 1000 - 1, 0)
+    data, start_ts, end_ts = _load_cached_data_for_updating(
+        "UNITTEST/BTC", "1m", timerange, data_handler, CandleType.SPOT
+    )
+    assert not data.empty
+    # Last candle was removed - so 1 candle overlap
+    assert start_ts == test_data[-1][0] - 60 * 1000
+    assert end_ts is None
+
+    # timeframe starts earlier than the cached data - prepending
+
+    timerange = TimeRange("date", None, test_data[0][0] / 1000 - 1, 0)
+    data, start_ts, end_ts = _load_cached_data_for_updating(
+        "UNITTEST/BTC", "1m", timerange, data_handler, CandleType.SPOT, True
+    )
+    assert_frame_equal(data, test_data_df.iloc[:-1])
+    assert start_ts == test_data[0][0] - 1000
+    assert end_ts == test_data[0][0]
+
+    # timeframe starts in the center of the cached data
+    # should return the cached data w/o the last item
+    timerange = TimeRange("date", None, test_data[0][0] / 1000 + 1, 0)
+    data, start_ts, end_ts = _load_cached_data_for_updating(
+        "UNITTEST/BTC", "1m", timerange, data_handler, CandleType.SPOT
+    )
+
+    assert_frame_equal(data, test_data_df.iloc[:-1])
+    assert test_data[-2][0] <= start_ts < test_data[-1][0]
+    assert end_ts is None
+
+    # timeframe starts after the cached data
+    # should return the cached data w/o the last item
+    timerange = TimeRange("date", None, test_data[-1][0] / 1000 + 100, 0)
+    data, start_ts, end_ts = _load_cached_data_for_updating(
+        "UNITTEST/BTC", "1m", timerange, data_handler, CandleType.SPOT
+    )
+    assert_frame_equal(data, test_data_df.iloc[:-1])
+    assert test_data[-2][0] <= start_ts < test_data[-1][0]
+    assert end_ts is None
+
+    # no datafile exist
+    # should return timestamp start time
+    timerange = TimeRange("date", None, now_ts - 10000, 0)
+    data, start_ts, end_ts = _load_cached_data_for_updating(
+        "NONEXIST/BTC", "1m", timerange, data_handler, CandleType.SPOT
+    )
+    assert data.empty
+    assert start_ts == (now_ts - 10000) * 1000
+    assert end_ts is None
+
+    # no datafile exist
+    # should return timestamp start and end time time
+    timerange = TimeRange("date", "date", now_ts - 1000000, now_ts - 100000)
+    data, start_ts, end_ts = _load_cached_data_for_updating(
+        "NONEXIST/BTC", "1m", timerange, data_handler, CandleType.SPOT
+    )
+    assert data.empty
+    assert start_ts == (now_ts - 1000000) * 1000
+    assert end_ts == (now_ts - 100000) * 1000
+
+    # no datafile exist, no timeframe is set
+    # should return an empty array and None
+    data, start_ts, end_ts = _load_cached_data_for_updating(
+        "NONEXIST/BTC", "1m", None, data_handler, CandleType.SPOT
+    )
+    assert data.empty
+    assert start_ts is None
+    assert end_ts is None
+
+
+@pytest.mark.parametrize(
+    "candle_type,subdir,file_tail",
+    [
+        ("mark", "futures/", "-mark"),
+        ("spot", "", ""),
+    ],
+)
+def test_download_pair_history(
+    ohlcv_history, mocker, default_conf, tmp_path, candle_type, subdir, file_tail
+) -> None:
+    exchange = get_patched_exchange(mocker, default_conf)
+    mocker.patch.object(exchange, "get_historic_ohlcv", return_value=ohlcv_history)
+    file1_1 = tmp_path / f"{subdir}MEME_BTC-1m{file_tail}.feather"
+    file1_5 = tmp_path / f"{subdir}MEME_BTC-5m{file_tail}.feather"
+    file2_1 = tmp_path / f"{subdir}CFI_BTC-1m{file_tail}.feather"
+    file2_5 = tmp_path / f"{subdir}CFI_BTC-5m{file_tail}.feather"
+
+    assert not file1_1.is_file()
+    assert not file2_1.is_file()
+
+    assert _download_pair_history(
+        datadir=tmp_path,
+        exchange=exchange,
+        pair="MEME/BTC",
+        timeframe="1m",
+        candle_type=candle_type,
+    )
+    assert _download_pair_history(
+        datadir=tmp_path, exchange=exchange, pair="CFI/BTC", timeframe="1m", candle_type=candle_type
+    )
+    assert not exchange._pairs_last_refresh_time
+    assert file1_1.is_file()
+    assert file2_1.is_file()
+
+    # clean files freshly downloaded
+    _clean_test_file(file1_1)
+    _clean_test_file(file2_1)
+
+    assert not file1_5.is_file()
+    assert not file2_5.is_file()
+
+    assert _download_pair_history(
+        datadir=tmp_path,
+        exchange=exchange,
+        pair="MEME/BTC",
+        timeframe="5m",
+        candle_type=candle_type,
+    )
+    assert _download_pair_history(
+        datadir=tmp_path, exchange=exchange, pair="CFI/BTC", timeframe="5m", candle_type=candle_type
+    )
+    assert not exchange._pairs_last_refresh_time
+    assert file1_5.is_file()
+    assert file2_5.is_file()
+
+
+def test_download_pair_history2(mocker, default_conf, testdatadir, ohlcv_history) -> None:
+    json_dump_mock = mocker.patch(
+        "freqtrade.data.history.datahandlers.featherdatahandler.FeatherDataHandler.ohlcv_store",
+        return_value=None,
+    )
+    exchange = get_patched_exchange(mocker, default_conf)
+    mocker.patch.object(exchange, "get_historic_ohlcv", return_value=ohlcv_history)
+    _download_pair_history(
+        datadir=testdatadir,
+        exchange=exchange,
+        pair="UNITTEST/BTC",
+        timeframe="1m",
+        candle_type="spot",
+    )
+    _download_pair_history(
+        datadir=testdatadir,
+        exchange=exchange,
+        pair="UNITTEST/BTC",
+        timeframe="3m",
+        candle_type="spot",
+    )
+    _download_pair_history(
+        datadir=testdatadir,
+        exchange=exchange,
+        pair="UNITTEST/USDT",
+        timeframe="1h",
+        candle_type="mark",
+    )
+    assert json_dump_mock.call_count == 3
+
+
+def test_download_backtesting_data_exception(mocker, caplog, default_conf, tmp_path) -> None:
+    mocker.patch(f"{EXMS}.get_historic_ohlcv", side_effect=Exception("File Error"))
+    exchange = get_patched_exchange(mocker, default_conf)
+
+    assert not _download_pair_history(
+        datadir=tmp_path, exchange=exchange, pair="MEME/BTC", timeframe="1m", candle_type="spot"
+    )
+    assert log_has('Failed to download history data for pair: "MEME/BTC", timeframe: 1m.', caplog)
+
+
+def test_load_partial_missing(testdatadir, caplog) -> None:
+    # Make sure we start fresh - test missing data at start
+    start = dt_utc(2018, 1, 1)
+    end = dt_utc(2018, 1, 11)
+    data = load_data(
+        testdatadir,
+        "5m",
+        ["UNITTEST/BTC"],
+        startup_candles=20,
+        timerange=TimeRange("date", "date", start.timestamp(), end.timestamp()),
+    )
+    assert log_has("Using indicator startup period: 20 ...", caplog)
+    # timedifference in 5 minutes
+    td = ((end - start).total_seconds() // 60 // 5) + 1
+    assert td != len(data["UNITTEST/BTC"])
+    start_real = data["UNITTEST/BTC"].iloc[0, 0]
+    assert log_has(
+        f"UNITTEST/BTC, spot, 5m, data starts at {start_real.strftime(DATETIME_PRINT_FORMAT)}",
+        caplog,
+    )
+    # Make sure we start fresh - test missing data at end
+    caplog.clear()
+    start = dt_utc(2018, 1, 10)
+    end = dt_utc(2018, 2, 20)
+    data = load_data(
+        datadir=testdatadir,
+        timeframe="5m",
+        pairs=["UNITTEST/BTC"],
+        timerange=TimeRange("date", "date", start.timestamp(), end.timestamp()),
+    )
+    # timedifference in 5 minutes
+    td = ((end - start).total_seconds() // 60 // 5) + 1
+    assert td != len(data["UNITTEST/BTC"])
+
+    # Shift endtime with +5
+    end_real = data["UNITTEST/BTC"].iloc[-1, 0].to_pydatetime()
+    assert log_has(
+        f"UNITTEST/BTC, spot, 5m, data ends at {end_real.strftime(DATETIME_PRINT_FORMAT)}",
+        caplog,
+    )
+
+
+def test_init(default_conf) -> None:
+    assert {} == load_data(datadir=Path(), pairs=[], timeframe=default_conf["timeframe"])
+
+
+def test_init_with_refresh(default_conf, mocker) -> None:
+    exchange = get_patched_exchange(mocker, default_conf)
+    refresh_data(
+        datadir=Path(),
+        pairs=[],
+        timeframe=default_conf["timeframe"],
+        exchange=exchange,
+        candle_type=CandleType.SPOT,
+    )
+    assert {} == load_data(datadir=Path(), pairs=[], timeframe=default_conf["timeframe"])
+
+
+def test_file_dump_json_tofile(testdatadir) -> None:
+    file = testdatadir / f"test_{uuid.uuid4()}.json"
+    data = {"bar": "foo"}
+
+    # check the file we will create does not exist
+    assert not file.is_file()
+
+    # Create the Json file
+    file_dump_json(file, data)
+
+    # Check the file was create
+    assert file.is_file()
+
+    # Open the Json file created and test the data is in it
+    with file.open() as data_file:
+        json_from_file = json.load(data_file)
+
+    assert "bar" in json_from_file
+    assert json_from_file["bar"] == "foo"
+
+    # Remove the file
+    _clean_test_file(file)
+
+
+def test_get_timerange(default_conf, mocker, testdatadir) -> None:
+    patch_exchange(mocker)
+
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY})
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    data = strategy.advise_all_indicators(
+        load_data(datadir=testdatadir, timeframe="1m", pairs=["UNITTEST/BTC"])
+    )
+    min_date, max_date = get_timerange(data)
+    assert min_date.isoformat() == "2017-11-04T23:02:00+00:00"
+    assert max_date.isoformat() == "2017-11-14T22:59:00+00:00"
+
+
+def test_validate_backtest_data_warn(default_conf, mocker, caplog, testdatadir) -> None:
+    patch_exchange(mocker)
+
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY})
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    data = strategy.advise_all_indicators(
+        load_data(
+            datadir=testdatadir, timeframe="1m", pairs=["UNITTEST/BTC"], fill_up_missing=False
+        )
+    )
+    min_date, max_date = get_timerange(data)
+    caplog.clear()
+    assert validate_backtest_data(
+        data["UNITTEST/BTC"], "UNITTEST/BTC", min_date, max_date, timeframe_to_minutes("1m")
+    )
+    assert len(caplog.record_tuples) == 1
+    assert log_has(
+        "UNITTEST/BTC has missing frames: expected 14397, got 13681, that's 716 missing values",
+        caplog,
+    )
+
+
+def test_validate_backtest_data(default_conf, mocker, caplog, testdatadir) -> None:
+    patch_exchange(mocker)
+
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY})
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    timerange = TimeRange()
+    data = strategy.advise_all_indicators(
+        load_data(datadir=testdatadir, timeframe="5m", pairs=["UNITTEST/BTC"], timerange=timerange)
+    )
+
+    min_date, max_date = get_timerange(data)
+    caplog.clear()
+    assert not validate_backtest_data(
+        data["UNITTEST/BTC"], "UNITTEST/BTC", min_date, max_date, timeframe_to_minutes("5m")
+    )
+    assert len(caplog.record_tuples) == 0
+
+
+@pytest.mark.parametrize(
+    "trademode,callcount",
+    [
+        ("spot", 4),
+        ("margin", 4),
+        ("futures", 8),  # Called 8 times - 4 normal, 2 funding and 2 mark/index calls
+    ],
+)
+def test_refresh_backtest_ohlcv_data(
+    mocker, default_conf, markets, caplog, testdatadir, trademode, callcount
+):
+    caplog.set_level(logging.DEBUG)
+    dl_mock = mocker.patch("freqtrade.data.history.history_utils._download_pair_history")
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+
+    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
+    mocker.patch.object(Path, "unlink", MagicMock())
+    default_conf["trading_mode"] = trademode
+
+    ex = get_patched_exchange(mocker, default_conf, exchange="bybit")
+    timerange = TimeRange.parse_timerange("20190101-20190102")
+    refresh_backtest_ohlcv_data(
+        exchange=ex,
+        pairs=["ETH/BTC", "XRP/BTC"],
+        timeframes=["1m", "5m"],
+        datadir=testdatadir,
+        timerange=timerange,
+        erase=True,
+        trading_mode=trademode,
+    )
+
+    assert dl_mock.call_count == callcount
+    assert dl_mock.call_args[1]["timerange"].starttype == "date"
+
+    assert log_has_re(r"Downloading pair ETH/BTC, .* interval 1m\.", caplog)
+    if trademode == "futures":
+        assert log_has_re(r"Downloading pair ETH/BTC, funding_rate, interval 8h\.", caplog)
+        assert log_has_re(r"Downloading pair ETH/BTC, mark, interval 4h\.", caplog)
+
+
+def test_download_data_no_markets(mocker, default_conf, caplog, testdatadir):
+    dl_mock = mocker.patch(
+        "freqtrade.data.history.history_utils._download_pair_history", MagicMock()
+    )
+
+    ex = get_patched_exchange(mocker, default_conf)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value={}))
+    timerange = TimeRange.parse_timerange("20190101-20190102")
+    unav_pairs = refresh_backtest_ohlcv_data(
+        exchange=ex,
+        pairs=["BTT/BTC", "LTC/USDT"],
+        timeframes=["1m", "5m"],
+        datadir=testdatadir,
+        timerange=timerange,
+        erase=False,
+        trading_mode="spot",
+    )
+
+    assert dl_mock.call_count == 0
+    assert "BTT/BTC: Pair not available on exchange." in unav_pairs
+    assert "LTC/USDT: Pair not available on exchange." in unav_pairs
+    assert log_has("Skipping pair BTT/BTC...", caplog)
+
+
+def test_refresh_backtest_trades_data(mocker, default_conf, markets, caplog, testdatadir):
+    dl_mock = mocker.patch(
+        "freqtrade.data.history.history_utils._download_trades_history", MagicMock()
+    )
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
+    mocker.patch.object(Path, "unlink", MagicMock())
+
+    ex = get_patched_exchange(mocker, default_conf)
+    timerange = TimeRange.parse_timerange("20190101-20190102")
+    unavailable_pairs = refresh_backtest_trades_data(
+        exchange=ex,
+        pairs=["ETH/BTC", "XRP/BTC", "XRP/ETH"],
+        datadir=testdatadir,
+        timerange=timerange,
+        erase=True,
+        trading_mode=TradingMode.SPOT,
+    )
+
+    assert dl_mock.call_count == 2
+    assert dl_mock.call_args[1]["timerange"].starttype == "date"
+
+    assert log_has("Downloading trades for pair ETH/BTC.", caplog)
+    assert [p for p in unavailable_pairs if "XRP/ETH" in p]
+    assert log_has("Skipping pair XRP/ETH...", caplog)
+
+
+def test_download_trades_history(
+    trades_history, mocker, default_conf, testdatadir, caplog, tmp_path, time_machine
+) -> None:
+    start_dt = dt_utc(2023, 1, 1)
+    time_machine.move_to(start_dt, tick=False)
+
+    ght_mock = MagicMock(side_effect=lambda pair, *args, **kwargs: (pair, trades_history))
+    mocker.patch(f"{EXMS}.get_historic_trades", ght_mock)
+    exchange = get_patched_exchange(mocker, default_conf)
+    file1 = tmp_path / "ETH_BTC-trades.json.gz"
+    data_handler = get_datahandler(tmp_path, data_format="jsongz")
+
+    assert not file1.is_file()
+
+    assert _download_trades_history(
+        data_handler=data_handler, exchange=exchange, pair="ETH/BTC", trading_mode=TradingMode.SPOT
+    )
+    assert log_has("Current Amount of trades: 0", caplog)
+    assert log_has("New Amount of trades: 6", caplog)
+    assert ght_mock.call_count == 1
+    # Default "since" - 30 days before current day.
+    assert ght_mock.call_args_list[0][1]["since"] == dt_ts(start_dt - timedelta(days=30))
+    assert file1.is_file()
+    caplog.clear()
+
+    ght_mock.reset_mock()
+    since_time = int(trades_history[-3][0] // 1000)
+    since_time2 = int(trades_history[-1][0] // 1000)
+    timerange = TimeRange("date", None, since_time, 0)
+    assert _download_trades_history(
+        data_handler=data_handler,
+        exchange=exchange,
+        pair="ETH/BTC",
+        timerange=timerange,
+        trading_mode=TradingMode.SPOT,
+    )
+
+    assert ght_mock.call_count == 1
+    # Check this in seconds - since we had to convert to seconds above too.
+    assert int(ght_mock.call_args_list[0][1]["since"] // 1000) == since_time2 - 5
+    assert ght_mock.call_args_list[0][1]["from_id"] is not None
+
+    file1.unlink()
+
+    mocker.patch(f"{EXMS}.get_historic_trades", MagicMock(side_effect=ValueError("he ho!")))
+    caplog.clear()
+
+    with pytest.raises(ValueError, match="he ho!"):
+        _download_trades_history(
+            data_handler=data_handler,
+            exchange=exchange,
+            pair="ETH/BTC",
+            trading_mode=TradingMode.SPOT,
+        )
+
+    file2 = tmp_path / "XRP_ETH-trades.json.gz"
+    copyfile(testdatadir / file2.name, file2)
+
+    ght_mock.reset_mock()
+    mocker.patch(f"{EXMS}.get_historic_trades", ght_mock)
+    # Since before first start date
+    since_time = int(trades_history[0][0] // 1000) - 500
+    timerange = TimeRange("date", None, since_time, 0)
+
+    with pytest.raises(ValueError, match=r"Start .* earlier than available data"):
+        _download_trades_history(
+            data_handler=data_handler,
+            exchange=exchange,
+            pair="XRP/ETH",
+            timerange=timerange,
+            trading_mode=TradingMode.SPOT,
+        )
+
+    assert ght_mock.call_count == 0
+
+    _clean_test_file(file2)
diff --git a/tests/data/test_trade_converter_kraken.py b/tests/data/test_trade_converter_kraken.py
new file mode 100644
index 000000000..d8d20fd88
--- /dev/null
+++ b/tests/data/test_trade_converter_kraken.py
@@ -0,0 +1,66 @@
+from datetime import datetime, timezone
+from shutil import copytree
+from unittest.mock import PropertyMock
+
+import pytest
+
+from freqtrade.data.converter.trade_converter_kraken import import_kraken_trades_from_csv
+from freqtrade.data.history import get_datahandler
+from freqtrade.enums import TradingMode
+from freqtrade.exceptions import OperationalException
+from tests.conftest import EXMS, log_has, log_has_re, patch_exchange
+
+
+def test_import_kraken_trades_from_csv(testdatadir, tmp_path, caplog, default_conf_usdt, mocker):
+    with pytest.raises(OperationalException, match="This function is only for the kraken exchange"):
+        import_kraken_trades_from_csv(default_conf_usdt, "feather")
+
+    default_conf_usdt["exchange"]["name"] = "kraken"
+
+    patch_exchange(mocker, exchange="kraken")
+    mocker.patch(
+        f"{EXMS}.markets",
+        PropertyMock(
+            return_value={
+                "BCH/EUR": {"symbol": "BCH/EUR", "id": "BCHEUR", "altname": "BCHEUR"},
+            }
+        ),
+    )
+    dstfile = tmp_path / "BCH_EUR-trades.feather"
+    assert not dstfile.is_file()
+    default_conf_usdt["datadir"] = tmp_path
+    # There's 2 files in this tree, containing a total of 2 days.
+    # tests/testdata/kraken/
+    # └── trades_csv
+    # ├── BCHEUR.csv       <-- 2023-01-01
+    # └── incremental_q2
+    #     └── BCHEUR.csv   <-- 2023-01-02
+
+    copytree(testdatadir / "kraken/trades_csv", tmp_path / "trades_csv")
+
+    import_kraken_trades_from_csv(default_conf_usdt, "feather")
+    assert log_has("Found csv files for BCHEUR.", caplog)
+    assert log_has("Converting pairs: BCH/EUR.", caplog)
+    assert log_has_re(r"BCH/EUR: 340 trades.* 2023-01-01.* 2023-01-02.*", caplog)
+
+    assert dstfile.is_file()
+
+    dh = get_datahandler(tmp_path, "feather")
+    trades = dh.trades_load("BCH_EUR", TradingMode.SPOT)
+    assert len(trades) == 340
+
+    assert trades["date"].min().to_pydatetime() == datetime(
+        2023, 1, 1, 0, 3, 56, tzinfo=timezone.utc
+    )
+    assert trades["date"].max().to_pydatetime() == datetime(
+        2023, 1, 2, 23, 17, 3, tzinfo=timezone.utc
+    )
+    # ID is not filled
+    assert len(trades.loc[trades["id"] != ""]) == 0
+
+    caplog.clear()
+    default_conf_usdt["pairs"] = ["XRP/EUR"]
+    # Filtered to non-existing pair
+    import_kraken_trades_from_csv(default_conf_usdt, "feather")
+    assert log_has("Found csv files for BCHEUR.", caplog)
+    assert log_has("No data found for pairs XRP/EUR.", caplog)
diff --git a/tests/edge/__init__.py b/tests/edge/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/edge/test_edge.py b/tests/edge/test_edge.py
new file mode 100644
index 000000000..50c03f0f3
--- /dev/null
+++ b/tests/edge/test_edge.py
@@ -0,0 +1,606 @@
+# pragma pylint: disable=missing-docstring, C0103, C0330
+# pragma pylint: disable=protected-access, too-many-lines, invalid-name, too-many-arguments
+
+import logging
+import math
+from datetime import timedelta
+from unittest.mock import MagicMock
+
+import numpy as np
+import pytest
+from pandas import DataFrame
+
+from freqtrade.data.converter import ohlcv_to_dataframe
+from freqtrade.edge import Edge, PairInfo
+from freqtrade.enums import ExitType
+from freqtrade.exceptions import OperationalException
+from freqtrade.util.datetime_helpers import dt_ts, dt_utc
+from tests.conftest import EXMS, get_patched_freqtradebot, log_has
+from tests.optimize import (
+    BTContainer,
+    BTrade,
+    _build_backtest_dataframe,
+    _get_frame_time_from_offset,
+)
+
+
+# Cases to be tested:
+# 1) Open trade should be removed from the end
+# 2) Two complete trades within dataframe (with sell hit for all)
+# 3) Entered, sl 1%, candle drops 8% => Trade closed, 1% loss
+# 4) Entered, sl 3%, candle drops 4%, recovers to 1% => Trade closed, 3% loss
+# 5) Stoploss and sell are hit. should sell on stoploss
+####################################################################
+
+tests_start_time = dt_utc(2018, 10, 3)
+timeframe_in_minute = 60
+
+# End helper functions
+# Open trade should be removed from the end
+tc0 = BTContainer(
+    data=[
+        # D  O     H     L     C     V    B  S
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 1],
+    ],  # enter trade (signal on last candle)
+    stop_loss=-0.99,
+    roi={"0": float("inf")},
+    profit_perc=0.00,
+    trades=[],
+)
+
+# Two complete trades within dataframe(with sell hit for all)
+tc1 = BTContainer(
+    data=[
+        # D  O     H     L     C     V    B  S
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 1],  # enter trade (signal on last candle)
+        [2, 5000, 5025, 4975, 4987, 6172, 0, 0],  # exit at open
+        [3, 5000, 5025, 4975, 4987, 6172, 1, 0],  # no action
+        [4, 5000, 5025, 4975, 4987, 6172, 0, 0],  # should enter the trade
+        [5, 5000, 5025, 4975, 4987, 6172, 0, 1],  # no action
+        [6, 5000, 5025, 4975, 4987, 6172, 0, 0],  # should sell
+    ],
+    stop_loss=-0.99,
+    roi={"0": float("inf")},
+    profit_perc=0.00,
+    trades=[
+        BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=2),
+        BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=4, close_tick=6),
+    ],
+)
+
+# 3) Entered, sl 1%, candle drops 8% => Trade closed, 1% loss
+tc2 = BTContainer(
+    data=[
+        # D  O     H     L     C     V    B  S
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4600, 4987, 6172, 0, 0],  # enter trade, stoploss hit
+        [2, 5000, 5025, 4975, 4987, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": float("inf")},
+    profit_perc=-0.01,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=1)],
+)
+
+# 4) Entered, sl 3 %, candle drops 4%, recovers to 1 % = > Trade closed, 3 % loss
+tc3 = BTContainer(
+    data=[
+        # D  O     H     L     C     V    B  S
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4800, 4987, 6172, 0, 0],  # enter trade, stoploss hit
+        [2, 5000, 5025, 4975, 4987, 6172, 0, 0],
+    ],
+    stop_loss=-0.03,
+    roi={"0": float("inf")},
+    profit_perc=-0.03,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=1)],
+)
+
+# 5) Stoploss and sell are hit. should sell on stoploss
+tc4 = BTContainer(
+    data=[
+        # D  O     H     L     C     V    B  S
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4800, 4987, 6172, 0, 1],  # enter trade, stoploss hit, sell signal
+        [2, 5000, 5025, 4975, 4987, 6172, 0, 0],
+    ],
+    stop_loss=-0.03,
+    roi={"0": float("inf")},
+    profit_perc=-0.03,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=1)],
+)
+
+TESTS = [tc0, tc1, tc2, tc3, tc4]
+
+
+@pytest.mark.parametrize("data", TESTS)
+def test_edge_results(edge_conf, mocker, caplog, data) -> None:
+    """
+    run functional tests
+    """
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+    frame = _build_backtest_dataframe(data.data)
+    caplog.set_level(logging.DEBUG)
+    edge.fee = 0
+
+    trades = edge._find_trades_for_stoploss_range(frame, "TEST/BTC", [data.stop_loss])
+    results = edge._fill_calculable_fields(DataFrame(trades)) if trades else DataFrame()
+
+    assert len(trades) == len(data.trades)
+
+    if not results.empty:
+        assert round(results["profit_ratio"].sum(), 3) == round(data.profit_perc, 3)
+
+    for c, trade in enumerate(data.trades):
+        res = results.iloc[c]
+        assert res.exit_type == trade.exit_reason
+        assert res.open_date == _get_frame_time_from_offset(trade.open_tick).replace(tzinfo=None)
+        assert res.close_date == _get_frame_time_from_offset(trade.close_tick).replace(tzinfo=None)
+
+
+def test_adjust(mocker, edge_conf):
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+    mocker.patch(
+        "freqtrade.edge.Edge._cached_pairs",
+        mocker.PropertyMock(
+            return_value={
+                "E/F": PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
+                "C/D": PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
+                "N/O": PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
+            }
+        ),
+    )
+
+    pairs = ["A/B", "C/D", "E/F", "G/H"]
+    assert edge.adjust(pairs) == ["E/F", "C/D"]
+
+
+def test_edge_get_stoploss(mocker, edge_conf):
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+    mocker.patch(
+        "freqtrade.edge.Edge._cached_pairs",
+        mocker.PropertyMock(
+            return_value={
+                "E/F": PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
+                "C/D": PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
+                "N/O": PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
+            }
+        ),
+    )
+
+    assert edge.get_stoploss("E/F") == -0.01
+
+
+def test_nonexisting_get_stoploss(mocker, edge_conf):
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+    mocker.patch(
+        "freqtrade.edge.Edge._cached_pairs",
+        mocker.PropertyMock(
+            return_value={
+                "E/F": PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
+            }
+        ),
+    )
+
+    assert edge.get_stoploss("N/O") == -0.1
+
+
+def test_edge_stake_amount(mocker, edge_conf):
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+    mocker.patch(
+        "freqtrade.edge.Edge._cached_pairs",
+        mocker.PropertyMock(
+            return_value={
+                "E/F": PairInfo(-0.02, 0.66, 3.71, 0.50, 1.71, 10, 60),
+            }
+        ),
+    )
+    assert edge._capital_ratio == 0.5
+    assert (
+        edge.stake_amount("E/F", free_capital=100, total_capital=100, capital_in_trade=25) == 31.25
+    )
+
+    assert edge.stake_amount("E/F", free_capital=20, total_capital=100, capital_in_trade=25) == 20
+
+    assert edge.stake_amount("E/F", free_capital=0, total_capital=100, capital_in_trade=25) == 0
+
+    # Test with increased allowed_risk
+    # Result should be no more than allowed capital
+    edge._allowed_risk = 0.4
+    edge._capital_ratio = 0.5
+    assert (
+        edge.stake_amount("E/F", free_capital=100, total_capital=100, capital_in_trade=25) == 62.5
+    )
+
+    assert edge.stake_amount("E/F", free_capital=100, total_capital=100, capital_in_trade=0) == 50
+
+    edge._capital_ratio = 1
+    # Full capital is available
+    assert edge.stake_amount("E/F", free_capital=100, total_capital=100, capital_in_trade=0) == 100
+    # Full capital is available
+    assert edge.stake_amount("E/F", free_capital=0, total_capital=100, capital_in_trade=0) == 0
+
+
+def test_nonexisting_stake_amount(mocker, edge_conf):
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+    mocker.patch(
+        "freqtrade.edge.Edge._cached_pairs",
+        mocker.PropertyMock(
+            return_value={
+                "E/F": PairInfo(-0.11, 0.66, 3.71, 0.50, 1.71, 10, 60),
+            }
+        ),
+    )
+    # should use strategy stoploss
+    assert edge.stake_amount("N/O", 1, 2, 1) == 0.15
+
+
+def test_edge_heartbeat_calculate(mocker, edge_conf):
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+    heartbeat = edge_conf["edge"]["process_throttle_secs"]
+
+    # should not recalculate if heartbeat not reached
+    edge._last_updated = dt_ts() - heartbeat + 1
+
+    assert edge.calculate(edge_conf["exchange"]["pair_whitelist"]) is False
+
+
+def mocked_load_data(datadir, pairs=None, timeframe="0m", timerange=None, *args, **kwargs):
+    if pairs is None:
+        pairs = []
+    hz = 0.1
+    base = 0.001
+
+    NEOBTC = [
+        [
+            dt_ts(tests_start_time + timedelta(minutes=(x * timeframe_in_minute))),
+            math.sin(x * hz) / 1000 + base,
+            math.sin(x * hz) / 1000 + base + 0.0001,
+            math.sin(x * hz) / 1000 + base - 0.0001,
+            math.sin(x * hz) / 1000 + base,
+            123.45,
+        ]
+        for x in range(0, 500)
+    ]
+
+    hz = 0.2
+    base = 0.002
+    LTCBTC = [
+        [
+            dt_ts(tests_start_time + timedelta(minutes=(x * timeframe_in_minute))),
+            math.sin(x * hz) / 1000 + base,
+            math.sin(x * hz) / 1000 + base + 0.0001,
+            math.sin(x * hz) / 1000 + base - 0.0001,
+            math.sin(x * hz) / 1000 + base,
+            123.45,
+        ]
+        for x in range(0, 500)
+    ]
+
+    pairdata = {
+        "NEO/BTC": ohlcv_to_dataframe(NEOBTC, "1h", pair="NEO/BTC", fill_missing=True),
+        "LTC/BTC": ohlcv_to_dataframe(LTCBTC, "1h", pair="LTC/BTC", fill_missing=True),
+    }
+    return pairdata
+
+
+def test_edge_process_downloaded_data(mocker, edge_conf):
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    mocker.patch(f"{EXMS}.get_fee", MagicMock(return_value=0.001))
+    mocker.patch("freqtrade.edge.edge_positioning.refresh_data", MagicMock())
+    mocker.patch("freqtrade.edge.edge_positioning.load_data", mocked_load_data)
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+
+    assert edge.calculate(edge_conf["exchange"]["pair_whitelist"])
+    assert len(edge._cached_pairs) == 2
+    assert edge._last_updated <= dt_ts() + 2
+
+
+def test_edge_process_no_data(mocker, edge_conf, caplog):
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    mocker.patch(f"{EXMS}.get_fee", MagicMock(return_value=0.001))
+    mocker.patch("freqtrade.edge.edge_positioning.refresh_data", MagicMock())
+    mocker.patch("freqtrade.edge.edge_positioning.load_data", MagicMock(return_value={}))
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+
+    assert not edge.calculate(edge_conf["exchange"]["pair_whitelist"])
+    assert len(edge._cached_pairs) == 0
+    assert log_has("No data found. Edge is stopped ...", caplog)
+    assert edge._last_updated == 0
+
+
+def test_edge_process_no_trades(mocker, edge_conf, caplog):
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    mocker.patch(f"{EXMS}.get_fee", return_value=0.001)
+    mocker.patch(
+        "freqtrade.edge.edge_positioning.refresh_data",
+    )
+    mocker.patch("freqtrade.edge.edge_positioning.load_data", mocked_load_data)
+    # Return empty
+    mocker.patch("freqtrade.edge.Edge._find_trades_for_stoploss_range", return_value=[])
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+
+    assert not edge.calculate(edge_conf["exchange"]["pair_whitelist"])
+    assert len(edge._cached_pairs) == 0
+    assert log_has("No trades found.", caplog)
+
+
+def test_edge_process_no_pairs(mocker, edge_conf, caplog):
+    edge_conf["exchange"]["pair_whitelist"] = []
+    mocker.patch("freqtrade.freqtradebot.validate_config_consistency")
+
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+    fee_mock = mocker.patch(f"{EXMS}.get_fee", return_value=0.001)
+    mocker.patch("freqtrade.edge.edge_positioning.refresh_data")
+    mocker.patch("freqtrade.edge.edge_positioning.load_data", mocked_load_data)
+    # Return empty
+    mocker.patch("freqtrade.edge.Edge._find_trades_for_stoploss_range", return_value=[])
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+    assert fee_mock.call_count == 0
+    assert edge.fee is None
+
+    assert not edge.calculate(["XRP/USDT"])
+    assert fee_mock.call_count == 1
+    assert edge.fee == 0.001
+
+
+def test_edge_init_error(mocker, edge_conf):
+    edge_conf["stake_amount"] = 0.5
+    mocker.patch(f"{EXMS}.get_fee", MagicMock(return_value=0.001))
+    with pytest.raises(OperationalException, match="Edge works only with unlimited stake amount"):
+        get_patched_freqtradebot(mocker, edge_conf)
+
+
+@pytest.mark.parametrize(
+    "fee,risk_reward_ratio,expectancy",
+    [
+        (0.0005, 306.5384615384, 101.5128205128),
+        (0.001, 152.6923076923, 50.2307692308),
+    ],
+)
+def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectancy):
+    edge_conf["edge"]["min_trade_number"] = 2
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+
+    def get_fee(*args, **kwargs):
+        return fee
+
+    freqtrade.exchange.get_fee = get_fee
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+
+    trades = [
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:05:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:10:00.000000000"),
+            "trade_duration": "",
+            "open_rate": 17,
+            "close_rate": 17,
+            "exit_type": "exit_signal",
+        },
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:20:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:25:00.000000000"),
+            "trade_duration": "",
+            "open_rate": 20,
+            "close_rate": 20,
+            "exit_type": "exit_signal",
+        },
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:30:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:40:00.000000000"),
+            "trade_duration": "",
+            "open_rate": 26,
+            "close_rate": 34,
+            "exit_type": "exit_signal",
+        },
+    ]
+
+    trades_df = DataFrame(trades)
+    trades_df = edge._fill_calculable_fields(trades_df)
+    final = edge._process_expectancy(trades_df)
+    assert len(final) == 1
+
+    assert "TEST/BTC" in final
+    assert final["TEST/BTC"].stoploss == -0.9
+    assert round(final["TEST/BTC"].winrate, 10) == 0.3333333333
+    assert round(final["TEST/BTC"].risk_reward_ratio, 10) == risk_reward_ratio
+    assert round(final["TEST/BTC"].required_risk_reward, 10) == 2.0
+    assert round(final["TEST/BTC"].expectancy, 10) == expectancy
+
+    # Pop last item so no trade is profitable
+    trades.pop()
+    trades_df = DataFrame(trades)
+    trades_df = edge._fill_calculable_fields(trades_df)
+    final = edge._process_expectancy(trades_df)
+    assert len(final) == 0
+    assert isinstance(final, dict)
+
+
+def test_process_expectancy_remove_pumps(mocker, edge_conf, fee):
+    edge_conf["edge"]["min_trade_number"] = 2
+    edge_conf["edge"]["remove_pumps"] = True
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+
+    freqtrade.exchange.get_fee = fee
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+
+    trades = [
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:05:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:10:00.000000000"),
+            "open_index": 1,
+            "close_index": 1,
+            "trade_duration": "",
+            "open_rate": 17,
+            "close_rate": 15,
+            "exit_type": "sell_signal",
+        },
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:20:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:25:00.000000000"),
+            "open_index": 4,
+            "close_index": 4,
+            "trade_duration": "",
+            "open_rate": 20,
+            "close_rate": 10,
+            "exit_type": "sell_signal",
+        },
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:20:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:25:00.000000000"),
+            "open_index": 4,
+            "close_index": 4,
+            "trade_duration": "",
+            "open_rate": 20,
+            "close_rate": 10,
+            "exit_type": "sell_signal",
+        },
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:20:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:25:00.000000000"),
+            "open_index": 4,
+            "close_index": 4,
+            "trade_duration": "",
+            "open_rate": 20,
+            "close_rate": 10,
+            "exit_type": "sell_signal",
+        },
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:20:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:25:00.000000000"),
+            "open_index": 4,
+            "close_index": 4,
+            "trade_duration": "",
+            "open_rate": 20,
+            "close_rate": 10,
+            "exit_type": "sell_signal",
+        },
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:30:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:40:00.000000000"),
+            "open_index": 6,
+            "close_index": 7,
+            "trade_duration": "",
+            "open_rate": 26,
+            "close_rate": 134,
+            "exit_type": "sell_signal",
+        },
+    ]
+
+    trades_df = DataFrame(trades)
+    trades_df = edge._fill_calculable_fields(trades_df)
+    final = edge._process_expectancy(trades_df)
+
+    assert "TEST/BTC" in final
+    assert final["TEST/BTC"].stoploss == -0.9
+    assert final["TEST/BTC"].nb_trades == len(trades_df) - 1
+    assert round(final["TEST/BTC"].winrate, 10) == 0.0
+
+
+def test_process_expectancy_only_wins(mocker, edge_conf, fee):
+    edge_conf["edge"]["min_trade_number"] = 2
+    freqtrade = get_patched_freqtradebot(mocker, edge_conf)
+
+    freqtrade.exchange.get_fee = fee
+    edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
+
+    trades = [
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:05:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:10:00.000000000"),
+            "open_index": 1,
+            "close_index": 1,
+            "trade_duration": "",
+            "open_rate": 15,
+            "close_rate": 17,
+            "exit_type": "sell_signal",
+        },
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:20:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:25:00.000000000"),
+            "open_index": 4,
+            "close_index": 4,
+            "trade_duration": "",
+            "open_rate": 10,
+            "close_rate": 20,
+            "exit_type": "sell_signal",
+        },
+        {
+            "pair": "TEST/BTC",
+            "stoploss": -0.9,
+            "profit_percent": "",
+            "profit_abs": "",
+            "open_date": np.datetime64("2018-10-03T00:30:00.000000000"),
+            "close_date": np.datetime64("2018-10-03T00:40:00.000000000"),
+            "open_index": 6,
+            "close_index": 7,
+            "trade_duration": "",
+            "open_rate": 26,
+            "close_rate": 134,
+            "exit_type": "sell_signal",
+        },
+    ]
+
+    trades_df = DataFrame(trades)
+    trades_df = edge._fill_calculable_fields(trades_df)
+    final = edge._process_expectancy(trades_df)
+
+    assert "TEST/BTC" in final
+    assert final["TEST/BTC"].stoploss == -0.9
+    assert final["TEST/BTC"].nb_trades == len(trades_df)
+    assert round(final["TEST/BTC"].winrate, 10) == 1.0
+    assert round(final["TEST/BTC"].risk_reward_ratio, 10) == float("inf")
+    assert round(final["TEST/BTC"].expectancy, 10) == float("inf")
diff --git a/tests/exchange/__init__.py b/tests/exchange/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/exchange/test_binance.py b/tests/exchange/test_binance.py
new file mode 100644
index 000000000..5241b3b69
--- /dev/null
+++ b/tests/exchange/test_binance.py
@@ -0,0 +1,1110 @@
+from datetime import datetime, timedelta
+from random import randint
+from unittest.mock import MagicMock, PropertyMock
+
+import ccxt
+import pandas as pd
+import pytest
+
+from freqtrade.data.converter.trade_converter import trades_dict_to_list
+from freqtrade.enums import CandleType, MarginMode, TradingMode
+from freqtrade.exceptions import DependencyException, InvalidOrderException, OperationalException
+from freqtrade.exchange.exchange_utils_timeframe import timeframe_to_seconds
+from freqtrade.persistence import Trade
+from freqtrade.util.datetime_helpers import dt_from_ts, dt_ts, dt_utc
+from tests.conftest import EXMS, get_patched_exchange
+from tests.exchange.test_exchange import ccxt_exceptionhandlers
+
+
+@pytest.mark.parametrize(
+    "side,order_type,time_in_force,expected",
+    [
+        ("buy", "limit", "gtc", {"timeInForce": "GTC"}),
+        ("buy", "limit", "IOC", {"timeInForce": "IOC"}),
+        ("buy", "market", "IOC", {}),
+        ("buy", "limit", "PO", {"timeInForce": "PO"}),
+        ("sell", "limit", "PO", {"timeInForce": "PO"}),
+        ("sell", "market", "PO", {}),
+    ],
+)
+def test__get_params_binance(default_conf, mocker, side, order_type, time_in_force, expected):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    assert exchange._get_params(side, order_type, 1, False, time_in_force) == expected
+
+
+@pytest.mark.parametrize("trademode", [TradingMode.FUTURES, TradingMode.SPOT])
+@pytest.mark.parametrize(
+    "limitratio,expected,side",
+    [
+        (None, 220 * 0.99, "sell"),
+        (0.99, 220 * 0.99, "sell"),
+        (0.98, 220 * 0.98, "sell"),
+        (None, 220 * 1.01, "buy"),
+        (0.99, 220 * 1.01, "buy"),
+        (0.98, 220 * 1.02, "buy"),
+    ],
+)
+def test_create_stoploss_order_binance(default_conf, mocker, limitratio, expected, side, trademode):
+    api_mock = MagicMock()
+    order_id = f"test_prod_buy_{randint(0, 10**6)}"
+    order_type = "stop_loss_limit" if trademode == TradingMode.SPOT else "stop"
+
+    api_mock.create_order = MagicMock(return_value={"id": order_id, "info": {"foo": "bar"}})
+    default_conf["dry_run"] = False
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+    default_conf["trading_mode"] = trademode
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, "binance")
+
+    with pytest.raises(InvalidOrderException):
+        order = exchange.create_stoploss(
+            pair="ETH/BTC",
+            amount=1,
+            stop_price=190,
+            side=side,
+            order_types={"stoploss": "limit", "stoploss_on_exchange_limit_ratio": 1.05},
+            leverage=1.0,
+        )
+
+    api_mock.create_order.reset_mock()
+    order_types = {"stoploss": "limit", "stoploss_price_type": "mark"}
+    if limitratio is not None:
+        order_types.update({"stoploss_on_exchange_limit_ratio": limitratio})
+
+    order = exchange.create_stoploss(
+        pair="ETH/BTC", amount=1, stop_price=220, order_types=order_types, side=side, leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args_list[0][1]["symbol"] == "ETH/BTC"
+    assert api_mock.create_order.call_args_list[0][1]["type"] == order_type
+    assert api_mock.create_order.call_args_list[0][1]["side"] == side
+    assert api_mock.create_order.call_args_list[0][1]["amount"] == 1
+    # Price should be 1% below stopprice
+    assert api_mock.create_order.call_args_list[0][1]["price"] == expected
+    if trademode == TradingMode.SPOT:
+        params_dict = {"stopPrice": 220}
+    else:
+        params_dict = {"stopPrice": 220, "reduceOnly": True, "workingType": "MARK_PRICE"}
+    assert api_mock.create_order.call_args_list[0][1]["params"] == params_dict
+
+    # test exception handling
+    with pytest.raises(DependencyException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds("0 balance"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, "binance")
+        exchange.create_stoploss(
+            pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side=side, leverage=1.0
+        )
+
+    with pytest.raises(InvalidOrderException):
+        api_mock.create_order = MagicMock(
+            side_effect=ccxt.InvalidOrder("binance Order would trigger immediately.")
+        )
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, "binance")
+        exchange.create_stoploss(
+            pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side=side, leverage=1.0
+        )
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        "binance",
+        "create_stoploss",
+        "create_order",
+        retries=1,
+        pair="ETH/BTC",
+        amount=1,
+        stop_price=220,
+        order_types={},
+        side=side,
+        leverage=1.0,
+    )
+
+
+def test_create_stoploss_order_dry_run_binance(default_conf, mocker):
+    api_mock = MagicMock()
+    order_type = "stop_loss_limit"
+    default_conf["dry_run"] = True
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, "binance")
+
+    with pytest.raises(InvalidOrderException):
+        order = exchange.create_stoploss(
+            pair="ETH/BTC",
+            amount=1,
+            stop_price=190,
+            side="sell",
+            order_types={"stoploss_on_exchange_limit_ratio": 1.05},
+            leverage=1.0,
+        )
+
+    api_mock.create_order.reset_mock()
+
+    order = exchange.create_stoploss(
+        pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side="sell", leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert "type" in order
+
+    assert order["type"] == order_type
+    assert order["price"] == 220
+    assert order["amount"] == 1
+
+
+@pytest.mark.parametrize(
+    "sl1,sl2,sl3,side", [(1501, 1499, 1501, "sell"), (1499, 1501, 1499, "buy")]
+)
+def test_stoploss_adjust_binance(mocker, default_conf, sl1, sl2, sl3, side):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    order = {
+        "type": "stop_loss_limit",
+        "price": 1500,
+        "stopPrice": 1500,
+        "info": {"stopPrice": 1500},
+    }
+    assert exchange.stoploss_adjust(sl1, order, side=side)
+    assert not exchange.stoploss_adjust(sl2, order, side=side)
+
+
+@pytest.mark.parametrize(
+    "pair, is_short, trading_mode, margin_mode, wallet_balance, "
+    "maintenance_amt, amount, open_rate, open_trades,"
+    "mm_ratio, expected",
+    [
+        (
+            "ETH/USDT:USDT",
+            False,
+            "futures",
+            "isolated",
+            1535443.01,
+            135365.00,
+            3683.979,
+            1456.84,
+            [],
+            0.10,
+            1114.78,
+        ),
+        (
+            "ETH/USDT:USDT",
+            False,
+            "futures",
+            "isolated",
+            1535443.01,
+            16300.000,
+            109.488,
+            32481.980,
+            [],
+            0.025,
+            18778.73,
+        ),
+        (
+            "ETH/USDT:USDT",
+            False,
+            "futures",
+            "cross",
+            1535443.01,
+            135365.00,
+            3683.979,  # amount
+            1456.84,  # open_rate
+            [
+                {
+                    # From calc example
+                    "pair": "BTC/USDT:USDT",
+                    "open_rate": 32481.98,
+                    "amount": 109.488,
+                    "stake_amount": 3556387.02624,  # open_rate * amount
+                    "mark_price": 31967.27,
+                    "mm_ratio": 0.025,
+                    "maintenance_amt": 16300.0,
+                },
+                {
+                    # From calc example
+                    "pair": "ETH/USDT:USDT",
+                    "open_rate": 1456.84,
+                    "amount": 3683.979,
+                    "stake_amount": 5366967.96,
+                    "mark_price": 1335.18,
+                    "mm_ratio": 0.10,
+                    "maintenance_amt": 135365.00,
+                },
+            ],
+            0.10,
+            1153.26,
+        ),
+        (
+            "BTC/USDT:USDT",
+            False,
+            "futures",
+            "cross",
+            1535443.01,
+            16300.0,
+            109.488,  # amount
+            32481.980,  # open_rate
+            [
+                {
+                    # From calc example
+                    "pair": "BTC/USDT:USDT",
+                    "open_rate": 32481.98,
+                    "amount": 109.488,
+                    "stake_amount": 3556387.02624,  # open_rate * amount
+                    "mark_price": 31967.27,
+                    "mm_ratio": 0.025,
+                    "maintenance_amt": 16300.0,
+                },
+                {
+                    # From calc example
+                    "pair": "ETH/USDT:USDT",
+                    "open_rate": 1456.84,
+                    "amount": 3683.979,
+                    "stake_amount": 5366967.96,
+                    "mark_price": 1335.18,
+                    "mm_ratio": 0.10,
+                    "maintenance_amt": 135365.00,
+                },
+            ],
+            0.025,
+            26316.89,
+        ),
+    ],
+)
+def test_liquidation_price_binance(
+    mocker,
+    default_conf,
+    pair,
+    is_short,
+    trading_mode,
+    margin_mode,
+    wallet_balance,
+    maintenance_amt,
+    amount,
+    open_rate,
+    open_trades,
+    mm_ratio,
+    expected,
+):
+    default_conf["trading_mode"] = trading_mode
+    default_conf["margin_mode"] = margin_mode
+    default_conf["liquidation_buffer"] = 0.0
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+
+    def get_maint_ratio(pair_, stake_amount):
+        if pair_ != pair:
+            oc = next(c for c in open_trades if c["pair"] == pair_)
+            return oc["mm_ratio"], oc["maintenance_amt"]
+        return mm_ratio, maintenance_amt
+
+    def fetch_funding_rates(*args, **kwargs):
+        return {
+            t["pair"]: {
+                "symbol": t["pair"],
+                "markPrice": t["mark_price"],
+            }
+            for t in open_trades
+        }
+
+    exchange.get_maintenance_ratio_and_amt = get_maint_ratio
+    exchange.fetch_funding_rates = fetch_funding_rates
+
+    open_trade_objects = [
+        Trade(
+            pair=t["pair"],
+            open_rate=t["open_rate"],
+            amount=t["amount"],
+            stake_amount=t["stake_amount"],
+            fee_open=0,
+        )
+        for t in open_trades
+    ]
+
+    assert (
+        pytest.approx(
+            round(
+                exchange.get_liquidation_price(
+                    pair=pair,
+                    open_rate=open_rate,
+                    is_short=is_short,
+                    wallet_balance=wallet_balance,
+                    amount=amount,
+                    stake_amount=open_rate * amount,
+                    leverage=5,
+                    open_trades=open_trade_objects,
+                ),
+                2,
+            )
+        )
+        == expected
+    )
+
+
+def test_fill_leverage_tiers_binance(default_conf, mocker):
+    api_mock = MagicMock()
+    api_mock.fetch_leverage_tiers = MagicMock(
+        return_value={
+            "ADA/BUSD": [
+                {
+                    "tier": 1,
+                    "minNotional": 0,
+                    "maxNotional": 100000,
+                    "maintenanceMarginRate": 0.025,
+                    "maxLeverage": 20,
+                    "info": {
+                        "bracket": "1",
+                        "initialLeverage": "20",
+                        "maxNotional": "100000",
+                        "minNotional": "0",
+                        "maintMarginRatio": "0.025",
+                        "cum": "0.0",
+                    },
+                },
+                {
+                    "tier": 2,
+                    "minNotional": 100000,
+                    "maxNotional": 500000,
+                    "maintenanceMarginRate": 0.05,
+                    "maxLeverage": 10,
+                    "info": {
+                        "bracket": "2",
+                        "initialLeverage": "10",
+                        "maxNotional": "500000",
+                        "minNotional": "100000",
+                        "maintMarginRatio": "0.05",
+                        "cum": "2500.0",
+                    },
+                },
+                {
+                    "tier": 3,
+                    "minNotional": 500000,
+                    "maxNotional": 1000000,
+                    "maintenanceMarginRate": 0.1,
+                    "maxLeverage": 5,
+                    "info": {
+                        "bracket": "3",
+                        "initialLeverage": "5",
+                        "maxNotional": "1000000",
+                        "minNotional": "500000",
+                        "maintMarginRatio": "0.1",
+                        "cum": "27500.0",
+                    },
+                },
+                {
+                    "tier": 4,
+                    "minNotional": 1000000,
+                    "maxNotional": 2000000,
+                    "maintenanceMarginRate": 0.15,
+                    "maxLeverage": 3,
+                    "info": {
+                        "bracket": "4",
+                        "initialLeverage": "3",
+                        "maxNotional": "2000000",
+                        "minNotional": "1000000",
+                        "maintMarginRatio": "0.15",
+                        "cum": "77500.0",
+                    },
+                },
+                {
+                    "tier": 5,
+                    "minNotional": 2000000,
+                    "maxNotional": 5000000,
+                    "maintenanceMarginRate": 0.25,
+                    "maxLeverage": 2,
+                    "info": {
+                        "bracket": "5",
+                        "initialLeverage": "2",
+                        "maxNotional": "5000000",
+                        "minNotional": "2000000",
+                        "maintMarginRatio": "0.25",
+                        "cum": "277500.0",
+                    },
+                },
+                {
+                    "tier": 6,
+                    "minNotional": 5000000,
+                    "maxNotional": 30000000,
+                    "maintenanceMarginRate": 0.5,
+                    "maxLeverage": 1,
+                    "info": {
+                        "bracket": "6",
+                        "initialLeverage": "1",
+                        "maxNotional": "30000000",
+                        "minNotional": "5000000",
+                        "maintMarginRatio": "0.5",
+                        "cum": "1527500.0",
+                    },
+                },
+            ],
+            "ZEC/USDT": [
+                {
+                    "tier": 1,
+                    "minNotional": 0,
+                    "maxNotional": 50000,
+                    "maintenanceMarginRate": 0.01,
+                    "maxLeverage": 50,
+                    "info": {
+                        "bracket": "1",
+                        "initialLeverage": "50",
+                        "maxNotional": "50000",
+                        "minNotional": "0",
+                        "maintMarginRatio": "0.01",
+                        "cum": "0.0",
+                    },
+                },
+                {
+                    "tier": 2,
+                    "minNotional": 50000,
+                    "maxNotional": 150000,
+                    "maintenanceMarginRate": 0.025,
+                    "maxLeverage": 20,
+                    "info": {
+                        "bracket": "2",
+                        "initialLeverage": "20",
+                        "maxNotional": "150000",
+                        "minNotional": "50000",
+                        "maintMarginRatio": "0.025",
+                        "cum": "750.0",
+                    },
+                },
+                {
+                    "tier": 3,
+                    "minNotional": 150000,
+                    "maxNotional": 250000,
+                    "maintenanceMarginRate": 0.05,
+                    "maxLeverage": 10,
+                    "info": {
+                        "bracket": "3",
+                        "initialLeverage": "10",
+                        "maxNotional": "250000",
+                        "minNotional": "150000",
+                        "maintMarginRatio": "0.05",
+                        "cum": "4500.0",
+                    },
+                },
+                {
+                    "tier": 4,
+                    "minNotional": 250000,
+                    "maxNotional": 500000,
+                    "maintenanceMarginRate": 0.1,
+                    "maxLeverage": 5,
+                    "info": {
+                        "bracket": "4",
+                        "initialLeverage": "5",
+                        "maxNotional": "500000",
+                        "minNotional": "250000",
+                        "maintMarginRatio": "0.1",
+                        "cum": "17000.0",
+                    },
+                },
+                {
+                    "tier": 5,
+                    "minNotional": 500000,
+                    "maxNotional": 1000000,
+                    "maintenanceMarginRate": 0.125,
+                    "maxLeverage": 4,
+                    "info": {
+                        "bracket": "5",
+                        "initialLeverage": "4",
+                        "maxNotional": "1000000",
+                        "minNotional": "500000",
+                        "maintMarginRatio": "0.125",
+                        "cum": "29500.0",
+                    },
+                },
+                {
+                    "tier": 6,
+                    "minNotional": 1000000,
+                    "maxNotional": 2000000,
+                    "maintenanceMarginRate": 0.25,
+                    "maxLeverage": 2,
+                    "info": {
+                        "bracket": "6",
+                        "initialLeverage": "2",
+                        "maxNotional": "2000000",
+                        "minNotional": "1000000",
+                        "maintMarginRatio": "0.25",
+                        "cum": "154500.0",
+                    },
+                },
+                {
+                    "tier": 7,
+                    "minNotional": 2000000,
+                    "maxNotional": 30000000,
+                    "maintenanceMarginRate": 0.5,
+                    "maxLeverage": 1,
+                    "info": {
+                        "bracket": "7",
+                        "initialLeverage": "1",
+                        "maxNotional": "30000000",
+                        "minNotional": "2000000",
+                        "maintMarginRatio": "0.5",
+                        "cum": "654500.0",
+                    },
+                },
+            ],
+        }
+    )
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="binance")
+    exchange.fill_leverage_tiers()
+
+    assert exchange._leverage_tiers == {
+        "ADA/BUSD": [
+            {
+                "minNotional": 0,
+                "maxNotional": 100000,
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 100000,
+                "maxNotional": 500000,
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 2500.0,
+            },
+            {
+                "minNotional": 500000,
+                "maxNotional": 1000000,
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 27500.0,
+            },
+            {
+                "minNotional": 1000000,
+                "maxNotional": 2000000,
+                "maintenanceMarginRate": 0.15,
+                "maxLeverage": 3,
+                "maintAmt": 77500.0,
+            },
+            {
+                "minNotional": 2000000,
+                "maxNotional": 5000000,
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
+                "maintAmt": 277500.0,
+            },
+            {
+                "minNotional": 5000000,
+                "maxNotional": 30000000,
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 1527500.0,
+            },
+        ],
+        "ZEC/USDT": [
+            {
+                "minNotional": 0,
+                "maxNotional": 50000,
+                "maintenanceMarginRate": 0.01,
+                "maxLeverage": 50,
+                "maintAmt": 0.0,
+            },
+            {
+                "minNotional": 50000,
+                "maxNotional": 150000,
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 20,
+                "maintAmt": 750.0,
+            },
+            {
+                "minNotional": 150000,
+                "maxNotional": 250000,
+                "maintenanceMarginRate": 0.05,
+                "maxLeverage": 10,
+                "maintAmt": 4500.0,
+            },
+            {
+                "minNotional": 250000,
+                "maxNotional": 500000,
+                "maintenanceMarginRate": 0.1,
+                "maxLeverage": 5,
+                "maintAmt": 17000.0,
+            },
+            {
+                "minNotional": 500000,
+                "maxNotional": 1000000,
+                "maintenanceMarginRate": 0.125,
+                "maxLeverage": 4,
+                "maintAmt": 29500.0,
+            },
+            {
+                "minNotional": 1000000,
+                "maxNotional": 2000000,
+                "maintenanceMarginRate": 0.25,
+                "maxLeverage": 2,
+                "maintAmt": 154500.0,
+            },
+            {
+                "minNotional": 2000000,
+                "maxNotional": 30000000,
+                "maintenanceMarginRate": 0.5,
+                "maxLeverage": 1,
+                "maintAmt": 654500.0,
+            },
+        ],
+    }
+
+    api_mock = MagicMock()
+    api_mock.load_leverage_tiers = MagicMock()
+    type(api_mock).has = PropertyMock(return_value={"fetchLeverageTiers": True})
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        "binance",
+        "fill_leverage_tiers",
+        "fetch_leverage_tiers",
+    )
+
+
+def test_fill_leverage_tiers_binance_dryrun(default_conf, mocker, leverage_tiers):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="binance")
+    exchange.fill_leverage_tiers()
+    assert len(exchange._leverage_tiers.keys()) > 100
+    for key, value in leverage_tiers.items():
+        v = exchange._leverage_tiers[key]
+        assert isinstance(v, list)
+        # Assert if conftest leverage tiers have less or equal tiers than the exchange
+        assert len(v) >= len(value)
+
+
+def test_additional_exchange_init_binance(default_conf, mocker):
+    api_mock = MagicMock()
+    api_mock.fapiPrivateGetPositionSideDual = MagicMock(return_value={"dualSidePosition": True})
+    api_mock.fapiPrivateGetMultiAssetsMargin = MagicMock(return_value={"multiAssetsMargin": True})
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+    with pytest.raises(
+        OperationalException,
+        match=r"Hedge Mode is not supported.*\nMulti-Asset Mode is not supported.*",
+    ):
+        get_patched_exchange(mocker, default_conf, exchange="binance", api_mock=api_mock)
+    api_mock.fapiPrivateGetPositionSideDual = MagicMock(return_value={"dualSidePosition": False})
+    api_mock.fapiPrivateGetMultiAssetsMargin = MagicMock(return_value={"multiAssetsMargin": False})
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance", api_mock=api_mock)
+    assert exchange
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        "binance",
+        "additional_exchange_init",
+        "fapiPrivateGetPositionSideDual",
+    )
+
+
+def test__set_leverage_binance(mocker, default_conf):
+    api_mock = MagicMock()
+    api_mock.set_leverage = MagicMock()
+    type(api_mock).has = PropertyMock(return_value={"setLeverage": True})
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="binance")
+    exchange._set_leverage(3.2, "BTC/USDT:USDT")
+    assert api_mock.set_leverage.call_count == 1
+    # Leverage is rounded to 3.
+    assert api_mock.set_leverage.call_args_list[0][1]["leverage"] == 3
+    assert api_mock.set_leverage.call_args_list[0][1]["symbol"] == "BTC/USDT:USDT"
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        "binance",
+        "_set_leverage",
+        "set_leverage",
+        pair="XRP/USDT",
+        leverage=5.0,
+    )
+
+
+def patch_binance_vision_ohlcv(mocker, start, archive_end, api_end, timeframe):
+    def make_storage(start: datetime, end: datetime, timeframe: str):
+        date = pd.date_range(start, end, freq=timeframe.replace("m", "min"))
+        df = pd.DataFrame(
+            data=dict(date=date, open=1.0, high=1.0, low=1.0, close=1.0),
+        )
+        return df
+
+    archive_storage = make_storage(start, archive_end, timeframe)
+    api_storage = make_storage(start, api_end, timeframe)
+
+    ohlcv = [[dt_ts(start), 1, 1, 1, 1]]
+    # (pair, timeframe, candle_type, ohlcv, True)
+    candle_history = [None, None, None, ohlcv, None]
+
+    def get_historic_ohlcv(
+        # self,
+        pair: str,
+        timeframe: str,
+        since_ms: int,
+        candle_type: CandleType,
+        is_new_pair: bool = False,
+        until_ms: int | None = None,
+    ):
+        since = dt_from_ts(since_ms)
+        until = dt_from_ts(until_ms) if until_ms else api_end + timedelta(seconds=1)
+        return api_storage.loc[(api_storage["date"] >= since) & (api_storage["date"] < until)]
+
+    async def download_archive_ohlcv(
+        candle_type,
+        pair,
+        timeframe,
+        since_ms,
+        until_ms,
+        markets=None,
+        stop_on_404=False,
+    ):
+        since = dt_from_ts(since_ms)
+        until = dt_from_ts(until_ms) if until_ms else archive_end + timedelta(seconds=1)
+        if since < start:
+            pass
+        return archive_storage.loc[
+            (archive_storage["date"] >= since) & (archive_storage["date"] < until)
+        ]
+
+    candle_mock = mocker.patch(f"{EXMS}._async_get_candle_history", return_value=candle_history)
+    api_mock = mocker.patch(f"{EXMS}.get_historic_ohlcv", side_effect=get_historic_ohlcv)
+    archive_mock = mocker.patch(
+        "freqtrade.exchange.binance.download_archive_ohlcv", side_effect=download_archive_ohlcv
+    )
+    return candle_mock, api_mock, archive_mock
+
+
+@pytest.mark.parametrize(
+    "timeframe,is_new_pair,since,until,first_date,last_date,candle_called,archive_called,"
+    "api_called",
+    [
+        (
+            "1m",
+            True,
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 2),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23, 59),
+            True,
+            True,
+            False,
+        ),
+        (
+            "1m",
+            True,
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 3),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 2, 23, 59),
+            True,
+            True,
+            True,
+        ),
+        (
+            "1m",
+            True,
+            dt_utc(2020, 1, 2),
+            dt_utc(2020, 1, 2, 1),
+            dt_utc(2020, 1, 2),
+            dt_utc(2020, 1, 2, 0, 59),
+            True,
+            False,
+            True,
+        ),
+        (
+            "1m",
+            False,
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 2),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23, 59),
+            False,
+            True,
+            False,
+        ),
+        (
+            "1m",
+            True,
+            dt_utc(2019, 1, 1),
+            dt_utc(2020, 1, 2),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23, 59),
+            True,
+            True,
+            False,
+        ),
+        (
+            "1m",
+            False,
+            dt_utc(2019, 1, 1),
+            dt_utc(2020, 1, 2),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23, 59),
+            False,
+            True,
+            False,
+        ),
+        (
+            "1m",
+            False,
+            dt_utc(2019, 1, 1),
+            dt_utc(2019, 1, 2),
+            None,
+            None,
+            False,
+            True,
+            True,
+        ),
+        (
+            "1m",
+            True,
+            dt_utc(2019, 1, 1),
+            dt_utc(2019, 1, 2),
+            None,
+            None,
+            True,
+            False,
+            False,
+        ),
+        (
+            "1m",
+            False,
+            dt_utc(2021, 1, 1),
+            dt_utc(2021, 1, 2),
+            None,
+            None,
+            False,
+            False,
+            False,
+        ),
+        (
+            "1m",
+            True,
+            dt_utc(2021, 1, 1),
+            dt_utc(2021, 1, 2),
+            None,
+            None,
+            True,
+            False,
+            False,
+        ),
+        (
+            "1h",
+            False,
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 2),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23),
+            False,
+            False,
+            True,
+        ),
+        (
+            "1m",
+            False,
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 3, 50, 30),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 3, 50),
+            False,
+            True,
+            False,
+        ),
+    ],
+)
+def test_get_historic_ohlcv_binance(
+    mocker,
+    default_conf,
+    timeframe,
+    is_new_pair,
+    since,
+    until,
+    first_date,
+    last_date,
+    candle_called,
+    archive_called,
+    api_called,
+):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+
+    start = dt_utc(2020, 1, 1)
+    archive_end = dt_utc(2020, 1, 2)
+    api_end = dt_utc(2020, 1, 3)
+    candle_mock, api_mock, archive_mock = patch_binance_vision_ohlcv(
+        mocker, start=start, archive_end=archive_end, api_end=api_end, timeframe=timeframe
+    )
+
+    candle_type = CandleType.SPOT
+    pair = "BTC/USDT"
+
+    since_ms = dt_ts(since)
+    until_ms = dt_ts(until)
+
+    df = exchange.get_historic_ohlcv(pair, timeframe, since_ms, candle_type, is_new_pair, until_ms)
+
+    if df.empty:
+        assert first_date is None
+        assert last_date is None
+    else:
+        assert df["date"].iloc[0] == first_date
+        assert df["date"].iloc[-1] == last_date
+        assert (
+            df["date"].diff().iloc[1:] == timedelta(seconds=timeframe_to_seconds(timeframe))
+        ).all()
+
+    if candle_called:
+        candle_mock.assert_called_once()
+    if archive_called:
+        archive_mock.assert_called_once()
+    if api_called:
+        api_mock.assert_called_once()
+
+
+@pytest.mark.parametrize(
+    "pair,notional_value,mm_ratio,amt",
+    [
+        ("XRP/USDT:USDT", 0.0, 0.025, 0),
+        ("BNB/USDT:USDT", 100.0, 0.0065, 0),
+        ("BTC/USDT:USDT", 170.30, 0.004, 0),
+        ("XRP/USDT:USDT", 999999.9, 0.1, 27500.0),
+        ("BNB/USDT:USDT", 5000000.0, 0.15, 233035.0),
+        ("BTC/USDT:USDT", 600000000, 0.5, 1.997038e8),
+    ],
+)
+def test_get_maintenance_ratio_and_amt_binance(
+    default_conf,
+    mocker,
+    leverage_tiers,
+    pair,
+    notional_value,
+    mm_ratio,
+    amt,
+):
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    exchange._leverage_tiers = leverage_tiers
+    (result_ratio, result_amt) = exchange.get_maintenance_ratio_and_amt(pair, notional_value)
+    assert (round(result_ratio, 8), round(result_amt, 8)) == (mm_ratio, amt)
+
+
+async def test__async_get_trade_history_id_binance(default_conf_usdt, mocker, fetch_trades_result):
+    default_conf_usdt["exchange"]["only_from_ccxt"] = True
+    exchange = get_patched_exchange(mocker, default_conf_usdt, exchange="binance")
+
+    async def mock_get_trade_hist(pair, *args, **kwargs):
+        if "since" in kwargs:
+            # older than initial call
+            if kwargs["since"] < 1565798399752:
+                return []
+            else:
+                # Don't expect to get here
+                raise ValueError("Unexpected call")
+                # return fetch_trades_result[:-2]
+        elif kwargs.get("params", {}).get(exchange._trades_pagination_arg) == "0":
+            # Return first 3
+            return fetch_trades_result[:-2]
+        elif kwargs.get("params", {}).get(exchange._trades_pagination_arg) in (
+            fetch_trades_result[-3]["id"],
+            1565798399752,
+        ):
+            # Return 2
+            return fetch_trades_result[-3:-1]
+        else:
+            # Return last 2
+            return fetch_trades_result[-2:]
+
+    exchange._api_async.fetch_trades = MagicMock(side_effect=mock_get_trade_hist)
+
+    pair = "ETH/BTC"
+    ret = await exchange._async_get_trade_history_id(
+        pair,
+        since=fetch_trades_result[0]["timestamp"],
+        until=fetch_trades_result[-1]["timestamp"] - 1,
+    )
+    assert ret[0] == pair
+    assert isinstance(ret[1], list)
+    assert exchange._api_async.fetch_trades.call_count == 4
+
+    fetch_trades_cal = exchange._api_async.fetch_trades.call_args_list
+    # first call (using since, not fromId)
+    assert fetch_trades_cal[0][0][0] == pair
+    assert fetch_trades_cal[0][1]["since"] == fetch_trades_result[0]["timestamp"]
+
+    # 2nd call
+    assert fetch_trades_cal[1][0][0] == pair
+    assert "params" in fetch_trades_cal[1][1]
+    pagination_arg = exchange._ft_has["trades_pagination_arg"]
+    assert pagination_arg in fetch_trades_cal[1][1]["params"]
+    # Initial call was with from_id = "0"
+    assert fetch_trades_cal[1][1]["params"][pagination_arg] == "0"
+
+    assert fetch_trades_cal[2][1]["params"][pagination_arg] != "0"
+    assert fetch_trades_cal[3][1]["params"][pagination_arg] != "0"
+
+    # Clean up event loop to avoid warnings
+    exchange.close()
+
+
+async def test__async_get_trade_history_id_binance_fast(
+    default_conf_usdt, mocker, fetch_trades_result
+):
+    default_conf_usdt["exchange"]["only_from_ccxt"] = False
+    exchange = get_patched_exchange(mocker, default_conf_usdt, exchange="binance")
+
+    async def mock_get_trade_hist(pair, *args, **kwargs):
+        if "since" in kwargs:
+            pass
+            # older than initial call
+            # if kwargs["since"] < 1565798399752:
+            #     return []
+            # else:
+            #     # Don't expect to get here
+            #     raise ValueError("Unexpected call")
+            #     # return fetch_trades_result[:-2]
+        elif kwargs.get("params", {}).get(exchange._trades_pagination_arg) == "0":
+            # Return first 3
+            return fetch_trades_result[:-2]
+        # elif kwargs.get("params", {}).get(exchange._trades_pagination_arg) in (
+        #     fetch_trades_result[-3]["id"],
+        #     1565798399752,
+        # ):
+        #     # Return 2
+        #     return fetch_trades_result[-3:-1]
+        # else:
+        #     # Return last 2
+        #     return fetch_trades_result[-2:]
+
+    pair = "ETH/BTC"
+    mocker.patch(
+        "freqtrade.exchange.binance.download_archive_trades",
+        return_value=(pair, trades_dict_to_list(fetch_trades_result[-2:])),
+    )
+
+    exchange._api_async.fetch_trades = MagicMock(side_effect=mock_get_trade_hist)
+
+    ret = await exchange._async_get_trade_history(
+        pair,
+        since=fetch_trades_result[0]["timestamp"],
+        until=fetch_trades_result[-1]["timestamp"] - 1,
+    )
+
+    assert ret[0] == pair
+    assert isinstance(ret[1], list)
+
+    # Clean up event loop to avoid warnings
+    exchange.close()
diff --git a/tests/exchange/test_binance_public_data.py b/tests/exchange/test_binance_public_data.py
new file mode 100644
index 000000000..ab299321b
--- /dev/null
+++ b/tests/exchange/test_binance_public_data.py
@@ -0,0 +1,496 @@
+import asyncio
+import datetime
+import io
+import re
+import sys
+import zipfile
+from datetime import timedelta
+
+import aiohttp
+import pandas as pd
+import pytest
+
+from freqtrade.enums import CandleType
+from freqtrade.exchange.binance_public_data import (
+    BadHttpStatus,
+    Http404,
+    binance_vision_trades_zip_url,
+    binance_vision_zip_name,
+    download_archive_ohlcv,
+    download_archive_trades,
+    get_daily_ohlcv,
+    get_daily_trades,
+)
+from freqtrade.util.datetime_helpers import dt_ts, dt_utc
+from ft_client.test_client.test_rest_client import log_has_re
+
+
+@pytest.fixture(scope="module")
+def event_loop_policy(request):
+    if sys.platform == "win32":
+        return asyncio.WindowsSelectorEventLoopPolicy()
+    else:
+        return asyncio.DefaultEventLoopPolicy()
+
+
+class MockResponse:
+    """AioHTTP response mock"""
+
+    def __init__(self, content, status, reason=""):
+        self._content = content
+        self.status = status
+        self.reason = reason
+
+    async def read(self):
+        return self._content
+
+    async def __aexit__(self, exc_type, exc, tb):
+        pass
+
+    async def __aenter__(self):
+        return self
+
+
+# spot klines archive csv file format, the futures/um klines don't have the header line
+#
+# open_time,open,high,low,close,volume,close_time,quote_volume,count,taker_buy_volume,taker_buy_quote_volume,ignore  # noqa: E501
+# 1698364800000,34161.6,34182.5,33977.4,34024.2,409953,1698368399999,1202.97118037,15095,192220,564.12041453,0  # noqa: E501
+# 1698368400000,34024.2,34060.1,33776.4,33848.4,740960,1698371999999,2183.75671155,23938,368266,1085.17080793,0  # noqa: E501
+# 1698372000000,33848.5,34150.0,33815.1,34094.2,390376,1698375599999,1147.73267094,13854,231446,680.60405822,0  # noqa: E501
+
+
+def make_response_from_url(start_date, end_date):
+    def make_daily_df(date, timeframe):
+        start = dt_utc(date.year, date.month, date.day)
+        end = start + timedelta(days=1)
+        date_col = pd.date_range(start, end, freq=timeframe.replace("m", "min"), inclusive="left")
+        cols = (
+            "open_time,open,high,low,close,volume,close_time,quote_volume,count,taker_buy_volume,"
+            "taker_buy_quote_volume,ignore"
+        )
+        df = pd.DataFrame(columns=cols.split(","), dtype=float)
+        df["open_time"] = date_col.astype("int64") // 10**6
+        df["open"] = df["high"] = df["low"] = df["close"] = df["volume"] = 1.0
+        return df
+
+    def make_daily_zip(asset_type_url_segment, symbol, timeframe, date) -> bytes:
+        df = make_daily_df(date, timeframe)
+        if asset_type_url_segment == "spot":
+            header = True
+        elif asset_type_url_segment == "futures/um":
+            header = None
+        else:
+            raise ValueError
+        csv = df.to_csv(index=False, header=header)
+        zip_buffer = io.BytesIO()
+        with zipfile.ZipFile(zip_buffer, "w") as zipf:
+            zipf.writestr(binance_vision_zip_name(symbol, timeframe, date), csv)
+        return zip_buffer.getvalue()
+
+    def make_response(url):
+        pattern = (
+            r"https://data.binance.vision/data/(?P<asset_type_url_segment>spot|futures/um)"
+            r"/daily/klines/(?P<symbol>.*?)/(?P<timeframe>.*?)/(?P=symbol)-(?P=timeframe)-"
+            r"(?P<date>\d{4}-\d{2}-\d{2}).zip"
+        )
+        m = re.match(pattern, url)
+        if not m:
+            return MockResponse(content="", status=404)
+
+        date = datetime.datetime.strptime(m["date"], "%Y-%m-%d").date()
+        if date < start_date or date > end_date:
+            return MockResponse(content="", status=404)
+
+        zip_file = make_daily_zip(m["asset_type_url_segment"], m["symbol"], m["timeframe"], date)
+        return MockResponse(content=zip_file, status=200)
+
+    return make_response
+
+
+@pytest.mark.parametrize(
+    "candle_type,pair,since,until,first_date,last_date,stop_on_404",
+    [
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 2),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23),
+            False,
+        ),
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23, 59, 59),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23),
+            False,
+        ),
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 5),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 3, 23),
+            False,
+        ),
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2019, 12, 25),
+            dt_utc(2020, 1, 5),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 3, 23),
+            False,
+        ),
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2019, 1, 1),
+            dt_utc(2019, 1, 5),
+            None,
+            None,
+            False,
+        ),
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2021, 1, 1),
+            dt_utc(2021, 1, 5),
+            None,
+            None,
+            False,
+        ),
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2020, 1, 2),
+            None,
+            dt_utc(2020, 1, 2),
+            dt_utc(2020, 1, 3, 23),
+            False,
+        ),
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2020, 1, 5),
+            dt_utc(2020, 1, 1),
+            None,
+            None,
+            False,
+        ),
+        (
+            CandleType.FUTURES,
+            "BTC/USDT:USDT",
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23, 59, 59),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23),
+            False,
+        ),
+        (
+            CandleType.INDEX,
+            "N/A",
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 1, 23, 59, 59),
+            None,
+            None,
+            False,
+        ),
+        # stop_on_404 = True
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2019, 12, 25),
+            dt_utc(2020, 1, 5),
+            None,
+            None,
+            True,
+        ),
+        (
+            CandleType.SPOT,
+            "BTC/USDT",
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 5),
+            dt_utc(2020, 1, 1),
+            dt_utc(2020, 1, 3, 23),
+            True,
+        ),
+        (
+            CandleType.FUTURES,
+            "BTC/USDT:USDT",
+            dt_utc(2019, 12, 25),
+            dt_utc(2020, 1, 5),
+            None,
+            None,
+            True,
+        ),
+    ],
+)
+async def test_download_archive_ohlcv(
+    mocker, candle_type, pair, since, until, first_date, last_date, stop_on_404
+):
+    history_start = dt_utc(2020, 1, 1).date()
+    history_end = dt_utc(2020, 1, 3).date()
+    timeframe = "1h"
+
+    since_ms = dt_ts(since)
+    until_ms = dt_ts(until)
+
+    mocker.patch(
+        "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+        side_effect=make_response_from_url(history_start, history_end),
+    )
+    markets = {"BTC/USDT": {"id": "BTCUSDT"}, "BTC/USDT:USDT": {"id": "BTCUSDT"}}
+
+    df = await download_archive_ohlcv(
+        candle_type,
+        pair,
+        timeframe,
+        since_ms=since_ms,
+        until_ms=until_ms,
+        markets=markets,
+        stop_on_404=stop_on_404,
+    )
+
+    if df.empty:
+        assert first_date is None and last_date is None
+    else:
+        assert candle_type in [CandleType.SPOT, CandleType.FUTURES]
+        assert df["date"].iloc[0] == first_date
+        assert df["date"].iloc[-1] == last_date
+
+
+async def test_download_archive_ohlcv_exception(mocker):
+    timeframe = "1h"
+    pair = "BTC/USDT"
+
+    since_ms = dt_ts(dt_utc(2020, 1, 1))
+    until_ms = dt_ts(dt_utc(2020, 1, 2))
+
+    markets = {"BTC/USDT": {"id": "BTCUSDT"}, "BTC/USDT:USDT": {"id": "BTCUSDT"}}
+    mocker.patch(
+        "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get", side_effect=RuntimeError
+    )
+
+    df = await download_archive_ohlcv(
+        CandleType.SPOT, pair, timeframe, since_ms=since_ms, until_ms=until_ms, markets=markets
+    )
+
+    assert df.empty
+
+
+async def test_get_daily_ohlcv(mocker, testdatadir):
+    symbol = "BTCUSDT"
+    timeframe = "1h"
+    date = dt_utc(2024, 10, 28).date()
+    first_date = dt_utc(2024, 10, 28)
+    last_date = dt_utc(2024, 10, 28, 23)
+
+    async with aiohttp.ClientSession() as session:
+        spot_path = (
+            testdatadir / "binance/binance_public_data/spot-klines-BTCUSDT-1h-2024-10-28.zip"
+        )
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(spot_path.read_bytes(), 200),
+        )
+        df = await get_daily_ohlcv(symbol, timeframe, CandleType.SPOT, date, session)
+        assert get.call_count == 1
+        assert df["date"].iloc[0] == first_date
+        assert df["date"].iloc[-1] == last_date
+
+        futures_path = (
+            testdatadir / "binance/binance_public_data/futures-um-klines-BTCUSDT-1h-2024-10-28.zip"
+        )
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(futures_path.read_bytes(), 200),
+        )
+        df = await get_daily_ohlcv(symbol, timeframe, CandleType.FUTURES, date, session)
+        assert get.call_count == 1
+        assert df["date"].iloc[0] == first_date
+        assert df["date"].iloc[-1] == last_date
+
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(b"", 404),
+        )
+        with pytest.raises(Http404):
+            df = await get_daily_ohlcv(
+                symbol, timeframe, CandleType.SPOT, date, session, retry_delay=0
+            )
+        assert get.call_count == 1
+
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(b"", 500),
+        )
+        mocker.patch("asyncio.sleep")
+        with pytest.raises(BadHttpStatus):
+            df = await get_daily_ohlcv(symbol, timeframe, CandleType.SPOT, date, session)
+        assert get.call_count == 4  # 1 + 3 default retries
+
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(b"nop", 200),
+        )
+        with pytest.raises(zipfile.BadZipFile):
+            df = await get_daily_ohlcv(symbol, timeframe, CandleType.SPOT, date, session)
+        assert get.call_count == 4  # 1 + 3 default retries
+
+
+async def test_download_archive_trades(mocker, caplog):
+    pair = "BTC/USDT"
+
+    since_ms = dt_ts(dt_utc(2020, 1, 1))
+    until_ms = dt_ts(dt_utc(2020, 1, 2))
+    markets = {"BTC/USDT": {"id": "BTCUSDT"}, "BTC/USDT:USDT": {"id": "BTCUSDT"}}
+
+    mocker.patch("freqtrade.exchange.binance_public_data.get_daily_trades", return_value=[[2, 3]])
+
+    pair1, res = await download_archive_trades(
+        CandleType.SPOT, pair, since_ms=since_ms, until_ms=until_ms, markets=markets
+    )
+    assert pair1 == pair
+    assert res == [[2, 3], [2, 3]]
+
+    mocker.patch(
+        "freqtrade.exchange.binance_public_data.get_daily_trades",
+        side_effect=Http404("xxx", dt_utc(2020, 1, 1), "http://example.com/something"),
+    )
+
+    pair1, res = await download_archive_trades(
+        CandleType.SPOT, pair, since_ms=since_ms, until_ms=until_ms, markets=markets
+    )
+
+    assert pair1 == pair
+    assert res == []
+    # exit on day 1
+    assert log_has_re("Fast download is unavailable", caplog)
+
+    # Test fail on day 2
+    caplog.clear()
+    mocker.patch(
+        "freqtrade.exchange.binance_public_data.get_daily_trades",
+        side_effect=[
+            [[2, 3]],
+            [[2, 3]],
+            Http404("xxx", dt_utc(2020, 1, 2), "http://example.com/something"),
+            [[2, 3]],
+        ],
+    )
+    # Download 3 days
+    until_ms = dt_ts(dt_utc(2020, 1, 3))
+
+    pair1, res = await download_archive_trades(
+        CandleType.SPOT, pair, since_ms=since_ms, until_ms=until_ms, markets=markets
+    )
+
+    assert pair1 == pair
+    assert res == [[2, 3], [2, 3]]
+    assert log_has_re(r"Binance fast download .*stopped", caplog)
+
+
+async def test_download_archive_trades_exception(mocker, caplog):
+    pair = "BTC/USDT"
+
+    since_ms = dt_ts(dt_utc(2020, 1, 1))
+    until_ms = dt_ts(dt_utc(2020, 1, 2))
+
+    markets = {"BTC/USDT": {"id": "BTCUSDT"}, "BTC/USDT:USDT": {"id": "BTCUSDT"}}
+    mocker.patch(
+        "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get", side_effect=RuntimeError
+    )
+
+    pair1, res = await download_archive_trades(
+        CandleType.SPOT, pair, since_ms=since_ms, until_ms=until_ms, markets=markets
+    )
+
+    assert pair1 == pair
+    assert res == []
+    mocker.patch(
+        "freqtrade.exchange.binance_public_data._download_archive_trades", side_effect=RuntimeError
+    )
+
+    await download_archive_trades(
+        CandleType.SPOT, pair, since_ms=since_ms, until_ms=until_ms, markets=markets
+    )
+    assert pair1 == pair
+    assert res == []
+    assert log_has_re("An exception occurred during fast trades download", caplog)
+
+
+async def test_binance_vision_trades_zip_url():
+    url = binance_vision_trades_zip_url("BTCUSDT", CandleType.SPOT, dt_utc(2023, 10, 27))
+    assert (
+        url == "https://data.binance.vision/data/spot/daily/aggTrades/"
+        "BTCUSDT/BTCUSDT-aggTrades-2023-10-27.zip"
+    )
+
+    url = binance_vision_trades_zip_url("BTCUSDT", CandleType.FUTURES, dt_utc(2023, 10, 28))
+    assert (
+        url == "https://data.binance.vision/data/futures/um/daily/aggTrades/"
+        "BTCUSDT/BTCUSDT-aggTrades-2023-10-28.zip"
+    )
+
+
+async def test_get_daily_trades(mocker, testdatadir):
+    symbol = "PEPEUSDT"
+    symbol_futures = "APEUSDT"
+    date = dt_utc(2024, 10, 28).date()
+    first_date = 1729987202368
+    last_date = 1730073596350
+
+    async with aiohttp.ClientSession() as session:
+        spot_path = (
+            testdatadir / "binance/binance_public_data/spot-PEPEUSDT-aggTrades-2024-10-27.zip"
+        )
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(spot_path.read_bytes(), 200),
+        )
+        res = await get_daily_trades(symbol, CandleType.SPOT, date, session)
+        assert get.call_count == 1
+        assert res[0][0] == first_date
+        assert res[-1][0] == last_date
+
+        futures_path = (
+            testdatadir / "binance/binance_public_data/futures-APEUSDT-aggTrades-2024-10-18.zip"
+        )
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(futures_path.read_bytes(), 200),
+        )
+        res_fut = await get_daily_trades(symbol_futures, CandleType.FUTURES, date, session)
+        assert get.call_count == 1
+        assert res_fut[0][0] == 1729209603958
+        assert res_fut[-1][0] == 1729295981272
+
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(b"", 404),
+        )
+        with pytest.raises(Http404):
+            await get_daily_trades(symbol, CandleType.SPOT, date, session, retry_delay=0)
+        assert get.call_count == 1
+
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(b"", 500),
+        )
+        mocker.patch("asyncio.sleep")
+        with pytest.raises(BadHttpStatus):
+            await get_daily_trades(symbol, CandleType.SPOT, date, session)
+        assert get.call_count == 4  # 1 + 3 default retries
+
+        get = mocker.patch(
+            "freqtrade.exchange.binance_public_data.aiohttp.ClientSession.get",
+            return_value=MockResponse(b"nop", 200),
+        )
+        with pytest.raises(zipfile.BadZipFile):
+            await get_daily_trades(symbol, CandleType.SPOT, date, session)
+        assert get.call_count == 4  # 1 + 3 default retries
diff --git a/tests/exchange/test_bitpanda.py b/tests/exchange/test_bitpanda.py
new file mode 100644
index 000000000..b007bea5c
--- /dev/null
+++ b/tests/exchange/test_bitpanda.py
@@ -0,0 +1,54 @@
+from datetime import datetime
+from unittest.mock import MagicMock
+
+from tests.conftest import EXMS, get_patched_exchange
+
+
+def test_get_trades_for_order(default_conf, mocker):
+    exchange_name = "bitpanda"
+    order_id = "ABCD-ABCD"
+    since = datetime(2018, 5, 5, 0, 0, 0)
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    api_mock = MagicMock()
+
+    api_mock.fetch_my_trades = MagicMock(
+        return_value=[
+            {
+                "id": "TTR67E-3PFBD-76IISV",
+                "order": "ABCD-ABCD",
+                "info": {
+                    "pair": "XLTCZBTC",
+                    "time": 1519860024.4388,
+                    "type": "buy",
+                    "ordertype": "limit",
+                    "price": "20.00000",
+                    "cost": "38.62000",
+                    "fee": "0.06179",
+                    "vol": "5",
+                    "id": "ABCD-ABCD",
+                },
+                "timestamp": 1519860024438,
+                "datetime": "2018-02-28T23:20:24.438Z",
+                "symbol": "LTC/BTC",
+                "type": "limit",
+                "side": "buy",
+                "price": 165.0,
+                "amount": 0.2340606,
+                "fee": {"cost": 0.06179, "currency": "BTC"},
+            }
+        ]
+    )
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    orders = exchange.get_trades_for_order(order_id, "LTC/BTC", since)
+    assert len(orders) == 1
+    assert orders[0]["price"] == 165
+    assert api_mock.fetch_my_trades.call_count == 1
+    # since argument should be
+    assert isinstance(api_mock.fetch_my_trades.call_args[0][1], int)
+    assert api_mock.fetch_my_trades.call_args[0][0] == "LTC/BTC"
+    # Same test twice, hardcoded number and doing the same calculation
+    assert api_mock.fetch_my_trades.call_args[0][1] == 1525478395000
+    # bitpanda requires "to" argument.
+    assert "to" in api_mock.fetch_my_trades.call_args[1]["params"]
diff --git a/tests/exchange/test_bybit.py b/tests/exchange/test_bybit.py
new file mode 100644
index 000000000..4448f49de
--- /dev/null
+++ b/tests/exchange/test_bybit.py
@@ -0,0 +1,199 @@
+from datetime import datetime, timedelta, timezone
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.enums.marginmode import MarginMode
+from freqtrade.enums.tradingmode import TradingMode
+from tests.conftest import EXMS, get_mock_coro, get_patched_exchange, log_has
+from tests.exchange.test_exchange import ccxt_exceptionhandlers
+
+
+def test_additional_exchange_init_bybit(default_conf, mocker, caplog):
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+    api_mock = MagicMock()
+    api_mock.set_position_mode = MagicMock(return_value={"dualSidePosition": False})
+    api_mock.is_unified_enabled = MagicMock(return_value=[False, False])
+
+    exchange = get_patched_exchange(mocker, default_conf, exchange="bybit", api_mock=api_mock)
+    assert api_mock.set_position_mode.call_count == 1
+    assert api_mock.is_unified_enabled.call_count == 1
+    assert exchange.unified_account is False
+
+    assert log_has("Bybit: Standard account.", caplog)
+
+    api_mock.set_position_mode.reset_mock()
+    api_mock.is_unified_enabled = MagicMock(return_value=[False, True])
+    exchange = get_patched_exchange(mocker, default_conf, exchange="bybit", api_mock=api_mock)
+    assert log_has("Bybit: Unified account. Assuming dedicated subaccount for this bot.", caplog)
+    assert api_mock.set_position_mode.call_count == 1
+    assert api_mock.is_unified_enabled.call_count == 1
+    assert exchange.unified_account is True
+
+    ccxt_exceptionhandlers(
+        mocker, default_conf, api_mock, "bybit", "additional_exchange_init", "set_position_mode"
+    )
+
+
+async def test_bybit_fetch_funding_rate(default_conf, mocker):
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    api_mock = MagicMock()
+    api_mock.fetch_funding_rate_history = get_mock_coro(return_value=[])
+    exchange = get_patched_exchange(mocker, default_conf, exchange="bybit", api_mock=api_mock)
+    limit = 200
+    # Test fetch_funding_rate_history (current data)
+    await exchange._fetch_funding_rate_history(
+        pair="BTC/USDT:USDT",
+        timeframe="4h",
+        limit=limit,
+    )
+
+    assert api_mock.fetch_funding_rate_history.call_count == 1
+    assert api_mock.fetch_funding_rate_history.call_args_list[0][0][0] == "BTC/USDT:USDT"
+    kwargs = api_mock.fetch_funding_rate_history.call_args_list[0][1]
+    assert kwargs["since"] is None
+
+    api_mock.fetch_funding_rate_history.reset_mock()
+    since_ms = 1610000000000
+    # Test fetch_funding_rate_history (current data)
+    await exchange._fetch_funding_rate_history(
+        pair="BTC/USDT:USDT",
+        timeframe="4h",
+        limit=limit,
+        since_ms=since_ms,
+    )
+
+    assert api_mock.fetch_funding_rate_history.call_count == 1
+    assert api_mock.fetch_funding_rate_history.call_args_list[0][0][0] == "BTC/USDT:USDT"
+    kwargs = api_mock.fetch_funding_rate_history.call_args_list[0][1]
+    assert kwargs["since"] == since_ms
+
+
+def test_bybit_get_funding_fees(default_conf, mocker):
+    now = datetime.now(timezone.utc)
+    exchange = get_patched_exchange(mocker, default_conf, exchange="bybit")
+    exchange._fetch_and_calculate_funding_fees = MagicMock()
+    exchange.get_funding_fees("BTC/USDT:USDT", 1, False, now)
+    assert exchange._fetch_and_calculate_funding_fees.call_count == 0
+
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, exchange="bybit")
+    exchange._fetch_and_calculate_funding_fees = MagicMock()
+    exchange.get_funding_fees("BTC/USDT:USDT", 1, False, now)
+
+    assert exchange._fetch_and_calculate_funding_fees.call_count == 1
+
+
+def test_bybit_fetch_orders(default_conf, mocker, limit_order):
+    api_mock = MagicMock()
+    api_mock.fetch_orders = MagicMock(
+        return_value=[
+            limit_order["buy"],
+            limit_order["sell"],
+        ]
+    )
+    api_mock.fetch_open_orders = MagicMock(return_value=[limit_order["buy"]])
+    api_mock.fetch_closed_orders = MagicMock(return_value=[limit_order["buy"]])
+
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    start_time = datetime.now(timezone.utc) - timedelta(days=20)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="bybit")
+    # Not available in dry-run
+    assert exchange.fetch_orders("mocked", start_time) == []
+    assert api_mock.fetch_orders.call_count == 0
+    default_conf["dry_run"] = False
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="bybit")
+    res = exchange.fetch_orders("mocked", start_time)
+    # Bybit will call the endpoint 3 times, as it has a limit of 7 days per call
+    assert api_mock.fetch_orders.call_count == 3
+    assert api_mock.fetch_open_orders.call_count == 0
+    assert api_mock.fetch_closed_orders.call_count == 0
+    assert len(res) == 2 * 3
+
+
+def test_bybit_fetch_order_canceled_empty(default_conf_usdt, mocker):
+    default_conf_usdt["dry_run"] = False
+
+    api_mock = MagicMock()
+    api_mock.fetch_order = MagicMock(
+        return_value={
+            "id": "123",
+            "symbol": "BTC/USDT",
+            "status": "canceled",
+            "filled": 0.0,
+            "remaining": 0.0,
+            "amount": 20.0,
+        }
+    )
+
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf_usdt, api_mock, exchange="bybit")
+
+    res = exchange.fetch_order("123", "BTC/USDT")
+    assert res["remaining"] is None
+    assert res["filled"] == 0.0
+    assert res["amount"] == 20.0
+    assert res["status"] == "canceled"
+
+    api_mock.fetch_order = MagicMock(
+        return_value={
+            "id": "123",
+            "symbol": "BTC/USDT",
+            "status": "canceled",
+            "filled": 0.0,
+            "remaining": 20.0,
+            "amount": 20.0,
+        }
+    )
+    # Don't touch orders which return correctly.
+    res1 = exchange.fetch_order("123", "BTC/USDT")
+    assert res1["remaining"] == 20.0
+    assert res1["filled"] == 0.0
+    assert res1["amount"] == 20.0
+    assert res1["status"] == "canceled"
+
+    # Reverse test - remaining is not touched
+    api_mock.fetch_order = MagicMock(
+        return_value={
+            "id": "124",
+            "symbol": "BTC/USDT",
+            "status": "open",
+            "filled": 0.0,
+            "remaining": 20.0,
+            "amount": 20.0,
+        }
+    )
+    res2 = exchange.fetch_order("123", "BTC/USDT")
+    assert res2["remaining"] == 20.0
+    assert res2["filled"] == 0.0
+    assert res2["amount"] == 20.0
+    assert res2["status"] == "open"
+
+
+@pytest.mark.parametrize(
+    "side,order_type,uta,tradingmode,expected",
+    [
+        ("buy", "limit", False, "spot", True),
+        ("buy", "limit", False, "futures", True),
+        ("sell", "limit", False, "spot", True),
+        ("sell", "limit", False, "futures", True),
+        ("buy", "market", False, "spot", True),
+        ("buy", "market", False, "futures", False),
+        ("buy", "market", True, "spot", False),
+        ("buy", "market", True, "futures", False),
+    ],
+)
+def test_bybit__order_needs_price(
+    default_conf, mocker, side, order_type, uta, tradingmode, expected
+):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="bybit")
+    exchange.trading_mode = tradingmode
+    exchange.unified_account = uta
+
+    assert exchange._order_needs_price(side, order_type) == expected
diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py
new file mode 100644
index 000000000..c0023f0ad
--- /dev/null
+++ b/tests/exchange/test_exchange.py
@@ -0,0 +1,6294 @@
+import copy
+import logging
+from copy import deepcopy
+from datetime import datetime, timedelta, timezone
+from random import randint
+from unittest.mock import MagicMock, Mock, PropertyMock, patch
+
+import ccxt
+import pytest
+from numpy import nan
+from pandas import DataFrame, to_datetime
+
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
+from freqtrade.enums import CandleType, MarginMode, RunMode, TradingMode
+from freqtrade.exceptions import (
+    ConfigurationError,
+    DDosProtection,
+    DependencyException,
+    ExchangeError,
+    InsufficientFundsError,
+    InvalidOrderException,
+    OperationalException,
+    PricingError,
+    TemporaryError,
+)
+from freqtrade.exchange import (
+    Binance,
+    Bybit,
+    Exchange,
+    Kraken,
+    market_is_active,
+    timeframe_to_prev_date,
+)
+from freqtrade.exchange.common import (
+    API_FETCH_ORDER_RETRY_COUNT,
+    API_RETRY_COUNT,
+    calculate_backoff,
+    remove_exchange_credentials,
+)
+from freqtrade.resolvers.exchange_resolver import ExchangeResolver
+from freqtrade.util import dt_now, dt_ts
+from tests.conftest import (
+    EXMS,
+    generate_test_data_raw,
+    get_mock_coro,
+    get_patched_exchange,
+    log_has,
+    log_has_re,
+    num_log_has_re,
+)
+
+
+# Make sure to always keep one exchange here which is NOT subclassed!!
+EXCHANGES = ["binance", "kraken", "gate", "kucoin", "bybit", "okx"]
+
+get_entry_rate_data = [
+    ("other", 20, 19, 10, 0.0, 20),  # Full ask side
+    ("ask", 20, 19, 10, 0.0, 20),  # Full ask side
+    ("ask", 20, 19, 10, 1.0, 10),  # Full last side
+    ("ask", 20, 19, 10, 0.5, 15),  # Between ask and last
+    ("ask", 20, 19, 10, 0.7, 13),  # Between ask and last
+    ("ask", 20, 19, 10, 0.3, 17),  # Between ask and last
+    ("ask", 5, 6, 10, 1.0, 5),  # last bigger than ask
+    ("ask", 5, 6, 10, 0.5, 5),  # last bigger than ask
+    ("ask", 20, 19, 10, None, 20),  # price_last_balance missing
+    ("ask", 10, 20, None, 0.5, 10),  # last not available - uses ask
+    ("ask", 4, 5, None, 0.5, 4),  # last not available - uses ask
+    ("ask", 4, 5, None, 1, 4),  # last not available - uses ask
+    ("ask", 4, 5, None, 0, 4),  # last not available - uses ask
+    ("same", 21, 20, 10, 0.0, 20),  # Full bid side
+    ("bid", 21, 20, 10, 0.0, 20),  # Full bid side
+    ("bid", 21, 20, 10, 1.0, 10),  # Full last side
+    ("bid", 21, 20, 10, 0.5, 15),  # Between bid and last
+    ("bid", 21, 20, 10, 0.7, 13),  # Between bid and last
+    ("bid", 21, 20, 10, 0.3, 17),  # Between bid and last
+    ("bid", 6, 5, 10, 1.0, 5),  # last bigger than bid
+    ("bid", 21, 20, 10, None, 20),  # price_last_balance missing
+    ("bid", 6, 5, 10, 0.5, 5),  # last bigger than bid
+    ("bid", 21, 20, None, 0.5, 20),  # last not available - uses bid
+    ("bid", 6, 5, None, 0.5, 5),  # last not available - uses bid
+    ("bid", 6, 5, None, 1, 5),  # last not available - uses bid
+    ("bid", 6, 5, None, 0, 5),  # last not available - uses bid
+]
+
+get_exit_rate_data = [
+    ("bid", 12.0, 11.0, 11.5, 0.0, 11.0),  # full bid side
+    ("bid", 12.0, 11.0, 11.5, 1.0, 11.5),  # full last side
+    ("bid", 12.0, 11.0, 11.5, 0.5, 11.25),  # between bid and lat
+    ("bid", 12.0, 11.2, 10.5, 0.0, 11.2),  # Last smaller than bid
+    ("bid", 12.0, 11.2, 10.5, 1.0, 11.2),  # Last smaller than bid - uses bid
+    ("bid", 12.0, 11.2, 10.5, 0.5, 11.2),  # Last smaller than bid - uses bid
+    ("bid", 0.003, 0.002, 0.005, 0.0, 0.002),
+    ("bid", 0.003, 0.002, 0.005, None, 0.002),
+    ("ask", 12.0, 11.0, 12.5, 0.0, 12.0),  # full ask side
+    ("ask", 12.0, 11.0, 12.5, 1.0, 12.5),  # full last side
+    ("ask", 12.0, 11.0, 12.5, 0.5, 12.25),  # between bid and lat
+    ("ask", 12.2, 11.2, 10.5, 0.0, 12.2),  # Last smaller than ask
+    ("ask", 12.0, 11.0, 10.5, 1.0, 12.0),  # Last smaller than ask - uses ask
+    ("ask", 12.0, 11.2, 10.5, 0.5, 12.0),  # Last smaller than ask - uses ask
+    ("ask", 10.0, 11.0, 11.0, 0.0, 10.0),
+    ("ask", 10.11, 11.2, 11.0, 0.0, 10.11),
+    ("ask", 0.001, 0.002, 11.0, 0.0, 0.001),
+    ("ask", 0.006, 1.0, 11.0, 0.0, 0.006),
+    ("ask", 0.006, 1.0, 11.0, None, 0.006),
+]
+
+
+def ccxt_exceptionhandlers(
+    mocker,
+    default_conf,
+    api_mock,
+    exchange_name,
+    fun,
+    mock_ccxt_fun,
+    retries=API_RETRY_COUNT + 1,
+    **kwargs,
+):
+    with patch("freqtrade.exchange.common.time.sleep"):
+        with pytest.raises(DDosProtection):
+            api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.DDoSProtection("DDos"))
+            exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+            getattr(exchange, fun)(**kwargs)
+        assert api_mock.__dict__[mock_ccxt_fun].call_count == retries
+
+    with pytest.raises(TemporaryError):
+        api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.OperationFailed("DeaDBeef"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        getattr(exchange, fun)(**kwargs)
+    assert api_mock.__dict__[mock_ccxt_fun].call_count == retries
+
+    with pytest.raises(OperationalException):
+        api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.BaseError("DeadBeef"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        getattr(exchange, fun)(**kwargs)
+    assert api_mock.__dict__[mock_ccxt_fun].call_count == 1
+
+
+async def async_ccxt_exception(
+    mocker, default_conf, api_mock, fun, mock_ccxt_fun, retries=API_RETRY_COUNT + 1, **kwargs
+):
+    with patch("freqtrade.exchange.common.asyncio.sleep", get_mock_coro(None)):
+        with pytest.raises(DDosProtection):
+            api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.DDoSProtection("Dooh"))
+            exchange = get_patched_exchange(mocker, default_conf, api_mock)
+            await getattr(exchange, fun)(**kwargs)
+        assert api_mock.__dict__[mock_ccxt_fun].call_count == retries
+    exchange.close()
+
+    with pytest.raises(TemporaryError):
+        api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.NetworkError("DeadBeef"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock)
+        await getattr(exchange, fun)(**kwargs)
+    assert api_mock.__dict__[mock_ccxt_fun].call_count == retries
+    exchange.close()
+
+    with pytest.raises(OperationalException):
+        api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.BaseError("DeadBeef"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock)
+        await getattr(exchange, fun)(**kwargs)
+    assert api_mock.__dict__[mock_ccxt_fun].call_count == 1
+    exchange.close()
+
+
+def test_init(default_conf, mocker, caplog):
+    caplog.set_level(logging.INFO)
+    get_patched_exchange(mocker, default_conf)
+    assert log_has("Instance is running with dry_run enabled", caplog)
+
+
+def test_remove_exchange_credentials(default_conf) -> None:
+    conf = deepcopy(default_conf)
+    remove_exchange_credentials(conf["exchange"], False)
+
+    assert conf["exchange"]["key"] != ""
+    assert conf["exchange"]["secret"] != ""
+
+    remove_exchange_credentials(conf["exchange"], True)
+    assert conf["exchange"]["key"] == ""
+    assert conf["exchange"]["secret"] == ""
+    assert conf["exchange"]["password"] == ""
+    assert conf["exchange"]["uid"] == ""
+
+
+def test_init_ccxt_kwargs(default_conf, mocker, caplog):
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    aei_mock = mocker.patch(f"{EXMS}.additional_exchange_init")
+
+    caplog.set_level(logging.INFO)
+    conf = copy.deepcopy(default_conf)
+    conf["exchange"]["ccxt_async_config"] = {"aiohttp_trust_env": True, "asyncio_loop": True}
+    ex = Exchange(conf)
+    assert log_has(
+        "Applying additional ccxt config: {'aiohttp_trust_env': True, 'asyncio_loop': True}", caplog
+    )
+    assert ex._api_async.aiohttp_trust_env
+    assert not ex._api.aiohttp_trust_env
+    assert aei_mock.call_count == 1
+
+    # Reset logging and config
+    caplog.clear()
+    conf = copy.deepcopy(default_conf)
+    conf["exchange"]["ccxt_config"] = {"TestKWARG": 11}
+    conf["exchange"]["ccxt_sync_config"] = {"TestKWARG44": 11}
+    conf["exchange"]["ccxt_async_config"] = {"asyncio_loop": True}
+    asynclogmsg = "Applying additional ccxt config: {'TestKWARG': 11, 'asyncio_loop': True}"
+    ex = Exchange(conf)
+    assert not ex._api_async.aiohttp_trust_env
+    assert hasattr(ex._api, "TestKWARG")
+    assert ex._api.TestKWARG == 11
+    # ccxt_config is assigned to both sync and async
+    assert not hasattr(ex._api_async, "TestKWARG44")
+
+    assert hasattr(ex._api_async, "TestKWARG")
+    assert log_has("Applying additional ccxt config: {'TestKWARG': 11, 'TestKWARG44': 11}", caplog)
+    assert log_has(asynclogmsg, caplog)
+    # Test additional headers case
+    Exchange._ccxt_params = {"hello": "world"}
+    ex = Exchange(conf)
+
+    assert log_has("Applying additional ccxt config: {'TestKWARG': 11, 'TestKWARG44': 11}", caplog)
+    assert ex._api.hello == "world"
+    assert ex._ccxt_config == {}
+    Exchange._headers = {}
+
+
+def test_destroy(default_conf, mocker, caplog):
+    caplog.set_level(logging.DEBUG)
+    get_patched_exchange(mocker, default_conf)
+    assert log_has("Exchange object destroyed, closing async loop", caplog)
+
+
+def test_init_exception(default_conf, mocker):
+    default_conf["exchange"]["name"] = "wrong_exchange_name"
+
+    with pytest.raises(
+        OperationalException, match=f"Exchange {default_conf['exchange']['name']} is not supported"
+    ):
+        Exchange(default_conf)
+
+    default_conf["exchange"]["name"] = "binance"
+    with pytest.raises(
+        OperationalException, match=f"Exchange {default_conf['exchange']['name']} is not supported"
+    ):
+        mocker.patch("ccxt.binance", MagicMock(side_effect=AttributeError))
+        Exchange(default_conf)
+
+    with pytest.raises(
+        OperationalException, match=r"Initialization of ccxt failed. Reason: DeadBeef"
+    ):
+        mocker.patch("ccxt.binance", MagicMock(side_effect=ccxt.BaseError("DeadBeef")))
+        Exchange(default_conf)
+
+
+def test_exchange_resolver(default_conf, mocker, caplog):
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=MagicMock()))
+    mocker.patch(f"{EXMS}._load_async_markets")
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    default_conf["exchange"]["name"] = "zaif"
+    exchange = ExchangeResolver.load_exchange(default_conf)
+    assert isinstance(exchange, Exchange)
+    assert log_has_re(r"No .* specific subclass found. Using the generic class instead.", caplog)
+    caplog.clear()
+
+    default_conf["exchange"]["name"] = "Bybit"
+    exchange = ExchangeResolver.load_exchange(default_conf)
+    assert isinstance(exchange, Exchange)
+    assert isinstance(exchange, Bybit)
+    assert not log_has_re(
+        r"No .* specific subclass found. Using the generic class instead.", caplog
+    )
+    caplog.clear()
+
+    default_conf["exchange"]["name"] = "kraken"
+    exchange = ExchangeResolver.load_exchange(default_conf)
+    assert isinstance(exchange, Exchange)
+    assert isinstance(exchange, Kraken)
+    assert not isinstance(exchange, Binance)
+    assert not log_has_re(
+        r"No .* specific subclass found. Using the generic class instead.", caplog
+    )
+
+    default_conf["exchange"]["name"] = "binance"
+    exchange = ExchangeResolver.load_exchange(default_conf)
+    assert isinstance(exchange, Exchange)
+    assert isinstance(exchange, Binance)
+    assert not isinstance(exchange, Kraken)
+
+    assert not log_has_re(
+        r"No .* specific subclass found. Using the generic class instead.", caplog
+    )
+
+    # Test mapping
+    default_conf["exchange"]["name"] = "binanceus"
+    exchange = ExchangeResolver.load_exchange(default_conf)
+    assert isinstance(exchange, Exchange)
+    assert isinstance(exchange, Binance)
+    assert not isinstance(exchange, Kraken)
+
+
+def test_validate_order_time_in_force(default_conf, mocker, caplog):
+    caplog.set_level(logging.INFO)
+    # explicitly test bybit, exchanges implementing other policies need separate tests
+    ex = get_patched_exchange(mocker, default_conf, exchange="bybit")
+    tif = {
+        "buy": "gtc",
+        "sell": "gtc",
+    }
+
+    ex.validate_order_time_in_force(tif)
+    tif2 = {
+        "buy": "fok",
+        "sell": "ioc22",
+    }
+    with pytest.raises(OperationalException, match=r"Time in force.*not supported for .*"):
+        ex.validate_order_time_in_force(tif2)
+    tif2 = {
+        "buy": "fok",
+        "sell": "ioc",
+    }
+    # Patch to see if this will pass if the values are in the ft dict
+    ex._ft_has.update({"order_time_in_force": ["GTC", "FOK", "IOC"]})
+    ex.validate_order_time_in_force(tif2)
+
+
+def test_validate_orderflow(default_conf, mocker, caplog):
+    caplog.set_level(logging.INFO)
+    # Test bybit - as it doesn't support historic trades data.
+    ex = get_patched_exchange(mocker, default_conf, exchange="bybit")
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    ex.validate_orderflow({"use_public_trades": False})
+
+    with pytest.raises(ConfigurationError, match=r"Trade data not available for.*"):
+        ex.validate_orderflow({"use_public_trades": True})
+
+    # Binance supports orderflow.
+    ex = get_patched_exchange(mocker, default_conf, exchange="binance")
+    ex.validate_orderflow({"use_public_trades": False})
+    ex.validate_orderflow({"use_public_trades": True})
+
+
+def test_validate_freqai_compat(default_conf, mocker, caplog):
+    caplog.set_level(logging.INFO)
+    # Test kraken - as it doesn't support historic trades data.
+    ex = get_patched_exchange(mocker, default_conf, exchange="kraken")
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+
+    default_conf["freqai"] = {"enabled": False}
+    ex.validate_freqai(default_conf)
+
+    default_conf["freqai"] = {"enabled": True}
+    with pytest.raises(ConfigurationError, match=r"Historic OHLCV data not available for.*"):
+        ex.validate_freqai(default_conf)
+
+    # Binance supports historic data.
+    ex = get_patched_exchange(mocker, default_conf, exchange="binance")
+    default_conf["freqai"] = {"enabled": True}
+    ex.validate_freqai(default_conf)
+    default_conf["freqai"] = {"enabled": False}
+    ex.validate_freqai(default_conf)
+
+
+@pytest.mark.parametrize(
+    "price,precision_mode,precision,expected",
+    [
+        (2.34559, 2, 4, 0.0001),
+        (2.34559, 2, 5, 0.00001),
+        (2.34559, 2, 3, 0.001),
+        (2.9999, 2, 3, 0.001),
+        (200.0511, 2, 3, 0.001),
+        # Tests for Tick_size
+        (2.34559, 4, 0.0001, 0.0001),
+        (2.34559, 4, 0.00001, 0.00001),
+        (2.34559, 4, 0.0025, 0.0025),
+        (2.9909, 4, 0.0025, 0.0025),
+        (234.43, 4, 0.5, 0.5),
+        (234.43, 4, 0.0025, 0.0025),
+        (234.43, 4, 0.00013, 0.00013),
+    ],
+)
+def test_price_get_one_pip(default_conf, mocker, price, precision_mode, precision, expected):
+    markets = PropertyMock(return_value={"ETH/BTC": {"precision": {"price": precision}}})
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    mocker.patch(f"{EXMS}.markets", markets)
+    mocker.patch(f"{EXMS}.precisionMode", PropertyMock(return_value=precision_mode))
+    mocker.patch(f"{EXMS}.precision_mode_price", PropertyMock(return_value=precision_mode))
+    pair = "ETH/BTC"
+    assert pytest.approx(exchange.price_get_one_pip(pair, price)) == expected
+
+
+def test__get_stake_amount_limit(mocker, default_conf) -> None:
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    stoploss = -0.05
+    markets = {"ETH/BTC": {"symbol": "ETH/BTC"}}
+
+    # no pair found
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    with pytest.raises(ValueError, match=r".*get market information.*"):
+        exchange.get_min_pair_stake_amount("BNB/BTC", 1, stoploss)
+
+    # no cost/amount Min
+    markets["ETH/BTC"]["limits"] = {
+        "cost": {"min": None, "max": None},
+        "amount": {"min": None, "max": None},
+    }
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 1, stoploss)
+    assert result is None
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 1)
+    assert result == float("inf")
+
+    # min/max cost is set
+    markets["ETH/BTC"]["limits"] = {
+        "cost": {"min": 2, "max": 10000},
+        "amount": {"min": None, "max": None},
+    }
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    # min
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 1, stoploss)
+    expected_result = 2 * (1 + 0.05) / (1 - abs(stoploss))
+    assert pytest.approx(result) == expected_result
+    # With Leverage
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 1, stoploss, 3.0)
+    assert pytest.approx(result) == expected_result / 3
+    # max
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 2)
+    assert result == 10000
+
+    # min amount is set
+    markets["ETH/BTC"]["limits"] = {
+        "cost": {"min": None, "max": None},
+        "amount": {"min": 2, "max": 10000},
+    }
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, stoploss)
+    expected_result = 2 * 2 * (1 + 0.05)
+    assert pytest.approx(result) == expected_result
+    # With Leverage
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, stoploss, 5.0)
+    assert pytest.approx(result) == expected_result / 5
+    # max
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 2)
+    assert result == 20000
+
+    # min amount and cost are set (cost is minimal and therefore ignored)
+    markets["ETH/BTC"]["limits"] = {
+        "cost": {"min": 2, "max": None},
+        "amount": {"min": 2, "max": None},
+    }
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, stoploss)
+    expected_result = max(2, 2 * 2) * (1 + 0.05)
+    assert pytest.approx(result) == expected_result
+    # With Leverage
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, stoploss, 10)
+    assert pytest.approx(result) == expected_result / 10
+
+    # min amount and cost are set (amount is minial)
+    markets["ETH/BTC"]["limits"] = {
+        "cost": {"min": 8, "max": 10000},
+        "amount": {"min": 2, "max": 500},
+    }
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, stoploss)
+    expected_result = max(8, 2 * 2) * (1 + 0.05) / (1 - abs(stoploss))
+    assert pytest.approx(result) == expected_result
+    # With Leverage
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, stoploss, 7.0)
+    assert pytest.approx(result) == expected_result / 7.0
+    # Max
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 2)
+    assert result == 1000
+
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, -0.4)
+    expected_result = max(8, 2 * 2) * 1.5
+    assert pytest.approx(result) == expected_result
+    # With Leverage
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, -0.4, 8.0)
+    assert pytest.approx(result) == expected_result / 8.0
+    # Max
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 2)
+    assert result == 1000
+
+    # Really big stoploss
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, -1)
+    expected_result = max(8, 2 * 2) * 1.5
+    assert pytest.approx(result) == expected_result
+    # With Leverage
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, -1, 12.0)
+    assert pytest.approx(result) == expected_result / 12
+    # Max
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 2)
+    assert result == 1000
+
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 2, 12.0)
+    assert result == 1000 / 12
+
+    markets["ETH/BTC"]["contractSize"] = "0.01"
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+
+    # Contract size 0.01
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, -1)
+    assert pytest.approx(result) == expected_result * 0.01
+    # Max
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 2)
+    assert result == 10
+
+    markets["ETH/BTC"]["contractSize"] = "10"
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    # With Leverage, Contract size 10
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 2, -1, 12.0)
+    assert pytest.approx(result) == (expected_result / 12) * 10.0
+    # Max
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 2)
+    assert result == 10000
+
+
+def test_get_min_pair_stake_amount_real_data(mocker, default_conf) -> None:
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    stoploss = -0.05
+    markets = {"ETH/BTC": {"symbol": "ETH/BTC"}}
+
+    # ~Real Binance data
+    markets["ETH/BTC"]["limits"] = {
+        "cost": {"min": 0.0001, "max": 4000},
+        "amount": {"min": 0.001, "max": 10000},
+    }
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 0.020405, stoploss)
+    expected_result = max(0.0001, 0.001 * 0.020405) * (1 + 0.05) / (1 - abs(stoploss))
+    assert round(result, 8) == round(expected_result, 8)
+    # Max
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 2.0)
+    assert result == 4000
+
+    # Leverage
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 0.020405, stoploss, 3.0)
+    assert round(result, 8) == round(expected_result / 3, 8)
+
+    # Contract_size
+    markets["ETH/BTC"]["contractSize"] = 0.1
+    result = exchange.get_min_pair_stake_amount("ETH/BTC", 0.020405, stoploss, 3.0)
+    assert round(result, 8) == round((expected_result / 3), 8)
+
+    # Max
+    result = exchange.get_max_pair_stake_amount("ETH/BTC", 12.0)
+    assert result == 4000
+
+
+def test__load_async_markets(default_conf, mocker, caplog):
+    mocker.patch(f"{EXMS}._init_ccxt")
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    exchange = Exchange(default_conf)
+    exchange._api_async.load_markets = get_mock_coro(None)
+    exchange._load_async_markets()
+    assert exchange._api_async.load_markets.call_count == 1
+    caplog.set_level(logging.DEBUG)
+
+    exchange._api_async.load_markets = get_mock_coro(side_effect=ccxt.BaseError("deadbeef"))
+    with pytest.raises(TemporaryError, match="deadbeef"):
+        exchange._load_async_markets()
+
+    exchange._api_async.load_markets = get_mock_coro(side_effect=ccxt.DDoSProtection("deadbeef"))
+    with pytest.raises(DDosProtection, match="deadbeef"):
+        exchange._load_async_markets()
+
+    exchange._api_async.load_markets = get_mock_coro(side_effect=ccxt.OperationFailed("deadbeef"))
+    with pytest.raises(TemporaryError, match="deadbeef"):
+        exchange._load_async_markets()
+
+
+def test__load_markets(default_conf, mocker, caplog):
+    caplog.set_level(logging.INFO)
+    api_mock = MagicMock()
+    api_mock.load_markets = get_mock_coro(side_effect=ccxt.BaseError("SomeError"))
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    Exchange(default_conf)
+    assert log_has("Could not load markets.", caplog)
+
+    expected_return = {"ETH/BTC": "available"}
+    api_mock = MagicMock()
+    api_mock.load_markets = get_mock_coro(return_value=expected_return)
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    default_conf["exchange"]["pair_whitelist"] = ["ETH/BTC"]
+    ex = Exchange(default_conf)
+
+    assert ex.markets == expected_return
+
+
+def test_reload_markets(default_conf, mocker, caplog, time_machine):
+    caplog.set_level(logging.DEBUG)
+    initial_markets = {"ETH/BTC": {}}
+    updated_markets = {"ETH/BTC": {}, "LTC/BTC": {}}
+    start_dt = dt_now()
+    time_machine.move_to(start_dt, tick=False)
+    api_mock = MagicMock()
+    api_mock.load_markets = get_mock_coro(return_value=initial_markets)
+    default_conf["exchange"]["markets_refresh_interval"] = 10
+    exchange = get_patched_exchange(
+        mocker, default_conf, api_mock, exchange="binance", mock_markets=False
+    )
+    lam_spy = mocker.spy(exchange, "_load_async_markets")
+    assert exchange._last_markets_refresh == dt_ts()
+
+    assert exchange.markets == initial_markets
+
+    time_machine.move_to(start_dt + timedelta(minutes=8), tick=False)
+    # less than 10 minutes have passed, no reload
+    exchange.reload_markets()
+    assert exchange.markets == initial_markets
+    assert lam_spy.call_count == 0
+
+    api_mock.load_markets = get_mock_coro(return_value=updated_markets)
+    # more than 10 minutes have passed, reload is executed
+    time_machine.move_to(start_dt + timedelta(minutes=11), tick=False)
+    exchange.reload_markets()
+    assert exchange.markets == updated_markets
+    assert lam_spy.call_count == 1
+    assert log_has("Performing scheduled market reload..", caplog)
+
+    # Not called again
+    lam_spy.reset_mock()
+
+    exchange.reload_markets()
+    assert lam_spy.call_count == 0
+
+    # Another reload should happen but it fails.
+    time_machine.move_to(start_dt + timedelta(minutes=51), tick=False)
+    api_mock.load_markets = get_mock_coro(side_effect=ccxt.NetworkError("LoadError"))
+
+    exchange.reload_markets(force=False)
+    assert exchange.markets == updated_markets
+    assert lam_spy.call_count == 1
+    # Tried once, failed
+
+    lam_spy.reset_mock()
+    # When forcing (bot startup), it should retry 3 times.
+    exchange.reload_markets(force=True)
+    assert lam_spy.call_count == 4
+    assert exchange.markets == updated_markets
+
+
+def test_reload_markets_exception(default_conf, mocker, caplog):
+    caplog.set_level(logging.DEBUG)
+
+    api_mock = MagicMock()
+    api_mock.load_markets = get_mock_coro(side_effect=ccxt.NetworkError("LoadError"))
+    default_conf["exchange"]["markets_refresh_interval"] = 10
+    exchange = get_patched_exchange(
+        mocker, default_conf, api_mock, exchange="binance", mock_markets=False
+    )
+
+    exchange._last_markets_refresh = 2
+    # less than 10 minutes have passed, no reload
+    exchange.reload_markets()
+    assert exchange._last_markets_refresh == 2
+    assert log_has_re(r"Could not load markets\..*", caplog)
+
+
+@pytest.mark.parametrize("stake_currency", ["ETH", "BTC", "USDT"])
+def test_validate_stakecurrency(default_conf, stake_currency, mocker, caplog):
+    default_conf["stake_currency"] = stake_currency
+    api_mock = MagicMock()
+    type(api_mock).load_markets = get_mock_coro(
+        return_value={
+            "ETH/BTC": {"quote": "BTC"},
+            "LTC/BTC": {"quote": "BTC"},
+            "XRP/ETH": {"quote": "ETH"},
+            "NEO/USDT": {"quote": "USDT"},
+        }
+    )
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    Exchange(default_conf)
+
+
+def test_validate_stakecurrency_error(default_conf, mocker, caplog):
+    default_conf["stake_currency"] = "XRP"
+    api_mock = MagicMock()
+    type(api_mock).load_markets = get_mock_coro(
+        return_value={
+            "ETH/BTC": {"quote": "BTC"},
+            "LTC/BTC": {"quote": "BTC"},
+            "XRP/ETH": {"quote": "ETH"},
+            "NEO/USDT": {"quote": "USDT"},
+        }
+    )
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    with pytest.raises(
+        ConfigurationError,
+        match=r"XRP is not available as stake on .*Available currencies are: BTC, ETH, USDT",
+    ):
+        Exchange(default_conf)
+
+    type(api_mock).load_markets = get_mock_coro(side_effect=ccxt.NetworkError("No connection."))
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+
+    with pytest.raises(
+        OperationalException, match=r"Could not load markets, therefore cannot start\. Please.*"
+    ):
+        Exchange(default_conf)
+
+
+def test_get_quote_currencies(default_conf, mocker):
+    ex = get_patched_exchange(mocker, default_conf)
+
+    assert set(ex.get_quote_currencies()) == set(["USD", "ETH", "BTC", "USDT", "BUSD"])
+
+
+@pytest.mark.parametrize(
+    "pair,expected",
+    [
+        ("XRP/BTC", "BTC"),
+        ("LTC/USD", "USD"),
+        ("ETH/USDT", "USDT"),
+        ("XLTCUSDT", "USDT"),
+        ("XRP/NOCURRENCY", ""),
+    ],
+)
+def test_get_pair_quote_currency(default_conf, mocker, pair, expected):
+    ex = get_patched_exchange(mocker, default_conf)
+    assert ex.get_pair_quote_currency(pair) == expected
+
+
+@pytest.mark.parametrize(
+    "pair,expected",
+    [
+        ("XRP/BTC", "XRP"),
+        ("LTC/USD", "LTC"),
+        ("ETH/USDT", "ETH"),
+        ("XLTCUSDT", "LTC"),
+        ("XRP/NOCURRENCY", ""),
+    ],
+)
+def test_get_pair_base_currency(default_conf, mocker, pair, expected):
+    ex = get_patched_exchange(mocker, default_conf)
+    assert ex.get_pair_base_currency(pair) == expected
+
+
+@pytest.mark.parametrize("timeframe", [("5m"), ("1m"), ("15m"), ("1h")])
+def test_validate_timeframes(default_conf, mocker, timeframe):
+    default_conf["timeframe"] = timeframe
+    api_mock = MagicMock()
+    id_mock = PropertyMock(return_value="test_exchange")
+    type(api_mock).id = id_mock
+    timeframes = PropertyMock(return_value={"1m": "1m", "5m": "5m", "15m": "15m", "1h": "1h"})
+    type(api_mock).timeframes = timeframes
+
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    Exchange(default_conf)
+
+
+def test_validate_timeframes_failed(default_conf, mocker):
+    default_conf["timeframe"] = "3m"
+    api_mock = MagicMock()
+    id_mock = PropertyMock(return_value="test_exchange")
+    type(api_mock).id = id_mock
+    timeframes = PropertyMock(
+        return_value={"15s": "15s", "1m": "1m", "5m": "5m", "15m": "15m", "1h": "1h"}
+    )
+    type(api_mock).timeframes = timeframes
+
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    with pytest.raises(
+        ConfigurationError, match=r"Invalid timeframe '3m'. This exchange supports.*"
+    ):
+        Exchange(default_conf)
+    default_conf["timeframe"] = "15s"
+
+    with pytest.raises(
+        ConfigurationError, match=r"Timeframes < 1m are currently not supported by Freqtrade."
+    ):
+        Exchange(default_conf)
+
+    # Will not raise an exception in util mode.
+    default_conf["runmode"] = RunMode.UTIL_EXCHANGE
+    Exchange(default_conf)
+
+
+def test_validate_timeframes_emulated_ohlcv_1(default_conf, mocker):
+    default_conf["timeframe"] = "3m"
+    api_mock = MagicMock()
+    id_mock = PropertyMock(return_value="test_exchange")
+    type(api_mock).id = id_mock
+
+    # delete timeframes so magicmock does not autocreate it
+    del api_mock.timeframes
+
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    with pytest.raises(
+        OperationalException,
+        match=r"The ccxt library does not provide the list of timeframes "
+        r"for the exchange .* and this exchange "
+        r"is therefore not supported. *",
+    ):
+        Exchange(default_conf)
+
+
+def test_validate_timeframes_emulated_ohlcvi_2(default_conf, mocker):
+    default_conf["timeframe"] = "3m"
+    api_mock = MagicMock()
+    id_mock = PropertyMock(return_value="test_exchange")
+    type(api_mock).id = id_mock
+
+    # delete timeframes so magicmock does not autocreate it
+    del api_mock.timeframes
+
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    with pytest.raises(
+        OperationalException,
+        match=r"The ccxt library does not provide the list of timeframes "
+        r"for the exchange .* and this exchange "
+        r"is therefore not supported. *",
+    ):
+        Exchange(default_conf)
+
+
+def test_validate_timeframes_not_in_config(default_conf, mocker):
+    # TODO: this test does not assert ...
+    del default_conf["timeframe"]
+    api_mock = MagicMock()
+    id_mock = PropertyMock(return_value="test_exchange")
+    type(api_mock).id = id_mock
+    timeframes = PropertyMock(return_value={"1m": "1m", "5m": "5m", "15m": "15m", "1h": "1h"})
+    type(api_mock).timeframes = timeframes
+
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    mocker.patch(f"{EXMS}.validate_required_startup_candles")
+    Exchange(default_conf)
+
+
+def test_validate_pricing(default_conf, mocker):
+    api_mock = MagicMock()
+    has = {
+        "fetchL2OrderBook": True,
+        "fetchTicker": True,
+    }
+    type(api_mock).has = PropertyMock(return_value=has)
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_trading_mode_and_margin_mode")
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    mocker.patch(f"{EXMS}.name", "Binance")
+    default_conf["exchange"]["name"] = "binance"
+    ExchangeResolver.load_exchange(default_conf)
+    has.update({"fetchTicker": False})
+    with pytest.raises(OperationalException, match="Ticker pricing not available for .*"):
+        ExchangeResolver.load_exchange(default_conf)
+
+    has.update({"fetchTicker": True})
+
+    default_conf["exit_pricing"]["use_order_book"] = True
+    ExchangeResolver.load_exchange(default_conf)
+    has.update({"fetchL2OrderBook": False})
+
+    with pytest.raises(OperationalException, match="Orderbook not available for .*"):
+        ExchangeResolver.load_exchange(default_conf)
+
+    has.update({"fetchL2OrderBook": True})
+
+    # Binance has no tickers on futures
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+
+    with pytest.raises(OperationalException, match="Ticker pricing not available for .*"):
+        ExchangeResolver.load_exchange(default_conf)
+
+
+def test_validate_ordertypes(default_conf, mocker):
+    api_mock = MagicMock()
+
+    type(api_mock).has = PropertyMock(return_value={"createMarketOrder": True})
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    mocker.patch(f"{EXMS}.validate_pricing")
+
+    default_conf["order_types"] = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+    Exchange(default_conf)
+
+    type(api_mock).has = PropertyMock(return_value={"createMarketOrder": False})
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+
+    default_conf["order_types"] = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+    with pytest.raises(OperationalException, match=r"Exchange .* does not support market orders."):
+        Exchange(default_conf)
+
+    default_conf["order_types"] = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": True,
+    }
+    with pytest.raises(OperationalException, match=r"On exchange stoploss is not supported for .*"):
+        Exchange(default_conf)
+
+
+@pytest.mark.parametrize(
+    "exchange_name,stopadv, expected",
+    [
+        ("binance", "last", True),
+        ("binance", "mark", True),
+        ("binance", "index", False),
+        ("bybit", "last", True),
+        ("bybit", "mark", True),
+        ("bybit", "index", True),
+        ("okx", "last", True),
+        ("okx", "mark", True),
+        ("okx", "index", True),
+        ("gate", "last", True),
+        ("gate", "mark", True),
+        ("gate", "index", True),
+    ],
+)
+def test_validate_ordertypes_stop_advanced(default_conf, mocker, exchange_name, stopadv, expected):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+    type(api_mock).has = PropertyMock(return_value={"createMarketOrder": True})
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    default_conf["order_types"] = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": True,
+        "stoploss_price_type": stopadv,
+    }
+    default_conf["exchange"]["name"] = exchange_name
+    if expected:
+        ExchangeResolver.load_exchange(default_conf)
+    else:
+        with pytest.raises(
+            OperationalException, match=r"On exchange stoploss price type is not supported for .*"
+        ):
+            ExchangeResolver.load_exchange(default_conf)
+
+
+def test_validate_order_types_not_in_config(default_conf, mocker):
+    api_mock = MagicMock()
+    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+
+    conf = copy.deepcopy(default_conf)
+    Exchange(conf)
+
+
+def test_validate_required_startup_candles(default_conf, mocker, caplog):
+    api_mock = MagicMock()
+    mocker.patch(f"{EXMS}.name", PropertyMock(return_value="Binance"))
+
+    mocker.patch(f"{EXMS}._init_ccxt", api_mock)
+    mocker.patch(f"{EXMS}.validate_timeframes")
+    mocker.patch(f"{EXMS}._load_async_markets")
+    mocker.patch(f"{EXMS}.validate_pricing")
+    mocker.patch(f"{EXMS}.validate_stakecurrency")
+
+    default_conf["startup_candle_count"] = 20
+    ex = Exchange(default_conf)
+    assert ex
+    # assumption is that the exchange provides 500 candles per call.s
+    assert ex.validate_required_startup_candles(200, "5m") == 1
+    assert ex.validate_required_startup_candles(499, "5m") == 1
+    assert ex.validate_required_startup_candles(600, "5m") == 2
+    assert ex.validate_required_startup_candles(501, "5m") == 2
+    assert ex.validate_required_startup_candles(499, "5m") == 1
+    assert ex.validate_required_startup_candles(1000, "5m") == 3
+    assert ex.validate_required_startup_candles(2499, "5m") == 5
+    assert log_has_re(r"Using 5 calls to get OHLCV. This.*", caplog)
+
+    with pytest.raises(OperationalException, match=r"This strategy requires 2500.*"):
+        ex.validate_required_startup_candles(2500, "5m")
+
+    # Ensure the same also happens on init
+    default_conf["startup_candle_count"] = 6000
+    with pytest.raises(OperationalException, match=r"This strategy requires 6000.*"):
+        Exchange(default_conf)
+
+    # Emulate kraken mode
+    ex._ft_has["ohlcv_has_history"] = False
+    with pytest.raises(
+        OperationalException,
+        match=r"This strategy requires 2500.*, " r"which is more than the amount.*",
+    ):
+        ex.validate_required_startup_candles(2500, "5m")
+
+
+def test_exchange_has(default_conf, mocker):
+    exchange = get_patched_exchange(mocker, default_conf)
+    assert not exchange.exchange_has("ASDFASDF")
+    api_mock = MagicMock()
+
+    type(api_mock).has = PropertyMock(return_value={"deadbeef": True})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    assert exchange.exchange_has("deadbeef")
+
+    type(api_mock).has = PropertyMock(return_value={"deadbeef": False})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    assert not exchange.exchange_has("deadbeef")
+
+    exchange._ft_has["exchange_has_overrides"] = {"deadbeef": True}
+    assert exchange.exchange_has("deadbeef")
+
+
+@pytest.mark.parametrize(
+    "side,leverage",
+    [
+        ("buy", 1),
+        ("buy", 5),
+        ("sell", 1.0),
+        ("sell", 5.0),
+    ],
+)
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_create_dry_run_order(default_conf, mocker, side, exchange_name, leverage):
+    default_conf["dry_run"] = True
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+
+    order = exchange.create_dry_run_order(
+        pair="ETH/BTC", ordertype="limit", side=side, amount=1, rate=200, leverage=leverage
+    )
+    assert "id" in order
+    assert f"dry_run_{side}_" in order["id"]
+    assert order["side"] == side
+    assert order["type"] == "limit"
+    assert order["symbol"] == "ETH/BTC"
+    assert order["amount"] == 1
+    assert order["cost"] == 1 * 200
+
+
+@pytest.mark.parametrize(
+    "side,is_short,order_reason",
+    [
+        ("buy", False, "entry"),
+        ("sell", False, "exit"),
+        ("buy", True, "exit"),
+        ("sell", True, "entry"),
+    ],
+)
+@pytest.mark.parametrize(
+    "order_type,price_side,fee",
+    [
+        ("limit", "same", 1.0),
+        ("limit", "other", 2.0),
+        ("market", "same", 2.0),
+        ("market", "other", 2.0),
+    ],
+)
+def test_create_dry_run_order_fees(
+    default_conf,
+    mocker,
+    side,
+    order_type,
+    is_short,
+    order_reason,
+    price_side,
+    fee,
+):
+    exchange = get_patched_exchange(mocker, default_conf)
+    mocker.patch(
+        f"{EXMS}.get_fee",
+        side_effect=lambda symbol, taker_or_maker: 2.0 if taker_or_maker == "taker" else 1.0,
+    )
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=price_side == "other")
+
+    order = exchange.create_dry_run_order(
+        pair="LTC/USDT", ordertype=order_type, side=side, amount=10, rate=2.0, leverage=1.0
+    )
+    if price_side == "other" or order_type == "market":
+        assert order["fee"]["rate"] == fee
+        return
+    else:
+        assert order["fee"] is None
+
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=price_side != "other")
+
+    order1 = exchange.fetch_dry_run_order(order["id"])
+    assert order1["fee"]["rate"] == fee
+
+
+@pytest.mark.parametrize(
+    "side,price,filled,converted",
+    [
+        # order_book_l2_usd spread:
+        # best ask: 25.566
+        # best bid: 25.563
+        ("buy", 25.563, False, False),
+        ("buy", 25.566, True, False),
+        ("sell", 25.566, False, False),
+        ("sell", 25.563, True, False),
+        ("buy", 29.563, True, True),
+        ("sell", 21.563, True, True),
+    ],
+)
+@pytest.mark.parametrize("leverage", [1, 2, 5])
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_create_dry_run_order_limit_fill(
+    default_conf,
+    mocker,
+    side,
+    price,
+    filled,
+    caplog,
+    exchange_name,
+    order_book_l2_usd,
+    converted,
+    leverage,
+):
+    default_conf["dry_run"] = True
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+        fetch_l2_order_book=order_book_l2_usd,
+    )
+
+    order = exchange.create_order(
+        pair="LTC/USDT",
+        ordertype="limit",
+        side=side,
+        amount=1,
+        rate=price,
+        leverage=leverage,
+    )
+    assert order_book_l2_usd.call_count == 1
+    assert "id" in order
+    assert f"dry_run_{side}_" in order["id"]
+    assert order["side"] == side
+    if not converted:
+        assert order["average"] == price
+        assert order["type"] == "limit"
+    else:
+        # Converted to market order
+        assert order["type"] == "market"
+        assert 25.5 < order["average"] < 25.6
+        assert log_has_re(r"Converted .* to market order.*", caplog)
+
+    assert order["symbol"] == "LTC/USDT"
+    assert order["status"] == "open" if not filled else "closed"
+    order_book_l2_usd.reset_mock()
+
+    # fetch order again...
+    order_closed = exchange.fetch_dry_run_order(order["id"])
+    assert order_book_l2_usd.call_count == (1 if not filled else 0)
+    assert order_closed["status"] == ("open" if not filled else "closed")
+    assert order_closed["filled"] == (0 if not filled else 1)
+    assert order_closed["cost"] == 1 * order_closed["average"]
+
+    order_book_l2_usd.reset_mock()
+
+    # Empty orderbook test
+    mocker.patch(f"{EXMS}.fetch_l2_order_book", return_value={"asks": [], "bids": []})
+    exchange._dry_run_open_orders[order["id"]]["status"] = "open"
+    order_closed = exchange.fetch_dry_run_order(order["id"])
+
+
+@pytest.mark.parametrize(
+    "side,rate,amount,endprice",
+    [
+        # spread is 25.263-25.266
+        ("buy", 25.564, 1, 25.566),
+        ("buy", 25.564, 100, 25.5672),  # Requires interpolation
+        ("buy", 25.590, 100, 25.5672),  # Price above spread ... average is lower
+        ("buy", 25.564, 1000, 25.575),  # More than orderbook return
+        ("buy", 24.000, 100000, 25.200),  # Run into max_slippage of 5%
+        ("sell", 25.564, 1, 25.563),
+        ("sell", 25.564, 100, 25.5625),  # Requires interpolation
+        ("sell", 25.510, 100, 25.5625),  # price below spread - average is higher
+        ("sell", 25.564, 1000, 25.5555),  # More than orderbook return
+        ("sell", 27, 10000, 25.65),  # max-slippage 5%
+    ],
+)
+@pytest.mark.parametrize("leverage", [1, 2, 5])
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_create_dry_run_order_market_fill(
+    default_conf, mocker, side, rate, amount, endprice, exchange_name, order_book_l2_usd, leverage
+):
+    default_conf["dry_run"] = True
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+        fetch_l2_order_book=order_book_l2_usd,
+    )
+
+    order = exchange.create_order(
+        pair="LTC/USDT",
+        ordertype="market",
+        side=side,
+        amount=amount,
+        rate=rate,
+        leverage=leverage,
+    )
+    assert "id" in order
+    assert f"dry_run_{side}_" in order["id"]
+    assert order["side"] == side
+    assert order["type"] == "market"
+    assert order["symbol"] == "LTC/USDT"
+    assert order["status"] == "closed"
+    assert order["filled"] == amount
+    assert order["amount"] == amount
+    assert pytest.approx(order["cost"]) == amount * order["average"]
+    assert round(order["average"], 4) == round(endprice, 4)
+
+
+@pytest.mark.parametrize("side", ["buy", "sell"])
+@pytest.mark.parametrize(
+    "ordertype,rate,marketprice",
+    [
+        ("market", None, None),
+        ("market", 200, True),
+        ("limit", 200, None),
+        ("stop_loss_limit", 200, None),
+    ],
+)
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_create_order(default_conf, mocker, side, ordertype, rate, marketprice, exchange_name):
+    api_mock = MagicMock()
+    order_id = f"test_prod_{side}_{randint(0, 10**6)}"
+    api_mock.options = {} if not marketprice else {"createMarketBuyOrderRequiresPrice": True}
+    api_mock.create_order = MagicMock(
+        return_value={"id": order_id, "info": {"foo": "bar"}, "symbol": "XLTCUSDT", "amount": 1}
+    )
+    default_conf["dry_run"] = False
+    default_conf["margin_mode"] = "isolated"
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y: y)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    exchange._set_leverage = MagicMock()
+    exchange.set_margin_mode = MagicMock()
+
+    # Only applies to gate
+    price_req = exchange._ft_has.get("marketOrderRequiresPrice", False)
+
+    order = exchange.create_order(
+        pair="XLTCUSDT", ordertype=ordertype, side=side, amount=1, rate=rate, leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert order["amount"] == 1
+    assert api_mock.create_order.call_args[0][0] == "XLTCUSDT"
+    assert api_mock.create_order.call_args[0][1] == ordertype
+    assert api_mock.create_order.call_args[0][2] == side
+    assert api_mock.create_order.call_args[0][3] == 1
+    assert api_mock.create_order.call_args[0][4] == (
+        rate if price_req or not (bool(marketprice) and side == "sell") else None
+    )
+    assert exchange._set_leverage.call_count == 0
+    assert exchange.set_margin_mode.call_count == 0
+
+    api_mock.create_order = MagicMock(
+        return_value={
+            "id": order_id,
+            "info": {"foo": "bar"},
+            "symbol": "ADA/USDT:USDT",
+            "amount": 1,
+        }
+    )
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    exchange.trading_mode = TradingMode.FUTURES
+    exchange._set_leverage = MagicMock()
+    exchange.set_margin_mode = MagicMock()
+    order = exchange.create_order(
+        pair="ADA/USDT:USDT", ordertype=ordertype, side=side, amount=1, rate=200, leverage=3.0
+    )
+
+    if exchange_name != "okx":
+        assert exchange._set_leverage.call_count == 1
+        assert exchange.set_margin_mode.call_count == 1
+    else:
+        assert api_mock.set_leverage.call_count == 1
+    assert order["amount"] == 0.01
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_buy_dry_run(default_conf, mocker, exchange_name):
+    default_conf["dry_run"] = True
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+
+    order = exchange.create_order(
+        pair="ETH/BTC",
+        ordertype="limit",
+        side="buy",
+        amount=1,
+        rate=200,
+        leverage=1.0,
+        time_in_force="gtc",
+    )
+    assert "id" in order
+    assert "dry_run_buy_" in order["id"]
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_buy_prod(default_conf, mocker, exchange_name):
+    api_mock = MagicMock()
+    order_id = f"test_prod_buy_{randint(0, 10**6)}"
+    order_type = "market"
+    time_in_force = "gtc"
+    api_mock.options = {}
+    api_mock.create_order = MagicMock(
+        return_value={"id": order_id, "symbol": "ETH/BTC", "info": {"foo": "bar"}}
+    )
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y: y)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    order = exchange.create_order(
+        pair="ETH/BTC",
+        ordertype=order_type,
+        side="buy",
+        amount=1,
+        rate=200,
+        leverage=1.0,
+        time_in_force=time_in_force,
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args[0][0] == "ETH/BTC"
+    assert api_mock.create_order.call_args[0][1] == order_type
+    assert api_mock.create_order.call_args[0][2] == "buy"
+    assert api_mock.create_order.call_args[0][3] == 1
+    if exchange._order_needs_price("buy", order_type):
+        assert api_mock.create_order.call_args[0][4] == 200
+    else:
+        assert api_mock.create_order.call_args[0][4] is None
+
+    api_mock.create_order.reset_mock()
+    order_type = "limit"
+    order = exchange.create_order(
+        pair="ETH/BTC",
+        ordertype=order_type,
+        side="buy",
+        amount=1,
+        rate=200,
+        leverage=1.0,
+        time_in_force=time_in_force,
+    )
+    assert api_mock.create_order.call_args[0][0] == "ETH/BTC"
+    assert api_mock.create_order.call_args[0][1] == order_type
+    assert api_mock.create_order.call_args[0][2] == "buy"
+    assert api_mock.create_order.call_args[0][3] == 1
+    assert api_mock.create_order.call_args[0][4] == 200
+
+    # test exception handling
+    with pytest.raises(DependencyException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds("Not enough funds"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC",
+            ordertype=order_type,
+            side="buy",
+            amount=1,
+            rate=200,
+            leverage=1.0,
+            time_in_force=time_in_force,
+        )
+
+    with pytest.raises(DependencyException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC",
+            ordertype="limit",
+            side="buy",
+            amount=1,
+            rate=200,
+            leverage=1.0,
+            time_in_force=time_in_force,
+        )
+
+    with pytest.raises(DependencyException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC",
+            ordertype="market",
+            side="buy",
+            amount=1,
+            rate=200,
+            leverage=1.0,
+            time_in_force=time_in_force,
+        )
+
+    with pytest.raises(TemporaryError):
+        api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError("Network disconnect"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC",
+            ordertype=order_type,
+            side="buy",
+            amount=1,
+            rate=200,
+            leverage=1.0,
+            time_in_force=time_in_force,
+        )
+
+    with pytest.raises(OperationalException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.BaseError("Unknown error"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC",
+            ordertype=order_type,
+            side="buy",
+            amount=1,
+            rate=200,
+            leverage=1.0,
+            time_in_force=time_in_force,
+        )
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_buy_considers_time_in_force(default_conf, mocker, exchange_name):
+    api_mock = MagicMock()
+    order_id = f"test_prod_buy_{randint(0, 10**6)}"
+    api_mock.options = {}
+    api_mock.create_order = MagicMock(
+        return_value={"id": order_id, "symbol": "ETH/BTC", "info": {"foo": "bar"}}
+    )
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y: y)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    order_type = "limit"
+    time_in_force = "ioc"
+
+    order = exchange.create_order(
+        pair="ETH/BTC",
+        ordertype=order_type,
+        side="buy",
+        amount=1,
+        rate=200,
+        leverage=1.0,
+        time_in_force=time_in_force,
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["status"] == "open"
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args[0][0] == "ETH/BTC"
+    assert api_mock.create_order.call_args[0][1] == order_type
+    assert api_mock.create_order.call_args[0][2] == "buy"
+    assert api_mock.create_order.call_args[0][3] == 1
+    assert api_mock.create_order.call_args[0][4] == 200
+    assert "timeInForce" in api_mock.create_order.call_args[0][5]
+    assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force.upper()
+
+    order_type = "market"
+    time_in_force = "ioc"
+
+    order = exchange.create_order(
+        pair="ETH/BTC",
+        ordertype=order_type,
+        side="buy",
+        amount=1,
+        rate=200,
+        leverage=1.0,
+        time_in_force=time_in_force,
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args[0][0] == "ETH/BTC"
+    assert api_mock.create_order.call_args[0][1] == order_type
+    assert api_mock.create_order.call_args[0][2] == "buy"
+    assert api_mock.create_order.call_args[0][3] == 1
+    if exchange._order_needs_price("buy", order_type):
+        assert api_mock.create_order.call_args[0][4] == 200
+    else:
+        assert api_mock.create_order.call_args[0][4] is None
+    # Market orders should not send timeInForce!!
+    assert "timeInForce" not in api_mock.create_order.call_args[0][5]
+
+
+def test_sell_dry_run(default_conf, mocker):
+    default_conf["dry_run"] = True
+    exchange = get_patched_exchange(mocker, default_conf)
+
+    order = exchange.create_order(
+        pair="ETH/BTC", ordertype="limit", side="sell", amount=1, rate=200, leverage=1.0
+    )
+    assert "id" in order
+    assert "dry_run_sell_" in order["id"]
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_sell_prod(default_conf, mocker, exchange_name):
+    api_mock = MagicMock()
+    order_id = f"test_prod_sell_{randint(0, 10**6)}"
+    order_type = "market"
+    api_mock.options = {}
+    api_mock.create_order = MagicMock(
+        return_value={"id": order_id, "symbol": "ETH/BTC", "info": {"foo": "bar"}}
+    )
+    default_conf["dry_run"] = False
+
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y: y)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    order = exchange.create_order(
+        pair="ETH/BTC", ordertype=order_type, side="sell", amount=1, rate=200, leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args[0][0] == "ETH/BTC"
+    assert api_mock.create_order.call_args[0][1] == order_type
+    assert api_mock.create_order.call_args[0][2] == "sell"
+    assert api_mock.create_order.call_args[0][3] == 1
+    if exchange._order_needs_price("sell", order_type):
+        assert api_mock.create_order.call_args[0][4] == 200
+    else:
+        assert api_mock.create_order.call_args[0][4] is None
+
+    api_mock.create_order.reset_mock()
+    order_type = "limit"
+    order = exchange.create_order(
+        pair="ETH/BTC", ordertype=order_type, side="sell", amount=1, rate=200, leverage=1.0
+    )
+    assert api_mock.create_order.call_args[0][0] == "ETH/BTC"
+    assert api_mock.create_order.call_args[0][1] == order_type
+    assert api_mock.create_order.call_args[0][2] == "sell"
+    assert api_mock.create_order.call_args[0][3] == 1
+    assert api_mock.create_order.call_args[0][4] == 200
+
+    # test exception handling
+    with pytest.raises(InsufficientFundsError):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds("0 balance"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC", ordertype=order_type, side="sell", amount=1, rate=200, leverage=1.0
+        )
+
+    with pytest.raises(InvalidOrderException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC", ordertype="limit", side="sell", amount=1, rate=200, leverage=1.0
+        )
+
+    # Market orders don't require price, so the behaviour is slightly different
+    with pytest.raises(DependencyException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC", ordertype="market", side="sell", amount=1, rate=200, leverage=1.0
+        )
+
+    with pytest.raises(TemporaryError):
+        api_mock.create_order = MagicMock(side_effect=ccxt.NetworkError("No Connection"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC", ordertype=order_type, side="sell", amount=1, rate=200, leverage=1.0
+        )
+
+    with pytest.raises(OperationalException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.BaseError("DeadBeef"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.create_order(
+            pair="ETH/BTC", ordertype=order_type, side="sell", amount=1, rate=200, leverage=1.0
+        )
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_sell_considers_time_in_force(default_conf, mocker, exchange_name):
+    api_mock = MagicMock()
+    order_id = f"test_prod_sell_{randint(0, 10**6)}"
+    api_mock.create_order = MagicMock(
+        return_value={"id": order_id, "symbol": "ETH/BTC", "info": {"foo": "bar"}}
+    )
+    api_mock.options = {}
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y: y)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    order_type = "limit"
+    time_in_force = "ioc"
+
+    order = exchange.create_order(
+        pair="ETH/BTC",
+        ordertype=order_type,
+        side="sell",
+        amount=1,
+        rate=200,
+        leverage=1.0,
+        time_in_force=time_in_force,
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args[0][0] == "ETH/BTC"
+    assert api_mock.create_order.call_args[0][1] == order_type
+    assert api_mock.create_order.call_args[0][2] == "sell"
+    assert api_mock.create_order.call_args[0][3] == 1
+    assert api_mock.create_order.call_args[0][4] == 200
+    assert "timeInForce" in api_mock.create_order.call_args[0][5]
+    assert api_mock.create_order.call_args[0][5]["timeInForce"] == time_in_force.upper()
+
+    order_type = "market"
+    time_in_force = "IOC"
+    order = exchange.create_order(
+        pair="ETH/BTC",
+        ordertype=order_type,
+        side="sell",
+        amount=1,
+        rate=200,
+        leverage=1.0,
+        time_in_force=time_in_force,
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args[0][0] == "ETH/BTC"
+    assert api_mock.create_order.call_args[0][1] == order_type
+    assert api_mock.create_order.call_args[0][2] == "sell"
+    assert api_mock.create_order.call_args[0][3] == 1
+    if exchange._order_needs_price("sell", order_type):
+        assert api_mock.create_order.call_args[0][4] == 200
+    else:
+        assert api_mock.create_order.call_args[0][4] is None
+    # Market orders should not send timeInForce!!
+    assert "timeInForce" not in api_mock.create_order.call_args[0][5]
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_get_balances_prod(default_conf, mocker, exchange_name):
+    balance_item = {"free": 10.0, "total": 10.0, "used": 0.0}
+
+    api_mock = MagicMock()
+    api_mock.fetch_balance = MagicMock(
+        return_value={"1ST": balance_item, "2ND": balance_item, "3RD": balance_item}
+    )
+    api_mock.commonCurrencies = {}
+    default_conf["dry_run"] = False
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    assert len(exchange.get_balances()) == 3
+    assert exchange.get_balances()["1ST"]["free"] == 10.0
+    assert exchange.get_balances()["1ST"]["total"] == 10.0
+    assert exchange.get_balances()["1ST"]["used"] == 0.0
+
+    ccxt_exceptionhandlers(
+        mocker, default_conf, api_mock, exchange_name, "get_balances", "fetch_balance"
+    )
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_fetch_positions(default_conf, mocker, exchange_name):
+    mocker.patch(f"{EXMS}.validate_trading_mode_and_margin_mode")
+    api_mock = MagicMock()
+    api_mock.fetch_positions = MagicMock(
+        return_value=[
+            {"symbol": "ETH/USDT:USDT", "leverage": 5},
+            {"symbol": "XRP/USDT:USDT", "leverage": 5},
+        ]
+    )
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    assert exchange.fetch_positions() == []
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = "futures"
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    res = exchange.fetch_positions()
+    assert len(res) == 2
+
+    ccxt_exceptionhandlers(
+        mocker, default_conf, api_mock, exchange_name, "fetch_positions", "fetch_positions"
+    )
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_fetch_orders(default_conf, mocker, exchange_name, limit_order):
+    api_mock = MagicMock()
+    api_mock.fetch_orders = MagicMock(
+        return_value=[
+            limit_order["buy"],
+            limit_order["sell"],
+        ]
+    )
+    api_mock.fetch_open_orders = MagicMock(return_value=[limit_order["buy"]])
+    api_mock.fetch_closed_orders = MagicMock(return_value=[limit_order["buy"]])
+
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    start_time = datetime.now(timezone.utc) - timedelta(days=20)
+    expected = 1
+    if exchange_name == "bybit":
+        expected = 3
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    # Not available in dry-run
+    assert exchange.fetch_orders("mocked", start_time) == []
+    assert api_mock.fetch_orders.call_count == 0
+    default_conf["dry_run"] = False
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    res = exchange.fetch_orders("mocked", start_time)
+    assert api_mock.fetch_orders.call_count == expected
+    assert api_mock.fetch_open_orders.call_count == 0
+    assert api_mock.fetch_closed_orders.call_count == 0
+    assert len(res) == 2 * expected
+
+    res = exchange.fetch_orders("mocked", start_time)
+
+    api_mock.fetch_orders.reset_mock()
+
+    def has_resp(_, endpoint):
+        if endpoint == "fetchOrders":
+            return False
+        if endpoint == "fetchClosedOrders":
+            return True
+        if endpoint == "fetchOpenOrders":
+            return True
+
+    if exchange_name == "okx":
+        # Special OKX case is tested separately
+        return
+
+    mocker.patch(f"{EXMS}.exchange_has", has_resp)
+
+    # happy path without fetchOrders
+    exchange.fetch_orders("mocked", start_time)
+    assert api_mock.fetch_orders.call_count == 0
+    assert api_mock.fetch_open_orders.call_count == expected
+    assert api_mock.fetch_closed_orders.call_count == expected
+
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "fetch_orders",
+        "fetch_orders",
+        retries=1,
+        pair="mocked",
+        since=start_time,
+    )
+
+    # Unhappy path - first fetch-orders call fails.
+    api_mock.fetch_orders = MagicMock(side_effect=ccxt.NotSupported())
+    api_mock.fetch_open_orders.reset_mock()
+    api_mock.fetch_closed_orders.reset_mock()
+
+    exchange.fetch_orders("mocked", start_time)
+
+    assert api_mock.fetch_orders.call_count == expected
+    assert api_mock.fetch_open_orders.call_count == expected
+    assert api_mock.fetch_closed_orders.call_count == expected
+
+
+def test_fetch_trading_fees(default_conf, mocker):
+    api_mock = MagicMock()
+    tick = {
+        "1INCH/USDT:USDT": {
+            "info": {
+                "user_id": "",
+                "taker_fee": "0.0018",
+                "maker_fee": "0.0018",
+                "gt_discount": False,
+                "gt_taker_fee": "0",
+                "gt_maker_fee": "0",
+                "loan_fee": "0.18",
+                "point_type": "1",
+                "futures_taker_fee": "0.0005",
+                "futures_maker_fee": "0",
+            },
+            "symbol": "1INCH/USDT:USDT",
+            "maker": 0.0,
+            "taker": 0.0005,
+        },
+        "ETH/USDT:USDT": {
+            "info": {
+                "user_id": "",
+                "taker_fee": "0.0018",
+                "maker_fee": "0.0018",
+                "gt_discount": False,
+                "gt_taker_fee": "0",
+                "gt_maker_fee": "0",
+                "loan_fee": "0.18",
+                "point_type": "1",
+                "futures_taker_fee": "0.0005",
+                "futures_maker_fee": "0",
+            },
+            "symbol": "ETH/USDT:USDT",
+            "maker": 0.0,
+            "taker": 0.0005,
+        },
+    }
+    exchange_name = "gate"
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+    api_mock.fetch_trading_fees = MagicMock(return_value=tick)
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    assert "1INCH/USDT:USDT" in exchange._trading_fees
+    assert "ETH/USDT:USDT" in exchange._trading_fees
+    assert api_mock.fetch_trading_fees.call_count == 1
+
+    api_mock.fetch_trading_fees.reset_mock()
+    # Reload-markets calls fetch_trading_fees, too - so the explicit calls in the below
+    # exception test would be called twice.
+    mocker.patch(f"{EXMS}.reload_markets")
+    ccxt_exceptionhandlers(
+        mocker, default_conf, api_mock, exchange_name, "fetch_trading_fees", "fetch_trading_fees"
+    )
+
+    api_mock.fetch_trading_fees = MagicMock(return_value={})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    exchange.fetch_trading_fees()
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    assert exchange.fetch_trading_fees() == {}
+
+
+def test_fetch_bids_asks(default_conf, mocker):
+    api_mock = MagicMock()
+    tick = {
+        "ETH/BTC": {
+            "symbol": "ETH/BTC",
+            "bid": 0.5,
+            "ask": 1,
+            "last": 42,
+        },
+        "BCH/BTC": {
+            "symbol": "BCH/BTC",
+            "bid": 0.6,
+            "ask": 0.5,
+            "last": 41,
+        },
+    }
+    exchange_name = "binance"
+    api_mock.fetch_bids_asks = MagicMock(return_value=tick)
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    # retrieve original ticker
+    bidsasks = exchange.fetch_bids_asks()
+
+    assert "ETH/BTC" in bidsasks
+    assert "BCH/BTC" in bidsasks
+    assert bidsasks["ETH/BTC"]["bid"] == 0.5
+    assert bidsasks["ETH/BTC"]["ask"] == 1
+    assert bidsasks["BCH/BTC"]["bid"] == 0.6
+    assert bidsasks["BCH/BTC"]["ask"] == 0.5
+    assert api_mock.fetch_bids_asks.call_count == 1
+
+    api_mock.fetch_bids_asks.reset_mock()
+
+    # Cached ticker should not call api again
+    tickers2 = exchange.fetch_bids_asks(cached=True)
+    assert tickers2 == bidsasks
+    assert api_mock.fetch_bids_asks.call_count == 0
+    tickers2 = exchange.fetch_bids_asks(cached=False)
+    assert api_mock.fetch_bids_asks.call_count == 1
+
+    ccxt_exceptionhandlers(
+        mocker, default_conf, api_mock, exchange_name, "fetch_bids_asks", "fetch_bids_asks"
+    )
+
+    with pytest.raises(OperationalException):
+        api_mock.fetch_bids_asks = MagicMock(side_effect=ccxt.NotSupported("DeadBeef"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.fetch_bids_asks()
+
+    api_mock.fetch_bids_asks = MagicMock(return_value={})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    exchange.fetch_bids_asks()
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    assert exchange.fetch_bids_asks() == {}
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_get_tickers(default_conf, mocker, exchange_name, caplog):
+    api_mock = MagicMock()
+    tick = {
+        "ETH/BTC": {
+            "symbol": "ETH/BTC",
+            "bid": 0.5,
+            "ask": 1,
+            "last": 42,
+        },
+        "BCH/BTC": {
+            "symbol": "BCH/BTC",
+            "bid": 0.6,
+            "ask": 0.5,
+            "last": 41,
+        },
+    }
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    api_mock.fetch_tickers = MagicMock(return_value=tick)
+    api_mock.fetch_bids_asks = MagicMock(return_value={})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    # retrieve original ticker
+    tickers = exchange.get_tickers()
+
+    assert "ETH/BTC" in tickers
+    assert "BCH/BTC" in tickers
+    assert tickers["ETH/BTC"]["bid"] == 0.5
+    assert tickers["ETH/BTC"]["ask"] == 1
+    assert tickers["BCH/BTC"]["bid"] == 0.6
+    assert tickers["BCH/BTC"]["ask"] == 0.5
+    assert api_mock.fetch_tickers.call_count == 1
+    assert api_mock.fetch_bids_asks.call_count == 0
+
+    api_mock.fetch_tickers.reset_mock()
+
+    # Cached ticker should not call api again
+    tickers2 = exchange.get_tickers(cached=True)
+    assert tickers2 == tickers
+    assert api_mock.fetch_tickers.call_count == 0
+    assert api_mock.fetch_bids_asks.call_count == 0
+    tickers2 = exchange.get_tickers(cached=False)
+    assert api_mock.fetch_tickers.call_count == 1
+    assert api_mock.fetch_bids_asks.call_count == 0
+
+    ccxt_exceptionhandlers(
+        mocker, default_conf, api_mock, exchange_name, "get_tickers", "fetch_tickers"
+    )
+
+    with pytest.raises(OperationalException):
+        api_mock.fetch_tickers = MagicMock(side_effect=ccxt.NotSupported("DeadBeef"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.get_tickers()
+
+    caplog.clear()
+    api_mock.fetch_tickers = MagicMock(side_effect=[ccxt.BadSymbol("SomeSymbol"), []])
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    x = exchange.get_tickers()
+    assert x == []
+    assert log_has_re(r"Could not load tickers due to BadSymbol\..*SomeSymbol", caplog)
+    caplog.clear()
+
+    api_mock.fetch_tickers = MagicMock(return_value={})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    exchange.get_tickers()
+
+    api_mock.fetch_tickers.reset_mock()
+    api_mock.fetch_bids_asks.reset_mock()
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    exchange.get_tickers()
+    assert api_mock.fetch_tickers.call_count == 1
+    assert api_mock.fetch_bids_asks.call_count == (1 if exchange_name == "binance" else 0)
+
+    api_mock.fetch_tickers.reset_mock()
+    api_mock.fetch_bids_asks.reset_mock()
+    mocker.patch(f"{EXMS}.exchange_has", return_value=False)
+    assert exchange.get_tickers() == {}
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_get_conversion_rate(default_conf_usdt, mocker, exchange_name):
+    api_mock = MagicMock()
+    tick = {
+        "ETH/USDT": {
+            "last": 42,
+        },
+        "BCH/USDT": {
+            "last": 41,
+        },
+        "ETH/BTC": {
+            "last": 250,
+        },
+    }
+    tick2 = {
+        "ADA/USDT:USDT": {
+            "last": 2.5,
+        }
+    }
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    api_mock.fetch_tickers = MagicMock(side_effect=[tick, tick2])
+    api_mock.fetch_bids_asks = MagicMock(return_value={})
+    default_conf_usdt["trading_mode"] = "futures"
+
+    exchange = get_patched_exchange(mocker, default_conf_usdt, api_mock, exchange=exchange_name)
+    # retrieve original ticker
+    assert exchange.get_conversion_rate("USDT", "USDT") == 1
+    assert api_mock.fetch_tickers.call_count == 0
+    assert exchange.get_conversion_rate("ETH", "USDT") == 42
+    assert exchange.get_conversion_rate("ETH", "USDC") is None
+    assert exchange.get_conversion_rate("ETH", "BTC") == 250
+    assert exchange.get_conversion_rate("BTC", "ETH") == 0.004
+
+    assert api_mock.fetch_tickers.call_count == 1
+    api_mock.fetch_tickers.reset_mock()
+
+    assert exchange.get_conversion_rate("ADA", "USDT") == 2.5
+    # Only the call to the "others" market
+    assert api_mock.fetch_tickers.call_count == 1
+
+    if exchange_name == "binance":
+        # Special binance case of BNFCR matching USDT.
+        assert exchange.get_conversion_rate("BNFCR", "USDT") is None
+        assert exchange.get_conversion_rate("BNFCR", "USDC") == 1
+        assert exchange.get_conversion_rate("USDT", "BNFCR") is None
+        assert exchange.get_conversion_rate("USDC", "BNFCR") == 1
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_fetch_ticker(default_conf, mocker, exchange_name):
+    api_mock = MagicMock()
+    tick = {
+        "symbol": "ETH/BTC",
+        "bid": 0.00001098,
+        "ask": 0.00001099,
+        "last": 0.0001,
+    }
+    api_mock.fetch_ticker = MagicMock(return_value=tick)
+    api_mock.markets = {"ETH/BTC": {"active": True}}
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    # retrieve original ticker
+    ticker = exchange.fetch_ticker(pair="ETH/BTC")
+
+    assert ticker["bid"] == 0.00001098
+    assert ticker["ask"] == 0.00001099
+
+    # change the ticker
+    tick = {
+        "symbol": "ETH/BTC",
+        "bid": 0.5,
+        "ask": 1,
+        "last": 42,
+    }
+    api_mock.fetch_ticker = MagicMock(return_value=tick)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    # if not caching the result we should get the same ticker
+    # if not fetching a new result we should get the cached ticker
+    ticker = exchange.fetch_ticker(pair="ETH/BTC")
+
+    assert api_mock.fetch_ticker.call_count == 1
+    assert ticker["bid"] == 0.5
+    assert ticker["ask"] == 1
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "fetch_ticker",
+        "fetch_ticker",
+        pair="ETH/BTC",
+    )
+
+    api_mock.fetch_ticker = MagicMock(return_value={})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    exchange.fetch_ticker(pair="ETH/BTC")
+
+    with pytest.raises(DependencyException, match=r"Pair XRP/ETH not available"):
+        exchange.fetch_ticker(pair="XRP/ETH")
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test___now_is_time_to_refresh(default_conf, mocker, exchange_name, time_machine):
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    pair = "BTC/USDT"
+    candle_type = CandleType.SPOT
+    start_dt = datetime(2023, 12, 1, 0, 10, 0, tzinfo=timezone.utc)
+    time_machine.move_to(start_dt, tick=False)
+    assert (pair, "5m", candle_type) not in exchange._pairs_last_refresh_time
+
+    # not refreshed yet
+    assert exchange._now_is_time_to_refresh(pair, "5m", candle_type) is True
+
+    last_closed_candle = dt_ts(start_dt - timedelta(minutes=5))
+    exchange._pairs_last_refresh_time[(pair, "5m", candle_type)] = last_closed_candle
+
+    # next candle not closed yet
+    time_machine.move_to(start_dt + timedelta(minutes=4, seconds=59), tick=False)
+    assert exchange._now_is_time_to_refresh(pair, "5m", candle_type) is False
+
+    # next candle closed
+    time_machine.move_to(start_dt + timedelta(minutes=5, seconds=0), tick=False)
+    assert exchange._now_is_time_to_refresh(pair, "5m", candle_type) is True
+
+    # 1 second later (last_refresh_time didn't change)
+    time_machine.move_to(start_dt + timedelta(minutes=5, seconds=1), tick=False)
+    assert exchange._now_is_time_to_refresh(pair, "5m", candle_type) is True
+
+
+@pytest.mark.parametrize("candle_type", ["mark", ""])
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_get_historic_ohlcv(default_conf, mocker, caplog, exchange_name, candle_type):
+    caplog.set_level(logging.DEBUG)
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    pair = "ETH/BTC"
+    calls = 0
+    now = dt_now()
+
+    async def mock_candle_hist(pair, timeframe, candle_type, since_ms):
+        nonlocal calls
+        calls += 1
+        ohlcv = [
+            [
+                dt_ts(now + timedelta(minutes=5 * (calls + i))),  # unix timestamp ms
+                1,  # open
+                2,  # high
+                3,  # low
+                4,  # close
+                5,  # volume (in quote currency)
+            ]
+            for i in range(2)
+        ]
+        return (pair, timeframe, candle_type, ohlcv, True)
+
+    exchange._async_get_candle_history = Mock(wraps=mock_candle_hist)
+    # one_call calculation * 1.8 should do 2 calls
+
+    since = 5 * 60 * exchange.ohlcv_candle_limit("5m", candle_type) * 1.8
+    ret = exchange.get_historic_ohlcv(
+        pair, "5m", dt_ts(dt_now() - timedelta(seconds=since)), candle_type=candle_type
+    )
+
+    assert exchange._async_get_candle_history.call_count == 2
+    # Returns twice the above OHLCV data after truncating the open candle.
+    assert len(ret) == 2
+    assert log_has_re(r"Downloaded data for .* from ccxt with length .*\.", caplog)
+
+    caplog.clear()
+
+    exchange._async_get_candle_history = get_mock_coro(side_effect=TimeoutError())
+    with pytest.raises(TimeoutError):
+        exchange.get_historic_ohlcv(
+            pair, "5m", dt_ts(dt_now() - timedelta(seconds=since)), candle_type=candle_type
+        )
+    assert log_has_re(r"Async code raised an exception: .*", caplog)
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+@pytest.mark.parametrize("candle_type", [CandleType.MARK, CandleType.SPOT])
+async def test__async_get_historic_ohlcv(default_conf, mocker, caplog, exchange_name, candle_type):
+    ohlcv = [
+        [
+            int((datetime.now(timezone.utc).timestamp() - 1000) * 1000),
+            1,  # open
+            2,  # high
+            3,  # low
+            4,  # close
+            5,  # volume (in quote currency)
+        ]
+    ]
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    # Monkey-patch async function
+    exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
+
+    pair = "ETH/USDT"
+    respair, restf, _, res, _ = await exchange._async_get_historic_ohlcv(
+        pair, "5m", 1500000000000, candle_type=candle_type
+    )
+    assert respair == pair
+    assert restf == "5m"
+    # Call with very old timestamp - causes tons of requests
+    assert exchange._api_async.fetch_ohlcv.call_count > 200
+    assert res[0] == ohlcv[0]
+
+    exchange._api_async.fetch_ohlcv.reset_mock()
+    end_ts = 1_500_500_000_000
+    start_ts = 1_500_000_000_000
+    respair, restf, _, res, _ = await exchange._async_get_historic_ohlcv(
+        pair, "5m", since_ms=start_ts, candle_type=candle_type, until_ms=end_ts
+    )
+    # Required candles
+    candles = (end_ts - start_ts) / 300_000
+    exp = candles // exchange.ohlcv_candle_limit("5m", candle_type, start_ts) + 1
+
+    # Depending on the exchange, this should be called between 1 and 6 times.
+    assert exchange._api_async.fetch_ohlcv.call_count == exp
+
+
+@pytest.mark.parametrize("candle_type", [CandleType.FUTURES, CandleType.MARK, CandleType.SPOT])
+def test_refresh_latest_ohlcv(mocker, default_conf, caplog, candle_type) -> None:
+    ohlcv = [
+        [
+            dt_ts(dt_now() - timedelta(minutes=5)),  # unix timestamp ms
+            1,  # open
+            2,  # high
+            3,  # low
+            4,  # close
+            5,  # volume (in quote currency)
+        ],
+        [
+            dt_ts(),  # unix timestamp ms
+            3,  # open
+            1,  # high
+            4,  # low
+            6,  # close
+            5,  # volume (in quote currency)
+        ],
+    ]
+
+    caplog.set_level(logging.DEBUG)
+    exchange = get_patched_exchange(mocker, default_conf)
+    exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
+
+    pairs = [("IOTA/ETH", "5m", candle_type), ("XRP/ETH", "5m", candle_type)]
+    # empty dicts
+    assert not exchange._klines
+    res = exchange.refresh_latest_ohlcv(pairs, cache=False)
+    # No caching
+    assert not exchange._klines
+
+    assert len(res) == len(pairs)
+    assert exchange._api_async.fetch_ohlcv.call_count == 2
+    exchange._api_async.fetch_ohlcv.reset_mock()
+
+    exchange.required_candle_call_count = 2
+    res = exchange.refresh_latest_ohlcv(pairs)
+    assert len(res) == len(pairs)
+
+    assert log_has(f"Refreshing candle (OHLCV) data for {len(pairs)} pairs", caplog)
+    assert exchange._klines
+    assert exchange._api_async.fetch_ohlcv.call_count == 4
+    exchange._api_async.fetch_ohlcv.reset_mock()
+    for pair in pairs:
+        assert isinstance(exchange.klines(pair), DataFrame)
+        assert len(exchange.klines(pair)) > 0
+
+        # klines function should return a different object on each call
+        # if copy is "True"
+        assert exchange.klines(pair) is not exchange.klines(pair)
+        assert exchange.klines(pair) is not exchange.klines(pair, copy=True)
+        assert exchange.klines(pair, copy=True) is not exchange.klines(pair, copy=True)
+        assert exchange.klines(pair, copy=False) is exchange.klines(pair, copy=False)
+
+    # test caching
+    res = exchange.refresh_latest_ohlcv(
+        [("IOTA/ETH", "5m", candle_type), ("XRP/ETH", "5m", candle_type)]
+    )
+    assert len(res) == len(pairs)
+
+    assert exchange._api_async.fetch_ohlcv.call_count == 0
+    assert log_has(
+        f"Using cached candle (OHLCV) data for {pairs[0][0]}, {pairs[0][1]}, {candle_type} ...",
+        caplog,
+    )
+    caplog.clear()
+    # Reset refresh times - must do 2 call per pair as cache is expired
+    exchange._pairs_last_refresh_time = {}
+    res = exchange.refresh_latest_ohlcv(
+        [("IOTA/ETH", "5m", candle_type), ("XRP/ETH", "5m", candle_type)]
+    )
+    assert len(res) == len(pairs)
+
+    assert exchange._api_async.fetch_ohlcv.call_count == 4
+
+    # cache - but disabled caching
+    exchange._api_async.fetch_ohlcv.reset_mock()
+    exchange.required_candle_call_count = 1
+
+    pairlist = [
+        ("IOTA/ETH", "5m", candle_type),
+        ("XRP/ETH", "5m", candle_type),
+        ("XRP/ETH", "1d", candle_type),
+    ]
+    res = exchange.refresh_latest_ohlcv(pairlist, cache=False)
+    assert len(res) == 3
+    assert exchange._api_async.fetch_ohlcv.call_count == 3
+
+    # Test the same again, should NOT return from cache!
+    exchange._api_async.fetch_ohlcv.reset_mock()
+    res = exchange.refresh_latest_ohlcv(pairlist, cache=False)
+    assert len(res) == 3
+    assert exchange._api_async.fetch_ohlcv.call_count == 3
+    exchange._api_async.fetch_ohlcv.reset_mock()
+    caplog.clear()
+
+    # Call with invalid timeframe
+    res = exchange.refresh_latest_ohlcv([("IOTA/ETH", "3m", candle_type)], cache=False)
+    if candle_type != CandleType.MARK:
+        assert not res
+        assert len(res) == 0
+        assert log_has_re(r"Cannot download \(IOTA\/ETH, 3m\).*", caplog)
+    else:
+        assert len(res) == 1
+
+
+@pytest.mark.parametrize("candle_type", [CandleType.FUTURES, CandleType.SPOT])
+def test_refresh_latest_trades(
+    mocker, default_conf, caplog, candle_type, tmp_path, time_machine
+) -> None:
+    time_machine.move_to(dt_now(), tick=False)
+    trades = [
+        {
+            # unix timestamp ms
+            "timestamp": dt_ts(dt_now() - timedelta(minutes=5)),
+            "amount": 16.512,
+            "cost": 10134.07488,
+            "fee": None,
+            "fees": [],
+            "id": "354669639",
+            "order": None,
+            "price": 613.74,
+            "side": "sell",
+            "takerOrMaker": None,
+            "type": None,
+        },
+        {
+            "timestamp": dt_ts(),  # unix timestamp ms
+            "amount": 12.512,
+            "cost": 1000,
+            "fee": None,
+            "fees": [],
+            "id": "354669640",
+            "order": None,
+            "price": 613.84,
+            "side": "buy",
+            "takerOrMaker": None,
+            "type": None,
+        },
+    ]
+
+    caplog.set_level(logging.DEBUG)
+    use_trades_conf = default_conf
+    use_trades_conf["exchange"]["use_public_trades"] = True
+    use_trades_conf["exchange"]["only_from_ccxt"] = True
+
+    use_trades_conf["datadir"] = tmp_path
+    use_trades_conf["orderflow"] = {"max_candles": 1500}
+    exchange = get_patched_exchange(mocker, use_trades_conf)
+    exchange._api_async.fetch_trades = get_mock_coro(trades)
+    exchange._ft_has["exchange_has_overrides"]["fetchTrades"] = True
+
+    pairs = [("IOTA/USDT:USDT", "5m", candle_type), ("XRP/USDT:USDT", "5m", candle_type)]
+    # empty dicts
+    assert not exchange._trades
+    res = exchange.refresh_latest_trades(pairs, cache=False)
+    # No caching
+    assert not exchange._trades
+
+    assert len(res) == len(pairs)
+    assert exchange._api_async.fetch_trades.call_count == 4
+    exchange._api_async.fetch_trades.reset_mock()
+
+    exchange.required_candle_call_count = 2
+    res = exchange.refresh_latest_trades(pairs)
+    assert len(res) == len(pairs)
+
+    assert log_has(f"Refreshing TRADES data for {len(pairs)} pairs", caplog)
+    assert exchange._trades
+    assert exchange._api_async.fetch_trades.call_count == 4
+    exchange._api_async.fetch_trades.reset_mock()
+    for pair in pairs:
+        assert isinstance(exchange.trades(pair), DataFrame)
+        assert len(exchange.trades(pair)) > 0
+
+        # trades function should return a different object on each call
+        # if copy is "True"
+        assert exchange.trades(pair) is not exchange.trades(pair)
+        assert exchange.trades(pair) is not exchange.trades(pair, copy=True)
+        assert exchange.trades(pair, copy=True) is not exchange.trades(pair, copy=True)
+        assert exchange.trades(pair, copy=False) is exchange.trades(pair, copy=False)
+
+        # test caching
+        ohlcv = [
+            [
+                dt_ts(dt_now() - timedelta(minutes=5)),  # unix timestamp ms
+                1,  # open
+                2,  # high
+                3,  # low
+                4,  # close
+                5,  # volume (in quote currency)
+            ],
+            [
+                dt_ts(),  # unix timestamp ms
+                3,  # open
+                1,  # high
+                4,  # low
+                6,  # close
+                5,  # volume (in quote currency)
+            ],
+        ]
+        cols = DEFAULT_DATAFRAME_COLUMNS
+        trades_df = DataFrame(ohlcv, columns=cols)
+
+        trades_df["date"] = to_datetime(trades_df["date"], unit="ms", utc=True)
+        trades_df["date"] = trades_df["date"].apply(lambda date: timeframe_to_prev_date("5m", date))
+        exchange._klines[pair] = trades_df
+    res = exchange.refresh_latest_trades(
+        [("IOTA/USDT:USDT", "5m", candle_type), ("XRP/USDT:USDT", "5m", candle_type)]
+    )
+    assert len(res) == 0
+    assert exchange._api_async.fetch_trades.call_count == 0
+    caplog.clear()
+
+    # Reset refresh times
+    for pair in pairs:
+        # test caching with "expired" candle
+        trades = [
+            {
+                # unix timestamp ms
+                "timestamp": dt_ts(exchange._klines[pair].iloc[-1].date - timedelta(minutes=5)),
+                "amount": 16.512,
+                "cost": 10134.07488,
+                "fee": None,
+                "fees": [],
+                "id": "354669639",
+                "order": None,
+                "price": 613.74,
+                "side": "sell",
+                "takerOrMaker": None,
+                "type": None,
+            }
+        ]
+        trades_df = DataFrame(trades)
+        trades_df["date"] = to_datetime(trades_df["timestamp"], unit="ms", utc=True)
+        exchange._trades[pair] = trades_df
+    res = exchange.refresh_latest_trades(
+        [("IOTA/USDT:USDT", "5m", candle_type), ("XRP/USDT:USDT", "5m", candle_type)]
+    )
+    assert len(res) == len(pairs)
+
+    assert exchange._api_async.fetch_trades.call_count == 4
+
+    # cache - but disabled caching
+    exchange._api_async.fetch_trades.reset_mock()
+    exchange.required_candle_call_count = 1
+
+    pairlist = [
+        ("IOTA/ETH", "5m", candle_type),
+        ("XRP/ETH", "5m", candle_type),
+        ("XRP/ETH", "1d", candle_type),
+    ]
+    res = exchange.refresh_latest_trades(pairlist, cache=False)
+    assert len(res) == 3
+    assert exchange._api_async.fetch_trades.call_count == 6
+
+    # Test the same again, should NOT return from cache!
+    exchange._api_async.fetch_trades.reset_mock()
+    res = exchange.refresh_latest_trades(pairlist, cache=False)
+    assert len(res) == 3
+    assert exchange._api_async.fetch_trades.call_count == 6
+    exchange._api_async.fetch_trades.reset_mock()
+    caplog.clear()
+
+
+@pytest.mark.parametrize("candle_type", [CandleType.FUTURES, CandleType.MARK, CandleType.SPOT])
+def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_machine) -> None:
+    start = datetime(2021, 8, 1, 0, 0, 0, 0, tzinfo=timezone.utc)
+    ohlcv = generate_test_data_raw("1h", 100, start.strftime("%Y-%m-%d"))
+    time_machine.move_to(start + timedelta(hours=99, minutes=30))
+
+    exchange = get_patched_exchange(mocker, default_conf)
+    mocker.patch(f"{EXMS}.ohlcv_candle_limit", return_value=100)
+    assert exchange._startup_candle_count == 0
+
+    exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
+    pair1 = ("IOTA/ETH", "1h", candle_type)
+    pair2 = ("XRP/ETH", "1h", candle_type)
+    pairs = [pair1, pair2]
+
+    # No caching
+    assert not exchange._klines
+    res = exchange.refresh_latest_ohlcv(pairs, cache=False)
+    assert exchange._api_async.fetch_ohlcv.call_count == 2
+    assert len(res) == 2
+    assert len(res[pair1]) == 99
+    assert len(res[pair2]) == 99
+    assert not exchange._klines
+    exchange._api_async.fetch_ohlcv.reset_mock()
+
+    # With caching
+    res = exchange.refresh_latest_ohlcv(pairs)
+    assert exchange._api_async.fetch_ohlcv.call_count == 2
+    assert len(res) == 2
+    assert len(res[pair1]) == 99
+    assert len(res[pair2]) == 99
+    assert exchange._klines
+    assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-2][0]
+    exchange._api_async.fetch_ohlcv.reset_mock()
+
+    # Returned from cache
+    res = exchange.refresh_latest_ohlcv(pairs)
+    assert exchange._api_async.fetch_ohlcv.call_count == 0
+    assert len(res) == 2
+    assert len(res[pair1]) == 99
+    assert len(res[pair2]) == 99
+    assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-2][0]
+
+    # Move time 1 candle further but result didn't change yet
+    time_machine.move_to(start + timedelta(hours=101))
+    res = exchange.refresh_latest_ohlcv(pairs)
+    assert exchange._api_async.fetch_ohlcv.call_count == 2
+    assert len(res) == 2
+    assert len(res[pair1]) == 99
+    assert len(res[pair2]) == 99
+    assert res[pair2].at[0, "open"]
+    assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-2][0]
+    refresh_pior = exchange._pairs_last_refresh_time[pair1]
+
+    # New candle on exchange - return 100 candles - but skip one candle so we actually get 2 candles
+    # in one go
+    new_startdate = (start + timedelta(hours=2)).strftime("%Y-%m-%d %H:%M")
+    # mocker.patch(f"{EXMS}.ohlcv_candle_limit", return_value=100)
+    ohlcv = generate_test_data_raw("1h", 100, new_startdate)
+    exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
+    res = exchange.refresh_latest_ohlcv(pairs)
+    assert exchange._api_async.fetch_ohlcv.call_count == 2
+    assert len(res) == 2
+    assert len(res[pair1]) == 100
+    assert len(res[pair2]) == 100
+    # Verify index starts at 0
+    assert res[pair2].at[0, "open"]
+    assert refresh_pior != exchange._pairs_last_refresh_time[pair1]
+
+    assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-2][0]
+    assert exchange._pairs_last_refresh_time[pair2] == ohlcv[-2][0]
+    exchange._api_async.fetch_ohlcv.reset_mock()
+
+    # Retry same call - from cache
+    res = exchange.refresh_latest_ohlcv(pairs)
+    assert exchange._api_async.fetch_ohlcv.call_count == 0
+    assert len(res) == 2
+    assert len(res[pair1]) == 100
+    assert len(res[pair2]) == 100
+    assert res[pair2].at[0, "open"]
+
+    # Move to distant future (so a 1 call would cause a hole in the data)
+    time_machine.move_to(start + timedelta(hours=2000))
+    ohlcv = generate_test_data_raw("1h", 100, start + timedelta(hours=1900))
+    exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
+    res = exchange.refresh_latest_ohlcv(pairs)
+
+    assert exchange._api_async.fetch_ohlcv.call_count == 2
+    assert len(res) == 2
+    # Cache eviction - new data.
+    assert len(res[pair1]) == 99
+    assert len(res[pair2]) == 99
+    assert res[pair2].at[0, "open"]
+
+
+def test_refresh_ohlcv_with_cache(mocker, default_conf, time_machine) -> None:
+    start = datetime(2021, 8, 1, 0, 0, 0, 0, tzinfo=timezone.utc)
+    ohlcv = generate_test_data_raw("1h", 100, start.strftime("%Y-%m-%d"))
+    time_machine.move_to(start, tick=False)
+    pairs = [
+        ("ETH/BTC", "1d", CandleType.SPOT),
+        ("TKN/BTC", "1d", CandleType.SPOT),
+        ("LTC/BTC", "1d", CandleType.SPOT),
+        ("LTC/BTC", "5m", CandleType.SPOT),
+        ("LTC/BTC", "1h", CandleType.SPOT),
+    ]
+
+    ohlcv_data = {p: ohlcv for p in pairs}
+    ohlcv_mock = mocker.patch(f"{EXMS}.refresh_latest_ohlcv", return_value=ohlcv_data)
+    mocker.patch(f"{EXMS}.ohlcv_candle_limit", return_value=100)
+    exchange = get_patched_exchange(mocker, default_conf)
+
+    assert len(exchange._expiring_candle_cache) == 0
+
+    res = exchange.refresh_ohlcv_with_cache(pairs, start.timestamp())
+    assert ohlcv_mock.call_count == 1
+    assert ohlcv_mock.call_args_list[0][0][0] == pairs
+    assert len(ohlcv_mock.call_args_list[0][0][0]) == 5
+
+    assert len(res) == 5
+    # length of 3 - as we have 3 different timeframes
+    assert len(exchange._expiring_candle_cache) == 3
+
+    ohlcv_mock.reset_mock()
+    res = exchange.refresh_ohlcv_with_cache(pairs, start.timestamp())
+    assert ohlcv_mock.call_count == 0
+
+    # Expire 5m cache
+    time_machine.move_to(start + timedelta(minutes=6), tick=False)
+
+    ohlcv_mock.reset_mock()
+    res = exchange.refresh_ohlcv_with_cache(pairs, start.timestamp())
+    assert ohlcv_mock.call_count == 1
+    assert len(ohlcv_mock.call_args_list[0][0][0]) == 1
+
+    # Expire 5m and 1h cache
+    time_machine.move_to(start + timedelta(hours=2), tick=False)
+
+    ohlcv_mock.reset_mock()
+    res = exchange.refresh_ohlcv_with_cache(pairs, start.timestamp())
+    assert ohlcv_mock.call_count == 1
+    assert len(ohlcv_mock.call_args_list[0][0][0]) == 2
+
+    # Expire all caches
+    time_machine.move_to(start + timedelta(days=1, hours=2), tick=False)
+
+    ohlcv_mock.reset_mock()
+    res = exchange.refresh_ohlcv_with_cache(pairs, start.timestamp())
+    assert ohlcv_mock.call_count == 1
+    assert len(ohlcv_mock.call_args_list[0][0][0]) == 5
+    assert ohlcv_mock.call_args_list[0][0][0] == pairs
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_name):
+    ohlcv = [
+        [
+            dt_ts(),  # unix timestamp ms
+            1,  # open
+            2,  # high
+            3,  # low
+            4,  # close
+            5,  # volume (in quote currency)
+        ]
+    ]
+
+    caplog.set_level(logging.DEBUG)
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    # Monkey-patch async function
+    exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
+
+    pair = "ETH/BTC"
+    res = await exchange._async_get_candle_history(pair, "5m", CandleType.SPOT)
+    assert type(res) is tuple
+    assert len(res) == 5
+    assert res[0] == pair
+    assert res[1] == "5m"
+    assert res[2] == CandleType.SPOT
+    assert res[3] == ohlcv
+    assert exchange._api_async.fetch_ohlcv.call_count == 1
+    assert not log_has(f"Using cached candle (OHLCV) data for {pair} ...", caplog)
+    exchange.close()
+    # exchange = Exchange(default_conf)
+    await async_ccxt_exception(
+        mocker,
+        default_conf,
+        MagicMock(),
+        "_async_get_candle_history",
+        "fetch_ohlcv",
+        pair="ABCD/BTC",
+        timeframe=default_conf["timeframe"],
+        candle_type=CandleType.SPOT,
+    )
+
+    api_mock = MagicMock()
+    with pytest.raises(
+        OperationalException, match=r"Could not fetch historical candle \(OHLCV\) data.*"
+    ):
+        api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.BaseError("Unknown error"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        await exchange._async_get_candle_history(
+            pair, "5m", CandleType.SPOT, dt_ts(dt_now() - timedelta(seconds=2000))
+        )
+
+    exchange.close()
+
+    with pytest.raises(
+        OperationalException,
+        match=r"Exchange.* does not support fetching " r"historical candle \(OHLCV\) data\..*",
+    ):
+        api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.NotSupported("Not supported"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        await exchange._async_get_candle_history(
+            pair, "5m", CandleType.SPOT, dt_ts(dt_now() - timedelta(seconds=2000))
+        )
+    exchange.close()
+
+
+async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog):
+    from freqtrade.exchange.common import _reset_logging_mixin
+
+    _reset_logging_mixin()
+    caplog.set_level(logging.INFO)
+    api_mock = MagicMock()
+    api_mock.fetch_ohlcv = MagicMock(
+        side_effect=ccxt.DDoSProtection(
+            "kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
+            "symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
+            "429 Too Many Requests"
+            '{"code":"429000","msg":"Too Many Requests"}'
+        )
+    )
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="kucoin")
+    mocker.patch(f"{EXMS}.name", PropertyMock(return_value="KuCoin"))
+
+    msg = "Kucoin 429 error, avoid triggering DDosProtection backoff delay"
+    assert not num_log_has_re(msg, caplog)
+
+    for _ in range(3):
+        with pytest.raises(DDosProtection, match=r"429 Too Many Requests"):
+            await exchange._async_get_candle_history(
+                "ETH/BTC",
+                "5m",
+                CandleType.SPOT,
+                since_ms=dt_ts(dt_now() - timedelta(seconds=2000)),
+                count=3,
+            )
+    assert num_log_has_re(msg, caplog) == 3
+
+    caplog.clear()
+    # Test regular non-kucoin message
+    api_mock.fetch_ohlcv = MagicMock(
+        side_effect=ccxt.DDoSProtection(
+            "kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
+            "symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
+            "429 Too Many Requests"
+            '{"code":"2222222","msg":"Too Many Requests"}'
+        )
+    )
+
+    msg = r"_async_get_candle_history\(\) returned exception: .*"
+    msg2 = r"Applying DDosProtection backoff delay: .*"
+    with patch("freqtrade.exchange.common.asyncio.sleep", get_mock_coro(None)):
+        for _ in range(3):
+            with pytest.raises(DDosProtection, match=r"429 Too Many Requests"):
+                await exchange._async_get_candle_history(
+                    "ETH/BTC",
+                    "5m",
+                    CandleType.SPOT,
+                    dt_ts(dt_now() - timedelta(seconds=2000)),
+                    count=3,
+                )
+        # Expect the "returned exception" message 12 times (4 retries * 3 (loop))
+        assert num_log_has_re(msg, caplog) == 12
+        assert num_log_has_re(msg2, caplog) == 9
+    exchange.close()
+
+
+async def test__async_get_candle_history_empty(default_conf, mocker, caplog):
+    """Test empty exchange result"""
+    ohlcv = []
+
+    caplog.set_level(logging.DEBUG)
+    exchange = get_patched_exchange(mocker, default_conf)
+    # Monkey-patch async function
+    exchange._api_async.fetch_ohlcv = get_mock_coro([])
+
+    exchange = Exchange(default_conf)
+    pair = "ETH/BTC"
+    res = await exchange._async_get_candle_history(pair, "5m", CandleType.SPOT)
+    assert type(res) is tuple
+    assert len(res) == 5
+    assert res[0] == pair
+    assert res[1] == "5m"
+    assert res[2] == CandleType.SPOT
+    assert res[3] == ohlcv
+    assert exchange._api_async.fetch_ohlcv.call_count == 1
+    exchange.close()
+
+
+def test_refresh_latest_ohlcv_inv_result(default_conf, mocker, caplog):
+    async def mock_get_candle_hist(pair, *args, **kwargs):
+        if pair == "ETH/BTC":
+            return [[]]
+        else:
+            raise TypeError()
+
+    exchange = get_patched_exchange(mocker, default_conf)
+
+    # Monkey-patch async function with empty result
+    exchange._api_async.fetch_ohlcv = MagicMock(side_effect=mock_get_candle_hist)
+
+    pairs = [("ETH/BTC", "5m", ""), ("XRP/BTC", "5m", "")]
+    res = exchange.refresh_latest_ohlcv(pairs)
+    assert exchange._klines
+    assert exchange._api_async.fetch_ohlcv.call_count == 2
+
+    assert isinstance(res, dict)
+    assert len(res) == 1
+    # Test that each is in list at least once as order is not guaranteed
+    assert log_has("Error loading ETH/BTC. Result was [[]].", caplog)
+    assert log_has("Async code raised an exception: TypeError()", caplog)
+
+
+def test_get_next_limit_in_list():
+    limit_range = [5, 10, 20, 50, 100, 500, 1000]
+    assert Exchange.get_next_limit_in_list(1, limit_range) == 5
+    assert Exchange.get_next_limit_in_list(5, limit_range) == 5
+    assert Exchange.get_next_limit_in_list(6, limit_range) == 10
+    assert Exchange.get_next_limit_in_list(9, limit_range) == 10
+    assert Exchange.get_next_limit_in_list(10, limit_range) == 10
+    assert Exchange.get_next_limit_in_list(11, limit_range) == 20
+    assert Exchange.get_next_limit_in_list(19, limit_range) == 20
+    assert Exchange.get_next_limit_in_list(21, limit_range) == 50
+    assert Exchange.get_next_limit_in_list(51, limit_range) == 100
+    assert Exchange.get_next_limit_in_list(1000, limit_range) == 1000
+    # Going over the limit ...
+    assert Exchange.get_next_limit_in_list(1001, limit_range) == 1000
+    assert Exchange.get_next_limit_in_list(2000, limit_range) == 1000
+    # Without required range
+    assert Exchange.get_next_limit_in_list(2000, limit_range, False) is None
+    assert Exchange.get_next_limit_in_list(15, limit_range, False) == 20
+
+    assert Exchange.get_next_limit_in_list(21, None) == 21
+    assert Exchange.get_next_limit_in_list(100, None) == 100
+    assert Exchange.get_next_limit_in_list(1000, None) == 1000
+    # With upper limit
+    assert Exchange.get_next_limit_in_list(1000, None, upper_limit=None) == 1000
+    assert Exchange.get_next_limit_in_list(1000, None, upper_limit=500) == 500
+    # with upper limit and range, limit_range wins
+    assert Exchange.get_next_limit_in_list(1000, limit_range, upper_limit=500) == 1000
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_fetch_l2_order_book(default_conf, mocker, order_book_l2, exchange_name):
+    default_conf["exchange"]["name"] = exchange_name
+    api_mock = MagicMock()
+
+    api_mock.fetch_l2_order_book = order_book_l2
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    order_book = exchange.fetch_l2_order_book(pair="ETH/BTC", limit=10)
+    assert "bids" in order_book
+    assert "asks" in order_book
+    assert len(order_book["bids"]) == 10
+    assert len(order_book["asks"]) == 10
+    assert api_mock.fetch_l2_order_book.call_args_list[0][0][0] == "ETH/BTC"
+
+    for val in [1, 5, 10, 12, 20, 50, 100]:
+        api_mock.fetch_l2_order_book.reset_mock()
+
+        order_book = exchange.fetch_l2_order_book(pair="ETH/BTC", limit=val)
+        assert api_mock.fetch_l2_order_book.call_args_list[0][0][0] == "ETH/BTC"
+        # Not all exchanges support all limits for orderbook
+        if not exchange.get_option("l2_limit_range") or val in exchange.get_option(
+            "l2_limit_range"
+        ):
+            assert api_mock.fetch_l2_order_book.call_args_list[0][0][1] == val
+        else:
+            next_limit = exchange.get_next_limit_in_list(val, exchange.get_option("l2_limit_range"))
+            assert api_mock.fetch_l2_order_book.call_args_list[0][0][1] == next_limit
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_fetch_l2_order_book_exception(default_conf, mocker, exchange_name):
+    api_mock = MagicMock()
+    with pytest.raises(OperationalException):
+        api_mock.fetch_l2_order_book = MagicMock(side_effect=ccxt.NotSupported("Not supported"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.fetch_l2_order_book(pair="ETH/BTC", limit=50)
+    with pytest.raises(TemporaryError):
+        api_mock.fetch_l2_order_book = MagicMock(side_effect=ccxt.NetworkError("DeadBeef"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.fetch_l2_order_book(pair="ETH/BTC", limit=50)
+    with pytest.raises(OperationalException):
+        api_mock.fetch_l2_order_book = MagicMock(side_effect=ccxt.BaseError("DeadBeef"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.fetch_l2_order_book(pair="ETH/BTC", limit=50)
+
+
+@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", get_entry_rate_data)
+def test_get_entry_rate(
+    mocker, default_conf, caplog, side, ask, bid, last, last_ab, expected, time_machine
+) -> None:
+    caplog.set_level(logging.DEBUG)
+    start_dt = datetime(2023, 12, 1, 0, 10, 0, tzinfo=timezone.utc)
+    time_machine.move_to(start_dt, tick=False)
+    if last_ab is None:
+        del default_conf["entry_pricing"]["price_last_balance"]
+    else:
+        default_conf["entry_pricing"]["price_last_balance"] = last_ab
+    default_conf["entry_pricing"]["price_side"] = side
+    exchange = get_patched_exchange(mocker, default_conf)
+    mocker.patch(f"{EXMS}.fetch_ticker", return_value={"ask": ask, "last": last, "bid": bid})
+    log_msg = "Using cached entry rate for ETH/BTC."
+
+    assert exchange.get_rate("ETH/BTC", side="entry", is_short=False, refresh=True) == expected
+    assert not log_has(log_msg, caplog)
+
+    time_machine.move_to(start_dt + timedelta(minutes=4), tick=False)
+    # Running a 2nd time without Refresh!
+    caplog.clear()
+    assert exchange.get_rate("ETH/BTC", side="entry", is_short=False, refresh=False) == expected
+    assert log_has(log_msg, caplog)
+
+    time_machine.move_to(start_dt + timedelta(minutes=6), tick=False)
+    # Running a 2nd time - forces refresh due to ttl timeout
+    caplog.clear()
+    assert exchange.get_rate("ETH/BTC", side="entry", is_short=False, refresh=False) == expected
+    assert not log_has(log_msg, caplog)
+
+    # Running a 2nd time with Refresh on!
+    caplog.clear()
+    assert exchange.get_rate("ETH/BTC", side="entry", is_short=False, refresh=True) == expected
+    assert not log_has(log_msg, caplog)
+
+
+@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", get_exit_rate_data)
+def test_get_exit_rate(
+    default_conf, mocker, caplog, side, bid, ask, last, last_ab, expected, time_machine
+) -> None:
+    caplog.set_level(logging.DEBUG)
+    start_dt = datetime(2023, 12, 1, 0, 10, 0, tzinfo=timezone.utc)
+    time_machine.move_to(start_dt, tick=False)
+
+    default_conf["exit_pricing"]["price_side"] = side
+    if last_ab is not None:
+        default_conf["exit_pricing"]["price_last_balance"] = last_ab
+    mocker.patch(f"{EXMS}.fetch_ticker", return_value={"ask": ask, "bid": bid, "last": last})
+    pair = "ETH/BTC"
+    log_msg = "Using cached exit rate for ETH/BTC."
+
+    # Test regular mode
+    exchange = get_patched_exchange(mocker, default_conf)
+    rate = exchange.get_rate(pair, side="exit", is_short=False, refresh=True)
+    assert not log_has(log_msg, caplog)
+    assert isinstance(rate, float)
+    assert rate == expected
+    # Use caching
+    caplog.clear()
+    assert exchange.get_rate(pair, side="exit", is_short=False, refresh=False) == expected
+    assert log_has(log_msg, caplog)
+
+    time_machine.move_to(start_dt + timedelta(minutes=4), tick=False)
+    # Caching still active - TTL didn't expire
+    caplog.clear()
+    assert exchange.get_rate(pair, side="exit", is_short=False, refresh=False) == expected
+    assert log_has(log_msg, caplog)
+
+    time_machine.move_to(start_dt + timedelta(minutes=6), tick=False)
+    # Caching expired - refresh forced
+    caplog.clear()
+    assert exchange.get_rate(pair, side="exit", is_short=False, refresh=False) == expected
+    assert not log_has(log_msg, caplog)
+
+
+@pytest.mark.parametrize(
+    "entry,is_short,side,ask,bid,last,last_ab,expected",
+    [
+        ("entry", False, "ask", None, 4, 4, 0, 4),  # ask not available
+        ("entry", False, "ask", None, None, 4, 0, 4),  # ask not available
+        ("entry", False, "bid", 6, None, 4, 0, 5),  # bid not available
+        ("entry", False, "bid", None, None, 4, 0, 5),  # No rate available
+        ("exit", False, "ask", None, 4, 4, 0, 4),  # ask not available
+        ("exit", False, "ask", None, None, 4, 0, 4),  # ask not available
+        ("exit", False, "bid", 6, None, 4, 0, 5),  # bid not available
+        ("exit", False, "bid", None, None, 4, 0, 5),  # bid not available
+    ],
+)
+def test_get_ticker_rate_error(
+    mocker, entry, default_conf, caplog, side, is_short, ask, bid, last, last_ab, expected
+) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf["entry_pricing"]["price_last_balance"] = last_ab
+    default_conf["entry_pricing"]["price_side"] = side
+    default_conf["exit_pricing"]["price_side"] = side
+    default_conf["exit_pricing"]["price_last_balance"] = last_ab
+    exchange = get_patched_exchange(mocker, default_conf)
+    mocker.patch(f"{EXMS}.fetch_ticker", return_value={"ask": ask, "last": last, "bid": bid})
+
+    with pytest.raises(PricingError):
+        exchange.get_rate("ETH/BTC", refresh=True, side=entry, is_short=is_short)
+
+
+@pytest.mark.parametrize(
+    "is_short,side,expected",
+    [
+        (False, "bid", 0.043936),  # Value from order_book_l2 fixture - bids side
+        (False, "ask", 0.043949),  # Value from order_book_l2 fixture - asks side
+        (False, "other", 0.043936),  # Value from order_book_l2 fixture - bids side
+        (False, "same", 0.043949),  # Value from order_book_l2 fixture - asks side
+        (True, "bid", 0.043936),  # Value from order_book_l2 fixture - bids side
+        (True, "ask", 0.043949),  # Value from order_book_l2 fixture - asks side
+        (True, "other", 0.043949),  # Value from order_book_l2 fixture - asks side
+        (True, "same", 0.043936),  # Value from order_book_l2 fixture - bids side
+    ],
+)
+def test_get_exit_rate_orderbook(
+    default_conf, mocker, caplog, is_short, side, expected, order_book_l2
+):
+    caplog.set_level(logging.DEBUG)
+    # Test orderbook mode
+    default_conf["exit_pricing"]["price_side"] = side
+    default_conf["exit_pricing"]["use_order_book"] = True
+    default_conf["exit_pricing"]["order_book_top"] = 1
+    pair = "ETH/BTC"
+    mocker.patch(f"{EXMS}.fetch_l2_order_book", order_book_l2)
+    exchange = get_patched_exchange(mocker, default_conf)
+    rate = exchange.get_rate(pair, refresh=True, side="exit", is_short=is_short)
+    assert not log_has("Using cached exit rate for ETH/BTC.", caplog)
+    assert isinstance(rate, float)
+    assert rate == expected
+    rate = exchange.get_rate(pair, refresh=False, side="exit", is_short=is_short)
+    assert rate == expected
+    assert log_has("Using cached exit rate for ETH/BTC.", caplog)
+
+
+def test_get_exit_rate_orderbook_exception(default_conf, mocker, caplog):
+    # Test orderbook mode
+    default_conf["exit_pricing"]["price_side"] = "ask"
+    default_conf["exit_pricing"]["use_order_book"] = True
+    default_conf["exit_pricing"]["order_book_top"] = 1
+    pair = "ETH/BTC"
+    # Test What happens if the exchange returns an empty orderbook.
+    mocker.patch(f"{EXMS}.fetch_l2_order_book", return_value={"bids": [[]], "asks": [[]]})
+    exchange = get_patched_exchange(mocker, default_conf)
+    with pytest.raises(PricingError):
+        exchange.get_rate(pair, refresh=True, side="exit", is_short=False)
+    assert log_has_re(
+        rf"{pair} - Exit Price at location 1 from orderbook " rf"could not be determined\..*",
+        caplog,
+    )
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_get_exit_rate_exception(default_conf, mocker, is_short):
+    # Ticker on one side can be empty in certain circumstances.
+    default_conf["exit_pricing"]["price_side"] = "ask"
+    pair = "ETH/BTC"
+    mocker.patch(f"{EXMS}.fetch_ticker", return_value={"ask": None, "bid": 0.12, "last": None})
+    exchange = get_patched_exchange(mocker, default_conf)
+    with pytest.raises(PricingError, match=r"Exit-Rate for ETH/BTC was empty."):
+        exchange.get_rate(pair, refresh=True, side="exit", is_short=is_short)
+
+    exchange._config["exit_pricing"]["price_side"] = "bid"
+    assert exchange.get_rate(pair, refresh=True, side="exit", is_short=is_short) == 0.12
+    # Reverse sides
+    mocker.patch(f"{EXMS}.fetch_ticker", return_value={"ask": 0.13, "bid": None, "last": None})
+    with pytest.raises(PricingError, match=r"Exit-Rate for ETH/BTC was empty."):
+        exchange.get_rate(pair, refresh=True, side="exit", is_short=is_short)
+
+    exchange._config["exit_pricing"]["price_side"] = "ask"
+    assert exchange.get_rate(pair, refresh=True, side="exit", is_short=is_short) == 0.13
+
+
+@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", get_entry_rate_data)
+@pytest.mark.parametrize("side2", ["bid", "ask"])
+@pytest.mark.parametrize("use_order_book", [True, False])
+def test_get_rates_testing_entry(
+    mocker,
+    default_conf,
+    caplog,
+    side,
+    ask,
+    bid,
+    last,
+    last_ab,
+    expected,
+    side2,
+    use_order_book,
+    order_book_l2,
+) -> None:
+    caplog.set_level(logging.DEBUG)
+    if last_ab is None:
+        del default_conf["entry_pricing"]["price_last_balance"]
+    else:
+        default_conf["entry_pricing"]["price_last_balance"] = last_ab
+    default_conf["entry_pricing"]["price_side"] = side
+    default_conf["exit_pricing"]["price_side"] = side2
+    default_conf["exit_pricing"]["use_order_book"] = use_order_book
+    api_mock = MagicMock()
+    api_mock.fetch_l2_order_book = order_book_l2
+    api_mock.fetch_ticker = MagicMock(return_value={"ask": ask, "last": last, "bid": bid})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+
+    assert exchange.get_rates("ETH/BTC", refresh=True, is_short=False)[0] == expected
+    assert not log_has("Using cached buy rate for ETH/BTC.", caplog)
+
+    api_mock.fetch_l2_order_book.reset_mock()
+    api_mock.fetch_ticker.reset_mock()
+    assert exchange.get_rates("ETH/BTC", refresh=False, is_short=False)[0] == expected
+    assert log_has("Using cached buy rate for ETH/BTC.", caplog)
+    assert api_mock.fetch_l2_order_book.call_count == 0
+    assert api_mock.fetch_ticker.call_count == 0
+    # Running a 2nd time with Refresh on!
+    caplog.clear()
+
+    assert exchange.get_rates("ETH/BTC", refresh=True, is_short=False)[0] == expected
+    assert not log_has("Using cached buy rate for ETH/BTC.", caplog)
+
+    assert api_mock.fetch_l2_order_book.call_count == int(use_order_book)
+    assert api_mock.fetch_ticker.call_count == 1
+
+
+@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", get_exit_rate_data)
+@pytest.mark.parametrize("side2", ["bid", "ask"])
+@pytest.mark.parametrize("use_order_book", [True, False])
+def test_get_rates_testing_exit(
+    default_conf,
+    mocker,
+    caplog,
+    side,
+    bid,
+    ask,
+    last,
+    last_ab,
+    expected,
+    side2,
+    use_order_book,
+    order_book_l2,
+) -> None:
+    caplog.set_level(logging.DEBUG)
+
+    default_conf["exit_pricing"]["price_side"] = side
+    if last_ab is not None:
+        default_conf["exit_pricing"]["price_last_balance"] = last_ab
+
+    default_conf["entry_pricing"]["price_side"] = side2
+    default_conf["entry_pricing"]["use_order_book"] = use_order_book
+    api_mock = MagicMock()
+    api_mock.fetch_l2_order_book = order_book_l2
+    api_mock.fetch_ticker = MagicMock(return_value={"ask": ask, "last": last, "bid": bid})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+
+    pair = "ETH/BTC"
+
+    # Test regular mode
+    rate = exchange.get_rates(pair, refresh=True, is_short=False)[1]
+    assert not log_has("Using cached sell rate for ETH/BTC.", caplog)
+    assert isinstance(rate, float)
+    assert rate == expected
+    # Use caching
+    api_mock.fetch_l2_order_book.reset_mock()
+    api_mock.fetch_ticker.reset_mock()
+
+    rate = exchange.get_rates(pair, refresh=False, is_short=False)[1]
+    assert rate == expected
+    assert log_has("Using cached sell rate for ETH/BTC.", caplog)
+
+    assert api_mock.fetch_l2_order_book.call_count == 0
+    assert api_mock.fetch_ticker.call_count == 0
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+async def test___async_get_candle_history_sort(default_conf, mocker, exchange_name):
+    def sort_data(data, key):
+        return sorted(data, key=key)
+
+    # GDAX use-case (real data from GDAX)
+    # This OHLCV data is ordered DESC (newest first, oldest last)
+    ohlcv = [
+        [1527833100000, 0.07666, 0.07671, 0.07666, 0.07668, 16.65244264],
+        [1527832800000, 0.07662, 0.07666, 0.07662, 0.07666, 1.30051526],
+        [1527832500000, 0.07656, 0.07661, 0.07656, 0.07661, 12.034778840000001],
+        [1527832200000, 0.07658, 0.07658, 0.07655, 0.07656, 0.59780186],
+        [1527831900000, 0.07658, 0.07658, 0.07658, 0.07658, 1.76278136],
+        [1527831600000, 0.07658, 0.07658, 0.07658, 0.07658, 2.22646521],
+        [1527831300000, 0.07655, 0.07657, 0.07655, 0.07657, 1.1753],
+        [1527831000000, 0.07654, 0.07654, 0.07651, 0.07651, 0.8073060299999999],
+        [1527830700000, 0.07652, 0.07652, 0.07651, 0.07652, 10.04822687],
+        [1527830400000, 0.07649, 0.07651, 0.07649, 0.07651, 2.5734867],
+    ]
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
+    sort_mock = mocker.patch("freqtrade.exchange.exchange.sorted", MagicMock(side_effect=sort_data))
+    # Test the OHLCV data sort
+    res = await exchange._async_get_candle_history(
+        "ETH/BTC", default_conf["timeframe"], CandleType.SPOT
+    )
+    assert res[0] == "ETH/BTC"
+    res_ohlcv = res[3]
+
+    assert sort_mock.call_count == 1
+    assert res_ohlcv[0][0] == 1527830400000
+    assert res_ohlcv[0][1] == 0.07649
+    assert res_ohlcv[0][2] == 0.07651
+    assert res_ohlcv[0][3] == 0.07649
+    assert res_ohlcv[0][4] == 0.07651
+    assert res_ohlcv[0][5] == 2.5734867
+
+    assert res_ohlcv[9][0] == 1527833100000
+    assert res_ohlcv[9][1] == 0.07666
+    assert res_ohlcv[9][2] == 0.07671
+    assert res_ohlcv[9][3] == 0.07666
+    assert res_ohlcv[9][4] == 0.07668
+    assert res_ohlcv[9][5] == 16.65244264
+
+    # This OHLCV data is ordered ASC (oldest first, newest last)
+    ohlcv = [
+        [1527827700000, 0.07659999, 0.0766, 0.07627, 0.07657998, 1.85216924],
+        [1527828000000, 0.07657995, 0.07657995, 0.0763, 0.0763, 26.04051037],
+        [1527828300000, 0.0763, 0.07659998, 0.0763, 0.0764, 10.36434124],
+        [1527828600000, 0.0764, 0.0766, 0.0764, 0.0766, 5.71044773],
+        [1527828900000, 0.0764, 0.07666998, 0.0764, 0.07666998, 47.48888565],
+        [1527829200000, 0.0765, 0.07672999, 0.0765, 0.07672999, 3.37640326],
+        [1527829500000, 0.0766, 0.07675, 0.0765, 0.07675, 8.36203831],
+        [1527829800000, 0.07675, 0.07677999, 0.07620002, 0.076695, 119.22963884],
+        [1527830100000, 0.076695, 0.07671, 0.07624171, 0.07671, 1.80689244],
+        [1527830400000, 0.07671, 0.07674399, 0.07629216, 0.07655213, 2.31452783],
+    ]
+    exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
+    # Reset sort mock
+    sort_mock = mocker.patch("freqtrade.exchange.sorted", MagicMock(side_effect=sort_data))
+    # Test the OHLCV data sort
+    res = await exchange._async_get_candle_history(
+        "ETH/BTC", default_conf["timeframe"], CandleType.SPOT
+    )
+    assert res[0] == "ETH/BTC"
+    assert res[1] == default_conf["timeframe"]
+    res_ohlcv = res[3]
+    # Sorted not called again - data is already in order
+    assert sort_mock.call_count == 0
+    assert res_ohlcv[0][0] == 1527827700000
+    assert res_ohlcv[0][1] == 0.07659999
+    assert res_ohlcv[0][2] == 0.0766
+    assert res_ohlcv[0][3] == 0.07627
+    assert res_ohlcv[0][4] == 0.07657998
+    assert res_ohlcv[0][5] == 1.85216924
+
+    assert res_ohlcv[9][0] == 1527830400000
+    assert res_ohlcv[9][1] == 0.07671
+    assert res_ohlcv[9][2] == 0.07674399
+    assert res_ohlcv[9][3] == 0.07629216
+    assert res_ohlcv[9][4] == 0.07655213
+    assert res_ohlcv[9][5] == 2.31452783
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+async def test__async_fetch_trades(
+    default_conf, mocker, caplog, exchange_name, fetch_trades_result
+):
+    caplog.set_level(logging.DEBUG)
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    # Monkey-patch async function
+    exchange._api_async.fetch_trades = get_mock_coro(fetch_trades_result)
+
+    pair = "ETH/BTC"
+    res, pagid = await exchange._async_fetch_trades(pair, since=None, params=None)
+    assert isinstance(res, list)
+    assert isinstance(res[0], list)
+    assert isinstance(res[1], list)
+    if exchange._trades_pagination == "id":
+        if exchange_name == "kraken":
+            assert pagid == 1565798399872512133
+        else:
+            assert pagid == "126181333"
+    else:
+        assert pagid == 1565798399872
+
+    assert exchange._api_async.fetch_trades.call_count == 1
+    assert exchange._api_async.fetch_trades.call_args[0][0] == pair
+    assert exchange._api_async.fetch_trades.call_args[1]["limit"] == 1000
+
+    assert log_has_re(f"Fetching trades for pair {pair}, since .*", caplog)
+    caplog.clear()
+    exchange._api_async.fetch_trades.reset_mock()
+    res, pagid = await exchange._async_fetch_trades(pair, since=None, params={"from": "123"})
+    assert exchange._api_async.fetch_trades.call_count == 1
+    assert exchange._api_async.fetch_trades.call_args[0][0] == pair
+    assert exchange._api_async.fetch_trades.call_args[1]["limit"] == 1000
+    assert exchange._api_async.fetch_trades.call_args[1]["params"] == {"from": "123"}
+
+    if exchange._trades_pagination == "id":
+        if exchange_name == "kraken":
+            assert pagid == 1565798399872512133
+        else:
+            assert pagid == "126181333"
+    else:
+        assert pagid == 1565798399872
+
+    assert log_has_re(f"Fetching trades for pair {pair}, params: .*", caplog)
+    exchange.close()
+
+    await async_ccxt_exception(
+        mocker,
+        default_conf,
+        MagicMock(),
+        "_async_fetch_trades",
+        "fetch_trades",
+        pair="ABCD/BTC",
+        since=None,
+    )
+
+    api_mock = MagicMock()
+    with pytest.raises(OperationalException, match=r"Could not fetch trade data*"):
+        api_mock.fetch_trades = MagicMock(side_effect=ccxt.BaseError("Unknown error"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        await exchange._async_fetch_trades(pair, since=dt_ts(dt_now() - timedelta(seconds=2000)))
+    exchange.close()
+
+    with pytest.raises(
+        OperationalException,
+        match=r"Exchange.* does not support fetching " r"historical trade data\..*",
+    ):
+        api_mock.fetch_trades = MagicMock(side_effect=ccxt.NotSupported("Not supported"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        await exchange._async_fetch_trades(pair, since=dt_ts(dt_now() - timedelta(seconds=2000)))
+    exchange.close()
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+async def test__async_fetch_trades_contract_size(
+    default_conf, mocker, caplog, exchange_name, fetch_trades_result
+):
+    caplog.set_level(logging.DEBUG)
+    default_conf["margin_mode"] = "isolated"
+    default_conf["trading_mode"] = "futures"
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    # Monkey-patch async function
+    exchange._api_async.fetch_trades = get_mock_coro(
+        [
+            {
+                "info": {
+                    "a": 126181333,
+                    "p": "0.01952600",
+                    "q": "0.01200000",
+                    "f": 138604158,
+                    "l": 138604158,
+                    "T": 1565798399872,
+                    "m": True,
+                    "M": True,
+                },
+                "timestamp": 1565798399872,
+                "datetime": "2019-08-14T15:59:59.872Z",
+                "symbol": "ETH/USDT:USDT",
+                "id": "126181383",
+                "order": None,
+                "type": None,
+                "takerOrMaker": None,
+                "side": "sell",
+                "price": 2.0,
+                "amount": 30.0,
+                "cost": 60.0,
+                "fee": None,
+            }
+        ]
+    )
+
+    pair = "ETH/USDT:USDT"
+    res, pagid = await exchange._async_fetch_trades(pair, since=None, params=None)
+    assert res[0][5] == 300
+    assert pagid is not None
+    exchange.close()
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+async def test__async_get_trade_history_id(
+    default_conf, mocker, exchange_name, fetch_trades_result
+):
+    default_conf["exchange"]["only_from_ccxt"] = True
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    if exchange._trades_pagination != "id":
+        exchange.close()
+        pytest.skip("Exchange does not support pagination by trade id")
+    pagination_arg = exchange._trades_pagination_arg
+
+    async def mock_get_trade_hist(pair, *args, **kwargs):
+        if "since" in kwargs:
+            # Return first 3
+            return fetch_trades_result[:-2]
+        elif kwargs.get("params", {}).get(pagination_arg) in (
+            fetch_trades_result[-3]["id"],
+            1565798399752,
+        ):
+            # Return 2
+            return fetch_trades_result[-3:-1]
+        else:
+            # Return last 2
+            return fetch_trades_result[-2:]
+
+    # Monkey-patch async function
+    exchange._api_async.fetch_trades = MagicMock(side_effect=mock_get_trade_hist)
+
+    pair = "ETH/BTC"
+    ret = await exchange._async_get_trade_history_id(
+        pair,
+        since=fetch_trades_result[0]["timestamp"],
+        until=fetch_trades_result[-1]["timestamp"] - 1,
+    )
+    assert isinstance(ret, tuple)
+    assert ret[0] == pair
+    assert isinstance(ret[1], list)
+    if exchange_name != "kraken":
+        assert len(ret[1]) == len(fetch_trades_result)
+    assert exchange._api_async.fetch_trades.call_count == 3
+    fetch_trades_cal = exchange._api_async.fetch_trades.call_args_list
+    # first call (using since, not fromId)
+    assert fetch_trades_cal[0][0][0] == pair
+    assert fetch_trades_cal[0][1]["since"] == fetch_trades_result[0]["timestamp"]
+
+    # 2nd call
+    assert fetch_trades_cal[1][0][0] == pair
+    assert "params" in fetch_trades_cal[1][1]
+    assert exchange._ft_has["trades_pagination_arg"] in fetch_trades_cal[1][1]["params"]
+
+
+@pytest.mark.parametrize(
+    "trade_id, expected",
+    [
+        ("1234", True),
+        ("170544369512007228", True),
+        ("1705443695120072285", True),
+        ("170544369512007228555", True),
+    ],
+)
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test__valid_trade_pagination_id(mocker, default_conf_usdt, exchange_name, trade_id, expected):
+    if exchange_name == "kraken":
+        pytest.skip("Kraken has a different pagination id format, and an explicit test.")
+    exchange = get_patched_exchange(mocker, default_conf_usdt, exchange=exchange_name)
+
+    assert exchange._valid_trade_pagination_id("XRP/USDT", trade_id) == expected
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+async def test__async_get_trade_history_time(
+    default_conf, mocker, caplog, exchange_name, fetch_trades_result
+):
+    caplog.set_level(logging.DEBUG)
+
+    async def mock_get_trade_hist(pair, *args, **kwargs):
+        if kwargs["since"] == fetch_trades_result[0]["timestamp"]:
+            return fetch_trades_result[:-1]
+        else:
+            return fetch_trades_result[-1:]
+
+    caplog.set_level(logging.DEBUG)
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    if exchange._trades_pagination != "time":
+        exchange.close()
+        pytest.skip("Exchange does not support pagination by timestamp")
+    # Monkey-patch async function
+    exchange._api_async.fetch_trades = MagicMock(side_effect=mock_get_trade_hist)
+    pair = "ETH/BTC"
+    ret = await exchange._async_get_trade_history_time(
+        pair,
+        since=fetch_trades_result[0]["timestamp"],
+        until=fetch_trades_result[-1]["timestamp"] - 1,
+    )
+    assert isinstance(ret, tuple)
+    assert ret[0] == pair
+    assert isinstance(ret[1], list)
+    assert len(ret[1]) == len(fetch_trades_result)
+    assert exchange._api_async.fetch_trades.call_count == 2
+    fetch_trades_cal = exchange._api_async.fetch_trades.call_args_list
+    # first call (using since, not fromId)
+    assert fetch_trades_cal[0][0][0] == pair
+    assert fetch_trades_cal[0][1]["since"] == fetch_trades_result[0]["timestamp"]
+
+    # 2nd call
+    assert fetch_trades_cal[1][0][0] == pair
+    assert fetch_trades_cal[1][1]["since"] == fetch_trades_result[-2]["timestamp"]
+    assert log_has_re(r"Stopping because until was reached.*", caplog)
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+async def test__async_get_trade_history_time_empty(
+    default_conf, mocker, caplog, exchange_name, trades_history
+):
+    caplog.set_level(logging.DEBUG)
+
+    async def mock_get_trade_hist(pair, *args, **kwargs):
+        if kwargs["since"] == trades_history[0][0]:
+            return trades_history[:-1], trades_history[:-1][-1][0]
+        else:
+            return [], None
+
+    caplog.set_level(logging.DEBUG)
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    # Monkey-patch async function
+    exchange._async_fetch_trades = MagicMock(side_effect=mock_get_trade_hist)
+    pair = "ETH/BTC"
+    ret = await exchange._async_get_trade_history_time(
+        pair, since=trades_history[0][0], until=trades_history[-1][0] - 1
+    )
+    assert isinstance(ret, tuple)
+    assert ret[0] == pair
+    assert isinstance(ret[1], list)
+    assert len(ret[1]) == len(trades_history) - 1
+    assert exchange._async_fetch_trades.call_count == 2
+    fetch_trades_cal = exchange._async_fetch_trades.call_args_list
+    # first call (using since, not fromId)
+    assert fetch_trades_cal[0][0][0] == pair
+    assert fetch_trades_cal[0][1]["since"] == trades_history[0][0]
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_get_historic_trades(default_conf, mocker, caplog, exchange_name, trades_history):
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+
+    pair = "ETH/BTC"
+
+    exchange._async_get_trade_history_id = get_mock_coro((pair, trades_history))
+    exchange._async_get_trade_history_time = get_mock_coro((pair, trades_history))
+    ret = exchange.get_historic_trades(
+        pair, since=trades_history[0][0], until=trades_history[-1][0]
+    )
+
+    # Depending on the exchange, one or the other method should be called
+    assert (
+        sum(
+            [
+                exchange._async_get_trade_history_id.call_count,
+                exchange._async_get_trade_history_time.call_count,
+            ]
+        )
+        == 1
+    )
+
+    assert len(ret) == 2
+    assert ret[0] == pair
+    assert len(ret[1]) == len(trades_history)
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_get_historic_trades_notsupported(
+    default_conf, mocker, caplog, exchange_name, trades_history
+):
+    mocker.patch(f"{EXMS}.exchange_has", return_value=False)
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+
+    pair = "ETH/BTC"
+
+    with pytest.raises(
+        OperationalException, match="This exchange does not support downloading Trades."
+    ):
+        exchange.get_historic_trades(pair, since=trades_history[0][0], until=trades_history[-1][0])
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_cancel_order_dry_run(default_conf, mocker, exchange_name):
+    default_conf["dry_run"] = True
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=True)
+    assert exchange.cancel_order(order_id="123", pair="TKN/BTC") == {}
+    assert exchange.cancel_stoploss_order(order_id="123", pair="TKN/BTC") == {}
+
+    order = exchange.create_order(
+        pair="ETH/BTC",
+        ordertype="limit",
+        side="buy",
+        amount=5,
+        rate=0.55,
+        time_in_force="gtc",
+        leverage=1.0,
+    )
+
+    cancel_order = exchange.cancel_order(order_id=order["id"], pair="ETH/BTC")
+    assert order["id"] == cancel_order["id"]
+    assert order["amount"] == cancel_order["amount"]
+    assert order["symbol"] == cancel_order["symbol"]
+    assert cancel_order["status"] == "canceled"
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+@pytest.mark.parametrize(
+    "order,result",
+    [
+        ({"status": "closed", "filled": 10}, False),
+        ({"status": "closed", "filled": 0.0}, True),
+        ({"status": "canceled", "filled": 0.0}, True),
+        ({"status": "canceled", "filled": 10.0}, False),
+        ({"status": "unknown", "filled": 10.0}, False),
+        ({"result": "testest123"}, False),
+    ],
+)
+def test_check_order_canceled_empty(mocker, default_conf, exchange_name, order, result):
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    assert exchange.check_order_canceled_empty(order) == result
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+@pytest.mark.parametrize(
+    "order,result",
+    [
+        ({"status": "closed", "amount": 10, "fee": {}}, True),
+        ({"status": "closed", "amount": 0.0, "fee": {}}, True),
+        ({"status": "canceled", "amount": 0.0, "fee": {}}, True),
+        ({"status": "canceled", "amount": 10.0}, False),
+        ({"amount": 10.0, "fee": {}}, False),
+        ({"result": "testest123"}, False),
+        ("hello_world", False),
+        ({"status": "canceled", "amount": None, "fee": None}, False),
+        ({"status": "canceled", "filled": None, "amount": None, "fee": None}, False),
+    ],
+)
+def test_is_cancel_order_result_suitable(mocker, default_conf, exchange_name, order, result):
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    assert exchange.is_cancel_order_result_suitable(order) == result
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+@pytest.mark.parametrize(
+    "corder,call_corder,call_forder",
+    [
+        ({"status": "closed", "amount": 10, "fee": {}}, 1, 0),
+        ({"amount": 10, "fee": {}}, 1, 1),
+    ],
+)
+def test_cancel_order_with_result(
+    default_conf, mocker, exchange_name, corder, call_corder, call_forder
+):
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    api_mock = MagicMock()
+    api_mock.cancel_order = MagicMock(return_value=corder)
+    api_mock.fetch_order = MagicMock(return_value={"id": "1234"})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    res = exchange.cancel_order_with_result("1234", "ETH/BTC", 1234)
+    assert isinstance(res, dict)
+    assert api_mock.cancel_order.call_count == call_corder
+    assert api_mock.fetch_order.call_count == call_forder
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_cancel_order_with_result_error(default_conf, mocker, exchange_name, caplog):
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    api_mock = MagicMock()
+    api_mock.cancel_order = MagicMock(side_effect=ccxt.InvalidOrder("Did not find order"))
+    api_mock.fetch_order = MagicMock(side_effect=ccxt.InvalidOrder("Did not find order"))
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    res = exchange.cancel_order_with_result("1234", "ETH/BTC", 1541)
+    assert isinstance(res, dict)
+    assert log_has("Could not cancel order 1234 for ETH/BTC.", caplog)
+    assert log_has("Could not fetch cancelled order 1234.", caplog)
+    assert res["amount"] == 1541
+
+
+# Ensure that if not dry_run, we should call API
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_cancel_order(default_conf, mocker, exchange_name):
+    default_conf["dry_run"] = False
+    api_mock = MagicMock()
+    api_mock.cancel_order = MagicMock(return_value={"id": "123"})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    assert exchange.cancel_order(order_id="_", pair="TKN/BTC") == {"id": "123"}
+
+    with pytest.raises(InvalidOrderException):
+        api_mock.cancel_order = MagicMock(side_effect=ccxt.InvalidOrder("Did not find order"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.cancel_order(order_id="_", pair="TKN/BTC")
+    assert api_mock.cancel_order.call_count == 1
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "cancel_order",
+        "cancel_order",
+        order_id="_",
+        pair="TKN/BTC",
+    )
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_cancel_stoploss_order(default_conf, mocker, exchange_name):
+    default_conf["dry_run"] = False
+    api_mock = MagicMock()
+    api_mock.cancel_order = MagicMock(return_value={"id": "123"})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    assert exchange.cancel_stoploss_order(order_id="_", pair="TKN/BTC") == {"id": "123"}
+
+    with pytest.raises(InvalidOrderException):
+        api_mock.cancel_order = MagicMock(side_effect=ccxt.InvalidOrder("Did not find order"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.cancel_stoploss_order(order_id="_", pair="TKN/BTC")
+    assert api_mock.cancel_order.call_count == 1
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "cancel_stoploss_order",
+        "cancel_order",
+        order_id="_",
+        pair="TKN/BTC",
+    )
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_cancel_stoploss_order_with_result(default_conf, mocker, exchange_name):
+    default_conf["dry_run"] = False
+    mock_prefix = "freqtrade.exchange.gate.Gate"
+    if exchange_name == "okx":
+        mock_prefix = "freqtrade.exchange.okx.Okx"
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", return_value={"for": 123})
+    mocker.patch(f"{mock_prefix}.fetch_stoploss_order", return_value={"for": 123})
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+
+    res = {"fee": {}, "status": "canceled", "amount": 1234}
+    mocker.patch(f"{EXMS}.cancel_stoploss_order", return_value=res)
+    mocker.patch(f"{mock_prefix}.cancel_stoploss_order", return_value=res)
+    co = exchange.cancel_stoploss_order_with_result(order_id="_", pair="TKN/BTC", amount=555)
+    assert co == res
+
+    mocker.patch(f"{EXMS}.cancel_stoploss_order", return_value="canceled")
+    mocker.patch(f"{mock_prefix}.cancel_stoploss_order", return_value="canceled")
+    # Fall back to fetch_stoploss_order
+    co = exchange.cancel_stoploss_order_with_result(order_id="_", pair="TKN/BTC", amount=555)
+    assert co == {"for": 123}
+
+    exc = InvalidOrderException("")
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", side_effect=exc)
+    mocker.patch(f"{mock_prefix}.fetch_stoploss_order", side_effect=exc)
+    co = exchange.cancel_stoploss_order_with_result(order_id="_", pair="TKN/BTC", amount=555)
+    assert co["amount"] == 555
+    assert co == {"id": "_", "fee": {}, "status": "canceled", "amount": 555, "info": {}}
+
+    with pytest.raises(InvalidOrderException):
+        exc = InvalidOrderException("Did not find order")
+        mocker.patch(f"{EXMS}.cancel_stoploss_order", side_effect=exc)
+        mocker.patch(f"{mock_prefix}.cancel_stoploss_order", side_effect=exc)
+        exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+        exchange.cancel_stoploss_order_with_result(order_id="_", pair="TKN/BTC", amount=123)
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_fetch_order(default_conf, mocker, exchange_name, caplog):
+    default_conf["dry_run"] = True
+    default_conf["exchange"]["log_responses"] = True
+    order = MagicMock()
+    order.myid = 123
+    order.symbol = "TKN/BTC"
+
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    exchange._dry_run_open_orders["X"] = order
+    assert exchange.fetch_order("X", "TKN/BTC").myid == 123
+
+    with pytest.raises(InvalidOrderException, match=r"Tried to get an invalid dry-run-order.*"):
+        exchange.fetch_order("Y", "TKN/BTC")
+
+    default_conf["dry_run"] = False
+    api_mock = MagicMock()
+    api_mock.fetch_order = MagicMock(return_value={"id": "123", "amount": 2, "symbol": "TKN/BTC"})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    assert exchange.fetch_order("X", "TKN/BTC") == {"id": "123", "amount": 2, "symbol": "TKN/BTC"}
+    assert log_has(("API fetch_order: {'id': '123', 'amount': 2, 'symbol': 'TKN/BTC'}"), caplog)
+
+    with pytest.raises(InvalidOrderException):
+        api_mock.fetch_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.fetch_order(order_id="_", pair="TKN/BTC")
+    assert api_mock.fetch_order.call_count == 1
+
+    api_mock.fetch_order = MagicMock(side_effect=ccxt.OrderNotFound("Order not found"))
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    with patch("freqtrade.exchange.common.time.sleep") as tm:
+        with pytest.raises(InvalidOrderException):
+            exchange.fetch_order(order_id="_", pair="TKN/BTC")
+        # Ensure backoff is called
+        assert tm.call_args_list[0][0][0] == 1
+        assert tm.call_args_list[1][0][0] == 2
+        if API_FETCH_ORDER_RETRY_COUNT > 2:
+            assert tm.call_args_list[2][0][0] == 5
+        if API_FETCH_ORDER_RETRY_COUNT > 3:
+            assert tm.call_args_list[3][0][0] == 10
+    assert api_mock.fetch_order.call_count == API_FETCH_ORDER_RETRY_COUNT + 1
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "fetch_order",
+        "fetch_order",
+        retries=API_FETCH_ORDER_RETRY_COUNT + 1,
+        order_id="_",
+        pair="TKN/BTC",
+    )
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_fetch_order_emulated(default_conf, mocker, exchange_name, caplog):
+    default_conf["dry_run"] = True
+    default_conf["exchange"]["log_responses"] = True
+    order = MagicMock()
+    order.myid = 123
+    order.symbol = "TKN/BTC"
+
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    mocker.patch(f"{EXMS}.exchange_has", return_value=False)
+    exchange._dry_run_open_orders["X"] = order
+    # Dry run - regular fetch_order behavior
+    assert exchange.fetch_order("X", "TKN/BTC").myid == 123
+
+    with pytest.raises(InvalidOrderException, match=r"Tried to get an invalid dry-run-order.*"):
+        exchange.fetch_order("Y", "TKN/BTC")
+
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.exchange_has", return_value=False)
+    api_mock = MagicMock()
+    api_mock.fetch_open_order = MagicMock(
+        return_value={"id": "123", "amount": 2, "symbol": "TKN/BTC"}
+    )
+    api_mock.fetch_closed_order = MagicMock(
+        return_value={"id": "123", "amount": 2, "symbol": "TKN/BTC"}
+    )
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    assert exchange.fetch_order("X", "TKN/BTC") == {"id": "123", "amount": 2, "symbol": "TKN/BTC"}
+    assert log_has(
+        ("API fetch_open_order: {'id': '123', 'amount': 2, 'symbol': 'TKN/BTC'}"), caplog
+    )
+    assert api_mock.fetch_open_order.call_count == 1
+    assert api_mock.fetch_closed_order.call_count == 0
+    caplog.clear()
+
+    # open_order doesn't find order
+    api_mock.fetch_open_order = MagicMock(side_effect=ccxt.OrderNotFound("Order not found"))
+    api_mock.fetch_closed_order = MagicMock(
+        return_value={"id": "123", "amount": 2, "symbol": "TKN/BTC"}
+    )
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    assert exchange.fetch_order("X", "TKN/BTC") == {"id": "123", "amount": 2, "symbol": "TKN/BTC"}
+    assert log_has(
+        ("API fetch_closed_order: {'id': '123', 'amount': 2, 'symbol': 'TKN/BTC'}"), caplog
+    )
+    assert api_mock.fetch_open_order.call_count == 1
+    assert api_mock.fetch_closed_order.call_count == 1
+    caplog.clear()
+
+    with pytest.raises(InvalidOrderException):
+        api_mock.fetch_open_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
+        api_mock.fetch_closed_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.fetch_order(order_id="_", pair="TKN/BTC")
+    assert api_mock.fetch_open_order.call_count == 1
+
+    api_mock.fetch_open_order = MagicMock(side_effect=ccxt.OrderNotFound("Order not found"))
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "fetch_order_emulated",
+        "fetch_open_order",
+        retries=1,
+        order_id="_",
+        pair="TKN/BTC",
+        params={},
+    )
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_fetch_stoploss_order(default_conf, mocker, exchange_name):
+    default_conf["dry_run"] = True
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    order = MagicMock()
+    order.myid = 123
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    exchange._dry_run_open_orders["X"] = order
+    assert exchange.fetch_stoploss_order("X", "TKN/BTC").myid == 123
+
+    with pytest.raises(InvalidOrderException, match=r"Tried to get an invalid dry-run-order.*"):
+        exchange.fetch_stoploss_order("Y", "TKN/BTC")
+
+    default_conf["dry_run"] = False
+    api_mock = MagicMock()
+    api_mock.fetch_order = MagicMock(return_value={"id": "123", "symbol": "TKN/BTC"})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    res = {"id": "123", "symbol": "TKN/BTC"}
+    if exchange_name == "okx":
+        res = {"id": "123", "symbol": "TKN/BTC", "type": "stoploss"}
+    assert exchange.fetch_stoploss_order("X", "TKN/BTC") == res
+
+    if exchange_name == "okx":
+        # Tested separately.
+        return
+    with pytest.raises(InvalidOrderException):
+        api_mock.fetch_order = MagicMock(side_effect=ccxt.InvalidOrder("Order not found"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        exchange.fetch_stoploss_order(order_id="_", pair="TKN/BTC")
+    assert api_mock.fetch_order.call_count == 1
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "fetch_stoploss_order",
+        "fetch_order",
+        retries=API_FETCH_ORDER_RETRY_COUNT + 1,
+        order_id="_",
+        pair="TKN/BTC",
+    )
+
+
+def test_fetch_order_or_stoploss_order(default_conf, mocker):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    fetch_order_mock = MagicMock()
+    fetch_stoploss_order_mock = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        fetch_order=fetch_order_mock,
+        fetch_stoploss_order=fetch_stoploss_order_mock,
+    )
+
+    exchange.fetch_order_or_stoploss_order("1234", "ETH/BTC", False)
+    assert fetch_order_mock.call_count == 1
+    assert fetch_order_mock.call_args_list[0][0][0] == "1234"
+    assert fetch_order_mock.call_args_list[0][0][1] == "ETH/BTC"
+    assert fetch_stoploss_order_mock.call_count == 0
+
+    fetch_order_mock.reset_mock()
+    fetch_stoploss_order_mock.reset_mock()
+
+    exchange.fetch_order_or_stoploss_order("1234", "ETH/BTC", True)
+    assert fetch_order_mock.call_count == 0
+    assert fetch_stoploss_order_mock.call_count == 1
+    assert fetch_stoploss_order_mock.call_args_list[0][0][0] == "1234"
+    assert fetch_stoploss_order_mock.call_args_list[0][0][1] == "ETH/BTC"
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_name(default_conf, mocker, exchange_name):
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+
+    assert exchange.name == exchange_name.title()
+    assert exchange.id == exchange_name
+
+
+@pytest.mark.parametrize(
+    "trading_mode,amount",
+    [
+        ("spot", 0.2340606),
+        ("futures", 2.340606),
+    ],
+)
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_get_trades_for_order(default_conf, mocker, exchange_name, trading_mode, amount):
+    order_id = "ABCD-ABCD"
+    since = datetime(2018, 5, 5, 0, 0, 0)
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = trading_mode
+    default_conf["margin_mode"] = "isolated"
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    api_mock = MagicMock()
+
+    api_mock.fetch_my_trades = MagicMock(
+        return_value=[
+            {
+                "id": "TTR67E-3PFBD-76IISV",
+                "order": "ABCD-ABCD",
+                "info": {
+                    "pair": "XLTCZBTC",
+                    "time": 1519860024.4388,
+                    "type": "buy",
+                    "ordertype": "limit",
+                    "price": "20.00000",
+                    "cost": "38.62000",
+                    "fee": "0.06179",
+                    "vol": "5",
+                    "id": "ABCD-ABCD",
+                },
+                "timestamp": 1519860024438,
+                "datetime": "2018-02-28T23:20:24.438Z",
+                "symbol": "ETH/USDT:USDT",
+                "type": "limit",
+                "side": "buy",
+                "price": 165.0,
+                "amount": 0.2340606,
+                "fee": {"cost": 0.06179, "currency": "BTC"},
+            }
+        ]
+    )
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+
+    orders = exchange.get_trades_for_order(order_id, "ETH/USDT:USDT", since)
+    assert len(orders) == 1
+    assert orders[0]["price"] == 165
+    assert pytest.approx(orders[0]["amount"]) == amount
+    assert api_mock.fetch_my_trades.call_count == 1
+    # since argument should be
+    assert isinstance(api_mock.fetch_my_trades.call_args[0][1], int)
+    assert api_mock.fetch_my_trades.call_args[0][0] == "ETH/USDT:USDT"
+    # Same test twice, hardcoded number and doing the same calculation
+    assert api_mock.fetch_my_trades.call_args[0][1] == 1525478395000
+    assert (
+        api_mock.fetch_my_trades.call_args[0][1]
+        == int(since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000
+    )
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "get_trades_for_order",
+        "fetch_my_trades",
+        order_id=order_id,
+        pair="ETH/USDT:USDT",
+        since=since,
+    )
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=False))
+    assert exchange.get_trades_for_order(order_id, "ETH/USDT:USDT", since) == []
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_get_fee(default_conf, mocker, exchange_name):
+    api_mock = MagicMock()
+    api_mock.calculate_fee = MagicMock(
+        return_value={"type": "taker", "currency": "BTC", "rate": 0.025, "cost": 0.05}
+    )
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    exchange._config.pop("fee", None)
+
+    assert exchange.get_fee("ETH/BTC") == 0.025
+    assert api_mock.calculate_fee.call_count == 1
+
+    ccxt_exceptionhandlers(
+        mocker, default_conf, api_mock, exchange_name, "get_fee", "calculate_fee", symbol="ETH/BTC"
+    )
+
+    api_mock.calculate_fee.reset_mock()
+    exchange._config["fee"] = 0.001
+
+    assert exchange.get_fee("ETH/BTC") == 0.001
+    assert api_mock.calculate_fee.call_count == 0
+
+
+def test_stoploss_order_unsupported_exchange(default_conf, mocker):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="bitpanda")
+    with pytest.raises(OperationalException, match=r"stoploss is not implemented .*"):
+        exchange.create_stoploss(
+            pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side="sell", leverage=1.0
+        )
+
+    with pytest.raises(OperationalException, match=r"stoploss is not implemented .*"):
+        exchange.stoploss_adjust(1, {}, side="sell")
+
+
+@pytest.mark.parametrize(
+    "side,ratio,expected",
+    [
+        ("sell", 0.99, 99.0),  # Default
+        ("sell", 0.999, 99.9),
+        ("sell", 1, 100),
+        ("sell", 1.1, InvalidOrderException),
+        ("buy", 0.99, 101.0),  # Default
+        ("buy", 0.999, 100.1),
+        ("buy", 1, 100),
+        ("buy", 1.1, InvalidOrderException),
+    ],
+)
+def test__get_stop_limit_rate(default_conf_usdt, mocker, side, ratio, expected):
+    exchange = get_patched_exchange(mocker, default_conf_usdt, exchange="binance")
+
+    order_types = {"stoploss_on_exchange_limit_ratio": ratio}
+    if isinstance(expected, type) and issubclass(expected, Exception):
+        with pytest.raises(expected):
+            exchange._get_stop_limit_rate(100, order_types, side)
+    else:
+        assert exchange._get_stop_limit_rate(100, order_types, side) == expected
+
+
+def test_merge_ft_has_dict(default_conf, mocker):
+    mocker.patch.multiple(
+        EXMS,
+        _init_ccxt=MagicMock(return_value=MagicMock()),
+        _load_async_markets=MagicMock(),
+        validate_timeframes=MagicMock(),
+        validate_stakecurrency=MagicMock(),
+        validate_pricing=MagicMock(),
+    )
+    ex = Exchange(default_conf)
+    assert ex._ft_has == Exchange._ft_has_default
+
+    ex = Kraken(default_conf)
+    assert ex._ft_has != Exchange._ft_has_default
+    assert ex.get_option("trades_pagination") == "id"
+    assert ex.get_option("trades_pagination_arg") == "since"
+
+    # Binance defines different values
+    ex = Binance(default_conf)
+    assert ex._ft_has != Exchange._ft_has_default
+    assert ex.get_option("stoploss_on_exchange")
+    assert ex.get_option("order_time_in_force") == ["GTC", "FOK", "IOC", "PO"]
+    assert ex.get_option("trades_pagination") == "id"
+    assert ex.get_option("trades_pagination_arg") == "fromId"
+
+    conf = copy.deepcopy(default_conf)
+    conf["exchange"]["_ft_has_params"] = {"DeadBeef": 20, "stoploss_on_exchange": False}
+    # Use settings from configuration (overriding stoploss_on_exchange)
+    ex = Binance(conf)
+    assert ex._ft_has != Exchange._ft_has_default
+    assert not ex._ft_has["stoploss_on_exchange"]
+    assert ex._ft_has["DeadBeef"] == 20
+
+
+def test_get_valid_pair_combination(default_conf, mocker, markets):
+    mocker.patch.multiple(
+        EXMS,
+        _init_ccxt=MagicMock(return_value=MagicMock()),
+        _load_async_markets=MagicMock(),
+        validate_timeframes=MagicMock(),
+        validate_pricing=MagicMock(),
+        markets=PropertyMock(return_value=markets),
+    )
+    ex = Exchange(default_conf)
+
+    assert next(ex.get_valid_pair_combination("ETH", "BTC")) == "ETH/BTC"
+    assert next(ex.get_valid_pair_combination("BTC", "ETH")) == "ETH/BTC"
+    multicombs = list(ex.get_valid_pair_combination("ETH", "USDT"))
+    assert len(multicombs) == 2
+    assert "ETH/USDT" in multicombs
+    assert "ETH/USDT:USDT" in multicombs
+
+    with pytest.raises(ValueError, match=r"Could not combine.* to get a valid pair."):
+        for x in ex.get_valid_pair_combination("NOPAIR", "ETH"):
+            pass
+
+
+@pytest.mark.parametrize(
+    "base_currencies,quote_currencies,tradable_only,active_only,spot_only,"
+    "futures_only,expected_keys,test_comment",
+    [
+        # Testing markets (in conftest.py):
+        # 'BLK/BTC':  'active': True
+        # 'BTT/BTC':  'active': True
+        # 'ETH/BTC':  'active': True
+        # 'ETH/USDT': 'active': True
+        # 'LTC/BTC':  'active': False
+        # 'LTC/ETH':  'active': True
+        # 'LTC/USD':  'active': True
+        # 'LTC/USDT': 'active': True
+        # 'NEO/BTC':  'active': False
+        # 'TKN/BTC':  'active'  not set
+        # 'XLTCUSDT': 'active': True, not a pair
+        # 'XRP/BTC':  'active': False
+        (
+            [],
+            [],
+            False,
+            False,
+            False,
+            False,
+            [
+                "BLK/BTC",
+                "BTT/BTC",
+                "ETH/BTC",
+                "ETH/USDT",
+                "LTC/BTC",
+                "LTC/ETH",
+                "LTC/USD",
+                "LTC/USDT",
+                "NEO/BTC",
+                "TKN/BTC",
+                "XLTCUSDT",
+                "XRP/BTC",
+                "ADA/USDT:USDT",
+                "ETH/USDT:USDT",
+            ],
+            "all markets",
+        ),
+        (
+            [],
+            [],
+            False,
+            False,
+            True,
+            False,
+            [
+                "BLK/BTC",
+                "BTT/BTC",
+                "ETH/BTC",
+                "ETH/USDT",
+                "LTC/BTC",
+                "LTC/ETH",
+                "LTC/USD",
+                "LTC/USDT",
+                "NEO/BTC",
+                "TKN/BTC",
+                "XRP/BTC",
+            ],
+            "all markets, only spot pairs",
+        ),
+        (
+            [],
+            [],
+            False,
+            True,
+            False,
+            False,
+            [
+                "BLK/BTC",
+                "ETH/BTC",
+                "ETH/USDT",
+                "LTC/BTC",
+                "LTC/ETH",
+                "LTC/USD",
+                "NEO/BTC",
+                "TKN/BTC",
+                "XLTCUSDT",
+                "XRP/BTC",
+                "ADA/USDT:USDT",
+                "ETH/USDT:USDT",
+            ],
+            "active markets",
+        ),
+        (
+            [],
+            [],
+            True,
+            False,
+            False,
+            False,
+            [
+                "BLK/BTC",
+                "BTT/BTC",
+                "ETH/BTC",
+                "ETH/USDT",
+                "LTC/BTC",
+                "LTC/ETH",
+                "LTC/USD",
+                "LTC/USDT",
+                "NEO/BTC",
+                "TKN/BTC",
+                "XRP/BTC",
+            ],
+            "all pairs",
+        ),
+        (
+            [],
+            [],
+            True,
+            True,
+            False,
+            False,
+            [
+                "BLK/BTC",
+                "ETH/BTC",
+                "ETH/USDT",
+                "LTC/BTC",
+                "LTC/ETH",
+                "LTC/USD",
+                "NEO/BTC",
+                "TKN/BTC",
+                "XRP/BTC",
+            ],
+            "active pairs",
+        ),
+        (
+            ["ETH", "LTC"],
+            [],
+            False,
+            False,
+            False,
+            False,
+            [
+                "ETH/BTC",
+                "ETH/USDT",
+                "LTC/BTC",
+                "LTC/ETH",
+                "LTC/USD",
+                "LTC/USDT",
+                "XLTCUSDT",
+                "ETH/USDT:USDT",
+            ],
+            "all markets, base=ETH, LTC",
+        ),
+        (
+            ["LTC"],
+            [],
+            False,
+            False,
+            False,
+            False,
+            ["LTC/BTC", "LTC/ETH", "LTC/USD", "LTC/USDT", "XLTCUSDT"],
+            "all markets, base=LTC",
+        ),
+        (
+            ["LTC"],
+            [],
+            False,
+            False,
+            True,
+            False,
+            ["LTC/BTC", "LTC/ETH", "LTC/USD", "LTC/USDT"],
+            "spot markets, base=LTC",
+        ),
+        (
+            [],
+            ["USDT"],
+            False,
+            False,
+            False,
+            False,
+            ["ETH/USDT", "LTC/USDT", "XLTCUSDT", "ADA/USDT:USDT", "ETH/USDT:USDT"],
+            "all markets, quote=USDT",
+        ),
+        (
+            [],
+            ["USDT"],
+            False,
+            False,
+            False,
+            True,
+            ["ADA/USDT:USDT", "ETH/USDT:USDT"],
+            "Futures markets, quote=USDT",
+        ),
+        (
+            [],
+            ["USDT", "USD"],
+            False,
+            False,
+            False,
+            False,
+            ["ETH/USDT", "LTC/USD", "LTC/USDT", "XLTCUSDT", "ADA/USDT:USDT", "ETH/USDT:USDT"],
+            "all markets, quote=USDT, USD",
+        ),
+        (
+            [],
+            ["USDT", "USD"],
+            False,
+            False,
+            True,
+            False,
+            ["ETH/USDT", "LTC/USD", "LTC/USDT"],
+            "spot markets, quote=USDT, USD",
+        ),
+        (
+            ["LTC"],
+            ["USDT"],
+            False,
+            False,
+            False,
+            False,
+            ["LTC/USDT", "XLTCUSDT"],
+            "all markets, base=LTC, quote=USDT",
+        ),
+        (
+            ["LTC"],
+            ["USDT"],
+            True,
+            False,
+            False,
+            False,
+            ["LTC/USDT"],
+            "all pairs, base=LTC, quote=USDT",
+        ),
+        (
+            ["LTC"],
+            ["USDT", "NONEXISTENT"],
+            False,
+            False,
+            False,
+            False,
+            ["LTC/USDT", "XLTCUSDT"],
+            "all markets, base=LTC, quote=USDT, NONEXISTENT",
+        ),
+        (
+            ["LTC"],
+            ["NONEXISTENT"],
+            False,
+            False,
+            False,
+            False,
+            [],
+            "all markets, base=LTC, quote=NONEXISTENT",
+        ),
+    ],
+)
+def test_get_markets(
+    default_conf,
+    mocker,
+    markets_static,
+    base_currencies,
+    quote_currencies,
+    tradable_only,
+    active_only,
+    spot_only,
+    futures_only,
+    expected_keys,
+    test_comment,  # Here for debugging purposes (Not used within method)
+):
+    mocker.patch.multiple(
+        EXMS,
+        _init_ccxt=MagicMock(return_value=MagicMock()),
+        _load_async_markets=MagicMock(),
+        validate_timeframes=MagicMock(),
+        validate_pricing=MagicMock(),
+        markets=PropertyMock(return_value=markets_static),
+    )
+    ex = Exchange(default_conf)
+    pairs = ex.get_markets(
+        base_currencies,
+        quote_currencies,
+        tradable_only=tradable_only,
+        spot_only=spot_only,
+        futures_only=futures_only,
+        active_only=active_only,
+    )
+    assert sorted(pairs.keys()) == sorted(expected_keys)
+
+
+def test_get_markets_error(default_conf, mocker):
+    ex = get_patched_exchange(mocker, default_conf)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=None))
+    with pytest.raises(OperationalException, match="Markets were not loaded."):
+        ex.get_markets("LTC", "USDT", True, False)
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_ohlcv_candle_limit(default_conf, mocker, exchange_name):
+    if exchange_name == "okx":
+        pytest.skip("Tested separately for okx")
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    timeframes = ("1m", "5m", "1h")
+    expected = exchange._ft_has.get("ohlcv_candle_limit", 500)
+    for timeframe in timeframes:
+        # if 'ohlcv_candle_limit_per_timeframe' in exchange._ft_has:
+        # expected = exchange._ft_has['ohlcv_candle_limit_per_timeframe'][timeframe]
+        # This should only run for bittrex
+        # assert exchange_name == 'bittrex'
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.SPOT) == expected
+
+
+@pytest.mark.parametrize(
+    "market_symbol,base,quote,exchange,spot,margin,futures,trademode,add_dict,expected_result",
+    [
+        ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, "spot", {}, True),
+        ("USDT/BTC", "USDT", "BTC", "binance", True, False, False, "spot", {}, True),
+        # No separating /
+        ("BTCUSDT", "BTC", "USDT", "binance", True, False, False, "spot", {}, True),
+        ("BTCUSDT", None, "USDT", "binance", True, False, False, "spot", {}, False),
+        ("USDT/BTC", "BTC", None, "binance", True, False, False, "spot", {}, False),
+        ("BTCUSDT", "BTC", None, "binance", True, False, False, "spot", {}, False),
+        ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, "spot", {}, True),
+        # Futures mode, spot pair
+        ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, "futures", {}, False),
+        ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, "margin", {}, False),
+        ("BTC/USDT", "BTC", "USDT", "binance", True, True, True, "margin", {}, True),
+        ("BTC/USDT", "BTC", "USDT", "binance", False, True, False, "margin", {}, True),
+        # Futures mode, futures pair
+        ("BTC/USDT", "BTC", "USDT", "binance", False, False, True, "futures", {}, True),
+        # Futures market
+        ("BTC/UNK", "BTC", "UNK", "binance", False, False, True, "spot", {}, False),
+        ("BTC/EUR", "BTC", "EUR", "kraken", True, False, False, "spot", {"darkpool": False}, True),
+        ("EUR/BTC", "EUR", "BTC", "kraken", True, False, False, "spot", {"darkpool": False}, True),
+        # no darkpools
+        ("BTC/EUR", "BTC", "EUR", "kraken", True, False, False, "spot", {"darkpool": True}, False),
+        # no darkpools
+        (
+            "BTC/EUR.d",
+            "BTC",
+            "EUR",
+            "kraken",
+            True,
+            False,
+            False,
+            "spot",
+            {"darkpool": True},
+            False,
+        ),
+        ("BTC/USDT:USDT", "BTC", "USD", "okx", False, False, True, "spot", {}, False),
+        ("BTC/USDT:USDT", "BTC", "USD", "okx", False, False, True, "margin", {}, False),
+        ("BTC/USDT:USDT", "BTC", "USD", "okx", False, False, True, "futures", {}, True),
+    ],
+)
+def test_market_is_tradable(
+    mocker,
+    default_conf,
+    market_symbol,
+    base,
+    quote,
+    spot,
+    margin,
+    futures,
+    trademode,
+    add_dict,
+    exchange,
+    expected_result,
+) -> None:
+    default_conf["trading_mode"] = trademode
+    mocker.patch(f"{EXMS}.validate_trading_mode_and_margin_mode")
+    ex = get_patched_exchange(mocker, default_conf, exchange=exchange)
+    market = {
+        "symbol": market_symbol,
+        "type": "swap",
+        "base": base,
+        "quote": quote,
+        "spot": spot,
+        "future": futures,
+        "swap": futures,
+        "margin": margin,
+        "linear": True,
+        **(add_dict),
+    }
+    assert ex.market_is_tradable(market) == expected_result
+
+
+@pytest.mark.parametrize(
+    "market,expected_result",
+    [
+        ({"symbol": "ETH/BTC", "active": True}, True),
+        ({"symbol": "ETH/BTC", "active": False}, False),
+        (
+            {
+                "symbol": "ETH/BTC",
+            },
+            True,
+        ),
+    ],
+)
+def test_market_is_active(market, expected_result) -> None:
+    assert market_is_active(market) == expected_result
+
+
+@pytest.mark.parametrize(
+    "order,expected",
+    [
+        ([{"fee"}], False),
+        ({"fee": None}, False),
+        ({"fee": {"currency": "ETH/BTC"}}, False),
+        ({"fee": {"currency": "ETH/BTC", "cost": None}}, False),
+        ({"fee": {"currency": "ETH/BTC", "cost": 0.01}}, True),
+    ],
+)
+def test_order_has_fee(order, expected) -> None:
+    assert Exchange.order_has_fee(order) == expected
+
+
+@pytest.mark.parametrize(
+    "order,expected",
+    [
+        ({"symbol": "ETH/BTC", "fee": {"currency": "ETH", "cost": 0.43}}, (0.43, "ETH", 0.01)),
+        ({"symbol": "ETH/USDT", "fee": {"currency": "USDT", "cost": 0.01}}, (0.01, "USDT", 0.01)),
+        (
+            {"symbol": "BTC/USDT", "fee": {"currency": "USDT", "cost": 0.34, "rate": 0.01}},
+            (0.34, "USDT", 0.01),
+        ),
+    ],
+)
+def test_extract_cost_curr_rate(mocker, default_conf, order, expected) -> None:
+    mocker.patch(f"{EXMS}.calculate_fee_rate", MagicMock(return_value=0.01))
+    ex = get_patched_exchange(mocker, default_conf)
+    assert ex.extract_cost_curr_rate(order["fee"], order["symbol"], cost=20, amount=1) == expected
+
+
+@pytest.mark.parametrize(
+    "order,unknown_fee_rate,expected",
+    [
+        # Using base-currency
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.05,
+                "fee": {"currency": "ETH", "cost": 0.004, "rate": None},
+            },
+            None,
+            0.1,
+        ),
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.05,
+                "cost": 0.05,
+                "fee": {"currency": "ETH", "cost": 0.004, "rate": None},
+            },
+            None,
+            0.08,
+        ),
+        # Using quote currency
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.05,
+                "fee": {"currency": "BTC", "cost": 0.005},
+            },
+            None,
+            0.1,
+        ),
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.05,
+                "fee": {"currency": "BTC", "cost": 0.002, "rate": None},
+            },
+            None,
+            0.04,
+        ),
+        # Using foreign currency
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.05,
+                "fee": {"currency": "NEO", "cost": 0.0012},
+            },
+            None,
+            0.001944,
+        ),
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 2.21,
+                "cost": 0.02992561,
+                "fee": {"currency": "NEO", "cost": 0.00027452},
+            },
+            None,
+            0.00074305,
+        ),
+        # Rate included in return - return as is
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.05,
+                "fee": {"currency": "USDT", "cost": 0.34, "rate": 0.01},
+            },
+            None,
+            0.01,
+        ),
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.05,
+                "fee": {"currency": "USDT", "cost": 0.34, "rate": 0.005},
+            },
+            None,
+            0.005,
+        ),
+        # 0.1% filled - no costs (kraken - #3431)
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.0,
+                "fee": {"currency": "BTC", "cost": 0.0, "rate": None},
+            },
+            None,
+            None,
+        ),
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.0,
+                "fee": {"currency": "ETH", "cost": 0.0, "rate": None},
+            },
+            None,
+            0.0,
+        ),
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.0,
+                "fee": {"currency": "NEO", "cost": 0.0, "rate": None},
+            },
+            None,
+            None,
+        ),
+        # Invalid pair combination - POINT/BTC is not a pair
+        (
+            {
+                "symbol": "POINT/BTC",
+                "amount": 0.04,
+                "cost": 0.5,
+                "fee": {"currency": "POINT", "cost": 2.0, "rate": None},
+            },
+            None,
+            None,
+        ),
+        (
+            {
+                "symbol": "POINT/BTC",
+                "amount": 0.04,
+                "cost": 0.5,
+                "fee": {"currency": "POINT", "cost": 2.0, "rate": None},
+            },
+            1,
+            4.0,
+        ),
+        (
+            {
+                "symbol": "POINT/BTC",
+                "amount": 0.04,
+                "cost": 0.5,
+                "fee": {"currency": "POINT", "cost": 2.0, "rate": None},
+            },
+            2,
+            8.0,
+        ),
+        # Missing currency
+        (
+            {
+                "symbol": "ETH/BTC",
+                "amount": 0.04,
+                "cost": 0.05,
+                "fee": {"currency": None, "cost": 0.005},
+            },
+            None,
+            None,
+        ),
+    ],
+)
+def test_calculate_fee_rate(mocker, default_conf, order, expected, unknown_fee_rate) -> None:
+    mocker.patch(f"{EXMS}.get_tickers", return_value={"NEO/BTC": {"last": 0.081}})
+    if unknown_fee_rate:
+        default_conf["exchange"]["unknown_fee_rate"] = unknown_fee_rate
+
+    ex = get_patched_exchange(mocker, default_conf)
+
+    assert (
+        ex.calculate_fee_rate(
+            order["fee"], order["symbol"], cost=order["cost"], amount=order["amount"]
+        )
+        == expected
+    )
+
+
+@pytest.mark.parametrize(
+    "retrycount,max_retries,expected",
+    [
+        (0, 3, 10),
+        (1, 3, 5),
+        (2, 3, 2),
+        (3, 3, 1),
+        (0, 1, 2),
+        (1, 1, 1),
+        (0, 4, 17),
+        (1, 4, 10),
+        (2, 4, 5),
+        (3, 4, 2),
+        (4, 4, 1),
+        (0, 5, 26),
+        (1, 5, 17),
+        (2, 5, 10),
+        (3, 5, 5),
+        (4, 5, 2),
+        (5, 5, 1),
+    ],
+)
+def test_calculate_backoff(retrycount, max_retries, expected):
+    assert calculate_backoff(retrycount, max_retries) == expected
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_get_funding_fees(default_conf_usdt, mocker, exchange_name, caplog):
+    now = datetime.now(timezone.utc)
+    default_conf_usdt["trading_mode"] = "futures"
+    default_conf_usdt["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf_usdt, exchange=exchange_name)
+    exchange._fetch_and_calculate_funding_fees = MagicMock(side_effect=ExchangeError)
+    assert exchange.get_funding_fees("BTC/USDT:USDT", 1, False, now) == 0.0
+    assert exchange._fetch_and_calculate_funding_fees.call_count == 1
+    assert log_has("Could not update funding fees for BTC/USDT:USDT.", caplog)
+
+
+@pytest.mark.parametrize("exchange_name", ["binance"])
+def test__get_funding_fees_from_exchange(default_conf, mocker, exchange_name):
+    api_mock = MagicMock()
+    api_mock.fetch_funding_history = MagicMock(
+        return_value=[
+            {
+                "amount": 0.14542,
+                "code": "USDT",
+                "datetime": "2021-09-01T08:00:01.000Z",
+                "id": "485478",
+                "info": {
+                    "asset": "USDT",
+                    "income": "0.14542",
+                    "incomeType": "FUNDING_FEE",
+                    "info": "FUNDING_FEE",
+                    "symbol": "XRPUSDT",
+                    "time": "1630382001000",
+                    "tradeId": "",
+                    "tranId": "993203",
+                },
+                "symbol": "XRP/USDT",
+                "timestamp": 1630382001000,
+            },
+            {
+                "amount": -0.14642,
+                "code": "USDT",
+                "datetime": "2021-09-01T16:00:01.000Z",
+                "id": "485479",
+                "info": {
+                    "asset": "USDT",
+                    "income": "-0.14642",
+                    "incomeType": "FUNDING_FEE",
+                    "info": "FUNDING_FEE",
+                    "symbol": "XRPUSDT",
+                    "time": "1630314001000",
+                    "tradeId": "",
+                    "tranId": "993204",
+                },
+                "symbol": "XRP/USDT",
+                "timestamp": 1630314001000,
+            },
+        ]
+    )
+    type(api_mock).has = PropertyMock(return_value={"fetchFundingHistory": True})
+
+    # mocker.patch(f'{EXMS}.get_funding_fees', lambda pair, since: y)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    date_time = datetime.strptime("2021-09-01T00:00:01.000Z", "%Y-%m-%dT%H:%M:%S.%fZ")
+    unix_time = int(date_time.timestamp())
+    expected_fees = -0.001  # 0.14542341 + -0.14642341
+    fees_from_datetime = exchange._get_funding_fees_from_exchange(pair="XRP/USDT", since=date_time)
+    fees_from_unix_time = exchange._get_funding_fees_from_exchange(pair="XRP/USDT", since=unix_time)
+
+    assert pytest.approx(expected_fees) == fees_from_datetime
+    assert pytest.approx(expected_fees) == fees_from_unix_time
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "_get_funding_fees_from_exchange",
+        "fetch_funding_history",
+        pair="XRP/USDT",
+        since=unix_time,
+    )
+
+
+@pytest.mark.parametrize("exchange", ["binance", "kraken"])
+@pytest.mark.parametrize(
+    "stake_amount,leverage,min_stake_with_lev",
+    [(9.0, 3.0, 3.0), (20.0, 5.0, 4.0), (100.0, 100.0, 1.0)],
+)
+def test_get_stake_amount_considering_leverage(
+    exchange, stake_amount, leverage, min_stake_with_lev, mocker, default_conf
+):
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange)
+    assert (
+        exchange._get_stake_amount_considering_leverage(stake_amount, leverage)
+        == min_stake_with_lev
+    )
+
+
+@pytest.mark.parametrize("margin_mode", [(MarginMode.CROSS), (MarginMode.ISOLATED)])
+def test_set_margin_mode(mocker, default_conf, margin_mode):
+    api_mock = MagicMock()
+    api_mock.set_margin_mode = MagicMock()
+    type(api_mock).has = PropertyMock(return_value={"setMarginMode": True})
+    default_conf["dry_run"] = False
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        "binance",
+        "set_margin_mode",
+        "set_margin_mode",
+        pair="XRP/USDT",
+        margin_mode=margin_mode,
+    )
+
+
+@pytest.mark.parametrize(
+    "exchange_name, trading_mode, margin_mode, exception_thrown",
+    [
+        ("binance", TradingMode.SPOT, None, False),
+        ("binance", TradingMode.MARGIN, MarginMode.ISOLATED, True),
+        ("kraken", TradingMode.SPOT, None, False),
+        ("kraken", TradingMode.MARGIN, MarginMode.ISOLATED, True),
+        ("kraken", TradingMode.FUTURES, MarginMode.ISOLATED, True),
+        ("bitmart", TradingMode.SPOT, None, False),
+        ("bitmart", TradingMode.MARGIN, MarginMode.CROSS, True),
+        ("bitmart", TradingMode.MARGIN, MarginMode.ISOLATED, True),
+        ("bitmart", TradingMode.FUTURES, MarginMode.CROSS, True),
+        ("bitmart", TradingMode.FUTURES, MarginMode.ISOLATED, True),
+        ("gate", TradingMode.MARGIN, MarginMode.ISOLATED, True),
+        ("okx", TradingMode.SPOT, None, False),
+        ("okx", TradingMode.MARGIN, MarginMode.CROSS, True),
+        ("okx", TradingMode.MARGIN, MarginMode.ISOLATED, True),
+        ("okx", TradingMode.FUTURES, MarginMode.CROSS, True),
+        ("binance", TradingMode.FUTURES, MarginMode.ISOLATED, False),
+        ("gate", TradingMode.FUTURES, MarginMode.ISOLATED, False),
+        ("okx", TradingMode.FUTURES, MarginMode.ISOLATED, False),
+        # * Remove once implemented
+        ("binance", TradingMode.MARGIN, MarginMode.CROSS, True),
+        ("binance", TradingMode.FUTURES, MarginMode.CROSS, False),
+        ("kraken", TradingMode.MARGIN, MarginMode.CROSS, True),
+        ("kraken", TradingMode.FUTURES, MarginMode.CROSS, True),
+        ("gate", TradingMode.MARGIN, MarginMode.CROSS, True),
+        ("gate", TradingMode.FUTURES, MarginMode.CROSS, True),
+        # * Uncomment once implemented
+        # ("binance", TradingMode.MARGIN, MarginMode.CROSS, False),
+        # ("binance", TradingMode.FUTURES, MarginMode.CROSS, False),
+        # ("kraken", TradingMode.MARGIN, MarginMode.CROSS, False),
+        # ("kraken", TradingMode.FUTURES, MarginMode.CROSS, False),
+        # ("gate", TradingMode.MARGIN, MarginMode.CROSS, False),
+        # ("gate", TradingMode.FUTURES, MarginMode.CROSS, False),
+    ],
+)
+def test_validate_trading_mode_and_margin_mode(
+    default_conf, mocker, exchange_name, trading_mode, margin_mode, exception_thrown
+):
+    exchange = get_patched_exchange(
+        mocker, default_conf, exchange=exchange_name, mock_supported_modes=False
+    )
+    if exception_thrown:
+        with pytest.raises(OperationalException):
+            exchange.validate_trading_mode_and_margin_mode(trading_mode, margin_mode)
+    else:
+        exchange.validate_trading_mode_and_margin_mode(trading_mode, margin_mode)
+
+
+@pytest.mark.parametrize(
+    "exchange_name,trading_mode,ccxt_config",
+    [
+        ("binance", "spot", {}),
+        ("binance", "margin", {"options": {"defaultType": "margin"}}),
+        ("binance", "futures", {"options": {"defaultType": "swap"}}),
+        ("bybit", "spot", {"options": {"defaultType": "spot"}}),
+        ("bybit", "futures", {"options": {"defaultType": "swap"}}),
+        ("gate", "futures", {"options": {"defaultType": "swap"}}),
+        ("hitbtc", "futures", {"options": {"defaultType": "swap"}}),
+        ("kraken", "futures", {"options": {"defaultType": "swap"}}),
+        ("kucoin", "futures", {"options": {"defaultType": "swap"}}),
+        ("okx", "futures", {"options": {"defaultType": "swap"}}),
+    ],
+)
+def test__ccxt_config(default_conf, mocker, exchange_name, trading_mode, ccxt_config):
+    default_conf["trading_mode"] = trading_mode
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    assert exchange._ccxt_config == ccxt_config
+
+
+@pytest.mark.parametrize(
+    "pair,nominal_value,max_lev",
+    [
+        ("ETH/BTC", 0.0, 2.0),
+        ("TKN/BTC", 100.0, 5.0),
+        ("BLK/BTC", 173.31, 3.0),
+        ("LTC/BTC", 0.0, 1.0),
+        ("TKN/USDT", 210.30, 1.0),
+    ],
+)
+def test_get_max_leverage_from_margin(default_conf, mocker, pair, nominal_value, max_lev):
+    default_conf["trading_mode"] = "margin"
+    default_conf["margin_mode"] = "isolated"
+    api_mock = MagicMock()
+    type(api_mock).has = PropertyMock(return_value={"fetchLeverageTiers": False})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="gate")
+    assert exchange.get_max_leverage(pair, nominal_value) == max_lev
+
+
+@pytest.mark.parametrize(
+    "size,funding_rate,mark_price,time_in_ratio,funding_fee,kraken_fee",
+    [
+        (10, 0.0001, 2.0, 1.0, 0.002, 0.002),
+        (10, 0.0002, 2.0, 0.01, 0.004, 0.00004),
+        (10, 0.0002, 2.5, None, 0.005, None),
+        (10, 0.0002, nan, None, 0.0, None),
+    ],
+)
+def test_calculate_funding_fees(
+    default_conf, mocker, size, funding_rate, mark_price, funding_fee, kraken_fee, time_in_ratio
+):
+    exchange = get_patched_exchange(mocker, default_conf)
+    kraken = get_patched_exchange(mocker, default_conf, exchange="kraken")
+    prior_date = timeframe_to_prev_date("1h", datetime.now(timezone.utc) - timedelta(hours=1))
+    trade_date = timeframe_to_prev_date("1h", datetime.now(timezone.utc))
+    funding_rates = DataFrame(
+        [
+            {"date": prior_date, "open": funding_rate},  # Line not used.
+            {"date": trade_date, "open": funding_rate},
+        ]
+    )
+    mark_rates = DataFrame(
+        [
+            {"date": prior_date, "open": mark_price},
+            {"date": trade_date, "open": mark_price},
+        ]
+    )
+    df = exchange.combine_funding_and_mark(funding_rates, mark_rates)
+
+    assert (
+        exchange.calculate_funding_fees(
+            df,
+            amount=size,
+            is_short=True,
+            open_date=trade_date,
+            close_date=trade_date,
+            time_in_ratio=time_in_ratio,
+        )
+        == funding_fee
+    )
+
+    if kraken_fee is None:
+        with pytest.raises(OperationalException):
+            kraken.calculate_funding_fees(
+                df,
+                amount=size,
+                is_short=True,
+                open_date=trade_date,
+                close_date=trade_date,
+                time_in_ratio=time_in_ratio,
+            )
+
+    else:
+        assert (
+            kraken.calculate_funding_fees(
+                df,
+                amount=size,
+                is_short=True,
+                open_date=trade_date,
+                close_date=trade_date,
+                time_in_ratio=time_in_ratio,
+            )
+            == kraken_fee
+        )
+
+
+@pytest.mark.parametrize(
+    "mark_price,funding_rate,futures_funding_rate",
+    [
+        (1000, 0.001, None),
+        (1000, 0.001, 0.01),
+        (1000, 0.001, 0.0),
+        (1000, 0.001, -0.01),
+    ],
+)
+def test_combine_funding_and_mark(
+    default_conf,
+    mocker,
+    funding_rate,
+    mark_price,
+    futures_funding_rate,
+):
+    exchange = get_patched_exchange(mocker, default_conf)
+    prior2_date = timeframe_to_prev_date("1h", datetime.now(timezone.utc) - timedelta(hours=2))
+    prior_date = timeframe_to_prev_date("1h", datetime.now(timezone.utc) - timedelta(hours=1))
+    trade_date = timeframe_to_prev_date("1h", datetime.now(timezone.utc))
+    funding_rates = DataFrame(
+        [
+            {"date": prior2_date, "open": funding_rate},
+            {"date": prior_date, "open": funding_rate},
+            {"date": trade_date, "open": funding_rate},
+        ]
+    )
+    mark_rates = DataFrame(
+        [
+            {"date": prior2_date, "open": mark_price},
+            {"date": prior_date, "open": mark_price},
+            {"date": trade_date, "open": mark_price},
+        ]
+    )
+
+    df = exchange.combine_funding_and_mark(funding_rates, mark_rates, futures_funding_rate)
+    assert "open_mark" in df.columns
+    assert "open_fund" in df.columns
+    assert len(df) == 3
+
+    funding_rates = DataFrame(
+        [
+            {"date": trade_date, "open": funding_rate},
+        ]
+    )
+    mark_rates = DataFrame(
+        [
+            {"date": prior2_date, "open": mark_price},
+            {"date": prior_date, "open": mark_price},
+            {"date": trade_date, "open": mark_price},
+        ]
+    )
+    df = exchange.combine_funding_and_mark(funding_rates, mark_rates, futures_funding_rate)
+
+    if futures_funding_rate is not None:
+        assert len(df) == 3
+        assert df.iloc[0]["open_fund"] == futures_funding_rate
+        assert df.iloc[1]["open_fund"] == futures_funding_rate
+        assert df.iloc[2]["open_fund"] == funding_rate
+    else:
+        assert len(df) == 1
+
+    # Empty funding rates
+    funding_rates2 = DataFrame([], columns=["date", "open"])
+    df = exchange.combine_funding_and_mark(funding_rates2, mark_rates, futures_funding_rate)
+    if futures_funding_rate is not None:
+        assert len(df) == 3
+        assert df.iloc[0]["open_fund"] == futures_funding_rate
+        assert df.iloc[1]["open_fund"] == futures_funding_rate
+        assert df.iloc[2]["open_fund"] == futures_funding_rate
+    else:
+        assert len(df) == 0
+
+    # Empty mark candles
+    mark_candles = DataFrame([], columns=["date", "open"])
+    df = exchange.combine_funding_and_mark(funding_rates, mark_candles, futures_funding_rate)
+
+    assert len(df) == 0
+
+
+@pytest.mark.parametrize(
+    "exchange,rate_start,rate_end,d1,d2,amount,expected_fees",
+    [
+        ("binance", 0, 2, "2021-09-01 01:00:00", "2021-09-01 04:00:00", 30.0, 0.0),
+        ("binance", 0, 2, "2021-09-01 00:00:00", "2021-09-01 08:00:00", 30.0, -0.00091409999),
+        ("binance", 0, 2, "2021-09-01 00:00:15", "2021-09-01 08:00:00", 30.0, -0.0002493),
+        ("binance", 1, 2, "2021-09-01 01:00:14", "2021-09-01 08:00:00", 30.0, -0.0002493),
+        ("binance", 1, 2, "2021-09-01 00:00:16", "2021-09-01 08:00:00", 30.0, -0.0002493),
+        ("binance", 0, 1, "2021-09-01 00:00:00", "2021-09-01 07:59:59", 30.0, -0.00066479999),
+        ("binance", 0, 2, "2021-09-01 00:00:00", "2021-09-01 12:00:00", 30.0, -0.00091409999),
+        # :01 must be rounded down.
+        ("binance", 0, 2, "2021-09-01 00:00:01", "2021-09-01 08:00:00", 30.0, -0.00091409999),
+        ("binance", 0, 2, "2021-08-31 23:58:00", "2021-09-01 08:00:00", 30.0, -0.00091409999),
+        ("binance", 0, 2, "2021-09-01 00:10:01", "2021-09-01 08:00:00", 30.0, -0.0002493),
+        # TODO: Uncomment once _calculate_funding_fees can pass time_in_ratio to exchange.
+        # ('kraken', "2021-09-01 00:00:00", "2021-09-01 08:00:00",  30.0, -0.0014937),
+        # ('kraken', "2021-09-01 00:00:15", "2021-09-01 08:00:00",  30.0, -0.0008289),
+        # ('kraken', "2021-09-01 01:00:14", "2021-09-01 08:00:00",  30.0, -0.0008289),
+        # ('kraken', "2021-09-01 00:00:00", "2021-09-01 07:59:59",  30.0, -0.0012443999999999999),
+        # ('kraken', "2021-09-01 00:00:00", "2021-09-01 12:00:00", 30.0,  0.0045759),
+        # ('kraken', "2021-09-01 00:00:01", "2021-09-01 08:00:00",  30.0, -0.0008289),
+        ("gate", 0, 2, "2021-09-01 00:10:00", "2021-09-01 04:00:00", 30.0, 0.0),
+        ("gate", 0, 2, "2021-09-01 00:00:00", "2021-09-01 08:00:00", 30.0, -0.0009140999),
+        ("gate", 0, 2, "2021-09-01 00:00:00", "2021-09-01 12:00:00", 30.0, -0.0009140999),
+        ("gate", 1, 2, "2021-09-01 00:00:01", "2021-09-01 08:00:00", 30.0, -0.0002493),
+        ("binance", 0, 2, "2021-09-01 00:00:00", "2021-09-01 08:00:00", 50.0, -0.0015235),
+        # TODO: Uncomment once _calculate_funding_fees can pass time_in_ratio to exchange.
+        # ('kraken', "2021-09-01 00:00:00", "2021-09-01 08:00:00",  50.0, -0.0024895),
+    ],
+)
+def test__fetch_and_calculate_funding_fees(
+    mocker,
+    default_conf,
+    funding_rate_history_hourly,
+    funding_rate_history_octohourly,
+    rate_start,
+    rate_end,
+    mark_ohlcv,
+    exchange,
+    d1,
+    d2,
+    amount,
+    expected_fees,
+):
+    """
+    nominal_value = mark_price * size
+    funding_fee = nominal_value * funding_rate
+    size: 30
+        time: 0, mark: 2.77, nominal_value: 83.1, fundRate: -0.000008, fundFee: -0.0006648
+        time: 1, mark: 2.73, nominal_value: 81.9, fundRate: -0.000004, fundFee: -0.0003276
+        time: 2, mark: 2.74, nominal_value: 82.2, fundRate: 0.000012, fundFee: 0.0009864
+        time: 3, mark: 2.76, nominal_value: 82.8, fundRate: -0.000003, fundFee: -0.0002484
+        time: 4, mark: 2.76, nominal_value: 82.8, fundRate: -0.000007, fundFee: -0.0005796
+        time: 5, mark: 2.77, nominal_value: 83.1, fundRate: 0.000003, fundFee: 0.0002493
+        time: 6, mark: 2.78, nominal_value: 83.4, fundRate: 0.000019, fundFee: 0.0015846
+        time: 7, mark: 2.78, nominal_value: 83.4, fundRate: 0.000003, fundFee: 0.0002502
+        time: 8, mark: 2.77, nominal_value: 83.1, fundRate: -0.000003, fundFee: -0.0002493
+        time: 9, mark: 2.77, nominal_value: 83.1, fundRate: 0, fundFee: 0.0
+        time: 10, mark: 2.84, nominal_value: 85.2, fundRate: 0.000013, fundFee: 0.0011076
+        time: 11, mark: 2.81, nominal_value: 84.3, fundRate: 0.000077, fundFee: 0.0064911
+        time: 12, mark: 2.81, nominal_value: 84.3, fundRate: 0.000072, fundFee: 0.0060696
+        time: 13, mark: 2.82, nominal_value: 84.6, fundRate: 0.000097, fundFee: 0.0082062
+
+    size: 50
+        time: 0, mark: 2.77, nominal_value: 138.5, fundRate: -0.000008, fundFee: -0.001108
+        time: 1, mark: 2.73, nominal_value: 136.5, fundRate: -0.000004, fundFee: -0.000546
+        time: 2, mark: 2.74, nominal_value: 137.0, fundRate: 0.000012, fundFee: 0.001644
+        time: 3, mark: 2.76, nominal_value: 138.0, fundRate: -0.000003, fundFee: -0.000414
+        time: 4, mark: 2.76, nominal_value: 138.0, fundRate: -0.000007, fundFee: -0.000966
+        time: 5, mark: 2.77, nominal_value: 138.5, fundRate: 0.000003, fundFee: 0.0004155
+        time: 6, mark: 2.78, nominal_value: 139.0, fundRate: 0.000019, fundFee: 0.002641
+        time: 7, mark: 2.78, nominal_value: 139.0, fundRate: 0.000003, fundFee: 0.000417
+        time: 8, mark: 2.77, nominal_value: 138.5, fundRate: -0.000003, fundFee: -0.0004155
+        time: 9, mark: 2.77, nominal_value: 138.5, fundRate: 0, fundFee: 0.0
+        time: 10, mark: 2.84, nominal_value: 142.0, fundRate: 0.000013, fundFee: 0.001846
+        time: 11, mark: 2.81, nominal_value: 140.5, fundRate: 0.000077, fundFee: 0.0108185
+        time: 12, mark: 2.81, nominal_value: 140.5, fundRate: 0.000072, fundFee: 0.010116
+        time: 13, mark: 2.82, nominal_value: 141.0, fundRate: 0.000097, fundFee: 0.013677
+    """
+    d1 = datetime.strptime(f"{d1} +0000", "%Y-%m-%d %H:%M:%S %z")
+    d2 = datetime.strptime(f"{d2} +0000", "%Y-%m-%d %H:%M:%S %z")
+    funding_rate_history = {
+        "binance": funding_rate_history_octohourly,
+        "gate": funding_rate_history_octohourly,
+    }[exchange][rate_start:rate_end]
+    api_mock = MagicMock()
+    api_mock.fetch_funding_rate_history = get_mock_coro(return_value=funding_rate_history)
+    api_mock.fetch_ohlcv = get_mock_coro(return_value=mark_ohlcv)
+    type(api_mock).has = PropertyMock(return_value={"fetchOHLCV": True})
+    type(api_mock).has = PropertyMock(return_value={"fetchFundingRateHistory": True})
+
+    ex = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange)
+    mocker.patch(f"{EXMS}.timeframes", PropertyMock(return_value=["1h", "4h", "8h"]))
+    funding_fees = ex._fetch_and_calculate_funding_fees(
+        pair="ADA/USDT:USDT", amount=amount, is_short=True, open_date=d1, close_date=d2
+    )
+    assert pytest.approx(funding_fees) == expected_fees
+    # Fees for Longs are inverted
+    funding_fees = ex._fetch_and_calculate_funding_fees(
+        pair="ADA/USDT:USDT", amount=amount, is_short=False, open_date=d1, close_date=d2
+    )
+    assert pytest.approx(funding_fees) == -expected_fees
+
+    # Return empty "refresh_latest"
+    mocker.patch(f"{EXMS}.refresh_latest_ohlcv", return_value={})
+    ex = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange)
+    with pytest.raises(ExchangeError, match="Could not find funding rates."):
+        ex._fetch_and_calculate_funding_fees(
+            pair="ADA/USDT:USDT", amount=amount, is_short=False, open_date=d1, close_date=d2
+        )
+
+
+@pytest.mark.parametrize(
+    "exchange,expected_fees",
+    [
+        ("binance", -0.0009140999999999999),
+        ("gate", -0.0009140999999999999),
+    ],
+)
+def test__fetch_and_calculate_funding_fees_datetime_called(
+    mocker,
+    default_conf,
+    funding_rate_history_octohourly,
+    mark_ohlcv,
+    exchange,
+    time_machine,
+    expected_fees,
+):
+    api_mock = MagicMock()
+    api_mock.fetch_ohlcv = get_mock_coro(return_value=mark_ohlcv)
+    api_mock.fetch_funding_rate_history = get_mock_coro(
+        return_value=funding_rate_history_octohourly
+    )
+    type(api_mock).has = PropertyMock(return_value={"fetchOHLCV": True})
+    type(api_mock).has = PropertyMock(return_value={"fetchFundingRateHistory": True})
+    mocker.patch(f"{EXMS}.timeframes", PropertyMock(return_value=["4h", "8h"]))
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange)
+    d1 = datetime.strptime("2021-08-31 23:00:01 +0000", "%Y-%m-%d %H:%M:%S %z")
+
+    time_machine.move_to("2021-09-01 08:00:00 +00:00")
+    funding_fees = exchange._fetch_and_calculate_funding_fees("ADA/USDT", 30.0, True, d1)
+    assert funding_fees == expected_fees
+    funding_fees = exchange._fetch_and_calculate_funding_fees("ADA/USDT", 30.0, False, d1)
+    assert funding_fees == 0 - expected_fees
+
+
+@pytest.mark.parametrize(
+    "pair,expected_size,trading_mode",
+    [
+        ("XLTCUSDT", 1, "spot"),
+        ("LTC/USD", 1, "futures"),
+        ("XLTCUSDT", 0.01, "futures"),
+        ("ETH/USDT:USDT", 10, "futures"),
+        ("TORN/USDT:USDT", None, "futures"),  # Don't fail for unavailable pairs.
+    ],
+)
+def test__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = trading_mode
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    mocker.patch(
+        f"{EXMS}.markets",
+        {
+            "LTC/USD": {
+                "symbol": "LTC/USD",
+                "contractSize": None,
+            },
+            "XLTCUSDT": {
+                "symbol": "XLTCUSDT",
+                "contractSize": "0.01",
+            },
+            "ETH/USDT:USDT": {
+                "symbol": "ETH/USDT:USDT",
+                "contractSize": "10",
+            },
+        },
+    )
+    size = exchange.get_contract_size(pair)
+    assert expected_size == size
+
+
+@pytest.mark.parametrize(
+    "pair,contract_size,trading_mode",
+    [
+        ("XLTCUSDT", 1, "spot"),
+        ("LTC/USD", 1, "futures"),
+        ("ADA/USDT:USDT", 0.01, "futures"),
+        ("LTC/ETH", 1, "futures"),
+        ("ETH/USDT:USDT", 10, "futures"),
+    ],
+)
+def test__order_contracts_to_amount(
+    mocker,
+    default_conf,
+    markets,
+    pair,
+    contract_size,
+    trading_mode,
+):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = trading_mode
+    default_conf["margin_mode"] = "isolated"
+    mocker.patch(f"{EXMS}.markets", markets)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+
+    orders = [
+        {
+            "id": "123456320",
+            "clientOrderId": "12345632018",
+            "timestamp": 1640124992000,
+            "datetime": "Tue 21 Dec 2021 22:16:32 UTC",
+            "lastTradeTimestamp": 1640124911000,
+            "status": "active",
+            "symbol": pair,
+            "type": "limit",
+            "timeInForce": "gtc",
+            "postOnly": None,
+            "side": "buy",
+            "price": 2.0,
+            "stopPrice": None,
+            "average": None,
+            "amount": 30.0,
+            "cost": 60.0,
+            "filled": None,
+            "remaining": 30.0,
+            "fee": {
+                "currency": "USDT",
+                "cost": 0.06,
+            },
+            "fees": [
+                {
+                    "currency": "USDT",
+                    "cost": 0.06,
+                }
+            ],
+            "trades": None,
+            "info": {},
+        },
+        {
+            "id": "123456380",
+            "clientOrderId": "12345638203",
+            "timestamp": 1640124992000,
+            "datetime": "Tue 21 Dec 2021 22:16:32 UTC",
+            "lastTradeTimestamp": 1640124911000,
+            "status": "active",
+            "symbol": pair,
+            "type": "limit",
+            "timeInForce": "gtc",
+            "postOnly": None,
+            "side": "sell",
+            "price": 2.2,
+            "stopPrice": None,
+            "average": None,
+            "amount": 40.0,
+            "cost": 80.0,
+            "filled": None,
+            "remaining": 40.0,
+            "fee": {
+                "currency": "USDT",
+                "cost": 0.08,
+            },
+            "fees": [
+                {
+                    "currency": "USDT",
+                    "cost": 0.08,
+                }
+            ],
+            "trades": None,
+            "info": {},
+        },
+        {
+            # Realistic stoploss order on gate.
+            "id": "123456380",
+            "clientOrderId": "12345638203",
+            "timestamp": None,
+            "datetime": None,
+            "lastTradeTimestamp": None,
+            "status": None,
+            "symbol": None,
+            "type": None,
+            "timeInForce": None,
+            "postOnly": None,
+            "side": None,
+            "price": None,
+            "stopPrice": None,
+            "average": None,
+            "amount": None,
+            "cost": None,
+            "filled": None,
+            "remaining": None,
+            "fee": None,
+            "fees": [],
+            "trades": None,
+            "info": {},
+        },
+    ]
+    order1_bef = orders[0]
+    order2_bef = orders[1]
+    order1 = exchange._order_contracts_to_amount(deepcopy(order1_bef))
+    order2 = exchange._order_contracts_to_amount(deepcopy(order2_bef))
+    assert order1["amount"] == order1_bef["amount"] * contract_size
+    assert order1["cost"] == order1_bef["cost"] * contract_size
+
+    assert order2["amount"] == order2_bef["amount"] * contract_size
+    assert order2["cost"] == order2_bef["cost"] * contract_size
+
+    # Don't fail
+    exchange._order_contracts_to_amount(orders[2])
+
+
+@pytest.mark.parametrize(
+    "pair,contract_size,trading_mode",
+    [
+        ("XLTCUSDT", 1, "spot"),
+        ("LTC/USD", 1, "futures"),
+        ("ADA/USDT:USDT", 0.01, "futures"),
+        ("LTC/ETH", 1, "futures"),
+        ("ETH/USDT:USDT", 10, "futures"),
+    ],
+)
+def test__trades_contracts_to_amount(
+    mocker,
+    default_conf,
+    markets,
+    pair,
+    contract_size,
+    trading_mode,
+):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = trading_mode
+    default_conf["margin_mode"] = "isolated"
+    mocker.patch(f"{EXMS}.markets", markets)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+
+    trades = [
+        {
+            "symbol": pair,
+            "amount": 30.0,
+        },
+        {
+            "symbol": pair,
+            "amount": 40.0,
+        },
+    ]
+
+    new_amount_trades = exchange._trades_contracts_to_amount(trades)
+    assert new_amount_trades[0]["amount"] == 30.0 * contract_size
+    assert new_amount_trades[1]["amount"] == 40.0 * contract_size
+
+
+@pytest.mark.parametrize(
+    "pair,param_amount,param_size",
+    [
+        ("ADA/USDT:USDT", 40, 4000),
+        ("LTC/ETH", 30, 30),
+        ("LTC/USD", 30, 30),
+        ("ETH/USDT:USDT", 10, 1),
+    ],
+)
+def test__amount_to_contracts(mocker, default_conf, pair, param_amount, param_size):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = "spot"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    mocker.patch(
+        f"{EXMS}.markets",
+        {
+            "LTC/USD": {
+                "symbol": "LTC/USD",
+                "contractSize": None,
+            },
+            "XLTCUSDT": {
+                "symbol": "XLTCUSDT",
+                "contractSize": "0.01",
+            },
+            "LTC/ETH": {
+                "symbol": "LTC/ETH",
+            },
+            "ETH/USDT:USDT": {
+                "symbol": "ETH/USDT:USDT",
+                "contractSize": "10",
+            },
+        },
+    )
+    result_size = exchange._amount_to_contracts(pair, param_amount)
+    assert result_size == param_amount
+    result_amount = exchange._contracts_to_amount(pair, param_size)
+    assert result_amount == param_size
+
+    default_conf["trading_mode"] = "futures"
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    result_size = exchange._amount_to_contracts(pair, param_amount)
+    assert result_size == param_size
+    result_amount = exchange._contracts_to_amount(pair, param_size)
+    assert result_amount == param_amount
+
+
+@pytest.mark.parametrize(
+    "pair,amount,expected_spot,expected_fut",
+    [
+        # Contract size of 0.01
+        ("ADA/USDT:USDT", 40, 40, 40),
+        ("ADA/USDT:USDT", 10.4445555, 10.4, 10.444),
+        ("LTC/ETH", 30, 30, 30),
+        ("LTC/USD", 30, 30, 30),
+        ("ADA/USDT:USDT", 1.17, 1.1, 1.17),
+        # contract size of 10
+        ("ETH/USDT:USDT", 10.111, 10.1, 10),
+        ("ETH/USDT:USDT", 10.188, 10.1, 10),
+        ("ETH/USDT:USDT", 10.988, 10.9, 10),
+    ],
+)
+def test_amount_to_contract_precision(
+    mocker,
+    default_conf,
+    pair,
+    amount,
+    expected_spot,
+    expected_fut,
+):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = "spot"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+
+    result_size = exchange.amount_to_contract_precision(pair, amount)
+    assert result_size == expected_spot
+
+    default_conf["trading_mode"] = "futures"
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    result_size = exchange.amount_to_contract_precision(pair, amount)
+    assert result_size == expected_fut
+
+
+@pytest.mark.parametrize(
+    "exchange_name,open_rate,is_short,trading_mode,margin_mode",
+    [
+        # Bybit
+        ("bybit", 2.0, False, "spot", None),
+        ("bybit", 2.0, False, "spot", "cross"),
+        ("bybit", 2.0, True, "spot", "isolated"),
+        # Binance
+        ("binance", 2.0, False, "spot", None),
+        ("binance", 2.0, False, "spot", "cross"),
+        ("binance", 2.0, True, "spot", "isolated"),
+    ],
+)
+def test_liquidation_price_is_none(
+    mocker, default_conf, exchange_name, open_rate, is_short, trading_mode, margin_mode
+):
+    default_conf["trading_mode"] = trading_mode
+    default_conf["margin_mode"] = margin_mode
+    exchange = get_patched_exchange(mocker, default_conf, exchange=exchange_name)
+    assert (
+        exchange.get_liquidation_price(
+            pair="DOGE/USDT",
+            open_rate=open_rate,
+            is_short=is_short,
+            amount=71200.81144,
+            stake_amount=open_rate * 71200.81144,
+            leverage=5,
+            wallet_balance=-56354.57,
+        )
+        is None
+    )
+
+
+def test_get_max_pair_stake_amount(
+    mocker,
+    default_conf,
+):
+    api_mock = MagicMock()
+    default_conf["margin_mode"] = "isolated"
+    default_conf["trading_mode"] = "futures"
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    markets = {
+        "XRP/USDT:USDT": {
+            "limits": {
+                "amount": {"min": 0.001, "max": 10000},
+                "cost": {"min": 5, "max": None},
+            },
+            "contractSize": None,
+            "spot": False,
+        },
+        "LTC/USDT:USDT": {
+            "limits": {
+                "amount": {"min": 0.001, "max": None},
+                "cost": {"min": 5, "max": None},
+            },
+            "contractSize": 0.01,
+            "spot": False,
+        },
+        "ETH/USDT:USDT": {
+            "limits": {
+                "amount": {"min": 0.001, "max": 10000},
+                "cost": {
+                    "min": 5,
+                    "max": 30000,
+                },
+            },
+            "contractSize": 0.01,
+            "spot": False,
+        },
+        "BTC/USDT": {
+            "limits": {
+                "amount": {"min": 0.001, "max": 10000},
+                "cost": {"min": 5, "max": None},
+            },
+            "contractSize": 0.01,
+            "spot": True,
+        },
+        "ADA/USDT": {
+            "limits": {
+                "amount": {"min": 0.001, "max": 10000},
+                "cost": {
+                    "min": 5,
+                    "max": 500,
+                },
+            },
+            "contractSize": 0.01,
+            "spot": True,
+        },
+        "DOGE/USDT:USDT": {
+            "limits": {
+                "amount": {"min": 0.001, "max": 10000},
+                "cost": {"min": 5, "max": 500},
+            },
+            "contractSize": None,
+            "spot": False,
+        },
+        "LUNA/USDT:USDT": {
+            "limits": {
+                "amount": {"min": 0.001, "max": 10000},
+                "cost": {"min": 5, "max": 500},
+            },
+            "contractSize": 0.01,
+            "spot": False,
+        },
+    }
+
+    mocker.patch(f"{EXMS}.markets", markets)
+    assert exchange.get_max_pair_stake_amount("XRP/USDT:USDT", 2.0) == 20000
+    assert exchange.get_max_pair_stake_amount("XRP/USDT:USDT", 2.0, 5) == 4000
+    assert exchange.get_max_pair_stake_amount("LTC/USDT:USDT", 2.0) == float("inf")
+    assert exchange.get_max_pair_stake_amount("ETH/USDT:USDT", 2.0) == 200
+    assert exchange.get_max_pair_stake_amount("DOGE/USDT:USDT", 2.0) == 500
+    assert exchange.get_max_pair_stake_amount("LUNA/USDT:USDT", 2.0) == 5.0
+
+    default_conf["trading_mode"] = "spot"
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    mocker.patch(f"{EXMS}.markets", markets)
+    assert exchange.get_max_pair_stake_amount("BTC/USDT", 2.0) == 20000
+    assert exchange.get_max_pair_stake_amount("ADA/USDT", 2.0) == 500
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_load_leverage_tiers(mocker, default_conf, exchange_name):
+    if exchange_name == "bybit":
+        # TODO: remove once get_leverage_tiers workaround has been removed.
+        pytest.skip("Currently skipping")
+    api_mock = MagicMock()
+    api_mock.fetch_leverage_tiers = MagicMock()
+    type(api_mock).has = PropertyMock(return_value={"fetchLeverageTiers": True})
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.validate_trading_mode_and_margin_mode")
+
+    api_mock.fetch_leverage_tiers = MagicMock(
+        return_value={
+            "ADA/USDT:USDT": [
+                {
+                    "tier": 1,
+                    "minNotional": 0,
+                    "maxNotional": 500,
+                    "maintenanceMarginRate": 0.02,
+                    "maxLeverage": 75,
+                    "info": {
+                        "baseMaxLoan": "",
+                        "imr": "0.013",
+                        "instId": "",
+                        "maxLever": "75",
+                        "maxSz": "500",
+                        "minSz": "0",
+                        "mmr": "0.01",
+                        "optMgnFactor": "0",
+                        "quoteMaxLoan": "",
+                        "tier": "1",
+                        "uly": "ADA-USDT",
+                    },
+                },
+            ]
+        }
+    )
+
+    # SPOT
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    assert exchange.load_leverage_tiers() == {}
+
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+
+    if exchange_name != "binance":
+        # FUTURES has.fetchLeverageTiers == False
+        type(api_mock).has = PropertyMock(return_value={"fetchLeverageTiers": False})
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+        assert exchange.load_leverage_tiers() == {}
+
+    # FUTURES regular
+    type(api_mock).has = PropertyMock(return_value={"fetchLeverageTiers": True})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    assert exchange.load_leverage_tiers() == {
+        "ADA/USDT:USDT": [
+            {
+                "tier": 1,
+                "minNotional": 0,
+                "maxNotional": 500,
+                "maintenanceMarginRate": 0.02,
+                "maxLeverage": 75,
+                "info": {
+                    "baseMaxLoan": "",
+                    "imr": "0.013",
+                    "instId": "",
+                    "maxLever": "75",
+                    "maxSz": "500",
+                    "minSz": "0",
+                    "mmr": "0.01",
+                    "optMgnFactor": "0",
+                    "quoteMaxLoan": "",
+                    "tier": "1",
+                    "uly": "ADA-USDT",
+                },
+            },
+        ]
+    }
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        exchange_name,
+        "load_leverage_tiers",
+        "fetch_leverage_tiers",
+    )
+
+
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+async def test_get_market_leverage_tiers(mocker, default_conf, exchange_name):
+    default_conf["exchange"]["name"] = exchange_name
+    await async_ccxt_exception(
+        mocker,
+        default_conf,
+        MagicMock(),
+        "get_market_leverage_tiers",
+        "fetch_market_leverage_tiers",
+        symbol="BTC/USDT:USDT",
+    )
+
+
+def test_parse_leverage_tier(mocker, default_conf):
+    exchange = get_patched_exchange(mocker, default_conf)
+
+    tier = {
+        "tier": 1,
+        "minNotional": 0,
+        "maxNotional": 100000,
+        "maintenanceMarginRate": 0.025,
+        "maxLeverage": 20,
+        "info": {
+            "bracket": "1",
+            "initialLeverage": "20",
+            "maxNotional": "100000",
+            "minNotional": "0",
+            "maintMarginRatio": "0.025",
+            "cum": "0.0",
+        },
+    }
+
+    assert exchange.parse_leverage_tier(tier) == {
+        "minNotional": 0,
+        "maxNotional": 100000,
+        "maintenanceMarginRate": 0.025,
+        "maxLeverage": 20,
+        "maintAmt": 0.0,
+    }
+
+    tier2 = {
+        "tier": 1,
+        "minNotional": 0,
+        "maxNotional": 2000,
+        "maintenanceMarginRate": 0.01,
+        "maxLeverage": 75,
+        "info": {
+            "baseMaxLoan": "",
+            "imr": "0.013",
+            "instId": "",
+            "maxLever": "75",
+            "maxSz": "2000",
+            "minSz": "0",
+            "mmr": "0.01",
+            "optMgnFactor": "0",
+            "quoteMaxLoan": "",
+            "tier": "1",
+            "uly": "SHIB-USDT",
+        },
+    }
+
+    assert exchange.parse_leverage_tier(tier2) == {
+        "minNotional": 0,
+        "maxNotional": 2000,
+        "maintenanceMarginRate": 0.01,
+        "maxLeverage": 75,
+        "maintAmt": None,
+    }
+
+
+def test_get_maintenance_ratio_and_amt_exceptions(mocker, default_conf, leverage_tiers):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+
+    exchange._leverage_tiers = leverage_tiers
+    with pytest.raises(
+        DependencyException,
+        match="nominal value can not be lower than 0",
+    ):
+        exchange.get_maintenance_ratio_and_amt("1000SHIB/USDT:USDT", -1)
+
+    exchange._leverage_tiers = {}
+
+    with pytest.raises(
+        InvalidOrderException,
+        match="Maintenance margin rate for 1000SHIB/USDT:USDT is unavailable for",
+    ):
+        exchange.get_maintenance_ratio_and_amt("1000SHIB/USDT:USDT", 10000)
+
+
+@pytest.mark.parametrize(
+    "pair,value,mmr,maintAmt",
+    [
+        ("ADA/USDT:USDT", 500, 0.025, 0.0),
+        ("ADA/USDT:USDT", 20000000, 0.5, 1527500.0),
+        ("ZEC/USDT:USDT", 500, 0.01, 0.0),
+        ("ZEC/USDT:USDT", 20000000, 0.5, 654500.0),
+    ],
+)
+def test_get_maintenance_ratio_and_amt(
+    mocker, default_conf, leverage_tiers, pair, value, mmr, maintAmt
+):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    exchange._leverage_tiers = leverage_tiers
+    assert exchange.get_maintenance_ratio_and_amt(pair, value) == (mmr, maintAmt)
+
+
+def test_get_max_leverage_futures(default_conf, mocker, leverage_tiers):
+    # Test Spot
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+    assert exchange.get_max_leverage("BNB/USDT", 100.0) == 1.0
+
+    # Test Futures
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, exchange="binance")
+
+    exchange._leverage_tiers = leverage_tiers
+
+    assert exchange.get_max_leverage("XRP/USDT:USDT", 1.0) == 20.0
+    assert exchange.get_max_leverage("BNB/USDT:USDT", 100.0) == 75.0
+    assert exchange.get_max_leverage("BTC/USDT:USDT", 170.30) == 125.0
+    assert pytest.approx(exchange.get_max_leverage("XRP/USDT:USDT", 99999.9)) == 5.000005
+    assert pytest.approx(exchange.get_max_leverage("BNB/USDT:USDT", 1500)) == 33.333333333333333
+    assert exchange.get_max_leverage("BTC/USDT:USDT", 300000000) == 2.0
+    assert exchange.get_max_leverage("BTC/USDT:USDT", 600000000) == 1.0  # Last tier
+
+    assert exchange.get_max_leverage("SPONGE/USDT:USDT", 200) == 1.0  # Pair not in leverage_tiers
+    assert exchange.get_max_leverage("BTC/USDT:USDT", 0.0) == 125.0  # No stake amount
+    with pytest.raises(
+        InvalidOrderException, match=r"Amount 1000000000.01 too high for BTC/USDT:USDT"
+    ):
+        exchange.get_max_leverage("BTC/USDT:USDT", 1000000000.01)
+
+
+@pytest.mark.parametrize("exchange_name", ["binance", "kraken", "gate", "okx", "bybit"])
+def test__get_params(mocker, default_conf, exchange_name):
+    api_mock = MagicMock()
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    exchange._params = {"test": True}
+
+    params1 = {"test": True}
+    params2 = {
+        "test": True,
+        "timeInForce": "IOC",
+        "reduceOnly": True,
+    }
+
+    if exchange_name == "kraken":
+        params2["leverage"] = 3.0
+
+    if exchange_name == "okx":
+        params2["tdMode"] = "isolated"
+        params2["posSide"] = "net"
+
+    if exchange_name == "bybit":
+        params2["position_idx"] = 0
+
+    assert (
+        exchange._get_params(
+            side="buy",
+            ordertype="market",
+            reduceOnly=False,
+            time_in_force="GTC",
+            leverage=1.0,
+        )
+        == params1
+    )
+
+    assert (
+        exchange._get_params(
+            side="buy",
+            ordertype="market",
+            reduceOnly=False,
+            time_in_force="IOC",
+            leverage=1.0,
+        )
+        == params1
+    )
+
+    assert (
+        exchange._get_params(
+            side="buy",
+            ordertype="limit",
+            reduceOnly=False,
+            time_in_force="GTC",
+            leverage=1.0,
+        )
+        == params1
+    )
+
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange=exchange_name)
+    exchange._params = {"test": True}
+
+    assert (
+        exchange._get_params(
+            side="buy",
+            ordertype="limit",
+            reduceOnly=True,
+            time_in_force="IOC",
+            leverage=3.0,
+        )
+        == params2
+    )
+
+
+def test_get_liquidation_price1(mocker, default_conf):
+    api_mock = MagicMock()
+    leverage = 9.97
+    positions = [
+        {
+            "info": {},
+            "symbol": "NEAR/USDT:USDT",
+            "timestamp": 1642164737148,
+            "datetime": "2022-01-14T12:52:17.148Z",
+            "initialMargin": 1.51072,
+            "initialMarginPercentage": 0.1,
+            "maintenanceMargin": 0.38916147,
+            "maintenanceMarginPercentage": 0.025,
+            "entryPrice": 18.884,
+            "notional": 15.1072,
+            "leverage": leverage,
+            "unrealizedPnl": 0.0048,
+            "contracts": 8,
+            "contractSize": 0.1,
+            "marginRatio": None,
+            "liquidationPrice": 17.47,
+            "markPrice": 18.89,
+            "margin_mode": 1.52549075,
+            "marginType": "isolated",
+            "side": "buy",
+            "percentage": 0.003177292946409658,
+        }
+    ]
+    api_mock.fetch_positions = MagicMock(return_value=positions)
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+    )
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    default_conf["liquidation_buffer"] = 0.0
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    liq_price = exchange.get_liquidation_price(
+        pair="NEAR/USDT:USDT",
+        open_rate=18.884,
+        is_short=False,
+        amount=0.8,
+        stake_amount=18.884 * 0.8,
+        leverage=leverage,
+        wallet_balance=18.884 * 0.8,
+    )
+    assert liq_price == 17.47
+
+    default_conf["liquidation_buffer"] = 0.05
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    liq_price = exchange.get_liquidation_price(
+        pair="NEAR/USDT:USDT",
+        open_rate=18.884,
+        is_short=False,
+        amount=0.8,
+        stake_amount=18.884 * 0.8,
+        leverage=leverage,
+        wallet_balance=18.884 * 0.8,
+    )
+    assert liq_price == 17.540699999999998
+
+    api_mock.fetch_positions = MagicMock(return_value=[])
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    liq_price = exchange.get_liquidation_price(
+        pair="NEAR/USDT:USDT",
+        open_rate=18.884,
+        is_short=False,
+        amount=0.8,
+        stake_amount=18.884 * 0.8,
+        leverage=leverage,
+        wallet_balance=18.884 * 0.8,
+    )
+    assert liq_price is None
+    default_conf["trading_mode"] = "margin"
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    with pytest.raises(OperationalException, match=r".*does not support .* margin"):
+        exchange.get_liquidation_price(
+            pair="NEAR/USDT:USDT",
+            open_rate=18.884,
+            is_short=False,
+            amount=0.8,
+            stake_amount=18.884 * 0.8,
+            leverage=leverage,
+            wallet_balance=18.884 * 0.8,
+            open_trades=[],
+        )
+
+
+@pytest.mark.parametrize("liquidation_buffer", [0.0])
+@pytest.mark.parametrize(
+    "is_short,trading_mode,exchange_name,margin_mode,leverage,open_rate,amount,mramt,expected_liq",
+    [
+        (False, "spot", "binance", "", 5.0, 10.0, 1.0, (0.01, 0.01), None),
+        (True, "spot", "binance", "", 5.0, 10.0, 1.0, (0.01, 0.01), None),
+        (False, "spot", "gate", "", 5.0, 10.0, 1.0, (0.01, 0.01), None),
+        (True, "spot", "gate", "", 5.0, 10.0, 1.0, (0.01, 0.01), None),
+        (False, "spot", "okx", "", 5.0, 10.0, 1.0, (0.01, 0.01), None),
+        (True, "spot", "okx", "", 5.0, 10.0, 1.0, (0.01, 0.01), None),
+        # Binance, short
+        (True, "futures", "binance", "isolated", 5.0, 10.0, 1.0, (0.01, 0.01), 11.89108910891089),
+        (True, "futures", "binance", "isolated", 3.0, 10.0, 1.0, (0.01, 0.01), 13.211221122079207),
+        (True, "futures", "binance", "isolated", 5.0, 8.0, 1.0, (0.01, 0.01), 9.514851485148514),
+        (True, "futures", "binance", "isolated", 5.0, 10.0, 0.6, (0.01, 0.01), 11.897689768976898),
+        # Binance, long
+        (False, "futures", "binance", "isolated", 5, 10, 1.0, (0.01, 0.01), 8.070707070707071),
+        (False, "futures", "binance", "isolated", 5, 8, 1.0, (0.01, 0.01), 6.454545454545454),
+        (False, "futures", "binance", "isolated", 3, 10, 1.0, (0.01, 0.01), 6.723905723905723),
+        (False, "futures", "binance", "isolated", 5, 10, 0.6, (0.01, 0.01), 8.063973063973064),
+        # Gate/okx, short
+        (True, "futures", "gate", "isolated", 5, 10, 1.0, (0.01, 0.01), 11.87413417771621),
+        (True, "futures", "gate", "isolated", 5, 10, 2.0, (0.01, 0.01), 11.87413417771621),
+        (True, "futures", "gate", "isolated", 3, 10, 1.0, (0.01, 0.01), 13.193482419684678),
+        (True, "futures", "gate", "isolated", 5, 8, 1.0, (0.01, 0.01), 9.499307342172967),
+        (True, "futures", "okx", "isolated", 3, 10, 1.0, (0.01, 0.01), 13.193482419684678),
+        # Gate/okx, long
+        (False, "futures", "gate", "isolated", 5.0, 10.0, 1.0, (0.01, 0.01), 8.085708510208207),
+        (False, "futures", "gate", "isolated", 3.0, 10.0, 1.0, (0.01, 0.01), 6.738090425173506),
+        (False, "futures", "okx", "isolated", 3.0, 10.0, 1.0, (0.01, 0.01), 6.738090425173506),
+        # bybit, long
+        (False, "futures", "bybit", "isolated", 1.0, 10.0, 1.0, (0.01, 0.01), 0.1),
+        (False, "futures", "bybit", "isolated", 3.0, 10.0, 1.0, (0.01, 0.01), 6.7666666),
+        (False, "futures", "bybit", "isolated", 5.0, 10.0, 1.0, (0.01, 0.01), 8.1),
+        (False, "futures", "bybit", "isolated", 10.0, 10.0, 1.0, (0.01, 0.01), 9.1),
+        # From the bybit example - without additional margin
+        (False, "futures", "bybit", "isolated", 50.0, 40000.0, 1.0, (0.005, None), 39400),
+        (False, "futures", "bybit", "isolated", 50.0, 20000.0, 1.0, (0.005, None), 19700),
+        # bybit, short
+        (True, "futures", "bybit", "isolated", 1.0, 10.0, 1.0, (0.01, 0.01), 19.9),
+        (True, "futures", "bybit", "isolated", 3.0, 10.0, 1.0, (0.01, 0.01), 13.233333),
+        (True, "futures", "bybit", "isolated", 5.0, 10.0, 1.0, (0.01, 0.01), 11.9),
+        (True, "futures", "bybit", "isolated", 10.0, 10.0, 1.0, (0.01, 0.01), 10.9),
+    ],
+)
+def test_get_liquidation_price(
+    mocker,
+    default_conf_usdt,
+    is_short,
+    trading_mode,
+    exchange_name,
+    margin_mode,
+    leverage,
+    open_rate,
+    amount,
+    mramt,
+    expected_liq,
+    liquidation_buffer,
+):
+    """
+    position = 0.2 * 5
+    wb: wallet balance (stake_amount if isolated)
+    cum_b: maintenance amount
+    side_1: -1 if is_short else 1
+    ep1: entry price
+    mmr_b: maintenance margin ratio
+
+    Binance, Short
+    leverage = 5, open_rate = 10, amount = 1.0
+        ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
+        ((2 + 0.01) - ((-1) * 1 * 10)) / ((1 * 0.01) - ((-1) * 1)) = 11.89108910891089
+    leverage = 3, open_rate = 10, amount = 1.0
+        ((3.3333333333 + 0.01) - ((-1) * 1.0 * 10)) / ((1.0 * 0.01) - ((-1) * 1.0)) = 13.2112211220
+    leverage = 5, open_rate = 8, amount = 1.0
+        ((1.6 + 0.01) - ((-1) * 1 * 8)) / ((1 * 0.01) - ((-1) * 1)) = 9.514851485148514
+    leverage = 5, open_rate = 10, amount = 0.6
+        ((1.6 + 0.01) - ((-1) * 0.6 * 10)) / ((0.6 * 0.01) - ((-1) * 0.6)) = 12.557755775577558
+
+    Binance, Long
+    leverage = 5, open_rate = 10, amount = 1.0
+        ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
+        ((2 + 0.01) - (1 * 1 * 10)) / ((1 * 0.01) - (1 * 1)) = 8.070707070707071
+    leverage = 5, open_rate = 8, amount = 1.0
+        ((1.6 + 0.01) - (1 * 1 * 8)) / ((1 * 0.01) - (1 * 1)) = 6.454545454545454
+    leverage = 3, open_rate = 10, amount = 1.0
+        ((2 + 0.01) - (1 * 0.6 * 10)) / ((0.6 * 0.01) - (1 * 0.6)) = 6.717171717171718
+    leverage = 5, open_rate = 10, amount = 0.6
+        ((1.6 + 0.01) - (1 * 0.6 * 10)) / ((0.6 * 0.01) - (1 * 0.6)) = 7.39057239057239
+
+    Gate/Okx, Short
+    leverage = 5, open_rate = 10, amount = 1.0
+        (open_rate + (wallet_balance / position)) / (1 + (mm_ratio + taker_fee_rate))
+        (10 + (2 / 1.0)) / (1 + (0.01 + 0.0006)) = 11.87413417771621
+    leverage = 5, open_rate = 10, amount = 2.0
+        (10 + (4 / 2.0)) / (1 + (0.01 + 0.0006)) = 11.87413417771621
+    leverage = 3, open_rate = 10, amount = 1.0
+        (10 + (3.3333333333333 / 1.0)) / (1 - (0.01 + 0.0006)) = 13.476180850346978
+    leverage = 5, open_rate = 8, amount = 1.0
+        (8 + (1.6 / 1.0)) / (1 + (0.01 + 0.0006)) = 9.499307342172967
+
+    Gate/Okx, Long
+    leverage = 5, open_rate = 10, amount = 1.0
+        (open_rate - (wallet_balance / position)) / (1 - (mm_ratio + taker_fee_rate))
+        (10 - (2 / 1)) / (1 - (0.01 + 0.0006)) = 8.085708510208207
+    leverage = 5, open_rate = 10, amount = 2.0
+        (10 - (4 / 2.0)) / (1 + (0.01 + 0.0006)) = 7.916089451810806
+    leverage = 3, open_rate = 10, amount = 1.0
+        (10 - (3.333333333333333333 / 1.0)) / (1 - (0.01 + 0.0006)) = 6.738090425173506
+    leverage = 5, open_rate = 8, amount = 1.0
+        (8 - (1.6 / 1.0)) / (1 + (0.01 + 0.0006)) = 6.332871561448645
+    """
+    default_conf_usdt["liquidation_buffer"] = liquidation_buffer
+    default_conf_usdt["trading_mode"] = trading_mode
+    default_conf_usdt["exchange"]["name"] = exchange_name
+    default_conf_usdt["margin_mode"] = margin_mode
+    mocker.patch("freqtrade.exchange.gate.Gate.validate_ordertypes")
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+    exchange = get_patched_exchange(mocker, default_conf_usdt, exchange=exchange_name)
+
+    exchange.get_maintenance_ratio_and_amt = MagicMock(return_value=mramt)
+    exchange.name = exchange_name
+    # default_conf_usdt.update({
+    #     "dry_run": False,
+    # })
+    liq = exchange.get_liquidation_price(
+        pair="ETH/USDT:USDT",
+        open_rate=open_rate,
+        amount=amount,
+        stake_amount=amount * open_rate / leverage,
+        wallet_balance=amount * open_rate / leverage,
+        leverage=leverage,
+        is_short=is_short,
+        open_trades=[],
+    )
+    if expected_liq is None:
+        assert liq is None
+    else:
+        buffer_amount = liquidation_buffer * abs(open_rate - expected_liq)
+        expected_liq = expected_liq - buffer_amount if is_short else expected_liq + buffer_amount
+        assert pytest.approx(expected_liq) == liq
+
+
+@pytest.mark.parametrize(
+    "contract_size,order_amount",
+    [
+        (10, 10),
+        (0.01, 10000),
+    ],
+)
+def test_stoploss_contract_size(mocker, default_conf, contract_size, order_amount):
+    api_mock = MagicMock()
+    order_id = f"test_prod_buy_{randint(0, 10**6)}"
+
+    api_mock.create_order = MagicMock(
+        return_value={
+            "id": order_id,
+            "info": {"foo": "bar"},
+            "amount": order_amount,
+            "cost": order_amount,
+            "filled": order_amount,
+            "remaining": order_amount,
+            "symbol": "ETH/BTC",
+        }
+    )
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock)
+    exchange.get_contract_size = MagicMock(return_value=contract_size)
+
+    api_mock.create_order.reset_mock()
+    order = exchange.create_stoploss(
+        pair="ETH/BTC", amount=100, stop_price=220, order_types={}, side="buy", leverage=1.0
+    )
+
+    assert api_mock.create_order.call_args_list[0][1]["amount"] == order_amount
+    assert order["amount"] == 100
+    assert order["cost"] == order_amount
+    assert order["filled"] == 100
+    assert order["remaining"] == 100
+
+
+def test_price_to_precision_with_default_conf(default_conf, mocker):
+    conf = copy.deepcopy(default_conf)
+    patched_ex = get_patched_exchange(mocker, conf)
+    prec_price = patched_ex.price_to_precision("XRP/USDT", 1.0000000101)
+    assert prec_price == 1.00000001
+    assert prec_price == 1.00000001
+
+
+def test_exchange_features(default_conf, mocker):
+    conf = copy.deepcopy(default_conf)
+    exchange = get_patched_exchange(mocker, conf)
+    exchange._api_async.features = {
+        "spot": {
+            "fetchOHLCV": {
+                "limit": 995,
+            }
+        },
+        "swap": {
+            "linear": {
+                "fetchOHLCV": {
+                    "limit": 997,
+                }
+            }
+        },
+    }
+    assert exchange.features("spot", "fetchOHLCV", "limit", 500) == 995
+    assert exchange.features("futures", "fetchOHLCV", "limit", 500) == 997
+    # Fall back to default
+    assert exchange.features("futures", "fetchOHLCV_else", "limit", 601) == 601
diff --git a/tests/exchange/test_exchange_utils.py b/tests/exchange/test_exchange_utils.py
new file mode 100644
index 000000000..a298599c4
--- /dev/null
+++ b/tests/exchange/test_exchange_utils.py
@@ -0,0 +1,387 @@
+# pragma pylint: disable=missing-docstring, protected-access, invalid-name
+from datetime import datetime, timedelta, timezone
+from math import isnan, nan
+
+import pytest
+from ccxt import (
+    DECIMAL_PLACES,
+    ROUND,
+    ROUND_DOWN,
+    ROUND_UP,
+    SIGNIFICANT_DIGITS,
+    TICK_SIZE,
+    TRUNCATE,
+)
+
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import (
+    amount_to_contract_precision,
+    amount_to_precision,
+    date_minus_candles,
+    price_to_precision,
+    timeframe_to_minutes,
+    timeframe_to_msecs,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    timeframe_to_resample_freq,
+    timeframe_to_seconds,
+)
+from freqtrade.exchange.check_exchange import check_exchange
+from tests.conftest import log_has_re
+
+
+def test_check_exchange(default_conf, caplog) -> None:
+    # Test an officially supported by Freqtrade team exchange
+    default_conf["runmode"] = RunMode.DRY_RUN
+    default_conf.get("exchange").update({"name": "BINANCE"})
+    assert check_exchange(default_conf)
+    assert log_has_re(
+        r"Exchange .* is officially supported by the Freqtrade development team\.", caplog
+    )
+    caplog.clear()
+
+    # Test an officially supported by Freqtrade team exchange
+    default_conf.get("exchange").update({"name": "binance"})
+    assert check_exchange(default_conf)
+    assert log_has_re(
+        r"Exchange \"binance\" is officially supported by the Freqtrade development team\.", caplog
+    )
+    caplog.clear()
+
+    # Test an officially supported by Freqtrade team exchange
+    default_conf.get("exchange").update({"name": "binanceus"})
+    assert check_exchange(default_conf)
+    assert log_has_re(
+        r"Exchange \"binanceus\" is officially supported by the Freqtrade development team\.",
+        caplog,
+    )
+    caplog.clear()
+
+    # Test an officially supported by Freqtrade team exchange - with remapping
+    default_conf.get("exchange").update({"name": "okx"})
+    assert check_exchange(default_conf)
+    assert log_has_re(
+        r"Exchange \"okx\" is officially supported by the Freqtrade development team\.", caplog
+    )
+    caplog.clear()
+    # Test an available exchange, supported by ccxt
+    default_conf.get("exchange").update({"name": "huobijp"})
+    assert check_exchange(default_conf)
+    assert log_has_re(
+        r"Exchange .* is known to the ccxt library, available for the bot, "
+        r"but not officially supported "
+        r"by the Freqtrade development team\. .*",
+        caplog,
+    )
+    caplog.clear()
+
+    # Test a 'bad' exchange, which known to have serious problems
+    default_conf.get("exchange").update({"name": "bitmex"})
+    with pytest.raises(OperationalException, match=r"Exchange .* will not work with Freqtrade\..*"):
+        check_exchange(default_conf)
+    caplog.clear()
+
+    # Test a 'bad' exchange with check_for_bad=False
+    default_conf.get("exchange").update({"name": "bitmex"})
+    assert check_exchange(default_conf, False)
+    assert log_has_re(
+        r"Exchange .* is known to the ccxt library, available for the bot, "
+        r"but not officially supported "
+        r"by the Freqtrade development team\. .*",
+        caplog,
+    )
+    caplog.clear()
+
+    # Test an invalid exchange
+    default_conf.get("exchange").update({"name": "unknown_exchange"})
+    with pytest.raises(
+        OperationalException,
+        match=r'Exchange "unknown_exchange" is not known to the ccxt library '
+        r"and therefore not available for the bot.*",
+    ):
+        check_exchange(default_conf)
+
+    # Test no exchange...
+    default_conf.get("exchange").update({"name": ""})
+    default_conf["runmode"] = RunMode.PLOT
+    assert check_exchange(default_conf)
+
+    # Test no exchange...
+    default_conf.get("exchange").update({"name": ""})
+    default_conf["runmode"] = RunMode.UTIL_EXCHANGE
+    with pytest.raises(
+        OperationalException, match=r"This command requires a configured exchange.*"
+    ):
+        check_exchange(default_conf)
+
+
+def test_date_minus_candles():
+    date = datetime(2019, 8, 12, 13, 25, 0, tzinfo=timezone.utc)
+
+    assert date_minus_candles("5m", 3, date) == date - timedelta(minutes=15)
+    assert date_minus_candles("5m", 5, date) == date - timedelta(minutes=25)
+    assert date_minus_candles("1m", 6, date) == date - timedelta(minutes=6)
+    assert date_minus_candles("1h", 3, date) == date - timedelta(hours=3, minutes=25)
+    assert date_minus_candles("1h", 3) == timeframe_to_prev_date("1h") - timedelta(hours=3)
+
+
+def test_timeframe_to_minutes():
+    assert timeframe_to_minutes("5m") == 5
+    assert timeframe_to_minutes("10m") == 10
+    assert timeframe_to_minutes("1h") == 60
+    assert timeframe_to_minutes("1d") == 1440
+
+
+def test_timeframe_to_seconds():
+    assert timeframe_to_seconds("5m") == 300
+    assert timeframe_to_seconds("10m") == 600
+    assert timeframe_to_seconds("1h") == 3600
+    assert timeframe_to_seconds("1d") == 86400
+
+
+def test_timeframe_to_msecs():
+    assert timeframe_to_msecs("5m") == 300000
+    assert timeframe_to_msecs("10m") == 600000
+    assert timeframe_to_msecs("1h") == 3600000
+    assert timeframe_to_msecs("1d") == 86400000
+
+
+@pytest.mark.parametrize(
+    "timeframe,expected",
+    [
+        ("1s", "1s"),
+        ("15s", "15s"),
+        ("5m", "300s"),
+        ("10m", "600s"),
+        ("1h", "3600s"),
+        ("1d", "86400s"),
+        ("1w", "1W-MON"),
+        ("1M", "1MS"),
+        ("1y", "1YS"),
+    ],
+)
+def test_timeframe_to_resample_freq(timeframe, expected):
+    assert timeframe_to_resample_freq(timeframe) == expected
+
+
+def test_timeframe_to_prev_date():
+    # 2019-08-12 13:22:08
+    date = datetime.fromtimestamp(1565616128, tz=timezone.utc)
+
+    tf_list = [
+        # 5m -> 2019-08-12 13:20:00
+        ("5m", datetime(2019, 8, 12, 13, 20, 0, tzinfo=timezone.utc)),
+        # 10m -> 2019-08-12 13:20:00
+        ("10m", datetime(2019, 8, 12, 13, 20, 0, tzinfo=timezone.utc)),
+        # 1h -> 2019-08-12 13:00:00
+        ("1h", datetime(2019, 8, 12, 13, 00, 0, tzinfo=timezone.utc)),
+        # 2h -> 2019-08-12 12:00:00
+        ("2h", datetime(2019, 8, 12, 12, 00, 0, tzinfo=timezone.utc)),
+        # 4h -> 2019-08-12 12:00:00
+        ("4h", datetime(2019, 8, 12, 12, 00, 0, tzinfo=timezone.utc)),
+        # 1d -> 2019-08-12 00:00:00
+        ("1d", datetime(2019, 8, 12, 00, 00, 0, tzinfo=timezone.utc)),
+    ]
+    for interval, result in tf_list:
+        assert timeframe_to_prev_date(interval, date) == result
+
+    date = datetime.now(tz=timezone.utc)
+    assert timeframe_to_prev_date("5m") < date
+    # Does not round
+    time = datetime(2019, 8, 12, 13, 20, 0, tzinfo=timezone.utc)
+    assert timeframe_to_prev_date("5m", time) == time
+    time = datetime(2019, 8, 12, 13, 0, 0, tzinfo=timezone.utc)
+    assert timeframe_to_prev_date("1h", time) == time
+
+
+def test_timeframe_to_next_date():
+    # 2019-08-12 13:22:08
+    date = datetime.fromtimestamp(1565616128, tz=timezone.utc)
+    tf_list = [
+        # 5m -> 2019-08-12 13:25:00
+        ("5m", datetime(2019, 8, 12, 13, 25, 0, tzinfo=timezone.utc)),
+        # 10m -> 2019-08-12 13:30:00
+        ("10m", datetime(2019, 8, 12, 13, 30, 0, tzinfo=timezone.utc)),
+        # 1h -> 2019-08-12 14:00:00
+        ("1h", datetime(2019, 8, 12, 14, 00, 0, tzinfo=timezone.utc)),
+        # 2h -> 2019-08-12 14:00:00
+        ("2h", datetime(2019, 8, 12, 14, 00, 0, tzinfo=timezone.utc)),
+        # 4h -> 2019-08-12 14:00:00
+        ("4h", datetime(2019, 8, 12, 16, 00, 0, tzinfo=timezone.utc)),
+        # 1d -> 2019-08-13 00:00:00
+        ("1d", datetime(2019, 8, 13, 0, 0, 0, tzinfo=timezone.utc)),
+    ]
+
+    for interval, result in tf_list:
+        assert timeframe_to_next_date(interval, date) == result
+
+    date = datetime.now(tz=timezone.utc)
+    assert timeframe_to_next_date("5m") > date
+
+    date = datetime(2019, 8, 12, 13, 30, 0, tzinfo=timezone.utc)
+    assert timeframe_to_next_date("5m", date) == date + timedelta(minutes=5)
+
+
+@pytest.mark.parametrize(
+    "amount,precision_mode,precision,expected",
+    [
+        (2.34559, DECIMAL_PLACES, 4, 2.3455),
+        (2.34559, DECIMAL_PLACES, 5, 2.34559),
+        (2.34559, DECIMAL_PLACES, 3, 2.345),
+        (2.9999, DECIMAL_PLACES, 3, 2.999),
+        (2.9909, DECIMAL_PLACES, 3, 2.990),
+        (2.9909, DECIMAL_PLACES, 0, 2),
+        (29991.5555, DECIMAL_PLACES, 0, 29991),
+        (29991.5555, DECIMAL_PLACES, -1, 29990),
+        (29991.5555, DECIMAL_PLACES, -2, 29900),
+        # Tests for
+        (2.34559, SIGNIFICANT_DIGITS, 4, 2.345),
+        (2.34559, SIGNIFICANT_DIGITS, 5, 2.3455),
+        (2.34559, SIGNIFICANT_DIGITS, 3, 2.34),
+        (2.9999, SIGNIFICANT_DIGITS, 3, 2.99),
+        (2.9909, SIGNIFICANT_DIGITS, 3, 2.99),
+        (0.0000077723, SIGNIFICANT_DIGITS, 5, 0.0000077723),
+        (0.0000077723, SIGNIFICANT_DIGITS, 3, 0.00000777),
+        (0.0000077723, SIGNIFICANT_DIGITS, 1, 0.000007),
+        # Tests for Tick-size
+        (2.34559, TICK_SIZE, 0.0001, 2.3455),
+        (2.34559, TICK_SIZE, 0.00001, 2.34559),
+        (2.34559, TICK_SIZE, 0.001, 2.345),
+        (2.9999, TICK_SIZE, 0.001, 2.999),
+        (2.9909, TICK_SIZE, 0.001, 2.990),
+        (2.9909, TICK_SIZE, 0.005, 2.99),
+        (2.9999, TICK_SIZE, 0.005, 2.995),
+    ],
+)
+def test_amount_to_precision(
+    amount,
+    precision_mode,
+    precision,
+    expected,
+):
+    """
+    Test rounds down
+    """
+    # digits counting mode
+    # DECIMAL_PLACES = 2
+    # SIGNIFICANT_DIGITS = 3
+    # TICK_SIZE = 4
+
+    assert amount_to_precision(amount, precision, precision_mode) == expected
+
+
+@pytest.mark.parametrize(
+    "price,precision_mode,precision,expected,rounding_mode",
+    [
+        # Tests for DECIMAL_PLACES, ROUND_UP
+        (2.34559, DECIMAL_PLACES, 4, 2.3456, ROUND_UP),
+        (2.34559, DECIMAL_PLACES, 5, 2.34559, ROUND_UP),
+        (2.34559, DECIMAL_PLACES, 3, 2.346, ROUND_UP),
+        (2.9999, DECIMAL_PLACES, 3, 3.000, ROUND_UP),
+        (2.9909, DECIMAL_PLACES, 3, 2.991, ROUND_UP),
+        (2.9901, DECIMAL_PLACES, 3, 2.991, ROUND_UP),
+        (2.34559, DECIMAL_PLACES, 5, 2.34559, ROUND_DOWN),
+        (2.34559, DECIMAL_PLACES, 4, 2.3455, ROUND_DOWN),
+        (2.9901, DECIMAL_PLACES, 3, 2.990, ROUND_DOWN),
+        (0.00299, DECIMAL_PLACES, 3, 0.002, ROUND_DOWN),
+        # Tests for DECIMAL_PLACES, ROUND
+        (2.345600000000001, DECIMAL_PLACES, 4, 2.3456, ROUND),
+        (2.345551, DECIMAL_PLACES, 4, 2.3456, ROUND),
+        (2.49, DECIMAL_PLACES, 0, 2.0, ROUND),
+        (2.51, DECIMAL_PLACES, 0, 3.0, ROUND),
+        (5.1, DECIMAL_PLACES, -1, 10.0, ROUND),
+        (4.9, DECIMAL_PLACES, -1, 0.0, ROUND),
+        (0.000007222, SIGNIFICANT_DIGITS, 1, 0.000007, ROUND),
+        (0.000007222, SIGNIFICANT_DIGITS, 2, 0.0000072, ROUND),
+        (0.000007777, SIGNIFICANT_DIGITS, 2, 0.0000078, ROUND),
+        # Tests for TICK_SIZE, ROUND_UP
+        (2.34559, TICK_SIZE, 0.0001, 2.3456, ROUND_UP),
+        (2.34559, TICK_SIZE, 0.00001, 2.34559, ROUND_UP),
+        (2.34559, TICK_SIZE, 0.001, 2.346, ROUND_UP),
+        (2.9999, TICK_SIZE, 0.001, 3.000, ROUND_UP),
+        (2.9909, TICK_SIZE, 0.001, 2.991, ROUND_UP),
+        (2.9909, TICK_SIZE, 0.001, 2.990, ROUND_DOWN),
+        (2.9909, TICK_SIZE, 0.005, 2.995, ROUND_UP),
+        (2.9973, TICK_SIZE, 0.005, 3.0, ROUND_UP),
+        (2.9977, TICK_SIZE, 0.005, 3.0, ROUND_UP),
+        (234.43, TICK_SIZE, 0.5, 234.5, ROUND_UP),
+        (234.43, TICK_SIZE, 0.5, 234.0, ROUND_DOWN),
+        (234.53, TICK_SIZE, 0.5, 235.0, ROUND_UP),
+        (234.53, TICK_SIZE, 0.5, 234.5, ROUND_DOWN),
+        (0.891534, TICK_SIZE, 0.0001, 0.8916, ROUND_UP),
+        (64968.89, TICK_SIZE, 0.01, 64968.89, ROUND_UP),
+        (0.000000003483, TICK_SIZE, 1e-12, 0.000000003483, ROUND_UP),
+        # Tests for TICK_SIZE, ROUND
+        (2.49, TICK_SIZE, 1.0, 2.0, ROUND),
+        (2.51, TICK_SIZE, 1.0, 3.0, ROUND),
+        (2.000000051, TICK_SIZE, 0.0000001, 2.0000001, ROUND),
+        (2.000000049, TICK_SIZE, 0.0000001, 2.0, ROUND),
+        (2.9909, TICK_SIZE, 0.005, 2.990, ROUND),
+        (2.9973, TICK_SIZE, 0.005, 2.995, ROUND),
+        (2.9977, TICK_SIZE, 0.005, 3.0, ROUND),
+        (234.24, TICK_SIZE, 0.5, 234.0, ROUND),
+        (234.26, TICK_SIZE, 0.5, 234.5, ROUND),
+        (nan, TICK_SIZE, 3, nan, ROUND),
+        # Tests for TRUNCATTE
+        (2.34559, DECIMAL_PLACES, 4, 2.3455, TRUNCATE),
+        (2.34559, DECIMAL_PLACES, 5, 2.34559, TRUNCATE),
+        (2.34559, DECIMAL_PLACES, 3, 2.345, TRUNCATE),
+        (2.9999, DECIMAL_PLACES, 3, 2.999, TRUNCATE),
+        (2.9909, DECIMAL_PLACES, 3, 2.990, TRUNCATE),
+        (2.9909, TICK_SIZE, 0.001, 2.990, TRUNCATE),
+        (2.9909, TICK_SIZE, 0.01, 2.99, TRUNCATE),
+        (2.9909, TICK_SIZE, 0.1, 2.9, TRUNCATE),
+        # Tests for Significant
+        (2.34559, SIGNIFICANT_DIGITS, 4, 2.345, TRUNCATE),
+        (2.34559, SIGNIFICANT_DIGITS, 5, 2.3455, TRUNCATE),
+        (2.34559, SIGNIFICANT_DIGITS, 3, 2.34, TRUNCATE),
+        (2.9999, SIGNIFICANT_DIGITS, 3, 2.99, TRUNCATE),
+        (2.9909, SIGNIFICANT_DIGITS, 2, 2.9, TRUNCATE),
+        (0.00000777, SIGNIFICANT_DIGITS, 2, 0.0000077, TRUNCATE),
+        (0.00000729, SIGNIFICANT_DIGITS, 2, 0.0000072, TRUNCATE),
+        # ROUND
+        (722.2, SIGNIFICANT_DIGITS, 1, 700.0, ROUND),
+        (790.2, SIGNIFICANT_DIGITS, 1, 800.0, ROUND),
+        (722.2, SIGNIFICANT_DIGITS, 2, 720.0, ROUND),
+        (722.2, SIGNIFICANT_DIGITS, 1, 800.0, ROUND_UP),
+        (722.2, SIGNIFICANT_DIGITS, 2, 730.0, ROUND_UP),
+        (777.7, SIGNIFICANT_DIGITS, 2, 780.0, ROUND_UP),
+        (777.7, SIGNIFICANT_DIGITS, 3, 778.0, ROUND_UP),
+        (722.2, SIGNIFICANT_DIGITS, 1, 700.0, ROUND_DOWN),
+        (722.2, SIGNIFICANT_DIGITS, 2, 720.0, ROUND_DOWN),
+        (777.7, SIGNIFICANT_DIGITS, 2, 770.0, ROUND_DOWN),
+        (777.7, SIGNIFICANT_DIGITS, 3, 777.0, ROUND_DOWN),
+        (0.000007222, SIGNIFICANT_DIGITS, 1, 0.000008, ROUND_UP),
+        (0.000007222, SIGNIFICANT_DIGITS, 2, 0.0000073, ROUND_UP),
+        (0.000007777, SIGNIFICANT_DIGITS, 2, 0.0000078, ROUND_UP),
+        (0.000007222, SIGNIFICANT_DIGITS, 1, 0.000007, ROUND_DOWN),
+        (0.000007222, SIGNIFICANT_DIGITS, 2, 0.0000072, ROUND_DOWN),
+        (0.000007777, SIGNIFICANT_DIGITS, 2, 0.0000077, ROUND_DOWN),
+    ],
+)
+def test_price_to_precision(price, precision_mode, precision, expected, rounding_mode):
+    result = price_to_precision(price, precision, precision_mode, rounding_mode=rounding_mode)
+    if not isnan(expected):
+        assert result == expected
+    else:
+        assert isnan(result)
+
+
+@pytest.mark.parametrize(
+    "amount,precision,precision_mode,contract_size,expected",
+    [
+        (1.17, 1.0, 4, 0.01, 1.17),  # Tick size
+        (1.17, 1.0, 2, 0.01, 1.17),  #
+        (1.16, 1.0, 4, 0.01, 1.16),  #
+        (1.16, 1.0, 2, 0.01, 1.16),  #
+        (1.13, 1.0, 2, 0.01, 1.13),  #
+        (10.988, 1.0, 2, 10, 10),
+        (10.988, 1.0, 4, 10, 10),
+    ],
+)
+def test_amount_to_contract_precision_standalone(
+    amount, precision, precision_mode, contract_size, expected
+):
+    res = amount_to_contract_precision(amount, precision, precision_mode, contract_size)
+    assert pytest.approx(res) == expected
diff --git a/tests/exchange/test_exchange_ws.py b/tests/exchange/test_exchange_ws.py
new file mode 100644
index 000000000..6dee7f339
--- /dev/null
+++ b/tests/exchange/test_exchange_ws.py
@@ -0,0 +1,199 @@
+import asyncio
+import logging
+import threading
+from datetime import timedelta
+from time import sleep
+from unittest.mock import AsyncMock, MagicMock
+
+from ccxt import NotSupported
+
+from freqtrade.enums import CandleType
+from freqtrade.exchange.exchange_ws import ExchangeWS
+from ft_client.test_client.test_rest_client import log_has_re
+
+
+def test_exchangews_init(mocker):
+    config = MagicMock()
+    ccxt_object = MagicMock()
+    mocker.patch("freqtrade.exchange.exchange_ws.ExchangeWS._start_forever", MagicMock())
+
+    exchange_ws = ExchangeWS(config, ccxt_object)
+    sleep(0.1)
+
+    assert exchange_ws.config == config
+    assert exchange_ws._ccxt_object == ccxt_object
+    assert exchange_ws._thread.name == "ccxt_ws"
+    assert exchange_ws._background_tasks == set()
+    assert exchange_ws._klines_watching == set()
+    assert exchange_ws._klines_scheduled == set()
+    assert exchange_ws.klines_last_refresh == {}
+    assert exchange_ws.klines_last_request == {}
+    # Cleanup
+    exchange_ws.cleanup()
+
+
+def test_exchangews_cleanup_error(mocker, caplog):
+    config = MagicMock()
+    ccxt_object = MagicMock()
+    ccxt_object.close = AsyncMock(side_effect=Exception("Test"))
+    mocker.patch("freqtrade.exchange.exchange_ws.ExchangeWS._start_forever", MagicMock())
+
+    exchange_ws = ExchangeWS(config, ccxt_object)
+    patch_eventloop_threading(exchange_ws)
+
+    sleep(0.1)
+    exchange_ws.reset_connections()
+
+    assert log_has_re("Exception in _cleanup_async", caplog)
+
+    exchange_ws.cleanup()
+
+
+def patch_eventloop_threading(exchange):
+    is_init = False
+
+    def thread_fuck():
+        nonlocal is_init
+        exchange._loop = asyncio.new_event_loop()
+        is_init = True
+        exchange._loop.run_forever()
+
+    x = threading.Thread(target=thread_fuck, daemon=True)
+    x.start()
+    while not is_init:
+        pass
+
+
+async def test_exchangews_ohlcv(mocker, time_machine, caplog):
+    config = MagicMock()
+    ccxt_object = MagicMock()
+    caplog.set_level(logging.DEBUG)
+
+    async def sleeper(*args, **kwargs):
+        # pass
+        await asyncio.sleep(0.12)
+        return MagicMock()
+
+    ccxt_object.un_watch_ohlcv_for_symbols = AsyncMock(side_effect=NotSupported)
+
+    ccxt_object.watch_ohlcv = AsyncMock(side_effect=sleeper)
+    ccxt_object.close = AsyncMock()
+    time_machine.move_to("2024-11-01 01:00:02 +00:00")
+
+    mocker.patch("freqtrade.exchange.exchange_ws.ExchangeWS._start_forever", MagicMock())
+
+    exchange_ws = ExchangeWS(config, ccxt_object)
+    patch_eventloop_threading(exchange_ws)
+    try:
+        assert exchange_ws._klines_watching == set()
+        assert exchange_ws._klines_scheduled == set()
+
+        exchange_ws.schedule_ohlcv("ETH/BTC", "1m", CandleType.SPOT)
+        exchange_ws.schedule_ohlcv("XRP/BTC", "1m", CandleType.SPOT)
+        await asyncio.sleep(0.2)
+
+        assert exchange_ws._klines_watching == {
+            ("ETH/BTC", "1m", CandleType.SPOT),
+            ("XRP/BTC", "1m", CandleType.SPOT),
+        }
+        assert exchange_ws._klines_scheduled == {
+            ("ETH/BTC", "1m", CandleType.SPOT),
+            ("XRP/BTC", "1m", CandleType.SPOT),
+        }
+        await asyncio.sleep(0.1)
+        assert ccxt_object.watch_ohlcv.call_count == 6
+        ccxt_object.watch_ohlcv.reset_mock()
+
+        time_machine.shift(timedelta(minutes=5))
+        exchange_ws.schedule_ohlcv("ETH/BTC", "1m", CandleType.SPOT)
+        await asyncio.sleep(1)
+        assert log_has_re("un_watch_ohlcv_for_symbols not supported: ", caplog)
+        # XRP/BTC should be cleaned up.
+        assert exchange_ws._klines_watching == {
+            ("ETH/BTC", "1m", CandleType.SPOT),
+        }
+
+        # Cleanup happened.
+        ccxt_object.un_watch_ohlcv_for_symbols = AsyncMock(side_effect=ValueError)
+        exchange_ws.schedule_ohlcv("ETH/BTC", "1m", CandleType.SPOT)
+        assert exchange_ws._klines_watching == {
+            ("ETH/BTC", "1m", CandleType.SPOT),
+        }
+        assert exchange_ws._klines_scheduled == {
+            ("ETH/BTC", "1m", CandleType.SPOT),
+        }
+
+    finally:
+        # Cleanup
+        exchange_ws.cleanup()
+    assert log_has_re("Exception in _unwatch_ohlcv", caplog)
+
+
+async def test_exchangews_get_ohlcv(mocker, caplog):
+    config = MagicMock()
+    ccxt_object = MagicMock()
+    ccxt_object.ohlcvs = {
+        "ETH/USDT": {
+            "1m": [
+                [1635840000000, 100, 200, 300, 400, 500],
+                [1635840060000, 101, 201, 301, 401, 501],
+                [1635840120000, 102, 202, 302, 402, 502],
+            ],
+            "5m": [
+                [1635840000000, 100, 200, 300, 400, 500],
+                [1635840300000, 105, 201, 301, 401, 501],
+                [1635840600000, 102, 202, 302, 402, 502],
+            ],
+        }
+    }
+    mocker.patch("freqtrade.exchange.exchange_ws.ExchangeWS._start_forever", MagicMock())
+
+    exchange_ws = ExchangeWS(config, ccxt_object)
+    exchange_ws.klines_last_refresh = {
+        ("ETH/USDT", "1m", CandleType.SPOT): 1635840120000,
+        ("ETH/USDT", "5m", CandleType.SPOT): 1635840600000,
+    }
+
+    # Matching last candle time - drop hint is true
+    resp = await exchange_ws.get_ohlcv("ETH/USDT", "1m", CandleType.SPOT, 1635840120000)
+    assert resp[0] == "ETH/USDT"
+    assert resp[1] == "1m"
+    assert resp[3] == [
+        [1635840000000, 100, 200, 300, 400, 500],
+        [1635840060000, 101, 201, 301, 401, 501],
+        [1635840120000, 102, 202, 302, 402, 502],
+    ]
+    assert resp[4] is True
+
+    # expected time > last candle time - drop hint is false
+    resp = await exchange_ws.get_ohlcv("ETH/USDT", "1m", CandleType.SPOT, 1635840180000)
+    assert resp[0] == "ETH/USDT"
+    assert resp[1] == "1m"
+    assert resp[3] == [
+        [1635840000000, 100, 200, 300, 400, 500],
+        [1635840060000, 101, 201, 301, 401, 501],
+        [1635840120000, 102, 202, 302, 402, 502],
+    ]
+    assert resp[4] is False
+
+    # Change "received" times to be before the candle starts.
+    # This should trigger the "time sync" warning.
+    exchange_ws.klines_last_refresh = {
+        ("ETH/USDT", "1m", CandleType.SPOT): 1635840110000,
+        ("ETH/USDT", "5m", CandleType.SPOT): 1635840600000,
+    }
+    msg = r".*Candle date > last refresh.*"
+    assert not log_has_re(msg, caplog)
+    resp = await exchange_ws.get_ohlcv("ETH/USDT", "1m", CandleType.SPOT, 1635840120000)
+    assert resp[0] == "ETH/USDT"
+    assert resp[1] == "1m"
+    assert resp[3] == [
+        [1635840000000, 100, 200, 300, 400, 500],
+        [1635840060000, 101, 201, 301, 401, 501],
+        [1635840120000, 102, 202, 302, 402, 502],
+    ]
+    assert resp[4] is True
+
+    assert log_has_re(msg, caplog)
+
+    exchange_ws.cleanup()
diff --git a/tests/exchange/test_gate.py b/tests/exchange/test_gate.py
new file mode 100644
index 000000000..3f5ecacf6
--- /dev/null
+++ b/tests/exchange/test_gate.py
@@ -0,0 +1,121 @@
+from datetime import datetime, timezone
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.enums import MarginMode, TradingMode
+from tests.conftest import EXMS, get_patched_exchange
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_fetch_stoploss_order_gate(default_conf, mocker):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="gate")
+
+    fetch_order_mock = MagicMock()
+    exchange.fetch_order = fetch_order_mock
+
+    exchange.fetch_stoploss_order("1234", "ETH/BTC")
+    assert fetch_order_mock.call_count == 1
+    assert fetch_order_mock.call_args_list[0][1]["order_id"] == "1234"
+    assert fetch_order_mock.call_args_list[0][1]["pair"] == "ETH/BTC"
+    assert fetch_order_mock.call_args_list[0][1]["params"] == {"stop": True}
+
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+
+    exchange = get_patched_exchange(mocker, default_conf, exchange="gate")
+
+    exchange.fetch_order = MagicMock(
+        return_value={
+            "status": "closed",
+            "id": "1234",
+            "stopPrice": 5.62,
+            "info": {"trade_id": "222555"},
+        }
+    )
+
+    exchange.fetch_stoploss_order("1234", "ETH/BTC")
+    assert exchange.fetch_order.call_count == 2
+    assert exchange.fetch_order.call_args_list[0][1]["order_id"] == "1234"
+    assert exchange.fetch_order.call_args_list[1][1]["order_id"] == "222555"
+
+
+def test_cancel_stoploss_order_gate(default_conf, mocker):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="gate")
+
+    cancel_order_mock = MagicMock()
+    exchange.cancel_order = cancel_order_mock
+
+    exchange.cancel_stoploss_order("1234", "ETH/BTC")
+    assert cancel_order_mock.call_count == 1
+    assert cancel_order_mock.call_args_list[0][1]["order_id"] == "1234"
+    assert cancel_order_mock.call_args_list[0][1]["pair"] == "ETH/BTC"
+    assert cancel_order_mock.call_args_list[0][1]["params"] == {"stop": True}
+
+
+@pytest.mark.parametrize(
+    "sl1,sl2,sl3,side", [(1501, 1499, 1501, "sell"), (1499, 1501, 1499, "buy")]
+)
+def test_stoploss_adjust_gate(mocker, default_conf, sl1, sl2, sl3, side):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="gate")
+    order = {
+        "price": 1500,
+        "stopPrice": 1500,
+    }
+    assert exchange.stoploss_adjust(sl1, order, side)
+    assert not exchange.stoploss_adjust(sl2, order, side)
+
+
+@pytest.mark.parametrize(
+    "takerormaker,rate,cost",
+    [
+        ("taker", 0.0005, 0.0001554325),
+        ("maker", 0.0, 0.0),
+    ],
+)
+def test_fetch_my_trades_gate(mocker, default_conf, takerormaker, rate, cost):
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    tick = {
+        "ETH/USDT:USDT": {
+            "info": {
+                "user_id": "",
+                "taker_fee": "0.0018",
+                "maker_fee": "0.0018",
+                "gt_discount": False,
+                "gt_taker_fee": "0",
+                "gt_maker_fee": "0",
+                "loan_fee": "0.18",
+                "point_type": "1",
+                "futures_taker_fee": "0.0005",
+                "futures_maker_fee": "0",
+            },
+            "symbol": "ETH/USDT:USDT",
+            "maker": 0.0,
+            "taker": 0.0005,
+        }
+    }
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+
+    api_mock = MagicMock()
+    api_mock.fetch_my_trades = MagicMock(
+        return_value=[
+            {
+                "fee": {"cost": None},
+                "price": 3108.65,
+                "cost": 0.310865,
+                "order": "22255",
+                "takerOrMaker": takerormaker,
+                "amount": 1,  # 1 contract
+            }
+        ]
+    )
+    exchange = get_patched_exchange(mocker, default_conf, api_mock=api_mock, exchange="gate")
+    exchange._trading_fees = tick
+    trades = exchange.get_trades_for_order("22255", "ETH/USDT:USDT", datetime.now(timezone.utc))
+    trade = trades[0]
+    assert trade["fee"]
+    assert trade["fee"]["rate"] == rate
+    assert trade["fee"]["currency"] == "USDT"
+    assert trade["fee"]["cost"] == cost
diff --git a/tests/exchange/test_htx.py b/tests/exchange/test_htx.py
new file mode 100644
index 000000000..e32d7e85e
--- /dev/null
+++ b/tests/exchange/test_htx.py
@@ -0,0 +1,140 @@
+from random import randint
+from unittest.mock import MagicMock
+
+import ccxt
+import pytest
+
+from freqtrade.exceptions import DependencyException, InvalidOrderException
+from tests.conftest import EXMS, get_patched_exchange
+from tests.exchange.test_exchange import ccxt_exceptionhandlers
+
+
+@pytest.mark.parametrize(
+    "limitratio,expected,side",
+    [
+        (None, 220 * 0.99, "sell"),
+        (0.99, 220 * 0.99, "sell"),
+        (0.98, 220 * 0.98, "sell"),
+    ],
+)
+def test_create_stoploss_order_htx(default_conf, mocker, limitratio, expected, side):
+    api_mock = MagicMock()
+    order_id = f"test_prod_buy_{randint(0, 10**6)}"
+    order_type = "stop-limit"
+
+    api_mock.create_order = MagicMock(return_value={"id": order_id, "info": {"foo": "bar"}})
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, "htx")
+
+    with pytest.raises(InvalidOrderException):
+        order = exchange.create_stoploss(
+            pair="ETH/BTC",
+            amount=1,
+            stop_price=190,
+            order_types={"stoploss_on_exchange_limit_ratio": 1.05},
+            side=side,
+            leverage=1.0,
+        )
+
+    api_mock.create_order.reset_mock()
+    order_types = {} if limitratio is None else {"stoploss_on_exchange_limit_ratio": limitratio}
+    order = exchange.create_stoploss(
+        pair="ETH/BTC", amount=1, stop_price=220, order_types=order_types, side=side, leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args_list[0][1]["symbol"] == "ETH/BTC"
+    assert api_mock.create_order.call_args_list[0][1]["type"] == order_type
+    assert api_mock.create_order.call_args_list[0][1]["side"] == "sell"
+    assert api_mock.create_order.call_args_list[0][1]["amount"] == 1
+    # Price should be 1% below stopprice
+    assert api_mock.create_order.call_args_list[0][1]["price"] == expected
+    assert api_mock.create_order.call_args_list[0][1]["params"] == {
+        "stopPrice": 220,
+        "operator": "lte",
+    }
+
+    # test exception handling
+    with pytest.raises(DependencyException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds("0 balance"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, "htx")
+        exchange.create_stoploss(
+            pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side=side, leverage=1.0
+        )
+
+    with pytest.raises(InvalidOrderException):
+        api_mock.create_order = MagicMock(
+            side_effect=ccxt.InvalidOrder("binance Order would trigger immediately.")
+        )
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, "binance")
+        exchange.create_stoploss(
+            pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side=side, leverage=1.0
+        )
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        "htx",
+        "create_stoploss",
+        "create_order",
+        retries=1,
+        pair="ETH/BTC",
+        amount=1,
+        stop_price=220,
+        order_types={},
+        side=side,
+        leverage=1.0,
+    )
+
+
+def test_create_stoploss_order_dry_run_htx(default_conf, mocker):
+    api_mock = MagicMock()
+    order_type = "stop-limit"
+    default_conf["dry_run"] = True
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, "htx")
+
+    with pytest.raises(InvalidOrderException):
+        order = exchange.create_stoploss(
+            pair="ETH/BTC",
+            amount=1,
+            stop_price=190,
+            order_types={"stoploss_on_exchange_limit_ratio": 1.05},
+            side="sell",
+            leverage=1.0,
+        )
+
+    api_mock.create_order.reset_mock()
+
+    order = exchange.create_stoploss(
+        pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side="sell", leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert "type" in order
+
+    assert order["type"] == order_type
+    assert order["price"] == 220
+    assert order["amount"] == 1
+
+
+def test_stoploss_adjust_htx(mocker, default_conf):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="htx")
+    order = {
+        "type": "stop",
+        "price": 1500,
+        "stopPrice": "1500",
+    }
+    assert exchange.stoploss_adjust(1501, order, "sell")
+    assert not exchange.stoploss_adjust(1499, order, "sell")
+    # Test with invalid order case
+    assert exchange.stoploss_adjust(1501, order, "sell")
diff --git a/tests/exchange/test_hyperliquid.py b/tests/exchange/test_hyperliquid.py
new file mode 100644
index 000000000..808bef802
--- /dev/null
+++ b/tests/exchange/test_hyperliquid.py
@@ -0,0 +1,417 @@
+from datetime import datetime, timezone
+from unittest.mock import MagicMock, PropertyMock
+
+import pytest
+
+from tests.conftest import EXMS, get_mock_coro, get_patched_exchange
+
+
+def test_hyperliquid_dry_run_liquidation_price(default_conf, mocker):
+    # test if liq price calculated by dry_run_liquidation_price() is close to ccxt liq price
+    # testing different pairs with large/small prices, different leverages, long, short
+    markets = {
+        "BTC/USDC:USDC": {"limits": {"leverage": {"max": 50}}},
+        "ETH/USDC:USDC": {"limits": {"leverage": {"max": 50}}},
+        "SOL/USDC:USDC": {"limits": {"leverage": {"max": 20}}},
+        "DOGE/USDC:USDC": {"limits": {"leverage": {"max": 20}}},
+    }
+    positions = [
+        {
+            "symbol": "ETH/USDC:USDC",
+            "entryPrice": 2458.5,
+            "side": "long",
+            "contracts": 0.015,
+            "collateral": 36.864593,
+            "leverage": 1.0,
+            "liquidationPrice": 0.86915825,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 63287.0,
+            "side": "long",
+            "contracts": 0.00039,
+            "collateral": 24.673292,
+            "leverage": 1.0,
+            "liquidationPrice": 22.37166537,
+        },
+        {
+            "symbol": "SOL/USDC:USDC",
+            "entryPrice": 146.82,
+            "side": "long",
+            "contracts": 0.16,
+            "collateral": 23.482979,
+            "leverage": 1.0,
+            "liquidationPrice": 0.05269872,
+        },
+        {
+            "symbol": "SOL/USDC:USDC",
+            "entryPrice": 145.83,
+            "side": "long",
+            "contracts": 0.33,
+            "collateral": 24.045107,
+            "leverage": 2.0,
+            "liquidationPrice": 74.83696193,
+        },
+        {
+            "symbol": "ETH/USDC:USDC",
+            "entryPrice": 2459.5,
+            "side": "long",
+            "contracts": 0.0199,
+            "collateral": 24.454895,
+            "leverage": 2.0,
+            "liquidationPrice": 1243.0411908,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 62739.0,
+            "side": "long",
+            "contracts": 0.00077,
+            "collateral": 24.137992,
+            "leverage": 2.0,
+            "liquidationPrice": 31708.03843631,
+        },
+        {
+            "symbol": "DOGE/USDC:USDC",
+            "entryPrice": 0.11586,
+            "side": "long",
+            "contracts": 437.0,
+            "collateral": 25.29769,
+            "leverage": 2.0,
+            "liquidationPrice": 0.05945697,
+        },
+        {
+            "symbol": "ETH/USDC:USDC",
+            "entryPrice": 2642.8,
+            "side": "short",
+            "contracts": 0.019,
+            "collateral": 25.091876,
+            "leverage": 2.0,
+            "liquidationPrice": 3924.18322043,
+        },
+        {
+            "symbol": "SOL/USDC:USDC",
+            "entryPrice": 155.89,
+            "side": "short",
+            "contracts": 0.32,
+            "collateral": 24.924941,
+            "leverage": 2.0,
+            "liquidationPrice": 228.07847866,
+        },
+        {
+            "symbol": "DOGE/USDC:USDC",
+            "entryPrice": 0.14333,
+            "side": "short",
+            "contracts": 351.0,
+            "collateral": 25.136807,
+            "leverage": 2.0,
+            "liquidationPrice": 0.20970228,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 68595.0,
+            "side": "short",
+            "contracts": 0.00069,
+            "collateral": 23.64871,
+            "leverage": 2.0,
+            "liquidationPrice": 101849.99354283,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 65536.0,
+            "side": "short",
+            "contracts": 0.00099,
+            "collateral": 21.604172,
+            "leverage": 3.0,
+            "liquidationPrice": 86493.46174617,
+        },
+        {
+            "symbol": "SOL/USDC:USDC",
+            "entryPrice": 173.06,
+            "side": "long",
+            "contracts": 0.6,
+            "collateral": 20.735658,
+            "leverage": 5.0,
+            "liquidationPrice": 142.05186667,
+        },
+        {
+            "symbol": "ETH/USDC:USDC",
+            "entryPrice": 2545.5,
+            "side": "long",
+            "contracts": 0.0329,
+            "collateral": 20.909894,
+            "leverage": 4.0,
+            "liquidationPrice": 1929.23322895,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 67400.0,
+            "side": "short",
+            "contracts": 0.00031,
+            "collateral": 20.887308,
+            "leverage": 1.0,
+            "liquidationPrice": 133443.97317151,
+        },
+        {
+            "symbol": "ETH/USDC:USDC",
+            "entryPrice": 2552.0,
+            "side": "short",
+            "contracts": 0.0327,
+            "collateral": 20.833393,
+            "leverage": 4.0,
+            "liquidationPrice": 3157.53150453,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 66930.0,
+            "side": "long",
+            "contracts": 0.0015,
+            "collateral": 20.043862,
+            "leverage": 5.0,
+            "liquidationPrice": 54108.51043771,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 67033.0,
+            "side": "long",
+            "contracts": 0.00121,
+            "collateral": 20.251817,
+            "leverage": 4.0,
+            "liquidationPrice": 50804.00091827,
+        },
+        {
+            "symbol": "ETH/USDC:USDC",
+            "entryPrice": 2521.9,
+            "side": "long",
+            "contracts": 0.0237,
+            "collateral": 19.902091,
+            "leverage": 3.0,
+            "liquidationPrice": 1699.14071943,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 68139.0,
+            "side": "short",
+            "contracts": 0.00145,
+            "collateral": 19.72573,
+            "leverage": 5.0,
+            "liquidationPrice": 80933.61590987,
+        },
+        {
+            "symbol": "SOL/USDC:USDC",
+            "entryPrice": 178.29,
+            "side": "short",
+            "contracts": 0.11,
+            "collateral": 19.605036,
+            "leverage": 1.0,
+            "liquidationPrice": 347.82205322,
+        },
+        {
+            "symbol": "SOL/USDC:USDC",
+            "entryPrice": 176.23,
+            "side": "long",
+            "contracts": 0.33,
+            "collateral": 19.364946,
+            "leverage": 3.0,
+            "liquidationPrice": 120.56240404,
+        },
+        {
+            "symbol": "SOL/USDC:USDC",
+            "entryPrice": 173.08,
+            "side": "short",
+            "contracts": 0.33,
+            "collateral": 19.01881,
+            "leverage": 3.0,
+            "liquidationPrice": 225.08561715,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 68240.0,
+            "side": "short",
+            "contracts": 0.00105,
+            "collateral": 17.887922,
+            "leverage": 4.0,
+            "liquidationPrice": 84431.79820839,
+        },
+        {
+            "symbol": "ETH/USDC:USDC",
+            "entryPrice": 2518.4,
+            "side": "short",
+            "contracts": 0.007,
+            "collateral": 17.62263,
+            "leverage": 1.0,
+            "liquidationPrice": 4986.05799151,
+        },
+        {
+            "symbol": "ETH/USDC:USDC",
+            "entryPrice": 2533.2,
+            "side": "long",
+            "contracts": 0.0347,
+            "collateral": 17.555195,
+            "leverage": 5.0,
+            "liquidationPrice": 2047.7642302,
+        },
+        {
+            "symbol": "DOGE/USDC:USDC",
+            "entryPrice": 0.13284,
+            "side": "long",
+            "contracts": 360.0,
+            "collateral": 15.943218,
+            "leverage": 3.0,
+            "liquidationPrice": 0.09082388,
+        },
+        {
+            "symbol": "SOL/USDC:USDC",
+            "entryPrice": 163.11,
+            "side": "short",
+            "contracts": 0.48,
+            "collateral": 15.650731,
+            "leverage": 5.0,
+            "liquidationPrice": 190.94213618,
+        },
+        {
+            "symbol": "BTC/USDC:USDC",
+            "entryPrice": 67141.0,
+            "side": "long",
+            "contracts": 0.00067,
+            "collateral": 14.979079,
+            "leverage": 3.0,
+            "liquidationPrice": 45236.52992613,
+        },
+    ]
+
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    default_conf["stake_currency"] = "USDC"
+    api_mock.load_markets = get_mock_coro(return_value=markets)
+    exchange = get_patched_exchange(
+        mocker, default_conf, api_mock, exchange="hyperliquid", mock_markets=False
+    )
+
+    for position in positions:
+        is_short = True if position["side"] == "short" else False
+        liq_price_returned = position["liquidationPrice"]
+        liq_price_calculated = exchange.dry_run_liquidation_price(
+            position["symbol"],
+            position["entryPrice"],
+            is_short,
+            position["contracts"],
+            position["collateral"],
+            position["leverage"],
+            position["collateral"],
+            [],
+        )
+        assert pytest.approx(liq_price_returned, rel=0.0001) == liq_price_calculated
+
+
+def test_hyperliquid_get_funding_fees(default_conf, mocker):
+    now = datetime.now(timezone.utc)
+    exchange = get_patched_exchange(mocker, default_conf, exchange="hyperliquid")
+    exchange._fetch_and_calculate_funding_fees = MagicMock()
+    exchange.get_funding_fees("BTC/USDC:USDC", 1, False, now)
+    assert exchange._fetch_and_calculate_funding_fees.call_count == 0
+
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, exchange="hyperliquid")
+    exchange._fetch_and_calculate_funding_fees = MagicMock()
+    exchange.get_funding_fees("BTC/USDC:USDC", 1, False, now)
+
+    assert exchange._fetch_and_calculate_funding_fees.call_count == 1
+
+
+def test_hyperliquid_get_max_leverage(default_conf, mocker):
+    markets = {
+        "BTC/USDC:USDC": {"limits": {"leverage": {"max": 50}}},
+        "ETH/USDC:USDC": {"limits": {"leverage": {"max": 50}}},
+        "SOL/USDC:USDC": {"limits": {"leverage": {"max": 20}}},
+        "DOGE/USDC:USDC": {"limits": {"leverage": {"max": 20}}},
+    }
+    exchange = get_patched_exchange(mocker, default_conf, exchange="hyperliquid")
+    assert exchange.get_max_leverage("BTC/USDC:USDC", 1) == 1.0
+
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, exchange="hyperliquid")
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+    )
+
+    assert exchange.get_max_leverage("BTC/USDC:USDC", 1) == 50
+    assert exchange.get_max_leverage("ETH/USDC:USDC", 20) == 50
+    assert exchange.get_max_leverage("SOL/USDC:USDC", 50) == 20
+    assert exchange.get_max_leverage("DOGE/USDC:USDC", 3) == 20
+
+
+def test_hyperliquid__lev_prep(default_conf, mocker):
+    api_mock = MagicMock()
+    api_mock.set_margin_mode = MagicMock()
+    type(api_mock).has = PropertyMock(return_value={"setMarginMode": True})
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="hyperliquid")
+    exchange._lev_prep("BTC/USDC:USDC", 3.2, "buy")
+
+    assert api_mock.set_margin_mode.call_count == 0
+
+    # test in futures mode
+    api_mock.set_margin_mode.reset_mock()
+    default_conf["dry_run"] = False
+
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="hyperliquid")
+    exchange._lev_prep("BTC/USDC:USDC", 3.2, "buy")
+
+    assert api_mock.set_margin_mode.call_count == 1
+    api_mock.set_margin_mode.assert_called_with("isolated", "BTC/USDC:USDC", {"leverage": 3})
+
+    api_mock.reset_mock()
+
+    exchange._lev_prep("BTC/USDC:USDC", 19.99, "sell")
+
+    assert api_mock.set_margin_mode.call_count == 1
+    api_mock.set_margin_mode.assert_called_with("isolated", "BTC/USDC:USDC", {"leverage": 19})
+
+
+def test_hyperliquid_fetch_order(default_conf_usdt, mocker):
+    default_conf_usdt["dry_run"] = False
+
+    api_mock = MagicMock()
+    api_mock.fetch_order = MagicMock(
+        return_value={
+            "id": "12345",
+            "symbol": "ETH/USDC:USDC",
+            "status": "closed",
+            "filled": 0.1,
+            "average": None,
+            "timestamp": 1630000000,
+        }
+    )
+
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    gtfo_mock = mocker.patch(
+        f"{EXMS}.get_trades_for_order",
+        return_value=[
+            {
+                "order_id": "12345",
+                "price": 1000,
+                "amount": 3,
+                "filled": 3,
+                "remaining": 0,
+            },
+            {
+                "order_id": "12345",
+                "price": 3000,
+                "amount": 1,
+                "filled": 1,
+                "remaining": 0,
+            },
+        ],
+    )
+    exchange = get_patched_exchange(mocker, default_conf_usdt, api_mock, exchange="hyperliquid")
+    o = exchange.fetch_order("12345", "ETH/USDC:USDC")
+    # Uses weighted average
+    assert o["average"] == 1500
+
+    assert gtfo_mock.call_count == 1
diff --git a/tests/exchange/test_kraken.py b/tests/exchange/test_kraken.py
new file mode 100644
index 000000000..fd36a359d
--- /dev/null
+++ b/tests/exchange/test_kraken.py
@@ -0,0 +1,291 @@
+from random import randint
+from unittest.mock import MagicMock
+
+import ccxt
+import pytest
+
+from freqtrade.exceptions import DependencyException, InvalidOrderException
+from tests.conftest import EXMS, get_patched_exchange
+from tests.exchange.test_exchange import ccxt_exceptionhandlers
+
+
+STOPLOSS_ORDERTYPE = "stop-loss"
+STOPLOSS_LIMIT_ORDERTYPE = "stop-loss-limit"
+
+
+@pytest.mark.parametrize(
+    "order_type,time_in_force,expected_params",
+    [
+        ("limit", "ioc", {"timeInForce": "IOC", "trading_agreement": "agree"}),
+        ("limit", "PO", {"postOnly": True, "trading_agreement": "agree"}),
+        ("market", None, {"trading_agreement": "agree"}),
+    ],
+)
+def test_kraken_trading_agreement(default_conf, mocker, order_type, time_in_force, expected_params):
+    api_mock = MagicMock()
+    order_id = f"test_prod_{order_type}_{randint(0, 10**6)}"
+    api_mock.options = {}
+    api_mock.create_order = MagicMock(
+        return_value={"id": order_id, "symbol": "ETH/BTC", "info": {"foo": "bar"}}
+    )
+    default_conf["dry_run"] = False
+
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="kraken")
+
+    order = exchange.create_order(
+        pair="ETH/BTC",
+        ordertype=order_type,
+        side="buy",
+        amount=1,
+        rate=200,
+        leverage=1.0,
+        time_in_force=time_in_force,
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args[0][0] == "ETH/BTC"
+    assert api_mock.create_order.call_args[0][1] == order_type
+    assert api_mock.create_order.call_args[0][2] == "buy"
+    assert api_mock.create_order.call_args[0][3] == 1
+    assert api_mock.create_order.call_args[0][4] == (200 if order_type == "limit" else None)
+
+    assert api_mock.create_order.call_args[0][5] == expected_params
+
+
+def test_get_balances_prod_kraken(default_conf, mocker):
+    balance_item = {"free": 0.0, "total": 10.0, "used": 0.0}
+    kraken = ccxt.kraken()
+
+    api_mock = MagicMock()
+    api_mock.commonCurrencies = kraken.commonCurrencies
+    api_mock.fetch_balance = MagicMock(
+        return_value={
+            "1ST": {"free": 0.0, "total": 0.0, "used": 0.0},
+            "1ST.F": balance_item.copy(),  # When "rewards" is enabled, the balance is in ".F"
+            "2ND": balance_item.copy(),
+            "3RD": balance_item.copy(),
+            "4TH": balance_item.copy(),
+            "EUR": balance_item.copy(),
+            "BTC": {"free": 0.0, "total": 0.0, "used": 0.0},
+            "BTC.F": balance_item.copy(),
+            "timestamp": 123123,
+        }
+    )
+    kraken_open_orders = [
+        {
+            "symbol": "1ST/EUR",
+            "type": "limit",
+            "side": "sell",
+            "price": 20,
+            "cost": 0.0,
+            "amount": 1.0,
+            "filled": 0.0,
+            "average": 0.0,
+            "remaining": 1.0,
+        },
+        {
+            "status": "open",
+            "symbol": "2ND/EUR",
+            "type": "limit",
+            "side": "sell",
+            "price": 20.0,
+            "cost": 0.0,
+            "amount": 2.0,
+            "filled": 0.0,
+            "average": 0.0,
+            "remaining": 2.0,
+        },
+        {
+            "status": "open",
+            "symbol": "2ND/USD",
+            "type": "limit",
+            "side": "sell",
+            "price": 20.0,
+            "cost": 0.0,
+            "amount": 2.0,
+            "filled": 0.0,
+            "average": 0.0,
+            "remaining": 2.0,
+        },
+        {
+            "status": "open",
+            "symbol": "3RD/EUR",
+            "type": "limit",
+            "side": "buy",
+            "price": 0.02,
+            "cost": 0.0,
+            "amount": 100.0,
+            "filled": 0.0,
+            "average": 0.0,
+            "remaining": 100.0,
+        },
+    ]
+    api_mock.fetch_open_orders = MagicMock(return_value=kraken_open_orders)
+    default_conf["dry_run"] = False
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="kraken")
+    balances = exchange.get_balances()
+    assert len(balances) == 7
+
+    assert balances["1ST"]["free"] == 9.0
+    assert balances["1ST"]["total"] == 10.0
+    assert balances["1ST"]["used"] == 1.0
+
+    assert balances["2ND"]["free"] == 6.0
+    assert balances["2ND"]["total"] == 10.0
+    assert balances["2ND"]["used"] == 4.0
+
+    assert balances["3RD"]["free"] == 10.0
+    assert balances["3RD"]["total"] == 10.0
+    assert balances["3RD"]["used"] == 0.0
+
+    assert balances["4TH"]["free"] == 10.0
+    assert balances["4TH"]["total"] == 10.0
+    assert balances["4TH"]["used"] == 0.0
+
+    assert balances["EUR"]["free"] == 8.0
+    assert balances["EUR"]["total"] == 10.0
+    assert balances["EUR"]["used"] == 2.0
+
+    assert balances["BTC"]["free"] == 10.0
+    assert balances["BTC"]["total"] == 10.0
+    assert balances["BTC"]["used"] == 0.0
+    ccxt_exceptionhandlers(
+        mocker, default_conf, api_mock, "kraken", "get_balances", "fetch_balance"
+    )
+
+
+@pytest.mark.parametrize("ordertype", ["market", "limit"])
+@pytest.mark.parametrize(
+    "side,adjustedprice",
+    [
+        ("sell", 217.8),
+        ("buy", 222.2),
+    ],
+)
+def test_create_stoploss_order_kraken(default_conf, mocker, ordertype, side, adjustedprice):
+    api_mock = MagicMock()
+    order_id = f"test_prod_buy_{randint(0, 10**6)}"
+
+    api_mock.create_order = MagicMock(return_value={"id": order_id, "info": {"foo": "bar"}})
+
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, "kraken")
+
+    order = exchange.create_stoploss(
+        pair="ETH/BTC",
+        amount=1,
+        stop_price=220,
+        side=side,
+        order_types={"stoploss": ordertype, "stoploss_on_exchange_limit_ratio": 0.99},
+        leverage=1.0,
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args_list[0][1]["symbol"] == "ETH/BTC"
+    assert api_mock.create_order.call_args_list[0][1]["type"] == ordertype
+    assert api_mock.create_order.call_args_list[0][1]["params"] == {
+        "trading_agreement": "agree",
+        "stopLossPrice": 220,
+    }
+    assert api_mock.create_order.call_args_list[0][1]["side"] == side
+    assert api_mock.create_order.call_args_list[0][1]["amount"] == 1
+    if ordertype == "limit":
+        assert api_mock.create_order.call_args_list[0][1]["price"] == adjustedprice
+    else:
+        assert api_mock.create_order.call_args_list[0][1]["price"] is None
+
+    # test exception handling
+    with pytest.raises(DependencyException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds("0 balance"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, "kraken")
+        exchange.create_stoploss(
+            pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side=side, leverage=1.0
+        )
+
+    with pytest.raises(InvalidOrderException):
+        api_mock.create_order = MagicMock(
+            side_effect=ccxt.InvalidOrder("kraken Order would trigger immediately.")
+        )
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, "kraken")
+        exchange.create_stoploss(
+            pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side=side, leverage=1.0
+        )
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        "kraken",
+        "create_stoploss",
+        "create_order",
+        retries=1,
+        pair="ETH/BTC",
+        amount=1,
+        stop_price=220,
+        order_types={},
+        side=side,
+        leverage=1.0,
+    )
+
+
+@pytest.mark.parametrize("side", ["buy", "sell"])
+def test_create_stoploss_order_dry_run_kraken(default_conf, mocker, side):
+    api_mock = MagicMock()
+    default_conf["dry_run"] = True
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, "kraken")
+
+    api_mock.create_order.reset_mock()
+
+    order = exchange.create_stoploss(
+        pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side=side, leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert "type" in order
+
+    assert order["type"] == "market"
+    assert order["price"] == 220
+    assert order["amount"] == 1
+
+
+@pytest.mark.parametrize(
+    "sl1,sl2,sl3,side", [(1501, 1499, 1501, "sell"), (1499, 1501, 1499, "buy")]
+)
+def test_stoploss_adjust_kraken(mocker, default_conf, sl1, sl2, sl3, side):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="kraken")
+    order = {
+        "type": "market",
+        "stopLossPrice": 1500,
+    }
+    assert exchange.stoploss_adjust(sl1, order, side=side)
+    assert not exchange.stoploss_adjust(sl2, order, side=side)
+    # diff. order type ...
+    order["type"] = "limit"
+    assert exchange.stoploss_adjust(sl3, order, side=side)
+
+
+@pytest.mark.parametrize(
+    "trade_id, expected",
+    [
+        ("1234", False),
+        ("170544369512007228", False),
+        ("1705443695120072285", True),
+        ("170544369512007228555", True),
+    ],
+)
+def test__valid_trade_pagination_id_kraken(mocker, default_conf_usdt, trade_id, expected):
+    exchange = get_patched_exchange(mocker, default_conf_usdt, exchange="kraken")
+    assert exchange._valid_trade_pagination_id("XRP/USDT", trade_id) == expected
diff --git a/tests/exchange/test_kucoin.py b/tests/exchange/test_kucoin.py
new file mode 100644
index 000000000..cfddfa5e9
--- /dev/null
+++ b/tests/exchange/test_kucoin.py
@@ -0,0 +1,177 @@
+from random import randint
+from unittest.mock import MagicMock
+
+import ccxt
+import pytest
+
+from freqtrade.exceptions import DependencyException, InvalidOrderException
+from tests.conftest import EXMS, get_patched_exchange
+from tests.exchange.test_exchange import ccxt_exceptionhandlers
+
+
+@pytest.mark.parametrize("order_type", ["market", "limit"])
+@pytest.mark.parametrize(
+    "limitratio,expected,side",
+    [
+        (None, 220 * 0.99, "sell"),
+        (0.99, 220 * 0.99, "sell"),
+        (0.98, 220 * 0.98, "sell"),
+    ],
+)
+def test_create_stoploss_order_kucoin(default_conf, mocker, limitratio, expected, side, order_type):
+    api_mock = MagicMock()
+    order_id = f"test_prod_buy_{randint(0, 10**6)}"
+
+    api_mock.create_order = MagicMock(return_value={"id": order_id, "info": {"foo": "bar"}})
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, "kucoin")
+    if order_type == "limit":
+        with pytest.raises(InvalidOrderException):
+            order = exchange.create_stoploss(
+                pair="ETH/BTC",
+                amount=1,
+                stop_price=190,
+                order_types={"stoploss": order_type, "stoploss_on_exchange_limit_ratio": 1.05},
+                side=side,
+                leverage=1.0,
+            )
+
+    api_mock.create_order.reset_mock()
+    order_types = {"stoploss": order_type}
+    if limitratio is not None:
+        order_types.update({"stoploss_on_exchange_limit_ratio": limitratio})
+    order = exchange.create_stoploss(
+        pair="ETH/BTC", amount=1, stop_price=220, order_types=order_types, side=side, leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert api_mock.create_order.call_args_list[0][1]["symbol"] == "ETH/BTC"
+    assert api_mock.create_order.call_args_list[0][1]["type"] == order_type
+    assert api_mock.create_order.call_args_list[0][1]["side"] == "sell"
+    assert api_mock.create_order.call_args_list[0][1]["amount"] == 1
+    # Price should be 1% below stopprice
+    if order_type == "limit":
+        assert api_mock.create_order.call_args_list[0][1]["price"] == expected
+    else:
+        assert api_mock.create_order.call_args_list[0][1]["price"] is None
+
+    assert api_mock.create_order.call_args_list[0][1]["params"] == {
+        "stopPrice": 220,
+        "stop": "loss",
+    }
+
+    # test exception handling
+    with pytest.raises(DependencyException):
+        api_mock.create_order = MagicMock(side_effect=ccxt.InsufficientFunds("0 balance"))
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, "kucoin")
+        exchange.create_stoploss(
+            pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side=side, leverage=1.0
+        )
+
+    with pytest.raises(InvalidOrderException):
+        api_mock.create_order = MagicMock(
+            side_effect=ccxt.InvalidOrder("kucoin Order would trigger immediately.")
+        )
+        exchange = get_patched_exchange(mocker, default_conf, api_mock, "kucoin")
+        exchange.create_stoploss(
+            pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side=side, leverage=1.0
+        )
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        "kucoin",
+        "create_stoploss",
+        "create_order",
+        retries=1,
+        pair="ETH/BTC",
+        amount=1,
+        stop_price=220,
+        order_types={},
+        side=side,
+        leverage=1.0,
+    )
+
+
+def test_stoploss_order_dry_run_kucoin(default_conf, mocker):
+    api_mock = MagicMock()
+    order_type = "market"
+    default_conf["dry_run"] = True
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, "kucoin")
+
+    with pytest.raises(InvalidOrderException):
+        order = exchange.create_stoploss(
+            pair="ETH/BTC",
+            amount=1,
+            stop_price=190,
+            order_types={"stoploss": "limit", "stoploss_on_exchange_limit_ratio": 1.05},
+            side="sell",
+            leverage=1.0,
+        )
+
+    api_mock.create_order.reset_mock()
+
+    order = exchange.create_stoploss(
+        pair="ETH/BTC", amount=1, stop_price=220, order_types={}, side="sell", leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert "type" in order
+
+    assert order["type"] == order_type
+    assert order["price"] == 220
+    assert order["amount"] == 1
+
+
+def test_stoploss_adjust_kucoin(mocker, default_conf):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="kucoin")
+    order = {
+        "type": "limit",
+        "price": 1500,
+        "stopPrice": 1500,
+        "info": {"stopPrice": 1500, "stop": "limit"},
+    }
+    assert exchange.stoploss_adjust(1501, order, "sell")
+    assert not exchange.stoploss_adjust(1499, order, "sell")
+    # Test with invalid order case
+    order["stopPrice"] = None
+    assert exchange.stoploss_adjust(1501, order, "sell")
+
+
+@pytest.mark.parametrize("side", ["buy", "sell"])
+@pytest.mark.parametrize(
+    "ordertype,rate", [("market", None), ("market", 200), ("limit", 200), ("stop_loss_limit", 200)]
+)
+def test_kucoin_create_order(default_conf, mocker, side, ordertype, rate):
+    api_mock = MagicMock()
+    order_id = f"test_prod_{side}_{randint(0, 10**6)}"
+    api_mock.create_order = MagicMock(
+        return_value={"id": order_id, "info": {"foo": "bar"}, "symbol": "XRP/USDT", "amount": 1}
+    )
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.amount_to_precision", lambda s, x, y: y)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y: y)
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="kucoin")
+    exchange._set_leverage = MagicMock()
+    exchange.set_margin_mode = MagicMock()
+
+    order = exchange.create_order(
+        pair="XRP/USDT", ordertype=ordertype, side=side, amount=1, rate=rate, leverage=1.0
+    )
+
+    assert "id" in order
+    assert "info" in order
+    assert order["id"] == order_id
+    assert order["amount"] == 1
+    # Status must be faked to open for kucoin.
+    assert order["status"] == "open"
diff --git a/tests/exchange/test_okx.py b/tests/exchange/test_okx.py
new file mode 100644
index 000000000..1bf783daa
--- /dev/null
+++ b/tests/exchange/test_okx.py
@@ -0,0 +1,749 @@
+from datetime import datetime, timedelta, timezone
+from unittest.mock import AsyncMock, MagicMock, PropertyMock
+
+import ccxt
+import pytest
+
+from freqtrade.enums import CandleType, MarginMode, TradingMode
+from freqtrade.exceptions import RetryableOrderError, TemporaryError
+from freqtrade.exchange.common import API_RETRY_COUNT
+from freqtrade.exchange.exchange import timeframe_to_minutes
+from tests.conftest import EXMS, get_patched_exchange, log_has
+from tests.exchange.test_exchange import ccxt_exceptionhandlers
+
+
+def test_okx_ohlcv_candle_limit(default_conf, mocker):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="okx")
+    timeframes = ("1m", "5m", "1h")
+    start_time = int(datetime(2021, 1, 1, tzinfo=timezone.utc).timestamp() * 1000)
+
+    for timeframe in timeframes:
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.SPOT) == 300
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.FUTURES) == 300
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.MARK) == 100
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.FUNDING_RATE) == 100
+
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.SPOT, start_time) == 100
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.FUTURES, start_time) == 100
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.MARK, start_time) == 100
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.FUNDING_RATE, start_time) == 100
+        one_call = int(
+            (
+                datetime.now(timezone.utc)
+                - timedelta(minutes=290 * timeframe_to_minutes(timeframe))
+            ).timestamp()
+            * 1000
+        )
+
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.SPOT, one_call) == 300
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.FUTURES, one_call) == 300
+
+        one_call = int(
+            (
+                datetime.now(timezone.utc)
+                - timedelta(minutes=320 * timeframe_to_minutes(timeframe))
+            ).timestamp()
+            * 1000
+        )
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.SPOT, one_call) == 100
+        assert exchange.ohlcv_candle_limit(timeframe, CandleType.FUTURES, one_call) == 100
+
+
+def test_get_maintenance_ratio_and_amt_okx(
+    default_conf,
+    mocker,
+):
+    api_mock = MagicMock()
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    default_conf["dry_run"] = False
+    mocker.patch.multiple(
+        "freqtrade.exchange.okx.Okx",
+        exchange_has=MagicMock(return_value=True),
+        load_leverage_tiers=MagicMock(
+            return_value={
+                "ETH/USDT:USDT": [
+                    {
+                        "tier": 1,
+                        "minNotional": 0,
+                        "maxNotional": 2000,
+                        "maintenanceMarginRate": 0.01,
+                        "maxLeverage": 75,
+                        "info": {
+                            "baseMaxLoan": "",
+                            "imr": "0.013",
+                            "instId": "",
+                            "maxLever": "75",
+                            "maxSz": "2000",
+                            "minSz": "0",
+                            "mmr": "0.01",
+                            "optMgnFactor": "0",
+                            "quoteMaxLoan": "",
+                            "tier": "1",
+                            "uly": "ETH-USDT",
+                        },
+                    },
+                    {
+                        "tier": 2,
+                        "minNotional": 2001,
+                        "maxNotional": 4000,
+                        "maintenanceMarginRate": 0.015,
+                        "maxLeverage": 50,
+                        "info": {
+                            "baseMaxLoan": "",
+                            "imr": "0.02",
+                            "instId": "",
+                            "maxLever": "50",
+                            "maxSz": "4000",
+                            "minSz": "2001",
+                            "mmr": "0.015",
+                            "optMgnFactor": "0",
+                            "quoteMaxLoan": "",
+                            "tier": "2",
+                            "uly": "ETH-USDT",
+                        },
+                    },
+                    {
+                        "tier": 3,
+                        "minNotional": 4001,
+                        "maxNotional": 8000,
+                        "maintenanceMarginRate": 0.02,
+                        "maxLeverage": 20,
+                        "info": {
+                            "baseMaxLoan": "",
+                            "imr": "0.05",
+                            "instId": "",
+                            "maxLever": "20",
+                            "maxSz": "8000",
+                            "minSz": "4001",
+                            "mmr": "0.02",
+                            "optMgnFactor": "0",
+                            "quoteMaxLoan": "",
+                            "tier": "3",
+                            "uly": "ETH-USDT",
+                        },
+                    },
+                ],
+                "ADA/USDT:USDT": [
+                    {
+                        "tier": 1,
+                        "minNotional": 0,
+                        "maxNotional": 500,
+                        "maintenanceMarginRate": 0.02,
+                        "maxLeverage": 75,
+                        "info": {
+                            "baseMaxLoan": "",
+                            "imr": "0.013",
+                            "instId": "",
+                            "maxLever": "75",
+                            "maxSz": "500",
+                            "minSz": "0",
+                            "mmr": "0.01",
+                            "optMgnFactor": "0",
+                            "quoteMaxLoan": "",
+                            "tier": "1",
+                            "uly": "ADA-USDT",
+                        },
+                    },
+                    {
+                        "tier": 2,
+                        "minNotional": 501,
+                        "maxNotional": 1000,
+                        "maintenanceMarginRate": 0.025,
+                        "maxLeverage": 50,
+                        "info": {
+                            "baseMaxLoan": "",
+                            "imr": "0.02",
+                            "instId": "",
+                            "maxLever": "50",
+                            "maxSz": "1000",
+                            "minSz": "501",
+                            "mmr": "0.015",
+                            "optMgnFactor": "0",
+                            "quoteMaxLoan": "",
+                            "tier": "2",
+                            "uly": "ADA-USDT",
+                        },
+                    },
+                    {
+                        "tier": 3,
+                        "minNotional": 1001,
+                        "maxNotional": 2000,
+                        "maintenanceMarginRate": 0.03,
+                        "maxLeverage": 20,
+                        "info": {
+                            "baseMaxLoan": "",
+                            "imr": "0.05",
+                            "instId": "",
+                            "maxLever": "20",
+                            "maxSz": "2000",
+                            "minSz": "1001",
+                            "mmr": "0.02",
+                            "optMgnFactor": "0",
+                            "quoteMaxLoan": "",
+                            "tier": "3",
+                            "uly": "ADA-USDT",
+                        },
+                    },
+                ],
+            }
+        ),
+    )
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="okx")
+    assert exchange.get_maintenance_ratio_and_amt("ETH/USDT:USDT", 2000) == (0.01, None)
+    assert exchange.get_maintenance_ratio_and_amt("ETH/USDT:USDT", 2001) == (0.015, None)
+    assert exchange.get_maintenance_ratio_and_amt("ETH/USDT:USDT", 4001) == (0.02, None)
+    assert exchange.get_maintenance_ratio_and_amt("ETH/USDT:USDT", 8000) == (0.02, None)
+
+    assert exchange.get_maintenance_ratio_and_amt("ADA/USDT:USDT", 1) == (0.02, None)
+    assert exchange.get_maintenance_ratio_and_amt("ADA/USDT:USDT", 2000) == (0.03, None)
+
+
+def test_get_max_pair_stake_amount_okx(default_conf, mocker, leverage_tiers):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="okx")
+    assert exchange.get_max_pair_stake_amount("BNB/BUSD", 1.0) == float("inf")
+
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, exchange="okx")
+    exchange._leverage_tiers = leverage_tiers
+
+    assert exchange.get_max_pair_stake_amount("XRP/USDT:USDT", 1.0) == 30000000
+    assert exchange.get_max_pair_stake_amount("BNB/USDT:USDT", 1.0) == 50000000
+    assert exchange.get_max_pair_stake_amount("BTC/USDT:USDT", 1.0) == 1000000000
+    assert exchange.get_max_pair_stake_amount("BTC/USDT:USDT", 1.0, 10.0) == 100000000
+
+    assert exchange.get_max_pair_stake_amount("TTT/USDT:USDT", 1.0) == float("inf")  # Not in tiers
+
+
+@pytest.mark.parametrize(
+    "mode,side,reduceonly,result",
+    [
+        ("net", "buy", False, "net"),
+        ("net", "sell", True, "net"),
+        ("net", "sell", False, "net"),
+        ("net", "buy", True, "net"),
+        ("longshort", "buy", False, "long"),
+        ("longshort", "sell", True, "long"),
+        ("longshort", "sell", False, "short"),
+        ("longshort", "buy", True, "short"),
+    ],
+)
+def test__get_posSide(default_conf, mocker, mode, side, reduceonly, result):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="okx")
+    exchange.net_only = mode == "net"
+    assert exchange._get_posSide(side, reduceonly) == result
+
+
+def test_additional_exchange_init_okx(default_conf, mocker):
+    api_mock = MagicMock()
+    api_mock.fetch_accounts = MagicMock(
+        return_value=[
+            {
+                "id": "2555",
+                "type": "2",
+                "currency": None,
+                "info": {
+                    "acctLv": "2",
+                    "autoLoan": False,
+                    "ctIsoMode": "automatic",
+                    "greeksType": "PA",
+                    "level": "Lv1",
+                    "levelTmp": "",
+                    "mgnIsoMode": "automatic",
+                    "posMode": "long_short_mode",
+                    "uid": "2555",
+                },
+            }
+        ]
+    )
+    default_conf["dry_run"] = False
+    exchange = get_patched_exchange(mocker, default_conf, exchange="okx", api_mock=api_mock)
+    assert api_mock.fetch_accounts.call_count == 0
+    exchange.trading_mode = TradingMode.FUTURES
+    # Default to netOnly
+    assert exchange.net_only
+    exchange.additional_exchange_init()
+    assert api_mock.fetch_accounts.call_count == 1
+    assert not exchange.net_only
+
+    api_mock.fetch_accounts = MagicMock(
+        return_value=[
+            {
+                "id": "2555",
+                "type": "2",
+                "currency": None,
+                "info": {
+                    "acctLv": "2",
+                    "autoLoan": False,
+                    "ctIsoMode": "automatic",
+                    "greeksType": "PA",
+                    "level": "Lv1",
+                    "levelTmp": "",
+                    "mgnIsoMode": "automatic",
+                    "posMode": "net_mode",
+                    "uid": "2555",
+                },
+            }
+        ]
+    )
+    exchange.additional_exchange_init()
+    assert api_mock.fetch_accounts.call_count == 1
+    assert exchange.net_only
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    ccxt_exceptionhandlers(
+        mocker, default_conf, api_mock, "okx", "additional_exchange_init", "fetch_accounts"
+    )
+
+
+def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmp_path, caplog, time_machine):
+    default_conf["datadir"] = tmp_path
+    # fd_mock = mocker.patch('freqtrade.exchange.exchange.file_dump_json')
+    api_mock = MagicMock()
+    type(api_mock).has = PropertyMock(
+        return_value={
+            "fetchLeverageTiers": False,
+            "fetchMarketLeverageTiers": True,
+        }
+    )
+    api_mock.fetch_market_leverage_tiers = AsyncMock(
+        side_effect=[
+            [
+                {
+                    "tier": 1,
+                    "minNotional": 0,
+                    "maxNotional": 500,
+                    "maintenanceMarginRate": 0.02,
+                    "maxLeverage": 75,
+                    "info": {
+                        "baseMaxLoan": "",
+                        "imr": "0.013",
+                        "instId": "",
+                        "maxLever": "75",
+                        "maxSz": "500",
+                        "minSz": "0",
+                        "mmr": "0.01",
+                        "optMgnFactor": "0",
+                        "quoteMaxLoan": "",
+                        "tier": "1",
+                        "uly": "ADA-USDT",
+                    },
+                },
+                {
+                    "tier": 2,
+                    "minNotional": 501,
+                    "maxNotional": 1000,
+                    "maintenanceMarginRate": 0.025,
+                    "maxLeverage": 50,
+                    "info": {
+                        "baseMaxLoan": "",
+                        "imr": "0.02",
+                        "instId": "",
+                        "maxLever": "50",
+                        "maxSz": "1000",
+                        "minSz": "501",
+                        "mmr": "0.015",
+                        "optMgnFactor": "0",
+                        "quoteMaxLoan": "",
+                        "tier": "2",
+                        "uly": "ADA-USDT",
+                    },
+                },
+                {
+                    "tier": 3,
+                    "minNotional": 1001,
+                    "maxNotional": 2000,
+                    "maintenanceMarginRate": 0.03,
+                    "maxLeverage": 20,
+                    "info": {
+                        "baseMaxLoan": "",
+                        "imr": "0.05",
+                        "instId": "",
+                        "maxLever": "20",
+                        "maxSz": "2000",
+                        "minSz": "1001",
+                        "mmr": "0.02",
+                        "optMgnFactor": "0",
+                        "quoteMaxLoan": "",
+                        "tier": "3",
+                        "uly": "ADA-USDT",
+                    },
+                },
+            ],
+            TemporaryError("this Failed"),
+            [
+                {
+                    "tier": 1,
+                    "minNotional": 0,
+                    "maxNotional": 2000,
+                    "maintenanceMarginRate": 0.01,
+                    "maxLeverage": 75,
+                    "info": {
+                        "baseMaxLoan": "",
+                        "imr": "0.013",
+                        "instId": "",
+                        "maxLever": "75",
+                        "maxSz": "2000",
+                        "minSz": "0",
+                        "mmr": "0.01",
+                        "optMgnFactor": "0",
+                        "quoteMaxLoan": "",
+                        "tier": "1",
+                        "uly": "ETH-USDT",
+                    },
+                },
+                {
+                    "tier": 2,
+                    "minNotional": 2001,
+                    "maxNotional": 4000,
+                    "maintenanceMarginRate": 0.015,
+                    "maxLeverage": 50,
+                    "info": {
+                        "baseMaxLoan": "",
+                        "imr": "0.02",
+                        "instId": "",
+                        "maxLever": "50",
+                        "maxSz": "4000",
+                        "minSz": "2001",
+                        "mmr": "0.015",
+                        "optMgnFactor": "0",
+                        "quoteMaxLoan": "",
+                        "tier": "2",
+                        "uly": "ETH-USDT",
+                    },
+                },
+                {
+                    "tier": 3,
+                    "minNotional": 4001,
+                    "maxNotional": 8000,
+                    "maintenanceMarginRate": 0.02,
+                    "maxLeverage": 20,
+                    "info": {
+                        "baseMaxLoan": "",
+                        "imr": "0.05",
+                        "instId": "",
+                        "maxLever": "20",
+                        "maxSz": "8000",
+                        "minSz": "4001",
+                        "mmr": "0.02",
+                        "optMgnFactor": "0",
+                        "quoteMaxLoan": "",
+                        "tier": "3",
+                        "uly": "ETH-USDT",
+                    },
+                },
+            ],
+        ]
+    )
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    default_conf["stake_currency"] = "USDT"
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="okx")
+    exchange.trading_mode = TradingMode.FUTURES
+    exchange.margin_mode = MarginMode.ISOLATED
+    exchange.markets = markets
+    # Initialization of load_leverage_tiers happens as part of exchange init.
+    assert exchange._leverage_tiers == {
+        "ADA/USDT:USDT": [
+            {
+                "minNotional": 0,
+                "maxNotional": 500,
+                "maintenanceMarginRate": 0.02,
+                "maxLeverage": 75,
+                "maintAmt": None,
+            },
+            {
+                "minNotional": 501,
+                "maxNotional": 1000,
+                "maintenanceMarginRate": 0.025,
+                "maxLeverage": 50,
+                "maintAmt": None,
+            },
+            {
+                "minNotional": 1001,
+                "maxNotional": 2000,
+                "maintenanceMarginRate": 0.03,
+                "maxLeverage": 20,
+                "maintAmt": None,
+            },
+        ],
+        "ETH/USDT:USDT": [
+            {
+                "minNotional": 0,
+                "maxNotional": 2000,
+                "maintenanceMarginRate": 0.01,
+                "maxLeverage": 75,
+                "maintAmt": None,
+            },
+            {
+                "minNotional": 2001,
+                "maxNotional": 4000,
+                "maintenanceMarginRate": 0.015,
+                "maxLeverage": 50,
+                "maintAmt": None,
+            },
+            {
+                "minNotional": 4001,
+                "maxNotional": 8000,
+                "maintenanceMarginRate": 0.02,
+                "maxLeverage": 20,
+                "maintAmt": None,
+            },
+        ],
+    }
+    filename = (
+        default_conf["datadir"] / f"futures/leverage_tiers_{default_conf['stake_currency']}.json"
+    )
+    assert filename.is_file()
+
+    logmsg = "Cached leverage tiers are outdated. Will update."
+    assert not log_has(logmsg, caplog)
+
+    api_mock.fetch_market_leverage_tiers.reset_mock()
+
+    exchange.load_leverage_tiers()
+    assert not log_has(logmsg, caplog)
+
+    assert api_mock.fetch_market_leverage_tiers.call_count == 0
+    # 2 day passes ...
+    time_machine.move_to(datetime.now() + timedelta(weeks=5))
+    exchange.load_leverage_tiers()
+
+    assert log_has(logmsg, caplog)
+
+
+def test__set_leverage_okx(mocker, default_conf):
+    api_mock = MagicMock()
+    api_mock.set_leverage = MagicMock()
+    type(api_mock).has = PropertyMock(return_value={"setLeverage": True})
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = TradingMode.FUTURES
+    default_conf["margin_mode"] = MarginMode.ISOLATED
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="okx")
+    exchange._lev_prep("BTC/USDT:USDT", 3.2, "buy")
+    assert api_mock.set_leverage.call_count == 1
+    # Leverage is rounded to 3.
+    assert api_mock.set_leverage.call_args_list[0][1]["leverage"] == 3.2
+    assert api_mock.set_leverage.call_args_list[0][1]["symbol"] == "BTC/USDT:USDT"
+    assert api_mock.set_leverage.call_args_list[0][1]["params"] == {
+        "mgnMode": "isolated",
+        "posSide": "net",
+    }
+    api_mock.set_leverage = MagicMock(side_effect=ccxt.NetworkError())
+    exchange._lev_prep("BTC/USDT:USDT", 3.2, "buy")
+    assert api_mock.fetch_leverage.call_count == 1
+
+    api_mock.fetch_leverage = MagicMock(side_effect=ccxt.NetworkError())
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf,
+        api_mock,
+        "okx",
+        "_lev_prep",
+        "set_leverage",
+        pair="XRP/USDT:USDT",
+        leverage=5.0,
+        side="buy",
+    )
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_fetch_stoploss_order_okx(default_conf, mocker):
+    default_conf["dry_run"] = False
+    mocker.patch("freqtrade.exchange.common.time.sleep")
+    api_mock = MagicMock()
+    api_mock.fetch_order = MagicMock()
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="okx")
+
+    exchange.fetch_stoploss_order("1234", "ETH/BTC")
+    assert api_mock.fetch_order.call_count == 1
+    assert api_mock.fetch_order.call_args_list[0][0][0] == "1234"
+    assert api_mock.fetch_order.call_args_list[0][0][1] == "ETH/BTC"
+    assert api_mock.fetch_order.call_args_list[0][1]["params"] == {"stop": True}
+
+    api_mock.fetch_order = MagicMock(side_effect=ccxt.OrderNotFound)
+    api_mock.fetch_open_orders = MagicMock(return_value=[])
+    api_mock.fetch_closed_orders = MagicMock(return_value=[])
+    api_mock.fetch_canceled_orders = MagicMock(creturn_value=[])
+
+    with pytest.raises(RetryableOrderError):
+        exchange.fetch_stoploss_order("1234", "ETH/BTC")
+    assert api_mock.fetch_order.call_count == API_RETRY_COUNT + 1
+    assert api_mock.fetch_open_orders.call_count == API_RETRY_COUNT + 1
+    assert api_mock.fetch_closed_orders.call_count == API_RETRY_COUNT + 1
+    assert api_mock.fetch_canceled_orders.call_count == API_RETRY_COUNT + 1
+
+    api_mock.fetch_order.reset_mock()
+    api_mock.fetch_open_orders.reset_mock()
+    api_mock.fetch_closed_orders.reset_mock()
+    api_mock.fetch_canceled_orders.reset_mock()
+
+    api_mock.fetch_closed_orders = MagicMock(
+        return_value=[{"id": "1234", "status": "closed", "info": {"ordId": "123455"}}]
+    )
+    mocker.patch(f"{EXMS}.fetch_order", MagicMock(return_value={"id": "123455"}))
+    resp = exchange.fetch_stoploss_order("1234", "ETH/BTC")
+    assert api_mock.fetch_order.call_count == 1
+    assert api_mock.fetch_open_orders.call_count == 1
+    assert api_mock.fetch_closed_orders.call_count == 1
+    assert api_mock.fetch_canceled_orders.call_count == 0
+
+    assert resp["id"] == "1234"
+    assert resp["id_stop"] == "123455"
+    assert resp["type"] == "stoploss"
+
+    default_conf["dry_run"] = True
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="okx")
+    dro_mock = mocker.patch(f"{EXMS}.fetch_dry_run_order", MagicMock(return_value={"id": "123455"}))
+
+    api_mock.fetch_order.reset_mock()
+    api_mock.fetch_open_orders.reset_mock()
+    api_mock.fetch_closed_orders.reset_mock()
+    api_mock.fetch_canceled_orders.reset_mock()
+    resp = exchange.fetch_stoploss_order("1234", "ETH/BTC")
+
+    assert api_mock.fetch_order.call_count == 0
+    assert api_mock.fetch_open_orders.call_count == 0
+    assert api_mock.fetch_closed_orders.call_count == 0
+    assert api_mock.fetch_canceled_orders.call_count == 0
+    assert dro_mock.call_count == 1
+
+
+def test_fetch_stoploss_order_okx_exceptions(default_conf_usdt, mocker):
+    default_conf_usdt["dry_run"] = False
+    api_mock = MagicMock()
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf_usdt,
+        api_mock,
+        "okx",
+        "fetch_stoploss_order",
+        "fetch_order",
+        retries=API_RETRY_COUNT + 1,
+        order_id="12345",
+        pair="ETH/USDT",
+    )
+
+    # Test 2nd part of the function
+    api_mock.fetch_order = MagicMock(side_effect=ccxt.OrderNotFound())
+    api_mock.fetch_closed_orders = MagicMock(return_value=[])
+    api_mock.fetch_canceled_orders = MagicMock(return_value=[])
+
+    ccxt_exceptionhandlers(
+        mocker,
+        default_conf_usdt,
+        api_mock,
+        "okx",
+        "fetch_stoploss_order",
+        "fetch_open_orders",
+        retries=API_RETRY_COUNT + 1,
+        order_id="12345",
+        pair="ETH/USDT",
+    )
+
+
+@pytest.mark.parametrize(
+    "sl1,sl2,sl3,side", [(1501, 1499, 1501, "sell"), (1499, 1501, 1499, "buy")]
+)
+def test_stoploss_adjust_okx(mocker, default_conf, sl1, sl2, sl3, side):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="okx")
+    order = {
+        "type": "stoploss",
+        "price": 1500,
+        "stopLossPrice": 1500,
+    }
+    assert exchange.stoploss_adjust(sl1, order, side=side)
+    assert not exchange.stoploss_adjust(sl2, order, side=side)
+
+
+def test_stoploss_cancel_okx(mocker, default_conf):
+    exchange = get_patched_exchange(mocker, default_conf, exchange="okx")
+
+    exchange.cancel_order = MagicMock()
+
+    exchange.cancel_stoploss_order("1234", "ETH/USDT")
+    assert exchange.cancel_order.call_count == 1
+    assert exchange.cancel_order.call_args_list[0][1]["order_id"] == "1234"
+    assert exchange.cancel_order.call_args_list[0][1]["pair"] == "ETH/USDT"
+    assert exchange.cancel_order.call_args_list[0][1]["params"] == {"stop": True}
+
+
+def test__get_stop_params_okx(mocker, default_conf):
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    exchange = get_patched_exchange(mocker, default_conf, exchange="okx")
+    params = exchange._get_stop_params("sell", "market", 1500)
+
+    assert params["tdMode"] == "isolated"
+    assert params["posSide"] == "net"
+
+
+def test_fetch_orders_okx(default_conf, mocker, limit_order):
+    api_mock = MagicMock()
+    api_mock.fetch_orders = MagicMock(
+        return_value=[
+            limit_order["buy"],
+            limit_order["sell"],
+        ]
+    )
+    api_mock.fetch_open_orders = MagicMock(return_value=[limit_order["buy"]])
+    api_mock.fetch_closed_orders = MagicMock(return_value=[limit_order["buy"]])
+
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    start_time = datetime.now(timezone.utc) - timedelta(days=20)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="okx")
+    # Not available in dry-run
+    assert exchange.fetch_orders("mocked", start_time) == []
+    assert api_mock.fetch_orders.call_count == 0
+    default_conf["dry_run"] = False
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, exchange="okx")
+
+    def has_resp(_, endpoint):
+        if endpoint == "fetchOrders":
+            return False
+        if endpoint == "fetchClosedOrders":
+            return True
+        if endpoint == "fetchOpenOrders":
+            return True
+
+    mocker.patch(f"{EXMS}.exchange_has", has_resp)
+
+    history_params = {"method": "privateGetTradeOrdersHistoryArchive"}
+
+    # happy path without fetchOrders
+    exchange.fetch_orders("mocked", start_time)
+    assert api_mock.fetch_orders.call_count == 0
+    assert api_mock.fetch_open_orders.call_count == 1
+    assert api_mock.fetch_closed_orders.call_count == 2
+    assert "params" not in api_mock.fetch_closed_orders.call_args_list[0][1]
+    assert api_mock.fetch_closed_orders.call_args_list[1][1]["params"] == history_params
+
+    api_mock.fetch_open_orders.reset_mock()
+    api_mock.fetch_closed_orders.reset_mock()
+
+    # regular closed_orders endpoint only has history for 7 days.
+    exchange.fetch_orders("mocked", datetime.now(timezone.utc) - timedelta(days=6))
+    assert api_mock.fetch_orders.call_count == 0
+    assert api_mock.fetch_open_orders.call_count == 1
+    assert api_mock.fetch_closed_orders.call_count == 1
+    assert "params" not in api_mock.fetch_closed_orders.call_args_list[0][1]
+
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+
+    # Unhappy path - first fetch-orders call fails.
+    api_mock.fetch_orders = MagicMock(side_effect=ccxt.NotSupported())
+    api_mock.fetch_open_orders.reset_mock()
+    api_mock.fetch_closed_orders.reset_mock()
+
+    exchange.fetch_orders("mocked", start_time)
+
+    assert api_mock.fetch_orders.call_count == 1
+    assert api_mock.fetch_open_orders.call_count == 1
+    assert api_mock.fetch_closed_orders.call_count == 2
+    assert "params" not in api_mock.fetch_closed_orders.call_args_list[0][1]
+    assert api_mock.fetch_closed_orders.call_args_list[1][1]["params"] == history_params
diff --git a/tests/exchange_online/__init__.py b/tests/exchange_online/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/exchange_online/conftest.py b/tests/exchange_online/conftest.py
new file mode 100644
index 000000000..f7cf49aa4
--- /dev/null
+++ b/tests/exchange_online/conftest.py
@@ -0,0 +1,605 @@
+from copy import deepcopy
+from pathlib import Path
+
+import pytest
+
+from freqtrade.constants import Config
+from freqtrade.exchange.exchange import Exchange
+from freqtrade.resolvers.exchange_resolver import ExchangeResolver
+from tests.conftest import EXMS, get_default_conf_usdt
+
+
+EXCHANGE_FIXTURE_TYPE = tuple[Exchange, str]
+EXCHANGE_WS_FIXTURE_TYPE = tuple[Exchange, str, str]
+
+
+# Exchanges that should be tested online
+EXCHANGES = {
+    "binance": {
+        "pair": "BTC/USDT",
+        "stake_currency": "USDT",
+        "use_ci_proxy": True,
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 1000,
+        "futures": True,
+        "futures_pair": "BTC/USDT:USDT",
+        "hasQuoteVolumeFutures": True,
+        "leverage_tiers_public": False,
+        "leverage_in_spot_market": False,
+        "trades_lookback_hours": 4,
+        "private_methods": ["fapiPrivateGetPositionSideDual", "fapiPrivateGetMultiAssetsMargin"],
+        "sample_order": [
+            {
+                "exchange_response": {
+                    "symbol": "SOLUSDT",
+                    "orderId": 3551312894,
+                    "orderListId": -1,
+                    "clientOrderId": "x-R4DD3S8297c73a11ccb9dc8f2811ba",
+                    "transactTime": 1674493798550,
+                    "price": "15.50000000",
+                    "origQty": "1.10000000",
+                    "executedQty": "0.00000000",
+                    "cummulativeQuoteQty": "0.00000000",
+                    "status": "NEW",
+                    "timeInForce": "GTC",
+                    "type": "LIMIT",
+                    "side": "BUY",
+                    "workingTime": 1674493798550,
+                    "fills": [],
+                    "selfTradePreventionMode": "NONE",
+                },
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            },
+            {
+                "exchange_response": {
+                    "symbol": "SOLUSDT",
+                    "orderId": 3551312894,
+                    "orderListId": -1,
+                    "clientOrderId": "x-R4DD3S8297c73a11ccb9dc8f2811ba",
+                    "transactTime": 1674493798550,
+                    "price": "15.50000000",
+                    "origQty": "1.10000000",
+                    "executedQty": "1.10000000",
+                    "cummulativeQuoteQty": "17.05",
+                    "status": "FILLED",
+                    "timeInForce": "GTC",
+                    "type": "LIMIT",
+                    "side": "BUY",
+                    "workingTime": 1674493798550,
+                    "fills": [],
+                    "selfTradePreventionMode": "NONE",
+                },
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            },
+        ],
+    },
+    "binanceus": {
+        "pair": "BTC/USDT",
+        "stake_currency": "USDT",
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 1000,
+        "futures": False,
+        "skip_ws_tests": True,
+        "sample_order": [
+            {
+                "exchange_response": {
+                    "symbol": "SOLUSDT",
+                    "orderId": 3551312894,
+                    "orderListId": -1,
+                    "clientOrderId": "x-R4DD3S8297c73a11ccb9dc8f2811ba",
+                    "transactTime": 1674493798550,
+                    "price": "15.50000000",
+                    "origQty": "1.10000000",
+                    "executedQty": "0.00000000",
+                    "cummulativeQuoteQty": "0.00000000",
+                    "status": "NEW",
+                    "timeInForce": "GTC",
+                    "type": "LIMIT",
+                    "side": "BUY",
+                    "workingTime": 1674493798550,
+                    "fills": [],
+                    "selfTradePreventionMode": "NONE",
+                },
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            }
+        ],
+    },
+    "kraken": {
+        "pair": "BTC/USD",
+        "stake_currency": "USD",
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 720,
+        "leverage_tiers_public": False,
+        "leverage_in_spot_market": True,
+        "trades_lookback_hours": 12,
+        "sample_balances": {
+            "exchange_response": {
+                "result": {
+                    "ADA": {"balance": "0.00000000", "hold_trade": "0.00000000"},
+                    "ADA.F": {"balance": "2.00000000", "hold_trade": "0.00000000"},
+                    "XBT": {"balance": "0.00060000", "hold_trade": "0.00000000"},
+                    "XBT.F": {"balance": "0.00100000", "hold_trade": "0.00000000"},
+                }
+            },
+            "expected": {
+                "ADA": {"free": 0.0, "total": 0.0, "used": 0.0},
+                "ADA.F": {"free": 2.0, "total": 2.0, "used": 0.0},
+                "BTC": {"free": 0.0006, "total": 0.0006, "used": 0.0},
+                # XBT.F should be mapped to BTC.F
+                "BTC.F": {"free": 0.001, "total": 0.001, "used": 0.0},
+            },
+        },
+    },
+    "kucoin": {
+        "pair": "XRP/USDT",
+        "stake_currency": "USDT",
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 1500,
+        "leverage_tiers_public": False,
+        "leverage_in_spot_market": True,
+        "sample_order": [
+            {
+                "exchange_response": {"id": "63d6742d0adc5570001d2bbf7"},
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            },  # create order
+            {
+                "exchange_response": {
+                    "id": "63d6742d0adc5570001d2bbf7",
+                    "symbol": "SOL-USDT",
+                    "opType": "DEAL",
+                    "type": "limit",
+                    "side": "buy",
+                    "price": "15.5",
+                    "size": "1.1",
+                    "funds": "0",
+                    "dealFunds": "17.05",
+                    "dealSize": "1.1",
+                    "fee": "0.000065252",
+                    "feeCurrency": "USDT",
+                    "stp": "",
+                    "stop": "",
+                    "stopTriggered": False,
+                    "stopPrice": "0",
+                    "timeInForce": "GTC",
+                    "postOnly": False,
+                    "hidden": False,
+                    "iceberg": False,
+                    "visibleSize": "0",
+                    "cancelAfter": 0,
+                    "channel": "API",
+                    "clientOid": "0a053870-11bf-41e5-be61-b272a4cb62e1",
+                    "remark": None,
+                    "tags": "partner:ccxt",
+                    "isActive": False,
+                    "cancelExist": False,
+                    "createdAt": 1674493798550,
+                    "tradeType": "TRADE",
+                },
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            },
+        ],
+    },
+    "gate": {
+        "pair": "BTC/USDT",
+        "stake_currency": "USDT",
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 1000,
+        "futures": True,
+        "futures_pair": "BTC/USDT:USDT",
+        "hasQuoteVolumeFutures": True,
+        "leverage_tiers_public": True,
+        "leverage_in_spot_market": True,
+        "sample_order": [
+            {
+                "exchange_response": {
+                    "id": "276266139423",
+                    "text": "apiv4",
+                    "create_time": "1674493798",
+                    "update_time": "1674493798",
+                    "create_time_ms": "1674493798550",
+                    "update_time_ms": "1674493798550",
+                    "status": "closed",
+                    "currency_pair": "SOL_USDT",
+                    "type": "limit",
+                    "account": "spot",
+                    "side": "buy",
+                    "amount": "1.1",
+                    "price": "15.5",
+                    "time_in_force": "gtc",
+                    "iceberg": "0",
+                    "left": "0",
+                    "fill_price": "17.05",
+                    "filled_total": "17.05",
+                    "avg_deal_price": "15.5",
+                    "fee": "0.0000018",
+                    "fee_currency": "SOL",
+                    "point_fee": "0",
+                    "gt_fee": "0",
+                    "gt_maker_fee": "0",
+                    "gt_taker_fee": "0.0015",
+                    "gt_discount": True,
+                    "rebated_fee": "0",
+                    "rebated_fee_currency": "USDT",
+                },
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            },
+            {
+                "exchange_response": {
+                    # market order
+                    "id": "276401180529",
+                    "text": "apiv4",
+                    "create_time": "1674493798",
+                    "update_time": "1674493798",
+                    "create_time_ms": "1674493798550",
+                    "update_time_ms": "1674493798550",
+                    "status": "cancelled",
+                    "currency_pair": "SOL_USDT",
+                    "type": "market",
+                    "account": "spot",
+                    "side": "buy",
+                    "amount": "17.05",
+                    "price": "0",
+                    "time_in_force": "ioc",
+                    "iceberg": "0",
+                    "left": "0.0000000016228",
+                    "fill_price": "17.05",
+                    "filled_total": "17.05",
+                    "avg_deal_price": "15.5",
+                    "fee": "0",
+                    "fee_currency": "SOL",
+                    "point_fee": "0.0199999999967544",
+                    "gt_fee": "0",
+                    "gt_maker_fee": "0",
+                    "gt_taker_fee": "0",
+                    "gt_discount": False,
+                    "rebated_fee": "0",
+                    "rebated_fee_currency": "USDT",
+                },
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            },
+        ],
+        "sample_my_trades": [
+            {
+                "id": "123412341234",
+                "create_time": "167997798",
+                "create_time_ms": "167997798825.566200",
+                "currency_pair": "SOL_USDT",
+                "side": "sell",
+                "role": "taker",
+                "amount": "0.0115",
+                "price": "1712.63",
+                "order_id": "1234123412",
+                "fee": "0.0",
+                "fee_currency": "USDT",
+                "point_fee": "0.03939049",
+                "gt_fee": "0.0",
+                "amend_text": "-",
+            }
+        ],
+    },
+    "okx": {
+        "pair": "BTC/USDT",
+        "stake_currency": "USDT",
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 300,
+        "futures": True,
+        "futures_pair": "BTC/USDT:USDT",
+        "hasQuoteVolumeFutures": False,
+        "leverage_tiers_public": True,
+        "leverage_in_spot_market": True,
+        "private_methods": ["fetch_accounts"],
+    },
+    "bybit": {
+        "pair": "BTC/USDT",
+        "stake_currency": "USDT",
+        "hasQuoteVolume": True,
+        "use_ci_proxy": True,
+        "timeframe": "1h",
+        "candle_count": 1000,
+        "futures_pair": "BTC/USDT:USDT",
+        "futures": True,
+        "orderbook_max_entries": 50,
+        "leverage_tiers_public": True,
+        "leverage_in_spot_market": True,
+        "sample_order": [
+            {
+                "exchange_response": {
+                    "orderId": "1274754916287346280",
+                    "orderLinkId": "1666798627015730",
+                    "symbol": "SOLUSDT",
+                    "createdTime": "1674493798550",
+                    "price": "15.5",
+                    "qty": "1.1",
+                    "orderType": "Limit",
+                    "side": "Buy",
+                    "orderStatus": "New",
+                    "timeInForce": "GTC",
+                    "accountId": "5555555",
+                    "execQty": "0",
+                    "orderCategory": "0",
+                },
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            }
+        ],
+    },
+    "bitmart": {
+        "pair": "BTC/USDT",
+        "stake_currency": "USDT",
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 200,
+        "orderbook_max_entries": 50,
+    },
+    "htx": {
+        "pair": "ETH/BTC",
+        "stake_currency": "BTC",
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 1000,
+    },
+    "bitvavo": {
+        "pair": "BTC/EUR",
+        "stake_currency": "EUR",
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 1440,
+        "leverage_tiers_public": False,
+        "leverage_in_spot_market": False,
+    },
+    "bingx": {
+        "pair": "BTC/USDT",
+        "stake_currency": "USDT",
+        "hasQuoteVolume": True,
+        "timeframe": "1h",
+        "candle_count": 1000,
+        "futures": False,
+        "sample_order": [
+            {
+                "exchange_response": {
+                    "symbol": "SOL-USDT",
+                    "orderId": "1762393630149869568",
+                    "transactTime": "1674493798550",
+                    "price": "15.5",
+                    "stopPrice": "0",
+                    "origQty": "1.1",
+                    "executedQty": "1.1",
+                    "cummulativeQuoteQty": "17.05",
+                    "status": "FILLED",
+                    "type": "LIMIT",
+                    "side": "BUY",
+                    "clientOrderID": "",
+                },
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            },
+            {
+                "exchange_response": {
+                    "symbol": "SOL-USDT",
+                    "orderId": "1762393630149869568",
+                    "transactTime": "1674493798550",
+                    "price": "15.5",
+                    "stopPrice": "0",
+                    "origQty": "1.1",
+                    "executedQty": "1.1",
+                    "cummulativeQuoteQty": "17.05",
+                    "status": "FILLED",
+                    "type": "MARKET",
+                    "side": "BUY",
+                    "clientOrderID": "",
+                },
+                "pair": "SOL/USDT",
+                "expected": {
+                    "symbol": "SOL/USDT",
+                    "orderId": "3551312894",
+                    "timestamp": 1674493798550,
+                    "datetime": "2023-03-25T15:49:58.550Z",
+                    "price": 15.5,
+                    "status": "open",
+                    "amount": 1.1,
+                },
+            },
+        ],
+    },
+    "hyperliquid": {
+        "pair": "PURR/USDC",
+        "stake_currency": "USDC",
+        "hasQuoteVolume": False,
+        "timeframe": "1h",
+        "futures": True,
+        "candle_count": 5000,
+        "orderbook_max_entries": 20,
+        "futures_pair": "BTC/USDC:USDC",
+        "hasQuoteVolumeFutures": True,
+        "leverage_tiers_public": False,
+        "leverage_in_spot_market": False,
+    },
+}
+
+
+@pytest.fixture(scope="class")
+def exchange_conf():
+    config = get_default_conf_usdt((Path(__file__).parent / "testdata").resolve())
+    config["exchange"]["pair_whitelist"] = []
+    config["exchange"]["key"] = ""
+    config["exchange"]["secret"] = ""
+    config["dry_run"] = False
+    config["entry_pricing"]["use_order_book"] = True
+    config["exit_pricing"]["use_order_book"] = True
+    return config
+
+
+def set_test_proxy(config: Config, use_proxy: bool) -> Config:
+    # Set proxy to test in CI.
+    import os
+
+    if use_proxy and (proxy := os.environ.get("CI_WEB_PROXY")):
+        config1 = deepcopy(config)
+        config1["exchange"]["ccxt_config"] = {
+            "httpsProxy": proxy,
+            "wsProxy": proxy,
+        }
+        return config1
+
+    return config
+
+
+def get_exchange(exchange_name, exchange_conf):
+    exchange_conf = set_test_proxy(
+        exchange_conf, EXCHANGES[exchange_name].get("use_ci_proxy", False)
+    )
+    exchange_conf["exchange"]["name"] = exchange_name
+    exchange_conf["stake_currency"] = EXCHANGES[exchange_name]["stake_currency"]
+    exchange = ExchangeResolver.load_exchange(
+        exchange_conf, validate=True, load_leverage_tiers=True
+    )
+
+    return exchange, exchange_name
+
+
+def get_futures_exchange(exchange_name, exchange_conf, class_mocker):
+    if EXCHANGES[exchange_name].get("futures") is not True:
+        pytest.skip(f"Exchange {exchange_name} does not support futures.")
+    else:
+        exchange_conf = deepcopy(exchange_conf)
+        exchange_conf = set_test_proxy(
+            exchange_conf, EXCHANGES[exchange_name].get("use_ci_proxy", False)
+        )
+        exchange_conf["trading_mode"] = "futures"
+        exchange_conf["margin_mode"] = "isolated"
+
+        class_mocker.patch("freqtrade.exchange.binance.Binance.fill_leverage_tiers")
+        class_mocker.patch(f"{EXMS}.fetch_trading_fees")
+        class_mocker.patch("freqtrade.exchange.okx.Okx.additional_exchange_init")
+        class_mocker.patch("freqtrade.exchange.binance.Binance.additional_exchange_init")
+        class_mocker.patch("freqtrade.exchange.bybit.Bybit.additional_exchange_init")
+        class_mocker.patch("freqtrade.exchange.gate.Gate.additional_exchange_init")
+        class_mocker.patch(f"{EXMS}.load_cached_leverage_tiers", return_value=None)
+        class_mocker.patch(f"{EXMS}.cache_leverage_tiers")
+
+        return get_exchange(exchange_name, exchange_conf)
+
+
+@pytest.fixture(params=EXCHANGES, scope="class")
+def exchange(request, exchange_conf, class_mocker):
+    class_mocker.patch("freqtrade.exchange.bybit.Bybit.additional_exchange_init")
+    return get_exchange(request.param, exchange_conf)
+
+
+@pytest.fixture(params=EXCHANGES, scope="class")
+def exchange_futures(request, exchange_conf, class_mocker):
+    return get_futures_exchange(request.param, exchange_conf, class_mocker)
+
+
+@pytest.fixture(params=["spot", "futures"], scope="class")
+def exchange_mode(request):
+    return request.param
+
+
+@pytest.fixture(params=EXCHANGES, scope="class")
+def exchange_ws(request, exchange_conf, exchange_mode, class_mocker):
+    class_mocker.patch("freqtrade.exchange.bybit.Bybit.additional_exchange_init")
+    exchange_conf["exchange"]["enable_ws"] = True
+    exchange_param = EXCHANGES[request.param]
+    if exchange_param.get("skip_ws_tests"):
+        pytest.skip(f"{request.param} does not support websocket tests.")
+    if exchange_mode == "spot":
+        exchange, name = get_exchange(request.param, exchange_conf)
+        pair = exchange_param["pair"]
+    elif exchange_param.get("futures"):
+        exchange, name = get_futures_exchange(
+            request.param, exchange_conf, class_mocker=class_mocker
+        )
+        pair = exchange_param["futures_pair"]
+    else:
+        pytest.skip("Exchange does not support futures.")
+
+    if not exchange._has_watch_ohlcv:
+        pytest.skip("Exchange does not support watch_ohlcv.")
+    yield exchange, name, pair
+    exchange.close()
diff --git a/tests/exchange_online/test_ccxt_compat.py b/tests/exchange_online/test_ccxt_compat.py
new file mode 100644
index 000000000..85de9b5ec
--- /dev/null
+++ b/tests/exchange_online/test_ccxt_compat.py
@@ -0,0 +1,522 @@
+"""
+Tests in this file do NOT mock network calls, so they are expected to be fluky at times.
+
+However, these tests should give a good idea to determine if a new exchange is
+suitable to run with freqtrade.
+"""
+
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from freqtrade.enums import CandleType
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
+from freqtrade.exchange.exchange import timeframe_to_msecs
+from freqtrade.util import dt_floor_day, dt_now, dt_ts
+from tests.exchange_online.conftest import EXCHANGE_FIXTURE_TYPE, EXCHANGES
+
+
+@pytest.mark.longrun
+class TestCCXTExchange:
+    def test_load_markets(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+        pair = EXCHANGES[exchangename]["pair"]
+        markets = exch.markets
+        assert pair in markets
+        assert isinstance(markets[pair], dict)
+        assert exch.market_is_spot(markets[pair])
+
+    def test_has_validations(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+
+        exch.validate_ordertypes(
+            {
+                "entry": "limit",
+                "exit": "limit",
+                "stoploss": "limit",
+            }
+        )
+
+        if exchangename == "gate":
+            # gate doesn't have market orders on spot
+            return
+        exch.validate_ordertypes(
+            {
+                "entry": "market",
+                "exit": "market",
+                "stoploss": "market",
+            }
+        )
+
+    def test_ohlcv_limit(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+        expected_count = EXCHANGES[exchangename].get("candle_count")
+        if not expected_count:
+            pytest.skip("No expected candle count for exchange")
+
+        assert exch.ohlcv_candle_limit("1m", CandleType.SPOT) == expected_count
+
+    def test_ohlcv_limit_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange_futures
+        expected_count = EXCHANGES[exchangename].get("candle_count")
+        if not expected_count:
+            pytest.skip("No expected candle count for exchange")
+
+        assert exch.ohlcv_candle_limit("1m", CandleType.SPOT) == expected_count
+
+    def test_load_markets_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        exchange, exchangename = exchange_futures
+        pair = EXCHANGES[exchangename]["pair"]
+        pair = EXCHANGES[exchangename].get("futures_pair", pair)
+        markets = exchange.markets
+        assert pair in markets
+        assert isinstance(markets[pair], dict)
+
+        assert exchange.market_is_future(markets[pair])
+
+    def test_ccxt_order_parse(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchange_name = exchange
+        if orders := EXCHANGES[exchange_name].get("sample_order"):
+            for order in orders:
+                pair = order["pair"]
+                exchange_response: dict = order["exchange_response"]
+
+                market = exch._api.markets[pair]
+                po = exch._api.parse_order(exchange_response, market)
+                expected = order["expected"]
+                assert isinstance(po["id"], str)
+                assert po["id"] is not None
+                if len(exchange_response.keys()) < 5:
+                    # Kucoin case
+                    assert po["status"] is None
+                    continue
+                assert po["timestamp"] == expected["timestamp"]
+                assert isinstance(po["datetime"], str)
+                assert isinstance(po["timestamp"], int)
+                assert isinstance(po["price"], float)
+                assert po["price"] == expected["price"]
+                if po["status"] == "closed":
+                    # Filled orders should have average assigned.
+                    assert isinstance(po["average"], float)
+                    assert po["average"] == 15.5
+                assert po["symbol"] == pair
+                assert isinstance(po["amount"], float)
+                assert po["amount"] == expected["amount"]
+                assert isinstance(po["status"], str)
+        else:
+            pytest.skip(f"No sample order available for exchange {exchange_name}")
+
+    def test_ccxt_my_trades_parse(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchange_name = exchange
+        if trades := EXCHANGES[exchange_name].get("sample_my_trades"):
+            pair = "SOL/USDT"
+            for trade in trades:
+                po = exch._api.parse_trade(trade)
+                assert po["symbol"] == pair
+                assert isinstance(po["id"], str)
+                assert isinstance(po["side"], str)
+                assert isinstance(po["amount"], float)
+                assert isinstance(po["price"], float)
+                assert isinstance(po["datetime"], str)
+                assert isinstance(po["timestamp"], int)
+
+                if fees := po.get("fees"):
+                    assert isinstance(fees, list)
+                    for fee in fees:
+                        assert isinstance(fee, dict)
+                        assert isinstance(fee["cost"], float)
+                        assert isinstance(fee["currency"], str)
+
+        else:
+            pytest.skip(f"No sample Trades available for exchange {exchange_name}")
+
+    def test_ccxt_balances_parse(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchange_name = exchange
+        if balance_response := EXCHANGES[exchange_name].get("sample_balances"):
+            balances = exch._api.parse_balance(balance_response["exchange_response"])
+            expected = balance_response["expected"]
+            for currency, balance in expected.items():
+                assert currency in balances
+                assert isinstance(balance, dict)
+                assert balance == balances[currency]
+            pass
+        else:
+            pytest.skip(f"No sample Balances available for exchange {exchange_name}")
+
+    def test_ccxt_fetch_tickers(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+        pair = EXCHANGES[exchangename]["pair"]
+
+        tickers = exch.get_tickers()
+        assert pair in tickers
+        assert "ask" in tickers[pair]
+        assert "bid" in tickers[pair]
+        if EXCHANGES[exchangename].get("tickers_have_bid_ask"):
+            assert tickers[pair]["bid"] is not None
+            assert tickers[pair]["ask"] is not None
+        assert "quoteVolume" in tickers[pair]
+        if EXCHANGES[exchangename].get("hasQuoteVolume"):
+            assert tickers[pair]["quoteVolume"] is not None
+
+    def test_ccxt_fetch_tickers_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange_futures
+        if not exch or exchangename in ("gate"):
+            # exchange_futures only returns values for supported exchanges
+            return
+
+        pair = EXCHANGES[exchangename]["pair"]
+        pair = EXCHANGES[exchangename].get("futures_pair", pair)
+
+        tickers = exch.get_tickers()
+        assert pair in tickers
+        assert "ask" in tickers[pair]
+        assert tickers[pair]["ask"] is not None
+        assert "bid" in tickers[pair]
+        assert tickers[pair]["bid"] is not None
+        assert "quoteVolume" in tickers[pair]
+        if EXCHANGES[exchangename].get("hasQuoteVolumeFutures"):
+            assert tickers[pair]["quoteVolume"] is not None
+
+    def test_ccxt_fetch_ticker(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+        pair = EXCHANGES[exchangename]["pair"]
+
+        ticker = exch.fetch_ticker(pair)
+        assert "ask" in ticker
+        assert "bid" in ticker
+        if EXCHANGES[exchangename].get("tickers_have_bid_ask"):
+            assert ticker["ask"] is not None
+            assert ticker["bid"] is not None
+        assert "quoteVolume" in ticker
+        if EXCHANGES[exchangename].get("hasQuoteVolume"):
+            assert ticker["quoteVolume"] is not None
+
+    def test_ccxt_fetch_l2_orderbook(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+        pair = EXCHANGES[exchangename]["pair"]
+        l2 = exch.fetch_l2_order_book(pair)
+        orderbook_max_entries = EXCHANGES[exchangename].get("orderbook_max_entries")
+        assert "asks" in l2
+        assert "bids" in l2
+        assert len(l2["asks"]) >= 1
+        assert len(l2["bids"]) >= 1
+        l2_limit_range = exch._ft_has["l2_limit_range"]
+        l2_limit_range_required = exch._ft_has["l2_limit_range_required"]
+        if exchangename == "gate":
+            # TODO: Gate is unstable here at the moment, ignoring the limit partially.
+            return
+        for val in [1, 2, 5, 25, 50, 100]:
+            if orderbook_max_entries and val > orderbook_max_entries:
+                continue
+            l2 = exch.fetch_l2_order_book(pair, val)
+            if not l2_limit_range or val in l2_limit_range:
+                if val > 50:
+                    # Orderbooks are not always this deep.
+                    assert val - 5 < len(l2["asks"]) <= val
+                    assert val - 5 < len(l2["bids"]) <= val
+                else:
+                    assert len(l2["asks"]) == val
+                    assert len(l2["bids"]) == val
+            else:
+                next_limit = exch.get_next_limit_in_list(
+                    val, l2_limit_range, l2_limit_range_required
+                )
+                if next_limit is None:
+                    assert len(l2["asks"]) > 100
+                    assert len(l2["asks"]) > 100
+                elif next_limit > 200:
+                    # Large orderbook sizes can be a problem for some exchanges (bitrex ...)
+                    assert len(l2["asks"]) > 200
+                    assert len(l2["asks"]) > 200
+                else:
+                    assert len(l2["asks"]) == next_limit
+                    assert len(l2["asks"]) == next_limit
+
+    def test_ccxt_fetch_ohlcv(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+        pair = EXCHANGES[exchangename]["pair"]
+        timeframe = EXCHANGES[exchangename]["timeframe"]
+
+        pair_tf = (pair, timeframe, CandleType.SPOT)
+
+        ohlcv = exch.refresh_latest_ohlcv([pair_tf])
+        assert isinstance(ohlcv, dict)
+        assert len(ohlcv[pair_tf]) == len(exch.klines(pair_tf))
+        # assert len(exch.klines(pair_tf)) > 200
+        # Assume 90% uptime ...
+        assert (
+            len(exch.klines(pair_tf)) > exch.ohlcv_candle_limit(timeframe, CandleType.SPOT) * 0.90
+        )
+        # Check if last-timeframe is within the last 2 intervals
+        now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2))
+        assert exch.klines(pair_tf).iloc[-1]["date"] >= timeframe_to_prev_date(timeframe, now)
+
+    def test_ccxt_fetch_ohlcv_startdate(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        """
+        Test that pair data starts at the provided startdate
+        """
+        exch, exchangename = exchange
+        pair = EXCHANGES[exchangename]["pair"]
+        timeframe = "1d"
+
+        pair_tf = (pair, timeframe, CandleType.SPOT)
+        # last 5 days ...
+        since_ms = dt_ts(dt_floor_day(dt_now()) - timedelta(days=6))
+        ohlcv = exch.refresh_latest_ohlcv([pair_tf], since_ms=since_ms)
+        assert isinstance(ohlcv, dict)
+        assert len(ohlcv[pair_tf]) == len(exch.klines(pair_tf))
+        # Check if last-timeframe is within the last 2 intervals
+        now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2))
+        assert exch.klines(pair_tf).iloc[-1]["date"] >= timeframe_to_prev_date(timeframe, now)
+        assert exch.klines(pair_tf)["date"].astype(int).iloc[0] // 1e6 == since_ms
+
+    def ccxt__async_get_candle_history(
+        self, exchange, exchangename, pair, timeframe, candle_type, factor=0.9
+    ):
+        timeframe_ms = timeframe_to_msecs(timeframe)
+        now = timeframe_to_prev_date(timeframe, datetime.now(timezone.utc))
+        for offset in (360, 120, 30, 10, 5, 2):
+            since = now - timedelta(days=offset)
+            since_ms = int(since.timestamp() * 1000)
+
+            res = exchange.loop.run_until_complete(
+                exchange._async_get_candle_history(
+                    pair=pair, timeframe=timeframe, since_ms=since_ms, candle_type=candle_type
+                )
+            )
+            assert res
+            assert res[0] == pair
+            assert res[1] == timeframe
+            assert res[2] == candle_type
+            candles = res[3]
+            candle_count = exchange.ohlcv_candle_limit(timeframe, candle_type, since_ms) * factor
+            candle_count1 = (now.timestamp() * 1000 - since_ms) // timeframe_ms * factor
+            assert len(candles) >= min(candle_count, candle_count1), (
+                f"{len(candles)} < {candle_count} in {timeframe}, Offset: {offset} {factor}"
+            )
+            # Check if first-timeframe is either the start, or start + 1
+            assert candles[0][0] == since_ms or (since_ms + timeframe_ms)
+
+    def test_ccxt__async_get_candle_history(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exc, exchangename = exchange
+
+        if not exc._ft_has["ohlcv_has_history"]:
+            pytest.skip("Exchange does not support candle history")
+        pair = EXCHANGES[exchangename]["pair"]
+        timeframe = EXCHANGES[exchangename]["timeframe"]
+        self.ccxt__async_get_candle_history(exc, exchangename, pair, timeframe, CandleType.SPOT)
+
+    @pytest.mark.parametrize(
+        "candle_type",
+        [
+            CandleType.FUTURES,
+            CandleType.FUNDING_RATE,
+            CandleType.MARK,
+        ],
+    )
+    def test_ccxt__async_get_candle_history_futures(
+        self, exchange_futures: EXCHANGE_FIXTURE_TYPE, candle_type
+    ):
+        exchange, exchangename = exchange_futures
+        pair = EXCHANGES[exchangename].get("futures_pair", EXCHANGES[exchangename]["pair"])
+        timeframe = EXCHANGES[exchangename]["timeframe"]
+        if candle_type == CandleType.FUNDING_RATE:
+            timeframe = exchange._ft_has.get(
+                "funding_fee_timeframe", exchange._ft_has["mark_ohlcv_timeframe"]
+            )
+        self.ccxt__async_get_candle_history(
+            exchange,
+            exchangename,
+            pair=pair,
+            timeframe=timeframe,
+            candle_type=candle_type,
+        )
+
+    def test_ccxt_fetch_funding_rate_history(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        exchange, exchangename = exchange_futures
+
+        pair = EXCHANGES[exchangename].get("futures_pair", EXCHANGES[exchangename]["pair"])
+        since = int((datetime.now(timezone.utc) - timedelta(days=5)).timestamp() * 1000)
+        timeframe_ff = exchange._ft_has.get(
+            "funding_fee_timeframe", exchange._ft_has["mark_ohlcv_timeframe"]
+        )
+        pair_tf = (pair, timeframe_ff, CandleType.FUNDING_RATE)
+
+        funding_ohlcv = exchange.refresh_latest_ohlcv(
+            [pair_tf], since_ms=since, drop_incomplete=False
+        )
+
+        assert isinstance(funding_ohlcv, dict)
+        rate = funding_ohlcv[pair_tf]
+
+        this_hour = timeframe_to_prev_date(timeframe_ff)
+        hour1 = timeframe_to_prev_date(timeframe_ff, this_hour - timedelta(minutes=1))
+        hour2 = timeframe_to_prev_date(timeframe_ff, hour1 - timedelta(minutes=1))
+        hour3 = timeframe_to_prev_date(timeframe_ff, hour2 - timedelta(minutes=1))
+        val0 = rate[rate["date"] == this_hour].iloc[0]["open"]
+        val1 = rate[rate["date"] == hour1].iloc[0]["open"]
+        val2 = rate[rate["date"] == hour2].iloc[0]["open"]
+        val3 = rate[rate["date"] == hour3].iloc[0]["open"]
+
+        # Test For last 4 hours
+        # Avoids random test-failure when funding-fees are 0 for a few hours.
+        assert val0 != 0.0 or val1 != 0.0 or val2 != 0.0 or val3 != 0.0
+        # We expect funding rates to be different from 0.0 - or moving around.
+        assert (
+            rate["open"].max() != 0.0
+            or rate["open"].min() != 0.0
+            or (rate["open"].min() != rate["open"].max())
+        )
+
+    def test_ccxt_fetch_mark_price_history(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        exchange, exchangename = exchange_futures
+        pair = EXCHANGES[exchangename].get("futures_pair", EXCHANGES[exchangename]["pair"])
+        since = int((datetime.now(timezone.utc) - timedelta(days=5)).timestamp() * 1000)
+        pair_tf = (pair, "1h", CandleType.MARK)
+
+        mark_ohlcv = exchange.refresh_latest_ohlcv([pair_tf], since_ms=since, drop_incomplete=False)
+
+        assert isinstance(mark_ohlcv, dict)
+        expected_tf = "1h"
+        mark_candles = mark_ohlcv[pair_tf]
+
+        this_hour = timeframe_to_prev_date(expected_tf)
+        prev_hour = timeframe_to_prev_date(expected_tf, this_hour - timedelta(minutes=1))
+
+        assert mark_candles[mark_candles["date"] == prev_hour].iloc[0]["open"] != 0.0
+        assert mark_candles[mark_candles["date"] == this_hour].iloc[0]["open"] != 0.0
+
+    def test_ccxt__calculate_funding_fees(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        exchange, exchangename = exchange_futures
+        pair = EXCHANGES[exchangename].get("futures_pair", EXCHANGES[exchangename]["pair"])
+        since = datetime.now(timezone.utc) - timedelta(days=5)
+
+        funding_fee = exchange._fetch_and_calculate_funding_fees(
+            pair, 20, is_short=False, open_date=since
+        )
+
+        assert isinstance(funding_fee, float)
+        # assert funding_fee > 0
+
+    def test_ccxt__async_get_trade_history(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+        if not (lookback := EXCHANGES[exchangename].get("trades_lookback_hours")):
+            pytest.skip("test_fetch_trades not enabled for this exchange")
+        pair = EXCHANGES[exchangename]["pair"]
+        since = int((datetime.now(timezone.utc) - timedelta(hours=lookback)).timestamp() * 1000)
+        res = exch.loop.run_until_complete(exch._async_get_trade_history(pair, since, None, None))
+        assert len(res) == 2
+        res_pair, res_trades = res
+        assert res_pair == pair
+        assert isinstance(res_trades, list)
+        assert res_trades[0][0] >= since
+        assert len(res_trades) > 1200
+
+    def test_ccxt_get_fee(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+        pair = EXCHANGES[exchangename]["pair"]
+        threshold = 0.01
+        assert 0 < exch.get_fee(pair, "limit", "buy") < threshold
+        assert 0 < exch.get_fee(pair, "limit", "sell") < threshold
+        assert 0 < exch.get_fee(pair, "market", "buy") < threshold
+        assert 0 < exch.get_fee(pair, "market", "sell") < threshold
+
+    def test_ccxt_get_max_leverage_spot(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        spot, spot_name = exchange
+        if spot:
+            leverage_in_market_spot = EXCHANGES[spot_name].get("leverage_in_spot_market")
+            if leverage_in_market_spot:
+                spot_pair = EXCHANGES[spot_name].get("pair", EXCHANGES[spot_name]["pair"])
+                spot_leverage = spot.get_max_leverage(spot_pair, 20)
+                assert isinstance(spot_leverage, float) or isinstance(spot_leverage, int)
+                assert spot_leverage >= 1.0
+
+    def test_ccxt_get_max_leverage_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        futures, futures_name = exchange_futures
+        leverage_tiers_public = EXCHANGES[futures_name].get("leverage_tiers_public")
+        if leverage_tiers_public:
+            futures_pair = EXCHANGES[futures_name].get(
+                "futures_pair", EXCHANGES[futures_name]["pair"]
+            )
+            futures_leverage = futures.get_max_leverage(futures_pair, 20)
+            assert isinstance(futures_leverage, float) or isinstance(futures_leverage, int)
+            assert futures_leverage >= 1.0
+
+    def test_ccxt_get_contract_size(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        futures, futures_name = exchange_futures
+        futures_pair = EXCHANGES[futures_name].get("futures_pair", EXCHANGES[futures_name]["pair"])
+        contract_size = futures.get_contract_size(futures_pair)
+        assert isinstance(contract_size, float) or isinstance(contract_size, int)
+        assert contract_size >= 0.0
+
+    def test_ccxt_load_leverage_tiers(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        futures, futures_name = exchange_futures
+        if EXCHANGES[futures_name].get("leverage_tiers_public"):
+            leverage_tiers = futures.load_leverage_tiers()
+            futures_pair = EXCHANGES[futures_name].get(
+                "futures_pair", EXCHANGES[futures_name]["pair"]
+            )
+            assert isinstance(leverage_tiers, dict)
+            assert futures_pair in leverage_tiers
+            pair_tiers = leverage_tiers[futures_pair]
+            assert len(pair_tiers) > 0
+            oldLeverage = float("inf")
+            oldMaintenanceMarginRate = oldminNotional = oldmaxNotional = -1
+            for tier in pair_tiers:
+                for key in ["maintenanceMarginRate", "minNotional", "maxNotional", "maxLeverage"]:
+                    assert key in tier
+                    assert tier[key] >= 0.0
+                assert tier["maxNotional"] > tier["minNotional"]
+                assert tier["maxLeverage"] <= oldLeverage
+                assert tier["maintenanceMarginRate"] >= oldMaintenanceMarginRate
+                assert tier["minNotional"] > oldminNotional
+                assert tier["maxNotional"] > oldmaxNotional
+                oldLeverage = tier["maxLeverage"]
+                oldMaintenanceMarginRate = tier["maintenanceMarginRate"]
+                oldminNotional = tier["minNotional"]
+                oldmaxNotional = tier["maxNotional"]
+
+    def test_ccxt_dry_run_liquidation_price(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        futures, futures_name = exchange_futures
+        if EXCHANGES[futures_name].get("leverage_tiers_public"):
+            futures_pair = EXCHANGES[futures_name].get(
+                "futures_pair", EXCHANGES[futures_name]["pair"]
+            )
+
+            liquidation_price = futures.dry_run_liquidation_price(
+                pair=futures_pair,
+                open_rate=40000,
+                is_short=False,
+                amount=100,
+                stake_amount=100,
+                leverage=5,
+                wallet_balance=100,
+                open_trades=[],
+            )
+            assert isinstance(liquidation_price, float)
+            assert liquidation_price >= 0.0
+
+            liquidation_price = futures.dry_run_liquidation_price(
+                pair=futures_pair,
+                open_rate=40000,
+                is_short=False,
+                amount=100,
+                stake_amount=100,
+                leverage=5,
+                wallet_balance=100,
+                open_trades=[],
+            )
+            assert isinstance(liquidation_price, float)
+            assert liquidation_price >= 0.0
+
+    def test_ccxt_get_max_pair_stake_amount(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
+        futures, futures_name = exchange_futures
+        futures_pair = EXCHANGES[futures_name].get("futures_pair", EXCHANGES[futures_name]["pair"])
+        max_stake_amount = futures.get_max_pair_stake_amount(futures_pair, 40000)
+        assert isinstance(max_stake_amount, float)
+        assert max_stake_amount >= 0.0
+
+    def test_private_method_presence(self, exchange: EXCHANGE_FIXTURE_TYPE):
+        exch, exchangename = exchange
+        for method in EXCHANGES[exchangename].get("private_methods", []):
+            assert hasattr(exch._api, method)
diff --git a/tests/exchange_online/test_ccxt_ws_compat.py b/tests/exchange_online/test_ccxt_ws_compat.py
new file mode 100644
index 000000000..6ab7600f4
--- /dev/null
+++ b/tests/exchange_online/test_ccxt_ws_compat.py
@@ -0,0 +1,68 @@
+"""
+Tests in this file do NOT mock network calls, so they are expected to be fluky at times.
+
+However, these tests aim to test ccxt compatibility, specifically regarding websockets.
+"""
+
+import logging
+from datetime import timedelta
+from time import sleep
+
+import pytest
+
+from freqtrade.enums import CandleType
+from freqtrade.exchange.exchange_utils import timeframe_to_prev_date
+from freqtrade.util.datetime_helpers import dt_now
+from tests.conftest import log_has_re
+from tests.exchange_online.conftest import EXCHANGE_WS_FIXTURE_TYPE
+
+
+@pytest.mark.longrun
+@pytest.mark.timeout(3 * 60)
+class TestCCXTExchangeWs:
+    def test_ccxt_watch_ohlcv(self, exchange_ws: EXCHANGE_WS_FIXTURE_TYPE, caplog, mocker):
+        exch, _exchangename, pair = exchange_ws
+
+        assert exch._ws_async is not None
+        timeframe = "1m"
+        pair_tf = (pair, timeframe, CandleType.SPOT)
+        m_hist = mocker.spy(exch, "_async_get_historic_ohlcv")
+        m_cand = mocker.spy(exch, "_async_get_candle_history")
+
+        while True:
+            # Don't start the test if we are too close to the end of the minute.
+            if dt_now().second < 50 and dt_now().second > 1:
+                break
+            sleep(1)
+
+        res = exch.refresh_latest_ohlcv([pair_tf])
+        assert m_cand.call_count == 1
+
+        # Currently open candle
+        next_candle = timeframe_to_prev_date(timeframe, dt_now())
+        now = next_candle - timedelta(seconds=1)
+        # Currently closed candle
+        curr_candle = timeframe_to_prev_date(timeframe, now)
+
+        assert pair_tf in exch._exchange_ws._klines_watching
+        assert pair_tf in exch._exchange_ws._klines_scheduled
+        assert res[pair_tf] is not None
+        df1 = res[pair_tf]
+        caplog.set_level(logging.DEBUG)
+        assert df1.iloc[-1]["date"] == curr_candle
+
+        # Wait until the next candle (might be up to 1 minute).
+        while True:
+            caplog.clear()
+            res = exch.refresh_latest_ohlcv([pair_tf])
+            df2 = res[pair_tf]
+            assert df2 is not None
+            if df2.iloc[-1]["date"] == next_candle:
+                break
+            assert df2.iloc[-1]["date"] == curr_candle
+            sleep(1)
+
+        assert m_hist.call_count == 0
+        # shouldn't have tried fetch_ohlcv a second time.
+        assert m_cand.call_count == 1
+        assert log_has_re(r"watch result.*", caplog)
diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py
new file mode 100644
index 000000000..9e14111af
--- /dev/null
+++ b/tests/freqai/conftest.py
@@ -0,0 +1,251 @@
+import sys
+from copy import deepcopy
+from pathlib import Path
+from typing import Any
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.freqai.data_drawer import FreqaiDataDrawer
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.resolvers import StrategyResolver
+from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
+from tests.conftest import get_patched_exchange
+
+
+def is_py12() -> bool:
+    return sys.version_info >= (3, 12)
+
+
+@pytest.fixture(scope="function")
+def freqai_conf(default_conf, tmp_path):
+    freqaiconf = deepcopy(default_conf)
+    freqaiconf.update(
+        {
+            "datadir": Path(default_conf["datadir"]),
+            "runmode": "backtest",
+            "strategy": "freqai_test_strat",
+            "user_data_dir": tmp_path,
+            "strategy-path": "freqtrade/tests/strategy/strats",
+            "freqaimodel": "LightGBMRegressor",
+            "freqaimodel_path": "freqai/prediction_models",
+            "timerange": "20180110-20180115",
+            "freqai": {
+                "enabled": True,
+                "purge_old_models": 2,
+                "train_period_days": 2,
+                "backtest_period_days": 10,
+                "live_retrain_hours": 0,
+                "expiration_hours": 1,
+                "identifier": "unique-id100",
+                "live_trained_timestamp": 0,
+                "data_kitchen_thread_count": 2,
+                "activate_tensorboard": False,
+                "feature_parameters": {
+                    "include_timeframes": ["5m"],
+                    "include_corr_pairlist": ["ADA/BTC"],
+                    "label_period_candles": 20,
+                    "include_shifted_candles": 1,
+                    "DI_threshold": 0.9,
+                    "weight_factor": 0.9,
+                    "principal_component_analysis": False,
+                    "use_SVM_to_remove_outliers": True,
+                    "stratify_training_data": 0,
+                    "indicator_periods_candles": [10],
+                    "shuffle_after_split": False,
+                    "buffer_train_data_candles": 0,
+                },
+                "data_split_parameters": {"test_size": 0.33, "shuffle": False},
+                "model_training_parameters": {"n_estimators": 100},
+            },
+            "config_files": [Path("config_examples", "config_freqai.example.json")],
+        }
+    )
+    freqaiconf["exchange"].update({"pair_whitelist": ["ADA/BTC", "DASH/BTC", "ETH/BTC", "LTC/BTC"]})
+    return freqaiconf
+
+
+def make_rl_config(conf):
+    conf.update({"strategy": "freqai_rl_test_strat"})
+    conf["freqai"].update(
+        {"model_training_parameters": {"learning_rate": 0.00025, "gamma": 0.9, "verbose": 1}}
+    )
+    conf["freqai"]["rl_config"] = {
+        "train_cycles": 1,
+        "thread_count": 2,
+        "max_trade_duration_candles": 300,
+        "model_type": "PPO",
+        "policy_type": "MlpPolicy",
+        "max_training_drawdown_pct": 0.5,
+        "net_arch": [32, 32],
+        "model_reward_parameters": {"rr": 1, "profit_aim": 0.02, "win_reward_factor": 2},
+        "drop_ohlc_from_features": False,
+    }
+
+    return conf
+
+
+def mock_pytorch_mlp_model_training_parameters() -> dict[str, Any]:
+    return {
+        "learning_rate": 3e-4,
+        "trainer_kwargs": {
+            "n_steps": None,
+            "batch_size": 64,
+            "n_epochs": 1,
+        },
+        "model_kwargs": {
+            "hidden_dim": 32,
+            "dropout_percent": 0.2,
+            "n_layer": 1,
+        },
+    }
+
+
+def get_patched_data_kitchen(mocker, freqaiconf):
+    dk = FreqaiDataKitchen(freqaiconf)
+    return dk
+
+
+def get_patched_data_drawer(mocker, freqaiconf):
+    # dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
+    dd = FreqaiDataDrawer(freqaiconf)
+    return dd
+
+
+def get_patched_freqai_strategy(mocker, freqaiconf):
+    strategy = StrategyResolver.load_strategy(freqaiconf)
+    strategy.ft_bot_start()
+
+    return strategy
+
+
+def get_patched_freqaimodel(mocker, freqaiconf):
+    freqaimodel = FreqaiModelResolver.load_freqaimodel(freqaiconf)
+
+    return freqaimodel
+
+
+def make_unfiltered_dataframe(mocker, freqai_conf):
+    freqai_conf.update({"timerange": "20180110-20180130"})
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    freqai.dk.pair = "ADA/BTC"
+    data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk)
+
+    freqai.dd.pair_dict = MagicMock()
+
+    new_timerange = TimeRange.parse_timerange("20180120-20180130")
+
+    corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(
+        data_load_timerange, freqai.dk.pair, freqai.dk
+    )
+
+    unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
+        strategy, corr_dataframes, base_dataframes, freqai.dk.pair
+    )
+    for i in range(5):
+        unfiltered_dataframe[f"constant_{i}"] = i
+
+    unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
+
+    return freqai, unfiltered_dataframe
+
+
+def make_data_dictionary(mocker, freqai_conf):
+    freqai_conf.update({"timerange": "20180110-20180130"})
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    freqai.dk.pair = "ADA/BTC"
+    data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk)
+
+    freqai.dd.pair_dict = MagicMock()
+
+    new_timerange = TimeRange.parse_timerange("20180120-20180130")
+
+    corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(
+        data_load_timerange, freqai.dk.pair, freqai.dk
+    )
+
+    unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
+        strategy, corr_dataframes, base_dataframes, freqai.dk.pair
+    )
+
+    unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
+
+    freqai.dk.find_features(unfiltered_dataframe)
+
+    features_filtered, labels_filtered = freqai.dk.filter_features(
+        unfiltered_dataframe,
+        freqai.dk.training_features_list,
+        freqai.dk.label_list,
+        training_filter=True,
+    )
+
+    data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
+
+    data_dictionary = freqai.dk.normalize_data(data_dictionary)
+
+    return freqai
+
+
+def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
+    strategy = get_patched_freqai_strategy(mocker, freqaiconf)
+    exchange = get_patched_exchange(mocker, freqaiconf)
+    strategy.dp = DataProvider(freqaiconf, exchange)
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
+    timerange = TimeRange.parse_timerange("20180110-20180114")
+    freqai.dk.load_all_pair_histories(timerange)
+
+    strategy.analyze_pair("ADA/BTC", "5m")
+    return strategy.dp.get_analyzed_dataframe("ADA/BTC", "5m")
+
+
+def get_freqai_analyzed_dataframe(mocker, freqaiconf):
+    strategy = get_patched_freqai_strategy(mocker, freqaiconf)
+    exchange = get_patched_exchange(mocker, freqaiconf)
+    strategy.dp = DataProvider(freqaiconf, exchange)
+    strategy.freqai_info = freqaiconf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
+    timerange = TimeRange.parse_timerange("20180110-20180114")
+    freqai.dk.load_all_pair_histories(timerange)
+    sub_timerange = TimeRange.parse_timerange("20180111-20180114")
+    corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
+
+    return freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
+
+
+def get_ready_to_train(mocker, freqaiconf):
+    strategy = get_patched_freqai_strategy(mocker, freqaiconf)
+    exchange = get_patched_exchange(mocker, freqaiconf)
+    strategy.dp = DataProvider(freqaiconf, exchange)
+    strategy.freqai_info = freqaiconf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
+    timerange = TimeRange.parse_timerange("20180110-20180114")
+    freqai.dk.load_all_pair_histories(timerange)
+    sub_timerange = TimeRange.parse_timerange("20180111-20180114")
+    corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
+    return corr_df, base_df, freqai, strategy
diff --git a/tests/freqai/test_freqai_backtesting.py b/tests/freqai/test_freqai_backtesting.py
new file mode 100644
index 000000000..e689d3927
--- /dev/null
+++ b/tests/freqai/test_freqai_backtesting.py
@@ -0,0 +1,168 @@
+from copy import deepcopy
+from datetime import datetime, timezone
+from pathlib import Path
+from unittest.mock import PropertyMock
+
+import pytest
+
+from freqtrade.commands.optimize_commands import setup_optimize_configuration
+from freqtrade.configuration.timerange import TimeRange
+from freqtrade.data import history
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import RunMode
+from freqtrade.enums.candletype import CandleType
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.optimize.backtesting import Backtesting
+from tests.conftest import (
+    CURRENT_TEST_STRATEGY,
+    get_args,
+    get_patched_exchange,
+    log_has_re,
+    patch_exchange,
+    patched_configuration_load_config_file,
+)
+from tests.freqai.conftest import get_patched_freqai_strategy
+
+
+def test_freqai_backtest_start_backtest_list(freqai_conf, mocker, testdatadir, caplog):
+    patch_exchange(mocker)
+
+    now = datetime.now(timezone.utc)
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["HULUMULU/USDT", "XRP/USDT"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.history.load_data")
+    mocker.patch("freqtrade.optimize.backtesting.history.get_timerange", return_value=(now, now))
+
+    patched_configuration_load_config_file(mocker, freqai_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--strategy-path",
+        str(Path(__file__).parents[1] / "strategy/strats"),
+        "--timeframe",
+        "1m",
+        "--strategy-list",
+        CURRENT_TEST_STRATEGY,
+    ]
+    args = get_args(args)
+    bt_config = setup_optimize_configuration(args, RunMode.BACKTEST)
+    Backtesting(bt_config)
+    assert log_has_re(
+        "Using --strategy-list with FreqAI REQUIRES all strategies to have identical", caplog
+    )
+    Backtesting.cleanup()
+
+
+@pytest.mark.parametrize(
+    "timeframe, expected_startup_candle_count",
+    [
+        ("5m", 876),
+        ("15m", 492),
+        ("1d", 302),
+    ],
+)
+def test_freqai_backtest_load_data(
+    freqai_conf, mocker, caplog, timeframe, expected_startup_candle_count
+):
+    patch_exchange(mocker)
+
+    now = datetime.now(timezone.utc)
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["HULUMULU/USDT", "XRP/USDT"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.history.load_data")
+    mocker.patch("freqtrade.optimize.backtesting.history.get_timerange", return_value=(now, now))
+    freqai_conf["timeframe"] = timeframe
+    freqai_conf.get("freqai", {}).get("feature_parameters", {}).update({"include_timeframes": []})
+    backtesting = Backtesting(deepcopy(freqai_conf))
+    backtesting.load_bt_data()
+
+    assert log_has_re(
+        f"Increasing startup_candle_count for freqai on {timeframe} "
+        f"to {expected_startup_candle_count}",
+        caplog,
+    )
+    assert history.load_data.call_args[1]["startup_candles"] == expected_startup_candle_count
+
+    Backtesting.cleanup()
+
+
+def test_freqai_backtest_live_models_model_not_found(freqai_conf, mocker, testdatadir, caplog):
+    patch_exchange(mocker)
+
+    now = datetime.now(timezone.utc)
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["HULUMULU/USDT", "XRP/USDT"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.history.load_data")
+    mocker.patch("freqtrade.optimize.backtesting.history.get_timerange", return_value=(now, now))
+    freqai_conf["timerange"] = ""
+    freqai_conf.get("freqai", {}).update({"backtest_using_historic_predictions": False})
+
+    patched_configuration_load_config_file(mocker, freqai_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--strategy-path",
+        str(Path(__file__).parents[1] / "strategy/strats"),
+        "--timeframe",
+        "5m",
+        "--freqai-backtest-live-models",
+    ]
+    args = get_args(args)
+    bt_config = setup_optimize_configuration(args, RunMode.BACKTEST)
+
+    with pytest.raises(
+        OperationalException, match=r".* Historic predictions data is required to run backtest .*"
+    ):
+        Backtesting(bt_config)
+
+    Backtesting.cleanup()
+
+
+def test_freqai_backtest_consistent_timerange(mocker, freqai_conf):
+    freqai_conf["runmode"] = "backtest"
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["XRP/USDT:USDT"]),
+    )
+
+    gbs = mocker.patch("freqtrade.optimize.backtesting.generate_backtest_stats")
+
+    freqai_conf["candle_type_def"] = CandleType.FUTURES
+    freqai_conf.get("exchange", {}).update({"pair_whitelist": ["XRP/USDT:USDT"]})
+    freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
+        {"include_timeframes": ["5m", "1h"], "include_corr_pairlist": []}
+    )
+    freqai_conf["timerange"] = "20211120-20211121"
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+
+    timerange = TimeRange.parse_timerange("20211115-20211122")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    backtesting = Backtesting(deepcopy(freqai_conf))
+    backtesting.start()
+
+    assert gbs.call_args[1]["min_date"] == datetime(2021, 11, 20, 0, 0, tzinfo=timezone.utc)
+    assert gbs.call_args[1]["max_date"] == datetime(2021, 11, 21, 0, 0, tzinfo=timezone.utc)
+    Backtesting.cleanup()
diff --git a/tests/freqai/test_freqai_datadrawer.py b/tests/freqai/test_freqai_datadrawer.py
new file mode 100644
index 000000000..037691d50
--- /dev/null
+++ b/tests/freqai/test_freqai_datadrawer.py
@@ -0,0 +1,239 @@
+import shutil
+from pathlib import Path
+from unittest.mock import patch
+
+import pandas as pd
+import pytest
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from tests.conftest import get_patched_exchange
+from tests.freqai.conftest import get_patched_freqai_strategy
+
+
+def test_update_historic_data(mocker, freqai_conf):
+    freqai_conf["runmode"] = "backtest"
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    timerange = TimeRange.parse_timerange("20180110-20180114")
+
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
+    dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
+    candle_difference = dp_candles - historic_candles
+    freqai.dk.pair = "ADA/BTC"
+    freqai.dd.update_historic_data(strategy, freqai.dk)
+
+    updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
+
+    assert updated_historic_candles - historic_candles == candle_difference
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_load_all_pairs_histories(mocker, freqai_conf):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    timerange = TimeRange.parse_timerange("20180110-20180114")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    assert len(freqai.dd.historic_data.keys()) == len(
+        freqai_conf.get("exchange", {}).get("pair_whitelist")
+    )
+    assert len(freqai.dd.historic_data["ADA/BTC"]) == len(
+        freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
+    )
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_get_base_and_corr_dataframes(mocker, freqai_conf):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    timerange = TimeRange.parse_timerange("20180110-20180114")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180111-20180114")
+    corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
+
+    num_tfs = len(
+        freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
+    )
+
+    assert len(base_df.keys()) == num_tfs
+
+    assert len(corr_df.keys()) == len(
+        freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
+    )
+
+    assert len(corr_df["ADA/BTC"].keys()) == num_tfs
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    timerange = TimeRange.parse_timerange("20180110-20180114")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180111-20180114")
+    corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
+
+    df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
+
+    assert len(df.columns) == 33
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_get_timerange_from_live_historic_predictions(mocker, freqai_conf):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    freqai = strategy.freqai
+    freqai.live = False
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = False
+    timerange = TimeRange.parse_timerange("20180126-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180128-20180130")
+    _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "ADA/BTC", freqai.dk)
+    base_df["5m"]["date_pred"] = base_df["5m"]["date"]
+    freqai.dd.historic_predictions = {}
+    freqai.dd.historic_predictions["ADA/USDT"] = base_df["5m"]
+    freqai.dd.save_historic_predictions_to_disk()
+    freqai.dd.save_global_metadata_to_disk({"start_dry_live_date": 1516406400})
+
+    timerange = freqai.dd.get_timerange_from_live_historic_predictions()
+    assert timerange.startts == 1516406400
+    assert timerange.stopts == 1517356500
+
+
+def test_get_timerange_from_backtesting_live_df_pred_not_found(mocker, freqai_conf):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    freqai = strategy.freqai
+    with pytest.raises(OperationalException, match=r"Historic predictions not found.*"):
+        freqai.dd.get_timerange_from_live_historic_predictions()
+
+
+def test_set_initial_return_values(mocker, freqai_conf):
+    """
+    Simple test of the set initial return values that ensures
+    we are concatenating and ffilling values properly.
+    """
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    freqai = strategy.freqai
+    freqai.live = False
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    # Setup
+    pair = "BTC/USD"
+    end_x = "2023-08-31"
+    start_x_plus_1 = "2023-08-30"
+    end_x_plus_5 = "2023-09-03"
+
+    historic_data = {"date_pred": pd.date_range(end=end_x, periods=5), "value": range(1, 6)}
+    new_data = {
+        "date": pd.date_range(start=start_x_plus_1, end=end_x_plus_5),
+        "value": range(6, 11),
+    }
+
+    freqai.dd.historic_predictions[pair] = pd.DataFrame(historic_data)
+
+    new_pred_df = pd.DataFrame(new_data)
+    dataframe = pd.DataFrame(new_data)
+
+    # Action
+    with patch("logging.Logger.warning") as mock_logger_warning:
+        freqai.dd.set_initial_return_values(pair, new_pred_df, dataframe)
+
+    # Assertions
+    hist_pred_df = freqai.dd.historic_predictions[pair]
+    model_return_df = freqai.dd.model_return_values[pair]
+
+    assert hist_pred_df["date_pred"].iloc[-1] == pd.Timestamp(end_x_plus_5)
+    assert "date_pred" in hist_pred_df.columns
+    assert hist_pred_df.shape[0] == 8
+
+    # compare values in model_return_df with hist_pred_df
+    assert (
+        model_return_df["value"].values == hist_pred_df.tail(len(dataframe))["value"].values
+    ).all()
+    assert model_return_df.shape[0] == len(dataframe)
+
+    # Ensure logger error is not called
+    mock_logger_warning.assert_not_called()
+
+
+def test_set_initial_return_values_warning(mocker, freqai_conf):
+    """
+    Simple test of set_initial_return_values that hits the warning
+    associated with leaving a FreqAI bot offline so long that the
+    exchange candles have no common date with the historic predictions
+    """
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    freqai = strategy.freqai
+    freqai.live = False
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    # Setup
+    pair = "BTC/USD"
+    end_x = "2023-08-31"
+    start_x_plus_1 = "2023-09-01"
+    end_x_plus_5 = "2023-09-05"
+
+    historic_data = {"date_pred": pd.date_range(end=end_x, periods=5), "value": range(1, 6)}
+    new_data = {
+        "date": pd.date_range(start=start_x_plus_1, end=end_x_plus_5),
+        "value": range(6, 11),
+    }
+
+    freqai.dd.historic_predictions[pair] = pd.DataFrame(historic_data)
+
+    new_pred_df = pd.DataFrame(new_data)
+    dataframe = pd.DataFrame(new_data)
+
+    # Action
+    with patch("logging.Logger.warning") as mock_logger_warning:
+        freqai.dd.set_initial_return_values(pair, new_pred_df, dataframe)
+
+    # Assertions
+    hist_pred_df = freqai.dd.historic_predictions[pair]
+    model_return_df = freqai.dd.model_return_values[pair]
+
+    assert hist_pred_df["date_pred"].iloc[-1] == pd.Timestamp(end_x_plus_5)
+    assert "date_pred" in hist_pred_df.columns
+    assert hist_pred_df.shape[0] == 10
+
+    # compare values in model_return_df with hist_pred_df
+    assert (
+        model_return_df["value"].values == hist_pred_df.tail(len(dataframe))["value"].values
+    ).all()
+    assert model_return_df.shape[0] == len(dataframe)
+
+    # Ensure logger error is not called
+    mock_logger_warning.assert_called()
diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py
new file mode 100644
index 000000000..2e92ae447
--- /dev/null
+++ b/tests/freqai/test_freqai_datakitchen.py
@@ -0,0 +1,206 @@
+import shutil
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pandas as pd
+import pytest
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from tests.conftest import get_patched_exchange, is_mac
+from tests.freqai.conftest import (
+    get_patched_data_kitchen,
+    get_patched_freqai_strategy,
+    make_unfiltered_dataframe,
+)
+
+
+@pytest.mark.parametrize(
+    "timerange, train_period_days, expected_result",
+    [
+        ("20220101-20220201", 30, "20211202-20220201"),
+        ("20220301-20220401", 15, "20220214-20220401"),
+    ],
+)
+def test_create_fulltimerange(
+    timerange, train_period_days, expected_result, freqai_conf, mocker, caplog
+):
+    dk = get_patched_data_kitchen(mocker, freqai_conf)
+    assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
+    shutil.rmtree(Path(dk.full_path))
+
+
+def test_create_fulltimerange_incorrect_backtest_period(mocker, freqai_conf):
+    dk = get_patched_data_kitchen(mocker, freqai_conf)
+    with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"):
+        dk.create_fulltimerange("20220101-20220201", 0.5)
+    with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"):
+        dk.create_fulltimerange("20220101-20220201", -1)
+    shutil.rmtree(Path(dk.full_path))
+
+
+@pytest.mark.parametrize(
+    "timerange, train_period_days, backtest_period_days, expected_result",
+    [
+        ("20220101-20220201", 30, 7, 9),
+        ("20220101-20220201", 30, 0.5, 120),
+        ("20220101-20220201", 10, 1, 80),
+    ],
+)
+def test_split_timerange(
+    mocker, freqai_conf, timerange, train_period_days, backtest_period_days, expected_result
+):
+    freqai_conf.update({"timerange": "20220101-20220401"})
+    dk = get_patched_data_kitchen(mocker, freqai_conf)
+    tr_list, bt_list = dk.split_timerange(timerange, train_period_days, backtest_period_days)
+    assert len(tr_list) == len(bt_list) == expected_result
+
+    with pytest.raises(
+        OperationalException, match=r"train_period_days must be an integer greater than 0."
+    ):
+        dk.split_timerange("20220101-20220201", -1, 0.5)
+    shutil.rmtree(Path(dk.full_path))
+
+
+def test_check_if_model_expired(mocker, freqai_conf):
+    dk = get_patched_data_kitchen(mocker, freqai_conf)
+    now = datetime.now(tz=timezone.utc).timestamp()
+    assert dk.check_if_model_expired(now) is False
+    now = (datetime.now(tz=timezone.utc) - timedelta(hours=2)).timestamp()
+    assert dk.check_if_model_expired(now) is True
+    shutil.rmtree(Path(dk.full_path))
+
+
+def test_filter_features(mocker, freqai_conf):
+    freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
+    freqai.dk.find_features(unfiltered_dataframe)
+
+    filtered_df, _labels = freqai.dk.filter_features(
+        unfiltered_dataframe,
+        freqai.dk.training_features_list,
+        freqai.dk.label_list,
+        training_filter=True,
+    )
+
+    assert len(filtered_df.columns) == 14
+
+
+def test_make_train_test_datasets(mocker, freqai_conf):
+    freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
+    freqai.dk.find_features(unfiltered_dataframe)
+
+    features_filtered, labels_filtered = freqai.dk.filter_features(
+        unfiltered_dataframe,
+        freqai.dk.training_features_list,
+        freqai.dk.label_list,
+        training_filter=True,
+    )
+
+    data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
+
+    assert data_dictionary
+    assert len(data_dictionary) == 7
+    assert len(data_dictionary["train_features"].index) == 1916
+
+
+@pytest.mark.parametrize("model", ["LightGBMRegressor"])
+def test_get_full_model_path(mocker, freqai_conf, model):
+    freqai_conf.update({"freqaimodel": model})
+    freqai_conf.update({"timerange": "20180110-20180130"})
+    freqai_conf.update({"strategy": "freqai_test_strat"})
+
+    if is_mac():
+        pytest.skip("Mac is confused during this test for unknown reasons")
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    freqai.dd.pair_dict = MagicMock()
+
+    data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
+    new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths("ADA/BTC", None)
+    freqai.extract_data_and_train_model(
+        new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange
+    )
+
+    model_path = freqai.dk.get_full_models_path(freqai_conf)
+    assert model_path.is_dir() is True
+
+
+def test_get_pair_data_for_features_with_prealoaded_data(mocker, freqai_conf):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    _, base_df = freqai.dd.get_base_and_corr_dataframes(timerange, "LTC/BTC", freqai.dk)
+    df = freqai.dk.get_pair_data_for_features(
+        "LTC/BTC", "5m", strategy, {}, base_dataframes=base_df
+    )
+
+    assert df is base_df["5m"]
+    assert not df.empty
+
+
+def test_get_pair_data_for_features_without_preloaded_data(mocker, freqai_conf):
+    freqai_conf.update({"timerange": "20180115-20180130"})
+    freqai_conf["runmode"] = "backtest"
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    base_df = {"5m": pd.DataFrame()}
+    df = freqai.dk.get_pair_data_for_features(
+        "LTC/BTC", "5m", strategy, {}, base_dataframes=base_df
+    )
+
+    assert df is not base_df["5m"]
+    assert not df.empty
+    assert df.iloc[0]["date"].strftime("%Y-%m-%d %H:%M:%S") == "2018-01-11 23:00:00"
+    assert df.iloc[-1]["date"].strftime("%Y-%m-%d %H:%M:%S") == "2018-01-30 00:00:00"
+
+
+def test_populate_features(mocker, freqai_conf):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180115-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(timerange, "LTC/BTC", freqai.dk)
+    mocker.patch.object(strategy, "feature_engineering_expand_all", return_value=base_df["5m"])
+    df = freqai.dk.populate_features(
+        base_df["5m"], "LTC/BTC", strategy, base_dataframes=base_df, corr_dataframes=corr_df
+    )
+
+    strategy.feature_engineering_expand_all.assert_called_once()
+    pd.testing.assert_frame_equal(
+        base_df["5m"], strategy.feature_engineering_expand_all.call_args[0][0]
+    )
+
+    assert df.iloc[0]["date"].strftime("%Y-%m-%d %H:%M:%S") == "2018-01-15 00:00:00"
diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py
new file mode 100644
index 000000000..0710591c9
--- /dev/null
+++ b/tests/freqai/test_freqai_interface.py
@@ -0,0 +1,591 @@
+import logging
+import shutil
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import RunMode
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.utils import download_all_data_for_training, get_required_data_timerange
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.persistence import Trade
+from freqtrade.plugins.pairlistmanager import PairListManager
+from tests.conftest import (
+    EXMS,
+    create_mock_trades,
+    get_patched_exchange,
+    is_arm,
+    is_mac,
+    log_has_re,
+)
+from tests.freqai.conftest import (
+    get_patched_freqai_strategy,
+    make_rl_config,
+    mock_pytorch_mlp_model_training_parameters,
+)
+
+
+def can_run_model(model: str) -> None:
+    is_pytorch_model = "Reinforcement" in model or "PyTorch" in model
+
+    if is_arm() and "Catboost" in model:
+        pytest.skip("CatBoost is not supported on ARM.")
+
+    if is_pytorch_model and is_mac():
+        pytest.skip("Reinforcement learning / PyTorch module not available on intel based Mac OS.")
+
+
+@pytest.mark.parametrize(
+    "model, pca, dbscan, float32, can_short, shuffle, buffer, noise",
+    [
+        ("LightGBMRegressor", True, False, True, True, False, 0, 0),
+        ("XGBoostRegressor", False, True, False, True, False, 10, 0.05),
+        ("XGBoostRFRegressor", False, False, False, True, False, 0, 0),
+        ("CatboostRegressor", False, False, False, True, True, 0, 0),
+        ("PyTorchMLPRegressor", False, False, False, False, False, 0, 0),
+        ("PyTorchTransformerRegressor", False, False, False, False, False, 0, 0),
+        ("ReinforcementLearner", False, True, False, True, False, 0, 0),
+        ("ReinforcementLearner_multiproc", False, False, False, True, False, 0, 0),
+        ("ReinforcementLearner_test_3ac", False, False, False, False, False, 0, 0),
+        ("ReinforcementLearner_test_3ac", False, False, False, True, False, 0, 0),
+        ("ReinforcementLearner_test_4ac", False, False, False, True, False, 0, 0),
+    ],
+)
+def test_extract_data_and_train_model_Standard(
+    mocker, freqai_conf, model, pca, dbscan, float32, can_short, shuffle, buffer, noise
+):
+    can_run_model(model)
+
+    test_tb = True
+    if is_mac():
+        test_tb = False
+
+    model_save_ext = "joblib"
+    freqai_conf.update({"freqaimodel": model})
+    freqai_conf.update({"timerange": "20180110-20180130"})
+    freqai_conf.update({"strategy": "freqai_test_strat"})
+    freqai_conf["freqai"]["feature_parameters"].update({"principal_component_analysis": pca})
+    freqai_conf["freqai"]["feature_parameters"].update({"use_DBSCAN_to_remove_outliers": dbscan})
+    freqai_conf.update({"reduce_df_footprint": float32})
+    freqai_conf["freqai"]["feature_parameters"].update({"shuffle_after_split": shuffle})
+    freqai_conf["freqai"]["feature_parameters"].update({"buffer_train_data_candles": buffer})
+    freqai_conf["freqai"]["feature_parameters"].update({"noise_standard_deviation": noise})
+
+    if "ReinforcementLearner" in model:
+        model_save_ext = "zip"
+        freqai_conf = make_rl_config(freqai_conf)
+        # test the RL guardrails
+        freqai_conf["freqai"]["feature_parameters"].update({"use_SVM_to_remove_outliers": True})
+        freqai_conf["freqai"]["feature_parameters"].update({"DI_threshold": 2})
+        freqai_conf["freqai"]["data_split_parameters"].update({"shuffle": True})
+
+    if "test_3ac" in model or "test_4ac" in model:
+        freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
+        freqai_conf["freqai"]["rl_config"]["drop_ohlc_from_features"] = True
+
+    if "PyTorch" in model:
+        model_save_ext = "zip"
+        pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
+        freqai_conf["freqai"]["model_training_parameters"].update(pytorch_mlp_mtp)
+        if "Transformer" in model:
+            # transformer model takes a window, unlike the MLP regressor
+            freqai_conf.update({"conv_width": 10})
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.activate_tensorboard = test_tb
+    freqai.can_short = can_short
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    freqai.dk.set_paths("ADA/BTC", 10000)
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    freqai.dd.pair_dict = MagicMock()
+
+    data_load_timerange = TimeRange.parse_timerange("20180125-20180130")
+    new_timerange = TimeRange.parse_timerange("20180127-20180130")
+    freqai.dk.set_paths("ADA/BTC", None)
+
+    freqai.train_timer("start", "ADA/BTC")
+    freqai.extract_data_and_train_model(
+        new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange
+    )
+    freqai.train_timer("stop", "ADA/BTC")
+    freqai.dd.save_metric_tracker_to_disk()
+    freqai.dd.save_drawer_to_disk()
+
+    assert Path(freqai.dk.full_path / "metric_tracker.json").is_file()
+    assert Path(freqai.dk.full_path / "pair_dictionary.json").is_file()
+    assert Path(
+        freqai.dk.data_path / f"{freqai.dk.model_filename}_model.{model_save_ext}"
+    ).is_file()
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
+
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+@pytest.mark.parametrize(
+    "model, strat",
+    [
+        ("LightGBMRegressorMultiTarget", "freqai_test_multimodel_strat"),
+        ("XGBoostRegressorMultiTarget", "freqai_test_multimodel_strat"),
+        ("CatboostRegressorMultiTarget", "freqai_test_multimodel_strat"),
+        ("LightGBMClassifierMultiTarget", "freqai_test_multimodel_classifier_strat"),
+        ("CatboostClassifierMultiTarget", "freqai_test_multimodel_classifier_strat"),
+    ],
+)
+def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, strat):
+    can_run_model(model)
+
+    freqai_conf.update({"timerange": "20180110-20180130"})
+    freqai_conf.update({"strategy": strat})
+    freqai_conf.update({"freqaimodel": model})
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    freqai.dd.pair_dict = MagicMock()
+
+    data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
+    new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths("ADA/BTC", None)
+
+    freqai.extract_data_and_train_model(
+        new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange
+    )
+
+    assert len(freqai.dk.label_list) == 2
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
+    assert len(freqai.dk.data["training_features_list"]) == 14
+
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        "LightGBMClassifier",
+        "CatboostClassifier",
+        "XGBoostClassifier",
+        "XGBoostRFClassifier",
+        "SKLearnRandomForestClassifier",
+        "PyTorchMLPClassifier",
+    ],
+)
+def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
+    can_run_model(model)
+
+    freqai_conf.update({"freqaimodel": model})
+    freqai_conf.update({"strategy": "freqai_test_classifier"})
+    freqai_conf.update({"timerange": "20180110-20180130"})
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    freqai.dd.pair_dict = MagicMock()
+
+    data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
+    new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths("ADA/BTC", None)
+
+    freqai.extract_data_and_train_model(
+        new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange
+    )
+
+    if "PyTorchMLPClassifier":
+        pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
+        freqai_conf["freqai"]["model_training_parameters"].update(pytorch_mlp_mtp)
+
+    if freqai.dd.model_type == "joblib":
+        model_file_extension = ".joblib"
+    elif freqai.dd.model_type == "pytorch":
+        model_file_extension = ".zip"
+    else:
+        raise Exception(
+            f"Unsupported model type: {freqai.dd.model_type}, can't assign model_file_extension"
+        )
+
+    assert Path(
+        freqai.dk.data_path / f"{freqai.dk.model_filename}_model{model_file_extension}"
+    ).exists()
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
+
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+@pytest.mark.parametrize(
+    "model, num_files, strat",
+    [
+        ("LightGBMRegressor", 2, "freqai_test_strat"),
+        ("XGBoostRegressor", 2, "freqai_test_strat"),
+        ("CatboostRegressor", 2, "freqai_test_strat"),
+        ("PyTorchMLPRegressor", 2, "freqai_test_strat"),
+        ("PyTorchTransformerRegressor", 2, "freqai_test_strat"),
+        ("ReinforcementLearner", 3, "freqai_rl_test_strat"),
+        ("XGBoostClassifier", 2, "freqai_test_classifier"),
+        ("LightGBMClassifier", 2, "freqai_test_classifier"),
+        ("CatboostClassifier", 2, "freqai_test_classifier"),
+        ("PyTorchMLPClassifier", 2, "freqai_test_classifier"),
+    ],
+)
+def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
+    can_run_model(model)
+    test_tb = True
+    if is_mac() and not is_arm():
+        test_tb = False
+
+    freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
+    freqai_conf["runmode"] = RunMode.BACKTEST
+
+    Trade.use_db = False
+
+    freqai_conf.update({"freqaimodel": model})
+    freqai_conf.update({"timerange": "20180120-20180130"})
+    freqai_conf.update({"strategy": strat})
+
+    if "ReinforcementLearner" in model:
+        freqai_conf = make_rl_config(freqai_conf)
+
+    if "test_4ac" in model:
+        freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
+
+    if "PyTorch" in model:
+        pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
+        freqai_conf["freqai"]["model_training_parameters"].update(pytorch_mlp_mtp)
+        if "Transformer" in model:
+            # transformer model takes a window, unlike the MLP regressor
+            freqai_conf.update({"conv_width": 10})
+
+    freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
+        {"indicator_periods_candles": [2]}
+    )
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = False
+    freqai.activate_tensorboard = test_tb
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180110-20180130")
+    _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
+    df = base_df[freqai_conf["timeframe"]]
+
+    metadata = {"pair": "LTC/BTC"}
+    freqai.dk.set_paths("LTC/BTC", None)
+    freqai.start_backtesting(df, metadata, freqai.dk, strategy)
+    model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
+
+    assert len(model_folders) == num_files
+    Trade.use_db = True
+    Backtesting.cleanup()
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
+    freqai_conf.update({"timerange": "20180120-20180124"})
+    freqai_conf["runmode"] = "backtest"
+    freqai_conf.get("freqai", {}).update(
+        {
+            "backtest_period_days": 0.5,
+            "save_backtest_models": True,
+        }
+    )
+    freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
+        {"indicator_periods_candles": [2]}
+    )
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = False
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180110-20180130")
+    _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
+    df = base_df[freqai_conf["timeframe"]]
+
+    metadata = {"pair": "LTC/BTC"}
+    freqai.start_backtesting(df, metadata, freqai.dk, strategy)
+    model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
+
+    assert len(model_folders) == 9
+
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
+    freqai_conf.update({"timerange": "20180120-20180130"})
+    freqai_conf["runmode"] = "backtest"
+    freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
+    freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
+        {"indicator_periods_candles": [2]}
+    )
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = False
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180101-20180130")
+    _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
+    df = base_df[freqai_conf["timeframe"]]
+
+    pair = "ADA/BTC"
+    metadata = {"pair": pair}
+    freqai.dk.pair = pair
+    freqai.start_backtesting(df, metadata, freqai.dk, strategy)
+    model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
+
+    assert len(model_folders) == 2
+
+    # without deleting the existing folder structure, re-run
+
+    freqai_conf.update({"timerange": "20180120-20180130"})
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = False
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180110-20180130")
+    _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
+    df = base_df[freqai_conf["timeframe"]]
+
+    pair = "ADA/BTC"
+    metadata = {"pair": pair}
+    freqai.dk.pair = pair
+    freqai.start_backtesting(df, metadata, freqai.dk, strategy)
+
+    assert log_has_re(
+        "Found backtesting prediction file ",
+        caplog,
+    )
+
+    pair = "ETH/BTC"
+    metadata = {"pair": pair}
+    freqai.dk.pair = pair
+    freqai.start_backtesting(df, metadata, freqai.dk, strategy)
+
+    path = freqai.dd.full_path / freqai.dk.backtest_predictions_folder
+    prediction_files = [x for x in path.iterdir() if x.is_file()]
+    assert len(prediction_files) == 2
+
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_backtesting_fit_live_predictions(mocker, freqai_conf, caplog):
+    freqai_conf["runmode"] = "backtest"
+    freqai_conf.get("freqai", {}).update({"fit_live_predictions_candles": 10})
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = False
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180128-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180129-20180130")
+    corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
+    df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
+    df = strategy.set_freqai_targets(df.copy(), metadata={"pair": "LTC/BTC"})
+    df = freqai.dk.remove_special_chars_from_feature_names(df)
+    freqai.dk.get_unique_classes_from_labels(df)
+    freqai.dk.pair = "ADA/BTC"
+    freqai.dk.full_df = df.fillna(0)
+
+    assert "&-s_close_mean" not in freqai.dk.full_df.columns
+    assert "&-s_close_std" not in freqai.dk.full_df.columns
+    freqai.backtesting_fit_live_predictions(freqai.dk)
+    assert "&-s_close_mean" in freqai.dk.full_df.columns
+    assert "&-s_close_std" in freqai.dk.full_df.columns
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_plot_feature_importance(mocker, freqai_conf):
+    from freqtrade.freqai.utils import plot_feature_importance
+
+    freqai_conf.update({"timerange": "20180110-20180130"})
+    freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
+        {"princpial_component_analysis": "true"}
+    )
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    freqai.dk.live = True
+    timerange = TimeRange.parse_timerange("20180110-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+
+    freqai.dd.pair_dict = {
+        "ADA/BTC": {
+            "model_filename": "fake_name",
+            "trained_timestamp": 1,
+            "data_path": "",
+            "extras": {},
+        }
+    }
+
+    data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
+    new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths("ADA/BTC", None)
+
+    freqai.extract_data_and_train_model(
+        new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange
+    )
+
+    model = freqai.dd.load_data("ADA/BTC", freqai.dk)
+
+    plot_feature_importance(model, "ADA/BTC", freqai.dk)
+
+    assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}.html")
+
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
+@pytest.mark.parametrize(
+    "timeframes,corr_pairs",
+    [
+        (["5m"], ["ADA/BTC", "DASH/BTC"]),
+        (["5m"], ["ADA/BTC", "DASH/BTC", "ETH/USDT"]),
+        (["5m", "15m"], ["ADA/BTC", "DASH/BTC", "ETH/USDT"]),
+    ],
+)
+def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs):
+    freqai_conf["freqai"]["feature_parameters"].update(
+        {
+            "include_timeframes": timeframes,
+            "include_corr_pairlist": corr_pairs,
+        }
+    )
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    pairlists = PairListManager(exchange, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange, pairlists)
+    pairlist = strategy.dp.current_whitelist()
+
+    pairs_a = strategy.informative_pairs()
+    assert len(pairs_a) == 0
+    pairs_b = strategy.gather_informative_pairs()
+    # we expect unique pairs * timeframes
+    assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes)
+
+
+def test_start_set_train_queue(mocker, freqai_conf, caplog):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    pairlist = PairListManager(exchange, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = False
+
+    freqai.train_queue = freqai._set_train_queue()
+
+    assert log_has_re(
+        "Set fresh train queue from whitelist.",
+        caplog,
+    )
+
+
+def test_get_required_data_timerange(mocker, freqai_conf):
+    time_range = get_required_data_timerange(freqai_conf)
+    assert (time_range.stopts - time_range.startts) == 177300
+
+
+def test_download_all_data_for_training(mocker, freqai_conf, caplog, tmp_path):
+    caplog.set_level(logging.DEBUG)
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    pairlist = PairListManager(exchange, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
+    freqai_conf["pairs"] = freqai_conf["exchange"]["pair_whitelist"]
+    freqai_conf["datadir"] = tmp_path
+    download_all_data_for_training(strategy.dp, freqai_conf)
+
+    assert log_has_re(
+        "Downloading",
+        caplog,
+    )
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("dp_exists", [(False), (True)])
+def test_get_state_info(mocker, freqai_conf, dp_exists, caplog, tickers):
+    if is_mac():
+        pytest.skip("Reinforcement learning module not available on intel based Mac OS")
+
+    freqai_conf.update({"freqaimodel": "ReinforcementLearner"})
+    freqai_conf.update({"timerange": "20180110-20180130"})
+    freqai_conf.update({"strategy": "freqai_rl_test_strat"})
+    freqai_conf = make_rl_config(freqai_conf)
+    freqai_conf["entry_pricing"]["price_side"] = "same"
+    freqai_conf["exit_pricing"]["price_side"] = "same"
+
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    ticker_mock = MagicMock(return_value=tickers()["ETH/BTC"])
+    mocker.patch(f"{EXMS}.fetch_ticker", ticker_mock)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+
+    if not dp_exists:
+        strategy.dp._exchange = None
+
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.data_provider = strategy.dp
+    freqai.live = True
+
+    Trade.use_db = True
+    create_mock_trades(MagicMock(return_value=0.0025), False, True)
+    freqai.get_state_info("ADA/BTC")
+    freqai.get_state_info("ETH/BTC")
+
+    if not dp_exists:
+        assert log_has_re(
+            "No exchange available",
+            caplog,
+        )
diff --git a/tests/freqai/test_models/ReinforcementLearner_test_3ac.py b/tests/freqai/test_models/ReinforcementLearner_test_3ac.py
new file mode 100644
index 000000000..ec7679883
--- /dev/null
+++ b/tests/freqai/test_models/ReinforcementLearner_test_3ac.py
@@ -0,0 +1,71 @@
+import logging
+
+import numpy as np
+
+from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
+from freqtrade.freqai.RL.Base3ActionRLEnv import Actions, Base3ActionRLEnv, Positions
+
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearner_test_3ac(ReinforcementLearner):
+    """
+    User created Reinforcement Learning Model prediction model.
+    """
+
+    class MyRLEnv(Base3ActionRLEnv):
+        """
+        User can override any function in BaseRLEnv and gym.Env. Here the user
+        sets a custom reward based on profit and trade duration.
+
+        Warning!
+        This is function is a showcase of functionality designed to show as many possible
+        environment control features as possible. It is also designed to run quickly
+        on small computers. This is a benchmark, it is *not* for live production.
+        """
+
+        def calculate_reward(self, action: int) -> float:
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+
+            pnl = self.get_unrealized_profit()
+            rew = np.sign(pnl) * (pnl + 1)
+            factor = 100.0
+
+            # reward agent for entering trades
+            if (
+                action in (Actions.Buy.value, Actions.Sell.value)
+                and self._position == Positions.Neutral
+            ):
+                return 25
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+
+            max_trade_duration = self.rl_config.get("max_trade_duration_candles", 300)
+            trade_duration = self._current_tick - self._last_trade_tick  # type: ignore
+
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+
+            # discourage sitting in position
+            if self._position in (Positions.Short, Positions.Long) and (
+                action == Actions.Neutral.value
+                or (action == Actions.Sell.value and self._position == Positions.Short)
+                or (action == Actions.Buy.value and self._position == Positions.Long)
+            ):
+                return -1 * trade_duration / max_trade_duration
+
+            # close position
+            if (action == Actions.Buy.value and self._position == Positions.Short) or (
+                action == Actions.Sell.value and self._position == Positions.Long
+            ):
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
+                return float(rew * factor)
+
+            return 0.0
diff --git a/tests/freqai/test_models/ReinforcementLearner_test_4ac.py b/tests/freqai/test_models/ReinforcementLearner_test_4ac.py
new file mode 100644
index 000000000..4044fc41d
--- /dev/null
+++ b/tests/freqai/test_models/ReinforcementLearner_test_4ac.py
@@ -0,0 +1,74 @@
+import logging
+
+import numpy as np
+
+from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
+from freqtrade.freqai.RL.Base4ActionRLEnv import Actions, Base4ActionRLEnv, Positions
+
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearner_test_4ac(ReinforcementLearner):
+    """
+    User created Reinforcement Learning Model prediction model.
+    """
+
+    class MyRLEnv(Base4ActionRLEnv):
+        """
+        User can override any function in BaseRLEnv and gym.Env. Here the user
+        sets a custom reward based on profit and trade duration.
+
+        Warning!
+        This is function is a showcase of functionality designed to show as many possible
+        environment control features as possible. It is also designed to run quickly
+        on small computers. This is a benchmark, it is *not* for live production.
+        """
+
+        def calculate_reward(self, action: int) -> float:
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+
+            pnl = self.get_unrealized_profit()
+            rew = np.sign(pnl) * (pnl + 1)
+            factor = 100.0
+
+            # reward agent for entering trades
+            if (
+                action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                and self._position == Positions.Neutral
+            ):
+                return 25
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+
+            max_trade_duration = self.rl_config.get("max_trade_duration_candles", 300)
+            trade_duration = self._current_tick - self._last_trade_tick  # type: ignore
+
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+
+            # discourage sitting in position
+            if (
+                self._position in (Positions.Short, Positions.Long)
+                and action == Actions.Neutral.value
+            ):
+                return -1 * trade_duration / max_trade_duration
+
+            # close long
+            if action == Actions.Exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
+                return float(rew * factor)
+
+            # close short
+            if action == Actions.Exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
+                return float(rew * factor)
+
+            return 0.0
diff --git a/tests/freqtradebot/__init__.py b/tests/freqtradebot/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/freqtradebot/test_freqtradebot.py b/tests/freqtradebot/test_freqtradebot.py
new file mode 100644
index 000000000..3ff8fcb5e
--- /dev/null
+++ b/tests/freqtradebot/test_freqtradebot.py
@@ -0,0 +1,5978 @@
+# pragma pylint: disable=missing-docstring, C0103
+# pragma pylint: disable=protected-access, too-many-lines, invalid-name, too-many-arguments
+
+import logging
+import time
+from copy import deepcopy
+from datetime import timedelta
+from unittest.mock import ANY, MagicMock, PropertyMock, patch
+
+import pytest
+from pandas import DataFrame
+from sqlalchemy import select
+
+from freqtrade.constants import CANCEL_REASON, UNLIMITED_STAKE_AMOUNT
+from freqtrade.enums import (
+    CandleType,
+    ExitCheckTuple,
+    ExitType,
+    RPCMessageType,
+    RunMode,
+    SignalDirection,
+    State,
+)
+from freqtrade.exceptions import (
+    DependencyException,
+    ExchangeError,
+    InsufficientFundsError,
+    InvalidOrderException,
+    OperationalException,
+    PricingError,
+    TemporaryError,
+)
+from freqtrade.freqtradebot import FreqtradeBot
+from freqtrade.persistence import Order, PairLocks, Trade
+from freqtrade.plugins.protections.iprotection import ProtectionReturn
+from freqtrade.util.datetime_helpers import dt_now, dt_utc
+from freqtrade.worker import Worker
+from tests.conftest import (
+    EXMS,
+    create_mock_trades,
+    create_mock_trades_usdt,
+    get_patched_freqtradebot,
+    get_patched_worker,
+    log_has,
+    log_has_re,
+    patch_edge,
+    patch_exchange,
+    patch_get_signal,
+    patch_wallet,
+    patch_whitelist,
+)
+from tests.conftest_trades import (
+    MOCK_TRADE_COUNT,
+    entry_side,
+    exit_side,
+    mock_order_2,
+    mock_order_2_sell,
+    mock_order_3,
+    mock_order_3_sell,
+    mock_order_4,
+    mock_order_5_stoploss,
+    mock_order_6_sell,
+)
+from tests.conftest_trades_usdt import mock_trade_usdt_4
+
+
+def patch_RPCManager(mocker) -> MagicMock:
+    """
+    This function mock RPC manager to avoid repeating this code in almost every tests
+    :param mocker: mocker to patch RPCManager class
+    :return: RPCManager.send_msg MagicMock to track if this method is called
+    """
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    rpc_mock = mocker.patch("freqtrade.freqtradebot.RPCManager.send_msg", MagicMock())
+    return rpc_mock
+
+
+# Unit tests
+
+
+def test_freqtradebot_state(mocker, default_conf_usdt, markets) -> None:
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    assert freqtrade.state is State.RUNNING
+
+    default_conf_usdt.pop("initial_state")
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    assert freqtrade.state is State.STOPPED
+
+
+def test_process_stopped(mocker, default_conf_usdt) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    coo_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.cancel_all_open_orders")
+    freqtrade.process_stopped()
+    assert coo_mock.call_count == 0
+
+    default_conf_usdt["cancel_open_orders_on_exit"] = True
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    freqtrade.process_stopped()
+    assert coo_mock.call_count == 1
+
+
+def test_process_calls_sendmsg(mocker, default_conf_usdt) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    freqtrade.process()
+    assert freqtrade.rpc.process_msg_queue.call_count == 1
+
+
+def test_bot_cleanup(mocker, default_conf_usdt, caplog) -> None:
+    mock_cleanup = mocker.patch("freqtrade.freqtradebot.Trade.commit")
+    coo_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.cancel_all_open_orders")
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    freqtrade.cleanup()
+    assert log_has("Cleaning up modules ...", caplog)
+    assert mock_cleanup.call_count == 1
+    assert coo_mock.call_count == 0
+
+    freqtrade.config["cancel_open_orders_on_exit"] = True
+    freqtrade.cleanup()
+    assert coo_mock.call_count == 1
+
+
+def test_bot_cleanup_db_errors(mocker, default_conf_usdt, caplog) -> None:
+    mocker.patch("freqtrade.freqtradebot.Trade.commit", side_effect=OperationalException())
+    mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot.check_for_open_trades",
+        side_effect=OperationalException(),
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    freqtrade.emc = MagicMock()
+    freqtrade.emc.shutdown = MagicMock()
+    freqtrade.cleanup()
+    assert freqtrade.emc.shutdown.call_count == 1
+
+
+@pytest.mark.parametrize("runmode", [RunMode.DRY_RUN, RunMode.LIVE])
+def test_order_dict(default_conf_usdt, mocker, runmode, caplog) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    conf = default_conf_usdt.copy()
+    conf["runmode"] = runmode
+    conf["order_types"] = {
+        "entry": "market",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": True,
+    }
+    conf["entry_pricing"]["price_side"] = "ask"
+
+    freqtrade = FreqtradeBot(conf)
+    if runmode == RunMode.LIVE:
+        assert not log_has_re(r".*stoploss_on_exchange .* dry-run", caplog)
+    assert freqtrade.strategy.order_types["stoploss_on_exchange"]
+
+    caplog.clear()
+    # is left untouched
+    conf = default_conf_usdt.copy()
+    conf["runmode"] = runmode
+    conf["order_types"] = {
+        "entry": "market",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": False,
+    }
+    freqtrade = FreqtradeBot(conf)
+    assert not freqtrade.strategy.order_types["stoploss_on_exchange"]
+    assert not log_has_re(r".*stoploss_on_exchange .* dry-run", caplog)
+
+
+def test_get_trade_stake_amount(default_conf_usdt, mocker) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    result = freqtrade.wallets.get_trade_stake_amount("ETH/USDT", 1)
+    assert result == default_conf_usdt["stake_amount"]
+
+
+@pytest.mark.parametrize("runmode", [RunMode.DRY_RUN, RunMode.LIVE])
+def test_load_strategy_no_keys(default_conf_usdt, mocker, runmode, caplog) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    conf = deepcopy(default_conf_usdt)
+    conf["runmode"] = runmode
+    erm = mocker.patch("freqtrade.freqtradebot.ExchangeResolver.load_exchange")
+
+    freqtrade = FreqtradeBot(conf)
+    strategy_config = freqtrade.strategy.config
+    assert id(strategy_config["exchange"]) == id(conf["exchange"])
+    # Keys have been removed and are not passed to the exchange
+    assert strategy_config["exchange"]["key"] == ""
+    assert strategy_config["exchange"]["secret"] == ""
+
+    assert erm.call_count == 1
+    ex_conf = erm.call_args_list[0][1]["exchange_config"]
+    assert id(ex_conf) != id(conf["exchange"])
+    # Keys are still present
+    assert ex_conf["key"] != ""
+    assert ex_conf["key"] == default_conf_usdt["exchange"]["key"]
+    assert ex_conf["secret"] != ""
+    assert ex_conf["secret"] == default_conf_usdt["exchange"]["secret"]
+
+
+@pytest.mark.parametrize(
+    "amend_last,wallet,max_open,lsamr,expected",
+    [
+        (False, 120, 2, 0.5, [60, None]),
+        (True, 120, 2, 0.5, [60, 58.8]),
+        (False, 180, 3, 0.5, [60, 60, None]),
+        (True, 180, 3, 0.5, [60, 60, 58.2]),
+        (False, 122, 3, 0.5, [60, 60, None]),
+        (True, 122, 3, 0.5, [60, 60, 0.0]),
+        (True, 167, 3, 0.5, [60, 60, 45.33]),
+        (True, 122, 3, 1, [60, 60, 0.0]),
+    ],
+)
+def test_check_available_stake_amount(
+    default_conf_usdt,
+    ticker_usdt,
+    mocker,
+    fee,
+    limit_buy_order_usdt_open,
+    amend_last,
+    wallet,
+    max_open,
+    lsamr,
+    expected,
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value=limit_buy_order_usdt_open),
+        get_fee=fee,
+    )
+    default_conf_usdt["dry_run_wallet"] = wallet
+
+    default_conf_usdt["amend_last_stake_amount"] = amend_last
+    default_conf_usdt["last_stake_amount_min_ratio"] = lsamr
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    for i in range(0, max_open):
+        if expected[i] is not None:
+            limit_buy_order_usdt_open["id"] = str(i)
+            result = freqtrade.wallets.get_trade_stake_amount("ETH/USDT", 1)
+            assert pytest.approx(result) == expected[i]
+            freqtrade.execute_entry("ETH/USDT", result)
+        else:
+            with pytest.raises(DependencyException):
+                freqtrade.wallets.get_trade_stake_amount("ETH/USDT", 1)
+
+
+def test_edge_called_in_process(mocker, edge_conf) -> None:
+    patch_RPCManager(mocker)
+    patch_edge(mocker)
+
+    patch_exchange(mocker)
+    freqtrade = FreqtradeBot(edge_conf)
+    patch_get_signal(freqtrade)
+    freqtrade.process()
+    assert freqtrade.active_pair_whitelist == ["NEO/BTC", "LTC/BTC"]
+
+
+def test_edge_overrides_stake_amount(mocker, edge_conf) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    patch_edge(mocker)
+    edge_conf["dry_run_wallet"] = 999.9
+    freqtrade = FreqtradeBot(edge_conf)
+
+    assert (
+        freqtrade.wallets.get_trade_stake_amount("NEO/BTC", 1, freqtrade.edge)
+        == (999.9 * 0.5 * 0.01) / 0.20
+    )
+    assert (
+        freqtrade.wallets.get_trade_stake_amount("LTC/BTC", 1, freqtrade.edge)
+        == (999.9 * 0.5 * 0.01) / 0.21
+    )
+
+
+@pytest.mark.parametrize(
+    "buy_price_mult,ignore_strat_sl",
+    [
+        (0.79, False),  # Override stoploss
+        (0.85, True),  # Override strategy stoploss
+    ],
+)
+def test_edge_overrides_stoploss(
+    limit_order, fee, caplog, mocker, buy_price_mult, ignore_strat_sl, edge_conf
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    patch_edge(mocker)
+    edge_conf["max_open_trades"] = float("inf")
+
+    # Strategy stoploss is -0.1 but Edge imposes a stoploss at -0.2
+    # Thus, if price falls 21%, stoploss should be triggered
+    #
+    # mocking the ticker: price is falling ...
+    enter_price = limit_order["buy"]["price"]
+    ticker_val = {
+        "bid": enter_price,
+        "ask": enter_price,
+        "last": enter_price,
+    }
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value=ticker_val),
+        get_fee=fee,
+    )
+    #############################################
+
+    # Create a trade with "limit_buy_order_usdt" price
+    freqtrade = FreqtradeBot(edge_conf)
+    freqtrade.active_pair_whitelist = ["NEO/BTC"]
+    patch_get_signal(freqtrade)
+    freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    caplog.clear()
+    #############################################
+    ticker_val.update(
+        {
+            "bid": enter_price * buy_price_mult,
+            "ask": enter_price * buy_price_mult,
+            "last": enter_price * buy_price_mult,
+        }
+    )
+
+    # stoploss should be hit
+    assert freqtrade.handle_trade(trade) is not ignore_strat_sl
+    if not ignore_strat_sl:
+        assert log_has_re("Exit for NEO/BTC detected. Reason: stop_loss.*", caplog)
+        assert trade.exit_reason == ExitType.STOP_LOSS.value
+        # Test compatibility ...
+        assert trade.sell_reason == ExitType.STOP_LOSS.value
+
+
+def test_total_open_trades_stakes(mocker, default_conf_usdt, ticker_usdt, fee) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    default_conf_usdt["max_open_trades"] = 2
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=False),
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+
+    assert trade is not None
+    assert trade.stake_amount == 60.0
+    assert trade.is_open
+    assert trade.open_date is not None
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade).order_by(Trade.id.desc())).first()
+
+    assert trade is not None
+    assert trade.stake_amount == 60.0
+    assert trade.is_open
+    assert trade.open_date is not None
+
+    assert Trade.total_open_trades_stakes() == 120.0
+
+
+@pytest.mark.parametrize("is_short,open_rate", [(False, 2.0), (True, 2.2)])
+def test_create_trade(
+    default_conf_usdt, ticker_usdt, limit_order, fee, mocker, is_short, open_rate
+) -> None:
+    send_msg_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=False),
+    )
+
+    # Save state of current whitelist
+    whitelist = deepcopy(default_conf_usdt["exchange"]["pair_whitelist"])
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    send_msg_mock.reset_mock()
+    freqtrade.create_trade("ETH/USDT")
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade is not None
+    assert pytest.approx(trade.stake_amount) == 60.0
+    assert trade.is_open
+    assert trade.open_date is not None
+    assert trade.exchange == "binance"
+
+    # Simulate fulfilled LIMIT_BUY order for trade
+    oobj = Order.parse_from_ccxt_object(
+        limit_order[entry_side(is_short)], "ADA/USDT", entry_side(is_short)
+    )
+    trade.update_trade(oobj)
+    assert send_msg_mock.call_count == 1
+    entry_msg = send_msg_mock.call_args_list[0][0][0]
+    assert entry_msg["type"] == RPCMessageType.ENTRY
+    assert entry_msg["stake_amount"] == trade.stake_amount
+    assert entry_msg["stake_currency"] == default_conf_usdt["stake_currency"]
+    assert entry_msg["pair"] == "ETH/USDT"
+    assert entry_msg["direction"] == ("Short" if is_short else "Long")
+    assert entry_msg["sub_trade"] is False
+
+    assert trade.open_rate == open_rate
+    assert trade.amount == 30.0
+
+    assert whitelist == default_conf_usdt["exchange"]["pair_whitelist"]
+
+
+def test_create_trade_no_stake_amount(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    patch_wallet(mocker, free=default_conf_usdt["stake_amount"] * 0.5)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+
+    with pytest.raises(DependencyException, match=r".*stake amount.*"):
+        freqtrade.create_trade("ETH/USDT")
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.parametrize(
+    "stake_amount,create,amount_enough,max_open_trades",
+    [
+        (5.0, True, True, 99),
+        (0.042, True, False, 99),  # Amount will be adjusted to min - which is 0.051
+        (0, False, True, 99),
+        (UNLIMITED_STAKE_AMOUNT, False, True, 0),
+    ],
+)
+def test_create_trade_minimal_amount(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_order_open,
+    fee,
+    mocker,
+    stake_amount,
+    create,
+    amount_enough,
+    max_open_trades,
+    caplog,
+    is_short,
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    enter_mock = MagicMock(return_value=limit_order_open[entry_side(is_short)])
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=enter_mock,
+        get_fee=fee,
+    )
+    default_conf_usdt["max_open_trades"] = max_open_trades
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade.config["stake_amount"] = stake_amount
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    if create:
+        assert freqtrade.create_trade("ETH/USDT")
+        if amount_enough:
+            rate, amount = enter_mock.call_args[1]["rate"], enter_mock.call_args[1]["amount"]
+            assert rate * amount <= default_conf_usdt["stake_amount"]
+        else:
+            assert log_has_re(r"Stake amount for pair .* is too small.*", caplog)
+    else:
+        assert not freqtrade.create_trade("ETH/USDT")
+        if not max_open_trades:
+            assert (
+                freqtrade.wallets.get_trade_stake_amount(
+                    "ETH/USDT", default_conf_usdt["max_open_trades"], freqtrade.edge
+                )
+                == 0
+            )
+
+
+@pytest.mark.parametrize(
+    "whitelist,positions",
+    [
+        (["ETH/USDT"], 1),  # No pairs left
+        ([], 0),  # No pairs in whitelist
+    ],
+)
+def test_enter_positions_no_pairs_left(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_buy_order_usdt_open,
+    fee,
+    whitelist,
+    positions,
+    mocker,
+    caplog,
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value=limit_buy_order_usdt_open),
+        get_fee=fee,
+    )
+    mocker.patch("freqtrade.configuration.config_validation._validate_whitelist")
+    default_conf_usdt["exchange"]["pair_whitelist"] = whitelist
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+
+    n = freqtrade.enter_positions()
+    assert n == positions
+    if positions:
+        assert not log_has_re(r"No currency pair in active pair whitelist.*", caplog)
+        n = freqtrade.enter_positions()
+        assert n == 0
+        assert log_has_re(r"No currency pair in active pair whitelist.*", caplog)
+    else:
+        assert n == 0
+        assert log_has("Active pair whitelist is empty.", caplog)
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_enter_positions_global_pairlock(
+    default_conf_usdt, ticker_usdt, limit_buy_order_usdt, fee, mocker, caplog
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value={"id": limit_buy_order_usdt["id"]}),
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+    n = freqtrade.enter_positions()
+    message = r"Global pairlock active until.* Not creating new trades."
+    n = freqtrade.enter_positions()
+    # 0 trades, but it's not because of pairlock.
+    assert n == 0
+    assert not log_has_re(message, caplog)
+    caplog.clear()
+
+    PairLocks.lock_pair("*", dt_now() + timedelta(minutes=20), "Just because", side="*")
+    n = freqtrade.enter_positions()
+    assert n == 0
+    assert log_has_re(message, caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_protections(mocker, default_conf_usdt, fee, is_short):
+    default_conf_usdt["_strategy_protections"] = [
+        {"method": "CooldownPeriod", "stop_duration": 60},
+        {
+            "method": "StoplossGuard",
+            "lookback_period_candles": 24,
+            "trade_limit": 4,
+            "stop_duration_candles": 4,
+            "only_per_pair": False,
+        },
+    ]
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    freqtrade.protections._protection_handlers[1].global_stop = MagicMock(
+        return_value=ProtectionReturn(True, dt_now() + timedelta(hours=1), "asdf")
+    )
+    create_mock_trades(fee, is_short)
+    freqtrade.handle_protections("ETC/BTC", "*")
+    send_msg_mock = freqtrade.rpc.send_msg
+    assert send_msg_mock.call_count == 2
+    assert send_msg_mock.call_args_list[0][0][0]["type"] == RPCMessageType.PROTECTION_TRIGGER
+    assert send_msg_mock.call_args_list[1][0][0]["type"] == RPCMessageType.PROTECTION_TRIGGER_GLOBAL
+
+
+def test_create_trade_no_signal(default_conf_usdt, fee, mocker) -> None:
+    default_conf_usdt["dry_run"] = True
+
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        get_fee=fee,
+    )
+    default_conf_usdt["stake_amount"] = 10
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_long=False, exit_long=False)
+
+    assert not freqtrade.create_trade("ETH/USDT")
+
+
+@pytest.mark.parametrize("max_open", range(0, 5))
+@pytest.mark.parametrize("tradable_balance_ratio,modifier", [(1.0, 1), (0.99, 0.8), (0.5, 0.5)])
+def test_create_trades_multiple_trades(
+    default_conf_usdt,
+    ticker_usdt,
+    fee,
+    mocker,
+    limit_buy_order_usdt_open,
+    max_open,
+    tradable_balance_ratio,
+    modifier,
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    default_conf_usdt["max_open_trades"] = max_open
+    default_conf_usdt["tradable_balance_ratio"] = tradable_balance_ratio
+    default_conf_usdt["dry_run_wallet"] = 60.0 * max_open
+
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value=limit_buy_order_usdt_open),
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+
+    n = freqtrade.enter_positions()
+    trades = Trade.get_open_trades()
+    # Expected trades should be max_open * a modified value
+    # depending on the configured tradable_balance
+    assert n == max(int(max_open * modifier), 0)
+    assert len(trades) == max(int(max_open * modifier), 0)
+
+
+def test_create_trades_preopen(
+    default_conf_usdt, ticker_usdt, fee, mocker, limit_buy_order_usdt_open, caplog
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    default_conf_usdt["max_open_trades"] = 4
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value=limit_buy_order_usdt_open),
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+
+    # Create 2 existing trades
+    freqtrade.execute_entry("ETH/USDT", default_conf_usdt["stake_amount"])
+    freqtrade.execute_entry("NEO/BTC", default_conf_usdt["stake_amount"])
+
+    assert len(Trade.get_open_trades()) == 2
+    # Change order_id for new orders
+    limit_buy_order_usdt_open["id"] = "123444"
+
+    # Create 2 new trades using create_trades
+    assert freqtrade.create_trade("ETH/USDT")
+    assert freqtrade.create_trade("NEO/BTC")
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 4
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_process_trade_creation(
+    default_conf_usdt, ticker_usdt, limit_order, limit_order_open, is_short, fee, mocker, caplog
+) -> None:
+    ticker_side = "ask" if is_short else "bid"
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value=limit_order_open[entry_side(is_short)]),
+        fetch_order=MagicMock(return_value=limit_order[entry_side(is_short)]),
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    trades = Trade.get_open_trades()
+    assert not trades
+
+    freqtrade.process()
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade = trades[0]
+    assert trade is not None
+    assert pytest.approx(trade.stake_amount) == default_conf_usdt["stake_amount"]
+    assert trade.is_open
+    assert trade.open_date is not None
+    assert trade.exchange == "binance"
+    assert trade.open_rate == ticker_usdt.return_value[ticker_side]
+    # Trade opens with 0 amount. Only trade filling will set the amount
+    assert pytest.approx(trade.amount) == 0
+    assert pytest.approx(trade.amount_requested) == 60 / ticker_usdt.return_value[ticker_side]
+
+    assert log_has_re(
+        f"{'Short' if is_short else 'Long'} signal found: about create a new trade for ETH/USDT "
+        r"with stake_amount: 60.0 and price: .*",
+        caplog,
+    )
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot._check_and_execute_exit")
+
+    # Fill trade.
+    freqtrade.process()
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade = trades[0]
+    assert trade is not None
+    assert trade.is_open
+    assert trade.open_date is not None
+    assert trade.exchange == "binance"
+    assert trade.open_rate == limit_order[entry_side(is_short)]["price"]
+    # Filled trade has amount set to filled order amount
+    assert pytest.approx(trade.amount) == limit_order[entry_side(is_short)]["filled"]
+
+
+def test_process_exchange_failures(default_conf_usdt, ticker_usdt, mocker) -> None:
+    # TODO: Move this test to test_worker
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        reload_markets=MagicMock(),
+        create_order=MagicMock(side_effect=TemporaryError),
+    )
+    sleep_mock = mocker.patch("time.sleep")
+
+    worker = Worker(args=None, config=default_conf_usdt)
+    patch_get_signal(worker.freqtrade)
+    mocker.patch(f"{EXMS}.reload_markets", MagicMock(side_effect=TemporaryError))
+
+    worker._process_running()
+    assert sleep_mock.called is True
+
+
+def test_process_operational_exception(default_conf_usdt, ticker_usdt, mocker) -> None:
+    msg_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS, fetch_ticker=ticker_usdt, create_order=MagicMock(side_effect=OperationalException)
+    )
+    worker = Worker(args=None, config=default_conf_usdt)
+    patch_get_signal(worker.freqtrade)
+
+    assert worker.freqtrade.state == State.RUNNING
+
+    worker._process_running()
+    assert worker.freqtrade.state == State.STOPPED
+    assert "OperationalException" in msg_mock.call_args_list[-1][0][0]["status"]
+
+
+def test_process_trade_handling(
+    default_conf_usdt, ticker_usdt, limit_buy_order_usdt_open, fee, mocker
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value=limit_buy_order_usdt_open),
+        fetch_order=MagicMock(return_value=limit_buy_order_usdt_open),
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+
+    trades = Trade.get_open_trades()
+    assert not trades
+    freqtrade.process()
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+
+    # Nothing happened ...
+    freqtrade.process()
+    assert len(trades) == 1
+
+
+def test_process_trade_no_whitelist_pair(
+    default_conf_usdt, ticker_usdt, limit_buy_order_usdt, fee, mocker
+) -> None:
+    """Test process with trade not in pair list"""
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value={"id": limit_buy_order_usdt["id"]}),
+        fetch_order=MagicMock(return_value=limit_buy_order_usdt),
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+    pair = "BLK/BTC"
+    # Ensure the pair is not in the whitelist!
+    assert pair not in default_conf_usdt["exchange"]["pair_whitelist"]
+
+    # create open trade not in whitelist
+    Trade.session.add(
+        Trade(
+            pair=pair,
+            stake_amount=0.001,
+            fee_open=fee.return_value,
+            fee_close=fee.return_value,
+            is_open=True,
+            amount=20,
+            open_rate=0.01,
+            exchange="binance",
+        )
+    )
+    Trade.session.add(
+        Trade(
+            pair="ETH/USDT",
+            stake_amount=0.001,
+            fee_open=fee.return_value,
+            fee_close=fee.return_value,
+            is_open=True,
+            amount=12,
+            open_rate=0.001,
+            exchange="binance",
+        )
+    )
+    Trade.commit()
+
+    assert pair not in freqtrade.active_pair_whitelist
+    freqtrade.process()
+    assert pair in freqtrade.active_pair_whitelist
+    # Make sure each pair is only in the list once
+    assert len(freqtrade.active_pair_whitelist) == len(set(freqtrade.active_pair_whitelist))
+
+
+def test_process_informative_pairs_added(default_conf_usdt, ticker_usdt, mocker) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+
+    refresh_mock = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(side_effect=TemporaryError),
+        refresh_latest_ohlcv=refresh_mock,
+    )
+    inf_pairs = MagicMock(
+        return_value=[("BTC/ETH", "1m", CandleType.SPOT), ("ETH/USDT", "1h", CandleType.SPOT)]
+    )
+    mocker.patch.multiple(
+        "freqtrade.strategy.interface.IStrategy",
+        get_exit_signal=MagicMock(return_value=(False, False)),
+        get_entry_signal=MagicMock(return_value=(None, None)),
+    )
+    mocker.patch("time.sleep", return_value=None)
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade.strategy.informative_pairs = inf_pairs
+    # patch_get_signal(freqtrade)
+
+    freqtrade.process()
+    assert inf_pairs.call_count == 1
+    assert refresh_mock.call_count == 1
+    assert ("BTC/ETH", "1m", CandleType.SPOT) in refresh_mock.call_args[0][0]
+    assert ("ETH/USDT", "1h", CandleType.SPOT) in refresh_mock.call_args[0][0]
+    assert ("ETH/USDT", default_conf_usdt["timeframe"], CandleType.SPOT) in refresh_mock.call_args[
+        0
+    ][0]
+
+
+@pytest.mark.parametrize(
+    "is_short,trading_mode,exchange_name,margin_mode,liq_buffer,liq_price",
+    [
+        (False, "spot", "binance", None, 0.0, None),
+        (True, "spot", "binance", None, 0.0, None),
+        (False, "spot", "gate", None, 0.0, None),
+        (True, "spot", "gate", None, 0.0, None),
+        (False, "spot", "okx", None, 0.0, None),
+        (True, "spot", "okx", None, 0.0, None),
+        (True, "futures", "binance", "isolated", 0.0, 11.88151815181518),
+        (False, "futures", "binance", "isolated", 0.0, 8.080471380471382),
+        (True, "futures", "gate", "isolated", 0.0, 11.87413417771621),
+        (False, "futures", "gate", "isolated", 0.0, 8.085708510208207),
+        (True, "futures", "binance", "isolated", 0.05, 11.7874422442244),
+        (False, "futures", "binance", "isolated", 0.05, 8.17644781144781),
+        (True, "futures", "gate", "isolated", 0.05, 11.7804274688304),
+        (False, "futures", "gate", "isolated", 0.05, 8.181423084697796),
+        (True, "futures", "okx", "isolated", 0.0, 11.87413417771621),
+        (False, "futures", "okx", "isolated", 0.0, 8.085708510208207),
+        (True, "futures", "bybit", "isolated", 0.0, 11.9),
+        (False, "futures", "bybit", "isolated", 0.0, 8.1),
+    ],
+)
+def test_execute_entry(
+    mocker,
+    default_conf_usdt,
+    fee,
+    limit_order,
+    limit_order_open,
+    is_short,
+    trading_mode,
+    exchange_name,
+    margin_mode,
+    liq_buffer,
+    liq_price,
+) -> None:
+    """
+    exchange_name = binance, is_short = true
+        leverage = 5
+        position = 0.2 * 5
+        ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
+        ((2 + 0.01) - ((-1) * 1 * 10)) / ((1 * 0.01) - ((-1) * 1)) = 11.89108910891089
+
+    exchange_name = binance, is_short = false
+        ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
+        ((2 + 0.01) - (1 * 1 * 10)) / ((1 * 0.01) - (1 * 1)) = 8.070707070707071
+
+    exchange_name = gate/okx, is_short = true
+        (open_rate + (wallet_balance / position)) / (1 + (mm_ratio + taker_fee_rate))
+        (10 + (2 / 1)) / (1 + (0.01 + 0.0006)) = 11.87413417771621
+
+    exchange_name = gate/okx, is_short = false
+        (open_rate - (wallet_balance / position)) / (1 - (mm_ratio + taker_fee_rate))
+        (10 - (2 / 1)) / (1 - (0.01 + 0.0006)) = 8.085708510208207
+    """
+    # TODO: Split this test into multiple tests to improve readability
+    open_order = limit_order_open[entry_side(is_short)]
+    order = limit_order[entry_side(is_short)]
+    default_conf_usdt["trading_mode"] = trading_mode
+    default_conf_usdt["liquidation_buffer"] = liq_buffer
+    leverage = 1.0 if trading_mode == "spot" else 5.0
+    default_conf_usdt["exchange"]["name"] = exchange_name
+    if margin_mode:
+        default_conf_usdt["margin_mode"] = margin_mode
+    mocker.patch("freqtrade.exchange.gate.Gate.validate_ordertypes")
+    patch_RPCManager(mocker)
+    patch_exchange(mocker, exchange=exchange_name)
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=False)
+    freqtrade.strategy.leverage = MagicMock(return_value=leverage)
+    stake_amount = 2
+    bid = 0.11
+    enter_rate_mock = MagicMock(return_value=bid)
+    enter_mm = MagicMock(return_value=open_order)
+    mocker.patch.multiple(
+        EXMS,
+        get_rate=enter_rate_mock,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=enter_mm,
+        get_min_pair_stake_amount=MagicMock(return_value=1),
+        get_max_pair_stake_amount=MagicMock(return_value=500000),
+        get_fee=fee,
+        get_funding_fees=MagicMock(return_value=0),
+        name=exchange_name,
+        get_maintenance_ratio_and_amt=MagicMock(return_value=(0.01, 0.01)),
+        get_max_leverage=MagicMock(return_value=10),
+    )
+    mocker.patch.multiple(
+        "freqtrade.exchange.okx.Okx",
+        get_max_pair_stake_amount=MagicMock(return_value=500000),
+    )
+    pair = "ETH/USDT"
+
+    assert not freqtrade.execute_entry(pair, stake_amount, is_short=is_short)
+    assert enter_rate_mock.call_count == 1
+    assert enter_mm.call_count == 0
+    assert freqtrade.strategy.confirm_trade_entry.call_count == 1
+    enter_rate_mock.reset_mock()
+
+    open_order["id"] = "22"
+    freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True)
+    assert freqtrade.execute_entry(pair, stake_amount)
+    assert enter_rate_mock.call_count == 2
+    assert enter_mm.call_count == 1
+    call_args = enter_mm.call_args_list[0][1]
+    assert call_args["pair"] == pair
+    assert call_args["rate"] == bid
+    assert pytest.approx(call_args["amount"]) == round(stake_amount / bid * leverage, 8)
+    enter_rate_mock.reset_mock()
+
+    # Should create an open trade with an open order id
+    # As the order is not fulfilled yet
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+    assert trade.is_open is True
+    assert trade.has_open_orders
+    assert "22" in trade.open_orders_ids
+
+    # Test calling with price
+    open_order["id"] = "33"
+    fix_price = 0.06
+    assert freqtrade.execute_entry(pair, stake_amount, fix_price, is_short=is_short)
+    # Make sure get_rate wasn't called again
+    assert enter_rate_mock.call_count == 1
+
+    assert enter_mm.call_count == 2
+    call_args = enter_mm.call_args_list[1][1]
+    assert call_args["pair"] == pair
+    assert call_args["rate"] == fix_price
+    assert pytest.approx(call_args["amount"]) == round(stake_amount / fix_price * leverage, 8)
+
+    # In case of closed order
+    order["status"] = "closed"
+    order["average"] = 10
+    order["cost"] = 300
+    order["id"] = "444"
+
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=order))
+    assert freqtrade.execute_entry(pair, stake_amount, is_short=is_short)
+    trade = Trade.session.scalars(select(Trade)).all()[2]
+    trade.is_short = is_short
+    assert trade
+    assert not trade.has_open_orders
+    assert trade.open_rate == 10
+    assert trade.stake_amount == round(order["average"] * order["filled"] / leverage, 8)
+    assert pytest.approx(trade.liquidation_price) == liq_price
+
+    # In case of rejected or expired order and partially filled
+    order["status"] = "expired"
+    order["amount"] = 30.0
+    order["filled"] = 20.0
+    order["remaining"] = 10.00
+    order["average"] = 0.5
+    order["cost"] = 10.0
+    order["id"] = "555"
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=order))
+    assert freqtrade.execute_entry(pair, stake_amount)
+    trade = Trade.session.scalars(select(Trade)).all()[3]
+    trade.is_short = is_short
+    assert trade
+    assert not trade.has_open_orders
+    assert trade.open_rate == 0.5
+    assert trade.stake_amount == round(order["average"] * order["filled"] / leverage, 8)
+
+    # Test with custom stake
+    order["status"] = "open"
+    order["id"] = "556"
+
+    freqtrade.strategy.custom_stake_amount = lambda **kwargs: 150.0
+    assert freqtrade.execute_entry(pair, stake_amount, is_short=is_short)
+    trade = Trade.session.scalars(select(Trade)).all()[4]
+    trade.is_short = is_short
+    assert trade
+    assert pytest.approx(trade.stake_amount) == 150
+
+    # Exception case
+    order["id"] = "557"
+    freqtrade.strategy.custom_stake_amount = lambda **kwargs: 20 / 0
+    assert freqtrade.execute_entry(pair, stake_amount, is_short=is_short)
+    trade = Trade.session.scalars(select(Trade)).all()[5]
+    trade.is_short = is_short
+    assert trade
+    assert pytest.approx(trade.stake_amount) == 2.0
+
+    # In case of the order is rejected and not filled at all
+    order["status"] = "rejected"
+    order["amount"] = 30.0 * leverage
+    order["filled"] = 0.0
+    order["remaining"] = 30.0
+    order["average"] = 0.5
+    order["cost"] = 0.0
+    order["id"] = "66"
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=order))
+    assert not freqtrade.execute_entry(pair, stake_amount)
+    assert freqtrade.strategy.leverage.call_count == 0 if trading_mode == "spot" else 2
+
+    # Fail to get price...
+    mocker.patch(f"{EXMS}.get_rate", MagicMock(return_value=0.0))
+
+    with pytest.raises(PricingError, match="Could not determine entry price."):
+        freqtrade.execute_entry(pair, stake_amount, is_short=is_short)
+
+    # In case of custom entry price
+    mocker.patch(f"{EXMS}.get_rate", return_value=0.50)
+    order["status"] = "open"
+    order["id"] = "5566"
+    freqtrade.strategy.custom_entry_price = lambda **kwargs: 0.508
+    assert freqtrade.execute_entry(pair, stake_amount, is_short=is_short)
+    trade = Trade.session.scalars(select(Trade)).all()[6]
+    trade.is_short = is_short
+    assert trade
+    assert trade.open_rate_requested == 0.508
+
+    # In case of custom entry price set to None
+
+    order["status"] = "open"
+    order["id"] = "5567"
+    freqtrade.strategy.custom_entry_price = lambda **kwargs: None
+
+    mocker.patch.multiple(
+        EXMS,
+        get_rate=MagicMock(return_value=10),
+    )
+
+    assert freqtrade.execute_entry(pair, stake_amount, is_short=is_short)
+    trade = Trade.session.scalars(select(Trade)).all()[7]
+    trade.is_short = is_short
+    assert trade
+    assert trade.open_rate_requested == 10
+
+    # In case of custom entry price not float type
+    order["status"] = "open"
+    order["id"] = "5568"
+    freqtrade.strategy.custom_entry_price = lambda **kwargs: "string price"
+    assert freqtrade.execute_entry(pair, stake_amount, is_short=is_short)
+    trade = Trade.session.scalars(select(Trade)).all()[8]
+    # Trade(id=9, pair=ETH/USDT, amount=0.20000000, is_short=False,
+    #   leverage=1.0, open_rate=10.00000000, open_since=...)
+    # Trade(id=9, pair=ETH/USDT, amount=0.60000000, is_short=True,
+    #   leverage=3.0, open_rate=10.00000000, open_since=...)
+    trade.is_short = is_short
+    assert trade
+    assert trade.open_rate_requested == 10
+
+    # In case of too high stake amount
+
+    order["status"] = "open"
+    order["id"] = "55672"
+
+    mocker.patch.multiple(
+        EXMS,
+        get_max_pair_stake_amount=MagicMock(return_value=500),
+    )
+    freqtrade.exchange.get_max_pair_stake_amount = MagicMock(return_value=500)
+
+    assert freqtrade.execute_entry(pair, 2000, is_short=is_short)
+    trade = Trade.session.scalars(select(Trade)).all()[9]
+    trade.is_short = is_short
+    assert pytest.approx(trade.stake_amount) == 500
+
+    order["id"] = "55673"
+
+    freqtrade.strategy.leverage.reset_mock()
+    assert freqtrade.execute_entry(pair, 200, leverage_=3)
+    assert freqtrade.strategy.leverage.call_count == 0
+    trade = Trade.session.scalars(select(Trade)).all()[10]
+    assert trade.leverage == 1 if trading_mode == "spot" else 3
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_execute_entry_confirm_error(mocker, default_conf_usdt, fee, limit_order, is_short) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(return_value=limit_order[entry_side(is_short)]),
+        get_rate=MagicMock(return_value=0.11),
+        get_min_pair_stake_amount=MagicMock(return_value=1),
+        get_fee=fee,
+    )
+    stake_amount = 2
+    pair = "ETH/USDT"
+
+    freqtrade.strategy.confirm_trade_entry = MagicMock(side_effect=ValueError)
+    assert freqtrade.execute_entry(pair, stake_amount)
+
+    limit_order[entry_side(is_short)]["id"] = "222"
+    freqtrade.strategy.confirm_trade_entry = MagicMock(side_effect=Exception)
+    assert freqtrade.execute_entry(pair, stake_amount)
+
+    limit_order[entry_side(is_short)]["id"] = "2223"
+    freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True)
+    assert freqtrade.execute_entry(pair, stake_amount)
+
+    freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=False)
+    assert not freqtrade.execute_entry(pair, stake_amount)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_execute_entry_fully_canceled_on_create(
+    mocker, default_conf_usdt, fee, limit_order_open, is_short
+) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    mock_hce = mocker.spy(freqtrade, "handle_cancel_enter")
+    order = limit_order_open[entry_side(is_short)]
+    pair = "ETH/USDT"
+    order["symbol"] = pair
+    order["status"] = "canceled"
+    order["filled"] = 0.0
+
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(return_value=order),
+        get_rate=MagicMock(return_value=0.11),
+        get_min_pair_stake_amount=MagicMock(return_value=1),
+        get_fee=fee,
+    )
+    stake_amount = 2
+
+    assert freqtrade.execute_entry(pair, stake_amount)
+    assert mock_hce.call_count == 1
+    # an order that immediately cancels completely should delete the order.
+    trades = Trade.get_trades().all()
+    assert len(trades) == 0
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_execute_entry_min_leverage(mocker, default_conf_usdt, fee, limit_order, is_short) -> None:
+    default_conf_usdt["trading_mode"] = "futures"
+    default_conf_usdt["margin_mode"] = "isolated"
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(return_value=limit_order[entry_side(is_short)]),
+        get_rate=MagicMock(return_value=0.11),
+        # Minimum stake-amount is ~5$
+        get_maintenance_ratio_and_amt=MagicMock(return_value=(0.0, 0.0)),
+        _fetch_and_calculate_funding_fees=MagicMock(return_value=0),
+        get_fee=fee,
+        get_max_leverage=MagicMock(return_value=5.0),
+    )
+    stake_amount = 2
+    pair = "SOL/BUSD:BUSD"
+    freqtrade.strategy.leverage = MagicMock(return_value=5.0)
+
+    assert freqtrade.execute_entry(pair, stake_amount, is_short=is_short)
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.leverage == 5.0
+    # assert trade.stake_amount == 2
+
+
+@pytest.mark.parametrize(
+    "return_value,side_effect,log_message",
+    [
+        (False, None, "Found no enter signals for whitelisted currencies. Trying again..."),
+        (None, DependencyException, "Unable to create trade for ETH/USDT: "),
+    ],
+)
+def test_enter_positions(
+    mocker, default_conf_usdt, return_value, side_effect, log_message, caplog
+) -> None:
+    caplog.set_level(logging.DEBUG)
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    mock_ct = mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot.create_trade",
+        MagicMock(return_value=return_value, side_effect=side_effect),
+    )
+    n = freqtrade.enter_positions()
+    assert n == 0
+    assert log_has(log_message, caplog)
+    # create_trade should be called once for every pair in the whitelist.
+    assert mock_ct.call_count == len(default_conf_usdt["exchange"]["pair_whitelist"])
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_exit_positions(mocker, default_conf_usdt, limit_order, is_short, caplog) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_trade", MagicMock(return_value=True))
+    mocker.patch(f"{EXMS}.fetch_order", return_value=limit_order[entry_side(is_short)])
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=[])
+
+    order_id = "123"
+    trade = Trade(
+        pair="ETH/USDT",
+        fee_open=0.001,
+        fee_close=0.001,
+        open_rate=0.01,
+        open_date=dt_now(),
+        stake_amount=0.01,
+        amount=11,
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+    )
+    trade.orders.append(
+        Order(
+            ft_order_side=entry_side(is_short),
+            price=0.01,
+            ft_pair=trade.pair,
+            ft_amount=trade.amount,
+            ft_price=trade.open_rate,
+            order_id=order_id,
+        )
+    )
+    Trade.session.add(trade)
+    Trade.commit()
+    trades = [trade]
+    freqtrade.wallets.update()
+    n = freqtrade.exit_positions(trades)
+    assert n == 1
+    # Test amount not modified by fee-logic
+    assert not log_has_re(r"Applying fee to amount for Trade .*", caplog)
+
+    gra = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.get_real_amount", return_value=0.0)
+    # test amount modified by fee-logic
+    n = freqtrade.exit_positions(trades)
+    assert n == 1
+    assert gra.call_count == 0
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_exit_positions_exception(mocker, default_conf_usdt, limit_order, caplog, is_short) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    order = limit_order[entry_side(is_short)]
+    mocker.patch(f"{EXMS}.fetch_order", return_value=order)
+
+    order_id = "123"
+    trade = Trade(
+        pair="ETH/USDT",
+        fee_open=0.001,
+        fee_close=0.001,
+        open_rate=0.01,
+        open_date=dt_now(),
+        stake_amount=0.01,
+        amount=11,
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+    )
+    trade.orders.append(
+        Order(
+            ft_order_side=entry_side(is_short),
+            price=0.01,
+            ft_pair=trade.pair,
+            ft_amount=trade.amount,
+            ft_price=trade.open_rate,
+            order_id=order_id,
+            ft_is_open=False,
+            filled=11,
+        )
+    )
+    Trade.session.add(trade)
+    Trade.commit()
+    freqtrade.wallets.update()
+    trades = [trade]
+
+    # Test raise of DependencyException exception
+    mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot.handle_trade", side_effect=DependencyException()
+    )
+    caplog.clear()
+    n = freqtrade.exit_positions(trades)
+    assert n == 0
+    assert log_has("Unable to exit trade ETH/USDT: ", caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_update_trade_state(mocker, default_conf_usdt, limit_order, is_short, caplog) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    order = limit_order[entry_side(is_short)]
+
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_trade", MagicMock(return_value=True))
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot._notify_enter")
+    mocker.patch(f"{EXMS}.fetch_order", return_value=order)
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=[])
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.get_real_amount", return_value=0.0)
+    order_id = order["id"]
+
+    trade = Trade(
+        fee_open=0.001,
+        fee_close=0.001,
+        open_rate=0.01,
+        open_date=dt_now(),
+        amount=11,
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+    )
+    trade.orders.append(
+        Order(
+            ft_order_side=entry_side(is_short),
+            price=0.01,
+            order_id=order_id,
+        )
+    )
+    freqtrade.strategy.order_filled = MagicMock(return_value=None)
+    assert not freqtrade.update_trade_state(trade, None)
+    assert log_has_re(r"Orderid for trade .* is empty.", caplog)
+    caplog.clear()
+    # Add datetime explicitly since sqlalchemy defaults apply only once written to database
+    freqtrade.update_trade_state(trade, order_id)
+    # Test amount not modified by fee-logic
+    assert not log_has_re(r"Applying fee to .*", caplog)
+    caplog.clear()
+    assert not trade.has_open_orders
+    assert trade.amount == order["amount"]
+    assert freqtrade.strategy.order_filled.call_count == 1
+
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.get_real_amount", return_value=0.01)
+    assert trade.amount == 30.0
+    # test amount modified by fee-logic
+    freqtrade.update_trade_state(trade, order_id)
+    assert trade.amount == 29.99
+    assert not trade.has_open_orders
+
+    trade.is_open = True
+    # Assert we call handle_trade() if trade is feasible for execution
+    freqtrade.update_trade_state(trade, order_id)
+
+    assert log_has_re("Found open order for.*", caplog)
+    limit_buy_order_usdt_new = deepcopy(limit_order)
+    limit_buy_order_usdt_new["filled"] = 0.0
+    limit_buy_order_usdt_new["status"] = "canceled"
+
+    freqtrade.strategy.order_filled = MagicMock(return_value=None)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.get_real_amount", side_effect=ValueError)
+    mocker.patch(f"{EXMS}.fetch_order", return_value=limit_buy_order_usdt_new)
+    res = freqtrade.update_trade_state(trade, order_id)
+    # Cancelled empty
+    assert res is True
+    assert freqtrade.strategy.order_filled.call_count == 0
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.parametrize("initial_amount,has_rounding_fee", [(30.0 + 1e-14, True), (8.0, False)])
+def test_update_trade_state_withorderdict(
+    default_conf_usdt,
+    trades_for_order,
+    limit_order,
+    fee,
+    mocker,
+    initial_amount,
+    has_rounding_fee,
+    is_short,
+    caplog,
+):
+    order = limit_order[entry_side(is_short)]
+    trades_for_order[0]["amount"] = initial_amount
+    order_id = "oid_123456"
+    order["id"] = order_id
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=trades_for_order)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot._notify_enter")
+    # fetch_order should not be called!!
+    mocker.patch(f"{EXMS}.fetch_order", MagicMock(side_effect=ValueError))
+    patch_exchange(mocker)
+    amount = sum(x["amount"] for x in trades_for_order)
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    caplog.clear()
+    trade = Trade(
+        pair="LTC/USDT",
+        amount=amount,
+        exchange="binance",
+        open_rate=2.0,
+        open_date=dt_now(),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        is_open=True,
+        leverage=1,
+        is_short=is_short,
+    )
+    trade.orders.append(
+        Order(
+            ft_order_side=entry_side(is_short),
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            order_id=order_id,
+        )
+    )
+    log_text = r"Applying fee on amount for .*"
+    freqtrade.update_trade_state(trade, order_id, order)
+    assert trade.amount != amount
+    if has_rounding_fee:
+        assert pytest.approx(trade.amount) == 29.992
+        assert log_has_re(log_text, caplog)
+    else:
+        assert pytest.approx(trade.amount) == order["amount"]
+        assert not log_has_re(log_text, caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_update_trade_state_exception(
+    mocker, default_conf_usdt, is_short, limit_order, caplog
+) -> None:
+    order = limit_order[entry_side(is_short)]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch(f"{EXMS}.fetch_order", return_value=order)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot._notify_enter")
+
+    # TODO: should not be magicmock
+    trade = MagicMock()
+    trade.amount = 123
+    open_order_id = "123"
+
+    # Test raise of OperationalException exception
+    mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot.get_real_amount", side_effect=DependencyException()
+    )
+    freqtrade.update_trade_state(trade, open_order_id)
+    assert log_has("Could not update trade amount: ", caplog)
+
+
+def test_update_trade_state_orderexception(mocker, default_conf_usdt, caplog) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch(f"{EXMS}.fetch_order", MagicMock(side_effect=InvalidOrderException))
+
+    # TODO: should not be magicmock
+    trade = MagicMock()
+    open_order_id = "123"
+
+    # Test raise of OperationalException exception
+    grm_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.get_real_amount", MagicMock())
+    freqtrade.update_trade_state(trade, open_order_id)
+    assert grm_mock.call_count == 0
+    assert log_has(f"Unable to fetch order {open_order_id}: ", caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_update_trade_state_sell(
+    default_conf_usdt, trades_for_order, limit_order_open, limit_order, is_short, mocker
+):
+    buy_order = limit_order[entry_side(is_short)]
+    open_order = limit_order_open[exit_side(is_short)]
+    l_order = limit_order[exit_side(is_short)]
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=trades_for_order)
+    # fetch_order should not be called!!
+    mocker.patch(f"{EXMS}.fetch_order", MagicMock(side_effect=ValueError))
+    wallet_mock = MagicMock()
+    mocker.patch("freqtrade.wallets.Wallets.update", wallet_mock)
+
+    patch_exchange(mocker)
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    amount = l_order["amount"]
+    wallet_mock.reset_mock()
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        open_rate=0.245441,
+        fee_open=0.0025,
+        fee_close=0.0025,
+        open_date=dt_now(),
+        is_open=True,
+        interest_rate=0.0005,
+        leverage=1,
+        is_short=is_short,
+    )
+    order = Order.parse_from_ccxt_object(buy_order, "LTC/ETH", entry_side(is_short))
+    trade.orders.append(order)
+
+    order = Order.parse_from_ccxt_object(open_order, "LTC/ETH", exit_side(is_short))
+    trade.orders.append(order)
+    assert order.status == "open"
+    freqtrade.update_trade_state(trade, trade.open_orders_ids[-1], l_order)
+    assert trade.amount == l_order["amount"]
+    # Wallet needs to be updated after closing a limit-sell order to re-enable buying
+    assert wallet_mock.call_count == 1
+    assert not trade.is_open
+    # Order is updated by update_trade_state
+    assert order.status == "closed"
+
+
+@pytest.mark.parametrize(
+    "is_short,close_profit",
+    [
+        (False, 0.09451372),
+        (True, 0.08635224),
+    ],
+)
+def test_handle_trade(
+    default_conf_usdt, limit_order_open, limit_order, fee, mocker, is_short, close_profit
+) -> None:
+    open_order = limit_order_open[exit_side(is_short)]
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 2.19, "ask": 2.2, "last": 2.19}),
+        create_order=MagicMock(
+            side_effect=[
+                enter_order,
+                open_order,
+            ]
+        ),
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+
+    time.sleep(0.01)  # Race condition fix
+    assert trade.is_open is True
+    freqtrade.wallets.update()
+
+    patch_get_signal(
+        freqtrade,
+        enter_long=False,
+        exit_short=is_short,
+        exit_long=not is_short,
+        exit_tag="sell_signal1",
+    )
+    assert freqtrade.handle_trade(trade) is True
+    assert trade.open_orders_ids[-1] == exit_order["id"]
+
+    # Simulate fulfilled LIMIT_SELL order for trade
+    trade.orders[-1].ft_is_open = False
+    trade.orders[-1].status = "closed"
+    trade.orders[-1].filled = trade.orders[-1].remaining
+    trade.orders[-1].remaining = 0.0
+
+    trade.update_trade(trade.orders[-1])
+
+    assert trade.close_rate == (2.0 if is_short else 2.2)
+    assert pytest.approx(trade.close_profit) == close_profit
+    assert pytest.approx(trade.calc_profit(trade.close_rate)) == 5.685
+    assert trade.close_date is not None
+    assert trade.exit_reason == "sell_signal1"
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_overlapping_signals(
+    default_conf_usdt, ticker_usdt, limit_order_open, fee, mocker, is_short
+) -> None:
+    open_order = limit_order_open[exit_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(
+            side_effect=[
+                open_order,
+                {"id": 1234553382},
+            ]
+        ),
+        get_fee=fee,
+    )
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    if is_short:
+        patch_get_signal(freqtrade, enter_long=False, enter_short=True, exit_short=True)
+    else:
+        patch_get_signal(freqtrade, enter_long=True, exit_long=True)
+    freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
+
+    freqtrade.enter_positions()
+
+    # Buy and Sell triggering, so doing nothing ...
+    trades = Trade.session.scalars(select(Trade)).all()
+
+    nb_trades = len(trades)
+    assert nb_trades == 0
+
+    # Buy is triggering, so buying ...
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.enter_positions()
+    trades = Trade.session.scalars(select(Trade)).all()
+    for trade in trades:
+        trade.is_short = is_short
+    nb_trades = len(trades)
+    assert nb_trades == 1
+    assert trades[0].is_open is True
+
+    # Buy and Sell are not triggering, so doing nothing ...
+    patch_get_signal(freqtrade, enter_long=False)
+    assert freqtrade.handle_trade(trades[0]) is False
+    trades = Trade.session.scalars(select(Trade)).all()
+    for trade in trades:
+        trade.is_short = is_short
+    nb_trades = len(trades)
+    assert nb_trades == 1
+    assert trades[0].is_open is True
+
+    # Buy and Sell are triggering, so doing nothing ...
+    if is_short:
+        patch_get_signal(freqtrade, enter_long=False, enter_short=True, exit_short=True)
+    else:
+        patch_get_signal(freqtrade, enter_long=True, exit_long=True)
+    assert freqtrade.handle_trade(trades[0]) is False
+    trades = Trade.session.scalars(select(Trade)).all()
+    for trade in trades:
+        trade.is_short = is_short
+    nb_trades = len(trades)
+    assert nb_trades == 1
+    assert trades[0].is_open is True
+
+    # Sell is triggering, guess what : we are Selling!
+    if is_short:
+        patch_get_signal(freqtrade, enter_long=False, exit_short=True)
+    else:
+        patch_get_signal(freqtrade, enter_long=False, exit_long=True)
+    trades = Trade.session.scalars(select(Trade)).all()
+    for trade in trades:
+        trade.is_short = is_short
+    assert freqtrade.handle_trade(trades[0]) is True
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_trade_roi(
+    default_conf_usdt, ticker_usdt, limit_order_open, fee, mocker, caplog, is_short
+) -> None:
+    open_order = limit_order_open[entry_side(is_short)]
+
+    caplog.set_level(logging.DEBUG)
+
+    patch_RPCManager(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(
+            side_effect=[
+                open_order,
+                {"id": 1234553382, "amount": open_order["amount"]},
+            ]
+        ),
+        get_fee=fee,
+    )
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.min_roi_reached = MagicMock(return_value=True)
+
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+
+    # FIX: sniffing logs, suggest handle_trade should not execute_trade_exit
+    #      instead that responsibility should be moved out of handle_trade(),
+    #      we might just want to check if we are in a sell condition without
+    #      executing
+    # if ROI is reached we must sell
+    caplog.clear()
+    patch_get_signal(freqtrade)
+    assert freqtrade.handle_trade(trade)
+    assert log_has("ETH/USDT - Required profit reached. exit_type=ExitType.ROI", caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_trade_use_exit_signal(
+    default_conf_usdt, ticker_usdt, limit_order_open, fee, mocker, caplog, is_short
+) -> None:
+    enter_open_order = limit_order_open[exit_side(is_short)]
+    exit_open_order = limit_order_open[entry_side(is_short)]
+
+    # use_exit_signal is True buy default
+    caplog.set_level(logging.DEBUG)
+    patch_RPCManager(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(
+            side_effect=[
+                enter_open_order,
+                exit_open_order,
+            ]
+        ),
+        get_fee=fee,
+    )
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+
+    patch_get_signal(freqtrade, enter_long=False, exit_long=False)
+    assert not freqtrade.handle_trade(trade)
+
+    if is_short:
+        patch_get_signal(freqtrade, enter_long=False, exit_short=True)
+    else:
+        patch_get_signal(freqtrade, enter_long=False, exit_long=True)
+    assert freqtrade.handle_trade(trade)
+    assert log_has("ETH/USDT - Sell signal received. exit_type=ExitType.EXIT_SIGNAL", caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_close_trade(
+    default_conf_usdt, ticker_usdt, limit_order_open, limit_order, fee, mocker, is_short
+) -> None:
+    open_order = limit_order_open[exit_side(is_short)]
+    enter_order = limit_order[exit_side(is_short)]
+    exit_order = limit_order[entry_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value=open_order),
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    # Create trade and sell it
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+
+    oobj = Order.parse_from_ccxt_object(enter_order, enter_order["symbol"], trade.entry_side)
+    trade.update_trade(oobj)
+    oobj = Order.parse_from_ccxt_object(exit_order, exit_order["symbol"], trade.exit_side)
+    trade.update_trade(oobj)
+    assert trade.is_open is False
+
+    with pytest.raises(DependencyException, match=r".*closed trade.*"):
+        freqtrade.handle_trade(trade)
+
+
+def test_bot_loop_start_called_once(mocker, default_conf_usdt, caplog):
+    ftbot = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.create_trade")
+    patch_get_signal(ftbot)
+    ftbot.strategy.bot_loop_start = MagicMock(side_effect=ValueError)
+    ftbot.strategy.analyze = MagicMock()
+
+    ftbot.process()
+    assert log_has_re(r"Strategy caused the following exception.*", caplog)
+    assert ftbot.strategy.bot_loop_start.call_count == 1
+    assert ftbot.strategy.analyze.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_manage_open_orders_entry_usercustom(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_buy_order_old,
+    open_trade,
+    limit_sell_order_old,
+    fee,
+    mocker,
+    is_short,
+) -> None:
+    old_order = limit_sell_order_old if is_short else limit_buy_order_old
+    old_order["id"] = open_trade.open_orders_ids[0]
+
+    default_conf_usdt["unfilledtimeout"] = {"entry": 1400, "exit": 30}
+
+    rpc_mock = patch_RPCManager(mocker)
+    cancel_order_mock = MagicMock(return_value=old_order)
+    cancel_enter_order = deepcopy(old_order)
+    cancel_enter_order["status"] = "canceled"
+    cancel_order_wr_mock = MagicMock(return_value=cancel_enter_order)
+
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=old_order),
+        cancel_order=cancel_order_mock,
+        cancel_order_with_result=cancel_order_wr_mock,
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    open_trade.is_short = is_short
+    open_trade.orders[0].side = "sell" if is_short else "buy"
+    open_trade.orders[0].ft_order_side = "sell" if is_short else "buy"
+    Trade.session.add(open_trade)
+    Trade.commit()
+
+    # Ensure default is to return empty (so not mocked yet)
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 0
+
+    # Return false - trade remains open
+    freqtrade.strategy.check_entry_timeout = MagicMock(return_value=False)
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 0
+    trades = Trade.session.scalars(
+        select(Trade)
+        .where(Order.ft_is_open.is_(True))
+        .where(Order.ft_order_side != "stoploss")
+        .where(Order.ft_trade_id == Trade.id)
+    ).all()
+    nb_trades = len(trades)
+    assert nb_trades == 1
+    assert freqtrade.strategy.check_entry_timeout.call_count == 1
+    freqtrade.strategy.check_entry_timeout = MagicMock(side_effect=KeyError)
+
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 0
+    trades = Trade.session.scalars(
+        select(Trade)
+        .where(Order.ft_is_open.is_(True))
+        .where(Order.ft_order_side != "stoploss")
+        .where(Order.ft_trade_id == Trade.id)
+    ).all()
+    nb_trades = len(trades)
+    assert nb_trades == 1
+    assert freqtrade.strategy.check_entry_timeout.call_count == 1
+    freqtrade.strategy.check_entry_timeout = MagicMock(return_value=True)
+
+    # Trade should be closed since the function returns true
+    freqtrade.manage_open_orders()
+    assert cancel_order_wr_mock.call_count == 1
+    assert rpc_mock.call_count == 2
+    trades = Trade.session.scalars(
+        select(Trade)
+        .where(Order.ft_is_open.is_(True))
+        .where(Order.ft_order_side != "stoploss")
+        .where(Order.ft_trade_id == Trade.id)
+    ).all()
+    nb_trades = len(trades)
+    assert nb_trades == 0
+    assert freqtrade.strategy.check_entry_timeout.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_manage_open_orders_entry(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_buy_order_old,
+    open_trade,
+    limit_sell_order_old,
+    fee,
+    mocker,
+    is_short,
+) -> None:
+    old_order = limit_sell_order_old if is_short else limit_buy_order_old
+    rpc_mock = patch_RPCManager(mocker)
+
+    order = Order.parse_from_ccxt_object(old_order, "mocked", "buy")
+    open_trade.orders[0] = order
+    limit_entry_cancel = deepcopy(old_order)
+    limit_entry_cancel["status"] = "canceled"
+    cancel_order_mock = MagicMock(return_value=limit_entry_cancel)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=old_order),
+        cancel_order_with_result=cancel_order_mock,
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    open_trade.is_short = is_short
+    Trade.session.add(open_trade)
+    Trade.commit()
+
+    freqtrade.strategy.check_entry_timeout = MagicMock(return_value=False)
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1234)
+    # check it does cancel entry orders over the time limit
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 1
+    assert rpc_mock.call_count == 2
+    trades = Trade.session.scalars(
+        select(Trade)
+        .where(Order.ft_is_open.is_(True))
+        .where(Order.ft_order_side != "stoploss")
+        .where(Order.ft_trade_id == Trade.id)
+    ).all()
+    nb_trades = len(trades)
+    assert nb_trades == 0
+    # Custom user entry-timeout is never called
+    assert freqtrade.strategy.check_entry_timeout.call_count == 0
+    # Entry adjustment is never called
+    assert freqtrade.strategy.adjust_entry_price.call_count == 0
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_adjust_entry_cancel(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_buy_order_old,
+    open_trade,
+    limit_sell_order_old,
+    fee,
+    mocker,
+    caplog,
+    is_short,
+) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    old_order = limit_sell_order_old if is_short else limit_buy_order_old
+    old_order["id"] = open_trade.open_orders[0].order_id
+    limit_entry_cancel = deepcopy(old_order)
+    limit_entry_cancel["status"] = "canceled"
+    cancel_order_mock = MagicMock(return_value=limit_entry_cancel)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=old_order),
+        cancel_order_with_result=cancel_order_mock,
+        get_fee=fee,
+    )
+
+    open_trade.is_short = is_short
+    Trade.session.add(open_trade)
+    Trade.commit()
+
+    # Timeout to not interfere
+    freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False)
+
+    # check that order is cancelled
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=None)
+    freqtrade.manage_open_orders()
+    trades = Trade.session.scalars(select(Trade).where(Order.ft_trade_id == Trade.id)).all()
+
+    assert len(trades) == 0
+    assert len(Order.session.scalars(select(Order)).all()) == 0
+    assert log_has_re(f"{'Sell' if is_short else 'Buy'} order user requested order cancel*", caplog)
+    assert log_has_re(f"{'Sell' if is_short else 'Buy'} order fully cancelled.*", caplog)
+
+    # Entry adjustment is called
+    assert freqtrade.strategy.adjust_entry_price.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_adjust_entry_replace_fail(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_buy_order_old,
+    open_trade,
+    limit_sell_order_old,
+    fee,
+    mocker,
+    caplog,
+    is_short,
+) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    old_order = limit_sell_order_old if is_short else limit_buy_order_old
+    old_order["id"] = open_trade.open_orders[0].order_id
+    limit_entry_cancel = deepcopy(old_order)
+    limit_entry_cancel["status"] = "open"
+    cancel_order_mock = MagicMock(return_value=limit_entry_cancel)
+    fetch_order_mock = MagicMock(return_value=old_order)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=fetch_order_mock,
+        cancel_order_with_result=cancel_order_mock,
+        get_fee=fee,
+    )
+    mocker.patch("freqtrade.freqtradebot.sleep")
+
+    open_trade.is_short = is_short
+    Trade.session.add(open_trade)
+    Trade.commit()
+
+    # Timeout to not interfere
+    freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False)
+
+    # Attempt replace order - which fails
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=12234)
+    freqtrade.manage_open_orders()
+    trades = Trade.session.scalars(select(Trade).where(Order.ft_trade_id == Trade.id)).all()
+
+    assert len(trades) == 0
+    assert len(Order.session.scalars(select(Order)).all()) == 0
+    assert fetch_order_mock.call_count == 4
+    assert log_has_re(r"Could not fully cancel order.*, therefore not replacing\.", caplog)
+
+    # Entry adjustment is called
+    assert freqtrade.strategy.adjust_entry_price.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_adjust_entry_replace_fail_create_order(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_buy_order_old,
+    open_trade,
+    limit_sell_order_old,
+    fee,
+    mocker,
+    caplog,
+    is_short,
+) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    old_order = limit_sell_order_old if is_short else limit_buy_order_old
+    old_order["id"] = open_trade.open_orders[0].order_id
+    limit_entry_cancel = deepcopy(old_order)
+    limit_entry_cancel["status"] = "canceled"
+    cancel_order_mock = MagicMock(return_value=limit_entry_cancel)
+    fetch_order_mock = MagicMock(return_value=old_order)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=fetch_order_mock,
+        cancel_order_with_result=cancel_order_mock,
+        get_fee=fee,
+    )
+    mocker.patch("freqtrade.freqtradebot.sleep")
+    mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot.execute_entry", side_effect=DependencyException()
+    )
+
+    open_trade.is_short = is_short
+    Trade.session.add(open_trade)
+    Trade.commit()
+
+    # Timeout to not interfere
+    freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False)
+
+    # Attempt replace order - which fails
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=12234)
+    freqtrade.manage_open_orders()
+    trades = Trade.session.scalars(select(Trade).where(Trade.is_open.is_(True))).all()
+
+    assert len(trades) == 0
+    assert len(Order.session.scalars(select(Order)).all()) == 0
+    assert fetch_order_mock.call_count == 1
+    assert log_has_re(r"Could not replace order for.*", caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_adjust_entry_maintain_replace(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_buy_order_old,
+    open_trade,
+    limit_sell_order_old,
+    fee,
+    mocker,
+    caplog,
+    is_short,
+) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    old_order = limit_sell_order_old if is_short else limit_buy_order_old
+    old_order["id"] = open_trade.open_orders_ids[0]
+    limit_entry_cancel = deepcopy(old_order)
+    limit_entry_cancel["status"] = "canceled"
+    cancel_order_mock = MagicMock(return_value=limit_entry_cancel)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=old_order),
+        cancel_order_with_result=cancel_order_mock,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=False),
+    )
+
+    open_trade.is_short = is_short
+    Trade.session.add(open_trade)
+    Trade.commit()
+
+    # Timeout to not interfere
+    freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False)
+
+    # Check that order is maintained
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=old_order["price"])
+    freqtrade.manage_open_orders()
+    trades = Trade.session.scalars(
+        select(Trade).where(Order.ft_is_open.is_(True)).where(Order.ft_trade_id == Trade.id)
+    ).all()
+    assert len(trades) == 1
+    assert len(Order.get_open_orders()) == 1
+    # Entry adjustment is called
+    assert freqtrade.strategy.adjust_entry_price.call_count == 1
+
+    # Check that order is replaced
+    freqtrade.get_valid_enter_price_and_stake = MagicMock(return_value={100, 10, 1})
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1234)
+
+    freqtrade.manage_open_orders()
+
+    assert freqtrade.strategy.adjust_entry_price.call_count == 1
+
+    trades = Trade.session.scalars(
+        select(Trade).where(Order.ft_is_open.is_(True)).where(Order.ft_trade_id == Trade.id)
+    ).all()
+    assert len(trades) == 1
+    nb_all_orders = len(Order.session.scalars(select(Order)).all())
+    assert nb_all_orders == 2
+    # New order seems to be in closed status?
+    # nb_open_orders = len(Order.get_open_orders())
+    # assert nb_open_orders == 1
+    assert log_has_re(f"{'Sell' if is_short else 'Buy'} order cancelled to be replaced*", caplog)
+    # Entry adjustment is called
+    assert freqtrade.strategy.adjust_entry_price.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_check_handle_cancelled_buy(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_buy_order_old,
+    open_trade,
+    limit_sell_order_old,
+    fee,
+    mocker,
+    caplog,
+    is_short,
+) -> None:
+    """Handle Buy order cancelled on exchange"""
+    old_order = limit_sell_order_old if is_short else limit_buy_order_old
+    rpc_mock = patch_RPCManager(mocker)
+    cancel_order_mock = MagicMock()
+    patch_exchange(mocker)
+    old_order.update({"status": "canceled", "filled": 0.0})
+    old_order["side"] = "buy" if is_short else "sell"
+    old_order["id"] = open_trade.open_orders[0].order_id
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=old_order),
+        cancel_order=cancel_order_mock,
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    open_trade.is_short = is_short
+    Trade.session.add(open_trade)
+    Trade.commit()
+
+    # check it does cancel buy orders over the time limit
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 0
+    assert rpc_mock.call_count == 2
+    trades = Trade.session.scalars(
+        select(Trade).where(Order.ft_is_open.is_(True)).where(Order.ft_trade_id == Trade.id)
+    ).all()
+    assert len(trades) == 0
+    exit_name = "Buy" if is_short else "Sell"
+    assert log_has_re(f"{exit_name} order cancelled on exchange for Trade.*", caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_manage_open_orders_buy_exception(
+    default_conf_usdt, ticker_usdt, open_trade, is_short, fee, mocker
+) -> None:
+    rpc_mock = patch_RPCManager(mocker)
+    cancel_order_mock = MagicMock()
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(side_effect=ExchangeError),
+        cancel_order=cancel_order_mock,
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    open_trade.is_short = is_short
+    Trade.session.add(open_trade)
+    Trade.commit()
+
+    # check it does cancel buy orders over the time limit
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 0
+    assert rpc_mock.call_count == 1
+    assert len(open_trade.open_orders) == 1
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_manage_open_orders_exit_usercustom(
+    default_conf_usdt, ticker_usdt, limit_sell_order_old, mocker, is_short, open_trade_usdt, caplog
+) -> None:
+    default_conf_usdt["unfilledtimeout"] = {"entry": 1440, "exit": 1440, "exit_timeout_count": 1}
+    limit_sell_order_old["amount"] = open_trade_usdt.amount
+    limit_sell_order_old["remaining"] = open_trade_usdt.amount
+
+    if is_short:
+        limit_sell_order_old["side"] = "buy"
+        open_trade_usdt.is_short = is_short
+    open_exit_order = Order.parse_from_ccxt_object(
+        limit_sell_order_old, "mocked", "buy" if is_short else "sell"
+    )
+    open_trade_usdt.orders[-1] = open_exit_order
+
+    rpc_mock = patch_RPCManager(mocker)
+    cancel_order_mock = MagicMock()
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.0)
+    et_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.execute_trade_exit")
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=limit_sell_order_old),
+        cancel_order=cancel_order_mock,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    open_trade_usdt.open_date = dt_now() - timedelta(hours=5)
+    open_trade_usdt.close_date = dt_now() - timedelta(minutes=601)
+    open_trade_usdt.close_profit_abs = 0.001
+
+    Trade.session.add(open_trade_usdt)
+    Trade.commit()
+    # Ensure default is false
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 0
+
+    freqtrade.strategy.check_exit_timeout = MagicMock(return_value=False)
+    freqtrade.strategy.check_entry_timeout = MagicMock(return_value=False)
+    # Return false - No impact
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 0
+    assert rpc_mock.call_count == 1
+    assert freqtrade.strategy.check_exit_timeout.call_count == 1
+    assert freqtrade.strategy.check_entry_timeout.call_count == 0
+
+    freqtrade.strategy.check_exit_timeout = MagicMock(side_effect=KeyError)
+    freqtrade.strategy.check_entry_timeout = MagicMock(side_effect=KeyError)
+    # Return Error - No impact
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 0
+    assert rpc_mock.call_count == 1
+    assert freqtrade.strategy.check_exit_timeout.call_count == 1
+    assert freqtrade.strategy.check_entry_timeout.call_count == 0
+
+    # Return True - sells!
+    freqtrade.strategy.check_exit_timeout = MagicMock(return_value=True)
+    freqtrade.strategy.check_entry_timeout = MagicMock(return_value=True)
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 1
+    assert rpc_mock.call_count == 2
+    assert freqtrade.strategy.check_exit_timeout.call_count == 1
+    assert freqtrade.strategy.check_entry_timeout.call_count == 0
+
+    # 2nd canceled trade - Fail execute exit
+    caplog.clear()
+
+    mocker.patch("freqtrade.persistence.Trade.get_canceled_exit_order_count", return_value=1)
+    mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot.execute_trade_exit", side_effect=DependencyException
+    )
+    freqtrade.manage_open_orders()
+    assert log_has_re("Unable to emergency exit .*", caplog)
+
+    et_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.execute_trade_exit")
+    caplog.clear()
+    # 2nd canceled trade ...
+
+    # If cancelling fails - no emergency exit!
+    with patch("freqtrade.freqtradebot.FreqtradeBot.handle_cancel_exit", return_value=False):
+        freqtrade.manage_open_orders()
+        assert et_mock.call_count == 0
+
+    freqtrade.manage_open_orders()
+    assert log_has_re("Emergency exiting trade.*", caplog)
+    assert et_mock.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_manage_open_orders_exit(
+    default_conf_usdt, ticker_usdt, limit_sell_order_old, mocker, is_short, open_trade_usdt
+) -> None:
+    rpc_mock = patch_RPCManager(mocker)
+    cancel_order_mock = MagicMock()
+    limit_sell_order_old["id"] = "123456789_exit"
+    limit_sell_order_old["side"] = "buy" if is_short else "sell"
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=limit_sell_order_old),
+        cancel_order=cancel_order_mock,
+        get_min_pair_stake_amount=MagicMock(return_value=0),
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    open_trade_usdt.open_date = dt_now() - timedelta(hours=5)
+    open_trade_usdt.close_date = dt_now() - timedelta(minutes=601)
+    open_trade_usdt.close_profit_abs = 0.001
+    open_trade_usdt.is_short = is_short
+
+    Trade.session.add(open_trade_usdt)
+    Trade.commit()
+
+    freqtrade.strategy.check_exit_timeout = MagicMock(return_value=False)
+    freqtrade.strategy.check_entry_timeout = MagicMock(return_value=False)
+    # check it does cancel sell orders over the time limit
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 1
+    assert rpc_mock.call_count == 2
+    assert open_trade_usdt.is_open is True
+    # Custom user sell-timeout is never called
+    assert freqtrade.strategy.check_exit_timeout.call_count == 0
+    assert freqtrade.strategy.check_entry_timeout.call_count == 0
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_check_handle_cancelled_exit(
+    default_conf_usdt, ticker_usdt, limit_sell_order_old, open_trade_usdt, is_short, mocker, caplog
+) -> None:
+    """Handle sell order cancelled on exchange"""
+    rpc_mock = patch_RPCManager(mocker)
+    cancel_order_mock = MagicMock()
+    limit_sell_order_old.update({"status": "canceled", "filled": 0.0})
+    limit_sell_order_old["side"] = "buy" if is_short else "sell"
+    limit_sell_order_old["id"] = open_trade_usdt.open_orders[0].order_id
+
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=limit_sell_order_old),
+        cancel_order_with_result=cancel_order_mock,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    open_trade_usdt.open_date = dt_now() - timedelta(hours=5)
+    open_trade_usdt.close_date = dt_now() - timedelta(minutes=601)
+    open_trade_usdt.is_short = is_short
+
+    Trade.session.add(open_trade_usdt)
+    Trade.commit()
+
+    # check it does cancel sell orders over the time limit
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 0
+    assert rpc_mock.call_count == 2
+    assert open_trade_usdt.is_open is True
+    exit_name = "Buy" if is_short else "Sell"
+    assert log_has_re(f"{exit_name} order cancelled on exchange for Trade.*", caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.parametrize("leverage", [1, 3, 5, 10])
+def test_manage_open_orders_partial(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_buy_order_old_partial,
+    is_short,
+    leverage,
+    open_trade,
+    mocker,
+) -> None:
+    rpc_mock = patch_RPCManager(mocker)
+    open_trade.is_short = is_short
+    open_trade.leverage = leverage
+    open_trade.orders[0].ft_order_side = "sell" if is_short else "buy"
+
+    limit_buy_order_old_partial["id"] = open_trade.orders[0].order_id
+    limit_buy_order_old_partial["side"] = "sell" if is_short else "buy"
+    limit_buy_canceled = deepcopy(limit_buy_order_old_partial)
+    limit_buy_canceled["status"] = "canceled"
+
+    cancel_order_mock = MagicMock(return_value=limit_buy_canceled)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=limit_buy_order_old_partial),
+        cancel_order_with_result=cancel_order_mock,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    prior_stake = open_trade.stake_amount
+    Trade.session.add(open_trade)
+    Trade.commit()
+
+    # check it does cancel buy orders over the time limit
+    # note this is for a partially-complete buy order
+    freqtrade.manage_open_orders()
+    assert cancel_order_mock.call_count == 1
+    assert rpc_mock.call_count == 3
+    trades = Trade.session.scalars(select(Trade)).all()
+    assert len(trades) == 1
+    assert trades[0].amount == 23.0
+    assert trades[0].stake_amount == open_trade.open_rate * trades[0].amount / leverage
+    assert trades[0].stake_amount != prior_stake
+    assert not trades[0].has_open_orders
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_manage_open_orders_partial_fee(
+    default_conf_usdt,
+    ticker_usdt,
+    open_trade,
+    caplog,
+    fee,
+    is_short,
+    limit_buy_order_old_partial,
+    trades_for_order,
+    limit_buy_order_old_partial_canceled,
+    mocker,
+) -> None:
+    open_trade.is_short = is_short
+    open_trade.orders[0].ft_order_side = "sell" if is_short else "buy"
+    rpc_mock = patch_RPCManager(mocker)
+    limit_buy_order_old_partial["id"] = open_trade.orders[0].order_id
+    limit_buy_order_old_partial_canceled["id"] = open_trade.open_orders_ids[0]
+    limit_buy_order_old_partial["side"] = "sell" if is_short else "buy"
+    limit_buy_order_old_partial_canceled["side"] = "sell" if is_short else "buy"
+
+    cancel_order_mock = MagicMock(return_value=limit_buy_order_old_partial_canceled)
+    mocker.patch("freqtrade.wallets.Wallets.get_free", MagicMock(return_value=0))
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=limit_buy_order_old_partial),
+        cancel_order_with_result=cancel_order_mock,
+        get_trades_for_order=MagicMock(return_value=trades_for_order),
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    assert open_trade.amount == limit_buy_order_old_partial["amount"]
+
+    open_trade.fee_open = fee()
+    open_trade.fee_close = fee()
+    Trade.session.add(open_trade)
+    Trade.commit()
+    # cancelling a half-filled order should update the amount to the bought amount
+    # and apply fees if necessary.
+    freqtrade.manage_open_orders()
+
+    assert log_has_re(r"Applying fee on amount for Trade.*", caplog)
+
+    assert cancel_order_mock.call_count == 1
+    assert rpc_mock.call_count == 3
+    trades = Trade.session.scalars(select(Trade).where(Order.ft_trade_id == Trade.id)).all()
+    assert len(trades) == 1
+    # Verify that trade has been updated
+    assert (
+        trades[0].amount
+        == (limit_buy_order_old_partial["amount"] - limit_buy_order_old_partial["remaining"])
+        - 0.023
+    )
+    assert not trades[0].has_open_orders
+    assert trades[0].fee_updated(open_trade.entry_side)
+    assert pytest.approx(trades[0].fee_open) == 0.001
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_manage_open_orders_partial_except(
+    default_conf_usdt,
+    ticker_usdt,
+    open_trade,
+    caplog,
+    fee,
+    is_short,
+    limit_buy_order_old_partial,
+    trades_for_order,
+    limit_buy_order_old_partial_canceled,
+    mocker,
+) -> None:
+    open_trade.is_short = is_short
+    open_trade.orders[0].ft_order_side = "sell" if is_short else "buy"
+    rpc_mock = patch_RPCManager(mocker)
+    limit_buy_order_old_partial_canceled["id"] = open_trade.open_orders_ids[0]
+    limit_buy_order_old_partial["id"] = open_trade.open_orders_ids[0]
+    if is_short:
+        limit_buy_order_old_partial["side"] = "sell"
+    cancel_order_mock = MagicMock(return_value=limit_buy_order_old_partial_canceled)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(return_value=limit_buy_order_old_partial),
+        cancel_order_with_result=cancel_order_mock,
+        get_trades_for_order=MagicMock(return_value=trades_for_order),
+    )
+    mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot.get_real_amount",
+        MagicMock(side_effect=DependencyException),
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    assert open_trade.amount == limit_buy_order_old_partial["amount"]
+
+    open_trade.fee_open = fee()
+    open_trade.fee_close = fee()
+    Trade.session.add(open_trade)
+    Trade.commit()
+    # cancelling a half-filled order should update the amount to the bought amount
+    # and apply fees if necessary.
+    freqtrade.manage_open_orders()
+
+    assert log_has_re(r"Could not update trade amount: .*", caplog)
+
+    assert cancel_order_mock.call_count == 1
+    assert rpc_mock.call_count == 3
+    trades = Trade.session.scalars(select(Trade)).all()
+    assert len(trades) == 1
+    # Verify that trade has been updated
+
+    assert trades[0].amount == (
+        limit_buy_order_old_partial["amount"] - limit_buy_order_old_partial["remaining"]
+    )
+    assert not trades[0].has_open_orders
+    assert trades[0].fee_open == fee()
+
+
+def test_manage_open_orders_exception(
+    default_conf_usdt, ticker_usdt, open_trade_usdt, mocker, caplog
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    cancel_order_mock = MagicMock()
+
+    mocker.patch.multiple(
+        "freqtrade.freqtradebot.FreqtradeBot",
+        handle_cancel_enter=MagicMock(),
+        handle_cancel_exit=MagicMock(),
+    )
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        fetch_order=MagicMock(side_effect=ExchangeError("Oh snap")),
+        cancel_order=cancel_order_mock,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    Trade.session.add(open_trade_usdt)
+    Trade.commit()
+
+    caplog.clear()
+    freqtrade.manage_open_orders()
+    assert log_has_re(
+        r"Cannot query order for Trade\(id=1, pair=ADA/USDT, amount=30.00000000, "
+        r"is_short=False, leverage=1.0, "
+        r"open_rate=2.00000000, open_since="
+        f"{open_trade_usdt.open_date.strftime('%Y-%m-%d %H:%M:%S')}"
+        r"\) due to Traceback \(most recent call last\):\n*",
+        caplog,
+    )
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_cancel_enter(mocker, caplog, default_conf_usdt, limit_order, is_short, fee) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    l_order = deepcopy(limit_order[entry_side(is_short)])
+    cancel_entry_order = deepcopy(limit_order[entry_side(is_short)])
+    cancel_entry_order["status"] = "canceled"
+    del cancel_entry_order["filled"]
+
+    cancel_order_mock = MagicMock(return_value=cancel_entry_order)
+    mocker.patch(f"{EXMS}.cancel_order_with_result", cancel_order_mock)
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade._notify_enter_cancel = MagicMock()
+
+    trade = mock_trade_usdt_4(fee, is_short)
+    Trade.session.add(trade)
+    Trade.commit()
+
+    l_order["filled"] = 0.0
+    l_order["status"] = "open"
+    reason = CANCEL_REASON["TIMEOUT"]
+    assert freqtrade.handle_cancel_enter(trade, l_order, trade.open_orders[0], reason)
+    assert cancel_order_mock.call_count == 1
+
+    cancel_order_mock.reset_mock()
+    caplog.clear()
+    l_order["filled"] = 0.01
+    assert not freqtrade.handle_cancel_enter(trade, l_order, trade.open_orders[0], reason)
+    assert cancel_order_mock.call_count == 0
+    assert log_has_re("Order .* for .* not cancelled, as the filled amount.* unexitable.*", caplog)
+
+    caplog.clear()
+    cancel_order_mock.reset_mock()
+    l_order["filled"] = 2
+    assert not freqtrade.handle_cancel_enter(trade, l_order, trade.open_orders[0], reason)
+    assert cancel_order_mock.call_count == 1
+
+    # Order remained open for some reason (cancel failed)
+    cancel_entry_order["status"] = "open"
+    cancel_order_mock = MagicMock(return_value=cancel_entry_order)
+
+    mocker.patch(f"{EXMS}.cancel_order_with_result", cancel_order_mock)
+    assert not freqtrade.handle_cancel_enter(trade, l_order, trade.open_orders[0], reason)
+    assert log_has_re(r"Order .* for .* not cancelled.", caplog)
+    # min_pair_stake empty should not crash
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=None)
+    assert not freqtrade.handle_cancel_enter(
+        trade, limit_order[entry_side(is_short)], trade.open_orders[0], reason
+    )
+
+    # Retry ...
+    cbo = limit_order[entry_side(is_short)]
+
+    mocker.patch("freqtrade.freqtradebot.sleep")
+    cbo["status"] = "open"
+    co_mock = mocker.patch(f"{EXMS}.cancel_order_with_result", return_value=cbo)
+    fo_mock = mocker.patch(f"{EXMS}.fetch_order", return_value=cbo)
+    assert not freqtrade.handle_cancel_enter(
+        trade, cbo, trade.open_orders[0], reason, replacing=True
+    )
+    assert co_mock.call_count == 1
+    assert fo_mock.call_count == 3
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.parametrize(
+    "limit_buy_order_canceled_empty",
+    ["binance", "kraken", "bybit"],
+    indirect=["limit_buy_order_canceled_empty"],
+)
+def test_handle_cancel_enter_exchanges(
+    mocker, caplog, default_conf_usdt, is_short, fee, limit_buy_order_canceled_empty
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    cancel_order_mock = mocker.patch(
+        f"{EXMS}.cancel_order_with_result", return_value=limit_buy_order_canceled_empty
+    )
+    notify_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot._notify_enter_cancel")
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    reason = CANCEL_REASON["TIMEOUT"]
+
+    trade = mock_trade_usdt_4(fee, is_short)
+    Trade.session.add(trade)
+    Trade.commit()
+    assert freqtrade.handle_cancel_enter(
+        trade, limit_buy_order_canceled_empty, trade.open_orders[0], reason
+    )
+    assert cancel_order_mock.call_count == 0
+    assert log_has_re(
+        f"{trade.entry_side.capitalize()} order fully cancelled. " r"Removing .* from database\.",
+        caplog,
+    )
+    assert notify_mock.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.parametrize("cancelorder", [{}, {"remaining": None}, "String Return value", 123])
+def test_handle_cancel_enter_corder_empty(
+    mocker, default_conf_usdt, limit_order, is_short, fee, cancelorder
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    l_order = limit_order[entry_side(is_short)]
+    cancel_order_mock = MagicMock(return_value=cancelorder)
+    mocker.patch.multiple(
+        EXMS,
+        cancel_order=cancel_order_mock,
+        fetch_order=MagicMock(side_effect=InvalidOrderException),
+    )
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade._notify_enter_cancel = MagicMock()
+    trade = mock_trade_usdt_4(fee, is_short)
+    Trade.session.add(trade)
+    Trade.commit()
+    l_order["filled"] = 0.0
+    l_order["status"] = "open"
+    reason = CANCEL_REASON["TIMEOUT"]
+    assert freqtrade.handle_cancel_enter(trade, l_order, trade.open_orders[0], reason)
+    assert cancel_order_mock.call_count == 1
+
+    cancel_order_mock.reset_mock()
+    l_order["filled"] = 1.0
+    order = deepcopy(l_order)
+    order["status"] = "canceled"
+    mocker.patch(f"{EXMS}.fetch_order", return_value=order)
+    assert not freqtrade.handle_cancel_enter(trade, l_order, trade.open_orders[0], reason)
+    assert cancel_order_mock.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+@pytest.mark.parametrize("leverage", [1, 5])
+@pytest.mark.parametrize("amount", [2, 50])
+def test_handle_cancel_exit_limit(
+    mocker, default_conf_usdt, fee, is_short, leverage, amount
+) -> None:
+    send_msg_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    cancel_order_mock = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        cancel_order=cancel_order_mock,
+    )
+    entry_price = 0.245441
+
+    mocker.patch(f"{EXMS}.get_rate", return_value=entry_price)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.2)
+
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_order_fee")
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    trade = Trade(
+        pair="LTC/USDT",
+        amount=amount * leverage,
+        exchange="binance",
+        open_rate=entry_price,
+        open_date=dt_now() - timedelta(days=2),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        close_rate=0.555,
+        close_date=dt_now(),
+        exit_reason="sell_reason_whatever",
+        stake_amount=entry_price * amount,
+        leverage=leverage,
+        is_short=is_short,
+    )
+    trade.orders = [
+        Order(
+            ft_order_side=entry_side(is_short),
+            ft_pair=trade.pair,
+            ft_is_open=False,
+            order_id="buy_123456",
+            status="closed",
+            symbol=trade.pair,
+            order_type="market",
+            side=entry_side(is_short),
+            price=trade.open_rate,
+            average=trade.open_rate,
+            filled=trade.amount,
+            remaining=0,
+            cost=trade.open_rate * trade.amount,
+            order_date=trade.open_date,
+            order_filled_date=trade.open_date,
+        ),
+        Order(
+            ft_order_side=exit_side(is_short),
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            order_id="sell_123456",
+            status="open",
+            symbol=trade.pair,
+            order_type="limit",
+            side=exit_side(is_short),
+            price=trade.open_rate,
+            average=trade.open_rate,
+            filled=0.0,
+            remaining=trade.amount,
+            cost=trade.open_rate * trade.amount,
+            order_date=trade.open_date,
+            order_filled_date=trade.open_date,
+        ),
+    ]
+    order = {"id": "sell_123456", "remaining": 1, "amount": 1, "status": "open"}
+    reason = CANCEL_REASON["TIMEOUT"]
+    order_obj = trade.open_orders[-1]
+    send_msg_mock.reset_mock()
+    assert freqtrade.handle_cancel_exit(trade, order, order_obj, reason)
+    assert cancel_order_mock.call_count == 1
+    assert send_msg_mock.call_count == 1
+    assert trade.close_rate is None
+    assert trade.exit_reason is None
+    assert not trade.has_open_orders
+
+    send_msg_mock.reset_mock()
+
+    # Partial exit - below exit threshold
+    order["amount"] = amount * leverage
+    order["filled"] = amount * 0.99 * leverage
+    assert not freqtrade.handle_cancel_exit(trade, order, order_obj, reason)
+    # Assert cancel_order was not called (callcount remains unchanged)
+    assert cancel_order_mock.call_count == 1
+    assert send_msg_mock.call_count == 1
+    assert (
+        send_msg_mock.call_args_list[0][0][0]["reason"]
+        == CANCEL_REASON["PARTIALLY_FILLED_KEEP_OPEN"]
+    )
+
+    assert not freqtrade.handle_cancel_exit(trade, order, order_obj, reason)
+
+    assert (
+        send_msg_mock.call_args_list[0][0][0]["reason"]
+        == CANCEL_REASON["PARTIALLY_FILLED_KEEP_OPEN"]
+    )
+
+    # Message should not be iterated again
+    assert trade.exit_order_status == CANCEL_REASON["PARTIALLY_FILLED_KEEP_OPEN"]
+    assert send_msg_mock.call_count == 1
+
+    send_msg_mock.reset_mock()
+
+    order["filled"] = amount * 0.5 * leverage
+    assert freqtrade.handle_cancel_exit(trade, order, order_obj, reason)
+    assert send_msg_mock.call_count == 1
+    assert send_msg_mock.call_args_list[0][0][0]["reason"] == CANCEL_REASON["PARTIALLY_FILLED"]
+
+
+def test_handle_cancel_exit_cancel_exception(mocker, default_conf_usdt) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.0)
+    mocker.patch(f"{EXMS}.cancel_order_with_result", side_effect=InvalidOrderException())
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    # TODO: should not be magicmock
+    trade = MagicMock()
+    order_obj = MagicMock()
+    order_obj.order_id = "125"
+    reason = CANCEL_REASON["TIMEOUT"]
+    order = {"remaining": 1, "id": "125", "amount": 1, "status": "open"}
+    assert not freqtrade.handle_cancel_exit(trade, order, order_obj, reason)
+
+    # mocker.patch(f'{EXMS}.cancel_order_with_result', return_value=order)
+    # assert not freqtrade.handle_cancel_exit(trade, order, reason)
+
+
+@pytest.mark.parametrize(
+    "is_short, open_rate, amt",
+    [
+        (False, 2.0, 30.0),
+        (True, 2.02, 29.70297029),
+    ],
+)
+def test_execute_trade_exit_up(
+    default_conf_usdt,
+    ticker_usdt,
+    fee,
+    ticker_usdt_sell_up,
+    mocker,
+    ticker_usdt_sell_down,
+    is_short,
+    open_rate,
+    amt,
+) -> None:
+    rpc_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(side_effect=[True, False]),
+    )
+    patch_whitelist(mocker, default_conf_usdt)
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.confirm_trade_exit = MagicMock(return_value=False)
+
+    # Create some test data
+    freqtrade.enter_positions()
+    rpc_mock.reset_mock()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.is_short == is_short
+    assert trade
+    assert freqtrade.strategy.confirm_trade_exit.call_count == 0
+
+    # Increase the price and sell it
+    mocker.patch.multiple(
+        EXMS, fetch_ticker=ticker_usdt_sell_down if is_short else ticker_usdt_sell_up
+    )
+    # Prevented sell ...
+    freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=(ticker_usdt_sell_down()["ask"] if is_short else ticker_usdt_sell_up()["bid"]),
+        exit_check=ExitCheckTuple(exit_type=ExitType.ROI),
+    )
+    assert rpc_mock.call_count == 0
+    assert freqtrade.strategy.confirm_trade_exit.call_count == 1
+    assert id(freqtrade.strategy.confirm_trade_exit.call_args_list[0][1]["trade"]) != id(trade)
+    assert freqtrade.strategy.confirm_trade_exit.call_args_list[0][1]["trade"].id == trade.id
+
+    # Repatch with true
+    freqtrade.strategy.confirm_trade_exit = MagicMock(return_value=True)
+    freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=(ticker_usdt_sell_down()["ask"] if is_short else ticker_usdt_sell_up()["bid"]),
+        exit_check=ExitCheckTuple(exit_type=ExitType.ROI),
+    )
+    assert freqtrade.strategy.confirm_trade_exit.call_count == 1
+
+    assert rpc_mock.call_count == 1
+    last_msg = rpc_mock.call_args_list[-1][0][0]
+    assert {
+        "trade_id": 1,
+        "type": RPCMessageType.EXIT,
+        "exchange": "Binance",
+        "pair": "ETH/USDT",
+        "gain": "profit",
+        "limit": 2.0 if is_short else 2.2,
+        "order_rate": 2.0 if is_short else 2.2,
+        "amount": pytest.approx(amt),
+        "order_type": "limit",
+        "buy_tag": None,
+        "direction": "Short" if trade.is_short else "Long",
+        "leverage": 1.0,
+        "enter_tag": None,
+        "open_rate": open_rate,
+        "current_rate": 2.01 if is_short else 2.3,
+        "profit_amount": 0.29554455 if is_short else 5.685,
+        "profit_ratio": 0.00493809 if is_short else 0.09451372,
+        "stake_currency": "USDT",
+        "quote_currency": "USDT",
+        "fiat_currency": "USD",
+        "base_currency": "ETH",
+        "exit_reason": ExitType.ROI.value,
+        "open_date": ANY,
+        "close_date": ANY,
+        "close_rate": ANY,
+        "sub_trade": False,
+        "cumulative_profit": 0.0,
+        "stake_amount": pytest.approx(60),
+        "is_final_exit": False,
+        "final_profit_ratio": None,
+    } == last_msg
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_execute_trade_exit_down(
+    default_conf_usdt,
+    ticker_usdt,
+    fee,
+    ticker_usdt_sell_down,
+    ticker_usdt_sell_up,
+    mocker,
+    is_short,
+) -> None:
+    rpc_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(side_effect=[True, False]),
+    )
+    patch_whitelist(mocker, default_conf_usdt)
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    # Create some test data
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+
+    # Decrease the price and sell it
+    mocker.patch.multiple(
+        EXMS, fetch_ticker=ticker_usdt_sell_up if is_short else ticker_usdt_sell_down
+    )
+    freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=(ticker_usdt_sell_up if is_short else ticker_usdt_sell_down)()["bid"],
+        exit_check=ExitCheckTuple(exit_type=ExitType.STOP_LOSS),
+    )
+
+    assert rpc_mock.call_count == 3
+    last_msg = rpc_mock.call_args_list[-1][0][0]
+    assert {
+        "type": RPCMessageType.EXIT,
+        "trade_id": 1,
+        "exchange": "Binance",
+        "pair": "ETH/USDT",
+        "direction": "Short" if trade.is_short else "Long",
+        "leverage": 1.0,
+        "gain": "loss",
+        "limit": 2.2 if is_short else 2.01,
+        "order_rate": 2.2 if is_short else 2.01,
+        "amount": pytest.approx(29.70297029) if is_short else 30.0,
+        "order_type": "limit",
+        "buy_tag": None,
+        "enter_tag": None,
+        "open_rate": 2.02 if is_short else 2.0,
+        "current_rate": 2.2 if is_short else 2.0,
+        "profit_amount": -5.65990099 if is_short else -0.00075,
+        "profit_ratio": -0.0945681 if is_short else -1.247e-05,
+        "stake_currency": "USDT",
+        "quote_currency": "USDT",
+        "base_currency": "ETH",
+        "fiat_currency": "USD",
+        "exit_reason": ExitType.STOP_LOSS.value,
+        "open_date": ANY,
+        "close_date": ANY,
+        "close_rate": ANY,
+        "sub_trade": False,
+        "cumulative_profit": 0.0,
+        "stake_amount": pytest.approx(60),
+        "is_final_exit": False,
+        "final_profit_ratio": None,
+    } == last_msg
+
+
+@pytest.mark.parametrize(
+    "is_short,amount,open_rate,current_rate,limit,profit_amount,profit_ratio,profit_or_loss",
+    [
+        (False, 30, 2.0, 2.3, 2.25, 7.18125, 0.11938903, "profit"),
+        (True, 29.70297029, 2.02, 2.2, 2.25, -7.14876237, -0.11944465, "loss"),
+    ],
+)
+def test_execute_trade_exit_custom_exit_price(
+    default_conf_usdt,
+    ticker_usdt,
+    fee,
+    ticker_usdt_sell_up,
+    is_short,
+    amount,
+    open_rate,
+    current_rate,
+    limit,
+    profit_amount,
+    profit_ratio,
+    profit_or_loss,
+    mocker,
+) -> None:
+    rpc_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(side_effect=[True, False]),
+    )
+    config = deepcopy(default_conf_usdt)
+    config["custom_price_max_distance_ratio"] = 0.1
+    patch_whitelist(mocker, config)
+    freqtrade = FreqtradeBot(config)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.confirm_trade_exit = MagicMock(return_value=False)
+
+    # Create some test data
+    freqtrade.enter_positions()
+    rpc_mock.reset_mock()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+    assert freqtrade.strategy.confirm_trade_exit.call_count == 0
+
+    # Increase the price and sell it
+    mocker.patch.multiple(EXMS, fetch_ticker=ticker_usdt_sell_up)
+
+    freqtrade.strategy.confirm_trade_exit = MagicMock(return_value=True)
+
+    # Set a custom exit price
+    freqtrade.strategy.custom_exit_price = lambda **kwargs: 2.25
+    freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=ticker_usdt_sell_up()["ask" if is_short else "bid"],
+        exit_check=ExitCheckTuple(exit_type=ExitType.EXIT_SIGNAL, exit_reason="foo"),
+    )
+
+    # Sell price must be different to default bid price
+
+    assert freqtrade.strategy.confirm_trade_exit.call_count == 1
+
+    assert rpc_mock.call_count == 1
+    last_msg = rpc_mock.call_args_list[-1][0][0]
+    assert {
+        "trade_id": 1,
+        "type": RPCMessageType.EXIT,
+        "exchange": "Binance",
+        "pair": "ETH/USDT",
+        "direction": "Short" if trade.is_short else "Long",
+        "leverage": 1.0,
+        "gain": profit_or_loss,
+        "limit": limit,
+        "order_rate": limit,
+        "amount": pytest.approx(amount),
+        "order_type": "limit",
+        "buy_tag": None,
+        "enter_tag": None,
+        "open_rate": open_rate,
+        "current_rate": current_rate,
+        "profit_amount": pytest.approx(profit_amount),
+        "profit_ratio": profit_ratio,
+        "stake_currency": "USDT",
+        "quote_currency": "USDT",
+        "base_currency": "ETH",
+        "fiat_currency": "USD",
+        "exit_reason": "foo",
+        "open_date": ANY,
+        "close_date": ANY,
+        "close_rate": ANY,
+        "sub_trade": False,
+        "cumulative_profit": 0.0,
+        "stake_amount": pytest.approx(60),
+        "is_final_exit": False,
+        "final_profit_ratio": None,
+    } == last_msg
+
+
+@pytest.mark.parametrize(
+    "is_short,amount,current_rate,limit,profit_amount,profit_ratio,profit_or_loss",
+    [
+        (False, 30, 2.3, 2.2, 5.685, 0.09451372, "profit"),
+        (True, 29.70297029, 2.2, 2.3, -8.63762376, -0.1443212, "loss"),
+    ],
+)
+def test_execute_trade_exit_market_order(
+    default_conf_usdt,
+    ticker_usdt,
+    fee,
+    is_short,
+    current_rate,
+    amount,
+    caplog,
+    limit,
+    profit_amount,
+    profit_ratio,
+    profit_or_loss,
+    ticker_usdt_sell_up,
+    mocker,
+) -> None:
+    """
+    amount
+        long: 60 / 2.0 = 30
+        short: 60 / 2.02 = 29.70297029
+    open_value
+        long: (30 * 2.0) + (30 * 2.0 * 0.0025) = 60.15
+        short: (29.702970297029704 * 2.02) - (29.702970297029704 * 2.02 * 0.0025) = 59.85
+    close_value
+        long: (30 * 2.2) - (30 * 2.2 * 0.0025) = 65.835
+        short: (29.702970297029704 * 2.3) + (29.702970297029704 * 2.3 * 0.0025) = 68.48762376237624
+    profit
+        long: 65.835 - 60.15 = 5.684999999999995
+        short: 59.85 - 68.48762376237624 = -8.637623762376244
+    profit_ratio
+        long: (65.835/60.15) - 1 = 0.0945137157107232
+        short: 1 - (68.48762376237624/59.85) = -0.1443211990371971
+    """
+    open_rate = ticker_usdt.return_value["ask" if is_short else "bid"]
+    rpc_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=True),
+    )
+    patch_whitelist(mocker, default_conf_usdt)
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    # Create some test data
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+
+    # Increase the price and sell it
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt_sell_up,
+        _dry_is_price_crossed=MagicMock(return_value=False),
+    )
+    freqtrade.config["order_types"]["exit"] = "market"
+
+    freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=ticker_usdt_sell_up()["ask" if is_short else "bid"],
+        exit_check=ExitCheckTuple(exit_type=ExitType.ROI),
+    )
+
+    assert not trade.is_open
+    assert pytest.approx(trade.close_profit) == profit_ratio
+
+    assert rpc_mock.call_count == 4
+    last_msg = rpc_mock.call_args_list[-2][0][0]
+    assert {
+        "type": RPCMessageType.EXIT,
+        "trade_id": 1,
+        "exchange": "Binance",
+        "pair": "ETH/USDT",
+        "direction": "Short" if trade.is_short else "Long",
+        "leverage": 1.0,
+        "gain": profit_or_loss,
+        "limit": limit,
+        "order_rate": limit,
+        "amount": pytest.approx(amount),
+        "order_type": "market",
+        "buy_tag": None,
+        "enter_tag": None,
+        "open_rate": open_rate,
+        "current_rate": current_rate,
+        "profit_amount": pytest.approx(profit_amount),
+        "profit_ratio": profit_ratio,
+        "stake_currency": "USDT",
+        "quote_currency": "USDT",
+        "base_currency": "ETH",
+        "fiat_currency": "USD",
+        "exit_reason": ExitType.ROI.value,
+        "open_date": ANY,
+        "close_date": ANY,
+        "close_rate": ANY,
+        "sub_trade": False,
+        "cumulative_profit": 0.0,
+        "stake_amount": pytest.approx(60),
+        "is_final_exit": False,
+        "final_profit_ratio": None,
+    } == last_msg
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_execute_trade_exit_insufficient_funds_error(
+    default_conf_usdt, ticker_usdt, fee, is_short, ticker_usdt_sell_up, mocker
+) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mock_insuf = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_insufficient_funds")
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        create_order=MagicMock(
+            side_effect=[
+                {"id": 1234553382},
+                InsufficientFundsError(),
+            ]
+        ),
+    )
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    # Create some test data
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+
+    # Increase the price and sell it
+    mocker.patch.multiple(EXMS, fetch_ticker=ticker_usdt_sell_up)
+
+    sell_reason = ExitCheckTuple(exit_type=ExitType.ROI)
+    assert not freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=ticker_usdt_sell_up()["ask" if is_short else "bid"],
+        exit_check=sell_reason,
+    )
+    assert mock_insuf.call_count == 1
+
+
+@pytest.mark.parametrize(
+    "profit_only,bid,ask,handle_first,handle_second,exit_type,is_short",
+    [
+        # Enable profit
+        (True, 2.18, 2.2, False, True, ExitType.EXIT_SIGNAL.value, False),
+        (True, 2.18, 2.2, False, True, ExitType.EXIT_SIGNAL.value, True),
+        # # Disable profit
+        (False, 3.19, 3.2, True, False, ExitType.EXIT_SIGNAL.value, False),
+        (False, 3.19, 3.2, True, False, ExitType.EXIT_SIGNAL.value, True),
+        # # Enable loss
+        # # * Shouldn't this be ExitType.STOP_LOSS.value
+        (True, 0.21, 0.22, False, False, None, False),
+        (True, 2.41, 2.42, False, False, None, True),
+        # Disable loss
+        (False, 0.10, 0.22, True, False, ExitType.EXIT_SIGNAL.value, False),
+        (False, 0.10, 0.22, True, False, ExitType.EXIT_SIGNAL.value, True),
+    ],
+)
+def test_exit_profit_only(
+    default_conf_usdt,
+    limit_order,
+    limit_order_open,
+    is_short,
+    fee,
+    mocker,
+    profit_only,
+    bid,
+    ask,
+    handle_first,
+    handle_second,
+    exit_type,
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    eside = entry_side(is_short)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": bid, "ask": ask, "last": bid}),
+        create_order=MagicMock(
+            side_effect=[
+                limit_order[eside],
+                {"id": 1234553382},
+            ]
+        ),
+        get_fee=fee,
+    )
+    default_conf_usdt.update(
+        {
+            "use_exit_signal": True,
+            "exit_profit_only": profit_only,
+            "exit_profit_offset": 0.1,
+        }
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.custom_exit = MagicMock(return_value=None)
+    if exit_type == ExitType.EXIT_SIGNAL.value:
+        freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
+    else:
+        freqtrade.strategy.ft_stoploss_reached = MagicMock(
+            return_value=ExitCheckTuple(exit_type=ExitType.NONE)
+        )
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.is_short == is_short
+    oobj = Order.parse_from_ccxt_object(limit_order[eside], limit_order[eside]["symbol"], eside)
+    trade.update_order(limit_order[eside])
+    trade.update_trade(oobj)
+    freqtrade.wallets.update()
+    if profit_only:
+        assert freqtrade.handle_trade(trade) is False
+        # Custom-exit is called
+        assert freqtrade.strategy.custom_exit.call_count == 1
+
+    patch_get_signal(freqtrade, enter_long=False, exit_short=is_short, exit_long=not is_short)
+    assert freqtrade.handle_trade(trade) is handle_first
+
+    if handle_second:
+        freqtrade.strategy.exit_profit_offset = 0.0
+        assert freqtrade.handle_trade(trade) is True
+
+
+def test_sell_not_enough_balance(
+    default_conf_usdt, limit_order, limit_order_open, fee, mocker, caplog
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(
+            return_value={"bid": 0.00002172, "ask": 0.00002173, "last": 0.00002172}
+        ),
+        create_order=MagicMock(
+            side_effect=[
+                limit_order_open["buy"],
+                {"id": 1234553382},
+            ]
+        ),
+        get_fee=fee,
+    )
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+    freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
+
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    amnt = trade.amount
+
+    oobj = Order.parse_from_ccxt_object(limit_order["buy"], limit_order["buy"]["symbol"], "buy")
+    trade.update_trade(oobj)
+    patch_get_signal(freqtrade, enter_long=False, exit_long=True)
+    mocker.patch("freqtrade.wallets.Wallets.get_free", MagicMock(return_value=trade.amount * 0.985))
+
+    assert freqtrade.handle_trade(trade) is True
+    assert log_has_re(r".*Falling back to wallet-amount.", caplog)
+    assert trade.amount != amnt
+
+
+@pytest.mark.parametrize("amount_wallet,has_err", [(95.29, False), (91.29, True)])
+def test__safe_exit_amount(default_conf_usdt, fee, caplog, mocker, amount_wallet, has_err):
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    amount = 95.33
+    amount_wallet = amount_wallet
+    mocker.patch("freqtrade.wallets.Wallets.get_free", MagicMock(return_value=amount_wallet))
+    wallet_update = mocker.patch("freqtrade.wallets.Wallets.update")
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        open_rate=0.245441,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+    if has_err:
+        with pytest.raises(DependencyException, match=r"Not enough amount to exit trade."):
+            assert freqtrade._safe_exit_amount(trade, trade.pair, trade.amount)
+    else:
+        wallet_update.reset_mock()
+        assert trade.amount != amount_wallet
+        assert freqtrade._safe_exit_amount(trade, trade.pair, trade.amount) == amount_wallet
+        assert log_has_re(r".*Falling back to wallet-amount.", caplog)
+        assert trade.amount == amount_wallet
+        assert wallet_update.call_count == 1
+        caplog.clear()
+        wallet_update.reset_mock()
+        assert freqtrade._safe_exit_amount(trade, trade.pair, amount_wallet) == amount_wallet
+        assert not log_has_re(r".*Falling back to wallet-amount.", caplog)
+        assert wallet_update.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_locked_pairs(
+    default_conf_usdt, ticker_usdt, fee, ticker_usdt_sell_down, mocker, caplog, is_short
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    # Create some test data
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+
+    # Decrease the price and sell it
+    mocker.patch.multiple(EXMS, fetch_ticker=ticker_usdt_sell_down)
+
+    freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=ticker_usdt_sell_down()["ask" if is_short else "bid"],
+        exit_check=ExitCheckTuple(exit_type=ExitType.STOP_LOSS),
+    )
+    trade.close(ticker_usdt_sell_down()["bid"])
+    assert not freqtrade.strategy.is_pair_locked(trade.pair, side="*")
+    # Both sides are locked
+    assert freqtrade.strategy.is_pair_locked(trade.pair, side="long") != is_short
+    assert freqtrade.strategy.is_pair_locked(trade.pair, side="short") == is_short
+
+    # reinit - should buy other pair.
+    caplog.clear()
+    freqtrade.enter_positions()
+    direction = "short" if is_short else "long"
+
+    assert log_has_re(rf"Pair {trade.pair} {direction} is locked.*", caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_ignore_roi_if_entry_signal(
+    default_conf_usdt, limit_order, limit_order_open, is_short, fee, mocker
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    eside = entry_side(is_short)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 2.19, "ask": 2.2, "last": 2.19}),
+        create_order=MagicMock(
+            side_effect=[
+                limit_order_open[eside],
+                {"id": 1234553382},
+            ]
+        ),
+        get_fee=fee,
+    )
+    default_conf_usdt["ignore_roi_if_entry_signal"] = True
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.min_roi_reached = MagicMock(return_value=True)
+
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    oobj = Order.parse_from_ccxt_object(limit_order[eside], limit_order[eside]["symbol"], eside)
+    trade.update_trade(oobj)
+    freqtrade.wallets.update()
+    if is_short:
+        patch_get_signal(freqtrade, enter_long=False, enter_short=True, exit_short=True)
+    else:
+        patch_get_signal(freqtrade, enter_long=True, exit_long=True)
+
+    assert freqtrade.handle_trade(trade) is False
+
+    # Test if entry-signal is absent (should sell due to roi = true)
+    if is_short:
+        patch_get_signal(freqtrade, enter_long=False, exit_short=False, exit_tag="something")
+    else:
+        patch_get_signal(freqtrade, enter_long=False, exit_long=False, exit_tag="something")
+    assert freqtrade.handle_trade(trade) is True
+    assert trade.exit_reason == ExitType.ROI.value
+
+
+@pytest.mark.parametrize("is_short,val1,val2", [(False, 1.5, 1.1), (True, 0.5, 0.9)])
+def test_trailing_stop_loss(
+    default_conf_usdt, limit_order_open, is_short, val1, val2, fee, caplog, mocker
+) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 2.0, "ask": 2.0, "last": 2.0}),
+        create_order=MagicMock(
+            side_effect=[
+                limit_order_open[entry_side(is_short)],
+                {"id": 1234553382},
+            ]
+        ),
+        get_fee=fee,
+    )
+    default_conf_usdt["trailing_stop"] = True
+    patch_whitelist(mocker, default_conf_usdt)
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.is_short == is_short
+    assert freqtrade.handle_trade(trade) is False
+
+    # Raise praise into profits
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        MagicMock(return_value={"bid": 2.0 * val1, "ask": 2.0 * val1, "last": 2.0 * val1}),
+    )
+
+    # Stoploss should be adjusted
+    assert freqtrade.handle_trade(trade) is False
+    caplog.clear()
+    # Price fell
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        MagicMock(return_value={"bid": 2.0 * val2, "ask": 2.0 * val2, "last": 2.0 * val2}),
+    )
+
+    caplog.set_level(logging.DEBUG)
+    # Sell as trailing-stop is reached
+    assert freqtrade.handle_trade(trade) is True
+    stop_multi = 1.1 if is_short else 0.9
+    assert log_has(
+        f"ETH/USDT - HIT STOP: current price at {(2.0 * val2):6f}, "
+        f"stoploss is {(2.0 * val1 * stop_multi):6f}, "
+        f"initial stoploss was at {(2.0 * stop_multi):6f}, trade opened at 2.000000",
+        caplog,
+    )
+    assert trade.exit_reason == ExitType.TRAILING_STOP_LOSS.value
+
+
+@pytest.mark.parametrize(
+    "offset,trail_if_reached,second_sl,is_short",
+    [
+        (0, False, 2.0394, False),
+        (0.011, False, 2.0394, False),
+        (0.055, True, 1.8, False),
+        (0, False, 2.1614, True),
+        (0.011, False, 2.1614, True),
+        (0.055, True, 2.42, True),
+    ],
+)
+def test_trailing_stop_loss_positive(
+    default_conf_usdt,
+    limit_order,
+    limit_order_open,
+    offset,
+    fee,
+    caplog,
+    mocker,
+    trail_if_reached,
+    second_sl,
+    is_short,
+) -> None:
+    enter_price = limit_order[entry_side(is_short)]["price"]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    eside = entry_side(is_short)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(
+            return_value={
+                "bid": enter_price - (-0.01 if is_short else 0.01),
+                "ask": enter_price - (-0.01 if is_short else 0.01),
+                "last": enter_price - (-0.01 if is_short else 0.01),
+            }
+        ),
+        create_order=MagicMock(
+            side_effect=[
+                limit_order[eside],
+                {"id": 1234553382},
+            ]
+        ),
+        get_fee=fee,
+    )
+    default_conf_usdt["trailing_stop"] = True
+    default_conf_usdt["trailing_stop_positive"] = 0.01
+    if offset:
+        default_conf_usdt["trailing_stop_positive_offset"] = offset
+        default_conf_usdt["trailing_only_offset_is_reached"] = trail_if_reached
+    patch_whitelist(mocker, default_conf_usdt)
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.is_short == is_short
+    oobj = Order.parse_from_ccxt_object(limit_order[eside], limit_order[eside]["symbol"], eside)
+    trade.update_order(limit_order[eside])
+    trade.update_trade(oobj)
+    caplog.set_level(logging.DEBUG)
+    # stop-loss not reached
+    assert freqtrade.handle_trade(trade) is False
+
+    # Raise ticker_usdt above buy price
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        MagicMock(
+            return_value={
+                "bid": enter_price + (-0.06 if is_short else 0.06),
+                "ask": enter_price + (-0.06 if is_short else 0.06),
+                "last": enter_price + (-0.06 if is_short else 0.06),
+            }
+        ),
+    )
+    caplog.clear()
+    # stop-loss not reached, adjusted stoploss
+    assert freqtrade.handle_trade(trade) is False
+    caplog_text = (
+        f"ETH/USDT - Using positive stoploss: 0.01 offset: {offset} profit: "
+        f"{'2.49' if not is_short else '2.24'}%"
+    )
+    if trail_if_reached:
+        assert not log_has(caplog_text, caplog)
+        assert not log_has("ETH/USDT - Adjusting stoploss...", caplog)
+    else:
+        assert log_has(caplog_text, caplog)
+        assert log_has("ETH/USDT - Adjusting stoploss...", caplog)
+    assert pytest.approx(trade.stop_loss) == second_sl
+    caplog.clear()
+
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        MagicMock(
+            return_value={
+                "bid": enter_price + (-0.135 if is_short else 0.125),
+                "ask": enter_price + (-0.135 if is_short else 0.125),
+                "last": enter_price + (-0.135 if is_short else 0.125),
+            }
+        ),
+    )
+    assert freqtrade.handle_trade(trade) is False
+    assert log_has(
+        f"ETH/USDT - Using positive stoploss: 0.01 offset: {offset} profit: "
+        f"{'5.72' if not is_short else '5.67'}%",
+        caplog,
+    )
+    assert log_has("ETH/USDT - Adjusting stoploss...", caplog)
+
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        MagicMock(
+            return_value={
+                "bid": enter_price + (-0.02 if is_short else 0.02),
+                "ask": enter_price + (-0.02 if is_short else 0.02),
+                "last": enter_price + (-0.02 if is_short else 0.02),
+            }
+        ),
+    )
+    # Lower price again (but still positive)
+    assert freqtrade.handle_trade(trade) is True
+    assert log_has(
+        f"ETH/USDT - HIT STOP: current price at {enter_price + (-0.02 if is_short else 0.02):.6f}, "
+        f"stoploss is {trade.stop_loss:.6f}, "
+        f"initial stoploss was at {'2.42' if is_short else '1.80'}0000, "
+        f"trade opened at {2.2 if is_short else 2.0}00000",
+        caplog,
+    )
+    assert trade.exit_reason == ExitType.TRAILING_STOP_LOSS.value
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_disable_ignore_roi_if_entry_signal(
+    default_conf_usdt, limit_order, limit_order_open, is_short, fee, mocker, time_machine
+) -> None:
+    time_machine.move_to("2025-01-10 08:00:16 +00:00")
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    eside = entry_side(is_short)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 2.0, "ask": 2.0, "last": 2.0}),
+        create_order=MagicMock(
+            side_effect=[limit_order_open[eside], {"id": 1234553382}, {"id": 1234553383}]
+        ),
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=False),
+    )
+    default_conf_usdt["exit_pricing"] = {"ignore_roi_if_entry_signal": False}
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.min_roi_reached = MagicMock(return_value=True)
+
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+
+    oobj = Order.parse_from_ccxt_object(limit_order[eside], limit_order[eside]["symbol"], eside)
+    trade.update_trade(oobj)
+    # Sell due to min_roi_reached
+    patch_get_signal(freqtrade, enter_long=not is_short, enter_short=is_short, exit_short=is_short)
+    assert freqtrade.handle_trade(trade) is True
+
+    # Test if entry-signal is absent
+    patch_get_signal(freqtrade)
+    # Signal was evaluated already - no action.
+    assert freqtrade.handle_trade(trade) is False
+
+    # Move to after the candle expired
+    time_machine.shift(timedelta(hours=5))
+    # Test if entry-signal is absent
+    patch_get_signal(freqtrade)
+    assert freqtrade.handle_trade(trade) is True
+    assert trade.exit_reason == ExitType.ROI.value
+
+
+def test_get_real_amount_quote(
+    default_conf_usdt, trades_for_order, buy_order_fee, fee, caplog, mocker
+):
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=trades_for_order)
+    amount = sum(x["amount"] for x in trades_for_order)
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        open_rate=0.245441,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    caplog.clear()
+    order_obj = Order.parse_from_ccxt_object(buy_order_fee, "LTC/ETH", "buy")
+    # Amount is reduced by "fee"
+    assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == (amount * 0.001)
+    assert log_has(
+        "Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, is_short=False,"
+        " leverage=1.0, open_rate=0.24544100, open_since=closed), fee=0.008.",
+        caplog,
+    )
+
+
+def test_get_real_amount_quote_dust(
+    default_conf_usdt, trades_for_order, buy_order_fee, fee, caplog, mocker
+):
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=trades_for_order)
+    walletmock = mocker.patch("freqtrade.wallets.Wallets.update")
+    mocker.patch("freqtrade.wallets.Wallets.get_free", return_value=8.1122)
+    amount = sum(x["amount"] for x in trades_for_order)
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        open_rate=0.245441,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    walletmock.reset_mock()
+    order_obj = Order.parse_from_ccxt_object(buy_order_fee, "LTC/ETH", "buy")
+    # Amount is kept as is
+    assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) is None
+    assert walletmock.call_count == 1
+    assert log_has_re(
+        r"Fee amount for Trade.* was in base currency - Eating Fee 0.008 into dust", caplog
+    )
+
+
+def test_get_real_amount_no_trade(default_conf_usdt, buy_order_fee, caplog, mocker, fee):
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=[])
+
+    # Invalid nested trade object
+    buy_order_fee["trades"] = [{"amount": None, "cost": 22}]
+
+    amount = buy_order_fee["amount"]
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        open_rate=0.245441,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    order_obj = Order.parse_from_ccxt_object(buy_order_fee, "LTC/ETH", "buy")
+    # Amount is reduced by "fee"
+    assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) is None
+    assert log_has(
+        "Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, "
+        "is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed) failed: "
+        "myTrade-dict empty found",
+        caplog,
+    )
+
+
+@pytest.mark.parametrize(
+    "fee_par,fee_reduction_amount,use_ticker_usdt_rate,expected_log",
+    [
+        # basic, amount does not change
+        ({"cost": 0.008, "currency": "ETH"}, 0, False, None),
+        # no currency in fee
+        ({"cost": 0.004, "currency": None}, 0, True, None),
+        # BNB no rate
+        (
+            {"cost": 0.00094518, "currency": "BNB"},
+            0,
+            True,
+            (
+                "Fee for Trade Trade(id=None, pair=LTC/ETH, amount=8.00000000, is_short=False, "
+                "leverage=1.0, open_rate=0.24544100, open_since=closed) [buy]: 0.00094518 BNB -"
+                " rate: None"
+            ),
+        ),
+        # from order
+        (
+            {"cost": 0.004, "currency": "LTC"},
+            0.004,
+            False,
+            (
+                "Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, "
+                "is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed), fee=0.004."
+            ),
+        ),
+        # invalid, no currency in from fee dict
+        ({"cost": 0.008, "currency": None}, 0, True, None),
+    ],
+)
+def test_get_real_amount(
+    default_conf_usdt,
+    trades_for_order,
+    buy_order_fee,
+    fee,
+    mocker,
+    caplog,
+    fee_par,
+    fee_reduction_amount,
+    use_ticker_usdt_rate,
+    expected_log,
+):
+    buy_order = deepcopy(buy_order_fee)
+    buy_order["fee"] = fee_par
+    trades_for_order[0]["fee"] = fee_par
+
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=trades_for_order)
+    amount = sum(x["amount"] for x in trades_for_order)
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.245441,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    if not use_ticker_usdt_rate:
+        mocker.patch(f"{EXMS}.fetch_ticker", side_effect=ExchangeError)
+
+    caplog.clear()
+    order_obj = Order.parse_from_ccxt_object(buy_order_fee, "LTC/ETH", "buy")
+    res = freqtrade.get_real_amount(trade, buy_order, order_obj)
+    if fee_reduction_amount == 0:
+        assert res is None
+    else:
+        assert res == fee_reduction_amount
+
+    if expected_log:
+        assert log_has(expected_log, caplog)
+
+
+@pytest.mark.parametrize(
+    "fee_cost, fee_currency, fee_reduction_amount, expected_fee, expected_log_amount",
+    [
+        # basic, amount is reduced by fee
+        (None, None, 0.001, 0.001, 7.992),
+        # different fee currency on both trades, fee is average of both trade's fee
+        (0.02, "BNB", 0.0005, 0.001518575, 7.996),
+    ],
+)
+def test_get_real_amount_multi(
+    default_conf_usdt,
+    trades_for_order2,
+    buy_order_fee,
+    caplog,
+    fee,
+    mocker,
+    markets,
+    fee_cost,
+    fee_currency,
+    fee_reduction_amount,
+    expected_fee,
+    expected_log_amount,
+):
+    trades_for_order = deepcopy(trades_for_order2)
+    if fee_cost:
+        trades_for_order[0]["fee"]["cost"] = fee_cost
+    if fee_currency:
+        trades_for_order[0]["fee"]["currency"] = fee_currency
+
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=trades_for_order)
+    amount = float(sum(x["amount"] for x in trades_for_order))
+    default_conf_usdt["stake_currency"] = "ETH"
+
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.245441,
+    )
+
+    # Fake markets entry to enable fee parsing
+    markets["BNB/ETH"] = markets["ETH/USDT"]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    mocker.patch(f"{EXMS}.get_conversion_rate", return_value=0.2)
+
+    # Amount is reduced by "fee"
+    expected_amount = amount * fee_reduction_amount
+    order_obj = Order.parse_from_ccxt_object(buy_order_fee, "LTC/ETH", "buy")
+    assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == expected_amount
+    assert log_has(
+        (
+            "Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, "
+            "is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed), "
+            f"fee={expected_amount}."
+        ),
+        caplog,
+    )
+
+    assert trade.fee_open == expected_fee
+    assert trade.fee_close == expected_fee
+    assert trade.fee_open_cost is not None
+    assert trade.fee_open_currency is not None
+    assert trade.fee_close_cost is None
+    assert trade.fee_close_currency is None
+
+
+def test_get_real_amount_invalid_order(
+    default_conf_usdt, trades_for_order, buy_order_fee, fee, mocker
+):
+    limit_buy_order_usdt = deepcopy(buy_order_fee)
+    limit_buy_order_usdt["fee"] = {"cost": 0.004}
+
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=[])
+    amount = float(sum(x["amount"] for x in trades_for_order))
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.245441,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    order_obj = Order.parse_from_ccxt_object(buy_order_fee, "LTC/ETH", "buy")
+    # Amount does not change
+    assert freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj) is None
+
+
+def test_get_real_amount_fees_order(
+    default_conf_usdt, market_buy_order_usdt_doublefee, fee, mocker
+):
+    tfo_mock = mocker.patch(f"{EXMS}.get_trades_for_order", return_value=[])
+    mocker.patch(f"{EXMS}.get_valid_pair_combination", return_value=["BNB/USDT"])
+    mocker.patch(f"{EXMS}.fetch_ticker", return_value={"last": 200})
+    trade = Trade(
+        pair="LTC/USDT",
+        amount=30.0,
+        exchange="binance",
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.245441,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    # Amount does not change
+    assert trade.fee_open == 0.0025
+    order_obj = Order.parse_from_ccxt_object(market_buy_order_usdt_doublefee, "LTC/ETH", "buy")
+    assert freqtrade.get_real_amount(trade, market_buy_order_usdt_doublefee, order_obj) is None
+    assert tfo_mock.call_count == 0
+    # Fetch fees from trades dict if available to get "proper" values
+    assert round(trade.fee_open, 4) == 0.001
+
+
+def test_get_real_amount_wrong_amount(
+    default_conf_usdt, trades_for_order, buy_order_fee, fee, mocker
+):
+    limit_buy_order_usdt = deepcopy(buy_order_fee)
+    limit_buy_order_usdt["amount"] = limit_buy_order_usdt["amount"] - 0.001
+
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=trades_for_order)
+    amount = float(sum(x["amount"] for x in trades_for_order))
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        open_rate=0.245441,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    order_obj = Order.parse_from_ccxt_object(buy_order_fee, "LTC/ETH", "buy")
+    # Amount does not change
+    with pytest.raises(DependencyException, match=r"Half bought\? Amounts don't match"):
+        freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj)
+
+
+def test_get_real_amount_wrong_amount_rounding(
+    default_conf_usdt, trades_for_order, buy_order_fee, fee, mocker
+):
+    # Floats should not be compared directly.
+    limit_buy_order_usdt = deepcopy(buy_order_fee)
+    trades_for_order[0]["amount"] = trades_for_order[0]["amount"] + 1e-15
+
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=trades_for_order)
+    amount = float(sum(x["amount"] for x in trades_for_order))
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.245441,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    order_obj = Order.parse_from_ccxt_object(buy_order_fee, "LTC/ETH", "buy")
+    # Amount changes by fee amount.
+    assert pytest.approx(freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj)) == (
+        amount * 0.001
+    )
+
+
+def test_get_real_amount_open_trade_usdt(default_conf_usdt, fee, mocker):
+    amount = 12345
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        open_rate=0.245441,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+    )
+    order = {
+        "id": "mocked_order",
+        "amount": amount,
+        "status": "open",
+        "side": "buy",
+        "price": 0.245441,
+    }
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    order_obj = Order.parse_from_ccxt_object(order, "LTC/ETH", "buy")
+    assert freqtrade.get_real_amount(trade, order, order_obj) is None
+
+
+def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker, caplog):
+    limit_buy_order_usdt = deepcopy(buy_order_fee)
+
+    # Fees amount in "POINT"
+    trades = [
+        {
+            "info": {},
+            "id": "some_trade_id",
+            "timestamp": 1660092505903,
+            "datetime": "2022-08-10T00:48:25.903Z",
+            "symbol": "CEL/USDT",
+            "order": "some_order_id",
+            "type": None,
+            "side": "sell",
+            "takerOrMaker": "taker",
+            "price": 1.83255,
+            "amount": 83.126,
+            "cost": 152.3325513,
+            "fee": {"currency": "POINT", "cost": 0.3046651026},
+            "fees": [
+                {"cost": "0", "currency": "USDT"},
+                {"cost": "0", "currency": "GT"},
+                {"cost": "0.3046651026", "currency": "POINT"},
+            ],
+        }
+    ]
+
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=trades)
+    amount = float(sum(x["amount"] for x in trades))
+    trade = Trade(
+        pair="CEL/USDT",
+        amount=amount,
+        exchange="binance",
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_rate=0.245441,
+    )
+    limit_buy_order_usdt["amount"] = amount
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    order_obj = Order.parse_from_ccxt_object(buy_order_fee, "LTC/ETH", "buy")
+    res = freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj)
+    assert res is None
+    assert trade.fee_open_currency is None
+    assert trade.fee_open_cost is None
+    message = "Not updating buy-fee - rate: None, POINT."
+    assert log_has(message, caplog)
+    caplog.clear()
+    freqtrade.config["exchange"]["unknown_fee_rate"] = 1
+    res = freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj)
+    assert res is None
+    assert trade.fee_open_currency == "POINT"
+    assert pytest.approx(trade.fee_open_cost) == 0.3046651026
+    assert trade.fee_open == 0.002
+    assert trade.fee_open != fee.return_value
+    assert not log_has(message, caplog)
+
+
+@pytest.mark.parametrize(
+    "amount,fee_abs,wallet,amount_exp",
+    [
+        (8.0, 0.0, 10, None),
+        (8.0, 0.0, 0, None),
+        (8.0, 0.1, 0, 0.1),
+        (8.0, 0.1, 10, None),
+        (8.0, 0.1, 8.0, None),
+        (8.0, 0.1, 7.9, 0.1),
+    ],
+)
+def test_apply_fee_conditional(
+    default_conf_usdt, fee, mocker, caplog, amount, fee_abs, wallet, amount_exp
+):
+    walletmock = mocker.patch("freqtrade.wallets.Wallets.update")
+    mocker.patch("freqtrade.wallets.Wallets.get_free", return_value=wallet)
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        open_rate=0.245441,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+    )
+    order = Order(
+        ft_order_side="buy",
+        order_id="100",
+        ft_pair=trade.pair,
+        ft_is_open=True,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    walletmock.reset_mock()
+    # Amount is kept as is
+    assert freqtrade.apply_fee_conditional(trade, "LTC", amount, fee_abs, order) == amount_exp
+    assert walletmock.call_count == 1
+    if fee_abs != 0 and amount_exp is None:
+        assert log_has_re(r"Fee amount.*Eating.*dust\.", caplog)
+
+
+@pytest.mark.parametrize(
+    "amount,fee_abs,wallet,amount_exp",
+    [
+        (8.0, 0.0, 16, None),
+        (8.0, 0.0, 0, None),
+        (8.0, 0.1, 8, 0.1),
+        (8.0, 0.1, 20, None),
+        (8.0, 0.1, 16.0, None),
+        (8.0, 0.1, 7.9, 0.1),
+        (8.0, 0.1, 12, 0.1),
+        (8.0, 0.1, 15.9, 0.1),
+    ],
+)
+def test_apply_fee_conditional_multibuy(
+    default_conf_usdt, fee, mocker, caplog, amount, fee_abs, wallet, amount_exp
+):
+    walletmock = mocker.patch("freqtrade.wallets.Wallets.update")
+    mocker.patch("freqtrade.wallets.Wallets.get_free", return_value=wallet)
+    trade = Trade(
+        pair="LTC/ETH",
+        amount=amount,
+        exchange="binance",
+        open_rate=0.245441,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+    )
+    # One closed order
+    order = Order(
+        ft_order_side="buy",
+        order_id="10",
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        filled=amount,
+        status="closed",
+    )
+    trade.orders.append(order)
+    # Add additional order - this should NOT eat into dust unless the wallet was bigger already.
+    order1 = Order(
+        ft_order_side="buy",
+        order_id="100",
+        ft_pair=trade.pair,
+        ft_is_open=True,
+    )
+    trade.orders.append(order1)
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    walletmock.reset_mock()
+    # The new trade amount will be 2x amount - fee / wallet will have to be adapted to this.
+    assert freqtrade.apply_fee_conditional(trade, "LTC", amount, fee_abs, order1) == amount_exp
+    assert walletmock.call_count == 1
+    if fee_abs != 0 and amount_exp is None:
+        assert log_has_re(r"Fee amount.*Eating.*dust\.", caplog)
+
+
+@pytest.mark.parametrize(
+    "delta, is_high_delta",
+    [
+        (0.1, False),
+        (100, True),
+    ],
+)
+@pytest.mark.parametrize("is_short", [False, True])
+def test_order_book_depth_of_market(
+    default_conf_usdt,
+    ticker_usdt,
+    limit_order_open,
+    fee,
+    mocker,
+    order_book_l2,
+    delta,
+    is_high_delta,
+    is_short,
+):
+    ticker_side = "ask" if is_short else "bid"
+
+    default_conf_usdt["entry_pricing"]["check_depth_of_market"]["enabled"] = True
+    default_conf_usdt["entry_pricing"]["check_depth_of_market"]["bids_to_ask_delta"] = delta
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.fetch_l2_order_book", order_book_l2)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value=limit_order_open[entry_side(is_short)]),
+        get_fee=fee,
+    )
+
+    # Save state of current whitelist
+    whitelist = deepcopy(default_conf_usdt["exchange"]["pair_whitelist"])
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    if is_high_delta:
+        assert trade is None
+    else:
+        trade.is_short = is_short
+        assert trade is not None
+        assert pytest.approx(trade.stake_amount) == 60.0
+        assert trade.is_open
+        assert trade.open_date is not None
+        assert trade.exchange == "binance"
+
+        assert len(Trade.session.scalars(select(Trade)).all()) == 1
+
+        # Simulate fulfilled LIMIT_BUY order for trade
+        oobj = Order.parse_from_ccxt_object(
+            limit_order_open[entry_side(is_short)], "ADA/USDT", entry_side(is_short)
+        )
+        trade.update_trade(oobj)
+
+        assert trade.open_rate == ticker_usdt.return_value[ticker_side]
+        assert whitelist == default_conf_usdt["exchange"]["pair_whitelist"]
+
+
+@pytest.mark.parametrize(
+    "exception_thrown,ask,last,order_book_top,order_book",
+    [(False, 0.045, 0.046, 2, None), (True, 0.042, 0.046, 1, {"bids": [[]], "asks": [[]]})],
+)
+def test_order_book_entry_pricing1(
+    mocker,
+    default_conf_usdt,
+    order_book_l2,
+    exception_thrown,
+    ask,
+    last,
+    order_book_top,
+    order_book,
+    caplog,
+) -> None:
+    """
+    test if function get_rate will return the order book price instead of the ask rate
+    """
+    patch_exchange(mocker)
+    ticker_usdt_mock = MagicMock(return_value={"ask": ask, "last": last})
+    mocker.patch.multiple(
+        EXMS,
+        fetch_l2_order_book=MagicMock(return_value=order_book) if order_book else order_book_l2,
+        fetch_ticker=ticker_usdt_mock,
+    )
+    default_conf_usdt["exchange"]["name"] = "binance"
+    default_conf_usdt["entry_pricing"]["use_order_book"] = True
+    default_conf_usdt["entry_pricing"]["order_book_top"] = order_book_top
+    default_conf_usdt["entry_pricing"]["price_last_balance"] = 0
+    default_conf_usdt["telegram"]["enabled"] = False
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    if exception_thrown:
+        with pytest.raises(PricingError):
+            freqtrade.exchange.get_rate("ETH/USDT", side="entry", is_short=False, refresh=True)
+        assert log_has_re(
+            r"ETH/USDT - Entry Price at location 1 from orderbook could not be determined.", caplog
+        )
+    else:
+        assert (
+            freqtrade.exchange.get_rate("ETH/USDT", side="entry", is_short=False, refresh=True)
+            == 0.043935
+        )
+        assert ticker_usdt_mock.call_count == 0
+
+
+def test_check_depth_of_market(default_conf_usdt, mocker, order_book_l2) -> None:
+    """
+    test check depth of market
+    """
+    patch_exchange(mocker)
+    mocker.patch.multiple(EXMS, fetch_l2_order_book=order_book_l2)
+    default_conf_usdt["telegram"]["enabled"] = False
+    default_conf_usdt["exchange"]["name"] = "binance"
+    default_conf_usdt["entry_pricing"]["check_depth_of_market"]["enabled"] = True
+    # delta is 100 which is impossible to reach. hence function will return false
+    default_conf_usdt["entry_pricing"]["check_depth_of_market"]["bids_to_ask_delta"] = 100
+    freqtrade = FreqtradeBot(default_conf_usdt)
+
+    conf = default_conf_usdt["entry_pricing"]["check_depth_of_market"]
+    assert freqtrade._check_depth_of_market("ETH/BTC", conf, side=SignalDirection.LONG) is False
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_order_book_exit_pricing(
+    default_conf_usdt,
+    limit_buy_order_usdt_open,
+    limit_buy_order_usdt,
+    fee,
+    is_short,
+    limit_sell_order_usdt_open,
+    mocker,
+    order_book_l2,
+    caplog,
+) -> None:
+    """
+    test order book ask strategy
+    """
+    mocker.patch(f"{EXMS}.fetch_l2_order_book", order_book_l2)
+    default_conf_usdt["exchange"]["name"] = "binance"
+    default_conf_usdt["exit_pricing"]["use_order_book"] = True
+    default_conf_usdt["exit_pricing"]["order_book_top"] = 1
+    default_conf_usdt["telegram"]["enabled"] = False
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(
+            side_effect=[
+                limit_buy_order_usdt_open,
+                limit_sell_order_usdt_open,
+            ]
+        ),
+        get_fee=fee,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade)
+
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+
+    time.sleep(0.01)  # Race condition fix
+    oobj = Order.parse_from_ccxt_object(limit_buy_order_usdt, limit_buy_order_usdt["symbol"], "buy")
+    trade.update_trade(oobj)
+    freqtrade.wallets.update()
+    assert trade.is_open is True
+
+    if is_short:
+        patch_get_signal(freqtrade, enter_long=False, exit_short=True)
+    else:
+        patch_get_signal(freqtrade, enter_long=False, exit_long=True)
+    assert freqtrade.handle_trade(trade) is True
+    assert trade.close_rate_requested == order_book_l2.return_value["asks"][0][0]
+
+    mocker.patch(f"{EXMS}.fetch_l2_order_book", return_value={"bids": [[]], "asks": [[]]})
+    with pytest.raises(PricingError):
+        freqtrade.handle_trade(trade)
+    assert log_has_re(
+        r"ETH/USDT - Exit Price at location 1 from orderbook could not be determined\..*", caplog
+    )
+
+
+def test_startup_state(default_conf_usdt, mocker):
+    default_conf_usdt["pairlist"] = {"method": "VolumePairList", "config": {"number_assets": 20}}
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    worker = get_patched_worker(mocker, default_conf_usdt)
+    assert worker.freqtrade.state is State.RUNNING
+
+
+def test_startup_trade_reinit(default_conf_usdt, edge_conf, mocker):
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    reinit_mock = MagicMock()
+    mocker.patch("freqtrade.persistence.Trade.stoploss_reinitialization", reinit_mock)
+
+    ftbot = get_patched_freqtradebot(mocker, default_conf_usdt)
+    ftbot.startup()
+    assert reinit_mock.call_count == 1
+
+    reinit_mock.reset_mock()
+
+    ftbot = get_patched_freqtradebot(mocker, edge_conf)
+    ftbot.startup()
+    assert reinit_mock.call_count == 0
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_sync_wallet_dry_run(
+    mocker, default_conf_usdt, ticker_usdt, fee, limit_buy_order_usdt_open, caplog
+):
+    default_conf_usdt["dry_run"] = True
+    # Initialize to 2 times stake amount
+    default_conf_usdt["dry_run_wallet"] = 120.0
+    default_conf_usdt["max_open_trades"] = 2
+    default_conf_usdt["tradable_balance_ratio"] = 1.0
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        create_order=MagicMock(return_value=limit_buy_order_usdt_open),
+        get_fee=fee,
+    )
+
+    bot = get_patched_freqtradebot(mocker, default_conf_usdt)
+    patch_get_signal(bot)
+    assert bot.wallets.get_free("USDT") == 120.0
+
+    n = bot.enter_positions()
+    assert n == 2
+    trades = Trade.session.scalars(select(Trade)).all()
+    assert len(trades) == 2
+
+    bot.config["max_open_trades"] = 3
+    n = bot.enter_positions()
+    assert n == 0
+    assert log_has_re(
+        r"Unable to create trade for XRP/USDT: "
+        r"Available balance \(0.0 USDT\) is lower than stake amount \(60.0 USDT\)",
+        caplog,
+    )
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize(
+    "is_short,buy_calls,sell_calls",
+    [
+        (False, 1, 1),
+        (True, 1, 1),
+    ],
+)
+def test_cancel_all_open_orders(
+    mocker, default_conf_usdt, fee, limit_order, limit_order_open, is_short, buy_calls, sell_calls
+):
+    default_conf_usdt["cancel_open_orders_on_exit"] = True
+    mocker.patch(
+        f"{EXMS}.fetch_order",
+        side_effect=[
+            ExchangeError(),
+            limit_order[exit_side(is_short)],
+            limit_order_open[entry_side(is_short)],
+            limit_order_open[exit_side(is_short)],
+        ],
+    )
+    buy_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_cancel_enter")
+    sell_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_cancel_exit")
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    create_mock_trades(fee, is_short=is_short)
+    trades = Trade.session.scalars(select(Trade)).all()
+    assert len(trades) == MOCK_TRADE_COUNT
+    freqtrade.cancel_all_open_orders()
+    assert buy_mock.call_count == buy_calls
+    assert sell_mock.call_count == sell_calls
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_check_for_open_trades(mocker, default_conf_usdt, fee, is_short):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    freqtrade.check_for_open_trades()
+    assert freqtrade.rpc.send_msg.call_count == 0
+
+    create_mock_trades(fee, is_short)
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+
+    freqtrade.check_for_open_trades()
+    assert freqtrade.rpc.send_msg.call_count == 1
+    assert "Handle these trades manually" in freqtrade.rpc.send_msg.call_args[0][0]["status"]
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.usefixtures("init_persistence")
+def test_startup_update_open_orders(mocker, default_conf_usdt, fee, caplog, is_short):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    create_mock_trades(fee, is_short=is_short)
+
+    freqtrade.startup_update_open_orders()
+    assert not log_has_re(r"Error updating Order .*", caplog)
+    caplog.clear()
+
+    freqtrade.config["dry_run"] = False
+    freqtrade.startup_update_open_orders()
+
+    assert len(Order.get_open_orders()) == 4
+    matching_buy_order = mock_order_4(is_short=is_short)
+    matching_buy_order.update(
+        {
+            "status": "closed",
+        }
+    )
+    mocker.patch(f"{EXMS}.fetch_order", return_value=matching_buy_order)
+    freqtrade.startup_update_open_orders()
+    # Only stoploss and sell orders are kept open
+    assert len(Order.get_open_orders()) == 3
+
+    caplog.clear()
+    mocker.patch(f"{EXMS}.fetch_order", side_effect=ExchangeError)
+    freqtrade.startup_update_open_orders()
+    assert log_has_re(r"Error updating Order .*", caplog)
+
+    mocker.patch(f"{EXMS}.fetch_order", side_effect=InvalidOrderException)
+    hto_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_cancel_order")
+    # Orders which are no longer found after X days should be assumed as canceled.
+    freqtrade.startup_update_open_orders()
+    assert log_has_re(r"Order is older than \d days.*", caplog)
+    assert hto_mock.call_count == 3
+    assert hto_mock.call_args_list[0][0][0]["status"] == "canceled"
+    assert hto_mock.call_args_list[1][0][0]["status"] == "canceled"
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_startup_backpopulate_precision(mocker, default_conf_usdt, fee, caplog):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    create_mock_trades_usdt(fee)
+
+    trades = Trade.get_trades().all()
+    trades[-1].exchange = "some_other_exchange"
+    for trade in trades:
+        assert trade.price_precision is None
+        assert trade.amount_precision is None
+        assert trade.precision_mode is None
+
+    freqtrade.startup_backpopulate_precision()
+    trades = Trade.get_trades().all()
+    for trade in trades:
+        if trade.exchange == "some_other_exchange":
+            assert trade.price_precision is None
+            assert trade.amount_precision is None
+            assert trade.precision_mode is None
+        else:
+            assert trade.price_precision is not None
+            assert trade.amount_precision is not None
+            assert trade.precision_mode is not None
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_update_trades_without_assigned_fees(mocker, default_conf_usdt, fee, is_short):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    def patch_with_fee(order):
+        order.update(
+            {"fee": {"cost": 0.1, "rate": 0.01, "currency": order["symbol"].split("/")[0]}}
+        )
+        return order
+
+    mocker.patch(
+        f"{EXMS}.fetch_order_or_stoploss_order",
+        side_effect=[
+            patch_with_fee(mock_order_2_sell(is_short=is_short)),
+            patch_with_fee(mock_order_3_sell(is_short=is_short)),
+            patch_with_fee(mock_order_2(is_short=is_short)),
+            patch_with_fee(mock_order_3(is_short=is_short)),
+            patch_with_fee(mock_order_4(is_short=is_short)),
+        ],
+    )
+
+    create_mock_trades(fee, is_short=is_short)
+    trades = Trade.get_trades().all()
+    assert len(trades) == MOCK_TRADE_COUNT
+    for trade in trades:
+        trade.is_short = is_short
+        assert trade.fee_open_cost is None
+        assert trade.fee_open_currency is None
+        assert trade.fee_close_cost is None
+        assert trade.fee_close_currency is None
+
+    freqtrade.update_trades_without_assigned_fees()
+
+    # Does nothing for dry-run
+    trades = Trade.get_trades().all()
+    assert len(trades) == MOCK_TRADE_COUNT
+    for trade in trades:
+        assert trade.fee_open_cost is None
+        assert trade.fee_open_currency is None
+        assert trade.fee_close_cost is None
+        assert trade.fee_close_currency is None
+
+    freqtrade.config["dry_run"] = False
+
+    freqtrade.update_trades_without_assigned_fees()
+
+    trades = Trade.get_trades().all()
+    assert len(trades) == MOCK_TRADE_COUNT
+
+    for trade in trades:
+        if trade.is_open:
+            # Exclude Trade 4 - as the order is still open.
+            if trade.select_order(entry_side(is_short), False):
+                assert trade.fee_open_cost is not None
+                assert trade.fee_open_currency is not None
+            else:
+                assert trade.fee_open_cost is None
+                assert trade.fee_open_currency is None
+
+        else:
+            assert trade.fee_close_cost is not None
+            assert trade.fee_close_currency is not None
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_reupdate_enter_order_fees(mocker, default_conf_usdt, fee, caplog, is_short):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mock_uts = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.update_trade_state")
+
+    mocker.patch(f"{EXMS}.fetch_order_or_stoploss_order", return_value={"status": "open"})
+    create_mock_trades(fee, is_short)
+    trades = Trade.get_trades().all()
+
+    freqtrade.handle_insufficient_funds(trades[3])
+    # assert log_has_re(r"Trying to reupdate buy fees for .*", caplog)
+    assert mock_uts.call_count == 1
+    assert mock_uts.call_args_list[0][0][0] == trades[3]
+    assert mock_uts.call_args_list[0][0][1] == mock_order_4(is_short)["id"]
+    assert log_has_re(r"Trying to refind lost order for .*", caplog)
+    mock_uts.reset_mock()
+    caplog.clear()
+
+    # Test with trade without orders
+    trade = Trade(
+        pair="XRP/ETH",
+        stake_amount=60.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_date=dt_now(),
+        is_open=True,
+        amount=30,
+        open_rate=2.0,
+        exchange="binance",
+        is_short=is_short,
+    )
+    Trade.session.add(trade)
+
+    freqtrade.handle_insufficient_funds(trade)
+    # assert log_has_re(r"Trying to reupdate buy fees for .*", caplog)
+    assert mock_uts.call_count == 0
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_insufficient_funds(mocker, default_conf_usdt, fee, is_short, caplog):
+    caplog.set_level(logging.DEBUG)
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mock_uts = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.update_trade_state")
+
+    mock_fo = mocker.patch(f"{EXMS}.fetch_order_or_stoploss_order", return_value={"status": "open"})
+
+    def reset_open_orders(trade):
+        trade.is_short = is_short
+
+    create_mock_trades(fee, is_short=is_short)
+    trades = Trade.get_trades().all()
+
+    caplog.clear()
+
+    # No open order
+    trade = trades[1]
+    reset_open_orders(trade)
+    assert not trade.has_open_orders
+    assert trade.has_open_sl_orders is False
+
+    freqtrade.handle_insufficient_funds(trade)
+    order = trade.orders[0]
+    assert log_has_re(
+        r"Order Order(.*order_id=" + order.order_id + ".*) is no longer open.", caplog
+    )
+    assert mock_fo.call_count == 0
+    assert mock_uts.call_count == 0
+    # No change to orderid - as update_trade_state is mocked
+    assert not trade.has_open_orders
+    assert trade.has_open_sl_orders is False
+
+    caplog.clear()
+    mock_fo.reset_mock()
+
+    # Open buy order
+    trade = trades[3]
+    reset_open_orders(trade)
+
+    # This part in not relevant anymore
+    # assert not trade.has_open_orders
+    assert trade.has_open_sl_orders is False
+
+    freqtrade.handle_insufficient_funds(trade)
+    order = mock_order_4(is_short=is_short)
+    assert log_has_re(r"Trying to refind Order\(.*", caplog)
+    assert mock_fo.call_count == 1
+    assert mock_uts.call_count == 1
+    # Found open buy order
+    assert trade.has_open_orders is True
+    assert trade.has_open_sl_orders is False
+
+    caplog.clear()
+    mock_fo.reset_mock()
+
+    # Open stoploss order
+    trade = trades[4]
+    reset_open_orders(trade)
+    assert not trade.has_open_orders
+    assert trade.has_open_sl_orders
+
+    freqtrade.handle_insufficient_funds(trade)
+    order = mock_order_5_stoploss(is_short=is_short)
+    assert log_has_re(r"Trying to refind Order\(.*", caplog)
+    assert mock_fo.call_count == 1
+    assert mock_uts.call_count == 2
+    # stoploss order is "refound" and added to the trade
+    assert not trade.has_open_orders
+    assert trade.has_open_sl_orders is True
+
+    caplog.clear()
+    mock_fo.reset_mock()
+    mock_uts.reset_mock()
+
+    # Open sell order
+    trade = trades[5]
+    reset_open_orders(trade)
+    # This part in not relevant anymore
+    # assert not trade.has_open_orders
+    assert trade.has_open_sl_orders is False
+
+    freqtrade.handle_insufficient_funds(trade)
+    order = mock_order_6_sell(is_short=is_short)
+    assert log_has_re(r"Trying to refind Order\(.*", caplog)
+    assert mock_fo.call_count == 1
+    assert mock_uts.call_count == 1
+    # sell-orderid is "refound" and added to the trade
+    assert trade.open_orders_ids[0] == order["id"]
+    assert trade.has_open_sl_orders is False
+
+    caplog.clear()
+
+    # Test error case
+    mock_fo = mocker.patch(f"{EXMS}.fetch_order_or_stoploss_order", side_effect=ExchangeError())
+    order = mock_order_5_stoploss(is_short=is_short)
+
+    freqtrade.handle_insufficient_funds(trades[4])
+    assert log_has(f"Error updating {order['id']}.", caplog)
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_onexchange_order(mocker, default_conf_usdt, limit_order, is_short, caplog):
+    default_conf_usdt["dry_run"] = False
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mock_uts = mocker.spy(freqtrade, "update_trade_state")
+
+    entry_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    mock_fo = mocker.patch(
+        f"{EXMS}.fetch_orders",
+        return_value=[
+            entry_order,
+            exit_order,
+        ],
+    )
+
+    trade = Trade(
+        pair="ETH/USDT",
+        fee_open=0.001,
+        fee_close=0.001,
+        open_rate=entry_order["price"],
+        open_date=dt_now(),
+        stake_amount=entry_order["cost"],
+        amount=entry_order["amount"],
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+    )
+
+    trade.orders.append(Order.parse_from_ccxt_object(entry_order, "ADA/USDT", entry_side(is_short)))
+    Trade.session.add(trade)
+    freqtrade.handle_onexchange_order(trade)
+    assert log_has_re(r"Found previously unknown order .*", caplog)
+    # Update trade state is called twice, once for the known and once for the unknown order.
+    assert mock_uts.call_count == 2
+    assert mock_fo.call_count == 1
+
+    trade = Trade.session.scalars(select(Trade)).first()
+
+    assert len(trade.orders) == 2
+    assert trade.is_open is False
+    assert trade.exit_reason == ExitType.SOLD_ON_EXCHANGE.value
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.parametrize(
+    "factor,adjusts",
+    [
+        (0.99, True),
+        (0.97, False),
+    ],
+)
+def test_handle_onexchange_order_changed_amount(
+    mocker,
+    default_conf_usdt,
+    limit_order,
+    is_short,
+    caplog,
+    factor,
+    adjusts,
+):
+    default_conf_usdt["dry_run"] = False
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mock_uts = mocker.spy(freqtrade, "update_trade_state")
+
+    entry_order = limit_order[entry_side(is_short)]
+    mock_fo = mocker.patch(
+        f"{EXMS}.fetch_orders",
+        return_value=[
+            entry_order,
+        ],
+    )
+
+    trade = Trade(
+        pair="ETH/USDT",
+        fee_open=0.001,
+        base_currency="ETH",
+        fee_close=0.001,
+        open_rate=entry_order["price"],
+        open_date=dt_now(),
+        stake_amount=entry_order["cost"],
+        amount=entry_order["amount"],
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+    )
+    freqtrade.wallets = MagicMock()
+    freqtrade.wallets.get_owned = MagicMock(return_value=entry_order["amount"] * factor)
+
+    trade.orders.append(Order.parse_from_ccxt_object(entry_order, "ADA/USDT", entry_side(is_short)))
+    Trade.session.add(trade)
+
+    # assert trade.amount > entry_order['amount']
+
+    freqtrade.handle_onexchange_order(trade)
+    assert mock_uts.call_count == 1
+    assert mock_fo.call_count == 1
+
+    trade = Trade.session.scalars(select(Trade)).first()
+
+    assert log_has_re(r".*has a total of .* but the Wallet shows.*", caplog)
+    if adjusts:
+        # Trade amount is updated
+        assert trade.amount == entry_order["amount"] * factor
+        assert log_has_re(r".*Adjusting trade amount to.*", caplog)
+    else:
+        assert log_has_re(r".*Refusing to adjust as the difference.*", caplog)
+        assert trade.amount == entry_order["amount"]
+
+    assert len(trade.orders) == 1
+    assert trade.is_open is True
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_onexchange_order_exit(mocker, default_conf_usdt, limit_order, is_short, caplog):
+    default_conf_usdt["dry_run"] = False
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mock_uts = mocker.spy(freqtrade, "update_trade_state")
+
+    entry_order = limit_order[entry_side(is_short)]
+    add_entry_order = deepcopy(entry_order)
+    add_entry_order.update(
+        {
+            "id": "_partial_entry_id",
+            "amount": add_entry_order["amount"] / 1.5,
+            "cost": add_entry_order["cost"] / 1.5,
+            "filled": add_entry_order["filled"] / 1.5,
+        }
+    )
+
+    exit_order_part = deepcopy(limit_order[exit_side(is_short)])
+    exit_order_part.update(
+        {
+            "id": "some_random_partial_id",
+            "amount": exit_order_part["amount"] / 2,
+            "cost": exit_order_part["cost"] / 2,
+            "filled": exit_order_part["filled"] / 2,
+        }
+    )
+    exit_order = limit_order[exit_side(is_short)]
+
+    # Orders intentionally in the wrong sequence
+    mock_fo = mocker.patch(
+        f"{EXMS}.fetch_orders",
+        return_value=[
+            entry_order,
+            exit_order_part,
+            exit_order,
+            add_entry_order,
+        ],
+    )
+
+    trade = Trade(
+        pair="ETH/USDT",
+        fee_open=0.001,
+        fee_close=0.001,
+        open_rate=entry_order["price"],
+        open_date=dt_now(),
+        stake_amount=entry_order["cost"],
+        amount=entry_order["amount"],
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+        is_open=True,
+    )
+
+    trade.orders = [
+        Order.parse_from_ccxt_object(entry_order, trade.pair, entry_side(is_short)),
+        Order.parse_from_ccxt_object(exit_order_part, trade.pair, exit_side(is_short)),
+        Order.parse_from_ccxt_object(add_entry_order, trade.pair, entry_side(is_short)),
+        Order.parse_from_ccxt_object(exit_order, trade.pair, exit_side(is_short)),
+    ]
+    trade.recalc_trade_from_orders()
+    Trade.session.add(trade)
+    Trade.commit()
+
+    freqtrade.handle_onexchange_order(trade)
+    # assert log_has_re(r"Found previously unknown order .*", caplog)
+    # Update trade state is called three times, once for every order
+    assert mock_uts.call_count == 4
+    assert mock_fo.call_count == 1
+
+    trade = Trade.session.scalars(select(Trade)).first()
+
+    assert len(trade.orders) == 4
+    assert trade.is_open is True
+    assert trade.exit_reason is None
+    assert trade.amount == 5.0
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_onexchange_order_fully_canceled_enter(
+    mocker, default_conf_usdt, limit_order, is_short, caplog
+):
+    default_conf_usdt["dry_run"] = False
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    entry_order = limit_order[entry_side(is_short)]
+    entry_order["status"] = "canceled"
+    entry_order["filled"] = 0.0
+    mock_fo = mocker.patch(
+        f"{EXMS}.fetch_orders",
+        return_value=[
+            entry_order,
+        ],
+    )
+    mocker.patch(f"{EXMS}.get_rate", return_value=entry_order["price"])
+
+    trade = Trade(
+        pair="ETH/USDT",
+        fee_open=0.001,
+        fee_close=0.001,
+        open_rate=entry_order["price"],
+        open_date=dt_now(),
+        stake_amount=entry_order["cost"],
+        amount=entry_order["amount"],
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+    )
+
+    trade.orders.append(Order.parse_from_ccxt_object(entry_order, "ADA/USDT", entry_side(is_short)))
+    Trade.session.add(trade)
+    assert freqtrade.handle_onexchange_order(trade) is True
+    assert log_has_re(r"Trade only had fully canceled entry orders\. .*", caplog)
+    assert mock_fo.call_count == 1
+    trades = Trade.get_trades().all()
+    assert len(trades) == 0
+
+
+def test_get_valid_price(mocker, default_conf_usdt) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade.config["custom_price_max_distance_ratio"] = 0.02
+
+    custom_price_string = "10"
+    custom_price_badstring = "10abc"
+    custom_price_float = 10.0
+    custom_price_int = 10
+
+    custom_price_over_max_alwd = 11.0
+    custom_price_under_min_alwd = 9.0
+    proposed_price = 10.1
+
+    valid_price_from_string = freqtrade.get_valid_price(custom_price_string, proposed_price)
+    valid_price_from_badstring = freqtrade.get_valid_price(custom_price_badstring, proposed_price)
+    valid_price_from_int = freqtrade.get_valid_price(custom_price_int, proposed_price)
+    valid_price_from_float = freqtrade.get_valid_price(custom_price_float, proposed_price)
+
+    valid_price_at_max_alwd = freqtrade.get_valid_price(custom_price_over_max_alwd, proposed_price)
+    valid_price_at_min_alwd = freqtrade.get_valid_price(custom_price_under_min_alwd, proposed_price)
+
+    assert isinstance(valid_price_from_string, float)
+    assert isinstance(valid_price_from_badstring, float)
+    assert isinstance(valid_price_from_int, float)
+    assert isinstance(valid_price_from_float, float)
+
+    assert valid_price_from_string == custom_price_float
+    assert valid_price_from_badstring == proposed_price
+    assert valid_price_from_int == custom_price_int
+    assert valid_price_from_float == custom_price_float
+
+    assert valid_price_at_max_alwd < custom_price_over_max_alwd
+    assert valid_price_at_max_alwd > proposed_price
+
+    assert valid_price_at_min_alwd > custom_price_under_min_alwd
+    assert valid_price_at_min_alwd < proposed_price
+
+
+@pytest.mark.parametrize(
+    "trading_mode,calls,t1,t2",
+    [
+        ("spot", 0, "2021-09-01 00:00:00", "2021-09-01 08:00:00"),
+        ("margin", 0, "2021-09-01 00:00:00", "2021-09-01 08:00:00"),
+        ("futures", 15, "2021-09-01 00:01:02", "2021-09-01 08:00:01"),
+        ("futures", 16, "2021-09-01 00:00:02", "2021-09-01 08:00:01"),
+        ("futures", 16, "2021-08-31 23:59:59", "2021-09-01 08:00:01"),
+        ("futures", 16, "2021-09-01 00:00:02", "2021-09-01 08:00:02"),
+        ("futures", 16, "2021-08-31 23:59:59", "2021-09-01 08:00:02"),
+        ("futures", 16, "2021-08-31 23:59:59", "2021-09-01 08:00:03"),
+        ("futures", 16, "2021-08-31 23:59:59", "2021-09-01 08:00:04"),
+        ("futures", 17, "2021-08-31 23:59:59", "2021-09-01 08:01:05"),
+        ("futures", 17, "2021-08-31 23:59:59", "2021-09-01 08:01:06"),
+        ("futures", 17, "2021-08-31 23:59:59", "2021-09-01 08:01:07"),
+        ("futures", 17, "2021-08-31 23:59:58", "2021-09-01 08:01:07"),
+    ],
+)
+@pytest.mark.parametrize(
+    "tzoffset",
+    [
+        "+00:00",
+        "+01:00",
+        "-02:00",
+    ],
+)
+def test_update_funding_fees_schedule(
+    mocker, default_conf, trading_mode, calls, time_machine, t1, t2, tzoffset
+):
+    time_machine.move_to(f"{t1} {tzoffset}", tick=False)
+
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.update_funding_fees", return_value=True)
+    default_conf["trading_mode"] = trading_mode
+    default_conf["margin_mode"] = "isolated"
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    time_machine.move_to(f"{t2} {tzoffset}", tick=False)
+    # Check schedule jobs in debugging with freqtrade._schedule.jobs
+    freqtrade._schedule.run_pending()
+
+    assert freqtrade.update_funding_fees.call_count == calls
+
+
+@pytest.mark.parametrize("schedule_off", [False, True])
+@pytest.mark.parametrize("is_short", [True, False])
+def test_update_funding_fees(
+    mocker,
+    default_conf,
+    time_machine,
+    fee,
+    ticker_usdt_sell_up,
+    is_short,
+    limit_order_open,
+    schedule_off,
+):
+    """
+    nominal_value = mark_price * size
+    funding_fee = nominal_value * funding_rate
+    size = 123
+    "LTC/USDT"
+        time: 0, mark: 3.3, fundRate: 0.00032583, nominal_value: 405.9, fundFee: 0.132254397
+        time: 8, mark: 3.2, fundRate: 0.00024472, nominal_value: 393.6, fundFee: 0.096321792
+    "ETH/USDT"
+        time: 0, mark: 2.4, fundRate: 0.0001, nominal_value: 295.2, fundFee: 0.02952
+        time: 8, mark: 2.5, fundRate: 0.0001, nominal_value: 307.5, fundFee: 0.03075
+    "ETC/USDT"
+        time: 0, mark: 4.3, fundRate: 0.00031077, nominal_value: 528.9, fundFee: 0.164366253
+        time: 8, mark: 4.1, fundRate: 0.00022655, nominal_value: 504.3, fundFee: 0.114249165
+    "XRP/USDT"
+        time: 0, mark: 1.2, fundRate: 0.00049426, nominal_value: 147.6, fundFee: 0.072952776
+        time: 8, mark: 1.2, fundRate: 0.00032715, nominal_value: 147.6, fundFee: 0.04828734
+    """
+    # SETUP
+    time_machine.move_to("2021-09-01 00:00:16 +00:00")
+
+    open_order = limit_order_open[entry_side(is_short)]
+    open_exit_order = limit_order_open[exit_side(is_short)]
+    bid = 0.11
+    enter_rate_mock = MagicMock(return_value=bid)
+    open_order.update(
+        {
+            "status": "closed",
+            "filled": open_order["amount"],
+            "remaining": 0,
+        }
+    )
+    enter_mm = MagicMock(return_value=open_order)
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+
+    date_midnight = dt_utc(2021, 9, 1)
+    date_eight = dt_utc(2021, 9, 1, 8)
+    date_sixteen = dt_utc(2021, 9, 1, 16)
+    columns = ["date", "open", "high", "low", "close", "volume"]
+    # 16:00 entry is actually never used
+    # But should be kept in the test to ensure we're filtering correctly.
+    funding_rates = {
+        "LTC/USDT": DataFrame(
+            [
+                [date_midnight, 0.00032583, 0, 0, 0, 0],
+                [date_eight, 0.00024472, 0, 0, 0, 0],
+                [date_sixteen, 0.00024472, 0, 0, 0, 0],
+            ],
+            columns=columns,
+        ),
+        "ETH/USDT": DataFrame(
+            [
+                [date_midnight, 0.0001, 0, 0, 0, 0],
+                [date_eight, 0.0001, 0, 0, 0, 0],
+                [date_sixteen, 0.0001, 0, 0, 0, 0],
+            ],
+            columns=columns,
+        ),
+        "XRP/USDT": DataFrame(
+            [
+                [date_midnight, 0.00049426, 0, 0, 0, 0],
+                [date_eight, 0.00032715, 0, 0, 0, 0],
+                [date_sixteen, 0.00032715, 0, 0, 0, 0],
+            ],
+            columns=columns,
+        ),
+    }
+
+    mark_prices = {
+        "LTC/USDT": DataFrame(
+            [
+                [date_midnight, 3.3, 0, 0, 0, 0],
+                [date_eight, 3.2, 0, 0, 0, 0],
+                [date_sixteen, 3.2, 0, 0, 0, 0],
+            ],
+            columns=columns,
+        ),
+        "ETH/USDT": DataFrame(
+            [
+                [date_midnight, 2.4, 0, 0, 0, 0],
+                [date_eight, 2.5, 0, 0, 0, 0],
+                [date_sixteen, 2.5, 0, 0, 0, 0],
+            ],
+            columns=columns,
+        ),
+        "XRP/USDT": DataFrame(
+            [
+                [date_midnight, 1.2, 0, 0, 0, 0],
+                [date_eight, 1.2, 0, 0, 0, 0],
+                [date_sixteen, 1.2, 0, 0, 0, 0],
+            ],
+            columns=columns,
+        ),
+    }
+
+    def refresh_latest_ohlcv_mock(pairlist, **kwargs):
+        ret = {}
+        for p, tf, ct in pairlist:
+            if ct == CandleType.MARK:
+                ret[(p, tf, ct)] = mark_prices[p]
+            else:
+                ret[(p, tf, ct)] = funding_rates[p]
+
+        return ret
+
+    mocker.patch(f"{EXMS}.refresh_latest_ohlcv", side_effect=refresh_latest_ohlcv_mock)
+
+    mocker.patch.multiple(
+        EXMS,
+        get_rate=enter_rate_mock,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=enter_mm,
+        get_min_pair_stake_amount=MagicMock(return_value=1),
+        get_fee=fee,
+        get_maintenance_ratio_and_amt=MagicMock(return_value=(0.01, 0.01)),
+    )
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    # initial funding fees,
+    freqtrade.execute_entry("ETH/USDT", 123, is_short=is_short)
+    freqtrade.execute_entry("LTC/USDT", 2.0, is_short=is_short)
+    freqtrade.execute_entry("XRP/USDT", 123, is_short=is_short)
+    multiple = 1 if is_short else -1
+    trades = Trade.get_open_trades()
+    assert len(trades) == 3
+    for trade in trades:
+        assert pytest.approx(trade.funding_fees) == 0
+    mocker.patch(f"{EXMS}.create_order", return_value=open_exit_order)
+    time_machine.move_to("2021-09-01 08:00:00 +00:00")
+    if schedule_off:
+        for trade in trades:
+            freqtrade.execute_trade_exit(
+                trade=trade,
+                # The values of the next 2 params are irrelevant for this test
+                limit=ticker_usdt_sell_up()["bid"],
+                exit_check=ExitCheckTuple(exit_type=ExitType.ROI),
+            )
+            assert trade.funding_fees == pytest.approx(
+                sum(
+                    trade.amount
+                    * mark_prices[trade.pair].iloc[1:2]["open"]
+                    * funding_rates[trade.pair].iloc[1:2]["open"]
+                    * multiple
+                )
+            )
+
+    else:
+        freqtrade._schedule.run_pending()
+
+    # Funding fees for 00:00 and 08:00
+    for trade in trades:
+        assert trade.funding_fees == pytest.approx(
+            sum(
+                trade.amount
+                * mark_prices[trade.pair].iloc[1:2]["open"]
+                * funding_rates[trade.pair].iloc[1:2]["open"]
+                * multiple
+            )
+        )
+
+
+def test_update_funding_fees_error(mocker, default_conf, caplog):
+    mocker.patch(f"{EXMS}.get_funding_fees", side_effect=ExchangeError())
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    freqtrade.update_funding_fees()
+
+    log_has("Could not update funding fees for open trades.", caplog)
+
+
+def test_position_adjust(mocker, default_conf_usdt, fee) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    patch_wallet(mocker, free=10000)
+    default_conf_usdt.update(
+        {
+            "position_adjustment_enable": True,
+            "dry_run": False,
+            "stake_amount": 10.0,
+            "dry_run_wallet": 1000.0,
+        }
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True)
+    bid = 11
+    stake_amount = 10
+    buy_rate_mock = MagicMock(return_value=bid)
+    mocker.patch.multiple(
+        EXMS,
+        get_rate=buy_rate_mock,
+        fetch_ticker=MagicMock(return_value={"bid": 10, "ask": 12, "last": 11}),
+        get_min_pair_stake_amount=MagicMock(return_value=1),
+        get_fee=fee,
+    )
+    pair = "ETH/USDT"
+
+    # Initial buy
+    closed_successful_buy_order = {
+        "pair": pair,
+        "ft_pair": pair,
+        "ft_order_side": "buy",
+        "side": "buy",
+        "type": "limit",
+        "status": "closed",
+        "price": bid,
+        "average": bid,
+        "cost": bid * stake_amount,
+        "amount": stake_amount,
+        "filled": stake_amount,
+        "ft_is_open": False,
+        "id": "650",
+        "order_id": "650",
+    }
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=closed_successful_buy_order))
+    mocker.patch(
+        f"{EXMS}.fetch_order_or_stoploss_order", MagicMock(return_value=closed_successful_buy_order)
+    )
+    assert freqtrade.execute_entry(pair, stake_amount)
+    # Should create an closed trade with an no open order id
+    # Order is filled and trade is open
+    orders = Order.session.scalars(select(Order)).all()
+    assert orders
+    assert len(orders) == 1
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert trade.is_open is True
+    assert not trade.has_open_orders
+    assert trade.open_rate == 11
+    assert trade.stake_amount == 110
+
+    # Assume it does nothing since order is closed and trade is open
+    freqtrade.update_trades_without_assigned_fees()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert trade.is_open is True
+    assert not trade.has_open_orders
+    assert trade.open_rate == 11
+    assert trade.stake_amount == 110
+    assert not trade.fee_updated("buy")
+
+    freqtrade.manage_open_orders()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert trade.is_open is True
+    assert not trade.has_open_orders
+    assert trade.open_rate == 11
+    assert trade.stake_amount == 110
+    assert not trade.fee_updated("buy")
+
+    # First position adjustment buy.
+    open_dca_order_1 = {
+        "ft_pair": pair,
+        "ft_order_side": "buy",
+        "side": "buy",
+        "type": "limit",
+        "status": None,
+        "price": 9,
+        "amount": 12,
+        "cost": 108,
+        "ft_is_open": True,
+        "id": "651",
+        "order_id": "651",
+    }
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=open_dca_order_1))
+    mocker.patch(f"{EXMS}.fetch_order_or_stoploss_order", MagicMock(return_value=open_dca_order_1))
+    assert freqtrade.execute_entry(pair, stake_amount, trade=trade)
+
+    orders = Order.session.scalars(select(Order)).all()
+    assert orders
+    assert len(orders) == 2
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert "651" in trade.open_orders_ids
+    assert trade.open_rate == 11
+    assert trade.amount == 10
+    assert trade.stake_amount == 110
+    assert not trade.fee_updated("buy")
+    trades: list[Trade] = Trade.get_open_trades_without_assigned_fees()
+    assert len(trades) == 1
+    assert trade.is_open
+    assert not trade.fee_updated("buy")
+    order = trade.select_order("buy", False)
+    assert order
+    assert order.order_id == "650"
+
+    def make_sure_its_651(*args, **kwargs):
+        if args[0] == "650":
+            return closed_successful_buy_order
+        if args[0] == "651":
+            return open_dca_order_1
+        return None
+
+    # Assume it does nothing since order is still open
+    fetch_order_mm = MagicMock(side_effect=make_sure_its_651)
+    mocker.patch(f"{EXMS}.create_order", fetch_order_mm)
+    mocker.patch(f"{EXMS}.fetch_order", fetch_order_mm)
+    mocker.patch(f"{EXMS}.fetch_order_or_stoploss_order", fetch_order_mm)
+    freqtrade.update_trades_without_assigned_fees()
+
+    orders = Order.session.scalars(select(Order)).all()
+    assert orders
+    assert len(orders) == 2
+    # Assert that the trade is found as open and without fees
+    trades: list[Trade] = Trade.get_open_trades_without_assigned_fees()
+    assert len(trades) == 1
+    # Assert trade is as expected
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert "651" in trade.open_orders_ids
+    assert trade.open_rate == 11
+    assert trade.amount == 10
+    assert trade.stake_amount == 110
+    assert not trade.fee_updated("buy")
+
+    # Make sure the closed order is found as the first order.
+    order = trade.select_order("buy", False)
+    assert order.order_id == "650"
+
+    # Now close the order so it should update.
+    closed_dca_order_1 = {
+        "ft_pair": pair,
+        "ft_order_side": "buy",
+        "side": "buy",
+        "type": "limit",
+        "status": "closed",
+        "price": 9,
+        "average": 9,
+        "amount": 12,
+        "filled": 12,
+        "cost": 108,
+        "ft_is_open": False,
+        "id": "651",
+        "order_id": "651",
+        "datetime": dt_now().isoformat(),
+    }
+
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=closed_dca_order_1))
+    mocker.patch(f"{EXMS}.fetch_order", MagicMock(return_value=closed_dca_order_1))
+    mocker.patch(
+        f"{EXMS}.fetch_order_or_stoploss_order", MagicMock(return_value=closed_dca_order_1)
+    )
+    freqtrade.manage_open_orders()
+
+    # Assert trade is as expected (averaged dca)
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert not trade.has_open_orders
+    assert pytest.approx(trade.open_rate) == 9.90909090909
+    assert trade.amount == 22
+    assert pytest.approx(trade.stake_amount) == 218
+
+    orders = Order.session.scalars(select(Order)).all()
+    assert orders
+    assert len(orders) == 2
+
+    # Make sure the closed order is found as the second order.
+    order = trade.select_order("buy", False)
+    assert order.order_id == "651"
+
+    # Assert that the trade is not found as open and without fees
+    trades: list[Trade] = Trade.get_open_trades_without_assigned_fees()
+    assert len(trades) == 1
+
+    # Add a second DCA
+    closed_dca_order_2 = {
+        "ft_pair": pair,
+        "status": "closed",
+        "ft_order_side": "buy",
+        "side": "buy",
+        "type": "limit",
+        "price": 7,
+        "average": 7,
+        "amount": 15,
+        "filled": 15,
+        "cost": 105,
+        "ft_is_open": False,
+        "id": "652",
+        "order_id": "652",
+    }
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=closed_dca_order_2))
+    mocker.patch(f"{EXMS}.fetch_order", MagicMock(return_value=closed_dca_order_2))
+    mocker.patch(
+        f"{EXMS}.fetch_order_or_stoploss_order", MagicMock(return_value=closed_dca_order_2)
+    )
+    assert freqtrade.execute_entry(pair, stake_amount, trade=trade)
+
+    # Assert trade is as expected (averaged dca)
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert not trade.has_open_orders
+    assert pytest.approx(trade.open_rate) == 8.729729729729
+    assert trade.amount == 37
+    assert trade.stake_amount == 323
+
+    orders = Order.session.scalars(select(Order)).all()
+    assert orders
+    assert len(orders) == 3
+
+    # Make sure the closed order is found as the second order.
+    order = trade.select_order("buy", False)
+    assert order.order_id == "652"
+    closed_sell_dca_order_1 = {
+        "ft_pair": pair,
+        "status": "closed",
+        "ft_order_side": "sell",
+        "side": "sell",
+        "type": "limit",
+        "price": 8,
+        "average": 8,
+        "amount": 15,
+        "filled": 15,
+        "cost": 120,
+        "ft_is_open": False,
+        "id": "653",
+        "order_id": "653",
+    }
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=closed_sell_dca_order_1))
+    mocker.patch(f"{EXMS}.fetch_order", MagicMock(return_value=closed_sell_dca_order_1))
+    mocker.patch(
+        f"{EXMS}.fetch_order_or_stoploss_order", MagicMock(return_value=closed_sell_dca_order_1)
+    )
+    assert freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=8,
+        exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
+        sub_trade_amt=15,
+    )
+
+    # Assert trade is as expected (averaged dca)
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert not trade.has_open_orders
+    assert trade.is_open
+    assert trade.amount == 22
+    assert trade.stake_amount == 192.05405405405406
+    assert pytest.approx(trade.open_rate) == 8.729729729729
+
+    orders = Order.session.scalars(select(Order)).all()
+    assert orders
+    assert len(orders) == 4
+
+    # Make sure the closed order is found as the second order.
+    order = trade.select_order("sell", False)
+    assert order.order_id == "653"
+
+
+def test_position_adjust2(mocker, default_conf_usdt, fee) -> None:
+    """
+    TODO: Should be adjusted to test both long and short
+    buy 100 @ 11
+    sell 50 @ 8
+    sell 50 @ 16
+    """
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    patch_wallet(mocker, free=10000)
+    default_conf_usdt.update(
+        {
+            "position_adjustment_enable": True,
+            "dry_run": False,
+            "stake_amount": 200.0,
+            "dry_run_wallet": 1000.0,
+        }
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True)
+    bid = 11
+    amount = 100
+    buy_rate_mock = MagicMock(return_value=bid)
+    mocker.patch.multiple(
+        EXMS,
+        get_rate=buy_rate_mock,
+        fetch_ticker=MagicMock(return_value={"bid": 10, "ask": 12, "last": 11}),
+        get_min_pair_stake_amount=MagicMock(return_value=1),
+        get_fee=fee,
+    )
+    pair = "ETH/USDT"
+    # Initial buy
+    closed_successful_buy_order = {
+        "pair": pair,
+        "ft_pair": pair,
+        "ft_order_side": "buy",
+        "side": "buy",
+        "type": "limit",
+        "status": "closed",
+        "price": bid,
+        "average": bid,
+        "cost": bid * amount,
+        "amount": amount,
+        "filled": amount,
+        "ft_is_open": False,
+        "id": "600",
+        "order_id": "600",
+    }
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=closed_successful_buy_order))
+    mocker.patch(
+        f"{EXMS}.fetch_order_or_stoploss_order", MagicMock(return_value=closed_successful_buy_order)
+    )
+    assert freqtrade.execute_entry(pair, amount)
+    # Should create an closed trade with an no open order id
+    # Order is filled and trade is open
+    orders = Order.session.scalars(select(Order)).all()
+    assert orders
+    assert len(orders) == 1
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert trade.is_open is True
+    assert not trade.has_open_orders
+    assert trade.open_rate == bid
+    assert trade.stake_amount == bid * amount
+
+    # Assume it does nothing since order is closed and trade is open
+    freqtrade.update_trades_without_assigned_fees()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert trade.is_open is True
+    assert not trade.has_open_orders
+    assert trade.open_rate == bid
+    assert trade.stake_amount == bid * amount
+    assert not trade.fee_updated(trade.entry_side)
+
+    freqtrade.manage_open_orders()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert trade.is_open is True
+    assert not trade.has_open_orders
+    assert trade.open_rate == bid
+    assert trade.stake_amount == bid * amount
+    assert not trade.fee_updated(trade.entry_side)
+
+    amount = 50
+    ask = 8
+    closed_sell_dca_order_1 = {
+        "ft_pair": pair,
+        "status": "closed",
+        "ft_order_side": "sell",
+        "side": "sell",
+        "type": "limit",
+        "price": ask,
+        "average": ask,
+        "amount": amount,
+        "filled": amount,
+        "cost": amount * ask,
+        "ft_is_open": False,
+        "id": "601",
+        "order_id": "601",
+    }
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=closed_sell_dca_order_1))
+    mocker.patch(f"{EXMS}.fetch_order", MagicMock(return_value=closed_sell_dca_order_1))
+    mocker.patch(
+        f"{EXMS}.fetch_order_or_stoploss_order", MagicMock(return_value=closed_sell_dca_order_1)
+    )
+    assert freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=ask,
+        exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
+        sub_trade_amt=amount,
+    )
+    trades: list[Trade] = trade.get_open_trades_without_assigned_fees()
+    assert len(trades) == 1
+    # Assert trade is as expected (averaged dca)
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert not trade.has_open_orders
+    assert trade.amount == 50
+    assert trade.open_rate == 11
+    assert trade.stake_amount == 550
+    assert pytest.approx(trade.realized_profit) == -152.375
+    assert pytest.approx(trade.close_profit_abs) == -152.375
+
+    orders = Order.session.scalars(select(Order)).all()
+    assert orders
+    assert len(orders) == 2
+    # Make sure the closed order is found as the second order.
+    order = trade.select_order("sell", False)
+    assert order.order_id == "601"
+
+    amount = 50
+    ask = 16
+    closed_sell_dca_order_2 = {
+        "ft_pair": pair,
+        "status": "closed",
+        "ft_order_side": "sell",
+        "side": "sell",
+        "type": "limit",
+        "price": ask,
+        "average": ask,
+        "amount": amount,
+        "filled": amount,
+        "cost": amount * ask,
+        "ft_is_open": False,
+        "id": "602",
+        "order_id": "602",
+    }
+    mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=closed_sell_dca_order_2))
+    mocker.patch(f"{EXMS}.fetch_order", MagicMock(return_value=closed_sell_dca_order_2))
+    mocker.patch(
+        f"{EXMS}.fetch_order_or_stoploss_order", MagicMock(return_value=closed_sell_dca_order_2)
+    )
+    assert freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=ask,
+        exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
+        sub_trade_amt=amount,
+    )
+    # Assert trade is as expected (averaged dca)
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert not trade.has_open_orders
+    assert trade.amount == 50
+    assert trade.open_rate == 11
+    assert trade.stake_amount == 550
+    # Trade fully realized
+    assert pytest.approx(trade.realized_profit) == 94.25
+    assert pytest.approx(trade.close_profit_abs) == 94.25
+    orders = Order.session.scalars(select(Order)).all()
+    assert orders
+    assert len(orders) == 3
+
+    # Make sure the closed order is found as the second order.
+    order = trade.select_order("sell", False)
+    assert order.order_id == "602"
+    assert trade.is_open is False
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        # tuple 1 - side amount, price
+        # tuple 2 - amount, open_rate, stake_amount, cumulative_profit, realized_profit, rel_profit
+        (
+            (("buy", 100, 10), (100.0, 10.0, 1000.0, 0.0, None, None)),
+            (("buy", 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)),
+            (("sell", 50, 12), (150.0, 12.5, 1875.0, -28.0625, -28.0625, -0.011197)),
+            (("sell", 100, 20), (50.0, 12.5, 625.0, 713.8125, 741.875, 0.2848129)),
+            (
+                ("sell", 50, 5),
+                (50.0, 12.5, 625.0, 336.625, 336.625, 0.1343142),
+            ),  # final profit (sum)
+        ),
+        (
+            (("buy", 100, 3), (100.0, 3.0, 300.0, 0.0, None, None)),
+            (("buy", 100, 7), (200.0, 5.0, 1000.0, 0.0, None, None)),
+            (("sell", 100, 11), (100.0, 5.0, 500.0, 596.0, 596.0, 0.5945137)),
+            (("buy", 150, 15), (250.0, 11.0, 2750.0, 596.0, 596.0, 0.5945137)),
+            (("sell", 100, 19), (150.0, 11.0, 1650.0, 1388.5, 792.5, 0.4261653)),
+            (("sell", 150, 23), (150.0, 11.0, 1650.0, 3175.75, 3175.75, 0.9747170)),  # final profit
+        ),
+    ],
+)
+def test_position_adjust3(mocker, default_conf_usdt, fee, data) -> None:
+    default_conf_usdt.update(
+        {
+            "position_adjustment_enable": True,
+            "dry_run": False,
+            "stake_amount": 200.0,
+            "dry_run_wallet": 1000.0,
+        }
+    )
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    patch_wallet(mocker, free=10000)
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    trade = None
+    freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True)
+    for idx, (order, result) in enumerate(data):
+        amount = order[1]
+        price = order[2]
+        price_mock = MagicMock(return_value=price)
+        mocker.patch.multiple(
+            EXMS,
+            get_rate=price_mock,
+            fetch_ticker=MagicMock(return_value={"bid": 10, "ask": 12, "last": 11}),
+            get_min_pair_stake_amount=MagicMock(return_value=1),
+            get_fee=fee,
+        )
+        pair = "ETH/USDT"
+        closed_successful_order = {
+            "pair": pair,
+            "ft_pair": pair,
+            "ft_order_side": order[0],
+            "side": order[0],
+            "type": "limit",
+            "status": "closed",
+            "price": price,
+            "average": price,
+            "cost": price * amount,
+            "amount": amount,
+            "filled": amount,
+            "ft_is_open": False,
+            "id": f"60{idx}",
+            "order_id": f"60{idx}",
+        }
+        mocker.patch(f"{EXMS}.create_order", MagicMock(return_value=closed_successful_order))
+        mocker.patch(
+            f"{EXMS}.fetch_order_or_stoploss_order", MagicMock(return_value=closed_successful_order)
+        )
+        if order[0] == "buy":
+            assert freqtrade.execute_entry(pair, amount, trade=trade)
+        else:
+            assert freqtrade.execute_trade_exit(
+                trade=trade,
+                limit=price,
+                exit_check=ExitCheckTuple(exit_type=ExitType.PARTIAL_EXIT),
+                sub_trade_amt=amount,
+            )
+
+        orders1 = Order.session.scalars(select(Order)).all()
+        assert orders1
+        assert len(orders1) == idx + 1
+
+        trade = Trade.session.scalars(select(Trade)).first()
+        assert trade
+        if idx < len(data) - 1:
+            assert trade.is_open is True
+        assert not trade.has_open_orders
+        assert trade.amount == result[0]
+        assert trade.open_rate == result[1]
+        assert trade.stake_amount == result[2]
+        assert pytest.approx(trade.realized_profit) == result[3]
+        assert pytest.approx(trade.close_profit_abs) == result[4]
+        assert pytest.approx(trade.close_profit) == result[5]
+
+        order_obj = trade.select_order(order[0], False)
+        assert order_obj.order_id == f"60{idx}"
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert not trade.has_open_orders
+    assert trade.is_open is False
+
+
+def test_process_open_trade_positions_exception(mocker, default_conf_usdt, fee, caplog) -> None:
+    default_conf_usdt.update(
+        {
+            "position_adjustment_enable": True,
+        }
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot.check_and_call_adjust_trade_position",
+        side_effect=DependencyException(),
+    )
+
+    create_mock_trades(fee)
+
+    freqtrade.process_open_trade_positions()
+    assert log_has_re(r"Unable to adjust position of trade for .*", caplog)
+
+
+def test_check_and_call_adjust_trade_position(mocker, default_conf_usdt, fee, caplog) -> None:
+    default_conf_usdt.update(
+        {
+            "position_adjustment_enable": True,
+            "max_entry_position_adjustment": 0,
+        }
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    buy_rate_mock = MagicMock(return_value=10)
+    mocker.patch.multiple(
+        EXMS,
+        get_rate=buy_rate_mock,
+        fetch_ticker=MagicMock(return_value={"bid": 10, "ask": 12, "last": 11}),
+        get_min_pair_stake_amount=MagicMock(return_value=1),
+        get_fee=fee,
+    )
+    create_mock_trades(fee)
+    caplog.set_level(logging.DEBUG)
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=(10, "aaaa"))
+    freqtrade.process_open_trade_positions()
+    assert log_has_re(r"Max adjustment entries for .* has been reached\.", caplog)
+    assert freqtrade.strategy.adjust_trade_position.call_count == 4
+
+    caplog.clear()
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=(-0.0005, "partial_exit_c"))
+    freqtrade.process_open_trade_positions()
+    assert log_has_re(r"LIMIT_SELL has been fulfilled.*", caplog)
+    assert freqtrade.strategy.adjust_trade_position.call_count == 4
+    trade = Trade.get_trades(trade_filter=[Trade.id == 5]).first()
+    assert trade.orders[-1].ft_order_tag == "partial_exit_c"
+    assert trade.is_open
diff --git a/tests/freqtradebot/test_integration.py b/tests/freqtradebot/test_integration.py
new file mode 100644
index 000000000..2cb4b6aa8
--- /dev/null
+++ b/tests/freqtradebot/test_integration.py
@@ -0,0 +1,845 @@
+import time
+from unittest.mock import MagicMock
+
+import pytest
+from sqlalchemy import select
+
+from freqtrade.enums import ExitCheckTuple, ExitType, TradingMode
+from freqtrade.persistence import Trade
+from freqtrade.persistence.models import Order
+from freqtrade.rpc.rpc import RPC
+from tests.conftest import EXMS, get_patched_freqtradebot, log_has_re, patch_get_signal
+
+
+def test_may_execute_exit_stoploss_on_exchange_multi(default_conf, ticker, fee, mocker) -> None:
+    """
+    Tests workflow of selling stoploss_on_exchange.
+    Sells
+    * first trade as stoploss
+    * 2nd trade is kept
+    * 3rd trade is sold via sell-signal
+    """
+    default_conf["max_open_trades"] = 3
+    default_conf["exchange"]["name"] = "binance"
+
+    stoploss = {"id": 123, "info": {}}
+    stoploss_order_open = {
+        "id": "123",
+        "timestamp": 1542707426845,
+        "datetime": "2018-11-20T09:50:26.845Z",
+        "lastTradeTimestamp": None,
+        "symbol": "BTC/USDT",
+        "type": "stop_loss_limit",
+        "side": "sell",
+        "price": 1.08801,
+        "amount": 91.07468123,
+        "cost": 0.0,
+        "average": 0.0,
+        "filled": 0.0,
+        "remaining": 0.0,
+        "status": "open",
+        "fee": None,
+        "trades": None,
+    }
+    stoploss_order_closed = stoploss_order_open.copy()
+    stoploss_order_closed["status"] = "closed"
+    stoploss_order_closed["filled"] = stoploss_order_closed["amount"]
+
+    # Sell first trade based on stoploss, keep 2nd and 3rd trade open
+    stop_orders = [stoploss_order_closed, stoploss_order_open.copy(), stoploss_order_open.copy()]
+    stoploss_order_mock = MagicMock(side_effect=stop_orders)
+    # Sell 3rd trade (not called for the first trade)
+    should_sell_mock = MagicMock(side_effect=[[], [ExitCheckTuple(exit_type=ExitType.EXIT_SIGNAL)]])
+    cancel_order_mock = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        create_stoploss=stoploss,
+        fetch_ticker=ticker,
+        get_fee=fee,
+        amount_to_precision=lambda s, x, y: y,
+        price_to_precision=lambda s, x, y: y,
+        fetch_stoploss_order=stoploss_order_mock,
+        cancel_stoploss_order_with_result=cancel_order_mock,
+    )
+
+    mocker.patch.multiple(
+        "freqtrade.freqtradebot.FreqtradeBot",
+        create_stoploss_order=MagicMock(return_value=True),
+        _notify_exit=MagicMock(),
+    )
+    mocker.patch("freqtrade.strategy.interface.IStrategy.should_exit", should_sell_mock)
+    wallets_mock = mocker.patch("freqtrade.wallets.Wallets.update")
+    mocker.patch("freqtrade.wallets.Wallets.get_free", return_value=1000)
+    mocker.patch("freqtrade.wallets.Wallets.check_exit_amount", return_value=True)
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+    # Switch ordertype to market to close trade immediately
+    freqtrade.strategy.order_types["exit"] = "market"
+    freqtrade.strategy.confirm_trade_entry = MagicMock(return_value=True)
+    freqtrade.strategy.confirm_trade_exit = MagicMock(return_value=True)
+    patch_get_signal(freqtrade)
+
+    # Create some test data
+    freqtrade.enter_positions()
+    assert freqtrade.strategy.confirm_trade_entry.call_count == 3
+    freqtrade.strategy.confirm_trade_entry.reset_mock()
+    assert freqtrade.strategy.confirm_trade_exit.call_count == 0
+    wallets_mock.reset_mock()
+
+    trades = Trade.session.scalars(select(Trade)).all()
+    # Make sure stoploss-order is open and trade is bought
+    for idx, trade in enumerate(trades):
+        stop_order = stop_orders[idx]
+        stop_order["id"] = f"stop{idx}"
+        oobj = Order.parse_from_ccxt_object(stop_order, trade.pair, "stoploss")
+        oobj.ft_is_open = True
+
+        trade.orders.append(oobj)
+        assert len(trade.open_sl_orders) == 1
+
+    n = freqtrade.exit_positions(trades)
+    assert n == 2
+    assert should_sell_mock.call_count == 2
+    assert freqtrade.strategy.confirm_trade_entry.call_count == 0
+    assert freqtrade.strategy.confirm_trade_exit.call_count == 1
+    freqtrade.strategy.confirm_trade_exit.reset_mock()
+
+    # Only order for 3rd trade needs to be cancelled
+    assert cancel_order_mock.call_count == 1
+    assert stoploss_order_mock.call_count == 3
+    # Wallets must be updated between stoploss cancellation and selling, and will be updated again
+    # during update_trade_state
+    assert wallets_mock.call_count == 4
+
+    trade = trades[0]
+    assert trade.exit_reason == ExitType.STOPLOSS_ON_EXCHANGE.value
+    assert not trade.is_open
+
+    trade = trades[1]
+    assert not trade.exit_reason
+    assert trade.is_open
+
+    trade = trades[2]
+    assert trade.exit_reason == ExitType.EXIT_SIGNAL.value
+    assert not trade.is_open
+
+
+@pytest.mark.parametrize(
+    "balance_ratio,result1",
+    [
+        (1, 200),
+        (0.99, 198),
+    ],
+)
+def test_forcebuy_last_unlimited(default_conf, ticker, fee, mocker, balance_ratio, result1) -> None:
+    """
+    Tests workflow unlimited stake-amount
+    Buy 4 trades, forcebuy a 5th trade
+    Sell one trade, calculated stake amount should now be lower than before since
+    one trade was sold at a loss.
+    """
+    default_conf["max_open_trades"] = 5
+    default_conf["force_entry_enable"] = True
+    default_conf["stake_amount"] = "unlimited"
+    default_conf["tradable_balance_ratio"] = balance_ratio
+    default_conf["dry_run_wallet"] = 1000
+    default_conf["exchange"]["name"] = "binance"
+    default_conf["telegram"]["enabled"] = True
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+        amount_to_precision=lambda s, x, y: y,
+        price_to_precision=lambda s, x, y: y,
+    )
+
+    mocker.patch.multiple(
+        "freqtrade.freqtradebot.FreqtradeBot",
+        create_stoploss_order=MagicMock(return_value=True),
+        _notify_exit=MagicMock(),
+    )
+    should_sell_mock = MagicMock(
+        side_effect=[[], [ExitCheckTuple(exit_type=ExitType.EXIT_SIGNAL)], [], [], []]
+    )
+    mocker.patch("freqtrade.strategy.interface.IStrategy.should_exit", should_sell_mock)
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    rpc = RPC(freqtrade)
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+    # Switch ordertype to market to close trade immediately
+    freqtrade.strategy.order_types["exit"] = "market"
+    patch_get_signal(freqtrade)
+
+    # Create 4 trades
+    n = freqtrade.enter_positions()
+    assert n == 4
+
+    trades = Trade.session.scalars(select(Trade)).all()
+    assert len(trades) == 4
+    assert freqtrade.wallets.get_trade_stake_amount("XRP/BTC", 5) == result1
+
+    rpc._rpc_force_entry("TKN/BTC", None)
+
+    trades = Trade.session.scalars(select(Trade)).all()
+    assert len(trades) == 5
+
+    for trade in trades:
+        assert pytest.approx(trade.stake_amount) == result1
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 5
+    bals = freqtrade.wallets.get_all_balances()
+
+    n = freqtrade.exit_positions(trades)
+    assert n == 1
+    trades = Trade.get_open_trades()
+    # One trade sold
+    assert len(trades) == 4
+    # stake-amount should now be reduced, since one trade was sold at a loss.
+    assert freqtrade.wallets.get_trade_stake_amount("XRP/BTC", 5) < result1
+    # Validate that balance of sold trade is not in dry-run balances anymore.
+    bals2 = freqtrade.wallets.get_all_balances()
+    assert bals != bals2
+    assert len(bals) == 6
+    assert len(bals2) == 5
+    assert "LTC" in bals
+    assert "LTC" not in bals2
+
+
+def test_dca_buying(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
+    default_conf_usdt["position_adjustment_enable"] = True
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+    )
+
+    patch_get_signal(freqtrade)
+    freqtrade.enter_positions()
+
+    assert len(Trade.get_trades().all()) == 1
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 1
+    assert pytest.approx(trade.stake_amount) == 60
+    assert trade.open_rate == 2.0
+    # No adjustment
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 1
+    assert pytest.approx(trade.stake_amount) == 60
+
+    # Reduce bid amount
+    ticker_usdt_modif = ticker_usdt.return_value
+    ticker_usdt_modif["bid"] = ticker_usdt_modif["bid"] * 0.995
+    mocker.patch(f"{EXMS}.fetch_ticker", return_value=ticker_usdt_modif)
+
+    # additional buy order
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 2
+    for o in trade.orders:
+        assert o.status == "closed"
+    assert pytest.approx(trade.stake_amount) == 120
+
+    # Open-rate averaged between 2.0 and 2.0 * 0.995
+    assert trade.open_rate < 2.0
+    assert trade.open_rate > 2.0 * 0.995
+
+    # No action - profit raised above 1% (the bar set in the strategy).
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 2
+    assert pytest.approx(trade.stake_amount) == 120
+    assert trade.orders[0].amount == 30
+    assert pytest.approx(trade.orders[1].amount) == 60 / ticker_usdt_modif["bid"]
+
+    assert pytest.approx(trade.amount) == trade.orders[0].amount + trade.orders[1].amount
+    assert trade.nr_of_successful_buys == 2
+    assert trade.nr_of_successful_entries == 2
+
+    # Sell
+    patch_get_signal(freqtrade, enter_long=False, exit_long=True)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert trade.is_open is False
+    assert trade.orders[0].amount == 30
+    assert trade.orders[0].side == "buy"
+    assert pytest.approx(trade.orders[1].amount) == 60 / ticker_usdt_modif["bid"]
+    # Sold everything
+    assert trade.orders[-1].side == "sell"
+    assert trade.orders[2].amount == trade.amount
+
+    assert trade.nr_of_successful_buys == 2
+    assert trade.nr_of_successful_entries == 2
+
+
+def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
+    default_conf_usdt["position_adjustment_enable"] = True
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        amount_to_precision=lambda s, x, y: round(y, 4),
+        price_to_precision=lambda s, x, y: y,
+    )
+
+    patch_get_signal(freqtrade, enter_long=False, enter_short=True)
+    freqtrade.enter_positions()
+
+    assert len(Trade.get_trades().all()) == 1
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 1
+    assert pytest.approx(trade.stake_amount) == 60
+    assert trade.open_rate == 2.02
+    assert trade.orders[0].amount == trade.amount
+    # No adjustment
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 1
+    assert pytest.approx(trade.stake_amount) == 60
+
+    # Reduce bid amount
+    ticker_usdt_modif = ticker_usdt.return_value
+    ticker_usdt_modif["ask"] = ticker_usdt_modif["ask"] * 1.004
+    mocker.patch(f"{EXMS}.fetch_ticker", return_value=ticker_usdt_modif)
+
+    # additional buy order
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 2
+    for o in trade.orders:
+        assert o.status == "closed"
+    assert pytest.approx(trade.stake_amount) == 120
+
+    # Open-rate averaged between 2.0 and 2.0 * 1.015
+    assert trade.open_rate >= 2.02
+    assert trade.open_rate < 2.02 * 1.015
+
+    # No action - profit raised above 1% (the bar set in the strategy).
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 2
+    assert pytest.approx(trade.stake_amount) == 120
+    assert trade.orders[1].amount == round(60 / ticker_usdt_modif["ask"], 4)
+
+    assert trade.amount == trade.orders[0].amount + trade.orders[1].amount
+    assert trade.nr_of_successful_entries == 2
+
+    # Buy
+    patch_get_signal(freqtrade, enter_long=False, exit_short=True)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert trade.is_open is False
+    # assert trade.orders[0].amount == 30
+    assert trade.orders[0].side == "sell"
+    assert trade.orders[1].amount == round(60 / ticker_usdt_modif["ask"], 4)
+    # Sold everything
+    assert trade.orders[-1].side == "buy"
+    assert trade.orders[2].amount == trade.amount
+
+    assert trade.nr_of_successful_entries == 2
+    assert trade.nr_of_successful_exits == 1
+
+
+@pytest.mark.parametrize("leverage", [1, 2])
+def test_dca_order_adjust(default_conf_usdt, ticker_usdt, leverage, fee, mocker) -> None:
+    default_conf_usdt["position_adjustment_enable"] = True
+    default_conf_usdt["trading_mode"] = "futures"
+    default_conf_usdt["margin_mode"] = "isolated"
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        amount_to_precision=lambda s, x, y: y,
+        price_to_precision=lambda s, x, y: y,
+    )
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=False)
+    mocker.patch(f"{EXMS}.get_max_leverage", return_value=10)
+    mocker.patch(f"{EXMS}.get_funding_fees", return_value=0)
+    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt", return_value=(0, 0))
+
+    patch_get_signal(freqtrade)
+    freqtrade.strategy.custom_entry_price = lambda **kwargs: ticker_usdt["ask"] * 0.96
+    freqtrade.strategy.leverage = MagicMock(return_value=leverage)
+    freqtrade.strategy.minimal_roi = {0: 0.2}
+
+    freqtrade.enter_positions()
+
+    assert len(Trade.get_trades().all()) == 1
+    trade: Trade = Trade.get_trades().first()
+    assert len(trade.orders) == 1
+    assert trade.has_open_orders
+    assert pytest.approx(trade.stake_amount) == 60
+    assert trade.open_rate == 1.96
+    assert trade.stop_loss_pct == -0.1
+    assert pytest.approx(trade.stop_loss) == trade.open_rate * (1 - 0.1 / leverage)
+    assert pytest.approx(trade.initial_stop_loss) == trade.open_rate * (1 - 0.1 / leverage)
+    assert trade.initial_stop_loss_pct == -0.1
+    assert trade.leverage == leverage
+    assert trade.stake_amount == 60
+    # No adjustment
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 1
+    assert trade.has_open_orders
+    assert pytest.approx(trade.stake_amount) == 60
+
+    # Cancel order and place new one
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1.99)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 2
+    assert trade.has_open_orders
+    # Open rate is not adjusted yet
+    assert trade.open_rate == 1.96
+    assert trade.stop_loss_pct == -0.1
+    assert pytest.approx(trade.stop_loss) == trade.open_rate * (1 - 0.1 / leverage)
+    assert pytest.approx(trade.initial_stop_loss) == trade.open_rate * (1 - 0.1 / leverage)
+    assert trade.stake_amount == 60
+    assert trade.initial_stop_loss_pct == -0.1
+
+    # Fill order
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=True)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 2
+    assert not trade.has_open_orders
+    # Open rate is not adjusted yet
+    assert trade.open_rate == 1.99
+    assert pytest.approx(trade.stake_amount) == 60
+    assert trade.stop_loss_pct == -0.1
+    assert pytest.approx(trade.stop_loss) == 1.99 * (1 - 0.1 / leverage)
+    assert pytest.approx(trade.initial_stop_loss) == 1.96 * (1 - 0.1 / leverage)
+    assert trade.initial_stop_loss_pct == -0.1
+    assert pytest.approx(trade.orders[-1].stake_amount) == trade.stake_amount
+
+    # 2nd order - not filling
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=120)
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=False)
+
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 3
+    assert trade.has_open_orders
+    assert trade.open_rate == 1.99
+    assert trade.orders[-1].price == 1.96
+    assert trade.orders[-1].cost == 120 * leverage
+    time.sleep(0.1)
+
+    # Replace new order with diff. order at a lower price
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1.95)
+    freqtrade.strategy.adjust_exit_price = MagicMock(side_effect=ValueError)
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=None)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 4
+    assert trade.has_open_orders
+    assert trade.open_rate == 1.99
+    assert pytest.approx(trade.stake_amount) == 60
+    assert trade.orders[-1].price == 1.95
+    assert pytest.approx(trade.orders[-1].cost) == 120 * leverage
+    assert freqtrade.strategy.adjust_entry_price.call_count == 1
+    assert freqtrade.strategy.adjust_exit_price.call_count == 0
+
+    # Fill DCA order
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=None)
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=True)
+    freqtrade.strategy.adjust_entry_price = MagicMock(side_effect=ValueError)
+
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 4
+    assert not trade.has_open_orders
+    assert pytest.approx(trade.open_rate) == 1.963153456
+    assert trade.orders[-1].price == 1.95
+    assert pytest.approx(trade.orders[-1].cost) == 120 * leverage
+    assert trade.orders[-1].status == "closed"
+
+    assert pytest.approx(trade.amount) == 91.689215 * leverage
+    # Check the 2 filled orders equal the above amount
+    assert pytest.approx(trade.orders[1].amount) == 30.150753768 * leverage
+    assert pytest.approx(trade.orders[-1].amount) == 61.538461232 * leverage
+
+    # Full exit
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=False)
+    freqtrade.strategy.custom_exit = MagicMock(return_value="Exit now")
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=2.02)
+    freqtrade.strategy.adjust_exit_price = MagicMock(side_effect=ValueError)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 5
+    assert trade.orders[-1].side == trade.exit_side
+    assert trade.orders[-1].status == "open"
+    assert trade.orders[-1].price == 2.02
+    assert pytest.approx(trade.amount) == 91.689215 * leverage
+    assert pytest.approx(trade.orders[-1].amount) == 91.689215 * leverage
+    assert freqtrade.strategy.adjust_entry_price.call_count == 0
+    assert freqtrade.strategy.adjust_exit_price.call_count == 0
+
+    # Process again, should not adjust price
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+
+    assert trade.orders[-2].status == "closed"
+    assert len(trade.orders) == 5
+    assert trade.orders[-1].side == trade.exit_side
+    assert trade.orders[-1].status == "open"
+    assert trade.orders[-1].price == 2.02
+    # Adjust entry price cannot be called - this is an exit order
+    assert freqtrade.strategy.adjust_entry_price.call_count == 0
+    assert freqtrade.strategy.adjust_exit_price.call_count == 1
+
+    freqtrade.strategy.adjust_exit_price = MagicMock(return_value=2.03)
+
+    # Process again, should adjust exit price
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+
+    assert trade.orders[-2].status == "canceled"
+    assert len(trade.orders) == 6
+    assert trade.orders[-1].side == trade.exit_side
+    assert trade.orders[-1].status == "open"
+    assert trade.orders[-1].price == 2.03
+    assert freqtrade.strategy.adjust_entry_price.call_count == 0
+    assert freqtrade.strategy.adjust_exit_price.call_count == 1
+
+
+@pytest.mark.parametrize("leverage", [1, 2])
+@pytest.mark.parametrize("is_short", [False, True])
+def test_dca_order_adjust_entry_replace_fails(
+    default_conf_usdt, ticker_usdt, fee, mocker, caplog, is_short, leverage
+) -> None:
+    spot = leverage == 1
+    if not spot:
+        default_conf_usdt["trading_mode"] = "futures"
+        default_conf_usdt["margin_mode"] = "isolated"
+    default_conf_usdt["position_adjustment_enable"] = True
+    default_conf_usdt["max_open_trades"] = 2
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        get_funding_fees=MagicMock(return_value=0),
+    )
+
+    # no order fills.
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", side_effect=[False, True])
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.enter_positions()
+
+    trades = Trade.session.scalars(
+        select(Trade)
+        .where(Order.ft_is_open.is_(True))
+        .where(Order.ft_order_side != "stoploss")
+        .where(Order.ft_trade_id == Trade.id)
+    ).all()
+    assert len(trades) == 1
+
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=False)
+
+    # Timeout to not interfere
+    freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False)
+
+    # Create DCA order for 2nd trade (so we have 2 open orders on 2 trades)
+    # this 2nd order won't fill.
+
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=(20, "PeNF"))
+
+    freqtrade.process()
+
+    assert freqtrade.strategy.adjust_trade_position.call_count == 2
+    trades = Trade.session.scalars(
+        select(Trade)
+        .where(Order.ft_is_open.is_(True))
+        .where(Order.ft_order_side != "stoploss")
+        .where(Order.ft_trade_id == Trade.id)
+    ).all()
+    assert len(trades) == 2
+
+    # We now have 2 orders open
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=2.05)
+    freqtrade.manage_open_orders()
+    trades = Trade.session.scalars(
+        select(Trade)
+        .where(Order.ft_is_open.is_(True))
+        .where(Order.ft_order_side != "stoploss")
+        .where(Order.ft_trade_id == Trade.id)
+    ).all()
+    assert len(trades) == 2
+    assert len(Order.get_open_orders()) == 2
+    # Entry adjustment is called
+    assert freqtrade.strategy.adjust_entry_price.call_count == 2
+
+    # Attempt order replacement - fails.
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1234)
+
+    entry_mock = mocker.patch(
+        "freqtrade.freqtradebot.FreqtradeBot.execute_entry", return_value=False
+    )
+    msg = r"Could not replace order for.*"
+    assert not log_has_re(msg, caplog)
+    freqtrade.manage_open_orders()
+
+    assert log_has_re(msg, caplog)
+    assert entry_mock.call_count == 2
+    assert len(Trade.get_trades().all()) == 1
+    assert len(Order.get_open_orders()) == 0
+
+
+@pytest.mark.parametrize("leverage", [1, 2])
+def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog, leverage) -> None:
+    default_conf_usdt["position_adjustment_enable"] = True
+    spot = leverage == 1
+    if not spot:
+        default_conf_usdt["trading_mode"] = "futures"
+        default_conf_usdt["margin_mode"] = "isolated"
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    assert freqtrade.trading_mode == TradingMode.FUTURES if not spot else TradingMode.SPOT
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        amount_to_precision=lambda s, x, y: y,
+        price_to_precision=lambda s, x, y: y,
+        get_min_pair_stake_amount=MagicMock(return_value=10),
+        get_funding_fees=MagicMock(return_value=0),
+    )
+    mocker.patch(f"{EXMS}.get_max_leverage", return_value=10)
+    starting_amount = freqtrade.wallets.get_total("USDT")
+    assert starting_amount == 1000
+
+    patch_get_signal(freqtrade)
+    freqtrade.strategy.leverage = MagicMock(return_value=leverage)
+    freqtrade.enter_positions()
+
+    assert len(Trade.get_trades().all()) == 1
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 1
+    assert pytest.approx(trade.stake_amount) == 60
+    assert trade.leverage == leverage
+    assert pytest.approx(trade.amount) == 30.0 * leverage
+    assert trade.open_rate == 2.0
+    assert pytest.approx(freqtrade.wallets.get_free("USDT")) == starting_amount - 60
+    if spot:
+        assert pytest.approx(freqtrade.wallets.get_total("USDT")) == starting_amount - 60
+    else:
+        assert freqtrade.wallets.get_total("USDT") == starting_amount
+
+    # Too small size
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-59)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 1
+    assert pytest.approx(trade.stake_amount) == 60
+    assert pytest.approx(trade.amount) == 30.0 * leverage
+    assert log_has_re(
+        r"Remaining amount of \d\.\d+.* would be smaller than the minimum of 10.", caplog
+    )
+
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=(-20, "PES"))
+
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 2
+    assert trade.orders[-1].ft_order_side == "sell"
+    assert trade.orders[-1].ft_order_tag == "PES"
+    assert pytest.approx(trade.stake_amount) == 40
+    assert pytest.approx(trade.amount) == 20 * leverage
+    assert trade.open_rate == 2.0
+    assert trade.is_open
+    assert trade.realized_profit > 0.098 * leverage
+    expected_profit = starting_amount - 40 + trade.realized_profit
+    assert pytest.approx(freqtrade.wallets.get_free("USDT")) == expected_profit
+
+    if spot:
+        assert pytest.approx(freqtrade.wallets.get_total("USDT")) == expected_profit
+    else:
+        # total won't change in futures mode, only free / used will.
+        assert freqtrade.wallets.get_total("USDT") == starting_amount + trade.realized_profit
+    caplog.clear()
+
+    # Sell more than what we got (we got ~20 coins left)
+    # Doesn't exit, as the amount is too high.
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-50)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 2
+
+    # Amount too low...
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-(trade.stake_amount * 0.99))
+    freqtrade.process()
+
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 2
+
+    # Amount exactly comes out as exactly 0
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-trade.stake_amount)
+    freqtrade.process()
+
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 3
+
+    assert trade.orders[-1].ft_order_side == "sell"
+    assert pytest.approx(trade.stake_amount) == 40
+    assert trade.is_open is False
+
+    # use amount that would trunc to 0.0 once selling
+    mocker.patch(f"{EXMS}.amount_to_contract_precision", lambda s, p, v: round(v, 1))
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-0.01)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 3
+    assert trade.orders[-1].ft_order_side == "sell"
+    assert pytest.approx(trade.stake_amount) == 40
+    assert trade.is_open is False
+    assert log_has_re(
+        "Wanted to exit of -0.01 amount, but exit amount is now 0.0 due to exchange limits "
+        "- not exiting.",
+        caplog,
+    )
+    expected_profit = starting_amount - 60 + trade.realized_profit
+    assert pytest.approx(freqtrade.wallets.get_free("USDT")) == expected_profit
+    if spot:
+        assert pytest.approx(freqtrade.wallets.get_total("USDT")) == expected_profit
+    else:
+        # total won't change in futures mode, only free / used will.
+        assert freqtrade.wallets.get_total("USDT") == starting_amount + trade.realized_profit
+
+
+@pytest.mark.parametrize("leverage", [1, 2])
+@pytest.mark.parametrize("is_short", [False, True])
+def test_dca_handle_similar_open_order(
+    default_conf_usdt, ticker_usdt, is_short, leverage, fee, mocker, caplog
+) -> None:
+    default_conf_usdt["position_adjustment_enable"] = True
+    default_conf_usdt["trading_mode"] = "futures"
+    default_conf_usdt["margin_mode"] = "isolated"
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        amount_to_precision=lambda s, x, y: y,
+        price_to_precision=lambda s, x, y: y,
+    )
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=False)
+    mocker.patch(f"{EXMS}.get_max_leverage", return_value=10)
+    mocker.patch(f"{EXMS}.get_funding_fees", return_value=0)
+    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt", return_value=(0, 0))
+
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.custom_entry_price = lambda **kwargs: ticker_usdt["ask"] * 0.96
+    freqtrade.strategy.leverage = MagicMock(return_value=leverage)
+    freqtrade.strategy.custom_exit = MagicMock(return_value=False)
+    freqtrade.strategy.minimal_roi = {0: 0.2}
+
+    # Create trade and initial entry order
+    freqtrade.enter_positions()
+
+    assert len(Trade.get_trades().all()) == 1
+    trade: Trade = Trade.get_trades().first()
+    assert len(trade.orders) == 1
+    assert trade.orders[-1].side == trade.entry_side
+    assert trade.orders[-1].status == "open"
+
+    assert trade.has_open_orders
+    # Process - shouldn't do anything
+    freqtrade.process()
+    # Doesn't try to exit, as we're not in a position yet
+    assert freqtrade.strategy.custom_exit.call_count == 0
+
+    # Adjust with new price, cancel initial entry order and place new one
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1.99)
+    freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    freqtrade.strategy.ft_check_timed_out = MagicMock(return_value=False)
+
+    assert len(trade.orders) == 2
+    assert len(trade.open_orders) == 1
+
+    # Adjust with new amount, should cancel and replace existing order
+    freqtrade.strategy.adjust_trade_position = MagicMock(
+        return_value=21
+    )  # -(trade.stake_amount * 0.5)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+
+    assert len(trade.orders) == 3
+    assert len(trade.open_orders) == 1
+
+    # Fill entry order
+    assert freqtrade.strategy.custom_exit.call_count == 0
+
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=True)
+    freqtrade.process()
+
+    trade = Trade.get_trades().first()
+    assert trade.amount > 0
+
+    assert freqtrade.strategy.custom_exit.call_count == 1
+    freqtrade.strategy.custom_exit.reset_mock()
+
+    # Should Create a new exit order
+    freqtrade.exchange.amount_to_contract_precision = MagicMock(return_value=2)
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-2)
+
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=False)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+
+    assert trade.orders[-2].status == "closed"
+    assert trade.orders[-1].status == "open"
+    assert trade.orders[-1].side == trade.exit_side
+    assert len(trade.orders) == 5
+    assert len(trade.open_orders) == 1
+    assert freqtrade.strategy.custom_exit.call_count == 1
+    freqtrade.strategy.custom_exit.reset_mock()
+
+    # Adjust with new exit amount, should cancel and replace existing exit order
+    freqtrade.exchange.amount_to_contract_precision = MagicMock(return_value=3)
+    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-3)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    # Even with open order, trying to exit...
+    assert freqtrade.strategy.custom_exit.call_count == 1
+    freqtrade.strategy.custom_exit.reset_mock()
+
+    assert trade.orders[-2].status == "canceled"
+    assert len(trade.orders) == 6
+    assert len(trade.open_orders) == 1
+
+    # Adjust with new exit price, should cancel and replace existing exit order
+    freqtrade.strategy.custom_exit_price = MagicMock(return_value=1.95)
+    freqtrade.process()
+    # Even with open order, trying to exit...
+    assert freqtrade.strategy.custom_exit.call_count == 1
+    freqtrade.strategy.custom_exit.reset_mock()
+
+    trade = Trade.get_trades().first()
+
+    assert trade.orders[-2].status == "canceled"
+    assert len(trade.orders) == 7
+    assert len(trade.open_orders) == 1
+    similar_msg = r"A similar open order was found for.*"
+
+    assert not log_has_re(similar_msg, caplog)
+
+    # Adjust with same params, should keep existing order as price and amount are similar
+    freqtrade.strategy.custom_exit_price = MagicMock(return_value=1.95)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert log_has_re(similar_msg, caplog)
+
+    assert len(trade.orders) == 7
+    assert len(trade.open_orders) == 1
diff --git a/tests/freqtradebot/test_stoploss_on_exchange.py b/tests/freqtradebot/test_stoploss_on_exchange.py
new file mode 100644
index 000000000..c5dc01b7e
--- /dev/null
+++ b/tests/freqtradebot/test_stoploss_on_exchange.py
@@ -0,0 +1,1344 @@
+from copy import deepcopy
+from datetime import timedelta
+from unittest.mock import ANY, MagicMock
+
+import pytest
+from sqlalchemy import select
+
+from freqtrade.enums import ExitCheckTuple, ExitType, RPCMessageType
+from freqtrade.exceptions import ExchangeError, InsufficientFundsError, InvalidOrderException
+from freqtrade.freqtradebot import FreqtradeBot
+from freqtrade.persistence import Order, Trade
+from freqtrade.persistence.models import PairLock
+from freqtrade.util.datetime_helpers import dt_now
+from tests.conftest import (
+    EXMS,
+    get_patched_freqtradebot,
+    log_has,
+    log_has_re,
+    patch_edge,
+    patch_exchange,
+    patch_get_signal,
+    patch_whitelist,
+)
+from tests.conftest_trades import entry_side, exit_side
+from tests.freqtradebot.test_freqtradebot import patch_RPCManager
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_add_stoploss_on_exchange(mocker, default_conf_usdt, limit_order, is_short, fee) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(return_value=limit_order[entry_side(is_short)]),
+        get_fee=fee,
+    )
+    order = limit_order[entry_side(is_short)]
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_trade", MagicMock(return_value=True))
+    mocker.patch(f"{EXMS}.fetch_order", return_value=order)
+    mocker.patch(f"{EXMS}.get_trades_for_order", return_value=[])
+
+    stoploss = MagicMock(return_value={"id": 13434334})
+    mocker.patch(f"{EXMS}.create_stoploss", stoploss)
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+    trades = [trade]
+
+    freqtrade.exit_positions(trades)
+    assert trade.has_open_sl_orders is True
+    assert stoploss.call_count == 1
+    assert trade.is_open is True
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_stoploss_on_exchange(
+    mocker, default_conf_usdt, fee, caplog, is_short, limit_order
+) -> None:
+    stop_order_dict = {"id": "13434334"}
+    stoploss = MagicMock(return_value=stop_order_dict)
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(
+            side_effect=[
+                enter_order,
+                exit_order,
+            ]
+        ),
+        get_fee=fee,
+        create_stoploss=stoploss,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    # First case: when stoploss is not yet set but the order is open
+    # should get the stoploss order id immediately
+    # and should return false as no trade actually happened
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.is_short == is_short
+    assert trade.is_open
+    assert trade.has_open_sl_orders is False
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert stoploss.call_count == 1
+    assert trade.open_sl_orders[-1].order_id == "13434334"
+
+    # Second case: when stoploss is set but it is not yet hit
+    # should do nothing and return false
+    trade.is_open = True
+
+    hanging_stoploss_order = MagicMock(return_value={"id": "13434334", "status": "open"})
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", hanging_stoploss_order)
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    hanging_stoploss_order.assert_called_once_with("13434334", trade.pair)
+    assert len(trade.open_sl_orders) == 1
+    assert trade.open_sl_orders[-1].order_id == "13434334"
+
+    # Third case: when stoploss was set but it was canceled for some reason
+    # should set a stoploss immediately and return False
+    caplog.clear()
+    trade.is_open = True
+
+    canceled_stoploss_order = MagicMock(return_value={"id": "13434334", "status": "canceled"})
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", canceled_stoploss_order)
+    stoploss.reset_mock()
+    amount_before = trade.amount
+
+    stop_order_dict.update({"id": "103_1"})
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert stoploss.call_count == 1
+    assert len(trade.open_sl_orders) == 1
+    assert trade.open_sl_orders[-1].order_id == "103_1"
+    assert trade.amount == amount_before
+
+    # Fourth case: when stoploss is set and it is hit
+    # should return true as a trade actually happened
+    caplog.clear()
+    stop_order_dict.update({"id": "103_1"})
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+
+    stoploss_order_hit = MagicMock(
+        return_value={
+            "id": "103_1",
+            "status": "closed",
+            "type": "stop_loss_limit",
+            "price": 3,
+            "average": 2,
+            "filled": enter_order["amount"],
+            "remaining": 0,
+            "amount": enter_order["amount"],
+        }
+    )
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", stoploss_order_hit)
+    freqtrade.strategy.order_filled = MagicMock(return_value=None)
+    assert freqtrade.handle_stoploss_on_exchange(trade) is True
+    assert log_has_re(r"STOP_LOSS_LIMIT is hit for Trade\(id=1, .*\)\.", caplog)
+    assert len(trade.open_sl_orders) == 0
+    assert trade.is_open is False
+    assert freqtrade.strategy.order_filled.call_count == 1
+    caplog.clear()
+
+    mocker.patch(f"{EXMS}.create_stoploss", side_effect=ExchangeError())
+    trade.is_open = True
+    freqtrade.handle_stoploss_on_exchange(trade)
+    assert log_has("Unable to place a stoploss order on exchange.", caplog)
+    assert len(trade.open_sl_orders) == 0
+
+    # Fifth case: fetch_order returns InvalidOrder
+    # It should try to add stoploss order
+    stop_order_dict.update({"id": "105"})
+    stoploss.reset_mock()
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", side_effect=InvalidOrderException())
+    mocker.patch(f"{EXMS}.create_stoploss", stoploss)
+    freqtrade.handle_stoploss_on_exchange(trade)
+    assert len(trade.open_sl_orders) == 1
+    assert stoploss.call_count == 1
+
+    # Sixth case: Closed Trade
+    # Should not create new order
+    trade.is_open = False
+    trade.open_sl_orders[-1].ft_is_open = False
+    stoploss.reset_mock()
+    mocker.patch(f"{EXMS}.fetch_order")
+    mocker.patch(f"{EXMS}.create_stoploss", stoploss)
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert trade.has_open_sl_orders is False
+    assert stoploss.call_count == 0
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_stoploss_on_exchange_emergency(
+    mocker, default_conf_usdt, fee, is_short, limit_order
+) -> None:
+    stop_order_dict = {"id": "13434334"}
+    stoploss = MagicMock(return_value=stop_order_dict)
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(
+            side_effect=[
+                enter_order,
+                exit_order,
+            ]
+        ),
+        get_fee=fee,
+        create_stoploss=stoploss,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.is_short == is_short
+    assert trade.is_open
+    assert trade.has_open_sl_orders is False
+
+    # emergency exit triggered
+    # Trailing stop should not act anymore
+    stoploss_order_cancelled = MagicMock(
+        side_effect=[
+            {
+                "id": "107",
+                "status": "canceled",
+                "type": "stop_loss_limit",
+                "price": 3,
+                "average": 2,
+                "amount": enter_order["amount"],
+                "filled": 0,
+                "remaining": enter_order["amount"],
+                "info": {"stopPrice": 22},
+            }
+        ]
+    )
+    trade.stoploss_last_update = dt_now() - timedelta(hours=1)
+    trade.stop_loss = 24
+    trade.exit_reason = None
+    trade.orders.append(
+        Order(
+            ft_order_side="stoploss",
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id="107",
+            status="open",
+        )
+    )
+    freqtrade.config["trailing_stop"] = True
+    stoploss = MagicMock(side_effect=InvalidOrderException())
+    assert trade.has_open_sl_orders is True
+    Trade.commit()
+    mocker.patch(f"{EXMS}.cancel_stoploss_order_with_result", side_effect=InvalidOrderException())
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", stoploss_order_cancelled)
+    mocker.patch(f"{EXMS}.create_stoploss", stoploss)
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert trade.has_open_sl_orders is False
+    assert trade.is_open is False
+    assert trade.exit_reason == str(ExitType.EMERGENCY_EXIT)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_stoploss_on_exchange_partial(
+    mocker, default_conf_usdt, fee, is_short, limit_order
+) -> None:
+    stop_order_dict = {"id": "101", "status": "open"}
+    stoploss = MagicMock(return_value=stop_order_dict)
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(
+            side_effect=[
+                enter_order,
+                exit_order,
+            ]
+        ),
+        get_fee=fee,
+        create_stoploss=stoploss,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert stoploss.call_count == 1
+    assert trade.has_open_sl_orders is True
+    assert trade.open_sl_orders[-1].order_id == "101"
+    assert trade.amount == 30
+    stop_order_dict.update({"id": "102"})
+    # Stoploss on exchange is cancelled on exchange, but filled partially.
+    # Must update trade amount to guarantee successful exit.
+    stoploss_order_hit = MagicMock(
+        return_value={
+            "id": "101",
+            "status": "canceled",
+            "type": "stop_loss_limit",
+            "price": 3,
+            "average": 2,
+            "filled": trade.amount / 2,
+            "remaining": trade.amount / 2,
+            "amount": enter_order["amount"],
+        }
+    )
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", stoploss_order_hit)
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    # Stoploss filled partially ...
+    assert trade.amount == 15
+
+    assert trade.open_sl_orders[-1].order_id == "102"
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_stoploss_on_exchange_partial_cancel_here(
+    mocker, default_conf_usdt, fee, is_short, limit_order, caplog, time_machine
+) -> None:
+    stop_order_dict = {"id": "101", "status": "open"}
+    time_machine.move_to(dt_now())
+    default_conf_usdt["trailing_stop"] = True
+    stoploss = MagicMock(return_value=stop_order_dict)
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(
+            side_effect=[
+                enter_order,
+                exit_order,
+            ]
+        ),
+        get_fee=fee,
+        create_stoploss=stoploss,
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert stoploss.call_count == 1
+    assert trade.has_open_sl_orders is True
+    assert trade.open_sl_orders[-1].order_id == "101"
+    assert trade.amount == 30
+    stop_order_dict.update({"id": "102"})
+    # Stoploss on exchange is open.
+    # Freqtrade cancels the stop - but cancel returns a partial filled order.
+    stoploss_order_hit = MagicMock(
+        return_value={
+            "id": "101",
+            "status": "open",
+            "type": "stop_loss_limit",
+            "price": 3,
+            "average": 2,
+            "filled": 0,
+            "remaining": trade.amount,
+            "amount": enter_order["amount"],
+        }
+    )
+    stoploss_order_cancel = MagicMock(
+        return_value={
+            "id": "101",
+            "status": "canceled",
+            "type": "stop_loss_limit",
+            "price": 3,
+            "average": 2,
+            "filled": trade.amount / 2,
+            "remaining": trade.amount / 2,
+            "amount": enter_order["amount"],
+        }
+    )
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", stoploss_order_hit)
+    mocker.patch(f"{EXMS}.cancel_stoploss_order_with_result", stoploss_order_cancel)
+    time_machine.shift(timedelta(minutes=15))
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    # Canceled Stoploss filled partially ...
+    assert log_has_re("Cancelling current stoploss on exchange.*", caplog)
+
+    assert trade.has_open_sl_orders is True
+    assert trade.open_sl_orders[-1].order_id == "102"
+    assert trade.amount == 15
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_sle_cancel_cant_recreate(
+    mocker, default_conf_usdt, fee, caplog, is_short, limit_order
+) -> None:
+    # Sixth case: stoploss order was cancelled but couldn't create new one
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(
+            side_effect=[
+                enter_order,
+                exit_order,
+            ]
+        ),
+        get_fee=fee,
+    )
+    mocker.patch.multiple(
+        EXMS,
+        fetch_stoploss_order=MagicMock(return_value={"status": "canceled", "id": "100"}),
+        create_stoploss=MagicMock(side_effect=ExchangeError()),
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.is_short == is_short
+    trade.is_open = True
+    trade.orders.append(
+        Order(
+            ft_order_side="stoploss",
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id="100",
+            status="open",
+        )
+    )
+    assert trade
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert log_has_re(r"All Stoploss orders are cancelled, but unable to recreate one\.", caplog)
+    assert trade.has_open_sl_orders is False
+    assert trade.is_open is True
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_create_stoploss_order_invalid_order(
+    mocker, default_conf_usdt, caplog, fee, is_short, limit_order
+):
+    open_order = limit_order[entry_side(is_short)]
+    order = limit_order[exit_side(is_short)]
+    rpc_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    create_order_mock = MagicMock(
+        side_effect=[
+            open_order,
+            order,
+        ]
+    )
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=create_order_mock,
+        get_fee=fee,
+    )
+    mocker.patch.multiple(
+        EXMS,
+        fetch_order=MagicMock(return_value={"status": "canceled"}),
+        create_stoploss=MagicMock(side_effect=InvalidOrderException()),
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    caplog.clear()
+    rpc_mock.reset_mock()
+    freqtrade.create_stoploss_order(trade, 200)
+    assert trade.has_open_sl_orders is False
+    assert trade.exit_reason == ExitType.EMERGENCY_EXIT.value
+    assert log_has("Unable to place a stoploss order on exchange. ", caplog)
+    assert log_has("Exiting the trade forcefully", caplog)
+
+    # Should call a market sell
+    assert create_order_mock.call_count == 2
+    assert create_order_mock.call_args[1]["ordertype"] == "market"
+    assert create_order_mock.call_args[1]["pair"] == trade.pair
+    assert create_order_mock.call_args[1]["amount"] == trade.amount
+
+    # Rpc is sending first buy, then sell
+    assert rpc_mock.call_count == 2
+    assert rpc_mock.call_args_list[0][0][0]["exit_reason"] == ExitType.EMERGENCY_EXIT.value
+    assert rpc_mock.call_args_list[0][0][0]["order_type"] == "market"
+    assert rpc_mock.call_args_list[0][0][0]["type"] == "exit"
+    assert rpc_mock.call_args_list[1][0][0]["type"] == "exit_fill"
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_create_stoploss_order_insufficient_funds(
+    mocker, default_conf_usdt, caplog, fee, limit_order, is_short
+):
+    exit_order = limit_order[exit_side(is_short)]["id"]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    mock_insuf = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_insufficient_funds")
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(
+            side_effect=[
+                limit_order[entry_side(is_short)],
+                exit_order,
+            ]
+        ),
+        get_fee=fee,
+        fetch_order=MagicMock(return_value={"status": "canceled"}),
+    )
+    mocker.patch.multiple(
+        EXMS,
+        create_stoploss=MagicMock(side_effect=InsufficientFundsError()),
+    )
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    caplog.clear()
+    freqtrade.create_stoploss_order(trade, 200)
+    # stoploss_orderid was empty before
+    assert trade.has_open_sl_orders is False
+    assert mock_insuf.call_count == 1
+    mock_insuf.reset_mock()
+
+    freqtrade.create_stoploss_order(trade, 200)
+    # No change to stoploss-orderid
+    assert trade.has_open_sl_orders is False
+    assert mock_insuf.call_count == 1
+
+
+@pytest.mark.parametrize(
+    "is_short,bid,ask,stop_price,hang_price",
+    [
+        (False, [4.38, 4.16], [4.4, 4.17], ["2.0805", 4.4 * 0.95], 3),
+        (True, [1.09, 1.21], [1.1, 1.22], ["2.321", 1.09 * 1.05], 1.5),
+    ],
+)
+@pytest.mark.usefixtures("init_persistence")
+def test_handle_stoploss_on_exchange_trailing(
+    mocker,
+    default_conf_usdt,
+    fee,
+    is_short,
+    bid,
+    ask,
+    limit_order,
+    stop_price,
+    hang_price,
+    time_machine,
+) -> None:
+    # When trailing stoploss is set
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    stoploss = MagicMock(return_value={"id": "13434334", "status": "open"})
+    start_dt = dt_now()
+    time_machine.move_to(start_dt, tick=False)
+    patch_RPCManager(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(
+            return_value={
+                "bid": 2.19,
+                "ask": 2.2,
+                "last": 2.19,
+            }
+        ),
+        create_order=MagicMock(
+            side_effect=[
+                enter_order,
+                exit_order,
+            ]
+        ),
+        get_fee=fee,
+    )
+    mocker.patch.multiple(
+        EXMS,
+        create_stoploss=stoploss,
+        stoploss_adjust=MagicMock(return_value=True),
+    )
+
+    # enabling TSL
+    default_conf_usdt["trailing_stop"] = True
+
+    # disabling ROI
+    default_conf_usdt["minimal_roi"]["0"] = 999999999
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    # enabling stoploss on exchange
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+
+    # setting stoploss
+    freqtrade.strategy.stoploss = 0.05 if is_short else -0.05
+
+    # setting stoploss_on_exchange_interval to 60 seconds
+    freqtrade.strategy.order_types["stoploss_on_exchange_interval"] = 60
+
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+    assert trade.has_open_sl_orders is False
+    trade.stoploss_last_update = dt_now() - timedelta(minutes=20)
+    trade.orders.append(
+        Order(
+            ft_order_side="stoploss",
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id="100",
+            order_date=dt_now() - timedelta(minutes=20),
+        )
+    )
+
+    stoploss_order_hanging = {
+        "id": "100",
+        "status": "open",
+        "type": "stop_loss_limit",
+        "price": hang_price,
+        "average": 2,
+        "fee": {},
+        "amount": 0,
+        "info": {"stopPrice": stop_price[0]},
+    }
+    stoploss_order_cancel = deepcopy(stoploss_order_hanging)
+    stoploss_order_cancel["status"] = "canceled"
+
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", return_value=stoploss_order_hanging)
+    mocker.patch(f"{EXMS}.cancel_stoploss_order", return_value=stoploss_order_cancel)
+
+    # stoploss initially at 5%
+    assert freqtrade.handle_trade(trade) is False
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+
+    assert len(trade.open_sl_orders) == 1
+
+    assert trade.open_sl_orders[-1].order_id == "13434334"
+
+    # price jumped 2x
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        MagicMock(
+            return_value={
+                "bid": bid[0],
+                "ask": ask[0],
+                "last": bid[0],
+            }
+        ),
+    )
+
+    cancel_order_mock = MagicMock(
+        return_value={"id": "13434334", "status": "canceled", "fee": {}, "amount": trade.amount}
+    )
+    stoploss_order_mock = MagicMock(return_value={"id": "so1", "status": "open"})
+    mocker.patch(f"{EXMS}.fetch_stoploss_order")
+    mocker.patch(f"{EXMS}.cancel_stoploss_order", cancel_order_mock)
+    mocker.patch(f"{EXMS}.create_stoploss", stoploss_order_mock)
+
+    # stoploss should not be updated as the interval is 60 seconds
+    assert freqtrade.handle_trade(trade) is False
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert len(trade.open_sl_orders) == 1
+    cancel_order_mock.assert_not_called()
+    stoploss_order_mock.assert_not_called()
+
+    # Move time by 10s ... so stoploss order should be replaced.
+    time_machine.move_to(start_dt + timedelta(minutes=10), tick=False)
+
+    assert freqtrade.handle_trade(trade) is False
+    assert trade.stop_loss == stop_price[1]
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+
+    cancel_order_mock.assert_called_once_with("13434334", "ETH/USDT")
+    stoploss_order_mock.assert_called_once_with(
+        amount=30,
+        pair="ETH/USDT",
+        order_types=freqtrade.strategy.order_types,
+        stop_price=stop_price[1],
+        side=exit_side(is_short),
+        leverage=1.0,
+    )
+
+    # price fell below stoploss, so dry-run sells trade.
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        MagicMock(
+            return_value={
+                "bid": bid[1],
+                "ask": ask[1],
+                "last": bid[1],
+            }
+        ),
+    )
+    mocker.patch(
+        f"{EXMS}.cancel_stoploss_order_with_result",
+        return_value={"id": "so1", "status": "canceled"},
+    )
+    assert len(trade.open_sl_orders) == 1
+    assert trade.open_sl_orders[-1].order_id == "so1"
+
+    assert freqtrade.handle_trade(trade) is True
+    assert trade.is_open is False
+    assert trade.has_open_sl_orders is False
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_stoploss_on_exchange_trailing_error(
+    mocker, default_conf_usdt, fee, caplog, limit_order, is_short, time_machine
+) -> None:
+    time_machine.move_to(dt_now() - timedelta(minutes=601))
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    # When trailing stoploss is set
+    stoploss = MagicMock(return_value={"id": "13434334", "status": "open"})
+    patch_exchange(mocker)
+
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(
+            side_effect=[
+                {"id": enter_order["id"]},
+                {"id": exit_order["id"]},
+            ]
+        ),
+        get_fee=fee,
+        create_stoploss=stoploss,
+        stoploss_adjust=MagicMock(return_value=True),
+    )
+
+    # enabling TSL
+    default_conf_usdt["trailing_stop"] = True
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    # enabling stoploss on exchange
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+
+    # setting stoploss
+    freqtrade.strategy.stoploss = 0.05 if is_short else -0.05
+
+    # setting stoploss_on_exchange_interval to 60 seconds
+    freqtrade.strategy.order_types["stoploss_on_exchange_interval"] = 60
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+    trade.stop_loss = 0.2
+
+    stoploss_order_hanging = {
+        "id": "abcd",
+        "status": "open",
+        "type": "stop_loss_limit",
+        "price": 3,
+        "average": 2,
+        "info": {"stopPrice": "0.1"},
+    }
+    trade.orders.append(
+        Order(
+            ft_order_side="stoploss",
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=3,
+            order_id="abcd",
+            order_date=dt_now(),
+        )
+    )
+    mocker.patch(f"{EXMS}.cancel_stoploss_order", side_effect=InvalidOrderException())
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", return_value=stoploss_order_hanging)
+    time_machine.shift(timedelta(minutes=50))
+    freqtrade.handle_trailing_stoploss_on_exchange(trade, stoploss_order_hanging)
+    assert log_has_re(r"Could not cancel stoploss order abcd for pair ETH/USDT.*", caplog)
+
+    # Still try to create order
+    assert stoploss.call_count == 1
+    # TODO: Is this actually correct ? This will create a new order every time,
+    assert len(trade.open_sl_orders) == 2
+
+    # Fail creating stoploss order
+    caplog.clear()
+    cancel_mock = mocker.patch(f"{EXMS}.cancel_stoploss_order")
+    mocker.patch(f"{EXMS}.create_stoploss", side_effect=ExchangeError())
+    time_machine.shift(timedelta(minutes=50))
+    freqtrade.handle_trailing_stoploss_on_exchange(trade, stoploss_order_hanging)
+    assert cancel_mock.call_count == 2
+    assert log_has_re(r"Could not create trailing stoploss order for pair ETH/USDT\..*", caplog)
+
+
+def test_stoploss_on_exchange_price_rounding(
+    mocker, default_conf_usdt, fee, open_trade_usdt
+) -> None:
+    patch_RPCManager(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        get_fee=fee,
+    )
+    price_mock = MagicMock(side_effect=lambda p, s, **kwargs: int(s))
+    stoploss_mock = MagicMock(return_value={"id": "13434334"})
+    adjust_mock = MagicMock(return_value=False)
+    mocker.patch.multiple(
+        EXMS,
+        create_stoploss=stoploss_mock,
+        stoploss_adjust=adjust_mock,
+        price_to_precision=price_mock,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    open_trade_usdt.stop_loss = 222.55
+
+    freqtrade.handle_trailing_stoploss_on_exchange(open_trade_usdt, {})
+    assert price_mock.call_count == 1
+    assert adjust_mock.call_count == 1
+    assert adjust_mock.call_args_list[0][0][0] == 222
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.usefixtures("init_persistence")
+def test_handle_stoploss_on_exchange_custom_stop(
+    mocker, default_conf_usdt, fee, is_short, limit_order
+) -> None:
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    # When trailing stoploss is set
+    stoploss = MagicMock(return_value={"id": 13434334, "status": "open"})
+    patch_RPCManager(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 1.9, "ask": 2.2, "last": 1.9}),
+        create_order=MagicMock(
+            side_effect=[
+                enter_order,
+                exit_order,
+            ]
+        ),
+        get_fee=fee,
+        is_cancel_order_result_suitable=MagicMock(return_value=True),
+    )
+    mocker.patch.multiple(
+        EXMS,
+        create_stoploss=stoploss,
+        stoploss_adjust=MagicMock(return_value=True),
+    )
+
+    # enabling TSL
+    default_conf_usdt["use_custom_stoploss"] = True
+
+    # disabling ROI
+    default_conf_usdt["minimal_roi"]["0"] = 999999999
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    # enabling stoploss on exchange
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+
+    # setting stoploss
+    freqtrade.strategy.custom_stoploss = lambda *args, **kwargs: -0.04
+
+    # setting stoploss_on_exchange_interval to 60 seconds
+    freqtrade.strategy.order_types["stoploss_on_exchange_interval"] = 60
+
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+    trade.orders.append(
+        Order(
+            ft_order_side="stoploss",
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_date=dt_now() - timedelta(minutes=601),
+            order_id="100",
+        )
+    )
+    Trade.commit()
+    slo = {
+        "id": "100",
+        "status": "open",
+        "type": "stop_loss_limit",
+        "price": 3,
+        "average": 2,
+        "info": {"stopPrice": "2.0805"},
+    }
+    slo_canceled = deepcopy(slo)
+    slo_canceled.update({"status": "canceled"})
+
+    def fetch_stoploss_order_mock(order_id, *args, **kwargs):
+        x = deepcopy(slo)
+        x["id"] = order_id
+        return x
+
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", MagicMock(fetch_stoploss_order_mock))
+    mocker.patch(f"{EXMS}.cancel_stoploss_order", return_value=slo_canceled)
+
+    assert freqtrade.handle_trade(trade) is False
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+
+    # price jumped 2x
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        MagicMock(
+            return_value={
+                "bid": 4.38 if not is_short else 1.9 / 2,
+                "ask": 4.4 if not is_short else 2.2 / 2,
+                "last": 4.38 if not is_short else 1.9 / 2,
+            }
+        ),
+    )
+
+    cancel_order_mock = MagicMock()
+    stoploss_order_mock = MagicMock(return_value={"id": "so1", "status": "open"})
+    mocker.patch(f"{EXMS}.cancel_stoploss_order", cancel_order_mock)
+    mocker.patch(f"{EXMS}.create_stoploss", stoploss_order_mock)
+
+    # stoploss should not be updated as the interval is 60 seconds
+    assert freqtrade.handle_trade(trade) is False
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    cancel_order_mock.assert_not_called()
+    stoploss_order_mock.assert_not_called()
+
+    assert freqtrade.handle_trade(trade) is False
+    assert trade.stop_loss == 4.4 * 0.96 if not is_short else 1.1
+    assert trade.stop_loss_pct == -0.04 if not is_short else 0.04
+
+    # setting stoploss_on_exchange_interval to 0 seconds
+    freqtrade.strategy.order_types["stoploss_on_exchange_interval"] = 0
+    cancel_order_mock.assert_not_called()
+    stoploss_order_mock.assert_not_called()
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+
+    cancel_order_mock.assert_called_once_with("13434334", "ETH/USDT")
+    # Long uses modified ask - offset, short modified bid + offset
+    stoploss_order_mock.assert_called_once_with(
+        amount=pytest.approx(trade.amount),
+        pair="ETH/USDT",
+        order_types=freqtrade.strategy.order_types,
+        stop_price=4.4 * 0.96 if not is_short else 0.95 * 1.04,
+        side=exit_side(is_short),
+        leverage=1.0,
+    )
+
+    # price fell below stoploss, so dry-run sells trade.
+    mocker.patch(
+        f"{EXMS}.fetch_ticker", MagicMock(return_value={"bid": 4.17, "ask": 4.19, "last": 4.17})
+    )
+    assert freqtrade.handle_trade(trade) is True
+
+
+def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, limit_order) -> None:
+    enter_order = limit_order["buy"]
+    exit_order = limit_order["sell"]
+    enter_order["average"] = 2.19
+    # When trailing stoploss is set
+    stoploss = MagicMock(return_value={"id": "13434334", "status": "open"})
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    patch_edge(mocker)
+    edge_conf["max_open_trades"] = float("inf")
+    edge_conf["dry_run_wallet"] = 999.9
+    edge_conf["exchange"]["name"] = "binance"
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={"bid": 2.19, "ask": 2.2, "last": 2.19}),
+        create_order=MagicMock(
+            side_effect=[
+                enter_order,
+                exit_order,
+            ]
+        ),
+        get_fee=fee,
+        create_stoploss=stoploss,
+    )
+
+    # enabling TSL
+    edge_conf["trailing_stop"] = True
+    edge_conf["trailing_stop_positive"] = 0.01
+    edge_conf["trailing_stop_positive_offset"] = 0.011
+
+    # disabling ROI
+    edge_conf["minimal_roi"]["0"] = 999999999
+
+    freqtrade = FreqtradeBot(edge_conf)
+
+    # enabling stoploss on exchange
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+
+    # setting stoploss
+    freqtrade.strategy.stoploss = -0.02
+
+    # setting stoploss_on_exchange_interval to 0 seconds
+    freqtrade.strategy.order_types["stoploss_on_exchange_interval"] = 0
+
+    patch_get_signal(freqtrade)
+
+    freqtrade.active_pair_whitelist = freqtrade.edge.adjust(freqtrade.active_pair_whitelist)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_open = True
+
+    trade.stoploss_last_update = dt_now()
+    trade.orders.append(
+        Order(
+            ft_order_side="stoploss",
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id="100",
+        )
+    )
+
+    stoploss_order_hanging = MagicMock(
+        return_value={
+            "id": "100",
+            "status": "open",
+            "type": "stop_loss_limit",
+            "price": 3,
+            "average": 2,
+            "stopPrice": "2.178",
+        }
+    )
+
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", stoploss_order_hanging)
+
+    # stoploss initially at 20% as edge dictated it.
+    assert freqtrade.handle_trade(trade) is False
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert pytest.approx(trade.stop_loss) == 1.76
+
+    cancel_order_mock = MagicMock()
+    stoploss_order_mock = MagicMock()
+    mocker.patch(f"{EXMS}.cancel_stoploss_order", cancel_order_mock)
+    mocker.patch(f"{EXMS}.create_stoploss", stoploss_order_mock)
+
+    # price goes down 5%
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        MagicMock(return_value={"bid": 2.19 * 0.95, "ask": 2.2 * 0.95, "last": 2.19 * 0.95}),
+    )
+    assert freqtrade.handle_trade(trade) is False
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+
+    # stoploss should remain the same
+    assert pytest.approx(trade.stop_loss) == 1.76
+
+    # stoploss on exchange should not be canceled
+    cancel_order_mock.assert_not_called()
+
+    # price jumped 2x
+    mocker.patch(
+        f"{EXMS}.fetch_ticker", MagicMock(return_value={"bid": 4.38, "ask": 4.4, "last": 4.38})
+    )
+
+    assert freqtrade.handle_trade(trade) is False
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+
+    # stoploss should be set to 1% as trailing is on
+    assert trade.stop_loss == 4.4 * 0.99
+    cancel_order_mock.assert_called_once_with("100", "NEO/BTC")
+    stoploss_order_mock.assert_called_once_with(
+        amount=30,
+        pair="NEO/BTC",
+        order_types=freqtrade.strategy.order_types,
+        stop_price=4.4 * 0.99,
+        side="sell",
+        leverage=1.0,
+    )
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_execute_trade_exit_down_stoploss_on_exchange_dry_run(
+    default_conf_usdt,
+    ticker_usdt,
+    fee,
+    is_short,
+    ticker_usdt_sell_down,
+    ticker_usdt_sell_up,
+    mocker,
+) -> None:
+    rpc_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(side_effect=[True, False]),
+    )
+    patch_whitelist(mocker, default_conf_usdt)
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    # Create some test data
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.is_short == is_short
+    assert trade
+
+    # Decrease the price and sell it
+    mocker.patch.multiple(
+        EXMS, fetch_ticker=ticker_usdt_sell_up if is_short else ticker_usdt_sell_down
+    )
+
+    default_conf_usdt["dry_run"] = True
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+    # Setting trade stoploss to 0.01
+
+    trade.stop_loss = 2.0 * 1.01 if is_short else 2.0 * 0.99
+    freqtrade.execute_trade_exit(
+        trade=trade, limit=trade.stop_loss, exit_check=ExitCheckTuple(exit_type=ExitType.STOP_LOSS)
+    )
+
+    # assert rpc_mock.call_count == 2
+    last_msg = rpc_mock.call_args_list[-1][0][0]
+
+    assert {
+        "type": RPCMessageType.EXIT,
+        "trade_id": 1,
+        "exchange": "Binance",
+        "pair": "ETH/USDT",
+        "direction": "Short" if trade.is_short else "Long",
+        "leverage": 1.0,
+        "gain": "loss",
+        "limit": 2.02 if is_short else 1.98,
+        "order_rate": 2.02 if is_short else 1.98,
+        "amount": pytest.approx(29.70297029 if is_short else 30.0),
+        "order_type": "limit",
+        "buy_tag": None,
+        "enter_tag": None,
+        "open_rate": 2.02 if is_short else 2.0,
+        "current_rate": 2.2 if is_short else 2.0,
+        "profit_amount": -0.3 if is_short else -0.8985,
+        "profit_ratio": -0.00501253 if is_short else -0.01493766,
+        "stake_currency": "USDT",
+        "quote_currency": "USDT",
+        "fiat_currency": "USD",
+        "base_currency": "ETH",
+        "exit_reason": ExitType.STOP_LOSS.value,
+        "open_date": ANY,
+        "close_date": ANY,
+        "close_rate": ANY,
+        "sub_trade": False,
+        "cumulative_profit": 0.0,
+        "stake_amount": pytest.approx(60),
+        "is_final_exit": False,
+        "final_profit_ratio": ANY,
+    } == last_msg
+
+
+def test_execute_trade_exit_sloe_cancel_exception(
+    mocker, default_conf_usdt, ticker_usdt, fee, caplog
+) -> None:
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mocker.patch(f"{EXMS}.cancel_stoploss_order", side_effect=InvalidOrderException())
+    mocker.patch("freqtrade.wallets.Wallets.get_free", MagicMock(return_value=300))
+    create_order_mock = MagicMock(
+        side_effect=[
+            {"id": "12345554"},
+            {"id": "12345555"},
+        ]
+    )
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        create_order=create_order_mock,
+    )
+
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+    patch_get_signal(freqtrade)
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    PairLock.session = MagicMock()
+
+    freqtrade.config["dry_run"] = False
+    trade.orders.append(
+        Order(
+            ft_order_side="stoploss",
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id="abcd",
+            status="open",
+        )
+    )
+
+    freqtrade.execute_trade_exit(
+        trade=trade, limit=1234, exit_check=ExitCheckTuple(exit_type=ExitType.STOP_LOSS)
+    )
+    assert create_order_mock.call_count == 2
+    assert log_has("Could not cancel stoploss order abcd for pair ETH/USDT", caplog)
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_execute_trade_exit_with_stoploss_on_exchange(
+    default_conf_usdt, ticker_usdt, fee, ticker_usdt_sell_up, is_short, mocker
+) -> None:
+    default_conf_usdt["exchange"]["name"] = "binance"
+    rpc_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    stoploss = MagicMock(return_value={"id": 123, "status": "open", "info": {"foo": "bar"}})
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_order_fee")
+
+    cancel_order = MagicMock(return_value=True)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        amount_to_precision=lambda s, x, y: y,
+        price_to_precision=lambda s, x, y: y,
+        create_stoploss=stoploss,
+        cancel_stoploss_order=cancel_order,
+        _dry_is_price_crossed=MagicMock(side_effect=[True, False]),
+    )
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    # Create some test data
+    freqtrade.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+    trades = [trade]
+
+    freqtrade.manage_open_orders()
+    freqtrade.exit_positions(trades)
+
+    # Increase the price and sell it
+    mocker.patch.multiple(EXMS, fetch_ticker=ticker_usdt_sell_up)
+
+    freqtrade.execute_trade_exit(
+        trade=trade,
+        limit=ticker_usdt_sell_up()["ask" if is_short else "bid"],
+        exit_check=ExitCheckTuple(exit_type=ExitType.STOP_LOSS),
+    )
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    assert trade
+    assert cancel_order.call_count == 1
+    assert rpc_mock.call_count == 4
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_may_execute_trade_exit_after_stoploss_on_exchange_hit(
+    default_conf_usdt, ticker_usdt, fee, mocker, is_short
+) -> None:
+    default_conf_usdt["exchange"]["name"] = "binance"
+    rpc_mock = patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+        amount_to_precision=lambda s, x, y: y,
+        price_to_precision=lambda s, x, y: y,
+        _dry_is_price_crossed=MagicMock(side_effect=[False, True]),
+    )
+
+    stoploss = MagicMock(return_value={"id": 123, "info": {"foo": "bar"}})
+
+    mocker.patch(f"{EXMS}.create_stoploss", stoploss)
+
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    freqtrade.strategy.order_types["stoploss_on_exchange"] = True
+    patch_get_signal(freqtrade, enter_long=not is_short, enter_short=is_short)
+
+    # Create some test data
+    freqtrade.enter_positions()
+    freqtrade.manage_open_orders()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trades = [trade]
+    assert trade.has_open_sl_orders is False
+
+    freqtrade.exit_positions(trades)
+    assert trade
+    assert trade.has_open_sl_orders is True
+    assert not trade.has_open_orders
+
+    # Assuming stoploss on exchange is hit
+    # trade should be sold at the price of stoploss, with exit_reason STOPLOSS_ON_EXCHANGE
+    stoploss_executed = MagicMock(
+        return_value={
+            "id": "123",
+            "timestamp": 1542707426845,
+            "datetime": "2018-11-20T09:50:26.845Z",
+            "lastTradeTimestamp": None,
+            "symbol": "BTC/USDT",
+            "type": "stop_loss_limit",
+            "side": "buy" if is_short else "sell",
+            "price": 1.08801,
+            "amount": trade.amount,
+            "cost": 1.08801 * trade.amount,
+            "average": 1.08801,
+            "filled": trade.amount,
+            "remaining": 0.0,
+            "status": "closed",
+            "fee": None,
+            "trades": None,
+        }
+    )
+    mocker.patch(f"{EXMS}.fetch_stoploss_order", stoploss_executed)
+
+    freqtrade.exit_positions(trades)
+    assert trade.has_open_sl_orders is False
+    assert trade.is_open is False
+    assert trade.exit_reason == ExitType.STOPLOSS_ON_EXCHANGE.value
+    assert rpc_mock.call_count == 4
+    assert rpc_mock.call_args_list[1][0][0]["type"] == RPCMessageType.ENTRY
+    assert rpc_mock.call_args_list[1][0][0]["amount"] > 20
+    assert rpc_mock.call_args_list[2][0][0]["type"] == RPCMessageType.ENTRY_FILL
+    assert rpc_mock.call_args_list[3][0][0]["type"] == RPCMessageType.EXIT_FILL
diff --git a/tests/freqtradebot/test_worker.py b/tests/freqtradebot/test_worker.py
new file mode 100644
index 000000000..6423e63ee
--- /dev/null
+++ b/tests/freqtradebot/test_worker.py
@@ -0,0 +1,259 @@
+import logging
+import time
+from datetime import timedelta
+from unittest.mock import MagicMock, PropertyMock
+
+import pytest
+import time_machine
+
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import State
+from freqtrade.worker import Worker
+from tests.conftest import EXMS, get_patched_worker, log_has, log_has_re
+
+
+def test_worker_state(mocker, default_conf, markets) -> None:
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    worker = get_patched_worker(mocker, default_conf)
+    assert worker.freqtrade.state is State.RUNNING
+
+    default_conf.pop("initial_state")
+    worker = Worker(args=None, config=default_conf)
+    assert worker.freqtrade.state is State.STOPPED
+
+
+def test_worker_running(mocker, default_conf, caplog) -> None:
+    mock_throttle = MagicMock()
+    mocker.patch("freqtrade.worker.Worker._throttle", mock_throttle)
+    mocker.patch("freqtrade.persistence.Trade.stoploss_reinitialization", MagicMock())
+
+    worker = get_patched_worker(mocker, default_conf)
+
+    state = worker._worker(old_state=None)
+    assert state is State.RUNNING
+    assert log_has("Changing state to: RUNNING", caplog)
+    assert mock_throttle.call_count == 1
+    # Check strategy is loaded, and received a dataprovider object
+    assert worker.freqtrade.strategy
+    assert worker.freqtrade.strategy.dp
+    assert isinstance(worker.freqtrade.strategy.dp, DataProvider)
+
+
+def test_worker_paused(mocker, default_conf, caplog) -> None:
+    mock_throttle = MagicMock()
+    mocker.patch("freqtrade.worker.Worker._throttle", mock_throttle)
+    mocker.patch("freqtrade.persistence.Trade.stoploss_reinitialization", MagicMock())
+
+    worker = get_patched_worker(mocker, default_conf)
+
+    worker.freqtrade.state = State.PAUSED
+    state = worker._worker(old_state=State.RUNNING)
+
+    assert state is State.PAUSED
+    assert log_has("Changing state from RUNNING to: PAUSED", caplog)
+    assert mock_throttle.call_count == 1
+    # Check strategy is loaded, and received a dataprovider object
+    assert worker.freqtrade.strategy
+    assert worker.freqtrade.strategy.dp
+    assert isinstance(worker.freqtrade.strategy.dp, DataProvider)
+
+
+def test_worker_stopped(mocker, default_conf, caplog) -> None:
+    mock_throttle = MagicMock()
+    mocker.patch("freqtrade.worker.Worker._throttle", mock_throttle)
+
+    worker = get_patched_worker(mocker, default_conf)
+    worker.freqtrade.state = State.STOPPED
+    state = worker._worker(old_state=State.RUNNING)
+    assert state is State.STOPPED
+    assert log_has("Changing state from RUNNING to: STOPPED", caplog)
+    assert mock_throttle.call_count == 1
+
+
+@pytest.mark.parametrize(
+    "old_state,target_state,startup_call,log_fragment",
+    [
+        (State.STOPPED, State.PAUSED, True, "Changing state from STOPPED to: PAUSED"),
+        (State.RUNNING, State.PAUSED, False, "Changing state from RUNNING to: PAUSED"),
+        (State.PAUSED, State.RUNNING, False, "Changing state from PAUSED to: RUNNING"),
+        (State.PAUSED, State.STOPPED, False, "Changing state from PAUSED to: STOPPED"),
+        (State.RELOAD_CONFIG, State.RUNNING, True, "Changing state from RELOAD_CONFIG to: RUNNING"),
+        (
+            State.RELOAD_CONFIG,
+            State.STOPPED,
+            False,
+            "Changing state from RELOAD_CONFIG to: STOPPED",
+        ),
+    ],
+)
+def test_worker_lifecycle(
+    mocker,
+    default_conf,
+    caplog,
+    old_state,
+    target_state,
+    startup_call,
+    log_fragment,
+):
+    mock_throttle = mocker.MagicMock()
+    mocker.patch("freqtrade.worker.Worker._throttle", mock_throttle)
+    mocker.patch("freqtrade.persistence.Trade.stoploss_reinitialization")
+    startup = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.startup")
+
+    worker = get_patched_worker(mocker, default_conf)
+    worker.freqtrade.state = target_state
+
+    new_state = worker._worker(old_state=old_state)
+
+    assert new_state is target_state
+    assert log_has(log_fragment, caplog)
+    assert mock_throttle.call_count == 1
+    assert startup.call_count == (1 if startup_call else 0)
+
+    # For any state where the strategy should be initialized
+    if target_state in (State.RUNNING, State.PAUSED):
+        assert worker.freqtrade.strategy
+        assert isinstance(worker.freqtrade.strategy.dp, DataProvider)
+    else:
+        assert new_state is State.STOPPED
+
+
+def test_throttle(mocker, default_conf, caplog) -> None:
+    def throttled_func():
+        return 42
+
+    caplog.set_level(logging.DEBUG)
+    worker = get_patched_worker(mocker, default_conf)
+
+    start = time.time()
+    result = worker._throttle(throttled_func, throttle_secs=0.1)
+    end = time.time()
+
+    assert result == 42
+    assert 0.3 > end - start > 0.1
+    assert log_has_re(r"Throttling with 'throttled_func\(\)': sleep for \d\.\d{2} s.*", caplog)
+
+    result = worker._throttle(throttled_func, throttle_secs=-1)
+    assert result == 42
+
+
+def test_throttle_sleep_time(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    worker = get_patched_worker(mocker, default_conf)
+    sleep_mock = mocker.patch("freqtrade.worker.Worker._sleep")
+    with time_machine.travel("2022-09-01 05:00:00 +00:00") as t:
+
+        def throttled_func(x=1):
+            t.shift(timedelta(seconds=x))
+            return 42
+
+        assert worker._throttle(throttled_func, throttle_secs=5) == 42
+        # This moves the clock by 1 second
+        assert sleep_mock.call_count == 1
+        assert 3.8 < sleep_mock.call_args[0][0] < 4.1
+
+        sleep_mock.reset_mock()
+        # This moves the clock by 1 second
+        assert worker._throttle(throttled_func, throttle_secs=10) == 42
+        assert sleep_mock.call_count == 1
+        assert 8.8 < sleep_mock.call_args[0][0] < 9.1
+
+        sleep_mock.reset_mock()
+        # This moves the clock by 5 second, so we only throttle by 5s
+        assert worker._throttle(throttled_func, throttle_secs=10, x=5) == 42
+        assert sleep_mock.call_count == 1
+        assert 4.8 < sleep_mock.call_args[0][0] < 5.1
+
+        t.move_to("2022-09-01 05:01:00 +00:00")
+        sleep_mock.reset_mock()
+        # Throttle for more than 5m (1 timeframe)
+        assert worker._throttle(throttled_func, throttle_secs=400, x=5) == 42
+        assert sleep_mock.call_count == 1
+        assert 394.8 < sleep_mock.call_args[0][0] < 395.1
+
+        t.move_to("2022-09-01 05:01:00 +00:00")
+
+        sleep_mock.reset_mock()
+        # Throttle for more than 5m (1 timeframe)
+        assert (
+            worker._throttle(
+                throttled_func, throttle_secs=400, timeframe="5m", timeframe_offset=0.4, x=5
+            )
+            == 42
+        )
+        assert sleep_mock.call_count == 1
+        # 300 (5m) - 60 (1m - see set time above) - 5 (duration of throttled_func) = 235
+        assert 235.2 < sleep_mock.call_args[0][0] < 235.6
+
+        t.move_to("2022-09-01 05:04:51 +00:00")
+        sleep_mock.reset_mock()
+        # Offset of 5s, so we hit the sweet-spot between "candle" and "candle offset"
+        # Which should not get a throttle iteration to avoid late candle fetching
+        assert (
+            worker._throttle(
+                throttled_func, throttle_secs=10, timeframe="5m", timeframe_offset=5, x=1.2
+            )
+            == 42
+        )
+        assert sleep_mock.call_count == 1
+        # Time is slightly bigger than throttle secs due to the high timeframe offset.
+        assert 11.1 < sleep_mock.call_args[0][0] < 13.2
+
+
+def test_throttle_with_assets(mocker, default_conf) -> None:
+    def throttled_func(nb_assets=-1):
+        return nb_assets
+
+    worker = get_patched_worker(mocker, default_conf)
+
+    result = worker._throttle(throttled_func, throttle_secs=0.1, nb_assets=666)
+    assert result == 666
+
+    result = worker._throttle(throttled_func, throttle_secs=0.1)
+    assert result == -1
+
+
+def test_worker_heartbeat_running(default_conf, mocker, caplog):
+    message = r"Bot heartbeat\. PID=.*state='RUNNING'"
+
+    mock_throttle = MagicMock()
+    mocker.patch("freqtrade.worker.Worker._throttle", mock_throttle)
+    worker = get_patched_worker(mocker, default_conf)
+
+    worker.freqtrade.state = State.RUNNING
+    worker._worker(old_state=State.STOPPED)
+    assert log_has_re(message, caplog)
+
+    caplog.clear()
+    # Message is not shown before interval is up
+    worker._worker(old_state=State.RUNNING)
+    assert not log_has_re(message, caplog)
+
+    caplog.clear()
+    # Set clock - 70 seconds
+    worker._heartbeat_msg -= 70
+    worker._worker(old_state=State.RUNNING)
+    assert log_has_re(message, caplog)
+
+
+def test_worker_heartbeat_stopped(default_conf, mocker, caplog):
+    message = r"Bot heartbeat\. PID=.*state='STOPPED'"
+
+    mock_throttle = MagicMock()
+    mocker.patch("freqtrade.worker.Worker._throttle", mock_throttle)
+    worker = get_patched_worker(mocker, default_conf)
+
+    worker.freqtrade.state = State.STOPPED
+    worker._worker(old_state=State.RUNNING)
+    assert log_has_re(message, caplog)
+
+    caplog.clear()
+    # Message is not shown before interval is up
+    worker._worker(old_state=State.STOPPED)
+    assert not log_has_re(message, caplog)
+
+    caplog.clear()
+    # Set clock - 70 seconds
+    worker._heartbeat_msg -= 70
+    worker._worker(old_state=State.STOPPED)
+    assert log_has_re(message, caplog)
diff --git a/tests/leverage/test_candletype.py b/tests/leverage/test_candletype.py
new file mode 100644
index 000000000..8a2c4ba3e
--- /dev/null
+++ b/tests/leverage/test_candletype.py
@@ -0,0 +1,33 @@
+import pytest
+
+from freqtrade.enums import CandleType
+
+
+@pytest.mark.parametrize(
+    "candle_type,expected",
+    [
+        ("", CandleType.SPOT),
+        ("spot", CandleType.SPOT),
+        (CandleType.SPOT, CandleType.SPOT),
+        (CandleType.FUTURES, CandleType.FUTURES),
+        (CandleType.INDEX, CandleType.INDEX),
+        (CandleType.MARK, CandleType.MARK),
+        ("futures", CandleType.FUTURES),
+        ("mark", CandleType.MARK),
+        ("premiumIndex", CandleType.PREMIUMINDEX),
+    ],
+)
+def test_CandleType_from_string(candle_type, expected):
+    assert CandleType.from_string(candle_type) == expected
+
+
+@pytest.mark.parametrize(
+    "candle_type,expected",
+    [
+        ("futures", CandleType.FUTURES),
+        ("spot", CandleType.SPOT),
+        ("margin", CandleType.SPOT),
+    ],
+)
+def test_CandleType_get_default(candle_type, expected):
+    assert CandleType.get_default(candle_type) == expected
diff --git a/tests/leverage/test_interest.py b/tests/leverage/test_interest.py
new file mode 100644
index 000000000..6df94bca4
--- /dev/null
+++ b/tests/leverage/test_interest.py
@@ -0,0 +1,49 @@
+import pytest
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.leverage import interest
+from freqtrade.util import FtPrecise
+
+
+ten_mins = FtPrecise(1 / 6)
+five_hours = FtPrecise(5.0)
+twentyfive_hours = FtPrecise(25.0)
+
+
+@pytest.mark.parametrize(
+    "exchange,interest_rate,hours,expected",
+    [
+        ("binance", 0.0005, ten_mins, 0.00125),
+        ("binance", 0.00025, ten_mins, 0.000625),
+        ("binance", 0.00025, five_hours, 0.003125),
+        ("binance", 0.00025, twentyfive_hours, 0.015625),
+        # Kraken
+        ("kraken", 0.0005, ten_mins, 0.06),
+        ("kraken", 0.00025, ten_mins, 0.03),
+        ("kraken", 0.00025, five_hours, 0.045),
+        ("kraken", 0.00025, twentyfive_hours, 0.12),
+    ],
+)
+def test_interest(exchange, interest_rate, hours, expected):
+    borrowed = FtPrecise(60.0)
+
+    assert (
+        pytest.approx(
+            float(
+                interest(
+                    exchange_name=exchange,
+                    borrowed=borrowed,
+                    rate=FtPrecise(interest_rate),
+                    hours=hours,
+                )
+            )
+        )
+        == expected
+    )
+
+
+def test_interest_exception():
+    with pytest.raises(OperationalException, match=r"Leverage not available on .* with freqtrade"):
+        interest(
+            exchange_name="bitmex", borrowed=FtPrecise(60.0), rate=FtPrecise(0.0005), hours=ten_mins
+        )
diff --git a/tests/leverage/test_update_liquidation_price.py b/tests/leverage/test_update_liquidation_price.py
new file mode 100644
index 000000000..68c35509f
--- /dev/null
+++ b/tests/leverage/test_update_liquidation_price.py
@@ -0,0 +1,57 @@
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.enums.marginmode import MarginMode
+from freqtrade.leverage.liquidation_price import update_liquidation_prices
+
+
+@pytest.mark.parametrize("dry_run", [False, True])
+@pytest.mark.parametrize("margin_mode", [MarginMode.CROSS, MarginMode.ISOLATED])
+def test_update_liquidation_prices(mocker, margin_mode, dry_run):
+    # Heavily mocked test - Only testing the logic of the function
+    # update liquidation price for trade in isolated mode
+    # update liquidation price for all trades in cross mode
+    exchange = MagicMock()
+    exchange.margin_mode = margin_mode
+    wallets = MagicMock()
+    trade_mock = MagicMock()
+
+    mocker.patch("freqtrade.persistence.Trade.get_open_trades", return_value=[trade_mock])
+
+    update_liquidation_prices(
+        trade=trade_mock,
+        exchange=exchange,
+        wallets=wallets,
+        stake_currency="USDT",
+        dry_run=dry_run,
+    )
+
+    assert trade_mock.set_liquidation_price.call_count == 1
+
+    assert wallets.get_collateral.call_count == (
+        0 if margin_mode == MarginMode.ISOLATED or not dry_run else 1
+    )
+
+    # Test with multiple trades
+    trade_mock.reset_mock()
+    trade_mock_2 = MagicMock()
+
+    mocker.patch(
+        "freqtrade.persistence.Trade.get_open_trades", return_value=[trade_mock, trade_mock_2]
+    )
+
+    update_liquidation_prices(
+        trade=trade_mock,
+        exchange=exchange,
+        wallets=wallets,
+        stake_currency="USDT",
+        dry_run=dry_run,
+    )
+    # Trade2 is only updated in cross mode
+    assert trade_mock_2.set_liquidation_price.call_count == (
+        1 if margin_mode == MarginMode.CROSS else 0
+    )
+    assert trade_mock.set_liquidation_price.call_count == 1
+
+    assert wallets.call_count == 0 if not dry_run else 1
diff --git a/tests/optimize/__init__.py b/tests/optimize/__init__.py
new file mode 100644
index 000000000..38d86439c
--- /dev/null
+++ b/tests/optimize/__init__.py
@@ -0,0 +1,86 @@
+from datetime import timedelta
+from typing import NamedTuple
+
+from pandas import DataFrame
+
+from freqtrade.enums import ExitType
+from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.util.datetime_helpers import dt_utc
+
+
+tests_start_time = dt_utc(2018, 10, 3)
+tests_timeframe = "1h"
+
+
+class BTrade(NamedTuple):
+    """
+    Minimalistic Trade result used for functional backtesting
+    """
+
+    exit_reason: ExitType
+    open_tick: int
+    close_tick: int
+    enter_tag: str | None = None
+    is_short: bool = False
+
+
+class BTContainer(NamedTuple):
+    """
+    Minimal BacktestContainer defining Backtest inputs and results.
+    """
+
+    data: list[list[float]]
+    stop_loss: float
+    roi: dict[str, float]
+    trades: list[BTrade]
+    profit_perc: float
+    trailing_stop: bool = False
+    trailing_only_offset_is_reached: bool = False
+    trailing_stop_positive: float | None = None
+    trailing_stop_positive_offset: float = 0.0
+    use_exit_signal: bool = False
+    use_custom_stoploss: bool = False
+    custom_entry_price: float | None = None
+    custom_exit_price: float | None = None
+    leverage: float = 1.0
+    timeout: int | None = None
+    adjust_entry_price: float | None = None
+    adjust_exit_price: float | None = None
+    adjust_trade_position: list[float] | None = None
+
+
+def _get_frame_time_from_offset(offset):
+    minutes = offset * timeframe_to_minutes(tests_timeframe)
+    return tests_start_time + timedelta(minutes=minutes)
+
+
+def _build_backtest_dataframe(data):
+    columns = [
+        "date",
+        "open",
+        "high",
+        "low",
+        "close",
+        "volume",
+        "enter_long",
+        "exit_long",
+        "enter_short",
+        "exit_short",
+    ]
+    if len(data[0]) == 8:
+        # No short columns
+        data = [[*d, 0, 0] for d in data]
+    columns = [*columns, "enter_tag"] if len(data[0]) == 11 else columns
+
+    frame = DataFrame.from_records(data, columns=columns)
+    frame["date"] = frame["date"].apply(_get_frame_time_from_offset)
+    # Ensure floats are in place
+    for column in ["open", "high", "low", "close", "volume"]:
+        frame[column] = frame[column].astype("float64")
+
+    # Ensure all candles make kindof sense
+    assert all(frame["low"] <= frame["close"])
+    assert all(frame["low"] <= frame["open"])
+    assert all(frame["high"] >= frame["close"])
+    assert all(frame["high"] >= frame["open"])
+    return frame
diff --git a/tests/optimize/conftest.py b/tests/optimize/conftest.py
new file mode 100644
index 000000000..b2833ef64
--- /dev/null
+++ b/tests/optimize/conftest.py
@@ -0,0 +1,70 @@
+from copy import deepcopy
+from datetime import datetime
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from freqtrade.enums import ExitType, RunMode
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.optimize.hyperopt import Hyperopt
+from tests.conftest import patch_exchange
+
+
+@pytest.fixture(scope="function")
+def hyperopt_conf(default_conf):
+    hyperconf = deepcopy(default_conf)
+    hyperconf.update(
+        {
+            "datadir": Path(default_conf["datadir"]),
+            "runmode": RunMode.HYPEROPT,
+            "strategy": "HyperoptableStrategy",
+            "hyperopt_loss": "ShortTradeDurHyperOptLoss",
+            "hyperopt_path": str(Path(__file__).parent / "hyperopts"),
+            "epochs": 1,
+            "timerange": None,
+            "spaces": ["default"],
+            "hyperopt_jobs": 1,
+            "hyperopt_min_trades": 1,
+        }
+    )
+    return hyperconf
+
+
+@pytest.fixture(autouse=True)
+def backtesting_cleanup():
+    yield None
+
+    Backtesting.cleanup()
+
+
+@pytest.fixture(scope="function")
+def hyperopt(hyperopt_conf, mocker):
+    patch_exchange(mocker)
+    return Hyperopt(hyperopt_conf)
+
+
+@pytest.fixture(scope="function")
+def hyperopt_results():
+    return pd.DataFrame(
+        {
+            "pair": ["ETH/USDT", "ETH/USDT", "ETH/USDT", "ETH/USDT"],
+            "profit_ratio": [-0.1, 0.2, -0.12, 0.3],
+            "profit_abs": [-0.2, 0.4, -0.21, 0.6],
+            "trade_duration": [10, 30, 10, 10],
+            "amount": [0.1, 0.1, 0.1, 0.1],
+            "exit_reason": [ExitType.STOP_LOSS, ExitType.ROI, ExitType.STOP_LOSS, ExitType.ROI],
+            "open_date": [
+                datetime(2019, 1, 1, 9, 15, 0),
+                datetime(2019, 1, 2, 8, 55, 0),
+                datetime(2019, 1, 3, 9, 15, 0),
+                datetime(2019, 1, 4, 9, 15, 0),
+            ],
+            "close_date": [
+                datetime(2019, 1, 1, 9, 25, 0),
+                datetime(2019, 1, 2, 9, 25, 0),
+                datetime(2019, 1, 3, 9, 25, 0),
+                datetime(2019, 1, 4, 9, 25, 0),
+            ],
+        }
+    )
diff --git a/tests/optimize/test_backtest_detail.py b/tests/optimize/test_backtest_detail.py
new file mode 100644
index 000000000..2e5ab41de
--- /dev/null
+++ b/tests/optimize/test_backtest_detail.py
@@ -0,0 +1,1406 @@
+# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, C0330, unused-argument
+import logging
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.data.history import get_timerange
+from freqtrade.enums import ExitType, TradingMode
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.persistence.trade_model import LocalTrade
+from tests.conftest import EXMS, patch_exchange
+from tests.optimize import (
+    BTContainer,
+    BTrade,
+    _build_backtest_dataframe,
+    _get_frame_time_from_offset,
+    tests_timeframe,
+)
+
+
+# Test 0: Sell with signal sell in candle 3
+# Test with Stop-loss at 1%
+tc0 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4986, 4986, 6172, 0, 0],  # exit with stoploss hit
+        [3, 5010, 5010, 4980, 5010, 6172, 0, 1],
+        [4, 5010, 5011, 4977, 4995, 6172, 0, 0],
+        [5, 4995, 4995, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 1},
+    profit_perc=0.002,
+    use_exit_signal=True,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4)],
+)
+
+# Test 1: Stop-Loss Triggered 1% loss
+# Test with Stop-loss at 1%
+tc1 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4600, 4600, 6172, 0, 0],  # exit with stoploss hit
+        [3, 4975, 5000, 4975, 4977, 6172, 0, 0],
+        [4, 4977, 4995, 4977, 4995, 6172, 0, 0],
+        [5, 4995, 4995, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 1},
+    profit_perc=-0.01,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=2)],
+)
+
+
+# Test 2: Minus 4% Low, minus 1% close
+# Test with Stop-Loss at 3%
+tc2 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4962, 4975, 6172, 0, 0],
+        [3, 4975, 5000, 4800, 4962, 6172, 0, 0],  # exit with stoploss hit
+        [4, 4962, 4987, 4937, 4950, 6172, 0, 0],
+        [5, 4950, 4975, 4925, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.03,
+    roi={"0": 1},
+    profit_perc=-0.03,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=3)],
+)
+
+
+# Test 3: Multiple trades.
+#         Candle drops 4%, Recovers 1%.
+#         Entry Criteria Met
+#         Candle drops 20%
+#  Trade-A: Stop-Loss Triggered 2% Loss
+#           Trade-B: Stop-Loss Triggered 2% Loss
+tc3 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4800, 4975, 6172, 0, 0],  # exit with stoploss hit
+        [3, 4975, 5000, 4950, 4962, 6172, 1, 0],
+        [4, 4975, 5000, 4950, 4962, 6172, 0, 0],  # enter trade 2 (signal on last candle)
+        [5, 4962, 4987, 4000, 4000, 6172, 0, 0],  # exit with stoploss hit
+        [6, 4950, 4975, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.02,
+    roi={"0": 1},
+    profit_perc=-0.04,
+    trades=[
+        BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=2),
+        BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=4, close_tick=5),
+    ],
+)
+
+# Test 4: Minus 3% / recovery +15%
+# Candle Data for test 3 - Candle drops 3% Closed 15% up
+# Test with Stop-loss at 2% ROI 6%
+# Stop-Loss Triggered 2% Loss
+tc4 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5750, 4850, 5750, 6172, 0, 0],  # Exit with stoploss hit
+        [3, 4975, 5000, 4950, 4962, 6172, 0, 0],
+        [4, 4962, 4987, 4937, 4950, 6172, 0, 0],
+        [5, 4950, 4975, 4925, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.02,
+    roi={"0": 0.06},
+    profit_perc=-0.02,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=2)],
+)
+
+# Test 5: Drops 0.5% Closes +20%, ROI triggers 3% Gain
+# stop-loss: 1%, ROI: 3%
+tc5 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4980, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4980, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5025, 4975, 4987, 6172, 0, 0],
+        [3, 4975, 6000, 4975, 6000, 6172, 0, 0],  # ROI
+        [4, 4962, 4987, 4962, 4972, 6172, 0, 0],
+        [5, 4950, 4975, 4925, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 0.03},
+    profit_perc=0.03,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)],
+)
+
+# Test 6: Drops 3% / Recovers 6% Positive / Closes 1% positive, Stop-Loss triggers 2% Loss
+# stop-loss: 2% ROI: 5%
+tc6 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5300, 4850, 5050, 6172, 0, 0],  # Exit with stoploss
+        [3, 4975, 5000, 4950, 4962, 6172, 0, 0],
+        [4, 4962, 4987, 4950, 4950, 6172, 0, 0],
+        [5, 4950, 4975, 4925, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.02,
+    roi={"0": 0.05},
+    profit_perc=-0.02,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=2)],
+)
+
+# Test 7: 6% Positive / 1% Negative / Close 1% Positive, ROI Triggers 3% Gain
+# stop-loss: 2% ROI: 3%
+tc7 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],
+        [2, 4987, 5300, 4950, 5050, 6172, 0, 0],
+        [3, 4975, 5000, 4950, 4962, 6172, 0, 0],
+        [4, 4962, 4987, 4950, 4950, 6172, 0, 0],
+        [5, 4950, 4975, 4925, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.02,
+    roi={"0": 0.03},
+    profit_perc=0.03,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=2)],
+)
+
+
+# Test 8: trailing_stop should raise so candle 3 causes a stoploss.
+# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted in candle 2
+tc8 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5050, 4950, 5000, 6172, 0, 0],
+        [2, 5000, 5250, 4750, 4850, 6172, 0, 0],
+        [3, 4850, 5050, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=-0.055,
+    trailing_stop=True,
+    trades=[BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)],
+)
+
+
+# Test 9: trailing_stop should raise - high and low in same candle.
+# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted in candle 3
+tc9 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5050, 4950, 5000, 6172, 0, 0],
+        [2, 5000, 5050, 4950, 5000, 6172, 0, 0],
+        [3, 5000, 5200, 4550, 4850, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=-0.064,
+    trailing_stop=True,
+    trades=[BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)],
+)
+
+# Test 10: trailing_stop should raise so candle 3 causes a stoploss
+# without applying trailing_stop_positive since stoploss_offset is at 10%.
+# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
+tc10 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4950, 5100, 6172, 0, 0],
+        [2, 5100, 5251, 5100, 5100, 6172, 0, 0],
+        [3, 4850, 5050, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=-0.1,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.10,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=4)],
+)
+
+# Test 11: trailing_stop should raise so candle 3 causes a stoploss
+# applying a positive trailing stop of 3% since stop_positive_offset is reached.
+# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
+tc11 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4950, 5100, 6172, 0, 0],
+        [2, 5100, 5251, 5100, 5100, 6172, 0, 0],
+        [3, 5000, 5150, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=0.019,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.05,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)],
+)
+
+# Test 12: trailing_stop should raise in candle 2 and cause a stoploss in the same candle
+# applying a positive trailing stop of 3% since stop_positive_offset is reached.
+# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
+tc12 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4950, 5100, 6172, 0, 0],
+        [2, 5100, 5251, 4650, 5100, 6172, 0, 0],
+        [3, 4850, 5050, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=0.019,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.05,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=2)],
+)
+
+# Test 13: Buy and sell ROI on same candle
+# stop-loss: 10% (should not apply), ROI: 1%
+tc13 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4950, 5100, 6172, 0, 0],
+        [2, 5100, 5251, 4850, 5100, 6172, 0, 0],
+        [3, 4850, 5050, 4750, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4750, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.01},
+    profit_perc=0.01,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=1)],
+)
+
+# Test 14 - Buy and Stoploss on same candle
+# stop-loss: 5%, ROI: 10% (should not apply)
+tc14 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4600, 5100, 6172, 0, 0],
+        [2, 5100, 5251, 4850, 5100, 6172, 0, 0],
+        [3, 4850, 5050, 4750, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.05,
+    roi={"0": 0.10},
+    profit_perc=-0.05,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=1)],
+)
+
+
+# Test 15 - Buy and ROI on same candle, followed by buy and Stoploss on next candle
+# stop-loss: 5%, ROI: 10% (should not apply)
+tc15 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4900, 5100, 6172, 1, 0],
+        [2, 5100, 5251, 4650, 5100, 6172, 0, 0],
+        [3, 4850, 5050, 4750, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.05,
+    roi={"0": 0.01},
+    profit_perc=-0.04,
+    trades=[
+        BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=1),
+        BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=2, close_tick=2),
+    ],
+)
+
+# Test 16: Buy, hold for 65 min, then forceexit using roi=-1
+# Causes negative profit even though sell-reason is ROI.
+# stop-loss: 10%, ROI: 10% (should not apply), -100% after 65 minutes (limits trade duration)
+tc16 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],
+        [2, 4987, 5300, 4950, 5050, 6172, 0, 0],
+        [3, 4975, 5000, 4940, 4962, 6172, 0, 0],  # Forceexit on ROI (roi=-1)
+        [4, 4962, 4987, 4950, 4950, 6172, 0, 0],
+        [5, 4950, 4975, 4925, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10, "65": -1},
+    profit_perc=-0.012,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)],
+)
+
+# Test 17: Buy, hold for 120 mins, then forceexit using roi=-1
+# Causes negative profit even though sell-reason is ROI.
+# stop-loss: 10%, ROI: 10% (should not apply), -100% after 100 minutes (limits trade duration)
+# Uses open as sell-rate (special case) - since the roi-time is a multiple of the timeframe.
+tc17 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],
+        [2, 4987, 5300, 4950, 5050, 6172, 0, 0],
+        [3, 4980, 5000, 4940, 4962, 6172, 0, 0],  # Forceexit on ROI (roi=-1)
+        [4, 4962, 4987, 4950, 4950, 6172, 0, 0],
+        [5, 4950, 4975, 4925, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10, "120": -1},
+    profit_perc=-0.004,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)],
+)
+
+
+# Test 18: Buy, hold for 120 mins, then drop ROI to 1%, causing a sell in candle 3.
+# stop-loss: 10%, ROI: 10% (should not apply), -100% after 100 minutes (limits trade duration)
+# uses open_rate as sell-price
+tc18 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],
+        [2, 4987, 5300, 4950, 5200, 6172, 0, 0],
+        [3, 5200, 5220, 4940, 4962, 6172, 0, 0],  # Sell on ROI (sells on open)
+        [4, 4962, 4987, 4950, 4950, 6172, 0, 0],
+        [5, 4950, 4975, 4925, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10, "120": 0.01},
+    profit_perc=0.04,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)],
+)
+
+# Test 19: Buy, hold for 119 mins, then drop ROI to 1%, causing a sell in candle 3.
+# stop-loss: 10%, ROI: 10% (should not apply), -100% after 100 minutes (limits trade duration)
+# uses calculated ROI (1%) as sell rate, otherwise identical to tc18
+tc19 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],
+        [2, 4987, 5300, 4950, 5200, 6172, 0, 0],
+        [3, 5000, 5300, 4940, 4962, 6172, 0, 0],  # Sell on ROI
+        [4, 4962, 4987, 4950, 4950, 6172, 0, 0],
+        [5, 4550, 4975, 4550, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10, "120": 0.01},
+    profit_perc=0.01,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)],
+)
+
+# Test 20: Buy, hold for 119 mins, then drop ROI to 1%, causing a sell in candle 3.
+# stop-loss: 10%, ROI: 10% (should not apply), -100% after 100 minutes (limits trade duration)
+# uses calculated ROI (1%) as sell rate, otherwise identical to tc18
+tc20 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],
+        [2, 4987, 5300, 4950, 5200, 6172, 0, 0],
+        [3, 5200, 5300, 4940, 4962, 6172, 0, 0],  # Sell on ROI
+        [4, 4962, 4987, 4950, 4950, 6172, 0, 0],
+        [5, 4925, 4975, 4925, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10, "119": 0.01},
+    profit_perc=0.01,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)],
+)
+
+# Test 21: trailing_stop ROI collision.
+# Roi should trigger before Trailing stop - otherwise Trailing stop profits can be > ROI
+# which cannot happen in reality
+# stop-loss: 10%, ROI: 4%, Trailing stop adjusted at the sell candle
+tc21 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4950, 5100, 6172, 0, 0],
+        [2, 5100, 5251, 4650, 5100, 6172, 0, 0],
+        [3, 4850, 5050, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.04},
+    profit_perc=0.04,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.05,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=2)],
+)
+
+# Test 22: trailing_stop Raises in candle 2 - but ROI applies at the same time.
+# applying a positive trailing stop of 3% - ROI should apply before trailing stop.
+# stop-loss: 10%, ROI: 4%, stoploss adjusted candle 2
+tc22 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4950, 5100, 6172, 0, 0],
+        [2, 5100, 5251, 5100, 5100, 6172, 0, 0],
+        [3, 4850, 5050, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.04},
+    profit_perc=0.04,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.05,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=2)],
+)
+
+
+# Test 23: trailing_stop Raises in candle 2 - but ROI applies at the same time.
+# applying a positive trailing stop of 3% - ROI should apply before trailing stop.
+# stop-loss: 10%, ROI: 4%, stoploss adjusted candle 2
+tc23 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 0, 0, 1, 0],
+        [1, 5000, 5050, 4900, 4900, 6172, 0, 0, 0, 0],
+        [2, 4900, 4900, 4749, 4900, 6172, 0, 0, 0, 0],
+        [3, 4850, 5050, 4650, 4750, 6172, 0, 0, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.04},
+    profit_perc=0.04,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.05,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=2, is_short=True)],
+)
+
+# Test 24: trailing_stop Raises in candle 2 (does not trigger)
+# applying a positive trailing stop of 3% since stop_positive_offset is reached.
+# ROI is changed after this to 4%, dropping ROI below trailing_stop_positive, causing a sell
+# in the candle after the raised stoploss candle with ROI reason.
+# Stoploss would trigger in this candle too, but it's no longer relevant.
+# stop-loss: 10%, ROI: 4%, stoploss adjusted candle 2, ROI adjusted in candle 3 (causing the sell)
+tc24 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4950, 5100, 6172, 0, 0],
+        [2, 5100, 5251, 5100, 5100, 6172, 0, 0],
+        [3, 4850, 5251, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.1, "119": 0.03},
+    profit_perc=0.03,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.05,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)],
+)
+
+# Test 25: Sell with signal sell in candle 3 (stoploss also triggers on this candle)
+# Stoploss at 1%.
+# Stoploss wins over Sell-signal (because sell-signal is acted on in the next candle)
+tc25 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4986, 4986, 6172, 0, 0],
+        [3, 5010, 5010, 4855, 5010, 6172, 0, 1],  # Triggers stoploss + sellsignal
+        [4, 5010, 5010, 4977, 4995, 6172, 0, 0],
+        [5, 4995, 4995, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 1},
+    profit_perc=-0.01,
+    use_exit_signal=True,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=3)],
+)
+
+# Test 26: Sell with signal sell in candle 3 (stoploss also triggers on this candle)
+# Stoploss at 1%.
+# Sell-signal wins over stoploss
+tc26 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4986, 4986, 6172, 0, 0],
+        [3, 5010, 5010, 4986, 5010, 6172, 0, 1],
+        [4, 5010, 5010, 4855, 4995, 6172, 0, 0],  # Triggers stoploss + sellsignal acted on
+        [5, 4995, 4995, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 1},
+    profit_perc=0.002,
+    use_exit_signal=True,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4)],
+)
+
+# Test 27: (copy of test26 with leverage)
+# Sell with signal sell in candle 3 (stoploss also triggers on this candle)
+# Stoploss at 1%.
+# Sell-signal wins over stoploss
+tc27 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4986, 4986, 6172, 0, 0],
+        [3, 5010, 5010, 4986, 5010, 6172, 0, 1],
+        [4, 5010, 5010, 4855, 4995, 6172, 0, 0],  # Triggers stoploss + sellsignal acted on
+        [5, 4995, 4995, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.05,
+    roi={"0": 1},
+    profit_perc=0.002 * 5.0,
+    use_exit_signal=True,
+    leverage=5.0,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4)],
+)
+
+# Test 28: (copy of test26 with leverage and as short)
+# Sell with signal sell in candle 3 (stoploss also triggers on this candle)
+# Stoploss at 1%.
+# Sell-signal wins over stoploss
+tc28 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 0, 0, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4986, 4986, 6172, 0, 0, 0, 0],
+        [3, 5010, 5010, 4986, 5010, 6172, 0, 0, 0, 1],
+        [4, 4990, 5010, 4855, 4995, 6172, 0, 0, 0, 0],  # Triggers stoploss + sellsignal acted on
+        [5, 4995, 4995, 4950, 4950, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.05,
+    roi={"0": 1},
+    profit_perc=0.002 * 5.0,
+    use_exit_signal=True,
+    leverage=5.0,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4, is_short=True)],
+)
+# Test 29: Sell with signal sell in candle 3 (ROI at signal candle)
+# Stoploss at 10% (irrelevant), ROI at 5% (will trigger)
+# Sell-signal wins over stoploss
+tc29 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4986, 4986, 6172, 0, 0],
+        [3, 5010, 5251, 4986, 5010, 6172, 0, 1],  # Triggers ROI, sell-signal
+        [4, 5010, 5010, 4855, 4995, 6172, 0, 0],
+        [5, 4995, 4995, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.05},
+    profit_perc=0.05,
+    use_exit_signal=True,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)],
+)
+
+# Test 30: Sell with signal sell in candle 3 (ROI at signal candle)
+# Stoploss at 10% (irrelevant), ROI at 5% (will trigger) - Wins over Sell-signal
+tc30 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+        [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4987, 5012, 4986, 4986, 6172, 0, 0],
+        [3, 5010, 5012, 4986, 5010, 6172, 0, 1],  # sell-signal
+        [4, 5010, 5251, 4855, 4995, 6172, 0, 0],  # Triggers ROI, sell-signal acted on
+        [5, 4995, 4995, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.05},
+    profit_perc=0.002,
+    use_exit_signal=True,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4)],
+)
+
+# Test 31: trailing_stop should raise so candle 3 causes a stoploss
+# Same case than tc11 - but candle 3 "gaps down" - the stoploss will be above the candle,
+# therefore "open" will be used
+# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
+tc31 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5100, 4950, 5100, 6172, 0, 0],
+        [2, 5100, 5251, 5100, 5100, 6172, 0, 0],
+        [3, 4850, 5050, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=-0.03,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.05,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)],
+)
+
+# Test 32: (Short of test 31) trailing_stop should raise so candle 3 causes a stoploss
+# Same case than tc11 - but candle 3 "gaps down" - the stoploss will be above the candle,
+# therefore "open" will be used
+# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
+tc32 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 0, 0, 1, 0],
+        [1, 5000, 5050, 4890, 4890, 6172, 0, 0, 0, 0],
+        [2, 4890, 4890, 4749, 4890, 6172, 0, 0, 0, 0],
+        [3, 5150, 5350, 4950, 4950, 6172, 0, 0, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=-0.03,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.05,
+    trailing_stop_positive=0.03,
+    trades=[
+        BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3, is_short=True)
+    ],
+)
+
+# Test 33: trailing_stop should be triggered by low of next candle, without adjusting stoploss using
+# high of stoploss candle.
+# stop-loss: 10%, ROI: 10% (should not apply)
+tc33 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5050, 5000, 5000, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],  # Triggers trailing-stoploss
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=-0.02,
+    trailing_stop=True,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=2)],
+)
+
+# Test 34: trailing_stop should be triggered immediately on trade open candle.
+# stop-loss: 10%, ROI: 10% (should not apply)
+tc34 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4900, 4900, 6172, 0, 0],  # enter trade (signal on last candle) and stop
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=-0.01,
+    trailing_stop=True,
+    trailing_stop_positive=0.01,
+    trades=[BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=1)],
+)
+
+# Test 35: trailing_stop should be triggered immediately on trade open candle.
+# stop-loss: 10%, ROI: 10% (should not apply)
+tc35 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4900, 4900, 6172, 0, 0],  # enter trade (signal on last candle) and stop
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=0.01,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.02,
+    trailing_stop_positive=0.01,
+    trades=[BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=1)],
+)
+
+# Test 36: trailing_stop should be triggered immediately on trade open candle.
+# stop-loss: 1%, ROI: 10% (should not apply)
+tc36 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],  # enter trade and stop
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 0.10},
+    profit_perc=-0.01,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.02,
+    trailing_stop_positive=0.01,
+    use_custom_stoploss=True,
+    trades=[BTrade(exit_reason=ExitType.TRAILING_STOP_LOSS, open_tick=1, close_tick=1)],
+)
+
+# Test 37: trailing_stop should be triggered immediately on trade open candle.
+# stop-loss: 1%, ROI: 10% (should not apply)
+tc37 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0, 0, 0, "buy_signal_01"],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0, 0, 0, None],  # enter trade and stop
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0, 0, 0, None],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0, 0, 0, None],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0, 0, 0, None],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 0.10},
+    profit_perc=-0.01,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.02,
+    trailing_stop_positive=0.01,
+    use_custom_stoploss=True,
+    trades=[
+        BTrade(
+            exit_reason=ExitType.TRAILING_STOP_LOSS,
+            open_tick=1,
+            close_tick=1,
+            enter_tag="buy_signal_01",
+        )
+    ],
+)
+# Test 38: trailing_stop should be triggered immediately on trade open candle.
+# copy of Test37 using shorts.
+# stop-loss: 1%, ROI: 10% (should not apply)
+tc38 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 0, 0, 1, 0, "short_signal_01"],
+        [1, 5000, 5049, 4500, 5000, 6172, 0, 0, 0, 0, None],  # enter trade and stop
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0, 0, 0, None],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0, 0, 0, None],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0, 0, 0, None],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 0.10},
+    profit_perc=-0.01,
+    trailing_stop=True,
+    trailing_only_offset_is_reached=True,
+    trailing_stop_positive_offset=0.02,
+    trailing_stop_positive=0.01,
+    use_custom_stoploss=True,
+    trades=[
+        BTrade(
+            exit_reason=ExitType.TRAILING_STOP_LOSS,
+            open_tick=1,
+            close_tick=1,
+            enter_tag="short_signal_01",
+            is_short=True,
+        )
+    ],
+)
+
+# Test 39: Custom-entry-price below all candles should timeout - so no trade happens.
+tc39 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],  # timeout
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 0.10},
+    profit_perc=0.0,
+    custom_entry_price=4200,
+    trades=[],
+)
+
+# Test 40: Custom-entry-price above all candles should have rate adjusted to "entry candle high"
+tc40 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],  # Timeout
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 0.10},
+    profit_perc=-0.01,
+    custom_entry_price=7200,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=1)],
+)
+
+# Test 41: Custom-entry-price above all candles should have rate adjusted to "entry candle high"
+tc41 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 0, 0, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0, 0, 0],  # Timeout
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0, 0, 0],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 0.10},
+    profit_perc=-0.01,
+    custom_entry_price=4000,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=1, is_short=True)],
+)
+
+# Test 42: Custom-entry-price around candle low
+# Would cause immediate ROI exit, but since the trade was entered
+# below open, we treat this as cheating, and delay the sell by 1 candle.
+# details: https://github.com/freqtrade/freqtrade/issues/6261
+tc42 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4951, 4999, 6172, 0, 0],  # Enter and immediate ROI
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.01},
+    profit_perc=0.01,
+    custom_entry_price=4952,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=2)],
+)
+
+# Test 43: Custom-entry-price around candle low
+# Would cause immediate ROI exit below close
+# details: https://github.com/freqtrade/freqtrade/issues/6261
+tc43 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5400, 5500, 4951, 5100, 6172, 0, 0],  # Enter and immediate ROI
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.01},
+    profit_perc=0.01,
+    custom_entry_price=4952,
+    trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=1)],
+)
+
+# Test 44: Custom exit price below all candles
+# Price adjusted to candle Low.
+tc44 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],
+        [2, 4900, 5250, 4900, 5100, 6172, 0, 1],  # exit - but timeout
+        [3, 5100, 5100, 4950, 4950, 6172, 0, 0],
+        [4, 5000, 5100, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=-0.01,
+    use_exit_signal=True,
+    custom_exit_price=4552,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=3)],
+)
+
+# Test 45: Custom exit price above all candles
+# causes sell signal timeout
+tc45 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],
+        [2, 4950, 5250, 4900, 5100, 6172, 0, 1],  # exit - entry timeout
+        [3, 5100, 5100, 4950, 4950, 6172, 0, 0],
+        [4, 5000, 5100, 4950, 4950, 6172, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=0.0,
+    use_exit_signal=True,
+    custom_exit_price=6052,
+    trades=[BTrade(exit_reason=ExitType.FORCE_EXIT, open_tick=1, close_tick=4)],
+)
+
+# Test 46: (Short of tc45) Custom short exit price above below candles
+# causes sell signal timeout
+tc46 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 0, 0, 1, 0],
+        [1, 5000, 5000, 4951, 5000, 6172, 0, 0, 0, 0],
+        [2, 4910, 5150, 4910, 5100, 6172, 0, 0, 0, 1],  # exit - entry timeout
+        [3, 5100, 5100, 4950, 4950, 6172, 0, 0, 0, 0],
+        [4, 5000, 5100, 4950, 4950, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=0.0,
+    use_exit_signal=True,
+    custom_exit_price=4700,
+    trades=[BTrade(exit_reason=ExitType.FORCE_EXIT, open_tick=1, close_tick=4, is_short=True)],
+)
+
+# Test 47: Colliding long and short signal
+tc47 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0, 0, 0],
+        [2, 4900, 5250, 4900, 5100, 6172, 0, 0, 0, 0],
+        [3, 5100, 5100, 4950, 4950, 6172, 0, 0, 0, 0],
+        [4, 5000, 5100, 4950, 4950, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=0.0,
+    use_exit_signal=True,
+    trades=[],
+)
+
+# Test 48: Custom-entry-price below all candles - readjust order
+tc48 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],  # timeout
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],  # Order readjust
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 1],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.2,
+    roi={"0": 0.10},
+    profit_perc=-0.087,
+    use_exit_signal=True,
+    timeout=1000,
+    custom_entry_price=4200,
+    adjust_entry_price=5200,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4, is_short=False)],
+)
+
+
+# Test 49: Custom-entry-price short above all candles - readjust order
+tc49 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 0, 0, 1, 0],
+        [1, 5000, 5200, 4951, 5000, 6172, 0, 0, 0, 0],  # timeout
+        [2, 4900, 5250, 4900, 5100, 6172, 0, 0, 0, 0],  # Order readjust
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0, 0, 1],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.2,
+    roi={"0": 0.10},
+    profit_perc=0.05,
+    use_exit_signal=True,
+    timeout=1000,
+    custom_entry_price=5300,
+    adjust_entry_price=5000,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4, is_short=True)],
+)
+
+# Test 50: Custom-entry-price below all candles - readjust order cancels order
+tc50 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],  # Enter long - place order
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],  # Order readjust - cancel order
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 0.10},
+    profit_perc=0.0,
+    use_exit_signal=True,
+    timeout=1000,
+    custom_entry_price=4200,
+    adjust_entry_price=None,
+    trades=[],
+)
+
+# Test 51: Custom-entry-price below all candles - readjust order leaves order in place and timeout.
+tc51 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],  # Enter long - place order
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],  # Order readjust - replace order
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],  # Order readjust - maintain order
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],  # Timeout
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.01,
+    roi={"0": 0.10},
+    profit_perc=0.0,
+    use_exit_signal=True,
+    timeout=60,
+    custom_entry_price=4200,
+    adjust_entry_price=4100,
+    trades=[],
+)
+
+# Test 52: Custom-entry-price below all candles - readjust order - stoploss
+tc52 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],  # enter trade (signal on last candle)
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 0],  # Order readjust
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],  # stoploss hit?
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.03,
+    roi={},
+    profit_perc=-0.03,
+    use_exit_signal=True,
+    timeout=1000,
+    custom_entry_price=4200,
+    adjust_entry_price=5200,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=2, is_short=False)],
+)
+
+
+# Test 53: Custom-entry-price short above all candles - readjust order - stoploss
+tc53 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 0, 0, 1, 0],
+        [1, 5000, 5200, 4951, 5000, 6172, 0, 0, 0, 0],  # enter trade (signal on last candle)
+        [2, 4900, 5250, 4900, 5100, 6172, 0, 0, 0, 0],  # Order readjust
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0, 0, 1],  # stoploss hit?
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.03,
+    roi={"0": 0.10},
+    profit_perc=-0.03,
+    use_exit_signal=True,
+    timeout=1000,
+    custom_entry_price=5300,
+    adjust_entry_price=5000,
+    trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=2, is_short=True)],
+)
+
+# Test 54: Switch position from long to short
+tc54 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0, 0, 0],
+        [1, 5000, 5000, 4951, 5000, 6172, 0, 0, 0, 0],
+        [2, 4910, 5150, 4910, 5100, 6172, 0, 0, 1, 0],  # Enter short signal being ignored
+        [3, 5100, 5100, 4950, 4950, 6172, 0, 1, 1, 0],  # exit - re-enter short
+        [4, 5000, 5100, 4950, 4950, 6172, 0, 0, 0, 1],
+        [5, 5000, 5100, 4950, 4950, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=0.00,
+    use_exit_signal=True,
+    trades=[
+        BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4, is_short=False),
+        BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=4, close_tick=5, is_short=True),
+    ],
+)
+
+# Test 55: Switch position from short to long
+tc55 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 0, 0, 1, 0],
+        [1, 5000, 5000, 4951, 5000, 6172, 1, 0, 0, 0],  # Enter long signal being ignored
+        [2, 4910, 5150, 4910, 5100, 6172, 1, 0, 0, 1],  # Exit - reenter long
+        [3, 5100, 5100, 4950, 4950, 6172, 0, 0, 0, 0],
+        [4, 5000, 5100, 4950, 4950, 6172, 0, 1, 0, 0],
+        [5, 5000, 5100, 4950, 4950, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 0.10},
+    profit_perc=-0.04,
+    use_exit_signal=True,
+    trades=[
+        BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=3, is_short=True),
+        BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=3, close_tick=5, is_short=False),
+    ],
+)
+
+# Test 56: Switch position from long to short
+tc56 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0, 0, 0],
+        [1, 5000, 5000, 4951, 5000, 6172, 0, 0, 0, 0],
+        [2, 4910, 5150, 4910, 5100, 6172, 0, 0, 1, 0],  # exit on stoploss - re-enter short
+        [3, 5100, 5100, 4888, 4950, 6172, 0, 0, 0, 0],
+        [4, 5000, 5100, 4950, 4950, 6172, 0, 0, 0, 1],
+        [5, 5000, 5100, 4950, 4950, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.02,
+    roi={"0": 0.10},
+    profit_perc=-0.0,
+    use_exit_signal=True,
+    trades=[
+        BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=3, is_short=False),
+        BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=3, close_tick=5, is_short=True),
+    ],
+)
+
+
+# Test 57: Custom-entry-price for position adjustment which won't fill
+# Causing the negative adjustment to cancel the unfilled order and exit partially
+tc57 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0, 0, 0],
+        [1, 4598, 5200, 4498, 5000, 6172, 0, 0, 0, 0],
+        [2, 4900, 5250, 4900, 5100, 6172, 0, 0, 0, 0],  # Enhance position, but won't fill
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0, 0, 0],
+        [4, 4750, 4950, 4650, 4750, 6172, 0, 0, 0, 0],
+        [5, 4750, 4950, 4650, 4750, 6172, 0, 1, 0, 0],
+        [6, 4750, 4950, 4650, 4750, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.2,
+    roi={"0": 0.50},
+    profit_perc=0.033,
+    use_exit_signal=True,
+    timeout=1000,
+    custom_entry_price=4600,
+    adjust_trade_position=[
+        None,
+        0.001,
+        None,
+        -0.0001,  # Cancels the above unfilled order and exits partially
+        None,
+        None,
+    ],
+    trades=[
+        BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=6, is_short=False),
+    ],
+)
+
+# Test 58: Custom-exit-price short - below all candles
+tc58 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 0, 0, 1, 0],
+        [1, 5000, 5200, 4951, 5000, 6172, 0, 0, 0, 0],  # enter trade (signal on last candle)
+        [2, 4900, 5250, 4900, 5100, 6172, 0, 0, 0, 1],  # Exit - delayed
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0, 0, 0],  #
+        [4, 4750, 5100, 4350, 4750, 6172, 0, 0, 0, 0],
+    ],
+    stop_loss=-0.10,
+    roi={"0": 1.00},
+    profit_perc=-0.01,
+    use_exit_signal=True,
+    timeout=1000,
+    custom_exit_price=4300,
+    adjust_exit_price=5050,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4, is_short=True)],
+)
+
+# Test 59: Custom-exit-price above all candles - readjust order
+tc59 = BTContainer(
+    data=[
+        # D   O     H     L     C    V    EL XL ES Xs  BT
+        [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+        [1, 5000, 5500, 4951, 5000, 6172, 0, 0],
+        [2, 4900, 5250, 4500, 5100, 6172, 0, 1],  # exit
+        [3, 5100, 5100, 4650, 4750, 6172, 0, 0],  # order readjust
+        [4, 4750, 4950, 4350, 4750, 6172, 0, 0],
+    ],
+    stop_loss=-0.2,
+    roi={"0": 0.10},
+    profit_perc=-0.02,
+    use_exit_signal=True,
+    timeout=1000,
+    custom_exit_price=5300,
+    adjust_exit_price=4900,
+    trades=[BTrade(exit_reason=ExitType.EXIT_SIGNAL, open_tick=1, close_tick=4, is_short=False)],
+)
+
+
+TESTS = [
+    tc0,
+    tc1,
+    tc2,
+    tc3,
+    tc4,
+    tc5,
+    tc6,
+    tc7,
+    tc8,
+    tc9,
+    tc10,
+    tc11,
+    tc12,
+    tc13,
+    tc14,
+    tc15,
+    tc16,
+    tc17,
+    tc18,
+    tc19,
+    tc20,
+    tc21,
+    tc22,
+    tc23,
+    tc24,
+    tc25,
+    tc26,
+    tc27,
+    tc28,
+    tc29,
+    tc30,
+    tc31,
+    tc32,
+    tc33,
+    tc34,
+    tc35,
+    tc36,
+    tc37,
+    tc38,
+    tc39,
+    tc40,
+    tc41,
+    tc42,
+    tc43,
+    tc44,
+    tc45,
+    tc46,
+    tc47,
+    tc48,
+    tc49,
+    tc50,
+    tc51,
+    tc52,
+    tc53,
+    tc54,
+    tc55,
+    tc56,
+    tc57,
+    tc58,
+    tc59,
+]
+
+
+@pytest.mark.parametrize("data", TESTS)
+def test_backtest_results(default_conf, mocker, caplog, data: BTContainer) -> None:
+    """
+    run functional tests
+    """
+    default_conf["stoploss"] = data.stop_loss
+    default_conf["minimal_roi"] = data.roi
+    default_conf["timeframe"] = tests_timeframe
+    default_conf["trailing_stop"] = data.trailing_stop
+    default_conf["trailing_only_offset_is_reached"] = data.trailing_only_offset_is_reached
+    if data.timeout:
+        default_conf["unfilledtimeout"].update(
+            {
+                "entry": data.timeout,
+                "exit": data.timeout,
+            }
+        )
+    # Only add this to configuration If it's necessary
+    if data.trailing_stop_positive is not None:
+        default_conf["trailing_stop_positive"] = data.trailing_stop_positive
+    default_conf["trailing_stop_positive_offset"] = data.trailing_stop_positive_offset
+    default_conf["use_exit_signal"] = data.use_exit_signal
+    default_conf["max_open_trades"] = 10
+
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_fee", return_value=0.0)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(f"{EXMS}.get_max_leverage", return_value=100)
+    mocker.patch(f"{EXMS}.calculate_funding_fees", return_value=0)
+    frame = _build_backtest_dataframe(data.data)
+    backtesting = Backtesting(default_conf)
+    # TODO: Should we initialize this properly??
+    backtesting.trading_mode = TradingMode.MARGIN
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting._can_short = True
+    backtesting.required_startup = 0
+    backtesting.strategy.advise_entry = lambda a, m: frame
+    backtesting.strategy.advise_exit = lambda a, m: frame
+    if data.custom_entry_price:
+        backtesting.strategy.custom_entry_price = MagicMock(return_value=data.custom_entry_price)
+    if data.custom_exit_price:
+        backtesting.strategy.custom_exit_price = MagicMock(return_value=data.custom_exit_price)
+    if data.adjust_trade_position:
+        backtesting.strategy.position_adjustment_enable = True
+        backtesting.strategy.adjust_trade_position = MagicMock(
+            side_effect=data.adjust_trade_position
+        )
+    if data.adjust_entry_price:
+        backtesting.strategy.adjust_entry_price = MagicMock(return_value=data.adjust_entry_price)
+    if data.adjust_exit_price:
+        backtesting.strategy.adjust_exit_price = MagicMock(return_value=data.adjust_exit_price)
+
+    backtesting.strategy.use_custom_stoploss = data.use_custom_stoploss
+    backtesting.strategy.leverage = lambda **kwargs: data.leverage
+    caplog.set_level(logging.DEBUG)
+
+    pair = "UNITTEST/BTC"
+    # Dummy data as we mock the analyze functions
+    data_processed = {pair: frame.copy()}
+    min_date, max_date = get_timerange({pair: frame})
+    result = backtesting.backtest(
+        processed=data_processed,
+        start_date=min_date,
+        end_date=max_date,
+    )
+
+    results = result["results"]
+    assert len(results) == len(data.trades)
+    assert round(results["profit_ratio"].sum(), 3) == round(data.profit_perc, 3)
+
+    for c, trade in enumerate(data.trades):
+        res: BTrade = results.iloc[c]
+        assert res.exit_reason == trade.exit_reason.value
+        assert res.enter_tag == trade.enter_tag
+        assert res.open_date == _get_frame_time_from_offset(trade.open_tick)
+        assert res.close_date == _get_frame_time_from_offset(trade.close_tick)
+        assert res.is_short == trade.is_short
+    assert len(LocalTrade.bt_trades) == len(data.trades)
+    assert len(LocalTrade.bt_trades_open) == 0, "Left open trade"
+    backtesting.cleanup()
+    del backtesting
diff --git a/tests/optimize/test_backtesting.py b/tests/optimize/test_backtesting.py
new file mode 100644
index 000000000..d5bc7d5b4
--- /dev/null
+++ b/tests/optimize/test_backtesting.py
@@ -0,0 +1,2692 @@
+# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
+
+import random
+from collections import defaultdict
+from copy import deepcopy
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from unittest.mock import ANY, MagicMock, PropertyMock
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from freqtrade import constants
+from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_backtesting
+from freqtrade.configuration import TimeRange
+from freqtrade.data import history
+from freqtrade.data.btanalysis import BT_DATA_COLUMNS, evaluate_result_multi
+from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_fill_up_missing_data
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.data.history import get_timerange
+from freqtrade.enums import CandleType, ExitType, RunMode
+from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.exchange import timeframe_to_next_date, timeframe_to_prev_date
+from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename, get_strategy_run_id
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.persistence import LocalTrade, Trade
+from freqtrade.resolvers import StrategyResolver
+from freqtrade.util.datetime_helpers import dt_utc
+from tests.conftest import (
+    CURRENT_TEST_STRATEGY,
+    EXMS,
+    generate_test_data,
+    get_args,
+    log_has,
+    log_has_re,
+    patch_exchange,
+    patched_configuration_load_config_file,
+)
+
+
+ORDER_TYPES = [
+    {"entry": "limit", "exit": "limit", "stoploss": "limit", "stoploss_on_exchange": False},
+    {"entry": "limit", "exit": "limit", "stoploss": "limit", "stoploss_on_exchange": True},
+]
+
+
+def trim_dictlist(dict_list, num):
+    new = {}
+    for pair, pair_data in dict_list.items():
+        new[pair] = pair_data[num:].reset_index()
+    return new
+
+
+def load_data_test(what, testdatadir):
+    timerange = TimeRange.parse_timerange("1510694220-1510700340")
+    data = history.load_pair_history(
+        pair="UNITTEST/BTC",
+        datadir=testdatadir,
+        timeframe="1m",
+        timerange=timerange,
+        drop_incomplete=False,
+        fill_up_missing=False,
+    )
+
+    base = 0.001
+    if what == "raise":
+        data.loc[:, "open"] = data.index * base
+        data.loc[:, "high"] = data.index * base + 0.0001
+        data.loc[:, "low"] = data.index * base - 0.0001
+        data.loc[:, "close"] = data.index * base
+
+    if what == "lower":
+        data.loc[:, "open"] = 1 - data.index * base
+        data.loc[:, "high"] = 1 - data.index * base + 0.0001
+        data.loc[:, "low"] = 1 - data.index * base - 0.0001
+        data.loc[:, "close"] = 1 - data.index * base
+
+    if what == "sine":
+        hz = 0.1  # frequency
+        data.loc[:, "open"] = np.sin(data.index * hz) / 1000 + base
+        data.loc[:, "high"] = np.sin(data.index * hz) / 1000 + base + 0.0001
+        data.loc[:, "low"] = np.sin(data.index * hz) / 1000 + base - 0.0001
+        data.loc[:, "close"] = np.sin(data.index * hz) / 1000 + base
+
+    return {
+        "UNITTEST/BTC": clean_ohlcv_dataframe(
+            data, timeframe="1m", pair="UNITTEST/BTC", fill_missing=True, drop_incomplete=True
+        )
+    }
+
+
+# FIX: fixturize this?
+def _make_backtest_conf(mocker, datadir, conf=None, pair="UNITTEST/BTC"):
+    data = history.load_data(datadir=datadir, timeframe="1m", pairs=[pair])
+    data = trim_dictlist(data, -201)
+    patch_exchange(mocker)
+    backtesting = Backtesting(conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+    return {
+        "processed": processed,
+        "start_date": min_date,
+        "end_date": max_date,
+    }
+
+
+def _trend(signals, buy_value, sell_value):
+    n = len(signals["low"])
+    buy = np.zeros(n)
+    sell = np.zeros(n)
+    for i in range(0, len(signals["date"])):
+        if random.random() > 0.5:  # Both buy and sell signals at same timeframe
+            buy[i] = buy_value
+            sell[i] = sell_value
+    signals["enter_long"] = buy
+    signals["exit_long"] = sell
+    signals["enter_short"] = 0
+    signals["exit_short"] = 0
+    return signals
+
+
+def _trend_alternate(dataframe=None, metadata=None):
+    signals = dataframe
+    low = signals["low"]
+    n = len(low)
+    buy = np.zeros(n)
+    sell = np.zeros(n)
+    for i in range(0, len(buy)):
+        if i % 2 == 0:
+            buy[i] = 1
+        else:
+            sell[i] = 1
+    signals["enter_long"] = buy
+    signals["exit_long"] = sell
+    signals["enter_short"] = 0
+    signals["exit_short"] = 0
+    return dataframe
+
+
+# Unit tests
+def test_setup_optimize_configuration_without_arguments(mocker, default_conf, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--export",
+        "none",
+    ]
+
+    config = setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
+    assert "max_open_trades" in config
+    assert "stake_currency" in config
+    assert "stake_amount" in config
+    assert "exchange" in config
+    assert "pair_whitelist" in config["exchange"]
+    assert "datadir" in config
+    assert log_has("Using data directory: {} ...".format(config["datadir"]), caplog)
+    assert "timeframe" in config
+    assert not log_has_re("Parameter -i/--ticker-interval detected .*", caplog)
+
+    assert "position_stacking" not in config
+    assert not log_has("Parameter --enable-position-stacking detected ...", caplog)
+
+    assert "timerange" not in config
+    assert "export" in config
+    assert config["export"] == "none"
+    assert "runmode" in config
+    assert config["runmode"] == RunMode.BACKTEST
+
+
+def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.configuration.configuration.create_datadir", lambda c, x: x)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--datadir",
+        "/foo/bar",
+        "--timeframe",
+        "1m",
+        "--enable-position-stacking",
+        "--timerange",
+        ":100",
+        "--export-filename",
+        "foo_bar.json",
+        "--fee",
+        "0",
+    ]
+
+    config = setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
+    assert "max_open_trades" in config
+    assert "stake_currency" in config
+    assert "stake_amount" in config
+    assert "exchange" in config
+    assert "pair_whitelist" in config["exchange"]
+    assert "datadir" in config
+    assert config["runmode"] == RunMode.BACKTEST
+
+    assert log_has("Using data directory: {} ...".format(config["datadir"]), caplog)
+    assert "timeframe" in config
+    assert log_has("Parameter -i/--timeframe detected ... Using timeframe: 1m ...", caplog)
+
+    assert "position_stacking" in config
+    assert log_has("Parameter --enable-position-stacking detected ...", caplog)
+
+    assert "timerange" in config
+    assert log_has("Parameter --timerange detected: {} ...".format(config["timerange"]), caplog)
+
+    assert "export" in config
+    assert "exportfilename" in config
+    assert isinstance(config["exportfilename"], Path)
+    assert log_has("Storing backtest results to {} ...".format(config["exportfilename"]), caplog)
+
+    assert "fee" in config
+    assert log_has("Parameter --fee detected, setting fee to: {} ...".format(config["fee"]), caplog)
+
+
+def test_setup_optimize_configuration_stake_amount(mocker, default_conf, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--stake-amount",
+        "1",
+        "--starting-balance",
+        "2",
+    ]
+
+    conf = setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
+    assert isinstance(conf, dict)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--stake-amount",
+        "1",
+        "--starting-balance",
+        "0.5",
+    ]
+    with pytest.raises(OperationalException, match=r"Starting balance .* smaller .*"):
+        setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
+
+
+def test_start(mocker, fee, default_conf, caplog) -> None:
+    start_mock = MagicMock()
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.start", start_mock)
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+    ]
+    pargs = get_args(args)
+    start_backtesting(pargs)
+    assert log_has("Starting freqtrade in Backtesting mode", caplog)
+    assert start_mock.call_count == 1
+
+
+@pytest.mark.parametrize("order_types", ORDER_TYPES)
+def test_backtesting_init(mocker, default_conf, order_types) -> None:
+    """
+    Check that stoploss_on_exchange is set to False while backtesting
+    since backtesting assumes a perfect stoploss anyway.
+    """
+    default_conf["order_types"] = order_types
+    patch_exchange(mocker)
+    get_fee = mocker.patch(f"{EXMS}.get_fee", MagicMock(return_value=0.5))
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    assert backtesting.config == default_conf
+    assert backtesting.timeframe == "5m"
+    assert callable(backtesting.strategy.advise_all_indicators)
+    assert callable(backtesting.strategy.advise_entry)
+    assert callable(backtesting.strategy.advise_exit)
+    assert isinstance(backtesting.strategy.dp, DataProvider)
+    get_fee.assert_called()
+    assert backtesting.fee == 0.5
+    assert not backtesting.strategy.order_types["stoploss_on_exchange"]
+    assert backtesting.strategy.bot_started is True
+
+
+def test_backtesting_init_no_timeframe(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    del default_conf["timeframe"]
+    default_conf["strategy_list"] = [CURRENT_TEST_STRATEGY, "HyperoptableStrategy"]
+
+    mocker.patch(f"{EXMS}.get_fee", MagicMock(return_value=0.5))
+    with pytest.raises(
+        OperationalException, match=r"Timeframe needs to be set in either configuration"
+    ):
+        Backtesting(default_conf)
+
+
+def test_data_with_fee(default_conf, mocker) -> None:
+    patch_exchange(mocker)
+    default_conf["fee"] = 0.01234
+
+    fee_mock = mocker.patch(f"{EXMS}.get_fee", MagicMock(return_value=0.5))
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    assert backtesting.fee == 0.01234
+    assert fee_mock.call_count == 0
+
+    default_conf["fee"] = 0.0
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    assert backtesting.fee == 0.0
+    assert fee_mock.call_count == 0
+
+
+def test_data_to_dataframe_bt(default_conf, mocker, testdatadir) -> None:
+    patch_exchange(mocker)
+    timerange = TimeRange.parse_timerange("1510694220-1510700340")
+    data = history.load_data(
+        testdatadir, "1m", ["UNITTEST/BTC"], timerange=timerange, fill_up_missing=True
+    )
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    processed = backtesting.strategy.advise_all_indicators(data)
+    assert len(processed["UNITTEST/BTC"]) == 103
+
+    # Load strategy to compare the result between Backtesting function and strategy are the same
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    processed2 = strategy.advise_all_indicators(data)
+    assert processed["UNITTEST/BTC"].equals(processed2["UNITTEST/BTC"])
+
+
+def test_backtest_abort(default_conf, mocker, testdatadir) -> None:
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+    backtesting.check_abort()
+
+    backtesting.abort = True
+
+    with pytest.raises(DependencyException, match="Stop requested"):
+        backtesting.check_abort()
+    # abort flag resets
+    assert backtesting.abort is False
+    assert backtesting.progress.progress == 0
+
+
+def test_backtesting_start(default_conf, mocker, caplog) -> None:
+    def get_timerange(input1):
+        return dt_utc(2017, 11, 14, 21, 17), dt_utc(2017, 11, 14, 22, 59)
+
+    mocker.patch("freqtrade.data.history.get_timerange", get_timerange)
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest")
+    mocker.patch("freqtrade.optimize.backtesting.generate_backtest_stats")
+    mocker.patch("freqtrade.optimize.backtesting.show_backtest_results")
+    sbs = mocker.patch("freqtrade.optimize.backtesting.store_backtest_results")
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+
+    default_conf["timeframe"] = "1m"
+    default_conf["export"] = "signals"
+    default_conf["exportfilename"] = "export.txt"
+    default_conf["timerange"] = "-1510694220"
+    default_conf["runmode"] = RunMode.BACKTEST
+
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.bot_loop_start = MagicMock()
+    backtesting.strategy.bot_start = MagicMock()
+    backtesting.start()
+    # check the logs, that will contain the backtest result
+    exists = ["Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days)."]
+    for line in exists:
+        assert log_has(line, caplog)
+    assert backtesting.strategy.dp._pairlists is not None
+    assert backtesting.strategy.bot_start.call_count == 1
+    assert backtesting.strategy.bot_loop_start.call_count == 0
+    assert sbs.call_count == 1
+
+
+def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) -> None:
+    def get_timerange(input1):
+        return dt_utc(2017, 11, 14, 21, 17), dt_utc(2017, 11, 14, 22, 59)
+
+    mocker.patch(
+        "freqtrade.data.history.history_utils.load_pair_history",
+        MagicMock(return_value=pd.DataFrame()),
+    )
+    mocker.patch("freqtrade.data.history.get_timerange", get_timerange)
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest")
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+
+    default_conf["timeframe"] = "1m"
+    default_conf["export"] = "none"
+    default_conf["timerange"] = "20180101-20180102"
+
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    with pytest.raises(OperationalException, match="No data found. Terminating."):
+        backtesting.start()
+
+
+def test_backtesting_no_pair_left(default_conf, mocker) -> None:
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    mocker.patch(
+        "freqtrade.data.history.history_utils.load_pair_history",
+        MagicMock(return_value=pd.DataFrame()),
+    )
+    mocker.patch("freqtrade.data.history.get_timerange", get_timerange)
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest")
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist", PropertyMock(return_value=[])
+    )
+
+    default_conf["timeframe"] = "1m"
+    default_conf["export"] = "none"
+    default_conf["timerange"] = "20180101-20180102"
+
+    with pytest.raises(OperationalException, match="No pair in whitelist."):
+        Backtesting(default_conf)
+
+    default_conf.update(
+        {
+            "pairlists": [{"method": "StaticPairList"}],
+            "timeframe_detail": "1d",
+        }
+    )
+
+    with pytest.raises(
+        OperationalException, match="Detail timeframe must be smaller than strategy timeframe."
+    ):
+        Backtesting(default_conf)
+
+
+def test_backtesting_pairlist_list(default_conf, mocker, tickers) -> None:
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    mocker.patch(f"{EXMS}.get_tickers", tickers)
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y: y)
+    mocker.patch("freqtrade.data.history.get_timerange", get_timerange)
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest")
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["XRP/BTC"]),
+    )
+    mocker.patch("freqtrade.plugins.pairlistmanager.PairListManager.refresh_pairlist")
+
+    default_conf["ticker_interval"] = "1m"
+    default_conf["export"] = "none"
+    # Use stoploss from strategy
+    del default_conf["stoploss"]
+    default_conf["timerange"] = "20180101-20180102"
+
+    default_conf["pairlists"] = [{"method": "VolumePairList", "number_assets": 5}]
+    with pytest.raises(
+        OperationalException,
+        match=r"VolumePairList not allowed for backtesting\..*StaticPairList.*",
+    ):
+        Backtesting(default_conf)
+
+    default_conf["pairlists"] = [
+        {"method": "StaticPairList"},
+        {"method": "PrecisionFilter"},
+    ]
+    Backtesting(default_conf)
+
+    # Multiple strategies
+    default_conf["strategy_list"] = [CURRENT_TEST_STRATEGY, "StrategyTestV2"]
+    with pytest.raises(
+        OperationalException,
+        match="PrecisionFilter not allowed for backtesting multiple strategies.",
+    ):
+        Backtesting(default_conf)
+
+
+def test_backtest__enter_trade(default_conf, fee, mocker) -> None:
+    default_conf["use_exit_signal"] = False
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    patch_exchange(mocker)
+    default_conf["stake_amount"] = "unlimited"
+    default_conf["max_open_trades"] = 2
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    pair = "UNITTEST/BTC"
+    row = [
+        pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
+        1,  # Buy
+        0.001,  # Open
+        0.0011,  # Close
+        0,  # Sell
+        0.00099,  # Low
+        0.0012,  # High
+        "",  # Buy Signal Name
+    ]
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    assert isinstance(trade, LocalTrade)
+    assert trade.stake_amount == 495
+
+    # Fake 2 trades, so there's not enough amount for the next trade left.
+    LocalTrade.bt_trades_open.append(trade)
+    backtesting.wallets.update()
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    assert trade is None
+    LocalTrade.bt_trades_open.pop()
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    assert trade is not None
+    LocalTrade.bt_trades_open.pop()
+
+    backtesting.strategy.custom_stake_amount = lambda **kwargs: 123.5
+    backtesting.wallets.update()
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    LocalTrade.bt_trades_open.pop()
+    assert trade
+    assert trade.stake_amount == 123.5
+
+    # In case of error - use proposed stake
+    backtesting.strategy.custom_stake_amount = lambda **kwargs: 20 / 0
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    LocalTrade.bt_trades_open.pop()
+    assert trade
+    assert trade.stake_amount == 495
+    assert trade.is_short is False
+
+    trade = backtesting._enter_trade(pair, row=row, direction="short")
+    LocalTrade.bt_trades_open.pop()
+    assert trade
+    assert trade.stake_amount == 495
+    assert trade.is_short is True
+
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=300.0)
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    LocalTrade.bt_trades_open.pop()
+    assert trade
+    assert trade.stake_amount == 300.0
+
+
+def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None:
+    default_conf_usdt["use_exit_signal"] = False
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(
+        "freqtrade.persistence.trade_model.price_to_precision", lambda p, *args, **kwargs: p
+    )
+    mocker.patch(f"{EXMS}.get_max_leverage", return_value=100)
+    mocker.patch("freqtrade.optimize.backtesting.price_to_precision", lambda p, *args: p)
+    patch_exchange(mocker)
+    default_conf_usdt["stake_amount"] = 300
+    default_conf_usdt["max_open_trades"] = 2
+    default_conf_usdt["trading_mode"] = "futures"
+    default_conf_usdt["margin_mode"] = "isolated"
+    default_conf_usdt["stake_currency"] = "USDT"
+    default_conf_usdt["exchange"]["pair_whitelist"] = [".*"]
+    backtesting = Backtesting(default_conf_usdt)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting._run_funding_fees")
+    pair = "ETH/USDT:USDT"
+    row = [
+        pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
+        0.1,  # Open
+        0.12,  # High
+        0.099,  # Low
+        0.11,  # Close
+        1,  # enter_long
+        0,  # exit_long
+        1,  # enter_short
+        0,  # exit_hsort
+        "",  # Long Signal Name
+        "",  # Short Signal Name
+        "",  # Exit Signal Name
+    ]
+
+    backtesting.strategy.leverage = MagicMock(return_value=5.0)
+    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt", return_value=(0.01, 0.01))
+
+    # leverage = 5
+    # ep1(trade.open_rate) = 0.1
+    # position(trade.amount) = 15000
+    # stake_amount = 300 -> wb = 300 / 5 = 60
+    # mmr = 0.01
+    # cum_b = 0.01
+    # side_1: -1 if is_short else 1
+    # liq_buffer = 0.05
+    #
+    # Binance, Long
+    # liquidation_price
+    #   = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
+    #   = ((300 + 0.01) - (1 * 15000 * 0.1)) / ((15000 * 0.01) - (1 * 15000))
+    #   = 0.0008080740740740741
+    # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1)
+    #   = 0.08080740740740741 + ((0.1 - 0.08080740740740741) * 0.05 * 1)
+    #   = 0.08176703703703704
+
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    assert pytest.approx(trade.liquidation_price) == 0.081767037
+
+    # Binance, Short
+    # liquidation_price
+    #   = ((wb + cum_b) - (side_1 * position * ep1)) / ((position * mmr_b) - (side_1 * position))
+    #   = ((300 + 0.01) - ((-1) * 15000 * 0.1)) / ((15000 * 0.01) - ((-1) * 15000))
+    #   = 0.0011881254125412541
+    # freqtrade_liquidation_price = liq + (abs(open_rate - liq) * liq_buffer * side_1)
+    #   = 0.11881254125412541 + (abs(0.1 - 0.11881254125412541) * 0.05 * -1)
+    #   = 0.11787191419141915
+
+    trade = backtesting._enter_trade(pair, row=row, direction="short")
+    assert pytest.approx(trade.liquidation_price) == 0.11787191
+    assert pytest.approx(trade.orders[0].cost) == (
+        trade.stake_amount * trade.leverage * (1 + fee.return_value)
+    )
+    assert pytest.approx(trade.orders[-1].stake_amount) == trade.stake_amount
+
+    # Stake-amount too high!
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=600.0)
+
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    assert trade is None
+
+    # Stake-amount throwing error
+    mocker.patch(
+        "freqtrade.wallets.Wallets.get_trade_stake_amount", side_effect=DependencyException
+    )
+
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    assert trade is None
+
+
+def test_backtest__check_trade_exit(default_conf, mocker) -> None:
+    default_conf["use_exit_signal"] = False
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    default_conf["timeframe_detail"] = "1m"
+    default_conf["max_open_trades"] = 2
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    pair = "UNITTEST/BTC"
+    row = [
+        pd.Timestamp(year=2020, month=1, day=1, hour=4, minute=55, tzinfo=timezone.utc),
+        200,  # Open
+        201.5,  # High
+        195,  # Low
+        201,  # Close
+        1,  # enter_long
+        0,  # exit_long
+        0,  # enter_short
+        0,  # exit_hsort
+        "",  # Long Signal Name
+        "",  # Short Signal Name
+        "",  # Exit Signal Name
+    ]
+
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    assert isinstance(trade, LocalTrade)
+
+    row_sell = [
+        pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0, tzinfo=timezone.utc),
+        200,  # Open
+        210.5,  # High
+        195,  # Low
+        201,  # Close
+        0,  # enter_long
+        0,  # exit_long
+        0,  # enter_short
+        0,  # exit_short
+        "",  # long Signal Name
+        "",  # Short Signal Name
+        "",  # Exit Signal Name
+    ]
+
+    # No data available.
+    res = backtesting._check_trade_exit(trade, row_sell, row_sell[0].to_pydatetime())
+    assert res is not None
+    assert res.exit_reason == ExitType.ROI.value
+    assert res.close_date_utc == datetime(2020, 1, 1, 5, 0, tzinfo=timezone.utc)
+
+    # Enter new trade
+    trade = backtesting._enter_trade(pair, row=row, direction="long")
+    assert isinstance(trade, LocalTrade)
+    # Assign empty ... no result.
+    backtesting.detail_data[pair] = pd.DataFrame(
+        [],
+        columns=[
+            "date",
+            "open",
+            "high",
+            "low",
+            "close",
+            "enter_long",
+            "exit_long",
+            "enter_short",
+            "exit_short",
+            "long_tag",
+            "short_tag",
+            "exit_tag",
+        ],
+    )
+
+    res = backtesting._check_trade_exit(trade, row, row[0].to_pydatetime())
+    assert res is None
+
+
+def test_backtest_one(default_conf, mocker, testdatadir) -> None:
+    default_conf["use_exit_signal"] = False
+    default_conf["max_open_trades"] = 10
+
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    pair = "UNITTEST/BTC"
+    timerange = TimeRange("date", None, 1517227800, 0)
+    data = history.load_data(
+        datadir=testdatadir, timeframe="5m", pairs=["UNITTEST/BTC"], timerange=timerange
+    )
+    processed = backtesting.strategy.advise_all_indicators(data)
+    backtesting.strategy.order_filled = MagicMock()
+    min_date, max_date = get_timerange(processed)
+
+    result = backtesting.backtest(
+        processed=deepcopy(processed),
+        start_date=min_date,
+        end_date=max_date,
+    )
+    results = result["results"]
+    assert not results.empty
+    assert len(results) == 2
+
+    expected = pd.DataFrame(
+        {
+            "pair": [pair, pair],
+            "stake_amount": [0.001, 0.001],
+            "max_stake_amount": [0.001, 0.001],
+            "amount": [0.00957442, 0.0097064],
+            "open_date": pd.to_datetime(
+                [dt_utc(2018, 1, 29, 18, 40, 0), dt_utc(2018, 1, 30, 3, 30, 0)], utc=True
+            ),
+            "close_date": pd.to_datetime(
+                [dt_utc(2018, 1, 29, 22, 35, 0), dt_utc(2018, 1, 30, 4, 10, 0)], utc=True
+            ),
+            "open_rate": [0.104445, 0.10302485],
+            "close_rate": [0.104969, 0.103541],
+            "fee_open": [0.0025, 0.0025],
+            "fee_close": [0.0025, 0.0025],
+            "trade_duration": [235, 40],
+            "profit_ratio": [0.0, 0.0],
+            "profit_abs": [0.0, 0.0],
+            "exit_reason": [ExitType.ROI.value, ExitType.ROI.value],
+            "initial_stop_loss_abs": [0.0940005, 0.09272236],
+            "initial_stop_loss_ratio": [-0.1, -0.1],
+            "stop_loss_abs": [0.0940005, 0.09272236],
+            "stop_loss_ratio": [-0.1, -0.1],
+            "min_rate": [0.10370188, 0.10300000000000001],
+            "max_rate": [0.10501, 0.1038888],
+            "is_open": [False, False],
+            "enter_tag": ["", ""],
+            "leverage": [1.0, 1.0],
+            "is_short": [False, False],
+            "open_timestamp": [1517251200000, 1517283000000],
+            "close_timestamp": [1517265300000, 1517285400000],
+            "orders": [
+                [
+                    {
+                        "amount": 0.00957442,
+                        "safe_price": 0.104445,
+                        "ft_order_side": "buy",
+                        "order_filled_timestamp": 1517251200000,
+                        "ft_is_entry": True,
+                        "ft_order_tag": "",
+                        "cost": ANY,
+                    },
+                    {
+                        "amount": 0.00957442,
+                        "safe_price": 0.10496853383458644,
+                        "ft_order_side": "sell",
+                        "order_filled_timestamp": 1517265300000,
+                        "ft_is_entry": False,
+                        "ft_order_tag": "roi",
+                        "cost": ANY,
+                    },
+                ],
+                [
+                    {
+                        "amount": 0.0097064,
+                        "safe_price": 0.10302485,
+                        "ft_order_side": "buy",
+                        "order_filled_timestamp": 1517283000000,
+                        "ft_is_entry": True,
+                        "ft_order_tag": "",
+                        "cost": ANY,
+                    },
+                    {
+                        "amount": 0.0097064,
+                        "safe_price": 0.10354126528822055,
+                        "ft_order_side": "sell",
+                        "order_filled_timestamp": 1517285400000,
+                        "ft_is_entry": False,
+                        "ft_order_tag": "roi",
+                        "cost": ANY,
+                    },
+                ],
+            ],
+        }
+    )
+    pd.testing.assert_frame_equal(results, expected)
+    assert "orders" in results.columns
+    data_pair = processed[pair]
+    # Called once per order
+    assert backtesting.strategy.order_filled.call_count == 4
+    for _, t in results.iterrows():
+        assert len(t["orders"]) == 2
+        ln = data_pair.loc[data_pair["date"] == t["open_date"]]
+        # Check open trade rate aligns to open rate
+        assert not ln.empty
+        assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
+        # check close trade rate aligns to close rate or is between high and low
+        ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
+        assert round(ln1.iloc[0]["open"], 6) == round(t["close_rate"], 6) or round(
+            ln1.iloc[0]["low"], 6
+        ) < round(t["close_rate"], 6) < round(ln1.iloc[0]["high"], 6)
+
+
+@pytest.mark.parametrize("use_detail", [True, False])
+def test_backtest_one_detail(default_conf_usdt, mocker, testdatadir, use_detail) -> None:
+    default_conf_usdt["use_exit_signal"] = False
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    if use_detail:
+        default_conf_usdt["timeframe_detail"] = "1m"
+
+    def advise_entry(df, *args, **kwargs):
+        # Mock function to force several entries
+        df.loc[(df["rsi"] < 40), "enter_long"] = 1
+        return df
+
+    def custom_entry_price(proposed_rate, **kwargs):
+        return proposed_rate * 0.997
+
+    default_conf_usdt["max_open_trades"] = 10
+
+    backtesting = Backtesting(default_conf_usdt)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.populate_entry_trend = advise_entry
+    backtesting.strategy.ignore_buying_expired_candle_after = 59
+    backtesting.strategy.custom_entry_price = custom_entry_price
+    pair = "XRP/ETH"
+    # Pick a timerange adapted to the pair we use to test
+    timerange = TimeRange.parse_timerange("20191010-20191013")
+    data = history.load_data(datadir=testdatadir, timeframe="5m", pairs=[pair], timerange=timerange)
+    if use_detail:
+        data_1m = history.load_data(
+            datadir=testdatadir, timeframe="1m", pairs=[pair], timerange=timerange
+        )
+        backtesting.detail_data = data_1m
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+
+    result = backtesting.backtest(
+        processed=deepcopy(processed),
+        start_date=min_date,
+        end_date=max_date,
+    )
+    results = result["results"]
+    assert not results.empty
+    # Timeout settings from default_conf = entry: 10, exit: 30
+    assert len(results) == (2 if use_detail else 3)
+
+    assert "orders" in results.columns
+    data_pair = processed[pair]
+
+    data_1m_pair = data_1m[pair] if use_detail else pd.DataFrame()
+    late_entry = 0
+    for _, t in results.iterrows():
+        assert len(t["orders"]) == 2
+
+        entryo = t["orders"][0]
+        entry_ts = datetime.fromtimestamp(entryo["order_filled_timestamp"] // 1000, tz=timezone.utc)
+        if entry_ts > t["open_date"]:
+            late_entry += 1
+
+        # Get "entry fill" candle
+        ln = (
+            data_1m_pair.loc[data_1m_pair["date"] == entry_ts]
+            if use_detail
+            else data_pair.loc[data_pair["date"] == entry_ts]
+        )
+        # Check open trade rate aligns to open rate
+        assert not ln.empty
+
+        # assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
+        assert (
+            round(ln.iloc[0]["low"], 6) <= round(t["open_rate"], 6) <= round(ln.iloc[0]["high"], 6)
+        )
+        # check close trade rate aligns to close rate or is between high and low
+        ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
+        if use_detail:
+            ln1_1m = data_1m_pair.loc[data_1m_pair["date"] == t["close_date"]]
+            assert not ln1.empty or not ln1_1m.empty
+        else:
+            assert not ln1.empty
+        ln2 = ln1_1m if ln1.empty else ln1
+
+        assert (
+            round(ln2.iloc[0]["low"], 6)
+            <= round(t["close_rate"], 6)
+            <= round(ln2.iloc[0]["high"], 6)
+        )
+
+    assert late_entry > 0
+
+
+@pytest.mark.parametrize(
+    "use_detail,exp_funding_fee, exp_ff_updates",
+    [
+        (True, -0.018054162, 10),
+        (False, -0.01780296, 6),
+    ],
+)
+def test_backtest_one_detail_futures(
+    default_conf_usdt, mocker, testdatadir, use_detail, exp_funding_fee, exp_ff_updates
+) -> None:
+    default_conf_usdt["use_exit_signal"] = False
+    default_conf_usdt["trading_mode"] = "futures"
+    default_conf_usdt["margin_mode"] = "isolated"
+    default_conf_usdt["candle_type_def"] = CandleType.FUTURES
+
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["XRP/USDT:USDT"]),
+    )
+    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt", return_value=(0.01, 0.01))
+    default_conf_usdt["timeframe"] = "1h"
+    if use_detail:
+        default_conf_usdt["timeframe_detail"] = "5m"
+
+    def advise_entry(df, *args, **kwargs):
+        # Mock function to force several entries
+        df.loc[(df["rsi"] < 40), "enter_long"] = 1
+        return df
+
+    def custom_entry_price(proposed_rate, **kwargs):
+        return proposed_rate * 0.997
+
+    default_conf_usdt["max_open_trades"] = 10
+
+    backtesting = Backtesting(default_conf_usdt)
+    ff_spy = mocker.spy(backtesting.exchange, "calculate_funding_fees")
+
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.populate_entry_trend = advise_entry
+    backtesting.strategy.custom_entry_price = custom_entry_price
+    pair = "XRP/USDT:USDT"
+    # Pick a timerange adapted to the pair we use to test
+    timerange = TimeRange.parse_timerange("20211117-20211119")
+    data = history.load_data(
+        datadir=Path(testdatadir),
+        timeframe="1h",
+        pairs=[pair],
+        timerange=timerange,
+        candle_type=CandleType.FUTURES,
+    )
+    backtesting.load_bt_data_detail()
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+
+    result = backtesting.backtest(
+        processed=deepcopy(processed),
+        start_date=min_date,
+        end_date=max_date,
+    )
+    results = result["results"]
+    assert not results.empty
+    # Timeout settings from default_conf = entry: 10, exit: 30
+    assert len(results) == (4 if use_detail else 2)
+
+    assert "orders" in results.columns
+    data_pair = processed[pair]
+
+    data_1m_pair = backtesting.detail_data[pair] if use_detail else pd.DataFrame()
+    late_entry = 0
+    for _, t in results.iterrows():
+        assert len(t["orders"]) == 2
+
+        entryo = t["orders"][0]
+        entry_ts = datetime.fromtimestamp(entryo["order_filled_timestamp"] // 1000, tz=timezone.utc)
+        if entry_ts > t["open_date"]:
+            late_entry += 1
+
+        # Get "entry fill" candle
+        ln = (
+            data_1m_pair.loc[data_1m_pair["date"] == entry_ts]
+            if use_detail
+            else data_pair.loc[data_pair["date"] == entry_ts]
+        )
+        # Check open trade rate aligns to open rate
+        assert not ln.empty
+
+        assert (
+            round(ln.iloc[0]["low"], 6) <= round(t["open_rate"], 6) <= round(ln.iloc[0]["high"], 6)
+        )
+        # check close trade rate aligns to close rate or is between high and low
+        ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
+        if use_detail:
+            ln1_1m = data_1m_pair.loc[data_1m_pair["date"] == t["close_date"]]
+            assert not ln1.empty or not ln1_1m.empty
+        else:
+            assert not ln1.empty
+        ln2 = ln1_1m if ln1.empty else ln1
+
+        assert (
+            round(ln2.iloc[0]["low"], 6)
+            <= round(t["close_rate"], 6)
+            <= round(ln2.iloc[0]["high"], 6)
+        )
+    assert pytest.approx(Trade.bt_trades[1].funding_fees) == exp_funding_fee
+    assert ff_spy.call_count == exp_ff_updates
+    # assert late_entry > 0
+
+
+@pytest.mark.parametrize(
+    "use_detail,entries,max_stake,ff_updates,expected_ff",
+    [
+        (True, 50, 3000, 55, -1.18038144),
+        (False, 6, 360, 11, -0.14679994),
+    ],
+)
+def test_backtest_one_detail_futures_funding_fees(
+    default_conf_usdt,
+    fee,
+    mocker,
+    testdatadir,
+    use_detail,
+    entries,
+    max_stake,
+    ff_updates,
+    expected_ff,
+) -> None:
+    """
+    Funding fees are expected to differ, as the maximum position size differs.
+    """
+    default_conf_usdt["use_exit_signal"] = False
+    default_conf_usdt["trading_mode"] = "futures"
+    default_conf_usdt["margin_mode"] = "isolated"
+    default_conf_usdt["candle_type_def"] = CandleType.FUTURES
+    default_conf_usdt["minimal_roi"] = {"0": 1}
+    default_conf_usdt["dry_run_wallet"] = 100000
+
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["XRP/USDT:USDT"]),
+    )
+    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt", return_value=(0.01, 0.01))
+    default_conf_usdt["timeframe"] = "1h"
+    if use_detail:
+        default_conf_usdt["timeframe_detail"] = "5m"
+    patch_exchange(mocker)
+
+    def advise_entry(df, *args, **kwargs):
+        # Mock function to force several entries
+        df.loc[:, "enter_long"] = 1
+        return df
+
+    def adjust_trade_position(trade, current_time, **kwargs):
+        if current_time > datetime(2021, 11, 18, 2, 0, 0, tzinfo=timezone.utc):
+            return None
+        return default_conf_usdt["stake_amount"]
+
+    default_conf_usdt["max_open_trades"] = 1
+
+    backtesting = Backtesting(default_conf_usdt)
+    ff_spy = mocker.spy(backtesting.exchange, "calculate_funding_fees")
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.populate_entry_trend = advise_entry
+    backtesting.strategy.adjust_trade_position = adjust_trade_position
+    backtesting.strategy.leverage = lambda **kwargs: 1
+    backtesting.strategy.position_adjustment_enable = True
+    pair = "XRP/USDT:USDT"
+    # Pick a timerange adapted to the pair we use to test
+    timerange = TimeRange.parse_timerange("20211117-20211119")
+    data = history.load_data(
+        datadir=Path(testdatadir),
+        timeframe="1h",
+        pairs=[pair],
+        timerange=timerange,
+        candle_type=CandleType.FUTURES,
+    )
+    backtesting.load_bt_data_detail()
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+
+    result = backtesting.backtest(
+        processed=deepcopy(processed),
+        start_date=min_date,
+        end_date=max_date,
+    )
+    results = result["results"]
+    assert not results.empty
+    # Only one result - as we're not selling.
+    assert len(results) == 1
+
+    assert "orders" in results.columns
+    # funding_fees have been calculated for each funding-fee candle
+    # the trade is open for 26 hours - hence we expect the 8h fee to apply 4 times.
+    # Additional counts will happen due each successful entry, which needs to call this, too.
+    assert ff_spy.call_count == ff_updates
+
+    for t in Trade.bt_trades:
+        # At least 6 adjustment orders
+        assert t.nr_of_successful_entries == entries
+        # Funding fees will vary depending on the number of adjustment orders
+        # That number is a lot higher with detail data.
+        assert t.max_stake_amount == max_stake
+        assert pytest.approx(t.funding_fees) == expected_ff
+
+
+def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None:
+    # This strategy intentionally places unfillable orders.
+    default_conf["strategy"] = "StrategyTestV3CustomEntryPrice"
+    default_conf["startup_candle_count"] = 0
+    # Cancel unfilled order after 4 minutes on 5m timeframe.
+    default_conf["unfilledtimeout"] = {"entry": 4}
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    patch_exchange(mocker)
+    default_conf["max_open_trades"] = 1
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    # Testing dataframe contains 11 candles. Expecting 10 timed out orders.
+    timerange = TimeRange("date", "date", 1517227800, 1517231100)
+    data = history.load_data(
+        datadir=testdatadir, timeframe="5m", pairs=["UNITTEST/BTC"], timerange=timerange
+    )
+    min_date, max_date = get_timerange(data)
+
+    result = backtesting.backtest(
+        processed=deepcopy(data),
+        start_date=min_date,
+        end_date=max_date,
+    )
+
+    assert result["timedout_entry_orders"] == 10
+
+
+def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None:
+    default_conf["use_exit_signal"] = False
+    default_conf["max_open_trades"] = 1
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+
+    # Run a backtesting for an exiting 1min timeframe
+    timerange = TimeRange.parse_timerange("1510688220-1510700340")
+    data = history.load_data(
+        datadir=testdatadir, timeframe="1m", pairs=["UNITTEST/BTC"], timerange=timerange
+    )
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+    results = backtesting.backtest(
+        processed=processed,
+        start_date=min_date,
+        end_date=max_date,
+    )
+    assert not results["results"].empty
+    assert len(results["results"]) == 1
+
+
+def test_backtest_trim_no_data_left(default_conf, fee, mocker, testdatadir) -> None:
+    default_conf["use_exit_signal"] = False
+    default_conf["max_open_trades"] = 10
+
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    timerange = TimeRange("date", None, 1517227800, 0)
+    backtesting.required_startup = 100
+    backtesting.timerange = timerange
+    data = history.load_data(
+        datadir=testdatadir, timeframe="5m", pairs=["UNITTEST/BTC"], timerange=timerange
+    )
+    df = data["UNITTEST/BTC"]
+    df["date"] = df.loc[:, "date"] - timedelta(days=1)
+    # Trimming 100 candles, so after 2nd trimming, no candle is left.
+    df = df.iloc[:100]
+    data["XRP/USDT"] = df
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+
+    backtesting.backtest(
+        processed=deepcopy(processed),
+        start_date=min_date,
+        end_date=max_date,
+    )
+
+
+def test_processed(default_conf, mocker, testdatadir) -> None:
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+
+    dict_of_tickerrows = load_data_test("raise", testdatadir)
+    dataframes = backtesting.strategy.advise_all_indicators(dict_of_tickerrows)
+    dataframe = dataframes["UNITTEST/BTC"]
+    cols = dataframe.columns
+    # assert the dataframe got some of the indicator columns
+    for col in ["close", "high", "low", "open", "date", "ema10", "rsi", "fastd", "plus_di"]:
+        assert col in cols
+
+
+def test_backtest_dataprovider_analyzed_df(default_conf, fee, mocker, testdatadir) -> None:
+    default_conf["use_exit_signal"] = False
+    default_conf["max_open_trades"] = 10
+    default_conf["runmode"] = "backtest"
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=100000)
+    patch_exchange(mocker)
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    timerange = TimeRange("date", None, 1517227800, 0)
+    data = history.load_data(
+        datadir=testdatadir, timeframe="5m", pairs=["UNITTEST/BTC"], timerange=timerange
+    )
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+
+    count = 0
+
+    def tmp_confirm_entry(pair, current_time, **kwargs):
+        nonlocal count
+        dp = backtesting.strategy.dp
+        df, _ = dp.get_analyzed_dataframe(pair, backtesting.strategy.timeframe)
+        current_candle = df.iloc[-1].squeeze()
+        assert current_candle["enter_long"] == 1
+
+        candle_date = timeframe_to_next_date(backtesting.strategy.timeframe, current_candle["date"])
+        assert candle_date == current_time
+        # These asserts don't properly raise as they are nested,
+        # therefore we increment count and assert for that.
+        df = dp.get_pair_dataframe(pair, backtesting.strategy.timeframe)
+        prior_time = timeframe_to_prev_date(
+            backtesting.strategy.timeframe, candle_date - timedelta(seconds=1)
+        )
+        assert prior_time == df.iloc[-1].squeeze()["date"]
+        assert df.iloc[-1].squeeze()["date"] < current_time
+
+        count += 1
+
+    backtesting.strategy.confirm_trade_entry = tmp_confirm_entry
+    backtesting.backtest(
+        processed=deepcopy(processed),
+        start_date=min_date,
+        end_date=max_date,
+    )
+    assert count == 5
+
+
+def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatadir) -> None:
+    # While this test IS a copy of test_backtest_pricecontours, it's needed to ensure
+    # results do not carry-over to the next run, which is not given by using parametrize.
+    patch_exchange(mocker)
+    default_conf["_strategy_protections"] = [
+        {
+            "method": "CooldownPeriod",
+            "stop_duration": 3,
+        }
+    ]
+
+    default_conf["enable_protections"] = True
+    default_conf["timeframe"] = "1m"
+    default_conf["max_open_trades"] = 1
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    tests = [
+        ["sine", 9],
+        ["raise", 10],
+        ["lower", 0],
+        ["sine", 9],
+        ["raise", 10],
+    ]
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+
+    # While entry-signals are unrealistic, running backtesting
+    # over and over again should not cause different results
+    for [contour, numres] in tests:
+        # Debug output for random test failure
+        print(f"{contour}, {numres}")
+        data = load_data_test(contour, testdatadir)
+        processed = backtesting.strategy.advise_all_indicators(data)
+        min_date, max_date = get_timerange(processed)
+        assert isinstance(processed, dict)
+        results = backtesting.backtest(
+            processed=processed,
+            start_date=min_date,
+            end_date=max_date,
+        )
+        assert len(results["results"]) == numres
+
+
+@pytest.mark.parametrize(
+    "protections,contour,expected",
+    [
+        (None, "sine", 35),
+        (None, "raise", 19),
+        (None, "lower", 0),
+        (None, "sine", 35),
+        (None, "raise", 19),
+        ([{"method": "CooldownPeriod", "stop_duration": 3}], "sine", 9),
+        ([{"method": "CooldownPeriod", "stop_duration": 3}], "raise", 10),
+        ([{"method": "CooldownPeriod", "stop_duration": 3}], "lower", 0),
+        ([{"method": "CooldownPeriod", "stop_duration": 3}], "sine", 9),
+        ([{"method": "CooldownPeriod", "stop_duration": 3}], "raise", 10),
+    ],
+)
+def test_backtest_pricecontours(
+    default_conf, mocker, testdatadir, protections, contour, expected
+) -> None:
+    if protections:
+        default_conf["_strategy_protections"] = protections
+        default_conf["enable_protections"] = True
+
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    # While entry-signals are unrealistic, running backtesting
+    # over and over again should not cause different results
+
+    default_conf["timeframe"] = "1m"
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+
+    data = load_data_test(contour, testdatadir)
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+    assert isinstance(processed, dict)
+    backtesting.strategy.max_open_trades = 1
+    backtesting.config.update({"max_open_trades": 1})
+    results = backtesting.backtest(
+        processed=processed,
+        start_date=min_date,
+        end_date=max_date,
+    )
+    assert len(results["results"]) == expected
+
+
+def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
+    # Override the default buy trend function in our StrategyTest
+    def fun(dataframe=None, pair=None):
+        buy_value = 1
+        sell_value = 1
+        return _trend(dataframe, buy_value, sell_value)
+
+    default_conf["max_open_trades"] = 10
+    backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.advise_entry = fun  # Override
+    backtesting.strategy.advise_exit = fun  # Override
+    result = backtesting.backtest(**backtest_conf)
+    assert result["results"].empty
+
+
+def test_backtest_only_sell(mocker, default_conf, testdatadir):
+    # Override the default buy trend function in our StrategyTest
+    def fun(dataframe=None, pair=None):
+        buy_value = 0
+        sell_value = 1
+        return _trend(dataframe, buy_value, sell_value)
+
+    default_conf["max_open_trades"] = 10
+    backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.advise_entry = fun  # Override
+    backtesting.strategy.advise_exit = fun  # Override
+    result = backtesting.backtest(**backtest_conf)
+    assert result["results"].empty
+
+
+def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    default_conf["max_open_trades"] = 10
+    default_conf["runmode"] = "backtest"
+    backtest_conf = _make_backtest_conf(
+        mocker, conf=default_conf, pair="UNITTEST/BTC", datadir=testdatadir
+    )
+    default_conf["timeframe"] = "1m"
+    backtesting = Backtesting(default_conf)
+    backtesting.required_startup = 0
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.advise_entry = _trend_alternate  # Override
+    backtesting.strategy.advise_exit = _trend_alternate  # Override
+    result = backtesting.backtest(**backtest_conf)
+    # 200 candles in backtest data
+    # won't buy on first (shifted by 1)
+    # 100 buys signals
+    results = result["results"]
+    assert len(results) == 100
+    # Cached data should be 200
+    analyzed_df = backtesting.dataprovider.get_analyzed_dataframe("UNITTEST/BTC", "1m")[0]
+    assert len(analyzed_df) == 200
+    # Expect last candle to be 1 below end date (as the last candle is assumed as "incomplete"
+    # during backtesting)
+    expected_last_candle_date = backtest_conf["end_date"] - timedelta(minutes=1)
+    assert analyzed_df.iloc[-1]["date"].to_pydatetime() == expected_last_candle_date
+
+    # One trade was force-closed at the end
+    assert len(results.loc[results["is_open"]]) == 0
+
+
+@pytest.mark.parametrize("pair", ["ADA/BTC", "LTC/BTC"])
+@pytest.mark.parametrize("tres", [0, 20, 30])
+def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir):
+    def _trend_alternate_hold(dataframe=None, metadata=None):
+        """
+        Buy every xth candle - sell every other xth -2 (hold on to pairs a bit)
+        """
+        if metadata["pair"] in ("ETH/BTC", "LTC/BTC"):
+            multi = 20
+        else:
+            multi = 18
+        dataframe["enter_long"] = np.where(dataframe.index % multi == 0, 1, 0)
+        dataframe["exit_long"] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
+        dataframe["enter_short"] = 0
+        dataframe["exit_short"] = 0
+        return dataframe
+
+    default_conf["runmode"] = "backtest"
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    patch_exchange(mocker)
+
+    pairs = ["ADA/BTC", "DASH/BTC", "ETH/BTC", "LTC/BTC", "NXT/BTC"]
+    data = history.load_data(datadir=testdatadir, timeframe="5m", pairs=pairs)
+    # Only use 500 lines to increase performance
+    data = trim_dictlist(data, -500)
+
+    # Remove data for one pair from the beginning of the data
+    if tres > 0:
+        data[pair] = data[pair][tres:].reset_index()
+    default_conf["timeframe"] = "5m"
+    default_conf["max_open_trades"] = 3
+
+    backtesting = Backtesting(default_conf)
+    vr_spy = mocker.spy(backtesting, "validate_row")
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.bot_loop_start = MagicMock()
+    backtesting.strategy.advise_entry = _trend_alternate_hold  # Override
+    backtesting.strategy.advise_exit = _trend_alternate_hold  # Override
+
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+
+    backtest_conf = {
+        "processed": deepcopy(processed),
+        "start_date": min_date,
+        "end_date": max_date,
+    }
+
+    results = backtesting.backtest(**backtest_conf)
+
+    # bot_loop_start is called once per candle.
+    assert backtesting.strategy.bot_loop_start.call_count == 499
+    # Validated row once per candle and pair
+    assert vr_spy.call_count == 2495
+    # List of calls pair args - in batches of 5 (s)
+    calls_per_candle = defaultdict(list)
+    for call in vr_spy.call_args_list:
+        calls_per_candle[call[0][3]].append(call[0][1])
+
+    all_orients = [x for _, x in calls_per_candle.items()]
+
+    distinct_calls = [list(x) for x in set(tuple(x) for x in all_orients)]
+
+    # All calls must be made for the full pairlist
+    assert all(len(x) == 5 for x in distinct_calls)
+
+    # order varied - and is not always identical
+    assert not all(
+        x == ["ADA/BTC", "DASH/BTC", "ETH/BTC", "LTC/BTC", "NXT/BTC"] for x in distinct_calls
+    )
+    # But some calls should've kept the original ordering
+    assert any(
+        x == ["ADA/BTC", "DASH/BTC", "ETH/BTC", "LTC/BTC", "NXT/BTC"] for x in distinct_calls
+    )
+    assert (
+        # Ordering can be different, but should be one of the following
+        any(x == ["ETH/BTC", "ADA/BTC", "DASH/BTC", "LTC/BTC", "NXT/BTC"] for x in distinct_calls)
+        or any(
+            x == ["ETH/BTC", "LTC/BTC", "ADA/BTC", "DASH/BTC", "NXT/BTC"] for x in distinct_calls
+        )
+    )
+
+    # Make sure we have parallel trades
+    assert len(evaluate_result_multi(results["results"], "5m", 2)) > 0
+    # make sure we don't have trades with more than configured max_open_trades
+    assert len(evaluate_result_multi(results["results"], "5m", 3)) == 0
+
+    # Cached data correctly removed amounts
+    removed_candles = len(data[pair]) - 1
+    assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, "5m")[0]) == removed_candles
+    assert (
+        len(backtesting.dataprovider.get_analyzed_dataframe("NXT/BTC", "5m")[0])
+        == len(data["NXT/BTC"]) - 1
+    )
+
+    backtesting.strategy.max_open_trades = 1
+    backtesting.config.update({"max_open_trades": 1})
+    backtest_conf = {
+        "processed": deepcopy(processed),
+        "start_date": min_date,
+        "end_date": max_date,
+    }
+    results = backtesting.backtest(**backtest_conf)
+    assert len(evaluate_result_multi(results["results"], "5m", 1)) == 0
+
+
+@pytest.mark.parametrize("use_detail", [True, False])
+@pytest.mark.parametrize("pair", ["ADA/USDT", "LTC/USDT"])
+@pytest.mark.parametrize("tres", [0, 20, 30])
+def test_backtest_multi_pair_detail(
+    default_conf_usdt,
+    fee,
+    mocker,
+    tres,
+    pair,
+    use_detail,
+):
+    """
+    literally the same as test_backtest_multi_pair - but with artificial data
+    and detail timeframe.
+    """
+
+    def _trend_alternate_hold(dataframe=None, metadata=None):
+        """
+        Buy every xth candle - sell every other xth -2 (hold on to pairs a bit)
+        """
+        if metadata["pair"] in ("ETH/USDT", "LTC/USDT"):
+            multi = 20
+        else:
+            multi = 18
+        dataframe["enter_long"] = np.where(dataframe.index % multi == 0, 1, 0)
+        dataframe["exit_long"] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
+        dataframe["enter_short"] = 0
+        dataframe["exit_short"] = 0
+        return dataframe
+
+    default_conf_usdt.update(
+        {
+            "runmode": "backtest",
+            "stoploss": -1.0,
+            "minimal_roi": {"0": 100},
+        }
+    )
+
+    if use_detail:
+        default_conf_usdt["timeframe_detail"] = "1m"
+
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    patch_exchange(mocker)
+
+    raw_candles_1m = generate_test_data("1m", 1000, "2022-01-03 12:00:00+00:00")
+    raw_candles = ohlcv_fill_up_missing_data(raw_candles_1m, "5m", "dummy")
+
+    pairs = ["ADA/USDT", "DASH/USDT", "ETH/USDT", "LTC/USDT", "NXT/USDT"]
+    data = {pair: raw_candles for pair in pairs}
+    detail_data = {pair: raw_candles_1m for pair in pairs}
+
+    # Only use 500 lines to increase performance
+    data = trim_dictlist(data, -200)
+
+    # Remove data for one pair from the beginning of the data
+    if tres > 0:
+        data[pair] = data[pair][tres:].reset_index()
+    default_conf_usdt["timeframe"] = "5m"
+    default_conf_usdt["max_open_trades"] = 3
+
+    backtesting = Backtesting(default_conf_usdt)
+    vr_spy = mocker.spy(backtesting, "validate_row")
+    bl_spy = mocker.spy(backtesting, "backtest_loop")
+    backtesting.detail_data = detail_data
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.bot_loop_start = MagicMock()
+    backtesting.strategy.advise_entry = _trend_alternate_hold  # Override
+    backtesting.strategy.advise_exit = _trend_alternate_hold  # Override
+
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+
+    backtest_conf = {
+        "processed": deepcopy(processed),
+        "start_date": min_date,
+        "end_date": max_date,
+    }
+
+    results = backtesting.backtest(**backtest_conf)
+
+    # bot_loop_start is called once per candle.
+    assert backtesting.strategy.bot_loop_start.call_count == 199
+    # Validated row once per candle and pair
+    assert vr_spy.call_count == 995
+
+    if use_detail:
+        # Backtest loop is called once per candle per pair
+        # Exact numbers depend on trade state - but should be around 3_800
+        assert bl_spy.call_count > 1_220
+        assert bl_spy.call_count < 1_300
+    else:
+        assert bl_spy.call_count < 995
+
+    # Make sure we have parallel trades
+    assert len(evaluate_result_multi(results["results"], "5m", 2)) > 0
+    # make sure we don't have trades with more than configured max_open_trades
+    assert len(evaluate_result_multi(results["results"], "5m", 3)) == 0
+
+    # Cached data correctly removed amounts
+    removed_candles = len(data[pair]) - 1
+    assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, "5m")[0]) == removed_candles
+    assert (
+        len(backtesting.dataprovider.get_analyzed_dataframe("NXT/USDT", "5m")[0])
+        == len(data["NXT/USDT"]) - 1
+    )
+
+    backtesting.strategy.max_open_trades = 1
+    backtesting.config.update({"max_open_trades": 1})
+    backtest_conf = {
+        "processed": deepcopy(processed),
+        "start_date": min_date,
+        "end_date": max_date,
+    }
+    results = backtesting.backtest(**backtest_conf)
+    assert len(evaluate_result_multi(results["results"], "5m", 1)) == 0
+
+
+@pytest.mark.parametrize("use_detail", [True, False])
+@pytest.mark.parametrize("pair", ["ADA/USDT", "LTC/USDT"])
+@pytest.mark.parametrize("tres", [0, 20, 30])
+def test_backtest_multi_pair_detail_simplified(
+    default_conf_usdt,
+    fee,
+    mocker,
+    tres,
+    pair,
+    use_detail,
+):
+    """
+    literally the same as test_backtest_multi_pair_detail
+    but with an "always enter" strategy, exiting after about half of the candle duration.
+    """
+
+    def _always_buy(dataframe, metadata):
+        """
+        Buy every xth candle - sell every other xth -2 (hold on to pairs a bit)
+        """
+        dataframe["enter_long"] = 1
+        dataframe["enter_short"] = 0
+        dataframe["exit_short"] = 0
+        return dataframe
+
+    def custom_exit(
+        trade: Trade,
+        current_time: datetime,
+        **kwargs,
+    ) -> str | bool | None:
+        # Exit within the same candle.
+        if (trade.open_date_utc + timedelta(minutes=20)) < current_time:
+            return "exit after 20 minutes"
+
+    default_conf_usdt.update(
+        {
+            "runmode": "backtest",
+            "stoploss": -1.0,
+            "minimal_roi": {"0": 100},
+        }
+    )
+
+    if use_detail:
+        default_conf_usdt["timeframe_detail"] = "5m"
+
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    patch_exchange(mocker)
+
+    raw_candles_5m = generate_test_data("5m", 1000, "2022-01-03 12:00:00+00:00")
+    raw_candles = ohlcv_fill_up_missing_data(raw_candles_5m, "1h", "dummy")
+
+    pairs = ["ADA/USDT", "DASH/USDT", "ETH/USDT", "LTC/USDT", "NXT/USDT"]
+    data = {pair: raw_candles for pair in pairs}
+    detail_data = {pair: raw_candles_5m for pair in pairs}
+
+    # Only use 500 lines to increase performance
+    data = trim_dictlist(data, -200)
+
+    # Remove data for one pair from the beginning of the data
+    if tres > 0:
+        data[pair] = data[pair][tres:].reset_index()
+    default_conf_usdt["timeframe"] = "1h"
+    default_conf_usdt["max_open_trades"] = 3
+
+    backtesting = Backtesting(default_conf_usdt)
+    vr_spy = mocker.spy(backtesting, "validate_row")
+    bl_spy = mocker.spy(backtesting, "backtest_loop")
+    backtesting.detail_data = detail_data
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.bot_loop_start = MagicMock()
+    backtesting.strategy.advise_entry = _always_buy  # Override
+    backtesting.strategy.advise_exit = _always_buy  # Override
+    backtesting.strategy.custom_exit = custom_exit  # Override
+
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+
+    backtest_conf = {
+        "processed": deepcopy(processed),
+        "start_date": min_date,
+        "end_date": max_date,
+    }
+
+    results = backtesting.backtest(**backtest_conf)
+
+    # bot_loop_start is called once per candle.
+    # assert backtesting.strategy.bot_loop_start.call_count == 83
+    # Validated row once per candle and pair
+    assert vr_spy.call_count == 415
+
+    if use_detail:
+        # Backtest loop is called once per candle per pair
+        # Exact numbers depend on trade state - but should be around 2_600
+        assert bl_spy.call_count > 2_170
+        assert bl_spy.call_count < 2_800
+        assert len(evaluate_result_multi(results["results"], "1h", 3)) > 0
+    else:
+        assert bl_spy.call_count < 995
+        assert len(evaluate_result_multi(results["results"], "1h", 3)) == 0
+
+    # Make sure we have parallel trades
+    assert len(evaluate_result_multi(results["results"], "1h", 2)) > 0
+    assert len(evaluate_result_multi(results["results"], "5m", 2)) > 0
+    # make sure we don't have trades with more than configured max_open_trades
+    # This must evaluate on detail timeframe - as we can have entries within the candle.
+    assert len(evaluate_result_multi(results["results"], "5m", 3)) == 0
+    assert len(evaluate_result_multi(results["results"], "1m", 3)) == 0
+
+    # # Cached data correctly removed amounts
+    offset = 1
+    removed_candles = len(data[pair]) - offset
+    assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, "1h")[0]) == removed_candles
+    assert (
+        len(backtesting.dataprovider.get_analyzed_dataframe("NXT/USDT", "1h")[0])
+        == len(data["NXT/USDT"]) - 1
+    )
+
+    backtesting.strategy.max_open_trades = 1
+    backtesting.config.update({"max_open_trades": 1})
+    backtest_conf = {
+        "processed": deepcopy(processed),
+        "start_date": min_date,
+        "end_date": max_date,
+    }
+    results = backtesting.backtest(**backtest_conf)
+    if use_detail:
+        assert len(evaluate_result_multi(results["results"], "1h", 1)) > 0
+    else:
+        assert len(evaluate_result_multi(results["results"], "1h", 1)) == 0
+    assert len(evaluate_result_multi(results["results"], "5m", 1)) == 0
+    assert len(evaluate_result_multi(results["results"], "1m", 1)) == 0
+
+
+@pytest.mark.parametrize("use_detail", [True, False])
+def test_backtest_multi_pair_long_short_switch(
+    default_conf_usdt,
+    fee,
+    mocker,
+    use_detail,
+):
+    """
+    literally the same as test_backtest_multi_pair - but with artificial data
+    and detail timeframe.
+    """
+
+    def _trend_alternate_hold(dataframe=None, metadata=None):
+        """
+        Buy every xth candle - sell every other xth -2 (hold on to pairs a bit)
+        """
+        if metadata["pair"] in ("ETH/USDT", "LTC/USDT"):
+            multi = 20
+        else:
+            multi = 18
+        dataframe["enter_long"] = np.where(dataframe.index % multi == 0, 1, 0)
+        dataframe["exit_long"] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
+        dataframe["enter_short"] = dataframe["exit_long"]
+        dataframe["exit_short"] = dataframe["enter_long"]
+        return dataframe
+
+    default_conf_usdt.update(
+        {
+            "runmode": "backtest",
+            "timeframe": "5m",
+            "max_open_trades": 1,
+            "stoploss": -1.0,
+            "minimal_roi": {"0": 100},
+            "margin_mode": "isolated",
+            "trading_mode": "futures",
+        }
+    )
+
+    if use_detail:
+        default_conf_usdt["timeframe_detail"] = "1m"
+
+    mocker.patch(f"{EXMS}.price_to_precision", lambda s, x, y, **kwargs: y)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    patch_exchange(mocker)
+
+    raw_candles_1m = generate_test_data("1m", 2500, "2022-01-03 12:00:00+00:00")
+    raw_candles = ohlcv_fill_up_missing_data(raw_candles_1m, "5m", "dummy")
+
+    pairs = [
+        "ETH/USDT:USDT",
+    ]
+    default_conf_usdt["exchange"]["pair_whitelist"] = pairs
+    # Fake whitelist to avoid some mock data issues
+    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt", return_value=(0.01, 0.01))
+
+    data = {pair: raw_candles for pair in pairs}
+    detail_data = {pair: raw_candles_1m for pair in pairs}
+
+    # Only use 500 lines to increase performance
+    data = trim_dictlist(data, -500)
+
+    backtesting = Backtesting(default_conf_usdt)
+    vr_spy = mocker.spy(backtesting, "validate_row")
+    bl_spy = mocker.spy(backtesting, "backtest_loop")
+    backtesting.detail_data = detail_data
+    backtesting.funding_fee_timeframe_secs = 3600 * 8  # 8h
+    backtesting.futures_data = {pair: pd.DataFrame() for pair in pairs}
+
+    backtesting.strategylist[0].can_short = True
+    backtesting._set_strategy(backtesting.strategylist[0])
+    backtesting.strategy.bot_loop_start = MagicMock()
+    backtesting.strategy.advise_entry = _trend_alternate_hold  # Override
+    backtesting.strategy.advise_exit = _trend_alternate_hold  # Override
+
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+
+    backtest_conf = {
+        "processed": deepcopy(processed),
+        "start_date": min_date,
+        "end_date": max_date,
+    }
+
+    results = backtesting.backtest(**backtest_conf)
+
+    # bot_loop_start is called once per candle.
+    assert backtesting.strategy.bot_loop_start.call_count == 499
+    # Validated row once per candle and pair
+    assert vr_spy.call_count == 499
+
+    if use_detail:
+        # Backtest loop is called once per candle per pair
+        assert bl_spy.call_count == 1511
+    else:
+        assert bl_spy.call_count == 508
+
+    # Make sure we have parallel trades
+    assert len(evaluate_result_multi(results["results"], "5m", 0)) > 0
+    # make sure we don't have trades with more than configured max_open_trades
+    assert len(evaluate_result_multi(results["results"], "5m", 1)) == 0
+
+    # Expect 26 results initially
+    assert len(results["results"]) == 53
+
+
+def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest")
+    mocker.patch("freqtrade.optimize.backtesting.generate_backtest_stats")
+    mocker.patch("freqtrade.optimize.backtesting.show_backtest_results")
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--datadir",
+        str(testdatadir),
+        "--timeframe",
+        "1m",
+        "--timerange",
+        "1510694220-1510700340",
+        "--enable-position-stacking",
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+    # check the logs, that will contain the backtest result
+    exists = [
+        "Parameter -i/--timeframe detected ... Using timeframe: 1m ...",
+        "Parameter --timerange detected: 1510694220-1510700340 ...",
+        f"Using data directory: {testdatadir} ...",
+        "Loading data from 2017-11-14 20:57:00 up to 2017-11-14 22:59:00 (0 days).",
+        "Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days).",
+        "Parameter --enable-position-stacking detected ...",
+    ]
+
+    for line in exists:
+        assert log_has(line, caplog)
+
+
+@pytest.mark.filterwarnings("ignore:deprecated")
+def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
+    default_conf.update(
+        {
+            "use_exit_signal": True,
+            "exit_profit_only": False,
+            "exit_profit_offset": 0.0,
+            "ignore_roi_if_entry_signal": False,
+        }
+    )
+    patch_exchange(mocker)
+    backtestmock = MagicMock(
+        return_value={
+            "results": pd.DataFrame(columns=BT_DATA_COLUMNS),
+            "config": default_conf,
+            "locks": [],
+            "rejected_signals": 20,
+            "timedout_entry_orders": 0,
+            "timedout_exit_orders": 0,
+            "canceled_trade_entries": 0,
+            "canceled_entry_orders": 0,
+            "replaced_entry_orders": 0,
+            "final_balance": 1000,
+        }
+    )
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest", backtestmock)
+    text_table_mock = MagicMock()
+    tag_metrics_mock = MagicMock()
+    strattable_mock = MagicMock()
+    strat_summary = MagicMock()
+
+    mocker.patch.multiple(
+        "freqtrade.optimize.optimize_reports.bt_output",
+        text_table_bt_results=text_table_mock,
+        text_table_strategy=strattable_mock,
+    )
+    mocker.patch.multiple(
+        "freqtrade.optimize.optimize_reports.optimize_reports",
+        generate_pair_metrics=MagicMock(),
+        generate_tag_metrics=tag_metrics_mock,
+        generate_strategy_comparison=strat_summary,
+        generate_daily_stats=MagicMock(),
+    )
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--strategy-path",
+        str(Path(__file__).parents[1] / "strategy/strats"),
+        "--timeframe",
+        "1m",
+        "--timerange",
+        "1510694220-1510700340",
+        "--enable-position-stacking",
+        "--strategy-list",
+        CURRENT_TEST_STRATEGY,
+        "StrategyTestV2",
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+    # 2 backtests, 6 tables (entry, exit, mixed - each 2x)
+    assert backtestmock.call_count == 2
+    assert text_table_mock.call_count == 4
+    assert strattable_mock.call_count == 1
+    assert tag_metrics_mock.call_count == 6
+    assert strat_summary.call_count == 1
+
+    # check the logs, that will contain the backtest result
+    exists = [
+        "Parameter -i/--timeframe detected ... Using timeframe: 1m ...",
+        "Parameter --timerange detected: 1510694220-1510700340 ...",
+        f"Using data directory: {testdatadir} ...",
+        "Loading data from 2017-11-14 20:57:00 up to 2017-11-14 22:59:00 (0 days).",
+        "Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days).",
+        "Parameter --enable-position-stacking detected ...",
+        f"Running backtesting for Strategy {CURRENT_TEST_STRATEGY}",
+        "Running backtesting for Strategy StrategyTestV2",
+    ]
+
+    for line in exists:
+        assert log_has(line, caplog)
+
+
+def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdatadir, capsys):
+    default_conf.update(
+        {
+            "use_exit_signal": True,
+            "exit_profit_only": False,
+            "exit_profit_offset": 0.0,
+            "ignore_roi_if_entry_signal": False,
+        }
+    )
+    patch_exchange(mocker)
+    result1 = pd.DataFrame(
+        {
+            "pair": ["XRP/BTC", "LTC/BTC"],
+            "profit_ratio": [0.0, 0.0],
+            "profit_abs": [0.0, 0.0],
+            "open_date": pd.to_datetime(
+                [
+                    "2018-01-29 18:40:00",
+                    "2018-01-30 03:30:00",
+                ],
+                utc=True,
+            ),
+            "close_date": pd.to_datetime(
+                [
+                    "2018-01-29 20:45:00",
+                    "2018-01-30 05:35:00",
+                ],
+                utc=True,
+            ),
+            "trade_duration": [235, 40],
+            "is_open": [False, False],
+            "stake_amount": [0.01, 0.01],
+            "open_rate": [0.104445, 0.10302485],
+            "close_rate": [0.104969, 0.103541],
+            "is_short": [False, False],
+            "exit_reason": [ExitType.ROI.value, ExitType.ROI.value],
+        }
+    )
+    result2 = pd.DataFrame(
+        {
+            "pair": ["XRP/BTC", "LTC/BTC", "ETH/BTC"],
+            "profit_ratio": [0.03, 0.01, 0.1],
+            "profit_abs": [0.01, 0.02, 0.2],
+            "open_date": pd.to_datetime(
+                ["2018-01-29 18:40:00", "2018-01-30 03:30:00", "2018-01-30 05:30:00"], utc=True
+            ),
+            "close_date": pd.to_datetime(
+                ["2018-01-29 20:45:00", "2018-01-30 05:35:00", "2018-01-30 08:30:00"], utc=True
+            ),
+            "trade_duration": [47, 40, 20],
+            "is_open": [False, False, False],
+            "stake_amount": [0.01, 0.01, 0.01],
+            "open_rate": [0.104445, 0.10302485, 0.122541],
+            "close_rate": [0.104969, 0.103541, 0.123541],
+            "is_short": [False, False, False],
+            "exit_reason": [ExitType.ROI.value, ExitType.ROI.value, ExitType.STOP_LOSS.value],
+        }
+    )
+    backtestmock = MagicMock(
+        side_effect=[
+            {
+                "results": result1,
+                "config": default_conf,
+                "locks": [],
+                "rejected_signals": 20,
+                "timedout_entry_orders": 0,
+                "timedout_exit_orders": 0,
+                "canceled_trade_entries": 0,
+                "canceled_entry_orders": 0,
+                "replaced_entry_orders": 0,
+                "final_balance": 1000,
+            },
+            {
+                "results": result2,
+                "config": default_conf,
+                "locks": [],
+                "rejected_signals": 20,
+                "timedout_entry_orders": 0,
+                "timedout_exit_orders": 0,
+                "canceled_trade_entries": 0,
+                "canceled_entry_orders": 0,
+                "replaced_entry_orders": 0,
+                "final_balance": 1000,
+            },
+        ]
+    )
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest", backtestmock)
+
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--strategy-path",
+        str(Path(__file__).parents[1] / "strategy/strats"),
+        "--timeframe",
+        "1m",
+        "--timerange",
+        "1510694220-1510700340",
+        "--enable-position-stacking",
+        "--breakdown",
+        "day",
+        "--strategy-list",
+        CURRENT_TEST_STRATEGY,
+        "StrategyTestV2",
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+
+    # check the logs, that will contain the backtest result
+    exists = [
+        "Parameter -i/--timeframe detected ... Using timeframe: 1m ...",
+        "Parameter --timerange detected: 1510694220-1510700340 ...",
+        f"Using data directory: {testdatadir} ...",
+        "Loading data from 2017-11-14 20:57:00 up to 2017-11-14 22:59:00 (0 days).",
+        "Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days).",
+        "Parameter --enable-position-stacking detected ...",
+        f"Running backtesting for Strategy {CURRENT_TEST_STRATEGY}",
+        "Running backtesting for Strategy StrategyTestV2",
+    ]
+
+    for line in exists:
+        assert log_has(line, caplog)
+
+    captured = capsys.readouterr()
+    assert "BACKTESTING REPORT" in captured.out
+    assert "EXIT REASON STATS" in captured.out
+    assert "DAY BREAKDOWN" in captured.out
+    assert "LEFT OPEN TRADES REPORT" in captured.out
+    assert "2017-11-14 21:17:00 -> 2017-11-14 22:59:00 | Max open trades : 1" in captured.out
+    assert "STRATEGY SUMMARY" in captured.out
+
+
+@pytest.mark.filterwarnings("ignore:deprecated")
+def test_backtest_start_futures_noliq(default_conf_usdt, mocker, caplog, testdatadir, capsys):
+    # Tests detail-data loading
+    default_conf_usdt.update(
+        {
+            "trading_mode": "futures",
+            "margin_mode": "isolated",
+            "use_exit_signal": True,
+            "exit_profit_only": False,
+            "exit_profit_offset": 0.0,
+            "ignore_roi_if_entry_signal": False,
+            "strategy": CURRENT_TEST_STRATEGY,
+        }
+    )
+    patch_exchange(mocker)
+
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["HULUMULU/USDT", "XRP/USDT:USDT"]),
+    )
+    # mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
+
+    patched_configuration_load_config_file(mocker, default_conf_usdt)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--strategy-path",
+        str(Path(__file__).parents[1] / "strategy/strats"),
+        "--timeframe",
+        "1h",
+    ]
+    args = get_args(args)
+    with pytest.raises(OperationalException, match=r"Pairs .* got no leverage tiers available\."):
+        start_backtesting(args)
+
+
+@pytest.mark.filterwarnings("ignore:deprecated")
+def test_backtest_start_nomock_futures(default_conf_usdt, mocker, caplog, testdatadir, capsys):
+    # Tests detail-data loading
+    default_conf_usdt.update(
+        {
+            "trading_mode": "futures",
+            "margin_mode": "isolated",
+            "use_exit_signal": True,
+            "exit_profit_only": False,
+            "exit_profit_offset": 0.0,
+            "ignore_roi_if_entry_signal": False,
+            "strategy": CURRENT_TEST_STRATEGY,
+        }
+    )
+    patch_exchange(mocker)
+    result1 = pd.DataFrame(
+        {
+            "pair": ["XRP/USDT:USDT", "XRP/USDT:USDT"],
+            "profit_ratio": [0.0, 0.0],
+            "profit_abs": [0.0, 0.0],
+            "open_date": pd.to_datetime(
+                [
+                    "2021-11-18 18:00:00",
+                    "2021-11-18 03:00:00",
+                ],
+                utc=True,
+            ),
+            "close_date": pd.to_datetime(
+                [
+                    "2021-11-18 20:00:00",
+                    "2021-11-18 05:00:00",
+                ],
+                utc=True,
+            ),
+            "trade_duration": [235, 40],
+            "is_open": [False, False],
+            "is_short": [False, False],
+            "stake_amount": [0.01, 0.01],
+            "open_rate": [0.104445, 0.10302485],
+            "close_rate": [0.104969, 0.103541],
+            "exit_reason": [ExitType.ROI, ExitType.ROI],
+        }
+    )
+    result2 = pd.DataFrame(
+        {
+            "pair": ["XRP/USDT:USDT", "XRP/USDT:USDT", "XRP/USDT:USDT"],
+            "profit_ratio": [0.03, 0.01, 0.1],
+            "profit_abs": [0.01, 0.02, 0.2],
+            "open_date": pd.to_datetime(
+                ["2021-11-19 18:00:00", "2021-11-19 03:00:00", "2021-11-19 05:00:00"], utc=True
+            ),
+            "close_date": pd.to_datetime(
+                ["2021-11-19 20:00:00", "2021-11-19 05:00:00", "2021-11-19 08:00:00"], utc=True
+            ),
+            "trade_duration": [47, 40, 20],
+            "is_open": [False, False, False],
+            "is_short": [False, False, False],
+            "stake_amount": [0.01, 0.01, 0.01],
+            "open_rate": [0.104445, 0.10302485, 0.122541],
+            "close_rate": [0.104969, 0.103541, 0.123541],
+            "exit_reason": [ExitType.ROI, ExitType.ROI, ExitType.STOP_LOSS],
+        }
+    )
+    backtestmock = MagicMock(
+        side_effect=[
+            {
+                "results": result1,
+                "config": default_conf_usdt,
+                "locks": [],
+                "rejected_signals": 20,
+                "timedout_entry_orders": 0,
+                "timedout_exit_orders": 0,
+                "canceled_trade_entries": 0,
+                "canceled_entry_orders": 0,
+                "replaced_entry_orders": 0,
+                "final_balance": 1000,
+            },
+            {
+                "results": result2,
+                "config": default_conf_usdt,
+                "locks": [],
+                "rejected_signals": 20,
+                "timedout_entry_orders": 0,
+                "timedout_exit_orders": 0,
+                "canceled_trade_entries": 0,
+                "canceled_entry_orders": 0,
+                "replaced_entry_orders": 0,
+                "final_balance": 1000,
+            },
+        ]
+    )
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["XRP/USDT:USDT"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest", backtestmock)
+
+    patched_configuration_load_config_file(mocker, default_conf_usdt)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--strategy-path",
+        str(Path(__file__).parents[1] / "strategy/strats"),
+        "--timeframe",
+        "1h",
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+
+    # check the logs, that will contain the backtest result
+    exists = [
+        "Parameter -i/--timeframe detected ... Using timeframe: 1h ...",
+        f"Using data directory: {testdatadir} ...",
+        "Loading data from 2021-11-17 01:00:00 up to 2021-11-21 04:00:00 (4 days).",
+        "Backtesting with data from 2021-11-17 21:00:00 up to 2021-11-21 04:00:00 (3 days).",
+        "XRP/USDT:USDT, funding_rate, 8h, data starts at 2021-11-18 00:00:00",
+        "XRP/USDT:USDT, mark, 8h, data starts at 2021-11-18 00:00:00",
+        f"Running backtesting for Strategy {CURRENT_TEST_STRATEGY}",
+    ]
+
+    for line in exists:
+        assert log_has(line, caplog)
+
+    captured = capsys.readouterr()
+    assert "BACKTESTING REPORT" in captured.out
+    assert "EXIT REASON STATS" in captured.out
+    assert "LEFT OPEN TRADES REPORT" in captured.out
+
+
+@pytest.mark.filterwarnings("ignore:deprecated")
+def test_backtest_start_multi_strat_nomock_detail(
+    default_conf, mocker, caplog, testdatadir, capsys
+):
+    # Tests detail-data loading
+    default_conf.update(
+        {
+            "use_exit_signal": True,
+            "exit_profit_only": False,
+            "exit_profit_offset": 0.0,
+            "ignore_roi_if_entry_signal": False,
+        }
+    )
+    patch_exchange(mocker)
+    result1 = pd.DataFrame(
+        {
+            "pair": ["XRP/BTC", "LTC/BTC"],
+            "profit_ratio": [0.0, 0.0],
+            "profit_abs": [0.0, 0.0],
+            "open_date": pd.to_datetime(
+                [
+                    "2018-01-29 18:40:00",
+                    "2018-01-30 03:30:00",
+                ],
+                utc=True,
+            ),
+            "close_date": pd.to_datetime(
+                [
+                    "2018-01-29 20:45:00",
+                    "2018-01-30 05:35:00",
+                ],
+                utc=True,
+            ),
+            "trade_duration": [235, 40],
+            "is_open": [False, False],
+            "is_short": [False, False],
+            "stake_amount": [0.01, 0.01],
+            "open_rate": [0.104445, 0.10302485],
+            "close_rate": [0.104969, 0.103541],
+            "exit_reason": [ExitType.ROI, ExitType.ROI],
+        }
+    )
+    result2 = pd.DataFrame(
+        {
+            "pair": ["XRP/BTC", "LTC/BTC", "ETH/BTC"],
+            "profit_ratio": [0.03, 0.01, 0.1],
+            "profit_abs": [0.01, 0.02, 0.2],
+            "open_date": pd.to_datetime(
+                ["2018-01-29 18:40:00", "2018-01-30 03:30:00", "2018-01-30 05:30:00"], utc=True
+            ),
+            "close_date": pd.to_datetime(
+                ["2018-01-29 20:45:00", "2018-01-30 05:35:00", "2018-01-30 08:30:00"], utc=True
+            ),
+            "trade_duration": [47, 40, 20],
+            "is_open": [False, False, False],
+            "is_short": [False, False, False],
+            "stake_amount": [0.01, 0.01, 0.01],
+            "open_rate": [0.104445, 0.10302485, 0.122541],
+            "close_rate": [0.104969, 0.103541, 0.123541],
+            "exit_reason": [ExitType.ROI, ExitType.ROI, ExitType.STOP_LOSS],
+        }
+    )
+    backtestmock = MagicMock(
+        side_effect=[
+            {
+                "results": result1,
+                "config": default_conf,
+                "locks": [],
+                "rejected_signals": 20,
+                "timedout_entry_orders": 0,
+                "timedout_exit_orders": 0,
+                "canceled_trade_entries": 0,
+                "canceled_entry_orders": 0,
+                "replaced_entry_orders": 0,
+                "final_balance": 1000,
+            },
+            {
+                "results": result2,
+                "config": default_conf,
+                "locks": [],
+                "rejected_signals": 20,
+                "timedout_entry_orders": 0,
+                "timedout_exit_orders": 0,
+                "canceled_trade_entries": 0,
+                "canceled_entry_orders": 0,
+                "replaced_entry_orders": 0,
+                "final_balance": 1000,
+            },
+        ]
+    )
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["XRP/ETH"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest", backtestmock)
+
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--strategy-path",
+        str(Path(__file__).parents[1] / "strategy/strats"),
+        "--timeframe",
+        "5m",
+        "--timeframe-detail",
+        "1m",
+        "--strategy-list",
+        CURRENT_TEST_STRATEGY,
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+
+    # check the logs, that will contain the backtest result
+    exists = [
+        "Parameter -i/--timeframe detected ... Using timeframe: 5m ...",
+        "Parameter --timeframe-detail detected, using 1m for intra-candle backtesting ...",
+        f"Using data directory: {testdatadir} ...",
+        "Loading data from 2019-10-11 00:00:00 up to 2019-10-13 11:15:00 (2 days).",
+        "Backtesting with data from 2019-10-11 01:40:00 up to 2019-10-13 11:15:00 (2 days).",
+        f"Running backtesting for Strategy {CURRENT_TEST_STRATEGY}",
+    ]
+
+    for line in exists:
+        assert log_has(line, caplog)
+
+    captured = capsys.readouterr()
+    assert "BACKTESTING REPORT" in captured.out
+    assert "EXIT REASON STATS" in captured.out
+    assert "LEFT OPEN TRADES REPORT" in captured.out
+
+
+@pytest.mark.filterwarnings("ignore:deprecated")
+@pytest.mark.parametrize("run_id", ["2", "changed"])
+@pytest.mark.parametrize("start_delta", [{"days": 0}, {"days": 1}, {"weeks": 1}, {"weeks": 4}])
+@pytest.mark.parametrize("cache", constants.BACKTEST_CACHE_AGE)
+def test_backtest_start_multi_strat_caching(
+    default_conf, mocker, caplog, testdatadir, run_id, start_delta, cache
+):
+    default_conf.update(
+        {
+            "use_exit_signal": True,
+            "exit_profit_only": False,
+            "exit_profit_offset": 0.0,
+            "ignore_roi_if_entry_signal": False,
+        }
+    )
+    patch_exchange(mocker)
+    backtestmock = MagicMock(
+        return_value={
+            "results": pd.DataFrame(columns=BT_DATA_COLUMNS),
+            "config": default_conf,
+            "locks": [],
+            "rejected_signals": 20,
+            "timedout_entry_orders": 0,
+            "timedout_exit_orders": 0,
+            "canceled_trade_entries": 0,
+            "canceled_entry_orders": 0,
+            "replaced_entry_orders": 0,
+            "final_balance": 1000,
+        }
+    )
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting.backtest", backtestmock)
+    mocker.patch("freqtrade.optimize.backtesting.show_backtest_results", MagicMock())
+
+    now = min_backtest_date = datetime.now(tz=timezone.utc)
+    start_time = now - timedelta(**start_delta) + timedelta(hours=1)
+    if cache == "none":
+        min_backtest_date = now + timedelta(days=1)
+    elif cache == "day":
+        min_backtest_date = now - timedelta(days=1)
+    elif cache == "week":
+        min_backtest_date = now - timedelta(weeks=1)
+    elif cache == "month":
+        min_backtest_date = now - timedelta(weeks=4)
+    load_backtest_metadata = MagicMock(
+        return_value={
+            "StrategyTestV2": {"run_id": "1", "backtest_start_time": now.timestamp()},
+            "StrategyTestV3": {"run_id": run_id, "backtest_start_time": start_time.timestamp()},
+        }
+    )
+    load_backtest_stats = MagicMock(
+        side_effect=[
+            {
+                "metadata": {"StrategyTestV2": {"run_id": "1"}},
+                "strategy": {"StrategyTestV2": {}},
+                "strategy_comparison": [{"key": "StrategyTestV2"}],
+            },
+            {
+                "metadata": {"StrategyTestV3": {"run_id": "2"}},
+                "strategy": {"StrategyTestV3": {}},
+                "strategy_comparison": [{"key": "StrategyTestV3"}],
+            },
+        ]
+    )
+    mocker.patch(
+        "pathlib.Path.glob",
+        return_value=[
+            Path(datetime.strftime(datetime.now(), "backtest-result-%Y-%m-%d_%H-%M-%S.json"))
+        ],
+    )
+    mocker.patch.multiple(
+        "freqtrade.data.btanalysis",
+        load_backtest_metadata=load_backtest_metadata,
+        load_backtest_stats=load_backtest_stats,
+    )
+    mocker.patch("freqtrade.optimize.backtesting.get_strategy_run_id", side_effect=["1", "2", "2"])
+
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--datadir",
+        str(testdatadir),
+        "--strategy-path",
+        str(Path(__file__).parents[1] / "strategy/strats"),
+        "--timeframe",
+        "1m",
+        "--timerange",
+        "1510694220-1510700340",
+        "--enable-position-stacking",
+        "--cache",
+        cache,
+        "--strategy-list",
+        "StrategyTestV2",
+        "StrategyTestV3",
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+
+    # check the logs, that will contain the backtest result
+    exists = [
+        "Parameter -i/--timeframe detected ... Using timeframe: 1m ...",
+        "Parameter --timerange detected: 1510694220-1510700340 ...",
+        f"Using data directory: {testdatadir} ...",
+        "Loading data from 2017-11-14 20:57:00 up to 2017-11-14 22:59:00 (0 days).",
+        "Parameter --enable-position-stacking detected ...",
+    ]
+
+    for line in exists:
+        assert log_has(line, caplog)
+
+    if cache == "none":
+        assert backtestmock.call_count == 2
+        exists = [
+            "Running backtesting for Strategy StrategyTestV2",
+            "Running backtesting for Strategy StrategyTestV3",
+            "Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days).",
+        ]
+    elif run_id == "2" and min_backtest_date < start_time:
+        assert backtestmock.call_count == 0
+        exists = [
+            "Reusing result of previous backtest for StrategyTestV2",
+            "Reusing result of previous backtest for StrategyTestV3",
+        ]
+    else:
+        exists = [
+            "Reusing result of previous backtest for StrategyTestV2",
+            "Running backtesting for Strategy StrategyTestV3",
+            "Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days).",
+        ]
+        assert backtestmock.call_count == 1
+
+    for line in exists:
+        assert log_has(line, caplog)
+
+
+def test_get_strategy_run_id(default_conf_usdt):
+    default_conf_usdt.update({"strategy": "StrategyTestV2", "max_open_trades": float("inf")})
+    strategy = StrategyResolver.load_strategy(default_conf_usdt)
+    x = get_strategy_run_id(strategy)
+    assert isinstance(x, str)
+
+
+def test_get_backtest_metadata_filename():
+    # Test with a file path
+    filename = Path("backtest_results.json")
+    expected = Path("backtest_results.meta.json")
+    assert get_backtest_metadata_filename(filename) == expected
+
+    # Test with a file path with multiple dots in the name
+    filename = Path("/path/to/backtest.results.json")
+    expected = Path("/path/to/backtest.results.meta.json")
+    assert get_backtest_metadata_filename(filename) == expected
+
+    # Test with a file path with no parent directory
+    filename = Path("backtest_results.json")
+    expected = Path("backtest_results.meta.json")
+    assert get_backtest_metadata_filename(filename) == expected
+
+    # Test with a string file path
+    filename = "/path/to/backtest_results.json"
+    expected = Path("/path/to/backtest_results.meta.json")
+    assert get_backtest_metadata_filename(filename) == expected
+
+    # Test with a string file path with no extension
+    filename = "/path/to/backtest_results"
+    expected = Path("/path/to/backtest_results.meta.json")
+    assert get_backtest_metadata_filename(filename) == expected
+
+    # Test with a string file path with multiple dots in the name
+    filename = "/path/to/backtest.results.json"
+    expected = Path("/path/to/backtest.results.meta.json")
+    assert get_backtest_metadata_filename(filename) == expected
+
+    # Test with a string file path with no parent directory
+    filename = "backtest_results.json"
+    expected = Path("backtest_results.meta.json")
+    assert get_backtest_metadata_filename(filename) == expected
+    # Test with a string file path with no parent directory
+
+    filename = "backtest_results_zip.zip"
+    expected = Path("backtest_results_zip.meta.json")
+    assert get_backtest_metadata_filename(filename) == expected
diff --git a/tests/optimize/test_backtesting_adjust_position.py b/tests/optimize/test_backtesting_adjust_position.py
new file mode 100644
index 000000000..c9e8c9eeb
--- /dev/null
+++ b/tests/optimize/test_backtesting_adjust_position.py
@@ -0,0 +1,224 @@
+# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
+
+from copy import deepcopy
+from unittest.mock import MagicMock
+
+import pandas as pd
+import pytest
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data import history
+from freqtrade.data.history import get_timerange
+from freqtrade.enums import ExitType
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.util.datetime_helpers import dt_utc
+from tests.conftest import EXMS, patch_exchange
+
+
+def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None:
+    default_conf["use_exit_signal"] = False
+    default_conf["max_open_trades"] = 10
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(
+        "freqtrade.optimize.backtesting.amount_to_contract_precision",
+        lambda x, *args, **kwargs: round(x, 8),
+    )
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    patch_exchange(mocker)
+    default_conf.update(
+        {"stake_amount": 100.0, "dry_run_wallet": 1000.0, "strategy": "StrategyTestV3"}
+    )
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    pair = "UNITTEST/BTC"
+    timerange = TimeRange("date", None, 1517227800, 0)
+    data = history.load_data(
+        datadir=testdatadir, timeframe="5m", pairs=["UNITTEST/BTC"], timerange=timerange
+    )
+    backtesting.strategy.position_adjustment_enable = True
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+    result = backtesting.backtest(
+        processed=deepcopy(processed),
+        start_date=min_date,
+        end_date=max_date,
+    )
+    results = result["results"]
+    assert not results.empty
+    assert len(results) == 2
+
+    expected = pd.DataFrame(
+        {
+            "pair": [pair, pair],
+            "stake_amount": [500.0, 100.0],
+            "max_stake_amount": [500.0, 100],
+            "amount": [4806.87657523, 970.63960782],
+            "open_date": pd.to_datetime(
+                [dt_utc(2018, 1, 29, 18, 40, 0), dt_utc(2018, 1, 30, 3, 30, 0)], utc=True
+            ),
+            "close_date": pd.to_datetime(
+                [dt_utc(2018, 1, 29, 22, 00, 0), dt_utc(2018, 1, 30, 4, 10, 0)], utc=True
+            ),
+            "open_rate": [0.10401764891917063, 0.10302485],
+            "close_rate": [0.10453904064307624, 0.10354126528822055],
+            "fee_open": [0.0025, 0.0025],
+            "fee_close": [0.0025, 0.0025],
+            "trade_duration": [200, 40],
+            "profit_ratio": [0.0, 0.0],
+            "profit_abs": [0.0, 0.0],
+            "exit_reason": [ExitType.ROI.value, ExitType.ROI.value],
+            "initial_stop_loss_abs": [0.0940005, 0.092722365],
+            "initial_stop_loss_ratio": [-0.1, -0.1],
+            "stop_loss_abs": [0.0940005, 0.092722365],
+            "stop_loss_ratio": [-0.1, -0.1],
+            "min_rate": [0.10370188, 0.10300000000000001],
+            "max_rate": [0.10481985, 0.10388887000000001],
+            "is_open": [False, False],
+            "enter_tag": ["", ""],
+            "leverage": [1.0, 1.0],
+            "is_short": [False, False],
+            "open_timestamp": [1517251200000, 1517283000000],
+            "close_timestamp": [1517263200000, 1517285400000],
+        }
+    )
+    results_no = results.drop(columns=["orders"])
+    pd.testing.assert_frame_equal(results_no, expected, check_exact=True)
+
+    data_pair = processed[pair]
+    assert len(results.iloc[0]["orders"]) == 6
+    assert len(results.iloc[1]["orders"]) == 2
+
+    for _, t in results.iterrows():
+        ln = data_pair.loc[data_pair["date"] == t["open_date"]]
+        # Check open trade rate aligns to open rate
+        assert ln is not None
+        # check close trade rate aligns to close rate or is between high and low
+        ln = data_pair.loc[data_pair["date"] == t["close_date"]]
+        assert round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or round(
+            ln.iloc[0]["low"], 6
+        ) < round(t["close_rate"], 6) < round(ln.iloc[0]["high"], 6)
+
+
+@pytest.mark.parametrize("leverage", [1, 2])
+def test_backtest_position_adjustment_detailed(default_conf, fee, mocker, leverage) -> None:
+    default_conf["use_exit_signal"] = False
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=10)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(f"{EXMS}.get_max_leverage", return_value=10)
+    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt", return_value=(0.1, 0.1))
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting._run_funding_fees")
+
+    patch_exchange(mocker)
+    default_conf.update(
+        {
+            "stake_amount": 100.0,
+            "dry_run_wallet": 1000.0,
+            "strategy": "StrategyTestV3",
+            "trading_mode": "futures",
+            "margin_mode": "isolated",
+        }
+    )
+    default_conf["pairlists"] = [{"method": "StaticPairList", "allow_inactive": True}]
+    backtesting = Backtesting(default_conf)
+    backtesting._can_short = True
+    backtesting._set_strategy(backtesting.strategylist[0])
+    pair = "XRP/USDT:USDT"
+    row_enter = [
+        pd.Timestamp(year=2020, month=1, day=1, hour=4, minute=0),
+        2.1,  # Open
+        2.2,  # High
+        1.9,  # Low
+        2.1,  # Close
+        1,  # enter_long
+        0,  # exit_long
+        0,  # enter_short
+        0,  # exit_short
+        "",  # enter_tag
+        "",  # exit_tag
+    ]
+    # Exit row - with slightly different values
+    row_exit = [
+        pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
+        2.2,  # Open
+        2.3,  # High
+        2.0,  # Low
+        2.2,  # Close
+        1,  # enter_long
+        0,  # exit_long
+        0,  # enter_short
+        0,  # exit_short
+        "",  # enter_tag
+        "",  # exit_tag
+    ]
+    backtesting.strategy.leverage = MagicMock(return_value=leverage)
+    trade = backtesting._enter_trade(pair, row=row_enter, direction="long")
+    current_time = row_enter[0].to_pydatetime()
+    assert trade
+    assert pytest.approx(trade.stake_amount) == 100.0
+    assert pytest.approx(trade.amount) == 47.61904762 * leverage
+    assert len(trade.orders) == 1
+    backtesting.strategy.adjust_trade_position = MagicMock(return_value=None)
+    assert pytest.approx(trade.liquidation_price) == (0.10278333 if leverage == 1 else 1.2122249)
+
+    trade = backtesting._check_adjust_trade_for_candle(trade, row_enter, current_time)
+    assert trade
+    assert pytest.approx(trade.stake_amount) == 100.0
+    assert pytest.approx(trade.amount) == 47.61904762 * leverage
+    assert len(trade.orders) == 1
+    # Increase position by 100
+    backtesting.strategy.adjust_trade_position = MagicMock(return_value=(100, "PartIncrease"))
+
+    trade = backtesting._check_adjust_trade_for_candle(trade, row_enter, current_time)
+
+    liq_price = 0.1038916 if leverage == 1 else 1.2127791
+    assert trade
+    assert pytest.approx(trade.stake_amount) == 200.0
+    assert pytest.approx(trade.amount) == 95.23809524 * leverage
+    assert len(trade.orders) == 2
+    assert trade.orders[-1].ft_order_tag == "PartIncrease"
+    assert pytest.approx(trade.liquidation_price) == liq_price
+
+    # Reduce by more than amount - no change to trade.
+    backtesting.strategy.adjust_trade_position = MagicMock(return_value=-500)
+    current_time = row_exit[0].to_pydatetime()
+
+    trade = backtesting._check_adjust_trade_for_candle(trade, row_exit, current_time)
+
+    assert trade
+    assert pytest.approx(trade.stake_amount) == 200.0
+    assert pytest.approx(trade.amount) == 95.23809524 * leverage
+    assert len(trade.orders) == 2
+    assert trade.nr_of_successful_entries == 2
+    assert pytest.approx(trade.liquidation_price) == liq_price
+
+    # Reduce position by 50
+    backtesting.strategy.adjust_trade_position = MagicMock(return_value=(-100, "partDecrease"))
+    trade = backtesting._check_adjust_trade_for_candle(trade, row_exit, current_time)
+
+    assert trade
+    assert pytest.approx(trade.stake_amount) == 100.0
+    assert pytest.approx(trade.amount) == 47.61904762 * leverage
+    assert len(trade.orders) == 3
+    assert trade.orders[-1].ft_order_tag == "partDecrease"
+    assert trade.nr_of_successful_entries == 2
+    assert trade.nr_of_successful_exits == 1
+    assert pytest.approx(trade.liquidation_price) == liq_price
+
+    # Adjust below minimum
+    backtesting.strategy.adjust_trade_position = MagicMock(return_value=-99)
+    trade = backtesting._check_adjust_trade_for_candle(trade, row_exit, current_time)
+
+    assert trade
+    assert pytest.approx(trade.stake_amount) == 100.0
+    assert pytest.approx(trade.amount) == 47.61904762 * leverage
+    assert len(trade.orders) == 3
+    assert trade.nr_of_successful_entries == 2
+    assert trade.nr_of_successful_exits == 1
+    assert pytest.approx(trade.liquidation_price) == liq_price
+
+    # Adjust to close trade
+    backtesting.strategy.adjust_trade_position = MagicMock(return_value=-trade.stake_amount)
+    trade = backtesting._check_adjust_trade_for_candle(trade, row_exit, current_time)
+    assert trade.is_open is False
diff --git a/tests/optimize/test_edge_cli.py b/tests/optimize/test_edge_cli.py
new file mode 100644
index 000000000..3f515bebe
--- /dev/null
+++ b/tests/optimize/test_edge_cli.py
@@ -0,0 +1,133 @@
+# pragma pylint: disable=missing-docstring, C0103, C0330
+# pragma pylint: disable=protected-access, too-many-lines, invalid-name, too-many-arguments
+
+from unittest.mock import MagicMock
+
+from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
+from freqtrade.enums import RunMode
+from freqtrade.optimize.edge_cli import EdgeCli
+from tests.conftest import (
+    CURRENT_TEST_STRATEGY,
+    EXMS,
+    get_args,
+    log_has,
+    patch_exchange,
+    patched_configuration_load_config_file,
+)
+
+
+def test_setup_optimize_configuration_without_arguments(mocker, default_conf, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "edge",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+    ]
+
+    config = setup_optimize_configuration(get_args(args), RunMode.EDGE)
+    assert config["runmode"] == RunMode.EDGE
+
+    assert "max_open_trades" in config
+    assert "stake_currency" in config
+    assert "stake_amount" in config
+    assert "exchange" in config
+    assert "pair_whitelist" in config["exchange"]
+    assert "datadir" in config
+    assert log_has("Using data directory: {} ...".format(config["datadir"]), caplog)
+    assert "timeframe" in config
+
+    assert "timerange" not in config
+    assert "stoploss_range" not in config
+
+
+def test_setup_edge_configuration_with_arguments(mocker, edge_conf, caplog) -> None:
+    patched_configuration_load_config_file(mocker, edge_conf)
+    mocker.patch("freqtrade.configuration.configuration.create_datadir", lambda c, x: x)
+
+    args = [
+        "edge",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--datadir",
+        "/foo/bar",
+        "--timeframe",
+        "1m",
+        "--timerange",
+        ":100",
+        "--stoplosses=-0.01,-0.10,-0.001",
+    ]
+
+    config = setup_optimize_configuration(get_args(args), RunMode.EDGE)
+    assert "max_open_trades" in config
+    assert "stake_currency" in config
+    assert "stake_amount" in config
+    assert "exchange" in config
+    assert "pair_whitelist" in config["exchange"]
+    assert "datadir" in config
+    assert config["runmode"] == RunMode.EDGE
+    assert log_has("Using data directory: {} ...".format(config["datadir"]), caplog)
+    assert "timeframe" in config
+    assert log_has("Parameter -i/--timeframe detected ... Using timeframe: 1m ...", caplog)
+
+    assert "timerange" in config
+    assert log_has("Parameter --timerange detected: {} ...".format(config["timerange"]), caplog)
+
+
+def test_start(mocker, fee, edge_conf, caplog) -> None:
+    start_mock = MagicMock()
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.optimize.edge_cli.EdgeCli.start", start_mock)
+    patched_configuration_load_config_file(mocker, edge_conf)
+
+    args = [
+        "edge",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+    ]
+    pargs = get_args(args)
+    start_edge(pargs)
+    assert log_has("Starting freqtrade in Edge mode", caplog)
+    assert start_mock.call_count == 1
+
+
+def test_edge_init(mocker, edge_conf) -> None:
+    patch_exchange(mocker)
+    edge_conf["stake_amount"] = 20
+    edge_cli = EdgeCli(edge_conf)
+    assert edge_cli.config == edge_conf
+    assert edge_cli.config["stake_amount"] == "unlimited"
+    assert callable(edge_cli.edge.calculate)
+    assert edge_cli.strategy.bot_started is True
+
+
+def test_edge_init_fee(mocker, edge_conf) -> None:
+    patch_exchange(mocker)
+    edge_conf["fee"] = 0.01234
+    edge_conf["stake_amount"] = 20
+    fee_mock = mocker.patch(f"{EXMS}.get_fee", return_value=0.5)
+    edge_cli = EdgeCli(edge_conf)
+    assert edge_cli.edge.fee == 0.01234
+    assert fee_mock.call_count == 0
+
+
+def test_edge_start(mocker, edge_conf) -> None:
+    mock_calculate = mocker.patch(
+        "freqtrade.edge.edge_positioning.Edge.calculate", return_value=True
+    )
+    table_mock = mocker.patch("freqtrade.optimize.edge_cli.generate_edge_table")
+
+    patch_exchange(mocker)
+    edge_conf["stake_amount"] = 20
+
+    edge_cli = EdgeCli(edge_conf)
+    edge_cli.start()
+    assert mock_calculate.call_count == 1
+    assert table_mock.call_count == 1
diff --git a/tests/optimize/test_hyperopt.py b/tests/optimize/test_hyperopt.py
new file mode 100644
index 000000000..8bd2e95e0
--- /dev/null
+++ b/tests/optimize/test_hyperopt.py
@@ -0,0 +1,1333 @@
+# pragma pylint: disable=missing-docstring,W0212,C0103
+from datetime import datetime, timedelta
+from functools import wraps
+from pathlib import Path
+from unittest.mock import ANY, MagicMock, PropertyMock
+
+import pandas as pd
+import pytest
+from filelock import Timeout
+from skopt.space import Integer
+
+from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_hyperopt
+from freqtrade.data.history import load_data
+from freqtrade.enums import ExitType, RunMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.hyperopt import Hyperopt
+from freqtrade.optimize.hyperopt.hyperopt_auto import HyperOptAuto
+from freqtrade.optimize.hyperopt_tools import HyperoptTools
+from freqtrade.optimize.optimize_reports import generate_strategy_stats
+from freqtrade.optimize.space import SKDecimal
+from freqtrade.strategy import IntParameter
+from freqtrade.util import dt_utc
+from tests.conftest import (
+    CURRENT_TEST_STRATEGY,
+    EXMS,
+    get_args,
+    get_markets,
+    log_has,
+    log_has_re,
+    patch_exchange,
+    patched_configuration_load_config_file,
+)
+
+
+def generate_result_metrics():
+    return {
+        "trade_count": 1,
+        "total_trades": 1,
+        "avg_profit": 0.1,
+        "total_profit": 0.001,
+        "profit": 0.01,
+        "duration": 20.0,
+        "wins": 1,
+        "draws": 0,
+        "losses": 0,
+        "profit_mean": 0.01,
+        "profit_total_abs": 0.001,
+        "profit_total": 0.01,
+        "holding_avg": timedelta(minutes=20),
+        "max_drawdown_account": 0.001,
+        "max_drawdown_abs": 0.001,
+        "loss": 0.001,
+        "is_initial_point": 0.001,
+        "is_random": False,
+        "is_best": 1,
+    }
+
+
+def test_setup_hyperopt_configuration_without_arguments(mocker, default_conf, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "hyperopt",
+        "--config",
+        "config.json",
+        "--strategy",
+        "HyperoptableStrategy",
+    ]
+
+    config = setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
+    assert "max_open_trades" in config
+    assert "stake_currency" in config
+    assert "stake_amount" in config
+    assert "exchange" in config
+    assert "pair_whitelist" in config["exchange"]
+    assert "datadir" in config
+    assert log_has("Using data directory: {} ...".format(config["datadir"]), caplog)
+    assert "timeframe" in config
+
+    assert "position_stacking" not in config
+    assert not log_has("Parameter --enable-position-stacking detected ...", caplog)
+
+    assert "timerange" not in config
+    assert "runmode" in config
+    assert config["runmode"] == RunMode.HYPEROPT
+
+
+def test_setup_hyperopt_configuration_with_arguments(mocker, default_conf, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.configuration.configuration.create_datadir", lambda c, x: x)
+
+    args = [
+        "hyperopt",
+        "--config",
+        "config.json",
+        "--strategy",
+        "HyperoptableStrategy",
+        "--datadir",
+        "/foo/bar",
+        "--timeframe",
+        "1m",
+        "--timerange",
+        ":100",
+        "--enable-position-stacking",
+        "--epochs",
+        "1000",
+        "--spaces",
+        "default",
+        "--print-all",
+    ]
+
+    config = setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
+    assert "max_open_trades" in config
+    assert "stake_currency" in config
+    assert "stake_amount" in config
+    assert "exchange" in config
+    assert "pair_whitelist" in config["exchange"]
+    assert "datadir" in config
+    assert config["runmode"] == RunMode.HYPEROPT
+
+    assert log_has("Using data directory: {} ...".format(config["datadir"]), caplog)
+    assert "timeframe" in config
+    assert log_has("Parameter -i/--timeframe detected ... Using timeframe: 1m ...", caplog)
+
+    assert "position_stacking" in config
+    assert log_has("Parameter --enable-position-stacking detected ...", caplog)
+
+    assert "timerange" in config
+    assert log_has("Parameter --timerange detected: {} ...".format(config["timerange"]), caplog)
+
+    assert "epochs" in config
+    assert log_has(
+        "Parameter --epochs detected ... Will run Hyperopt with for 1000 epochs ...", caplog
+    )
+
+    assert "spaces" in config
+    assert log_has("Parameter -s/--spaces detected: {}".format(config["spaces"]), caplog)
+    assert "print_all" in config
+    assert log_has("Parameter --print-all detected ...", caplog)
+
+
+def test_setup_hyperopt_configuration_stake_amount(mocker, default_conf) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        "hyperopt",
+        "--config",
+        "config.json",
+        "--strategy",
+        "HyperoptableStrategy",
+        "--stake-amount",
+        "1",
+        "--starting-balance",
+        "2",
+    ]
+    conf = setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
+    assert isinstance(conf, dict)
+
+    args = [
+        "hyperopt",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--stake-amount",
+        "1",
+        "--starting-balance",
+        "0.5",
+    ]
+    with pytest.raises(OperationalException, match=r"Starting balance .* smaller .*"):
+        setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
+
+
+def test_start_not_installed(mocker, default_conf, import_fails) -> None:
+    start_mock = MagicMock()
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    mocker.patch("freqtrade.optimize.hyperopt.Hyperopt.start", start_mock)
+    patch_exchange(mocker)
+
+    args = [
+        "hyperopt",
+        "--config",
+        "config.json",
+        "--strategy",
+        "HyperoptableStrategy",
+        "--epochs",
+        "5",
+        "--hyperopt-loss",
+        "SharpeHyperOptLossDaily",
+    ]
+    pargs = get_args(args)
+
+    with pytest.raises(OperationalException, match=r"Please ensure that the hyperopt dependencies"):
+        start_hyperopt(pargs)
+
+
+def test_start_no_hyperopt_allowed(mocker, hyperopt_conf, caplog) -> None:
+    start_mock = MagicMock()
+    patched_configuration_load_config_file(mocker, hyperopt_conf)
+    mocker.patch("freqtrade.optimize.hyperopt.Hyperopt.start", start_mock)
+    patch_exchange(mocker)
+
+    args = [
+        "hyperopt",
+        "--config",
+        "config.json",
+        "--hyperopt",
+        "HyperoptTestSepFile",
+        "--hyperopt-loss",
+        "SharpeHyperOptLossDaily",
+        "--epochs",
+        "5",
+    ]
+    pargs = get_args(args)
+    with pytest.raises(OperationalException, match=r"Using separate Hyperopt files has been.*"):
+        start_hyperopt(pargs)
+
+
+def test_start_no_data(mocker, hyperopt_conf, tmp_path) -> None:
+    hyperopt_conf["user_data_dir"] = tmp_path
+    patched_configuration_load_config_file(mocker, hyperopt_conf)
+    mocker.patch("freqtrade.data.history.load_pair_history", MagicMock(return_value=pd.DataFrame))
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+
+    patch_exchange(mocker)
+    args = [
+        "hyperopt",
+        "--config",
+        "config.json",
+        "--strategy",
+        "HyperoptableStrategy",
+        "--hyperopt-loss",
+        "SharpeHyperOptLossDaily",
+        "--epochs",
+        "5",
+    ]
+    pargs = get_args(args)
+    with pytest.raises(OperationalException, match="No data found. Terminating."):
+        start_hyperopt(pargs)
+
+    # Cleanup since that failed hyperopt start leaves a lockfile.
+    try:
+        Path(Hyperopt.get_lock_filename(hyperopt_conf)).unlink()
+    except Exception:
+        pass
+
+
+def test_start_filelock(mocker, hyperopt_conf, caplog) -> None:
+    hyperopt_mock = MagicMock(side_effect=Timeout(Hyperopt.get_lock_filename(hyperopt_conf)))
+    patched_configuration_load_config_file(mocker, hyperopt_conf)
+    mocker.patch("freqtrade.optimize.hyperopt.Hyperopt.__init__", hyperopt_mock)
+    patch_exchange(mocker)
+
+    args = [
+        "hyperopt",
+        "--config",
+        "config.json",
+        "--strategy",
+        "HyperoptableStrategy",
+        "--hyperopt-loss",
+        "SharpeHyperOptLossDaily",
+        "--epochs",
+        "5",
+    ]
+    pargs = get_args(args)
+    start_hyperopt(pargs)
+    assert log_has("Another running instance of freqtrade Hyperopt detected.", caplog)
+
+
+def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
+    hyperopt.current_best_loss = 2
+    hyperopt.total_epochs = 2
+
+    hyperopt.print_results(
+        {
+            "loss": 1,
+            "results_metrics": generate_result_metrics(),
+            "total_profit": 0,
+            "current_epoch": 2,  # This starts from 1 (in a human-friendly manner)
+            "is_initial_point": False,
+            "is_random": False,
+            "is_best": True,
+        }
+    )
+    hyperopt._hyper_out.print()
+    out, _err = capsys.readouterr()
+    assert all(
+        x in out for x in ["Best", "2/2", "1", "0.10%", "0.00100000 BTC    (1.00%)", "0:20:00"]
+    )
+
+
+def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
+    hyperopt.current_best_loss = 2
+    hyperopt.print_results(
+        {
+            "is_best": False,
+            "loss": 3,
+            "current_epoch": 1,
+        }
+    )
+    assert caplog.record_tuples == []
+
+
+def test_roi_table_generation(hyperopt) -> None:
+    params = {
+        "roi_t1": 5,
+        "roi_t2": 10,
+        "roi_t3": 15,
+        "roi_p1": 1,
+        "roi_p2": 2,
+        "roi_p3": 3,
+    }
+
+    assert hyperopt.hyperopter.custom_hyperopt.generate_roi_table(params) == {
+        0: 6,
+        15: 3,
+        25: 1,
+        30: 0,
+    }
+
+
+def test_params_no_optimize_details(hyperopt) -> None:
+    hyperopt.hyperopter.config["spaces"] = ["buy"]
+    res = hyperopt.hyperopter._get_no_optimize_details()
+    assert isinstance(res, dict)
+    assert "trailing" in res
+    assert res["trailing"]["trailing_stop"] is False
+    assert "roi" in res
+    assert res["roi"]["0"] == 0.04
+    assert "stoploss" in res
+    assert res["stoploss"]["stoploss"] == -0.1
+    assert "max_open_trades" in res
+    assert res["max_open_trades"]["max_open_trades"] == 1
+
+
+def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
+    dumper = mocker.patch("freqtrade.optimize.hyperopt.hyperopt_optimizer.dump")
+    dumper2 = mocker.patch("freqtrade.optimize.hyperopt.Hyperopt._save_result")
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.calculate_market_change", return_value=1.5
+    )
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.file_dump_json")
+
+    mocker.patch(
+        "freqtrade.optimize.backtesting.Backtesting.load_bt_data",
+        MagicMock(return_value=(MagicMock(), None)),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+    # Dummy-reduce points to ensure scikit-learn is forced to generate new values
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.INITIAL_POINTS", 2)
+
+    parallel = mocker.patch(
+        "freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel",
+        MagicMock(
+            return_value=[
+                {
+                    "loss": 1,
+                    "results_explanation": "foo result",
+                    "params": {"buy": {}, "sell": {}, "roi": {}, "stoploss": 0.0},
+                    "results_metrics": generate_result_metrics(),
+                }
+            ]
+        ),
+    )
+    patch_exchange(mocker)
+    # Co-test loading timeframe from strategy
+    del hyperopt_conf["timeframe"]
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.backtesting.strategy.advise_all_indicators = MagicMock()
+    hyperopt.hyperopter.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
+
+    hyperopt.start()
+
+    parallel.assert_called_once()
+
+    out, _err = capsys.readouterr()
+    assert "Best result:\n\n*    1/1: foo result Objective: 1.00000\n" in out
+    # Should be called for historical candle data
+    assert dumper.call_count == 1
+    assert dumper2.call_count == 1
+    assert hasattr(hyperopt.hyperopter.backtesting.strategy, "advise_exit")
+    assert hasattr(hyperopt.hyperopter.backtesting.strategy, "advise_entry")
+    assert (
+        hyperopt.hyperopter.backtesting.strategy.max_open_trades == hyperopt_conf["max_open_trades"]
+    )
+    assert hasattr(hyperopt.hyperopter.backtesting, "_position_stacking")
+
+
+def test_hyperopt_format_results(hyperopt):
+    bt_result = {
+        "results": pd.DataFrame(
+            {
+                "pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
+                "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
+                "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
+                "open_date": [
+                    dt_utc(2017, 11, 14, 19, 32, 00),
+                    dt_utc(2017, 11, 14, 21, 36, 00),
+                    dt_utc(2017, 11, 14, 22, 12, 00),
+                    dt_utc(2017, 11, 14, 22, 44, 00),
+                ],
+                "close_date": [
+                    dt_utc(2017, 11, 14, 21, 35, 00),
+                    dt_utc(2017, 11, 14, 22, 10, 00),
+                    dt_utc(2017, 11, 14, 22, 43, 00),
+                    dt_utc(2017, 11, 14, 22, 58, 00),
+                ],
+                "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
+                "close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
+                "trade_duration": [123, 34, 31, 14],
+                "is_open": [False, False, False, True],
+                "is_short": [False, False, False, False],
+                "stake_amount": [0.01, 0.01, 0.01, 0.01],
+                "exit_reason": [
+                    ExitType.ROI.value,
+                    ExitType.STOP_LOSS.value,
+                    ExitType.ROI.value,
+                    ExitType.FORCE_EXIT.value,
+                ],
+            }
+        ),
+        "config": hyperopt.config,
+        "locks": [],
+        "final_balance": 0.02,
+        "rejected_signals": 2,
+        "timedout_entry_orders": 0,
+        "timedout_exit_orders": 0,
+        "canceled_trade_entries": 0,
+        "canceled_entry_orders": 0,
+        "replaced_entry_orders": 0,
+        "backtest_start_time": 1619718665,
+        "backtest_end_time": 1619718665,
+    }
+    results_metrics = generate_strategy_stats(
+        ["XRP/BTC"],
+        "",
+        bt_result,
+        dt_utc(2017, 11, 14, 19, 32, 00),
+        dt_utc(2017, 12, 14, 19, 32, 00),
+        market_change=0,
+    )
+
+    results_explanation = HyperoptTools.format_results_explanation_string(results_metrics, "BTC")
+    total_profit = results_metrics["profit_total_abs"]
+
+    results = {
+        "loss": 0.0,
+        "params_dict": None,
+        "params_details": None,
+        "results_metrics": results_metrics,
+        "results_explanation": results_explanation,
+        "total_profit": total_profit,
+        "current_epoch": 1,
+        "is_initial_point": True,
+    }
+
+    result = HyperoptTools._format_explanation_string(results, 1)
+    assert " 0.71%" in result
+    assert "Total profit  0.00003100 BTC" in result
+    assert "0:50:00 min" in result
+
+
+def test_populate_indicators(hyperopt, testdatadir) -> None:
+    data = load_data(testdatadir, "1m", ["UNITTEST/BTC"], fill_up_missing=True)
+    dataframes = hyperopt.hyperopter.backtesting.strategy.advise_all_indicators(data)
+    dataframe = dataframes["UNITTEST/BTC"]
+
+    # Check if some indicators are generated. We will not test all of them
+    assert "adx" in dataframe
+    assert "macd" in dataframe
+    assert "rsi" in dataframe
+
+
+def test_generate_optimizer(mocker, hyperopt_conf) -> None:
+    hyperopt_conf.update(
+        {
+            "spaces": "all",
+            "hyperopt_min_trades": 1,
+        }
+    )
+
+    backtest_result = {
+        "results": pd.DataFrame(
+            {
+                "pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
+                "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
+                "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
+                "open_date": [
+                    dt_utc(2017, 11, 14, 19, 32, 00),
+                    dt_utc(2017, 11, 14, 21, 36, 00),
+                    dt_utc(2017, 11, 14, 22, 12, 00),
+                    dt_utc(2017, 11, 14, 22, 44, 00),
+                ],
+                "close_date": [
+                    dt_utc(2017, 11, 14, 21, 35, 00),
+                    dt_utc(2017, 11, 14, 22, 10, 00),
+                    dt_utc(2017, 11, 14, 22, 43, 00),
+                    dt_utc(2017, 11, 14, 22, 58, 00),
+                ],
+                "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
+                "close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
+                "trade_duration": [123, 34, 31, 14],
+                "is_open": [False, False, False, True],
+                "is_short": [False, False, False, False],
+                "stake_amount": [0.01, 0.01, 0.01, 0.01],
+                "exit_reason": [
+                    ExitType.ROI.value,
+                    ExitType.STOP_LOSS.value,
+                    ExitType.ROI.value,
+                    ExitType.FORCE_EXIT.value,
+                ],
+            }
+        ),
+        "config": hyperopt_conf,
+        "locks": [],
+        "rejected_signals": 20,
+        "timedout_entry_orders": 0,
+        "timedout_exit_orders": 0,
+        "canceled_trade_entries": 0,
+        "canceled_entry_orders": 0,
+        "replaced_entry_orders": 0,
+        "final_balance": 1000,
+    }
+
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.Backtesting.backtest",
+        return_value=backtest_result,
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        return_value=(dt_utc(2017, 12, 10), dt_utc(2017, 12, 13)),
+    )
+    patch_exchange(mocker)
+    mocker.patch.object(Path, "open")
+    mocker.patch("freqtrade.configuration.config_validation.validate_config_schema")
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.load", return_value={"XRP/BTC": None}
+    )
+
+    optimizer_param = {
+        "buy_plusdi": 0.02,
+        "buy_rsi": 35,
+        "sell_minusdi": 0.02,
+        "sell_rsi": 75,
+        "protection_cooldown_lookback": 20,
+        "protection_enabled": True,
+        "roi_t1": 60.0,
+        "roi_t2": 30.0,
+        "roi_t3": 20.0,
+        "roi_p1": 0.01,
+        "roi_p2": 0.01,
+        "roi_p3": 0.1,
+        "stoploss": -0.4,
+        "trailing_stop": True,
+        "trailing_stop_positive": 0.02,
+        "trailing_stop_positive_offset_p1": 0.05,
+        "trailing_only_offset_is_reached": False,
+        "max_open_trades": 3,
+    }
+    response_expected = {
+        "loss": 1.9147239021396234,
+        "results_explanation": (
+            "     4 trades. 4/0/0 Wins/Draws/Losses. "
+            "Avg profit   0.77%. Median profit   0.71%. Total profit  "
+            "0.00003100 BTC (   0.00%). "
+            "Avg duration 0:50:00 min."
+        ),
+        "params_details": {
+            "buy": {
+                "buy_plusdi": 0.02,
+                "buy_rsi": 35,
+            },
+            "roi": {"0": 0.12000000000000001, "20.0": 0.02, "50.0": 0.01, "110.0": 0},
+            "protection": {
+                "protection_cooldown_lookback": 20,
+                "protection_enabled": True,
+            },
+            "sell": {
+                "sell_minusdi": 0.02,
+                "sell_rsi": 75,
+            },
+            "stoploss": {"stoploss": -0.4},
+            "trailing": {
+                "trailing_only_offset_is_reached": False,
+                "trailing_stop": True,
+                "trailing_stop_positive": 0.02,
+                "trailing_stop_positive_offset": 0.07,
+            },
+            "max_open_trades": {"max_open_trades": 3},
+        },
+        "params_dict": optimizer_param,
+        "params_not_optimized": {"buy": {}, "protection": {}, "sell": {}},
+        "results_metrics": ANY,
+        "total_profit": 3.1e-08,
+    }
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.min_date = dt_utc(2017, 12, 10)
+    hyperopt.hyperopter.max_date = dt_utc(2017, 12, 13)
+    hyperopt.hyperopter.init_spaces()
+    generate_optimizer_value = hyperopt.hyperopter.generate_optimizer(
+        list(optimizer_param.values())
+    )
+    assert generate_optimizer_value == response_expected
+
+
+def test_clean_hyperopt(mocker, hyperopt_conf, caplog):
+    patch_exchange(mocker)
+
+    mocker.patch(
+        "freqtrade.strategy.hyper.HyperStrategyMixin.load_params_from_file",
+        MagicMock(return_value={}),
+    )
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.Path.is_file", MagicMock(return_value=True))
+    unlinkmock = mocker.patch("freqtrade.optimize.hyperopt.hyperopt.Path.unlink", MagicMock())
+    h = Hyperopt(hyperopt_conf)
+
+    assert unlinkmock.call_count == 2
+    assert log_has(f"Removing `{h.data_pickle_file}`.", caplog)
+
+
+def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
+    dumper = mocker.patch("freqtrade.optimize.hyperopt.hyperopt_optimizer.dump")
+    dumper2 = mocker.patch("freqtrade.optimize.hyperopt.Hyperopt._save_result")
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.file_dump_json")
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.calculate_market_change", return_value=1.5
+    )
+
+    mocker.patch(
+        "freqtrade.optimize.backtesting.Backtesting.load_bt_data",
+        MagicMock(return_value=(MagicMock(), None)),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+
+    parallel = mocker.patch(
+        "freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel",
+        MagicMock(
+            return_value=[
+                {
+                    "loss": 1,
+                    "results_explanation": "foo result",
+                    "params": {},
+                    "params_details": {
+                        "buy": {"mfi-value": None},
+                        "sell": {"sell-mfi-value": None},
+                        "roi": {},
+                        "stoploss": {"stoploss": None},
+                        "trailing": {"trailing_stop": None},
+                        "max_open_trades": {"max_open_trades": None},
+                    },
+                    "results_metrics": generate_result_metrics(),
+                }
+            ]
+        ),
+    )
+    patch_exchange(mocker)
+
+    hyperopt_conf.update(
+        {
+            "spaces": "all",
+            "hyperopt_jobs": 1,
+            "print_json": True,
+        }
+    )
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.backtesting.strategy.advise_all_indicators = MagicMock()
+    hyperopt.hyperopter.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
+
+    hyperopt.start()
+
+    parallel.assert_called_once()
+
+    out, _err = capsys.readouterr()
+    result_str = (
+        '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi"'
+        ':{},"stoploss":null,"trailing_stop":null,"max_open_trades":null}'
+    )
+    assert result_str in out
+    # Should be called for historical candle data
+    assert dumper.call_count == 1
+    assert dumper2.call_count == 1
+
+
+def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
+    dumper = mocker.patch("freqtrade.optimize.hyperopt.hyperopt_optimizer.dump")
+    dumper2 = mocker.patch("freqtrade.optimize.hyperopt.Hyperopt._save_result")
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.file_dump_json")
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.calculate_market_change", return_value=1.5
+    )
+    mocker.patch(
+        "freqtrade.optimize.backtesting.Backtesting.load_bt_data",
+        MagicMock(return_value=(MagicMock(), None)),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+
+    parallel = mocker.patch(
+        "freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel",
+        MagicMock(
+            return_value=[
+                {
+                    "loss": 1,
+                    "results_explanation": "foo result",
+                    "params": {},
+                    "params_details": {
+                        "buy": {"mfi-value": None},
+                        "sell": {"sell-mfi-value": None},
+                        "roi": {},
+                        "stoploss": {"stoploss": None},
+                    },
+                    "results_metrics": generate_result_metrics(),
+                }
+            ]
+        ),
+    )
+    patch_exchange(mocker)
+
+    hyperopt_conf.update({"print_json": True})
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.backtesting.strategy.advise_all_indicators = MagicMock()
+    hyperopt.hyperopter.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
+
+    hyperopt.start()
+
+    parallel.assert_called_once()
+
+    out, _err = capsys.readouterr()
+    assert (
+        '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null}'
+        in out
+    )
+    # Should be called for historical candle data
+    assert dumper.call_count == 1
+    assert dumper2.call_count == 1
+
+
+def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
+    dumper = mocker.patch("freqtrade.optimize.hyperopt.hyperopt_optimizer.dump")
+    dumper2 = mocker.patch("freqtrade.optimize.hyperopt.Hyperopt._save_result")
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.calculate_market_change", return_value=1.5
+    )
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.file_dump_json")
+    mocker.patch(
+        "freqtrade.optimize.backtesting.Backtesting.load_bt_data",
+        MagicMock(return_value=(MagicMock(), None)),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+
+    parallel = mocker.patch(
+        "freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel",
+        MagicMock(
+            return_value=[
+                {
+                    "loss": 1,
+                    "results_explanation": "foo result",
+                    "params": {},
+                    "params_details": {"roi": {}, "stoploss": {"stoploss": None}},
+                    "results_metrics": generate_result_metrics(),
+                }
+            ]
+        ),
+    )
+    patch_exchange(mocker)
+
+    hyperopt_conf.update(
+        {
+            "spaces": "roi stoploss",
+            "hyperopt_jobs": 1,
+            "print_json": True,
+        }
+    )
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.backtesting.strategy.advise_all_indicators = MagicMock()
+    hyperopt.hyperopter.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
+
+    hyperopt.start()
+
+    parallel.assert_called_once()
+
+    out, _err = capsys.readouterr()
+    assert '{"minimal_roi":{},"stoploss":null}' in out
+
+    assert dumper.call_count == 1
+    assert dumper2.call_count == 1
+
+
+def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
+    dumper = mocker.patch("freqtrade.optimize.hyperopt.hyperopt_optimizer.dump")
+    dumper2 = mocker.patch("freqtrade.optimize.hyperopt.Hyperopt._save_result")
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.calculate_market_change", return_value=1.5
+    )
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.file_dump_json")
+    mocker.patch(
+        "freqtrade.optimize.backtesting.Backtesting.load_bt_data",
+        MagicMock(return_value=(MagicMock(), None)),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+
+    parallel = mocker.patch(
+        "freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel",
+        MagicMock(
+            return_value=[
+                {
+                    "loss": 1,
+                    "results_explanation": "foo result",
+                    "params": {"stoploss": 0.0},
+                    "results_metrics": generate_result_metrics(),
+                }
+            ]
+        ),
+    )
+    patch_exchange(mocker)
+
+    hyperopt_conf.update({"spaces": "roi stoploss"})
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.backtesting.strategy.advise_all_indicators = MagicMock()
+    hyperopt.hyperopter.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
+
+    hyperopt.start()
+
+    parallel.assert_called_once()
+
+    out, _err = capsys.readouterr()
+    assert "Best result:\n\n*    1/1: foo result Objective: 1.00000\n" in out
+    assert dumper.call_count == 1
+    assert dumper2.call_count == 1
+
+    assert hasattr(hyperopt.hyperopter.backtesting.strategy, "advise_exit")
+    assert hasattr(hyperopt.hyperopter.backtesting.strategy, "advise_entry")
+    assert (
+        hyperopt.hyperopter.backtesting.strategy.max_open_trades == hyperopt_conf["max_open_trades"]
+    )
+    assert hasattr(hyperopt.hyperopter.backtesting, "_position_stacking")
+
+
+def test_simplified_interface_all_failed(mocker, hyperopt_conf, caplog) -> None:
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt_optimizer.dump", MagicMock())
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.file_dump_json")
+    mocker.patch(
+        "freqtrade.optimize.backtesting.Backtesting.load_bt_data",
+        MagicMock(return_value=(MagicMock(), None)),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+
+    patch_exchange(mocker)
+
+    hyperopt_conf.update(
+        {
+            "spaces": "all",
+        }
+    )
+
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_auto.HyperOptAuto._generate_indicator_space",
+        return_value=[],
+    )
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.backtesting.strategy.advise_all_indicators = MagicMock()
+    hyperopt.hyperopter.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
+
+    with pytest.raises(OperationalException, match=r"The 'protection' space is included into *"):
+        hyperopt.hyperopter.init_spaces()
+
+    hyperopt.config["hyperopt_ignore_missing_space"] = True
+    caplog.clear()
+    hyperopt.hyperopter.init_spaces()
+    assert log_has_re(r"The 'protection' space is included into *", caplog)
+    assert hyperopt.hyperopter.protection_space == []
+
+
+def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
+    dumper = mocker.patch("freqtrade.optimize.hyperopt.hyperopt_optimizer.dump")
+    dumper2 = mocker.patch("freqtrade.optimize.hyperopt.Hyperopt._save_result")
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.calculate_market_change", return_value=1.5
+    )
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.file_dump_json")
+    mocker.patch(
+        "freqtrade.optimize.backtesting.Backtesting.load_bt_data",
+        MagicMock(return_value=(MagicMock(), None)),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+
+    parallel = mocker.patch(
+        "freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel",
+        MagicMock(
+            return_value=[
+                {
+                    "loss": 1,
+                    "results_explanation": "foo result",
+                    "params": {},
+                    "results_metrics": generate_result_metrics(),
+                }
+            ]
+        ),
+    )
+    patch_exchange(mocker)
+
+    hyperopt_conf.update({"spaces": "buy"})
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.backtesting.strategy.advise_all_indicators = MagicMock()
+    hyperopt.hyperopter.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
+
+    hyperopt.start()
+
+    parallel.assert_called_once()
+
+    out, _err = capsys.readouterr()
+    assert "Best result:\n\n*    1/1: foo result Objective: 1.00000\n" in out
+    assert dumper.called
+    assert dumper.call_count == 1
+    assert dumper2.call_count == 1
+    assert hasattr(hyperopt.hyperopter.backtesting.strategy, "advise_exit")
+    assert hasattr(hyperopt.hyperopter.backtesting.strategy, "advise_entry")
+    assert (
+        hyperopt.hyperopter.backtesting.strategy.max_open_trades == hyperopt_conf["max_open_trades"]
+    )
+    assert hasattr(hyperopt.hyperopter.backtesting, "_position_stacking")
+
+
+def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
+    dumper = mocker.patch("freqtrade.optimize.hyperopt.hyperopt_optimizer.dump")
+    dumper2 = mocker.patch("freqtrade.optimize.hyperopt.Hyperopt._save_result")
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.calculate_market_change", return_value=1.5
+    )
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.file_dump_json")
+    mocker.patch(
+        "freqtrade.optimize.backtesting.Backtesting.load_bt_data",
+        MagicMock(return_value=(MagicMock(), None)),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+
+    parallel = mocker.patch(
+        "freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel",
+        MagicMock(
+            return_value=[
+                {
+                    "loss": 1,
+                    "results_explanation": "foo result",
+                    "params": {},
+                    "results_metrics": generate_result_metrics(),
+                }
+            ]
+        ),
+    )
+    patch_exchange(mocker)
+
+    hyperopt_conf.update(
+        {
+            "spaces": "sell",
+        }
+    )
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.backtesting.strategy.advise_all_indicators = MagicMock()
+    hyperopt.hyperopter.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
+
+    hyperopt.start()
+
+    parallel.assert_called_once()
+
+    out, _err = capsys.readouterr()
+    assert "Best result:\n\n*    1/1: foo result Objective: 1.00000\n" in out
+    assert dumper.called
+    assert dumper.call_count == 1
+    assert dumper2.call_count == 1
+    assert hasattr(hyperopt.hyperopter.backtesting.strategy, "advise_exit")
+    assert hasattr(hyperopt.hyperopter.backtesting.strategy, "advise_entry")
+    assert (
+        hyperopt.hyperopter.backtesting.strategy.max_open_trades == hyperopt_conf["max_open_trades"]
+    )
+    assert hasattr(hyperopt.hyperopter.backtesting, "_position_stacking")
+
+
+@pytest.mark.parametrize(
+    "space",
+    [
+        ("buy"),
+        ("sell"),
+        ("protection"),
+    ],
+)
+def test_simplified_interface_failed(mocker, hyperopt_conf, space) -> None:
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt_optimizer.dump", MagicMock())
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.file_dump_json")
+    mocker.patch(
+        "freqtrade.optimize.backtesting.Backtesting.load_bt_data",
+        MagicMock(return_value=(MagicMock(), None)),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.get_timerange",
+        MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13))),
+    )
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_auto.HyperOptAuto._generate_indicator_space",
+        return_value=[],
+    )
+
+    patch_exchange(mocker)
+
+    hyperopt_conf.update({"spaces": space})
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    hyperopt.hyperopter.backtesting.strategy.advise_all_indicators = MagicMock()
+    hyperopt.hyperopter.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
+
+    with pytest.raises(OperationalException, match=f"The '{space}' space is included into *"):
+        hyperopt.start()
+
+
+def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmp_path, fee) -> None:
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    # Dummy-reduce points to ensure scikit-learn is forced to generate new values
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.INITIAL_POINTS", 2)
+    (tmp_path / "hyperopt_results").mkdir(parents=True)
+    # No hyperopt needed
+    hyperopt_conf.update(
+        {
+            "strategy": "HyperoptableStrategy",
+            "user_data_dir": tmp_path,
+            "hyperopt_random_state": 42,
+            "spaces": ["all"],
+        }
+    )
+    hyperopt = Hyperopt(hyperopt_conf)
+    opt = hyperopt.hyperopter
+    opt.backtesting.exchange.get_max_leverage = MagicMock(return_value=1.0)
+    assert isinstance(opt.custom_hyperopt, HyperOptAuto)
+    assert isinstance(opt.backtesting.strategy.buy_rsi, IntParameter)
+    assert opt.backtesting.strategy.bot_started is True
+    assert opt.backtesting.strategy.bot_loop_started is False
+
+    assert opt.backtesting.strategy.buy_rsi.in_space is True
+    assert opt.backtesting.strategy.buy_rsi.value == 35
+    assert opt.backtesting.strategy.sell_rsi.value == 74
+    assert opt.backtesting.strategy.protection_cooldown_lookback.value == 30
+    assert opt.backtesting.strategy.max_open_trades == 1
+    buy_rsi_range = opt.backtesting.strategy.buy_rsi.range
+    assert isinstance(buy_rsi_range, range)
+    # Range from 0 - 50 (inclusive)
+    assert len(list(buy_rsi_range)) == 51
+
+    hyperopt.start()
+    # All values should've changed.
+    assert opt.backtesting.strategy.protection_cooldown_lookback.value != 30
+    assert opt.backtesting.strategy.buy_rsi.value != 35
+    assert opt.backtesting.strategy.sell_rsi.value != 74
+    assert opt.backtesting.strategy.max_open_trades != 1
+
+    opt.custom_hyperopt.generate_estimator = lambda *args, **kwargs: "ET1"
+    with pytest.raises(OperationalException, match="Estimator ET1 not supported."):
+        opt.get_optimizer(2, 42, 2, 2)
+
+
+@pytest.mark.filterwarnings("ignore::DeprecationWarning")
+def test_in_strategy_auto_hyperopt_with_parallel(mocker, hyperopt_conf, tmp_path, fee) -> None:
+    mocker.patch(f"{EXMS}.validate_config", MagicMock())
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.reload_markets")
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=get_markets()))
+    (tmp_path / "hyperopt_results").mkdir(parents=True)
+    # Dummy-reduce points to ensure scikit-learn is forced to generate new values
+    mocker.patch("freqtrade.optimize.hyperopt.hyperopt.INITIAL_POINTS", 2)
+    # No hyperopt needed
+    hyperopt_conf.update(
+        {
+            "strategy": "HyperoptableStrategy",
+            "user_data_dir": tmp_path,
+            "hyperopt_random_state": 42,
+            "spaces": ["all"],
+            # Enforce parallelity
+            "epochs": 2,
+            "hyperopt_jobs": 2,
+            "fee": fee.return_value,
+        }
+    )
+    hyperopt = Hyperopt(hyperopt_conf)
+    opt = hyperopt.hyperopter
+    opt.backtesting.exchange.get_max_leverage = lambda *x, **xx: 1.0
+    opt.backtesting.exchange.get_min_pair_stake_amount = lambda *x, **xx: 0.00001
+    opt.backtesting.exchange.get_max_pair_stake_amount = lambda *x, **xx: 100.0
+    opt.backtesting.exchange._markets = get_markets()
+
+    assert isinstance(opt.custom_hyperopt, HyperOptAuto)
+    assert isinstance(opt.backtesting.strategy.buy_rsi, IntParameter)
+    assert opt.backtesting.strategy.bot_started is True
+    assert opt.backtesting.strategy.bot_loop_started is False
+
+    assert opt.backtesting.strategy.buy_rsi.in_space is True
+    assert opt.backtesting.strategy.buy_rsi.value == 35
+    assert opt.backtesting.strategy.sell_rsi.value == 74
+    assert opt.backtesting.strategy.protection_cooldown_lookback.value == 30
+    buy_rsi_range = opt.backtesting.strategy.buy_rsi.range
+    assert isinstance(buy_rsi_range, range)
+    # Range from 0 - 50 (inclusive)
+    assert len(list(buy_rsi_range)) == 51
+
+    hyperopt.start()
+
+
+def test_in_strategy_auto_hyperopt_per_epoch(mocker, hyperopt_conf, tmp_path, fee) -> None:
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    (tmp_path / "hyperopt_results").mkdir(parents=True)
+
+    hyperopt_conf.update(
+        {
+            "strategy": "HyperoptableStrategy",
+            "user_data_dir": tmp_path,
+            "hyperopt_random_state": 42,
+            "spaces": ["all"],
+            "epochs": 3,
+            "analyze_per_epoch": True,
+        }
+    )
+    go = mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.HyperOptimizer.generate_optimizer",
+        return_value={
+            "loss": 0.05,
+            "results_explanation": "foo result",
+            "params": {},
+            "results_metrics": generate_result_metrics(),
+        },
+    )
+    hyperopt = Hyperopt(hyperopt_conf)
+    opt = hyperopt.hyperopter
+    opt.backtesting.exchange.get_max_leverage = MagicMock(return_value=1.0)
+    assert isinstance(opt.custom_hyperopt, HyperOptAuto)
+    assert isinstance(opt.backtesting.strategy.buy_rsi, IntParameter)
+    assert opt.backtesting.strategy.bot_loop_started is False
+    assert opt.backtesting.strategy.bot_started is True
+
+    assert opt.backtesting.strategy.buy_rsi.in_space is True
+    assert opt.backtesting.strategy.buy_rsi.value == 35
+    assert opt.backtesting.strategy.sell_rsi.value == 74
+    assert opt.backtesting.strategy.protection_cooldown_lookback.value == 30
+    buy_rsi_range = opt.backtesting.strategy.buy_rsi.range
+    assert isinstance(buy_rsi_range, range)
+    # Range from 0 - 50 (inclusive)
+    assert len(list(buy_rsi_range)) == 51
+
+    hyperopt.start()
+    # backtesting should be called 3 times (once per epoch)
+    assert go.call_count == 3
+
+
+def test_SKDecimal():
+    space = SKDecimal(1, 2, decimals=2)
+    assert 1.5 in space
+    assert 2.5 not in space
+    assert space.low == 100
+    assert space.high == 200
+
+    assert space.inverse_transform([200]) == [2.0]
+    assert space.inverse_transform([100]) == [1.0]
+    assert space.inverse_transform([150, 160]) == [1.5, 1.6]
+
+    assert space.transform([1.5]) == [150]
+    assert space.transform([2.0]) == [200]
+    assert space.transform([1.0]) == [100]
+    assert space.transform([1.5, 1.6]) == [150, 160]
+
+
+def test_stake_amount_unlimited_max_open_trades(mocker, hyperopt_conf, tmp_path, fee) -> None:
+    # This test is to ensure that unlimited max_open_trades are ignored for the backtesting
+    # if we have an unlimited stake amount
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    (tmp_path / "hyperopt_results").mkdir(parents=True)
+    hyperopt_conf.update(
+        {
+            "strategy": "HyperoptableStrategy",
+            "user_data_dir": tmp_path,
+            "hyperopt_random_state": 42,
+            "spaces": ["trades"],
+            "stake_amount": "unlimited",
+        }
+    )
+    hyperopt = Hyperopt(hyperopt_conf)
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.HyperOptimizer._get_params_dict",
+        return_value={"max_open_trades": -1},
+    )
+
+    assert isinstance(hyperopt.hyperopter.custom_hyperopt, HyperOptAuto)
+
+    assert hyperopt.hyperopter.backtesting.strategy.max_open_trades == 1
+
+    hyperopt.start()
+
+    assert hyperopt.hyperopter.backtesting.strategy.max_open_trades == 1
+
+
+def test_max_open_trades_dump(mocker, hyperopt_conf, tmp_path, fee, capsys) -> None:
+    # This test is to ensure that after hyperopting, max_open_trades is never
+    # saved as inf in the output json params
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    (tmp_path / "hyperopt_results").mkdir(parents=True)
+    hyperopt_conf.update(
+        {
+            "strategy": "HyperoptableStrategy",
+            "user_data_dir": tmp_path,
+            "hyperopt_random_state": 42,
+            "spaces": ["trades"],
+        }
+    )
+    hyperopt = Hyperopt(hyperopt_conf)
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.HyperOptimizer._get_params_dict",
+        return_value={"max_open_trades": -1},
+    )
+
+    assert isinstance(hyperopt.hyperopter.custom_hyperopt, HyperOptAuto)
+
+    hyperopt.start()
+
+    out, _err = capsys.readouterr()
+
+    assert "max_open_trades = -1" in out
+    assert "max_open_trades = inf" not in out
+
+    ##############
+
+    hyperopt_conf.update({"print_json": True})
+
+    hyperopt = Hyperopt(hyperopt_conf)
+    mocker.patch(
+        "freqtrade.optimize.hyperopt.hyperopt_optimizer.HyperOptimizer._get_params_dict",
+        return_value={"max_open_trades": -1},
+    )
+
+    assert isinstance(hyperopt.hyperopter.custom_hyperopt, HyperOptAuto)
+
+    hyperopt.start()
+
+    out, _err = capsys.readouterr()
+
+    assert '"max_open_trades":-1' in out
+
+
+def test_max_open_trades_consistency(mocker, hyperopt_conf, tmp_path, fee) -> None:
+    # This test is to ensure that max_open_trades is the same across all functions needing it
+    # after it has been changed from the hyperopt
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_fee", return_value=0)
+
+    (tmp_path / "hyperopt_results").mkdir(parents=True)
+    hyperopt_conf.update(
+        {
+            "strategy": "HyperoptableStrategy",
+            "user_data_dir": tmp_path,
+            "hyperopt_random_state": 42,
+            "spaces": ["trades"],
+            "stake_amount": "unlimited",
+            "dry_run_wallet": 8,
+            "available_capital": 8,
+            "dry_run": True,
+            "epochs": 1,
+        }
+    )
+    hyperopt = Hyperopt(hyperopt_conf)
+
+    assert isinstance(hyperopt.hyperopter.custom_hyperopt, HyperOptAuto)
+
+    hyperopt.hyperopter.custom_hyperopt.max_open_trades_space = lambda: [
+        Integer(1, 10, name="max_open_trades")
+    ]
+
+    first_time_evaluated = False
+
+    def stake_amount_interceptor(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            nonlocal first_time_evaluated
+            stake_amount = func(*args, **kwargs)
+            if first_time_evaluated is False:
+                assert stake_amount == 1
+                first_time_evaluated = True
+            return stake_amount
+
+        return wrapper
+
+    hyperopt.hyperopter.backtesting.wallets._calculate_unlimited_stake_amount = (
+        stake_amount_interceptor(
+            hyperopt.hyperopter.backtesting.wallets._calculate_unlimited_stake_amount
+        )
+    )
+
+    hyperopt.start()
+
+    assert hyperopt.hyperopter.backtesting.strategy.max_open_trades == 8
+    assert hyperopt.config["max_open_trades"] == 8
diff --git a/tests/optimize/test_hyperopt_tools.py b/tests/optimize/test_hyperopt_tools.py
new file mode 100644
index 000000000..2351acbba
--- /dev/null
+++ b/tests/optimize/test_hyperopt_tools.py
@@ -0,0 +1,472 @@
+import logging
+import re
+from pathlib import Path
+
+import numpy as np
+import pytest
+import rapidjson
+
+from freqtrade.constants import FTHYPT_FILEVERSION
+from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
+from tests.conftest import CURRENT_TEST_STRATEGY, log_has, log_has_re
+
+
+# Functions for recurrent object patching
+def create_results() -> list[dict]:
+    return [{"loss": 1, "result": "foo", "params": {}, "is_best": True}]
+
+
+def test_save_results_saves_epochs(hyperopt, tmp_path, caplog) -> None:
+    hyperopt.results_file = tmp_path / "ut_results.fthypt"
+
+    hyperopt_epochs = HyperoptTools.load_filtered_results(hyperopt.results_file, {})
+    assert log_has_re("Hyperopt file .* not found.", caplog)
+    assert hyperopt_epochs == ([], 0)
+
+    # Test writing to temp dir and reading again
+    epochs = create_results()
+
+    caplog.set_level(logging.DEBUG)
+
+    for epoch in epochs:
+        hyperopt._save_result(epoch)
+    assert log_has(f"1 epoch saved to '{hyperopt.results_file}'.", caplog)
+
+    hyperopt._save_result(epochs[0])
+    assert log_has(f"2 epochs saved to '{hyperopt.results_file}'.", caplog)
+
+    hyperopt_epochs = HyperoptTools.load_filtered_results(hyperopt.results_file, {})
+    assert len(hyperopt_epochs) == 2
+    assert hyperopt_epochs[1] == 2
+    assert len(hyperopt_epochs[0]) == 2
+
+    result_gen = HyperoptTools._read_results(hyperopt.results_file, 1)
+    epoch = next(result_gen)
+    assert len(epoch) == 1
+    assert epoch[0] == epochs[0]
+    epoch = next(result_gen)
+    assert len(epoch) == 1
+    epoch = next(result_gen)
+    assert len(epoch) == 0
+    with pytest.raises(StopIteration):
+        next(result_gen)
+
+
+def test_load_previous_results2(mocker, testdatadir, caplog) -> None:
+    results_file = testdatadir / "hyperopt_results_SampleStrategy.pickle"
+    with pytest.raises(
+        OperationalException, match=r"Legacy hyperopt results are no longer supported.*"
+    ):
+        HyperoptTools.load_filtered_results(results_file, {})
+
+
+@pytest.mark.parametrize(
+    "spaces, expected_results",
+    [
+        (
+            ["buy"],
+            {
+                "buy": True,
+                "sell": False,
+                "roi": False,
+                "stoploss": False,
+                "trailing": False,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["sell"],
+            {
+                "buy": False,
+                "sell": True,
+                "roi": False,
+                "stoploss": False,
+                "trailing": False,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["roi"],
+            {
+                "buy": False,
+                "sell": False,
+                "roi": True,
+                "stoploss": False,
+                "trailing": False,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["stoploss"],
+            {
+                "buy": False,
+                "sell": False,
+                "roi": False,
+                "stoploss": True,
+                "trailing": False,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["trailing"],
+            {
+                "buy": False,
+                "sell": False,
+                "roi": False,
+                "stoploss": False,
+                "trailing": True,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["buy", "sell", "roi", "stoploss"],
+            {
+                "buy": True,
+                "sell": True,
+                "roi": True,
+                "stoploss": True,
+                "trailing": False,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["buy", "sell", "roi", "stoploss", "trailing"],
+            {
+                "buy": True,
+                "sell": True,
+                "roi": True,
+                "stoploss": True,
+                "trailing": True,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["buy", "roi"],
+            {
+                "buy": True,
+                "sell": False,
+                "roi": True,
+                "stoploss": False,
+                "trailing": False,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["all"],
+            {
+                "buy": True,
+                "sell": True,
+                "roi": True,
+                "stoploss": True,
+                "trailing": True,
+                "protection": True,
+                "trades": True,
+            },
+        ),
+        (
+            ["default"],
+            {
+                "buy": True,
+                "sell": True,
+                "roi": True,
+                "stoploss": True,
+                "trailing": False,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["default", "trailing"],
+            {
+                "buy": True,
+                "sell": True,
+                "roi": True,
+                "stoploss": True,
+                "trailing": True,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["all", "buy"],
+            {
+                "buy": True,
+                "sell": True,
+                "roi": True,
+                "stoploss": True,
+                "trailing": True,
+                "protection": True,
+                "trades": True,
+            },
+        ),
+        (
+            ["default", "buy"],
+            {
+                "buy": True,
+                "sell": True,
+                "roi": True,
+                "stoploss": True,
+                "trailing": False,
+                "protection": False,
+                "trades": False,
+            },
+        ),
+        (
+            ["all"],
+            {
+                "buy": True,
+                "sell": True,
+                "roi": True,
+                "stoploss": True,
+                "trailing": True,
+                "protection": True,
+                "trades": True,
+            },
+        ),
+        (
+            ["protection"],
+            {
+                "buy": False,
+                "sell": False,
+                "roi": False,
+                "stoploss": False,
+                "trailing": False,
+                "protection": True,
+                "trades": False,
+            },
+        ),
+        (
+            ["trades"],
+            {
+                "buy": False,
+                "sell": False,
+                "roi": False,
+                "stoploss": False,
+                "trailing": False,
+                "protection": False,
+                "trades": True,
+            },
+        ),
+        (
+            ["default", "trades"],
+            {
+                "buy": True,
+                "sell": True,
+                "roi": True,
+                "stoploss": True,
+                "trailing": False,
+                "protection": False,
+                "trades": True,
+            },
+        ),
+    ],
+)
+def test_has_space(hyperopt_conf, spaces, expected_results):
+    for s in ["buy", "sell", "roi", "stoploss", "trailing", "protection", "trades"]:
+        hyperopt_conf.update({"spaces": spaces})
+        assert HyperoptTools.has_space(hyperopt_conf, s) == expected_results[s]
+
+
+def test_show_epoch_details(capsys):
+    test_result = {
+        "params_details": {
+            "trailing": {
+                "trailing_stop": True,
+                "trailing_stop_positive": 0.02,
+                "trailing_stop_positive_offset": 0.04,
+                "trailing_only_offset_is_reached": True,
+            },
+            "roi": {0: 0.18, 90: 0.14, 225: 0.05, 430: 0},
+        },
+        "results_explanation": "foo result",
+        "is_initial_point": False,
+        "total_profit": 0,
+        "current_epoch": 2,  # This starts from 1 (in a human-friendly manner)
+        "is_best": True,
+    }
+
+    HyperoptTools.show_epoch_details(test_result, 5, False, no_header=True)
+    captured = capsys.readouterr()
+    assert "# Trailing stop:" in captured.out
+    # re.match(r"Pairs for .*", captured.out)
+    assert re.search(r"^\s+trailing_stop = True$", captured.out, re.MULTILINE)
+    assert re.search(r"^\s+trailing_stop_positive = 0.02$", captured.out, re.MULTILINE)
+    assert re.search(r"^\s+trailing_stop_positive_offset = 0.04$", captured.out, re.MULTILINE)
+    assert re.search(r"^\s+trailing_only_offset_is_reached = True$", captured.out, re.MULTILINE)
+
+    assert "# ROI table:" in captured.out
+    assert re.search(r"^\s+minimal_roi = \{$", captured.out, re.MULTILINE)
+    assert re.search(r"^\s+\"90\"\:\s0.14,\s*$", captured.out, re.MULTILINE)
+
+
+def test__pprint_dict():
+    params = {"buy_std": 1.2, "buy_rsi": 31, "buy_enable": True, "buy_what": "asdf"}
+    non_params = {"buy_notoptimied": 55}
+
+    x = HyperoptTools._pprint_dict(params, non_params)
+    assert (
+        x
+        == """{
+    "buy_std": 1.2,
+    "buy_rsi": 31,
+    "buy_enable": True,
+    "buy_what": "asdf",
+    "buy_notoptimied": 55,  # value loaded from strategy
+}"""
+    )
+
+
+def test_get_strategy_filename(default_conf, tmp_path):
+    default_conf["user_data_dir"] = tmp_path
+    x = HyperoptTools.get_strategy_filename(default_conf, "StrategyTestV3")
+    assert isinstance(x, Path)
+    assert x == Path(__file__).parents[1] / "strategy/strats/strategy_test_v3.py"
+
+    x = HyperoptTools.get_strategy_filename(default_conf, "NonExistingStrategy")
+    assert x is None
+
+
+def test_export_params(tmp_path):
+    filename = tmp_path / f"{CURRENT_TEST_STRATEGY}.json"
+    assert not filename.is_file()
+    params = {
+        "params_details": {
+            "buy": {"buy_rsi": 30},
+            "sell": {"sell_rsi": 70},
+            "roi": {"0": 0.528, "346": 0.08499, "507": 0.049, "1595": 0},
+            "max_open_trades": {"max_open_trades": 5},
+        },
+        "params_not_optimized": {
+            "stoploss": -0.05,
+            "trailing": {
+                "trailing_stop": False,
+                "trailing_stop_positive": 0.05,
+                "trailing_stop_positive_offset": 0.1,
+                "trailing_only_offset_is_reached": True,
+            },
+        },
+    }
+    HyperoptTools.export_params(params, CURRENT_TEST_STRATEGY, filename)
+
+    assert filename.is_file()
+
+    with filename.open("r") as f:
+        content = rapidjson.load(f)
+    assert content["strategy_name"] == CURRENT_TEST_STRATEGY
+    assert "params" in content
+    assert "buy" in content["params"]
+    assert "sell" in content["params"]
+    assert "roi" in content["params"]
+    assert "stoploss" in content["params"]
+    assert "trailing" in content["params"]
+    assert "max_open_trades" in content["params"]
+
+
+def test_try_export_params(default_conf, tmp_path, caplog, mocker):
+    default_conf["disableparamexport"] = False
+    default_conf["user_data_dir"] = tmp_path
+    export_mock = mocker.patch("freqtrade.optimize.hyperopt_tools.HyperoptTools.export_params")
+
+    filename = tmp_path / f"{CURRENT_TEST_STRATEGY}.json"
+    assert not filename.is_file()
+    params = {
+        "params_details": {
+            "buy": {"buy_rsi": 30},
+            "sell": {"sell_rsi": 70},
+            "roi": {"0": 0.528, "346": 0.08499, "507": 0.049, "1595": 0},
+        },
+        "params_not_optimized": {
+            "stoploss": -0.05,
+            "trailing": {
+                "trailing_stop": False,
+                "trailing_stop_positive": 0.05,
+                "trailing_stop_positive_offset": 0.1,
+                "trailing_only_offset_is_reached": True,
+            },
+        },
+        FTHYPT_FILEVERSION: 2,
+    }
+    HyperoptTools.try_export_params(default_conf, "StrategyTestVXXX", params)
+
+    assert log_has("Strategy not found, not exporting parameter file.", caplog)
+    assert export_mock.call_count == 0
+    caplog.clear()
+
+    HyperoptTools.try_export_params(default_conf, CURRENT_TEST_STRATEGY, params)
+
+    assert export_mock.call_count == 1
+    assert export_mock.call_args_list[0][0][1] == CURRENT_TEST_STRATEGY
+    assert export_mock.call_args_list[0][0][2].name == "strategy_test_v3.json"
+
+
+def test_params_print(capsys):
+    params = {
+        "buy": {"buy_rsi": 30},
+        "sell": {"sell_rsi": 70},
+    }
+    non_optimized = {
+        "buy": {"buy_adx": 44},
+        "sell": {"sell_adx": 65},
+        "stoploss": {
+            "stoploss": -0.05,
+        },
+        "roi": {
+            "0": 0.05,
+            "20": 0.01,
+        },
+        "trailing": {
+            "trailing_stop": False,
+            "trailing_stop_positive": 0.05,
+            "trailing_stop_positive_offset": 0.1,
+            "trailing_only_offset_is_reached": True,
+        },
+        "max_open_trades": {"max_open_trades": 5},
+    }
+    HyperoptTools._params_pretty_print(params, "buy", "No header", non_optimized)
+
+    captured = capsys.readouterr()
+    assert re.search("# No header", captured.out)
+    assert re.search('"buy_rsi": 30,\n', captured.out)
+    assert re.search('"buy_adx": 44,  # value loaded.*\n', captured.out)
+    assert not re.search("sell", captured.out)
+
+    HyperoptTools._params_pretty_print(params, "sell", "Sell Header", non_optimized)
+    captured = capsys.readouterr()
+    assert re.search("# Sell Header", captured.out)
+    assert re.search('"sell_rsi": 70,\n', captured.out)
+    assert re.search('"sell_adx": 65,  # value loaded.*\n', captured.out)
+
+    HyperoptTools._params_pretty_print(params, "roi", "ROI Table:", non_optimized)
+    captured = capsys.readouterr()
+    assert re.search("# ROI Table:  # value loaded.*\n", captured.out)
+    assert re.search("minimal_roi = {\n", captured.out)
+    assert re.search('"20": 0.01\n', captured.out)
+
+    HyperoptTools._params_pretty_print(params, "trailing", "Trailing stop:", non_optimized)
+    captured = capsys.readouterr()
+    assert re.search("# Trailing stop:", captured.out)
+    assert re.search("trailing_stop = False  # value loaded.*\n", captured.out)
+    assert re.search("trailing_stop_positive = 0.05  # value loaded.*\n", captured.out)
+    assert re.search("trailing_stop_positive_offset = 0.1  # value loaded.*\n", captured.out)
+    assert re.search("trailing_only_offset_is_reached = True  # value loaded.*\n", captured.out)
+
+    HyperoptTools._params_pretty_print(params, "max_open_trades", "Max Open Trades:", non_optimized)
+    captured = capsys.readouterr()
+
+    assert re.search("# Max Open Trades:", captured.out)
+    assert re.search("max_open_trades = 5  # value loaded.*\n", captured.out)
+
+
+def test_hyperopt_serializer():
+    assert isinstance(hyperopt_serializer(np.int_(5)), int)
+    assert isinstance(hyperopt_serializer(np.bool_(True)), bool)
+    assert isinstance(hyperopt_serializer(np.bool_(False)), bool)
diff --git a/tests/optimize/test_hyperoptloss.py b/tests/optimize/test_hyperoptloss.py
new file mode 100644
index 000000000..6d3110509
--- /dev/null
+++ b/tests/optimize/test_hyperoptloss.py
@@ -0,0 +1,240 @@
+from datetime import datetime
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.hyperopt_loss.hyperopt_loss_short_trade_dur import ShortTradeDurHyperOptLoss
+from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
+
+
+def test_hyperoptlossresolver_noname(default_conf):
+    with pytest.raises(
+        OperationalException,
+        match="No Hyperopt loss set. Please use `--hyperopt-loss` to specify "
+        "the Hyperopt-Loss class to use.",
+    ):
+        HyperOptLossResolver.load_hyperoptloss(default_conf)
+
+
+def test_hyperoptlossresolver(mocker, default_conf) -> None:
+    hl = ShortTradeDurHyperOptLoss
+    mocker.patch(
+        "freqtrade.resolvers.hyperopt_resolver.HyperOptLossResolver.load_object",
+        MagicMock(return_value=hl()),
+    )
+    default_conf.update({"hyperopt_loss": "SharpeHyperOptLossDaily"})
+    x = HyperOptLossResolver.load_hyperoptloss(default_conf)
+    assert hasattr(x, "hyperopt_loss_function")
+
+
+def test_hyperoptlossresolver_wrongname(default_conf) -> None:
+    default_conf.update({"hyperopt_loss": "NonExistingLossClass"})
+
+    with pytest.raises(OperationalException, match=r"Impossible to load HyperoptLoss.*"):
+        HyperOptLossResolver.load_hyperoptloss(default_conf)
+
+
+def test_loss_calculation_prefer_correct_trade_count(hyperopt_conf, hyperopt_results) -> None:
+    hyperopt_conf.update({"hyperopt_loss": "ShortTradeDurHyperOptLoss"})
+    hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
+    correct = hl.hyperopt_loss_function(
+        results=hyperopt_results,
+        trade_count=600,
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=hyperopt_conf,
+        processed=None,
+        backtest_stats={"profit_total": hyperopt_results["profit_abs"].sum()},
+        starting_balance=hyperopt_conf["dry_run_wallet"],
+    )
+    over = hl.hyperopt_loss_function(
+        results=hyperopt_results,
+        trade_count=600 + 100,
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=hyperopt_conf,
+        processed=None,
+        backtest_stats={"profit_total": hyperopt_results["profit_abs"].sum()},
+        starting_balance=hyperopt_conf["dry_run_wallet"],
+    )
+    under = hl.hyperopt_loss_function(
+        results=hyperopt_results,
+        trade_count=600 - 100,
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=hyperopt_conf,
+        processed=None,
+        backtest_stats={"profit_total": hyperopt_results["profit_abs"].sum()},
+        starting_balance=hyperopt_conf["dry_run_wallet"],
+    )
+    assert over > correct
+    assert under > correct
+
+
+def test_loss_calculation_prefer_shorter_trades(hyperopt_conf, hyperopt_results) -> None:
+    resultsb = hyperopt_results.copy()
+    resultsb.loc[1, "trade_duration"] = 20
+
+    hyperopt_conf.update({"hyperopt_loss": "ShortTradeDurHyperOptLoss"})
+    hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
+    longer = hl.hyperopt_loss_function(
+        results=hyperopt_results,
+        trade_count=100,
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=hyperopt_conf,
+        processed=None,
+        backtest_stats={"profit_total": hyperopt_results["profit_abs"].sum()},
+        starting_balance=hyperopt_conf["dry_run_wallet"],
+    )
+    shorter = hl.hyperopt_loss_function(
+        results=resultsb,
+        trade_count=100,
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=hyperopt_conf,
+        processed=None,
+        backtest_stats={"profit_total": resultsb["profit_abs"].sum()},
+        starting_balance=hyperopt_conf["dry_run_wallet"],
+    )
+    assert shorter < longer
+
+
+def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) -> None:
+    results_over = hyperopt_results.copy()
+    results_over["profit_ratio"] = hyperopt_results["profit_ratio"] * 2
+    results_under = hyperopt_results.copy()
+    results_under["profit_ratio"] = hyperopt_results["profit_ratio"] / 2
+
+    hyperopt_conf.update({"hyperopt_loss": "ShortTradeDurHyperOptLoss"})
+    hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
+    correct = hl.hyperopt_loss_function(
+        results=hyperopt_results,
+        trade_count=600,
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=hyperopt_conf,
+        processed=None,
+        backtest_stats={"profit_total": hyperopt_results["profit_abs"].sum()},
+        starting_balance=hyperopt_conf["dry_run_wallet"],
+    )
+    over = hl.hyperopt_loss_function(
+        results=results_over,
+        trade_count=600,
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=hyperopt_conf,
+        processed=None,
+        backtest_stats={"profit_total": results_over["profit_abs"].sum()},
+        starting_balance=hyperopt_conf["dry_run_wallet"],
+    )
+    under = hl.hyperopt_loss_function(
+        results=results_under,
+        trade_count=600,
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=hyperopt_conf,
+        processed=None,
+        backtest_stats={"profit_total": results_under["profit_abs"].sum()},
+        starting_balance=hyperopt_conf["dry_run_wallet"],
+    )
+    assert over < correct
+    assert under > correct
+
+
+@pytest.mark.parametrize(
+    "lossfunction",
+    [
+        "OnlyProfitHyperOptLoss",
+        "SortinoHyperOptLoss",
+        "SortinoHyperOptLossDaily",
+        "SharpeHyperOptLoss",
+        "SharpeHyperOptLossDaily",
+        "MaxDrawDownHyperOptLoss",
+        "MaxDrawDownRelativeHyperOptLoss",
+        "MaxDrawDownPerPairHyperOptLoss",
+        "CalmarHyperOptLoss",
+        "ProfitDrawDownHyperOptLoss",
+        "MultiMetricHyperOptLoss",
+    ],
+)
+def test_loss_functions_better_profits(default_conf, hyperopt_results, lossfunction) -> None:
+    results_over = hyperopt_results.copy()
+    results_over["profit_abs"] = hyperopt_results["profit_abs"] * 2 + 0.2
+    results_over["profit_ratio"] = hyperopt_results["profit_ratio"] * 2
+    results_under = hyperopt_results.copy()
+    results_under["profit_abs"] = hyperopt_results["profit_abs"] / 2 - 0.2
+    results_under["profit_ratio"] = hyperopt_results["profit_ratio"] / 2
+    pair_results = [
+        {
+            "key": "ETH/USDT",
+            "max_drawdown_abs": 50.0,
+            "profit_total_abs": 100.0,
+        },
+        {
+            "key": "BTC/USDT",
+            "max_drawdown_abs": 50.0,
+            "profit_total_abs": 100.0,
+        },
+    ]
+    pair_results_over = [
+        {
+            **p,
+            "max_drawdown_abs": p["max_drawdown_abs"] * 0.5,
+            "profit_total_abs": p["profit_total_abs"] * 2,
+        }
+        for p in pair_results
+    ]
+    pair_results_under = [
+        {
+            **p,
+            "max_drawdown_abs": p["max_drawdown_abs"] * 2,
+            "profit_total_abs": p["profit_total_abs"] * 0.5,
+        }
+        for p in pair_results
+    ]
+
+    default_conf.update({"hyperopt_loss": lossfunction})
+    hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
+    correct = hl.hyperopt_loss_function(
+        results=hyperopt_results,
+        trade_count=len(hyperopt_results),
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=default_conf,
+        processed=None,
+        backtest_stats={
+            "profit_total": hyperopt_results["profit_abs"].sum(),
+            "results_per_pair": pair_results,
+        },
+        starting_balance=default_conf["dry_run_wallet"],
+    )
+    over = hl.hyperopt_loss_function(
+        results=results_over,
+        trade_count=len(results_over),
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=default_conf,
+        processed=None,
+        backtest_stats={
+            "profit_total": results_over["profit_abs"].sum(),
+            "results_per_pair": pair_results_over,
+        },
+        starting_balance=default_conf["dry_run_wallet"],
+    )
+    under = hl.hyperopt_loss_function(
+        results=results_under,
+        trade_count=len(results_under),
+        min_date=datetime(2019, 1, 1),
+        max_date=datetime(2019, 5, 1),
+        config=default_conf,
+        processed=None,
+        backtest_stats={
+            "profit_total": results_under["profit_abs"].sum(),
+            "results_per_pair": pair_results_under,
+        },
+        starting_balance=default_conf["dry_run_wallet"],
+    )
+    assert over < correct
+    assert under > correct
diff --git a/tests/optimize/test_lookahead_analysis.py b/tests/optimize/test_lookahead_analysis.py
new file mode 100644
index 000000000..67c83762a
--- /dev/null
+++ b/tests/optimize/test_lookahead_analysis.py
@@ -0,0 +1,493 @@
+# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
+from copy import deepcopy
+from pathlib import Path
+from unittest.mock import MagicMock, PropertyMock
+
+import pytest
+
+from freqtrade.commands.optimize_commands import start_lookahead_analysis
+from freqtrade.data.history import get_timerange
+from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.analysis.lookahead import Analysis, LookaheadAnalysis
+from freqtrade.optimize.analysis.lookahead_helpers import LookaheadAnalysisSubFunctions
+from tests.conftest import EXMS, get_args, log_has_re, patch_exchange
+
+
+IGNORE_BIASED_INDICATORS_CAPTION = (
+    "Any indicators in 'biased_indicators' which are used within "
+    "set_freqai_targets() can be ignored."
+)
+
+
+@pytest.fixture
+def lookahead_conf(default_conf_usdt, tmp_path):
+    default_conf_usdt["user_data_dir"] = tmp_path
+    default_conf_usdt["minimum_trade_amount"] = 10
+    default_conf_usdt["targeted_trade_amount"] = 20
+    default_conf_usdt["timerange"] = "20220101-20220501"
+
+    default_conf_usdt["strategy_path"] = str(
+        Path(__file__).parent.parent / "strategy/strats/lookahead_bias"
+    )
+    default_conf_usdt["strategy"] = "strategy_test_v3_with_lookahead_bias"
+    default_conf_usdt["max_open_trades"] = 1
+    default_conf_usdt["dry_run_wallet"] = 1000000000
+    default_conf_usdt["pairs"] = ["UNITTEST/USDT"]
+    return default_conf_usdt
+
+
+def test_start_lookahead_analysis(mocker):
+    single_mock = MagicMock()
+    text_table_mock = MagicMock()
+    mocker.patch.multiple(
+        "freqtrade.optimize.analysis.lookahead_helpers.LookaheadAnalysisSubFunctions",
+        initialize_single_lookahead_analysis=single_mock,
+        text_table_lookahead_analysis_instances=text_table_mock,
+    )
+    args = [
+        "lookahead-analysis",
+        "--strategy",
+        "strategy_test_v3_with_lookahead_bias",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy/strats/lookahead_bias"),
+        "--pairs",
+        "UNITTEST/BTC",
+        "--max-open-trades",
+        "1",
+        "--timerange",
+        "20220101-20220201",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+
+    start_lookahead_analysis(pargs)
+    assert single_mock.call_count == 1
+    assert text_table_mock.call_count == 1
+
+    single_mock.reset_mock()
+
+    # Test invalid config
+    args = [
+        "lookahead-analysis",
+        "--strategy",
+        "strategy_test_v3_with_lookahead_bias",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy/strats/lookahead_bias"),
+        "--targeted-trade-amount",
+        "10",
+        "--minimum-trade-amount",
+        "20",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(
+        OperationalException,
+        match=r"Targeted trade amount can't be smaller than minimum trade amount.*",
+    ):
+        start_lookahead_analysis(pargs)
+
+    # Missing timerange
+    args = [
+        "lookahead-analysis",
+        "--strategy",
+        "strategy_test_v3_with_lookahead_bias",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy/strats/lookahead_bias"),
+        "--pairs",
+        "UNITTEST/BTC",
+        "--max-open-trades",
+        "1",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(OperationalException, match=r"Please set a timerange\..*"):
+        start_lookahead_analysis(pargs)
+
+
+def test_lookahead_helper_invalid_config(lookahead_conf) -> None:
+    conf = deepcopy(lookahead_conf)
+    conf["targeted_trade_amount"] = 10
+    conf["minimum_trade_amount"] = 40
+    with pytest.raises(
+        OperationalException,
+        match=r"Targeted trade amount can't be smaller than minimum trade amount.*",
+    ):
+        LookaheadAnalysisSubFunctions.start(conf)
+
+
+def test_lookahead_helper_no_strategy_defined(lookahead_conf):
+    conf = deepcopy(lookahead_conf)
+    conf["pairs"] = ["UNITTEST/USDT"]
+    del conf["strategy"]
+    with pytest.raises(OperationalException, match=r"No Strategy specified"):
+        LookaheadAnalysisSubFunctions.start(conf)
+
+
+def test_lookahead_helper_start(lookahead_conf, mocker) -> None:
+    single_mock = MagicMock()
+    text_table_mock = MagicMock()
+    mocker.patch.multiple(
+        "freqtrade.optimize.analysis.lookahead_helpers.LookaheadAnalysisSubFunctions",
+        initialize_single_lookahead_analysis=single_mock,
+        text_table_lookahead_analysis_instances=text_table_mock,
+    )
+    LookaheadAnalysisSubFunctions.start(lookahead_conf)
+    assert single_mock.call_count == 1
+    assert text_table_mock.call_count == 1
+
+    single_mock.reset_mock()
+    text_table_mock.reset_mock()
+
+
+@pytest.mark.parametrize(
+    "indicators, expected_caption_text",
+    [
+        (
+            ["&indicator1", "indicator2"],
+            IGNORE_BIASED_INDICATORS_CAPTION,
+        ),
+        (
+            ["indicator1", "&indicator2"],
+            IGNORE_BIASED_INDICATORS_CAPTION,
+        ),
+        (
+            ["&indicator1", "&indicator2"],
+            IGNORE_BIASED_INDICATORS_CAPTION,
+        ),
+        (["indicator1", "indicator2"], None),
+        ([], None),
+    ],
+    ids=(
+        "First of two biased indicators starts with '&'",
+        "Second of two biased indicators starts with '&'",
+        "Both biased indicators start with '&'",
+        "No biased indicators start with '&'",
+        "Empty biased indicators list",
+    ),
+)
+def test_lookahead_helper_start__caption_based_on_indicators(
+    indicators, expected_caption_text, lookahead_conf, mocker
+):
+    """Test that the table caption is only populated if a biased_indicator starts with '&'."""
+
+    single_mock = MagicMock()
+    lookahead_analysis = LookaheadAnalysis(
+        lookahead_conf,
+        {"name": "strategy_test_v3_with_lookahead_bias"},
+    )
+    lookahead_analysis.current_analysis.false_indicators = indicators
+    single_mock.return_value = lookahead_analysis
+    text_table_mock = MagicMock()
+    mocker.patch.multiple(
+        "freqtrade.optimize.analysis.lookahead_helpers.LookaheadAnalysisSubFunctions",
+        initialize_single_lookahead_analysis=single_mock,
+        text_table_lookahead_analysis_instances=text_table_mock,
+    )
+
+    LookaheadAnalysisSubFunctions.start(lookahead_conf)
+
+    text_table_mock.assert_called_once_with(
+        lookahead_conf, [lookahead_analysis], caption=expected_caption_text
+    )
+
+
+def test_lookahead_helper_text_table_lookahead_analysis_instances(lookahead_conf):
+    analysis = Analysis()
+    analysis.has_bias = True
+    analysis.total_signals = 5
+    analysis.false_entry_signals = 4
+    analysis.false_exit_signals = 3
+
+    strategy_obj = {
+        "name": "strategy_test_v3_with_lookahead_bias",
+        "location": Path(lookahead_conf["strategy_path"], f"{lookahead_conf['strategy']}.py"),
+    }
+
+    instance = LookaheadAnalysis(lookahead_conf, strategy_obj)
+    instance.current_analysis = analysis
+    data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances(
+        lookahead_conf, [instance]
+    )
+
+    # check row contents for a try that has too few signals
+    assert data[0][0] == "strategy_test_v3_with_lookahead_bias.py"
+    assert data[0][1] == "strategy_test_v3_with_lookahead_bias"
+    assert data[0][2].__contains__("too few trades")
+    assert len(data[0]) == 3
+
+    # now check for an error which occurred after enough trades
+    analysis.total_signals = 12
+    analysis.false_entry_signals = 11
+    analysis.false_exit_signals = 10
+    instance = LookaheadAnalysis(lookahead_conf, strategy_obj)
+    instance.current_analysis = analysis
+    data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances(
+        lookahead_conf, [instance]
+    )
+    assert data[0][2].__contains__("error")
+
+    # edit it into not showing an error
+    instance.failed_bias_check = False
+    data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances(
+        lookahead_conf, [instance]
+    )
+    assert data[0][0] == "strategy_test_v3_with_lookahead_bias.py"
+    assert data[0][1] == "strategy_test_v3_with_lookahead_bias"
+    assert data[0][2]  # True
+    assert data[0][3] == 12
+    assert data[0][4] == 11
+    assert data[0][5] == 10
+    assert data[0][6] == ""
+
+    analysis.false_indicators.append("falseIndicator1")
+    analysis.false_indicators.append("falseIndicator2")
+    data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances(
+        lookahead_conf, [instance]
+    )
+
+    assert data[0][6] == "falseIndicator1, falseIndicator2"
+
+    # check amount of returning rows
+    assert len(data) == 1
+
+    # check amount of multiple rows
+    data = LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances(
+        lookahead_conf, [instance, instance, instance]
+    )
+    assert len(data) == 3
+
+
+@pytest.mark.parametrize(
+    "caption",
+    [
+        "",
+        "A test caption",
+        None,
+        False,
+    ],
+    ids=(
+        "Pass empty string",
+        "Pass non-empty string",
+        "Pass None",
+        "Don't pass caption",
+    ),
+)
+def test_lookahead_helper_text_table_lookahead_analysis_instances__caption(
+    caption,
+    lookahead_conf,
+    mocker,
+):
+    """Test that the caption is passed in the table kwargs when calling print_rich_table()."""
+
+    print_rich_table_mock = MagicMock()
+    mocker.patch(
+        "freqtrade.optimize.analysis.lookahead_helpers.print_rich_table",
+        print_rich_table_mock,
+    )
+    lookahead_analysis = LookaheadAnalysis(
+        lookahead_conf,
+        {
+            "name": "strategy_test_v3_with_lookahead_bias",
+            "location": Path(lookahead_conf["strategy_path"], f"{lookahead_conf['strategy']}.py"),
+        },
+    )
+    kwargs = {}
+    if caption is not False:
+        kwargs["caption"] = caption
+
+    LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances(
+        lookahead_conf, [lookahead_analysis], **kwargs
+    )
+
+    assert print_rich_table_mock.call_args[-1]["table_kwargs"]["caption"] == (
+        caption if caption is not False else None
+    )
+
+
+def test_lookahead_helper_export_to_csv(lookahead_conf):
+    import pandas as pd
+
+    lookahead_conf["lookahead_analysis_exportfilename"] = "temp_csv_lookahead_analysis.csv"
+
+    # just to be sure the test won't fail: remove file if exists for some reason
+    # (repeat this at the end once again to clean up)
+    if Path(lookahead_conf["lookahead_analysis_exportfilename"]).exists():
+        Path(lookahead_conf["lookahead_analysis_exportfilename"]).unlink()
+
+    # before we can start we have to delete the
+
+    # 1st check: create a new file and verify its contents
+    analysis1 = Analysis()
+    analysis1.has_bias = True
+    analysis1.total_signals = 12
+    analysis1.false_entry_signals = 11
+    analysis1.false_exit_signals = 10
+    analysis1.false_indicators.append("falseIndicator1")
+    analysis1.false_indicators.append("falseIndicator2")
+    lookahead_conf["lookahead_analysis_exportfilename"] = "temp_csv_lookahead_analysis.csv"
+
+    strategy_obj1 = {
+        "name": "strat1",
+        "location": Path("file1.py"),
+    }
+
+    instance1 = LookaheadAnalysis(lookahead_conf, strategy_obj1)
+    instance1.failed_bias_check = False
+    instance1.current_analysis = analysis1
+
+    LookaheadAnalysisSubFunctions.export_to_csv(lookahead_conf, [instance1])
+    saved_data1 = pd.read_csv(lookahead_conf["lookahead_analysis_exportfilename"])
+
+    expected_values1 = [
+        ["file1.py", "strat1", True, 12, 11, 10, "falseIndicator1,falseIndicator2"],
+    ]
+    expected_columns = [
+        "filename",
+        "strategy",
+        "has_bias",
+        "total_signals",
+        "biased_entry_signals",
+        "biased_exit_signals",
+        "biased_indicators",
+    ]
+    expected_data1 = pd.DataFrame(expected_values1, columns=expected_columns)
+
+    assert Path(lookahead_conf["lookahead_analysis_exportfilename"]).exists()
+    assert expected_data1.equals(saved_data1)
+
+    # 2nd check: update the same strategy (which internally changed or is being retested)
+    expected_values2 = [
+        ["file1.py", "strat1", False, 22, 21, 20, "falseIndicator3,falseIndicator4"],
+    ]
+    expected_data2 = pd.DataFrame(expected_values2, columns=expected_columns)
+
+    analysis2 = Analysis()
+    analysis2.has_bias = False
+    analysis2.total_signals = 22
+    analysis2.false_entry_signals = 21
+    analysis2.false_exit_signals = 20
+    analysis2.false_indicators.append("falseIndicator3")
+    analysis2.false_indicators.append("falseIndicator4")
+
+    strategy_obj2 = {
+        "name": "strat1",
+        "location": Path("file1.py"),
+    }
+
+    instance2 = LookaheadAnalysis(lookahead_conf, strategy_obj2)
+    instance2.failed_bias_check = False
+    instance2.current_analysis = analysis2
+
+    LookaheadAnalysisSubFunctions.export_to_csv(lookahead_conf, [instance2])
+    saved_data2 = pd.read_csv(lookahead_conf["lookahead_analysis_exportfilename"])
+
+    assert expected_data2.equals(saved_data2)
+
+    # 3rd check: now we add a new row to an already existing file
+    expected_values3 = [
+        ["file1.py", "strat1", False, 22, 21, 20, "falseIndicator3,falseIndicator4"],
+        ["file3.py", "strat3", True, 32, 31, 30, "falseIndicator5,falseIndicator6"],
+    ]
+
+    expected_data3 = pd.DataFrame(expected_values3, columns=expected_columns)
+
+    analysis3 = Analysis()
+    analysis3.has_bias = True
+    analysis3.total_signals = 32
+    analysis3.false_entry_signals = 31
+    analysis3.false_exit_signals = 30
+    analysis3.false_indicators.append("falseIndicator5")
+    analysis3.false_indicators.append("falseIndicator6")
+    lookahead_conf["lookahead_analysis_exportfilename"] = "temp_csv_lookahead_analysis.csv"
+
+    strategy_obj3 = {
+        "name": "strat3",
+        "location": Path("file3.py"),
+    }
+
+    instance3 = LookaheadAnalysis(lookahead_conf, strategy_obj3)
+    instance3.failed_bias_check = False
+    instance3.current_analysis = analysis3
+
+    LookaheadAnalysisSubFunctions.export_to_csv(lookahead_conf, [instance3])
+    saved_data3 = pd.read_csv(lookahead_conf["lookahead_analysis_exportfilename"])
+    assert expected_data3.equals(saved_data3)
+
+    # remove csv file after the test is done
+    if Path(lookahead_conf["lookahead_analysis_exportfilename"]).exists():
+        Path(lookahead_conf["lookahead_analysis_exportfilename"]).unlink()
+
+
+def test_initialize_single_lookahead_analysis(lookahead_conf, mocker, caplog):
+    mocker.patch("freqtrade.data.history.get_timerange", get_timerange)
+    mocker.patch(f"{EXMS}.get_fee", return_value=0.0)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    patch_exchange(mocker)
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+    lookahead_conf["pairs"] = ["UNITTEST/USDT"]
+
+    lookahead_conf["timeframe"] = "5m"
+    lookahead_conf["timerange"] = "20180119-20180122"
+    start_mock = mocker.patch("freqtrade.optimize.analysis.lookahead.LookaheadAnalysis.start")
+    strategy_obj = {
+        "name": "strategy_test_v3_with_lookahead_bias",
+        "location": Path(lookahead_conf["strategy_path"], f"{lookahead_conf['strategy']}.py"),
+    }
+
+    instance = LookaheadAnalysisSubFunctions.initialize_single_lookahead_analysis(
+        lookahead_conf, strategy_obj
+    )
+    assert log_has_re(r"Bias test of .* started\.", caplog)
+    assert start_mock.call_count == 1
+
+    assert instance.strategy_obj["name"] == "strategy_test_v3_with_lookahead_bias"
+
+
+@pytest.mark.parametrize("scenario", ["no_bias", "bias1"])
+def test_biased_strategy(lookahead_conf, mocker, caplog, scenario) -> None:
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.data.history.get_timerange", get_timerange)
+    mocker.patch(f"{EXMS}.get_fee", return_value=0.0)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+    lookahead_conf["pairs"] = ["UNITTEST/USDT"]
+
+    lookahead_conf["timeframe"] = "5m"
+    lookahead_conf["timerange"] = "20180119-20180122"
+
+    # Patch scenario Parameter to allow for easy selection
+    mocker.patch(
+        "freqtrade.strategy.hyper.HyperStrategyMixin.load_params_from_file",
+        return_value={"params": {"buy": {"scenario": scenario}}},
+    )
+
+    strategy_obj = {"name": "strategy_test_v3_with_lookahead_bias"}
+    instance = LookaheadAnalysis(lookahead_conf, strategy_obj)
+    instance.start()
+    # Assert init correct
+    assert log_has_re(f"Strategy Parameter: scenario = {scenario}", caplog)
+
+    # check non-biased strategy
+    if scenario == "no_bias":
+        assert not instance.current_analysis.has_bias
+    # check biased strategy
+    elif scenario == "bias1":
+        assert instance.current_analysis.has_bias
+
+
+def test_config_overrides(lookahead_conf):
+    lookahead_conf["max_open_trades"] = 0
+    lookahead_conf["dry_run_wallet"] = 1
+    lookahead_conf["pairs"] = ["BTC/USDT", "ETH/USDT", "SOL/USDT"]
+    lookahead_conf = LookaheadAnalysisSubFunctions.calculate_config_overrides(lookahead_conf)
+
+    assert lookahead_conf["dry_run_wallet"] == 1000000000
+    assert lookahead_conf["max_open_trades"] == 3
diff --git a/tests/optimize/test_optimize_reports.py b/tests/optimize/test_optimize_reports.py
new file mode 100644
index 000000000..8943a7467
--- /dev/null
+++ b/tests/optimize/test_optimize_reports.py
@@ -0,0 +1,698 @@
+import json
+import re
+import shutil
+from datetime import timedelta
+from pathlib import Path
+from shutil import copyfile
+from zipfile import ZipFile
+
+import joblib
+import pandas as pd
+import pytest
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
+from freqtrade.data import history
+from freqtrade.data.btanalysis import (
+    get_latest_backtest_filename,
+    load_backtest_data,
+    load_backtest_stats,
+)
+from freqtrade.edge import PairInfo
+from freqtrade.enums import ExitType
+from freqtrade.optimize.optimize_reports import (
+    generate_backtest_stats,
+    generate_daily_stats,
+    generate_edge_table,
+    generate_pair_metrics,
+    generate_periodic_breakdown_stats,
+    generate_strategy_comparison,
+    generate_trading_stats,
+    show_sorted_pairlist,
+    store_backtest_results,
+    text_table_bt_results,
+    text_table_strategy,
+)
+from freqtrade.optimize.optimize_reports.bt_output import text_table_tags
+from freqtrade.optimize.optimize_reports.optimize_reports import (
+    _get_resample_from_period,
+    calc_streak,
+    generate_tag_metrics,
+)
+from freqtrade.resolvers.strategy_resolver import StrategyResolver
+from freqtrade.util import dt_ts
+from freqtrade.util.datetime_helpers import dt_from_ts, dt_utc
+from tests.conftest import CURRENT_TEST_STRATEGY, log_has_re
+from tests.data.test_history import _clean_test_file
+
+
+def _backup_file(file: Path, copy_file: bool = False) -> None:
+    """
+    Backup existing file to avoid deleting the user file
+    :param file: complete path to the file
+    :param copy_file: keep file in place too.
+    :return: None
+    """
+    file_swp = str(file) + ".swp"
+    if file.is_file():
+        file.rename(file_swp)
+
+        if copy_file:
+            copyfile(file_swp, file)
+
+
+def test_text_table_bt_results(capsys):
+    results = pd.DataFrame(
+        {
+            "pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"],
+            "profit_ratio": [0.1, 0.2, -0.05],
+            "profit_abs": [0.2, 0.4, -0.1],
+            "trade_duration": [10, 30, 20],
+            "close_date": [
+                dt_utc(2017, 11, 14, 21, 35, 00),
+                dt_utc(2017, 11, 14, 22, 10, 00),
+                dt_utc(2017, 11, 14, 22, 43, 00),
+            ],
+        }
+    )
+
+    pair_results = generate_pair_metrics(
+        ["ETH/BTC"],
+        stake_currency="BTC",
+        starting_balance=4,
+        results=results,
+        min_date=dt_from_ts(1510688220),
+        max_date=dt_from_ts(1510700340),
+    )
+    text_table_bt_results(pair_results, stake_currency="BTC", title="title")
+    text = capsys.readouterr().out
+    re.search(
+        r".* Pair .* Trades .* Avg Profit % .* Tot Profit BTC .* Tot Profit % .* "
+        r"Avg Duration .* Win  Draw  Loss  Win% .*",
+        text,
+    )
+    re.search(
+        r".* ETH/BTC .* 3 .* 8.33 .* 0.50000000 .* 12.50 .* 0:20:00 .* 2     0     1  66.7 .*",
+        text,
+    )
+    re.search(
+        r".* TOTAL .* 3 .* 8.33 .* 0.50000000 .* 12.50 .* 0:20:00 .* 2     0     1  66.7 .*", text
+    )
+
+
+def test_generate_backtest_stats(default_conf, testdatadir, tmp_path):
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY})
+    StrategyResolver.load_strategy(default_conf)
+
+    results = {
+        "DefStrat": {
+            "results": pd.DataFrame(
+                {
+                    "pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
+                    "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
+                    "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
+                    "open_date": [
+                        dt_utc(2017, 11, 14, 19, 32, 00),
+                        dt_utc(2017, 11, 14, 21, 36, 00),
+                        dt_utc(2017, 11, 14, 22, 12, 00),
+                        dt_utc(2017, 11, 14, 22, 44, 00),
+                    ],
+                    "close_date": [
+                        dt_utc(2017, 11, 14, 21, 35, 00),
+                        dt_utc(2017, 11, 14, 22, 10, 00),
+                        dt_utc(2017, 11, 14, 22, 43, 00),
+                        dt_utc(2017, 11, 14, 22, 58, 00),
+                    ],
+                    "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
+                    "close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
+                    "trade_duration": [123, 34, 31, 14],
+                    "is_open": [False, False, False, True],
+                    "is_short": [False, False, False, False],
+                    "stake_amount": [0.01, 0.01, 0.01, 0.01],
+                    "exit_reason": [
+                        ExitType.ROI.value,
+                        ExitType.STOP_LOSS.value,
+                        ExitType.ROI.value,
+                        ExitType.FORCE_EXIT.value,
+                    ],
+                }
+            ),
+            "config": default_conf,
+            "locks": [],
+            "final_balance": 1000.02,
+            "rejected_signals": 20,
+            "timedout_entry_orders": 0,
+            "timedout_exit_orders": 0,
+            "canceled_trade_entries": 0,
+            "canceled_entry_orders": 0,
+            "replaced_entry_orders": 0,
+            "backtest_start_time": dt_ts() // 1000,
+            "backtest_end_time": dt_ts() // 1000,
+            "run_id": "123",
+        }
+    }
+    timerange = TimeRange.parse_timerange("1510688220-1510700340")
+    min_date = dt_from_ts(1510688220)
+    max_date = dt_from_ts(1510700340)
+    btdata = history.load_data(
+        testdatadir, "1m", ["UNITTEST/BTC"], timerange=timerange, fill_up_missing=True
+    )
+
+    stats = generate_backtest_stats(btdata, results, min_date, max_date)
+    assert isinstance(stats, dict)
+    assert "strategy" in stats
+    assert "DefStrat" in stats["strategy"]
+    assert "strategy_comparison" in stats
+    strat_stats = stats["strategy"]["DefStrat"]
+    assert strat_stats["backtest_start"] == min_date.strftime(DATETIME_PRINT_FORMAT)
+    assert strat_stats["backtest_end"] == max_date.strftime(DATETIME_PRINT_FORMAT)
+    assert strat_stats["total_trades"] == len(results["DefStrat"]["results"])
+    # Above sample had no losing trade
+    assert strat_stats["max_drawdown_account"] == 0.0
+
+    # Retry with losing trade
+    results = {
+        "DefStrat": {
+            "results": pd.DataFrame(
+                {
+                    "pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
+                    "profit_ratio": [0.003312, 0.010801, -0.013803, 0.002780],
+                    "profit_abs": [0.000003, 0.000011, -0.000014, 0.000003],
+                    "open_date": [
+                        dt_utc(2017, 11, 14, 19, 32, 00),
+                        dt_utc(2017, 11, 14, 21, 36, 00),
+                        dt_utc(2017, 11, 14, 22, 12, 00),
+                        dt_utc(2017, 11, 14, 22, 44, 00),
+                    ],
+                    "close_date": [
+                        dt_utc(2017, 11, 14, 21, 35, 00),
+                        dt_utc(2017, 11, 14, 22, 10, 00),
+                        dt_utc(2017, 11, 14, 22, 43, 00),
+                        dt_utc(2017, 11, 14, 22, 58, 00),
+                    ],
+                    "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
+                    "close_rate": [0.002546, 0.003014, 0.0032903, 0.003217],
+                    "trade_duration": [123, 34, 31, 14],
+                    "is_open": [False, False, False, True],
+                    "is_short": [False, False, False, False],
+                    "stake_amount": [0.01, 0.01, 0.01, 0.01],
+                    "exit_reason": [
+                        ExitType.ROI.value,
+                        ExitType.ROI.value,
+                        ExitType.STOP_LOSS.value,
+                        ExitType.FORCE_EXIT.value,
+                    ],
+                }
+            ),
+            "config": default_conf,
+            "locks": [],
+            "final_balance": 1000.02,
+            "rejected_signals": 20,
+            "timedout_entry_orders": 0,
+            "timedout_exit_orders": 0,
+            "canceled_trade_entries": 0,
+            "canceled_entry_orders": 0,
+            "replaced_entry_orders": 0,
+            "backtest_start_time": dt_ts() // 1000,
+            "backtest_end_time": dt_ts() // 1000,
+            "run_id": "124",
+        }
+    }
+
+    stats = generate_backtest_stats(btdata, results, min_date, max_date)
+    assert isinstance(stats, dict)
+    assert "strategy" in stats
+    assert "DefStrat" in stats["strategy"]
+    assert "strategy_comparison" in stats
+    strat_stats = stats["strategy"]["DefStrat"]
+
+    assert pytest.approx(strat_stats["max_drawdown_account"]) == 1.399999e-08
+    assert strat_stats["drawdown_start"] == "2017-11-14 22:10:00"
+    assert strat_stats["drawdown_end"] == "2017-11-14 22:43:00"
+    assert strat_stats["drawdown_end_ts"] == 1510699380000
+    assert strat_stats["drawdown_start_ts"] == 1510697400000
+    assert strat_stats["pairlist"] == ["UNITTEST/BTC"]
+
+    # Test storing stats
+    filename = tmp_path / "btresult.json"
+    filename_last = tmp_path / LAST_BT_RESULT_FN
+    _backup_file(filename_last, copy_file=True)
+    assert not filename.is_file()
+    default_conf["exportfilename"] = filename
+
+    store_backtest_results(default_conf, stats, "2022_01_01_15_05_13")
+
+    # get real Filename (it's btresult-<date>.zip)
+    last_fn = get_latest_backtest_filename(filename_last.parent)
+    assert re.match(r"btresult-.*\.zip", last_fn)
+
+    filename1 = tmp_path / last_fn
+    assert filename1.is_file()
+
+    content = json.dumps(load_backtest_stats(filename1))
+    assert "max_drawdown_account" in content
+    assert "strategy" in content
+    assert "pairlist" in content
+
+    assert filename_last.is_file()
+
+    _clean_test_file(filename_last)
+    filename1.unlink()
+
+
+def test_store_backtest_results(testdatadir, mocker):
+    dump_mock = mocker.patch("freqtrade.optimize.optimize_reports.bt_storage.file_dump_json")
+    zip_mock = mocker.patch("freqtrade.optimize.optimize_reports.bt_storage.ZipFile")
+    data = {"metadata": {}, "strategy": {}, "strategy_comparison": []}
+    store_backtest_results(
+        {"exportfilename": testdatadir, "original_config": {}}, data, "2022_01_01_15_05_13"
+    )
+
+    assert dump_mock.call_count == 2
+    assert zip_mock.call_count == 1
+    assert isinstance(dump_mock.call_args_list[0][0][0], Path)
+    assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / "backtest-result"))
+
+    dump_mock.reset_mock()
+    zip_mock.reset_mock()
+    filename = testdatadir / "testresult.json"
+    store_backtest_results(
+        {"exportfilename": filename, "original_config": {}}, data, "2022_01_01_15_05_13"
+    )
+    assert dump_mock.call_count == 2
+    assert zip_mock.call_count == 1
+    assert isinstance(dump_mock.call_args_list[0][0][0], Path)
+    # result will be testdatadir / testresult-<timestamp>.json
+    assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / "testresult"))
+
+
+def test_store_backtest_results_real(tmp_path, caplog):
+    data = {"metadata": {}, "strategy": {}, "strategy_comparison": []}
+    config = {
+        "exportfilename": tmp_path,
+        "original_config": {},
+    }
+    store_backtest_results(
+        config, data, "2022_01_01_15_05_13", strategy_files={"DefStrat": "NoFile"}
+    )
+    assert log_has_re(r"Strategy file .* does not exist\. Skipping\.", caplog)
+
+    zip_file = tmp_path / "backtest-result-2022_01_01_15_05_13.zip"
+    assert zip_file.is_file()
+    assert (tmp_path / "backtest-result-2022_01_01_15_05_13.meta.json").is_file()
+    assert not (tmp_path / "backtest-result-2022_01_01_15_05_13_market_change.feather").is_file()
+    with ZipFile(zip_file, "r") as zipf:
+        assert "backtest-result-2022_01_01_15_05_13.json" in zipf.namelist()
+        assert "backtest-result-2022_01_01_15_05_13_market_change.feather" not in zipf.namelist()
+    assert (tmp_path / LAST_BT_RESULT_FN).is_file()
+    fn = get_latest_backtest_filename(tmp_path)
+    assert fn == "backtest-result-2022_01_01_15_05_13.zip"
+
+    strategy_test_dir = Path(__file__).parent.parent / "strategy" / "strats"
+
+    shutil.copy(strategy_test_dir / "strategy_test_v3.py", tmp_path)
+    params_file = tmp_path / "strategy_test_v3.json"
+    with params_file.open("w") as f:
+        f.write("""{"strategy_name": "TurtleStrategyX5","params":{}}""")
+
+    store_backtest_results(
+        config,
+        data,
+        "2024_01_01_15_05_25",
+        market_change_data=pd.DataFrame(),
+        strategy_files={"DefStrat": str(tmp_path / "strategy_test_v3.py")},
+    )
+    zip_file = tmp_path / "backtest-result-2024_01_01_15_05_25.zip"
+    assert zip_file.is_file()
+    assert (tmp_path / "backtest-result-2024_01_01_15_05_25.meta.json").is_file()
+    assert not (tmp_path / "backtest-result-2024_01_01_15_05_25_market_change.feather").is_file()
+
+    with ZipFile(zip_file, "r") as zipf:
+        assert "backtest-result-2024_01_01_15_05_25.json" in zipf.namelist()
+        assert "backtest-result-2024_01_01_15_05_25_market_change.feather" in zipf.namelist()
+        assert "backtest-result-2024_01_01_15_05_25_config.json" in zipf.namelist()
+        # strategy file is copied to the zip file
+        assert "backtest-result-2024_01_01_15_05_25_DefStrat.py" in zipf.namelist()
+        # compare the content of the strategy file
+        with zipf.open("backtest-result-2024_01_01_15_05_25_DefStrat.py") as strategy_file:
+            strategy_content = strategy_file.read()
+            with (strategy_test_dir / "strategy_test_v3.py").open("rb") as original_file:
+                original_content = original_file.read()
+                assert strategy_content == original_content
+        assert "backtest-result-2024_01_01_15_05_25_DefStrat.py" in zipf.namelist()
+        with zipf.open("backtest-result-2024_01_01_15_05_25_DefStrat.json") as pf:
+            params_content = pf.read()
+            with params_file.open("rb") as original_file:
+                original_content = original_file.read()
+                assert params_content == original_content
+
+    assert (tmp_path / LAST_BT_RESULT_FN).is_file()
+
+    # Last file reference should be updated
+    fn = get_latest_backtest_filename(tmp_path)
+    assert fn == "backtest-result-2024_01_01_15_05_25.zip"
+
+
+def test_write_read_backtest_candles(tmp_path):
+    candle_dict = {"DefStrat": {"UNITTEST/BTC": pd.DataFrame()}}
+    bt_results = {"metadata": {}, "strategy": {}, "strategy_comparison": []}
+
+    mock_conf = {
+        "exportfilename": tmp_path,
+        "export": "signals",
+        "runmode": "backtest",
+        "original_config": {},
+    }
+    # test directory exporting
+    sample_date = "2022_01_01_15_05_13"
+    data = {
+        "signals": candle_dict,
+        "rejected": {},
+        "exited": {},
+    }
+    store_backtest_results(mock_conf, bt_results, sample_date, analysis_results=data)
+    stored_file = tmp_path / f"backtest-result-{sample_date}.zip"
+    signals_pkl = f"backtest-result-{sample_date}_signals.pkl"
+    rejected_pkl = f"backtest-result-{sample_date}_rejected.pkl"
+    exited_pkl = f"backtest-result-{sample_date}_exited.pkl"
+    assert not (tmp_path / signals_pkl).is_file()
+    assert stored_file.is_file()
+
+    with ZipFile(stored_file, "r") as zipf:
+        assert signals_pkl in zipf.namelist()
+        assert rejected_pkl in zipf.namelist()
+        assert exited_pkl in zipf.namelist()
+
+        # open and read the file
+        with zipf.open(signals_pkl) as scp:
+            pickled_signal_candles = joblib.load(scp)
+
+    assert pickled_signal_candles.keys() == candle_dict.keys()
+    assert pickled_signal_candles["DefStrat"].keys() == pickled_signal_candles["DefStrat"].keys()
+    assert pickled_signal_candles["DefStrat"]["UNITTEST/BTC"].equals(
+        pickled_signal_candles["DefStrat"]["UNITTEST/BTC"]
+    )
+
+    _clean_test_file(stored_file)
+
+    # test file exporting
+    filename = tmp_path / "testresult"
+    mock_conf["exportfilename"] = filename
+    store_backtest_results(mock_conf, bt_results, sample_date, analysis_results=data)
+    stored_file = tmp_path / f"testresult-{sample_date}.zip"
+    signals_pkl = f"testresult-{sample_date}_signals.pkl"
+    rejected_pkl = f"testresult-{sample_date}_rejected.pkl"
+    exited_pkl = f"testresult-{sample_date}_exited.pkl"
+    assert not (tmp_path / signals_pkl).is_file()
+    assert stored_file.is_file()
+
+    with ZipFile(stored_file, "r") as zipf:
+        assert signals_pkl in zipf.namelist()
+        assert rejected_pkl in zipf.namelist()
+        assert exited_pkl in zipf.namelist()
+
+        with zipf.open(signals_pkl) as scp:
+            pickled_signal_candles2 = joblib.load(scp)
+
+    assert pickled_signal_candles2.keys() == candle_dict.keys()
+    assert pickled_signal_candles2["DefStrat"].keys() == pickled_signal_candles2["DefStrat"].keys()
+    assert pickled_signal_candles2["DefStrat"]["UNITTEST/BTC"].equals(
+        pickled_signal_candles2["DefStrat"]["UNITTEST/BTC"]
+    )
+
+    _clean_test_file(stored_file)
+
+
+def test_generate_pair_metrics():
+    results = pd.DataFrame(
+        {
+            "pair": ["ETH/BTC", "ETH/BTC"],
+            "profit_ratio": [0.1, 0.2],
+            "profit_abs": [0.2, 0.4],
+            "trade_duration": [10, 30],
+            "close_date": [
+                dt_utc(2017, 11, 14, 21, 35, 00),
+                dt_utc(2017, 11, 14, 22, 10, 00),
+            ],
+            "wins": [2, 0],
+            "draws": [0, 0],
+            "losses": [0, 0],
+        }
+    )
+
+    pair_results = generate_pair_metrics(
+        ["ETH/BTC"],
+        stake_currency="BTC",
+        starting_balance=2,
+        results=results,
+        min_date=dt_from_ts(1510688220),
+        max_date=dt_from_ts(1510700340),
+    )
+    assert isinstance(pair_results, list)
+    assert len(pair_results) == 2
+    assert pair_results[-1]["key"] == "TOTAL"
+    assert (
+        pytest.approx(pair_results[-1]["profit_mean_pct"]) == pair_results[-1]["profit_mean"] * 100
+    )
+    assert pytest.approx(pair_results[-1]["profit_sum_pct"]) == pair_results[-1]["profit_sum"] * 100
+
+
+def test_generate_daily_stats(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    res = generate_daily_stats(bt_data)
+    assert isinstance(res, dict)
+    assert round(res["backtest_best_day"], 4) == 0.1796
+    assert round(res["backtest_worst_day"], 4) == -0.1468
+    assert res["winning_days"] == 19
+    assert res["draw_days"] == 0
+    assert res["losing_days"] == 2
+
+    # Select empty dataframe!
+    res = generate_daily_stats(bt_data.loc[bt_data["open_date"] == "2000-01-01", :])
+    assert isinstance(res, dict)
+    assert round(res["backtest_best_day"], 4) == 0.0
+    assert res["winning_days"] == 0
+    assert res["draw_days"] == 0
+    assert res["losing_days"] == 0
+
+
+def test_generate_trading_stats(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    res = generate_trading_stats(bt_data)
+    assert isinstance(res, dict)
+    assert res["winner_holding_avg"] == timedelta(seconds=1440)
+    assert res["loser_holding_avg"] == timedelta(days=1, seconds=21420)
+    assert "wins" in res
+    assert "losses" in res
+    assert "draws" in res
+
+    # Select empty dataframe!
+    res = generate_trading_stats(bt_data.loc[bt_data["open_date"] == "2000-01-01", :])
+    assert res["wins"] == 0
+    assert res["losses"] == 0
+
+
+def test_calc_streak(testdatadir):
+    df = pd.DataFrame(
+        {
+            "profit_ratio": [0.05, -0.02, -0.03, -0.05, 0.01, 0.02, 0.03, 0.04, -0.02, -0.03],
+        }
+    )
+    # 4 consecutive wins, 3 consecutive losses
+    res = calc_streak(df)
+    assert res == (4, 3)
+    assert isinstance(res[0], int)
+    assert isinstance(res[1], int)
+
+    # invert situation
+    df1 = df.copy()
+    df1["profit_ratio"] = df1["profit_ratio"] * -1
+    assert calc_streak(df1) == (3, 4)
+
+    df_empty = pd.DataFrame(
+        {
+            "profit_ratio": [],
+        }
+    )
+    assert df_empty.empty
+    assert calc_streak(df_empty) == (0, 0)
+
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    assert calc_streak(bt_data) == (7, 18)
+
+
+def test_text_table_exit_reason(capsys):
+    results = pd.DataFrame(
+        {
+            "pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"],
+            "profit_ratio": [0.1, 0.2, -0.1],
+            "profit_abs": [0.2, 0.4, -0.2],
+            "trade_duration": [10, 30, 10],
+            "close_date": [
+                dt_utc(2017, 11, 14, 21, 35, 00),
+                dt_utc(2017, 11, 14, 22, 10, 00),
+                dt_utc(2017, 11, 14, 22, 43, 00),
+            ],
+            "wins": [2, 0, 0],
+            "draws": [0, 0, 0],
+            "losses": [0, 0, 1],
+            "exit_reason": [ExitType.ROI.value, ExitType.ROI.value, ExitType.STOP_LOSS.value],
+        }
+    )
+
+    exit_reason_stats = generate_tag_metrics(
+        "exit_reason",
+        starting_balance=22,
+        results=results,
+        min_date=dt_from_ts(1510688220),
+        max_date=dt_from_ts(1510700340),
+        skip_nan=False,
+    )
+    text_table_tags("exit_tag", exit_reason_stats, "BTC")
+    text = capsys.readouterr().out
+
+    assert re.search(
+        r".* Exit Reason .* Exits .* Avg Profit % .* Tot Profit BTC .* Tot Profit % .* "
+        r"Avg Duration .* Win  Draw  Loss  Win% .*",
+        text,
+    )
+    assert re.search(
+        r".* roi .* 2 .* 15.0 .* 0.60000000 .* 2.73 .* 0:20:00 .* 2     0     0   100 .*",
+        text,
+    )
+    assert re.search(
+        r".* stop_loss .* 1 .* -10.0 .* -0.20000000 .* -0.91 .* 0:10:00 .* 0     0     1     0 .*",
+        text,
+    )
+    assert re.search(
+        r".* TOTAL .* 3 .* 6.67 .* 0.40000000 .* 1.82 .* 0:17:00 .* 2     0     1  66.7 .*", text
+    )
+
+
+def test_generate_sell_reason_stats():
+    results = pd.DataFrame(
+        {
+            "pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"],
+            "profit_ratio": [0.1, 0.2, -0.1],
+            "profit_abs": [0.2, 0.4, -0.2],
+            "trade_duration": [10, 30, 10],
+            "close_date": [
+                dt_utc(2017, 11, 14, 21, 35, 00),
+                dt_utc(2017, 11, 14, 22, 10, 00),
+                dt_utc(2017, 11, 14, 22, 43, 00),
+            ],
+            "wins": [2, 0, 0],
+            "draws": [0, 0, 0],
+            "losses": [0, 0, 1],
+            "exit_reason": [ExitType.ROI.value, ExitType.ROI.value, ExitType.STOP_LOSS.value],
+        }
+    )
+
+    exit_reason_stats = generate_tag_metrics(
+        "exit_reason",
+        starting_balance=22,
+        results=results,
+        min_date=dt_from_ts(1510688220),
+        max_date=dt_from_ts(1510700340),
+        skip_nan=False,
+    )
+    roi_result = exit_reason_stats[0]
+    assert roi_result["key"] == "roi"
+    assert roi_result["trades"] == 2
+    assert pytest.approx(roi_result["profit_mean"]) == 0.15
+    assert roi_result["profit_mean_pct"] == round(roi_result["profit_mean"] * 100, 2)
+    assert pytest.approx(roi_result["profit_mean"]) == 0.15
+    assert roi_result["profit_mean_pct"] == round(roi_result["profit_mean"] * 100, 2)
+
+    stop_result = exit_reason_stats[1]
+
+    assert stop_result["key"] == "stop_loss"
+    assert stop_result["trades"] == 1
+    assert pytest.approx(stop_result["profit_mean"]) == -0.1
+    assert stop_result["profit_mean_pct"] == round(stop_result["profit_mean"] * 100, 2)
+    assert pytest.approx(stop_result["profit_mean"]) == -0.1
+    assert stop_result["profit_mean_pct"] == round(stop_result["profit_mean"] * 100, 2)
+
+
+def test_text_table_strategy(testdatadir, capsys):
+    filename = testdatadir / "backtest_results/backtest-result_multistrat.json"
+    bt_res_data = load_backtest_stats(filename)
+
+    bt_res_data_comparison = bt_res_data.pop("strategy_comparison")
+
+    strategy_results = generate_strategy_comparison(bt_stats=bt_res_data["strategy"])
+    assert strategy_results == bt_res_data_comparison
+    text_table_strategy(strategy_results, "BTC", "STRATEGY SUMMARY")
+
+    captured = capsys.readouterr()
+    text = captured.out
+    assert re.search(
+        r".* Strategy .* Trades .* Avg Profit % .* Tot Profit BTC .* Tot Profit % .* "
+        r"Avg Duration .* Win  Draw  Loss  Win% .* Drawdown .*",
+        text,
+    )
+    assert re.search(
+        r".*StrategyTestV2 .* 179 .* 0.08 .* 0.02608550 .* "
+        r"260.85 .* 3:40:00 .* 170     0     9  95.0 .* 0.00308222 BTC  8.67%.*",
+        text,
+    )
+    assert re.search(
+        r".*TestStrategy .* 179 .* 0.08 .* 0.02608550 .* "
+        r"260.85 .* 3:40:00 .* 170     0     9  95.0 .* 0.00308222 BTC  8.67%.*",
+        text,
+    )
+
+
+def test_generate_edge_table(capsys):
+    results = {}
+    results["ETH/BTC"] = PairInfo(-0.01, 0.60, 2, 1, 3, 10, 60)
+    generate_edge_table(results)
+    text = capsys.readouterr().out
+    assert re.search(r".* ETH/BTC .*", text)
+    assert re.search(r".* Risk Reward Ratio .* Required Risk Reward .* Expectancy .*", text)
+
+
+def test_generate_periodic_breakdown_stats(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename).to_dict(orient="records")
+
+    res = generate_periodic_breakdown_stats(bt_data, "day")
+    assert isinstance(res, list)
+    assert len(res) == 21
+    day = res[0]
+    assert "date" in day
+    assert "draws" in day
+    assert "losses" in day
+    assert "wins" in day
+    assert "profit_abs" in day
+
+    # Select empty dataframe!
+    res = generate_periodic_breakdown_stats([], "day")
+    assert res == []
+
+
+def test__get_resample_from_period():
+    assert _get_resample_from_period("day") == "1d"
+    assert _get_resample_from_period("week") == "1W-MON"
+    assert _get_resample_from_period("month") == "1ME"
+    with pytest.raises(ValueError, match=r"Period noooo is not supported."):
+        _get_resample_from_period("noooo")
+
+    for period in BACKTEST_BREAKDOWNS:
+        assert isinstance(_get_resample_from_period(period), str)
+
+
+def test_show_sorted_pairlist(testdatadir, default_conf, capsys):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_stats(filename)
+    default_conf["backtest_show_pair_list"] = True
+
+    show_sorted_pairlist(default_conf, bt_data)
+
+    out, _err = capsys.readouterr()
+    assert "Pairs for Strategy StrategyTestV3: \n[" in out
+    assert "TOTAL" not in out
+    assert '"ETH/BTC",  // ' in out
diff --git a/tests/optimize/test_recursive_analysis.py b/tests/optimize/test_recursive_analysis.py
new file mode 100644
index 000000000..e16c82d24
--- /dev/null
+++ b/tests/optimize/test_recursive_analysis.py
@@ -0,0 +1,184 @@
+# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
+from copy import deepcopy
+from pathlib import Path
+from unittest.mock import MagicMock, PropertyMock
+
+import pytest
+
+from freqtrade.commands.optimize_commands import start_recursive_analysis
+from freqtrade.data.history import get_timerange
+from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.analysis.recursive import RecursiveAnalysis
+from freqtrade.optimize.analysis.recursive_helpers import RecursiveAnalysisSubFunctions
+from tests.conftest import EXMS, get_args, log_has_re, patch_exchange
+
+
+@pytest.fixture
+def recursive_conf(default_conf_usdt, tmp_path):
+    default_conf_usdt["user_data_dir"] = tmp_path
+    default_conf_usdt["timerange"] = "20220101-20220501"
+
+    default_conf_usdt["strategy_path"] = str(Path(__file__).parent.parent / "strategy/strats")
+    default_conf_usdt["strategy"] = "strategy_test_v3_recursive_issue"
+    default_conf_usdt["pairs"] = ["UNITTEST/USDT"]
+    default_conf_usdt["startup_candle"] = [100]
+    return default_conf_usdt
+
+
+def test_start_recursive_analysis(mocker):
+    single_mock = MagicMock()
+    text_table_mock = MagicMock()
+    mocker.patch.multiple(
+        "freqtrade.optimize.analysis.recursive_helpers.RecursiveAnalysisSubFunctions",
+        initialize_single_recursive_analysis=single_mock,
+        text_table_recursive_analysis_instances=text_table_mock,
+    )
+    args = [
+        "recursive-analysis",
+        "--strategy",
+        "strategy_test_v3_recursive_issue",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy/strats"),
+        "--pairs",
+        "UNITTEST/BTC",
+        "--timerange",
+        "20220101-20220201",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+
+    start_recursive_analysis(pargs)
+    assert single_mock.call_count == 1
+    assert text_table_mock.call_count == 1
+
+    single_mock.reset_mock()
+
+    # Missing timerange
+    args = [
+        "recursive-analysis",
+        "--strategy",
+        "strategy_test_v3_with_recursive_bias",
+        "--strategy-path",
+        str(Path(__file__).parent.parent / "strategy/strats"),
+        "--pairs",
+        "UNITTEST/BTC",
+    ]
+    pargs = get_args(args)
+    pargs["config"] = None
+    with pytest.raises(OperationalException, match=r"Please set a timerange\..*"):
+        start_recursive_analysis(pargs)
+
+
+def test_recursive_helper_no_strategy_defined(recursive_conf):
+    conf = deepcopy(recursive_conf)
+    conf["pairs"] = ["UNITTEST/USDT"]
+    del conf["strategy"]
+    with pytest.raises(OperationalException, match=r"No Strategy specified"):
+        RecursiveAnalysisSubFunctions.start(conf)
+
+
+def test_recursive_helper_start(recursive_conf, mocker) -> None:
+    single_mock = MagicMock()
+    text_table_mock = MagicMock()
+    mocker.patch.multiple(
+        "freqtrade.optimize.analysis.recursive_helpers.RecursiveAnalysisSubFunctions",
+        initialize_single_recursive_analysis=single_mock,
+        text_table_recursive_analysis_instances=text_table_mock,
+    )
+    RecursiveAnalysisSubFunctions.start(recursive_conf)
+    assert single_mock.call_count == 1
+    assert text_table_mock.call_count == 1
+
+    single_mock.reset_mock()
+    text_table_mock.reset_mock()
+
+
+def test_recursive_helper_text_table_recursive_analysis_instances(recursive_conf):
+    dict_diff = dict()
+    dict_diff["rsi"] = {}
+    dict_diff["rsi"][100] = "0.078%"
+
+    strategy_obj = {
+        "name": "strategy_test_v3_recursive_issue",
+        "location": Path(recursive_conf["strategy_path"], f"{recursive_conf['strategy']}.py"),
+    }
+
+    instance = RecursiveAnalysis(recursive_conf, strategy_obj)
+    instance.dict_recursive = dict_diff
+    data = RecursiveAnalysisSubFunctions.text_table_recursive_analysis_instances([instance])
+
+    # check row contents for a try that has too few signals
+    assert data[0][0] == "rsi"
+    assert data[0][1] == "0.078%"
+    assert len(data[0]) == 2
+
+    # now check when there is no issue
+    dict_diff = dict()
+    instance = RecursiveAnalysis(recursive_conf, strategy_obj)
+    instance.dict_recursive = dict_diff
+    data = RecursiveAnalysisSubFunctions.text_table_recursive_analysis_instances([instance])
+    assert len(data) == 0
+
+
+def test_initialize_single_recursive_analysis(recursive_conf, mocker, caplog):
+    mocker.patch("freqtrade.data.history.get_timerange", get_timerange)
+    patch_exchange(mocker)
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+    recursive_conf["pairs"] = ["UNITTEST/BTC"]
+
+    recursive_conf["timeframe"] = "5m"
+    recursive_conf["timerange"] = "20180119-20180122"
+    start_mock = mocker.patch("freqtrade.optimize.analysis.recursive.RecursiveAnalysis.start")
+    strategy_obj = {
+        "name": "strategy_test_v3_recursive_issue",
+        "location": Path(recursive_conf["strategy_path"], f"{recursive_conf['strategy']}.py"),
+    }
+
+    instance = RecursiveAnalysisSubFunctions.initialize_single_recursive_analysis(
+        recursive_conf, strategy_obj
+    )
+    assert log_has_re(r"Recursive test of .* started\.", caplog)
+    assert start_mock.call_count == 1
+
+    assert instance.strategy_obj["name"] == "strategy_test_v3_recursive_issue"
+
+
+@pytest.mark.parametrize("scenario", ["no_bias", "bias1", "bias2"])
+def test_recursive_biased_strategy(recursive_conf, mocker, caplog, scenario) -> None:
+    patch_exchange(mocker)
+    mocker.patch(f"{EXMS}.get_fee", return_value=0.0)
+    mocker.patch("freqtrade.data.history.get_timerange", get_timerange)
+    mocker.patch(
+        "freqtrade.plugins.pairlistmanager.PairListManager.whitelist",
+        PropertyMock(return_value=["UNITTEST/BTC"]),
+    )
+    recursive_conf["pairs"] = ["UNITTEST/BTC"]
+
+    recursive_conf["timeframe"] = "5m"
+    recursive_conf["timerange"] = "20180119-20180122"
+    recursive_conf["startup_candle"] = [100]
+
+    # Patch scenario Parameter to allow for easy selection
+    mocker.patch(
+        "freqtrade.strategy.hyper.HyperStrategyMixin.load_params_from_file",
+        return_value={"params": {"buy": {"scenario": scenario}}},
+    )
+
+    strategy_obj = {"name": "strategy_test_v3_recursive_issue"}
+    instance = RecursiveAnalysis(recursive_conf, strategy_obj)
+    instance.start()
+    # Assert init correct
+    assert log_has_re(f"Strategy Parameter: scenario = {scenario}", caplog)
+
+    if scenario == "bias2":
+        assert log_has_re("=> found lookahead in indicator rsi", caplog)
+    diff_pct = abs(float(instance.dict_recursive["rsi"][100].replace("%", "")))
+    # check non-biased strategy
+    if scenario == "no_bias":
+        assert diff_pct < 0.01
+    # check biased strategy
+    elif scenario in ("bias1", "bias2"):
+        assert diff_pct >= 0.01
diff --git a/tests/persistence/__init__.py b/tests/persistence/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/persistence/test_db_context.py b/tests/persistence/test_db_context.py
new file mode 100644
index 000000000..164095d5d
--- /dev/null
+++ b/tests/persistence/test_db_context.py
@@ -0,0 +1,24 @@
+import pytest
+
+from freqtrade.persistence import FtNoDBContext, PairLocks, Trade
+
+
+@pytest.mark.parametrize("timeframe", ["", "5m", "1d"])
+def test_FtNoDBContext(timeframe):
+    PairLocks.timeframe = ""
+    assert Trade.use_db is True
+    assert PairLocks.use_db is True
+    assert PairLocks.timeframe == ""
+
+    with FtNoDBContext(timeframe):
+        assert Trade.use_db is False
+        assert PairLocks.use_db is False
+        assert PairLocks.timeframe == timeframe
+
+    with FtNoDBContext():
+        assert Trade.use_db is False
+        assert PairLocks.use_db is False
+        assert PairLocks.timeframe == ""
+
+    assert Trade.use_db is True
+    assert PairLocks.use_db is True
diff --git a/tests/persistence/test_key_value_store.py b/tests/persistence/test_key_value_store.py
new file mode 100644
index 000000000..e69ae0220
--- /dev/null
+++ b/tests/persistence/test_key_value_store.py
@@ -0,0 +1,69 @@
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from freqtrade.persistence.key_value_store import KeyValueStore, set_startup_time
+from tests.conftest import create_mock_trades_usdt
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_key_value_store(time_machine):
+    start = datetime(2023, 1, 1, 4, tzinfo=timezone.utc)
+    time_machine.move_to(start, tick=False)
+
+    KeyValueStore.store_value("test", "testStringValue")
+    KeyValueStore.store_value("test_dt", datetime.now(timezone.utc))
+    KeyValueStore.store_value("test_float", 22.51)
+    KeyValueStore.store_value("test_int", 15)
+
+    assert KeyValueStore.get_value("test") == "testStringValue"
+    assert KeyValueStore.get_value("test") == "testStringValue"
+    assert KeyValueStore.get_string_value("test") == "testStringValue"
+    assert KeyValueStore.get_value("test_dt") == datetime.now(timezone.utc)
+    assert KeyValueStore.get_datetime_value("test_dt") == datetime.now(timezone.utc)
+    assert KeyValueStore.get_string_value("test_dt") is None
+    assert KeyValueStore.get_float_value("test_dt") is None
+    assert KeyValueStore.get_int_value("test_dt") is None
+    assert KeyValueStore.get_value("test_float") == 22.51
+    assert KeyValueStore.get_float_value("test_float") == 22.51
+    assert KeyValueStore.get_value("test_int") == 15
+    assert KeyValueStore.get_int_value("test_int") == 15
+    assert KeyValueStore.get_datetime_value("test_int") is None
+
+    time_machine.move_to(start + timedelta(days=20, hours=5), tick=False)
+    assert KeyValueStore.get_value("test_dt") != datetime.now(timezone.utc)
+    assert KeyValueStore.get_value("test_dt") == start
+    # Test update works
+    KeyValueStore.store_value("test_dt", datetime.now(timezone.utc))
+    assert KeyValueStore.get_value("test_dt") == datetime.now(timezone.utc)
+
+    KeyValueStore.store_value("test_float", 23.51)
+    assert KeyValueStore.get_value("test_float") == 23.51
+    # test deleting
+    KeyValueStore.delete_value("test_float")
+    assert KeyValueStore.get_value("test_float") is None
+    # Delete same value again (should not fail)
+    KeyValueStore.delete_value("test_float")
+
+    with pytest.raises(ValueError, match=r"Unknown value type"):
+        KeyValueStore.store_value("test_float", {"some": "dict"})
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_set_startup_time(fee, time_machine):
+    create_mock_trades_usdt(fee)
+    start = datetime.now(timezone.utc)
+    time_machine.move_to(start, tick=False)
+    set_startup_time()
+
+    assert KeyValueStore.get_value("startup_time") == start
+    initial_time = KeyValueStore.get_value("bot_start_time")
+    assert initial_time <= start
+
+    # Simulate bot restart
+    new_start = start + timedelta(days=5)
+    time_machine.move_to(new_start, tick=False)
+    set_startup_time()
+
+    assert KeyValueStore.get_value("startup_time") == new_start
+    assert KeyValueStore.get_value("bot_start_time") == initial_time
diff --git a/tests/persistence/test_migrations.py b/tests/persistence/test_migrations.py
new file mode 100644
index 000000000..677ba9014
--- /dev/null
+++ b/tests/persistence/test_migrations.py
@@ -0,0 +1,454 @@
+# pragma pylint: disable=missing-docstring, C0103
+import logging
+from importlib import import_module
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+from sqlalchemy import create_engine, select, text
+from sqlalchemy.schema import CreateTable
+
+from freqtrade.constants import DEFAULT_DB_PROD_URL
+from freqtrade.enums import TradingMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence import Trade, init_db
+from freqtrade.persistence.base import ModelBase
+from freqtrade.persistence.migrations import get_last_sequence_ids, set_sequence_ids
+from freqtrade.persistence.models import PairLock
+from freqtrade.persistence.trade_model import Order
+from tests.conftest import log_has
+
+
+spot, margin, futures = TradingMode.SPOT, TradingMode.MARGIN, TradingMode.FUTURES
+
+
+def test_init_create_session(default_conf):
+    # Check if init create a session
+    init_db(default_conf["db_url"])
+    assert hasattr(Trade, "session")
+    assert "scoped_session" in type(Trade.session).__name__
+
+
+def test_init_custom_db_url(default_conf, tmp_path):
+    # Update path to a value other than default, but still in-memory
+    filename = tmp_path / "freqtrade2_test.sqlite"
+    assert not filename.is_file()
+
+    default_conf.update({"db_url": f"sqlite:///{filename}"})
+
+    init_db(default_conf["db_url"])
+    assert filename.is_file()
+    r = Trade.session.execute(text("PRAGMA journal_mode"))
+    assert r.first() == ("wal",)
+
+
+def test_init_invalid_db_url():
+    # Update path to a value other than default, but still in-memory
+    with pytest.raises(OperationalException, match=r".*no valid database URL*"):
+        init_db("unknown:///some.url")
+
+    with pytest.raises(OperationalException, match=r"Bad db-url.*For in-memory database, pl.*"):
+        init_db("sqlite:///")
+
+
+def test_init_prod_db(default_conf, mocker):
+    default_conf.update({"dry_run": False})
+    default_conf.update({"db_url": DEFAULT_DB_PROD_URL})
+
+    create_engine_mock = mocker.patch("freqtrade.persistence.models.create_engine", MagicMock())
+
+    init_db(default_conf["db_url"])
+    assert create_engine_mock.call_count == 1
+    assert create_engine_mock.mock_calls[0][1][0] == "sqlite:///tradesv3.sqlite"
+
+
+def test_init_dryrun_db(default_conf, tmpdir):
+    filename = f"{tmpdir}/freqtrade2_prod.sqlite"
+    assert not Path(filename).is_file()
+    default_conf.update({"dry_run": True, "db_url": f"sqlite:///{filename}"})
+
+    init_db(default_conf["db_url"])
+    assert Path(filename).is_file()
+
+
+def test_migrate(mocker, default_conf, fee, caplog):
+    """
+    Test Database migration (starting with new pairformat)
+    """
+    caplog.set_level(logging.DEBUG)
+    amount = 103.223
+    # Always create all columns apart from the last!
+    create_table_old = """CREATE TABLE IF NOT EXISTS "trades" (
+                                id INTEGER NOT NULL,
+                                exchange VARCHAR NOT NULL,
+                                pair VARCHAR NOT NULL,
+                                is_open BOOLEAN NOT NULL,
+                                fee FLOAT NOT NULL,
+                                open_rate FLOAT,
+                                close_rate FLOAT,
+                                close_profit FLOAT,
+                                stake_amount FLOAT NOT NULL,
+                                amount FLOAT,
+                                open_date DATETIME NOT NULL,
+                                close_date DATETIME,
+                                open_order_id VARCHAR,
+                                stop_loss FLOAT,
+                                initial_stop_loss FLOAT,
+                                max_rate FLOAT,
+                                sell_reason VARCHAR,
+                                strategy VARCHAR,
+                                ticker_interval INTEGER,
+                                stoploss_order_id VARCHAR,
+                                PRIMARY KEY (id),
+                                CHECK (is_open IN (0, 1))
+                                );"""
+    create_table_order = """CREATE TABLE orders (
+                                id INTEGER NOT NULL,
+                                ft_trade_id INTEGER,
+                                ft_order_side VARCHAR(25) NOT NULL,
+                                ft_pair VARCHAR(25) NOT NULL,
+                                ft_is_open BOOLEAN NOT NULL,
+                                order_id VARCHAR(255) NOT NULL,
+                                status VARCHAR(255),
+                                symbol VARCHAR(25),
+                                order_type VARCHAR(50),
+                                side VARCHAR(25),
+                                price FLOAT,
+                                amount FLOAT,
+                                filled FLOAT,
+                                remaining FLOAT,
+                                cost FLOAT,
+                                order_date DATETIME,
+                                order_filled_date DATETIME,
+                                order_update_date DATETIME,
+                                PRIMARY KEY (id)
+                            );"""
+    insert_table_old = """INSERT INTO trades (exchange, pair, is_open, fee,
+                          open_rate, stake_amount, amount, open_date,
+                          stop_loss, initial_stop_loss, max_rate, ticker_interval,
+                          open_order_id, stoploss_order_id)
+                          VALUES ('binance', 'ETC/BTC', 1, {fee},
+                          0.00258580, {stake}, {amount},
+                          '2019-11-28 12:44:24.000000',
+                          0.0, 0.0, 0.0, '5m',
+                          'buy_order', 'dry_stop_order_id222')
+                          """.format(
+        fee=fee.return_value, stake=default_conf.get("stake_amount"), amount=amount
+    )
+    insert_orders = f"""
+        insert into orders (
+            ft_trade_id,
+            ft_order_side,
+            ft_pair,
+            ft_is_open,
+            order_id,
+            status,
+            symbol,
+            order_type,
+            side,
+            price,
+            amount,
+            filled,
+            remaining,
+            cost)
+        values (
+            1,
+            'buy',
+            'ETC/BTC',
+            0,
+            'dry_buy_order',
+            'closed',
+            'ETC/BTC',
+            'limit',
+            'buy',
+            0.00258580,
+            {amount},
+            {amount},
+            0,
+            {amount * 0.00258580}
+        ),
+        (
+            1,
+            'buy',
+            'ETC/BTC',
+            1,
+            'dry_buy_order22',
+            'canceled',
+            'ETC/BTC',
+            'limit',
+            'buy',
+            0.00258580,
+            {amount},
+            {amount},
+            0,
+            {amount * 0.00258580}
+        ),
+         (
+            1,
+            'stoploss',
+            'ETC/BTC',
+            1,
+            'dry_stop_order_id11X',
+            'canceled',
+            'ETC/BTC',
+            'limit',
+            'sell',
+            0.00258580,
+            {amount},
+            {amount},
+            0,
+            {amount * 0.00258580}
+        ),
+        (
+            1,
+            'stoploss',
+            'ETC/BTC',
+            1,
+            'dry_stop_order_id222',
+            'open',
+            'ETC/BTC',
+            'limit',
+            'sell',
+            0.00258580,
+            {amount},
+            {amount},
+            0,
+            {amount * 0.00258580}
+        ),
+        (
+            -- Order without reference trade
+            2,
+            'buy',
+            'ETC/BTC',
+            1,
+            'dry_buy_order55',
+            'canceled',
+            'ETC/BTC',
+            'limit',
+            'buy',
+            0.00258580,
+            {amount},
+            {amount},
+            0,
+            {amount * 0.00258580}
+        )
+    """
+    engine = create_engine("sqlite://")
+    mocker.patch("freqtrade.persistence.models.create_engine", lambda *args, **kwargs: engine)
+
+    # Create table using the old format
+    with engine.begin() as connection:
+        connection.execute(text(create_table_old))
+        connection.execute(text(create_table_order))
+        connection.execute(text("create index ix_trades_is_open on trades(is_open)"))
+        connection.execute(text("create index ix_trades_pair on trades(pair)"))
+        connection.execute(text(insert_table_old))
+        connection.execute(text(insert_orders))
+
+        # fake previous backup
+        connection.execute(text("create table trades_bak as select * from trades"))
+
+        connection.execute(text("create table trades_bak1 as select * from trades"))
+    # Run init to test migration
+    init_db(default_conf["db_url"])
+
+    trades = Trade.session.scalars(select(Trade)).all()
+    assert len(trades) == 1
+    trade = trades[0]
+    assert trade.id == 1
+    assert trade.fee_open == fee.return_value
+    assert trade.fee_close == fee.return_value
+    assert trade.open_rate_requested is None
+    assert trade.close_rate_requested is None
+    assert trade.is_open == 1
+    assert trade.amount == amount
+    assert trade.amount_requested == amount
+    assert trade.stake_amount == default_conf.get("stake_amount")
+    assert trade.pair == "ETC/BTC"
+    assert trade.exchange == "binance"
+    assert trade.max_rate == 0.0
+    assert trade.min_rate is None
+    assert trade.stop_loss == 0.0
+    assert trade.initial_stop_loss == 0.0
+    assert trade.exit_reason is None
+    assert trade.strategy is None
+    assert trade.timeframe == "5m"
+    assert log_has("trying trades_bak1", caplog)
+    assert log_has("trying trades_bak2", caplog)
+    assert log_has(
+        "Running database migration for trades - backup: trades_bak2, orders_bak0", caplog
+    )
+    assert log_has("Database migration finished.", caplog)
+    assert pytest.approx(trade.open_trade_value) == trade._calc_open_trade_value(
+        trade.amount, trade.open_rate
+    )
+    assert trade.close_profit_abs is None
+    assert trade.stake_amount == trade.max_stake_amount
+
+    orders = trade.orders
+    assert len(orders) == 4
+    assert orders[0].order_id == "dry_buy_order"
+    assert orders[0].ft_order_side == "buy"
+
+    # All dry-run stoploss orders will be closed
+    assert orders[-1].order_id == "dry_stop_order_id222"
+    assert orders[-1].ft_order_side == "stoploss"
+    assert orders[-1].ft_is_open is False
+
+    assert orders[1].order_id == "dry_buy_order22"
+    assert orders[1].ft_order_side == "buy"
+    assert orders[1].ft_is_open is True
+
+    assert orders[2].order_id == "dry_stop_order_id11X"
+    assert orders[2].ft_order_side == "stoploss"
+    assert orders[2].ft_is_open is False
+
+    orders1 = Order.session.scalars(select(Order)).all()
+    assert len(orders1) == 5
+    order = orders1[4]
+    assert order.ft_trade_id == 2
+    assert order.ft_is_open is False
+
+
+def test_migrate_too_old(mocker, default_conf, fee, caplog):
+    """
+    Test Database migration (starting with new pairformat)
+    """
+    caplog.set_level(logging.DEBUG)
+    amount = 103.223
+    create_table_old = """CREATE TABLE IF NOT EXISTS "trades" (
+                                id INTEGER NOT NULL,
+                                exchange VARCHAR NOT NULL,
+                                pair VARCHAR NOT NULL,
+                                is_open BOOLEAN NOT NULL,
+                                fee_open FLOAT NOT NULL,
+                                fee_close FLOAT NOT NULL,
+                                open_rate FLOAT,
+                                close_rate FLOAT,
+                                close_profit FLOAT,
+                                stake_amount FLOAT NOT NULL,
+                                amount FLOAT,
+                                open_date DATETIME NOT NULL,
+                                close_date DATETIME,
+                                open_order_id VARCHAR,
+                                PRIMARY KEY (id),
+                                CHECK (is_open IN (0, 1))
+                                );"""
+
+    insert_table_old = """INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close,
+                          open_rate, stake_amount, amount, open_date)
+                          VALUES ('binance', 'ETC/BTC', 1, {fee}, {fee},
+                          0.00258580, {stake}, {amount},
+                          '2019-11-28 12:44:24.000000')
+                          """.format(
+        fee=fee.return_value, stake=default_conf.get("stake_amount"), amount=amount
+    )
+    engine = create_engine("sqlite://")
+    mocker.patch("freqtrade.persistence.models.create_engine", lambda *args, **kwargs: engine)
+
+    # Create table using the old format
+    with engine.begin() as connection:
+        connection.execute(text(create_table_old))
+        connection.execute(text(insert_table_old))
+
+    # Run init to test migration
+    with pytest.raises(OperationalException, match=r"Your database seems to be very old"):
+        init_db(default_conf["db_url"])
+
+
+def test_migrate_get_last_sequence_ids():
+    engine = MagicMock()
+    engine.begin = MagicMock()
+    engine.name = "postgresql"
+    get_last_sequence_ids(engine, "trades_bak", "orders_bak")
+
+    assert engine.begin.call_count == 2
+    engine.reset_mock()
+    engine.begin.reset_mock()
+
+    engine.name = "somethingelse"
+    get_last_sequence_ids(engine, "trades_bak", "orders_bak")
+
+    assert engine.begin.call_count == 0
+
+
+def test_migrate_set_sequence_ids():
+    engine = MagicMock()
+    engine.begin = MagicMock()
+    engine.name = "postgresql"
+    set_sequence_ids(engine, 22, 55, 5)
+
+    assert engine.begin.call_count == 1
+    engine.reset_mock()
+    engine.begin.reset_mock()
+
+    engine.name = "somethingelse"
+    set_sequence_ids(engine, 22, 55, 6)
+
+    assert engine.begin.call_count == 0
+
+
+def test_migrate_pairlocks(mocker, default_conf, fee, caplog):
+    """
+    Test Database migration (starting with new pairformat)
+    """
+    caplog.set_level(logging.DEBUG)
+    # Always create all columns apart from the last!
+    create_table_old = """CREATE TABLE pairlocks (
+                            id INTEGER NOT NULL,
+                            pair VARCHAR(25) NOT NULL,
+                            reason VARCHAR(255),
+                            lock_time DATETIME NOT NULL,
+                            lock_end_time DATETIME NOT NULL,
+                            active BOOLEAN NOT NULL,
+                            PRIMARY KEY (id)
+                        )
+                                """
+    create_index1 = "CREATE INDEX ix_pairlocks_pair ON pairlocks (pair)"
+    create_index2 = "CREATE INDEX ix_pairlocks_lock_end_time ON pairlocks (lock_end_time)"
+    create_index3 = "CREATE INDEX ix_pairlocks_active ON pairlocks (active)"
+    insert_table_old = """INSERT INTO pairlocks (
+        id, pair, reason, lock_time, lock_end_time, active)
+        VALUES (1, 'ETH/BTC', 'Auto lock', '2021-07-12 18:41:03', '2021-07-11 18:45:00', 1)
+                          """
+    insert_table_old2 = """INSERT INTO pairlocks (
+        id, pair, reason, lock_time, lock_end_time, active)
+        VALUES (2, '*', 'Lock all', '2021-07-12 18:41:03', '2021-07-12 19:00:00', 1)
+                          """
+    engine = create_engine("sqlite://")
+    mocker.patch("freqtrade.persistence.models.create_engine", lambda *args, **kwargs: engine)
+    # Create table using the old format
+    with engine.begin() as connection:
+        connection.execute(text(create_table_old))
+
+        connection.execute(text(insert_table_old))
+        connection.execute(text(insert_table_old2))
+        connection.execute(text(create_index1))
+        connection.execute(text(create_index2))
+        connection.execute(text(create_index3))
+
+    init_db(default_conf["db_url"])
+
+    assert len(PairLock.get_all_locks().all()) == 2
+    assert len(PairLock.session.scalars(select(PairLock).filter(PairLock.pair == "*")).all()) == 1
+    pairlocks = PairLock.session.scalars(select(PairLock).filter(PairLock.pair == "ETH/BTC")).all()
+    assert len(pairlocks) == 1
+    assert pairlocks[0].pair == "ETH/BTC"
+    assert pairlocks[0].side == "*"
+
+
+@pytest.mark.parametrize(
+    "dialect",
+    [
+        "sqlite",
+        "postgresql",
+        "mysql",
+        "oracle",
+        "mssql",
+    ],
+)
+def test_create_table_compiles(dialect):
+    dialect_mod = import_module(f"sqlalchemy.dialects.{dialect}")
+    for table in ModelBase.metadata.tables.values():
+        create_sql = str(CreateTable(table).compile(dialect=dialect_mod.dialect()))
+        assert "CREATE TABLE" in create_sql
diff --git a/tests/persistence/test_persistence.py b/tests/persistence/test_persistence.py
new file mode 100644
index 000000000..d07bd7192
--- /dev/null
+++ b/tests/persistence/test_persistence.py
@@ -0,0 +1,2891 @@
+# pragma pylint: disable=missing-docstring, C0103
+from datetime import datetime, timedelta, timezone
+from types import FunctionType
+
+import pytest
+from sqlalchemy import select
+
+from freqtrade.constants import CUSTOM_TAG_MAX_LENGTH, DATETIME_PRINT_FORMAT
+from freqtrade.enums import TradingMode
+from freqtrade.exceptions import DependencyException
+from freqtrade.exchange.exchange_utils import TICK_SIZE
+from freqtrade.persistence import LocalTrade, Order, Trade, init_db
+from freqtrade.util import dt_now
+from tests.conftest import (
+    create_mock_trades,
+    create_mock_trades_usdt,
+    create_mock_trades_with_leverage,
+    log_has,
+    log_has_re,
+)
+
+
+spot, margin, futures = TradingMode.SPOT, TradingMode.MARGIN, TradingMode.FUTURES
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.usefixtures("init_persistence")
+def test_enter_exit_side(fee, is_short):
+    entry_side, exit_side = ("sell", "buy") if is_short else ("buy", "sell")
+    trade = Trade(
+        id=2,
+        pair="ADA/USDT",
+        stake_amount=0.001,
+        open_rate=0.01,
+        amount=5,
+        is_open=True,
+        open_date=dt_now(),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        is_short=is_short,
+        leverage=2.0,
+        trading_mode=margin,
+    )
+    assert trade.entry_side == entry_side
+    assert trade.exit_side == exit_side
+    assert trade.trade_direction == "short" if is_short else "long"
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_set_stop_loss_liquidation(fee):
+    trade = Trade(
+        id=2,
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        open_rate=2.0,
+        amount=30.0,
+        is_open=True,
+        open_date=dt_now(),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        is_short=False,
+        leverage=2.0,
+        trading_mode=margin,
+    )
+    trade.set_liquidation_price(0.09)
+    assert trade.liquidation_price == 0.09
+    assert trade.stop_loss is None
+    assert trade.initial_stop_loss is None
+
+    trade.adjust_stop_loss(2.0, 0.2, True)
+    assert trade.liquidation_price == 0.09
+    assert trade.stop_loss == 1.8
+    assert trade.initial_stop_loss == 1.8
+
+    trade.set_liquidation_price(0.08)
+    assert trade.liquidation_price == 0.08
+    assert trade.stop_loss == 1.8
+    assert trade.initial_stop_loss == 1.8
+
+    trade.set_liquidation_price(0.11)
+    trade.adjust_stop_loss(2.0, 0.2)
+    assert trade.liquidation_price == 0.11
+    # Stoploss does not change from liquidation price
+    assert trade.stop_loss == 1.8
+    assert trade.stop_loss_pct == -0.2
+    assert trade.initial_stop_loss == 1.8
+
+    # lower stop doesn't move stoploss
+    trade.adjust_stop_loss(1.8, 0.2)
+    assert trade.liquidation_price == 0.11
+    assert trade.stop_loss == 1.8
+    assert trade.stop_loss_pct == -0.2
+    assert trade.initial_stop_loss == 1.8
+
+    # Lower stop with "allow_refresh" does move stoploss
+    trade.adjust_stop_loss(1.8, 0.22, allow_refresh=True)
+    assert trade.liquidation_price == 0.11
+    assert trade.stop_loss == 1.602
+    assert trade.stop_loss_pct == -0.22
+    assert trade.initial_stop_loss == 1.8
+
+    # higher stop does move stoploss
+    trade.adjust_stop_loss(2.1, 0.1)
+    assert trade.liquidation_price == 0.11
+    assert pytest.approx(trade.stop_loss) == 1.994999
+    assert trade.stop_loss_pct == -0.1
+    assert trade.initial_stop_loss == 1.8
+    assert trade.stoploss_or_liquidation == trade.stop_loss
+
+    trade.stop_loss = None
+    trade.liquidation_price = None
+    trade.initial_stop_loss = None
+    trade.initial_stop_loss_pct = None
+
+    trade.adjust_stop_loss(2.0, 0.1, True)
+    assert trade.liquidation_price is None
+    assert trade.stop_loss == 1.9
+    assert trade.initial_stop_loss == 1.9
+    assert trade.stoploss_or_liquidation == 1.9
+
+    trade.is_short = True
+    trade.recalc_open_trade_value()
+    trade.stop_loss = None
+    trade.initial_stop_loss = None
+    trade.initial_stop_loss_pct = None
+
+    trade.set_liquidation_price(3.09)
+    assert trade.liquidation_price == 3.09
+    assert trade.stop_loss is None
+    assert trade.initial_stop_loss is None
+
+    trade.adjust_stop_loss(2.0, 0.2)
+    assert trade.liquidation_price == 3.09
+    assert trade.stop_loss == 2.2
+    assert trade.initial_stop_loss == 2.2
+    assert trade.stoploss_or_liquidation == 2.2
+
+    trade.set_liquidation_price(3.1)
+    assert trade.liquidation_price == 3.1
+    assert trade.stop_loss == 2.2
+    assert trade.initial_stop_loss == 2.2
+    assert trade.stoploss_or_liquidation == 2.2
+
+    trade.set_liquidation_price(3.8)
+    assert trade.liquidation_price == 3.8
+    # Stoploss does not change from liquidation price
+    assert trade.stop_loss == 2.2
+    assert trade.stop_loss_pct == -0.2
+    assert trade.initial_stop_loss == 2.2
+
+    # Stop doesn't move stop higher
+    trade.adjust_stop_loss(2.0, 0.3)
+    assert trade.liquidation_price == 3.8
+    assert trade.stop_loss == 2.2
+    assert trade.stop_loss_pct == -0.2
+    assert trade.initial_stop_loss == 2.2
+
+    # Stop does move stop higher with "allow_refresh"
+    trade.adjust_stop_loss(2.0, 0.3, allow_refresh=True)
+    assert trade.liquidation_price == 3.8
+    assert trade.stop_loss == 2.3
+    assert trade.stop_loss_pct == -0.3
+    assert trade.initial_stop_loss == 2.2
+
+    # Stoploss does move lower
+    trade.set_liquidation_price(1.5)
+    trade.adjust_stop_loss(1.8, 0.1)
+    assert trade.liquidation_price == 1.5
+    assert pytest.approx(trade.stop_loss) == 1.89
+    assert trade.stop_loss_pct == -0.1
+    assert trade.initial_stop_loss == 2.2
+    assert trade.stoploss_or_liquidation == 1.5
+
+
+@pytest.mark.parametrize(
+    "exchange,is_short,lev,minutes,rate,interest,trading_mode",
+    [
+        ("binance", False, 3, 10, 0.0005, round(0.0008333333333333334, 8), margin),
+        ("binance", True, 3, 10, 0.0005, 0.000625, margin),
+        ("binance", False, 3, 295, 0.0005, round(0.004166666666666667, 8), margin),
+        ("binance", True, 3, 295, 0.0005, round(0.0031249999999999997, 8), margin),
+        ("binance", False, 3, 295, 0.00025, round(0.0020833333333333333, 8), margin),
+        ("binance", True, 3, 295, 0.00025, round(0.0015624999999999999, 8), margin),
+        ("binance", False, 5, 295, 0.0005, 0.005, margin),
+        ("binance", True, 5, 295, 0.0005, round(0.0031249999999999997, 8), margin),
+        ("binance", False, 1, 295, 0.0005, 0.0, spot),
+        ("binance", True, 1, 295, 0.0005, 0.003125, margin),
+        ("binance", False, 3, 10, 0.0005, 0.0, futures),
+        ("binance", True, 3, 295, 0.0005, 0.0, futures),
+        ("binance", False, 5, 295, 0.0005, 0.0, futures),
+        ("binance", True, 5, 295, 0.0005, 0.0, futures),
+        ("binance", False, 1, 295, 0.0005, 0.0, futures),
+        ("binance", True, 1, 295, 0.0005, 0.0, futures),
+        ("kraken", False, 3, 10, 0.0005, 0.040, margin),
+        ("kraken", True, 3, 10, 0.0005, 0.030, margin),
+        ("kraken", False, 3, 295, 0.0005, 0.06, margin),
+        ("kraken", True, 3, 295, 0.0005, 0.045, margin),
+        ("kraken", False, 3, 295, 0.00025, 0.03, margin),
+        ("kraken", True, 3, 295, 0.00025, 0.0225, margin),
+        ("kraken", False, 5, 295, 0.0005, round(0.07200000000000001, 8), margin),
+        ("kraken", True, 5, 295, 0.0005, 0.045, margin),
+        ("kraken", False, 1, 295, 0.0005, 0.0, spot),
+        ("kraken", True, 1, 295, 0.0005, 0.045, margin),
+    ],
+)
+@pytest.mark.usefixtures("init_persistence")
+def test_interest(fee, exchange, is_short, lev, minutes, rate, interest, trading_mode):
+    """
+    10min, 5hr limit trade on Binance/Kraken at 3x,5x leverage
+    fee: 0.25 % quote
+    interest_rate: 0.05 % per 4 hrs
+    open_rate: 2.00 quote
+    close_rate: 2.20 quote
+    amount: = 30.0 crypto
+    stake_amount
+        3x, -3x: 20.0  quote
+        5x, -5x: 12.0  quote
+    borrowed
+      10min
+         3x: 40 quote
+        -3x: 30 crypto
+         5x: 48 quote
+        -5x: 30 crypto
+         1x: 0
+        -1x: 30 crypto
+    hours: 1/6 (10 minutes)
+    time-periods:
+        10min
+            kraken: (1 + 1) 4hr_periods = 2 4hr_periods
+            binance: 1/24 24hr_periods
+        4.95hr
+            kraken: ceil(1 + 4.95/4) 4hr_periods = 3 4hr_periods
+            binance: ceil(4.95)/24 24hr_periods = 5/24 24hr_periods
+    interest: borrowed * interest_rate * time-periods
+      10min
+        binance     3x: 40 * 0.0005 * 1/24 = 0.0008333333333333334 quote
+        kraken      3x: 40 * 0.0005 * 2    = 0.040 quote
+        binace     -3x: 30 * 0.0005 * 1/24 = 0.000625 crypto
+        kraken     -3x: 30 * 0.0005 * 2    = 0.030 crypto
+      5hr
+        binance     3x: 40 * 0.0005 * 5/24 = 0.004166666666666667 quote
+        kraken      3x: 40 * 0.0005 * 3    = 0.06 quote
+        binace     -3x: 30 * 0.0005 * 5/24 = 0.0031249999999999997 crypto
+        kraken     -3x: 30 * 0.0005 * 3    = 0.045 crypto
+      0.00025 interest
+        binance     3x: 40 * 0.00025 * 5/24 = 0.0020833333333333333 quote
+        kraken      3x: 40 * 0.00025 * 3    = 0.03 quote
+        binace     -3x: 30 * 0.00025 * 5/24 = 0.0015624999999999999 crypto
+        kraken     -3x: 30 * 0.00025 * 3    = 0.0225 crypto
+      5x leverage, 0.0005 interest, 5hr
+        binance     5x: 48 * 0.0005 * 5/24 = 0.005 quote
+        kraken      5x: 48 * 0.0005 * 3    = 0.07200000000000001 quote
+        binace     -5x: 30 * 0.0005 * 5/24 = 0.0031249999999999997 crypto
+        kraken     -5x: 30 * 0.0005 * 3    = 0.045 crypto
+      1x leverage, 0.0005 interest, 5hr
+        binance,kraken 1x: 0.0 quote
+        binace        -1x: 30 * 0.0005 * 5/24 = 0.003125 crypto
+        kraken        -1x: 30 * 0.0005 * 3    = 0.045 crypto
+    """
+
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=20.0,
+        amount=30.0,
+        open_rate=2.0,
+        open_date=datetime.now(timezone.utc) - timedelta(minutes=minutes),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange=exchange,
+        leverage=lev,
+        interest_rate=rate,
+        is_short=is_short,
+        trading_mode=trading_mode,
+    )
+
+    assert round(float(trade.calculate_interest()), 8) == interest
+
+
+@pytest.mark.parametrize(
+    "is_short,lev,borrowed,trading_mode",
+    [
+        (False, 1.0, 0.0, spot),
+        (True, 1.0, 30.0, margin),
+        (False, 3.0, 40.0, margin),
+        (True, 3.0, 30.0, margin),
+    ],
+)
+@pytest.mark.usefixtures("init_persistence")
+def test_borrowed(fee, is_short, lev, borrowed, trading_mode):
+    """
+    10 minute limit trade on Binance/Kraken at 1x, 3x leverage
+    fee: 0.25% quote
+    interest_rate: 0.05% per 4 hrs
+    open_rate: 2.00 quote
+    close_rate: 2.20 quote
+    amount: = 30.0 crypto
+    stake_amount
+        1x,-1x: 60.0  quote
+        3x,-3x: 20.0  quote
+    borrowed
+         1x:  0 quote
+         3x: 40 quote
+        -1x: 30 crypto
+        -3x: 30 crypto
+    hours: 1/6 (10 minutes)
+    time-periods:
+        kraken: (1 + 1) 4hr_periods = 2 4hr_periods
+        binance: 1/24 24hr_periods
+    interest: borrowed * interest_rate * time-periods
+        1x            :  /
+        binance     3x: 40 * 0.0005 * 1/24 = 0.0008333333333333334 quote
+        kraken      3x: 40 * 0.0005 * 2 = 0.040 quote
+        binace -1x,-3x: 30 * 0.0005 * 1/24 = 0.000625 crypto
+        kraken -1x,-3x: 30 * 0.0005 * 2 = 0.030 crypto
+    open_value: (amount * open_rate) ± (amount * open_rate * fee)
+         1x, 3x: 30 * 2 + 30 * 2 * 0.0025 = 60.15 quote
+        -1x,-3x: 30 * 2 - 30 * 2 * 0.0025 = 59.850 quote
+    amount_closed:
+        1x, 3x         : amount
+        -1x, -3x       : amount + interest
+        binance -1x,-3x: 30 + 0.000625 = 30.000625 crypto
+        kraken  -1x,-3x: 30 + 0.03 = 30.03 crypto
+    close_value:
+         1x, 3x: (amount_closed * close_rate) - (amount_closed * close_rate * fee) - interest
+        -1x,-3x: (amount_closed * close_rate) + (amount_closed * close_rate * fee)
+        binance,kraken 1x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025)         = 65.835
+        binance        3x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025) - 0.00083333 = 65.83416667
+        kraken         3x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025) - 0.040 = 65.795
+        binance   -1x,-3x: (30.000625 * 2.20) + (30.000625 * 2.20 * 0.0025) = 66.16637843750001
+        kraken    -1x,-3x: (30.03 * 2.20) + (30.03 * 2.20 * 0.0025)         = 66.231165
+    total_profit:
+        1x, 3x : close_value - open_value
+        -1x,-3x: open_value  - close_value
+        binance,kraken 1x: 65.835 - 60.15             = 5.685
+        binance        3x: 65.83416667 - 60.15        = 5.684166670000003
+        kraken         3x: 65.795 - 60.15             = 5.645
+        binance   -1x,-3x: 59.850 - 66.16637843750001 = -6.316378437500013
+        kraken    -1x,-3x: 59.850 - 66.231165          = -6.381165
+    total_profit_ratio:
+        1x, 3x : ((close_value/open_value) - 1) * leverage
+        -1x,-3x: (1 - (close_value/open_value)) * leverage
+        binance  1x: ((65.835 / 60.15) - 1)  * 1 = 0.0945137157107232
+        binance  3x: ((65.83416667 / 60.15) - 1)  * 3 = 0.2834995845386534
+        kraken   1x: ((65.835 / 60.15) - 1)  * 1 = 0.0945137157107232
+        kraken   3x: ((65.795 / 60.15) - 1)  * 3 = 0.2815461346633419
+        binance -1x: (1-(66.1663784375 / 59.85)) * 1 = -0.1055368159983292
+        binance -3x: (1-(66.1663784375 / 59.85)) * 3 = -0.3166104479949876
+        kraken  -1x: (1-(66.2311650 / 59.85)) * 1    = -0.106619298245614
+        kraken  -3x: (1-(66.2311650 / 59.85)) * 3    = -0.319857894736842
+    """
+
+    trade = Trade(
+        id=2,
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        open_rate=2.0,
+        amount=30.0,
+        is_open=True,
+        open_date=dt_now(),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        is_short=is_short,
+        leverage=lev,
+        trading_mode=trading_mode,
+    )
+    assert trade.borrowed == borrowed
+
+
+@pytest.mark.parametrize(
+    "is_short,open_rate,close_rate,lev,profit,trading_mode",
+    [
+        (False, 2.0, 2.2, 1.0, 0.09451372, spot),
+        (True, 2.2, 2.0, 3.0, 0.25894253, margin),
+    ],
+)
+@pytest.mark.usefixtures("init_persistence")
+def test_update_limit_order(
+    fee,
+    caplog,
+    limit_buy_order_usdt,
+    limit_sell_order_usdt,
+    time_machine,
+    is_short,
+    open_rate,
+    close_rate,
+    lev,
+    profit,
+    trading_mode,
+):
+    """
+    10 minute limit trade on Binance/Kraken at 1x, 3x leverage
+    fee: 0.25% quote
+    interest_rate: 0.05% per 4 hrs
+    open_rate: 2.00 quote
+    close_rate: 2.20 quote
+    amount: = 30.0 crypto
+    stake_amount
+        1x,-1x: 60.0  quote
+        3x,-3x: 20.0  quote
+    borrowed
+         1x:  0 quote
+         3x: 40 quote
+        -1x: 30 crypto
+        -3x: 30 crypto
+    hours: 1/6 (10 minutes)
+    time-periods:
+        kraken: (1 + 1) 4hr_periods = 2 4hr_periods
+        binance: 1/24 24hr_periods
+    interest: borrowed * interest_rate * time-periods
+        1x            :  /
+        binance     3x: 40 * 0.0005 * 1/24 = 0.0008333333333333334 quote
+        kraken      3x: 40 * 0.0005 * 2 = 0.040 quote
+        binace -1x,-3x: 30 * 0.0005 * 1/24 = 0.000625 crypto
+        kraken -1x,-3x: 30 * 0.0005 * 2 = 0.030 crypto
+    open_value: (amount * open_rate) ± (amount * open_rate * fee)
+         1x, 3x: 30 * 2 + 30 * 2 * 0.0025 = 60.15 quote
+        -1x,-3x: 30 * 2 - 30 * 2 * 0.0025 = 59.850 quote
+    amount_closed:
+        1x, 3x         : amount
+        -1x, -3x       : amount + interest
+        binance -1x,-3x: 30 + 0.000625 = 30.000625 crypto
+        kraken  -1x,-3x: 30 + 0.03 = 30.03 crypto
+    close_value:
+         1x, 3x: (amount_closed * close_rate) - (amount_closed * close_rate * fee) - interest
+        -1x,-3x: (amount_closed * close_rate) + (amount_closed * close_rate * fee)
+        binance,kraken 1x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025)         = 65.835
+        binance        3x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025) - 0.00083333 = 65.83416667
+        kraken         3x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025) - 0.040 = 65.795
+        binance   -1x,-3x: (30.000625 * 2.20) + (30.000625 * 2.20 * 0.0025) = 66.16637843750001
+        kraken    -1x,-3x: (30.03 * 2.20) + (30.03 * 2.20 * 0.0025)         = 66.231165
+    total_profit:
+        1x, 3x : close_value - open_value
+        -1x,-3x: open_value  - close_value
+        binance,kraken 1x: 65.835 - 60.15             = 5.685
+        binance        3x: 65.83416667 - 60.15        = 5.684166670000003
+        kraken         3x: 65.795 - 60.15             = 5.645
+        binance   -1x,-3x: 59.850 - 66.16637843750001 = -6.316378437500013
+        kraken    -1x,-3x: 59.850 - 66.231165          = -6.381165
+    total_profit_ratio:
+        1x, 3x : ((close_value/open_value) - 1) * leverage
+        -1x,-3x: (1 - (close_value/open_value)) * leverage
+        binance  1x: ((65.835 / 60.15) - 1)  * 1 = 0.0945137157107232
+        binance  3x: ((65.83416667 / 60.15) - 1)  * 3 = 0.2834995845386534
+        kraken   1x: ((65.835 / 60.15) - 1)  * 1 = 0.0945137157107232
+        kraken   3x: ((65.795 / 60.15) - 1)  * 3 = 0.2815461346633419
+        binance -1x: (1-(66.1663784375 / 59.85)) * 1 = -0.1055368159983292
+        binance -3x: (1-(66.1663784375 / 59.85)) * 3 = -0.3166104479949876
+        kraken  -1x: (1-(66.2311650 / 59.85)) * 1    = -0.106619298245614
+        kraken  -3x: (1-(66.2311650 / 59.85)) * 3    = -0.319857894736842
+    open_rate: 2.2, close_rate: 2.0, -3x, binance, short
+        open_value: 30 * 2.2 - 30 * 2.2 * 0.0025 = 65.835 quote
+        amount_closed: 30 + 0.000625 = 30.000625 crypto
+        close_value: (30.000625 * 2.0) + (30.000625 * 2.0 * 0.0025) = 60.151253125
+        total_profit: 65.835 - 60.151253125 = 5.683746874999997
+        total_profit_ratio: (1-(60.151253125/65.835)) * 3 = 0.2589996297562085
+
+    """
+    time_machine.move_to("2022-03-31 20:45:00 +00:00")
+
+    enter_order = limit_sell_order_usdt if is_short else limit_buy_order_usdt
+    exit_order = limit_buy_order_usdt if is_short else limit_sell_order_usdt
+    entry_side, exit_side = ("sell", "buy") if is_short else ("buy", "sell")
+
+    trade = Trade(
+        id=2,
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        open_rate=open_rate,
+        amount=30.0,
+        is_open=True,
+        open_date=dt_now(),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        is_short=is_short,
+        interest_rate=0.0005,
+        leverage=lev,
+        trading_mode=trading_mode,
+    )
+    assert not trade.has_open_orders
+    assert trade.close_profit is None
+    assert trade.close_date is None
+
+    oobj = Order.parse_from_ccxt_object(enter_order, "ADA/USDT", entry_side)
+    trade.orders.append(oobj)
+    trade.update_trade(oobj)
+    assert not trade.has_open_orders
+    assert trade.open_rate == open_rate
+    assert trade.close_profit is None
+    assert trade.close_date is None
+    assert log_has_re(
+        f"LIMIT_{entry_side.upper()} has been fulfilled for "
+        r"Trade\(id=2, pair=ADA/USDT, amount=30.00000000, "
+        f"is_short={is_short}, leverage={lev}, open_rate={open_rate}0000000, "
+        r"open_since=.*\).",
+        caplog,
+    )
+
+    caplog.clear()
+    time_machine.move_to("2022-03-31 21:45:05 +00:00")
+    oobj = Order.parse_from_ccxt_object(exit_order, "ADA/USDT", exit_side)
+    trade.orders.append(oobj)
+    trade.update_trade(oobj)
+
+    assert not trade.has_open_orders
+    assert trade.close_rate == close_rate
+    assert pytest.approx(trade.close_profit) == profit
+    assert trade.close_date is not None
+    assert log_has_re(
+        f"LIMIT_{exit_side.upper()} has been fulfilled for "
+        r"Trade\(id=2, pair=ADA/USDT, amount=30.00000000, "
+        f"is_short={is_short}, leverage={lev}, open_rate={open_rate}0000000, "
+        r"open_since=.*\).",
+        caplog,
+    )
+    caplog.clear()
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_update_market_order(market_buy_order_usdt, market_sell_order_usdt, fee, caplog):
+    trade = Trade(
+        id=1,
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        open_rate=2.0,
+        amount=30.0,
+        is_open=True,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_date=dt_now(),
+        exchange="binance",
+        trading_mode=margin,
+        leverage=1.0,
+    )
+
+    oobj = Order.parse_from_ccxt_object(market_buy_order_usdt, "ADA/USDT", "buy")
+    trade.orders.append(oobj)
+    trade.update_trade(oobj)
+    assert not trade.has_open_orders
+    assert trade.open_rate == 2.0
+    assert trade.close_profit is None
+    assert trade.close_date is None
+    assert log_has_re(
+        r"MARKET_BUY has been fulfilled for Trade\(id=1, "
+        r"pair=ADA/USDT, amount=30.00000000, is_short=False, leverage=1.0, "
+        r"open_rate=2.00000000, open_since=.*\).",
+        caplog,
+    )
+
+    caplog.clear()
+    trade.is_open = True
+    oobj = Order.parse_from_ccxt_object(market_sell_order_usdt, "ADA/USDT", "sell")
+    trade.orders.append(oobj)
+    trade.update_trade(oobj)
+    assert not trade.has_open_orders
+    assert trade.close_rate == 2.2
+    assert pytest.approx(trade.close_profit) == 0.094513715710723
+    assert trade.close_date is not None
+    assert log_has_re(
+        r"MARKET_SELL has been fulfilled for Trade\(id=1, "
+        r"pair=ADA/USDT, amount=30.00000000, is_short=False, leverage=1.0, "
+        r"open_rate=2.00000000, open_since=.*\).",
+        caplog,
+    )
+
+
+@pytest.mark.parametrize(
+    "exchange,is_short,lev,open_value,close_value,profit,profit_ratio,trading_mode,funding_fees",
+    [
+        ("binance", False, 1, 60.15, 65.835, 5.685, 0.09451371, spot, 0.0),
+        ("binance", True, 1, 65.835, 60.151253125, 5.68374687, 0.08633321, margin, 0.0),
+        ("binance", False, 3, 60.15, 65.83416667, 5.68416667, 0.28349958, margin, 0.0),
+        ("binance", True, 3, 65.835, 60.151253125, 5.68374687, 0.25899963, margin, 0.0),
+        ("kraken", False, 1, 60.15, 65.835, 5.685, 0.09451371, spot, 0.0),
+        ("kraken", True, 1, 65.835, 60.21015, 5.62485, 0.0854386, margin, 0.0),
+        ("kraken", False, 3, 60.15, 65.795, 5.645, 0.28154613, margin, 0.0),
+        ("kraken", True, 3, 65.835, 60.21015, 5.62485, 0.25631579, margin, 0.0),
+        ("binance", False, 1, 60.15, 65.835, 5.685, 0.09451371, futures, 0.0),
+        ("binance", False, 1, 60.15, 66.835, 6.685, 0.11113881, futures, 1.0),
+        ("binance", True, 1, 65.835, 60.15, 5.685, 0.08635224, futures, 0.0),
+        ("binance", True, 1, 65.835, 61.15, 4.685, 0.07116276, futures, -1.0),
+        ("binance", True, 3, 65.835, 59.15, 6.685, 0.3046252, futures, 1.0),
+        ("binance", False, 3, 60.15, 64.835, 4.685, 0.23366583, futures, -1.0),
+    ],
+)
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_open_close_trade_price(
+    limit_order,
+    fee,
+    exchange,
+    is_short,
+    lev,
+    open_value,
+    close_value,
+    profit,
+    profit_ratio,
+    trading_mode,
+    funding_fees,
+):
+    trade: Trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        open_rate=2.0,
+        amount=30.0,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=10),
+        interest_rate=0.0005,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange=exchange,
+        is_short=is_short,
+        leverage=lev,
+        trading_mode=trading_mode,
+    )
+    entry_order = limit_order[trade.entry_side]
+    exit_order = limit_order[trade.exit_side]
+
+    oobj = Order.parse_from_ccxt_object(entry_order, "ADA/USDT", trade.entry_side)
+    oobj._trade_live = trade
+    oobj.update_from_ccxt_object(entry_order)
+    trade.update_trade(oobj)
+
+    trade.funding_fee_running = funding_fees
+
+    oobj = Order.parse_from_ccxt_object(exit_order, "ADA/USDT", trade.exit_side)
+    oobj._trade_live = trade
+    oobj.update_from_ccxt_object(exit_order)
+    trade.update_trade(oobj)
+
+    assert trade.is_open is False
+    # Funding fees transfer from funding_fee_running to funding_Fees
+    assert trade.funding_fees == funding_fees
+    assert trade.orders[-1].funding_fee == funding_fees
+
+    assert pytest.approx(trade._calc_open_trade_value(trade.amount, trade.open_rate)) == open_value
+    assert pytest.approx(trade.calc_close_trade_value(trade.close_rate)) == close_value
+    assert pytest.approx(trade.close_profit_abs) == profit
+    assert pytest.approx(trade.close_profit) == profit_ratio
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_trade_close(fee, time_machine):
+    time_machine.move_to("2022-09-01 05:00:00 +00:00", tick=False)
+
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        open_rate=2.0,
+        amount=30.0,
+        is_open=True,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_date=dt_now() - timedelta(minutes=10),
+        interest_rate=0.0005,
+        exchange="binance",
+        trading_mode=margin,
+        leverage=1.0,
+    )
+    trade.orders.append(
+        Order(
+            ft_order_side=trade.entry_side,
+            order_id=f"{trade.pair}-{trade.entry_side}-{trade.open_date}",
+            ft_is_open=False,
+            ft_pair=trade.pair,
+            amount=trade.amount,
+            filled=trade.amount,
+            remaining=0,
+            price=trade.open_rate,
+            average=trade.open_rate,
+            status="closed",
+            order_type="limit",
+            side=trade.entry_side,
+            order_filled_date=trade.open_date,
+        )
+    )
+    trade.orders.append(
+        Order(
+            ft_order_side=trade.exit_side,
+            order_id=f"{trade.pair}-{trade.exit_side}-{trade.open_date}",
+            ft_is_open=False,
+            ft_pair=trade.pair,
+            amount=trade.amount,
+            filled=trade.amount,
+            remaining=0,
+            price=2.2,
+            average=2.2,
+            status="closed",
+            order_type="limit",
+            side=trade.exit_side,
+            order_filled_date=dt_now(),
+        )
+    )
+    assert trade.close_profit is None
+    assert trade.close_date is None
+    assert trade.is_open is True
+    trade.close(2.2)
+    assert trade.is_open is False
+    assert pytest.approx(trade.close_profit) == 0.094513715
+    assert trade.close_date is not None
+    assert trade.close_date_utc == dt_now()
+
+    new_date = dt_now() + timedelta(minutes=5)
+    assert trade.close_date_utc != new_date
+    # Close should NOT update close_date if the trade has been closed already
+    assert trade.is_open is False
+    trade.close_date = new_date
+    trade.close(2.2)
+    assert trade.close_date_utc == new_date
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_close_trade_price_exception(limit_buy_order_usdt, fee):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        open_rate=2.0,
+        amount=30.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        trading_mode=margin,
+        leverage=1.0,
+    )
+
+    oobj = Order.parse_from_ccxt_object(limit_buy_order_usdt, "ADA/USDT", "buy")
+    trade.update_trade(oobj)
+    assert trade.calc_close_trade_value(trade.close_rate) == 0.0
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_update_open_order(limit_buy_order_usdt):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        open_rate=2.0,
+        amount=30.0,
+        fee_open=0.1,
+        fee_close=0.1,
+        exchange="binance",
+        trading_mode=margin,
+    )
+
+    assert not trade.has_open_orders
+    assert trade.close_profit is None
+    assert trade.close_date is None
+
+    limit_buy_order_usdt["status"] = "open"
+    oobj = Order.parse_from_ccxt_object(limit_buy_order_usdt, "ADA/USDT", "buy")
+    trade.update_trade(oobj)
+
+    assert not trade.has_open_orders
+    assert trade.close_profit is None
+    assert trade.close_date is None
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_update_invalid_order(limit_buy_order_usdt):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        amount=30.0,
+        open_rate=2.0,
+        fee_open=0.1,
+        fee_close=0.1,
+        exchange="binance",
+        trading_mode=margin,
+    )
+    limit_buy_order_usdt["type"] = "invalid"
+    oobj = Order.parse_from_ccxt_object(limit_buy_order_usdt, "ADA/USDT", "meep")
+    with pytest.raises(ValueError, match=r"Unknown order type"):
+        trade.update_trade(oobj)
+
+
+@pytest.mark.parametrize("exchange", ["binance", "kraken"])
+@pytest.mark.parametrize("trading_mode", [spot, margin, futures])
+@pytest.mark.parametrize("lev", [1, 3])
+@pytest.mark.parametrize(
+    "is_short,fee_rate,result",
+    [
+        (False, 0.003, 60.18),
+        (False, 0.0025, 60.15),
+        (False, 0.003, 60.18),
+        (False, 0.0025, 60.15),
+        (True, 0.003, 59.82),
+        (True, 0.0025, 59.85),
+        (True, 0.003, 59.82),
+        (True, 0.0025, 59.85),
+    ],
+)
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_open_trade_value(
+    limit_buy_order_usdt, exchange, lev, is_short, fee_rate, result, trading_mode
+):
+    # 10 minute limit trade on Binance/Kraken at 1x, 3x leverage
+    # fee: 0.25 %, 0.3% quote
+    # open_rate: 2.00 quote
+    # amount: = 30.0 crypto
+    # stake_amount
+    #     1x, -1x: 60.0  quote
+    #     3x, -3x: 20.0  quote
+    # open_value: (amount * open_rate) ± (amount * open_rate * fee)
+    # 0.25% fee
+    #      1x, 3x: 30 * 2 + 30 * 2 * 0.0025 = 60.15 quote
+    #     -1x,-3x: 30 * 2 - 30 * 2 * 0.0025 = 59.85 quote
+    # 0.3% fee
+    #      1x, 3x: 30 * 2 + 30 * 2 * 0.003  = 60.18 quote
+    #     -1x,-3x: 30 * 2 - 30 * 2 * 0.003  = 59.82 quote
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        amount=30.0,
+        open_rate=2.0,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=10),
+        fee_open=fee_rate,
+        fee_close=fee_rate,
+        exchange=exchange,
+        leverage=lev,
+        is_short=is_short,
+        trading_mode=trading_mode,
+    )
+    oobj = Order.parse_from_ccxt_object(
+        limit_buy_order_usdt, "ADA/USDT", "sell" if is_short else "buy"
+    )
+    trade.update_trade(oobj)  # Buy @ 2.0
+
+    # Get the open rate price with the standard fee rate
+    assert trade._calc_open_trade_value(trade.amount, trade.open_rate) == result
+
+
+@pytest.mark.parametrize(
+    "exchange,is_short,lev,open_rate,close_rate,fee_rate,result,trading_mode,funding_fees",
+    [
+        ("binance", False, 1, 2.0, 2.5, 0.0025, 74.8125, spot, 0),
+        ("binance", False, 1, 2.0, 2.5, 0.003, 74.775, spot, 0),
+        ("binance", False, 1, 2.0, 2.2, 0.005, 65.67, margin, 0),
+        ("binance", False, 3, 2.0, 2.5, 0.0025, 74.81166667, margin, 0),
+        ("binance", False, 3, 2.0, 2.5, 0.003, 74.77416667, margin, 0),
+        ("binance", True, 3, 2.2, 2.5, 0.0025, 75.18906641, margin, 0),
+        ("binance", True, 3, 2.2, 2.5, 0.003, 75.22656719, margin, 0),
+        ("binance", True, 1, 2.2, 2.5, 0.0025, 75.18906641, margin, 0),
+        ("binance", True, 1, 2.2, 2.5, 0.003, 75.22656719, margin, 0),
+        # Kraken
+        ("kraken", False, 3, 2.0, 2.5, 0.0025, 74.7725, margin, 0),
+        ("kraken", False, 3, 2.0, 2.5, 0.003, 74.735, margin, 0),
+        ("kraken", True, 3, 2.2, 2.5, 0.0025, 75.2626875, margin, 0),
+        ("kraken", True, 3, 2.2, 2.5, 0.003, 75.300225, margin, 0),
+        ("kraken", True, 1, 2.2, 2.5, 0.0025, 75.2626875, margin, 0),
+        ("kraken", True, 1, 2.2, 2.5, 0.003, 75.300225, margin, 0),
+        ("binance", False, 1, 2.0, 2.5, 0.0025, 75.8125, futures, 1),
+        ("binance", False, 3, 2.0, 2.5, 0.0025, 73.8125, futures, -1),
+        ("binance", True, 3, 2.0, 2.5, 0.0025, 74.1875, futures, 1),
+        ("binance", True, 1, 2.0, 2.5, 0.0025, 76.1875, futures, -1),
+    ],
+)
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_close_trade_price(
+    open_rate, exchange, is_short, lev, close_rate, fee_rate, result, trading_mode, funding_fees
+):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        amount=30.0,
+        open_rate=open_rate,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=10),
+        fee_open=fee_rate,
+        fee_close=fee_rate,
+        exchange=exchange,
+        interest_rate=0.0005,
+        is_short=is_short,
+        leverage=lev,
+        trading_mode=trading_mode,
+        funding_fees=funding_fees,
+    )
+    assert round(trade.calc_close_trade_value(rate=close_rate), 8) == result
+
+
+@pytest.mark.parametrize(
+    "exchange,is_short,lev,close_rate,fee_close,profit,profit_ratio,trading_mode,funding_fees",
+    [
+        ("binance", False, 1, 2.1, 0.0025, 2.6925, 0.044763092, spot, 0),
+        ("binance", False, 3, 2.1, 0.0025, 8.075, 0.134247714, margin, 0),
+        ("binance", True, 1, 2.1, 0.0025, -3.3088157, -0.055285142, margin, 0),
+        ("binance", True, 3, 2.1, 0.0025, -9.92644709, -0.16585542, margin, 0),
+        ("binance", False, 1, 1.9, 0.0025, -3.2925, -0.054738154, margin, 0),
+        ("binance", False, 3, 1.9, 0.0025, -9.88, -0.164256026, margin, 0),
+        ("binance", True, 1, 1.9, 0.0025, 2.70630953, 0.0452182043, margin, 0),
+        ("binance", True, 3, 1.9, 0.0025, 8.11892859, 0.135654613, margin, 0),
+        ("binance", False, 1, 2.2, 0.0025, 5.685, 0.09451371, margin, 0),
+        ("binance", False, 3, 2.2, 0.0025, 17.0525, 0.28349958, margin, 0),
+        ("binance", True, 1, 2.2, 0.0025, -6.3163784, -0.10553681, margin, 0),
+        ("binance", True, 3, 2.2, 0.0025, -18.94913, -0.31661044, margin, 0),
+        # Kraken
+        ("kraken", False, 1, 2.1, 0.0025, 2.6925, 0.044763092, spot, 0),
+        ("kraken", False, 3, 2.1, 0.0025, 7.9575, 0.132294264, margin, 0),
+        ("kraken", True, 1, 2.1, 0.0025, -3.3706575, -0.056318421, margin, 0),
+        ("kraken", True, 3, 2.1, 0.0025, -10.1119725, -0.168955263, margin, 0),
+        ("kraken", False, 1, 1.9, 0.0025, -3.2925, -0.054738154, margin, 0),
+        ("kraken", False, 3, 1.9, 0.0025, -9.9975, -0.166209476, margin, 0),
+        ("kraken", True, 1, 1.9, 0.0025, 2.6503575, 0.044283333, margin, 0),
+        ("kraken", True, 3, 1.9, 0.0025, 7.9510725, 0.132850000, margin, 0),
+        ("kraken", False, 1, 2.2, 0.0025, 5.685, 0.09451371, margin, 0),
+        ("kraken", False, 3, 2.2, 0.0025, 16.935, 0.28154613, margin, 0),
+        ("kraken", True, 1, 2.2, 0.0025, -6.381165, -0.1066192, margin, 0),
+        ("kraken", True, 3, 2.2, 0.0025, -19.143495, -0.3198578, margin, 0),
+        ("binance", False, 1, 2.1, 0.003, 2.66100000, 0.044239401, spot, 0),
+        ("binance", False, 1, 1.9, 0.003, -3.3209999, -0.055211970, spot, 0),
+        ("binance", False, 1, 2.2, 0.003, 5.6520000, 0.093965087, spot, 0),
+        # FUTURES, funding_fee=1
+        ("binance", False, 1, 2.1, 0.0025, 3.6925, 0.06138819, futures, 1),
+        ("binance", False, 3, 2.1, 0.0025, 9.0775, 0.15091438, futures, 1),
+        ("binance", True, 1, 2.1, 0.0025, -2.3074999, -0.03855472, futures, 1),
+        ("binance", True, 3, 2.1, 0.0025, -8.9225, -0.14908104, futures, 1),
+        ("binance", False, 1, 1.9, 0.0025, -2.2925, -0.03811305, futures, 1),
+        ("binance", False, 3, 1.9, 0.0025, -8.8775, -0.14758936, futures, 1),
+        ("binance", True, 1, 1.9, 0.0025, 3.7075, 0.06194653, futures, 1),
+        ("binance", True, 3, 1.9, 0.0025, 9.1225, 0.15242272, futures, 1),
+        ("binance", False, 1, 2.2, 0.0025, 6.685, 0.11113881, futures, 1),
+        ("binance", False, 3, 2.2, 0.0025, 18.055, 0.30016625, futures, 1),
+        ("binance", True, 1, 2.2, 0.0025, -5.315, -0.08880534, futures, 1),
+        ("binance", True, 3, 2.2, 0.0025, -17.945, -0.29983292, futures, 1),
+        # FUTURES, funding_fee=-1
+        ("binance", False, 1, 2.1, 0.0025, 1.6925, 0.02813798, futures, -1),
+        ("binance", False, 3, 2.1, 0.0025, 7.0775, 0.11766417, futures, -1),
+        ("binance", True, 1, 2.1, 0.0025, -4.307499, -0.07197159, futures, -1),
+        ("binance", True, 3, 2.1, 0.0025, -10.92249, -0.18249791, futures, -1),
+        ("binance", False, 1, 1.9, 0.0025, -4.292499, -0.07136325, futures, -1),
+        ("binance", False, 3, 1.9, 0.0025, -10.87749, -0.18083957, futures, -1),
+        ("binance", True, 1, 1.9, 0.0025, 1.7075, 0.02852965, futures, -1),
+        ("binance", True, 3, 1.9, 0.0025, 7.1225, 0.11900585, futures, -1),
+        ("binance", False, 1, 2.2, 0.0025, 4.684999, 0.07788861, futures, -1),
+        ("binance", False, 3, 2.2, 0.0025, 16.055, 0.26691604, futures, -1),
+        ("binance", True, 1, 2.2, 0.0025, -7.315, -0.12222222, futures, -1),
+        ("binance", True, 3, 2.2, 0.0025, -19.945, -0.33324979, futures, -1),
+        # FUTURES, funding_fee=0
+        ("binance", False, 1, 2.1, 0.0025, 2.6925, 0.04476309, futures, 0),
+        ("binance", False, 3, 2.1, 0.0025, 8.0775, 0.13428928, futures, 0),
+        ("binance", True, 1, 2.1, 0.0025, -3.3074999, -0.05526316, futures, 0),
+        ("binance", True, 3, 2.1, 0.0025, -9.9224997, -0.16578947, futures, 0),
+        ("binance", False, 1, 1.9, 0.0025, -3.2925, -0.05473815, futures, 0),
+        ("binance", False, 3, 1.9, 0.0025, -9.8775, -0.16421446, futures, 0),
+        ("binance", True, 1, 1.9, 0.0025, 2.7075, 0.0452381, futures, 0),
+        ("binance", True, 3, 1.9, 0.0025, 8.1225, 0.13571429, futures, 0),
+    ],
+)
+@pytest.mark.usefixtures("init_persistence")
+def test_calc_profit(
+    exchange, is_short, lev, close_rate, fee_close, profit, profit_ratio, trading_mode, funding_fees
+):
+    """
+    10 minute limit trade on Binance/Kraken at 1x, 3x leverage
+    arguments:
+        fee:
+            0.25% quote
+            0.30% quote
+        interest_rate: 0.05% per 4 hrs
+        open_rate: 2.0 quote
+        close_rate:
+            1.9 quote
+            2.1 quote
+            2.2 quote
+        amount: = 30.0 crypto
+        stake_amount
+            1x,-1x: 60.0  quote
+            3x,-3x: 20.0  quote
+        hours: 1/6 (10 minutes)
+        funding_fees: 1
+    borrowed
+         1x:  0 quote
+         3x: 40 quote
+        -1x: 30 crypto
+        -3x: 30 crypto
+    time-periods:
+        kraken: (1 + 1) 4hr_periods = 2 4hr_periods
+        binance: 1/24 24hr_periods
+    interest: borrowed * interest_rate * time-periods
+        1x            :  /
+        binance     3x: 40 * 0.0005 * 1/24 = 0.0008333333333333334 quote
+        kraken      3x: 40 * 0.0005 * 2    = 0.040 quote
+        binace -1x,-3x: 30 * 0.0005 * 1/24 = 0.000625 crypto
+        kraken -1x,-3x: 30 * 0.0005 * 2    = 0.030 crypto
+    open_value: (amount * open_rate) ± (amount * open_rate * fee)
+      0.0025 fee
+         1x, 3x: 30 * 2 + 30 * 2 * 0.0025 = 60.15 quote
+        -1x,-3x: 30 * 2 - 30 * 2 * 0.0025 = 59.85 quote
+      0.003 fee: Is only applied to close rate in this test
+    amount_closed:
+        1x, 3x                         = amount
+        -1x, -3x                       = amount + interest
+        binance -1x,-3x: 30 + 0.000625 = 30.000625 crypto
+        kraken  -1x,-3x: 30 + 0.03     = 30.03 crypto
+    close_value:
+        equations:
+            1x, 3x: (amount_closed * close_rate) - (amount_closed * close_rate * fee) - interest
+            -1x,-3x: (amount_closed * close_rate) + (amount_closed * close_rate * fee)
+        2.1 quote
+            bin,krak  1x: (30.00 * 2.1) - (30.00 * 2.1 * 0.0025)                = 62.8425
+            bin       3x: (30.00 * 2.1) - (30.00 * 2.1 * 0.0025) - 0.0008333333 = 62.8416666667
+            krak      3x: (30.00 * 2.1) - (30.00 * 2.1 * 0.0025) - 0.040        = 62.8025
+            bin  -1x,-3x: (30.000625 * 2.1) + (30.000625 * 2.1 * 0.0025)        = 63.15881578125
+            krak -1x,-3x: (30.03 * 2.1) + (30.03 * 2.1 * 0.0025)                = 63.2206575
+        1.9 quote
+            bin,krak  1x: (30.00 * 1.9) - (30.00 * 1.9 * 0.0025)                = 56.8575
+            bin       3x: (30.00 * 1.9) - (30.00 * 1.9 * 0.0025) - 0.0008333333 = 56.85666667
+            krak      3x: (30.00 * 1.9) - (30.00 * 1.9 * 0.0025) - 0.040        = 56.8175
+            bin  -1x,-3x: (30.000625 * 1.9) + (30.000625 * 1.9 * 0.0025)        = 57.14369046875
+            krak -1x,-3x: (30.03 * 1.9) + (30.03 * 1.9 * 0.0025)                = 57.1996425
+        2.2 quote
+            bin,krak  1x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025)              = 65.835
+            bin       3x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025) - 0.00083333 = 65.83416667
+            krak      3x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025) - 0.040      = 65.795
+            bin  -1x,-3x: (30.000625 * 2.20) + (30.000625 * 2.20 * 0.0025)      = 66.1663784375
+            krak -1x,-3x: (30.03 * 2.20) + (30.03 * 2.20 * 0.0025)              = 66.231165
+    total_profit:
+        equations:
+            1x, 3x : close_value - open_value
+            -1x,-3x: open_value - close_value
+        2.1 quote
+            binance,kraken 1x: 62.8425     - 60.15          = 2.6925
+            binance        3x: 62.84166667 - 60.15          = 2.69166667
+            kraken         3x: 62.8025     - 60.15          = 2.6525
+            binance   -1x,-3x: 59.850      - 63.15881578125 = -3.308815781249997
+            kraken    -1x,-3x: 59.850      - 63.2206575     = -3.3706575
+        1.9 quote
+            binance,kraken 1x: 56.8575     - 60.15          = -3.2925
+            binance        3x: 56.85666667 - 60.15          = -3.29333333
+            kraken         3x: 56.8175     - 60.15          = -3.3325
+            binance   -1x,-3x: 59.850      - 57.14369046875 = 2.7063095312499996
+            kraken    -1x,-3x: 59.850      - 57.1996425     = 2.6503575
+        2.2 quote
+            binance,kraken 1x: 65.835      - 60.15          = 5.685
+            binance        3x: 65.83416667 - 60.15          = 5.68416667
+            kraken         3x: 65.795      - 60.15          = 5.645
+            binance   -1x,-3x: 59.850      - 66.1663784375  = -6.316378437499999
+            kraken    -1x,-3x: 59.850      - 66.231165      = -6.381165
+    total_profit_ratio:
+        equations:
+            1x, 3x : ((close_value/open_value) - 1) * leverage
+            -1x,-3x: (1 - (close_value/open_value)) * leverage
+        2.1 quote
+            binance,kraken 1x: (62.8425 / 60.15) - 1             = 0.04476309226932673
+            binance        3x: ((62.84166667 / 60.15) - 1)*3     = 0.13424771421446402
+            kraken         3x: ((62.8025 / 60.15) - 1)*3         = 0.13229426433915248
+            binance       -1x: 1 - (63.15881578125 / 59.850)     = -0.05528514254385963
+            binance       -3x: (1 - (63.15881578125 / 59.850))*3 = -0.1658554276315789
+            kraken        -1x: 1 - (63.2206575 / 59.850)         = -0.05631842105263152
+            kraken        -3x: (1 - (63.2206575 / 59.850))*3     = -0.16895526315789455
+        1.9 quote
+            binance,kraken 1x: (56.8575 / 60.15) - 1             = -0.05473815461346632
+            binance        3x: ((56.85666667 / 60.15) - 1)*3     = -0.16425602643391513
+            kraken         3x: ((56.8175 / 60.15) - 1)*3         = -0.16620947630922667
+            binance       -1x: 1 - (57.14369046875 / 59.850)     = 0.045218204365079395
+            binance       -3x: (1 - (57.14369046875 / 59.850))*3 = 0.13565461309523819
+            kraken        -1x: 1 - (57.1996425 / 59.850)         = 0.04428333333333334
+            kraken        -3x: (1 - (57.1996425 / 59.850))*3     = 0.13285000000000002
+        2.2 quote
+            binance,kraken 1x: (65.835 / 60.15) - 1             = 0.0945137157107232
+            binance        3x: ((65.83416667 / 60.15) - 1)*3     = 0.2834995845386534
+            kraken         3x: ((65.795 / 60.15) - 1)*3         = 0.2815461346633419
+            binance       -1x: 1 - (66.1663784375 / 59.850)     = -0.1055368159983292
+            binance       -3x: (1 - (66.1663784375 / 59.850))*3 = -0.3166104479949876
+            kraken        -1x: 1 - (66.231165 / 59.850)         = -0.106619298245614
+            kraken        -3x: (1 - (66.231165 / 59.850))*3     = -0.319857894736842
+    fee: 0.003, 1x
+        close_value:
+            2.1 quote: (30.00 * 2.1) - (30.00 * 2.1 * 0.003) = 62.811
+            1.9 quote: (30.00 * 1.9) - (30.00 * 1.9 * 0.003) = 56.829
+            2.2 quote: (30.00 * 2.2) - (30.00 * 2.2 * 0.003) = 65.802
+        total_profit
+            fee: 0.003, 1x
+                2.1 quote: 62.811 - 60.15 = 2.6610000000000014
+                1.9 quote: 56.829 - 60.15 = -3.320999999999998
+                2.2 quote: 65.802 - 60.15 = 5.652000000000008
+        total_profit_ratio
+            fee: 0.003, 1x
+                2.1 quote: (62.811 / 60.15) - 1 = 0.04423940149625927
+                1.9 quote: (56.829 / 60.15) - 1 = -0.05521197007481293
+                2.2 quote: (65.802 / 60.15) - 1 = 0.09396508728179565
+    futures (live):
+        funding_fee: 1
+            close_value:
+                equations:
+                    1x,3x: (amount * close_rate) - (amount * close_rate * fee) + funding_fees
+                    -1x,-3x: (amount * close_rate) + (amount * close_rate * fee) - funding_fees
+                2.1 quote
+                    1x,3x: (30.00 * 2.1) - (30.00 * 2.1 * 0.0025) + 1   = 63.8425
+                    -1x,-3x: (30.00 * 2.1) + (30.00 * 2.1 * 0.0025) - 1   = 62.1575
+                1.9 quote
+                    1x,3x: (30.00 * 1.9) - (30.00 * 1.9 * 0.0025) + 1   = 57.8575
+                    -1x,-3x: (30.00 * 1.9) + (30.00 * 1.9 * 0.0025) - 1   = 56.1425
+                2.2 quote:
+                    1x,3x: (30.00 * 2.20) - (30.00 * 2.20 * 0.0025) + 1 = 66.835
+                    -1x,-3x: (30.00 * 2.20) + (30.00 * 2.20 * 0.0025) - 1 = 65.165
+            total_profit:
+                2.1 quote
+                    1x,3x:   63.8425     - 60.15          = 3.6925
+                    -1x,-3x: 59.850      - 62.1575        = -2.3074999999999974
+                1.9 quote
+                    1x,3x:   57.8575     - 60.15          = -2.2925
+                    -1x,-3x: 59.850      - 56.1425        = 3.707500000000003
+                2.2 quote:
+                    1x,3x:   66.835      - 60.15          = 6.685
+                    -1x,-3x: 59.850      - 65.165         = -5.315000000000005
+            total_profit_ratio:
+                2.1 quote
+                    1x: (63.8425 / 60.15) - 1             = 0.06138819617622615
+                    3x: ((63.8425 / 60.15) - 1)*3         = 0.18416458852867845
+                    -1x: 1 - (62.1575 / 59.850)           = -0.038554720133667564
+                    -3x: (1 - (62.1575 / 59.850))*3       = -0.11566416040100269
+                1.9 quote
+                    1x: (57.8575 / 60.15) - 1             = -0.0381130507065669
+                    3x: ((57.8575 / 60.15) - 1)*3         = -0.1143391521197007
+                    -1x: 1 - (56.1425 / 59.850)           = 0.06194653299916464
+                    -3x: (1 - (56.1425 / 59.850))*3       = 0.18583959899749392
+                2.2 quote
+                    1x: (66.835 / 60.15) - 1             = 0.11113881961762262
+                    3x: ((66.835 / 60.15) - 1)*3         = 0.33341645885286786
+                    -1x: 1 - (65.165 / 59.850)           = -0.08880534670008355
+                    -3x: (1 - (65.165 / 59.850))*3       = -0.26641604010025066
+        funding_fee: -1
+            close_value:
+                equations:
+                    (amount * close_rate) - (amount * close_rate * fee) + funding_fees
+                    (amount * close_rate) - (amount * close_rate * fee) - funding_fees
+                2.1 quote
+                    1x,3x:  (30.00 * 2.1) - (30.00 * 2.1 * 0.0025) + (-1)   = 61.8425
+                    -1x,-3x: (30.00 * 2.1) + (30.00 * 2.1 * 0.0025) - (-1)   = 64.1575
+                1.9 quote
+                    1x,3x:  (30.00 * 1.9) - (30.00 * 1.9 * 0.0025) + (-1)   = 55.8575
+                    -1x,-3x: (30.00 * 1.9) + (30.00 * 1.9 * 0.0025) - (-1)   = 58.1425
+                2.2 quote:
+                    1x,3x:  (30.00 * 2.20) - (30.00 * 2.20 * 0.0025) + (-1) = 64.835
+                    -1x,-3x: (30.00 * 2.20) + (30.00 * 2.20 * 0.0025) - (-1) = 67.165
+            total_profit:
+                2.1 quote
+                    1x,3x:   61.8425     - 60.15          = 1.6925000000000026
+                    -1x,-3x: 59.850      - 64.1575        = -4.307499999999997
+                1.9 quote
+                    1x,3x:   55.8575     - 60.15          = -4.292499999999997
+                    -1x,-3x: 59.850      - 58.1425        = 1.7075000000000031
+                2.2 quote:
+                    1x,3x:   64.835      - 60.15          = 4.684999999999995
+                    -1x,-3x: 59.850      - 67.165         = -7.315000000000005
+            total_profit_ratio:
+                2.1 quote
+                    1x: (61.8425 / 60.15) - 1             = 0.028137988362427313
+                    3x: ((61.8425 / 60.15) - 1)*3         = 0.08441396508728194
+                    -1x: 1 - (64.1575 / 59.850)           = -0.07197159565580624
+                    -3x: (1 - (64.1575 / 59.850))*3       = -0.21591478696741873
+                1.9 quote
+                    1x: (55.8575 / 60.15) - 1             = -0.07136325852036574
+                    3x: ((55.8575 / 60.15) - 1)*3         = -0.2140897755610972
+                    -1x: 1 - (58.1425 / 59.850)           = 0.02852965747702596
+                    -3x: (1 - (58.1425 / 59.850))*3       = 0.08558897243107788
+                2.2 quote
+                    1x: (64.835 / 60.15) - 1              = 0.07788861180382378
+                    3x: ((64.835 / 60.15) - 1)*3          = 0.23366583541147135
+                    -1x: 1 - (67.165 / 59.850)            = -0.12222222222222223
+                    -3x: (1 - (67.165 / 59.850))*3        = -0.3666666666666667
+    """
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=60.0,
+        amount=30.0 * lev,
+        open_rate=2.0,
+        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=10),
+        interest_rate=0.0005,
+        exchange=exchange,
+        is_short=is_short,
+        leverage=lev,
+        fee_open=0.0025,
+        fee_close=fee_close,
+        max_stake_amount=60.0,
+        trading_mode=trading_mode,
+        funding_fees=funding_fees,
+    )
+
+    profit_res = trade.calculate_profit(close_rate)
+    assert pytest.approx(profit_res.profit_abs) == round(profit, 8)
+    assert pytest.approx(profit_res.profit_ratio) == round(profit_ratio, 8)
+    val = trade.open_trade_value * (profit_res.profit_ratio) / lev
+    assert pytest.approx(val) == profit_res.profit_abs
+
+    assert pytest.approx(profit_res.total_profit) == round(profit, 8)
+    assert pytest.approx(profit_res.total_profit_ratio) == round(profit_ratio, 8)
+
+    assert pytest.approx(trade.calc_profit(rate=close_rate)) == round(profit, 8)
+    assert pytest.approx(trade.calc_profit_ratio(rate=close_rate)) == round(profit_ratio, 8)
+
+    profit_res2 = trade.calculate_profit(close_rate, trade.amount, trade.open_rate)
+    assert pytest.approx(profit_res2.profit_abs) == round(profit, 8)
+    assert pytest.approx(profit_res2.profit_ratio) == round(profit_ratio, 8)
+
+    assert pytest.approx(profit_res2.total_profit) == round(profit, 8)
+    assert pytest.approx(profit_res2.total_profit_ratio) == round(profit_ratio, 8)
+
+    assert pytest.approx(trade.calc_profit(close_rate, trade.amount, trade.open_rate)) == round(
+        profit, 8
+    )
+    assert pytest.approx(
+        trade.calc_profit_ratio(close_rate, trade.amount, trade.open_rate)
+    ) == round(profit_ratio, 8)
+
+
+def test_adjust_stop_loss(fee):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=30.0,
+        amount=30,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        max_rate=1,
+    )
+
+    trade.adjust_stop_loss(trade.open_rate, 0.05, True)
+    assert trade.stop_loss == 0.95
+    assert trade.stop_loss_pct == -0.05
+    assert trade.initial_stop_loss == 0.95
+    assert trade.initial_stop_loss_pct == -0.05
+
+    # Get percent of profit with a lower rate
+    trade.adjust_stop_loss(0.96, 0.05)
+    assert trade.stop_loss == 0.95
+    assert trade.stop_loss_pct == -0.05
+    assert trade.initial_stop_loss == 0.95
+    assert trade.initial_stop_loss_pct == -0.05
+
+    # Get percent of profit with a custom rate (Higher than open rate)
+    trade.adjust_stop_loss(1.3, -0.1)
+    assert pytest.approx(trade.stop_loss) == 1.17
+    assert trade.stop_loss_pct == -0.1
+    assert trade.initial_stop_loss == 0.95
+    assert trade.initial_stop_loss_pct == -0.05
+
+    # current rate lower again ... should not change
+    trade.adjust_stop_loss(1.2, 0.1)
+    assert pytest.approx(trade.stop_loss) == 1.17
+    assert trade.initial_stop_loss == 0.95
+    assert trade.initial_stop_loss_pct == -0.05
+
+    # current rate higher... should raise stoploss
+    trade.adjust_stop_loss(1.4, 0.1)
+    assert pytest.approx(trade.stop_loss) == 1.26
+    assert trade.initial_stop_loss == 0.95
+    assert trade.initial_stop_loss_pct == -0.05
+
+    #  Initial is true but stop_loss set - so doesn't do anything
+    trade.adjust_stop_loss(1.7, 0.1, True)
+    assert pytest.approx(trade.stop_loss) == 1.26
+    assert trade.initial_stop_loss == 0.95
+    assert trade.initial_stop_loss_pct == -0.05
+    assert trade.stop_loss_pct == -0.1
+
+
+def test_adjust_stop_loss_short(fee):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=0.001,
+        amount=5,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        max_rate=1,
+        is_short=True,
+    )
+    trade.adjust_stop_loss(trade.open_rate, 0.05, True)
+    assert trade.stop_loss == 1.05
+    assert trade.stop_loss_pct == -0.05
+    assert trade.initial_stop_loss == 1.05
+    assert trade.initial_stop_loss_pct == -0.05
+    # Get percent of profit with a lower rate
+    trade.adjust_stop_loss(1.04, 0.05)
+    assert trade.stop_loss == 1.05
+    assert trade.stop_loss_pct == -0.05
+    assert trade.initial_stop_loss == 1.05
+    assert trade.initial_stop_loss_pct == -0.05
+    # Get percent of profit with a custom rate (Higher than open rate)
+    trade.adjust_stop_loss(0.7, 0.1)
+    # If the price goes down to 0.7, with a trailing stop of 0.1,
+    # the new stoploss at 0.1 above 0.7 would be 0.7*0.1 higher
+    assert round(trade.stop_loss, 8) == 0.77
+    assert trade.stop_loss_pct == -0.1
+    assert trade.initial_stop_loss == 1.05
+    assert trade.initial_stop_loss_pct == -0.05
+    # current rate lower again ... should not change
+    trade.adjust_stop_loss(0.8, -0.1)
+    assert round(trade.stop_loss, 8) == 0.77
+    assert trade.initial_stop_loss == 1.05
+    assert trade.initial_stop_loss_pct == -0.05
+    # current rate higher... should raise stoploss
+    trade.adjust_stop_loss(0.6, -0.1)
+    assert round(trade.stop_loss, 8) == 0.66
+    assert trade.initial_stop_loss == 1.05
+    assert trade.initial_stop_loss_pct == -0.05
+    #  Initial is true but stop_loss set - so doesn't do anything
+    trade.adjust_stop_loss(0.3, -0.1, True)
+    assert round(trade.stop_loss, 8) == 0.66
+    assert trade.initial_stop_loss == 1.05
+    assert trade.initial_stop_loss_pct == -0.05
+    assert trade.stop_loss_pct == -0.1
+    # Liquidation price is lower than stoploss - so liquidation would trigger first.
+    trade.set_liquidation_price(0.63)
+    trade.adjust_stop_loss(0.59, -0.1)
+    assert trade.stop_loss == 0.649
+    assert trade.liquidation_price == 0.63
+
+
+def test_adjust_min_max_rates(fee):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=30.0,
+        amount=30.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+    )
+
+    trade.adjust_min_max_rates(trade.open_rate, trade.open_rate)
+    assert trade.max_rate == 1
+    assert trade.min_rate == 1
+
+    # check min adjusted, max remained
+    trade.adjust_min_max_rates(0.96, 0.96)
+    assert trade.max_rate == 1
+    assert trade.min_rate == 0.96
+
+    # check max adjusted, min remains
+    trade.adjust_min_max_rates(1.05, 1.05)
+    assert trade.max_rate == 1.05
+    assert trade.min_rate == 0.96
+
+    # current rate "in the middle" - no adjustment
+    trade.adjust_min_max_rates(1.03, 1.03)
+    assert trade.max_rate == 1.05
+    assert trade.min_rate == 0.96
+
+    # current rate "in the middle" - no adjustment
+    trade.adjust_min_max_rates(1.10, 0.91)
+    assert trade.max_rate == 1.10
+    assert trade.min_rate == 0.91
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("use_db", [True, False])
+@pytest.mark.parametrize("is_short", [True, False])
+def test_get_open(fee, is_short, use_db):
+    Trade.use_db = use_db
+    Trade.reset_trades()
+
+    create_mock_trades(fee, is_short, use_db)
+    assert len(Trade.get_open_trades()) == 4
+    assert Trade.get_open_trade_count() == 4
+
+    Trade.use_db = True
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("use_db", [True, False])
+def test_get_open_lev(fee, use_db):
+    Trade.use_db = use_db
+    Trade.reset_trades()
+
+    create_mock_trades_with_leverage(fee, use_db)
+    assert len(Trade.get_open_trades()) == 5
+    assert Trade.get_open_trade_count() == 5
+
+    Trade.use_db = True
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+@pytest.mark.parametrize("use_db", [True, False])
+@pytest.mark.usefixtures("init_persistence")
+def test_get_open_orders(fee, is_short, use_db):
+    Trade.use_db = use_db
+    Trade.reset_trades()
+
+    create_mock_trades_usdt(fee, is_short, use_db)
+    # Trade.commit()
+    trade = Trade.get_trades_proxy(pair="XRP/USDT")[0]
+    # assert trade.id == 3
+    assert len(trade.orders) == 2
+    assert len(trade.open_orders) == 0
+    assert not trade.has_open_orders
+
+    Trade.use_db = True
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_to_json(fee):
+    # Simulate dry_run entries
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=0.001,
+        amount=123.0,
+        amount_requested=123.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_date=dt_now() - timedelta(hours=2),
+        open_rate=0.123,
+        exchange="binance",
+        enter_tag=None,
+        precision_mode=1,
+        precision_mode_price=1,
+        amount_precision=8.0,
+        price_precision=7.0,
+        contract_size=1,
+    )
+    result = trade.to_json()
+    assert isinstance(result, dict)
+
+    assert result == {
+        "trade_id": None,
+        "pair": "ADA/USDT",
+        "base_currency": "ADA",
+        "quote_currency": "USDT",
+        "is_open": None,
+        "open_date": trade.open_date.strftime(DATETIME_PRINT_FORMAT),
+        "open_timestamp": int(trade.open_date.timestamp() * 1000),
+        "open_fill_date": None,
+        "open_fill_timestamp": None,
+        "close_date": None,
+        "close_timestamp": None,
+        "open_rate": 0.123,
+        "open_rate_requested": None,
+        "open_trade_value": 15.1668225,
+        "fee_close": 0.0025,
+        "fee_close_cost": None,
+        "fee_close_currency": None,
+        "fee_open": 0.0025,
+        "fee_open_cost": None,
+        "fee_open_currency": None,
+        "close_rate": None,
+        "close_rate_requested": None,
+        "amount": 123.0,
+        "amount_requested": 123.0,
+        "stake_amount": 0.001,
+        "max_stake_amount": None,
+        "trade_duration": None,
+        "trade_duration_s": None,
+        "realized_profit": 0.0,
+        "realized_profit_ratio": None,
+        "close_profit": None,
+        "close_profit_pct": None,
+        "close_profit_abs": None,
+        "profit_ratio": None,
+        "profit_pct": None,
+        "profit_abs": None,
+        "exit_reason": None,
+        "exit_order_status": None,
+        "stop_loss_abs": None,
+        "stop_loss_ratio": None,
+        "stop_loss_pct": None,
+        "stoploss_last_update": None,
+        "stoploss_last_update_timestamp": None,
+        "initial_stop_loss_abs": None,
+        "initial_stop_loss_pct": None,
+        "initial_stop_loss_ratio": None,
+        "min_rate": None,
+        "max_rate": None,
+        "strategy": None,
+        "enter_tag": None,
+        "timeframe": None,
+        "exchange": "binance",
+        "leverage": None,
+        "interest_rate": None,
+        "liquidation_price": None,
+        "is_short": None,
+        "trading_mode": None,
+        "funding_fees": None,
+        "amount_precision": 8.0,
+        "price_precision": 7.0,
+        "precision_mode": 1,
+        "precision_mode_price": 1,
+        "contract_size": 1,
+        "orders": [],
+        "has_open_orders": False,
+    }
+
+    # Simulate dry_run entries
+    trade = Trade(
+        pair="XRP/BTC",
+        stake_amount=0.001,
+        amount=100.0,
+        amount_requested=101.0,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_date=dt_now() - timedelta(hours=2),
+        close_date=dt_now() - timedelta(hours=1),
+        open_rate=0.123,
+        close_rate=0.125,
+        enter_tag="buys_signal_001",
+        exchange="binance",
+        precision_mode=2,
+        precision_mode_price=1,
+        amount_precision=7.0,
+        price_precision=8.0,
+        contract_size=1,
+    )
+    result = trade.to_json()
+    assert isinstance(result, dict)
+
+    assert result == {
+        "trade_id": None,
+        "pair": "XRP/BTC",
+        "base_currency": "XRP",
+        "quote_currency": "BTC",
+        "open_date": trade.open_date.strftime(DATETIME_PRINT_FORMAT),
+        "open_timestamp": int(trade.open_date.timestamp() * 1000),
+        "open_fill_date": None,
+        "open_fill_timestamp": None,
+        "close_date": trade.close_date.strftime(DATETIME_PRINT_FORMAT),
+        "close_timestamp": int(trade.close_date.timestamp() * 1000),
+        "open_rate": 0.123,
+        "close_rate": 0.125,
+        "amount": 100.0,
+        "amount_requested": 101.0,
+        "stake_amount": 0.001,
+        "max_stake_amount": None,
+        "trade_duration": 60,
+        "trade_duration_s": 3600,
+        "stop_loss_abs": None,
+        "stop_loss_pct": None,
+        "stop_loss_ratio": None,
+        "stoploss_last_update": None,
+        "stoploss_last_update_timestamp": None,
+        "initial_stop_loss_abs": None,
+        "initial_stop_loss_pct": None,
+        "initial_stop_loss_ratio": None,
+        "realized_profit": 0.0,
+        "realized_profit_ratio": None,
+        "close_profit": None,
+        "close_profit_pct": None,
+        "close_profit_abs": None,
+        "profit_ratio": None,
+        "profit_pct": None,
+        "profit_abs": None,
+        "close_rate_requested": None,
+        "fee_close": 0.0025,
+        "fee_close_cost": None,
+        "fee_close_currency": None,
+        "fee_open": 0.0025,
+        "fee_open_cost": None,
+        "fee_open_currency": None,
+        "is_open": None,
+        "max_rate": None,
+        "min_rate": None,
+        "open_rate_requested": None,
+        "open_trade_value": 12.33075,
+        "exit_reason": None,
+        "exit_order_status": None,
+        "strategy": None,
+        "enter_tag": "buys_signal_001",
+        "timeframe": None,
+        "exchange": "binance",
+        "leverage": None,
+        "interest_rate": None,
+        "liquidation_price": None,
+        "is_short": None,
+        "trading_mode": None,
+        "funding_fees": None,
+        "amount_precision": 7.0,
+        "price_precision": 8.0,
+        "precision_mode": 2,
+        "precision_mode_price": 1,
+        "contract_size": 1,
+        "orders": [],
+        "has_open_orders": False,
+    }
+
+
+def test_stoploss_reinitialization(default_conf, fee):
+    init_db(default_conf["db_url"])
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=30.0,
+        fee_open=fee.return_value,
+        open_date=dt_now() - timedelta(hours=2),
+        amount=30.0,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        max_rate=1,
+    )
+
+    trade.adjust_stop_loss(trade.open_rate, 0.05, True)
+    assert trade.stop_loss == 0.95
+    assert trade.stop_loss_pct == -0.05
+    assert trade.initial_stop_loss == 0.95
+    assert trade.initial_stop_loss_pct == -0.05
+    Trade.session.add(trade)
+    Trade.commit()
+
+    # Lower stoploss
+    Trade.stoploss_reinitialization(0.06)
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade_adj = trades[0]
+    assert trade_adj.stop_loss == 0.94
+    assert trade_adj.stop_loss_pct == -0.06
+    assert trade_adj.initial_stop_loss == 0.94
+    assert trade_adj.initial_stop_loss_pct == -0.06
+
+    # Raise stoploss
+    Trade.stoploss_reinitialization(0.04)
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade_adj = trades[0]
+    assert trade_adj.stop_loss == 0.96
+    assert trade_adj.stop_loss_pct == -0.04
+    assert trade_adj.initial_stop_loss == 0.96
+    assert trade_adj.initial_stop_loss_pct == -0.04
+
+    # Trailing stoploss (move stoplos up a bit)
+    trade.adjust_stop_loss(1.02, 0.04)
+    assert trade_adj.stop_loss == 0.9792
+    assert trade_adj.initial_stop_loss == 0.96
+
+    Trade.stoploss_reinitialization(0.04)
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade_adj = trades[0]
+    # Stoploss should not change in this case.
+    assert trade_adj.stop_loss == 0.9792
+    assert trade_adj.stop_loss_pct == -0.04
+    assert trade_adj.initial_stop_loss == 0.96
+    assert trade_adj.initial_stop_loss_pct == -0.04
+
+
+def test_stoploss_reinitialization_leverage(default_conf, fee):
+    init_db(default_conf["db_url"])
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=30.0,
+        fee_open=fee.return_value,
+        open_date=dt_now() - timedelta(hours=2),
+        amount=30.0,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        max_rate=1,
+        leverage=5.0,
+    )
+
+    trade.adjust_stop_loss(trade.open_rate, 0.1, True)
+    assert trade.stop_loss == 0.98
+    assert trade.stop_loss_pct == -0.1
+    assert trade.initial_stop_loss == 0.98
+    assert trade.initial_stop_loss_pct == -0.1
+    Trade.session.add(trade)
+    Trade.commit()
+
+    # Lower stoploss
+    Trade.stoploss_reinitialization(0.15)
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade_adj = trades[0]
+    assert trade_adj.stop_loss == 0.97
+    assert trade_adj.stop_loss_pct == -0.15
+    assert trade_adj.initial_stop_loss == 0.97
+    assert trade_adj.initial_stop_loss_pct == -0.15
+
+    # Raise stoploss
+    Trade.stoploss_reinitialization(0.05)
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade_adj = trades[0]
+    assert trade_adj.stop_loss == 0.99
+    assert trade_adj.stop_loss_pct == -0.05
+    assert trade_adj.initial_stop_loss == 0.99
+    assert trade_adj.initial_stop_loss_pct == -0.05
+
+    # Trailing stoploss (move stoplos up a bit)
+    trade.adjust_stop_loss(1.02, 0.05)
+    assert trade_adj.stop_loss == 1.0098
+    assert trade_adj.initial_stop_loss == 0.99
+
+    Trade.stoploss_reinitialization(0.05)
+
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade_adj = trades[0]
+    # Stoploss should not change in this case.
+    assert trade_adj.stop_loss == 1.0098
+    assert trade_adj.stop_loss_pct == -0.05
+    assert trade_adj.initial_stop_loss == 0.99
+    assert trade_adj.initial_stop_loss_pct == -0.05
+
+
+def test_stoploss_reinitialization_short(default_conf, fee):
+    init_db(default_conf["db_url"])
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=0.001,
+        fee_open=fee.return_value,
+        open_date=dt_now() - timedelta(hours=2),
+        amount=10,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        max_rate=1,
+        is_short=True,
+        leverage=5.0,
+    )
+    trade.adjust_stop_loss(trade.open_rate, -0.1, True)
+    assert trade.stop_loss == 1.02
+    assert trade.stop_loss_pct == -0.1
+    assert trade.initial_stop_loss == 1.02
+    assert trade.initial_stop_loss_pct == -0.1
+    Trade.session.add(trade)
+    Trade.commit()
+    # Lower stoploss
+    Trade.stoploss_reinitialization(-0.15)
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade_adj = trades[0]
+    assert trade_adj.stop_loss == 1.03
+    assert trade_adj.stop_loss_pct == -0.15
+    assert trade_adj.initial_stop_loss == 1.03
+    assert trade_adj.initial_stop_loss_pct == -0.15
+    # Raise stoploss
+    Trade.stoploss_reinitialization(-0.05)
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade_adj = trades[0]
+    assert trade_adj.stop_loss == 1.01
+    assert trade_adj.stop_loss_pct == -0.05
+    assert trade_adj.initial_stop_loss == 1.01
+    assert trade_adj.initial_stop_loss_pct == -0.05
+    # Trailing stoploss
+    trade.adjust_stop_loss(0.98, -0.05)
+    assert trade_adj.stop_loss == 0.9898
+    assert trade_adj.initial_stop_loss == 1.01
+    Trade.stoploss_reinitialization(-0.05)
+    trades = Trade.get_open_trades()
+    assert len(trades) == 1
+    trade_adj = trades[0]
+    # Stoploss should not change in this case.
+    assert trade_adj.stop_loss == 0.9898
+    assert trade_adj.stop_loss_pct == -0.05
+    assert trade_adj.initial_stop_loss == 1.01
+    assert trade_adj.initial_stop_loss_pct == -0.05
+    # Stoploss can't go above liquidation price
+    trade_adj.set_liquidation_price(0.985)
+    trade.adjust_stop_loss(0.9799, -0.05)
+    assert trade_adj.stop_loss == 0.989699
+    assert trade_adj.liquidation_price == 0.985
+
+
+def test_update_fee(fee):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=30.0,
+        fee_open=fee.return_value,
+        open_date=dt_now() - timedelta(hours=2),
+        amount=30.0,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        max_rate=1,
+    )
+    fee_cost = 0.15
+    fee_currency = "BTC"
+    fee_rate = 0.0075
+    assert trade.fee_open_currency is None
+    assert not trade.fee_updated("buy")
+    assert not trade.fee_updated("sell")
+
+    trade.update_fee(fee_cost, fee_currency, fee_rate, "buy")
+    assert trade.fee_updated("buy")
+    assert not trade.fee_updated("sell")
+    assert trade.fee_open_currency == fee_currency
+    assert trade.fee_open_cost == fee_cost
+    assert trade.fee_open == fee_rate
+    # Setting buy rate should "guess" close rate
+    assert trade.fee_close == fee_rate
+    assert trade.fee_close_currency is None
+    assert trade.fee_close_cost is None
+
+    fee_rate = 0.0076
+    trade.update_fee(fee_cost, fee_currency, fee_rate, "sell")
+    assert trade.fee_updated("buy")
+    assert trade.fee_updated("sell")
+    assert trade.fee_close == 0.0076
+    assert trade.fee_close_cost == fee_cost
+    assert trade.fee_close == fee_rate
+
+
+def test_fee_updated(fee):
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=30.0,
+        fee_open=fee.return_value,
+        open_date=dt_now() - timedelta(hours=2),
+        amount=30.0,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        max_rate=1,
+    )
+
+    assert trade.fee_open_currency is None
+    assert not trade.fee_updated("buy")
+    assert not trade.fee_updated("sell")
+    assert not trade.fee_updated("asdf")
+
+    trade.update_fee(0.15, "BTC", 0.0075, "buy")
+    assert trade.fee_updated("buy")
+    assert not trade.fee_updated("sell")
+    assert trade.fee_open_currency is not None
+    assert trade.fee_close_currency is None
+
+    trade.update_fee(0.15, "ABC", 0.0075, "sell")
+    assert trade.fee_updated("buy")
+    assert trade.fee_updated("sell")
+    assert not trade.fee_updated("asfd")
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [True, False])
+@pytest.mark.parametrize("use_db", [True, False])
+def test_total_open_trades_stakes(fee, is_short, use_db):
+    Trade.use_db = use_db
+    Trade.reset_trades()
+    res = Trade.total_open_trades_stakes()
+    assert res == 0
+    create_mock_trades(fee, is_short, use_db)
+    res = Trade.total_open_trades_stakes()
+    assert res == 0.004
+
+    Trade.use_db = True
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize(
+    "is_short,result",
+    [
+        (True, -0.006739127),
+        (False, 0.000739127),
+        (None, -0.005429127),
+    ],
+)
+@pytest.mark.parametrize("use_db", [True, False])
+def test_get_total_closed_profit(fee, use_db, is_short, result):
+    Trade.use_db = use_db
+    Trade.reset_trades()
+    res = Trade.get_total_closed_profit()
+    assert res == 0
+    create_mock_trades(fee, is_short, use_db)
+    res = Trade.get_total_closed_profit()
+    assert pytest.approx(res) == result
+
+    Trade.use_db = True
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [True, False])
+@pytest.mark.parametrize("use_db", [True, False])
+def test_get_trades_proxy(fee, use_db, is_short):
+    Trade.use_db = use_db
+    Trade.reset_trades()
+    create_mock_trades(fee, is_short, use_db)
+    trades = Trade.get_trades_proxy()
+    assert len(trades) == 6
+
+    assert isinstance(trades[0], Trade)
+
+    trades = Trade.get_trades_proxy(is_open=True)
+    assert len(trades) == 4
+    assert trades[0].is_open
+    trades = Trade.get_trades_proxy(is_open=False)
+
+    assert len(trades) == 2
+    assert not trades[0].is_open
+
+    opendate = datetime.now(tz=timezone.utc) - timedelta(minutes=15)
+
+    assert len(Trade.get_trades_proxy(open_date=opendate)) == 3
+
+    Trade.use_db = True
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [True, False])
+def test_get_trades__query(fee, is_short):
+    query = Trade.get_trades_query([])
+    # without orders there should be no join issued.
+    query1 = Trade.get_trades_query([], include_orders=False)
+
+    # Empty "with-options -> default - selection"
+    assert query._with_options == ()
+    assert query1._with_options != ()
+
+    create_mock_trades(fee, is_short)
+    query = Trade.get_trades_query([])
+    query1 = Trade.get_trades_query([], include_orders=False)
+
+    assert query._with_options == ()
+    assert query1._with_options != ()
+
+
+def test_get_trades_backtest():
+    Trade.use_db = False
+    with pytest.raises(NotImplementedError, match=r"`Trade.get_trades\(\)` not .*"):
+        Trade.get_trades([])
+    Trade.use_db = True
+
+
+@pytest.mark.usefixtures("init_persistence")
+# @pytest.mark.parametrize('is_short', [True, False])
+def test_get_overall_performance(fee):
+    create_mock_trades(fee, False)
+    res = Trade.get_overall_performance()
+
+    assert len(res) == 2
+    assert "pair" in res[0]
+    assert "profit" in res[0]
+    assert "count" in res[0]
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize(
+    "is_short,pair,profit",
+    [
+        (True, "XRP/BTC", -0.00018780487),
+        (False, "ETC/BTC", 0.00003860975),
+        (None, "XRP/BTC", 0.000025203252),
+    ],
+)
+def test_get_best_pair(fee, is_short, pair, profit):
+    res = Trade.get_best_pair()
+    assert res is None
+
+    create_mock_trades(fee, is_short)
+    res = Trade.get_best_pair()
+    assert len(res) == 4
+    assert res[0] == pair
+    assert pytest.approx(res[1]) == profit
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_get_best_pair_lev(fee):
+    res = Trade.get_best_pair()
+    assert res is None
+
+    create_mock_trades_with_leverage(fee)
+    res = Trade.get_best_pair()
+    assert len(res) == 4
+    assert res[0] == "ETC/BTC"
+    assert pytest.approx(res[1]) == 0.00003860975
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [True, False])
+def test_get_canceled_exit_order_count(fee, is_short):
+    create_mock_trades(fee, is_short=is_short)
+    trade = Trade.get_trades([Trade.pair == "ETC/BTC"]).first()
+    # No canceled order.
+    assert trade.get_canceled_exit_order_count() == 0
+    # Property returns the same result
+    assert trade.canceled_exit_order_count == 0
+
+    trade.orders[-1].status = "canceled"
+    assert trade.get_canceled_exit_order_count() == 1
+    assert trade.canceled_exit_order_count == 1
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [True, False])
+def test_fully_canceled_entry_order_count(fee, is_short):
+    create_mock_trades(fee, is_short=is_short)
+    trade = Trade.get_trades([Trade.pair == "ETC/BTC"]).first()
+    # No canceled order.
+    assert trade.fully_canceled_entry_order_count == 0
+
+    trade.orders[0].status = "canceled"
+    trade.orders[0].filled = 0
+    assert trade.fully_canceled_entry_order_count == 1
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_update_order_from_ccxt(caplog, time_machine):
+    start = datetime(2023, 1, 1, 4, tzinfo=timezone.utc)
+    time_machine.move_to(start, tick=False)
+
+    # Most basic order return (only has orderid)
+    o = Order.parse_from_ccxt_object({"id": "1234"}, "ADA/USDT", "buy", 20.01, 1234.6)
+    assert isinstance(o, Order)
+    assert o.ft_pair == "ADA/USDT"
+    assert o.ft_order_side == "buy"
+    assert o.order_id == "1234"
+    assert o.ft_price == 1234.6
+    assert o.ft_amount == 20.01
+    assert o.ft_is_open
+    ccxt_order = {
+        "id": "1234",
+        "side": "buy",
+        "symbol": "ADA/USDT",
+        "type": "limit",
+        "price": 1234.5,
+        "amount": 20.0,
+        "filled": 9,
+        "remaining": 11,
+        "status": "open",
+        "timestamp": 1599394315123,
+    }
+    o = Order.parse_from_ccxt_object(ccxt_order, "ADA/USDT", "buy", 20.01, 1234.6)
+    assert isinstance(o, Order)
+    assert o.ft_pair == "ADA/USDT"
+    assert o.ft_order_side == "buy"
+    assert o.order_id == "1234"
+    assert o.order_type == "limit"
+    assert o.price == 1234.5
+    assert o.ft_price == 1234.6
+    assert o.ft_amount == 20.01
+    assert o.filled == 9
+    assert o.remaining == 11
+    assert o.order_date is not None
+    assert o.ft_is_open
+    assert o.order_filled_date is None
+
+    # Order is unfilled, "filled" not set
+    # https://github.com/freqtrade/freqtrade/issues/5404
+    ccxt_order.update({"filled": None, "remaining": 20.0, "status": "canceled"})
+    o.update_from_ccxt_object(ccxt_order)
+
+    # Order has been closed
+    ccxt_order.update({"filled": 20.0, "remaining": 0.0, "status": "closed"})
+    o.update_from_ccxt_object(ccxt_order)
+
+    assert o.filled == 20.0
+    assert o.remaining == 0.0
+    assert not o.ft_is_open
+    assert o.order_filled_date == start
+    # Move time
+    time_machine.move_to(start + timedelta(hours=1), tick=False)
+
+    ccxt_order.update({"id": "somethingelse"})
+    with pytest.raises(DependencyException, match=r"Order-id's don't match"):
+        o.update_from_ccxt_object(ccxt_order)
+
+    message = "aaaa is not a valid response object."
+    assert not log_has(message, caplog)
+    Order.update_orders([o], "aaaa")
+    assert log_has(message, caplog)
+
+    # Call regular update - shouldn't fail.
+    Order.update_orders([o], {"id": "1234"})
+    assert o.order_filled_date == start
+
+    # Fill order again - shouldn't update filled date
+    ccxt_order.update({"id": "1234"})
+    Order.update_orders([o], ccxt_order)
+    assert o.order_filled_date == start
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [True, False])
+def test_select_order(fee, is_short):
+    create_mock_trades(fee, is_short)
+
+    trades = Trade.get_trades().all()
+
+    # Open buy order, no sell order
+    order = trades[0].select_order(trades[0].entry_side, True)
+    assert order is not None
+    order = trades[0].select_order(trades[0].entry_side, False)
+    assert order is None
+    order = trades[0].select_order(trades[0].exit_side, None)
+    assert order is None
+
+    # closed buy order, and open sell order
+    order = trades[1].select_order(trades[1].entry_side, True)
+    assert order is None
+    order = trades[1].select_order(trades[1].entry_side, False)
+    assert order is not None
+    order = trades[1].select_order(trades[1].entry_side, None)
+    assert order is not None
+    order = trades[1].select_order(trades[1].exit_side, True)
+    assert order is None
+    order = trades[1].select_order(trades[1].exit_side, False)
+    assert order is not None
+
+    # Has open buy order
+    order = trades[3].select_order(trades[3].entry_side, True)
+    assert order is not None
+    order = trades[3].select_order(trades[3].entry_side, False)
+    assert order is None
+
+    # Open sell order
+    order = trades[4].select_order(trades[4].entry_side, True)
+    assert order is None
+    order = trades[4].select_order(trades[4].entry_side, False)
+    assert order is not None
+
+    trades[4].orders[1].ft_order_side = trades[4].exit_side
+    order = trades[4].select_order(trades[4].exit_side, True)
+    assert order is not None
+
+    trades[4].orders[1].ft_order_side = "stoploss"
+    order = trades[4].select_order("stoploss", None)
+    assert order is not None
+    assert order.ft_order_side == "stoploss"
+
+
+def test_Trade_object_idem():
+    assert issubclass(Trade, LocalTrade)
+
+    trade = vars(Trade)
+    localtrade = vars(LocalTrade)
+
+    excludes = (
+        "delete",
+        "session",
+        "commit",
+        "rollback",
+        "query",
+        "open_date",
+        "get_best_pair",
+        "get_overall_performance",
+        "get_total_closed_profit",
+        "total_open_trades_stakes",
+        "get_closed_trades_without_assigned_fees",
+        "get_open_trades_without_assigned_fees",
+        "get_trades",
+        "get_trades_query",
+        "get_exit_reason_performance",
+        "get_enter_tag_performance",
+        "get_mix_tag_performance",
+        "get_trading_volume",
+        "validate_string_len",
+        "custom_data",
+    )
+    EXCLUDES2 = (
+        "bt_trades",
+        "bt_trades_open",
+        "bt_trades_open_pp",
+        "bt_open_open_trade_count",
+        "bt_total_profit",
+        "from_json",
+    )
+
+    # Parent (LocalTrade) should have the same attributes
+    for item in trade:
+        # Exclude private attributes and open_date (as it's not assigned a default)
+        if not item.startswith("_") and item not in excludes:
+            assert item in localtrade
+
+    # Fails if only a column is added without corresponding parent field
+    for item in localtrade:
+        if (
+            not item.startswith("__")
+            and item not in EXCLUDES2
+            and type(getattr(LocalTrade, item)) not in (property, FunctionType)
+        ):
+            assert item in trade
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_trade_truncates_string_fields():
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=20.0,
+        amount=30.0,
+        open_rate=2.0,
+        open_date=datetime.now(timezone.utc) - timedelta(minutes=20),
+        fee_open=0.001,
+        fee_close=0.001,
+        exchange="binance",
+        leverage=1.0,
+        trading_mode="futures",
+        enter_tag="a" * CUSTOM_TAG_MAX_LENGTH * 2,
+        exit_reason="b" * CUSTOM_TAG_MAX_LENGTH * 2,
+    )
+    Trade.session.add(trade)
+    Trade.commit()
+
+    trade1 = Trade.session.scalars(select(Trade)).first()
+
+    assert trade1.enter_tag == "a" * CUSTOM_TAG_MAX_LENGTH
+    assert trade1.exit_reason == "b" * CUSTOM_TAG_MAX_LENGTH
+
+
+def test_recalc_trade_from_orders(fee):
+    o1_amount = 100
+    o1_rate = 1
+    o1_cost = o1_amount * o1_rate
+    o1_fee_cost = o1_cost * fee.return_value
+    o1_trade_val = o1_cost + o1_fee_cost
+
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=o1_cost,
+        open_date=dt_now() - timedelta(hours=2),
+        amount=o1_amount,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=o1_rate,
+        max_rate=o1_rate,
+        leverage=1,
+    )
+
+    assert fee.return_value == 0.0025
+    assert trade._calc_open_trade_value(trade.amount, trade.open_rate) == o1_trade_val
+    assert trade.amount == o1_amount
+    assert trade.stake_amount == o1_cost
+    assert trade.open_rate == o1_rate
+    assert trade.open_trade_value == o1_trade_val
+
+    # Calling without orders should not throw exceptions and change nothing
+    trade.recalc_trade_from_orders()
+    assert trade.amount == o1_amount
+    assert trade.stake_amount == o1_cost
+    assert trade.open_rate == o1_rate
+    assert trade.open_trade_value == o1_trade_val
+
+    trade.update_fee(o1_fee_cost, "BNB", fee.return_value, "buy")
+
+    assert len(trade.orders) == 0
+
+    # Check with 1 order
+    order1 = Order(
+        ft_order_side="buy",
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side="buy",
+        price=o1_rate,
+        average=o1_rate,
+        filled=o1_amount,
+        remaining=0,
+        cost=o1_amount,
+        order_date=trade.open_date,
+        order_filled_date=trade.open_date,
+    )
+    trade.orders.append(order1)
+    trade.recalc_trade_from_orders()
+
+    # Calling recalc with single initial order should not change anything
+    assert trade.amount == o1_amount
+    assert trade.stake_amount == o1_amount
+    assert trade.open_rate == o1_rate
+    assert trade.fee_open_cost == o1_fee_cost
+    assert trade.open_trade_value == o1_trade_val
+
+    # One additional adjustment / DCA order
+    o2_amount = 125
+    o2_rate = 0.9
+    o2_cost = o2_amount * o2_rate
+    o2_fee_cost = o2_cost * fee.return_value
+    o2_trade_val = o2_cost + o2_fee_cost
+
+    order2 = Order(
+        ft_order_side="buy",
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side="buy",
+        price=o2_rate,
+        average=o2_rate,
+        filled=o2_amount,
+        remaining=0,
+        cost=o2_cost,
+        order_date=dt_now() - timedelta(hours=1),
+        order_filled_date=dt_now() - timedelta(hours=1),
+    )
+    trade.orders.append(order2)
+    trade.recalc_trade_from_orders()
+
+    # Validate that the trade now has new averaged open price and total values
+    avg_price = (o1_cost + o2_cost) / (o1_amount + o2_amount)
+    assert trade.amount == o1_amount + o2_amount
+    assert trade.stake_amount == o1_amount + o2_cost
+    assert trade.open_rate == avg_price
+    assert trade.fee_open_cost == o1_fee_cost + o2_fee_cost
+    assert trade.open_trade_value == o1_trade_val + o2_trade_val
+
+    # Let's try with multiple additional orders
+    o3_amount = 150
+    o3_rate = 0.85
+    o3_cost = o3_amount * o3_rate
+    o3_fee_cost = o3_cost * fee.return_value
+    o3_trade_val = o3_cost + o3_fee_cost
+
+    order3 = Order(
+        ft_order_side="buy",
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side="buy",
+        price=o3_rate,
+        average=o3_rate,
+        filled=o3_amount,
+        remaining=0,
+        cost=o3_cost,
+        order_date=dt_now() - timedelta(hours=1),
+        order_filled_date=dt_now() - timedelta(hours=1),
+    )
+    trade.orders.append(order3)
+    trade.recalc_trade_from_orders()
+
+    # Validate that the sum is still correct and open rate is averaged
+    avg_price = (o1_cost + o2_cost + o3_cost) / (o1_amount + o2_amount + o3_amount)
+    assert trade.amount == o1_amount + o2_amount + o3_amount
+    assert trade.stake_amount == o1_cost + o2_cost + o3_cost
+    assert trade.open_rate == avg_price
+    assert pytest.approx(trade.fee_open_cost) == o1_fee_cost + o2_fee_cost + o3_fee_cost
+    assert pytest.approx(trade.open_trade_value) == o1_trade_val + o2_trade_val + o3_trade_val
+
+    # Just to make sure full sell orders are ignored, let's calculate one more time.
+
+    sell1 = Order(
+        ft_order_side="sell",
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side="sell",
+        price=avg_price + 0.95,
+        average=avg_price + 0.95,
+        filled=o1_amount + o2_amount + o3_amount,
+        remaining=0,
+        cost=o1_cost + o2_cost + o3_cost,
+        order_date=trade.open_date,
+        order_filled_date=trade.open_date,
+    )
+    trade.orders.append(sell1)
+    trade.recalc_trade_from_orders()
+
+    assert trade.amount == o1_amount + o2_amount + o3_amount
+    assert trade.stake_amount == o1_cost + o2_cost + o3_cost
+    assert trade.open_rate == avg_price
+    assert pytest.approx(trade.fee_open_cost) == o1_fee_cost + o2_fee_cost + o3_fee_cost
+    assert pytest.approx(trade.open_trade_value) == o1_trade_val + o2_trade_val + o3_trade_val
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_recalc_trade_from_orders_kucoin():
+    # Taken from https://github.com/freqtrade/freqtrade/issues/9346
+    o1_amount = 11511963.8634448908
+    o2_amount = 11750101.7743937783
+    o3_amount = 23262065.6378386617  # Exit amount - barely doesn't even out
+
+    res = o1_amount + o2_amount - o3_amount
+    assert res > 0.0
+    assert res < 0.1
+    o1_rate = 0.000029901
+    o2_rate = 0.000029295
+    o3_rate = 0.000029822
+
+    o1_cost = o1_amount * o1_rate
+
+    trade = Trade(
+        pair="FLOKI/USDT",
+        stake_amount=o1_cost,
+        open_date=dt_now() - timedelta(hours=2),
+        amount=o1_amount,
+        fee_open=0.001,
+        fee_close=0.001,
+        exchange="binance",
+        open_rate=o1_rate,
+        max_rate=o1_rate,
+        leverage=1,
+    )
+    # Check with 1 order
+    order1 = Order(
+        ft_order_side="buy",
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side="buy",
+        price=o1_rate,
+        average=o1_rate,
+        filled=o1_amount,
+        remaining=0,
+        cost=o1_cost,
+        order_date=trade.open_date,
+        order_filled_date=trade.open_date,
+    )
+    trade.orders.append(order1)
+    order2 = Order(
+        ft_order_side="buy",
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side="buy",
+        price=o2_rate,
+        average=o2_rate,
+        filled=o2_amount,
+        remaining=0,
+        cost=o2_amount * o2_rate,
+        order_date=trade.open_date,
+        order_filled_date=trade.open_date,
+    )
+    trade.orders.append(order2)
+    trade.recalc_trade_from_orders()
+    assert trade.amount == o1_amount + o2_amount
+    profit = trade.calculate_profit(o3_rate)
+    assert profit.profit_abs == pytest.approx(3.90069871)
+    assert profit.profit_ratio == pytest.approx(0.00566035)
+
+    order3 = Order(
+        ft_order_side="sell",
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side="sell",
+        price=o3_rate,
+        average=o3_rate,
+        filled=o3_amount,
+        remaining=0,
+        cost=o2_amount * o2_rate,
+        order_date=trade.open_date,
+        order_filled_date=trade.open_date,
+    )
+
+    trade.orders.append(order3)
+    trade.update_trade(order3)
+    assert trade.is_open is False
+    # Trade closed correctly - but left a minimal amount.
+    assert trade.amount == 8e-09
+    assert pytest.approx(trade.close_profit_abs) == 3.90069871
+    assert pytest.approx(trade.close_profit) == 0.00566035
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_recalc_trade_from_orders_ignores_bad_orders(fee, is_short):
+    o1_amount = 100
+    o1_rate = 1
+    o1_cost = o1_amount * o1_rate
+    o1_fee_cost = o1_cost * fee.return_value
+    o1_trade_val = o1_cost - o1_fee_cost if is_short else o1_cost + o1_fee_cost
+    entry_side = "sell" if is_short else "buy"
+    exit_side = "buy" if is_short else "sell"
+
+    trade = Trade(
+        pair="ADA/USDT",
+        stake_amount=o1_cost,
+        open_date=dt_now() - timedelta(hours=2),
+        amount=o1_amount,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=o1_rate,
+        max_rate=o1_rate,
+        is_short=is_short,
+        leverage=1.0,
+    )
+    trade.update_fee(o1_fee_cost, "BNB", fee.return_value, entry_side)
+    # Check with 1 order
+    order1 = Order(
+        ft_order_side=entry_side,
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side=entry_side,
+        price=o1_rate,
+        average=o1_rate,
+        filled=o1_amount,
+        remaining=0,
+        cost=o1_amount,
+        order_date=trade.open_date,
+        order_filled_date=trade.open_date,
+    )
+    trade.orders.append(order1)
+    trade.recalc_trade_from_orders()
+
+    # Calling recalc with single initial order should not change anything
+    assert trade.amount == o1_amount
+    assert trade.stake_amount == o1_amount
+    assert trade.open_rate == o1_rate
+    assert trade.fee_open_cost == o1_fee_cost
+    assert trade.open_trade_value == o1_trade_val
+    assert trade.nr_of_successful_entries == 1
+
+    order2 = Order(
+        ft_order_side=entry_side,
+        ft_pair=trade.pair,
+        ft_is_open=True,
+        status="open",
+        symbol=trade.pair,
+        order_type="market",
+        side=entry_side,
+        price=o1_rate,
+        average=o1_rate,
+        filled=o1_amount,
+        remaining=0,
+        cost=o1_cost,
+        order_date=dt_now() - timedelta(hours=1),
+        order_filled_date=dt_now() - timedelta(hours=1),
+    )
+    trade.orders.append(order2)
+    trade.recalc_trade_from_orders()
+
+    # Validate that the trade values have not been changed
+    assert trade.amount == o1_amount
+    assert trade.stake_amount == o1_amount
+    assert trade.open_rate == o1_rate
+    assert trade.fee_open_cost == o1_fee_cost
+    assert trade.open_trade_value == o1_trade_val
+    assert trade.nr_of_successful_entries == 1
+
+    # Let's try with some other orders
+    order3 = Order(
+        ft_order_side=entry_side,
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="cancelled",
+        symbol=trade.pair,
+        order_type="market",
+        side=entry_side,
+        price=1,
+        average=2,
+        filled=0,
+        remaining=4,
+        cost=5,
+        order_date=dt_now() - timedelta(hours=1),
+        order_filled_date=dt_now() - timedelta(hours=1),
+    )
+    trade.orders.append(order3)
+    trade.recalc_trade_from_orders()
+
+    # Validate that the order values still are ignoring orders 2 and 3
+    assert trade.amount == o1_amount
+    assert trade.stake_amount == o1_amount
+    assert trade.open_rate == o1_rate
+    assert trade.fee_open_cost == o1_fee_cost
+    assert trade.open_trade_value == o1_trade_val
+    assert trade.nr_of_successful_entries == 1
+
+    order4 = Order(
+        ft_order_side=entry_side,
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side=entry_side,
+        price=o1_rate,
+        average=o1_rate,
+        filled=o1_amount,
+        remaining=0,
+        cost=o1_cost,
+        order_date=dt_now() - timedelta(hours=1),
+        order_filled_date=dt_now() - timedelta(hours=1),
+    )
+    trade.orders.append(order4)
+    trade.recalc_trade_from_orders()
+
+    # Validate that the trade values have been changed
+    assert trade.amount == 2 * o1_amount
+    assert trade.stake_amount == 2 * o1_amount
+    assert trade.open_rate == o1_rate
+    assert trade.fee_open_cost == trade.nr_of_successful_entries * o1_fee_cost
+    assert trade.open_trade_value == 2 * o1_trade_val
+    assert trade.nr_of_successful_entries == 2
+
+    # Reduce position - this will reduce amount again.
+    sell1 = Order(
+        ft_order_side=exit_side,
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side=exit_side,
+        price=4,
+        average=3,
+        filled=o1_amount,
+        remaining=1,
+        cost=5,
+        order_date=trade.open_date,
+        order_filled_date=trade.open_date,
+    )
+    trade.orders.append(sell1)
+    trade.recalc_trade_from_orders()
+
+    assert trade.amount == o1_amount
+    assert trade.stake_amount == o1_amount
+    assert trade.open_rate == o1_rate
+    assert trade.fee_open_cost == trade.nr_of_successful_entries * o1_fee_cost
+    assert trade.open_trade_value == o1_trade_val
+    assert trade.nr_of_successful_entries == 2
+
+    # Check with 1 order
+    order_noavg = Order(
+        ft_order_side=entry_side,
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        status="closed",
+        symbol=trade.pair,
+        order_type="market",
+        side=entry_side,
+        price=o1_rate,
+        average=None,
+        filled=o1_amount,
+        remaining=0,
+        cost=o1_amount,
+        order_date=trade.open_date,
+        order_filled_date=trade.open_date,
+    )
+    trade.orders.append(order_noavg)
+    trade.recalc_trade_from_orders()
+
+    # Calling recalc with single initial order should not change anything
+    assert trade.amount == 2 * o1_amount
+    assert trade.stake_amount == 2 * o1_amount
+    assert trade.open_rate == o1_rate
+    assert trade.fee_open_cost == trade.nr_of_successful_entries * o1_fee_cost
+    assert trade.open_trade_value == 2 * o1_trade_val
+    assert trade.nr_of_successful_entries == 3
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_select_filled_orders(fee):
+    create_mock_trades(fee)
+
+    trades = Trade.get_trades().all()
+
+    # Closed buy order, no sell order
+    orders = trades[0].select_filled_orders("buy")
+    assert isinstance(orders, list)
+    assert len(orders) == 0
+
+    orders = trades[0].select_filled_orders("sell")
+    assert orders is not None
+    assert len(orders) == 0
+
+    # closed buy order, and closed sell order
+    orders = trades[1].select_filled_orders("buy")
+    assert isinstance(orders, list)
+    assert len(orders) == 1
+    order = orders[0]
+    assert order.amount > 0
+    assert order.filled > 0
+    assert order.side == "buy"
+    assert order.ft_order_side == "buy"
+    assert order.status == "closed"
+
+    orders = trades[1].select_filled_orders("sell")
+    assert isinstance(orders, list)
+    assert len(orders) == 1
+
+    # Has open buy order
+    orders = trades[3].select_filled_orders("buy")
+    assert isinstance(orders, list)
+    assert len(orders) == 0
+    orders = trades[3].select_filled_orders("sell")
+    assert isinstance(orders, list)
+    assert len(orders) == 0
+
+    # Open sell order
+    orders = trades[4].select_filled_orders("buy")
+    assert isinstance(orders, list)
+    assert len(orders) == 1
+    orders = trades[4].select_filled_orders("sell")
+    assert isinstance(orders, list)
+    assert len(orders) == 0
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_select_filled_orders_usdt(fee):
+    create_mock_trades_usdt(fee)
+
+    trades = Trade.get_trades().all()
+
+    # Closed buy order, no sell order
+    orders = trades[0].select_filled_orders("buy")
+    assert isinstance(orders, list)
+    assert len(orders) == 1
+    assert orders[0].amount == 2.0
+    assert orders[0].filled == 2.0
+    assert orders[0].side == "buy"
+    assert orders[0].price == 10.0
+    assert orders[0].stake_amount == 20
+    assert orders[0].stake_amount_filled == 20
+
+    orders = trades[3].select_filled_orders("buy")
+    assert isinstance(orders, list)
+    assert len(orders) == 0
+    orders = trades[3].select_filled_or_open_orders()
+    assert isinstance(orders, list)
+    assert len(orders) == 1
+    assert orders[0].price == 2.0
+    assert orders[0].amount == 10
+    assert orders[0].filled == 0
+    assert orders[0].stake_amount == 20
+    assert orders[0].stake_amount_filled == 0
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_order_to_ccxt(limit_buy_order_open, limit_sell_order_usdt_open):
+    order = Order.parse_from_ccxt_object(limit_buy_order_open, "mocked", "buy")
+    order.ft_trade_id = 1
+    order.session.add(order)
+    Order.session.commit()
+
+    order_resp = Order.order_by_id(limit_buy_order_open["id"])
+    assert order_resp
+
+    raw_order = order_resp.to_ccxt_object()
+    del raw_order["fee"]
+    del raw_order["datetime"]
+    del raw_order["info"]
+    assert raw_order.get("stopPrice") is None
+    raw_order.pop("stopPrice", None)
+    del limit_buy_order_open["datetime"]
+    assert raw_order == limit_buy_order_open
+
+    order1 = Order.parse_from_ccxt_object(limit_sell_order_usdt_open, "mocked", "sell")
+    order1.ft_order_side = "stoploss"
+    order1.stop_price = order1.price * 0.9
+    order1.ft_trade_id = 1
+    order1.session.add(order1)
+    Order.session.commit()
+
+    order_resp1 = Order.order_by_id(limit_sell_order_usdt_open["id"])
+    raw_order1 = order_resp1.to_ccxt_object()
+
+    assert raw_order1.get("stopPrice") is not None
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize(
+    "data",
+    [
+        # tuple 1 - side, amount, price
+        # tuple 2 - amount, open_rate, stake_amount, cumulative_profit, realized_profit, rel_profit
+        {
+            "orders": [
+                (("buy", 100, 10), (100.0, 10.0, 1000.0, 0.0, None, None)),
+                (("buy", 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)),
+                (("sell", 50, 12), (150.0, 12.5, 1875.0, -25.0, -25.0, -0.01)),
+                (("sell", 100, 20), (50.0, 12.5, 625.0, 725.0, 750.0, 0.29)),
+                (("sell", 50, 5), (50.0, 12.5, 625.0, 350.0, -375.0, 0.14)),
+            ],
+            "end_profit": 350.0,
+            "end_profit_ratio": 0.14,
+            "fee": 0.0,
+        },
+        {
+            "orders": [
+                (("buy", 100, 10), (100.0, 10.0, 1000.0, 0.0, None, None)),
+                (("buy", 100, 15), (200.0, 12.5, 2500.0, 0.0, None, None)),
+                (("sell", 50, 12), (150.0, 12.5, 1875.0, -28.0625, -28.0625, -0.011197)),
+                (("sell", 100, 20), (50.0, 12.5, 625.0, 713.8125, 741.875, 0.2848129)),
+                (("sell", 50, 5), (50.0, 12.5, 625.0, 336.625, -377.1875, 0.1343142)),
+            ],
+            "end_profit": 336.625,
+            "end_profit_ratio": 0.1343142,
+            "fee": 0.0025,
+        },
+        {
+            "orders": [
+                (("buy", 100, 3), (100.0, 3.0, 300.0, 0.0, None, None)),
+                (("buy", 100, 7), (200.0, 5.0, 1000.0, 0.0, None, None)),
+                (("sell", 100, 11), (100.0, 5.0, 500.0, 596.0, 596.0, 0.5945137)),
+                (("buy", 150, 15), (250.0, 11.0, 2750.0, 596.0, 596.0, 0.5945137)),
+                (("sell", 100, 19), (150.0, 11.0, 1650.0, 1388.5, 792.5, 0.4261653)),
+                (("sell", 150, 23), (150.0, 11.0, 1650.0, 3175.75, 1787.25, 0.9747170)),
+            ],
+            "end_profit": 3175.75,
+            "end_profit_ratio": 0.9747170,
+            "fee": 0.0025,
+        },
+        {
+            # Test above without fees
+            "orders": [
+                (("buy", 100, 3), (100.0, 3.0, 300.0, 0.0, None, None)),
+                (("buy", 100, 7), (200.0, 5.0, 1000.0, 0.0, None, None)),
+                (("sell", 100, 11), (100.0, 5.0, 500.0, 600.0, 600.0, 0.6)),
+                (("buy", 150, 15), (250.0, 11.0, 2750.0, 600.0, 600.0, 0.6)),
+                (("sell", 100, 19), (150.0, 11.0, 1650.0, 1400.0, 800.0, 0.43076923)),
+                (("sell", 150, 23), (150.0, 11.0, 1650.0, 3200.0, 1800.0, 0.98461538)),
+            ],
+            "end_profit": 3200.0,
+            "end_profit_ratio": 0.98461538,
+            "fee": 0.0,
+        },
+        {
+            "orders": [
+                (("buy", 100, 8), (100.0, 8.0, 800.0, 0.0, None, None)),
+                (("buy", 100, 9), (200.0, 8.5, 1700.0, 0.0, None, None)),
+                (("sell", 100, 10), (100.0, 8.5, 850.0, 150.0, 150.0, 0.08823529)),
+                (("buy", 150, 11), (250.0, 10, 2500.0, 150.0, 150.0, 0.08823529)),
+                (("sell", 100, 12), (150.0, 10.0, 1500.0, 350.0, 200.0, 0.1044776)),
+                (("sell", 150, 14), (150.0, 10.0, 1500.0, 950.0, 600.0, 0.283582)),
+            ],
+            "end_profit": 950.0,
+            "end_profit_ratio": 0.283582,
+            "fee": 0.0,
+        },
+    ],
+)
+def test_recalc_trade_from_orders_dca(data) -> None:
+    pair = "ETH/USDT"
+    trade = Trade(
+        id=2,
+        pair=pair,
+        stake_amount=1000,
+        open_rate=data["orders"][0][0][2],
+        amount=data["orders"][0][0][1],
+        is_open=True,
+        open_date=dt_now(),
+        fee_open=data["fee"],
+        fee_close=data["fee"],
+        exchange="binance",
+        is_short=False,
+        leverage=1.0,
+        trading_mode=TradingMode.SPOT,
+        price_precision=0.001,
+        precision_mode_price=TICK_SIZE,
+    )
+    Trade.session.add(trade)
+
+    for idx, (order, result) in enumerate(data["orders"]):
+        amount = order[1]
+        price = order[2]
+
+        order_obj = Order(
+            ft_order_side=order[0],
+            ft_pair=trade.pair,
+            order_id=f"order_{order[0]}_{idx}",
+            ft_is_open=False,
+            ft_amount=amount,
+            ft_price=price,
+            status="closed",
+            symbol=trade.pair,
+            order_type="market",
+            side=order[0],
+            price=price,
+            average=price,
+            filled=amount,
+            remaining=0,
+            cost=amount * price,
+            order_date=dt_now() - timedelta(hours=10 + idx),
+            order_filled_date=dt_now() - timedelta(hours=10 + idx),
+        )
+        trade.orders.append(order_obj)
+        trade.recalc_trade_from_orders()
+        Trade.commit()
+
+        orders1 = Order.session.scalars(select(Order)).all()
+        assert orders1
+        assert len(orders1) == idx + 1
+
+        trade = Trade.session.scalars(select(Trade)).first()
+        assert trade
+        assert len(trade.orders) == idx + 1
+        if idx < len(data) - 1:
+            assert trade.is_open is True
+        assert not trade.has_open_orders
+        assert trade.amount == result[0]
+        assert trade.open_rate == result[1]
+        assert trade.stake_amount == result[2]
+        assert pytest.approx(trade.realized_profit) == result[3]
+        assert pytest.approx(trade.close_profit_abs) == result[4]
+        assert pytest.approx(trade.close_profit) == result[5]
+
+    trade.close(price)
+    assert pytest.approx(trade.close_profit_abs) == data["end_profit"]
+    assert pytest.approx(trade.close_profit) == data["end_profit_ratio"]
+    assert not trade.is_open
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+    assert not trade.has_open_orders
diff --git a/tests/persistence/test_trade_custom_data.py b/tests/persistence/test_trade_custom_data.py
new file mode 100644
index 000000000..e493276d7
--- /dev/null
+++ b/tests/persistence/test_trade_custom_data.py
@@ -0,0 +1,168 @@
+from copy import deepcopy
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.data.history.history_utils import get_timerange
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.persistence import Trade, disable_database_use, enable_database_use
+from freqtrade.persistence.custom_data import CustomDataWrapper
+from tests.conftest import (
+    EXMS,
+    create_mock_trades_usdt,
+    generate_test_data,
+    get_patched_freqtradebot,
+    patch_exchange,
+)
+
+
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("use_db", [True, False])
+def test_trade_custom_data(fee, use_db):
+    if not use_db:
+        disable_database_use("5m")
+    Trade.reset_trades()
+    CustomDataWrapper.reset_custom_data()
+
+    create_mock_trades_usdt(fee, use_db=use_db)
+
+    trade1 = Trade.get_trades_proxy()[0]
+    if not use_db:
+        trade1.id = 1
+
+    assert trade1.get_all_custom_data() == []
+    trade1.set_custom_data("test_str", "test_value")
+    trade1.set_custom_data("test_int", 1)
+    trade1.set_custom_data("test_float", 1.55)
+    trade1.set_custom_data("test_bool", True)
+    trade1.set_custom_data("test_dict", {"test": "dict"})
+
+    assert len(trade1.get_all_custom_data()) == 5
+    assert trade1.get_custom_data("test_str") == "test_value"
+    trade1.set_custom_data("test_str", "test_value_updated")
+    assert trade1.get_custom_data("test_str") == "test_value_updated"
+
+    assert trade1.get_custom_data("test_int") == 1
+    assert isinstance(trade1.get_custom_data("test_int"), int)
+
+    assert trade1.get_custom_data("test_float") == 1.55
+    assert isinstance(trade1.get_custom_data("test_float"), float)
+
+    assert trade1.get_custom_data("test_bool") is True
+    assert isinstance(trade1.get_custom_data("test_bool"), bool)
+
+    assert trade1.get_custom_data("test_dict") == {"test": "dict"}
+    assert isinstance(trade1.get_custom_data("test_dict"), dict)
+    if not use_db:
+        enable_database_use()
+
+
+def test_trade_custom_data_strategy_compat(mocker, default_conf_usdt, fee):
+    mocker.patch(f"{EXMS}.get_rate", return_value=0.50)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.get_real_amount", return_value=None)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.handle_cancel_exit", return_value=True)
+    default_conf_usdt["minimal_roi"] = {"0": 100}
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    create_mock_trades_usdt(fee)
+
+    trade1 = Trade.get_trades_proxy(pair="ADA/USDT")[0]
+    trade1.set_custom_data("test_str", "test_value")
+    trade1.set_custom_data("test_int", 1)
+
+    def custom_exit(pair, trade, **kwargs):
+        if pair == "ADA/USDT":
+            custom_val = trade.get_custom_data("test_str")
+            custom_val_i = trade.get_custom_data("test_int")
+
+            return f"{custom_val}_{custom_val_i}"
+
+    freqtrade.strategy.custom_exit = custom_exit
+    ff_spy = mocker.spy(freqtrade.strategy, "custom_exit")
+    trades = Trade.get_open_trades()
+    freqtrade.exit_positions(trades)
+    Trade.commit()
+
+    trade_after = Trade.get_trades_proxy(pair="ADA/USDT")[0]
+    assert trade_after.get_custom_data("test_str") == "test_value"
+    assert trade_after.get_custom_data("test_int") == 1
+    # 2 trades filled entry, with open exit order
+    # 1 trade with filled entry order
+    assert ff_spy.call_count == 3
+
+    assert trade_after.exit_reason == "test_value_1"
+
+
+def test_trade_custom_data_strategy_backtest_compat(mocker, default_conf_usdt, fee):
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=10)
+    mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float("inf"))
+    mocker.patch(f"{EXMS}.get_max_leverage", return_value=10)
+    mocker.patch(f"{EXMS}.get_maintenance_ratio_and_amt", return_value=(0.1, 0.1))
+    mocker.patch("freqtrade.optimize.backtesting.Backtesting._run_funding_fees")
+
+    patch_exchange(mocker)
+    default_conf_usdt.update(
+        {
+            "stake_amount": 100.0,
+            "max_open_trades": 2,
+            "dry_run_wallet": 1000.0,
+            "strategy": "StrategyTestV3",
+            "trading_mode": "futures",
+            "margin_mode": "isolated",
+            "stoploss": -2,
+            "minimal_roi": {"0": 100},
+        }
+    )
+    default_conf_usdt["pairlists"] = [{"method": "StaticPairList", "allow_inactive": True}]
+    backtesting = Backtesting(default_conf_usdt)
+
+    df = generate_test_data(default_conf_usdt["timeframe"], 100, "2022-01-01 00:00:00+00:00")
+
+    pair_exp = "XRP/USDT:USDT"
+
+    def custom_exit(pair, trade, **kwargs):
+        custom_val = trade.get_custom_data("test_str")
+        custom_val_i = trade.get_custom_data("test_int", 0)
+
+        if pair == pair_exp:
+            trade.set_custom_data("test_str", "test_value")
+            trade.set_custom_data("test_int", custom_val_i + 1)
+
+        if custom_val_i >= 2:
+            return f"{custom_val}_{custom_val_i}"
+
+    backtesting._set_strategy(backtesting.strategylist[0])
+    processed = backtesting.strategy.advise_all_indicators(
+        {
+            pair_exp: df,
+            "BTC/USDT:USDT": df,
+        }
+    )
+
+    def fun(dataframe, *args, **kwargs):
+        dataframe.loc[dataframe.index == 50, "enter_long"] = 1
+        return dataframe
+
+    backtesting.strategy.advise_entry = fun
+    backtesting.strategy.leverage = MagicMock(return_value=1)
+    backtesting.strategy.custom_exit = custom_exit
+    ff_spy = mocker.spy(backtesting.strategy, "custom_exit")
+
+    min_date, max_date = get_timerange(processed)
+
+    result = backtesting.backtest(
+        processed=deepcopy(processed),
+        start_date=min_date,
+        end_date=max_date,
+    )
+    results = result["results"]
+    assert not results.empty
+    assert len(results) == 2
+    assert results["pair"][0] == pair_exp
+    assert results["pair"][1] == "BTC/USDT:USDT"
+    assert results["exit_reason"][0] == "test_value_2"
+    assert results["exit_reason"][1] == "exit_signal"
+
+    assert ff_spy.call_count == 7
+    Backtesting.cleanup()
diff --git a/tests/persistence/test_trade_fromjson.py b/tests/persistence/test_trade_fromjson.py
new file mode 100644
index 000000000..ff717751e
--- /dev/null
+++ b/tests/persistence/test_trade_fromjson.py
@@ -0,0 +1,342 @@
+import json
+from datetime import datetime, timezone
+
+import pytest
+
+from freqtrade.persistence.trade_model import LocalTrade, Trade
+from tests.conftest import create_mock_trades_usdt
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_trade_fromjson():
+    """Test the Trade.from_json() method."""
+    trade_string = """{
+        "trade_id": 25,
+        "pair": "ETH/USDT",
+        "base_currency": "ETH",
+        "quote_currency": "USDT",
+        "is_open": false,
+        "exchange": "binance",
+        "amount": 407.0,
+        "amount_requested": 102.92547026,
+        "stake_amount": 102.7494348,
+        "strategy": "SampleStrategy55",
+        "buy_tag": "Strategy2",
+        "enter_tag": "Strategy2",
+        "timeframe": 5,
+        "fee_open": 0.001,
+        "fee_open_cost": 0.1027494,
+        "fee_open_currency": "ETH",
+        "fee_close": 0.001,
+        "fee_close_cost": 0.1054944,
+        "fee_close_currency": "USDT",
+        "open_date": "2022-10-18 09:12:42",
+        "open_timestamp": 1666084362912,
+        "open_rate": 0.2518998249562391,
+        "open_rate_requested": 0.2516,
+        "open_trade_value": 102.62575199,
+        "close_date": "2022-10-18 09:45:22",
+        "close_timestamp": 1666086322208,
+        "realized_profit": 2.76315361,
+        "close_rate": 0.2592,
+        "close_rate_requested": 0.2592,
+        "close_profit": 0.026865,
+        "close_profit_pct": 2.69,
+        "close_profit_abs": 2.76315361,
+        "trade_duration_s": 1959,
+        "trade_duration": 32,
+        "profit_ratio": 0.02686,
+        "profit_pct": 2.69,
+        "profit_abs": 2.76315361,
+        "sell_reason": "no longer good",
+        "exit_reason": "no longer good",
+        "exit_order_status": "closed",
+        "stop_loss_abs": 0.1981,
+        "stop_loss_ratio": -0.216,
+        "stop_loss_pct": -21.6,
+        "stoploss_last_update": "2022-10-18 09:13:42",
+        "stoploss_last_update_timestamp": 1666077222000,
+        "initial_stop_loss_abs": 0.1981,
+        "initial_stop_loss_ratio": -0.216,
+        "initial_stop_loss_pct": -21.6,
+        "min_rate": 0.2495,
+        "max_rate": 0.2592,
+        "leverage": 1.0,
+        "interest_rate": 0.0,
+        "liquidation_price": null,
+        "is_short": false,
+        "trading_mode": "spot",
+        "funding_fees": 0.0,
+        "amount_precision": 1.0,
+        "price_precision": 3.0,
+        "precision_mode": 2,
+        "contract_size": 1.0,
+        "open_order_id": null,
+        "orders": [
+            {
+                "amount": 102.0,
+                "safe_price": 0.2526,
+                "ft_order_side": "buy",
+                "order_filled_timestamp": 1666084370887,
+                "ft_is_entry": true,
+                "pair": "ETH/USDT",
+                "order_id": "78404228",
+                "status": "closed",
+                "average": 0.2526,
+                "cost": 25.7652,
+                "filled": 102.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:12:42",
+                "order_timestamp": 1666084362684,
+                "order_filled_date": "2022-10-18 09:12:50",
+                "order_type": "limit",
+                "price": 0.2526,
+                "remaining": 0.0
+            },
+            {
+                "amount": 102.0,
+                "safe_price": 0.2517,
+                "ft_order_side": "buy",
+                "order_filled_timestamp": 1666084379056,
+                "ft_is_entry": true,
+                "pair": "ETH/USDT",
+                "order_id": "78405139",
+                "status": "closed",
+                "average": 0.2517,
+                "cost": 25.6734,
+                "filled": 102.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:12:57",
+                "order_timestamp": 1666084377681,
+                "order_filled_date": "2022-10-18 09:12:59",
+                "order_type": "limit",
+                "price": 0.2517,
+                "remaining": 0.0
+            },
+            {
+                "amount": 102.0,
+                "safe_price": 0.2517,
+                "ft_order_side": "buy",
+                "order_filled_timestamp": 1666084389644,
+                "ft_is_entry": true,
+                "pair": "ETH/USDT",
+                "order_id": "78405265",
+                "status": "closed",
+                "average": 0.2517,
+                "cost": 25.6734,
+                "filled": 102.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:13:03",
+                "order_timestamp": 1666084383295,
+                "order_filled_date": "2022-10-18 09:13:09",
+                "order_type": "limit",
+                "price": 0.2517,
+                "remaining": 0.0
+            },
+            {
+                "amount": 102.0,
+                "safe_price": 0.2516,
+                "ft_order_side": "buy",
+                "order_filled_timestamp": 1666084723521,
+                "ft_is_entry": true,
+                "pair": "ETH/USDT",
+                "order_id": "78405395",
+                "status": "closed",
+                "average": 0.2516,
+                "cost": 25.6632,
+                "filled": 102.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:13:13",
+                "order_timestamp": 1666084393920,
+                "order_filled_date": "2022-10-18 09:18:43",
+                "order_type": "limit",
+                "price": 0.2516,
+                "remaining": 0.0
+            },
+            {
+                "amount": 407.0,
+                "safe_price": 0.2592,
+                "ft_order_side": "sell",
+                "order_filled_timestamp": 1666086322198,
+                "ft_is_entry": false,
+                "pair": "ETH/USDT",
+                "order_id": "78432649",
+                "status": "closed",
+                "average": 0.2592,
+                "cost": 105.4944,
+                "filled": 407.0,
+                "is_open": false,
+                "order_date": "2022-10-18 09:45:21",
+                "order_timestamp": 1666086321435,
+                "order_filled_date": "2022-10-18 09:45:22",
+                "order_type": "market",
+                "price": 0.2592,
+                "remaining": 0.0,
+                "funding_fee": -0.055
+            }
+        ]
+    }"""
+    trade = Trade.from_json(trade_string)
+    Trade.session.add(trade)
+    Trade.commit()
+
+    assert trade.id == 25
+    assert trade.pair == "ETH/USDT"
+    assert trade.open_date_utc == datetime(2022, 10, 18, 9, 12, 42, tzinfo=timezone.utc)
+    assert isinstance(trade.open_date, datetime)
+    assert trade.exit_reason == "no longer good"
+    assert trade.realized_profit == 2.76315361
+    assert trade.precision_mode == 2
+    assert trade.amount_precision == 1.0
+    assert trade.contract_size == 1.0
+
+    assert len(trade.orders) == 5
+    last_o = trade.orders[-1]
+    assert last_o.order_filled_utc == datetime(2022, 10, 18, 9, 45, 22, tzinfo=timezone.utc)
+    assert isinstance(last_o.order_date, datetime)
+    assert last_o.funding_fee == -0.055
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_trade_serialize_load_back(fee):
+    create_mock_trades_usdt(fee, None)
+
+    t = Trade.get_trades([Trade.id == 1]).first()
+    assert t.id == 1
+    t.funding_fees = 0.025
+    t.orders[0].funding_fee = 0.0125
+    assert len(t.orders) == 2
+    Trade.commit()
+
+    tjson = t.to_json(False)
+    assert isinstance(tjson, dict)
+    trade_string = json.dumps(tjson)
+    trade = Trade.from_json(trade_string)
+
+    assert trade.id == t.id
+    assert trade.funding_fees == t.funding_fees
+    assert len(trade.orders) == len(t.orders)
+    assert trade.orders[0].funding_fee == t.orders[0].funding_fee
+    excluded = [
+        "trade_id",
+        "quote_currency",
+        "open_timestamp",
+        "close_timestamp",
+        "realized_profit_ratio",
+        "close_profit_pct",
+        "trade_duration_s",
+        "trade_duration",
+        "profit_ratio",
+        "profit_pct",
+        "profit_abs",
+        "stop_loss_abs",
+        "initial_stop_loss_abs",
+        "open_fill_date",
+        "open_fill_timestamp",
+        "orders",
+    ]
+    failed = []
+    # Ensure all attributes written can be read.
+    for obj, value in tjson.items():
+        if obj in excluded:
+            continue
+        tattr = getattr(trade, obj, None)
+        if isinstance(tattr, datetime):
+            tattr = tattr.strftime("%Y-%m-%d %H:%M:%S")
+        if tattr != value:
+            failed.append((obj, tattr, value))
+
+    assert tjson.get("trade_id") == trade.id
+    assert tjson.get("quote_currency") == trade.stake_currency
+    assert tjson.get("stop_loss_abs") == trade.stop_loss
+    assert tjson.get("initial_stop_loss_abs") == trade.initial_stop_loss
+
+    excluded_o = [
+        "order_filled_timestamp",
+        "ft_is_entry",
+        "pair",
+        "is_open",
+        "order_timestamp",
+    ]
+    order_obj = trade.orders[0]
+    for obj, value in tjson["orders"][0].items():
+        if obj in excluded_o:
+            continue
+        tattr = getattr(order_obj, obj, None)
+        if isinstance(tattr, datetime):
+            tattr = tattr.strftime("%Y-%m-%d %H:%M:%S")
+        if tattr != value:
+            failed.append((obj, tattr, value))
+
+    assert tjson["orders"][0]["pair"] == order_obj.ft_pair
+    assert not failed
+
+    trade2 = LocalTrade.from_json(trade_string)
+    assert len(trade2.orders) == len(t.orders)
+
+    trade3 = LocalTrade.from_json(trade_string)
+    assert len(trade3.orders) == len(t.orders)
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_trade_fromjson_backtesting():
+    """
+    trade.from_json should be able to load a trade output via backtesting.
+    """
+    trade_string = """
+        {
+             "pair":"XRP/USDT:USDT",
+             "stake_amount":3015.294001,
+             "max_stake_amount":60305.88002,
+             "amount":94612.3,
+             "open_date":"2024-03-02 10:40:00+00:00",
+             "close_date":"2024-03-02 11:10:00+00:00",
+             "open_rate":0.6374,
+             "close_rate":0.6399,
+             "fee_open":0.0005,
+             "fee_close":0.0005,
+             "trade_duration":30,
+             "profit_ratio":-0.09853216535933962,
+             "profit_abs":-296.95489539,
+             "exit_reason":"trailing_stop_loss",
+             "initial_stop_loss_abs":0.6689,
+             "initial_stop_loss_ratio":-0.99,
+             "stop_loss_abs":0.6399,
+             "stop_loss_ratio":-0.3368287257705749,
+             "min_rate":0.6294,
+             "max_rate":0.6421,
+             "is_open":false,
+             "enter_tag":"[0.6373, 0.5993, 0.64]",
+             "leverage":20,
+             "is_short":true,
+             "open_timestamp":1709376000000,
+             "close_timestamp":1709377800000,
+             "orders":[
+                {
+                   "amount":94612.3,
+                   "safe_price":0.6374,
+                   "ft_order_side":"sell",
+                   "order_filled_timestamp":1709376000000,
+                   "ft_is_entry":true,
+                   "ft_order_tag":"[0.6373, 0.5993, 0.64]",
+                   "cost":60336.032960009994
+                },
+                {
+                   "amount":94612.3,
+                   "safe_price":0.6399,
+                   "ft_order_side":"buy",
+                   "order_filled_timestamp":1709377800000,
+                   "ft_is_entry":false,
+                   "ft_order_tag":"trailing_stop_loss",
+                   "cost":60572.681975385
+                }
+             ]
+          }
+        """
+
+    trade = Trade.from_json(trade_string)
+    Trade.session.add(trade)
+    Trade.commit()
+
+    # Trade-id not given - use first available
+    assert trade.id == 1
diff --git a/tests/plugins/__init__.py b/tests/plugins/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/plugins/test_pairlist.py b/tests/plugins/test_pairlist.py
new file mode 100644
index 000000000..0f133242b
--- /dev/null
+++ b/tests/plugins/test_pairlist.py
@@ -0,0 +1,2543 @@
+# pragma pylint: disable=missing-docstring,C0103,protected-access
+
+import logging
+import time
+from copy import deepcopy
+from datetime import timedelta
+from unittest.mock import MagicMock, PropertyMock
+
+import pandas as pd
+import pytest
+import time_machine
+
+from freqtrade.constants import AVAILABLE_PAIRLISTS
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import CandleType, RunMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence import LocalTrade, Trade
+from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
+from freqtrade.plugins.pairlistmanager import PairListManager
+from freqtrade.resolvers import PairListResolver
+from freqtrade.util.datetime_helpers import dt_now
+from tests.conftest import (
+    EXMS,
+    create_mock_trades_usdt,
+    generate_test_data,
+    get_patched_exchange,
+    get_patched_freqtradebot,
+    log_has,
+    log_has_re,
+    num_log_has,
+)
+
+
+# Exclude RemotePairList from tests.
+# It has a mandatory parameter, and requires special handling, which happens in test_remotepairlist.
+TESTABLE_PAIRLISTS = [p for p in AVAILABLE_PAIRLISTS if p not in ["RemotePairList"]]
+
+
+@pytest.fixture(scope="function")
+def whitelist_conf(default_conf):
+    default_conf["runmode"] = "dry_run"
+    default_conf["stake_currency"] = "BTC"
+    default_conf["exchange"]["pair_whitelist"] = [
+        "ETH/BTC",
+        "TKN/BTC",
+        "TRST/BTC",
+        "SWT/BTC",
+        "BCC/BTC",
+        "HOT/BTC",
+    ]
+    default_conf["exchange"]["pair_blacklist"] = ["BLK/BTC"]
+    default_conf["pairlists"] = [
+        {
+            "method": "VolumePairList",
+            "number_assets": 5,
+            "sort_key": "quoteVolume",
+        },
+    ]
+    default_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [],
+            }
+        }
+    )
+    return default_conf
+
+
+@pytest.fixture(scope="function")
+def whitelist_conf_2(default_conf):
+    default_conf["runmode"] = "dry_run"
+    default_conf["stake_currency"] = "BTC"
+    default_conf["exchange"]["pair_whitelist"] = [
+        "ETH/BTC",
+        "TKN/BTC",
+        "BLK/BTC",
+        "LTC/BTC",
+        "BTT/BTC",
+        "HOT/BTC",
+        "FUEL/BTC",
+        "XRP/BTC",
+    ]
+    default_conf["exchange"]["pair_blacklist"] = ["BLK/BTC"]
+    default_conf["pairlists"] = [
+        # {   "method": "StaticPairList"},
+        {
+            "method": "VolumePairList",
+            "number_assets": 5,
+            "sort_key": "quoteVolume",
+            "refresh_period": 0,
+        },
+    ]
+    return default_conf
+
+
+@pytest.fixture(scope="function")
+def whitelist_conf_agefilter(default_conf):
+    default_conf["runmode"] = "dry_run"
+    default_conf["stake_currency"] = "BTC"
+    default_conf["exchange"]["pair_whitelist"] = [
+        "ETH/BTC",
+        "TKN/BTC",
+        "BLK/BTC",
+        "LTC/BTC",
+        "BTT/BTC",
+        "HOT/BTC",
+        "FUEL/BTC",
+        "XRP/BTC",
+    ]
+    default_conf["exchange"]["pair_blacklist"] = ["BLK/BTC"]
+    default_conf["pairlists"] = [
+        {
+            "method": "VolumePairList",
+            "number_assets": 5,
+            "sort_key": "quoteVolume",
+            "refresh_period": -1,
+        },
+        {"method": "AgeFilter", "min_days_listed": 2, "max_days_listed": 100},
+    ]
+    return default_conf
+
+
+@pytest.fixture(scope="function")
+def static_pl_conf(whitelist_conf):
+    whitelist_conf["pairlists"] = [
+        {
+            "method": "StaticPairList",
+        },
+    ]
+    return whitelist_conf
+
+
+def test_log_cached(mocker, static_pl_conf, markets, tickers):
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, static_pl_conf)
+    logmock = MagicMock()
+    # Assign starting whitelist
+    pl = freqtrade.pairlists._pairlist_handlers[0]
+    pl.log_once("Hello world", logmock)
+    assert logmock.call_count == 1
+    pl.log_once("Hello world", logmock)
+    assert logmock.call_count == 1
+    assert pl._log_cache.currsize == 1
+    assert ("Hello world",) in pl._log_cache._Cache__data
+
+    pl.log_once("Hello world2", logmock)
+    assert logmock.call_count == 2
+    assert pl._log_cache.currsize == 2
+
+
+def test_load_pairlist_noexist(mocker, markets, default_conf):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    plm = PairListManager(freqtrade.exchange, default_conf, MagicMock())
+    with pytest.raises(
+        OperationalException,
+        match=r"Impossible to load Pairlist 'NonexistingPairList'. "
+        r"This class does not exist or contains Python code errors.",
+    ):
+        PairListResolver.load_pairlist(
+            "NonexistingPairList", freqtrade.exchange, plm, default_conf, {}, 1
+        )
+
+
+def test_load_pairlist_verify_multi(mocker, markets_static, default_conf):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets_static))
+    plm = PairListManager(freqtrade.exchange, default_conf, MagicMock())
+    # Call different versions one after the other, should always consider what was passed in
+    # and have no side-effects (therefore the same check multiple times)
+    assert plm.verify_whitelist(["ETH/BTC", "XRP/BTC"], print) == ["ETH/BTC", "XRP/BTC"]
+    assert plm.verify_whitelist(["ETH/BTC", "XRP/BTC", "BUUU/BTC"], print) == ["ETH/BTC", "XRP/BTC"]
+    assert plm.verify_whitelist(["XRP/BTC", "BUUU/BTC"], print) == ["XRP/BTC"]
+    assert plm.verify_whitelist(["ETH/BTC", "XRP/BTC"], print) == ["ETH/BTC", "XRP/BTC"]
+    assert plm.verify_whitelist(["ETH/USDT", "XRP/USDT"], print) == ["ETH/USDT"]
+    assert plm.verify_whitelist(["ETH/BTC", "XRP/BTC"], print) == ["ETH/BTC", "XRP/BTC"]
+
+
+def test_refresh_market_pair_not_in_whitelist(mocker, markets, static_pl_conf):
+    freqtrade = get_patched_freqtradebot(mocker, static_pl_conf)
+
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    freqtrade.pairlists.refresh_pairlist()
+    # List ordered by BaseVolume
+    whitelist = ["ETH/BTC", "TKN/BTC"]
+    # Ensure all except those in whitelist are removed
+    assert set(whitelist) == set(freqtrade.pairlists.whitelist)
+    # Ensure config dict hasn't been changed
+    assert (
+        static_pl_conf["exchange"]["pair_whitelist"]
+        == freqtrade.config["exchange"]["pair_whitelist"]
+    )
+
+
+def test_refresh_static_pairlist(mocker, markets, static_pl_conf):
+    freqtrade = get_patched_freqtradebot(mocker, static_pl_conf)
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+        markets=PropertyMock(return_value=markets),
+    )
+    freqtrade.pairlists.refresh_pairlist()
+    # List ordered by BaseVolume
+    whitelist = ["ETH/BTC", "TKN/BTC"]
+    # Ensure all except those in whitelist are removed
+    assert set(whitelist) == set(freqtrade.pairlists.whitelist)
+    assert static_pl_conf["exchange"]["pair_blacklist"] == freqtrade.pairlists.blacklist
+
+
+@pytest.mark.parametrize(
+    "pairs,expected",
+    [
+        (
+            ["NOEXIST/BTC", r"\+WHAT/BTC"],
+            ["ETH/BTC", "TKN/BTC", "TRST/BTC", "NOEXIST/BTC", "SWT/BTC", "BCC/BTC", "HOT/BTC"],
+        ),
+        (
+            ["NOEXIST/BTC", r"*/BTC"],  # This is an invalid regex
+            [],
+        ),
+    ],
+)
+def test_refresh_static_pairlist_noexist(mocker, markets, static_pl_conf, pairs, expected, caplog):
+    static_pl_conf["pairlists"][0]["allow_inactive"] = True
+    static_pl_conf["exchange"]["pair_whitelist"] += pairs
+    freqtrade = get_patched_freqtradebot(mocker, static_pl_conf)
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+        markets=PropertyMock(return_value=markets),
+    )
+    freqtrade.pairlists.refresh_pairlist()
+
+    # Ensure all except those in whitelist are removed
+    assert set(expected) == set(freqtrade.pairlists.whitelist)
+    assert static_pl_conf["exchange"]["pair_blacklist"] == freqtrade.pairlists.blacklist
+    if not expected:
+        assert log_has_re(r"Pair whitelist contains an invalid Wildcard: Wildcard error.*", caplog)
+
+
+def test_invalid_blacklist(mocker, markets, static_pl_conf, caplog):
+    static_pl_conf["exchange"]["pair_blacklist"] = ["*/BTC"]
+    freqtrade = get_patched_freqtradebot(mocker, static_pl_conf)
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+        markets=PropertyMock(return_value=markets),
+    )
+    freqtrade.pairlists.refresh_pairlist()
+    whitelist = []
+    # Ensure all except those in whitelist are removed
+    assert set(whitelist) == set(freqtrade.pairlists.whitelist)
+    assert static_pl_conf["exchange"]["pair_blacklist"] == freqtrade.pairlists.blacklist
+    log_has_re(r"Pair blacklist contains an invalid Wildcard.*", caplog)
+
+
+def test_remove_logs_for_pairs_already_in_blacklist(mocker, markets, static_pl_conf, caplog):
+    logger = logging.getLogger(__name__)
+    freqtrade = get_patched_freqtradebot(mocker, static_pl_conf)
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+        markets=PropertyMock(return_value=markets),
+    )
+    freqtrade.pairlists.refresh_pairlist()
+    whitelist = ["ETH/BTC", "TKN/BTC"]
+    caplog.clear()
+    caplog.set_level(logging.INFO)
+
+    # Ensure all except those in whitelist are removed.
+    assert set(whitelist) == set(freqtrade.pairlists.whitelist)
+    assert static_pl_conf["exchange"]["pair_blacklist"] == freqtrade.pairlists.blacklist
+    # Ensure that log message wasn't generated.
+    assert not log_has("Pair BLK/BTC in your blacklist. Removing it from whitelist...", caplog)
+
+    for _ in range(3):
+        new_whitelist = freqtrade.pairlists.verify_blacklist(
+            [*whitelist, "BLK/BTC"], logger.warning
+        )
+        # Ensure that the pair is removed from the white list, and properly logged.
+        assert set(whitelist) == set(new_whitelist)
+    assert num_log_has("Pair BLK/BTC in your blacklist. Removing it from whitelist...", caplog) == 1
+
+
+def test_refresh_pairlist_dynamic(mocker, shitcoinmarkets, tickers, whitelist_conf):
+    mocker.patch.multiple(
+        EXMS,
+        get_tickers=tickers,
+        exchange_has=MagicMock(return_value=True),
+    )
+    freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+    # Remock markets with shitcoinmarkets since get_patched_freqtradebot uses the markets fixture
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=shitcoinmarkets),
+    )
+    # argument: use the whitelist dynamically by exchange-volume
+    whitelist = ["ETH/BTC", "TKN/BTC", "LTC/BTC", "XRP/BTC", "HOT/BTC"]
+    freqtrade.pairlists.refresh_pairlist()
+    assert whitelist == freqtrade.pairlists.whitelist
+
+    whitelist_conf["pairlists"] = [{"method": "VolumePairList"}]
+    with pytest.raises(
+        OperationalException,
+        match=r"`number_assets` not specified. Please check your configuration "
+        r'for "pairlist.config.number_assets"',
+    ):
+        PairListManager(freqtrade.exchange, whitelist_conf, MagicMock())
+
+
+def test_refresh_pairlist_dynamic_2(mocker, shitcoinmarkets, tickers, whitelist_conf_2):
+    tickers_dict = tickers()
+
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+    )
+    # Remove caching of ticker data to emulate changing volume by the time of second call
+    mocker.patch.multiple(
+        "freqtrade.plugins.pairlistmanager.PairListManager",
+        _get_cached_tickers=MagicMock(return_value=tickers_dict),
+    )
+    freqtrade = get_patched_freqtradebot(mocker, whitelist_conf_2)
+    # Remock markets with shitcoinmarkets since get_patched_freqtradebot uses the markets fixture
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=shitcoinmarkets),
+    )
+
+    whitelist = ["ETH/BTC", "TKN/BTC", "LTC/BTC", "XRP/BTC", "HOT/BTC"]
+    freqtrade.pairlists.refresh_pairlist()
+    assert whitelist == freqtrade.pairlists.whitelist
+
+    # Delay to allow 0 TTL cache to expire...
+    time.sleep(1)
+    whitelist = ["FUEL/BTC", "ETH/BTC", "TKN/BTC", "LTC/BTC", "XRP/BTC"]
+    tickers_dict["FUEL/BTC"]["quoteVolume"] = 10000.0
+    freqtrade.pairlists.refresh_pairlist()
+    assert whitelist == freqtrade.pairlists.whitelist
+
+
+def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+    )
+    freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets_empty))
+
+    # argument: use the whitelist dynamically by exchange-volume
+    whitelist = []
+    whitelist_conf["exchange"]["pair_whitelist"] = []
+    freqtrade.pairlists.refresh_pairlist()
+    pairslist = whitelist_conf["exchange"]["pair_whitelist"]
+
+    assert set(whitelist) == set(pairslist)
+
+
+@pytest.mark.parametrize(
+    "pairlists,base_currency,whitelist_result",
+    [
+        # VolumePairList only
+        (
+            [{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC", "XRP/BTC", "HOT/BTC"],
+        ),
+        (
+            [{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}],
+            "USDT",
+            ["ETH/USDT", "NANO/USDT", "ADAHALF/USDT", "ADADOUBLE/USDT"],
+        ),
+        # No pair for ETH, VolumePairList
+        ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}], "ETH", []),
+        # No pair for ETH, StaticPairList
+        ([{"method": "StaticPairList"}], "ETH", []),
+        # No pair for ETH, all handlers
+        (
+            [
+                {"method": "StaticPairList"},
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "AgeFilter", "min_days_listed": 2, "max_days_listed": None},
+                {"method": "PrecisionFilter"},
+                {"method": "PriceFilter", "low_price_ratio": 0.03},
+                {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+                {"method": "ShuffleFilter"},
+                {"method": "PerformanceFilter"},
+            ],
+            "ETH",
+            [],
+        ),
+        # AgeFilter and VolumePairList (require 2 days only, all should pass age test)
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "AgeFilter", "min_days_listed": 2, "max_days_listed": 100},
+            ],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC", "XRP/BTC", "HOT/BTC"],
+        ),
+        # AgeFilter and VolumePairList (require 10 days, all should fail age test)
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "AgeFilter", "min_days_listed": 10, "max_days_listed": None},
+            ],
+            "BTC",
+            [],
+        ),
+        # AgeFilter and VolumePairList (all pair listed > 2, all should fail age test)
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "AgeFilter", "min_days_listed": 1, "max_days_listed": 2},
+            ],
+            "BTC",
+            [],
+        ),
+        # AgeFilter and VolumePairList LTC/BTC has 6 candles - removes all
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "AgeFilter", "min_days_listed": 4, "max_days_listed": 5},
+            ],
+            "BTC",
+            [],
+        ),
+        # AgeFilter and VolumePairList LTC/BTC has 6 candles - passes
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "AgeFilter", "min_days_listed": 4, "max_days_listed": 10},
+            ],
+            "BTC",
+            ["LTC/BTC"],
+        ),
+        # Precisionfilter and quote volume
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "PrecisionFilter"},
+            ],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC", "XRP/BTC"],
+        ),
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "PrecisionFilter"},
+            ],
+            "USDT",
+            ["ETH/USDT", "NANO/USDT"],
+        ),
+        # PriceFilter and VolumePairList
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "PriceFilter", "low_price_ratio": 0.03},
+            ],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC", "XRP/BTC"],
+        ),
+        # PriceFilter and VolumePairList
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "PriceFilter", "low_price_ratio": 0.03},
+            ],
+            "USDT",
+            ["ETH/USDT", "NANO/USDT"],
+        ),
+        # Hot is removed by precision_filter, Fuel by low_price_ratio, Ripple by min_price.
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 6, "sort_key": "quoteVolume"},
+                {"method": "PrecisionFilter"},
+                {"method": "PriceFilter", "low_price_ratio": 0.02, "min_price": 0.01},
+            ],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC"],
+        ),
+        # Hot is removed by precision_filter, Fuel by low_price_ratio, Ethereum by max_price.
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 6, "sort_key": "quoteVolume"},
+                {"method": "PrecisionFilter"},
+                {"method": "PriceFilter", "low_price_ratio": 0.02, "max_price": 0.05},
+            ],
+            "BTC",
+            ["TKN/BTC", "LTC/BTC", "XRP/BTC"],
+        ),
+        # HOT and XRP are removed because below 1250 quoteVolume
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "min_value": 1250,
+                }
+            ],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC"],
+        ),
+        # HOT, XRP and FUEL whitelisted because they are below 1300 quoteVolume.
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "max_value": 1300,
+                }
+            ],
+            "BTC",
+            ["XRP/BTC", "HOT/BTC", "FUEL/BTC"],
+        ),
+        # HOT, XRP whitelisted because they are between 100 and 1300 quoteVolume.
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "min_value": 100,
+                    "max_value": 1300,
+                }
+            ],
+            "BTC",
+            ["XRP/BTC", "HOT/BTC"],
+        ),
+        # StaticPairlist only
+        ([{"method": "StaticPairList"}], "BTC", ["ETH/BTC", "TKN/BTC", "HOT/BTC"]),
+        # Static Pairlist before VolumePairList - sorting changes
+        # SpreadFilter
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+            ],
+            "USDT",
+            ["ETH/USDT"],
+        ),
+        # ShuffleFilter
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "ShuffleFilter", "seed": 77},
+            ],
+            "USDT",
+            ["ADADOUBLE/USDT", "ETH/USDT", "NANO/USDT", "ADAHALF/USDT"],
+        ),
+        # ShuffleFilter, other seed
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "ShuffleFilter", "seed": 42},
+            ],
+            "USDT",
+            ["ADAHALF/USDT", "NANO/USDT", "ADADOUBLE/USDT", "ETH/USDT"],
+        ),
+        # ShuffleFilter, no seed
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
+                {"method": "ShuffleFilter"},
+            ],
+            "USDT",
+            4,
+        ),  # whitelist_result is integer -- check only length of randomized pairlist
+        # AgeFilter only
+        (
+            [{"method": "AgeFilter", "min_days_listed": 2}],
+            "BTC",
+            "filter_at_the_beginning",
+        ),  # OperationalException expected
+        # PrecisionFilter after StaticPairList
+        (
+            [{"method": "StaticPairList"}, {"method": "PrecisionFilter"}],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC"],
+        ),
+        # PrecisionFilter only
+        (
+            [{"method": "PrecisionFilter"}],
+            "BTC",
+            "filter_at_the_beginning",
+        ),  # OperationalException expected
+        # PriceFilter after StaticPairList
+        (
+            [
+                {"method": "StaticPairList"},
+                {
+                    "method": "PriceFilter",
+                    "low_price_ratio": 0.02,
+                    "min_price": 0.000001,
+                    "max_price": 0.1,
+                },
+            ],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC"],
+        ),
+        # PriceFilter only
+        (
+            [{"method": "PriceFilter", "low_price_ratio": 0.02}],
+            "BTC",
+            "filter_at_the_beginning",
+        ),  # OperationalException expected
+        # ShuffleFilter after StaticPairList
+        (
+            [{"method": "StaticPairList"}, {"method": "ShuffleFilter", "seed": 42}],
+            "BTC",
+            ["TKN/BTC", "ETH/BTC", "HOT/BTC"],
+        ),
+        # ShuffleFilter only
+        (
+            [{"method": "ShuffleFilter", "seed": 42}],
+            "BTC",
+            "filter_at_the_beginning",
+        ),  # OperationalException expected
+        # PerformanceFilter after StaticPairList
+        (
+            [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC", "HOT/BTC"],
+        ),
+        # PerformanceFilter only
+        (
+            [{"method": "PerformanceFilter"}],
+            "BTC",
+            "filter_at_the_beginning",
+        ),  # OperationalException expected
+        # SpreadFilter after StaticPairList
+        (
+            [{"method": "StaticPairList"}, {"method": "SpreadFilter", "max_spread_ratio": 0.005}],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC"],
+        ),
+        # SpreadFilter only
+        (
+            [{"method": "SpreadFilter", "max_spread_ratio": 0.005}],
+            "BTC",
+            "filter_at_the_beginning",
+        ),  # OperationalException expected
+        # Static Pairlist after VolumePairList, on a non-first position (appends pairs)
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 2, "sort_key": "quoteVolume"},
+                {"method": "StaticPairList"},
+            ],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC", "TRST/BTC", "SWT/BTC", "BCC/BTC", "HOT/BTC"],
+        ),
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
+                {"method": "PriceFilter", "low_price_ratio": 0.02},
+            ],
+            "USDT",
+            ["ETH/USDT", "NANO/USDT"],
+        ),
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
+                {"method": "PriceFilter", "max_value": 0.000001},
+            ],
+            "USDT",
+            ["NANO/USDT"],
+        ),
+        (
+            [
+                {"method": "StaticPairList"},
+                {
+                    "method": "RangeStabilityFilter",
+                    "lookback_days": 10,
+                    "min_rate_of_change": 0.01,
+                    "refresh_period": 1440,
+                },
+            ],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC", "HOT/BTC"],
+        ),
+        (
+            [
+                {"method": "StaticPairList"},
+                {
+                    "method": "RangeStabilityFilter",
+                    "lookback_days": 10,
+                    "max_rate_of_change": 0.01,
+                    "refresh_period": 1440,
+                },
+            ],
+            "BTC",
+            [],
+        ),  # All removed because of max_rate_of_change being 0.017
+        (
+            [
+                {"method": "StaticPairList"},
+                {
+                    "method": "RangeStabilityFilter",
+                    "lookback_days": 10,
+                    "min_rate_of_change": 0.018,
+                    "max_rate_of_change": 0.02,
+                    "refresh_period": 1440,
+                },
+            ],
+            "BTC",
+            [],
+        ),  # All removed - limits are above the highest change_rate
+        (
+            [
+                {"method": "StaticPairList"},
+                {
+                    "method": "VolatilityFilter",
+                    "lookback_days": 3,
+                    "min_volatility": 0.002,
+                    "max_volatility": 0.004,
+                    "refresh_period": 1440,
+                },
+            ],
+            "BTC",
+            ["ETH/BTC", "TKN/BTC"],
+        ),
+        # VolumePairList with no offset = unchanged pairlist
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
+                {"method": "OffsetFilter", "offset": 0, "number_assets": 0},
+            ],
+            "USDT",
+            ["ETH/USDT", "NANO/USDT", "ADAHALF/USDT", "ADADOUBLE/USDT"],
+        ),
+        # VolumePairList with offset = 2
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
+                {"method": "OffsetFilter", "offset": 2},
+            ],
+            "USDT",
+            ["ADAHALF/USDT", "ADADOUBLE/USDT"],
+        ),
+        # VolumePairList with offset and limit
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
+                {"method": "OffsetFilter", "offset": 1, "number_assets": 2},
+            ],
+            "USDT",
+            ["NANO/USDT", "ADAHALF/USDT"],
+        ),
+        # VolumePairList with higher offset, than total pairlist
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
+                {"method": "OffsetFilter", "offset": 100},
+            ],
+            "USDT",
+            [],
+        ),
+    ],
+)
+def test_VolumePairList_whitelist_gen(
+    mocker,
+    whitelist_conf,
+    shitcoinmarkets,
+    tickers,
+    ohlcv_history,
+    pairlists,
+    base_currency,
+    whitelist_result,
+    caplog,
+) -> None:
+    whitelist_conf["runmode"] = "util_exchange"
+    whitelist_conf["pairlists"] = pairlists
+    whitelist_conf["stake_currency"] = base_currency
+
+    ohlcv_history_high_vola = ohlcv_history.copy()
+    ohlcv_history_high_vola.loc[ohlcv_history_high_vola.index == 1, "close"] = 0.00090
+
+    ohlcv_data = {
+        ("ETH/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        ("TKN/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        ("LTC/BTC", "1d", CandleType.SPOT): pd.concat([ohlcv_history, ohlcv_history]),
+        ("XRP/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        ("HOT/BTC", "1d", CandleType.SPOT): ohlcv_history_high_vola,
+    }
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+
+    freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+    mocker.patch.multiple(
+        EXMS, get_tickers=tickers, markets=PropertyMock(return_value=shitcoinmarkets)
+    )
+    mocker.patch.multiple(
+        EXMS,
+        refresh_latest_ohlcv=MagicMock(return_value=ohlcv_data),
+    )
+
+    # Provide for PerformanceFilter's dependency
+    mocker.patch.multiple(
+        "freqtrade.persistence.Trade", get_overall_performance=MagicMock(return_value=[])
+    )
+
+    # Set whitelist_result to None if pairlist is invalid and should produce exception
+    if whitelist_result == "filter_at_the_beginning":
+        with pytest.raises(
+            OperationalException,
+            match=r"This Pairlist Handler should not be used at the first position "
+            r"in the list of Pairlist Handlers.",
+        ):
+            freqtrade.pairlists.refresh_pairlist()
+    else:
+        freqtrade.pairlists.refresh_pairlist()
+        whitelist = freqtrade.pairlists.whitelist
+
+        assert isinstance(whitelist, list)
+
+        # Verify length of pairlist matches (used for ShuffleFilter without seed)
+        if isinstance(whitelist_result, list):
+            assert whitelist == whitelist_result
+        else:
+            assert len(whitelist) == whitelist_result
+
+        for pairlist in pairlists:
+            if (
+                pairlist["method"] == "AgeFilter"
+                and pairlist["min_days_listed"]
+                and len(ohlcv_history) < pairlist["min_days_listed"]
+            ):
+                assert log_has_re(
+                    r"^Removed .* from whitelist, because age .* is less than " r".* day.*", caplog
+                )
+            if (
+                pairlist["method"] == "AgeFilter"
+                and pairlist["max_days_listed"]
+                and len(ohlcv_history) > pairlist["max_days_listed"]
+            ):
+                assert log_has_re(
+                    r"^Removed .* from whitelist, because age .* is less than "
+                    r".* day.* or more than .* day",
+                    caplog,
+                )
+            if pairlist["method"] == "PrecisionFilter" and whitelist_result:
+                assert log_has_re(
+                    r"^Removed .* from whitelist, because stop price .* "
+                    r"would be <= stop limit.*",
+                    caplog,
+                )
+            if pairlist["method"] == "PriceFilter" and whitelist_result:
+                assert (
+                    log_has_re(r"^Removed .* from whitelist, because 1 unit is .*%$", caplog)
+                    or log_has_re(
+                        r"^Removed .* from whitelist, " r"because last price < .*%$", caplog
+                    )
+                    or log_has_re(
+                        r"^Removed .* from whitelist, " r"because last price > .*%$", caplog
+                    )
+                    or log_has_re(
+                        r"^Removed .* from whitelist, " r"because min value change of .*", caplog
+                    )
+                    or log_has_re(
+                        r"^Removed .* from whitelist, because ticker\['last'\] " r"is empty.*",
+                        caplog,
+                    )
+                )
+            if pairlist["method"] == "VolumePairList":
+                logmsg = (
+                    "DEPRECATED: using any key other than quoteVolume for "
+                    "VolumePairList is deprecated."
+                )
+                if pairlist["sort_key"] != "quoteVolume":
+                    assert log_has(logmsg, caplog)
+                else:
+                    assert not log_has(logmsg, caplog)
+            if pairlist["method"] == "VolatilityFilter":
+                assert log_has_re(r"^Removed .* from whitelist, because volatility.*$", caplog)
+
+
+@pytest.mark.parametrize(
+    "pairlists,base_currency,exchange,volumefilter_result",
+    [
+        # default refresh of 1800 to small for daily candle lookback
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "lookback_days": 1,
+                }
+            ],
+            "BTC",
+            "binance",
+            "default_refresh_too_short",
+        ),  # OperationalException expected
+        # ambiguous configuration with lookback days and period
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "lookback_days": 1,
+                    "lookback_period": 1,
+                }
+            ],
+            "BTC",
+            "binance",
+            "lookback_days_and_period",
+        ),  # OperationalException expected
+        # negative lookback period
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "lookback_timeframe": "1d",
+                    "lookback_period": -1,
+                }
+            ],
+            "BTC",
+            "binance",
+            "lookback_period_negative",
+        ),  # OperationalException expected
+        # lookback range exceedes exchange limit
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "lookback_timeframe": "1m",
+                    "lookback_period": 2000,
+                    "refresh_period": 3600,
+                }
+            ],
+            "BTC",
+            "binance",
+            "lookback_exceeds_exchange_request_size",
+        ),  # OperationalException expected
+        # expecting pairs as given
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "lookback_timeframe": "1d",
+                    "lookback_period": 1,
+                    "refresh_period": 86400,
+                }
+            ],
+            "BTC",
+            "binance",
+            ["LTC/BTC", "ETH/BTC", "TKN/BTC", "XRP/BTC", "HOT/BTC"],
+        ),
+        # expecting pairs as input, because 1h candles are not available
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "lookback_timeframe": "1h",
+                    "lookback_period": 2,
+                    "refresh_period": 3600,
+                }
+            ],
+            "BTC",
+            "binance",
+            ["ETH/BTC", "LTC/BTC", "NEO/BTC", "TKN/BTC", "XRP/BTC"],
+        ),
+        # TKN/BTC is removed because it doesn't have enough candles
+        (
+            [
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "lookback_timeframe": "1d",
+                    "lookback_period": 6,
+                    "refresh_period": 86400,
+                }
+            ],
+            "BTC",
+            "binance",
+            ["LTC/BTC", "XRP/BTC", "ETH/BTC", "HOT/BTC", "NEO/BTC"],
+        ),
+        # VolumePairlist in range mode as filter.
+        # TKN/BTC is removed because it doesn't have enough candles
+        (
+            [
+                {"method": "VolumePairList", "number_assets": 5},
+                {
+                    "method": "VolumePairList",
+                    "number_assets": 5,
+                    "sort_key": "quoteVolume",
+                    "lookback_timeframe": "1d",
+                    "lookback_period": 2,
+                    "refresh_period": 86400,
+                },
+            ],
+            "BTC",
+            "binance",
+            ["LTC/BTC", "XRP/BTC", "ETH/BTC", "TKN/BTC", "HOT/BTC"],
+        ),
+        # ftx data is already in Quote currency, therefore won't require conversion
+        # ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
+        #    "lookback_timeframe": "1d", "lookback_period": 1, "refresh_period": 86400}],
+        #  "BTC", "ftx", ['HOT/BTC', 'LTC/BTC', 'ETH/BTC', 'TKN/BTC', 'XRP/BTC']),
+    ],
+)
+def test_VolumePairList_range(
+    mocker,
+    whitelist_conf,
+    shitcoinmarkets,
+    tickers,
+    ohlcv_history,
+    pairlists,
+    base_currency,
+    exchange,
+    volumefilter_result,
+    time_machine,
+) -> None:
+    whitelist_conf["pairlists"] = pairlists
+    whitelist_conf["stake_currency"] = base_currency
+    whitelist_conf["exchange"]["name"] = exchange
+    # Ensure we have 6 candles
+    ohlcv_history_long = pd.concat([ohlcv_history, ohlcv_history])
+
+    ohlcv_history_high_vola = ohlcv_history_long.copy()
+    ohlcv_history_high_vola.loc[ohlcv_history_high_vola.index == 1, "close"] = 0.00090
+
+    # create candles for medium overall volume with last candle high volume
+    ohlcv_history_medium_volume = ohlcv_history_long.copy()
+    ohlcv_history_medium_volume.loc[ohlcv_history_medium_volume.index == 2, "volume"] = 5
+
+    # create candles for high volume with all candles high volume, but very low price.
+    ohlcv_history_high_volume = ohlcv_history_long.copy()
+    ohlcv_history_high_volume["volume"] = 10
+    ohlcv_history_high_volume["low"] = ohlcv_history_high_volume.loc[:, "low"] * 0.01
+    ohlcv_history_high_volume["high"] = ohlcv_history_high_volume.loc[:, "high"] * 0.01
+    ohlcv_history_high_volume["close"] = ohlcv_history_high_volume.loc[:, "close"] * 0.01
+
+    ohlcv_data = {
+        ("ETH/BTC", "1d", CandleType.SPOT): ohlcv_history_long,
+        ("TKN/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        ("LTC/BTC", "1d", CandleType.SPOT): ohlcv_history_medium_volume,
+        ("XRP/BTC", "1d", CandleType.SPOT): ohlcv_history_high_vola,
+        ("HOT/BTC", "1d", CandleType.SPOT): ohlcv_history_high_volume,
+    }
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+
+    if volumefilter_result == "default_refresh_too_short":
+        with pytest.raises(
+            OperationalException,
+            match=r"Refresh period of [0-9]+ seconds is smaller than one timeframe "
+            r"of [0-9]+.*\. Please adjust refresh_period to at least [0-9]+ "
+            r"and restart the bot\.",
+        ):
+            freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+        return
+    elif volumefilter_result == "lookback_days_and_period":
+        with pytest.raises(
+            OperationalException,
+            match=r"Ambiguous configuration: lookback_days and lookback_period both "
+            r"set in pairlist config\..*",
+        ):
+            freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+    elif volumefilter_result == "lookback_period_negative":
+        with pytest.raises(
+            OperationalException, match=r"VolumeFilter requires lookback_period to be >= 0"
+        ):
+            freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+    elif volumefilter_result == "lookback_exceeds_exchange_request_size":
+        with pytest.raises(
+            OperationalException,
+            match=r"VolumeFilter requires lookback_period to not exceed "
+            r"exchange max request size \([0-9]+\)",
+        ):
+            freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+    else:
+        freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+        mocker.patch.multiple(
+            EXMS, get_tickers=tickers, markets=PropertyMock(return_value=shitcoinmarkets)
+        )
+        start_dt = dt_now()
+        time_machine.move_to(start_dt)
+        # remove ohlcv when looback_timeframe != 1d
+        # to enforce fallback to ticker data
+        if "lookback_timeframe" in pairlists[0]:
+            if pairlists[0]["lookback_timeframe"] != "1d":
+                ohlcv_data = {}
+
+        ohclv_mock = mocker.patch(f"{EXMS}.refresh_latest_ohlcv", return_value=ohlcv_data)
+
+        freqtrade.pairlists.refresh_pairlist()
+        whitelist = freqtrade.pairlists.whitelist
+        assert ohclv_mock.call_count == 1
+
+        assert isinstance(whitelist, list)
+        assert whitelist == volumefilter_result
+        # Test caching
+        ohclv_mock.reset_mock()
+        freqtrade.pairlists.refresh_pairlist()
+        # in "filter" mode, caching is disabled.
+        assert ohclv_mock.call_count == 0
+        whitelist = freqtrade.pairlists.whitelist
+        assert whitelist == volumefilter_result
+
+        time_machine.move_to(start_dt + timedelta(days=2))
+        ohclv_mock.reset_mock()
+        freqtrade.pairlists.refresh_pairlist()
+        assert ohclv_mock.call_count == 1
+        whitelist = freqtrade.pairlists.whitelist
+        assert whitelist == volumefilter_result
+
+
+def test_PrecisionFilter_error(mocker, whitelist_conf) -> None:
+    whitelist_conf["pairlists"] = [{"method": "StaticPairList"}, {"method": "PrecisionFilter"}]
+    del whitelist_conf["stoploss"]
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+
+    with pytest.raises(
+        OperationalException, match=r"PrecisionFilter can only work with stoploss defined\..*"
+    ):
+        PairListManager(MagicMock, whitelist_conf, MagicMock())
+
+
+def test_PerformanceFilter_error(mocker, whitelist_conf, caplog) -> None:
+    whitelist_conf["pairlists"] = [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}]
+    if hasattr(Trade, "session"):
+        del Trade.session
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    exchange = get_patched_exchange(mocker, whitelist_conf)
+    pm = PairListManager(exchange, whitelist_conf, MagicMock())
+    pm.refresh_pairlist()
+
+    assert log_has("PerformanceFilter is not available in this mode.", caplog)
+
+
+def test_VolatilityFilter_error(mocker, whitelist_conf) -> None:
+    volatility_filter = {"method": "VolatilityFilter", "lookback_days": -1}
+    whitelist_conf["pairlists"] = [{"method": "StaticPairList"}, volatility_filter]
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    exchange_mock = MagicMock()
+    exchange_mock.ohlcv_candle_limit = MagicMock(return_value=1000)
+
+    with pytest.raises(
+        OperationalException, match=r"VolatilityFilter requires lookback_days to be >= 1*"
+    ):
+        PairListManager(exchange_mock, whitelist_conf, MagicMock())
+
+    volatility_filter = {"method": "VolatilityFilter", "lookback_days": 2000}
+    whitelist_conf["pairlists"] = [{"method": "StaticPairList"}, volatility_filter]
+    with pytest.raises(
+        OperationalException,
+        match=r"VolatilityFilter requires lookback_days to not exceed exchange max",
+    ):
+        PairListManager(exchange_mock, whitelist_conf, MagicMock())
+
+    volatility_filter = {"method": "VolatilityFilter", "sort_direction": "Random"}
+    whitelist_conf["pairlists"] = [{"method": "StaticPairList"}, volatility_filter]
+    with pytest.raises(
+        OperationalException,
+        match=r"VolatilityFilter requires sort_direction to be either " r"None .*'asc'.*'desc'",
+    ):
+        PairListManager(exchange_mock, whitelist_conf, MagicMock())
+
+
+@pytest.mark.parametrize(
+    "pairlist,expected_pairlist",
+    [
+        (
+            {"method": "VolatilityFilter", "sort_direction": "asc"},
+            ["XRP/BTC", "ETH/BTC", "LTC/BTC", "TKN/BTC"],
+        ),
+        (
+            {"method": "VolatilityFilter", "sort_direction": "desc"},
+            ["TKN/BTC", "LTC/BTC", "ETH/BTC", "XRP/BTC"],
+        ),
+        (
+            {"method": "VolatilityFilter", "sort_direction": "desc", "min_volatility": 0.4},
+            ["TKN/BTC", "LTC/BTC", "ETH/BTC"],
+        ),
+        (
+            {"method": "VolatilityFilter", "sort_direction": "asc", "min_volatility": 0.4},
+            ["ETH/BTC", "LTC/BTC", "TKN/BTC"],
+        ),
+        (
+            {"method": "VolatilityFilter", "sort_direction": "desc", "max_volatility": 0.5},
+            ["LTC/BTC", "ETH/BTC", "XRP/BTC"],
+        ),
+        (
+            {"method": "VolatilityFilter", "sort_direction": "asc", "max_volatility": 0.5},
+            ["XRP/BTC", "ETH/BTC", "LTC/BTC"],
+        ),
+        (
+            {"method": "RangeStabilityFilter", "sort_direction": "asc"},
+            ["ETH/BTC", "XRP/BTC", "LTC/BTC", "TKN/BTC"],
+        ),
+        (
+            {"method": "RangeStabilityFilter", "sort_direction": "desc"},
+            ["TKN/BTC", "LTC/BTC", "XRP/BTC", "ETH/BTC"],
+        ),
+        (
+            {"method": "RangeStabilityFilter", "sort_direction": "asc", "min_rate_of_change": 0.4},
+            ["XRP/BTC", "LTC/BTC", "TKN/BTC"],
+        ),
+        (
+            {"method": "RangeStabilityFilter", "sort_direction": "desc", "min_rate_of_change": 0.4},
+            ["TKN/BTC", "LTC/BTC", "XRP/BTC"],
+        ),
+    ],
+)
+def test_VolatilityFilter_RangeStabilityFilter_sort(
+    mocker, whitelist_conf, tickers, time_machine, pairlist, expected_pairlist
+) -> None:
+    whitelist_conf["pairlists"] = [{"method": "VolumePairList", "number_assets": 10}, pairlist]
+
+    df1 = generate_test_data("1d", 10, "2022-01-05 00:00:00+00:00", random_seed=42)
+    df2 = generate_test_data("1d", 10, "2022-01-05 00:00:00+00:00", random_seed=2)
+    df3 = generate_test_data("1d", 10, "2022-01-05 00:00:00+00:00", random_seed=3)
+    df4 = generate_test_data("1d", 10, "2022-01-05 00:00:00+00:00", random_seed=4)
+    df5 = generate_test_data("1d", 10, "2022-01-05 00:00:00+00:00", random_seed=5)
+    df6 = generate_test_data("1d", 10, "2022-01-05 00:00:00+00:00", random_seed=6)
+
+    assert not df1.equals(df2)
+    time_machine.move_to("2022-01-15 00:00:00+00:00")
+
+    ohlcv_data = {
+        ("ETH/BTC", "1d", CandleType.SPOT): df1,
+        ("TKN/BTC", "1d", CandleType.SPOT): df2,
+        ("LTC/BTC", "1d", CandleType.SPOT): df3,
+        ("XRP/BTC", "1d", CandleType.SPOT): df4,
+        ("HOT/BTC", "1d", CandleType.SPOT): df5,
+        ("BLK/BTC", "1d", CandleType.SPOT): df6,
+    }
+    ohlcv_mock = MagicMock(return_value=ohlcv_data)
+    mocker.patch.multiple(
+        EXMS,
+        exchange_has=MagicMock(return_value=True),
+        refresh_latest_ohlcv=ohlcv_mock,
+        get_tickers=tickers,
+    )
+
+    exchange = get_patched_exchange(mocker, whitelist_conf)
+    exchange.ohlcv_candle_limit = MagicMock(return_value=1000)
+    plm = PairListManager(exchange, whitelist_conf, MagicMock())
+
+    assert exchange.ohlcv_candle_limit.call_count == 2
+    plm.refresh_pairlist()
+    assert ohlcv_mock.call_count == 1
+    assert exchange.ohlcv_candle_limit.call_count == 2
+    assert plm.whitelist == expected_pairlist
+
+    plm.refresh_pairlist()
+    assert exchange.ohlcv_candle_limit.call_count == 2
+    assert ohlcv_mock.call_count == 1
+
+
+def test_ShuffleFilter_init(mocker, whitelist_conf, caplog) -> None:
+    whitelist_conf["pairlists"] = [
+        {"method": "StaticPairList"},
+        {"method": "ShuffleFilter", "seed": 43},
+    ]
+    whitelist_conf["runmode"] = "backtest"
+
+    exchange = get_patched_exchange(mocker, whitelist_conf)
+    plm = PairListManager(exchange, whitelist_conf)
+    assert log_has("Backtesting mode detected, applying seed value: 43", caplog)
+
+    with time_machine.travel("2021-09-01 05:01:00 +00:00") as t:
+        plm.refresh_pairlist()
+        pl1 = deepcopy(plm.whitelist)
+        plm.refresh_pairlist()
+        assert plm.whitelist == pl1
+
+        t.shift(timedelta(minutes=10))
+        plm.refresh_pairlist()
+        assert plm.whitelist != pl1
+
+    caplog.clear()
+    whitelist_conf["runmode"] = RunMode.DRY_RUN
+    plm = PairListManager(exchange, whitelist_conf)
+    assert not log_has("Backtesting mode detected, applying seed value: 42", caplog)
+    assert log_has("Live mode detected, not applying seed.", caplog)
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_PerformanceFilter_lookback(mocker, default_conf_usdt, fee, caplog) -> None:
+    default_conf_usdt["exchange"]["pair_whitelist"].extend(["ADA/USDT", "XRP/USDT", "ETC/USDT"])
+    default_conf_usdt["pairlists"] = [
+        {"method": "StaticPairList"},
+        {"method": "PerformanceFilter", "minutes": 60, "min_profit": 0.01},
+    ]
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    exchange = get_patched_exchange(mocker, default_conf_usdt)
+    pm = PairListManager(exchange, default_conf_usdt)
+    pm.refresh_pairlist()
+
+    assert pm.whitelist == ["ETH/USDT", "XRP/USDT", "NEO/USDT", "TKN/USDT"]
+
+    with time_machine.travel("2021-09-01 05:00:00 +00:00") as t:
+        create_mock_trades_usdt(fee)
+        pm.refresh_pairlist()
+        assert pm.whitelist == ["XRP/USDT", "NEO/USDT"]
+        assert log_has_re(r"Removing pair .* since .* is below .*", caplog)
+
+        # Move to "outside" of lookback window, so original sorting is restored.
+        t.move_to("2021-09-01 07:00:00 +00:00")
+        pm.refresh_pairlist()
+        assert pm.whitelist == ["ETH/USDT", "XRP/USDT", "NEO/USDT", "TKN/USDT"]
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_PerformanceFilter_keep_mid_order(mocker, default_conf_usdt, fee, caplog) -> None:
+    default_conf_usdt["exchange"]["pair_whitelist"].extend(["ADA/USDT", "ETC/USDT"])
+    default_conf_usdt["pairlists"] = [
+        {"method": "StaticPairList", "allow_inactive": True},
+        {
+            "method": "PerformanceFilter",
+            "minutes": 60,
+        },
+    ]
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    exchange = get_patched_exchange(mocker, default_conf_usdt)
+    pm = PairListManager(exchange, default_conf_usdt)
+    pm.refresh_pairlist()
+
+    assert pm.whitelist == [
+        "ETH/USDT",
+        "LTC/USDT",
+        "XRP/USDT",
+        "NEO/USDT",
+        "TKN/USDT",
+        "ADA/USDT",
+        "ETC/USDT",
+    ]
+
+    with time_machine.travel("2021-09-01 05:00:00 +00:00") as t:
+        create_mock_trades_usdt(fee)
+        pm.refresh_pairlist()
+        assert pm.whitelist == [
+            "XRP/USDT",
+            "NEO/USDT",
+            "ETH/USDT",
+            "LTC/USDT",
+            "TKN/USDT",
+            "ADA/USDT",
+            "ETC/USDT",
+        ]
+        # assert log_has_re(r'Removing pair .* since .* is below .*', caplog)
+
+        # Move to "outside" of lookback window, so original sorting is restored.
+        t.move_to("2021-09-01 07:00:00 +00:00")
+        pm.refresh_pairlist()
+        assert pm.whitelist == [
+            "ETH/USDT",
+            "LTC/USDT",
+            "XRP/USDT",
+            "NEO/USDT",
+            "TKN/USDT",
+            "ADA/USDT",
+            "ETC/USDT",
+        ]
+
+
+def test_gen_pair_whitelist_not_supported(mocker, default_conf, tickers) -> None:
+    default_conf["pairlists"] = [{"method": "VolumePairList", "number_assets": 10}]
+
+    mocker.patch.multiple(
+        EXMS,
+        get_tickers=tickers,
+        exchange_has=MagicMock(return_value=False),
+    )
+
+    with pytest.raises(
+        OperationalException, match=r"Exchange does not support dynamic whitelist.*"
+    ):
+        get_patched_freqtradebot(mocker, default_conf)
+
+
+def test_pair_whitelist_not_supported_Spread(mocker, default_conf, tickers) -> None:
+    default_conf["pairlists"] = [{"method": "StaticPairList"}, {"method": "SpreadFilter"}]
+
+    mocker.patch.multiple(
+        EXMS,
+        get_tickers=tickers,
+        exchange_has=MagicMock(return_value=False),
+    )
+
+    with pytest.raises(OperationalException, match=r"Exchange does not support fetchTickers, .*"):
+        get_patched_freqtradebot(mocker, default_conf)
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    mocker.patch(f"{EXMS}.get_option", MagicMock(return_value=False))
+    with pytest.raises(OperationalException, match=r".*requires exchange to have bid/ask data"):
+        get_patched_freqtradebot(mocker, default_conf)
+
+
+@pytest.mark.parametrize("pairlist", TESTABLE_PAIRLISTS)
+def test_pairlist_class(mocker, whitelist_conf, markets, pairlist):
+    whitelist_conf["pairlists"][0]["method"] = pairlist
+    mocker.patch.multiple(
+        EXMS, markets=PropertyMock(return_value=markets), exchange_has=MagicMock(return_value=True)
+    )
+    freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+
+    assert freqtrade.pairlists.name_list == [pairlist]
+    assert pairlist in str(freqtrade.pairlists.short_desc())
+    assert isinstance(freqtrade.pairlists.whitelist, list)
+    assert isinstance(freqtrade.pairlists.blacklist, list)
+
+
+@pytest.mark.parametrize("pairlist", TESTABLE_PAIRLISTS)
+@pytest.mark.parametrize(
+    "whitelist,log_message",
+    [
+        (["ETH/BTC", "TKN/BTC"], ""),
+        # TRX/ETH not in markets
+        (["ETH/BTC", "TKN/BTC", "TRX/ETH"], "is not compatible with exchange"),
+        # wrong stake
+        (["ETH/BTC", "TKN/BTC", "ETH/USDT"], "is not compatible with your stake currency"),
+        # BCH/BTC not available
+        (["ETH/BTC", "TKN/BTC", "BCH/BTC"], "is not compatible with exchange"),
+        # BTT/BTC is inactive
+        (["ETH/BTC", "TKN/BTC", "BTT/BTC"], "Market is not active"),
+        # XLTCUSDT is not a valid pair
+        (["ETH/BTC", "TKN/BTC", "XLTCUSDT"], "is not tradable with Freqtrade"),
+    ],
+)
+def test__whitelist_for_active_markets(
+    mocker, whitelist_conf, markets, pairlist, whitelist, caplog, log_message, tickers
+):
+    whitelist_conf["pairlists"][0]["method"] = pairlist
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+    caplog.clear()
+
+    # Assign starting whitelist
+    pairlist_handler = freqtrade.pairlists._pairlist_handlers[0]
+    new_whitelist = pairlist_handler._whitelist_for_active_markets(whitelist)
+
+    assert set(new_whitelist) == set(["ETH/BTC", "TKN/BTC"])
+    assert log_message in caplog.text
+
+
+@pytest.mark.parametrize("pairlist", TESTABLE_PAIRLISTS)
+def test__whitelist_for_active_markets_empty(mocker, whitelist_conf, pairlist, tickers):
+    whitelist_conf["pairlists"][0]["method"] = pairlist
+
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+
+    freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+    mocker.patch.multiple(EXMS, markets=PropertyMock(return_value=None), get_tickers=tickers)
+    # Assign starting whitelist
+    pairlist_handler = freqtrade.pairlists._pairlist_handlers[0]
+    with pytest.raises(OperationalException, match=r"Markets not loaded.*"):
+        pairlist_handler._whitelist_for_active_markets(["ETH/BTC"])
+
+
+def test_volumepairlist_invalid_sortvalue(mocker, whitelist_conf):
+    whitelist_conf["pairlists"][0].update({"sort_key": "asdf"})
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    with pytest.raises(OperationalException, match=r"key asdf not in .*"):
+        get_patched_freqtradebot(mocker, whitelist_conf)
+
+
+def test_volumepairlist_caching(mocker, markets, whitelist_conf, tickers):
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+    freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+    assert len(freqtrade.pairlists._pairlist_handlers[0]._pair_cache) == 0
+    assert tickers.call_count == 0
+    freqtrade.pairlists.refresh_pairlist()
+    assert tickers.call_count == 1
+
+    assert len(freqtrade.pairlists._pairlist_handlers[0]._pair_cache) == 1
+    freqtrade.pairlists.refresh_pairlist()
+    assert tickers.call_count == 1
+
+
+def test_agefilter_min_days_listed_too_small(mocker, default_conf, markets, tickers):
+    default_conf["pairlists"] = [
+        {"method": "VolumePairList", "number_assets": 10},
+        {"method": "AgeFilter", "min_days_listed": -1},
+    ]
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+
+    with pytest.raises(
+        OperationalException, match=r"AgeFilter requires min_days_listed to be >= 1"
+    ):
+        get_patched_freqtradebot(mocker, default_conf)
+
+
+def test_agefilter_max_days_lower_than_min_days(mocker, default_conf, markets, tickers):
+    default_conf["pairlists"] = [
+        {"method": "VolumePairList", "number_assets": 10},
+        {"method": "AgeFilter", "min_days_listed": 3, "max_days_listed": 2},
+    ]
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+
+    with pytest.raises(
+        OperationalException, match=r"AgeFilter max_days_listed <= min_days_listed not permitted"
+    ):
+        get_patched_freqtradebot(mocker, default_conf)
+
+
+def test_agefilter_min_days_listed_too_large(mocker, default_conf, markets, tickers):
+    default_conf["pairlists"] = [
+        {"method": "VolumePairList", "number_assets": 10},
+        {"method": "AgeFilter", "min_days_listed": 99999},
+    ]
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+
+    with pytest.raises(
+        OperationalException,
+        match=r"AgeFilter requires min_days_listed to not exceed "
+        r"exchange max request size \([0-9]+\)",
+    ):
+        get_patched_freqtradebot(mocker, default_conf)
+
+
+def test_agefilter_caching(mocker, markets, whitelist_conf_agefilter, tickers, ohlcv_history):
+    with time_machine.travel("2021-09-01 05:00:00 +00:00") as t:
+        ohlcv_data = {
+            ("ETH/BTC", "1d", CandleType.SPOT): ohlcv_history,
+            ("TKN/BTC", "1d", CandleType.SPOT): ohlcv_history,
+            ("LTC/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        }
+        mocker.patch.multiple(
+            EXMS,
+            markets=PropertyMock(return_value=markets),
+            exchange_has=MagicMock(return_value=True),
+            get_tickers=tickers,
+            refresh_latest_ohlcv=MagicMock(return_value=ohlcv_data),
+        )
+
+        freqtrade = get_patched_freqtradebot(mocker, whitelist_conf_agefilter)
+        assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 0
+        freqtrade.pairlists.refresh_pairlist()
+        assert len(freqtrade.pairlists.whitelist) == 3
+        assert freqtrade.exchange.refresh_latest_ohlcv.call_count > 0
+
+        freqtrade.pairlists.refresh_pairlist()
+        assert len(freqtrade.pairlists.whitelist) == 3
+        # Call to XRP/BTC cached
+        assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 2
+
+        ohlcv_data = {
+            ("ETH/BTC", "1d", CandleType.SPOT): ohlcv_history,
+            ("TKN/BTC", "1d", CandleType.SPOT): ohlcv_history,
+            ("LTC/BTC", "1d", CandleType.SPOT): ohlcv_history,
+            ("XRP/BTC", "1d", CandleType.SPOT): ohlcv_history.iloc[[0]],
+        }
+        mocker.patch(f"{EXMS}.refresh_latest_ohlcv", return_value=ohlcv_data)
+        freqtrade.pairlists.refresh_pairlist()
+        assert len(freqtrade.pairlists.whitelist) == 3
+        assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 1
+
+        # Move to next day
+        t.move_to("2021-09-02 01:00:00 +00:00")
+        mocker.patch(f"{EXMS}.refresh_latest_ohlcv", return_value=ohlcv_data)
+        freqtrade.pairlists.refresh_pairlist()
+        assert len(freqtrade.pairlists.whitelist) == 3
+        assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 1
+
+        # Move another day with fresh mocks (now the pair is old enough)
+        t.move_to("2021-09-03 01:00:00 +00:00")
+        # Called once for XRP/BTC
+        ohlcv_data = {
+            ("ETH/BTC", "1d", CandleType.SPOT): ohlcv_history,
+            ("TKN/BTC", "1d", CandleType.SPOT): ohlcv_history,
+            ("LTC/BTC", "1d", CandleType.SPOT): ohlcv_history,
+            ("XRP/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        }
+        mocker.patch(f"{EXMS}.refresh_latest_ohlcv", return_value=ohlcv_data)
+        freqtrade.pairlists.refresh_pairlist()
+        assert len(freqtrade.pairlists.whitelist) == 4
+        # Called once (only for XRP/BTC)
+        assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 1
+
+
+def test_OffsetFilter_error(mocker, whitelist_conf) -> None:
+    whitelist_conf["pairlists"] = [
+        {"method": "StaticPairList"},
+        {"method": "OffsetFilter", "offset": -1},
+    ]
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+
+    with pytest.raises(OperationalException, match=r"OffsetFilter requires offset to be >= 0"):
+        PairListManager(MagicMock, whitelist_conf)
+
+
+def test_rangestabilityfilter_checks(mocker, default_conf, markets, tickers):
+    default_conf["pairlists"] = [
+        {"method": "VolumePairList", "number_assets": 10},
+        {"method": "RangeStabilityFilter", "lookback_days": 99999},
+    ]
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+
+    with pytest.raises(
+        OperationalException,
+        match=r"RangeStabilityFilter requires lookback_days to not exceed "
+        r"exchange max request size \([0-9]+\)",
+    ):
+        get_patched_freqtradebot(mocker, default_conf)
+
+    default_conf["pairlists"] = [
+        {"method": "VolumePairList", "number_assets": 10},
+        {"method": "RangeStabilityFilter", "lookback_days": 0},
+    ]
+
+    with pytest.raises(
+        OperationalException, match="RangeStabilityFilter requires lookback_days to be >= 1"
+    ):
+        get_patched_freqtradebot(mocker, default_conf)
+
+    default_conf["pairlists"] = [
+        {"method": "VolumePairList", "number_assets": 10},
+        {"method": "RangeStabilityFilter", "sort_direction": "something"},
+    ]
+
+    with pytest.raises(
+        OperationalException,
+        match="RangeStabilityFilter requires sort_direction to be either None.*",
+    ):
+        get_patched_freqtradebot(mocker, default_conf)
+
+
+@pytest.mark.parametrize(
+    "min_rate_of_change,max_rate_of_change,expected_length",
+    [
+        (0.01, 0.99, 5),
+        (0.05, 0.0, 0),  # Setting min rate_of_change to 5% removes all pairs from the whitelist.
+    ],
+)
+def test_rangestabilityfilter_caching(
+    mocker,
+    markets,
+    default_conf,
+    tickers,
+    ohlcv_history,
+    min_rate_of_change,
+    max_rate_of_change,
+    expected_length,
+):
+    default_conf["pairlists"] = [
+        {"method": "VolumePairList", "number_assets": 10},
+        {
+            "method": "RangeStabilityFilter",
+            "lookback_days": 2,
+            "min_rate_of_change": min_rate_of_change,
+            "max_rate_of_change": max_rate_of_change,
+        },
+    ]
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+    ohlcv_data = {
+        ("ETH/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        ("TKN/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        ("LTC/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        ("XRP/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        ("HOT/BTC", "1d", CandleType.SPOT): ohlcv_history,
+        ("BLK/BTC", "1d", CandleType.SPOT): ohlcv_history,
+    }
+    mocker.patch.multiple(
+        EXMS,
+        refresh_latest_ohlcv=MagicMock(return_value=ohlcv_data),
+    )
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 0
+    freqtrade.pairlists.refresh_pairlist()
+    assert len(freqtrade.pairlists.whitelist) == expected_length
+    assert freqtrade.exchange.refresh_latest_ohlcv.call_count > 0
+
+    previous_call_count = freqtrade.exchange.refresh_latest_ohlcv.call_count
+    freqtrade.pairlists.refresh_pairlist()
+    assert len(freqtrade.pairlists.whitelist) == expected_length
+    # Should not have increased since first call.
+    assert freqtrade.exchange.refresh_latest_ohlcv.call_count == previous_call_count
+
+
+def test_spreadfilter_invalid_data(mocker, default_conf, markets, tickers, caplog):
+    default_conf["pairlists"] = [
+        {"method": "VolumePairList", "number_assets": 10},
+        {"method": "SpreadFilter", "max_spread_ratio": 0.1},
+    ]
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+
+    ftbot = get_patched_freqtradebot(mocker, default_conf)
+    ftbot.pairlists.refresh_pairlist()
+
+    assert len(ftbot.pairlists.whitelist) == 5
+
+    tickers.return_value["ETH/BTC"]["ask"] = 0.0
+    del tickers.return_value["TKN/BTC"]
+    del tickers.return_value["LTC/BTC"]
+    mocker.patch.multiple(EXMS, get_tickers=tickers)
+
+    ftbot.pairlists.refresh_pairlist()
+    assert log_has_re(r"Removed .* invalid ticker data.*", caplog)
+
+    assert len(ftbot.pairlists.whitelist) == 2
+
+
+@pytest.mark.parametrize(
+    "pairlistconfig,desc_expected,exception_expected",
+    [
+        (
+            {
+                "method": "PriceFilter",
+                "low_price_ratio": 0.001,
+                "min_price": 0.00000010,
+                "max_price": 1.0,
+            },
+            "[{'PriceFilter': 'PriceFilter - Filtering pairs priced below "
+            "0.1% or below 0.00000010 or above 1.00000000.'}]",
+            None,
+        ),
+        (
+            {"method": "PriceFilter", "low_price_ratio": 0.001, "min_price": 0.00000010},
+            "[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.1% "
+            "or below 0.00000010.'}]",
+            None,
+        ),
+        (
+            {"method": "PriceFilter", "low_price_ratio": 0.001, "max_price": 1.00010000},
+            "[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.1% "
+            "or above 1.00010000.'}]",
+            None,
+        ),
+        (
+            {"method": "PriceFilter", "min_price": 0.00002000},
+            "[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.00002000.'}]",
+            None,
+        ),
+        (
+            {"method": "PriceFilter", "max_value": 0.00002000},
+            "[{'PriceFilter': 'PriceFilter - Filtering pairs priced Value above 0.00002000.'}]",
+            None,
+        ),
+        (
+            {"method": "PriceFilter"},
+            "[{'PriceFilter': 'PriceFilter - No price filters configured.'}]",
+            None,
+        ),
+        (
+            {"method": "PriceFilter", "low_price_ratio": -0.001},
+            None,
+            "PriceFilter requires low_price_ratio to be >= 0",
+        ),  # OperationalException expected
+        (
+            {"method": "PriceFilter", "min_price": -0.00000010},
+            None,
+            "PriceFilter requires min_price to be >= 0",
+        ),  # OperationalException expected
+        (
+            {"method": "PriceFilter", "max_price": -1.00010000},
+            None,
+            "PriceFilter requires max_price to be >= 0",
+        ),  # OperationalException expected
+        (
+            {"method": "PriceFilter", "max_value": -1.00010000},
+            None,
+            "PriceFilter requires max_value to be >= 0",
+        ),  # OperationalException expected
+        (
+            {"method": "RangeStabilityFilter", "lookback_days": 10, "min_rate_of_change": 0.01},
+            "[{'RangeStabilityFilter': 'RangeStabilityFilter - Filtering pairs with rate "
+            "of change below 0.01 over the last days.'}]",
+            None,
+        ),
+        (
+            {
+                "method": "RangeStabilityFilter",
+                "lookback_days": 10,
+                "min_rate_of_change": 0.01,
+                "max_rate_of_change": 0.99,
+            },
+            "[{'RangeStabilityFilter': 'RangeStabilityFilter - Filtering pairs with rate "
+            "of change below 0.01 and above 0.99 over the last days.'}]",
+            None,
+        ),
+        (
+            {"method": "OffsetFilter", "offset": 5, "number_assets": 10},
+            "[{'OffsetFilter': 'OffsetFilter - Taking 10 Pairs, starting from 5.'}]",
+            None,
+        ),
+        (
+            {"method": "ProducerPairList"},
+            "[{'ProducerPairList': 'ProducerPairList - default'}]",
+            None,
+        ),
+        (
+            {
+                "method": "RemotePairList",
+                "number_assets": 10,
+                "pairlist_url": "https://example.com",
+            },
+            "[{'RemotePairList': 'RemotePairList - 10 pairs from RemotePairlist.'}]",
+            None,
+        ),
+    ],
+)
+def test_pricefilter_desc(
+    mocker, whitelist_conf, markets, pairlistconfig, desc_expected, exception_expected
+):
+    mocker.patch.multiple(
+        EXMS, markets=PropertyMock(return_value=markets), exchange_has=MagicMock(return_value=True)
+    )
+    whitelist_conf["pairlists"] = [pairlistconfig]
+
+    if desc_expected is not None:
+        freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+        short_desc = str(freqtrade.pairlists.short_desc())
+        assert short_desc == desc_expected
+    else:  # OperationalException expected
+        with pytest.raises(OperationalException, match=exception_expected):
+            freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
+
+
+def test_pairlistmanager_no_pairlist(mocker, whitelist_conf):
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+
+    whitelist_conf["pairlists"] = []
+
+    with pytest.raises(OperationalException, match=r"No Pairlist Handlers defined"):
+        get_patched_freqtradebot(mocker, whitelist_conf)
+
+
+@pytest.mark.parametrize(
+    "pairlists,pair_allowlist,overall_performance,allowlist_result",
+    [
+        # No trades yet
+        (
+            [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}],
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC"],
+            [],
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC"],
+        ),
+        # Happy path: Descending order, all values filled
+        (
+            [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}],
+            ["ETH/BTC", "TKN/BTC"],
+            [
+                {"pair": "TKN/BTC", "profit_ratio": 0.05, "count": 3},
+                {"pair": "ETH/BTC", "profit_ratio": 0.04, "count": 2},
+            ],
+            ["TKN/BTC", "ETH/BTC"],
+        ),
+        # Performance data outside allow list ignored
+        (
+            [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}],
+            ["ETH/BTC", "TKN/BTC"],
+            [
+                {"pair": "OTHER/BTC", "profit_ratio": 0.05, "count": 3},
+                {"pair": "ETH/BTC", "profit_ratio": 0.04, "count": 2},
+            ],
+            ["ETH/BTC", "TKN/BTC"],
+        ),
+        # Partial performance data missing and sorted between positive and negative profit
+        (
+            [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}],
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC"],
+            [
+                {"pair": "ETH/BTC", "profit_ratio": -0.05, "count": 100},
+                {"pair": "TKN/BTC", "profit_ratio": 0.04, "count": 2},
+            ],
+            ["TKN/BTC", "LTC/BTC", "ETH/BTC"],
+        ),
+        # Tie in performance data broken by count (ascending)
+        (
+            [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}],
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC"],
+            [
+                {"pair": "LTC/BTC", "profit_ratio": -0.0501, "count": 101},
+                {"pair": "TKN/BTC", "profit_ratio": -0.0501, "count": 2},
+                {"pair": "ETH/BTC", "profit_ratio": -0.0501, "count": 100},
+            ],
+            ["TKN/BTC", "ETH/BTC", "LTC/BTC"],
+        ),
+        # Tie in performance and count, broken by prior sorting sort
+        (
+            [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}],
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC"],
+            [
+                {"pair": "LTC/BTC", "profit_ratio": -0.0501, "count": 1},
+                {"pair": "TKN/BTC", "profit_ratio": -0.0501, "count": 1},
+                {"pair": "ETH/BTC", "profit_ratio": -0.0501, "count": 1},
+            ],
+            ["ETH/BTC", "TKN/BTC", "LTC/BTC"],
+        ),
+    ],
+)
+def test_performance_filter(
+    mocker,
+    whitelist_conf,
+    pairlists,
+    pair_allowlist,
+    overall_performance,
+    allowlist_result,
+    tickers,
+    markets,
+    ohlcv_history_list,
+):
+    allowlist_conf = whitelist_conf
+    allowlist_conf["pairlists"] = pairlists
+    allowlist_conf["exchange"]["pair_whitelist"] = pair_allowlist
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+
+    freqtrade = get_patched_freqtradebot(mocker, allowlist_conf)
+    mocker.patch.multiple(EXMS, get_tickers=tickers, markets=PropertyMock(return_value=markets))
+    mocker.patch.multiple(
+        EXMS,
+        get_historic_ohlcv=MagicMock(return_value=ohlcv_history_list),
+    )
+    mocker.patch.multiple(
+        "freqtrade.persistence.Trade",
+        get_overall_performance=MagicMock(return_value=overall_performance),
+    )
+    freqtrade.pairlists.refresh_pairlist()
+    allowlist = freqtrade.pairlists.whitelist
+    assert allowlist == allowlist_result
+
+
+@pytest.mark.parametrize(
+    "wildcardlist,pairs,expected",
+    [
+        (["BTC/USDT"], ["BTC/USDT"], ["BTC/USDT"]),
+        (["BTC/USDT", "ETH/USDT"], ["BTC/USDT", "ETH/USDT"], ["BTC/USDT", "ETH/USDT"]),
+        (["BTC/USDT", "ETH/USDT"], ["BTC/USDT"], ["BTC/USDT"]),  # Test one too many
+        ([".*/USDT"], ["BTC/USDT", "ETH/USDT"], ["BTC/USDT", "ETH/USDT"]),  # Wildcard simple
+        (
+            [".*C/USDT"],
+            ["BTC/USDT", "ETC/USDT", "ETH/USDT"],
+            ["BTC/USDT", "ETC/USDT"],
+        ),  # Wildcard exclude one
+        (
+            [".*UP/USDT", "BTC/USDT", "ETH/USDT"],
+            ["BTC/USDT", "ETC/USDT", "ETH/USDT", "BTCUP/USDT", "XRPUP/USDT", "XRPDOWN/USDT"],
+            ["BTC/USDT", "ETH/USDT", "BTCUP/USDT", "XRPUP/USDT"],
+        ),  # Wildcard exclude one
+        (
+            ["BTC/.*", "ETH/.*"],
+            ["BTC/USDT", "ETC/USDT", "ETH/USDT", "BTC/USD", "ETH/EUR", "BTC/GBP"],
+            ["BTC/USDT", "ETH/USDT", "BTC/USD", "ETH/EUR", "BTC/GBP"],
+        ),  # Wildcard exclude one
+        (
+            ["*UP/USDT", "BTC/USDT", "ETH/USDT"],
+            ["BTC/USDT", "ETC/USDT", "ETH/USDT", "BTCUP/USDT", "XRPUP/USDT", "XRPDOWN/USDT"],
+            None,
+        ),
+        (["BTC/USD"], ["BTC/USD", "BTC/USDT"], ["BTC/USD"]),
+    ],
+)
+def test_expand_pairlist(wildcardlist, pairs, expected):
+    if expected is None:
+        with pytest.raises(ValueError, match=r"Wildcard error in \*UP/USDT,"):
+            expand_pairlist(wildcardlist, pairs)
+    else:
+        assert sorted(expand_pairlist(wildcardlist, pairs)) == sorted(expected)
+        conf = {
+            "pairs": wildcardlist,
+            "freqai": {
+                "enabled": True,
+                "feature_parameters": {
+                    "include_corr_pairlist": [
+                        "BTC/USDT:USDT",
+                        "XRP/BUSD",
+                    ]
+                },
+            },
+        }
+        assert sorted(dynamic_expand_pairlist(conf, pairs)) == sorted(
+            [*expected, "BTC/USDT:USDT", "XRP/BUSD"]
+        )
+
+
+@pytest.mark.parametrize(
+    "wildcardlist,pairs,expected",
+    [
+        (["BTC/USDT"], ["BTC/USDT"], ["BTC/USDT"]),
+        (["BTC/USDT", "ETH/USDT"], ["BTC/USDT", "ETH/USDT"], ["BTC/USDT", "ETH/USDT"]),
+        (["BTC/USDT", "ETH/USDT"], ["BTC/USDT"], ["BTC/USDT", "ETH/USDT"]),  # Test one too many
+        ([".*/USDT"], ["BTC/USDT", "ETH/USDT"], ["BTC/USDT", "ETH/USDT"]),  # Wildcard simple
+        (
+            [".*C/USDT"],
+            ["BTC/USDT", "ETC/USDT", "ETH/USDT"],
+            ["BTC/USDT", "ETC/USDT"],
+        ),  # Wildcard exclude one
+        (
+            [".*UP/USDT", "BTC/USDT", "ETH/USDT"],
+            ["BTC/USDT", "ETC/USDT", "ETH/USDT", "BTCUP/USDT", "XRPUP/USDT", "XRPDOWN/USDT"],
+            ["BTC/USDT", "ETH/USDT", "BTCUP/USDT", "XRPUP/USDT"],
+        ),  # Wildcard exclude one
+        (
+            ["BTC/.*", "ETH/.*"],
+            ["BTC/USDT", "ETC/USDT", "ETH/USDT", "BTC/USD", "ETH/EUR", "BTC/GBP"],
+            ["BTC/USDT", "ETH/USDT", "BTC/USD", "ETH/EUR", "BTC/GBP"],
+        ),  # Wildcard exclude one
+        (
+            ["*UP/USDT", "BTC/USDT", "ETH/USDT"],
+            ["BTC/USDT", "ETC/USDT", "ETH/USDT", "BTCUP/USDT", "XRPUP/USDT", "XRPDOWN/USDT"],
+            None,
+        ),
+        (["HELLO/WORLD"], [], ["HELLO/WORLD"]),  # Invalid pair kept
+        (["BTC/USD"], ["BTC/USD", "BTC/USDT"], ["BTC/USD"]),
+        (["BTC/USDT:USDT"], ["BTC/USDT:USDT", "BTC/USDT"], ["BTC/USDT:USDT"]),
+        (
+            ["BB_BTC/USDT", "CC_BTC/USDT", "AA_ETH/USDT", "XRP/USDT", "ETH/USDT", "XX_BTC/USDT"],
+            ["BTC/USDT", "ETH/USDT"],
+            ["XRP/USDT", "ETH/USDT"],
+        ),
+    ],
+)
+def test_expand_pairlist_keep_invalid(wildcardlist, pairs, expected):
+    if expected is None:
+        with pytest.raises(ValueError, match=r"Wildcard error in \*UP/USDT,"):
+            expand_pairlist(wildcardlist, pairs, keep_invalid=True)
+    else:
+        assert sorted(expand_pairlist(wildcardlist, pairs, keep_invalid=True)) == sorted(expected)
+
+
+def test_ProducerPairlist_no_emc(mocker, whitelist_conf):
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+
+    whitelist_conf["pairlists"] = [
+        {
+            "method": "ProducerPairList",
+            "number_assets": 10,
+            "producer_name": "hello_world",
+        }
+    ]
+    del whitelist_conf["external_message_consumer"]
+
+    with pytest.raises(
+        OperationalException,
+        match=r"ProducerPairList requires external_message_consumer to be enabled.",
+    ):
+        get_patched_freqtradebot(mocker, whitelist_conf)
+
+
+def test_ProducerPairlist(mocker, whitelist_conf, markets):
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+    )
+    whitelist_conf["pairlists"] = [
+        {
+            "method": "ProducerPairList",
+            "number_assets": 2,
+            "producer_name": "hello_world",
+        }
+    ]
+    whitelist_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {
+                        "name": "hello_world",
+                        "host": "null",
+                        "port": 9891,
+                        "ws_token": "dummy",
+                    }
+                ],
+            }
+        }
+    )
+
+    exchange = get_patched_exchange(mocker, whitelist_conf)
+    dp = DataProvider(whitelist_conf, exchange, None)
+    pairs = ["ETH/BTC", "LTC/BTC", "XRP/BTC"]
+    # different producer
+    dp._set_producer_pairs([*pairs, "MEEP/USDT"], "default")
+    pm = PairListManager(exchange, whitelist_conf, dp)
+    pm.refresh_pairlist()
+    assert pm.whitelist == []
+    # proper producer
+    dp._set_producer_pairs(pairs, "hello_world")
+    pm.refresh_pairlist()
+
+    # Pairlist reduced to 2
+    assert pm.whitelist == pairs[:2]
+    assert len(pm.whitelist) == 2
+    whitelist_conf["exchange"]["pair_whitelist"] = ["TKN/BTC"]
+
+    whitelist_conf["pairlists"] = [
+        {"method": "StaticPairList"},
+        {
+            "method": "ProducerPairList",
+            "producer_name": "hello_world",
+        },
+    ]
+    pm = PairListManager(exchange, whitelist_conf, dp)
+    pm.refresh_pairlist()
+    assert len(pm.whitelist) == 4
+    assert pm.whitelist == ["TKN/BTC", *pairs]
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_FullTradesFilter(mocker, default_conf_usdt, fee, caplog) -> None:
+    default_conf_usdt["exchange"]["pair_whitelist"].extend(["ADA/USDT", "XRP/USDT", "ETC/USDT"])
+    default_conf_usdt["pairlists"] = [{"method": "StaticPairList"}, {"method": "FullTradesFilter"}]
+    default_conf_usdt["max_open_trades"] = -1
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    exchange = get_patched_exchange(mocker, default_conf_usdt)
+    pm = PairListManager(exchange, default_conf_usdt)
+    pm.refresh_pairlist()
+
+    assert pm.whitelist == ["ETH/USDT", "XRP/USDT", "NEO/USDT", "TKN/USDT"]
+
+    with time_machine.travel("2021-09-01 05:00:00 +00:00") as t:
+        create_mock_trades_usdt(fee)
+        pm.refresh_pairlist()
+
+        # Unlimited max open trades, so no change to whitelist
+        pm.refresh_pairlist()
+        assert pm.whitelist == ["ETH/USDT", "XRP/USDT", "NEO/USDT", "TKN/USDT"]
+
+        # Set max_open_trades to 4, the filter should empty the whitelist
+        default_conf_usdt["max_open_trades"] = 4
+        pm.refresh_pairlist()
+        assert pm.whitelist == []
+        assert log_has_re(r"Whitelist with 0 pairs: \[]", caplog)
+
+        list_trades = LocalTrade.get_open_trades()
+        assert len(list_trades) == 4
+
+        # Move to 1 hour later, close a trade, so original sorting is restored.
+        t.move_to("2021-09-01 07:00:00 +00:00")
+        list_trades[2].close(12)
+        Trade.commit()
+
+        # open trades count below max_open_trades, whitelist restored
+        list_trades = LocalTrade.get_open_trades()
+        assert len(list_trades) == 3
+        pm.refresh_pairlist()
+        assert pm.whitelist == ["ETH/USDT", "XRP/USDT", "NEO/USDT", "TKN/USDT"]
+
+        # Set max_open_trades to 3, the filter should empty the whitelist
+        default_conf_usdt["max_open_trades"] = 3
+        pm.refresh_pairlist()
+        assert pm.whitelist == []
+        assert log_has_re(r"Whitelist with 0 pairs: \[]", caplog)
+
+
+@pytest.mark.parametrize(
+    "pairlists,trade_mode,result,coin_market_calls",
+    [
+        (
+            [
+                # Get 2 pairs
+                {"method": "StaticPairList", "allow_inactive": True},
+                {"method": "MarketCapPairList", "number_assets": 2},
+            ],
+            "spot",
+            ["BTC/USDT", "ETH/USDT"],
+            1,
+        ),
+        (
+            [
+                # Get 6 pairs
+                {"method": "StaticPairList", "allow_inactive": True},
+                {"method": "MarketCapPairList", "number_assets": 6},
+            ],
+            "spot",
+            ["BTC/USDT", "ETH/USDT", "XRP/USDT", "ADA/USDT"],
+            1,
+        ),
+        (
+            [
+                # Get 3 pairs within top 6 ranks
+                {"method": "StaticPairList", "allow_inactive": True},
+                {"method": "MarketCapPairList", "max_rank": 6, "number_assets": 3},
+            ],
+            "spot",
+            ["BTC/USDT", "ETH/USDT", "XRP/USDT"],
+            1,
+        ),
+        (
+            [
+                # Get 4 pairs within top 8 ranks
+                {"method": "StaticPairList", "allow_inactive": True},
+                {"method": "MarketCapPairList", "max_rank": 8, "number_assets": 4},
+            ],
+            "spot",
+            ["BTC/USDT", "ETH/USDT", "XRP/USDT"],
+            1,
+        ),
+        (
+            [
+                # MarketCapPairList as generator
+                {"method": "MarketCapPairList", "number_assets": 5}
+            ],
+            "spot",
+            ["BTC/USDT", "ETH/USDT", "XRP/USDT"],
+            1,
+        ),
+        (
+            [
+                # MarketCapPairList as generator - low max_rank
+                {"method": "MarketCapPairList", "max_rank": 2, "number_assets": 5}
+            ],
+            "spot",
+            ["BTC/USDT", "ETH/USDT"],
+            1,
+        ),
+        (
+            [
+                # MarketCapPairList as generator - futures - low max_rank
+                {"method": "MarketCapPairList", "max_rank": 2, "number_assets": 5}
+            ],
+            "futures",
+            ["ETH/USDT:USDT"],
+            1,
+        ),
+        (
+            [
+                # MarketCapPairList as generator - futures - low number_assets
+                {"method": "MarketCapPairList", "number_assets": 2}
+            ],
+            "futures",
+            ["ETH/USDT:USDT", "ADA/USDT:USDT"],
+            1,
+        ),
+        (
+            [
+                # MarketCapPairList as generator - futures, 1 category
+                {"method": "MarketCapPairList", "number_assets": 2, "categories": ["layer-1"]}
+            ],
+            "futures",
+            ["ETH/USDT:USDT", "ADA/USDT:USDT"],
+            ["layer-1"],
+        ),
+        (
+            [
+                # MarketCapPairList as generator - futures, 1 category
+                {
+                    "method": "MarketCapPairList",
+                    "number_assets": 2,
+                    "categories": ["layer-1", "protocol"],
+                }
+            ],
+            "futures",
+            ["ETH/USDT:USDT", "ADA/USDT:USDT"],
+            ["layer-1", "protocol"],
+        ),
+    ],
+)
+def test_MarketCapPairList_filter(
+    mocker, default_conf_usdt, trade_mode, markets, pairlists, result, coin_market_calls
+):
+    test_value = [
+        {"symbol": "btc"},
+        {"symbol": "eth"},
+        {"symbol": "usdt"},
+        {"symbol": "bnb"},
+        {"symbol": "sol"},
+        {"symbol": "xrp"},
+        {"symbol": "usdc"},
+        {"symbol": "steth"},
+        {"symbol": "ada"},
+        {"symbol": "avax"},
+    ]
+
+    default_conf_usdt["trading_mode"] = trade_mode
+    if trade_mode == "spot":
+        default_conf_usdt["exchange"]["pair_whitelist"].extend(["BTC/USDT", "ETC/USDT", "ADA/USDT"])
+    default_conf_usdt["pairlists"] = pairlists
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+    )
+    mocker.patch(
+        "freqtrade.plugins.pairlist.MarketCapPairList.FtCoinGeckoApi.get_coins_categories_list",
+        return_value=[
+            {"category_id": "layer-1"},
+            {"category_id": "protocol"},
+            {"category_id": "defi"},
+        ],
+    )
+
+    gcm_mock = mocker.patch(
+        "freqtrade.plugins.pairlist.MarketCapPairList.FtCoinGeckoApi.get_coins_markets",
+        return_value=test_value,
+    )
+
+    exchange = get_patched_exchange(mocker, default_conf_usdt)
+
+    pm = PairListManager(exchange, default_conf_usdt)
+    pm.refresh_pairlist()
+    if isinstance(coin_market_calls, int):
+        assert gcm_mock.call_count == coin_market_calls
+    else:
+        assert gcm_mock.call_count == len(coin_market_calls)
+        for call in coin_market_calls:
+            assert any(
+                "category" in c.kwargs and c.kwargs["category"] == call
+                for c in gcm_mock.call_args_list
+            )
+
+    assert pm.whitelist == result
+
+
+def test_MarketCapPairList_timing(mocker, default_conf_usdt, markets, time_machine):
+    test_value = [
+        {"symbol": "btc"},
+        {"symbol": "eth"},
+        {"symbol": "usdt"},
+        {"symbol": "bnb"},
+        {"symbol": "sol"},
+        {"symbol": "xrp"},
+        {"symbol": "usdc"},
+        {"symbol": "steth"},
+        {"symbol": "ada"},
+        {"symbol": "avax"},
+    ]
+
+    default_conf_usdt["trading_mode"] = "spot"
+    default_conf_usdt["exchange"]["pair_whitelist"].extend(["BTC/USDT", "ETC/USDT", "ADA/USDT"])
+    default_conf_usdt["pairlists"] = [{"method": "MarketCapPairList", "number_assets": 2}]
+
+    markets_mock = MagicMock(return_value=markets)
+    mocker.patch.multiple(
+        EXMS,
+        get_markets=markets_mock,
+        exchange_has=MagicMock(return_value=True),
+    )
+
+    mocker.patch(
+        "freqtrade.plugins.pairlist.MarketCapPairList.FtCoinGeckoApi.get_coins_markets",
+        return_value=test_value,
+    )
+
+    start_dt = dt_now()
+
+    exchange = get_patched_exchange(mocker, default_conf_usdt)
+    time_machine.move_to(start_dt)
+
+    pm = PairListManager(exchange, default_conf_usdt)
+    markets_mock.reset_mock()
+    pm.refresh_pairlist()
+    assert markets_mock.call_count == 3
+    markets_mock.reset_mock()
+
+    time_machine.move_to(start_dt + timedelta(hours=20))
+    pm.refresh_pairlist()
+    # Cached pairlist ...
+    assert markets_mock.call_count == 1
+
+    markets_mock.reset_mock()
+    time_machine.move_to(start_dt + timedelta(days=2))
+    pm.refresh_pairlist()
+    # No longer cached pairlist ...
+    assert markets_mock.call_count == 3
+
+
+def test_MarketCapPairList_filter_special_no_pair_from_coingecko(
+    mocker,
+    default_conf_usdt,
+    markets,
+):
+    default_conf_usdt["pairlists"] = [{"method": "MarketCapPairList", "number_assets": 2}]
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+    )
+
+    # Simulate no pair returned from coingecko
+    gcm_mock = mocker.patch(
+        "freqtrade.plugins.pairlist.MarketCapPairList.FtCoinGeckoApi.get_coins_markets",
+        return_value=[],
+    )
+
+    exchange = get_patched_exchange(mocker, default_conf_usdt)
+
+    pm = PairListManager(exchange, default_conf_usdt)
+    pm.refresh_pairlist()
+    assert gcm_mock.call_count == 1
+    assert pm.whitelist == []
+
+
+def test_MarketCapPairList_exceptions(mocker, default_conf_usdt, caplog):
+    exchange = get_patched_exchange(mocker, default_conf_usdt)
+    default_conf_usdt["pairlists"] = [{"method": "MarketCapPairList"}]
+    with pytest.raises(OperationalException, match=r"`number_assets` not specified.*"):
+        # No number_assets
+        PairListManager(exchange, default_conf_usdt)
+
+    default_conf_usdt["pairlists"] = [
+        {"method": "MarketCapPairList", "number_assets": 20, "max_rank": 500}
+    ]
+    with caplog.at_level(logging.WARNING):
+        PairListManager(exchange, default_conf_usdt)
+    assert log_has_re("The max rank you have set \\(500\\) is quite high", caplog)
+    # Test invalid coinmarkets list
+    mocker.patch(
+        "freqtrade.plugins.pairlist.MarketCapPairList.FtCoinGeckoApi.get_coins_categories_list",
+        return_value=[
+            {"category_id": "layer-1"},
+            {"category_id": "protocol"},
+            {"category_id": "defi"},
+        ],
+    )
+    default_conf_usdt["pairlists"] = [
+        {
+            "method": "MarketCapPairList",
+            "number_assets": 20,
+            "categories": ["layer-1", "defi", "layer250"],
+        }
+    ]
+    with pytest.raises(
+        OperationalException, match="Category layer250 not in coingecko category list."
+    ):
+        PairListManager(exchange, default_conf_usdt)
+
+
+@pytest.mark.parametrize(
+    "pairlists,expected_error,expected_warning",
+    [
+        (
+            [{"method": "StaticPairList"}],
+            None,  # Error
+            None,  # Warning
+        ),
+        (
+            [{"method": "VolumePairList", "number_assets": 10}],
+            "VolumePairList",  # Error
+            None,  # Warning
+        ),
+        (
+            [{"method": "MarketCapPairList", "number_assets": 10}],
+            None,  # Error
+            r"MarketCapPairList.*lookahead.*",  # Warning
+        ),
+        (
+            [{"method": "StaticPairList"}, {"method": "FullTradesFilter"}],
+            None,  # Error
+            r"FullTradesFilter do not generate.*",  # Warning
+        ),
+        (  # combi, fails and warns
+            [
+                {"method": "VolumePairList", "number_assets": 10},
+                {"method": "MarketCapPairList", "number_assets": 10},
+            ],
+            "VolumePairList",  # Error
+            r"MarketCapPairList.*lookahead.*",  # Warning
+        ),
+    ],
+)
+def test_backtesting_modes(
+    mocker, default_conf_usdt, pairlists, expected_error, expected_warning, caplog, markets, tickers
+):
+    default_conf_usdt["runmode"] = "dry_run"
+    default_conf_usdt["pairlists"] = pairlists
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+    exchange = get_patched_exchange(mocker, default_conf_usdt)
+
+    # Dry run mode - works always
+    PairListManager(exchange, default_conf_usdt)
+
+    default_conf_usdt["runmode"] = "backtest"
+    if expected_error:
+        with pytest.raises(OperationalException, match=f"Pairlist Handlers {expected_error}.*"):
+            PairListManager(exchange, default_conf_usdt)
+
+    if not expected_error:
+        PairListManager(exchange, default_conf_usdt)
+
+    if expected_warning:
+        assert log_has_re(f"Pairlist Handlers {expected_warning}", caplog)
diff --git a/tests/plugins/test_pairlocks.py b/tests/plugins/test_pairlocks.py
new file mode 100644
index 000000000..f227818b3
--- /dev/null
+++ b/tests/plugins/test_pairlocks.py
@@ -0,0 +1,160 @@
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from freqtrade.persistence import PairLocks
+from freqtrade.persistence.models import PairLock
+from freqtrade.util import dt_now
+
+
+@pytest.mark.parametrize("use_db", (False, True))
+@pytest.mark.usefixtures("init_persistence")
+def test_PairLocks(use_db):
+    PairLocks.timeframe = "5m"
+    PairLocks.use_db = use_db
+    # No lock should be present
+    if use_db:
+        assert len(PairLock.get_all_locks().all()) == 0
+
+    assert PairLocks.use_db == use_db
+
+    pair = "ETH/BTC"
+    assert not PairLocks.is_pair_locked(pair)
+    PairLocks.lock_pair(pair, dt_now() + timedelta(minutes=4))
+    # ETH/BTC locked for 4 minutes (on both sides)
+    assert PairLocks.is_pair_locked(pair)
+    assert PairLocks.is_pair_locked(pair, side="long")
+    assert PairLocks.is_pair_locked(pair, side="short")
+
+    pair = "BNB/BTC"
+    PairLocks.lock_pair(pair, dt_now() + timedelta(minutes=4), side="long")
+    assert not PairLocks.is_pair_locked(pair)
+    assert PairLocks.is_pair_locked(pair, side="long")
+    assert not PairLocks.is_pair_locked(pair, side="short")
+
+    pair = "BNB/USDT"
+    PairLocks.lock_pair(pair, dt_now() + timedelta(minutes=4), side="short")
+    assert not PairLocks.is_pair_locked(pair)
+    assert not PairLocks.is_pair_locked(pair, side="long")
+    assert PairLocks.is_pair_locked(pair, side="short")
+    assert len(PairLocks.get_pair_locks(pair)) == 1
+
+    # XRP/BTC should not be locked now
+    pair = "XRP/BTC"
+    assert not PairLocks.is_pair_locked(pair)
+    # Unlocking a pair that's not locked should not raise an error
+    PairLocks.unlock_pair(pair)
+
+    PairLocks.lock_pair(pair, dt_now() + timedelta(minutes=4))
+    assert PairLocks.is_pair_locked(pair)
+
+    # Get all locks from above
+    locks = PairLocks.get_pair_locks(None)
+    assert len(locks) == 4
+
+    assert len(PairLocks.get_pair_locks(None, side="*")) == 2
+
+    # Unlock original pair
+    pair = "ETH/BTC"
+    PairLocks.unlock_pair(pair)
+    assert not PairLocks.is_pair_locked(pair)
+    assert not PairLocks.is_global_lock()
+
+    pair = "BTC/USDT"
+    # Lock until 14:30
+    lock_time = datetime(2020, 5, 1, 14, 30, 0, tzinfo=timezone.utc)
+    PairLocks.lock_pair(pair, lock_time)
+
+    assert not PairLocks.is_pair_locked(pair)
+    assert PairLocks.is_pair_locked(pair, lock_time + timedelta(minutes=-10))
+    assert not PairLocks.is_global_lock(lock_time + timedelta(minutes=-10))
+    assert PairLocks.is_pair_locked(pair, lock_time + timedelta(minutes=-50))
+    assert not PairLocks.is_global_lock(lock_time + timedelta(minutes=-50))
+
+    # Should not be locked after time expired
+    assert not PairLocks.is_pair_locked(pair, lock_time + timedelta(minutes=10))
+
+    locks = PairLocks.get_pair_locks(pair, lock_time + timedelta(minutes=-2))
+    assert len(locks) == 1
+    assert "PairLock" in str(locks[0])
+
+    # Unlock all
+    PairLocks.unlock_pair(pair, lock_time + timedelta(minutes=-2))
+    assert not PairLocks.is_global_lock(lock_time + timedelta(minutes=-50))
+
+    # Global lock
+    PairLocks.lock_pair("*", lock_time)
+    assert PairLocks.is_global_lock(lock_time + timedelta(minutes=-50))
+    # Global lock also locks every pair separately
+    assert PairLocks.is_pair_locked(pair, lock_time + timedelta(minutes=-50))
+    assert PairLocks.is_pair_locked("XRP/USDT", lock_time + timedelta(minutes=-50))
+
+    if use_db:
+        locks = PairLocks.get_all_locks()
+        locks_db = PairLock.get_all_locks().all()
+        assert len(locks) == len(locks_db)
+        assert len(locks_db) > 0
+    else:
+        # Nothing was pushed to the database
+        assert len(PairLocks.get_all_locks()) > 0
+        assert len(PairLock.get_all_locks().all()) == 0
+    # Reset use-db variable
+    PairLocks.reset_locks()
+    PairLocks.use_db = True
+
+
+@pytest.mark.parametrize("use_db", (False, True))
+@pytest.mark.usefixtures("init_persistence")
+def test_PairLocks_getlongestlock(use_db):
+    PairLocks.timeframe = "5m"
+    # No lock should be present
+    PairLocks.use_db = use_db
+    if use_db:
+        assert len(PairLock.get_all_locks().all()) == 0
+
+    assert PairLocks.use_db == use_db
+
+    pair = "ETH/BTC"
+    assert not PairLocks.is_pair_locked(pair)
+    PairLocks.lock_pair(pair, dt_now() + timedelta(minutes=4))
+    # ETH/BTC locked for 4 minutes
+    assert PairLocks.is_pair_locked(pair)
+    lock = PairLocks.get_pair_longest_lock(pair)
+
+    assert lock.lock_end_time.replace(tzinfo=timezone.utc) > dt_now() + timedelta(minutes=3)
+    assert lock.lock_end_time.replace(tzinfo=timezone.utc) < dt_now() + timedelta(minutes=14)
+
+    PairLocks.lock_pair(pair, dt_now() + timedelta(minutes=15))
+    assert PairLocks.is_pair_locked(pair)
+
+    lock = PairLocks.get_pair_longest_lock(pair)
+    # Must be longer than above
+    assert lock.lock_end_time.replace(tzinfo=timezone.utc) > dt_now() + timedelta(minutes=14)
+
+    PairLocks.reset_locks()
+    PairLocks.use_db = True
+
+
+@pytest.mark.parametrize("use_db", (False, True))
+@pytest.mark.usefixtures("init_persistence")
+def test_PairLocks_reason(use_db):
+    PairLocks.timeframe = "5m"
+    PairLocks.use_db = use_db
+    # No lock should be present
+    if use_db:
+        assert len(PairLock.get_all_locks().all()) == 0
+
+    assert PairLocks.use_db == use_db
+
+    PairLocks.lock_pair("XRP/USDT", dt_now() + timedelta(minutes=4), "TestLock1")
+    PairLocks.lock_pair("ETH/USDT", dt_now() + timedelta(minutes=4), "TestLock2")
+
+    assert PairLocks.is_pair_locked("XRP/USDT")
+    assert PairLocks.is_pair_locked("ETH/USDT")
+
+    PairLocks.unlock_reason("TestLock1")
+    assert not PairLocks.is_pair_locked("XRP/USDT")
+    assert PairLocks.is_pair_locked("ETH/USDT")
+
+    PairLocks.reset_locks()
+    PairLocks.use_db = True
diff --git a/tests/plugins/test_percentchangepairlist.py b/tests/plugins/test_percentchangepairlist.py
new file mode 100644
index 000000000..7c2f16d76
--- /dev/null
+++ b/tests/plugins/test_percentchangepairlist.py
@@ -0,0 +1,377 @@
+from datetime import datetime, timezone
+from unittest.mock import MagicMock
+
+import pandas as pd
+import pytest
+
+from freqtrade.data.converter import ohlcv_to_dataframe
+from freqtrade.enums import CandleType
+from freqtrade.exceptions import OperationalException
+from freqtrade.plugins.pairlist.PercentChangePairList import PercentChangePairList
+from freqtrade.plugins.pairlistmanager import PairListManager
+from tests.conftest import (
+    EXMS,
+    generate_test_data_raw,
+    get_patched_exchange,
+    get_patched_freqtradebot,
+)
+
+
+@pytest.fixture(scope="function")
+def rpl_config(default_conf):
+    default_conf["stake_currency"] = "USDT"
+
+    default_conf["exchange"]["pair_whitelist"] = [
+        "ETH/USDT",
+        "XRP/USDT",
+    ]
+    default_conf["exchange"]["pair_blacklist"] = ["BLK/USDT"]
+
+    return default_conf
+
+
+def test_volume_change_pair_list_init_exchange_support(mocker, rpl_config):
+    rpl_config["pairlists"] = [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 2,
+            "sort_key": "percentage",
+            "min_value": 0,
+            "refresh_period": 86400,
+        }
+    ]
+
+    with pytest.raises(
+        OperationalException,
+        match=r"Exchange does not support dynamic whitelist in this configuration. "
+        r"Please edit your config and either remove PercentChangePairList, "
+        r"or switch to using candles. and restart the bot.",
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+
+def test_volume_change_pair_list_init_wrong_refresh_period(mocker, rpl_config):
+    rpl_config["pairlists"] = [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 2,
+            "sort_key": "percentage",
+            "min_value": 0,
+            "refresh_period": 1800,
+            "lookback_days": 4,
+        }
+    ]
+
+    with pytest.raises(
+        OperationalException,
+        match=r"Refresh period of 1800 seconds is smaller than one "
+        r"timeframe of 1d. Please adjust refresh_period "
+        r"to at least 86400 and restart the bot.",
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+
+def test_volume_change_pair_list_init_wrong_lookback_period(mocker, rpl_config):
+    rpl_config["pairlists"] = [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 2,
+            "sort_key": "percentage",
+            "min_value": 0,
+            "refresh_period": 86400,
+            "lookback_days": 3,
+            "lookback_period": 3,
+        }
+    ]
+
+    with pytest.raises(
+        OperationalException,
+        match=r"Ambiguous configuration: lookback_days "
+        r"and lookback_period both set in pairlist config. "
+        r"Please set lookback_days only or lookback_period "
+        r"and lookback_timeframe and restart the bot.",
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+    rpl_config["pairlists"] = [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 2,
+            "sort_key": "percentage",
+            "min_value": 0,
+            "refresh_period": 86400,
+            "lookback_days": 1001,
+        }
+    ]
+
+    with pytest.raises(
+        OperationalException,
+        match=r"ChangeFilter requires lookback_period to not exceed"
+        r" exchange max request size \(\d+\)",
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+
+def test_volume_change_pair_list_init_wrong_config(mocker, rpl_config):
+    rpl_config["pairlists"] = [
+        {
+            "method": "PercentChangePairList",
+            "sort_key": "percentage",
+            "min_value": 0,
+            "refresh_period": 86400,
+        }
+    ]
+
+    with pytest.raises(
+        OperationalException,
+        match=r"`number_assets` not specified. Please check your configuration "
+        r'for "pairlist.config.number_assets"',
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+
+def test_gen_pairlist_with_valid_change_pair_list_config(mocker, rpl_config, tickers, time_machine):
+    rpl_config["pairlists"] = [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 2,
+            "sort_key": "percentage",
+            "min_value": 0,
+            "refresh_period": 86400,
+            "lookback_days": 4,
+        }
+    ]
+    start = datetime(2024, 8, 1, 0, 0, 0, 0, tzinfo=timezone.utc)
+    time_machine.move_to(start, tick=False)
+
+    mock_ohlcv_data = {
+        ("ETH/USDT", "1d", CandleType.SPOT): pd.DataFrame(
+            ohlcv_to_dataframe(
+                generate_test_data_raw("1d", 100, start.strftime("%Y-%m-%d"), random_seed=12),
+                "1d",
+                pair="ETH/USDT",
+                fill_missing=True,
+            )
+        ),
+        ("BTC/USDT", "1d", CandleType.SPOT): pd.DataFrame(
+            ohlcv_to_dataframe(
+                generate_test_data_raw("1d", 100, start.strftime("%Y-%m-%d"), random_seed=13),
+                "1d",
+                pair="BTC/USDT",
+                fill_missing=True,
+            )
+        ),
+        ("XRP/USDT", "1d", CandleType.SPOT): pd.DataFrame(
+            ohlcv_to_dataframe(
+                generate_test_data_raw("1d", 100, start.strftime("%Y-%m-%d"), random_seed=14),
+                "1d",
+                pair="XRP/USDT",
+                fill_missing=True,
+            )
+        ),
+        ("NEO/USDT", "1d", CandleType.SPOT): pd.DataFrame(
+            ohlcv_to_dataframe(
+                generate_test_data_raw("1d", 100, start.strftime("%Y-%m-%d"), random_seed=15),
+                "1d",
+                pair="NEO/USDT",
+                fill_missing=True,
+            )
+        ),
+        ("TKN/USDT", "1d", CandleType.SPOT): pd.DataFrame(
+            # Make sure always have highest percentage
+            {
+                "timestamp": [
+                    "2024-07-01 00:00:00",
+                    "2024-07-01 01:00:00",
+                    "2024-07-01 02:00:00",
+                    "2024-07-01 03:00:00",
+                    "2024-07-01 04:00:00",
+                    "2024-07-01 05:00:00",
+                ],
+                "open": [100, 102, 101, 103, 104, 105],
+                "high": [102, 103, 102, 104, 105, 106],
+                "low": [99, 101, 100, 102, 103, 104],
+                "close": [101, 102, 103, 104, 105, 106],
+                "volume": [1000, 1500, 2000, 2500, 3000, 3500],
+            }
+        ),
+    }
+
+    mocker.patch(f"{EXMS}.refresh_latest_ohlcv", MagicMock(return_value=mock_ohlcv_data))
+
+    exchange = get_patched_exchange(mocker, rpl_config, exchange="binance")
+    pairlistmanager = PairListManager(exchange, rpl_config)
+
+    remote_pairlist = PercentChangePairList(
+        exchange, pairlistmanager, rpl_config, rpl_config["pairlists"][0], 0
+    )
+
+    result = remote_pairlist.gen_pairlist(tickers)
+
+    assert len(result) == 2
+    assert result == ["NEO/USDT", "TKN/USDT"]
+
+
+def test_filter_pairlist_with_empty_ticker(mocker, rpl_config, tickers, time_machine):
+    rpl_config["pairlists"] = [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 2,
+            "sort_key": "percentage",
+            "min_value": 0,
+            "refresh_period": 86400,
+            "sort_direction": "asc",
+            "lookback_days": 4,
+        }
+    ]
+    start = datetime(2024, 8, 1, 0, 0, 0, 0, tzinfo=timezone.utc)
+    time_machine.move_to(start, tick=False)
+
+    mock_ohlcv_data = {
+        ("ETH/USDT", "1d", CandleType.SPOT): pd.DataFrame(
+            {
+                "timestamp": [
+                    "2024-07-01 00:00:00",
+                    "2024-07-01 01:00:00",
+                    "2024-07-01 02:00:00",
+                    "2024-07-01 03:00:00",
+                    "2024-07-01 04:00:00",
+                    "2024-07-01 05:00:00",
+                ],
+                "open": [100, 102, 101, 103, 104, 105],
+                "high": [102, 103, 102, 104, 105, 106],
+                "low": [99, 101, 100, 102, 103, 104],
+                "close": [101, 102, 103, 104, 105, 105],
+                "volume": [1000, 1500, 2000, 2500, 3000, 3500],
+            }
+        ),
+        ("XRP/USDT", "1d", CandleType.SPOT): pd.DataFrame(
+            {
+                "timestamp": [
+                    "2024-07-01 00:00:00",
+                    "2024-07-01 01:00:00",
+                    "2024-07-01 02:00:00",
+                    "2024-07-01 03:00:00",
+                    "2024-07-01 04:00:00",
+                    "2024-07-01 05:00:00",
+                ],
+                "open": [100, 102, 101, 103, 104, 105],
+                "high": [102, 103, 102, 104, 105, 106],
+                "low": [99, 101, 100, 102, 103, 104],
+                "close": [101, 102, 103, 104, 105, 104],
+                "volume": [1000, 1500, 2000, 2500, 3000, 3400],
+            }
+        ),
+    }
+
+    mocker.patch(f"{EXMS}.refresh_latest_ohlcv", MagicMock(return_value=mock_ohlcv_data))
+    exchange = get_patched_exchange(mocker, rpl_config, exchange="binance")
+    pairlistmanager = PairListManager(exchange, rpl_config)
+
+    remote_pairlist = PercentChangePairList(
+        exchange, pairlistmanager, rpl_config, rpl_config["pairlists"][0], 0
+    )
+
+    result = remote_pairlist.filter_pairlist(rpl_config["exchange"]["pair_whitelist"], {})
+
+    assert len(result) == 2
+    assert result == ["XRP/USDT", "ETH/USDT"]
+
+
+def test_filter_pairlist_with_max_value_set(mocker, rpl_config, tickers, time_machine):
+    rpl_config["pairlists"] = [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 2,
+            "sort_key": "percentage",
+            "min_value": 0,
+            "max_value": 15,
+            "refresh_period": 86400,
+            "lookback_days": 4,
+        }
+    ]
+
+    start = datetime(2024, 8, 1, 0, 0, 0, 0, tzinfo=timezone.utc)
+    time_machine.move_to(start, tick=False)
+
+    mock_ohlcv_data = {
+        ("ETH/USDT", "1d", CandleType.SPOT): pd.DataFrame(
+            {
+                "timestamp": [
+                    "2024-07-01 00:00:00",
+                    "2024-07-01 01:00:00",
+                    "2024-07-01 02:00:00",
+                    "2024-07-01 03:00:00",
+                    "2024-07-01 04:00:00",
+                    "2024-07-01 05:00:00",
+                ],
+                "open": [100, 102, 101, 103, 104, 105],
+                "high": [102, 103, 102, 104, 105, 106],
+                "low": [99, 101, 100, 102, 103, 104],
+                "close": [101, 102, 103, 104, 105, 106],
+                "volume": [1000, 1500, 2000, 1800, 2400, 2500],
+            }
+        ),
+        ("XRP/USDT", "1d", CandleType.SPOT): pd.DataFrame(
+            {
+                "timestamp": [
+                    "2024-07-01 00:00:00",
+                    "2024-07-01 01:00:00",
+                    "2024-07-01 02:00:00",
+                    "2024-07-01 03:00:00",
+                    "2024-07-01 04:00:00",
+                    "2024-07-01 05:00:00",
+                ],
+                "open": [100, 102, 101, 103, 104, 105],
+                "high": [102, 103, 102, 104, 105, 106],
+                "low": [99, 101, 100, 102, 103, 104],
+                "close": [101, 102, 103, 104, 105, 101],
+                "volume": [1000, 1500, 2000, 2500, 3000, 3500],
+            }
+        ),
+    }
+
+    mocker.patch(f"{EXMS}.refresh_latest_ohlcv", MagicMock(return_value=mock_ohlcv_data))
+    exchange = get_patched_exchange(mocker, rpl_config, exchange="binance")
+    pairlistmanager = PairListManager(exchange, rpl_config)
+
+    remote_pairlist = PercentChangePairList(
+        exchange, pairlistmanager, rpl_config, rpl_config["pairlists"][0], 0
+    )
+
+    result = remote_pairlist.filter_pairlist(rpl_config["exchange"]["pair_whitelist"], {})
+
+    assert len(result) == 1
+    assert result == ["ETH/USDT"]
+
+
+def test_gen_pairlist_from_tickers(mocker, rpl_config, tickers):
+    rpl_config["pairlists"] = [
+        {
+            "method": "PercentChangePairList",
+            "number_assets": 2,
+            "sort_key": "percentage",
+            "min_value": 0,
+        }
+    ]
+
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+
+    exchange = get_patched_exchange(mocker, rpl_config, exchange="binance")
+    pairlistmanager = PairListManager(exchange, rpl_config)
+
+    remote_pairlist = pairlistmanager._pairlist_handlers[0]
+
+    # The generator returns BTC ETH and TKN - filtering the first ensures removing pairs
+    # in this step ain't problematic.
+    def _validate_pair(pair, ticker):
+        if pair == "BTC/USDT":
+            return False
+        return True
+
+    remote_pairlist._validate_pair = _validate_pair
+
+    result = remote_pairlist.gen_pairlist(tickers.return_value)
+
+    assert len(result) == 1
+    assert result == ["ETH/USDT"]
diff --git a/tests/plugins/test_protections.py b/tests/plugins/test_protections.py
new file mode 100644
index 000000000..ef2c7e1d3
--- /dev/null
+++ b/tests/plugins/test_protections.py
@@ -0,0 +1,879 @@
+import random
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from freqtrade.enums import ExitType
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence import PairLocks, Trade
+from freqtrade.persistence.trade_model import Order
+from freqtrade.plugins.protectionmanager import ProtectionManager
+from tests.conftest import get_patched_freqtradebot, log_has_re
+
+
+AVAILABLE_PROTECTIONS = ["CooldownPeriod", "LowProfitPairs", "MaxDrawdown", "StoplossGuard"]
+
+
+def generate_mock_trade(
+    pair: str,
+    fee: float,
+    is_open: bool,
+    exit_reason: str = ExitType.EXIT_SIGNAL,
+    min_ago_open: int | None = None,
+    min_ago_close: int | None = None,
+    profit_rate: float = 0.9,
+    is_short: bool = False,
+):
+    open_rate = random.random()
+
+    trade = Trade(
+        pair=pair,
+        stake_amount=0.01,
+        fee_open=fee,
+        fee_close=fee,
+        open_date=datetime.now(timezone.utc) - timedelta(minutes=min_ago_open or 200),
+        close_date=datetime.now(timezone.utc) - timedelta(minutes=min_ago_close or 30),
+        open_rate=open_rate,
+        is_open=is_open,
+        amount=0.01 / open_rate,
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+    )
+
+    trade.orders.append(
+        Order(
+            ft_order_side=trade.entry_side,
+            order_id=f"{pair}-{trade.entry_side}-{trade.open_date}",
+            ft_is_open=False,
+            ft_pair=pair,
+            ft_amount=trade.amount,
+            ft_price=trade.open_rate,
+            amount=trade.amount,
+            filled=trade.amount,
+            remaining=0,
+            price=open_rate,
+            average=open_rate,
+            status="closed",
+            order_type="market",
+            side=trade.entry_side,
+        )
+    )
+    if not is_open:
+        close_price = open_rate * (2 - profit_rate if is_short else profit_rate)
+        trade.orders.append(
+            Order(
+                ft_order_side=trade.exit_side,
+                order_id=f"{pair}-{trade.exit_side}-{trade.close_date}",
+                ft_is_open=False,
+                ft_pair=pair,
+                ft_amount=trade.amount,
+                ft_price=trade.open_rate,
+                amount=trade.amount,
+                filled=trade.amount,
+                remaining=0,
+                price=close_price,
+                average=close_price,
+                status="closed",
+                order_type="market",
+                side=trade.exit_side,
+            )
+        )
+
+    trade.recalc_open_trade_value()
+    if not is_open:
+        trade.close(close_price)
+        trade.exit_reason = exit_reason
+
+    Trade.session.add(trade)
+    Trade.commit()
+    return trade
+
+
+def test_protectionmanager(mocker, default_conf):
+    default_conf["_strategy_protections"] = [
+        {"method": protection} for protection in AVAILABLE_PROTECTIONS
+    ]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    for handler in freqtrade.protections._protection_handlers:
+        assert handler.name in AVAILABLE_PROTECTIONS
+        if not handler.has_global_stop:
+            assert handler.global_stop(datetime.now(timezone.utc), "*") is None
+        if not handler.has_local_stop:
+            assert handler.stop_per_pair("XRP/BTC", datetime.now(timezone.utc), "*") is None
+
+
+@pytest.mark.parametrize(
+    "protconf,expected",
+    [
+        ([], None),
+        ([{"method": "StoplossGuard", "lookback_period": 2000, "stop_duration_candles": 10}], None),
+        ([{"method": "StoplossGuard", "lookback_period_candles": 20, "stop_duration": 10}], None),
+        (
+            [
+                {
+                    "method": "StoplossGuard",
+                    "lookback_period_candles": 20,
+                    "lookback_period": 2000,
+                    "stop_duration": 10,
+                }
+            ],
+            r"Protections must specify either `lookback_period`.*",
+        ),
+        (
+            [
+                {
+                    "method": "StoplossGuard",
+                    "lookback_period": 20,
+                    "stop_duration": 10,
+                    "stop_duration_candles": 10,
+                }
+            ],
+            r"Protections must specify either `stop_duration`.*",
+        ),
+        (
+            [
+                {
+                    "method": "StoplossGuard",
+                    "lookback_period": 20,
+                    "stop_duration": 10,
+                    "unlock_at": "20:02",
+                }
+            ],
+            r"Protections must specify either `unlock_at`, `stop_duration` or.*",
+        ),
+        (
+            [{"method": "StoplossGuard", "lookback_period_candles": 20, "unlock_at": "20:02"}],
+            None,
+        ),
+        (
+            [{"method": "StoplossGuard", "lookback_period_candles": 20, "unlock_at": "55:102"}],
+            "Invalid date format for unlock_at: 55:102.",
+        ),
+    ],
+)
+def test_validate_protections(protconf, expected):
+    if expected:
+        with pytest.raises(OperationalException, match=expected):
+            ProtectionManager.validate_protections(protconf)
+    else:
+        ProtectionManager.validate_protections(protconf)
+
+
+@pytest.mark.parametrize(
+    "timeframe,expected_lookback,expected_stop,protconf",
+    [
+        (
+            "1m",
+            20,
+            10,
+            [{"method": "StoplossGuard", "lookback_period_candles": 20, "stop_duration": 10}],
+        ),
+        (
+            "5m",
+            100,
+            15,
+            [{"method": "StoplossGuard", "lookback_period_candles": 20, "stop_duration": 15}],
+        ),
+        (
+            "1h",
+            1200,
+            40,
+            [{"method": "StoplossGuard", "lookback_period_candles": 20, "stop_duration": 40}],
+        ),
+        (
+            "1d",
+            1440,
+            5,
+            [{"method": "StoplossGuard", "lookback_period_candles": 1, "stop_duration": 5}],
+        ),
+        (
+            "1m",
+            20,
+            5,
+            [{"method": "StoplossGuard", "lookback_period": 20, "stop_duration_candles": 5}],
+        ),
+        (
+            "5m",
+            15,
+            25,
+            [{"method": "StoplossGuard", "lookback_period": 15, "stop_duration_candles": 5}],
+        ),
+        (
+            "1h",
+            50,
+            600,
+            [{"method": "StoplossGuard", "lookback_period": 50, "stop_duration_candles": 10}],
+        ),
+        (
+            "1h",
+            60,
+            540,
+            [{"method": "StoplossGuard", "lookback_period_candles": 1, "stop_duration_candles": 9}],
+        ),
+        (
+            "1m",
+            20,
+            "01:00",
+            [{"method": "StoplossGuard", "lookback_period_candles": 20, "unlock_at": "01:00"}],
+        ),
+        (
+            "5m",
+            100,
+            "02:00",
+            [{"method": "StoplossGuard", "lookback_period_candles": 20, "unlock_at": "02:00"}],
+        ),
+        (
+            "1h",
+            1200,
+            "03:00",
+            [{"method": "StoplossGuard", "lookback_period_candles": 20, "unlock_at": "03:00"}],
+        ),
+        (
+            "1d",
+            1440,
+            "04:00",
+            [{"method": "StoplossGuard", "lookback_period_candles": 1, "unlock_at": "04:00"}],
+        ),
+    ],
+)
+def test_protections_init(default_conf, timeframe, expected_lookback, expected_stop, protconf):
+    """
+    Test the initialization of protections with different configurations, including unlock_at.
+    """
+    default_conf["timeframe"] = timeframe
+    man = ProtectionManager(default_conf, protconf)
+    assert len(man._protection_handlers) == len(protconf)
+    assert man._protection_handlers[0]._lookback_period == expected_lookback
+    if isinstance(expected_stop, int):
+        assert man._protection_handlers[0]._stop_duration == expected_stop
+    else:
+        assert man._protection_handlers[0]._unlock_at == expected_stop
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.usefixtures("init_persistence")
+def test_stoploss_guard(mocker, default_conf, fee, caplog, is_short):
+    # Active for both sides (long and short)
+    default_conf["_strategy_protections"] = [
+        {"method": "StoplossGuard", "lookback_period": 60, "stop_duration": 40, "trade_limit": 3}
+    ]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    message = r"Trading stopped due to .*"
+    assert not freqtrade.protections.global_stop()
+    assert not log_has_re(message, caplog)
+    caplog.clear()
+
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=200,
+        min_ago_close=30,
+        is_short=is_short,
+    )
+
+    assert not freqtrade.protections.global_stop()
+    assert not log_has_re(message, caplog)
+    caplog.clear()
+    # This trade does not count, as it's closed too long ago
+    generate_mock_trade(
+        "BCH/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=250,
+        min_ago_close=100,
+        is_short=is_short,
+    )
+
+    generate_mock_trade(
+        "ETH/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=240,
+        min_ago_close=30,
+        is_short=is_short,
+    )
+    # 3 Trades closed - but the 2nd has been closed too long ago.
+    assert not freqtrade.protections.global_stop()
+    assert not log_has_re(message, caplog)
+    caplog.clear()
+
+    generate_mock_trade(
+        "LTC/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=180,
+        min_ago_close=30,
+        is_short=is_short,
+    )
+
+    assert freqtrade.protections.global_stop()
+    assert log_has_re(message, caplog)
+    assert PairLocks.is_global_lock()
+
+    # Test 5m after lock-period - this should try and relock the pair, but end-time
+    # should be the previous end-time
+    end_time = PairLocks.get_pair_longest_lock("*").lock_end_time + timedelta(minutes=5)
+    freqtrade.protections.global_stop(end_time)
+    assert not PairLocks.is_global_lock(end_time)
+
+
+@pytest.mark.parametrize("only_per_pair", [False, True])
+@pytest.mark.parametrize("only_per_side", [False, True])
+@pytest.mark.usefixtures("init_persistence")
+def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair, only_per_side):
+    default_conf["_strategy_protections"] = [
+        {
+            "method": "StoplossGuard",
+            "lookback_period": 60,
+            "trade_limit": 2,
+            "stop_duration": 60,
+            "only_per_pair": only_per_pair,
+            "only_per_side": only_per_side,
+        }
+    ]
+    check_side = "long" if only_per_side else "*"
+    is_short = False
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    message = r"Trading stopped due to .*"
+    pair = "XRP/BTC"
+    assert not freqtrade.protections.stop_per_pair(pair)
+    assert not freqtrade.protections.global_stop()
+    assert not log_has_re(message, caplog)
+    caplog.clear()
+
+    generate_mock_trade(
+        pair,
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=200,
+        min_ago_close=30,
+        profit_rate=0.9,
+        is_short=is_short,
+    )
+
+    assert not freqtrade.protections.stop_per_pair(pair)
+    assert not freqtrade.protections.global_stop()
+    assert not log_has_re(message, caplog)
+    caplog.clear()
+    # This trade does not count, as it's closed too long ago
+    generate_mock_trade(
+        pair,
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=250,
+        min_ago_close=100,
+        profit_rate=0.9,
+        is_short=is_short,
+    )
+    # Trade does not count for per pair stop as it's the wrong pair.
+    generate_mock_trade(
+        "ETH/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=240,
+        min_ago_close=30,
+        profit_rate=0.9,
+        is_short=is_short,
+    )
+    # 3 Trades closed - but the 2nd has been closed too long ago.
+    assert not freqtrade.protections.stop_per_pair(pair)
+    assert freqtrade.protections.global_stop() != only_per_pair
+    if not only_per_pair:
+        assert log_has_re(message, caplog)
+    else:
+        assert not log_has_re(message, caplog)
+
+    caplog.clear()
+
+    # Trade does not count potentially, as it's in the wrong direction
+    generate_mock_trade(
+        pair,
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=150,
+        min_ago_close=25,
+        profit_rate=0.9,
+        is_short=not is_short,
+    )
+    freqtrade.protections.stop_per_pair(pair)
+    assert freqtrade.protections.global_stop() != only_per_pair
+    assert PairLocks.is_pair_locked(pair, side=check_side) != (only_per_side and only_per_pair)
+    assert PairLocks.is_global_lock(side=check_side) != only_per_pair
+    if only_per_side:
+        assert not PairLocks.is_pair_locked(pair, side="*")
+        assert not PairLocks.is_global_lock(side="*")
+
+    caplog.clear()
+
+    # 2nd Trade that counts with correct pair
+    generate_mock_trade(
+        pair,
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=180,
+        min_ago_close=31,
+        profit_rate=0.9,
+        is_short=is_short,
+    )
+
+    freqtrade.protections.stop_per_pair(pair)
+    assert freqtrade.protections.global_stop() != only_per_pair
+    assert PairLocks.is_pair_locked(pair, side=check_side)
+    assert PairLocks.is_global_lock(side=check_side) != only_per_pair
+    if only_per_side:
+        assert not PairLocks.is_pair_locked(pair, side="*")
+        assert not PairLocks.is_global_lock(side="*")
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_CooldownPeriod(mocker, default_conf, fee, caplog):
+    default_conf["_strategy_protections"] = [
+        {
+            "method": "CooldownPeriod",
+            "stop_duration": 60,
+        }
+    ]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    message = r"Trading stopped due to .*"
+    assert not freqtrade.protections.global_stop()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+
+    assert not log_has_re(message, caplog)
+    caplog.clear()
+
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=200,
+        min_ago_close=30,
+    )
+
+    assert not freqtrade.protections.global_stop()
+    assert freqtrade.protections.stop_per_pair("XRP/BTC")
+    assert PairLocks.is_pair_locked("XRP/BTC")
+    assert not PairLocks.is_global_lock()
+
+    generate_mock_trade(
+        "ETH/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.ROI.value,
+        min_ago_open=205,
+        min_ago_close=35,
+    )
+
+    assert not freqtrade.protections.global_stop()
+    assert not PairLocks.is_pair_locked("ETH/BTC")
+    assert freqtrade.protections.stop_per_pair("ETH/BTC")
+    assert PairLocks.is_pair_locked("ETH/BTC")
+    assert not PairLocks.is_global_lock()
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_CooldownPeriod_unlock_at(mocker, default_conf, fee, caplog, time_machine):
+    default_conf["_strategy_protections"] = [
+        {
+            "method": "CooldownPeriod",
+            "unlock_at": "05:00",
+        }
+    ]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    message = r"Trading stopped due to .*"
+    assert not freqtrade.protections.global_stop()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+
+    assert not log_has_re(message, caplog)
+    caplog.clear()
+
+    start_dt = datetime(2024, 5, 2, 0, 30, 0, tzinfo=timezone.utc)
+    time_machine.move_to(start_dt, tick=False)
+
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=20,
+        min_ago_close=10,
+    )
+
+    assert not freqtrade.protections.global_stop()
+    assert freqtrade.protections.stop_per_pair("XRP/BTC")
+    assert PairLocks.is_pair_locked("XRP/BTC")
+    assert not PairLocks.is_global_lock()
+
+    # Move time to "4:30"
+    time_machine.move_to(start_dt + timedelta(hours=4), tick=False)
+    assert PairLocks.is_pair_locked("XRP/BTC")
+    assert not PairLocks.is_global_lock()
+
+    # Move time to "past 5:00"
+    time_machine.move_to(start_dt + timedelta(hours=5), tick=False)
+    assert not PairLocks.is_pair_locked("XRP/BTC")
+    assert not PairLocks.is_global_lock()
+
+    # Force rollover to the next day.
+    start_dt = datetime(2024, 5, 2, 22, 00, 0, tzinfo=timezone.utc)
+    time_machine.move_to(start_dt, tick=False)
+    generate_mock_trade(
+        "ETH/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.ROI.value,
+        min_ago_open=20,
+        min_ago_close=10,
+    )
+
+    assert not freqtrade.protections.global_stop()
+    assert not PairLocks.is_pair_locked("ETH/BTC")
+    assert freqtrade.protections.stop_per_pair("ETH/BTC")
+    assert PairLocks.is_pair_locked("ETH/BTC")
+    assert not PairLocks.is_global_lock()
+    # Move to 23:00
+    time_machine.move_to(start_dt + timedelta(hours=1), tick=False)
+    assert PairLocks.is_pair_locked("ETH/BTC")
+    assert not PairLocks.is_global_lock()
+
+    # Move to 04:59 (should still be locked)
+    time_machine.move_to(start_dt + timedelta(hours=6, minutes=59), tick=False)
+    assert PairLocks.is_pair_locked("ETH/BTC")
+    assert not PairLocks.is_global_lock()
+
+    # Move to 05:01 (should still be locked - it unlocks once the 05:00 candle stops at 05:05)
+    time_machine.move_to(start_dt + timedelta(hours=7, minutes=1), tick=False)
+
+    assert PairLocks.is_pair_locked("ETH/BTC")
+    assert not PairLocks.is_global_lock()
+
+    # Move to 05:01 (unlocked).
+    time_machine.move_to(start_dt + timedelta(hours=7, minutes=5), tick=False)
+
+    assert not PairLocks.is_pair_locked("ETH/BTC")
+    assert not PairLocks.is_global_lock()
+
+
+@pytest.mark.parametrize("only_per_side", [False, True])
+@pytest.mark.usefixtures("init_persistence")
+def test_LowProfitPairs(mocker, default_conf, fee, caplog, only_per_side):
+    default_conf["_strategy_protections"] = [
+        {
+            "method": "LowProfitPairs",
+            "lookback_period": 400,
+            "stop_duration": 60,
+            "trade_limit": 2,
+            "required_profit": 0.0,
+            "only_per_side": only_per_side,
+        }
+    ]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    message = r"Trading stopped due to .*"
+    assert not freqtrade.protections.global_stop()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+
+    assert not log_has_re(message, caplog)
+    caplog.clear()
+
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=800,
+        min_ago_close=450,
+        profit_rate=0.9,
+    )
+
+    Trade.commit()
+    # Not locked with 1 trade
+    assert not freqtrade.protections.global_stop()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+    assert not PairLocks.is_pair_locked("XRP/BTC")
+    assert not PairLocks.is_global_lock()
+
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=200,
+        min_ago_close=120,
+        profit_rate=0.9,
+    )
+
+    Trade.commit()
+    # Not locked with 1 trade (first trade is outside of lookback_period)
+    assert not freqtrade.protections.global_stop()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+    assert not PairLocks.is_pair_locked("XRP/BTC")
+    assert not PairLocks.is_global_lock()
+
+    # Add positive trade
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.ROI.value,
+        min_ago_open=20,
+        min_ago_close=10,
+        profit_rate=1.15,
+        is_short=True,
+    )
+    Trade.commit()
+    assert freqtrade.protections.stop_per_pair("XRP/BTC") != only_per_side
+    assert not PairLocks.is_pair_locked("XRP/BTC", side="*")
+    assert PairLocks.is_pair_locked("XRP/BTC", side="long") == only_per_side
+
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=110,
+        min_ago_close=21,
+        profit_rate=0.8,
+    )
+    Trade.commit()
+
+    # Locks due to 2nd trade
+    assert freqtrade.protections.global_stop() != only_per_side
+    assert freqtrade.protections.stop_per_pair("XRP/BTC") != only_per_side
+    assert PairLocks.is_pair_locked("XRP/BTC", side="long")
+    assert PairLocks.is_pair_locked("XRP/BTC", side="*") != only_per_side
+    assert not PairLocks.is_global_lock()
+    Trade.commit()
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_MaxDrawdown(mocker, default_conf, fee, caplog):
+    default_conf["_strategy_protections"] = [
+        {
+            "method": "MaxDrawdown",
+            "lookback_period": 1000,
+            "stop_duration": 60,
+            "trade_limit": 3,
+            "max_allowed_drawdown": 0.15,
+        }
+    ]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    message = r"Trading stopped due to Max.*"
+
+    assert not freqtrade.protections.global_stop()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+    caplog.clear()
+
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=1000,
+        min_ago_close=900,
+        profit_rate=1.1,
+    )
+    generate_mock_trade(
+        "ETH/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=1000,
+        min_ago_close=900,
+        profit_rate=1.1,
+    )
+    generate_mock_trade(
+        "NEO/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=1000,
+        min_ago_close=900,
+        profit_rate=1.1,
+    )
+    Trade.commit()
+    # No losing trade yet ... so max_drawdown will raise exception
+    assert not freqtrade.protections.global_stop()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=500,
+        min_ago_close=400,
+        profit_rate=0.9,
+    )
+    # Not locked with one trade
+    assert not freqtrade.protections.global_stop()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+    assert not PairLocks.is_pair_locked("XRP/BTC")
+    assert not PairLocks.is_global_lock()
+
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.STOP_LOSS.value,
+        min_ago_open=1200,
+        min_ago_close=1100,
+        profit_rate=0.5,
+    )
+    Trade.commit()
+
+    # Not locked with 1 trade (2nd trade is outside of lookback_period)
+    assert not freqtrade.protections.global_stop()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+    assert not PairLocks.is_pair_locked("XRP/BTC")
+    assert not PairLocks.is_global_lock()
+    assert not log_has_re(message, caplog)
+
+    # Winning trade ... (should not lock, does not change drawdown!)
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.ROI.value,
+        min_ago_open=320,
+        min_ago_close=410,
+        profit_rate=1.5,
+    )
+    Trade.commit()
+    assert not freqtrade.protections.global_stop()
+    assert not PairLocks.is_global_lock()
+
+    caplog.clear()
+
+    # Add additional negative trade, causing a loss of > 15%
+    generate_mock_trade(
+        "XRP/BTC",
+        fee.return_value,
+        False,
+        exit_reason=ExitType.ROI.value,
+        min_ago_open=20,
+        min_ago_close=10,
+        profit_rate=0.8,
+    )
+    Trade.commit()
+    assert not freqtrade.protections.stop_per_pair("XRP/BTC")
+    # local lock not supported
+    assert not PairLocks.is_pair_locked("XRP/BTC")
+    assert freqtrade.protections.global_stop()
+    assert PairLocks.is_global_lock()
+    assert log_has_re(message, caplog)
+
+
+@pytest.mark.parametrize(
+    "protectionconf,desc_expected,exception_expected",
+    [
+        (
+            {
+                "method": "StoplossGuard",
+                "lookback_period": 60,
+                "trade_limit": 2,
+                "stop_duration": 60,
+            },
+            "[{'StoplossGuard': 'StoplossGuard - Frequent Stoploss Guard, "
+            "2 stoplosses with profit < 0.00% within 60 minutes.'}]",
+            None,
+        ),
+        (
+            {"method": "CooldownPeriod", "stop_duration": 60},
+            "[{'CooldownPeriod': 'CooldownPeriod - Cooldown period for 60 minutes.'}]",
+            None,
+        ),
+        (
+            {"method": "LowProfitPairs", "lookback_period": 60, "stop_duration": 60},
+            "[{'LowProfitPairs': 'LowProfitPairs - Low Profit Protection, locks pairs with "
+            "profit < 0.0 within 60 minutes.'}]",
+            None,
+        ),
+        (
+            {"method": "MaxDrawdown", "lookback_period": 60, "stop_duration": 60},
+            "[{'MaxDrawdown': 'MaxDrawdown - Max drawdown protection, stop trading "
+            "if drawdown is > 0.0 within 60 minutes.'}]",
+            None,
+        ),
+        (
+            {
+                "method": "StoplossGuard",
+                "lookback_period_candles": 12,
+                "trade_limit": 2,
+                "required_profit": -0.05,
+                "stop_duration": 60,
+            },
+            "[{'StoplossGuard': 'StoplossGuard - Frequent Stoploss Guard, "
+            "2 stoplosses with profit < -5.00% within 12 candles.'}]",
+            None,
+        ),
+        (
+            {"method": "CooldownPeriod", "stop_duration_candles": 5},
+            "[{'CooldownPeriod': 'CooldownPeriod - Cooldown period for 5 candles.'}]",
+            None,
+        ),
+        (
+            {"method": "LowProfitPairs", "lookback_period_candles": 11, "stop_duration": 60},
+            "[{'LowProfitPairs': 'LowProfitPairs - Low Profit Protection, locks pairs with "
+            "profit < 0.0 within 11 candles.'}]",
+            None,
+        ),
+        (
+            {"method": "MaxDrawdown", "lookback_period_candles": 20, "stop_duration": 60},
+            "[{'MaxDrawdown': 'MaxDrawdown - Max drawdown protection, stop trading "
+            "if drawdown is > 0.0 within 20 candles.'}]",
+            None,
+        ),
+        (
+            {
+                "method": "CooldownPeriod",
+                "unlock_at": "01:00",
+            },
+            "[{'CooldownPeriod': 'CooldownPeriod - Cooldown period until 01:00.'}]",
+            None,
+        ),
+        (
+            {
+                "method": "StoplossGuard",
+                "lookback_period_candles": 12,
+                "trade_limit": 2,
+                "required_profit": -0.05,
+                "unlock_at": "01:00",
+            },
+            "[{'StoplossGuard': 'StoplossGuard - Frequent Stoploss Guard, "
+            "2 stoplosses with profit < -5.00% within 12 candles.'}]",
+            None,
+        ),
+        (
+            {"method": "LowProfitPairs", "lookback_period_candles": 11, "unlock_at": "03:00"},
+            "[{'LowProfitPairs': 'LowProfitPairs - Low Profit Protection, locks pairs with "
+            "profit < 0.0 within 11 candles.'}]",
+            None,
+        ),
+        (
+            {"method": "MaxDrawdown", "lookback_period_candles": 20, "unlock_at": "04:00"},
+            "[{'MaxDrawdown': 'MaxDrawdown - Max drawdown protection, stop trading "
+            "if drawdown is > 0.0 within 20 candles.'}]",
+            None,
+        ),
+    ],
+)
+def test_protection_manager_desc(
+    mocker, default_conf, protectionconf, desc_expected, exception_expected
+):
+    default_conf["_strategy_protections"] = [protectionconf]
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    short_desc = str(freqtrade.protections.short_desc())
+    assert short_desc == desc_expected
diff --git a/tests/plugins/test_remotepairlist.py b/tests/plugins/test_remotepairlist.py
new file mode 100644
index 000000000..ed2cd8ac4
--- /dev/null
+++ b/tests/plugins/test_remotepairlist.py
@@ -0,0 +1,343 @@
+import json
+from unittest.mock import MagicMock, PropertyMock
+
+import pytest
+import requests
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.plugins.pairlist.RemotePairList import RemotePairList
+from freqtrade.plugins.pairlistmanager import PairListManager
+from tests.conftest import EXMS, get_patched_exchange, get_patched_freqtradebot, log_has
+
+
+@pytest.fixture(scope="function")
+def rpl_config(default_conf):
+    default_conf["stake_currency"] = "USDT"
+
+    default_conf["exchange"]["pair_whitelist"] = [
+        "ETH/USDT",
+        "XRP/USDT",
+    ]
+    default_conf["exchange"]["pair_blacklist"] = ["BLK/USDT"]
+
+    return default_conf
+
+
+def test_gen_pairlist_with_local_file(mocker, rpl_config):
+    mock_file = MagicMock()
+    mock_file.read.return_value = '{"pairs": ["TKN/USDT","ETH/USDT","NANO/USDT"]}'
+    mocker.patch("freqtrade.plugins.pairlist.RemotePairList.open", return_value=mock_file)
+
+    mock_file_path = mocker.patch("freqtrade.plugins.pairlist.RemotePairList.Path")
+    mock_file_path.exists.return_value = True
+
+    jsonparse = json.loads(mock_file.read.return_value)
+    mocker.patch("freqtrade.plugins.pairlist.RemotePairList.rapidjson.load", return_value=jsonparse)
+
+    rpl_config["pairlists"] = [
+        {
+            "method": "RemotePairList",
+            "number_assets": 2,
+            "refresh_period": 1800,
+            "keep_pairlist_on_failure": True,
+            "pairlist_url": "file:///pairlist.json",
+            "bearer_token": "",
+            "read_timeout": 60,
+        }
+    ]
+
+    exchange = get_patched_exchange(mocker, rpl_config)
+    pairlistmanager = PairListManager(exchange, rpl_config)
+
+    remote_pairlist = RemotePairList(
+        exchange, pairlistmanager, rpl_config, rpl_config["pairlists"][0], 0
+    )
+
+    result = remote_pairlist.gen_pairlist([])
+
+    assert result == ["TKN/USDT", "ETH/USDT"]
+
+
+def test_fetch_pairlist_mock_response_html(mocker, rpl_config):
+    mock_response = MagicMock()
+    mock_response.headers = {"content-type": "text/html"}
+
+    rpl_config["pairlists"] = [
+        {
+            "method": "RemotePairList",
+            "pairlist_url": "http://example.com/pairlist",
+            "number_assets": 10,
+            "read_timeout": 10,
+            "keep_pairlist_on_failure": True,
+        }
+    ]
+
+    exchange = get_patched_exchange(mocker, rpl_config)
+    pairlistmanager = PairListManager(exchange, rpl_config)
+
+    mocker.patch(
+        "freqtrade.plugins.pairlist.RemotePairList.requests.get", return_value=mock_response
+    )
+    remote_pairlist = RemotePairList(
+        exchange, pairlistmanager, rpl_config, rpl_config["pairlists"][0], 0
+    )
+
+    with pytest.raises(OperationalException, match="RemotePairList is not of type JSON."):
+        remote_pairlist.fetch_pairlist()
+
+
+def test_fetch_pairlist_timeout_keep_last_pairlist(mocker, rpl_config, caplog):
+    rpl_config["pairlists"] = [
+        {
+            "method": "RemotePairList",
+            "pairlist_url": "http://example.com/pairlist",
+            "number_assets": 10,
+            "read_timeout": 10,
+            "keep_pairlist_on_failure": True,
+        }
+    ]
+
+    exchange = get_patched_exchange(mocker, rpl_config)
+    pairlistmanager = PairListManager(exchange, rpl_config)
+
+    mocker.patch(
+        "freqtrade.plugins.pairlist.RemotePairList.requests.get",
+        side_effect=requests.exceptions.RequestException,
+    )
+
+    remote_pairlist = RemotePairList(
+        exchange, pairlistmanager, rpl_config, rpl_config["pairlists"][0], 0
+    )
+
+    remote_pairlist._last_pairlist = ["BTC/USDT", "ETH/USDT", "LTC/USDT"]
+    remote_pairlist._init_done = True
+    pairlist_url = rpl_config["pairlists"][0]["pairlist_url"]
+    pairs, _time_elapsed = remote_pairlist.fetch_pairlist()
+
+    assert log_has(f"Error: Was not able to fetch pairlist from: {pairlist_url}", caplog)
+    assert log_has("Keeping last fetched pairlist", caplog)
+    assert pairs == ["BTC/USDT", "ETH/USDT", "LTC/USDT"]
+
+
+def test_remote_pairlist_init_no_pairlist_url(mocker, rpl_config):
+    rpl_config["pairlists"] = [
+        {
+            "method": "RemotePairList",
+            "number_assets": 10,
+            "keep_pairlist_on_failure": True,
+        }
+    ]
+
+    get_patched_exchange(mocker, rpl_config)
+    with pytest.raises(
+        OperationalException,
+        match=r"`pairlist_url` not specified."
+        r' Please check your configuration for "pairlist.config.pairlist_url"',
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+
+def test_remote_pairlist_init_no_number_assets(mocker, rpl_config):
+    rpl_config["pairlists"] = [
+        {
+            "method": "RemotePairList",
+            "pairlist_url": "http://example.com/pairlist",
+            "keep_pairlist_on_failure": True,
+        }
+    ]
+
+    get_patched_exchange(mocker, rpl_config)
+
+    with pytest.raises(
+        OperationalException,
+        match=r"`number_assets` not specified. "
+        'Please check your configuration for "pairlist.config.number_assets"',
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+
+def test_fetch_pairlist_mock_response_valid(mocker, rpl_config):
+    rpl_config["pairlists"] = [
+        {
+            "method": "RemotePairList",
+            "pairlist_url": "http://example.com/pairlist",
+            "number_assets": 10,
+            "refresh_period": 10,
+            "read_timeout": 10,
+            "keep_pairlist_on_failure": True,
+        }
+    ]
+
+    mock_response = MagicMock()
+
+    mock_response.json.return_value = {
+        "pairs": ["ETH/USDT", "XRP/USDT", "LTC/USDT", "EOS/USDT"],
+        "refresh_period": 60,
+    }
+
+    mock_response.headers = {"content-type": "application/json"}
+
+    mock_response.elapsed.total_seconds.return_value = 0.4
+    mocker.patch(
+        "freqtrade.plugins.pairlist.RemotePairList.requests.get", return_value=mock_response
+    )
+
+    exchange = get_patched_exchange(mocker, rpl_config)
+    pairlistmanager = PairListManager(exchange, rpl_config)
+    remote_pairlist = RemotePairList(
+        exchange, pairlistmanager, rpl_config, rpl_config["pairlists"][0], 0
+    )
+    pairs, time_elapsed = remote_pairlist.fetch_pairlist()
+
+    assert pairs == ["ETH/USDT", "XRP/USDT", "LTC/USDT", "EOS/USDT"]
+    assert time_elapsed == 0.4
+    assert remote_pairlist._refresh_period == 60
+
+
+def test_remote_pairlist_init_wrong_mode(mocker, rpl_config):
+    rpl_config["pairlists"] = [
+        {
+            "method": "RemotePairList",
+            "mode": "blacklis",
+            "number_assets": 20,
+            "pairlist_url": "http://example.com/pairlist",
+            "keep_pairlist_on_failure": True,
+        }
+    ]
+
+    with pytest.raises(
+        OperationalException,
+        match=r'`mode` not configured correctly. Supported Modes are "whitelist","blacklist"',
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+    rpl_config["pairlists"] = [
+        {
+            "method": "RemotePairList",
+            "mode": "blacklist",
+            "number_assets": 20,
+            "pairlist_url": "http://example.com/pairlist",
+            "keep_pairlist_on_failure": True,
+        }
+    ]
+
+    with pytest.raises(
+        OperationalException, match=r"A `blacklist` mode RemotePairList can not be.*first.*"
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+
+def test_remote_pairlist_init_wrong_proc_mode(mocker, rpl_config):
+    rpl_config["pairlists"] = [
+        {
+            "method": "RemotePairList",
+            "processing_mode": "filler",
+            "mode": "whitelist",
+            "number_assets": 20,
+            "pairlist_url": "http://example.com/pairlist",
+            "keep_pairlist_on_failure": True,
+        }
+    ]
+
+    get_patched_exchange(mocker, rpl_config)
+    with pytest.raises(
+        OperationalException,
+        match=r'`processing_mode` not configured correctly. Supported Modes are "filter","append"',
+    ):
+        get_patched_freqtradebot(mocker, rpl_config)
+
+
+def test_remote_pairlist_blacklist(mocker, rpl_config, caplog, markets, tickers):
+    mock_response = MagicMock()
+
+    mock_response.json.return_value = {"pairs": ["XRP/USDT"], "refresh_period": 60}
+
+    mock_response.headers = {"content-type": "application/json"}
+
+    rpl_config["pairlists"] = [
+        {
+            "method": "StaticPairList",
+        },
+        {
+            "method": "RemotePairList",
+            "mode": "blacklist",
+            "pairlist_url": "http://example.com/pairlist",
+            "number_assets": 3,
+        },
+    ]
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+
+    mocker.patch(
+        "freqtrade.plugins.pairlist.RemotePairList.requests.get", return_value=mock_response
+    )
+
+    exchange = get_patched_exchange(mocker, rpl_config)
+
+    pairlistmanager = PairListManager(exchange, rpl_config)
+
+    remote_pairlist = RemotePairList(
+        exchange, pairlistmanager, rpl_config, rpl_config["pairlists"][1], 1
+    )
+
+    pairs, _time_elapsed = remote_pairlist.fetch_pairlist()
+
+    assert pairs == ["XRP/USDT"]
+
+    whitelist = remote_pairlist.filter_pairlist(rpl_config["exchange"]["pair_whitelist"], {})
+    assert whitelist == ["ETH/USDT"]
+
+    assert log_has(f"Blacklist - Filtered out pairs: {pairs}", caplog)
+
+
+@pytest.mark.parametrize("processing_mode", ["filter", "append"])
+def test_remote_pairlist_whitelist(mocker, rpl_config, processing_mode, markets, tickers):
+    mock_response = MagicMock()
+
+    mock_response.json.return_value = {"pairs": ["XRP/USDT"], "refresh_period": 60}
+
+    mock_response.headers = {"content-type": "application/json"}
+
+    rpl_config["pairlists"] = [
+        {
+            "method": "StaticPairList",
+        },
+        {
+            "method": "RemotePairList",
+            "mode": "whitelist",
+            "processing_mode": processing_mode,
+            "pairlist_url": "http://example.com/pairlist",
+            "number_assets": 3,
+        },
+    ]
+
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        exchange_has=MagicMock(return_value=True),
+        get_tickers=tickers,
+    )
+
+    mocker.patch(
+        "freqtrade.plugins.pairlist.RemotePairList.requests.get", return_value=mock_response
+    )
+
+    exchange = get_patched_exchange(mocker, rpl_config)
+
+    pairlistmanager = PairListManager(exchange, rpl_config)
+
+    remote_pairlist = RemotePairList(
+        exchange, pairlistmanager, rpl_config, rpl_config["pairlists"][1], 1
+    )
+
+    pairs, _time_elapsed = remote_pairlist.fetch_pairlist()
+
+    assert pairs == ["XRP/USDT"]
+
+    whitelist = remote_pairlist.filter_pairlist(rpl_config["exchange"]["pair_whitelist"], {})
+    assert whitelist == (["XRP/USDT"] if processing_mode == "filter" else ["ETH/USDT", "XRP/USDT"])
diff --git a/tests/pytest.sh b/tests/pytest.sh
new file mode 100755
index 000000000..d2ad223e7
--- /dev/null
+++ b/tests/pytest.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+
+echo "Running Unit tests"
+
+pytest --random-order --cov=freqtrade --cov-config=.coveragerc tests/
diff --git a/tests/rpc/__init__.py b/tests/rpc/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/rpc/test_fiat_convert.py b/tests/rpc/test_fiat_convert.py
new file mode 100644
index 000000000..4ae7441c8
--- /dev/null
+++ b/tests/rpc/test_fiat_convert.py
@@ -0,0 +1,221 @@
+# pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors,
+# pragma pylint: disable=protected-access, C0103
+
+import datetime
+from unittest.mock import MagicMock
+
+import pytest
+from requests.exceptions import RequestException
+
+from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
+from freqtrade.util.coin_gecko import FtCoinGeckoApi
+from tests.conftest import log_has, log_has_re
+
+
+def test_fiat_convert_is_singleton():
+    fiat_convert = CryptoToFiatConverter({"a": 22})
+    fiat_convert2 = CryptoToFiatConverter({})
+
+    assert fiat_convert is fiat_convert2
+    assert id(fiat_convert) == id(fiat_convert2)
+
+
+def test_fiat_convert_is_supported():
+    fiat_convert = CryptoToFiatConverter({})
+    assert fiat_convert._is_supported_fiat(fiat="USD") is True
+    assert fiat_convert._is_supported_fiat(fiat="usd") is True
+    assert fiat_convert._is_supported_fiat(fiat="abc") is False
+    assert fiat_convert._is_supported_fiat(fiat="ABC") is False
+
+
+def test_fiat_convert_find_price(mocker):
+    fiat_convert = CryptoToFiatConverter({})
+
+    fiat_convert._coinlistings = {}
+    fiat_convert._backoff = 0
+    mocker.patch(
+        "freqtrade.rpc.fiat_convert.CryptoToFiatConverter._load_cryptomap", return_value=None
+    )
+    assert fiat_convert.get_price(crypto_symbol="BTC", fiat_symbol="EUR") == 0.0
+
+    with pytest.raises(ValueError, match=r"The fiat ABC is not supported."):
+        fiat_convert._find_price(crypto_symbol="BTC", fiat_symbol="ABC")
+
+    assert fiat_convert.get_price(crypto_symbol="XRP", fiat_symbol="USD") == 0.0
+
+    mocker.patch(
+        "freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price", return_value=12345.0
+    )
+    assert fiat_convert.get_price(crypto_symbol="BTC", fiat_symbol="USD") == 12345.0
+    assert fiat_convert.get_price(crypto_symbol="btc", fiat_symbol="usd") == 12345.0
+
+    mocker.patch(
+        "freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price", return_value=13000.2
+    )
+    assert fiat_convert.get_price(crypto_symbol="BTC", fiat_symbol="EUR") == 13000.2
+
+
+def test_fiat_convert_unsupported_crypto(mocker, caplog):
+    mocker.patch("freqtrade.rpc.fiat_convert.CryptoToFiatConverter._coinlistings", return_value=[])
+    fiat_convert = CryptoToFiatConverter({})
+    assert fiat_convert._find_price(crypto_symbol="CRYPTO_123", fiat_symbol="EUR") == 0.0
+    assert log_has("unsupported crypto-symbol CRYPTO_123 - returning 0.0", caplog)
+
+
+def test_fiat_convert_get_price(mocker):
+    find_price = mocker.patch(
+        "freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price", return_value=28000.0
+    )
+
+    fiat_convert = CryptoToFiatConverter({})
+
+    with pytest.raises(ValueError, match=r"The fiat us dollar is not supported."):
+        fiat_convert.get_price(crypto_symbol="btc", fiat_symbol="US Dollar")
+
+    # Check the value return by the method
+    pair_len = len(fiat_convert._pair_price)
+    assert pair_len == 0
+    assert fiat_convert.get_price(crypto_symbol="btc", fiat_symbol="usd") == 28000.0
+    assert fiat_convert._pair_price["btc/usd"] == 28000.0
+    assert len(fiat_convert._pair_price) == 1
+    assert find_price.call_count == 1
+
+    # Verify the cached is used
+    fiat_convert._pair_price["btc/usd"] = 9867.543
+    assert fiat_convert.get_price(crypto_symbol="btc", fiat_symbol="usd") == 9867.543
+    assert find_price.call_count == 1
+
+
+def test_fiat_convert_same_currencies():
+    fiat_convert = CryptoToFiatConverter({})
+
+    assert fiat_convert.get_price(crypto_symbol="USD", fiat_symbol="USD") == 1.0
+
+
+def test_fiat_convert_two_FIAT():
+    fiat_convert = CryptoToFiatConverter({})
+
+    assert fiat_convert.get_price(crypto_symbol="USD", fiat_symbol="EUR") == 0.0
+
+
+def test_loadcryptomap():
+    fiat_convert = CryptoToFiatConverter({})
+    assert len(fiat_convert._coinlistings) == 2
+
+    assert fiat_convert._get_gecko_id("btc") == "bitcoin"
+
+
+def test_fiat_init_network_exception(mocker):
+    # Because CryptoToFiatConverter is a Singleton we reset the listings
+    listmock = MagicMock(side_effect=RequestException)
+    mocker.patch.multiple(
+        "freqtrade.rpc.fiat_convert.FtCoinGeckoApi",
+        get_coins_list=listmock,
+    )
+    # with pytest.raises(RequestEsxception):
+    fiat_convert = CryptoToFiatConverter({})
+    fiat_convert._coinlistings = {}
+    fiat_convert._load_cryptomap()
+
+    assert len(fiat_convert._coinlistings) == 0
+
+
+def test_fiat_convert_without_network():
+    # Because CryptoToFiatConverter is a Singleton we reset the value of _coingecko
+
+    fiat_convert = CryptoToFiatConverter({})
+
+    cmc_temp = fiat_convert._coingecko
+    fiat_convert._coingecko = None
+
+    assert fiat_convert._coingecko is None
+    assert fiat_convert._find_price(crypto_symbol="btc", fiat_symbol="usd") == 0.0
+    fiat_convert._coingecko = cmc_temp
+
+
+def test_fiat_too_many_requests_response(mocker, caplog):
+    # Because CryptoToFiatConverter is a Singleton we reset the listings
+    req_exception = "429 Too Many Requests"
+    listmock = MagicMock(return_value="{}", side_effect=RequestException(req_exception))
+    mocker.patch.multiple(
+        "freqtrade.rpc.fiat_convert.FtCoinGeckoApi",
+        get_coins_list=listmock,
+    )
+    # with pytest.raises(RequestEsxception):
+    fiat_convert = CryptoToFiatConverter({})
+    fiat_convert._coinlistings = {}
+    fiat_convert._load_cryptomap()
+
+    assert len(fiat_convert._coinlistings) == 0
+    assert fiat_convert._backoff > datetime.datetime.now().timestamp()
+    assert log_has(
+        "Too many requests for CoinGecko API, backing off and trying again later.", caplog
+    )
+
+
+def test_fiat_multiple_coins(caplog):
+    fiat_convert = CryptoToFiatConverter({})
+    fiat_convert._coinlistings = [
+        {"id": "helium", "symbol": "hnt", "name": "Helium"},
+        {"id": "hymnode", "symbol": "hnt", "name": "Hymnode"},
+        {"id": "bitcoin", "symbol": "btc", "name": "Bitcoin"},
+        {"id": "ethereum", "symbol": "eth", "name": "Ethereum"},
+        {"id": "ethereum-wormhole", "symbol": "eth", "name": "Ethereum Wormhole"},
+    ]
+
+    assert fiat_convert._get_gecko_id("btc") == "bitcoin"
+    assert fiat_convert._get_gecko_id("hnt") is None
+    assert fiat_convert._get_gecko_id("eth") == "ethereum"
+
+    assert log_has("Found multiple mappings in CoinGecko for hnt.", caplog)
+
+
+def test_fiat_invalid_response(mocker, caplog):
+    # Because CryptoToFiatConverter is a Singleton we reset the listings
+    listmock = MagicMock(return_value=None)
+    mocker.patch.multiple(
+        "freqtrade.rpc.fiat_convert.FtCoinGeckoApi",
+        get_coins_list=listmock,
+    )
+    # with pytest.raises(RequestEsxception):
+    fiat_convert = CryptoToFiatConverter({})
+    fiat_convert._coinlistings = []
+    fiat_convert._load_cryptomap()
+
+    assert len(fiat_convert._coinlistings) == 0
+    assert log_has_re(
+        "Could not load FIAT Cryptocurrency map for the following problem: .*", caplog
+    )
+
+
+def test_convert_amount(mocker):
+    mocker.patch("freqtrade.rpc.fiat_convert.CryptoToFiatConverter.get_price", return_value=12345.0)
+
+    fiat_convert = CryptoToFiatConverter({})
+    result = fiat_convert.convert_amount(crypto_amount=1.23, crypto_symbol="BTC", fiat_symbol="USD")
+    assert result == 15184.35
+
+    result = fiat_convert.convert_amount(crypto_amount=1.23, crypto_symbol="BTC", fiat_symbol="BTC")
+    assert result == 1.23
+
+    result = fiat_convert.convert_amount(
+        crypto_amount="1.23", crypto_symbol="BTC", fiat_symbol="BTC"
+    )
+    assert result == 1.23
+
+
+def test_FtCoinGeckoApi():
+    ftc = FtCoinGeckoApi()
+    assert ftc.extra_params is None
+    assert ftc.api_base_url == "https://api.coingecko.com/api/v3/"
+
+    # defaults to demo
+    ftc = FtCoinGeckoApi(api_key="123456")
+    assert ftc.extra_params is not None
+    assert ftc.extra_params["x_cg_demo_api_key"] == "123456"
+    assert ftc.api_base_url == "https://api.coingecko.com/api/v3/"
+
+    ftc = FtCoinGeckoApi(api_key="123456", is_demo=False)
+    assert ftc.extra_params is not None
+    assert ftc.extra_params["x_cg_pro_api_key"] == "123456"
+    assert ftc.api_base_url == "https://pro-api.coingecko.com/api/v3/"
diff --git a/tests/rpc/test_rpc.py b/tests/rpc/test_rpc.py
new file mode 100644
index 000000000..a57053210
--- /dev/null
+++ b/tests/rpc/test_rpc.py
@@ -0,0 +1,1433 @@
+from copy import deepcopy
+from datetime import datetime, timedelta, timezone
+from unittest.mock import ANY, MagicMock, PropertyMock
+
+import pytest
+from numpy import isnan
+from sqlalchemy import select
+
+from freqtrade.edge import PairInfo
+from freqtrade.enums import SignalDirection, State, TradingMode
+from freqtrade.exceptions import ExchangeError, InvalidOrderException, TemporaryError
+from freqtrade.persistence import Order, Trade
+from freqtrade.persistence.key_value_store import set_startup_time
+from freqtrade.rpc import RPC, RPCException
+from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
+from tests.conftest import (
+    EXMS,
+    create_mock_trades,
+    create_mock_trades_usdt,
+    get_patched_freqtradebot,
+    patch_get_signal,
+)
+
+
+def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
+    gen_response = {
+        "trade_id": 1,
+        "pair": "ETH/BTC",
+        "base_currency": "ETH",
+        "quote_currency": "BTC",
+        "open_date": ANY,
+        "open_timestamp": ANY,
+        "open_fill_date": ANY,
+        "open_fill_timestamp": ANY,
+        "is_open": ANY,
+        "fee_open": ANY,
+        "fee_open_cost": ANY,
+        "fee_open_currency": ANY,
+        "fee_close": fee.return_value,
+        "fee_close_cost": ANY,
+        "fee_close_currency": ANY,
+        "open_rate_requested": ANY,
+        "open_trade_value": 0.0010025,
+        "close_rate_requested": ANY,
+        "exit_reason": ANY,
+        "exit_order_status": ANY,
+        "min_rate": ANY,
+        "max_rate": ANY,
+        "strategy": ANY,
+        "enter_tag": ANY,
+        "timeframe": 5,
+        "close_date": None,
+        "close_timestamp": None,
+        "open_rate": 1.098e-05,
+        "close_rate": None,
+        "current_rate": 1.099e-05,
+        "amount": 91.07468123,
+        "amount_requested": 91.07468124,
+        "stake_amount": 0.001,
+        "max_stake_amount": None,
+        "trade_duration": None,
+        "trade_duration_s": None,
+        "close_profit": None,
+        "close_profit_pct": None,
+        "close_profit_abs": None,
+        "profit_ratio": -0.00408133,
+        "profit_pct": -0.41,
+        "profit_abs": -4.09e-06,
+        "profit_fiat": ANY,
+        "stop_loss_abs": 9.89e-06,
+        "stop_loss_pct": -10.0,
+        "stop_loss_ratio": -0.1,
+        "stoploss_last_update": ANY,
+        "stoploss_last_update_timestamp": ANY,
+        "initial_stop_loss_abs": 9.89e-06,
+        "initial_stop_loss_pct": -10.0,
+        "initial_stop_loss_ratio": -0.1,
+        "stoploss_current_dist": pytest.approx(-1.0999999e-06),
+        "stoploss_current_dist_ratio": -0.10009099,
+        "stoploss_current_dist_pct": -10.01,
+        "stoploss_entry_dist": -0.00010402,
+        "stoploss_entry_dist_ratio": -0.10376381,
+        "open_orders": "",
+        "realized_profit": 0.0,
+        "realized_profit_ratio": None,
+        "total_profit_abs": -4.09e-06,
+        "total_profit_fiat": ANY,
+        "total_profit_ratio": None,
+        "exchange": "binance",
+        "leverage": 1.0,
+        "interest_rate": 0.0,
+        "liquidation_price": None,
+        "is_short": False,
+        "funding_fees": 0.0,
+        "trading_mode": TradingMode.SPOT,
+        "amount_precision": 8.0,
+        "price_precision": 8.0,
+        "precision_mode": 2,
+        "precision_mode_price": 2,
+        "contract_size": 1,
+        "has_open_orders": False,
+        "nr_of_successful_entries": ANY,
+        "orders": [
+            {
+                "amount": 91.07468123,
+                "average": 1.098e-05,
+                "safe_price": 1.098e-05,
+                "cost": 0.0009999999999054,
+                "filled": 91.07468123,
+                "ft_order_side": "buy",
+                "order_date": ANY,
+                "order_timestamp": ANY,
+                "order_filled_date": ANY,
+                "order_filled_timestamp": ANY,
+                "order_type": "limit",
+                "price": 1.098e-05,
+                "is_open": False,
+                "pair": "ETH/BTC",
+                "order_id": ANY,
+                "remaining": ANY,
+                "status": ANY,
+                "ft_is_entry": True,
+                "ft_fee_base": None,
+                "funding_fee": ANY,
+                "ft_order_tag": None,
+            }
+        ],
+    }
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(side_effect=[False, True]),
+    )
+
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+
+    freqtradebot.state = State.RUNNING
+    with pytest.raises(RPCException, match=r".*no active trade*"):
+        rpc._rpc_trade_status()
+
+    freqtradebot.enter_positions()
+
+    # Open order...
+    results = rpc._rpc_trade_status()
+    response_unfilled = deepcopy(gen_response)
+    # Different from "filled" response:
+    response_unfilled.update(
+        {
+            "amount": 0.0,
+            "open_trade_value": 0.0,
+            "stoploss_entry_dist": 0.0,
+            "stoploss_entry_dist_ratio": 0.0,
+            "profit_ratio": 0.0,
+            "profit_pct": 0.0,
+            "profit_abs": 0.0,
+            "total_profit_abs": 0.0,
+            "open_orders": "(limit buy rem=91.07468123)",
+            "has_open_orders": True,
+        }
+    )
+    response_unfilled["orders"][0].update(
+        {"is_open": True, "filled": 0.0, "remaining": 91.07468123}
+    )
+    assert results[0] == response_unfilled
+
+    # Open order without remaining
+    trade = Trade.get_open_trades()[0]
+    # kucoin case (no remaining set).
+    trade.orders[0].remaining = None
+    Trade.commit()
+
+    results = rpc._rpc_trade_status()
+    # Reuse above object, only remaining changed.
+    response_unfilled["orders"][0].update(
+        {
+            "remaining": None,
+        }
+    )
+    assert results[0] == response_unfilled
+
+    trade = Trade.get_open_trades()[0]
+    trade.orders[0].remaining = trade.amount
+    Trade.commit()
+
+    # Fill open order ...
+    freqtradebot.manage_open_orders()
+    trades = Trade.get_open_trades()
+    freqtradebot.exit_positions(trades)
+
+    results = rpc._rpc_trade_status()
+
+    response = deepcopy(gen_response)
+    response.update(
+        {
+            "max_stake_amount": 0.001,
+            "total_profit_ratio": pytest.approx(-0.00408133),
+            "has_open_orders": False,
+        }
+    )
+    assert results[0] == response
+
+    mocker.patch(
+        f"{EXMS}.get_rate", MagicMock(side_effect=ExchangeError("Pair 'ETH/BTC' not available"))
+    )
+    results = rpc._rpc_trade_status()
+    assert isnan(results[0]["profit_ratio"])
+    assert isnan(results[0]["current_rate"])
+    response_norate = deepcopy(gen_response)
+    # Update elements that are NaN when no rate is available.
+    response_norate.update(
+        {
+            "stoploss_current_dist": ANY,
+            "stoploss_current_dist_ratio": ANY,
+            "stoploss_current_dist_pct": ANY,
+            "max_stake_amount": 0.001,
+            "profit_ratio": ANY,
+            "profit_pct": ANY,
+            "profit_abs": ANY,
+            "total_profit_abs": ANY,
+            "total_profit_ratio": ANY,
+            "current_rate": ANY,
+        }
+    )
+    assert results[0] == response_norate
+
+
+def test_rpc_status_table(default_conf, ticker, fee, mocker) -> None:
+    mocker.patch.multiple(
+        "freqtrade.rpc.fiat_convert.FtCoinGeckoApi",
+        get_price=MagicMock(return_value={"bitcoin": {"usd": 15000.0}}),
+    )
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=15000.0)
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+    del default_conf["fiat_display_currency"]
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+
+    freqtradebot.state = State.RUNNING
+    with pytest.raises(RPCException, match=r".*no active trade*"):
+        rpc._rpc_status_table(default_conf["stake_currency"], "USD")
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=False)
+    freqtradebot.enter_positions()
+
+    result, headers, fiat_profit_sum, total_sum = rpc._rpc_status_table(
+        default_conf["stake_currency"], "USD"
+    )
+    assert "Since" in headers
+    assert "Pair" in headers
+    assert "now" == result[0][2]
+    assert "ETH/BTC" in result[0][1]
+    assert "0.00% (0.00)" == result[0][3]
+    assert "0.00" == f"{fiat_profit_sum:.2f}"
+    assert "0.00" == f"{total_sum:.2f}"
+
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", return_value=True)
+    freqtradebot.process()
+
+    result, headers, fiat_profit_sum, total_sum = rpc._rpc_status_table(
+        default_conf["stake_currency"], "USD"
+    )
+    assert "Since" in headers
+    assert "Pair" in headers
+    assert "now" == result[0][2]
+    assert "ETH/BTC" in result[0][1]
+    assert "-0.41% (-0.00)" == result[0][3]
+    assert "-0.00" == f"{fiat_profit_sum:.2f}"
+
+    # Test with fiat convert
+    rpc._config["fiat_display_currency"] = "USD"
+    rpc._fiat_converter = CryptoToFiatConverter({})
+    result, headers, fiat_profit_sum, total_sum = rpc._rpc_status_table(
+        default_conf["stake_currency"], "USD"
+    )
+    assert "Since" in headers
+    assert "Pair" in headers
+    assert len(result[0]) == 4
+    assert "now" == result[0][2]
+    assert "ETH/BTC" in result[0][1]
+    assert "-0.41% (-0.06)" == result[0][3]
+    assert "-0.06" == f"{fiat_profit_sum:.2f}"
+    assert "-0.06" == f"{total_sum:.2f}"
+
+    rpc._config["position_adjustment_enable"] = True
+    rpc._config["max_entry_position_adjustment"] = 3
+    result, headers, fiat_profit_sum, total_sum = rpc._rpc_status_table(
+        default_conf["stake_currency"], "USD"
+    )
+    assert "# Entries" in headers
+    assert len(result[0]) == 5
+    # 4th column should be 1/4 - as 1 order filled (a total of 4 is possible)
+    # 3 on top of the initial one.
+    assert result[0][4] == "1/4"
+
+    mocker.patch(
+        f"{EXMS}.get_rate", MagicMock(side_effect=ExchangeError("Pair 'ETH/BTC' not available"))
+    )
+    result, headers, fiat_profit_sum, total_sum = rpc._rpc_status_table(
+        default_conf["stake_currency"], "USD"
+    )
+    assert "now" == result[0][2]
+    assert "ETH/BTC" in result[0][1]
+    assert "nan%" == result[0][3]
+    assert isnan(fiat_profit_sum)
+
+
+def test__rpc_timeunit_profit(
+    default_conf_usdt, ticker, fee, markets, mocker, time_machine
+) -> None:
+    time_machine.move_to("2023-09-05 10:00:00 +00:00", tick=False)
+
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS, fetch_ticker=ticker, get_fee=fee, markets=PropertyMock(return_value=markets)
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf_usdt)
+    create_mock_trades_usdt(fee)
+
+    stake_currency = default_conf_usdt["stake_currency"]
+    fiat_display_currency = default_conf_usdt["fiat_display_currency"]
+
+    rpc = RPC(freqtradebot)
+    rpc._fiat_converter = CryptoToFiatConverter({})
+
+    # Try valid data
+    days = rpc._rpc_timeunit_profit(7, stake_currency, fiat_display_currency)
+    assert len(days["data"]) == 7
+    assert days["stake_currency"] == default_conf_usdt["stake_currency"]
+    assert days["fiat_display_currency"] == default_conf_usdt["fiat_display_currency"]
+    for day in days["data"]:
+        # {'date': datetime.date(2022, 6, 11), 'abs_profit': 13.8299999,
+        #  'starting_balance': 1055.37, 'rel_profit': 0.0131044,
+        #  'fiat_value': 0.0, 'trade_count': 2}
+        assert day["abs_profit"] in (0.0, pytest.approx(6.83), pytest.approx(-4.09))
+        assert day["rel_profit"] in (0.0, pytest.approx(0.00642902), pytest.approx(-0.00383512))
+        assert day["trade_count"] in (0, 1, 2)
+        assert day["starting_balance"] in (pytest.approx(1062.37), pytest.approx(1066.46))
+        assert day["fiat_value"] in (0.0,)
+    # ensure first day is current date
+    assert str(days["data"][0]["date"]) == str(datetime.now(timezone.utc).date())
+
+    # Try invalid data
+    with pytest.raises(RPCException, match=r".*must be an integer greater than 0*"):
+        rpc._rpc_timeunit_profit(0, stake_currency, fiat_display_currency)
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_rpc_trade_history(mocker, default_conf, markets, fee, is_short):
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(EXMS, markets=PropertyMock(return_value=markets))
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    create_mock_trades(fee, is_short)
+    rpc = RPC(freqtradebot)
+    rpc._fiat_converter = CryptoToFiatConverter({})
+    trades = rpc._rpc_trade_history(2)
+    assert len(trades["trades"]) == 2
+    assert trades["trades_count"] == 2
+    assert isinstance(trades["trades"][0], dict)
+    assert isinstance(trades["trades"][1], dict)
+
+    trades = rpc._rpc_trade_history(0)
+    assert len(trades["trades"]) == 2
+    assert trades["trades_count"] == 2
+    # The first closed trade is for ETC ... sorting is descending
+    assert trades["trades"][-1]["pair"] == "ETC/BTC"
+    assert trades["trades"][0]["pair"] == "XRP/BTC"
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_rpc_delete_trade(mocker, default_conf, fee, markets, caplog, is_short):
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    stoploss_mock = MagicMock()
+    cancel_mock = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        cancel_order=cancel_mock,
+        cancel_stoploss_order=stoploss_mock,
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    freqtradebot.strategy.order_types["stoploss_on_exchange"] = True
+    create_mock_trades(fee, is_short)
+    rpc = RPC(freqtradebot)
+    with pytest.raises(RPCException, match="invalid argument"):
+        rpc._rpc_delete("200")
+
+    trades = Trade.session.scalars(select(Trade)).all()
+    trades[2].orders.append(
+        Order(
+            ft_order_side="stoploss",
+            ft_pair=trades[2].pair,
+            ft_is_open=True,
+            ft_amount=trades[2].amount,
+            ft_price=trades[2].stop_loss,
+            order_id="102",
+            status="open",
+        )
+    )
+    assert len(trades) > 2
+
+    res = rpc._rpc_delete("1")
+    assert isinstance(res, dict)
+    assert res["result"] == "success"
+    assert res["trade_id"] == "1"
+    assert res["cancel_order_count"] == 1
+    assert cancel_mock.call_count == 1
+    assert stoploss_mock.call_count == 0
+    cancel_mock.reset_mock()
+    stoploss_mock.reset_mock()
+
+    res = rpc._rpc_delete("5")
+    assert isinstance(res, dict)
+    assert stoploss_mock.call_count == 1
+    assert res["cancel_order_count"] == 1
+
+    stoploss_mock = mocker.patch(f"{EXMS}.cancel_stoploss_order", side_effect=InvalidOrderException)
+
+    res = rpc._rpc_delete("3")
+    assert stoploss_mock.call_count == 1
+    stoploss_mock.reset_mock()
+
+    cancel_mock = mocker.patch(f"{EXMS}.cancel_order", side_effect=InvalidOrderException)
+
+    res = rpc._rpc_delete("4")
+    assert cancel_mock.call_count == 1
+    assert stoploss_mock.call_count == 0
+
+
+def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=1.1)
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf_usdt)
+    stake_currency = default_conf_usdt["stake_currency"]
+    fiat_display_currency = default_conf_usdt["fiat_display_currency"]
+
+    rpc = RPC(freqtradebot)
+    rpc._fiat_converter = CryptoToFiatConverter({})
+
+    res = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
+    assert res["trade_count"] == 0
+    assert res["first_trade_date"] == ""
+    assert res["first_trade_timestamp"] == 0
+    assert res["latest_trade_date"] == ""
+    assert res["latest_trade_timestamp"] == 0
+    assert res["expectancy"] == 0
+    assert res["expectancy_ratio"] == 100
+
+    # Create some test data
+    create_mock_trades_usdt(fee)
+
+    stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
+    assert pytest.approx(stats["profit_closed_coin"]) == 2.74
+    assert pytest.approx(stats["profit_closed_percent_mean"]) == -1.67
+    assert pytest.approx(stats["profit_closed_fiat"]) == 3.014
+    assert pytest.approx(stats["profit_all_coin"]) == -57.40975881
+    assert pytest.approx(stats["profit_all_percent_mean"]) == -50.83
+    assert pytest.approx(stats["profit_all_fiat"]) == -63.150734691
+    assert pytest.approx(stats["winrate"]) == 0.666666667
+    assert pytest.approx(stats["expectancy"]) == 0.913333333
+    assert pytest.approx(stats["expectancy_ratio"]) == 0.223308883
+    assert stats["trade_count"] == 7
+    assert stats["first_trade_humanized"] == "2 days ago"
+    assert stats["latest_trade_humanized"] == "17 minutes ago"
+    assert stats["avg_duration"] in ("0:17:40")
+    assert stats["best_pair"] == "NEO/USDT"
+    assert stats["best_rate"] == 1.99
+
+    # Test non-available pair
+    mocker.patch(
+        f"{EXMS}.get_rate", MagicMock(side_effect=ExchangeError("Pair 'XRP/USDT' not available"))
+    )
+    stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
+    assert stats["trade_count"] == 7
+    assert stats["first_trade_humanized"] == "2 days ago"
+    assert stats["latest_trade_humanized"] == "17 minutes ago"
+    assert stats["avg_duration"] in ("0:17:40")
+    assert stats["best_pair"] == "NEO/USDT"
+    assert stats["best_rate"] == 1.99
+    assert isnan(stats["profit_all_coin"])
+
+
+def test_rpc_balance_handle_error(default_conf, mocker):
+    mock_balance = {
+        "BTC": {
+            "free": 10.0,
+            "total": 12.0,
+            "used": 2.0,
+        },
+        "ETH": {
+            "free": 1.0,
+            "total": 5.0,
+            "used": 4.0,
+        },
+    }
+    # ETH will be skipped due to mocked Error below
+
+    mocker.patch.multiple(
+        "freqtrade.rpc.fiat_convert.FtCoinGeckoApi",
+        get_price=MagicMock(return_value={"bitcoin": {"usd": 15000.0}}),
+    )
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=15000.0)
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=mock_balance),
+        get_tickers=MagicMock(side_effect=TemporaryError("Could not load ticker due to xxx")),
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    rpc._fiat_converter = CryptoToFiatConverter({})
+    res = rpc._rpc_balance(default_conf["stake_currency"], default_conf["fiat_display_currency"])
+    assert res["stake"] == "BTC"
+
+    assert len(res["currencies"]) == 1
+    assert res["currencies"][0]["currency"] == "BTC"
+    # ETH has not been converted.
+    assert all(currency["currency"] != "ETH" for currency in res["currencies"])
+
+
+@pytest.mark.parametrize("proxy_coin", [None, "BNFCR"])
+@pytest.mark.parametrize("margin_mode", ["isolated", "cross"])
+def test_rpc_balance_handle(default_conf_usdt, mocker, tickers, proxy_coin, margin_mode):
+    mock_balance = {
+        "BTC": {
+            "free": 0.01,
+            "total": 0.012,
+            "used": 0.002,
+        },
+        "ETH": {
+            "free": 1.0,
+            "total": 5.0,
+            "used": 4.0,
+        },
+        # Invalid coin not in tickers list.
+        # This triggers a 2nd call to get_tickers
+        "NotACoin": {
+            "free": 0.0,
+            "total": 2.0,
+            "used": 0.0,
+        },
+        "USDT": {
+            "free": 50.0,
+            "total": 100.0,
+            "used": 5.0,
+        },
+    }
+    if proxy_coin:
+        default_conf_usdt["proxy_coin"] = proxy_coin
+        mock_balance[proxy_coin] = {
+            "free": 1500.0,
+            "total": 0.0,
+            "used": 0.0,
+        }
+
+    mock_pos = [
+        {
+            "symbol": "ETH/USDT:USDT",
+            "timestamp": None,
+            "datetime": None,
+            "initialMargin": 20,
+            "initialMarginPercentage": None,
+            "maintenanceMargin": 0.0,
+            "maintenanceMarginPercentage": 0.005,
+            "entryPrice": 0.0,
+            "notional": 10.0,
+            "leverage": 5.0,
+            "unrealizedPnl": 0.0,
+            "contracts": 1.0,
+            "contractSize": 1,
+            "marginRatio": None,
+            "liquidationPrice": 0.0,
+            "markPrice": 2896.41,
+            # Collateral is in USDT - and can be higher than position size in cross mode
+            "collateral": 50,
+            "marginType": "cross",
+            "side": "short",
+            "percentage": None,
+        }
+    ]
+
+    mocker.patch.multiple(
+        "freqtrade.rpc.fiat_convert.FtCoinGeckoApi",
+        get_price=MagicMock(return_value={"bitcoin": {"usd": 1.2}}),
+    )
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=1.2)
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        validate_trading_mode_and_margin_mode=MagicMock(),
+        get_balances=MagicMock(return_value=mock_balance),
+        fetch_positions=MagicMock(return_value=mock_pos),
+        get_tickers=tickers,
+        get_valid_pair_combination=MagicMock(
+            side_effect=lambda a, b: [f"{b}/{a}" if a == "USDT" else f"{a}/{b}"]
+        ),
+    )
+    default_conf_usdt["dry_run"] = False
+    default_conf_usdt["trading_mode"] = "futures"
+    default_conf_usdt["margin_mode"] = margin_mode
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf_usdt)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    rpc._fiat_converter = CryptoToFiatConverter({})
+
+    result = rpc._rpc_balance(
+        default_conf_usdt["stake_currency"], default_conf_usdt["fiat_display_currency"]
+    )
+
+    assert tickers.call_count == 4 if not proxy_coin else 6
+    assert tickers.call_args_list[0][1]["cached"] is True
+    # Testing futures - so we should get spot tickers
+    assert tickers.call_args_list[-1][1]["market_type"] == "spot"
+    assert "USD" == result["symbol"]
+    expected_curr = [
+        {
+            "currency": "BTC",
+            "free": 0.01,
+            "balance": 0.012,
+            "used": 0.002,
+            "bot_owned": 0,
+            "est_stake": 86.4872,
+            "est_stake_bot": 0,
+            "stake": "USDT",
+            "side": "long",
+            "position": 0,
+            "is_bot_managed": False,
+            "is_position": False,
+        },
+        {
+            "currency": "ETH",
+            "free": 1.0,
+            "balance": 5.0,
+            "used": 4.0,
+            "bot_owned": 0,
+            "est_stake": 530.21,
+            "est_stake_bot": 0,
+            "stake": "USDT",
+            "side": "long",
+            "position": 0,
+            "is_bot_managed": False,
+            "is_position": False,
+        },
+        {
+            "currency": "NotACoin",
+            "balance": 2.0,
+            "bot_owned": 0,
+            "est_stake": 0,
+            "est_stake_bot": 0,
+            "free": 0.0,
+            "is_bot_managed": False,
+            "is_position": False,
+            "position": 0,
+            "side": "long",
+            "stake": "USDT",
+            "used": 0.0,
+        },
+        {
+            "currency": "USDT",
+            "free": 50.0,
+            "balance": 100.0,
+            "used": 5.0,
+            "bot_owned": 49.5,
+            "est_stake": 50.0,
+            "est_stake_bot": 49.5,
+            "stake": "USDT",
+            "side": "long",
+            "position": 0,
+            "is_bot_managed": True,
+            "is_position": False,
+        },
+        {
+            "currency": "ETH/USDT:USDT",
+            "free": 0,
+            "balance": 0,
+            "used": 0,
+            "position": 10.0,
+            "est_stake": 20,
+            "est_stake_bot": 20,
+            "stake": "USDT",
+            "side": "short",
+            "is_bot_managed": True,
+            "is_position": True,
+        },
+    ]
+    if proxy_coin:
+        if margin_mode == "cross":
+            # Insert before ETH - as positions are always last.
+            expected_curr.insert(
+                len(expected_curr) - 1,
+                {
+                    "currency": proxy_coin,
+                    "free": 1500.0,
+                    "balance": 0.0,
+                    "used": 0.0,
+                    "bot_owned": 1485.0,
+                    "est_stake": 1500.0,
+                    "est_stake_bot": 1485.0,
+                    "stake": "USDT",
+                    "side": "long",
+                    "position": 0,
+                    "is_bot_managed": True,
+                    "is_position": False,
+                },
+            )
+            expected_curr[-3] = {
+                "currency": "USDT",
+                "free": 50.0,
+                "balance": 100.0,
+                "used": 5.0,
+                "bot_owned": 0,
+                "est_stake": 50.0,
+                "est_stake_bot": 0,
+                "stake": "USDT",
+                "side": "long",
+                "position": 0,
+                "is_bot_managed": False,
+                "is_position": False,
+            }
+        else:
+            expected_curr.insert(
+                len(expected_curr) - 1,
+                {
+                    "currency": proxy_coin,
+                    "free": 1500.0,
+                    "balance": 0.0,
+                    "used": 0.0,
+                    "bot_owned": 0.0,
+                    "est_stake": 0,
+                    "est_stake_bot": 0,
+                    "stake": "USDT",
+                    "side": "long",
+                    "position": 0,
+                    "is_bot_managed": False,
+                    "is_position": False,
+                },
+            )
+
+    assert result["currencies"] == expected_curr
+    if proxy_coin and margin_mode == "cross":
+        assert pytest.approx(result["total_bot"]) == 1505.0
+        assert pytest.approx(result["total"]) == 2186.6972  # ETH stake is missing.
+        assert result["starting_capital"] == 1500 * default_conf_usdt["tradable_balance_ratio"]
+        assert result["starting_capital_ratio"] == pytest.approx(0.013468013468013407)
+    else:
+        assert pytest.approx(result["total_bot"]) == 69.5
+        assert pytest.approx(result["total"]) == 686.6972  # ETH stake is missing.
+        assert result["starting_capital"] == 50 * default_conf_usdt["tradable_balance_ratio"]
+        assert result["starting_capital_ratio"] == pytest.approx(0.4040404)
+    assert pytest.approx(result["value"]) == result["total"] * 1.2
+
+
+def test_rpc_start(mocker, default_conf) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(EXMS, fetch_ticker=MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    freqtradebot.state = State.STOPPED
+
+    result = rpc._rpc_start()
+    assert {"status": "starting trader ..."} == result
+    assert freqtradebot.state == State.RUNNING
+
+    result = rpc._rpc_start()
+    assert {"status": "already running"} == result
+    assert freqtradebot.state == State.RUNNING
+
+
+def test_rpc_stop(mocker, default_conf) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(EXMS, fetch_ticker=MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    freqtradebot.state = State.RUNNING
+
+    result = rpc._rpc_stop()
+    assert {"status": "stopping trader ..."} == result
+    assert freqtradebot.state == State.STOPPED
+
+    result = rpc._rpc_stop()
+
+    assert {"status": "already stopped"} == result
+    assert freqtradebot.state == State.STOPPED
+
+
+def test_rpc_pause(mocker, default_conf) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(EXMS, fetch_ticker=MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    freqtradebot.state = State.PAUSED
+
+    result = rpc._rpc_pause()
+    assert {
+        "status": "paused, no more entries will occur from now. Run /start to enable entries."
+    } == result
+
+
+def test_rpc_force_exit(default_conf, ticker, fee, mocker) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+
+    cancel_order_mock = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        cancel_order=cancel_order_mock,
+        fetch_order=MagicMock(
+            return_value={
+                "status": "closed",
+                "type": "limit",
+                "side": "buy",
+                "filled": 0.0,
+            }
+        ),
+        _dry_is_price_crossed=MagicMock(return_value=True),
+        get_fee=fee,
+    )
+    mocker.patch("freqtrade.wallets.Wallets.get_free", return_value=1000)
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+
+    freqtradebot.state = State.STOPPED
+    with pytest.raises(RPCException, match=r".*trader is not running*"):
+        rpc._rpc_force_exit(None)
+
+    freqtradebot.state = State.RUNNING
+    with pytest.raises(RPCException, match=r".*invalid argument*"):
+        rpc._rpc_force_exit(None)
+
+    msg = rpc._rpc_force_exit("all")
+    assert msg == {"result": "Created exit orders for all open trades."}
+
+    freqtradebot.enter_positions()
+    msg = rpc._rpc_force_exit("all")
+    assert msg == {"result": "Created exit orders for all open trades."}
+
+    freqtradebot.enter_positions()
+    msg = rpc._rpc_force_exit("2")
+    assert msg == {"result": "Created exit order for trade 2."}
+
+    freqtradebot.state = State.STOPPED
+    with pytest.raises(RPCException, match=r".*trader is not running*"):
+        rpc._rpc_force_exit(None)
+
+    with pytest.raises(RPCException, match=r".*trader is not running*"):
+        rpc._rpc_force_exit("all")
+
+    freqtradebot.state = State.RUNNING
+    assert cancel_order_mock.call_count == 0
+    mocker.patch(f"{EXMS}._dry_is_price_crossed", MagicMock(return_value=False))
+    freqtradebot.enter_positions()
+    # make an limit-buy open trade
+    trade = Trade.session.scalars(select(Trade).filter(Trade.id == "3")).first()
+    filled_amount = trade.amount_requested / 2
+    # Fetch order - it's open first, and closed after cancel_order is called.
+    mocker.patch(
+        f"{EXMS}.fetch_order",
+        side_effect=[
+            {
+                "id": trade.orders[0].order_id,
+                "status": "open",
+                "type": "limit",
+                "side": "buy",
+                "filled": filled_amount,
+            },
+            {
+                "id": trade.orders[0].order_id,
+                "status": "closed",
+                "type": "limit",
+                "side": "buy",
+                "filled": filled_amount,
+            },
+        ],
+    )
+    # check that the trade is called, which is done by ensuring exchange.cancel_order is called
+    # and trade amount is updated
+    rpc._rpc_force_exit("3")
+    assert cancel_order_mock.call_count == 1
+    assert pytest.approx(trade.amount) == filled_amount
+
+    mocker.patch(
+        f"{EXMS}.fetch_order",
+        return_value={"status": "open", "type": "limit", "side": "buy", "filled": filled_amount},
+    )
+
+    freqtradebot.config["max_open_trades"] = 3
+    freqtradebot.enter_positions()
+
+    cancel_order_mock.reset_mock()
+    trade = Trade.session.scalars(select(Trade).filter(Trade.id == "3")).first()
+    amount = trade.amount_requested
+    # make an limit-sell open order trade
+    mocker.patch(
+        f"{EXMS}.fetch_order",
+        return_value={
+            "status": "open",
+            "type": "limit",
+            "side": "sell",
+            "amount": amount,
+            "remaining": amount,
+            "filled": 0.0,
+            "id": trade.orders[-1].order_id,
+        },
+    )
+    cancel_order_3 = mocker.patch(
+        f"{EXMS}.cancel_order_with_result",
+        return_value={
+            "status": "canceled",
+            "type": "limit",
+            "side": "sell",
+            "amount": amount,
+            "remaining": amount,
+            "filled": 0.0,
+            "id": trade.orders[-1].order_id,
+        },
+    )
+    msg = rpc._rpc_force_exit("3")
+    assert msg == {"result": "Created exit order for trade 3."}
+    # status quo, no exchange calls
+    assert cancel_order_3.call_count == 1
+    assert cancel_order_mock.call_count == 0
+
+    trade = Trade.session.scalars(select(Trade).filter(Trade.id == "4")).first()
+    amount = trade.amount_requested
+    # make an limit-buy open trade, if there is no 'filled', don't sell it
+    mocker.patch(
+        f"{EXMS}.fetch_order",
+        return_value={"status": "open", "type": "limit", "side": "buy", "filled": None},
+    )
+    cancel_order_4 = mocker.patch(
+        f"{EXMS}.cancel_order_with_result",
+        return_value={
+            "status": "canceled",
+            "type": "limit",
+            "side": "sell",
+            "amount": amount,
+            "remaining": 0.0,
+            "filled": amount,
+            "id": trade.orders[0].order_id,
+        },
+    )
+    # check that the trade is called, which is done by ensuring exchange.cancel_order is called
+    msg = rpc._rpc_force_exit("4")
+    assert msg == {"result": "Created exit order for trade 4."}
+    assert cancel_order_4.call_count == 1
+    assert cancel_order_mock.call_count == 0
+    assert pytest.approx(trade.amount) == amount
+
+
+def test_performance_handle(default_conf_usdt, ticker, fee, mocker) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf_usdt)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    # Create some test data
+    create_mock_trades_usdt(fee)
+
+    res = rpc._rpc_performance()
+    assert len(res) == 3
+    assert res[0]["pair"] == "NEO/USDT"
+    assert res[0]["count"] == 1
+    assert res[0]["profit_abs"] == 3.9875
+    assert res[0]["profit_pct"] == 1.99
+
+
+def test_enter_tag_performance_handle(default_conf, ticker, fee, mocker) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+
+    # Create some test data
+    create_mock_trades_usdt(fee)
+    freqtradebot.enter_positions()
+
+    res = rpc._rpc_enter_tag_performance(None)
+
+    assert len(res) == 3
+    assert res[0]["enter_tag"] == "TEST1"
+    assert res[0]["count"] == 1
+    assert res[0]["profit_pct"] == 1.99
+
+    res = rpc._rpc_enter_tag_performance(None)
+
+    assert len(res) == 3
+    assert res[0]["enter_tag"] == "TEST1"
+    assert res[0]["count"] == 1
+    assert res[0]["profit_pct"] == 1.99
+
+
+def test_enter_tag_performance_handle_2(mocker, default_conf, markets, fee):
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(EXMS, markets=PropertyMock(return_value=markets))
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    create_mock_trades(fee)
+    rpc = RPC(freqtradebot)
+
+    res = rpc._rpc_enter_tag_performance(None)
+
+    assert len(res) == 2
+    assert res[0]["enter_tag"] == "TEST1"
+    assert res[0]["count"] == 1
+    assert pytest.approx(res[0]["profit_pct"]) == 0.0
+    assert pytest.approx(res[0]["profit_ratio"]) == 0.00003860975
+    assert res[1]["enter_tag"] == "Other"
+    assert res[1]["count"] == 1
+    assert pytest.approx(res[1]["profit_pct"]) == 0.0
+    assert pytest.approx(res[1]["profit_ratio"]) == 0.00002520325
+
+    # Test for a specific pair
+    res = rpc._rpc_enter_tag_performance("ETC/BTC")
+    assert len(res) == 1
+    assert res[0]["count"] == 1
+    assert res[0]["enter_tag"] == "TEST1"
+    assert pytest.approx(res[0]["profit_pct"]) == 0.0
+    assert pytest.approx(res[0]["profit_ratio"]) == 0.00003860975
+
+
+def test_exit_reason_performance_handle(default_conf_usdt, ticker, fee, mocker) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf_usdt)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+
+    # Create some test data
+    create_mock_trades_usdt(fee)
+
+    res = rpc._rpc_exit_reason_performance(None)
+
+    assert len(res) == 3
+    assert res[0]["exit_reason"] == "exit_signal"
+    assert res[0]["count"] == 1
+    assert res[0]["profit_pct"] == 1.99
+
+    assert res[1]["exit_reason"] == "roi"
+    assert res[2]["exit_reason"] == "Other"
+
+
+def test_exit_reason_performance_handle_2(mocker, default_conf, markets, fee):
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(EXMS, markets=PropertyMock(return_value=markets))
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    create_mock_trades(fee)
+    rpc = RPC(freqtradebot)
+
+    res = rpc._rpc_exit_reason_performance(None)
+
+    assert len(res) == 2
+    assert res[0]["exit_reason"] == "sell_signal"
+    assert res[0]["count"] == 1
+    assert pytest.approx(res[0]["profit_pct"]) == 0.0
+    assert pytest.approx(res[0]["profit_ratio"]) == 0.00003860975
+    assert res[1]["exit_reason"] == "roi"
+    assert res[1]["count"] == 1
+    assert pytest.approx(res[1]["profit_pct"]) == 0.0
+    assert pytest.approx(res[1]["profit_ratio"]) == 0.000025203252
+
+    # Test for a specific pair
+    res = rpc._rpc_exit_reason_performance("ETC/BTC")
+    assert len(res) == 1
+    assert res[0]["count"] == 1
+    assert res[0]["exit_reason"] == "sell_signal"
+    assert pytest.approx(res[0]["profit_pct"]) == 0.0
+    assert pytest.approx(res[0]["profit_ratio"]) == 0.00003860975
+
+
+def test_mix_tag_performance_handle(default_conf, ticker, fee, mocker) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+
+    # Create some test data
+    create_mock_trades_usdt(fee)
+
+    res = rpc._rpc_mix_tag_performance(None)
+
+    assert len(res) == 3
+    assert res[0]["mix_tag"] == "TEST1 exit_signal"
+    assert res[0]["count"] == 1
+    assert res[0]["profit_pct"] == 5.0
+
+
+def test_mix_tag_performance_handle_2(mocker, default_conf, markets, fee):
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(EXMS, markets=PropertyMock(return_value=markets))
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    create_mock_trades(fee)
+    rpc = RPC(freqtradebot)
+
+    res = rpc._rpc_mix_tag_performance(None)
+
+    assert len(res) == 2
+    assert res[0]["mix_tag"] == "TEST1 sell_signal"
+    assert res[0]["count"] == 1
+    assert pytest.approx(res[0]["profit_pct"]) == 0.5
+    assert res[1]["mix_tag"] == "Other roi"
+    assert res[1]["count"] == 1
+    assert pytest.approx(res[1]["profit_pct"]) == 1.0
+
+    # Test for a specific pair
+    res = rpc._rpc_mix_tag_performance("ETC/BTC")
+
+    assert len(res) == 1
+    assert res[0]["count"] == 1
+    assert res[0]["mix_tag"] == "TEST1 sell_signal"
+    assert pytest.approx(res[0]["profit_pct"]) == 0.5
+
+
+def test_rpc_count(mocker, default_conf, ticker, fee) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+
+    counts = rpc._rpc_count()
+    assert counts["current"] == 0
+
+    # Create some test data
+    freqtradebot.enter_positions()
+    counts = rpc._rpc_count()
+    assert counts["current"] == 1
+
+
+def test_rpc_force_entry(mocker, default_conf, ticker, fee, limit_buy_order_open) -> None:
+    default_conf["force_entry_enable"] = True
+    default_conf["max_open_trades"] = 0
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    buy_mm = MagicMock(return_value=limit_buy_order_open)
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+        create_order=buy_mm,
+    )
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    pair = "ETH/BTC"
+    with pytest.raises(RPCException, match="Maximum number of trades is reached."):
+        rpc._rpc_force_entry(pair, None)
+    freqtradebot.config["max_open_trades"] = 5
+
+    trade = rpc._rpc_force_entry(pair, None)
+    assert isinstance(trade, Trade)
+    assert trade.pair == pair
+    assert trade.open_rate == ticker()["bid"]
+
+    # Test buy duplicate
+    with pytest.raises(RPCException, match=r"position for ETH/BTC already open - id: 1"):
+        rpc._rpc_force_entry(pair, 0.0001)
+    pair = "XRP/BTC"
+    trade = rpc._rpc_force_entry(pair, 0.0001, order_type="limit")
+    assert isinstance(trade, Trade)
+    assert trade.pair == pair
+    assert trade.open_rate == 0.0001
+
+    with pytest.raises(RPCException, match=r"Symbol does not exist or market is not active."):
+        rpc._rpc_force_entry("LTC/NOTHING", 0.0001)
+
+    # Test buy pair not with stakes
+    with pytest.raises(
+        RPCException, match=r"Wrong pair selected. Only pairs with stake-currency.*"
+    ):
+        rpc._rpc_force_entry("LTC/ETH", 0.0001)
+
+    # Test with defined stake_amount
+    pair = "LTC/BTC"
+    trade = rpc._rpc_force_entry(pair, 0.0001, order_type="limit", stake_amount=0.05)
+    assert trade.stake_amount == 0.05
+    assert trade.buy_tag == "force_entry"
+
+    assert trade.open_orders_ids[-1] == "mocked_limit_buy"
+
+    freqtradebot.strategy.position_adjustment_enable = True
+    with pytest.raises(RPCException, match=r"position for LTC/BTC already open.*open order.*"):
+        rpc._rpc_force_entry(pair, 0.0001, order_type="limit", stake_amount=0.05)
+
+    # Test not buying
+    pair = "XRP/BTC"
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    freqtradebot.config["stake_amount"] = 0
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    pair = "TKN/BTC"
+    with pytest.raises(RPCException, match=r"Failed to enter position for TKN/BTC."):
+        trade = rpc._rpc_force_entry(pair, None)
+
+
+def test_rpc_force_entry_stopped(mocker, default_conf) -> None:
+    default_conf["force_entry_enable"] = True
+    default_conf["initial_state"] = "stopped"
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    pair = "ETH/BTC"
+    with pytest.raises(RPCException, match=r"trader is not running"):
+        rpc._rpc_force_entry(pair, None)
+
+
+def test_rpc_force_entry_disabled(mocker, default_conf) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    pair = "ETH/BTC"
+    with pytest.raises(RPCException, match=r"Force_entry not enabled."):
+        rpc._rpc_force_entry(pair, None)
+
+
+def test_rpc_force_entry_wrong_mode(mocker, default_conf) -> None:
+    default_conf["force_entry_enable"] = True
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    rpc = RPC(freqtradebot)
+    pair = "ETH/BTC"
+    with pytest.raises(RPCException, match="Can't go short on Spot markets."):
+        rpc._rpc_force_entry(pair, None, order_side=SignalDirection.SHORT)
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_rpc_add_and_delete_lock(mocker, default_conf):
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc = RPC(freqtradebot)
+    pair = "ETH/BTC"
+
+    rpc._rpc_add_lock(pair, datetime.now(timezone.utc) + timedelta(minutes=4), "", "*")
+    rpc._rpc_add_lock(pair, datetime.now(timezone.utc) + timedelta(minutes=5), "", "*")
+    rpc._rpc_add_lock(pair, datetime.now(timezone.utc) + timedelta(minutes=10), "", "*")
+
+    locks = rpc._rpc_locks()
+    assert locks["lock_count"] == 3
+    locks1 = rpc._rpc_delete_lock(lockid=locks["locks"][0]["id"])
+    assert locks1["lock_count"] == 2
+
+    locks2 = rpc._rpc_delete_lock(pair=pair)
+    assert locks2["lock_count"] == 0
+
+
+def test_rpc_whitelist(mocker, default_conf) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc = RPC(freqtradebot)
+    ret = rpc._rpc_whitelist()
+    assert len(ret["method"]) == 1
+    assert "StaticPairList" in ret["method"]
+    assert ret["whitelist"] == default_conf["exchange"]["pair_whitelist"]
+
+
+def test_rpc_whitelist_dynamic(mocker, default_conf) -> None:
+    default_conf["pairlists"] = [
+        {
+            "method": "VolumePairList",
+            "number_assets": 4,
+        }
+    ]
+    mocker.patch(f"{EXMS}.exchange_has", MagicMock(return_value=True))
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc = RPC(freqtradebot)
+    ret = rpc._rpc_whitelist()
+    assert len(ret["method"]) == 1
+    assert "VolumePairList" in ret["method"]
+    assert ret["length"] == 4
+    assert ret["whitelist"] == default_conf["exchange"]["pair_whitelist"]
+
+
+def test_rpc_blacklist(mocker, default_conf) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc = RPC(freqtradebot)
+    ret = rpc._rpc_blacklist(None)
+    assert len(ret["method"]) == 1
+    assert "StaticPairList" in ret["method"]
+    assert len(ret["blacklist"]) == 2
+    assert ret["blacklist"] == default_conf["exchange"]["pair_blacklist"]
+    assert ret["blacklist"] == ["DOGE/BTC", "HOT/BTC"]
+
+    ret = rpc._rpc_blacklist(["ETH/BTC"])
+    assert "StaticPairList" in ret["method"]
+    assert len(ret["blacklist"]) == 3
+    assert ret["blacklist"] == default_conf["exchange"]["pair_blacklist"]
+    assert ret["blacklist"] == ["DOGE/BTC", "HOT/BTC", "ETH/BTC"]
+
+    ret = rpc._rpc_blacklist(["ETH/BTC"])
+    assert "errors" in ret
+    assert isinstance(ret["errors"], dict)
+    assert ret["errors"]["ETH/BTC"]["error_msg"] == "Pair ETH/BTC already in pairlist."
+
+    ret = rpc._rpc_blacklist(["*/BTC"])
+    assert "StaticPairList" in ret["method"]
+    assert len(ret["blacklist"]) == 3
+    assert ret["blacklist"] == default_conf["exchange"]["pair_blacklist"]
+    assert ret["blacklist"] == ["DOGE/BTC", "HOT/BTC", "ETH/BTC"]
+    assert ret["blacklist_expanded"] == ["ETH/BTC"]
+    assert "errors" in ret
+    assert isinstance(ret["errors"], dict)
+    assert ret["errors"] == {"*/BTC": {"error_msg": "Pair */BTC is not a valid wildcard."}}
+
+    ret = rpc._rpc_blacklist(["XRP/.*"])
+    assert "StaticPairList" in ret["method"]
+    assert len(ret["blacklist"]) == 4
+    assert ret["blacklist"] == default_conf["exchange"]["pair_blacklist"]
+    assert ret["blacklist"] == ["DOGE/BTC", "HOT/BTC", "ETH/BTC", "XRP/.*"]
+    assert ret["blacklist_expanded"] == ["ETH/BTC", "XRP/BTC", "XRP/USDT"]
+    assert "errors" in ret
+    assert isinstance(ret["errors"], dict)
+
+    ret = rpc._rpc_blacklist_delete(["DOGE/BTC", "HOT/BTC"])
+
+    assert "StaticPairList" in ret["method"]
+    assert len(ret["blacklist"]) == 2
+    assert ret["blacklist"] == default_conf["exchange"]["pair_blacklist"]
+    assert ret["blacklist"] == ["ETH/BTC", "XRP/.*"]
+    assert ret["blacklist_expanded"] == ["ETH/BTC", "XRP/BTC", "XRP/USDT"]
+    assert "errors" in ret
+    assert isinstance(ret["errors"], dict)
+
+
+def test_rpc_edge_disabled(mocker, default_conf) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc = RPC(freqtradebot)
+    with pytest.raises(RPCException, match=r"Edge is not enabled."):
+        rpc._rpc_edge()
+
+
+def test_rpc_edge_enabled(mocker, edge_conf) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+    mocker.patch(
+        "freqtrade.edge.Edge._cached_pairs",
+        mocker.PropertyMock(
+            return_value={
+                "E/F": PairInfo(-0.02, 0.66, 3.71, 0.50, 1.71, 10, 60),
+            }
+        ),
+    )
+    freqtradebot = get_patched_freqtradebot(mocker, edge_conf)
+
+    rpc = RPC(freqtradebot)
+    ret = rpc._rpc_edge()
+
+    assert len(ret) == 1
+    assert ret[0]["Pair"] == "E/F"
+    assert ret[0]["Winrate"] == 0.66
+    assert ret[0]["Expectancy"] == 1.71
+    assert ret[0]["Stoploss"] == -0.02
+
+
+def test_rpc_health(mocker, default_conf) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram", MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    set_startup_time()
+    rpc = RPC(freqtradebot)
+    result = rpc.health()
+    assert result["last_process"] is None
+    assert result["last_process_ts"] is None
diff --git a/tests/rpc/test_rpc_apiserver.py b/tests/rpc/test_rpc_apiserver.py
new file mode 100644
index 000000000..6261f1ae1
--- /dev/null
+++ b/tests/rpc/test_rpc_apiserver.py
@@ -0,0 +1,3271 @@
+"""
+Unit test file for rpc/api_server.py
+"""
+
+import asyncio
+import logging
+import time
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from unittest.mock import ANY, MagicMock, PropertyMock
+
+import pandas as pd
+import pytest
+import rapidjson
+import uvicorn
+from fastapi import FastAPI, WebSocketDisconnect
+from fastapi.exceptions import HTTPException
+from fastapi.testclient import TestClient
+from requests.auth import _basic_auth_str
+from sqlalchemy import select
+
+from freqtrade.__init__ import __version__
+from freqtrade.enums import CandleType, RunMode, State, TradingMode
+from freqtrade.exceptions import DependencyException, ExchangeError, OperationalException
+from freqtrade.loggers import setup_logging, setup_logging_pre
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.persistence import CustomDataWrapper, Trade
+from freqtrade.rpc import RPC
+from freqtrade.rpc.api_server import ApiServer
+from freqtrade.rpc.api_server.api_auth import create_token, get_user_from_token
+from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
+from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
+from freqtrade.util.datetime_helpers import format_date
+from tests.conftest import (
+    CURRENT_TEST_STRATEGY,
+    EXMS,
+    create_mock_trades,
+    create_mock_trades_usdt,
+    generate_test_data,
+    get_mock_coro,
+    get_patched_freqtradebot,
+    log_has,
+    log_has_re,
+    patch_get_signal,
+)
+
+
+BASE_URI = "/api/v1"
+_TEST_USER = "FreqTrader"
+_TEST_PASS = "SuperSecurePassword1!"
+_TEST_WS_TOKEN = "secret_Ws_t0ken"
+
+
+@pytest.fixture
+def botclient(default_conf, mocker):
+    setup_logging_pre()
+    setup_logging(default_conf)
+    default_conf["runmode"] = RunMode.DRY_RUN
+    default_conf.update(
+        {
+            "api_server": {
+                "enabled": True,
+                "listen_ip_address": "127.0.0.1",
+                "listen_port": 8080,
+                "CORS_origins": ["http://example.com"],
+                "username": _TEST_USER,
+                "password": _TEST_PASS,
+                "ws_token": _TEST_WS_TOKEN,
+            }
+        }
+    )
+
+    ftbot = get_patched_freqtradebot(mocker, default_conf)
+    rpc = RPC(ftbot)
+    mocker.patch("freqtrade.rpc.api_server.ApiServer.start_api", MagicMock())
+    apiserver = None
+    try:
+        apiserver = ApiServer(default_conf)
+        apiserver.add_rpc_handler(rpc)
+        # We need to use the TestClient as a context manager to
+        # handle lifespan events correctly
+        with TestClient(apiserver.app) as client:
+            yield ftbot, client
+        # Cleanup ... ?
+    finally:
+        if apiserver:
+            apiserver.cleanup()
+        ApiServer.shutdown()
+
+
+def client_post(client: TestClient, url, data=None):
+    if data is None:
+        data = {}
+    return client.post(
+        url,
+        json=data,
+        headers={
+            "Authorization": _basic_auth_str(_TEST_USER, _TEST_PASS),
+            "Origin": "http://example.com",
+            "content-type": "application/json",
+        },
+    )
+
+
+def client_patch(client: TestClient, url, data=None):
+    if data is None:
+        data = {}
+    return client.patch(
+        url,
+        json=data,
+        headers={
+            "Authorization": _basic_auth_str(_TEST_USER, _TEST_PASS),
+            "Origin": "http://example.com",
+            "content-type": "application/json",
+        },
+    )
+
+
+def client_get(client: TestClient, url):
+    # Add fake Origin to ensure CORS kicks in
+    return client.get(
+        url,
+        headers={
+            "Authorization": _basic_auth_str(_TEST_USER, _TEST_PASS),
+            "Origin": "http://example.com",
+        },
+    )
+
+
+def client_delete(client: TestClient, url):
+    # Add fake Origin to ensure CORS kicks in
+    return client.delete(
+        url,
+        headers={
+            "Authorization": _basic_auth_str(_TEST_USER, _TEST_PASS),
+            "Origin": "http://example.com",
+        },
+    )
+
+
+def assert_response(response, expected_code=200, needs_cors=True):
+    assert response.status_code == expected_code
+    assert response.headers.get("content-type") == "application/json"
+    if needs_cors:
+        assert ("access-control-allow-credentials", "true") in response.headers.items()
+        assert ("access-control-allow-origin", "http://example.com") in response.headers.items()
+
+
+def test_api_not_found(botclient):
+    _ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/invalid_url")
+    assert_response(rc, 404)
+    assert rc.json() == {"detail": "Not Found"}
+
+
+def test_api_ui_fallback(botclient, mocker):
+    _ftbot, client = botclient
+
+    rc = client_get(client, "/favicon.ico")
+    assert rc.status_code == 200
+
+    rc = client_get(client, "/fallback_file.html")
+    assert rc.status_code == 200
+    assert "`freqtrade install-ui`" in rc.text
+
+    # Forwarded to fallback_html or index.html (depending if it's installed or not)
+    rc = client_get(client, "/something")
+    assert rc.status_code == 200
+
+    rc = client_get(client, "/something.js")
+    assert rc.status_code == 200
+
+    # Test directory traversal without mock
+    rc = client_get(client, "%2F%2F%2Fetc/passwd")
+    assert rc.status_code == 200
+    # Allow both fallback or real UI
+    assert "`freqtrade install-ui`" in rc.text or "<!DOCTYPE html>" in rc.text
+
+    mocker.patch.object(Path, "is_file", MagicMock(side_effect=[True, False]))
+    rc = client_get(client, "%2F%2F%2Fetc/passwd")
+    assert rc.status_code == 200
+
+    assert "`freqtrade install-ui`" in rc.text
+
+
+def test_api_ui_version(botclient, mocker):
+    _ftbot, client = botclient
+
+    mocker.patch("freqtrade.commands.deploy_ui.read_ui_version", return_value="0.1.2")
+    rc = client_get(client, "/ui_version")
+    assert rc.status_code == 200
+    assert rc.json()["version"] == "0.1.2"
+
+
+def test_api_auth():
+    with pytest.raises(ValueError):
+        create_token({"identity": {"u": "Freqtrade"}}, "secret1234", token_type="NotATokenType")
+
+    token = create_token({"identity": {"u": "Freqtrade"}}, "secret1234")
+    assert isinstance(token, str)
+
+    u = get_user_from_token(token, "secret1234")
+    assert u == "Freqtrade"
+    with pytest.raises(HTTPException):
+        get_user_from_token(token, "secret1234", token_type="refresh")
+    # Create invalid token
+    token = create_token({"identity": {"u1": "Freqrade"}}, "secret1234")
+    with pytest.raises(HTTPException):
+        get_user_from_token(token, "secret1234")
+
+    with pytest.raises(HTTPException):
+        get_user_from_token(b"not_a_token", "secret1234")
+
+
+def test_api_ws_auth(botclient):
+    ftbot, client = botclient
+
+    def url(token):
+        return f"/api/v1/message/ws?token={token}"
+
+    bad_token = "bad-ws_token"
+    with pytest.raises(WebSocketDisconnect):
+        with client.websocket_connect(url(bad_token)):
+            pass
+
+    good_token = _TEST_WS_TOKEN
+    with client.websocket_connect(url(good_token)):
+        pass
+
+    jwt_secret = ftbot.config["api_server"].get("jwt_secret_key", "super-secret")
+    jwt_token = create_token({"identity": {"u": "Freqtrade"}}, jwt_secret)
+    with client.websocket_connect(url(jwt_token)):
+        pass
+
+
+def test_api_unauthorized(botclient):
+    ftbot, client = botclient
+    rc = client.get(f"{BASE_URI}/ping")
+    assert_response(rc, needs_cors=False)
+    assert rc.json() == {"status": "pong"}
+
+    # Don't send user/pass information
+    rc = client.get(f"{BASE_URI}/version")
+    assert_response(rc, 401, needs_cors=False)
+    assert rc.json() == {"detail": "Unauthorized"}
+
+    # Change only username
+    ftbot.config["api_server"]["username"] = "Ftrader"
+    rc = client_get(client, f"{BASE_URI}/version")
+    assert_response(rc, 401)
+    assert rc.json() == {"detail": "Unauthorized"}
+
+    # Change only password
+    ftbot.config["api_server"]["username"] = _TEST_USER
+    ftbot.config["api_server"]["password"] = "WrongPassword"
+    rc = client_get(client, f"{BASE_URI}/version")
+    assert_response(rc, 401)
+    assert rc.json() == {"detail": "Unauthorized"}
+
+    ftbot.config["api_server"]["username"] = "Ftrader"
+    ftbot.config["api_server"]["password"] = "WrongPassword"
+
+    rc = client_get(client, f"{BASE_URI}/version")
+    assert_response(rc, 401)
+    assert rc.json() == {"detail": "Unauthorized"}
+
+
+def test_api_token_login(botclient):
+    _ftbot, client = botclient
+    rc = client.post(
+        f"{BASE_URI}/token/login",
+        data=None,
+        headers={
+            "Authorization": _basic_auth_str("WRONG_USER", "WRONG_PASS"),
+            "Origin": "http://example.com",
+        },
+    )
+    assert_response(rc, 401)
+    rc = client_post(client, f"{BASE_URI}/token/login")
+    assert_response(rc)
+    assert "access_token" in rc.json()
+    assert "refresh_token" in rc.json()
+
+    # test Authentication is working with JWT tokens too
+    rc = client.get(
+        f"{BASE_URI}/count",
+        headers={
+            "Authorization": f"Bearer {rc.json()['access_token']}",
+            "Origin": "http://example.com",
+        },
+    )
+    assert_response(rc)
+
+
+def test_api_token_refresh(botclient):
+    _ftbot, client = botclient
+    rc = client_post(client, f"{BASE_URI}/token/login")
+    assert_response(rc)
+    rc = client.post(
+        f"{BASE_URI}/token/refresh",
+        data=None,
+        headers={
+            "Authorization": f"Bearer {rc.json()['refresh_token']}",
+            "Origin": "http://example.com",
+        },
+    )
+    assert_response(rc)
+    assert "access_token" in rc.json()
+    assert "refresh_token" not in rc.json()
+
+
+def test_api_stop_workflow(botclient):
+    ftbot, client = botclient
+    assert ftbot.state == State.RUNNING
+    rc = client_post(client, f"{BASE_URI}/stop")
+    assert_response(rc)
+    assert rc.json() == {"status": "stopping trader ..."}
+    assert ftbot.state == State.STOPPED
+
+    # Stop bot again
+    rc = client_post(client, f"{BASE_URI}/stop")
+    assert_response(rc)
+    assert rc.json() == {"status": "already stopped"}
+
+    # Start bot
+    rc = client_post(client, f"{BASE_URI}/start")
+    assert_response(rc)
+    assert rc.json() == {"status": "starting trader ..."}
+    assert ftbot.state == State.RUNNING
+
+    # Call start again
+    rc = client_post(client, f"{BASE_URI}/start")
+    assert_response(rc)
+    assert rc.json() == {"status": "already running"}
+
+
+def test_api__init__(default_conf, mocker):
+    """
+    Test __init__() method
+    """
+    default_conf.update(
+        {
+            "api_server": {
+                "enabled": True,
+                "listen_ip_address": "127.0.0.1",
+                "listen_port": 8080,
+                "username": "TestUser",
+                "password": "testPass",
+            }
+        }
+    )
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init")
+    mocker.patch("freqtrade.rpc.api_server.webserver.ApiServer.start_api", MagicMock())
+    apiserver = ApiServer(default_conf)
+    apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf)))
+    assert apiserver._config == default_conf
+    with pytest.raises(OperationalException, match="RPC Handler already attached."):
+        apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf)))
+
+    apiserver.cleanup()
+    ApiServer.shutdown()
+
+
+def test_api_UvicornServer(mocker):
+    thread_mock = mocker.patch("freqtrade.rpc.api_server.uvicorn_threaded.threading.Thread")
+    s = UvicornServer(uvicorn.Config(MagicMock(), port=8080, host="127.0.0.1"))
+    assert thread_mock.call_count == 0
+
+    # Fake started to avoid sleeping forever
+    s.started = True
+    s.run_in_thread()
+    assert thread_mock.call_count == 1
+
+    s.cleanup()
+    assert s.should_exit is True
+
+
+def test_api_UvicornServer_run(mocker):
+    serve_mock = mocker.patch(
+        "freqtrade.rpc.api_server.uvicorn_threaded.UvicornServer.serve", get_mock_coro(None)
+    )
+    s = UvicornServer(uvicorn.Config(MagicMock(), port=8080, host="127.0.0.1"))
+    assert serve_mock.call_count == 0
+
+    # Fake started to avoid sleeping forever
+    s.started = True
+    s.run()
+    assert serve_mock.call_count == 1
+
+
+def test_api_UvicornServer_run_no_uvloop(mocker, import_fails):
+    serve_mock = mocker.patch(
+        "freqtrade.rpc.api_server.uvicorn_threaded.UvicornServer.serve", get_mock_coro(None)
+    )
+    asyncio.set_event_loop(asyncio.new_event_loop())
+    s = UvicornServer(uvicorn.Config(MagicMock(), port=8080, host="127.0.0.1"))
+    assert serve_mock.call_count == 0
+
+    # Fake started to avoid sleeping forever
+    s.started = True
+    s.run()
+    assert serve_mock.call_count == 1
+
+
+def test_api_run(default_conf, mocker, caplog):
+    default_conf.update(
+        {
+            "api_server": {
+                "enabled": True,
+                "listen_ip_address": "127.0.0.1",
+                "listen_port": 8080,
+                "username": "TestUser",
+                "password": "testPass",
+            }
+        }
+    )
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init")
+
+    server_inst_mock = MagicMock()
+    server_inst_mock.run_in_thread = MagicMock()
+    server_inst_mock.run = MagicMock()
+    server_mock = MagicMock(return_value=server_inst_mock)
+    mocker.patch("freqtrade.rpc.api_server.webserver.UvicornServer", server_mock)
+
+    apiserver = ApiServer(default_conf)
+    apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf)))
+
+    assert server_mock.call_count == 1
+    assert apiserver._config == default_conf
+    apiserver.start_api()
+    assert server_mock.call_count == 2
+    assert server_inst_mock.run_in_thread.call_count == 2
+    assert server_inst_mock.run.call_count == 0
+    assert server_mock.call_args_list[0][0][0].host == "127.0.0.1"
+    assert server_mock.call_args_list[0][0][0].port == 8080
+    assert isinstance(server_mock.call_args_list[0][0][0].app, FastAPI)
+
+    assert log_has("Starting HTTP Server at 127.0.0.1:8080", caplog)
+    assert log_has("Starting Local Rest Server.", caplog)
+
+    # Test binding to public
+    caplog.clear()
+    server_mock.reset_mock()
+    apiserver._config.update(
+        {
+            "api_server": {
+                "enabled": True,
+                "listen_ip_address": "0.0.0.0",
+                "listen_port": 8089,
+                "password": "",
+            }
+        }
+    )
+    apiserver.start_api()
+
+    assert server_mock.call_count == 1
+    assert server_inst_mock.run_in_thread.call_count == 1
+    assert server_inst_mock.run.call_count == 0
+    assert server_mock.call_args_list[0][0][0].host == "0.0.0.0"
+    assert server_mock.call_args_list[0][0][0].port == 8089
+    assert isinstance(server_mock.call_args_list[0][0][0].app, FastAPI)
+    assert log_has("Starting HTTP Server at 0.0.0.0:8089", caplog)
+    assert log_has("Starting Local Rest Server.", caplog)
+    assert log_has("SECURITY WARNING - Local Rest Server listening to external connections", caplog)
+    assert log_has(
+        "SECURITY WARNING - This is insecure please set to your loopback,"
+        "e.g 127.0.0.1 in config.json",
+        caplog,
+    )
+    assert log_has(
+        "SECURITY WARNING - No password for local REST Server defined. "
+        "Please make sure that this is intentional!",
+        caplog,
+    )
+    assert log_has_re("SECURITY WARNING - `jwt_secret_key` seems to be default.*", caplog)
+
+    server_mock.reset_mock()
+    apiserver._standalone = True
+    apiserver.start_api()
+    assert server_inst_mock.run_in_thread.call_count == 0
+    assert server_inst_mock.run.call_count == 1
+
+    apiserver1 = ApiServer(default_conf)
+    assert id(apiserver1) == id(apiserver)
+
+    apiserver._standalone = False
+
+    # Test crashing API server
+    caplog.clear()
+    mocker.patch(
+        "freqtrade.rpc.api_server.webserver.UvicornServer", MagicMock(side_effect=Exception)
+    )
+    apiserver.start_api()
+    assert log_has("Api server failed to start.", caplog)
+    apiserver.cleanup()
+    ApiServer.shutdown()
+
+
+def test_api_cleanup(default_conf, mocker, caplog):
+    default_conf.update(
+        {
+            "api_server": {
+                "enabled": True,
+                "listen_ip_address": "127.0.0.1",
+                "listen_port": 8080,
+                "username": "TestUser",
+                "password": "testPass",
+            }
+        }
+    )
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init")
+
+    server_mock = MagicMock()
+    server_mock.cleanup = MagicMock()
+    mocker.patch("freqtrade.rpc.api_server.webserver.UvicornServer", server_mock)
+
+    apiserver = ApiServer(default_conf)
+    apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf)))
+
+    apiserver.cleanup()
+    assert apiserver._server.cleanup.call_count == 1
+    assert log_has("Stopping API Server", caplog)
+    ApiServer.shutdown()
+
+
+def test_api_reloadconf(botclient):
+    ftbot, client = botclient
+
+    rc = client_post(client, f"{BASE_URI}/reload_config")
+    assert_response(rc)
+    assert rc.json() == {"status": "Reloading config ..."}
+    assert ftbot.state == State.RELOAD_CONFIG
+
+
+def test_api_pause(botclient):
+    ftbot, client = botclient
+
+    rc = client_post(client, f"{BASE_URI}/pause")
+    assert_response(rc)
+    assert rc.json() == {
+        "status": "paused, no more entries will occur from now. Run /start to enable entries."
+    }
+
+    rc = client_post(client, f"{BASE_URI}/pause")
+    assert_response(rc)
+    assert rc.json() == {
+        "status": "paused, no more entries will occur from now. Run /start to enable entries."
+    }
+
+    rc = client_post(client, f"{BASE_URI}/stopentry")
+    assert_response(rc)
+    assert rc.json() == {
+        "status": "paused, no more entries will occur from now. Run /start to enable entries."
+    }
+
+
+def test_api_balance(botclient, mocker, rpc_balance, tickers):
+    ftbot, client = botclient
+
+    ftbot.config["dry_run"] = False
+    mocker.patch(f"{EXMS}.get_balances", return_value=rpc_balance)
+    mocker.patch(f"{EXMS}.get_tickers", tickers)
+    mocker.patch(f"{EXMS}.get_valid_pair_combination", side_effect=lambda a, b: [f"{a}/{b}"])
+    ftbot.wallets.update()
+
+    rc = client_get(client, f"{BASE_URI}/balance")
+    assert_response(rc)
+    response = rc.json()
+    assert "currencies" in response
+    assert len(response["currencies"]) == 5
+    assert response["currencies"][0] == {
+        "currency": "BTC",
+        "free": 12.0,
+        "balance": 12.0,
+        "used": 0.0,
+        "bot_owned": pytest.approx(11.879999),
+        "est_stake": 12.0,
+        "est_stake_bot": pytest.approx(11.879999),
+        "stake": "BTC",
+        "is_position": False,
+        "position": 0.0,
+        "side": "long",
+        "is_bot_managed": True,
+    }
+    assert response["total"] == 12.159513094
+    assert response["total_bot"] == pytest.approx(11.879999)
+    assert "starting_capital" in response
+    assert "starting_capital_fiat" in response
+    assert "starting_capital_pct" in response
+    assert "starting_capital_ratio" in response
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_api_count(botclient, mocker, ticker, fee, markets, is_short):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+        markets=PropertyMock(return_value=markets),
+    )
+    rc = client_get(client, f"{BASE_URI}/count")
+    assert_response(rc)
+
+    assert rc.json()["current"] == 0
+    assert rc.json()["max"] == 1
+
+    # Create some test data
+    create_mock_trades(fee, is_short=is_short)
+    rc = client_get(client, f"{BASE_URI}/count")
+    assert_response(rc)
+    assert rc.json()["current"] == 4
+    assert rc.json()["max"] == 1
+
+    ftbot.config["max_open_trades"] = float("inf")
+    rc = client_get(client, f"{BASE_URI}/count")
+    assert rc.json()["max"] == -1
+
+
+def test_api_locks(botclient):
+    _ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/locks")
+    assert_response(rc)
+
+    assert "locks" in rc.json()
+
+    assert rc.json()["lock_count"] == 0
+    assert rc.json()["lock_count"] == len(rc.json()["locks"])
+
+    rc = client_post(
+        client,
+        f"{BASE_URI}/locks",
+        [
+            {
+                "pair": "ETH/BTC",
+                "until": f"{format_date(datetime.now(timezone.utc) + timedelta(minutes=4))}Z",
+                "reason": "randreason",
+            },
+            {
+                "pair": "XRP/BTC",
+                "until": f"{format_date(datetime.now(timezone.utc) + timedelta(minutes=20))}Z",
+                "reason": "deadbeef",
+            },
+        ],
+    )
+    assert_response(rc)
+    assert rc.json()["lock_count"] == 2
+
+    rc = client_get(client, f"{BASE_URI}/locks")
+    assert_response(rc)
+
+    assert rc.json()["lock_count"] == 2
+    assert rc.json()["lock_count"] == len(rc.json()["locks"])
+    assert "ETH/BTC" in (rc.json()["locks"][0]["pair"], rc.json()["locks"][1]["pair"])
+    assert "randreason" in (rc.json()["locks"][0]["reason"], rc.json()["locks"][1]["reason"])
+    assert "deadbeef" in (rc.json()["locks"][0]["reason"], rc.json()["locks"][1]["reason"])
+
+    # Test deletions
+    rc = client_delete(client, f"{BASE_URI}/locks/1")
+    assert_response(rc)
+    assert rc.json()["lock_count"] == 1
+
+    rc = client_post(client, f"{BASE_URI}/locks/delete", data={"pair": "XRP/BTC"})
+    assert_response(rc)
+    assert rc.json()["lock_count"] == 0
+
+
+def test_api_show_config(botclient):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+
+    rc = client_get(client, f"{BASE_URI}/show_config")
+    assert_response(rc)
+    response = rc.json()
+    assert "dry_run" in response
+    assert response["exchange"] == "binance"
+    assert response["timeframe"] == "5m"
+    assert response["timeframe_ms"] == 300000
+    assert response["timeframe_min"] == 5
+    assert response["state"] == "running"
+    assert response["bot_name"] == "freqtrade"
+    assert response["trading_mode"] == "spot"
+    assert response["strategy_version"] is None
+    assert not response["trailing_stop"]
+    assert "entry_pricing" in response
+    assert "exit_pricing" in response
+    assert "unfilledtimeout" in response
+    assert "version" in response
+    assert "api_version" in response
+    assert 2.1 <= response["api_version"] < 3.0
+
+
+def test_api_daily(botclient, mocker, ticker, fee, markets):
+    ftbot, client = botclient
+
+    ftbot.config["dry_run"] = False
+    mocker.patch(f"{EXMS}.get_balances", return_value=ticker)
+    mocker.patch(f"{EXMS}.get_tickers", ticker)
+    mocker.patch(f"{EXMS}.get_fee", fee)
+    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value=markets))
+    ftbot.wallets.update()
+
+    rc = client_get(client, f"{BASE_URI}/daily")
+    assert_response(rc)
+    response = rc.json()
+    assert "data" in response
+    assert len(response["data"]) == 7
+    assert response["stake_currency"] == "BTC"
+    assert response["fiat_display_currency"] == "USD"
+    assert response["data"][0]["date"] == str(datetime.now(timezone.utc).date())
+
+
+def test_api_weekly(botclient, mocker, ticker, fee, markets, time_machine):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+        markets=PropertyMock(return_value=markets),
+    )
+    time_machine.move_to("2023-03-31 21:45:05 +00:00")
+    rc = client_get(client, f"{BASE_URI}/weekly")
+    assert_response(rc)
+    assert len(rc.json()["data"]) == 4
+    assert rc.json()["stake_currency"] == "BTC"
+    assert rc.json()["fiat_display_currency"] == "USD"
+    # Moved to monday
+    assert rc.json()["data"][0]["date"] == "2023-03-27"
+    assert rc.json()["data"][1]["date"] == "2023-03-20"
+
+
+def test_api_monthly(botclient, mocker, ticker, fee, markets, time_machine):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+        markets=PropertyMock(return_value=markets),
+    )
+    time_machine.move_to("2023-03-31 21:45:05 +00:00")
+    rc = client_get(client, f"{BASE_URI}/monthly")
+    assert_response(rc)
+    assert len(rc.json()["data"]) == 3
+    assert rc.json()["stake_currency"] == "BTC"
+    assert rc.json()["fiat_display_currency"] == "USD"
+    assert rc.json()["data"][0]["date"] == "2023-03-01"
+    assert rc.json()["data"][1]["date"] == "2023-02-01"
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_api_trades(botclient, mocker, fee, markets, is_short):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+    mocker.patch.multiple(EXMS, markets=PropertyMock(return_value=markets))
+    rc = client_get(client, f"{BASE_URI}/trades")
+    assert_response(rc)
+    assert len(rc.json()) == 4
+    assert rc.json()["trades_count"] == 0
+    assert rc.json()["total_trades"] == 0
+    assert rc.json()["offset"] == 0
+
+    create_mock_trades(fee, is_short=is_short)
+    Trade.session.flush()
+
+    rc = client_get(client, f"{BASE_URI}/trades")
+    assert_response(rc)
+    assert len(rc.json()["trades"]) == 2
+    assert rc.json()["trades_count"] == 2
+    assert rc.json()["total_trades"] == 2
+    assert rc.json()["trades"][0]["is_short"] == is_short
+    rc = client_get(client, f"{BASE_URI}/trades?limit=1")
+    assert_response(rc)
+    assert len(rc.json()["trades"]) == 1
+    assert rc.json()["trades_count"] == 1
+    assert rc.json()["total_trades"] == 2
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_api_trade_single(botclient, mocker, fee, ticker, markets, is_short):
+    ftbot, client = botclient
+    patch_get_signal(ftbot, enter_long=not is_short, enter_short=is_short)
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        fetch_ticker=ticker,
+    )
+    rc = client_get(client, f"{BASE_URI}/trade/3")
+    assert_response(rc, 404)
+    assert rc.json()["detail"] == "Trade not found."
+
+    Trade.rollback()
+    create_mock_trades(fee, is_short=is_short)
+
+    rc = client_get(client, f"{BASE_URI}/trade/3")
+    assert_response(rc)
+    assert rc.json()["trade_id"] == 3
+    assert rc.json()["is_short"] == is_short
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_api_custom_data_single_trade(botclient, fee):
+    Trade.reset_trades()
+    CustomDataWrapper.reset_custom_data()
+
+    create_mock_trades_usdt(fee, use_db=True)
+
+    trade1 = Trade.get_trades_proxy()[0]
+
+    assert trade1.get_all_custom_data() == []
+
+    trade1.set_custom_data("test_str", "test_value")
+    trade1.set_custom_data("test_int", 0)
+    trade1.set_custom_data("test_float", 1.54)
+    trade1.set_custom_data("test_bool", True)
+    trade1.set_custom_data("test_dict", {"test": "vl"})
+
+    trade1.set_custom_data("test_int", 1)
+
+    _, client = botclient
+
+    # CASE 1 Checking all custom data of trade 1
+    rc = client_get(client, f"{BASE_URI}/trades/1/custom-data")
+    assert_response(rc)
+
+    # Validate response JSON structure
+    response_json = rc.json()
+
+    assert len(response_json) == 1
+
+    res_cust_data = response_json[0]["custom_data"]
+    expected_data_td_1 = [
+        {"key": "test_str", "type": "str", "value": "test_value"},
+        {"key": "test_int", "type": "int", "value": 1},
+        {"key": "test_float", "type": "float", "value": 1.54},
+        {"key": "test_bool", "type": "bool", "value": True},
+        {"key": "test_dict", "type": "dict", "value": {"test": "vl"}},
+    ]
+
+    # Ensure response contains exactly the expected number of entries
+    assert len(res_cust_data) == len(expected_data_td_1), (
+        f"Expected {len(expected_data_td_1)} entries, but got {len(res_cust_data)}.\n"
+    )
+
+    # Validate each expected entry
+    for expected in expected_data_td_1:
+        matched_item = None
+        for item in res_cust_data:
+            if item["key"] == expected["key"]:
+                matched_item = item
+                break
+
+        assert matched_item is not None, (
+            f"Missing expected entry for key '{expected['key']}'\nExpected: {expected}\n"
+        )
+
+        # Validate individual fields and print only incorrect values
+        mismatches = []
+        for field in ["key", "type", "value"]:
+            if matched_item[field] != expected[field]:
+                mismatches.append(f"{field}: Expected {expected[field]}, Got {matched_item[field]}")
+
+        assert not mismatches, f"Error in entry '{expected['key']}':\n" + "\n".join(mismatches)
+
+    # CASE 2 Checking specific existing key custom data of trade 1
+    rc = client_get(client, f"{BASE_URI}/trades/1/custom-data?key=test_dict")
+    assert_response(rc, 200)
+
+    # CASE 3 Checking specific not existing key custom data of trade 1
+    rc = client_get(client, f"{BASE_URI}/trades/1/custom-data&key=test")
+    assert_response(rc, 404)
+
+    # CASE 4 Trying to get custom-data from not existing trade
+    rc = client_get(client, f"{BASE_URI}/trades/13/custom-data")
+    assert_response(rc, 404)
+    assert rc.json()["detail"] == "No trade found for trade_id: 13"
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_api_custom_data_multiple_open_trades(botclient, fee):
+    use_db = True
+    Trade.use_db = use_db
+    Trade.reset_trades()
+    CustomDataWrapper.reset_custom_data()
+    create_mock_trades(fee, False, use_db)
+    trades = Trade.get_trades_proxy()
+    assert len(trades) == 6
+
+    assert isinstance(trades[0], Trade)
+
+    trades = Trade.get_trades_proxy(is_open=True)
+    assert len(trades) == 4
+
+    create_mock_trades_usdt(fee, use_db=True)
+
+    trade1 = Trade.get_trades_proxy(is_open=True)[0]
+    trade2 = Trade.get_trades_proxy(is_open=True)[1]
+
+    # Initially, no custom data should be present.
+    assert trade1.get_all_custom_data() == []
+    assert trade2.get_all_custom_data() == []
+
+    # Set custom data for the two open trades.
+    trade1.set_custom_data("test_str", "test_value_t1")
+    trade1.set_custom_data("test_float", 1.54)
+    trade1.set_custom_data("test_dict", {"test_t1": "vl_t1"})
+
+    trade2.set_custom_data("test_str", "test_value_t2")
+    trade2.set_custom_data("test_float", 1.55)
+    trade2.set_custom_data("test_dict", {"test_t2": "vl_t2"})
+
+    _, client = botclient
+
+    # CASE 1: Checking all custom data for both trades.
+    rc = client_get(client, f"{BASE_URI}/trades/open/custom-data")
+    assert_response(rc)
+
+    response_json = rc.json()
+
+    # Expecting two trade entries in the response
+    assert len(response_json) == 2, f"Expected 2 trade entries, but got {len(response_json)}.\n"
+
+    # Define expected custom data for each trade.
+    # The keys now use the actual trade_ids from the custom data.
+    expected_custom_data = {
+        1: [
+            {
+                "key": "test_str",
+                "type": "str",
+                "value": "test_value_t1",
+            },
+            {
+                "key": "test_float",
+                "type": "float",
+                "value": 1.54,
+            },
+            {
+                "key": "test_dict",
+                "type": "dict",
+                "value": {"test_t1": "vl_t1"},
+            },
+        ],
+        4: [
+            {
+                "key": "test_str",
+                "type": "str",
+                "value": "test_value_t2",
+            },
+            {
+                "key": "test_float",
+                "type": "float",
+                "value": 1.55,
+            },
+            {
+                "key": "test_dict",
+                "type": "dict",
+                "value": {"test_t2": "vl_t2"},
+            },
+        ],
+    }
+
+    # Iterate over each trade's data in the response and validate entries.
+    for trade_entry in response_json:
+        trade_id = trade_entry.get("trade_id")
+        assert trade_id in expected_custom_data, f"\nUnexpected trade_id: {trade_id}"
+
+        custom_data_list = trade_entry.get("custom_data")
+        expected_data = expected_custom_data[trade_id]
+        assert len(custom_data_list) == len(expected_data), (
+            f"Error for trade_id {trade_id}: "
+            f"Expected {len(expected_data)} entries, but got {len(custom_data_list)}.\n"
+        )
+
+        # For each expected entry, check that the response contains the correct entry.
+        for expected in expected_data:
+            matched_item = None
+            for item in custom_data_list:
+                if item["key"] == expected["key"]:
+                    matched_item = item
+                    break
+
+            assert matched_item is not None, (
+                f"For trade_id {trade_id}, "
+                f"missing expected entry for key '{expected['key']}'\n"
+                f"Expected: {expected}\n"
+            )
+
+            # Validate key fields.
+            mismatches = []
+            for field in ["key", "type", "value"]:
+                if matched_item[field] != expected[field]:
+                    mismatches.append(
+                        f"{field}: Expected {expected[field]}, Got {matched_item[field]}"
+                    )
+            # Check for field presence of created_at and updated_at without comparing values.
+            for field in ["created_at", "updated_at"]:
+                if field not in matched_item:
+                    mismatches.append(f"Missing field: {field}")
+
+            assert not mismatches, (
+                f"Error in entry '{expected['key']}' for trade_id {trade_id}:\n"
+                + "\n".join(mismatches)
+            )
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_api_delete_trade(botclient, mocker, fee, markets, is_short):
+    ftbot, client = botclient
+    patch_get_signal(ftbot, enter_long=not is_short, enter_short=is_short)
+    stoploss_mock = MagicMock()
+    cancel_mock = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        cancel_order=cancel_mock,
+        cancel_stoploss_order=stoploss_mock,
+    )
+
+    create_mock_trades(fee, is_short=is_short)
+
+    ftbot.strategy.order_types["stoploss_on_exchange"] = True
+    trades = Trade.session.scalars(select(Trade)).all()
+    Trade.commit()
+    assert len(trades) > 2
+
+    rc = client_delete(client, f"{BASE_URI}/trades/1")
+    assert_response(rc)
+    assert rc.json()["result_msg"] == "Deleted trade 1. Closed 1 open orders."
+    assert len(trades) - 1 == len(Trade.session.scalars(select(Trade)).all())
+    assert cancel_mock.call_count == 1
+
+    cancel_mock.reset_mock()
+    rc = client_delete(client, f"{BASE_URI}/trades/1")
+    # Trade is gone now.
+    assert_response(rc, 502)
+    assert cancel_mock.call_count == 0
+
+    assert len(trades) - 1 == len(Trade.session.scalars(select(Trade)).all())
+    rc = client_delete(client, f"{BASE_URI}/trades/5")
+    assert_response(rc)
+    assert rc.json()["result_msg"] == "Deleted trade 5. Closed 1 open orders."
+    assert len(trades) - 2 == len(Trade.session.scalars(select(Trade)).all())
+    assert stoploss_mock.call_count == 1
+
+    rc = client_delete(client, f"{BASE_URI}/trades/502")
+    # Error - trade won't exist.
+    assert_response(rc, 502)
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_api_delete_open_order(botclient, mocker, fee, markets, ticker, is_short):
+    ftbot, client = botclient
+    patch_get_signal(ftbot, enter_long=not is_short, enter_short=is_short)
+    stoploss_mock = MagicMock()
+    cancel_mock = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        fetch_ticker=ticker,
+        cancel_order=cancel_mock,
+        cancel_stoploss_order=stoploss_mock,
+    )
+
+    rc = client_delete(client, f"{BASE_URI}/trades/10/open-order")
+    assert_response(rc, 502)
+    assert "Invalid trade_id." in rc.json()["error"]
+
+    create_mock_trades(fee, is_short=is_short)
+    Trade.commit()
+
+    rc = client_delete(client, f"{BASE_URI}/trades/5/open-order")
+    assert_response(rc, 502)
+    assert "No open order for trade_id" in rc.json()["error"]
+    trade = Trade.get_trades([Trade.id == 6]).first()
+    mocker.patch(f"{EXMS}.fetch_order", side_effect=ExchangeError)
+    rc = client_delete(client, f"{BASE_URI}/trades/6/open-order")
+    assert_response(rc, 502)
+    assert "Order not found." in rc.json()["error"]
+
+    trade = Trade.get_trades([Trade.id == 6]).first()
+    mocker.patch(f"{EXMS}.fetch_order", return_value=trade.orders[-1].to_ccxt_object())
+
+    rc = client_delete(client, f"{BASE_URI}/trades/6/open-order")
+    assert_response(rc)
+    assert cancel_mock.call_count == 1
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_api_trade_reload_trade(botclient, mocker, fee, markets, ticker, is_short):
+    ftbot, client = botclient
+    patch_get_signal(ftbot, enter_long=not is_short, enter_short=is_short)
+    stoploss_mock = MagicMock()
+    cancel_mock = MagicMock()
+    ftbot.handle_onexchange_order = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        fetch_ticker=ticker,
+        cancel_order=cancel_mock,
+        cancel_stoploss_order=stoploss_mock,
+    )
+
+    rc = client_post(client, f"{BASE_URI}/trades/10/reload")
+    assert_response(rc, 502)
+    assert "Could not find trade with id 10." in rc.json()["error"]
+    assert ftbot.handle_onexchange_order.call_count == 0
+
+    create_mock_trades(fee, is_short=is_short)
+    Trade.commit()
+
+    rc = client_post(client, f"{BASE_URI}/trades/5/reload")
+    assert ftbot.handle_onexchange_order.call_count == 1
+
+
+def test_api_logs(botclient):
+    _ftbot, client = botclient
+    rc = client_get(client, f"{BASE_URI}/logs")
+    assert_response(rc)
+    assert len(rc.json()) == 2
+    assert "logs" in rc.json()
+    # Using a fixed comparison here would make this test fail!
+    assert rc.json()["log_count"] > 1
+    assert len(rc.json()["logs"]) == rc.json()["log_count"]
+
+    assert isinstance(rc.json()["logs"][0], list)
+    # date
+    assert isinstance(rc.json()["logs"][0][0], str)
+    # created_timestamp
+    assert isinstance(rc.json()["logs"][0][1], float)
+    assert isinstance(rc.json()["logs"][0][2], str)
+    assert isinstance(rc.json()["logs"][0][3], str)
+    assert isinstance(rc.json()["logs"][0][4], str)
+
+    rc1 = client_get(client, f"{BASE_URI}/logs?limit=5")
+    assert_response(rc1)
+    assert len(rc1.json()) == 2
+    assert "logs" in rc1.json()
+    # Using a fixed comparison here would make this test fail!
+    if rc1.json()["log_count"] < 5:
+        # Help debugging random test failure
+        print(f"rc={rc.json()}")
+        print(f"rc1={rc1.json()}")
+    assert rc1.json()["log_count"] > 2
+    assert len(rc1.json()["logs"]) == rc1.json()["log_count"]
+
+
+def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+        markets=PropertyMock(return_value=markets),
+    )
+    rc = client_get(client, f"{BASE_URI}/edge")
+    assert_response(rc, 502)
+    assert rc.json() == {"error": "Error querying /api/v1/edge: Edge is not enabled."}
+
+
+@pytest.mark.parametrize(
+    "is_short,expected",
+    [
+        (
+            True,
+            {
+                "best_pair": "XRP/BTC",
+                "best_rate": -0.02,
+                "best_pair_profit_ratio": -0.00018780487,
+                "best_pair_profit_abs": -0.001155,
+                "profit_all_coin": 15.382312,
+                "profit_all_fiat": 189894.6470718,
+                "profit_all_percent_mean": 49.62,
+                "profit_all_ratio_mean": 0.49620917,
+                "profit_all_percent_sum": 198.48,
+                "profit_all_ratio_sum": 1.98483671,
+                "profit_all_percent": 1.54,
+                "profit_all_ratio": 0.01538214,
+                "profit_closed_coin": -0.00673913,
+                "profit_closed_fiat": -83.19455985,
+                "profit_closed_ratio_mean": -0.0075,
+                "profit_closed_percent_mean": -0.75,
+                "profit_closed_ratio_sum": -0.015,
+                "profit_closed_percent_sum": -1.5,
+                "profit_closed_ratio": -6.739057628404269e-06,
+                "profit_closed_percent": -0.0,
+                "winning_trades": 0,
+                "losing_trades": 2,
+                "profit_factor": 0.0,
+                "winrate": 0.0,
+                "expectancy": -0.0033695635,
+                "expectancy_ratio": -1.0,
+                "trading_volume": 75.945,
+            },
+        ),
+        (
+            False,
+            {
+                "best_pair": "ETC/BTC",
+                "best_rate": 0.0,
+                "best_pair_profit_ratio": 0.00003860975,
+                "best_pair_profit_abs": 0.000584127,
+                "profit_all_coin": -15.46546305,
+                "profit_all_fiat": -190921.14135225,
+                "profit_all_percent_mean": -49.62,
+                "profit_all_ratio_mean": -0.49620955,
+                "profit_all_percent_sum": -198.48,
+                "profit_all_ratio_sum": -1.9848382,
+                "profit_all_percent": -1.55,
+                "profit_all_ratio": -0.0154654126,
+                "profit_closed_coin": 0.00073913,
+                "profit_closed_fiat": 9.124559849999999,
+                "profit_closed_ratio_mean": 0.0075,
+                "profit_closed_percent_mean": 0.75,
+                "profit_closed_ratio_sum": 0.015,
+                "profit_closed_percent_sum": 1.5,
+                "profit_closed_ratio": 7.391275897987988e-07,
+                "profit_closed_percent": 0.0,
+                "winning_trades": 2,
+                "losing_trades": 0,
+                "profit_factor": None,
+                "winrate": 1.0,
+                "expectancy": 0.0003695635,
+                "expectancy_ratio": 100,
+                "trading_volume": 75.945,
+            },
+        ),
+        (
+            None,
+            {
+                "best_pair": "XRP/BTC",
+                "best_rate": 0.0,
+                "best_pair_profit_ratio": 0.000025203252,
+                "best_pair_profit_abs": 0.000155,
+                "profit_all_coin": -14.87167525,
+                "profit_all_fiat": -183590.83096125,
+                "profit_all_percent_mean": 0.13,
+                "profit_all_ratio_mean": 0.0012538324,
+                "profit_all_percent_sum": 0.5,
+                "profit_all_ratio_sum": 0.005015329,
+                "profit_all_percent": -1.49,
+                "profit_all_ratio": -0.0148715350,
+                "profit_closed_coin": -0.00542913,
+                "profit_closed_fiat": -67.02260985,
+                "profit_closed_ratio_mean": 0.0025,
+                "profit_closed_percent_mean": 0.25,
+                "profit_closed_ratio_sum": 0.005,
+                "profit_closed_percent_sum": 0.5,
+                "profit_closed_ratio": -5.429078808526421e-06,
+                "profit_closed_percent": -0.0,
+                "winning_trades": 1,
+                "losing_trades": 1,
+                "profit_factor": 0.02775724835771106,
+                "winrate": 0.5,
+                "expectancy": -0.0027145635000000003,
+                "expectancy_ratio": -0.48612137582114445,
+                "trading_volume": 75.945,
+            },
+        ),
+    ],
+)
+def test_api_profit(botclient, mocker, ticker, fee, markets, is_short, expected):
+    ftbot, client = botclient
+    ftbot.config["tradable_balance_ratio"] = 1
+    patch_get_signal(ftbot)
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+        markets=PropertyMock(return_value=markets),
+    )
+
+    rc = client_get(client, f"{BASE_URI}/profit")
+    assert_response(rc, 200)
+    assert rc.json()["trade_count"] == 0
+
+    create_mock_trades(fee, is_short=is_short)
+    # Simulate fulfilled LIMIT_BUY order for trade
+
+    rc = client_get(client, f"{BASE_URI}/profit")
+    assert_response(rc)
+    # raise ValueError(rc.json())
+    assert rc.json() == {
+        "avg_duration": ANY,
+        "best_pair": expected["best_pair"],
+        "best_pair_profit_ratio": pytest.approx(expected["best_pair_profit_ratio"]),
+        "best_pair_profit_abs": expected["best_pair_profit_abs"],
+        "best_rate": expected["best_rate"],
+        "first_trade_date": ANY,
+        "first_trade_humanized": ANY,
+        "first_trade_timestamp": ANY,
+        "latest_trade_date": ANY,
+        "latest_trade_humanized": "5 minutes ago",
+        "latest_trade_timestamp": ANY,
+        "profit_all_coin": pytest.approx(expected["profit_all_coin"]),
+        "profit_all_fiat": pytest.approx(expected["profit_all_fiat"]),
+        "profit_all_percent_mean": pytest.approx(expected["profit_all_percent_mean"]),
+        "profit_all_ratio_mean": pytest.approx(expected["profit_all_ratio_mean"]),
+        "profit_all_percent_sum": pytest.approx(expected["profit_all_percent_sum"]),
+        "profit_all_ratio_sum": pytest.approx(expected["profit_all_ratio_sum"]),
+        "profit_all_percent": pytest.approx(expected["profit_all_percent"]),
+        "profit_all_ratio": pytest.approx(expected["profit_all_ratio"]),
+        "profit_closed_coin": pytest.approx(expected["profit_closed_coin"]),
+        "profit_closed_fiat": pytest.approx(expected["profit_closed_fiat"]),
+        "profit_closed_ratio_mean": pytest.approx(expected["profit_closed_ratio_mean"]),
+        "profit_closed_percent_mean": pytest.approx(expected["profit_closed_percent_mean"]),
+        "profit_closed_ratio_sum": pytest.approx(expected["profit_closed_ratio_sum"]),
+        "profit_closed_percent_sum": pytest.approx(expected["profit_closed_percent_sum"]),
+        "profit_closed_ratio": pytest.approx(expected["profit_closed_ratio"]),
+        "profit_closed_percent": pytest.approx(expected["profit_closed_percent"]),
+        "trade_count": 6,
+        "closed_trade_count": 2,
+        "winning_trades": expected["winning_trades"],
+        "losing_trades": expected["losing_trades"],
+        "profit_factor": expected["profit_factor"],
+        "winrate": expected["winrate"],
+        "expectancy": expected["expectancy"],
+        "expectancy_ratio": expected["expectancy_ratio"],
+        "max_drawdown": ANY,
+        "max_drawdown_abs": ANY,
+        "max_drawdown_start": ANY,
+        "max_drawdown_start_timestamp": ANY,
+        "max_drawdown_end": ANY,
+        "max_drawdown_end_timestamp": ANY,
+        "trading_volume": expected["trading_volume"],
+        "bot_start_timestamp": 0,
+        "bot_start_date": "",
+    }
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+def test_api_stats(botclient, mocker, ticker, fee, markets, is_short):
+    ftbot, client = botclient
+    patch_get_signal(ftbot, enter_long=not is_short, enter_short=is_short)
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+        markets=PropertyMock(return_value=markets),
+    )
+
+    rc = client_get(client, f"{BASE_URI}/stats")
+    assert_response(rc, 200)
+    assert "durations" in rc.json()
+    assert "exit_reasons" in rc.json()
+
+    create_mock_trades(fee, is_short=is_short)
+
+    rc = client_get(client, f"{BASE_URI}/stats")
+    assert_response(rc, 200)
+    assert "durations" in rc.json()
+    assert "exit_reasons" in rc.json()
+
+    assert "wins" in rc.json()["durations"]
+    assert "losses" in rc.json()["durations"]
+    assert "draws" in rc.json()["durations"]
+
+
+def test_api_performance(botclient, fee):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+
+    create_mock_trades_usdt(fee)
+
+    rc = client_get(client, f"{BASE_URI}/performance")
+    assert_response(rc)
+    assert len(rc.json()) == 3
+    assert rc.json() == [
+        {
+            "count": 1,
+            "pair": "NEO/USDT",
+            "profit": 1.99,
+            "profit_pct": 1.99,
+            "profit_ratio": 0.0199375,
+            "profit_abs": 3.9875,
+        },
+        {
+            "count": 1,
+            "pair": "XRP/USDT",
+            "profit": 9.47,
+            "profit_abs": 2.8425,
+            "profit_pct": 9.47,
+            "profit_ratio": pytest.approx(0.094749999),
+        },
+        {
+            "count": 1,
+            "pair": "LTC/USDT",
+            "profit": -20.45,
+            "profit_abs": -4.09,
+            "profit_pct": -20.45,
+            "profit_ratio": -0.2045,
+        },
+    ]
+
+
+def test_api_entries(botclient, fee):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+    # Empty
+    rc = client_get(client, f"{BASE_URI}/entries")
+    assert_response(rc)
+    assert len(rc.json()) == 0
+
+    create_mock_trades(fee)
+    rc = client_get(client, f"{BASE_URI}/entries")
+    assert_response(rc)
+    response = rc.json()
+    assert len(response) == 2
+    resp = response[0]
+    assert resp["enter_tag"] == "TEST1"
+    assert resp["count"] == 1
+    assert resp["profit_pct"] == 0.0
+    assert pytest.approx(resp["profit_ratio"]) == 0.000038609756
+
+
+def test_api_exits(botclient, fee):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+    # Empty
+    rc = client_get(client, f"{BASE_URI}/exits")
+    assert_response(rc)
+    assert len(rc.json()) == 0
+
+    create_mock_trades(fee)
+    rc = client_get(client, f"{BASE_URI}/exits")
+    assert_response(rc)
+    response = rc.json()
+    assert len(response) == 2
+    resp = response[0]
+    assert resp["exit_reason"] == "sell_signal"
+    assert resp["count"] == 1
+    assert resp["profit_pct"] == 0.0
+    assert pytest.approx(resp["profit_ratio"]) == 0.000038609756
+
+
+def test_api_mix_tag(botclient, fee):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+    # Empty
+    rc = client_get(client, f"{BASE_URI}/mix_tags")
+    assert_response(rc)
+    assert len(rc.json()) == 0
+
+    create_mock_trades(fee)
+    rc = client_get(client, f"{BASE_URI}/mix_tags")
+    assert_response(rc)
+    response = rc.json()
+    assert len(response) == 2
+    resp = response[0]
+    assert resp["mix_tag"] == "TEST1 sell_signal"
+    assert resp["count"] == 1
+    assert resp["profit_pct"] == 0.5
+
+
+@pytest.mark.parametrize(
+    "is_short,current_rate,open_trade_value",
+    [(True, 1.098e-05, 6.134625), (False, 1.099e-05, 6.165375)],
+)
+def test_api_status(
+    botclient, mocker, ticker, fee, markets, is_short, current_rate, open_trade_value
+):
+    ftbot, client = botclient
+    patch_get_signal(ftbot)
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+        markets=PropertyMock(return_value=markets),
+        fetch_order=MagicMock(return_value={}),
+    )
+
+    rc = client_get(client, f"{BASE_URI}/status")
+    assert_response(rc, 200)
+    assert rc.json() == []
+    create_mock_trades(fee, is_short=is_short)
+
+    rc = client_get(client, f"{BASE_URI}/status")
+    assert_response(rc)
+    assert len(rc.json()) == 4
+    assert rc.json()[0] == {
+        "amount": 50.0,
+        "amount_requested": 123.0,
+        "close_date": None,
+        "close_timestamp": None,
+        "close_profit": None,
+        "close_profit_pct": None,
+        "close_profit_abs": None,
+        "close_rate": None,
+        "profit_ratio": ANY,
+        "profit_pct": ANY,
+        "profit_abs": ANY,
+        "profit_fiat": ANY,
+        "total_profit_abs": ANY,
+        "total_profit_fiat": ANY,
+        "total_profit_ratio": ANY,
+        "realized_profit": 0.0,
+        "realized_profit_ratio": None,
+        "current_rate": current_rate,
+        "open_date": ANY,
+        "open_timestamp": ANY,
+        "open_fill_date": ANY,
+        "open_fill_timestamp": ANY,
+        "open_rate": 0.123,
+        "pair": "ETH/BTC",
+        "base_currency": "ETH",
+        "quote_currency": "BTC",
+        "stake_amount": 0.001,
+        "max_stake_amount": ANY,
+        "stop_loss_abs": ANY,
+        "stop_loss_pct": ANY,
+        "stop_loss_ratio": ANY,
+        "stoploss_last_update": ANY,
+        "stoploss_last_update_timestamp": ANY,
+        "initial_stop_loss_abs": 0.0,
+        "initial_stop_loss_pct": ANY,
+        "initial_stop_loss_ratio": ANY,
+        "stoploss_current_dist": ANY,
+        "stoploss_current_dist_ratio": ANY,
+        "stoploss_current_dist_pct": ANY,
+        "stoploss_entry_dist": ANY,
+        "stoploss_entry_dist_ratio": ANY,
+        "trade_id": 1,
+        "close_rate_requested": ANY,
+        "fee_close": 0.0025,
+        "fee_close_cost": None,
+        "fee_close_currency": None,
+        "fee_open": 0.0025,
+        "fee_open_cost": None,
+        "fee_open_currency": None,
+        "is_open": True,
+        "is_short": is_short,
+        "max_rate": ANY,
+        "min_rate": ANY,
+        "open_rate_requested": ANY,
+        "open_trade_value": open_trade_value,
+        "exit_reason": None,
+        "exit_order_status": None,
+        "strategy": CURRENT_TEST_STRATEGY,
+        "enter_tag": None,
+        "timeframe": 5,
+        "exchange": "binance",
+        "leverage": 1.0,
+        "interest_rate": 0.0,
+        "liquidation_price": None,
+        "funding_fees": None,
+        "trading_mode": ANY,
+        "amount_precision": None,
+        "price_precision": None,
+        "precision_mode": None,
+        "orders": [ANY],
+        "has_open_orders": True,
+    }
+
+    mocker.patch(
+        f"{EXMS}.get_rate", MagicMock(side_effect=ExchangeError("Pair 'ETH/BTC' not available"))
+    )
+
+    rc = client_get(client, f"{BASE_URI}/status")
+    assert_response(rc)
+    resp_values = rc.json()
+    assert len(resp_values) == 4
+    assert resp_values[0]["profit_abs"] == 0.0
+
+
+def test_api_version(botclient):
+    _ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/version")
+    assert_response(rc)
+    assert rc.json() == {"version": __version__}
+
+
+def test_api_blacklist(botclient, mocker):
+    _ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/blacklist")
+    assert_response(rc)
+    # DOGE and HOT are not in the markets mock!
+    assert rc.json() == {
+        "blacklist": ["DOGE/BTC", "HOT/BTC"],
+        "blacklist_expanded": [],
+        "length": 2,
+        "method": ["StaticPairList"],
+        "errors": {},
+    }
+
+    # Add ETH/BTC to blacklist
+    rc = client_post(client, f"{BASE_URI}/blacklist", data={"blacklist": ["ETH/BTC"]})
+    assert_response(rc)
+    assert rc.json() == {
+        "blacklist": ["DOGE/BTC", "HOT/BTC", "ETH/BTC"],
+        "blacklist_expanded": ["ETH/BTC"],
+        "length": 3,
+        "method": ["StaticPairList"],
+        "errors": {},
+    }
+
+    rc = client_post(client, f"{BASE_URI}/blacklist", data={"blacklist": ["XRP/.*"]})
+    assert_response(rc)
+    assert rc.json() == {
+        "blacklist": ["DOGE/BTC", "HOT/BTC", "ETH/BTC", "XRP/.*"],
+        "blacklist_expanded": ["ETH/BTC", "XRP/BTC", "XRP/USDT"],
+        "length": 4,
+        "method": ["StaticPairList"],
+        "errors": {},
+    }
+
+    rc = client_delete(client, f"{BASE_URI}/blacklist?pairs_to_delete=DOGE/BTC")
+    assert_response(rc)
+    assert rc.json() == {
+        "blacklist": ["HOT/BTC", "ETH/BTC", "XRP/.*"],
+        "blacklist_expanded": ["ETH/BTC", "XRP/BTC", "XRP/USDT"],
+        "length": 3,
+        "method": ["StaticPairList"],
+        "errors": {},
+    }
+
+    rc = client_delete(client, f"{BASE_URI}/blacklist?pairs_to_delete=NOTHING/BTC")
+    assert_response(rc)
+    assert rc.json() == {
+        "blacklist": ["HOT/BTC", "ETH/BTC", "XRP/.*"],
+        "blacklist_expanded": ["ETH/BTC", "XRP/BTC", "XRP/USDT"],
+        "length": 3,
+        "method": ["StaticPairList"],
+        "errors": {
+            "NOTHING/BTC": {"error_msg": "Pair NOTHING/BTC is not in the current blacklist."}
+        },
+    }
+    rc = client_delete(
+        client, f"{BASE_URI}/blacklist?pairs_to_delete=HOT/BTC&pairs_to_delete=ETH/BTC"
+    )
+    assert_response(rc)
+    assert rc.json() == {
+        "blacklist": ["XRP/.*"],
+        "blacklist_expanded": ["XRP/BTC", "XRP/USDT"],
+        "length": 1,
+        "method": ["StaticPairList"],
+        "errors": {},
+    }
+
+
+def test_api_whitelist(botclient):
+    _ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/whitelist")
+    assert_response(rc)
+    assert rc.json() == {
+        "whitelist": ["ETH/BTC", "LTC/BTC", "XRP/BTC", "NEO/BTC"],
+        "length": 4,
+        "method": ["StaticPairList"],
+    }
+
+
+@pytest.mark.parametrize(
+    "endpoint",
+    [
+        "forcebuy",
+        "forceenter",
+    ],
+)
+def test_api_force_entry(botclient, mocker, fee, endpoint):
+    ftbot, client = botclient
+
+    rc = client_post(client, f"{BASE_URI}/{endpoint}", data={"pair": "ETH/BTC"})
+    assert_response(rc, 502)
+    assert rc.json() == {"error": f"Error querying /api/v1/{endpoint}: Force_entry not enabled."}
+
+    # enable forcebuy
+    ftbot.config["force_entry_enable"] = True
+
+    fbuy_mock = MagicMock(return_value=None)
+    mocker.patch("freqtrade.rpc.rpc.RPC._rpc_force_entry", fbuy_mock)
+    rc = client_post(client, f"{BASE_URI}/{endpoint}", data={"pair": "ETH/BTC"})
+    assert_response(rc)
+    assert rc.json() == {"status": "Error entering long trade for pair ETH/BTC."}
+
+    # Test creating trade
+    fbuy_mock = MagicMock(
+        return_value=Trade(
+            pair="ETH/BTC",
+            amount=1,
+            amount_requested=1,
+            exchange="binance",
+            stake_amount=1,
+            open_rate=0.245441,
+            open_date=datetime.now(timezone.utc),
+            is_open=False,
+            is_short=False,
+            fee_close=fee.return_value,
+            fee_open=fee.return_value,
+            close_rate=0.265441,
+            id=22,
+            timeframe=5,
+            strategy=CURRENT_TEST_STRATEGY,
+            trading_mode=TradingMode.SPOT,
+        )
+    )
+    mocker.patch("freqtrade.rpc.rpc.RPC._rpc_force_entry", fbuy_mock)
+
+    rc = client_post(client, f"{BASE_URI}/{endpoint}", data={"pair": "ETH/BTC"})
+    assert_response(rc)
+    assert rc.json() == {
+        "amount": 1.0,
+        "amount_requested": 1.0,
+        "trade_id": 22,
+        "close_date": None,
+        "close_timestamp": None,
+        "close_rate": 0.265441,
+        "open_date": ANY,
+        "open_timestamp": ANY,
+        "open_fill_date": ANY,
+        "open_fill_timestamp": ANY,
+        "open_rate": 0.245441,
+        "pair": "ETH/BTC",
+        "base_currency": "ETH",
+        "quote_currency": "BTC",
+        "stake_amount": 1,
+        "max_stake_amount": ANY,
+        "stop_loss_abs": None,
+        "stop_loss_pct": None,
+        "stop_loss_ratio": None,
+        "stoploss_last_update": None,
+        "stoploss_last_update_timestamp": None,
+        "initial_stop_loss_abs": None,
+        "initial_stop_loss_pct": None,
+        "initial_stop_loss_ratio": None,
+        "close_profit": None,
+        "close_profit_pct": None,
+        "close_profit_abs": None,
+        "close_rate_requested": None,
+        "profit_ratio": None,
+        "profit_pct": None,
+        "profit_abs": None,
+        "profit_fiat": None,
+        "realized_profit": 0.0,
+        "realized_profit_ratio": None,
+        "fee_close": 0.0025,
+        "fee_close_cost": None,
+        "fee_close_currency": None,
+        "fee_open": 0.0025,
+        "fee_open_cost": None,
+        "fee_open_currency": None,
+        "is_open": False,
+        "is_short": False,
+        "max_rate": None,
+        "min_rate": None,
+        "open_rate_requested": None,
+        "open_trade_value": 0.24605460,
+        "exit_reason": None,
+        "exit_order_status": None,
+        "strategy": CURRENT_TEST_STRATEGY,
+        "enter_tag": None,
+        "timeframe": 5,
+        "exchange": "binance",
+        "leverage": None,
+        "interest_rate": None,
+        "liquidation_price": None,
+        "funding_fees": None,
+        "trading_mode": "spot",
+        "amount_precision": None,
+        "price_precision": None,
+        "precision_mode": None,
+        "has_open_orders": False,
+        "orders": [],
+    }
+
+
+def test_api_forceexit(botclient, mocker, ticker, fee, markets):
+    ftbot, client = botclient
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(return_value=ticker),
+        fetch_ticker=ticker,
+        get_fee=fee,
+        markets=PropertyMock(return_value=markets),
+        _dry_is_price_crossed=MagicMock(return_value=True),
+    )
+    patch_get_signal(ftbot)
+
+    rc = client_post(client, f"{BASE_URI}/forceexit", data={"tradeid": "1"})
+    assert_response(rc, 502)
+    assert rc.json() == {"error": "Error querying /api/v1/forceexit: invalid argument"}
+    Trade.rollback()
+
+    create_mock_trades(fee)
+    trade = Trade.get_trades([Trade.id == 5]).first()
+    assert pytest.approx(trade.amount) == 123
+    rc = client_post(
+        client, f"{BASE_URI}/forceexit", data={"tradeid": "5", "ordertype": "market", "amount": 23}
+    )
+    assert_response(rc)
+    assert rc.json() == {"result": "Created exit order for trade 5."}
+    Trade.rollback()
+
+    trade = Trade.get_trades([Trade.id == 5]).first()
+    assert pytest.approx(trade.amount) == 100
+    assert trade.is_open is True
+
+    rc = client_post(client, f"{BASE_URI}/forceexit", data={"tradeid": "5"})
+    assert_response(rc)
+    assert rc.json() == {"result": "Created exit order for trade 5."}
+    Trade.rollback()
+
+    trade = Trade.get_trades([Trade.id == 5]).first()
+    assert trade.is_open is False
+
+
+def test_api_pair_candles(botclient, ohlcv_history):
+    ftbot, client = botclient
+    timeframe = "5m"
+    amount = 3
+
+    # No pair
+    rc = client_get(client, f"{BASE_URI}/pair_candles?limit={amount}&timeframe={timeframe}")
+    assert_response(rc, 422)
+
+    # No timeframe
+    rc = client_get(client, f"{BASE_URI}/pair_candles?pair=XRP%2FBTC")
+    assert_response(rc, 422)
+
+    rc = client_get(
+        client, f"{BASE_URI}/pair_candles?limit={amount}&pair=XRP%2FBTC&timeframe={timeframe}"
+    )
+    assert_response(rc)
+    assert "columns" in rc.json()
+    assert "data_start_ts" in rc.json()
+    assert "data_start" in rc.json()
+    assert "data_stop" in rc.json()
+    assert "data_stop_ts" in rc.json()
+    assert len(rc.json()["data"]) == 0
+    ohlcv_history["sma"] = ohlcv_history["close"].rolling(2).mean()
+    ohlcv_history["sma2"] = ohlcv_history["close"].rolling(2).mean()
+    ohlcv_history["enter_long"] = 0
+    ohlcv_history.loc[1, "enter_long"] = 1
+    ohlcv_history["exit_long"] = 0
+    ohlcv_history["enter_short"] = 0
+    ohlcv_history["exit_short"] = 0
+
+    ftbot.dataprovider._set_cached_df("XRP/BTC", timeframe, ohlcv_history, CandleType.SPOT)
+    for call in ("get", "post"):
+        if call == "get":
+            rc = client_get(
+                client,
+                f"{BASE_URI}/pair_candles?limit={amount}&pair=XRP%2FBTC&timeframe={timeframe}",
+            )
+        else:
+            rc = client_post(
+                client,
+                f"{BASE_URI}/pair_candles",
+                data={
+                    "pair": "XRP/BTC",
+                    "timeframe": timeframe,
+                    "limit": amount,
+                    "columns": ["sma"],
+                },
+            )
+        assert_response(rc)
+        resp = rc.json()
+        assert "strategy" in resp
+        assert resp["strategy"] == CURRENT_TEST_STRATEGY
+        assert "columns" in resp
+        assert "data_start_ts" in resp
+        assert "data_start" in resp
+        assert "data_stop" in resp
+        assert "data_stop_ts" in resp
+        assert resp["data_start"] == "2017-11-26 08:50:00+00:00"
+        assert resp["data_start_ts"] == 1511686200000
+        assert resp["data_stop"] == "2017-11-26 09:00:00+00:00"
+        assert resp["data_stop_ts"] == 1511686800000
+        assert isinstance(resp["columns"], list)
+        base_cols = {
+            "date",
+            "open",
+            "high",
+            "low",
+            "close",
+            "volume",
+            "sma",
+            "enter_long",
+            "exit_long",
+            "enter_short",
+            "exit_short",
+            "__date_ts",
+            "_enter_long_signal_close",
+            "_exit_long_signal_close",
+            "_enter_short_signal_close",
+            "_exit_short_signal_close",
+        }
+        if call == "get":
+            assert set(resp["columns"]) == base_cols.union({"sma2"})
+        else:
+            assert set(resp["columns"]) == base_cols
+
+        # All columns doesn't include the internal columns
+        assert set(resp["all_columns"]) == {
+            "date",
+            "open",
+            "high",
+            "low",
+            "close",
+            "volume",
+            "sma",
+            "sma2",
+            "enter_long",
+            "exit_long",
+            "enter_short",
+            "exit_short",
+        }
+        assert "pair" in resp
+        assert resp["pair"] == "XRP/BTC"
+
+        assert "data" in resp
+        assert len(resp["data"]) == amount
+        if call == "get":
+            assert len(resp["data"][0]) == 17
+            assert resp["data"] == [
+                [
+                    "2017-11-26T08:50:00Z",
+                    8.794e-05,
+                    8.948e-05,
+                    8.794e-05,
+                    8.88e-05,
+                    0.0877869,
+                    None,
+                    None,
+                    0,
+                    0,
+                    0,
+                    0,
+                    1511686200000,
+                    None,
+                    None,
+                    None,
+                    None,
+                ],
+                [
+                    "2017-11-26T08:55:00Z",
+                    8.88e-05,
+                    8.942e-05,
+                    8.88e-05,
+                    8.893e-05,
+                    0.05874751,
+                    8.886500000000001e-05,
+                    8.886500000000001e-05,
+                    1,
+                    0,
+                    0,
+                    0,
+                    1511686500000,
+                    8.893e-05,
+                    None,
+                    None,
+                    None,
+                ],
+                [
+                    "2017-11-26T09:00:00Z",
+                    8.891e-05,
+                    8.893e-05,
+                    8.875e-05,
+                    8.877e-05,
+                    0.7039405,
+                    8.885e-05,
+                    8.885e-05,
+                    0,
+                    0,
+                    0,
+                    0,
+                    1511686800000,
+                    None,
+                    None,
+                    None,
+                    None,
+                ],
+            ]
+        else:
+            assert len(resp["data"][0]) == 16
+            assert resp["data"] == [
+                [
+                    "2017-11-26T08:50:00Z",
+                    8.794e-05,
+                    8.948e-05,
+                    8.794e-05,
+                    8.88e-05,
+                    0.0877869,
+                    None,
+                    0,
+                    0,
+                    0,
+                    0,
+                    1511686200000,
+                    None,
+                    None,
+                    None,
+                    None,
+                ],
+                [
+                    "2017-11-26T08:55:00Z",
+                    8.88e-05,
+                    8.942e-05,
+                    8.88e-05,
+                    8.893e-05,
+                    0.05874751,
+                    8.886500000000001e-05,
+                    1,
+                    0,
+                    0,
+                    0,
+                    1511686500000,
+                    8.893e-05,
+                    None,
+                    None,
+                    None,
+                ],
+                [
+                    "2017-11-26T09:00:00Z",
+                    8.891e-05,
+                    8.893e-05,
+                    8.875e-05,
+                    8.877e-05,
+                    0.7039405,
+                    8.885e-05,
+                    0,
+                    0,
+                    0,
+                    0,
+                    1511686800000,
+                    None,
+                    None,
+                    None,
+                    None,
+                ],
+            ]
+
+    # prep for next test
+    ohlcv_history["exit_long"] = ohlcv_history["exit_long"].astype("float64")
+    ohlcv_history.at[0, "exit_long"] = float("inf")
+    ohlcv_history["date1"] = ohlcv_history["date"]
+    ohlcv_history.at[0, "date1"] = pd.NaT
+
+    ftbot.dataprovider._set_cached_df("XRP/BTC", timeframe, ohlcv_history, CandleType.SPOT)
+    rc = client_get(
+        client, f"{BASE_URI}/pair_candles?limit={amount}&pair=XRP%2FBTC&timeframe={timeframe}"
+    )
+    assert_response(rc)
+    assert rc.json()["data"] == [
+        [
+            "2017-11-26T08:50:00Z",
+            8.794e-05,
+            8.948e-05,
+            8.794e-05,
+            8.88e-05,
+            0.0877869,
+            None,
+            None,
+            0,
+            None,
+            0,
+            0,
+            None,
+            1511686200000,
+            None,
+            None,
+            None,
+            None,
+        ],
+        [
+            "2017-11-26T08:55:00Z",
+            8.88e-05,
+            8.942e-05,
+            8.88e-05,
+            8.893e-05,
+            0.05874751,
+            8.886500000000001e-05,
+            8.886500000000001e-05,
+            1,
+            0.0,
+            0,
+            0,
+            "2017-11-26T08:55:00Z",
+            1511686500000,
+            8.893e-05,
+            None,
+            None,
+            None,
+        ],
+        [
+            "2017-11-26T09:00:00Z",
+            8.891e-05,
+            8.893e-05,
+            8.875e-05,
+            8.877e-05,
+            0.7039405,
+            8.885e-05,
+            8.885e-05,
+            0,
+            0.0,
+            0,
+            0,
+            "2017-11-26T09:00:00Z",
+            1511686800000,
+            None,
+            None,
+            None,
+            None,
+        ],
+    ]
+
+
+def test_api_pair_history(botclient, tmp_path, mocker):
+    _ftbot, client = botclient
+    _ftbot.config["user_data_dir"] = tmp_path
+
+    timeframe = "5m"
+    lfm = mocker.patch("freqtrade.strategy.interface.IStrategy.load_freqAI_model")
+    # Wrong mode
+    rc = client_get(
+        client,
+        f"{BASE_URI}/pair_history?timeframe={timeframe}"
+        f"&timerange=20180111-20180112&strategy={CURRENT_TEST_STRATEGY}",
+    )
+    assert_response(rc, 503)
+    _ftbot.config["runmode"] = RunMode.WEBSERVER
+
+    # No pair
+    rc = client_get(
+        client,
+        f"{BASE_URI}/pair_history?timeframe={timeframe}"
+        f"&timerange=20180111-20180112&strategy={CURRENT_TEST_STRATEGY}",
+    )
+    assert_response(rc, 422)
+
+    # No Timeframe
+    rc = client_get(
+        client,
+        f"{BASE_URI}/pair_history?pair=UNITTEST%2FBTC"
+        f"&timerange=20180111-20180112&strategy={CURRENT_TEST_STRATEGY}",
+    )
+    assert_response(rc, 422)
+
+    # No timerange
+    rc = client_get(
+        client,
+        f"{BASE_URI}/pair_history?pair=UNITTEST%2FBTC&timeframe={timeframe}"
+        f"&strategy={CURRENT_TEST_STRATEGY}",
+    )
+    assert_response(rc, 422)
+
+    # No strategy
+    rc = client_get(
+        client,
+        f"{BASE_URI}/pair_history?pair=UNITTEST%2FBTC&timeframe={timeframe}"
+        "&timerange=20180111-20180112",
+    )
+    assert_response(rc, 422)
+
+    # Invalid strategy
+    rc = client_get(
+        client,
+        f"{BASE_URI}/pair_history?pair=UNITTEST%2FBTC&timeframe={timeframe}"
+        "&timerange=20180111-20180112&strategy={CURRENT_TEST_STRATEGY}11",
+    )
+    assert_response(rc, 502)
+
+    # Working
+    for call in ("get", "post"):
+        if call == "get":
+            rc = client_get(
+                client,
+                f"{BASE_URI}/pair_history?pair=UNITTEST%2FBTC&timeframe={timeframe}"
+                f"&timerange=20180111-20180112&strategy={CURRENT_TEST_STRATEGY}",
+            )
+        else:
+            rc = client_post(
+                client,
+                f"{BASE_URI}/pair_history",
+                data={
+                    "pair": "UNITTEST/BTC",
+                    "timeframe": timeframe,
+                    "timerange": "20180111-20180112",
+                    "strategy": CURRENT_TEST_STRATEGY,
+                    "columns": ["rsi", "fastd", "fastk"],
+                },
+            )
+
+        assert_response(rc, 200)
+        result = rc.json()
+        assert result["length"] == 289
+        assert len(result["data"]) == result["length"]
+        assert "columns" in result
+        assert "data" in result
+        data = result["data"]
+        assert len(data) == 289
+        col_count = 30 if call == "get" else 18
+        # analyzed DF has 30 columns
+        assert len(result["columns"]) == col_count
+        assert len(result["all_columns"]) == 25
+        assert len(data[0]) == col_count
+        date_col_idx = next(idx for idx, c in enumerate(result["columns"]) if c == "date")
+        rsi_col_idx = next(idx for idx, c in enumerate(result["columns"]) if c == "rsi")
+
+        assert data[0][date_col_idx] == "2018-01-11T00:00:00Z"
+        assert data[0][rsi_col_idx] is not None
+        assert data[0][rsi_col_idx] > 0
+        assert lfm.call_count == 1
+        assert result["pair"] == "UNITTEST/BTC"
+        assert result["strategy"] == CURRENT_TEST_STRATEGY
+        assert result["data_start"] == "2018-01-11 00:00:00+00:00"
+        assert result["data_start_ts"] == 1515628800000
+        assert result["data_stop"] == "2018-01-12 00:00:00+00:00"
+        assert result["data_stop_ts"] == 1515715200000
+        lfm.reset_mock()
+
+        # No data found
+        if call == "get":
+            rc = client_get(
+                client,
+                f"{BASE_URI}/pair_history?pair=UNITTEST%2FBTC&timeframe={timeframe}"
+                f"&timerange=20200111-20200112&strategy={CURRENT_TEST_STRATEGY}",
+            )
+        else:
+            rc = client_post(
+                client,
+                f"{BASE_URI}/pair_history",
+                data={
+                    "pair": "UNITTEST/BTC",
+                    "timeframe": timeframe,
+                    "timerange": "20200111-20200112",
+                    "strategy": CURRENT_TEST_STRATEGY,
+                    "columns": ["rsi", "fastd", "fastk"],
+                },
+            )
+        assert_response(rc, 502)
+        assert rc.json()["detail"] == ("No data for UNITTEST/BTC, 5m in 20200111-20200112 found.")
+
+    # No strategy
+    rc = client_post(
+        client,
+        f"{BASE_URI}/pair_history",
+        data={
+            "pair": "UNITTEST/BTC",
+            "timeframe": timeframe,
+            "timerange": "20180111-20180112",
+            # "strategy": CURRENT_TEST_STRATEGY,
+            "columns": ["rsi", "fastd", "fastk"],
+        },
+    )
+    assert_response(rc, 200)
+    result = rc.json()
+    assert result["length"] == 289
+    assert len(result["data"]) == result["length"]
+    assert "columns" in result
+    assert "data" in result
+    # Result without strategy won't have enter_long assigned.
+    assert "enter_long" not in result["columns"]
+    assert result["columns"] == ["date", "open", "high", "low", "close", "volume", "__date_ts"]
+
+
+def test_api_pair_history_live_mode(botclient, tmp_path, mocker):
+    _ftbot, client = botclient
+    _ftbot.config["user_data_dir"] = tmp_path
+    _ftbot.config["runmode"] = RunMode.WEBSERVER
+
+    mocker.patch("freqtrade.strategy.interface.IStrategy.load_freqAI_model")
+    # no strategy, live data
+    gho = mocker.patch(
+        "freqtrade.exchange.binance.Binance.get_historic_ohlcv",
+        return_value=generate_test_data("1h", 100),
+    )
+    rc = client_post(
+        client,
+        f"{BASE_URI}/pair_history",
+        data={
+            "pair": "UNITTEST/BTC",
+            "timeframe": "1h",
+            "timerange": "20240101-",
+            # "strategy": CURRENT_TEST_STRATEGY,
+            "columns": ["rsi", "fastd", "fastk"],
+            "live_mode": True,
+        },
+    )
+
+    assert_response(rc, 200)
+    result = rc.json()
+    # 100 candles - as in the generate_test_data call above
+    assert result["length"] == 100
+    assert len(result["data"]) == result["length"]
+    assert result["columns"] == ["date", "open", "high", "low", "close", "volume", "__date_ts"]
+    assert gho.call_count == 1
+
+    gho.reset_mock()
+    rc = client_post(
+        client,
+        f"{BASE_URI}/pair_history",
+        data={
+            "pair": "UNITTEST/BTC",
+            "timeframe": "1h",
+            "timerange": "20240101-",
+            "strategy": CURRENT_TEST_STRATEGY,
+            "columns": ["rsi", "fastd", "fastk"],
+            "live_mode": True,
+        },
+    )
+
+    assert_response(rc, 200)
+    result = rc.json()
+    # 80 candles - as in the generate_test_data call above - 20 startup candles
+    assert result["length"] == 100 - 20
+    assert len(result["data"]) == result["length"]
+
+    assert "rsi" in result["columns"]
+    assert "enter_long" in result["columns"]
+    assert "fastd" in result["columns"]
+    assert "date" in result["columns"]
+    assert gho.call_count == 1
+
+
+def test_api_plot_config(botclient, mocker, tmp_path):
+    ftbot, client = botclient
+    ftbot.config["user_data_dir"] = tmp_path
+
+    rc = client_get(client, f"{BASE_URI}/plot_config")
+    assert_response(rc)
+    assert rc.json() == {}
+
+    ftbot.strategy.plot_config = {
+        "main_plot": {"sma": {}},
+        "subplots": {"RSI": {"rsi": {"color": "red"}}},
+    }
+    rc = client_get(client, f"{BASE_URI}/plot_config")
+    assert_response(rc)
+    assert rc.json() == ftbot.strategy.plot_config
+    assert isinstance(rc.json()["main_plot"], dict)
+    assert isinstance(rc.json()["subplots"], dict)
+
+    ftbot.strategy.plot_config = {"main_plot": {"sma": {}}}
+    rc = client_get(client, f"{BASE_URI}/plot_config")
+    assert_response(rc)
+
+    assert isinstance(rc.json()["main_plot"], dict)
+    assert isinstance(rc.json()["subplots"], dict)
+
+    rc = client_get(client, f"{BASE_URI}/plot_config?strategy=freqai_test_classifier")
+    assert_response(rc)
+    res = rc.json()
+    assert "target_roi" in res["subplots"]
+    assert "do_predict" in res["subplots"]
+
+    rc = client_get(client, f"{BASE_URI}/plot_config?strategy=HyperoptableStrategy")
+    assert_response(rc)
+    assert rc.json()["subplots"] == {}
+
+    rc = client_get(client, f"{BASE_URI}/plot_config?strategy=NotAStrategy")
+    assert_response(rc, 502)
+    assert rc.json()["detail"] is not None
+
+    mocker.patch("freqtrade.rpc.api_server.api_v1.get_rpc_optional", return_value=None)
+
+    rc = client_get(client, f"{BASE_URI}/plot_config")
+    assert_response(rc)
+
+
+def test_api_strategies(botclient, tmp_path):
+    ftbot, client = botclient
+    ftbot.config["user_data_dir"] = tmp_path
+
+    rc = client_get(client, f"{BASE_URI}/strategies")
+
+    assert_response(rc)
+
+    assert rc.json() == {
+        "strategies": [
+            "HyperoptableStrategy",
+            "HyperoptableStrategyV2",
+            "InformativeDecoratorTest",
+            "StrategyTestV2",
+            "StrategyTestV3",
+            "StrategyTestV3CustomEntryPrice",
+            "StrategyTestV3Futures",
+            "freqai_rl_test_strat",
+            "freqai_test_classifier",
+            "freqai_test_multimodel_classifier_strat",
+            "freqai_test_multimodel_strat",
+            "freqai_test_strat",
+            "strategy_test_v3_recursive_issue",
+        ]
+    }
+
+
+def test_api_strategy(botclient, tmp_path, mocker):
+    _ftbot, client = botclient
+    _ftbot.config["user_data_dir"] = tmp_path
+
+    rc = client_get(client, f"{BASE_URI}/strategy/{CURRENT_TEST_STRATEGY}")
+
+    assert_response(rc)
+    assert rc.json()["strategy"] == CURRENT_TEST_STRATEGY
+
+    data = (Path(__file__).parents[1] / "strategy/strats/strategy_test_v3.py").read_text()
+    assert rc.json()["code"] == data
+
+    rc = client_get(client, f"{BASE_URI}/strategy/NoStrat")
+    assert_response(rc, 404)
+
+    # Disallow base64 strategies
+    rc = client_get(client, f"{BASE_URI}/strategy/xx:cHJpbnQoImhlbGxvIHdvcmxkIik=")
+    assert_response(rc, 500)
+    mocker.patch(
+        "freqtrade.resolvers.strategy_resolver.StrategyResolver._load_strategy",
+        side_effect=Exception("Test"),
+    )
+
+    rc = client_get(client, f"{BASE_URI}/strategy/NoStrat")
+    assert_response(rc, 502)
+
+
+def test_api_exchanges(botclient):
+    _ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/exchanges")
+    assert_response(rc)
+    response = rc.json()
+    assert isinstance(response["exchanges"], list)
+    assert len(response["exchanges"]) > 20
+    okx = next(x for x in response["exchanges"] if x["classname"] == "okx")
+    assert okx == {
+        "classname": "okx",
+        "name": "OKX",
+        "valid": True,
+        "supported": True,
+        "comment": "",
+        "dex": False,
+        "is_alias": False,
+        "alias_for": None,
+        "trade_modes": [
+            {"trading_mode": "spot", "margin_mode": ""},
+            {"trading_mode": "futures", "margin_mode": "isolated"},
+        ],
+    }
+
+    mexc = next(x for x in response["exchanges"] if x["classname"] == "mexc")
+    assert mexc == {
+        "classname": "mexc",
+        "name": "MEXC Global",
+        "valid": True,
+        "supported": False,
+        "dex": False,
+        "comment": "",
+        "is_alias": False,
+        "alias_for": None,
+        "trade_modes": [{"trading_mode": "spot", "margin_mode": ""}],
+    }
+    waves = next(x for x in response["exchanges"] if x["classname"] == "wavesexchange")
+    assert waves == {
+        "classname": "wavesexchange",
+        "name": "Waves.Exchange",
+        "valid": True,
+        "supported": False,
+        "dex": True,
+        "comment": ANY,
+        "is_alias": False,
+        "alias_for": None,
+        "trade_modes": [{"trading_mode": "spot", "margin_mode": ""}],
+    }
+
+
+def test_list_hyperoptloss(botclient, tmp_path):
+    ftbot, client = botclient
+    ftbot.config["user_data_dir"] = tmp_path
+
+    rc = client_get(client, f"{BASE_URI}/hyperoptloss")
+    assert_response(rc)
+    response = rc.json()
+    assert isinstance(response["loss_functions"], list)
+    assert len(response["loss_functions"]) > 0
+
+    sharpeloss = [r for r in response["loss_functions"] if r["name"] == "SharpeHyperOptLoss"]
+    assert len(sharpeloss) == 1
+    assert "Sharpe Ratio calculation" in sharpeloss[0]["description"]
+    assert len([r for r in response["loss_functions"] if r["name"] == "SortinoHyperOptLoss"]) == 1
+
+
+def test_api_freqaimodels(botclient, tmp_path, mocker):
+    ftbot, client = botclient
+    ftbot.config["user_data_dir"] = tmp_path
+    mocker.patch(
+        "freqtrade.resolvers.freqaimodel_resolver.FreqaiModelResolver.search_all_objects",
+        return_value=[
+            {"name": "LightGBMClassifier"},
+            {"name": "LightGBMClassifierMultiTarget"},
+            {"name": "LightGBMRegressor"},
+            {"name": "LightGBMRegressorMultiTarget"},
+            {"name": "ReinforcementLearner"},
+            {"name": "ReinforcementLearner_multiproc"},
+            {"name": "SKlearnRandomForestClassifier"},
+            {"name": "XGBoostClassifier"},
+            {"name": "XGBoostRFClassifier"},
+            {"name": "XGBoostRFRegressor"},
+            {"name": "XGBoostRegressor"},
+            {"name": "XGBoostRegressorMultiTarget"},
+        ],
+    )
+
+    rc = client_get(client, f"{BASE_URI}/freqaimodels")
+
+    assert_response(rc)
+
+    assert rc.json() == {
+        "freqaimodels": [
+            "LightGBMClassifier",
+            "LightGBMClassifierMultiTarget",
+            "LightGBMRegressor",
+            "LightGBMRegressorMultiTarget",
+            "ReinforcementLearner",
+            "ReinforcementLearner_multiproc",
+            "SKlearnRandomForestClassifier",
+            "XGBoostClassifier",
+            "XGBoostRFClassifier",
+            "XGBoostRFRegressor",
+            "XGBoostRegressor",
+            "XGBoostRegressorMultiTarget",
+        ]
+    }
+
+
+def test_api_pairlists_available(botclient, tmp_path):
+    ftbot, client = botclient
+    ftbot.config["user_data_dir"] = tmp_path
+
+    rc = client_get(client, f"{BASE_URI}/pairlists/available")
+
+    assert_response(rc, 503)
+    assert rc.json()["detail"] == "Bot is not in the correct state."
+
+    ftbot.config["runmode"] = RunMode.WEBSERVER
+
+    rc = client_get(client, f"{BASE_URI}/pairlists/available")
+    assert_response(rc)
+    response = rc.json()
+    assert isinstance(response["pairlists"], list)
+    assert len(response["pairlists"]) > 0
+
+    assert len([r for r in response["pairlists"] if r["name"] == "AgeFilter"]) == 1
+    assert len([r for r in response["pairlists"] if r["name"] == "VolumePairList"]) == 1
+    assert len([r for r in response["pairlists"] if r["name"] == "StaticPairList"]) == 1
+
+    volumepl = next(r for r in response["pairlists"] if r["name"] == "VolumePairList")
+    assert volumepl["is_pairlist_generator"] is True
+    assert len(volumepl["params"]) > 1
+    age_pl = next(r for r in response["pairlists"] if r["name"] == "AgeFilter")
+    assert age_pl["is_pairlist_generator"] is False
+    assert len(volumepl["params"]) > 2
+
+
+def test_api_pairlists_evaluate(botclient, tmp_path, mocker):
+    ftbot, client = botclient
+    ftbot.config["user_data_dir"] = tmp_path
+
+    rc = client_get(client, f"{BASE_URI}/pairlists/evaluate/randomJob")
+
+    assert_response(rc, 503)
+    assert rc.json()["detail"] == "Bot is not in the correct state."
+
+    ftbot.config["runmode"] = RunMode.WEBSERVER
+
+    rc = client_get(client, f"{BASE_URI}/pairlists/evaluate/randomJob")
+    assert_response(rc, 404)
+    assert rc.json()["detail"] == "Job not found."
+
+    body = {
+        "pairlists": [
+            {
+                "method": "StaticPairList",
+            },
+        ],
+        "blacklist": [],
+        "stake_currency": "BTC",
+    }
+    # Fail, already running
+    ApiBG.pairlist_running = True
+    rc = client_post(client, f"{BASE_URI}/pairlists/evaluate", body)
+    assert_response(rc, 400)
+    assert rc.json()["detail"] == "Pairlist evaluation is already running."
+
+    # should start the run
+    ApiBG.pairlist_running = False
+    rc = client_post(client, f"{BASE_URI}/pairlists/evaluate", body)
+    assert_response(rc)
+    assert rc.json()["status"] == "Pairlist evaluation started in background."
+    job_id = rc.json()["job_id"]
+
+    rc = client_get(client, f"{BASE_URI}/background/RandomJob")
+    assert_response(rc, 404)
+    assert rc.json()["detail"] == "Job not found."
+
+    # Background list
+    rc = client_get(client, f"{BASE_URI}/background")
+    assert_response(rc)
+    response = rc.json()
+    assert isinstance(response, list)
+    assert len(response) == 1
+    assert response[0]["job_id"] == job_id
+
+    # Get individual job
+    rc = client_get(client, f"{BASE_URI}/background/{job_id}")
+    assert_response(rc)
+    response = rc.json()
+    assert response["job_id"] == job_id
+    assert response["job_category"] == "pairlist"
+
+    rc = client_get(client, f"{BASE_URI}/pairlists/evaluate/{job_id}")
+    assert_response(rc)
+    response = rc.json()
+    assert response["result"]["whitelist"] == ["ETH/BTC", "LTC/BTC", "XRP/BTC", "NEO/BTC"]
+    assert response["result"]["length"] == 4
+
+    # Restart with additional filter, reducing the list to 2
+    body["pairlists"].append({"method": "OffsetFilter", "number_assets": 2})
+    rc = client_post(client, f"{BASE_URI}/pairlists/evaluate", body)
+    assert_response(rc)
+    assert rc.json()["status"] == "Pairlist evaluation started in background."
+    job_id = rc.json()["job_id"]
+
+    rc = client_get(client, f"{BASE_URI}/pairlists/evaluate/{job_id}")
+    assert_response(rc)
+    response = rc.json()
+    assert response["result"]["whitelist"] == [
+        "ETH/BTC",
+        "LTC/BTC",
+    ]
+    assert response["result"]["length"] == 2
+    # Patch __run_pairlists
+    plm = mocker.patch("freqtrade.rpc.api_server.api_pairlists.__run_pairlist", return_value=None)
+    body = {
+        "pairlists": [
+            {
+                "method": "StaticPairList",
+            },
+        ],
+        "blacklist": [],
+        "stake_currency": "BTC",
+        "exchange": "randomExchange",
+        "trading_mode": "futures",
+        "margin_mode": "isolated",
+    }
+    rc = client_post(client, f"{BASE_URI}/pairlists/evaluate", body)
+    assert_response(rc)
+    assert plm.call_count == 1
+    call_config = plm.call_args_list[0][0][1]
+    assert call_config["exchange"]["name"] == "randomExchange"
+    assert call_config["trading_mode"] == "futures"
+    assert call_config["margin_mode"] == "isolated"
+
+
+def test_list_available_pairs(botclient):
+    ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/available_pairs")
+
+    assert_response(rc)
+    assert rc.json()["length"] == 12
+    assert isinstance(rc.json()["pairs"], list)
+
+    rc = client_get(client, f"{BASE_URI}/available_pairs?timeframe=5m")
+    assert_response(rc)
+    assert rc.json()["length"] == 12
+
+    rc = client_get(client, f"{BASE_URI}/available_pairs?stake_currency=ETH")
+    assert_response(rc)
+    assert rc.json()["length"] == 1
+    assert rc.json()["pairs"] == ["XRP/ETH"]
+    assert len(rc.json()["pair_interval"]) == 2
+
+    rc = client_get(client, f"{BASE_URI}/available_pairs?stake_currency=ETH&timeframe=5m")
+    assert_response(rc)
+    assert rc.json()["length"] == 1
+    assert rc.json()["pairs"] == ["XRP/ETH"]
+    assert len(rc.json()["pair_interval"]) == 1
+
+    ftbot.config["trading_mode"] = "futures"
+    rc = client_get(client, f"{BASE_URI}/available_pairs?timeframe=1h")
+    assert_response(rc)
+    assert rc.json()["length"] == 1
+    assert rc.json()["pairs"] == ["XRP/USDT:USDT"]
+
+    rc = client_get(client, f"{BASE_URI}/available_pairs?timeframe=1h&candletype=mark")
+    assert_response(rc)
+    assert rc.json()["length"] == 2
+    assert rc.json()["pairs"] == ["UNITTEST/USDT:USDT", "XRP/USDT:USDT"]
+    assert len(rc.json()["pair_interval"]) == 2
+
+
+def test_sysinfo(botclient):
+    _ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/sysinfo")
+    assert_response(rc)
+    result = rc.json()
+    assert "cpu_pct" in result
+    assert "ram_pct" in result
+
+
+def test_api_backtesting(botclient, mocker, fee, caplog, tmp_path):
+    try:
+        ftbot, client = botclient
+        mocker.patch(f"{EXMS}.get_fee", fee)
+
+        rc = client_get(client, f"{BASE_URI}/backtest")
+        # Backtest prevented in default mode
+        assert_response(rc, 503)
+        assert rc.json()["detail"] == "Bot is not in the correct state."
+
+        ftbot.config["runmode"] = RunMode.WEBSERVER
+        # Backtesting not started yet
+        rc = client_get(client, f"{BASE_URI}/backtest")
+        assert_response(rc)
+
+        result = rc.json()
+        assert result["status"] == "not_started"
+        assert not result["running"]
+        assert result["status_msg"] == "Backtest not yet executed"
+        assert result["progress"] == 0
+
+        # Reset backtesting
+        rc = client_delete(client, f"{BASE_URI}/backtest")
+        assert_response(rc)
+        result = rc.json()
+        assert result["status"] == "reset"
+        assert not result["running"]
+        assert result["status_msg"] == "Backtest reset"
+        ftbot.config["export"] = "trades"
+        ftbot.config["backtest_cache"] = "day"
+        ftbot.config["user_data_dir"] = tmp_path
+        ftbot.config["exportfilename"] = tmp_path / "backtest_results"
+        ftbot.config["exportfilename"].mkdir()
+
+        # start backtesting
+        data = {
+            "strategy": CURRENT_TEST_STRATEGY,
+            "timeframe": "5m",
+            "timerange": "20180110-20180111",
+            "max_open_trades": 3,
+            "stake_amount": 100,
+            "dry_run_wallet": 1000,
+            "enable_protections": False,
+        }
+        rc = client_post(client, f"{BASE_URI}/backtest", data=data)
+        assert_response(rc)
+        result = rc.json()
+
+        assert result["status"] == "running"
+        assert result["progress"] == 0
+        assert result["running"]
+        assert result["status_msg"] == "Backtest started"
+
+        rc = client_get(client, f"{BASE_URI}/backtest")
+        assert_response(rc)
+
+        result = rc.json()
+        assert result["status"] == "ended"
+        assert not result["running"]
+        assert result["status_msg"] == "Backtest ended"
+        assert result["progress"] == 1
+        assert result["backtest_result"]
+
+        rc = client_get(client, f"{BASE_URI}/backtest/abort")
+        assert_response(rc)
+        result = rc.json()
+        assert result["status"] == "not_running"
+        assert not result["running"]
+        assert result["status_msg"] == "Backtest ended"
+
+        # Simulate running backtest
+        ApiBG.bgtask_running = True
+        rc = client_get(client, f"{BASE_URI}/backtest/abort")
+        assert_response(rc)
+        result = rc.json()
+        assert result["status"] == "stopping"
+        assert not result["running"]
+        assert result["status_msg"] == "Backtest ended"
+
+        # Get running backtest...
+        rc = client_get(client, f"{BASE_URI}/backtest")
+        assert_response(rc)
+        result = rc.json()
+        assert result["status"] == "running"
+        assert result["running"]
+        assert result["step"] == "backtest"
+        assert result["status_msg"] == "Backtest running"
+
+        # Try delete with task still running
+        rc = client_delete(client, f"{BASE_URI}/backtest")
+        assert_response(rc)
+        result = rc.json()
+        assert result["status"] == "running"
+
+        # Post to backtest that's still running
+        rc = client_post(client, f"{BASE_URI}/backtest", data=data)
+        assert_response(rc, 502)
+        result = rc.json()
+        assert "Bot Background task already running" in result["error"]
+
+        ApiBG.bgtask_running = False
+
+        # Rerun backtest (should get previous result)
+        rc = client_post(client, f"{BASE_URI}/backtest", data=data)
+        assert_response(rc)
+        result = rc.json()
+        assert log_has_re("Reusing result of previous backtest.*", caplog)
+
+        data["stake_amount"] = 101
+
+        mocker.patch(
+            "freqtrade.optimize.backtesting.Backtesting.backtest_one_strategy",
+            side_effect=DependencyException("DeadBeef"),
+        )
+        rc = client_post(client, f"{BASE_URI}/backtest", data=data)
+        assert log_has("Backtesting caused an error: DeadBeef", caplog)
+
+        rc = client_get(client, f"{BASE_URI}/backtest")
+        assert_response(rc)
+        result = rc.json()
+        assert result["status"] == "error"
+        assert "Backtest failed" in result["status_msg"]
+
+        # Delete backtesting to avoid leakage since the backtest-object may stick around.
+        rc = client_delete(client, f"{BASE_URI}/backtest")
+        assert_response(rc)
+
+        result = rc.json()
+        assert result["status"] == "reset"
+        assert not result["running"]
+        assert result["status_msg"] == "Backtest reset"
+
+        # Disallow base64 strategies
+        data["strategy"] = "xx:cHJpbnQoImhlbGxvIHdvcmxkIik="
+        rc = client_post(client, f"{BASE_URI}/backtest", data=data)
+        assert_response(rc, 500)
+    finally:
+        Backtesting.cleanup()
+
+
+def test_api_backtest_history(botclient, mocker, testdatadir):
+    ftbot, client = botclient
+    mocker.patch(
+        "freqtrade.data.btanalysis._get_backtest_files",
+        return_value=[
+            testdatadir / "backtest_results/backtest-result_multistrat.json",
+            testdatadir / "backtest_results/backtest-result.json",
+        ],
+    )
+
+    rc = client_get(client, f"{BASE_URI}/backtest/history")
+    assert_response(rc, 503)
+    assert rc.json()["detail"] == "Bot is not in the correct state."
+
+    ftbot.config["user_data_dir"] = testdatadir
+    ftbot.config["runmode"] = RunMode.WEBSERVER
+
+    rc = client_get(client, f"{BASE_URI}/backtest/history")
+    assert_response(rc)
+    result = rc.json()
+    assert len(result) == 3
+    fn = result[0]["filename"]
+    assert fn == "backtest-result_multistrat"
+    assert result[0]["notes"] == ""
+    strategy = result[0]["strategy"]
+    rc = client_get(client, f"{BASE_URI}/backtest/history/result?filename={fn}&strategy={strategy}")
+    assert_response(rc)
+    result2 = rc.json()
+    assert result2
+    assert result2["status"] == "ended"
+    assert not result2["running"]
+    assert result2["progress"] == 1
+    # Only one strategy loaded - even though we use multiresult
+    assert len(result2["backtest_result"]["strategy"]) == 1
+    assert result2["backtest_result"]["strategy"][strategy]
+
+
+def test_api_delete_backtest_history_entry(botclient, tmp_path: Path):
+    ftbot, client = botclient
+
+    # Create a temporary directory and file
+    bt_results_base = tmp_path / "backtest_results"
+    bt_results_base.mkdir()
+    file_path = bt_results_base / "test.json"
+    file_path.touch()
+    meta_path = file_path.with_suffix(".meta.json")
+    meta_path.touch()
+    market_change_path = file_path.with_name(file_path.stem + "_market_change.feather")
+    market_change_path.touch()
+
+    rc = client_delete(client, f"{BASE_URI}/backtest/history/randomFile.json")
+    assert_response(rc, 503)
+    assert rc.json()["detail"] == "Bot is not in the correct state."
+
+    ftbot.config["user_data_dir"] = tmp_path
+    ftbot.config["runmode"] = RunMode.WEBSERVER
+    rc = client_delete(client, f"{BASE_URI}/backtest/history/randomFile.json")
+    assert rc.status_code == 404
+    assert rc.json()["detail"] == "File not found."
+
+    rc = client_delete(client, f"{BASE_URI}/backtest/history/{file_path.name}")
+    assert rc.status_code == 200
+
+    assert not file_path.exists()
+    assert not meta_path.exists()
+    assert not market_change_path.exists()
+
+
+def test_api_patch_backtest_history_entry(botclient, tmp_path: Path):
+    ftbot, client = botclient
+
+    # Create a temporary directory and file
+    bt_results_base = tmp_path / "backtest_results"
+    bt_results_base.mkdir()
+    file_path = bt_results_base / "test.json"
+    file_path.touch()
+    meta_path = file_path.with_suffix(".meta.json")
+    with meta_path.open("w") as metafile:
+        rapidjson.dump(
+            {
+                CURRENT_TEST_STRATEGY: {
+                    "run_id": "6e542efc8d5e62cef6e5be0ffbc29be81a6e751d",
+                    "backtest_start_time": 1690176003,
+                }
+            },
+            metafile,
+        )
+
+    def read_metadata():
+        with meta_path.open("r") as metafile:
+            return rapidjson.load(metafile)
+
+    rc = client_patch(client, f"{BASE_URI}/backtest/history/randomFile.json")
+    assert_response(rc, 503)
+
+    ftbot.config["user_data_dir"] = tmp_path
+    ftbot.config["runmode"] = RunMode.WEBSERVER
+
+    rc = client_patch(
+        client,
+        f"{BASE_URI}/backtest/history/randomFile.json",
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+        },
+    )
+    assert rc.status_code == 404
+
+    # Nonexisting strategy
+    rc = client_patch(
+        client,
+        f"{BASE_URI}/backtest/history/{file_path.name}",
+        {
+            "strategy": f"{CURRENT_TEST_STRATEGY}xxx",
+        },
+    )
+    assert rc.status_code == 400
+    assert rc.json()["detail"] == "Strategy not in metadata."
+
+    # no Notes
+    rc = client_patch(
+        client,
+        f"{BASE_URI}/backtest/history/{file_path.name}",
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+        },
+    )
+    assert rc.status_code == 200
+    res = rc.json()
+    assert isinstance(res, list)
+    assert len(res) == 1
+    assert res[0]["strategy"] == CURRENT_TEST_STRATEGY
+    assert res[0]["notes"] == ""
+
+    fileres = read_metadata()
+    assert fileres[CURRENT_TEST_STRATEGY]["run_id"] == res[0]["run_id"]
+    assert fileres[CURRENT_TEST_STRATEGY]["notes"] == ""
+
+    rc = client_patch(
+        client,
+        f"{BASE_URI}/backtest/history/{file_path.name}",
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+            "notes": "FooBar",
+        },
+    )
+    assert rc.status_code == 200
+    res = rc.json()
+    assert isinstance(res, list)
+    assert len(res) == 1
+    assert res[0]["strategy"] == CURRENT_TEST_STRATEGY
+    assert res[0]["notes"] == "FooBar"
+
+    fileres = read_metadata()
+    assert fileres[CURRENT_TEST_STRATEGY]["run_id"] == res[0]["run_id"]
+    assert fileres[CURRENT_TEST_STRATEGY]["notes"] == "FooBar"
+
+
+def test_api_patch_backtest_market_change(botclient, tmp_path: Path):
+    ftbot, client = botclient
+
+    # Create a temporary directory and file
+    bt_results_base = tmp_path / "backtest_results"
+    bt_results_base.mkdir()
+    file_path = bt_results_base / "test_22_market_change.feather"
+    df = pd.DataFrame(
+        {
+            "date": ["2018-01-01T00:00:00Z", "2018-01-01T00:05:00Z"],
+            "count": [2, 4],
+            "mean": [2555, 2556],
+            "rel_mean": [0, 0.022],
+        }
+    )
+    df["date"] = pd.to_datetime(df["date"])
+    df.to_feather(file_path, compression_level=9, compression="lz4")
+    # Nonexisting file
+    rc = client_get(client, f"{BASE_URI}/backtest/history/randomFile.json/market_change")
+    assert_response(rc, 503)
+
+    ftbot.config["user_data_dir"] = tmp_path
+    ftbot.config["runmode"] = RunMode.WEBSERVER
+
+    rc = client_get(client, f"{BASE_URI}/backtest/history/randomFile.json/market_change")
+    assert_response(rc, 404)
+
+    rc = client_get(client, f"{BASE_URI}/backtest/history/test_22/market_change")
+    assert_response(rc, 200)
+    result = rc.json()
+    assert result["length"] == 2
+    assert result["columns"] == ["date", "count", "mean", "rel_mean", "__date_ts"]
+    assert result["data"] == [
+        ["2018-01-01T00:00:00Z", 2, 2555, 0.0, 1514764800000],
+        ["2018-01-01T00:05:00Z", 4, 2556, 0.022, 1514765100000],
+    ]
+
+
+def test_health(botclient):
+    _ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/health")
+
+    assert_response(rc)
+    ret = rc.json()
+    assert ret["last_process_ts"] is None
+    assert ret["last_process"] is None
+
+
+def test_api_ws_subscribe(botclient, mocker):
+    _ftbot, client = botclient
+    ws_url = f"/api/v1/message/ws?token={_TEST_WS_TOKEN}"
+
+    sub_mock = mocker.patch("freqtrade.rpc.api_server.ws.WebSocketChannel.set_subscriptions")
+
+    with client.websocket_connect(ws_url) as ws:
+        ws.send_json({"type": "subscribe", "data": ["whitelist"]})
+        time.sleep(0.2)
+
+    # Check call count is now 1 as we sent a valid subscribe request
+    assert sub_mock.call_count == 1
+
+    with client.websocket_connect(ws_url) as ws:
+        ws.send_json({"type": "subscribe", "data": "whitelist"})
+        time.sleep(0.2)
+
+    # Call count hasn't changed as the subscribe request was invalid
+    assert sub_mock.call_count == 1
+
+
+def test_api_ws_requests(botclient, caplog):
+    caplog.set_level(logging.DEBUG)
+
+    _ftbot, client = botclient
+    ws_url = f"/api/v1/message/ws?token={_TEST_WS_TOKEN}"
+
+    # Test whitelist request
+    with client.websocket_connect(ws_url) as ws:
+        ws.send_json({"type": "whitelist", "data": None})
+        response = ws.receive_json()
+
+    assert log_has_re(r"Request of type whitelist from.+", caplog)
+    assert response["type"] == "whitelist"
+
+    # Test analyzed_df request
+    with client.websocket_connect(ws_url) as ws:
+        ws.send_json({"type": "analyzed_df", "data": {}})
+        response = ws.receive_json()
+
+    assert log_has_re(r"Request of type analyzed_df from.+", caplog)
+    assert response["type"] == "analyzed_df"
+
+    caplog.clear()
+    # Test analyzed_df request with data
+    with client.websocket_connect(ws_url) as ws:
+        ws.send_json({"type": "analyzed_df", "data": {"limit": 100}})
+        response = ws.receive_json()
+
+    assert log_has_re(r"Request of type analyzed_df from.+", caplog)
+    assert response["type"] == "analyzed_df"
+
+
+def test_api_ws_send_msg(default_conf, mocker, caplog):
+    try:
+        caplog.set_level(logging.DEBUG)
+
+        default_conf.update(
+            {
+                "api_server": {
+                    "enabled": True,
+                    "listen_ip_address": "127.0.0.1",
+                    "listen_port": 8080,
+                    "CORS_origins": ["http://example.com"],
+                    "username": _TEST_USER,
+                    "password": _TEST_PASS,
+                    "ws_token": _TEST_WS_TOKEN,
+                }
+            }
+        )
+        mocker.patch("freqtrade.rpc.telegram.Telegram._init")
+        mocker.patch("freqtrade.rpc.api_server.ApiServer.start_api")
+        apiserver = ApiServer(default_conf)
+        apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf)))
+
+        # Start test client context manager to run lifespan events
+        with TestClient(apiserver.app):
+            # Test message is published on the Message Stream
+            test_message = {"type": "status", "data": "test"}
+            first_waiter = apiserver._message_stream._waiter
+            apiserver.send_msg(test_message)
+            assert first_waiter.result()[0] == test_message
+
+            second_waiter = apiserver._message_stream._waiter
+            apiserver.send_msg(test_message)
+            assert first_waiter != second_waiter
+
+    finally:
+        ApiServer.shutdown()
+        ApiServer.shutdown()
+
+
+def test_api_download_data(botclient, mocker, tmp_path):
+    ftbot, client = botclient
+
+    rc = client_post(client, f"{BASE_URI}/download_data", data={})
+    assert_response(rc, 503)
+    assert rc.json()["detail"] == "Bot is not in the correct state."
+
+    ftbot.config["runmode"] = RunMode.WEBSERVER
+    ftbot.config["user_data_dir"] = tmp_path
+
+    body = {
+        "pairs": ["ETH/BTC", "XRP/BTC"],
+        "timeframes": ["5m"],
+    }
+
+    # Fail, already running
+    ApiBG.download_data_running = True
+    rc = client_post(client, f"{BASE_URI}/download_data", body)
+    assert_response(rc, 400)
+    assert rc.json()["detail"] == "Data Download is already running."
+
+    # Reset running state
+    ApiBG.download_data_running = False
+
+    # Test successful download
+    mocker.patch(
+        "freqtrade.data.history.history_utils.download_data",
+        return_value=None,
+    )
+
+    rc = client_post(client, f"{BASE_URI}/download_data", body)
+    assert_response(rc)
+    assert rc.json()["status"] == "Data Download started in background."
+    job_id = rc.json()["job_id"]
+
+    rc = client_get(client, f"{BASE_URI}/background/{job_id}")
+    assert_response(rc)
+    response = rc.json()
+    assert response["job_id"] == job_id
+    assert response["job_category"] == "download_data"
+    # Job finishes immediately due to mock.
+    assert response["status"] == "success"
+
+    # Background list contains the job
+    rc = client_get(client, f"{BASE_URI}/background")
+    assert_response(rc)
+    response = rc.json()
+    assert isinstance(response, list)
+    assert len(response) == 1
+    assert response[0]["job_id"] == job_id
+
+    # Test error case
+    ApiBG.download_data_running = False
+    mocker.patch(
+        "freqtrade.data.history.history_utils.download_data",
+        side_effect=OperationalException("Download error"),
+    )
+    rc = client_post(client, f"{BASE_URI}/download_data", body)
+    assert_response(rc)
+    assert rc.json()["status"] == "Data Download started in background."
+    job_id = rc.json()["job_id"]
+
+    rc = client_get(client, f"{BASE_URI}/background/{job_id}")
+    assert_response(rc)
+    response = rc.json()
+    assert response["job_id"] == job_id
+    assert response["job_category"] == "download_data"
+    assert response["status"] == "failed"
+    assert response["error"] == "Download error"
+
+
+def test_api_markets_live(botclient):
+    ftbot, client = botclient
+
+    rc = client_get(client, f"{BASE_URI}/markets")
+    assert_response(rc, 200)
+    response = rc.json()
+    assert "markets" in response
+    assert len(response["markets"]) >= 0
+    assert response["markets"]["XRP/USDT"] == {
+        "base": "XRP",
+        "quote": "USDT",
+        "symbol": "XRP/USDT",
+        "spot": True,
+        "swap": False,
+    }
+
+    assert "BTC/USDT" in response["markets"]
+    assert "XRP/BTC" in response["markets"]
+
+    rc = client_get(
+        client,
+        f"{BASE_URI}/markets?base=XRP",
+    )
+    assert_response(rc, 200)
+    response = rc.json()
+    assert "XRP/USDT" in response["markets"]
+    assert "XRP/BTC" in response["markets"]
+
+    assert "BTC/USDT" not in response["markets"]
+
+
+def test_api_markets_webserver(botclient):
+    # Ensure webserver exchanges are reset
+    ApiBG.exchanges = {}
+    ftbot, client = botclient
+    # Test in webserver mode
+    ftbot.config["runmode"] = RunMode.WEBSERVER
+
+    rc = client_get(client, f"{BASE_URI}/markets?exchange=binance")
+    assert_response(rc, 200)
+    response = rc.json()
+    assert "markets" in response
+    assert len(response["markets"]) >= 0
+    assert response["exchange_id"] == "binance"
+
+    rc = client_get(client, f"{BASE_URI}/markets?exchange=hyperliquid")
+    assert_response(rc, 200)
+
+    assert "hyperliquid_spot" in ApiBG.exchanges
+    assert "binance_spot" in ApiBG.exchanges
diff --git a/tests/rpc/test_rpc_emc.py b/tests/rpc/test_rpc_emc.py
new file mode 100644
index 000000000..678379e68
--- /dev/null
+++ b/tests/rpc/test_rpc_emc.py
@@ -0,0 +1,468 @@
+"""
+Unit test file for rpc/external_message_consumer.py
+"""
+
+import asyncio
+import logging
+from datetime import datetime, timezone
+from unittest.mock import MagicMock
+
+import pytest
+import websockets
+
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer
+from tests.conftest import log_has, log_has_re, log_has_when
+
+
+_TEST_WS_TOKEN = "secret_Ws_t0ken"
+_TEST_WS_HOST = "127.0.0.1"
+_TEST_WS_PORT = 9989
+
+
+@pytest.fixture
+def patched_emc(default_conf, mocker):
+    default_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {"name": "default", "host": "null", "port": 9891, "ws_token": _TEST_WS_TOKEN}
+                ],
+            }
+        }
+    )
+    dataprovider = DataProvider(default_conf, None, None, None)
+    emc = ExternalMessageConsumer(default_conf, dataprovider)
+
+    try:
+        yield emc
+    finally:
+        emc.shutdown()
+
+
+def test_emc_start(patched_emc, caplog):
+    # Test if the message was printed
+    assert log_has_when("Starting ExternalMessageConsumer", caplog, "setup")
+    # Test if the thread and loop objects were created
+    assert patched_emc._thread and patched_emc._loop
+
+    # Test we call start again nothing happens
+    prev_thread = patched_emc._thread
+    patched_emc.start()
+    assert prev_thread == patched_emc._thread
+
+
+def test_emc_shutdown(patched_emc, caplog):
+    patched_emc.shutdown()
+
+    assert log_has("Stopping ExternalMessageConsumer", caplog)
+    # Test the loop has stopped
+    assert patched_emc._loop is None
+    # Test if the thread has stopped
+    assert patched_emc._thread is None
+
+    caplog.clear()
+    patched_emc.shutdown()
+
+    # Test func didn't run again as it was called once already
+    assert not log_has("Stopping ExternalMessageConsumer", caplog)
+
+
+def test_emc_init(patched_emc):
+    # Test the settings were set correctly
+    assert patched_emc.initial_candle_limit <= 1500
+    assert patched_emc.wait_timeout > 0
+    assert patched_emc.sleep_time > 0
+
+
+# Parametrize this?
+def test_emc_handle_producer_message(patched_emc, caplog, ohlcv_history):
+    test_producer = {"name": "test", "url": "ws://test", "ws_token": "test"}
+    producer_name = test_producer["name"]
+    invalid_msg = r"Invalid message .+"
+
+    caplog.set_level(logging.DEBUG)
+
+    # Test handle whitelist message
+    whitelist_message = {"type": "whitelist", "data": ["BTC/USDT"]}
+    patched_emc.handle_producer_message(test_producer, whitelist_message)
+
+    assert log_has(f"Received message of type `whitelist` from `{producer_name}`", caplog)
+    assert log_has(
+        f"Consumed message from `{producer_name}` of type `RPCMessageType.WHITELIST`", caplog
+    )
+
+    # Test handle analyzed_df single candle message
+    df_message = {
+        "type": "analyzed_df",
+        "data": {
+            "key": ("BTC/USDT", "5m", "spot"),
+            "df": ohlcv_history,
+            "la": datetime.now(timezone.utc),
+        },
+    }
+    patched_emc.handle_producer_message(test_producer, df_message)
+
+    assert log_has(f"Received message of type `analyzed_df` from `{producer_name}`", caplog)
+    assert log_has_re(r"Holes in data or no existing df,.+", caplog)
+
+    # Test unhandled message
+    unhandled_message = {"type": "status", "data": "RUNNING"}
+    patched_emc.handle_producer_message(test_producer, unhandled_message)
+
+    assert log_has_re(r"Received unhandled message\: .*", caplog)
+
+    # Test malformed messages
+    caplog.clear()
+    malformed_message = {"type": "whitelist", "data": {"pair": "BTC/USDT"}}
+    patched_emc.handle_producer_message(test_producer, malformed_message)
+
+    assert log_has_re(invalid_msg, caplog)
+    caplog.clear()
+
+    malformed_message = {
+        "type": "analyzed_df",
+        "data": {"key": "BTC/USDT", "df": ohlcv_history, "la": datetime.now(timezone.utc)},
+    }
+    patched_emc.handle_producer_message(test_producer, malformed_message)
+
+    assert log_has(f"Received message of type `analyzed_df` from `{producer_name}`", caplog)
+    assert log_has_re(invalid_msg, caplog)
+    caplog.clear()
+
+    # Empty dataframe
+    malformed_message = {
+        "type": "analyzed_df",
+        "data": {
+            "key": ("BTC/USDT", "5m", "spot"),
+            "df": ohlcv_history.loc[ohlcv_history["open"] < 0],
+            "la": datetime.now(timezone.utc),
+        },
+    }
+    patched_emc.handle_producer_message(test_producer, malformed_message)
+
+    assert log_has(f"Received message of type `analyzed_df` from `{producer_name}`", caplog)
+    assert not log_has_re(invalid_msg, caplog)
+    assert log_has_re(r"Received Empty Dataframe for.+", caplog)
+
+    caplog.clear()
+    malformed_message = {"some": "stuff"}
+    patched_emc.handle_producer_message(test_producer, malformed_message)
+
+    assert log_has_re(invalid_msg, caplog)
+    caplog.clear()
+
+    caplog.clear()
+    malformed_message = {"type": "whitelist", "data": None}
+    patched_emc.handle_producer_message(test_producer, malformed_message)
+
+    assert log_has_re(r"Empty message .+", caplog)
+
+
+async def test_emc_create_connection_success(default_conf, caplog, mocker):
+    default_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {
+                        "name": "default",
+                        "host": _TEST_WS_HOST,
+                        "port": _TEST_WS_PORT,
+                        "ws_token": _TEST_WS_TOKEN,
+                    }
+                ],
+                "wait_timeout": 60,
+                "ping_timeout": 60,
+                "sleep_timeout": 60,
+            }
+        }
+    )
+
+    mocker.patch(
+        "freqtrade.rpc.external_message_consumer.ExternalMessageConsumer.start", MagicMock()
+    )
+    dp = DataProvider(default_conf, None, None, None)
+    emc = ExternalMessageConsumer(default_conf, dp)
+
+    test_producer = default_conf["external_message_consumer"]["producers"][0]
+    lock = asyncio.Lock()
+
+    emc._running = True
+
+    async def eat(websocket):
+        emc._running = False
+
+    try:
+        async with websockets.serve(eat, _TEST_WS_HOST, _TEST_WS_PORT):
+            await emc._create_connection(test_producer, lock)
+
+        assert log_has_re(r"Connected to channel.+", caplog)
+    finally:
+        emc.shutdown()
+
+
+@pytest.mark.parametrize(
+    "host,port",
+    [
+        (_TEST_WS_HOST, -1),
+        ("10000.1241..2121/", _TEST_WS_PORT),
+    ],
+)
+async def test_emc_create_connection_invalid_url(default_conf, caplog, mocker, host, port):
+    default_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {"name": "default", "host": host, "port": port, "ws_token": _TEST_WS_TOKEN}
+                ],
+                "wait_timeout": 60,
+                "ping_timeout": 60,
+                "sleep_timeout": 60,
+            }
+        }
+    )
+
+    dp = DataProvider(default_conf, None, None, None)
+    # Handle start explicitly to avoid messing with threading in tests
+    mocker.patch("freqtrade.rpc.external_message_consumer.ExternalMessageConsumer.start")
+    mocker.patch("freqtrade.rpc.api_server.ws.channel.create_channel")
+    emc = ExternalMessageConsumer(default_conf, dp)
+
+    try:
+        emc._running = True
+        await emc._create_connection(emc.producers[0], asyncio.Lock())
+        assert log_has_re(r".+ is an invalid WebSocket URL .+", caplog)
+    finally:
+        emc.shutdown()
+
+
+async def test_emc_create_connection_error(default_conf, caplog, mocker):
+    default_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {
+                        "name": "default",
+                        "host": _TEST_WS_HOST,
+                        "port": _TEST_WS_PORT,
+                        "ws_token": _TEST_WS_TOKEN,
+                    }
+                ],
+                "wait_timeout": 60,
+                "ping_timeout": 60,
+                "sleep_timeout": 60,
+            }
+        }
+    )
+
+    # Test unexpected error
+    mocker.patch("websockets.connect", side_effect=RuntimeError)
+
+    dp = DataProvider(default_conf, None, None, None)
+    emc = ExternalMessageConsumer(default_conf, dp)
+
+    try:
+        await asyncio.sleep(0.05)
+        assert log_has("Unexpected error has occurred:", caplog)
+    finally:
+        emc.shutdown()
+
+
+async def test_emc_receive_messages_valid(default_conf, caplog, mocker):
+    caplog.set_level(logging.DEBUG)
+
+    default_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {
+                        "name": "default",
+                        "host": _TEST_WS_HOST,
+                        "port": _TEST_WS_PORT,
+                        "ws_token": _TEST_WS_TOKEN,
+                    }
+                ],
+                "wait_timeout": 1,
+                "ping_timeout": 60,
+                "sleep_time": 60,
+            }
+        }
+    )
+
+    mocker.patch(
+        "freqtrade.rpc.external_message_consumer.ExternalMessageConsumer.start", MagicMock()
+    )
+
+    lock = asyncio.Lock()
+    test_producer = default_conf["external_message_consumer"]["producers"][0]
+
+    dp = DataProvider(default_conf, None, None, None)
+    emc = ExternalMessageConsumer(default_conf, dp)
+
+    class TestChannel:
+        async def recv(self, *args, **kwargs):
+            emc._running = False
+            return {"type": "whitelist", "data": ["BTC/USDT"]}
+
+        async def ping(self, *args, **kwargs):
+            return asyncio.Future()
+
+    try:
+        emc._running = True
+        await emc._receive_messages(TestChannel(), test_producer, lock)
+
+        assert log_has_re(r"Received message of type `whitelist`.+", caplog)
+    finally:
+        emc.shutdown()
+
+
+async def test_emc_receive_messages_invalid(default_conf, caplog, mocker):
+    default_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {
+                        "name": "default",
+                        "host": _TEST_WS_HOST,
+                        "port": _TEST_WS_PORT,
+                        "ws_token": _TEST_WS_TOKEN,
+                    }
+                ],
+                "wait_timeout": 1,
+                "ping_timeout": 60,
+                "sleep_time": 60,
+            }
+        }
+    )
+
+    mocker.patch(
+        "freqtrade.rpc.external_message_consumer.ExternalMessageConsumer.start", MagicMock()
+    )
+
+    lock = asyncio.Lock()
+    test_producer = default_conf["external_message_consumer"]["producers"][0]
+
+    dp = DataProvider(default_conf, None, None, None)
+    emc = ExternalMessageConsumer(default_conf, dp)
+
+    class TestChannel:
+        async def recv(self, *args, **kwargs):
+            emc._running = False
+            return {"type": ["BTC/USDT"]}
+
+        async def ping(self, *args, **kwargs):
+            return asyncio.Future()
+
+    try:
+        emc._running = True
+        await emc._receive_messages(TestChannel(), test_producer, lock)
+
+        assert log_has_re(r"Invalid message from.+", caplog)
+    finally:
+        emc.shutdown()
+
+
+async def test_emc_receive_messages_timeout(default_conf, caplog, mocker):
+    default_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {
+                        "name": "default",
+                        "host": _TEST_WS_HOST,
+                        "port": _TEST_WS_PORT,
+                        "ws_token": _TEST_WS_TOKEN,
+                    }
+                ],
+                "wait_timeout": 0.1,
+                "ping_timeout": 1,
+                "sleep_time": 1,
+            }
+        }
+    )
+
+    mocker.patch(
+        "freqtrade.rpc.external_message_consumer.ExternalMessageConsumer.start", MagicMock()
+    )
+
+    lock = asyncio.Lock()
+    test_producer = default_conf["external_message_consumer"]["producers"][0]
+
+    dp = DataProvider(default_conf, None, None, None)
+    emc = ExternalMessageConsumer(default_conf, dp)
+
+    def change_running():
+        emc._running = not emc._running
+
+    class TestChannel:
+        async def recv(self, *args, **kwargs):
+            await asyncio.sleep(0.2)
+
+        async def ping(self, *args, **kwargs):
+            return asyncio.Future()
+
+    try:
+        change_running()
+
+        with pytest.raises(asyncio.TimeoutError):
+            await emc._receive_messages(TestChannel(), test_producer, lock)
+
+        assert log_has_re(r"Ping error.+", caplog)
+    finally:
+        emc.shutdown()
+
+
+async def test_emc_receive_messages_handle_error(default_conf, caplog, mocker):
+    default_conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {
+                        "name": "default",
+                        "host": _TEST_WS_HOST,
+                        "port": _TEST_WS_PORT,
+                        "ws_token": _TEST_WS_TOKEN,
+                    }
+                ],
+                "wait_timeout": 1,
+                "ping_timeout": 1,
+                "sleep_time": 1,
+            }
+        }
+    )
+
+    mocker.patch(
+        "freqtrade.rpc.external_message_consumer.ExternalMessageConsumer.start", MagicMock()
+    )
+
+    lock = asyncio.Lock()
+    test_producer = default_conf["external_message_consumer"]["producers"][0]
+
+    dp = DataProvider(default_conf, None, None, None)
+    emc = ExternalMessageConsumer(default_conf, dp)
+
+    emc.handle_producer_message = MagicMock(side_effect=Exception)
+
+    class TestChannel:
+        async def recv(self, *args, **kwargs):
+            emc._running = False
+            return {"type": "whitelist", "data": ["BTC/USDT"]}
+
+        async def ping(self, *args, **kwargs):
+            return asyncio.Future()
+
+    try:
+        emc._running = True
+        await emc._receive_messages(TestChannel(), test_producer, lock)
+
+        assert log_has_re(r"Error handling producer message.+", caplog)
+    finally:
+        emc.shutdown()
diff --git a/tests/rpc/test_rpc_manager.py b/tests/rpc/test_rpc_manager.py
new file mode 100644
index 000000000..67755255f
--- /dev/null
+++ b/tests/rpc/test_rpc_manager.py
@@ -0,0 +1,220 @@
+# pragma pylint: disable=missing-docstring, C0103
+import logging
+import time
+from collections import deque
+from unittest.mock import MagicMock
+
+from freqtrade.enums import RPCMessageType
+from freqtrade.rpc import RPCManager
+from freqtrade.rpc.api_server.webserver import ApiServer
+from tests.conftest import get_patched_freqtradebot, log_has
+
+
+def test__init__(mocker, default_conf) -> None:
+    default_conf["telegram"]["enabled"] = False
+
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+    assert rpc_manager.registered_modules == []
+
+
+def test_init_telegram_disabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf["telegram"]["enabled"] = False
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert not log_has("Enabling rpc.telegram ...", caplog)
+    assert rpc_manager.registered_modules == []
+
+
+def test_init_telegram_enabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf["telegram"]["enabled"] = True
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert log_has("Enabling rpc.telegram ...", caplog)
+    len_modules = len(rpc_manager.registered_modules)
+    assert len_modules == 1
+    assert "telegram" in [mod.name for mod in rpc_manager.registered_modules]
+
+
+def test_cleanup_telegram_disabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    telegram_mock = mocker.patch("freqtrade.rpc.telegram.Telegram.cleanup", MagicMock())
+    default_conf["telegram"]["enabled"] = False
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+    rpc_manager.cleanup()
+
+    assert not log_has("Cleaning up rpc.telegram ...", caplog)
+    assert telegram_mock.call_count == 0
+
+
+def test_cleanup_telegram_enabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf["telegram"]["enabled"] = True
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+    telegram_mock = mocker.patch("freqtrade.rpc.telegram.Telegram.cleanup", MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+
+    # Check we have Telegram as a registered modules
+    assert "telegram" in [mod.name for mod in rpc_manager.registered_modules]
+
+    rpc_manager.cleanup()
+    assert log_has("Cleaning up rpc.telegram ...", caplog)
+    assert "telegram" not in [mod.name for mod in rpc_manager.registered_modules]
+    assert telegram_mock.call_count == 1
+
+
+def test_send_msg_telegram_disabled(mocker, default_conf, caplog) -> None:
+    telegram_mock = mocker.patch("freqtrade.rpc.telegram.Telegram.send_msg", MagicMock())
+    default_conf["telegram"]["enabled"] = False
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+    rpc_manager.send_msg({"type": RPCMessageType.STATUS, "status": "test"})
+
+    assert log_has("Sending rpc message: {'type': status, 'status': 'test'}", caplog)
+    assert telegram_mock.call_count == 0
+
+
+def test_send_msg_telegram_error(mocker, default_conf, caplog) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+    mocker.patch("freqtrade.rpc.telegram.Telegram.send_msg", side_effect=ValueError())
+    default_conf["telegram"]["enabled"] = True
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+    rpc_manager.send_msg({"type": RPCMessageType.STATUS, "status": "test"})
+
+    assert log_has("Sending rpc message: {'type': status, 'status': 'test'}", caplog)
+    assert log_has("Exception occurred within RPC module telegram", caplog)
+
+
+def test_process_msg_queue(mocker, default_conf, caplog) -> None:
+    telegram_mock = mocker.patch("freqtrade.rpc.telegram.Telegram.send_msg")
+    default_conf["telegram"]["enabled"] = True
+    default_conf["telegram"]["allow_custom_messages"] = True
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init")
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+    queue = deque()
+    queue.append("Test message")
+    queue.append("Test message 2")
+    rpc_manager.process_msg_queue(queue)
+
+    assert log_has("Sending rpc strategy_msg: Test message", caplog)
+    assert log_has("Sending rpc strategy_msg: Test message 2", caplog)
+    assert telegram_mock.call_count == 2
+
+
+def test_send_msg_telegram_enabled(mocker, default_conf, caplog) -> None:
+    default_conf["telegram"]["enabled"] = True
+    telegram_mock = mocker.patch("freqtrade.rpc.telegram.Telegram.send_msg")
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init")
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+    rpc_manager.send_msg({"type": RPCMessageType.STATUS, "status": "test"})
+
+    assert log_has("Sending rpc message: {'type': status, 'status': 'test'}", caplog)
+    assert telegram_mock.call_count == 1
+
+
+def test_init_webhook_disabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf["telegram"]["enabled"] = False
+    default_conf["webhook"] = {"enabled": False}
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert not log_has("Enabling rpc.webhook ...", caplog)
+    assert rpc_manager.registered_modules == []
+
+
+def test_init_webhook_enabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf["telegram"]["enabled"] = False
+    default_conf["webhook"] = {"enabled": True, "url": "https://DEADBEEF.com"}
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert log_has("Enabling rpc.webhook ...", caplog)
+    assert len(rpc_manager.registered_modules) == 1
+    assert "webhook" in [mod.name for mod in rpc_manager.registered_modules]
+
+
+def test_send_msg_webhook_CustomMessagetype(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    default_conf["telegram"]["enabled"] = False
+    default_conf["webhook"] = {"enabled": True, "url": "https://DEADBEEF.com"}
+    mocker.patch(
+        "freqtrade.rpc.webhook.Webhook.send_msg", MagicMock(side_effect=NotImplementedError)
+    )
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert "webhook" in [mod.name for mod in rpc_manager.registered_modules]
+    rpc_manager.send_msg({"type": RPCMessageType.STARTUP, "status": "TestMessage"})
+    assert log_has("Message type 'startup' not implemented by handler webhook.", caplog)
+
+
+def test_startupmessages_telegram_enabled(mocker, default_conf) -> None:
+    default_conf["telegram"]["enabled"] = True
+    telegram_mock = mocker.patch("freqtrade.rpc.telegram.Telegram.send_msg", MagicMock())
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc_manager = RPCManager(freqtradebot)
+    rpc_manager.startup_messages(default_conf, freqtradebot.pairlists, freqtradebot.protections)
+
+    assert telegram_mock.call_count == 3
+    assert "*Exchange:* `binance`" in telegram_mock.call_args_list[1][0][0]["status"]
+
+    telegram_mock.reset_mock()
+    default_conf["dry_run"] = True
+    default_conf["whitelist"] = {"method": "VolumePairList", "config": {"number_assets": 20}}
+    default_conf["_strategy_protections"] = [
+        {"method": "StoplossGuard", "lookback_period": 60, "trade_limit": 2, "stop_duration": 60}
+    ]
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+
+    rpc_manager.startup_messages(default_conf, freqtradebot.pairlists, freqtradebot.protections)
+    assert telegram_mock.call_count == 4
+    assert "Dry run is enabled." in telegram_mock.call_args_list[0][0][0]["status"]
+    assert "StoplossGuard" in telegram_mock.call_args_list[-1][0][0]["status"]
+
+
+def test_init_apiserver_disabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    run_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.api_server.ApiServer.start_api", run_mock)
+    default_conf["telegram"]["enabled"] = False
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    assert not log_has("Enabling rpc.api_server", caplog)
+    assert rpc_manager.registered_modules == []
+    assert run_mock.call_count == 0
+
+
+def test_init_apiserver_enabled(mocker, default_conf, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    run_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.api_server.ApiServer.start_api", run_mock)
+
+    default_conf["telegram"]["enabled"] = False
+    default_conf["api_server"] = {
+        "enabled": True,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "username": "TestUser",
+        "password": "TestPass",
+    }
+    rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
+
+    # Sleep to allow the thread to start
+    time.sleep(0.5)
+    assert log_has("Enabling rpc.api_server", caplog)
+    assert len(rpc_manager.registered_modules) == 1
+    assert "apiserver" in [mod.name for mod in rpc_manager.registered_modules]
+    assert run_mock.call_count == 1
+    ApiServer.shutdown()
diff --git a/tests/rpc/test_rpc_telegram.py b/tests/rpc/test_rpc_telegram.py
new file mode 100644
index 000000000..3409cc0e5
--- /dev/null
+++ b/tests/rpc/test_rpc_telegram.py
@@ -0,0 +1,3019 @@
+# pragma pylint: disable=missing-docstring, C0103
+# pragma pylint: disable=protected-access, unused-argument, invalid-name
+# pragma pylint: disable=too-many-lines, too-many-arguments
+
+import asyncio
+import logging
+import re
+import threading
+from datetime import timedelta
+from functools import reduce
+from random import choice, randint
+from string import ascii_uppercase
+from unittest.mock import ANY, AsyncMock, MagicMock
+
+import pytest
+import time_machine
+from pandas import DataFrame
+from sqlalchemy import select
+from telegram import Chat, Message, ReplyKeyboardMarkup, Update, User
+from telegram.error import BadRequest, NetworkError, TelegramError
+
+from freqtrade import __version__
+from freqtrade.constants import CANCEL_REASON
+from freqtrade.edge import PairInfo
+from freqtrade.enums import (
+    ExitType,
+    MarketDirection,
+    RPCMessageType,
+    RunMode,
+    SignalDirection,
+    State,
+)
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqtradebot import FreqtradeBot
+from freqtrade.loggers import setup_logging
+from freqtrade.persistence import PairLocks, Trade
+from freqtrade.persistence.models import Order
+from freqtrade.rpc import RPC
+from freqtrade.rpc.rpc import RPCException
+from freqtrade.rpc.telegram import Telegram, authorized_only
+from freqtrade.util.datetime_helpers import dt_now
+from tests.conftest import (
+    CURRENT_TEST_STRATEGY,
+    EXMS,
+    create_mock_trades,
+    create_mock_trades_usdt,
+    get_patched_freqtradebot,
+    log_has,
+    log_has_re,
+    patch_exchange,
+    patch_get_signal,
+    patch_whitelist,
+)
+
+
+@pytest.fixture(autouse=True)
+def mock_exchange_loop(mocker):
+    mocker.patch("freqtrade.exchange.exchange.Exchange._init_async_loop")
+
+
+@pytest.fixture
+def default_conf(default_conf) -> dict:
+    # Telegram is enabled by default
+    default_conf["telegram"]["enabled"] = True
+    return default_conf
+
+
+@pytest.fixture
+def update():
+    message = Message(
+        0,
+        dt_now(),
+        Chat(1235, 0),
+        from_user=User(5432, "test", is_bot=False),
+    )
+    _update = Update(0, message=message)
+
+    return _update
+
+
+def patch_eventloop_threading(telegrambot):
+    is_init = False
+
+    def thread_fuck():
+        nonlocal is_init
+        telegrambot._loop = asyncio.new_event_loop()
+        is_init = True
+        telegrambot._loop.run_forever()
+
+    x = threading.Thread(target=thread_fuck, daemon=True)
+    x.start()
+    while not is_init:
+        pass
+
+
+class DummyCls(Telegram):
+    """
+    Dummy class for testing the Telegram @authorized_only decorator
+    """
+
+    def __init__(self, rpc: RPC, config) -> None:
+        super().__init__(rpc, config)
+        self.state = {"called": False}
+
+    def _init(self):
+        pass
+
+    @authorized_only
+    async def dummy_handler(self, *args, **kwargs) -> None:
+        """
+        Fake method that only change the state of the object
+        """
+        self.state["called"] = True
+
+    @authorized_only
+    async def dummy_exception(self, *args, **kwargs) -> None:
+        """
+        Fake method that throw an exception
+        """
+        raise Exception("test")
+
+
+def get_telegram_testobject(mocker, default_conf, mock=True, ftbot=None):
+    msg_mock = AsyncMock()
+    if mock:
+        mocker.patch.multiple(
+            "freqtrade.rpc.telegram.Telegram",
+            _init=MagicMock(),
+            _send_msg=msg_mock,
+            _start_thread=MagicMock(),
+        )
+    if not ftbot:
+        ftbot = get_patched_freqtradebot(mocker, default_conf)
+    rpc = RPC(ftbot)
+    telegram = Telegram(rpc, default_conf)
+    telegram._loop = MagicMock()
+    patch_eventloop_threading(telegram)
+
+    return telegram, ftbot, msg_mock
+
+
+def test_telegram__init__(default_conf, mocker) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+
+    telegram, _, _ = get_telegram_testobject(mocker, default_conf)
+    assert telegram._config == default_conf
+
+
+def test_telegram_init(default_conf, mocker, caplog) -> None:
+    app_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.telegram.Telegram._start_thread", MagicMock())
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init_telegram_app", return_value=app_mock)
+    mocker.patch("freqtrade.rpc.telegram.Telegram._startup_telegram", AsyncMock())
+
+    telegram, _, _ = get_telegram_testobject(mocker, default_conf, mock=False)
+    telegram._init()
+    assert app_mock.call_count == 0
+
+    # number of handles registered
+    assert app_mock.add_handler.call_count > 0
+    # assert start_polling.start_polling.call_count == 1
+
+    message_str = (
+        "rpc.telegram is listening for following commands: [['status'], ['profit'], "
+        "['balance'], ['start'], ['stop'], "
+        "['forceexit', 'forcesell', 'fx'], ['forcebuy', 'forcelong'], ['forceshort'], "
+        "['reload_trade'], ['trades'], ['delete'], ['cancel_open_order', 'coo'], "
+        "['performance'], ['buys', 'entries'], ['exits', 'sells'], ['mix_tags'], "
+        "['stats'], ['daily'], ['weekly'], ['monthly'], "
+        "['count'], ['locks'], ['delete_locks', 'unlock'], "
+        "['reload_conf', 'reload_config'], ['show_conf', 'show_config'], "
+        "['pause', 'stopbuy', 'stopentry'], ['whitelist'], ['blacklist'], "
+        "['bl_delete', 'blacklist_delete'], "
+        "['logs'], ['edge'], ['health'], ['help'], ['version'], ['marketdir'], "
+        "['order'], ['list_custom_data'], ['tg_info']]"
+    )
+
+    assert log_has(message_str, caplog)
+
+
+async def test_telegram_startup(default_conf, mocker) -> None:
+    app_mock = MagicMock()
+    app_mock.initialize = AsyncMock()
+    app_mock.start = AsyncMock()
+    app_mock.updater.start_polling = AsyncMock()
+    app_mock.updater.running = False
+    sleep_mock = mocker.patch("freqtrade.rpc.telegram.asyncio.sleep", AsyncMock())
+
+    telegram, _, _ = get_telegram_testobject(mocker, default_conf)
+    telegram._app = app_mock
+    await telegram._startup_telegram()
+    assert app_mock.initialize.call_count == 1
+    assert app_mock.start.call_count == 1
+    assert app_mock.updater.start_polling.call_count == 1
+    assert sleep_mock.call_count == 1
+
+
+async def test_telegram_cleanup(
+    default_conf,
+    mocker,
+) -> None:
+    app_mock = MagicMock()
+    app_mock.stop = AsyncMock()
+    app_mock.initialize = AsyncMock()
+
+    updater_mock = MagicMock()
+    updater_mock.stop = AsyncMock()
+    app_mock.updater = updater_mock
+    # mocker.patch('freqtrade.rpc.telegram.Application', app_mock)
+
+    telegram, _, _ = get_telegram_testobject(mocker, default_conf)
+    telegram._app = app_mock
+    telegram._loop = asyncio.get_running_loop()
+    telegram._thread = MagicMock()
+    telegram.cleanup()
+    await asyncio.sleep(0.1)
+    assert app_mock.stop.call_count == 1
+    assert telegram._thread.join.call_count == 1
+
+
+async def test_authorized_only(default_conf, mocker, caplog, update) -> None:
+    patch_exchange(mocker)
+    caplog.set_level(logging.DEBUG)
+    default_conf["telegram"]["enabled"] = False
+    bot = FreqtradeBot(default_conf)
+    rpc = RPC(bot)
+    dummy = DummyCls(rpc, default_conf)
+
+    patch_get_signal(bot)
+    await dummy.dummy_handler(update=update, context=MagicMock())
+    assert dummy.state["called"] is True
+    assert log_has("Executing handler: dummy_handler for chat_id: 1235", caplog)
+    assert not log_has("Rejected unauthorized message from: 1235", caplog)
+    assert not log_has("Exception occurred within Telegram module", caplog)
+
+
+async def test_authorized_only_unauthorized(default_conf, mocker, caplog) -> None:
+    patch_exchange(mocker)
+    caplog.set_level(logging.DEBUG)
+    message = Message(
+        randint(1, 100),
+        dt_now(),
+        Chat(0xDEADBEEF, 0),
+        from_user=User(5432, "test", is_bot=False),
+    )
+    update = Update(randint(1, 100), message=message)
+
+    default_conf["telegram"]["enabled"] = False
+    bot = FreqtradeBot(default_conf)
+    rpc = RPC(bot)
+    dummy = DummyCls(rpc, default_conf)
+
+    patch_get_signal(bot)
+    await dummy.dummy_handler(update=update, context=MagicMock())
+    assert dummy.state["called"] is False
+    assert not log_has("Executing handler: dummy_handler for chat_id: 3735928559", caplog)
+    assert log_has("Rejected unauthorized message from: 3735928559", caplog)
+    assert not log_has("Exception occurred within Telegram module", caplog)
+
+
+async def test_authorized_users(default_conf, mocker, caplog, update) -> None:
+    patch_exchange(mocker)
+    caplog.set_level(logging.DEBUG)
+    default_conf["telegram"]["enabled"] = False
+    default_conf["telegram"]["authorized_users"] = ["5432"]
+    bot = FreqtradeBot(default_conf)
+    rpc = RPC(bot)
+    dummy = DummyCls(rpc, default_conf)
+
+    await dummy.dummy_handler(update=update, context=MagicMock())
+    assert dummy.state["called"] is True
+    assert log_has("Executing handler: dummy_handler for chat_id: 1235", caplog)
+    caplog.clear()
+    # Test empty case
+    default_conf["telegram"]["authorized_users"] = []
+    dummy1 = DummyCls(rpc, default_conf)
+    await dummy1.dummy_handler(update=update, context=MagicMock())
+    assert dummy1.state["called"] is False
+    assert log_has_re(r"Unauthorized user tried to .*5432", caplog)
+    caplog.clear()
+    # Test wrong user
+    default_conf["telegram"]["authorized_users"] = ["1234"]
+    dummy1 = DummyCls(rpc, default_conf)
+    await dummy1.dummy_handler(update=update, context=MagicMock())
+    assert dummy1.state["called"] is False
+    assert log_has_re(r"Unauthorized user tried to .*5432", caplog)
+    caplog.clear()
+
+    # Test reverse case again
+    default_conf["telegram"]["authorized_users"] = ["5432"]
+    dummy1 = DummyCls(rpc, default_conf)
+    await dummy1.dummy_handler(update=update, context=MagicMock())
+    assert dummy1.state["called"] is True
+    assert not log_has_re(r"Unauthorized user tried to .*5432", caplog)
+
+
+async def test_authorized_only_exception(default_conf, mocker, caplog, update) -> None:
+    patch_exchange(mocker)
+
+    default_conf["telegram"]["enabled"] = False
+
+    bot = FreqtradeBot(default_conf)
+    rpc = RPC(bot)
+    dummy = DummyCls(rpc, default_conf)
+    patch_get_signal(bot)
+
+    await dummy.dummy_exception(update=update, context=MagicMock())
+    assert dummy.state["called"] is False
+    assert not log_has("Executing handler: dummy_handler for chat_id: 0", caplog)
+    assert not log_has("Rejected unauthorized message from: 0", caplog)
+    assert log_has("Exception occurred within Telegram module", caplog)
+
+
+async def test_telegram_status(default_conf, update, mocker) -> None:
+    default_conf["telegram"]["enabled"] = False
+
+    status_table = MagicMock()
+    mocker.patch("freqtrade.rpc.telegram.Telegram._status_table", status_table)
+
+    mocker.patch.multiple(
+        "freqtrade.rpc.rpc.RPC",
+        _rpc_trade_status=MagicMock(
+            return_value=[
+                {
+                    "trade_id": 1,
+                    "pair": "ETH/BTC",
+                    "base_currency": "ETH",
+                    "quote_currency": "BTC",
+                    "open_date": dt_now(),
+                    "close_date": None,
+                    "open_rate": 1.099e-05,
+                    "close_rate": None,
+                    "current_rate": 1.098e-05,
+                    "amount": 90.99181074,
+                    "stake_amount": 90.99181074,
+                    "max_stake_amount": 90.99181074,
+                    "buy_tag": None,
+                    "enter_tag": None,
+                    "close_profit_ratio": None,
+                    "profit": -0.0059,
+                    "profit_ratio": -0.0059,
+                    "profit_abs": -0.225,
+                    "realized_profit": 0.0,
+                    "total_profit_abs": -0.225,
+                    "initial_stop_loss_abs": 1.098e-05,
+                    "stop_loss_abs": 1.099e-05,
+                    "exit_order_status": None,
+                    "initial_stop_loss_ratio": -0.0005,
+                    "stoploss_current_dist": 1e-08,
+                    "stoploss_current_dist_ratio": -0.0002,
+                    "stop_loss_ratio": -0.0001,
+                    "open_order": "(limit buy rem=0.00000000)",
+                    "is_open": True,
+                    "is_short": False,
+                    "filled_entry_orders": [],
+                    "orders": [],
+                }
+            ]
+        ),
+    )
+
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    await telegram._status(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+
+    context = MagicMock()
+    # /status table
+    context.args = ["table"]
+    await telegram._status(update=update, context=context)
+    assert status_table.call_count == 1
+
+
+@pytest.mark.usefixtures("init_persistence")
+async def test_telegram_status_multi_entry(default_conf, update, mocker, fee) -> None:
+    default_conf["telegram"]["enabled"] = False
+    default_conf["position_adjustment_enable"] = True
+    mocker.patch.multiple(
+        EXMS,
+        fetch_order=MagicMock(return_value=None),
+        get_rate=MagicMock(return_value=0.22),
+    )
+
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    create_mock_trades(fee)
+    trades = Trade.get_open_trades()
+    trade = trades[3]
+    # Average may be empty on some exchanges
+    trade.orders[0].average = 0
+    trade.orders.append(
+        Order(
+            order_id="5412vbb",
+            ft_order_side="buy",
+            ft_pair=trade.pair,
+            ft_is_open=False,
+            ft_amount=trade.amount,
+            ft_price=trade.open_rate,
+            status="closed",
+            symbol=trade.pair,
+            order_type="market",
+            side="buy",
+            price=trade.open_rate * 0.95,
+            average=0,
+            filled=trade.amount,
+            remaining=0,
+            cost=trade.amount,
+            order_date=trade.open_date,
+            order_filled_date=trade.open_date,
+        )
+    )
+    trade.recalc_trade_from_orders()
+    Trade.commit()
+
+    await telegram._status(update=update, context=MagicMock())
+    assert msg_mock.call_count == 4
+    msg = msg_mock.call_args_list[3][0][0]
+    assert re.search(r"Number of Entries.*2", msg)
+    assert re.search(r"Number of Exits.*1", msg)
+    assert re.search(r"Close Date:", msg) is None
+    assert re.search(r"Close Profit:", msg) is None
+
+
+@pytest.mark.usefixtures("init_persistence")
+async def test_telegram_status_closed_trade(default_conf, update, mocker, fee) -> None:
+    default_conf["position_adjustment_enable"] = True
+    mocker.patch.multiple(
+        EXMS,
+        fetch_order=MagicMock(return_value=None),
+        get_rate=MagicMock(return_value=0.22),
+    )
+
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    create_mock_trades(fee)
+    trade = Trade.get_trades([Trade.is_open.is_(False)]).first()
+    context = MagicMock()
+    context.args = [str(trade.id)]
+    await telegram._status(update=update, context=context)
+    assert msg_mock.call_count == 1
+    msg = msg_mock.call_args_list[0][0][0]
+    assert re.search(r"Close Date:", msg)
+    assert re.search(r"Close Profit:", msg)
+
+
+async def test_order_handle(default_conf, update, ticker, fee, mocker) -> None:
+    default_conf["max_open_trades"] = 3
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=True),
+    )
+    status_table = MagicMock()
+    mocker.patch.multiple(
+        "freqtrade.rpc.telegram.Telegram",
+        _status_table=status_table,
+    )
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    patch_get_signal(freqtradebot)
+
+    freqtradebot.state = State.RUNNING
+    msg_mock.reset_mock()
+
+    # Create some test data
+    freqtradebot.enter_positions()
+
+    mocker.patch("freqtrade.rpc.telegram.MAX_MESSAGE_LENGTH", 500)
+
+    msg_mock.reset_mock()
+    context = MagicMock()
+    context.args = ["2"]
+    await telegram._order(update=update, context=context)
+
+    assert msg_mock.call_count == 1
+
+    msg1 = msg_mock.call_args_list[0][0][0]
+
+    assert "Order List for Trade #*`2`" in msg1
+
+    msg_mock.reset_mock()
+    mocker.patch("freqtrade.rpc.telegram.MAX_MESSAGE_LENGTH", 50)
+    context = MagicMock()
+    context.args = ["2"]
+    await telegram._order(update=update, context=context)
+
+    assert msg_mock.call_count == 2
+
+    msg1 = msg_mock.call_args_list[0][0][0]
+    msg2 = msg_mock.call_args_list[1][0][0]
+
+    assert "Order List for Trade #*`2`" in msg1
+    assert "*Order List for Trade #*`2` - continued" in msg2
+
+
+@pytest.mark.usefixtures("init_persistence")
+async def test_telegram_order_multi_entry(default_conf, update, mocker, fee) -> None:
+    default_conf["telegram"]["enabled"] = False
+    default_conf["position_adjustment_enable"] = True
+    mocker.patch.multiple(
+        EXMS,
+        fetch_order=MagicMock(return_value=None),
+        get_rate=MagicMock(return_value=0.22),
+    )
+
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    create_mock_trades(fee)
+    trades = Trade.get_open_trades()
+    trade = trades[3]
+    # Average may be empty on some exchanges
+    trade.orders[0].average = 0
+    trade.orders.append(
+        Order(
+            order_id="5412vbb",
+            ft_order_side="buy",
+            ft_pair=trade.pair,
+            ft_is_open=False,
+            ft_amount=trade.amount,
+            ft_price=trade.open_rate,
+            status="closed",
+            symbol=trade.pair,
+            order_type="market",
+            side="buy",
+            price=trade.open_rate * 0.95,
+            average=0,
+            filled=trade.amount,
+            remaining=0,
+            cost=trade.amount,
+            order_date=trade.open_date,
+            order_filled_date=trade.open_date,
+        )
+    )
+    trade.recalc_trade_from_orders()
+    Trade.commit()
+
+    await telegram._order(update=update, context=MagicMock())
+    assert msg_mock.call_count == 4
+    msg = msg_mock.call_args_list[3][0][0]
+    assert re.search(r"from 1st entry rate", msg)
+    assert re.search(r"Order Filled", msg)
+
+
+async def test_status_handle(default_conf, update, ticker, fee, mocker) -> None:
+    default_conf["max_open_trades"] = 3
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=True),
+    )
+    status_table = MagicMock()
+    mocker.patch.multiple(
+        "freqtrade.rpc.telegram.Telegram",
+        _status_table=status_table,
+    )
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    patch_get_signal(freqtradebot)
+
+    freqtradebot.state = State.STOPPED
+    # Status is also enabled when stopped
+    await telegram._status(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "no active trade" in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    freqtradebot.state = State.RUNNING
+    await telegram._status(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "no active trade" in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    # Create some test data
+    freqtradebot.enter_positions()
+    # Trigger status while we have a fulfilled order for the open trade
+    await telegram._status(update=update, context=MagicMock())
+
+    # close_rate should not be included in the message as the trade is not closed
+    # and no line should be empty
+    lines = msg_mock.call_args_list[0][0][0].split("\n")
+    assert "" not in lines[:-1]
+    assert "Close Rate" not in "".join(lines)
+    assert "Close Profit" not in "".join(lines)
+
+    assert msg_mock.call_count == 3
+    assert "ETH/BTC" in msg_mock.call_args_list[0][0][0]
+    assert "LTC/BTC" in msg_mock.call_args_list[1][0][0]
+
+    msg_mock.reset_mock()
+    context = MagicMock()
+    context.args = ["2", "3"]
+
+    await telegram._status(update=update, context=context)
+
+    lines = msg_mock.call_args_list[0][0][0].split("\n")
+    assert "" not in lines[:-1]
+    assert "Close Rate" not in "".join(lines)
+    assert "Close Profit" not in "".join(lines)
+
+    assert msg_mock.call_count == 2
+    assert "LTC/BTC" in msg_mock.call_args_list[0][0][0]
+
+    mocker.patch("freqtrade.rpc.telegram.MAX_MESSAGE_LENGTH", 500)
+
+    msg_mock.reset_mock()
+    context = MagicMock()
+    context.args = ["2"]
+    await telegram._status(update=update, context=context)
+
+    assert msg_mock.call_count == 1
+
+    msg1 = msg_mock.call_args_list[0][0][0]
+
+    assert "Close Rate" not in msg1
+    assert "Trade ID:* `2`" in msg1
+
+
+async def test_status_table_handle(default_conf, update, ticker, fee, mocker) -> None:
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    default_conf["stake_amount"] = 15.0
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    patch_get_signal(freqtradebot)
+
+    freqtradebot.state = State.STOPPED
+    # Status table is also enabled when stopped
+    await telegram._status_table(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "no active trade" in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    freqtradebot.state = State.RUNNING
+    await telegram._status_table(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "no active trade" in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    # Create some test data
+    freqtradebot.enter_positions()
+
+    await telegram._status_table(update=update, context=MagicMock())
+
+    text = re.sub("</?pre>", "", msg_mock.call_args_list[-1][0][0])
+    line = text.split("\n")
+    fields = re.sub("[ ]+", " ", line[2].strip()).split(" ")
+
+    assert int(fields[0]) == 1
+    # assert 'L' in fields[1]
+    assert "ETH/BTC" in fields[1]
+    assert msg_mock.call_count == 1
+
+
+async def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time_machine) -> None:
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=1.1)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    telegram, _freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt)
+
+    # Move date to within day
+    time_machine.move_to("2022-06-11 08:00:00+00:00")
+    # Create some test data
+    create_mock_trades_usdt(fee)
+
+    # Try valid data
+    # /daily 2
+    context = MagicMock()
+    context.args = ["2"]
+    await telegram._daily(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Daily Profit over the last 2 days</b>:" in msg_mock.call_args_list[0][0][0]
+    assert "Day " in msg_mock.call_args_list[0][0][0]
+    assert str(dt_now().date()) in msg_mock.call_args_list[0][0][0]
+    assert "  6.83 USDT" in msg_mock.call_args_list[0][0][0]
+    assert "  7.51 USD" in msg_mock.call_args_list[0][0][0]
+    assert "(2)" in msg_mock.call_args_list[0][0][0]
+    assert "(2)  6.83 USDT  7.51 USD  0.64%" in msg_mock.call_args_list[0][0][0]
+    assert "(0)" in msg_mock.call_args_list[0][0][0]
+
+    # Reset msg_mock
+    msg_mock.reset_mock()
+    context.args = []
+    await telegram._daily(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Daily Profit over the last 7 days</b>:" in msg_mock.call_args_list[0][0][0]
+    assert str(dt_now().date()) in msg_mock.call_args_list[0][0][0]
+    assert str((dt_now() - timedelta(days=5)).date()) in msg_mock.call_args_list[0][0][0]
+    assert "  6.83 USDT" in msg_mock.call_args_list[0][0][0]
+    assert "  7.51 USD" in msg_mock.call_args_list[0][0][0]
+    assert "(2)" in msg_mock.call_args_list[0][0][0]
+    assert "(1)" in msg_mock.call_args_list[0][0][0]
+    assert "(0)" in msg_mock.call_args_list[0][0][0]
+
+    # Reset msg_mock
+    msg_mock.reset_mock()
+
+    # /daily 1
+    context = MagicMock()
+    context.args = ["1"]
+    await telegram._daily(update=update, context=context)
+    assert "  6.83 USDT" in msg_mock.call_args_list[0][0][0]
+    assert "  7.51 USD" in msg_mock.call_args_list[0][0][0]
+    assert "(2)" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_daily_wrong_input(default_conf, update, ticker, mocker) -> None:
+    mocker.patch.multiple(EXMS, fetch_ticker=ticker)
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    # Try invalid data
+    msg_mock.reset_mock()
+    freqtradebot.state = State.RUNNING
+    # /daily -2
+    context = MagicMock()
+    context.args = ["-2"]
+    await telegram._daily(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "must be an integer greater than 0" in msg_mock.call_args_list[0][0][0]
+
+    # Try invalid data
+    msg_mock.reset_mock()
+    freqtradebot.state = State.RUNNING
+    # /daily today
+    context = MagicMock()
+    context.args = ["today"]
+    await telegram._daily(update=update, context=context)
+    assert "Daily Profit over the last 7 days</b>:" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, time_machine) -> None:
+    default_conf_usdt["max_open_trades"] = 1
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=1.1)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt)
+    # Move to saturday - so all trades are within that week
+    time_machine.move_to("2022-06-11")
+    create_mock_trades_usdt(fee)
+
+    # Try valid data
+    # /weekly 2
+    context = MagicMock()
+    context.args = ["2"]
+    await telegram._weekly(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert (
+        "Weekly Profit over the last 2 weeks (starting from Monday)</b>:"
+        in msg_mock.call_args_list[0][0][0]
+    )
+    assert "Monday " in msg_mock.call_args_list[0][0][0]
+    today = dt_now().date()
+    first_iso_day_of_current_week = today - timedelta(days=today.weekday())
+    assert str(first_iso_day_of_current_week) in msg_mock.call_args_list[0][0][0]
+    assert "  2.74 USDT" in msg_mock.call_args_list[0][0][0]
+    assert "  3.01 USD" in msg_mock.call_args_list[0][0][0]
+    assert "(3)" in msg_mock.call_args_list[0][0][0]
+    assert "(0)" in msg_mock.call_args_list[0][0][0]
+
+    # Reset msg_mock
+    msg_mock.reset_mock()
+    context.args = []
+    await telegram._weekly(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert (
+        "Weekly Profit over the last 8 weeks (starting from Monday)</b>:"
+        in msg_mock.call_args_list[0][0][0]
+    )
+    assert "Weekly" in msg_mock.call_args_list[0][0][0]
+    assert "  2.74 USDT" in msg_mock.call_args_list[0][0][0]
+    assert "  3.01 USD" in msg_mock.call_args_list[0][0][0]
+    assert "(3)" in msg_mock.call_args_list[0][0][0]
+    assert "(0)" in msg_mock.call_args_list[0][0][0]
+
+    # Try invalid data
+    msg_mock.reset_mock()
+    freqtradebot.state = State.RUNNING
+    # /weekly -3
+    context = MagicMock()
+    context.args = ["-3"]
+    await telegram._weekly(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "must be an integer greater than 0" in msg_mock.call_args_list[0][0][0]
+
+    # Try invalid data
+    msg_mock.reset_mock()
+    freqtradebot.state = State.RUNNING
+    # /weekly this week
+    context = MagicMock()
+    context.args = ["this week"]
+    await telegram._weekly(update=update, context=context)
+    assert (
+        "Weekly Profit over the last 8 weeks (starting from Monday)</b>:"
+        in msg_mock.call_args_list[0][0][0]
+    )
+
+
+async def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_machine) -> None:
+    default_conf_usdt["max_open_trades"] = 1
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=1.1)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt)
+    # Move to day within the month so all mock trades fall into this week.
+    time_machine.move_to("2022-06-11")
+    create_mock_trades_usdt(fee)
+
+    # Try valid data
+    # /monthly 2
+    context = MagicMock()
+    context.args = ["2"]
+    await telegram._monthly(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Monthly Profit over the last 2 months</b>:" in msg_mock.call_args_list[0][0][0]
+    assert "Month " in msg_mock.call_args_list[0][0][0]
+    today = dt_now().date()
+    current_month = f"{today.year}-{today.month:02} "
+    assert current_month in msg_mock.call_args_list[0][0][0]
+    assert "  2.74 USDT" in msg_mock.call_args_list[0][0][0]
+    assert "  3.01 USD" in msg_mock.call_args_list[0][0][0]
+    assert "(3)" in msg_mock.call_args_list[0][0][0]
+    assert "(0)" in msg_mock.call_args_list[0][0][0]
+
+    # Reset msg_mock
+    msg_mock.reset_mock()
+    context.args = []
+    await telegram._monthly(update=update, context=context)
+    assert msg_mock.call_count == 1
+    # Default to 6 months
+    assert "Monthly Profit over the last 6 months</b>:" in msg_mock.call_args_list[0][0][0]
+    assert "Month " in msg_mock.call_args_list[0][0][0]
+    assert current_month in msg_mock.call_args_list[0][0][0]
+    assert "  2.74 USDT" in msg_mock.call_args_list[0][0][0]
+    assert "  3.01 USD" in msg_mock.call_args_list[0][0][0]
+    assert "(3)" in msg_mock.call_args_list[0][0][0]
+    assert "(0)" in msg_mock.call_args_list[0][0][0]
+
+    # Reset msg_mock
+    msg_mock.reset_mock()
+
+    # /monthly 12
+    context = MagicMock()
+    context.args = ["12"]
+    await telegram._monthly(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Monthly Profit over the last 12 months</b>:" in msg_mock.call_args_list[0][0][0]
+    assert "  2.74 USDT" in msg_mock.call_args_list[0][0][0]
+    assert "  3.01 USD" in msg_mock.call_args_list[0][0][0]
+    assert "(3)" in msg_mock.call_args_list[0][0][0]
+
+    # The one-digit months should contain a zero, Eg: September 2021 = "2021-09"
+    # Since we loaded the last 12 months, any month should appear
+    assert "-09" in msg_mock.call_args_list[0][0][0]
+
+    # Try invalid data
+    msg_mock.reset_mock()
+    freqtradebot.state = State.RUNNING
+    # /monthly -3
+    context = MagicMock()
+    context.args = ["-3"]
+    await telegram._monthly(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "must be an integer greater than 0" in msg_mock.call_args_list[0][0][0]
+
+    # Try invalid data
+    msg_mock.reset_mock()
+    freqtradebot.state = State.RUNNING
+    # /monthly february
+    context = MagicMock()
+    context.args = ["february"]
+    await telegram._monthly(update=update, context=context)
+    assert "Monthly Profit over the last 6 months</b>:" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_telegram_profit_handle(
+    default_conf_usdt, update, ticker_usdt, ticker_sell_up, fee, limit_sell_order_usdt, mocker
+) -> None:
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=1.1)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker_usdt,
+        get_fee=fee,
+    )
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt)
+    patch_get_signal(freqtradebot)
+
+    await telegram._profit(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "No trades yet." in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    # Create some test data
+    freqtradebot.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+
+    context = MagicMock()
+    # Test with invalid 2nd argument (should silently pass)
+    context.args = ["aaa"]
+    await telegram._profit(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "No closed trade" in msg_mock.call_args_list[-1][0][0]
+    assert "*ROI:* All trades" in msg_mock.call_args_list[-1][0][0]
+    mocker.patch("freqtrade.wallets.Wallets.get_starting_balance", return_value=1000)
+    assert (
+        "∙ `0.298 USDT (0.50%) (0.03 \N{GREEK CAPITAL LETTER SIGMA}%)`"
+        in msg_mock.call_args_list[-1][0][0]
+    )
+    msg_mock.reset_mock()
+
+    # Update the ticker with a market going up
+    mocker.patch(f"{EXMS}.fetch_ticker", ticker_sell_up)
+    # Simulate fulfilled LIMIT_SELL order for trade
+    trade = Trade.session.scalars(select(Trade)).first()
+    oobj = Order.parse_from_ccxt_object(
+        limit_sell_order_usdt, limit_sell_order_usdt["symbol"], "sell"
+    )
+    trade.orders.append(oobj)
+    trade.update_trade(oobj)
+
+    trade.close_date = dt_now()
+    trade.is_open = False
+    Trade.commit()
+
+    context.args = [3]
+    await telegram._profit(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "*ROI:* Closed trades" in msg_mock.call_args_list[-1][0][0]
+    assert (
+        "∙ `5.685 USDT (9.45%) (0.57 \N{GREEK CAPITAL LETTER SIGMA}%)`"
+        in msg_mock.call_args_list[-1][0][0]
+    )
+    assert "∙ `6.253 USD`" in msg_mock.call_args_list[-1][0][0]
+    assert "*ROI:* All trades" in msg_mock.call_args_list[-1][0][0]
+    assert (
+        "∙ `5.685 USDT (9.45%) (0.57 \N{GREEK CAPITAL LETTER SIGMA}%)`"
+        in msg_mock.call_args_list[-1][0][0]
+    )
+    assert "∙ `6.253 USD`" in msg_mock.call_args_list[-1][0][0]
+
+    assert "*Best Performing:* `ETH/USDT: 5.685 USDT (9.47%)`" in msg_mock.call_args_list[-1][0][0]
+    assert "*Max Drawdown:*" in msg_mock.call_args_list[-1][0][0]
+    assert "*Profit factor:*" in msg_mock.call_args_list[-1][0][0]
+    assert "*Winrate:*" in msg_mock.call_args_list[-1][0][0]
+    assert "*Expectancy (Ratio):*" in msg_mock.call_args_list[-1][0][0]
+    assert "*Trading volume:* `126 USDT`" in msg_mock.call_args_list[-1][0][0]
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+async def test_telegram_stats(default_conf, update, ticker, fee, mocker, is_short) -> None:
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=15000.0)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    await telegram._stats(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "No trades yet." in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    # Create some test data
+    create_mock_trades(fee, is_short=is_short)
+
+    await telegram._stats(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "Exit Reason" in msg_mock.call_args_list[-1][0][0]
+    assert "ROI" in msg_mock.call_args_list[-1][0][0]
+    assert "Avg. Duration" in msg_mock.call_args_list[-1][0][0]
+    # Duration is not only N/A
+    assert "0:19:00" in msg_mock.call_args_list[-1][0][0]
+    assert "N/A" in msg_mock.call_args_list[-1][0][0]
+    msg_mock.reset_mock()
+
+
+async def test_telegram_balance_handle(default_conf, update, mocker, rpc_balance, tickers) -> None:
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.get_balances", return_value=rpc_balance)
+    mocker.patch(f"{EXMS}.get_tickers", tickers)
+    mocker.patch(f"{EXMS}.get_valid_pair_combination", side_effect=lambda a, b: [f"{a}/{b}"])
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    await telegram._balance(update=update, context=MagicMock())
+    context = MagicMock()
+    context.args = ["full"]
+    await telegram._balance(update=update, context=context)
+    result = msg_mock.call_args_list[0][0][0]
+    result_full = msg_mock.call_args_list[1][0][0]
+    assert msg_mock.call_count == 2
+    assert "*BTC:*" in result
+    assert "*ETH:*" not in result
+    assert "*USDT:*" not in result
+    assert "*EUR:*" not in result
+    assert "*LTC:*" not in result
+
+    assert "*LTC:*" in result_full
+    assert "*XRP:*" not in result
+    assert "Balance:" in result
+    assert "Est. BTC:" in result
+    assert "BTC: 11" in result
+    assert "BTC: 12" in result_full
+    assert "*3 Other Currencies (< 0.0001 BTC):*" in result
+    assert "BTC: 0.00000309" in result
+    assert "*Estimated Value*:" in result_full
+    assert "*Estimated Value (Bot managed assets only)*:" in result
+
+
+async def test_telegram_balance_handle_futures(
+    default_conf, update, rpc_balance, mocker, tickers
+) -> None:
+    default_conf.update(
+        {
+            "dry_run": False,
+            "trading_mode": "futures",
+            "margin_mode": "isolated",
+        }
+    )
+    mock_pos = [
+        {
+            "symbol": "ETH/USDT:USDT",
+            "timestamp": None,
+            "datetime": None,
+            "initialMargin": 0.0,
+            "initialMarginPercentage": None,
+            "maintenanceMargin": 0.0,
+            "maintenanceMarginPercentage": 0.005,
+            "entryPrice": 0.0,
+            "notional": 10.0,
+            "leverage": 5.0,
+            "unrealizedPnl": 0.0,
+            "contracts": 1.0,
+            "contractSize": 1,
+            "marginRatio": None,
+            "liquidationPrice": 0.0,
+            "markPrice": 2896.41,
+            "collateral": 20,
+            "marginType": "isolated",
+            "side": "short",
+            "percentage": None,
+        },
+        {
+            "symbol": "XRP/USDT:USDT",
+            "timestamp": None,
+            "datetime": None,
+            "initialMargin": 0.0,
+            "initialMarginPercentage": None,
+            "maintenanceMargin": 0.0,
+            "maintenanceMarginPercentage": 0.005,
+            "entryPrice": 0.0,
+            "notional": 10.0,
+            "leverage": None,
+            "unrealizedPnl": 0.0,
+            "contracts": 1.0,
+            "contractSize": 1,
+            "marginRatio": None,
+            "liquidationPrice": 0.0,
+            "markPrice": 2896.41,
+            "collateral": 20,
+            "marginType": "isolated",
+            "side": "short",
+            "percentage": None,
+        },
+    ]
+    mocker.patch(f"{EXMS}.get_balances", return_value=rpc_balance)
+    mocker.patch(f"{EXMS}.fetch_positions", return_value=mock_pos)
+    mocker.patch(f"{EXMS}.get_tickers", tickers)
+    mocker.patch(f"{EXMS}.get_valid_pair_combination", side_effect=lambda a, b: [f"{a}/{b}"])
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    await telegram._balance(update=update, context=MagicMock())
+    result = msg_mock.call_args_list[0][0][0]
+    assert msg_mock.call_count == 1
+
+    assert "ETH/USDT:USDT" in result
+    assert "`short: 10" in result
+    assert "XRP/USDT:USDT" in result
+
+
+async def test_balance_handle_empty_response(default_conf, update, mocker) -> None:
+    default_conf["dry_run"] = False
+    mocker.patch(f"{EXMS}.get_balances", return_value={})
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    freqtradebot.config["dry_run"] = False
+    await telegram._balance(update=update, context=MagicMock())
+    result = msg_mock.call_args_list[0][0][0]
+    assert msg_mock.call_count == 1
+    assert "Starting capital: `0 BTC" in result
+
+
+async def test_balance_handle_empty_response_dry(default_conf, update, mocker) -> None:
+    mocker.patch(f"{EXMS}.get_balances", return_value={})
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    await telegram._balance(update=update, context=MagicMock())
+    result = msg_mock.call_args_list[0][0][0]
+    assert msg_mock.call_count == 1
+    assert "*Warning:* Simulated balances in Dry Mode." in result
+    assert "Starting capital: `990 BTC`" in result
+
+
+async def test_balance_handle_too_large_response(default_conf, update, mocker) -> None:
+    balances = []
+    for i in range(100):
+        curr = choice(ascii_uppercase) + choice(ascii_uppercase) + choice(ascii_uppercase)
+        balances.append(
+            {
+                "currency": curr,
+                "free": 1.0,
+                "used": 0.5,
+                "balance": i,
+                "bot_owned": 0.5,
+                "est_stake": 1,
+                "est_stake_bot": 1,
+                "stake": "BTC",
+                "is_position": False,
+                "leverage": 1.0,
+                "position": 0.0,
+                "side": "long",
+                "is_bot_managed": True,
+            }
+        )
+    mocker.patch(
+        "freqtrade.rpc.rpc.RPC._rpc_balance",
+        return_value={
+            "currencies": balances,
+            "total": 100.0,
+            "total_bot": 100.0,
+            "symbol": 100.0,
+            "value": 1000.0,
+            "value_bot": 1000.0,
+            "starting_capital": 1000,
+            "starting_capital_fiat": 1000,
+        },
+    )
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    await telegram._balance(update=update, context=MagicMock())
+    assert msg_mock.call_count > 1
+    # Test if wrap happens around 4000 -
+    # and each single currency-output is around 120 characters long so we need
+    # an offset to avoid random test failures
+    assert len(msg_mock.call_args_list[0][0][0]) < 4096
+    assert len(msg_mock.call_args_list[0][0][0]) > (4096 - 120)
+
+
+async def test_start_handle(default_conf, update, mocker) -> None:
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    freqtradebot.state = State.STOPPED
+    assert freqtradebot.state == State.STOPPED
+    await telegram._start(update=update, context=MagicMock())
+    assert freqtradebot.state == State.RUNNING
+    assert msg_mock.call_count == 1
+
+
+async def test_start_handle_already_running(default_conf, update, mocker) -> None:
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    freqtradebot.state = State.RUNNING
+    assert freqtradebot.state == State.RUNNING
+    await telegram._start(update=update, context=MagicMock())
+    assert freqtradebot.state == State.RUNNING
+    assert msg_mock.call_count == 1
+    assert "already running" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_stop_handle(default_conf, update, mocker) -> None:
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    freqtradebot.state = State.RUNNING
+    assert freqtradebot.state == State.RUNNING
+    await telegram._stop(update=update, context=MagicMock())
+    assert freqtradebot.state == State.STOPPED
+    assert msg_mock.call_count == 1
+    assert "stopping trader" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_stop_handle_already_stopped(default_conf, update, mocker) -> None:
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    freqtradebot.state = State.STOPPED
+    assert freqtradebot.state == State.STOPPED
+    await telegram._stop(update=update, context=MagicMock())
+    assert freqtradebot.state == State.STOPPED
+    assert msg_mock.call_count == 1
+    assert "already stopped" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_pause_handle(default_conf, update, mocker) -> None:
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    assert freqtradebot.state == State.RUNNING
+    await telegram._pause(update=update, context=MagicMock())
+    assert freqtradebot.state == State.PAUSED
+    assert msg_mock.call_count == 1
+    assert (
+        "paused, no more entries will occur from now. Run /start to enable entries."
+        in msg_mock.call_args_list[0][0][0]
+    )
+
+
+async def test_reload_config_handle(default_conf, update, mocker) -> None:
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    freqtradebot.state = State.RUNNING
+    assert freqtradebot.state == State.RUNNING
+    await telegram._reload_config(update=update, context=MagicMock())
+    assert freqtradebot.state == State.RELOAD_CONFIG
+    assert msg_mock.call_count == 1
+    assert "Reloading config" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_telegram_forceexit_handle(
+    default_conf, update, ticker, fee, ticker_sell_up, mocker
+) -> None:
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=15000.0)
+    msg_mock = mocker.patch("freqtrade.rpc.telegram.Telegram.send_msg", MagicMock())
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+    patch_exchange(mocker)
+    patch_whitelist(mocker, default_conf)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=True),
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    rpc = RPC(freqtradebot)
+    telegram = Telegram(rpc, default_conf)
+    patch_get_signal(freqtradebot)
+
+    # Create some test data
+    freqtradebot.enter_positions()
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+
+    # Increase the price and sell it
+    mocker.patch(f"{EXMS}.fetch_ticker", ticker_sell_up)
+
+    # /forceexit 1
+    context = MagicMock()
+    context.args = ["1"]
+    await telegram._force_exit(update=update, context=context)
+
+    assert msg_mock.call_count == 4
+    last_msg = msg_mock.call_args_list[-2][0][0]
+    assert {
+        "type": RPCMessageType.EXIT,
+        "trade_id": 1,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "gain": "profit",
+        "leverage": 1.0,
+        "limit": 1.173e-05,
+        "order_rate": 1.173e-05,
+        "amount": 91.07468123,
+        "order_type": "limit",
+        "open_rate": 1.098e-05,
+        "current_rate": 1.173e-05,
+        "direction": "Long",
+        "profit_amount": 6.314e-05,
+        "profit_ratio": 0.0629778,
+        "stake_currency": "BTC",
+        "quote_currency": "BTC",
+        "base_currency": "ETH",
+        "fiat_currency": "USD",
+        "buy_tag": ANY,
+        "enter_tag": ANY,
+        "exit_reason": ExitType.FORCE_EXIT.value,
+        "open_date": ANY,
+        "close_date": ANY,
+        "close_rate": ANY,
+        "stake_amount": 0.0009999999999054,
+        "sub_trade": False,
+        "cumulative_profit": 0.0,
+        "is_final_exit": False,
+        "final_profit_ratio": None,
+    } == last_msg
+
+
+async def test_telegram_force_exit_down_handle(
+    default_conf, update, ticker, fee, ticker_sell_down, mocker
+) -> None:
+    mocker.patch(
+        "freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price", return_value=15000.0
+    )
+    msg_mock = mocker.patch("freqtrade.rpc.telegram.Telegram.send_msg", MagicMock())
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+    patch_exchange(mocker)
+    patch_whitelist(mocker, default_conf)
+
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=True),
+    )
+
+    freqtradebot = FreqtradeBot(default_conf)
+    rpc = RPC(freqtradebot)
+    telegram = Telegram(rpc, default_conf)
+    patch_get_signal(freqtradebot)
+
+    # Create some test data
+    freqtradebot.enter_positions()
+
+    # Decrease the price and sell it
+    mocker.patch.multiple(EXMS, fetch_ticker=ticker_sell_down)
+
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade
+
+    # /forceexit 1
+    context = MagicMock()
+    context.args = ["1"]
+    await telegram._force_exit(update=update, context=context)
+
+    assert msg_mock.call_count == 4
+
+    last_msg = msg_mock.call_args_list[-2][0][0]
+    assert {
+        "type": RPCMessageType.EXIT,
+        "trade_id": 1,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "gain": "loss",
+        "leverage": 1.0,
+        "limit": 1.043e-05,
+        "order_rate": 1.043e-05,
+        "amount": 91.07468123,
+        "order_type": "limit",
+        "open_rate": 1.098e-05,
+        "current_rate": 1.043e-05,
+        "direction": "Long",
+        "profit_amount": -5.497e-05,
+        "profit_ratio": -0.05482878,
+        "stake_currency": "BTC",
+        "quote_currency": "BTC",
+        "base_currency": "ETH",
+        "fiat_currency": "USD",
+        "buy_tag": ANY,
+        "enter_tag": ANY,
+        "exit_reason": ExitType.FORCE_EXIT.value,
+        "open_date": ANY,
+        "close_date": ANY,
+        "close_rate": ANY,
+        "stake_amount": 0.0009999999999054,
+        "sub_trade": False,
+        "cumulative_profit": 0.0,
+        "is_final_exit": False,
+        "final_profit_ratio": None,
+    } == last_msg
+
+
+async def test_forceexit_all_handle(default_conf, update, ticker, fee, mocker) -> None:
+    patch_exchange(mocker)
+    mocker.patch(
+        "freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price", return_value=15000.0
+    )
+    msg_mock = mocker.patch("freqtrade.rpc.telegram.Telegram.send_msg", MagicMock())
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+    patch_whitelist(mocker, default_conf)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=True),
+    )
+    default_conf["max_open_trades"] = 4
+    freqtradebot = FreqtradeBot(default_conf)
+    rpc = RPC(freqtradebot)
+    telegram = Telegram(rpc, default_conf)
+    patch_get_signal(freqtradebot)
+
+    # Create some test data
+    freqtradebot.enter_positions()
+    msg_mock.reset_mock()
+
+    # /forceexit all
+    context = MagicMock()
+    context.args = ["all"]
+    await telegram._force_exit(update=update, context=context)
+
+    # Called for each trade 2 times
+    assert msg_mock.call_count == 8
+    msg = msg_mock.call_args_list[0][0][0]
+    assert {
+        "type": RPCMessageType.EXIT,
+        "trade_id": 1,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "gain": "loss",
+        "leverage": 1.0,
+        "order_rate": 1.099e-05,
+        "limit": 1.099e-05,
+        "amount": 91.07468123,
+        "order_type": "limit",
+        "open_rate": 1.098e-05,
+        "current_rate": 1.099e-05,
+        "direction": "Long",
+        "profit_amount": -4.09e-06,
+        "profit_ratio": -0.00408133,
+        "stake_currency": "BTC",
+        "quote_currency": "BTC",
+        "base_currency": "ETH",
+        "fiat_currency": "USD",
+        "buy_tag": ANY,
+        "enter_tag": ANY,
+        "exit_reason": ExitType.FORCE_EXIT.value,
+        "open_date": ANY,
+        "close_date": ANY,
+        "close_rate": ANY,
+        "stake_amount": 0.0009999999999054,
+        "sub_trade": False,
+        "cumulative_profit": 0.0,
+        "is_final_exit": False,
+        "final_profit_ratio": None,
+    } == msg
+
+
+async def test_forceexit_handle_invalid(default_conf, update, mocker) -> None:
+    mocker.patch(
+        "freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price", return_value=15000.0
+    )
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    # Trader is not running
+    freqtradebot.state = State.STOPPED
+    # /forceexit 1
+    context = MagicMock()
+    context.args = ["1"]
+    await telegram._force_exit(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "not running" in msg_mock.call_args_list[0][0][0]
+
+    # Invalid argument
+    msg_mock.reset_mock()
+    freqtradebot.state = State.RUNNING
+    # /forceexit 123456
+    context = MagicMock()
+    context.args = ["123456"]
+    await telegram._force_exit(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "invalid argument" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_force_exit_no_pair(default_conf, update, ticker, fee, mocker) -> None:
+    default_conf["max_open_trades"] = 4
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+        _dry_is_price_crossed=MagicMock(return_value=True),
+    )
+    femock = mocker.patch("freqtrade.rpc.rpc.RPC._rpc_force_exit")
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    patch_get_signal(freqtradebot)
+
+    # /forceexit
+    context = MagicMock()
+    context.args = []
+    await telegram._force_exit(update=update, context=context)
+    # No pair
+    assert msg_mock.call_args_list[0][1]["msg"] == "No open trade found."
+
+    # Create some test data
+    freqtradebot.enter_positions()
+    msg_mock.reset_mock()
+
+    # /forceexit
+    await telegram._force_exit(update=update, context=context)
+    keyboard = msg_mock.call_args_list[0][1]["keyboard"]
+    # 4 pairs + cancel
+    assert reduce(lambda acc, x: acc + len(x), keyboard, 0) == 5
+    assert keyboard[-1][0].text == "Cancel"
+
+    assert keyboard[1][0].callback_data == "force_exit__2 "
+    update = MagicMock()
+    update.callback_query = AsyncMock()
+    update.callback_query.data = keyboard[1][0].callback_data
+    await telegram._force_exit_inline(update, None)
+    assert update.callback_query.answer.call_count == 1
+    assert update.callback_query.edit_message_text.call_count == 1
+    assert femock.call_count == 1
+    assert femock.call_args_list[0][0][0] == "2"
+
+    # Retry exiting - but cancel instead
+    update.callback_query.reset_mock()
+    await telegram._force_exit(update=update, context=context)
+    # Use cancel button
+    update.callback_query.data = keyboard[-1][0].callback_data
+    await telegram._force_exit_inline(update, None)
+    query = update.callback_query
+    assert query.answer.call_count == 1
+    assert query.edit_message_text.call_count == 1
+    assert query.edit_message_text.call_args_list[-1][1]["text"] == "Force exit canceled."
+
+
+async def test_force_enter_handle(default_conf, update, mocker) -> None:
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=15000.0)
+
+    fbuy_mock = MagicMock(return_value=None)
+    mocker.patch("freqtrade.rpc.rpc.RPC._rpc_force_entry", fbuy_mock)
+
+    telegram, freqtradebot, _ = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    # /forcelong ETH/BTC
+    context = MagicMock()
+    context.args = ["ETH/BTC"]
+    await telegram._force_enter(update=update, context=context, order_side=SignalDirection.LONG)
+
+    assert fbuy_mock.call_count == 1
+    assert fbuy_mock.call_args_list[0][0][0] == "ETH/BTC"
+    assert fbuy_mock.call_args_list[0][0][1] is None
+    assert fbuy_mock.call_args_list[0][1]["order_side"] == SignalDirection.LONG
+
+    # Reset and retry with specified price
+    fbuy_mock = MagicMock(return_value=None)
+    mocker.patch("freqtrade.rpc.rpc.RPC._rpc_force_entry", fbuy_mock)
+    # /forcelong ETH/BTC 0.055
+    context = MagicMock()
+    context.args = ["ETH/BTC", "0.055"]
+    await telegram._force_enter(update=update, context=context, order_side=SignalDirection.LONG)
+
+    assert fbuy_mock.call_count == 1
+    assert fbuy_mock.call_args_list[0][0][0] == "ETH/BTC"
+    assert isinstance(fbuy_mock.call_args_list[0][0][1], float)
+    assert fbuy_mock.call_args_list[0][0][1] == 0.055
+
+
+async def test_force_enter_handle_exception(default_conf, update, mocker) -> None:
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=15000.0)
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    await telegram._force_enter(update=update, context=MagicMock(), order_side=SignalDirection.LONG)
+
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args_list[0][0][0] == "Force_entry not enabled."
+
+
+async def test_force_enter_no_pair(default_conf, update, mocker) -> None:
+    mocker.patch("freqtrade.rpc.rpc.CryptoToFiatConverter._find_price", return_value=15000.0)
+
+    fbuy_mock = MagicMock(return_value=None)
+    mocker.patch("freqtrade.rpc.rpc.RPC._rpc_force_entry", fbuy_mock)
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    patch_get_signal(freqtradebot)
+
+    context = MagicMock()
+    context.args = []
+    await telegram._force_enter(update=update, context=context, order_side=SignalDirection.LONG)
+
+    assert fbuy_mock.call_count == 0
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args_list[0][1]["msg"] == "Which pair?"
+    # assert msg_mock.call_args_list[0][1]['callback_query_handler'] == 'forcebuy'
+    keyboard = msg_mock.call_args_list[0][1]["keyboard"]
+    # One additional button - cancel
+    assert reduce(lambda acc, x: acc + len(x), keyboard, 0) == 5
+    update = MagicMock()
+    update.callback_query = AsyncMock()
+    update.callback_query.data = "force_enter__XRP/USDT_||_long"
+    await telegram._force_enter_inline(update, None)
+    assert fbuy_mock.call_count == 1
+
+    fbuy_mock.reset_mock()
+    update.callback_query = AsyncMock()
+    update.callback_query.data = "force_enter__cancel"
+    await telegram._force_enter_inline(update, None)
+    assert fbuy_mock.call_count == 0
+    query = update.callback_query
+    assert query.edit_message_text.call_count == 1
+    assert query.edit_message_text.call_args_list[-1][1]["text"] == "Force enter canceled."
+
+
+async def test_telegram_performance_handle(default_conf_usdt, update, ticker, fee, mocker) -> None:
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+    telegram, _freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt)
+
+    # Create some test data
+    create_mock_trades_usdt(fee)
+
+    await telegram._performance(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "Performance" in msg_mock.call_args_list[0][0][0]
+    assert "<code>XRP/USDT\t2.842 USDT (9.47%) (1)</code>" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_telegram_entry_tag_performance_handle(
+    default_conf_usdt, update, ticker, fee, mocker
+) -> None:
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt)
+    patch_get_signal(freqtradebot)
+
+    create_mock_trades_usdt(fee)
+
+    context = MagicMock()
+    await telegram._enter_tag_performance(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Entry Tag Performance" in msg_mock.call_args_list[0][0][0]
+    assert "`TEST1\t3.987 USDT (1.99%) (1)`" in msg_mock.call_args_list[0][0][0]
+
+    context.args = ["XRP/USDT"]
+    await telegram._enter_tag_performance(update=update, context=context)
+    assert msg_mock.call_count == 2
+
+    msg_mock.reset_mock()
+    mocker.patch(
+        "freqtrade.rpc.rpc.RPC._rpc_enter_tag_performance", side_effect=RPCException("Error")
+    )
+    await telegram._enter_tag_performance(update=update, context=MagicMock())
+
+    assert msg_mock.call_count == 1
+    assert "Error" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_telegram_exit_reason_performance_handle(
+    default_conf_usdt, update, ticker, fee, mocker
+) -> None:
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt)
+    patch_get_signal(freqtradebot)
+
+    create_mock_trades_usdt(fee)
+
+    context = MagicMock()
+    await telegram._exit_reason_performance(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Exit Reason Performance" in msg_mock.call_args_list[0][0][0]
+    assert "`roi\t2.842 USDT (9.47%) (1)`" in msg_mock.call_args_list[0][0][0]
+    context.args = ["XRP/USDT"]
+
+    await telegram._exit_reason_performance(update=update, context=context)
+    assert msg_mock.call_count == 2
+
+    msg_mock.reset_mock()
+    mocker.patch(
+        "freqtrade.rpc.rpc.RPC._rpc_exit_reason_performance", side_effect=RPCException("Error")
+    )
+    await telegram._exit_reason_performance(update=update, context=MagicMock())
+
+    assert msg_mock.call_count == 1
+    assert "Error" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_telegram_mix_tag_performance_handle(
+    default_conf_usdt, update, ticker, fee, mocker
+) -> None:
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt)
+    patch_get_signal(freqtradebot)
+
+    # Create some test data
+    create_mock_trades_usdt(fee)
+
+    context = MagicMock()
+    await telegram._mix_tag_performance(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Mix Tag Performance" in msg_mock.call_args_list[0][0][0]
+    assert "`TEST3 roi\t2.842 USDT (10.00%) (1)`" in msg_mock.call_args_list[0][0][0]
+
+    context.args = ["XRP/USDT"]
+    await telegram._mix_tag_performance(update=update, context=context)
+    assert msg_mock.call_count == 2
+
+    msg_mock.reset_mock()
+    mocker.patch(
+        "freqtrade.rpc.rpc.RPC._rpc_mix_tag_performance", side_effect=RPCException("Error")
+    )
+    await telegram._mix_tag_performance(update=update, context=MagicMock())
+
+    assert msg_mock.call_count == 1
+    assert "Error" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_count_handle(default_conf, update, ticker, fee, mocker) -> None:
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+
+    freqtradebot.state = State.STOPPED
+    await telegram._count(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "not running" in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+    freqtradebot.state = State.RUNNING
+
+    # Create some test data
+    freqtradebot.enter_positions()
+    msg_mock.reset_mock()
+    await telegram._count(update=update, context=MagicMock())
+
+    msg = (
+        "<pre>  current    max    total stake\n---------  -----  -------------\n"
+        "        1      {}          {}</pre>"
+    ).format(default_conf["max_open_trades"], default_conf["stake_amount"])
+    assert msg in msg_mock.call_args_list[0][0][0]
+
+
+async def test_telegram_lock_handle(default_conf, update, ticker, fee, mocker) -> None:
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    patch_get_signal(freqtradebot)
+    await telegram._locks(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "No active locks." in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+
+    PairLocks.lock_pair("ETH/BTC", dt_now() + timedelta(minutes=4), "randreason")
+    PairLocks.lock_pair("XRP/BTC", dt_now() + timedelta(minutes=20), "deadbeef")
+
+    await telegram._locks(update=update, context=MagicMock())
+
+    assert "Pair" in msg_mock.call_args_list[0][0][0]
+    assert "Until" in msg_mock.call_args_list[0][0][0]
+    assert "Reason\n" in msg_mock.call_args_list[0][0][0]
+    assert "ETH/BTC" in msg_mock.call_args_list[0][0][0]
+    assert "XRP/BTC" in msg_mock.call_args_list[0][0][0]
+    assert "deadbeef" in msg_mock.call_args_list[0][0][0]
+    assert "randreason" in msg_mock.call_args_list[0][0][0]
+
+    context = MagicMock()
+    context.args = ["XRP/BTC"]
+    msg_mock.reset_mock()
+    await telegram._delete_locks(update=update, context=context)
+
+    assert "ETH/BTC" in msg_mock.call_args_list[0][0][0]
+    assert "randreason" in msg_mock.call_args_list[0][0][0]
+    assert "XRP/BTC" not in msg_mock.call_args_list[0][0][0]
+    assert "deadbeef" not in msg_mock.call_args_list[0][0][0]
+
+
+async def test_whitelist_static(default_conf, update, mocker) -> None:
+    telegram, _freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    await telegram._whitelist(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert (
+        "Using whitelist `['StaticPairList']` with 4 pairs\n"
+        "`ETH/BTC, LTC/BTC, XRP/BTC, NEO/BTC`" in msg_mock.call_args_list[0][0][0]
+    )
+
+    context = MagicMock()
+    context.args = ["sorted"]
+    msg_mock.reset_mock()
+    await telegram._whitelist(update=update, context=context)
+    assert (
+        "Using whitelist `['StaticPairList']` with 4 pairs\n"
+        "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in msg_mock.call_args_list[0][0][0]
+    )
+
+    context = MagicMock()
+    context.args = ["baseonly"]
+    msg_mock.reset_mock()
+    await telegram._whitelist(update=update, context=context)
+    assert (
+        "Using whitelist `['StaticPairList']` with 4 pairs\n"
+        "`ETH, LTC, XRP, NEO`" in msg_mock.call_args_list[0][0][0]
+    )
+
+    context = MagicMock()
+    context.args = ["baseonly", "sorted"]
+    msg_mock.reset_mock()
+    await telegram._whitelist(update=update, context=context)
+    assert (
+        "Using whitelist `['StaticPairList']` with 4 pairs\n"
+        "`ETH, LTC, NEO, XRP`" in msg_mock.call_args_list[0][0][0]
+    )
+
+
+async def test_whitelist_dynamic(default_conf, update, mocker) -> None:
+    mocker.patch(f"{EXMS}.exchange_has", return_value=True)
+    default_conf["pairlists"] = [{"method": "VolumePairList", "number_assets": 4}]
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    await telegram._whitelist(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert (
+        "Using whitelist `['VolumePairList']` with 4 pairs\n"
+        "`ETH/BTC, LTC/BTC, XRP/BTC, NEO/BTC`" in msg_mock.call_args_list[0][0][0]
+    )
+
+    context = MagicMock()
+    context.args = ["sorted"]
+    msg_mock.reset_mock()
+    await telegram._whitelist(update=update, context=context)
+    assert (
+        "Using whitelist `['VolumePairList']` with 4 pairs\n"
+        "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in msg_mock.call_args_list[0][0][0]
+    )
+
+    context = MagicMock()
+    context.args = ["baseonly"]
+    msg_mock.reset_mock()
+    await telegram._whitelist(update=update, context=context)
+    assert (
+        "Using whitelist `['VolumePairList']` with 4 pairs\n"
+        "`ETH, LTC, XRP, NEO`" in msg_mock.call_args_list[0][0][0]
+    )
+
+    context = MagicMock()
+    context.args = ["baseonly", "sorted"]
+    msg_mock.reset_mock()
+    await telegram._whitelist(update=update, context=context)
+    assert (
+        "Using whitelist `['VolumePairList']` with 4 pairs\n"
+        "`ETH, LTC, NEO, XRP`" in msg_mock.call_args_list[0][0][0]
+    )
+
+
+async def test_blacklist_static(default_conf, update, mocker) -> None:
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    await telegram._blacklist(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "Blacklist contains 2 pairs\n`DOGE/BTC, HOT/BTC`" in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+
+    # /blacklist ETH/BTC
+    context = MagicMock()
+    context.args = ["ETH/BTC"]
+    await telegram._blacklist(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert (
+        "Blacklist contains 3 pairs\n`DOGE/BTC, HOT/BTC, ETH/BTC`"
+        in msg_mock.call_args_list[0][0][0]
+    )
+    assert freqtradebot.pairlists.blacklist == ["DOGE/BTC", "HOT/BTC", "ETH/BTC"]
+
+    msg_mock.reset_mock()
+    context = MagicMock()
+    context.args = ["XRP/.*"]
+    await telegram._blacklist(update=update, context=context)
+    assert msg_mock.call_count == 1
+
+    assert (
+        "Blacklist contains 4 pairs\n`DOGE/BTC, HOT/BTC, ETH/BTC, XRP/.*`"
+        in msg_mock.call_args_list[0][0][0]
+    )
+    assert freqtradebot.pairlists.blacklist == ["DOGE/BTC", "HOT/BTC", "ETH/BTC", "XRP/.*"]
+
+    msg_mock.reset_mock()
+    context.args = ["DOGE/BTC"]
+    await telegram._blacklist_delete(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert (
+        "Blacklist contains 3 pairs\n`HOT/BTC, ETH/BTC, XRP/.*`" in msg_mock.call_args_list[0][0][0]
+    )
+
+
+async def test_telegram_logs(default_conf, update, mocker) -> None:
+    mocker.patch.multiple(
+        "freqtrade.rpc.telegram.Telegram",
+        _init=MagicMock(),
+    )
+    setup_logging(default_conf)
+
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    context = MagicMock()
+    context.args = []
+    await telegram._logs(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "freqtrade\\.rpc\\.telegram" in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+    context.args = ["1"]
+    await telegram._logs(update=update, context=context)
+    assert msg_mock.call_count == 1
+
+    msg_mock.reset_mock()
+    # Test with changed MaxMessageLength
+    mocker.patch("freqtrade.rpc.telegram.MAX_MESSAGE_LENGTH", 200)
+    context = MagicMock()
+    context.args = []
+    await telegram._logs(update=update, context=context)
+    # Called at least 2 times. Exact times will change with unrelated changes to setup messages
+    # Therefore we don't test for this explicitly.
+    assert msg_mock.call_count >= 2
+
+
+async def test_edge_disabled(default_conf, update, mocker) -> None:
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    await telegram._edge(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "Edge is not enabled." in msg_mock.call_args_list[0][0][0]
+
+
+async def test_edge_enabled(edge_conf, update, mocker) -> None:
+    mocker.patch(
+        "freqtrade.edge.Edge._cached_pairs",
+        mocker.PropertyMock(
+            return_value={
+                "E/F": PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60),
+            }
+        ),
+    )
+
+    telegram, _, msg_mock = get_telegram_testobject(mocker, edge_conf)
+
+    await telegram._edge(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "<b>Edge only validated following pairs:</b>\n<pre>" in msg_mock.call_args_list[0][0][0]
+    assert "Pair      Winrate    Expectancy    Stoploss" in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+
+    mocker.patch("freqtrade.edge.Edge._cached_pairs", mocker.PropertyMock(return_value={}))
+    await telegram._edge(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "<b>Edge only validated following pairs:</b>" in msg_mock.call_args_list[0][0][0]
+    assert "Winrate" not in msg_mock.call_args_list[0][0][0]
+
+
+@pytest.mark.parametrize(
+    "is_short,regex_pattern",
+    [(True, r"now[ ]*XRP\/BTC \(#3\)  -1.00% \("), (False, r"now[ ]*XRP\/BTC \(#3\)  1.00% \(")],
+)
+async def test_telegram_trades(mocker, update, default_conf, fee, is_short, regex_pattern):
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    context = MagicMock()
+    context.args = []
+
+    await telegram._trades(update=update, context=context)
+    assert "<b>0 recent trades</b>:" in msg_mock.call_args_list[0][0][0]
+    assert "<pre>" not in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    context.args = ["hello"]
+    await telegram._trades(update=update, context=context)
+    assert "<b>0 recent trades</b>:" in msg_mock.call_args_list[0][0][0]
+    assert "<pre>" not in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    create_mock_trades(fee, is_short=is_short)
+
+    context = MagicMock()
+    context.args = [5]
+    await telegram._trades(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "2 recent trades</b>:" in msg_mock.call_args_list[0][0][0]
+    assert "Profit (" in msg_mock.call_args_list[0][0][0]
+    assert "Close Date" in msg_mock.call_args_list[0][0][0]
+    assert "<pre>" in msg_mock.call_args_list[0][0][0]
+    assert bool(re.search(regex_pattern, msg_mock.call_args_list[0][0][0]))
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+async def test_telegram_delete_trade(mocker, update, default_conf, fee, is_short):
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    context = MagicMock()
+    context.args = []
+
+    await telegram._delete_trade(update=update, context=context)
+    assert "Trade-id not set." in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+    create_mock_trades(fee, is_short=is_short)
+
+    context = MagicMock()
+    context.args = [1]
+    await telegram._delete_trade(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Deleted trade 1." in msg_mock.call_args_list[0][0][0]
+    assert "Please make sure to take care of this asset" in msg_mock.call_args_list[0][0][0]
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+async def test_telegram_reload_trade_from_exchange(mocker, update, default_conf, fee, is_short):
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    context = MagicMock()
+    context.args = []
+
+    await telegram._reload_trade_from_exchange(update=update, context=context)
+    assert "Trade-id not set." in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+    create_mock_trades(fee, is_short=is_short)
+
+    context.args = [5]
+
+    await telegram._reload_trade_from_exchange(update=update, context=context)
+    assert "Status: `Reloaded from orders from exchange`" in msg_mock.call_args_list[0][0][0]
+
+
+@pytest.mark.parametrize("is_short", [True, False])
+async def test_telegram_delete_open_order(mocker, update, default_conf, fee, is_short, ticker):
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+    )
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    context = MagicMock()
+    context.args = []
+
+    await telegram._cancel_open_order(update=update, context=context)
+    assert "Trade-id not set." in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+    create_mock_trades(fee, is_short=is_short)
+
+    context = MagicMock()
+    context.args = [5]
+    await telegram._cancel_open_order(update=update, context=context)
+    assert "No open order for trade_id" in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+
+    trade = Trade.get_trades([Trade.id == 6]).first()
+    mocker.patch(f"{EXMS}.fetch_order", return_value=trade.orders[-1].to_ccxt_object())
+    context = MagicMock()
+    context.args = [6]
+    await telegram._cancel_open_order(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Open order canceled." in msg_mock.call_args_list[0][0][0]
+
+
+async def test_help_handle(default_conf, update, mocker) -> None:
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    await telegram._help(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "*/help:* `This help message`" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_version_handle(default_conf, update, mocker) -> None:
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    await telegram._version(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert f"*Version:* `{__version__}`" in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+    freqtradebot.strategy.version = lambda: "1.1.1"
+
+    await telegram._version(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert f"*Version:* `{__version__}`" in msg_mock.call_args_list[0][0][0]
+    assert "*Strategy version: * `1.1.1`" in msg_mock.call_args_list[0][0][0]
+
+
+async def test_show_config_handle(default_conf, update, mocker) -> None:
+    default_conf["runmode"] = RunMode.DRY_RUN
+
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    await telegram._show_config(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "*Mode:* `{}`".format("Dry-run") in msg_mock.call_args_list[0][0][0]
+    assert "*Exchange:* `binance`" in msg_mock.call_args_list[0][0][0]
+    assert f"*Strategy:* `{CURRENT_TEST_STRATEGY}`" in msg_mock.call_args_list[0][0][0]
+    assert "*Stoploss:* `-0.1`" in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+    freqtradebot.config["trailing_stop"] = True
+    await telegram._show_config(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert "*Mode:* `{}`".format("Dry-run") in msg_mock.call_args_list[0][0][0]
+    assert "*Exchange:* `binance`" in msg_mock.call_args_list[0][0][0]
+    assert f"*Strategy:* `{CURRENT_TEST_STRATEGY}`" in msg_mock.call_args_list[0][0][0]
+    assert "*Initial Stoploss:* `-0.1`" in msg_mock.call_args_list[0][0][0]
+
+
+@pytest.mark.parametrize(
+    "message_type,enter,enter_signal,leverage",
+    [
+        (RPCMessageType.ENTRY, "Long", "long_signal_01", None),
+        (RPCMessageType.ENTRY, "Long", "long_signal_01", 1.0),
+        (RPCMessageType.ENTRY, "Long", "long_signal_01", 5.0),
+        (RPCMessageType.ENTRY, "Short", "short_signal_01", 2.0),
+    ],
+)
+def test_send_msg_enter_notification(
+    default_conf, mocker, caplog, message_type, enter, enter_signal, leverage
+) -> None:
+    default_conf["telegram"]["notification_settings"]["show_candle"] = "ohlc"
+    df = DataFrame(
+        {
+            "open": [1.1],
+            "high": [2.2],
+            "low": [1.0],
+            "close": [1.5],
+        }
+    )
+    mocker.patch(
+        "freqtrade.data.dataprovider.DataProvider.get_analyzed_dataframe", return_value=(df, 1)
+    )
+
+    msg = {
+        "type": message_type,
+        "trade_id": 1,
+        "enter_tag": enter_signal,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "leverage": leverage,
+        "open_rate": 1.099e-05,
+        "order_type": "limit",
+        "direction": enter,
+        "stake_amount": 0.01465333,
+        "stake_amount_fiat": 0.0,
+        "stake_currency": "BTC",
+        "quote_currency": "BTC",
+        "base_currency": "ETH",
+        "fiat_currency": "USD",
+        "sub_trade": False,
+        "current_rate": 1.099e-05,
+        "amount": 1333.3333333333335,
+        "analyzed_candle": {"open": 1.1, "high": 2.2, "low": 1.0, "close": 1.5},
+        "open_date": dt_now() + timedelta(hours=-1),
+    }
+    telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    telegram.send_msg(msg)
+    leverage_text = f" ({leverage:.3g}x)" if leverage and leverage != 1.0 else ""
+
+    assert msg_mock.call_args[0][0] == (
+        f"\N{LARGE BLUE CIRCLE} *Binance (dry):* New Trade (#1)\n"
+        f"*Pair:* `ETH/BTC`\n"
+        "*Candle OHLC*: `1.1, 2.2, 1.0, 1.5`\n"
+        f"*Enter Tag:* `{enter_signal}`\n"
+        "*Amount:* `1333.33333333`\n"
+        f"*Direction:* `{enter}"
+        f"{leverage_text}`\n"
+        "*Open Rate:* `0.00001099 BTC`\n"
+        "*Current Rate:* `0.00001099 BTC`\n"
+        "*Total:* `0.01465333 BTC / 180.895 USD`"
+    )
+
+    freqtradebot.config["telegram"]["notification_settings"] = {"entry": "off"}
+    caplog.clear()
+    msg_mock.reset_mock()
+    telegram.send_msg(msg)
+    assert msg_mock.call_count == 0
+    assert log_has("Notification 'entry' not sent.", caplog)
+
+    freqtradebot.config["telegram"]["notification_settings"] = {"entry": "silent"}
+    caplog.clear()
+    msg_mock.reset_mock()
+
+    telegram.send_msg(msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args_list[0][1]["disable_notification"] is True
+
+
+@pytest.mark.parametrize(
+    "message_type,enter_signal",
+    [
+        (RPCMessageType.ENTRY_CANCEL, "long_signal_01"),
+        (RPCMessageType.ENTRY_CANCEL, "short_signal_01"),
+    ],
+)
+def test_send_msg_enter_cancel_notification(
+    default_conf, mocker, message_type, enter_signal
+) -> None:
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    telegram.send_msg(
+        {
+            "type": message_type,
+            "enter_tag": enter_signal,
+            "trade_id": 1,
+            "exchange": "Binance",
+            "pair": "ETH/BTC",
+            "reason": CANCEL_REASON["TIMEOUT"],
+        }
+    )
+    assert (
+        msg_mock.call_args[0][0] == "\N{WARNING SIGN} *Binance (dry):* "
+        "Cancelling enter Order for ETH/BTC (#1). "
+        "Reason: cancelled due to timeout."
+    )
+
+
+def test_send_msg_protection_notification(default_conf, mocker, time_machine) -> None:
+    default_conf["telegram"]["notification_settings"]["protection_trigger"] = "on"
+
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    time_machine.move_to("2021-09-01 05:00:00 +00:00")
+    lock = PairLocks.lock_pair("ETH/BTC", dt_now() + timedelta(minutes=6), "randreason")
+    msg = {
+        "type": RPCMessageType.PROTECTION_TRIGGER,
+    }
+    msg.update(lock.to_json())
+    telegram.send_msg(msg)
+    assert (
+        msg_mock.call_args[0][0] == "*Protection* triggered due to randreason. "
+        "`ETH/BTC` will be locked until `2021-09-01 05:10:00`."
+    )
+
+    msg_mock.reset_mock()
+    # Test global protection
+
+    msg = {
+        "type": RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
+    }
+    lock = PairLocks.lock_pair("*", dt_now() + timedelta(minutes=100), "randreason")
+    msg.update(lock.to_json())
+    telegram.send_msg(msg)
+    assert (
+        msg_mock.call_args[0][0] == "*Protection* triggered due to randreason. "
+        "*All pairs* will be locked until `2021-09-01 06:45:00`."
+    )
+
+
+@pytest.mark.parametrize(
+    "message_type,entered,enter_signal,leverage",
+    [
+        (RPCMessageType.ENTRY_FILL, "Long", "long_signal_01", 1.0),
+        (RPCMessageType.ENTRY_FILL, "Long", "long_signal_02", 2.0),
+        (RPCMessageType.ENTRY_FILL, "Short", "short_signal_01", 2.0),
+    ],
+)
+def test_send_msg_entry_fill_notification(
+    default_conf, mocker, message_type, entered, enter_signal, leverage
+) -> None:
+    default_conf["telegram"]["notification_settings"]["entry_fill"] = "on"
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    telegram.send_msg(
+        {
+            "type": message_type,
+            "trade_id": 1,
+            "enter_tag": enter_signal,
+            "exchange": "Binance",
+            "pair": "ETH/BTC",
+            "leverage": leverage,
+            "stake_amount": 0.01465333,
+            "direction": entered,
+            "sub_trade": False,
+            "stake_currency": "BTC",
+            "quote_currency": "BTC",
+            "base_currency": "ETH",
+            "fiat_currency": "USD",
+            "open_rate": 1.099e-05,
+            "amount": 1333.3333333333335,
+            "open_date": dt_now() - timedelta(hours=1),
+        }
+    )
+    leverage_text = f" ({leverage:.3g}x)" if leverage != 1.0 else ""
+    assert msg_mock.call_args[0][0] == (
+        f"\N{CHECK MARK} *Binance (dry):* New Trade filled (#1)\n"
+        f"*Pair:* `ETH/BTC`\n"
+        f"*Enter Tag:* `{enter_signal}`\n"
+        "*Amount:* `1333.33333333`\n"
+        f"*Direction:* `{entered}"
+        f"{leverage_text}`\n"
+        "*Open Rate:* `0.00001099 BTC`\n"
+        "*Total:* `0.01465333 BTC / 180.895 USD`"
+    )
+
+    msg_mock.reset_mock()
+    telegram.send_msg(
+        {
+            "type": message_type,
+            "trade_id": 1,
+            "enter_tag": enter_signal,
+            "exchange": "Binance",
+            "pair": "ETH/BTC",
+            "leverage": leverage,
+            "stake_amount": 0.01465333,
+            "sub_trade": True,
+            "direction": entered,
+            "stake_currency": "BTC",
+            "quote_currency": "BTC",
+            "base_currency": "ETH",
+            "fiat_currency": "USD",
+            "open_rate": 1.099e-05,
+            "amount": 1333.3333333333335,
+            "open_date": dt_now() - timedelta(hours=1),
+        }
+    )
+
+    assert msg_mock.call_args[0][0] == (
+        f"\N{CHECK MARK} *Binance (dry):* Position increase filled (#1)\n"
+        f"*Pair:* `ETH/BTC`\n"
+        f"*Enter Tag:* `{enter_signal}`\n"
+        "*Amount:* `1333.33333333`\n"
+        f"*Direction:* `{entered}"
+        f"{leverage_text}`\n"
+        "*Open Rate:* `0.00001099 BTC`\n"
+        "*New Total:* `0.01465333 BTC / 180.895 USD`"
+    )
+
+
+def test_send_msg_exit_notification(default_conf, mocker) -> None:
+    with time_machine.travel("2022-09-01 05:00:00 +00:00", tick=False):
+        telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+        old_convamount = telegram._rpc._fiat_converter.convert_amount
+        telegram._rpc._fiat_converter.convert_amount = lambda a, b, c: -24.812
+        telegram.send_msg(
+            {
+                "type": RPCMessageType.EXIT,
+                "trade_id": 1,
+                "exchange": "Binance",
+                "pair": "KEY/ETH",
+                "leverage": 1.0,
+                "direction": "Long",
+                "gain": "loss",
+                "order_rate": 3.201e-04,
+                "amount": 1333.3333333333335,
+                "order_type": "market",
+                "open_rate": 7.5e-04,
+                "current_rate": 3.201e-04,
+                "profit_amount": -0.05746268,
+                "profit_ratio": -0.57405275,
+                "stake_currency": "ETH",
+                "quote_currency": "ETH",
+                "base_currency": "KEY",
+                "fiat_currency": "USD",
+                "enter_tag": "buy_signal1",
+                "exit_reason": ExitType.STOP_LOSS.value,
+                "open_date": dt_now() - timedelta(hours=1),
+                "close_date": dt_now(),
+            }
+        )
+        assert msg_mock.call_args[0][0] == (
+            "\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n"
+            "*Unrealized Profit:* `-57.41% (loss: -0.05746 ETH / -24.812 USD)`\n"
+            "*Enter Tag:* `buy_signal1`\n"
+            "*Exit Reason:* `stop_loss`\n"
+            "*Direction:* `Long`\n"
+            "*Amount:* `1333.33333333`\n"
+            "*Open Rate:* `0.00075 ETH`\n"
+            "*Current Rate:* `0.0003201 ETH`\n"
+            "*Exit Rate:* `0.0003201 ETH`\n"
+            "*Duration:* `1:00:00 (60.0 min)`"
+        )
+
+        msg_mock.reset_mock()
+        telegram.send_msg(
+            {
+                "type": RPCMessageType.EXIT,
+                "trade_id": 1,
+                "exchange": "Binance",
+                "pair": "KEY/ETH",
+                "direction": "Long",
+                "gain": "loss",
+                "order_rate": 3.201e-04,
+                "amount": 1333.3333333333335,
+                "order_type": "market",
+                "open_rate": 7.5e-04,
+                "current_rate": 3.201e-04,
+                "cumulative_profit": -0.15746268,
+                "profit_amount": -0.05746268,
+                "profit_ratio": -0.57405275,
+                "stake_currency": "ETH",
+                "quote_currency": "ETH",
+                "base_currency": "KEY",
+                "fiat_currency": "USD",
+                "enter_tag": "buy_signal1",
+                "exit_reason": ExitType.STOP_LOSS.value,
+                "open_date": dt_now() - timedelta(days=1, hours=2, minutes=30),
+                "close_date": dt_now(),
+                "stake_amount": 0.01,
+                "sub_trade": True,
+            }
+        )
+        assert msg_mock.call_args[0][0] == (
+            "\N{WARNING SIGN} *Binance (dry):* Partially exiting KEY/ETH (#1)\n"
+            "*Unrealized Sub Profit:* `-57.41% (loss: -0.05746 ETH / -24.812 USD)`\n"
+            "*Cumulative Profit:* `-0.15746 ETH / -24.812 USD`\n"
+            "*Enter Tag:* `buy_signal1`\n"
+            "*Exit Reason:* `stop_loss`\n"
+            "*Direction:* `Long`\n"
+            "*Amount:* `1333.33333333`\n"
+            "*Open Rate:* `0.00075 ETH`\n"
+            "*Current Rate:* `0.0003201 ETH`\n"
+            "*Exit Rate:* `0.0003201 ETH`\n"
+            "*Remaining:* `0.01 ETH / -24.812 USD`"
+        )
+
+        msg_mock.reset_mock()
+        telegram.send_msg(
+            {
+                "type": RPCMessageType.EXIT,
+                "trade_id": 1,
+                "exchange": "Binance",
+                "pair": "KEY/ETH",
+                "direction": "Long",
+                "gain": "loss",
+                "order_rate": 3.201e-04,
+                "amount": 1333.3333333333335,
+                "order_type": "market",
+                "open_rate": 7.5e-04,
+                "current_rate": 3.201e-04,
+                "profit_amount": -0.05746268,
+                "profit_ratio": -0.57405275,
+                "stake_currency": "ETH",
+                "quote_currency": "ETH",
+                "base_currency": "KEY",
+                "fiat_currency": None,
+                "enter_tag": "buy_signal1",
+                "exit_reason": ExitType.STOP_LOSS.value,
+                "open_date": dt_now() - timedelta(days=1, hours=2, minutes=30),
+                "close_date": dt_now(),
+            }
+        )
+        assert msg_mock.call_args[0][0] == (
+            "\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n"
+            "*Unrealized Profit:* `-57.41% (loss: -0.05746 ETH)`\n"
+            "*Enter Tag:* `buy_signal1`\n"
+            "*Exit Reason:* `stop_loss`\n"
+            "*Direction:* `Long`\n"
+            "*Amount:* `1333.33333333`\n"
+            "*Open Rate:* `0.00075 ETH`\n"
+            "*Current Rate:* `0.0003201 ETH`\n"
+            "*Exit Rate:* `0.0003201 ETH`\n"
+            "*Duration:* `1 day, 2:30:00 (1590.0 min)`"
+        )
+        # Reset singleton function to avoid random breaks
+        telegram._rpc._fiat_converter.convert_amount = old_convamount
+
+
+async def test_send_msg_exit_cancel_notification(default_conf, mocker) -> None:
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    old_convamount = telegram._rpc._fiat_converter.convert_amount
+    telegram._rpc._fiat_converter.convert_amount = lambda a, b, c: -24.812
+    telegram.send_msg(
+        {
+            "type": RPCMessageType.EXIT_CANCEL,
+            "trade_id": 1,
+            "exchange": "Binance",
+            "pair": "KEY/ETH",
+            "reason": "Cancelled on exchange",
+        }
+    )
+    assert msg_mock.call_args[0][0] == (
+        "\N{WARNING SIGN} *Binance (dry):* Cancelling exit Order for KEY/ETH (#1)."
+        " Reason: Cancelled on exchange."
+    )
+
+    msg_mock.reset_mock()
+    # Test with live mode (no dry appendix)
+    telegram._config["dry_run"] = False
+    telegram.send_msg(
+        {
+            "type": RPCMessageType.EXIT_CANCEL,
+            "trade_id": 1,
+            "exchange": "Binance",
+            "pair": "KEY/ETH",
+            "reason": "timeout",
+        }
+    )
+    assert msg_mock.call_args[0][0] == (
+        "\N{WARNING SIGN} *Binance:* Cancelling exit Order for KEY/ETH (#1). Reason: timeout."
+    )
+    # Reset singleton function to avoid random breaks
+    telegram._rpc._fiat_converter.convert_amount = old_convamount
+
+
+@pytest.mark.parametrize(
+    "direction,enter_signal,leverage",
+    [
+        ("Long", "long_signal_01", None),
+        ("Long", "long_signal_01", 1.0),
+        ("Long", "long_signal_01", 5.0),
+        ("Short", "short_signal_01", 2.0),
+    ],
+)
+def test_send_msg_exit_fill_notification(
+    default_conf, mocker, direction, enter_signal, leverage
+) -> None:
+    default_conf["telegram"]["notification_settings"]["exit_fill"] = "on"
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    with time_machine.travel("2022-09-01 05:00:00 +00:00", tick=False):
+        telegram.send_msg(
+            {
+                "type": RPCMessageType.EXIT_FILL,
+                "trade_id": 1,
+                "exchange": "Binance",
+                "pair": "KEY/ETH",
+                "leverage": leverage,
+                "direction": direction,
+                "gain": "loss",
+                "limit": 3.201e-04,
+                "amount": 1333.3333333333335,
+                "order_type": "market",
+                "open_rate": 7.5e-04,
+                "close_rate": 3.201e-04,
+                "profit_amount": -0.05746268,
+                "profit_ratio": -0.57405275,
+                "stake_currency": "ETH",
+                "quote_currency": "ETH",
+                "base_currency": "KEY",
+                "fiat_currency": None,
+                "enter_tag": enter_signal,
+                "exit_reason": ExitType.STOP_LOSS.value,
+                "open_date": dt_now() - timedelta(days=1, hours=2, minutes=30),
+                "close_date": dt_now(),
+            }
+        )
+
+        leverage_text = f" ({leverage:.3g}x)`\n" if leverage and leverage != 1.0 else "`\n"
+        assert msg_mock.call_args[0][0] == (
+            "\N{WARNING SIGN} *Binance (dry):* Exited KEY/ETH (#1)\n"
+            "*Profit:* `-57.41% (loss: -0.05746 ETH)`\n"
+            f"*Enter Tag:* `{enter_signal}`\n"
+            "*Exit Reason:* `stop_loss`\n"
+            f"*Direction:* `{direction}"
+            f"{leverage_text}"
+            "*Amount:* `1333.33333333`\n"
+            "*Open Rate:* `0.00075 ETH`\n"
+            "*Exit Rate:* `0.0003201 ETH`\n"
+            "*Duration:* `1 day, 2:30:00 (1590.0 min)`"
+        )
+
+
+def test_send_msg_status_notification(default_conf, mocker) -> None:
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    telegram.send_msg({"type": RPCMessageType.STATUS, "status": "running"})
+    assert msg_mock.call_args[0][0] == "*Status:* `running`"
+
+
+async def test_warning_notification(default_conf, mocker) -> None:
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    telegram.send_msg({"type": RPCMessageType.WARNING, "status": "message"})
+    assert msg_mock.call_args[0][0] == "\N{WARNING SIGN} *Warning:* `message`"
+
+
+def test_startup_notification(default_conf, mocker) -> None:
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    telegram.send_msg({"type": RPCMessageType.STARTUP, "status": "*Custom:* `Hello World`"})
+    assert msg_mock.call_args[0][0] == "*Custom:* `Hello World`"
+
+
+def test_send_msg_strategy_msg_notification(default_conf, mocker) -> None:
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    telegram.send_msg({"type": RPCMessageType.STRATEGY_MSG, "msg": "hello world, Test msg"})
+    assert msg_mock.call_args[0][0] == "hello world, Test msg"
+
+
+def test_send_msg_unknown_type(default_conf, mocker) -> None:
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    telegram.send_msg(
+        {
+            "type": None,
+        }
+    )
+    assert msg_mock.call_count == 0
+
+
+@pytest.mark.parametrize(
+    "message_type,enter,enter_signal,leverage",
+    [
+        (RPCMessageType.ENTRY, "Long", "long_signal_01", None),
+        (RPCMessageType.ENTRY, "Long", "long_signal_01", 2.0),
+        (RPCMessageType.ENTRY, "Short", "short_signal_01", 2.0),
+    ],
+)
+def test_send_msg_buy_notification_no_fiat(
+    default_conf, mocker, message_type, enter, enter_signal, leverage
+) -> None:
+    del default_conf["fiat_display_currency"]
+    default_conf["dry_run"] = False
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    telegram.send_msg(
+        {
+            "type": message_type,
+            "enter_tag": enter_signal,
+            "trade_id": 1,
+            "exchange": "Binance",
+            "pair": "ETH/BTC",
+            "leverage": leverage,
+            "open_rate": 1.099e-05,
+            "order_type": "limit",
+            "direction": enter,
+            "sub_trade": False,
+            "stake_amount": 0.01465333,
+            "stake_amount_fiat": 0.0,
+            "stake_currency": "BTC",
+            "quote_currency": "BTC",
+            "base_currency": "ETH",
+            "fiat_currency": None,
+            "current_rate": 1.099e-05,
+            "amount": 1333.3333333333335,
+            "open_date": dt_now() - timedelta(hours=1),
+        }
+    )
+
+    leverage_text = f" ({leverage:.3g}x)" if leverage and leverage != 1.0 else ""
+    assert msg_mock.call_args[0][0] == (
+        f"\N{LARGE BLUE CIRCLE} *Binance:* New Trade (#1)\n"
+        "*Pair:* `ETH/BTC`\n"
+        f"*Enter Tag:* `{enter_signal}`\n"
+        "*Amount:* `1333.33333333`\n"
+        f"*Direction:* `{enter}"
+        f"{leverage_text}`\n"
+        "*Open Rate:* `0.00001099 BTC`\n"
+        "*Current Rate:* `0.00001099 BTC`\n"
+        "*Total:* `0.01465333 BTC`"
+    )
+
+
+@pytest.mark.parametrize(
+    "direction,enter_signal,leverage",
+    [
+        ("Long", "long_signal_01", None),
+        ("Long", "long_signal_01", 1.0),
+        ("Long", "long_signal_01", 5.0),
+        ("Short", "short_signal_01", 2.0),
+    ],
+)
+@pytest.mark.parametrize("fiat", ["", None])
+def test_send_msg_exit_notification_no_fiat(
+    default_conf, mocker, direction, enter_signal, leverage, time_machine, fiat
+) -> None:
+    if fiat is None:
+        del default_conf["fiat_display_currency"]
+    else:
+        default_conf["fiat_display_currency"] = fiat
+    time_machine.move_to("2022-05-02 00:00:00 +00:00", tick=False)
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+
+    telegram.send_msg(
+        {
+            "type": RPCMessageType.EXIT,
+            "trade_id": 1,
+            "exchange": "Binance",
+            "pair": "KEY/ETH",
+            "gain": "loss",
+            "leverage": leverage,
+            "direction": direction,
+            "sub_trade": False,
+            "order_rate": 3.201e-04,
+            "amount": 1333.3333333333335,
+            "order_type": "limit",
+            "open_rate": 7.5e-04,
+            "current_rate": 3.201e-04,
+            "profit_amount": -0.05746268,
+            "profit_ratio": -0.57405275,
+            "stake_currency": "ETH",
+            "quote_currency": "ETH",
+            "base_currency": "KEY",
+            "fiat_currency": "USD",
+            "enter_tag": enter_signal,
+            "exit_reason": ExitType.STOP_LOSS.value,
+            "open_date": dt_now() - timedelta(hours=2, minutes=35, seconds=3),
+            "close_date": dt_now(),
+        }
+    )
+
+    leverage_text = f" ({leverage:.3g}x)" if leverage and leverage != 1.0 else ""
+    assert msg_mock.call_args[0][0] == (
+        "\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n"
+        "*Unrealized Profit:* `-57.41% (loss: -0.05746 ETH)`\n"
+        f"*Enter Tag:* `{enter_signal}`\n"
+        "*Exit Reason:* `stop_loss`\n"
+        f"*Direction:* `{direction}"
+        f"{leverage_text}`\n"
+        "*Amount:* `1333.33333333`\n"
+        "*Open Rate:* `0.00075 ETH`\n"
+        "*Current Rate:* `0.0003201 ETH`\n"
+        "*Exit Rate:* `0.0003201 ETH`\n"
+        "*Duration:* `2:35:03 (155.1 min)`"
+    )
+
+
+@pytest.mark.parametrize(
+    "msg,expected",
+    [
+        ({"profit_ratio": 0.201, "exit_reason": "roi"}, "\N{ROCKET}"),
+        ({"profit_ratio": 0.051, "exit_reason": "roi"}, "\N{ROCKET}"),
+        ({"profit_ratio": 0.0256, "exit_reason": "roi"}, "\N{EIGHT SPOKED ASTERISK}"),
+        ({"profit_ratio": 0.01, "exit_reason": "roi"}, "\N{EIGHT SPOKED ASTERISK}"),
+        ({"profit_ratio": 0.0, "exit_reason": "roi"}, "\N{EIGHT SPOKED ASTERISK}"),
+        ({"profit_ratio": -0.05, "exit_reason": "stop_loss"}, "\N{WARNING SIGN}"),
+        ({"profit_ratio": -0.02, "exit_reason": "sell_signal"}, "\N{CROSS MARK}"),
+    ],
+)
+def test__exit_emoji(default_conf, mocker, msg, expected):
+    del default_conf["fiat_display_currency"]
+
+    telegram, _, _ = get_telegram_testobject(mocker, default_conf)
+
+    assert telegram._get_exit_emoji(msg) == expected
+
+
+async def test_telegram__send_msg(default_conf, mocker, caplog) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+    bot = MagicMock()
+    bot.send_message = AsyncMock()
+    bot.edit_message_text = AsyncMock()
+    telegram, _, _ = get_telegram_testobject(mocker, default_conf, mock=False)
+    telegram._app = MagicMock()
+    telegram._app.bot = bot
+
+    await telegram._send_msg("test")
+    assert len(bot.method_calls) == 1
+
+    # Test update
+    query = MagicMock()
+    query.edit_message_text = AsyncMock()
+    await telegram._send_msg("test", callback_path="DeadBeef", query=query, reload_able=True)
+    assert query.edit_message_text.call_count == 1
+    assert "Updated: " in query.edit_message_text.call_args_list[0][1]["text"]
+
+    query.edit_message_text = AsyncMock(side_effect=BadRequest("not modified"))
+    await telegram._send_msg("test", callback_path="DeadBeef", query=query)
+    assert query.edit_message_text.call_count == 1
+    assert not log_has_re(r"TelegramError: .*", caplog)
+
+    query.edit_message_text = AsyncMock(side_effect=BadRequest(""))
+    await telegram._send_msg("test2", callback_path="DeadBeef", query=query)
+    assert query.edit_message_text.call_count == 1
+    assert log_has_re(r"TelegramError: .*", caplog)
+
+    query.edit_message_text = AsyncMock(side_effect=TelegramError("DeadBEEF"))
+    await telegram._send_msg("test3", callback_path="DeadBeef", query=query)
+
+    assert log_has_re(r"TelegramError: DeadBEEF! Giving up.*", caplog)
+
+
+async def test__send_msg_network_error(default_conf, mocker, caplog) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+    bot = MagicMock()
+    bot.send_message = MagicMock(side_effect=NetworkError("Oh snap"))
+    telegram, _, _ = get_telegram_testobject(mocker, default_conf, mock=False)
+    telegram._app = MagicMock()
+    telegram._app.bot = bot
+
+    telegram._config["telegram"]["enabled"] = True
+    await telegram._send_msg("test")
+
+    # Bot should've tried to send it twice
+    assert len(bot.method_calls) == 2
+    assert log_has("Telegram NetworkError: Oh snap! Trying one more time.", caplog)
+
+
+@pytest.mark.filterwarnings("ignore:.*ChatPermissions")
+async def test__send_msg_keyboard(default_conf, mocker, caplog) -> None:
+    mocker.patch("freqtrade.rpc.telegram.Telegram._init", MagicMock())
+    bot = MagicMock()
+    bot.send_message = AsyncMock()
+    freqtradebot = get_patched_freqtradebot(mocker, default_conf)
+    rpc = RPC(freqtradebot)
+
+    invalid_keys_list = [["/not_valid", "/profit"], ["/daily"], ["/alsoinvalid"]]
+    default_keys_list = [
+        ["/daily", "/profit", "/balance"],
+        ["/status", "/status table", "/performance"],
+        ["/count", "/start", "/stop", "/help"],
+    ]
+    default_keyboard = ReplyKeyboardMarkup(default_keys_list)
+
+    custom_keys_list = [
+        ["/daily", "/stats", "/balance", "/profit", "/profit 5"],
+        ["/count", "/start", "/reload_config", "/help"],
+    ]
+    custom_keyboard = ReplyKeyboardMarkup(custom_keys_list)
+
+    def init_telegram(freqtradebot):
+        telegram = Telegram(rpc, default_conf)
+        telegram._app = MagicMock()
+        telegram._app.bot = bot
+        return telegram
+
+    # no keyboard in config -> default keyboard
+    freqtradebot.config["telegram"]["enabled"] = True
+    telegram = init_telegram(freqtradebot)
+    await telegram._send_msg("test")
+    used_keyboard = bot.send_message.call_args[1]["reply_markup"]
+    assert used_keyboard == default_keyboard
+
+    # invalid keyboard in config -> default keyboard
+    freqtradebot.config["telegram"]["enabled"] = True
+    freqtradebot.config["telegram"]["keyboard"] = invalid_keys_list
+    err_msg = (
+        re.escape(
+            "config.telegram.keyboard: Invalid commands for custom "
+            "Telegram keyboard: ['/not_valid', '/alsoinvalid']"
+            "\nvalid commands are: "
+        )
+        + r"*"
+    )
+    with pytest.raises(OperationalException, match=err_msg):
+        telegram = init_telegram(freqtradebot)
+
+    # valid keyboard in config -> custom keyboard
+    freqtradebot.config["telegram"]["enabled"] = True
+    freqtradebot.config["telegram"]["keyboard"] = custom_keys_list
+    telegram = init_telegram(freqtradebot)
+    await telegram._send_msg("test")
+    used_keyboard = bot.send_message.call_args[1]["reply_markup"]
+    assert used_keyboard == custom_keyboard
+    assert log_has(
+        "using custom keyboard from config.json: "
+        "[['/daily', '/stats', '/balance', '/profit', '/profit 5'], ['/count', "
+        "'/start', '/reload_config', '/help']]",
+        caplog,
+    )
+
+
+async def test_change_market_direction(default_conf, mocker, update) -> None:
+    telegram, _, _msg_mock = get_telegram_testobject(mocker, default_conf)
+    assert telegram._rpc._freqtrade.strategy.market_direction == MarketDirection.NONE
+    context = MagicMock()
+    context.args = ["long"]
+    await telegram._changemarketdir(update, context)
+    assert telegram._rpc._freqtrade.strategy.market_direction == MarketDirection.LONG
+    context = MagicMock()
+    context.args = ["invalid"]
+    await telegram._changemarketdir(update, context)
+    assert telegram._rpc._freqtrade.strategy.market_direction == MarketDirection.LONG
+
+
+async def test_telegram_list_custom_data(default_conf_usdt, update, ticker, fee, mocker) -> None:
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        get_fee=fee,
+    )
+    telegram, _freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt)
+
+    # Create some test data
+    create_mock_trades_usdt(fee)
+    # No trade id
+    context = MagicMock()
+    await telegram._list_custom_data(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "Trade-id not set." in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    #
+    context.args = ["1"]
+    await telegram._list_custom_data(update=update, context=context)
+    assert msg_mock.call_count == 1
+    assert "No custom-data found for Trade ID: 1." in msg_mock.call_args_list[0][0][0]
+    msg_mock.reset_mock()
+
+    # Add some custom data
+    trade1 = Trade.get_trades_proxy()[0]
+    trade1.set_custom_data("test_int", 1)
+    trade1.set_custom_data("test_dict", {"test": "dict"})
+    Trade.commit()
+    context.args = [f"{trade1.id}"]
+    await telegram._list_custom_data(update=update, context=context)
+    assert msg_mock.call_count == 3
+    assert "Found custom-data entries: " in msg_mock.call_args_list[0][0][0]
+    assert (
+        "*Key:* `test_int`\n*Type:* `int`\n*Value:* `1`\n*Create Date:*"
+    ) in msg_mock.call_args_list[1][0][0]
+    assert (
+        "*Key:* `test_dict`\n*Type:* `dict`\n*Value:* `{'test': 'dict'}`\n*Create Date:* `"
+    ) in msg_mock.call_args_list[2][0][0]
+
+    msg_mock.reset_mock()
+
+
+def test_noficiation_settings(default_conf_usdt, mocker):
+    (telegram, _, _) = get_telegram_testobject(mocker, default_conf_usdt)
+    telegram._config["telegram"].update(
+        {
+            "notification_settings": {
+                "status": "silent",
+                "warning": "on",
+                "startup": "off",
+                "entry": "silent",
+                "entry_fill": "on",
+                "entry_cancel": "silent",
+                "exit": {
+                    "roi": "silent",
+                    "emergency_exit": "on",
+                    "force_exit": "on",
+                    "exit_signal": "silent",
+                    "trailing_stop_loss": "on",
+                    "stop_loss": "on",
+                    "stoploss_on_exchange": "on",
+                    "custom_exit": "silent",
+                    "partial_exit": "off",
+                },
+                "exit_fill": {
+                    "roi": "silent",
+                    "partial_exit": "off",
+                    "*": "silent",  # Default to silent
+                },
+                "exit_cancel": "on",
+                "protection_trigger": "off",
+                "protection_trigger_global": "on",
+                "strategy_msg": "off",
+                "show_candle": "off",
+            }
+        }
+    )
+
+    loudness = telegram._message_loudness
+
+    assert loudness({"type": RPCMessageType.ENTRY, "exit_reason": ""}) == "silent"
+    assert loudness({"type": RPCMessageType.ENTRY_FILL, "exit_reason": ""}) == "on"
+    assert loudness({"type": RPCMessageType.EXIT, "exit_reason": ""}) == "on"
+    # Default to silent due to "*" definition
+    assert loudness({"type": RPCMessageType.EXIT_FILL, "exit_reason": ""}) == "silent"
+    assert loudness({"type": RPCMessageType.PROTECTION_TRIGGER, "exit_reason": ""}) == "off"
+    assert loudness({"type": RPCMessageType.EXIT, "exit_reason": "roi"}) == "silent"
+    assert loudness({"type": RPCMessageType.EXIT, "exit_reason": "partial_exit"}) == "off"
+    # Not given key defaults to on
+    assert loudness({"type": RPCMessageType.EXIT, "exit_reason": "cust_exit112"}) == "on"
+
+    assert loudness({"type": RPCMessageType.EXIT_FILL, "exit_reason": "roi"}) == "silent"
+    assert loudness({"type": RPCMessageType.EXIT_FILL, "exit_reason": "partial_exit"}) == "off"
+    # Default to silent due to "*" definition
+    assert loudness({"type": RPCMessageType.EXIT_FILL, "exit_reason": "cust_exit112"}) == "silent"
+
+    # Simplified setup for exit
+    telegram._config["telegram"].update(
+        {
+            "notification_settings": {
+                "status": "silent",
+                "warning": "on",
+                "startup": "off",
+                "entry": "silent",
+                "entry_fill": "on",
+                "entry_cancel": "silent",
+                "exit": "off",
+                "exit_cancel": "on",
+                "exit_fill": "on",
+                "protection_trigger": "off",
+                "protection_trigger_global": "on",
+                "strategy_msg": "off",
+                "show_candle": "off",
+            }
+        }
+    )
+
+    assert loudness({"type": RPCMessageType.EXIT_FILL, "exit_reason": "roi"}) == "on"
+    # All regular exits are off
+    assert loudness({"type": RPCMessageType.EXIT, "exit_reason": "roi"}) == "off"
+    assert loudness({"type": RPCMessageType.EXIT, "exit_reason": "partial_exit"}) == "off"
+    assert loudness({"type": RPCMessageType.EXIT, "exit_reason": "cust_exit112"}) == "off"
+
+
+async def test__tg_info(default_conf_usdt, mocker, update):
+    (telegram, _, _) = get_telegram_testobject(mocker, default_conf_usdt)
+    context = AsyncMock()
+
+    await telegram._tg_info(update, context)
+
+    assert context.bot.send_message.call_count == 1
+    content = context.bot.send_message.call_args[1]["text"]
+    assert "Freqtrade Bot Info:\n" in content
+    assert '"chat_id": "1235"' in content
diff --git a/tests/rpc/test_rpc_webhook.py b/tests/rpc/test_rpc_webhook.py
new file mode 100644
index 000000000..b60e52177
--- /dev/null
+++ b/tests/rpc/test_rpc_webhook.py
@@ -0,0 +1,479 @@
+# pragma pylint: disable=missing-docstring, C0103, protected-access
+
+import logging
+from datetime import datetime, timedelta
+from unittest.mock import MagicMock
+
+from requests import RequestException
+
+from freqtrade.enums import ExitType, RPCMessageType
+from freqtrade.rpc import RPC
+from freqtrade.rpc.discord import Discord
+from freqtrade.rpc.webhook import Webhook
+from tests.conftest import get_patched_freqtradebot, log_has
+
+
+def get_webhook_dict() -> dict:
+    return {
+        "enabled": True,
+        "url": "https://maker.ifttt.com/trigger/freqtrade_test/with/key/c764udvJ5jfSlswVRukZZ2/",
+        "webhookentry": {
+            # Intentionally broken, as "entry" should have priority.
+            "value1": "Buying {pair55555}",
+        },
+        "entry": {
+            "value1": "Buying {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "{stake_amount:8f} {stake_currency}",
+            "value4": "leverage {leverage:.1f}",
+            "value5": "direction {direction}",
+        },
+        "webhookentrycancel": {
+            "value1": "Cancelling Open Buy Order for {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "{stake_amount:8f} {stake_currency}",
+            "value4": "leverage {leverage:.1f}",
+            "value5": "direction {direction}",
+        },
+        "webhookentryfill": {
+            "value1": "Buy Order for {pair} filled",
+            "value2": "at {open_rate:8f}",
+            "value3": "{stake_amount:8f} {stake_currency}",
+            "value4": "leverage {leverage:.1f}",
+            "value5": "direction {direction}",
+        },
+        "webhookexit": {
+            "value1": "Selling {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})",
+        },
+        "webhookexitcancel": {
+            "value1": "Cancelling Open Sell Order for {pair}",
+            "value2": "limit {limit:8f}",
+            "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})",
+        },
+        "webhookexitfill": {
+            "value1": "Sell Order for {pair} filled",
+            "value2": "at {close_rate:8f}",
+            "value3": "",
+        },
+        "webhookstatus": {"value1": "Status: {status}", "value2": "", "value3": ""},
+    }
+
+
+def test__init__(mocker, default_conf):
+    default_conf["webhook"] = {"enabled": True, "url": "https://DEADBEEF.com"}
+    webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
+    assert webhook._config == default_conf
+
+
+def test_send_msg_webhook(default_conf, mocker):
+    default_conf["webhook"] = get_webhook_dict()
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
+    # Test buy
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    msg = {
+        "type": RPCMessageType.ENTRY,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "leverage": 1.0,
+        "direction": "Long",
+        "limit": 0.005,
+        "stake_amount": 0.8,
+        "stake_amount_fiat": 500,
+        "stake_currency": "BTC",
+        "fiat_currency": "EUR",
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["entry"]["value1"].format(
+        **msg
+    )
+    assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["entry"]["value2"].format(
+        **msg
+    )
+    assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["entry"]["value3"].format(
+        **msg
+    )
+    assert msg_mock.call_args[0][0]["value4"] == default_conf["webhook"]["entry"]["value4"].format(
+        **msg
+    )
+    assert msg_mock.call_args[0][0]["value5"] == default_conf["webhook"]["entry"]["value5"].format(
+        **msg
+    )
+    # Test short
+    msg_mock.reset_mock()
+
+    msg = {
+        "type": RPCMessageType.ENTRY,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "leverage": 2.0,
+        "direction": "Short",
+        "limit": 0.005,
+        "stake_amount": 0.8,
+        "stake_amount_fiat": 500,
+        "stake_currency": "BTC",
+        "fiat_currency": "EUR",
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["entry"]["value1"].format(
+        **msg
+    )
+    assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["entry"]["value2"].format(
+        **msg
+    )
+    assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["entry"]["value3"].format(
+        **msg
+    )
+    assert msg_mock.call_args[0][0]["value4"] == default_conf["webhook"]["entry"]["value4"].format(
+        **msg
+    )
+    assert msg_mock.call_args[0][0]["value5"] == default_conf["webhook"]["entry"]["value5"].format(
+        **msg
+    )
+    # Test buy cancel
+    msg_mock.reset_mock()
+
+    msg = {
+        "type": RPCMessageType.ENTRY_CANCEL,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "leverage": 1.0,
+        "direction": "Long",
+        "limit": 0.005,
+        "stake_amount": 0.8,
+        "stake_amount_fiat": 500,
+        "stake_currency": "BTC",
+        "fiat_currency": "EUR",
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["webhookentrycancel"][
+        "value1"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["webhookentrycancel"][
+        "value2"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["webhookentrycancel"][
+        "value3"
+    ].format(**msg)
+    # Test short cancel
+    msg_mock.reset_mock()
+
+    msg = {
+        "type": RPCMessageType.ENTRY_CANCEL,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "leverage": 2.0,
+        "direction": "Short",
+        "limit": 0.005,
+        "stake_amount": 0.8,
+        "stake_amount_fiat": 500,
+        "stake_currency": "BTC",
+        "fiat_currency": "EUR",
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["webhookentrycancel"][
+        "value1"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["webhookentrycancel"][
+        "value2"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["webhookentrycancel"][
+        "value3"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value4"] == default_conf["webhook"]["webhookentrycancel"][
+        "value4"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value5"] == default_conf["webhook"]["webhookentrycancel"][
+        "value5"
+    ].format(**msg)
+    # Test buy fill
+    msg_mock.reset_mock()
+
+    msg = {
+        "type": RPCMessageType.ENTRY_FILL,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "leverage": 1.0,
+        "direction": "Long",
+        "open_rate": 0.005,
+        "stake_amount": 0.8,
+        "stake_amount_fiat": 500,
+        "stake_currency": "BTC",
+        "fiat_currency": "EUR",
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["webhookentryfill"][
+        "value1"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["webhookentryfill"][
+        "value2"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["webhookentryfill"][
+        "value3"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value4"] == default_conf["webhook"]["webhookentrycancel"][
+        "value4"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value5"] == default_conf["webhook"]["webhookentrycancel"][
+        "value5"
+    ].format(**msg)
+    # Test short fill
+    msg_mock.reset_mock()
+
+    msg = {
+        "type": RPCMessageType.ENTRY_FILL,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "leverage": 2.0,
+        "direction": "Short",
+        "open_rate": 0.005,
+        "stake_amount": 0.8,
+        "stake_amount_fiat": 500,
+        "stake_currency": "BTC",
+        "fiat_currency": "EUR",
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["webhookentryfill"][
+        "value1"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["webhookentryfill"][
+        "value2"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["webhookentryfill"][
+        "value3"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value4"] == default_conf["webhook"]["webhookentrycancel"][
+        "value4"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value5"] == default_conf["webhook"]["webhookentrycancel"][
+        "value5"
+    ].format(**msg)
+    # Test sell
+    msg_mock.reset_mock()
+
+    msg = {
+        "type": RPCMessageType.EXIT,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "gain": "profit",
+        "limit": 0.005,
+        "amount": 0.8,
+        "order_type": "limit",
+        "open_rate": 0.004,
+        "current_rate": 0.005,
+        "profit_amount": 0.001,
+        "profit_ratio": 0.20,
+        "stake_currency": "BTC",
+        "sell_reason": ExitType.STOP_LOSS.value,
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["webhookexit"][
+        "value1"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["webhookexit"][
+        "value2"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["webhookexit"][
+        "value3"
+    ].format(**msg)
+    # Test sell cancel
+    msg_mock.reset_mock()
+    msg = {
+        "type": RPCMessageType.EXIT_CANCEL,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "gain": "profit",
+        "limit": 0.005,
+        "amount": 0.8,
+        "order_type": "limit",
+        "open_rate": 0.004,
+        "current_rate": 0.005,
+        "profit_amount": 0.001,
+        "profit_ratio": 0.20,
+        "stake_currency": "BTC",
+        "sell_reason": ExitType.STOP_LOSS.value,
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["webhookexitcancel"][
+        "value1"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["webhookexitcancel"][
+        "value2"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["webhookexitcancel"][
+        "value3"
+    ].format(**msg)
+    # Test Sell fill
+    msg_mock.reset_mock()
+    msg = {
+        "type": RPCMessageType.EXIT_FILL,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "gain": "profit",
+        "close_rate": 0.005,
+        "amount": 0.8,
+        "order_type": "limit",
+        "open_rate": 0.004,
+        "current_rate": 0.005,
+        "profit_amount": 0.001,
+        "profit_ratio": 0.20,
+        "stake_currency": "BTC",
+        "sell_reason": ExitType.STOP_LOSS.value,
+    }
+    webhook.send_msg(msg=msg)
+    assert msg_mock.call_count == 1
+    assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["webhookexitfill"][
+        "value1"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["webhookexitfill"][
+        "value2"
+    ].format(**msg)
+    assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["webhookexitfill"][
+        "value3"
+    ].format(**msg)
+
+    for msgtype in [RPCMessageType.STATUS, RPCMessageType.WARNING, RPCMessageType.STARTUP]:
+        # Test notification
+        msg = {"type": msgtype, "status": "Unfilled sell order for BTC cancelled due to timeout"}
+        msg_mock = MagicMock()
+        mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+        webhook.send_msg(msg)
+        assert msg_mock.call_count == 1
+        assert msg_mock.call_args[0][0]["value1"] == default_conf["webhook"]["webhookstatus"][
+            "value1"
+        ].format(**msg)
+        assert msg_mock.call_args[0][0]["value2"] == default_conf["webhook"]["webhookstatus"][
+            "value2"
+        ].format(**msg)
+        assert msg_mock.call_args[0][0]["value3"] == default_conf["webhook"]["webhookstatus"][
+            "value3"
+        ].format(**msg)
+
+
+def test_exception_send_msg(default_conf, mocker, caplog):
+    caplog.set_level(logging.DEBUG)
+    default_conf["webhook"] = get_webhook_dict()
+    del default_conf["webhook"]["entry"]
+    del default_conf["webhook"]["webhookentry"]
+
+    webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
+    webhook.send_msg({"type": RPCMessageType.ENTRY})
+    assert log_has(f"Message type '{RPCMessageType.ENTRY}' not configured for webhooks", caplog)
+
+    default_conf["webhook"] = get_webhook_dict()
+    default_conf["webhook"]["strategy_msg"] = {"value1": "{DEADBEEF:8f}"}
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
+    msg = {
+        "type": RPCMessageType.STRATEGY_MSG,
+        "msg": "hello world",
+    }
+    webhook.send_msg(msg)
+    assert log_has(
+        "Problem calling Webhook. Please check your webhook configuration. Exception: 'DEADBEEF'",
+        caplog,
+    )
+
+    # Test no failure for not implemented but known messagetypes
+    for e in RPCMessageType:
+        msg = {"type": e, "status": "whatever"}
+        webhook.send_msg(msg)
+
+    # Test no failure for not implemented but known messagetypes
+    for e in RPCMessageType:
+        msg = {"type": e, "status": "whatever"}
+        webhook.send_msg(msg)
+
+
+def test__send_msg(default_conf, mocker, caplog):
+    default_conf["webhook"] = get_webhook_dict()
+    webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
+    msg = {"value1": "DEADBEEF", "value2": "ALIVEBEEF", "value3": "FREQTRADE"}
+    post = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.post", post)
+    webhook._send_msg(msg)
+
+    assert post.call_count == 1
+    assert post.call_args[1] == {"data": msg, "timeout": 10}
+    assert post.call_args[0] == (default_conf["webhook"]["url"],)
+
+    post = MagicMock(side_effect=RequestException)
+    mocker.patch("freqtrade.rpc.webhook.post", post)
+    webhook._send_msg(msg)
+    assert log_has("Could not call webhook url. Exception: ", caplog)
+
+
+def test__send_msg_with_json_format(default_conf, mocker, caplog):
+    default_conf["webhook"] = get_webhook_dict()
+    default_conf["webhook"]["format"] = "json"
+    webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
+    msg = {"text": "Hello"}
+    post = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.post", post)
+    webhook._send_msg(msg)
+
+    assert post.call_args[1] == {"json": msg, "timeout": 10}
+
+
+def test__send_msg_with_raw_format(default_conf, mocker, caplog):
+    default_conf["webhook"] = get_webhook_dict()
+    default_conf["webhook"]["format"] = "raw"
+    webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
+    msg = {"data": "Hello"}
+    post = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.post", post)
+    webhook._send_msg(msg)
+
+    assert post.call_args[1] == {
+        "data": msg["data"],
+        "headers": {"Content-Type": "text/plain"},
+        "timeout": 10,
+    }
+
+
+def test_send_msg_discord(default_conf, mocker):
+    default_conf["discord"] = {"enabled": True, "webhook_url": "https://webhookurl..."}
+    msg_mock = MagicMock()
+    mocker.patch("freqtrade.rpc.webhook.Webhook._send_msg", msg_mock)
+    discord = Discord(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
+
+    msg = {
+        "type": RPCMessageType.EXIT_FILL,
+        "trade_id": 1,
+        "exchange": "Binance",
+        "pair": "ETH/BTC",
+        "direction": "Long",
+        "gain": "profit",
+        "close_rate": 0.005,
+        "amount": 0.8,
+        "order_type": "limit",
+        "open_date": datetime.now() - timedelta(days=1),
+        "close_date": datetime.now(),
+        "open_rate": 0.004,
+        "current_rate": 0.005,
+        "profit_amount": 0.001,
+        "profit_ratio": 0.20,
+        "stake_currency": "BTC",
+        "enter_tag": "enter_tagggg",
+        "exit_reason": ExitType.STOP_LOSS.value,
+    }
+    discord.send_msg(msg=msg)
+
+    assert msg_mock.call_count == 1
+    assert "embeds" in msg_mock.call_args_list[0][0][0]
+    assert "title" in msg_mock.call_args_list[0][0][0]["embeds"][0]
+    assert "color" in msg_mock.call_args_list[0][0][0]["embeds"][0]
+    assert "fields" in msg_mock.call_args_list[0][0][0]["embeds"][0]
diff --git a/tests/setup.Tests.ps1 b/tests/setup.Tests.ps1
new file mode 100644
index 000000000..e58a4729d
--- /dev/null
+++ b/tests/setup.Tests.ps1
@@ -0,0 +1,177 @@
+
+Describe "Setup and Tests" {
+  BeforeAll {
+    # Setup variables
+    $SetupScriptPath = Join-Path $PSScriptRoot "..\setup.ps1"
+    $Global:LogFilePath = Join-Path $env:TEMP "script_log.txt"
+
+    # Check if the setup script exists
+    if (-Not (Test-Path -Path $SetupScriptPath)) {
+      Write-Host "Error: setup.ps1 script not found at path: $SetupScriptPath"
+      exit 1
+    }
+
+    # Mock main to prevent it from running
+    Mock Main {}
+
+    . $SetupScriptPath
+  }
+
+  Context "Write-Log Tests" -Tag "Unit" {
+    It "should write INFO level log" {
+      if (Test-Path $Global:LogFilePath){
+        Remove-Item $Global:LogFilePath -ErrorAction SilentlyContinue
+      }
+
+      Write-Log -Message "Test Info Message" -Level "INFO"
+      $Global:LogFilePath | Should -Exist
+
+      $LogContent = Get-Content $Global:LogFilePath
+      $LogContent | Should -Contain "INFO: Test Info Message"
+    }
+
+    It "should write ERROR level log" {
+      if (Test-Path $Global:LogFilePath){
+        Remove-Item $Global:LogFilePath -ErrorAction SilentlyContinue
+      }
+
+      Write-Log -Message "Test Error Message" -Level "ERROR"
+      $Global:LogFilePath | Should -Exist
+
+      $LogContent = Get-Content $Global:LogFilePath
+      $LogContent | Should -Contain "ERROR: Test Error Message"
+    }
+  }
+
+  Describe "Get-UserSelection Tests" {
+    Context "Valid input" {
+      It "Should return the correct index for a valid single selection" {
+        $Options = @("Option1", "Option2", "Option3")
+        Mock Read-Host { return "B" }
+        $Result = Get-UserSelection -prompt "Select an option" -options $Options
+        $Result | Should -Be 1
+      }
+
+      It "Should return the correct index for a valid single selection" {
+        $Options = @("Option1", "Option2", "Option3")
+        Mock Read-Host { return "b" }
+        $Result = Get-UserSelection -prompt "Select an option" -options $Options
+        $Result | Should -Be 1
+      }
+
+      It "Should return the default choice when no input is provided" {
+        $Options = @("Option1", "Option2", "Option3")
+        Mock Read-Host { return "" }
+        $Result = Get-UserSelection -prompt "Select an option" -options $Options -defaultChoice "C"
+        $Result | Should -Be 2
+      }
+    }
+
+    Context "Invalid input" {
+      It "Should return -1 for an invalid letter selection" {
+        $Options = @("Option1", "Option2", "Option3")
+        Mock Read-Host { return "X" }
+        $Result = Get-UserSelection -prompt "Select an option" -options $Options
+        $Result | Should -Be -1
+      }
+
+      It "Should return -1 for a selection outside the valid range" {
+        $Options = @("Option1", "Option2", "Option3")
+        Mock Read-Host { return "D" }
+        $Result = Get-UserSelection -prompt "Select an option" -options $Options
+        $Result | Should -Be -1
+      }
+
+      It "Should return -1 for a non-letter input" {
+        $Options = @("Option1", "Option2", "Option3")
+        Mock Read-Host { return "1" }
+        $Result = Get-UserSelection -prompt "Select an option" -options $Options
+        $Result | Should -Be -1
+      }
+
+      It "Should return -1 for mixed valid and invalid input" {
+        Mock Read-Host { return "A,X,B,Y,C,Z" }
+        $Options = @("Option1", "Option2", "Option3")
+        $Indices = Get-UserSelection -prompt "Select options" -options $Options -defaultChoice "A"
+        $Indices | Should -Be -1
+      }
+    }
+
+    Context "Multiple selections" {
+      It "Should handle valid input correctly" {
+        Mock Read-Host { return "A, B, C" }
+        $Options = @("Option1", "Option2", "Option3")
+        $Indices = Get-UserSelection -prompt "Select options" -options $Options -defaultChoice "A"
+        $Indices | Should -Be @(0, 1, 2)
+      }
+
+      It "Should handle valid input without whitespace correctly" {
+        Mock Read-Host { return "A,B,C" }
+        $Options = @("Option1", "Option2", "Option3")
+        $Indices = Get-UserSelection -prompt "Select options" -options $Options -defaultChoice "A"
+        $Indices | Should -Be @(0, 1, 2)
+      }
+
+      It "Should return indices for selected options" {
+        Mock Read-Host { return "a,b" }
+        $Options = @("Option1", "Option2", "Option3")
+        $Indices = Get-UserSelection -prompt "Select options" -options $Options
+        $Indices | Should -Be @(0, 1)
+      }
+
+      It "Should return default choice if no input" {
+        Mock Read-Host { return "" }
+        $Options = @("Option1", "Option2", "Option3")
+        $Indices = Get-UserSelection -prompt "Select options" -options $Options -defaultChoice "C"
+        $Indices | Should -Be @(2)
+      }
+
+      It "Should handle invalid input gracefully" {
+        Mock Read-Host { return "x,y,z" }
+        $Options = @("Option1", "Option2", "Option3")
+        $Indices = Get-UserSelection -prompt "Select options" -options $Options -defaultChoice "A"
+        $Indices | Should -Be -1
+      }
+
+      It "Should handle input without whitespace" {
+        Mock Read-Host { return "a,b,c" }
+        $Options = @("Option1", "Option2", "Option3")
+        $Indices = Get-UserSelection -prompt "Select options" -options $Options
+        $Indices | Should -Be @(0, 1, 2)
+      }
+    }
+  }
+
+  Describe "Exit-Script Tests" -Tag "Unit" {
+    BeforeEach {
+      Mock Write-Log {}
+      Mock Start-Process {}
+      Mock Read-Host { return "Y" }
+    }
+
+    It "should exit with the given exit code without waiting for key press" {
+      $ExitCode = Exit-Script -ExitCode 0 -isSubShell $true -waitForKeypress $false
+      $ExitCode | Should -Be 0
+    }
+
+    It "should prompt to open log file on error" {
+      Exit-Script -ExitCode 1 -isSubShell $true -waitForKeypress $false
+      Assert-MockCalled Read-Host -Exactly 1
+      Assert-MockCalled Start-Process -Exactly 1
+    }
+  }
+
+  Context 'Find-PythonExecutable' {
+    It 'Returns the first valid Python executable' {
+      Mock Test-PythonExecutable { $true } -ParameterFilter { $PythonExecutable -eq 'python' }
+      $Result = Find-PythonExecutable
+      $Result | Should -Be 'python'
+    }
+
+    It 'Returns null if no valid Python executable is found' {
+      Mock Test-PythonExecutable { $false }
+      $Result = Find-PythonExecutable
+      $Result | Should -Be $null
+    }
+  }
+}
diff --git a/tests/strategy/__init__.py b/tests/strategy/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/strategy/strats/broken_strats/broken_futures_strategies.py b/tests/strategy/strats/broken_strats/broken_futures_strategies.py
new file mode 100644
index 000000000..a3e51bc50
--- /dev/null
+++ b/tests/strategy/strats/broken_strats/broken_futures_strategies.py
@@ -0,0 +1,90 @@
+"""
+The strategies here are minimal strategies designed to fail loading in certain conditions.
+They are not operational, and don't aim to be.
+"""
+
+from datetime import datetime
+
+from pandas import DataFrame
+
+from freqtrade.persistence.trade_model import Order
+from freqtrade.strategy.interface import IStrategy
+
+
+class TestStrategyNoImplements(IStrategy):
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return super().populate_indicators(dataframe, metadata)
+
+
+class TestStrategyNoImplementSell(TestStrategyNoImplements):
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return super().populate_entry_trend(dataframe, metadata)
+
+
+class TestStrategyImplementEmptyWorking(TestStrategyNoImplementSell):
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return super().populate_exit_trend(dataframe, metadata)
+
+
+class TestStrategyImplementCustomSell(TestStrategyImplementEmptyWorking):
+    def custom_sell(
+        self,
+        pair: str,
+        trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        **kwargs,
+    ):
+        return False
+
+
+class TestStrategyImplementBuyTimeout(TestStrategyNoImplementSell):
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return super().populate_exit_trend(dataframe, metadata)
+
+    def check_buy_timeout(
+        self, pair: str, trade, order: Order, current_time: datetime, **kwargs
+    ) -> bool:
+        return False
+
+
+class TestStrategyImplementSellTimeout(TestStrategyNoImplementSell):
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return super().populate_exit_trend(dataframe, metadata)
+
+    def check_sell_timeout(
+        self, pair: str, trade, order: Order, current_time: datetime, **kwargs
+    ) -> bool:
+        return False
+
+
+class TestStrategyAdjustOrderPrice(TestStrategyImplementEmptyWorking):
+    def adjust_entry_price(
+        self,
+        trade,
+        order,
+        pair,
+        current_time,
+        proposed_rate,
+        current_order_rate,
+        entry_tag,
+        side,
+        **kwargs,
+    ):
+        return proposed_rate
+
+    def adjust_order_price(
+        self,
+        trade,
+        order,
+        pair,
+        current_time,
+        proposed_rate,
+        current_order_rate,
+        entry_tag,
+        side,
+        is_entry,
+        **kwargs,
+    ):
+        return proposed_rate
diff --git a/tests/strategy/strats/broken_strats/legacy_strategy_v1.py b/tests/strategy/strats/broken_strats/legacy_strategy_v1.py
new file mode 100644
index 000000000..b0d7a2ae6
--- /dev/null
+++ b/tests/strategy/strats/broken_strats/legacy_strategy_v1.py
@@ -0,0 +1,21 @@
+# type: ignore
+from pandas import DataFrame
+
+from freqtrade.strategy import IStrategy
+
+
+# Dummy strategy - no longer loads but raises an exception.
+class TestStrategyLegacyV1(IStrategy):
+    minimal_roi = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}
+    stoploss = -0.10
+
+    timeframe = "5m"
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        return dataframe
diff --git a/tests/strategy/strats/failing_strategy.py b/tests/strategy/strats/failing_strategy.py
new file mode 100644
index 000000000..a65a0ddc2
--- /dev/null
+++ b/tests/strategy/strats/failing_strategy.py
@@ -0,0 +1,9 @@
+# The strategy which fails to load due to non-existent dependency
+
+import nonexiting_module  # noqa
+
+from freqtrade.strategy.interface import IStrategy
+
+
+class TestStrategyLegacyV1(IStrategy):
+    pass
diff --git a/tests/strategy/strats/freqai_rl_test_strat.py b/tests/strategy/strats/freqai_rl_test_strat.py
new file mode 100644
index 000000000..3c505fa81
--- /dev/null
+++ b/tests/strategy/strats/freqai_rl_test_strat.py
@@ -0,0 +1,87 @@
+import logging
+from functools import reduce
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+from freqtrade.strategy import IStrategy
+
+
+logger = logging.getLogger(__name__)
+
+
+class freqai_rl_test_strat(IStrategy):
+    """
+    Test strategy - used for testing freqAI functionalities.
+    DO not use in production.
+    """
+
+    minimal_roi = {"0": 0.1, "240": -1}
+
+    process_only_new_candles = True
+    stoploss = -0.05
+    use_exit_signal = True
+    startup_candle_count: int = 300
+    can_short = False
+
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
+    ):
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+
+        dataframe["%-raw_close"] = dataframe["close"]
+        dataframe["%-raw_open"] = dataframe["open"]
+        dataframe["%-raw_high"] = dataframe["high"]
+        dataframe["%-raw_low"] = dataframe["low"]
+
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["&-action"] = 0
+
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
+
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
+            ] = (1, "long")
+
+        enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]
+
+        if enter_short_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
+            ] = (1, "short")
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
+        if exit_long_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
+
+        exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
+        if exit_short_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
+
+        return df
diff --git a/tests/strategy/strats/freqai_test_classifier.py b/tests/strategy/strats/freqai_test_classifier.py
new file mode 100644
index 000000000..f1b49fee6
--- /dev/null
+++ b/tests/strategy/strats/freqai_test_classifier.py
@@ -0,0 +1,114 @@
+import logging
+from functools import reduce
+
+import numpy as np
+import talib.abstract as ta
+from pandas import DataFrame
+
+from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
+
+
+logger = logging.getLogger(__name__)
+
+
+class freqai_test_classifier(IStrategy):
+    """
+    Test strategy - used for testing freqAI functionalities.
+    DO not use in production.
+    """
+
+    minimal_roi = {"0": 0.1, "240": -1}
+
+    plot_config = {
+        "main_plot": {},
+        "subplots": {
+            "prediction": {"prediction": {"color": "blue"}},
+            "target_roi": {
+                "target_roi": {"color": "brown"},
+            },
+            "do_predict": {
+                "do_predict": {"color": "brown"},
+            },
+        },
+    }
+
+    process_only_new_candles = True
+    stoploss = -0.05
+    use_exit_signal = True
+    startup_candle_count: int = 300
+    can_short = False
+
+    linear_roi_offset = DecimalParameter(
+        0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
+    )
+    max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
+
+    def informative_pairs(self):
+        whitelist_pairs = self.dp.current_whitelist()
+        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
+        informative_pairs = []
+        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
+            for pair in whitelist_pairs:
+                informative_pairs.append((pair, tf))
+            for pair in corr_pairs:
+                if pair in whitelist_pairs:
+                    continue  # avoid duplication
+                informative_pairs.append((pair, tf))
+        return informative_pairs
+
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
+    ):
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        self.freqai.class_names = ["down", "up"]
+        dataframe["&s-up_or_down"] = np.where(
+            dataframe["close"].shift(-100) > dataframe["close"], "up", "down"
+        )
+
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        self.freqai_info = self.config["freqai"]
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        enter_long_conditions = [df["&s-up_or_down"] == "up"]
+
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
+            ] = (1, "long")
+
+        enter_short_conditions = [df["&s-up_or_down"] == "down"]
+
+        if enter_short_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
+            ] = (1, "short")
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        return df
diff --git a/tests/strategy/strats/freqai_test_multimodel_classifier_strat.py b/tests/strategy/strats/freqai_test_multimodel_classifier_strat.py
new file mode 100644
index 000000000..61e1b5165
--- /dev/null
+++ b/tests/strategy/strats/freqai_test_multimodel_classifier_strat.py
@@ -0,0 +1,114 @@
+import logging
+from functools import reduce
+
+import numpy as np
+import talib.abstract as ta
+from pandas import DataFrame
+
+from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
+
+
+logger = logging.getLogger(__name__)
+
+
+class freqai_test_multimodel_classifier_strat(IStrategy):
+    """
+    Test strategy - used for testing freqAI multimodel functionalities.
+    DO not use in production.
+    """
+
+    minimal_roi = {"0": 0.1, "240": -1}
+
+    plot_config = {
+        "main_plot": {},
+        "subplots": {
+            "prediction": {"prediction": {"color": "blue"}},
+            "target_roi": {
+                "target_roi": {"color": "brown"},
+            },
+            "do_predict": {
+                "do_predict": {"color": "brown"},
+            },
+        },
+    }
+
+    process_only_new_candles = True
+    stoploss = -0.05
+    use_exit_signal = True
+    startup_candle_count: int = 300
+    can_short = False
+
+    linear_roi_offset = DecimalParameter(
+        0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
+    )
+    max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
+
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
+    ):
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["&s-up_or_down"] = np.where(
+            dataframe["close"].shift(-50) > dataframe["close"], "up", "down"
+        )
+
+        dataframe["&s-up_or_down2"] = np.where(
+            dataframe["close"].shift(-50) > dataframe["close"], "up2", "down2"
+        )
+
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        self.freqai_info = self.config["freqai"]
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
+        dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
+
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
+            ] = (1, "long")
+
+        enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
+
+        if enter_short_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
+            ] = (1, "short")
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
+        if exit_long_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
+
+        exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
+        if exit_short_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
+
+        return df
diff --git a/tests/strategy/strats/freqai_test_multimodel_strat.py b/tests/strategy/strats/freqai_test_multimodel_strat.py
new file mode 100644
index 000000000..e0d7cd2bc
--- /dev/null
+++ b/tests/strategy/strats/freqai_test_multimodel_strat.py
@@ -0,0 +1,125 @@
+import logging
+from functools import reduce
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
+
+
+logger = logging.getLogger(__name__)
+
+
+class freqai_test_multimodel_strat(IStrategy):
+    """
+    Test strategy - used for testing freqAI multimodel functionalities.
+    DO not use in production.
+    """
+
+    minimal_roi = {"0": 0.1, "240": -1}
+
+    plot_config = {
+        "main_plot": {},
+        "subplots": {
+            "prediction": {"prediction": {"color": "blue"}},
+            "target_roi": {
+                "target_roi": {"color": "brown"},
+            },
+            "do_predict": {
+                "do_predict": {"color": "brown"},
+            },
+        },
+    }
+
+    process_only_new_candles = True
+    stoploss = -0.05
+    use_exit_signal = True
+    startup_candle_count: int = 300
+    can_short = False
+
+    linear_roi_offset = DecimalParameter(
+        0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
+    )
+    max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
+
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
+    ):
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
+        )
+
+        dataframe["&-s_range"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .max()
+            - dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .min()
+        )
+
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        self.freqai_info = self.config["freqai"]
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
+        dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
+
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
+            ] = (1, "long")
+
+        enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
+
+        if enter_short_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
+            ] = (1, "short")
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
+        if exit_long_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
+
+        exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
+        if exit_short_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
+
+        return df
diff --git a/tests/strategy/strats/freqai_test_strat.py b/tests/strategy/strats/freqai_test_strat.py
new file mode 100644
index 000000000..349f4c531
--- /dev/null
+++ b/tests/strategy/strats/freqai_test_strat.py
@@ -0,0 +1,114 @@
+import logging
+from functools import reduce
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
+
+
+logger = logging.getLogger(__name__)
+
+
+class freqai_test_strat(IStrategy):
+    """
+    Test strategy - used for testing freqAI functionalities.
+    DO not use in production.
+    """
+
+    minimal_roi = {"0": 0.1, "240": -1}
+
+    plot_config = {
+        "main_plot": {},
+        "subplots": {
+            "prediction": {"prediction": {"color": "blue"}},
+            "target_roi": {
+                "target_roi": {"color": "brown"},
+            },
+            "do_predict": {
+                "do_predict": {"color": "brown"},
+            },
+        },
+    }
+
+    process_only_new_candles = True
+    stoploss = -0.05
+    use_exit_signal = True
+    startup_candle_count: int = 300
+    can_short = False
+
+    linear_roi_offset = DecimalParameter(
+        0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
+    )
+    max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
+
+    def feature_engineering_expand_all(
+        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
+    ):
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs):
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
+        )
+
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        self.freqai_info = self.config["freqai"]
+
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
+        dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
+
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
+            ] = (1, "long")
+
+        enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
+
+        if enter_short_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
+            ] = (1, "short")
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
+        if exit_long_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
+
+        exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
+        if exit_short_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
+
+        return df
diff --git a/tests/strategy/strats/hyperoptable_strategy.py b/tests/strategy/strats/hyperoptable_strategy.py
new file mode 100644
index 000000000..3a2b210be
--- /dev/null
+++ b/tests/strategy/strats/hyperoptable_strategy.py
@@ -0,0 +1,117 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+from pandas import DataFrame
+from strategy_test_v3 import StrategyTestV3
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, RealParameter
+
+
+class HyperoptableStrategy(StrategyTestV3):
+    """
+    Default Strategy provided by freqtrade bot.
+    Please do not modify this strategy, it's  intended for internal use only.
+    Please look at the SampleStrategy in the user_data/strategy directory
+    or strategy repository https://github.com/freqtrade/freqtrade-strategies
+    for samples and inspiration.
+    """
+
+    buy_params = {
+        "buy_rsi": 35,
+        # Intentionally not specified, so "default" is tested
+        # 'buy_plusdi': 0.4
+    }
+
+    sell_params = {"sell_rsi": 74, "sell_minusdi": 0.4}
+
+    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space="buy")
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell")
+    sell_minusdi = DecimalParameter(
+        low=0, high=1, default=0.5001, decimals=3, space="sell", load=False
+    )
+    protection_enabled = BooleanParameter(default=True)
+    protection_cooldown_lookback = IntParameter([0, 50], default=30)
+
+    # Invalid plot config ...
+    plot_config = {
+        "main_plot": {},
+    }
+
+    @property
+    def protections(self):
+        prot = []
+        if self.protection_enabled.value:
+            prot.append(
+                {
+                    "method": "CooldownPeriod",
+                    "stop_duration_candles": self.protection_cooldown_lookback.value,
+                }
+            )
+        return prot
+
+    bot_loop_started = False
+    bot_started = False
+
+    def bot_loop_start(self, **kwargs):
+        self.bot_loop_started = True
+
+    def bot_start(self, **kwargs) -> None:
+        """
+        Parameters can also be defined here ...
+        """
+        self.bot_started = True
+        self.buy_rsi = IntParameter([0, 50], default=30, space="buy")
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe["rsi"] < self.buy_rsi.value)
+                & (dataframe["fastd"] < 35)
+                & (dataframe["adx"] > 30)
+                & (dataframe["plus_di"] > self.buy_plusdi.value)
+            )
+            | ((dataframe["adx"] > 65) & (dataframe["plus_di"] > self.buy_plusdi.value)),
+            "buy",
+        ] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with sell column
+        """
+        dataframe.loc[
+            (
+                (
+                    (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
+                    | (qtpylib.crossed_above(dataframe["fastd"], 70))
+                )
+                & (dataframe["adx"] > 10)
+                & (dataframe["minus_di"] > 0)
+            )
+            | ((dataframe["adx"] > 70) & (dataframe["minus_di"] > self.sell_minusdi.value)),
+            "sell",
+        ] = 1
+        return dataframe
diff --git a/tests/strategy/strats/hyperoptable_strategy_v2.py b/tests/strategy/strats/hyperoptable_strategy_v2.py
new file mode 100644
index 000000000..40e139e1f
--- /dev/null
+++ b/tests/strategy/strats/hyperoptable_strategy_v2.py
@@ -0,0 +1,58 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+from strategy_test_v2 import StrategyTestV2
+
+from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, RealParameter
+
+
+class HyperoptableStrategyV2(StrategyTestV2):
+    """
+    Default Strategy provided by freqtrade bot.
+    Please do not modify this strategy, it's  intended for internal use only.
+    Please look at the SampleStrategy in the user_data/strategy directory
+    or strategy repository https://github.com/freqtrade/freqtrade-strategies
+    for samples and inspiration.
+    """
+
+    buy_params = {
+        "buy_rsi": 35,
+        # Intentionally not specified, so "default" is tested
+        # 'buy_plusdi': 0.4
+    }
+
+    sell_params = {
+        # Sell parameters
+        "sell_rsi": 74,
+        "sell_minusdi": 0.4,
+    }
+
+    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space="buy")
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell")
+    sell_minusdi = DecimalParameter(
+        low=0, high=1, default=0.5001, decimals=3, space="sell", load=False
+    )
+    protection_enabled = BooleanParameter(default=True)
+    protection_cooldown_lookback = IntParameter([0, 50], default=30)
+
+    @property
+    def protections(self):
+        prot = []
+        if self.protection_enabled.value:
+            prot.append(
+                {
+                    "method": "CooldownPeriod",
+                    "stop_duration_candles": self.protection_cooldown_lookback.value,
+                }
+            )
+        return prot
+
+    bot_loop_started = False
+
+    def bot_loop_start(self, **kwargs):
+        self.bot_loop_started = True
+
+    def bot_start(self, **kwargs) -> None:
+        """
+        Parameters can also be defined here ...
+        """
+        self.buy_rsi = IntParameter([0, 50], default=30, space="buy")
diff --git a/tests/strategy/strats/informative_decorator_strategy.py b/tests/strategy/strats/informative_decorator_strategy.py
new file mode 100644
index 000000000..7414981e3
--- /dev/null
+++ b/tests/strategy/strats/informative_decorator_strategy.py
@@ -0,0 +1,85 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+from pandas import DataFrame
+
+from freqtrade.strategy import IStrategy, informative, merge_informative_pair
+
+
+class InformativeDecoratorTest(IStrategy):
+    """
+    Strategy used by tests freqtrade bot.
+    Please do not modify this strategy, it's  intended for internal use only.
+    Please look at the SampleStrategy in the user_data/strategy directory
+    or strategy repository https://github.com/freqtrade/freqtrade-strategies
+    for samples and inspiration.
+    """
+
+    INTERFACE_VERSION = 2
+    stoploss = -0.10
+    timeframe = "5m"
+    startup_candle_count: int = 20
+
+    def informative_pairs(self):
+        # Intentionally return 2 tuples, must be converted to 3 in compatibility code
+        return [
+            ("NEO/USDT", "5m"),
+            ("NEO/USDT", "15m", ""),
+            ("NEO/USDT", "2h", "futures"),
+        ]
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe["buy"] = 0
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe["sell"] = 0
+        return dataframe
+
+    # Decorator stacking test.
+    @informative("30m")
+    @informative("1h")
+    def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe["rsi"] = 14
+        return dataframe
+
+    # Simple informative test.
+    @informative("1h", "NEO/{stake}")
+    def populate_indicators_neo_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe["rsi"] = 14
+        return dataframe
+
+    @informative("1h", "{base}/BTC")
+    def populate_indicators_base_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe["rsi"] = 14
+        return dataframe
+
+    # Quote currency different from stake currency test.
+    @informative("1h", "ETH/BTC", candle_type="spot")
+    def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe["rsi"] = 14
+        return dataframe
+
+    # Formatting test.
+    @informative("30m", "NEO/{stake}", "{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}")
+    def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe["rsi"] = 14
+        return dataframe
+
+    # Custom formatter test
+    @informative("30m", "ETH/{stake}", fmt=lambda column, **kwargs: column + "_from_callable")
+    def populate_indicators_eth_30m(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe["rsi"] = 14
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Strategy timeframe indicators for current pair.
+        dataframe["rsi"] = 14
+        # Informative pairs are available in this method.
+        dataframe["rsi_less"] = dataframe["rsi"] < dataframe["rsi_1h"]
+
+        # Mixing manual informative pairs with decorators.
+        informative = self.dp.get_pair_dataframe("NEO/USDT", "5m", "")
+        informative["rsi"] = 14
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "5m", ffill=True)
+
+        return dataframe
diff --git a/tests/strategy/strats/lookahead_bias/strategy_test_v3_with_lookahead_bias.py b/tests/strategy/strats/lookahead_bias/strategy_test_v3_with_lookahead_bias.py
new file mode 100644
index 000000000..618b401bb
--- /dev/null
+++ b/tests/strategy/strats/lookahead_bias/strategy_test_v3_with_lookahead_bias.py
@@ -0,0 +1,53 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+from pandas import DataFrame
+from technical.indicators import ichimoku
+
+from freqtrade.strategy import IStrategy
+from freqtrade.strategy.parameters import CategoricalParameter
+
+
+class strategy_test_v3_with_lookahead_bias(IStrategy):
+    INTERFACE_VERSION = 3
+
+    # Minimal ROI designed for the strategy
+    minimal_roi = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.10
+
+    # Optimal timeframe for the strategy
+    timeframe = "5m"
+    scenario = CategoricalParameter(["no_bias", "bias1"], default="bias1", space="buy")
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # bias is introduced here
+        if self.scenario.value != "no_bias":
+            ichi = ichimoku(
+                dataframe,
+                conversion_line_period=20,
+                base_line_periods=60,
+                laggin_span=120,
+                displacement=30,
+            )
+            dataframe["chikou_span"] = ichi["chikou_span"]
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        if self.scenario.value == "no_bias":
+            dataframe.loc[dataframe["close"].shift(10) < dataframe["close"], "enter_long"] = 1
+        else:
+            dataframe.loc[dataframe["close"].shift(-10) > dataframe["close"], "enter_long"] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        if self.scenario.value == "no_bias":
+            dataframe.loc[dataframe["close"].shift(10) < dataframe["close"], "exit"] = 1
+        else:
+            dataframe.loc[dataframe["close"].shift(-10) > dataframe["close"], "exit"] = 1
+
+        return dataframe
diff --git a/tests/strategy/strats/strategy_test_v2.py b/tests/strategy/strats/strategy_test_v2.py
new file mode 100644
index 000000000..58473e9c0
--- /dev/null
+++ b/tests/strategy/strats/strategy_test_v2.py
@@ -0,0 +1,140 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.strategy import IStrategy
+
+
+class StrategyTestV2(IStrategy):
+    """
+    Strategy used by tests freqtrade bot.
+    Please do not modify this strategy, it's  intended for internal use only.
+    Please look at the SampleStrategy in the user_data/strategy directory
+    or strategy repository https://github.com/freqtrade/freqtrade-strategies
+    for samples and inspiration.
+    """
+
+    INTERFACE_VERSION = 2
+
+    # Minimal ROI designed for the strategy
+    minimal_roi = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.10
+
+    # Optimal timeframe for the strategy
+    timeframe = "5m"
+
+    # Optional order type mapping
+    order_types = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": False,
+    }
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
+    # Optional time in force for orders
+    order_time_in_force = {
+        "entry": "gtc",
+        "exit": "gtc",
+    }
+    # Test legacy use_sell_signal definition
+    use_sell_signal = False
+
+    # By default this strategy does not use Position Adjustments
+    position_adjustment_enable = False
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Dataframe with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+        # Momentum Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe["adx"] = ta.ADX(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
+
+        # Minus Directional Indicator / Movement
+        dataframe["minus_di"] = ta.MINUS_DI(dataframe)
+
+        # Plus Directional Indicator / Movement
+        dataframe["plus_di"] = ta.PLUS_DI(dataframe)
+
+        # RSI
+        dataframe["rsi"] = ta.RSI(dataframe)
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe["fastd"] = stoch_fast["fastd"]
+        dataframe["fastk"] = stoch_fast["fastk"]
+
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe["bb_lowerband"] = bollinger["lower"]
+        dataframe["bb_middleband"] = bollinger["mid"]
+        dataframe["bb_upperband"] = bollinger["upper"]
+
+        # EMA - Exponential Moving Average
+        dataframe["ema10"] = ta.EMA(dataframe, timeperiod=10)
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe["rsi"] < 35)
+                & (dataframe["fastd"] < 35)
+                & (dataframe["adx"] > 30)
+                & (dataframe["plus_di"] > 0.5)
+            )
+            | ((dataframe["adx"] > 65) & (dataframe["plus_di"] > 0.5)),
+            "buy",
+        ] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (
+                    (qtpylib.crossed_above(dataframe["rsi"], 70))
+                    | (qtpylib.crossed_above(dataframe["fastd"], 70))
+                )
+                & (dataframe["adx"] > 10)
+                & (dataframe["minus_di"] > 0)
+            )
+            | ((dataframe["adx"] > 70) & (dataframe["minus_di"] > 0.5)),
+            "sell",
+        ] = 1
+        return dataframe
diff --git a/tests/strategy/strats/strategy_test_v3.py b/tests/strategy/strats/strategy_test_v3.py
new file mode 100644
index 000000000..e87810111
--- /dev/null
+++ b/tests/strategy/strats/strategy_test_v3.py
@@ -0,0 +1,208 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+from datetime import datetime
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.persistence import Trade
+from freqtrade.strategy import (
+    BooleanParameter,
+    DecimalParameter,
+    IntParameter,
+    IStrategy,
+    RealParameter,
+)
+
+
+class StrategyTestV3(IStrategy):
+    """
+    Strategy used by tests freqtrade bot.
+    Please do not modify this strategy, it's  intended for internal use only.
+    Please look at the SampleStrategy in the user_data/strategy directory
+    or strategy repository https://github.com/freqtrade/freqtrade-strategies
+    for samples and inspiration.
+    """
+
+    INTERFACE_VERSION = 3
+
+    # Minimal ROI designed for the strategy
+    minimal_roi = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}
+
+    # Optimal max_open_trades for the strategy
+    max_open_trades = -1
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.10
+
+    # Optimal timeframe for the strategy
+    timeframe = "5m"
+
+    # Optional order type mapping
+    order_types = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": False,
+    }
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
+    # Optional time in force for orders
+    order_time_in_force = {
+        "entry": "gtc",
+        "exit": "gtc",
+    }
+
+    buy_params = {
+        "buy_rsi": 35,
+        # Intentionally not specified, so "default" is tested
+        # 'buy_plusdi': 0.4
+    }
+
+    sell_params = {"sell_rsi": 74, "sell_minusdi": 0.4}
+
+    buy_rsi = IntParameter([0, 50], default=30, space="buy")
+    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space="buy")
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell")
+    sell_minusdi = DecimalParameter(
+        low=0, high=1, default=0.5001, decimals=3, space="sell", load=False
+    )
+    protection_enabled = BooleanParameter(default=True)
+    protection_cooldown_lookback = IntParameter([0, 50], default=30)
+
+    # TODO: Can this work with protection tests? (replace HyperoptableStrategy implicitly ... )
+    @property
+    def protections(self):
+        prot = []
+        if self.protection_enabled.value:
+            # Workaround to simplify tests. This will not work in real scenarios.
+            prot = self.config.get("_strategy_protections", {})
+        return prot
+
+    bot_started = False
+
+    def bot_start(self):
+        self.bot_started = True
+
+    def informative_pairs(self):
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Momentum Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe["adx"] = ta.ADX(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
+
+        # Minus Directional Indicator / Movement
+        dataframe["minus_di"] = ta.MINUS_DI(dataframe)
+
+        # Plus Directional Indicator / Movement
+        dataframe["plus_di"] = ta.PLUS_DI(dataframe)
+
+        # RSI
+        dataframe["rsi"] = ta.RSI(dataframe)
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe["fastd"] = stoch_fast["fastd"]
+        dataframe["fastk"] = stoch_fast["fastk"]
+
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe["bb_lowerband"] = bollinger["lower"]
+        dataframe["bb_middleband"] = bollinger["mid"]
+        dataframe["bb_upperband"] = bollinger["upper"]
+
+        # EMA - Exponential Moving Average
+        dataframe["ema10"] = ta.EMA(dataframe, timeperiod=10)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (dataframe["rsi"] < self.buy_rsi.value)
+                & (dataframe["fastd"] < 35)
+                & (dataframe["adx"] > 30)
+                & (dataframe["plus_di"] > self.buy_plusdi.value)
+            )
+            | ((dataframe["adx"] > 65) & (dataframe["plus_di"] > self.buy_plusdi.value)),
+            "enter_long",
+        ] = 1
+        dataframe.loc[
+            (qtpylib.crossed_below(dataframe["rsi"], self.sell_rsi.value)),
+            ("enter_short", "enter_tag"),
+        ] = (1, "short_Tag")
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (
+                    (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
+                    | (qtpylib.crossed_above(dataframe["fastd"], 70))
+                )
+                & (dataframe["adx"] > 10)
+                & (dataframe["minus_di"] > 0)
+            )
+            | ((dataframe["adx"] > 70) & (dataframe["minus_di"] > self.sell_minusdi.value)),
+            "exit_long",
+        ] = 1
+
+        dataframe.loc[
+            (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)),
+            ("exit_short", "exit_tag"),
+        ] = (1, "short_Tag")
+
+        return dataframe
+
+    def leverage(
+        self,
+        pair: str,
+        current_time: datetime,
+        current_rate: float,
+        proposed_leverage: float,
+        max_leverage: float,
+        entry_tag: str | None,
+        side: str,
+        **kwargs,
+    ) -> float:
+        # Return 3.0 in all cases.
+        # Bot-logic must make sure it's an allowed leverage and eventually adjust accordingly.
+
+        return 3.0
+
+    def adjust_trade_position(
+        self,
+        trade: Trade,
+        current_time: datetime,
+        current_rate: float,
+        current_profit: float,
+        min_stake: float | None,
+        max_stake: float,
+        current_entry_rate: float,
+        current_exit_rate: float,
+        current_entry_profit: float,
+        current_exit_profit: float,
+        **kwargs,
+    ) -> float | None:
+        if current_profit < -0.0075:
+            orders = trade.select_filled_orders(trade.entry_side)
+            return round(orders[0].stake_amount, 0)
+
+        return None
+
+
+class StrategyTestV3Futures(StrategyTestV3):
+    can_short = True
diff --git a/tests/strategy/strats/strategy_test_v3_custom_entry_price.py b/tests/strategy/strats/strategy_test_v3_custom_entry_price.py
new file mode 100644
index 000000000..2689db27a
--- /dev/null
+++ b/tests/strategy/strats/strategy_test_v3_custom_entry_price.py
@@ -0,0 +1,43 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+from datetime import datetime
+
+from pandas import DataFrame
+from strategy_test_v3 import StrategyTestV3
+
+from freqtrade.persistence import Trade
+
+
+class StrategyTestV3CustomEntryPrice(StrategyTestV3):
+    """
+    Strategy used by tests freqtrade bot.
+    Please do not modify this strategy, it's  intended for internal use only.
+    Please look at the SampleStrategy in the user_data/strategy directory
+    or strategy repository https://github.com/freqtrade/freqtrade-strategies
+    for samples and inspiration.
+    """
+
+    new_entry_price: float = 0.001
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[dataframe["volume"] > 0, "enter_long"] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return dataframe
+
+    def custom_entry_price(
+        self,
+        pair: str,
+        trade: Trade | None,
+        current_time: datetime,
+        proposed_rate: float,
+        entry_tag: str | None,
+        side: str,
+        **kwargs,
+    ) -> float:
+        return self.new_entry_price
diff --git a/tests/strategy/strats/strategy_test_v3_recursive_issue.py b/tests/strategy/strats/strategy_test_v3_recursive_issue.py
new file mode 100644
index 000000000..e709d7ad4
--- /dev/null
+++ b/tests/strategy/strats/strategy_test_v3_recursive_issue.py
@@ -0,0 +1,45 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+import talib.abstract as ta
+from pandas import DataFrame
+
+from freqtrade.strategy import IStrategy
+from freqtrade.strategy.parameters import CategoricalParameter
+
+
+class strategy_test_v3_recursive_issue(IStrategy):
+    INTERFACE_VERSION = 3
+
+    # Minimal ROI designed for the strategy
+    minimal_roi = {"0": 0.04}
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.10
+
+    # Optimal timeframe for the strategy
+    timeframe = "5m"
+    scenario = CategoricalParameter(["no_bias", "bias1", "bias2"], default="bias1", space="buy")
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 100
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # bias is introduced here
+        if self.scenario.value == "no_bias":
+            dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+        else:
+            dataframe["rsi"] = ta.RSI(dataframe, timeperiod=50)
+
+        if self.scenario.value == "bias2":
+            # Has both bias1 and bias2
+            dataframe["rsi_lookahead"] = ta.RSI(dataframe, timeperiod=50).shift(-1)
+
+        # String columns shouldn't cause issues
+        dataframe["test_string_column"] = f"a{len(dataframe)}"
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return dataframe
diff --git a/tests/strategy/test_default_strategy.py b/tests/strategy/test_default_strategy.py
new file mode 100644
index 000000000..494e374c4
--- /dev/null
+++ b/tests/strategy/test_default_strategy.py
@@ -0,0 +1,82 @@
+from datetime import datetime, timezone
+
+import pytest
+from pandas import DataFrame
+
+from freqtrade.persistence.models import Trade
+
+from .strats.strategy_test_v3 import StrategyTestV3
+
+
+def test_strategy_test_v3_structure():
+    assert hasattr(StrategyTestV3, "minimal_roi")
+    assert hasattr(StrategyTestV3, "stoploss")
+    assert hasattr(StrategyTestV3, "timeframe")
+    assert hasattr(StrategyTestV3, "populate_indicators")
+    assert hasattr(StrategyTestV3, "populate_entry_trend")
+    assert hasattr(StrategyTestV3, "populate_exit_trend")
+
+
+@pytest.mark.parametrize(
+    "is_short,side",
+    [
+        (True, "short"),
+        (False, "long"),
+    ],
+)
+def test_strategy_test_v3(dataframe_1m, fee, is_short, side):
+    strategy = StrategyTestV3({})
+
+    metadata = {"pair": "ETH/BTC"}
+    assert isinstance(strategy.minimal_roi, dict)
+    assert isinstance(strategy.stoploss, float)
+    assert isinstance(strategy.timeframe, str)
+    indicators = strategy.populate_indicators(dataframe_1m, metadata)
+    assert isinstance(indicators, DataFrame)
+    assert isinstance(strategy.populate_buy_trend(indicators, metadata), DataFrame)
+    assert isinstance(strategy.populate_sell_trend(indicators, metadata), DataFrame)
+
+    trade = Trade(
+        open_rate=19_000, amount=0.1, pair="ETH/BTC", fee_open=fee.return_value, is_short=is_short
+    )
+
+    assert (
+        strategy.confirm_trade_entry(
+            pair="ETH/BTC",
+            order_type="limit",
+            amount=0.1,
+            rate=20000,
+            time_in_force="gtc",
+            current_time=datetime.now(timezone.utc),
+            side=side,
+            entry_tag=None,
+        )
+        is True
+    )
+    assert (
+        strategy.confirm_trade_exit(
+            pair="ETH/BTC",
+            trade=trade,
+            order_type="limit",
+            amount=0.1,
+            rate=20000,
+            time_in_force="gtc",
+            exit_reason="roi",
+            sell_reason="roi",
+            current_time=datetime.now(timezone.utc),
+            side=side,
+        )
+        is True
+    )
+
+    assert (
+        strategy.custom_stoploss(
+            pair="ETH/BTC",
+            trade=trade,
+            current_time=datetime.now(),
+            current_rate=20_000,
+            current_profit=0.05,
+            after_fill=False,
+        )
+        == strategy.stoploss
+    )
diff --git a/tests/strategy/test_interface.py b/tests/strategy/test_interface.py
new file mode 100644
index 000000000..647484535
--- /dev/null
+++ b/tests/strategy/test_interface.py
@@ -0,0 +1,1086 @@
+# pragma pylint: disable=missing-docstring, C0103
+import logging
+import math
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+from pandas import DataFrame
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import CUSTOM_TAG_MAX_LENGTH
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.data.history import load_data
+from freqtrade.enums import ExitCheckTuple, ExitType, HyperoptState, SignalDirection
+from freqtrade.exceptions import OperationalException, StrategyError
+from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer
+from freqtrade.optimize.space import SKDecimal
+from freqtrade.persistence import PairLocks, Trade
+from freqtrade.resolvers import StrategyResolver
+from freqtrade.strategy.hyper import detect_parameters
+from freqtrade.strategy.parameters import (
+    BaseParameter,
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+)
+from freqtrade.util import dt_now
+from tests.conftest import CURRENT_TEST_STRATEGY, TRADE_SIDES, log_has, log_has_re
+
+from .strats.strategy_test_v3 import StrategyTestV3
+
+
+# Avoid to reinit the same object again and again
+_STRATEGY = StrategyTestV3(config={})
+_STRATEGY.dp = DataProvider({}, None, None)
+
+
+def test_returns_latest_signal(ohlcv_history):
+    ohlcv_history.loc[1, "date"] = dt_now()
+    # Take a copy to correctly modify the call
+    mocked_history = ohlcv_history.copy()
+    mocked_history["enter_long"] = 0
+    mocked_history["exit_long"] = 0
+    mocked_history["enter_short"] = 0
+    mocked_history["exit_short"] = 0
+    # Set tags in lines that don't matter to test nan in the sell line
+    mocked_history.loc[0, "enter_tag"] = "wrong_line"
+    mocked_history.loc[0, "exit_tag"] = "wrong_line"
+    mocked_history.loc[1, "exit_long"] = 1
+
+    assert _STRATEGY.get_entry_signal("ETH/BTC", "5m", mocked_history) == (None, None)
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history) == (False, True, None)
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history, True) == (False, False, None)
+    mocked_history.loc[1, "exit_long"] = 0
+    mocked_history.loc[1, "enter_long"] = 1
+
+    assert _STRATEGY.get_entry_signal("ETH/BTC", "5m", mocked_history) == (
+        SignalDirection.LONG,
+        None,
+    )
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history) == (True, False, None)
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history, True) == (False, False, None)
+    mocked_history.loc[1, "exit_long"] = 0
+    mocked_history.loc[1, "enter_long"] = 0
+
+    assert _STRATEGY.get_entry_signal("ETH/BTC", "5m", mocked_history) == (None, None)
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history) == (False, False, None)
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history, True) == (False, False, None)
+    mocked_history.loc[1, "exit_long"] = 0
+    mocked_history.loc[1, "enter_long"] = 1
+    mocked_history.loc[1, "enter_tag"] = "buy_signal_01"
+
+    assert _STRATEGY.get_entry_signal("ETH/BTC", "5m", mocked_history) == (
+        SignalDirection.LONG,
+        "buy_signal_01",
+    )
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history) == (True, False, None)
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history, True) == (False, False, None)
+
+    mocked_history.loc[1, "exit_long"] = 0
+    mocked_history.loc[1, "enter_long"] = 0
+    mocked_history.loc[1, "enter_short"] = 1
+    mocked_history.loc[1, "exit_short"] = 0
+    mocked_history.loc[1, "enter_tag"] = "sell_signal_01"
+
+    # Don't provide short signal while in spot mode
+    assert _STRATEGY.get_entry_signal("ETH/BTC", "5m", mocked_history) == (None, None)
+
+    _STRATEGY.config["trading_mode"] = "futures"
+    # Short signal gets ignored as can_short is not set.
+    assert _STRATEGY.get_entry_signal("ETH/BTC", "5m", mocked_history) == (None, None)
+
+    _STRATEGY.can_short = True
+
+    assert _STRATEGY.get_entry_signal("ETH/BTC", "5m", mocked_history) == (
+        SignalDirection.SHORT,
+        "sell_signal_01",
+    )
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history) == (False, False, None)
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history, True) == (True, False, None)
+
+    mocked_history.loc[1, "enter_short"] = 0
+    mocked_history.loc[1, "exit_short"] = 1
+    mocked_history.loc[1, "exit_tag"] = "sell_signal_02"
+    assert _STRATEGY.get_entry_signal("ETH/BTC", "5m", mocked_history) == (None, None)
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history) == (
+        False,
+        False,
+        "sell_signal_02",
+    )
+    assert _STRATEGY.get_exit_signal("ETH/BTC", "5m", mocked_history, True) == (
+        False,
+        True,
+        "sell_signal_02",
+    )
+
+    _STRATEGY.can_short = False
+    _STRATEGY.config["trading_mode"] = "spot"
+
+
+def test_analyze_pair_empty(mocker, caplog, ohlcv_history):
+    mocker.patch.object(_STRATEGY.dp, "ohlcv", return_value=ohlcv_history)
+    mocker.patch.object(_STRATEGY, "_analyze_ticker_internal", return_value=DataFrame([]))
+    mocker.patch.object(_STRATEGY, "assert_df")
+
+    _STRATEGY.analyze_pair("ETH/BTC")
+
+    assert log_has("Empty dataframe for pair ETH/BTC", caplog)
+
+
+def test_get_signal_empty(default_conf, caplog):
+    assert (None, None) == _STRATEGY.get_latest_candle(
+        "foo", default_conf["timeframe"], DataFrame()
+    )
+    assert log_has("Empty candle (OHLCV) data for pair foo", caplog)
+    caplog.clear()
+
+    assert (None, None) == _STRATEGY.get_latest_candle("bar", default_conf["timeframe"], None)
+    assert log_has("Empty candle (OHLCV) data for pair bar", caplog)
+    caplog.clear()
+
+    assert (None, None) == _STRATEGY.get_latest_candle(
+        "baz", default_conf["timeframe"], DataFrame([])
+    )
+    assert log_has("Empty candle (OHLCV) data for pair baz", caplog)
+
+
+def test_get_signal_exception_valueerror(mocker, caplog, ohlcv_history):
+    caplog.set_level(logging.INFO)
+    mocker.patch.object(_STRATEGY.dp, "ohlcv", return_value=ohlcv_history)
+    mocker.patch.object(_STRATEGY, "_analyze_ticker_internal", side_effect=ValueError("xyz"))
+    _STRATEGY.analyze_pair("foo")
+    assert log_has_re(r"Strategy caused the following exception: xyz.*", caplog)
+    caplog.clear()
+
+    mocker.patch.object(
+        _STRATEGY, "analyze_ticker", side_effect=Exception("invalid ticker history ")
+    )
+    _STRATEGY.analyze_pair("foo")
+    assert log_has_re(r"Strategy caused the following exception: xyz.*", caplog)
+
+
+def test_get_signal_old_dataframe(default_conf, mocker, caplog, ohlcv_history):
+    # default_conf defines a 5m interval. we check interval * 2 + 5m
+    # this is necessary as the last candle is removed (partial candles) by default
+    ohlcv_history.loc[1, "date"] = dt_now() - timedelta(minutes=16)
+    # Take a copy to correctly modify the call
+    mocked_history = ohlcv_history.copy()
+    mocked_history["exit_long"] = 0
+    mocked_history["enter_long"] = 0
+    mocked_history.loc[1, "enter_long"] = 1
+
+    caplog.set_level(logging.INFO)
+    mocker.patch.object(_STRATEGY, "assert_df")
+
+    assert (None, None) == _STRATEGY.get_latest_candle(
+        "xyz", default_conf["timeframe"], mocked_history
+    )
+    assert log_has("Outdated history for pair xyz. Last tick is 16 minutes old", caplog)
+
+
+def test_get_signal_no_sell_column(default_conf, mocker, caplog, ohlcv_history):
+    # default_conf defines a 5m interval. we check interval * 2 + 5m
+    # this is necessary as the last candle is removed (partial candles) by default
+    ohlcv_history.loc[1, "date"] = dt_now()
+    # Take a copy to correctly modify the call
+    mocked_history = ohlcv_history.copy()
+    # Intentionally don't set sell column
+    # mocked_history['sell'] = 0
+    mocked_history["enter_long"] = 0
+    mocked_history.loc[1, "enter_long"] = 1
+
+    caplog.set_level(logging.INFO)
+    mocker.patch.object(_STRATEGY, "assert_df")
+
+    assert (SignalDirection.LONG, None) == _STRATEGY.get_entry_signal(
+        "xyz", default_conf["timeframe"], mocked_history
+    )
+
+
+def test_ignore_expired_candle(default_conf):
+    strategy = StrategyResolver.load_strategy(default_conf)
+    strategy.ignore_buying_expired_candle_after = 60
+
+    latest_date = datetime(2020, 12, 30, 7, 0, 0, tzinfo=timezone.utc)
+    # Add 1 candle length as the "latest date" defines candle open.
+    current_time = latest_date + timedelta(seconds=80 + 300)
+
+    assert (
+        strategy.ignore_expired_candle(
+            latest_date=latest_date, current_time=current_time, timeframe_seconds=300, enter=True
+        )
+        is True
+    )
+
+    current_time = latest_date + timedelta(seconds=30 + 300)
+
+    assert (
+        strategy.ignore_expired_candle(
+            latest_date=latest_date, current_time=current_time, timeframe_seconds=300, enter=True
+        )
+        is not True
+    )
+
+
+def test_assert_df_raise(mocker, caplog, ohlcv_history):
+    ohlcv_history.loc[1, "date"] = dt_now() - timedelta(minutes=16)
+    # Take a copy to correctly modify the call
+    mocked_history = ohlcv_history.copy()
+    mocked_history["sell"] = 0
+    mocked_history["buy"] = 0
+    mocked_history.loc[1, "buy"] = 1
+
+    caplog.set_level(logging.INFO)
+    mocker.patch.object(_STRATEGY.dp, "ohlcv", return_value=ohlcv_history)
+    mocker.patch.object(_STRATEGY.dp, "get_analyzed_dataframe", return_value=(mocked_history, 0))
+    mocker.patch.object(_STRATEGY, "assert_df", side_effect=StrategyError("Dataframe returned..."))
+    _STRATEGY.analyze_pair("xyz")
+    assert log_has(
+        "Unable to analyze candle (OHLCV) data for pair xyz: Dataframe returned...", caplog
+    )
+
+
+def test_assert_df(ohlcv_history, caplog):
+    df_len = len(ohlcv_history) - 1
+    ohlcv_history.loc[:, "enter_long"] = 0
+    ohlcv_history.loc[:, "exit_long"] = 0
+    # Ensure it's running when passed correctly
+    _STRATEGY.assert_df(
+        ohlcv_history,
+        len(ohlcv_history),
+        ohlcv_history.loc[df_len, "close"],
+        ohlcv_history.loc[df_len, "date"],
+    )
+
+    with pytest.raises(StrategyError, match=r"Dataframe returned from strategy.*length\."):
+        _STRATEGY.assert_df(
+            ohlcv_history,
+            len(ohlcv_history) + 1,
+            ohlcv_history.loc[df_len, "close"],
+            ohlcv_history.loc[df_len, "date"],
+        )
+
+    with pytest.raises(
+        StrategyError, match=r"Dataframe returned from strategy.*last close price\."
+    ):
+        _STRATEGY.assert_df(
+            ohlcv_history,
+            len(ohlcv_history),
+            ohlcv_history.loc[df_len, "close"] + 0.01,
+            ohlcv_history.loc[df_len, "date"],
+        )
+    with pytest.raises(StrategyError, match=r"Dataframe returned from strategy.*last date\."):
+        _STRATEGY.assert_df(
+            ohlcv_history,
+            len(ohlcv_history),
+            ohlcv_history.loc[df_len, "close"],
+            ohlcv_history.loc[0, "date"],
+        )
+    with pytest.raises(
+        StrategyError, match=r"No dataframe returned \(return statement missing\?\)."
+    ):
+        _STRATEGY.assert_df(
+            None,
+            len(ohlcv_history),
+            ohlcv_history.loc[df_len, "close"],
+            ohlcv_history.loc[0, "date"],
+        )
+
+    _STRATEGY.disable_dataframe_checks = True
+    caplog.clear()
+    _STRATEGY.assert_df(
+        ohlcv_history,
+        len(ohlcv_history),
+        ohlcv_history.loc[2, "close"],
+        ohlcv_history.loc[0, "date"],
+    )
+    assert log_has_re(r"Dataframe returned from strategy.*last date\.", caplog)
+    # reset to avoid problems in other tests due to test leakage
+    _STRATEGY.disable_dataframe_checks = False
+
+
+def test_advise_all_indicators(default_conf, testdatadir) -> None:
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    timerange = TimeRange.parse_timerange("1510694220-1510700340")
+    data = load_data(testdatadir, "1m", ["UNITTEST/BTC"], timerange=timerange, fill_up_missing=True)
+    processed = strategy.advise_all_indicators(data)
+    assert len(processed["UNITTEST/BTC"]) == 103
+
+
+def test_freqai_not_initialized(default_conf) -> None:
+    strategy = StrategyResolver.load_strategy(default_conf)
+    strategy.ft_bot_start()
+    with pytest.raises(OperationalException, match=r"freqAI is not enabled\."):
+        strategy.freqai.start()
+
+
+def test_advise_all_indicators_copy(mocker, default_conf, testdatadir) -> None:
+    strategy = StrategyResolver.load_strategy(default_conf)
+    aimock = mocker.patch("freqtrade.strategy.interface.IStrategy.advise_indicators")
+    timerange = TimeRange.parse_timerange("1510694220-1510700340")
+    data = load_data(testdatadir, "1m", ["UNITTEST/BTC"], timerange=timerange, fill_up_missing=True)
+    strategy.advise_all_indicators(data)
+    assert aimock.call_count == 1
+    # Ensure that a copy of the dataframe is passed to advice_indicators
+    assert aimock.call_args_list[0][0][0] is not data
+
+
+def test_min_roi_reached(default_conf, fee) -> None:
+    # Use list to confirm sequence does not matter
+    min_roi_list = [{20: 0.05, 55: 0.01, 0: 0.1}, {0: 0.1, 20: 0.05, 55: 0.01}]
+    for roi in min_roi_list:
+        strategy = StrategyResolver.load_strategy(default_conf)
+        strategy.minimal_roi = roi
+        trade = Trade(
+            pair="ETH/BTC",
+            stake_amount=0.001,
+            amount=5,
+            open_date=dt_now() - timedelta(hours=1),
+            fee_open=fee.return_value,
+            fee_close=fee.return_value,
+            exchange="binance",
+            open_rate=1,
+        )
+
+        assert not strategy.min_roi_reached(trade, 0.02, dt_now() - timedelta(minutes=56))
+        assert strategy.min_roi_reached(trade, 0.12, dt_now() - timedelta(minutes=56))
+
+        assert not strategy.min_roi_reached(trade, 0.04, dt_now() - timedelta(minutes=39))
+        assert strategy.min_roi_reached(trade, 0.06, dt_now() - timedelta(minutes=39))
+
+        assert not strategy.min_roi_reached(trade, -0.01, dt_now() - timedelta(minutes=1))
+        assert strategy.min_roi_reached(trade, 0.02, dt_now() - timedelta(minutes=1))
+
+
+def test_min_roi_reached2(default_conf, fee) -> None:
+    # test with ROI raising after last interval
+    min_roi_list = [
+        {20: 0.07, 30: 0.05, 55: 0.30, 0: 0.1},
+        {0: 0.1, 20: 0.07, 30: 0.05, 55: 0.30},
+    ]
+    for roi in min_roi_list:
+        strategy = StrategyResolver.load_strategy(default_conf)
+        strategy.minimal_roi = roi
+        trade = Trade(
+            pair="ETH/BTC",
+            stake_amount=0.001,
+            amount=5,
+            open_date=dt_now() - timedelta(hours=1),
+            fee_open=fee.return_value,
+            fee_close=fee.return_value,
+            exchange="binance",
+            open_rate=1,
+        )
+
+        assert not strategy.min_roi_reached(trade, 0.02, dt_now() - timedelta(minutes=56))
+        assert strategy.min_roi_reached(trade, 0.12, dt_now() - timedelta(minutes=56))
+
+        assert not strategy.min_roi_reached(trade, 0.04, dt_now() - timedelta(minutes=39))
+        assert strategy.min_roi_reached(trade, 0.071, dt_now() - timedelta(minutes=39))
+
+        assert not strategy.min_roi_reached(trade, 0.04, dt_now() - timedelta(minutes=26))
+        assert strategy.min_roi_reached(trade, 0.06, dt_now() - timedelta(minutes=26))
+
+        # Should not trigger with 20% profit since after 55 minutes only 30% is active.
+        assert not strategy.min_roi_reached(trade, 0.20, dt_now() - timedelta(minutes=2))
+        assert strategy.min_roi_reached(trade, 0.31, dt_now() - timedelta(minutes=2))
+
+
+def test_min_roi_reached3(default_conf, fee) -> None:
+    # test for issue #1948
+    min_roi = {
+        20: 0.07,
+        30: 0.05,
+        55: 0.30,
+    }
+    strategy = StrategyResolver.load_strategy(default_conf)
+    strategy.minimal_roi = min_roi
+    trade = Trade(
+        pair="ETH/BTC",
+        stake_amount=0.001,
+        amount=5,
+        open_date=dt_now() - timedelta(hours=1),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+    )
+
+    assert not strategy.min_roi_reached(trade, 0.02, dt_now() - timedelta(minutes=56))
+    assert not strategy.min_roi_reached(trade, 0.12, dt_now() - timedelta(minutes=56))
+
+    assert not strategy.min_roi_reached(trade, 0.04, dt_now() - timedelta(minutes=39))
+    assert strategy.min_roi_reached(trade, 0.071, dt_now() - timedelta(minutes=39))
+
+    assert not strategy.min_roi_reached(trade, 0.04, dt_now() - timedelta(minutes=26))
+    assert strategy.min_roi_reached(trade, 0.06, dt_now() - timedelta(minutes=26))
+
+    # Should not trigger with 20% profit since after 55 minutes only 30% is active.
+    assert not strategy.min_roi_reached(trade, 0.20, dt_now() - timedelta(minutes=2))
+    assert strategy.min_roi_reached(trade, 0.31, dt_now() - timedelta(minutes=2))
+
+
+@pytest.mark.parametrize(
+    "profit,adjusted,expected,liq,trailing,custom,profit2,adjusted2,expected2,custom_stop",
+    [
+        # Profit, adjusted stoploss(absolute), profit for 2nd call, enable trailing,
+        #   enable custom stoploss, expected after 1st call, expected after 2nd call
+        (0.2, 0.9, ExitType.NONE, None, False, False, 0.3, 0.9, ExitType.NONE, None),
+        (0.2, 0.9, ExitType.NONE, None, False, False, -0.2, 0.9, ExitType.STOP_LOSS, None),
+        (0.2, 0.9, ExitType.NONE, 0.92, False, False, -0.09, 0.9, ExitType.LIQUIDATION, None),
+        (
+            0.2,
+            1.14,
+            ExitType.NONE,
+            None,
+            True,
+            False,
+            0.05,
+            1.14,
+            ExitType.TRAILING_STOP_LOSS,
+            None,
+        ),
+        (0.01, 0.96, ExitType.NONE, None, True, False, 0.05, 0.998, ExitType.NONE, None),
+        (
+            0.05,
+            0.998,
+            ExitType.NONE,
+            None,
+            True,
+            False,
+            -0.01,
+            0.998,
+            ExitType.TRAILING_STOP_LOSS,
+            None,
+        ),
+        # Default custom case - trails with 10%
+        (0.05, 0.945, ExitType.NONE, None, False, True, -0.02, 0.945, ExitType.NONE, None),
+        (
+            0.05,
+            0.945,
+            ExitType.NONE,
+            None,
+            False,
+            True,
+            -0.06,
+            0.945,
+            ExitType.TRAILING_STOP_LOSS,
+            None,
+        ),
+        (
+            0.05,
+            0.998,
+            ExitType.NONE,
+            None,
+            False,
+            True,
+            -0.06,
+            0.998,
+            ExitType.TRAILING_STOP_LOSS,
+            lambda **kwargs: -0.05,
+        ),
+        (
+            0.05,
+            0.998,
+            ExitType.NONE,
+            None,
+            False,
+            True,
+            0.09,
+            1.036,
+            ExitType.NONE,
+            lambda **kwargs: -0.05,
+        ),
+        (
+            0.05,
+            0.945,
+            ExitType.NONE,
+            None,
+            False,
+            True,
+            0.09,
+            0.981,
+            ExitType.NONE,
+            lambda current_profit, **kwargs: (
+                -0.1 if current_profit < 0.6 else -(current_profit * 2)
+            ),
+        ),
+        # Error case - static stoploss in place
+        (
+            0.05,
+            0.9,
+            ExitType.NONE,
+            None,
+            False,
+            True,
+            0.09,
+            0.9,
+            ExitType.NONE,
+            lambda **kwargs: None,
+        ),
+        # Error case - Returning inf.
+        (
+            0.05,
+            0.9,
+            ExitType.NONE,
+            None,
+            False,
+            True,
+            0.09,
+            0.9,
+            ExitType.NONE,
+            lambda **kwargs: math.inf,
+        ),
+    ],
+)
+def test_ft_stoploss_reached(
+    default_conf,
+    fee,
+    profit,
+    adjusted,
+    expected,
+    liq,
+    trailing,
+    custom,
+    profit2,
+    adjusted2,
+    expected2,
+    custom_stop,
+) -> None:
+    strategy = StrategyResolver.load_strategy(default_conf)
+    trade = Trade(
+        pair="ETH/BTC",
+        stake_amount=0.01,
+        amount=1,
+        open_date=dt_now() - timedelta(hours=1),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        liquidation_price=liq,
+        price_precision=4,
+        precision_mode=2,
+        precision_mode_price=2,
+    )
+    trade.adjust_min_max_rates(trade.open_rate, trade.open_rate)
+    strategy.trailing_stop = trailing
+    strategy.trailing_stop_positive = -0.05
+    strategy.use_custom_stoploss = custom
+    original_stopvalue = strategy.custom_stoploss
+    if custom_stop:
+        strategy.custom_stoploss = custom_stop
+
+    now = dt_now()
+    current_rate = trade.open_rate * (1 + profit)
+    sl_flag = strategy.ft_stoploss_reached(
+        current_rate=current_rate,
+        trade=trade,
+        current_time=now,
+        current_profit=profit,
+        force_stoploss=0,
+        high=None,
+    )
+    assert isinstance(sl_flag, ExitCheckTuple)
+    assert sl_flag.exit_type == expected
+    if expected == ExitType.NONE:
+        assert sl_flag.exit_flag is False
+    else:
+        assert sl_flag.exit_flag is True
+    assert round(trade.stop_loss, 3) == adjusted
+    current_rate2 = trade.open_rate * (1 + profit2)
+
+    sl_flag = strategy.ft_stoploss_reached(
+        current_rate=current_rate2,
+        trade=trade,
+        current_time=now,
+        current_profit=profit2,
+        force_stoploss=0,
+        high=None,
+    )
+    assert sl_flag.exit_type == expected2
+    if expected2 == ExitType.NONE:
+        assert sl_flag.exit_flag is False
+    else:
+        assert sl_flag.exit_flag is True
+    assert round(trade.stop_loss, 3) == adjusted2
+
+    strategy.custom_stoploss = original_stopvalue
+
+
+def test_custom_exit(default_conf, fee, caplog) -> None:
+    strategy = StrategyResolver.load_strategy(default_conf)
+    trade = Trade(
+        pair="ETH/BTC",
+        stake_amount=0.01,
+        amount=1,
+        open_date=dt_now() - timedelta(hours=1),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        leverage=1.0,
+    )
+
+    now = dt_now()
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=False, low=None, high=None)
+
+    assert res == []
+
+    strategy.custom_exit = MagicMock(return_value=True)
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=False, low=None, high=None)
+    assert res[0].exit_flag is True
+    assert res[0].exit_type == ExitType.CUSTOM_EXIT
+    assert res[0].exit_reason == "custom_exit"
+
+    strategy.custom_exit = MagicMock(return_value="hello world")
+
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=False, low=None, high=None)
+    assert res[0].exit_type == ExitType.CUSTOM_EXIT
+    assert res[0].exit_flag is True
+    assert res[0].exit_reason == "hello world"
+
+    caplog.clear()
+    strategy.custom_exit = MagicMock(return_value="h" * CUSTOM_TAG_MAX_LENGTH * 2)
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=False, low=None, high=None)
+    assert res[0].exit_type == ExitType.CUSTOM_EXIT
+    assert res[0].exit_flag is True
+    assert res[0].exit_reason == "h" * (CUSTOM_TAG_MAX_LENGTH)
+    assert log_has_re("Custom exit reason returned from custom_exit is too long.*", caplog)
+
+
+def test_should_sell(default_conf, fee) -> None:
+    strategy = StrategyResolver.load_strategy(default_conf)
+    trade = Trade(
+        pair="ETH/BTC",
+        stake_amount=0.01,
+        amount=1,
+        open_date=dt_now() - timedelta(hours=1),
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange="binance",
+        open_rate=1,
+        leverage=1.0,
+    )
+    now = dt_now()
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=False, low=None, high=None)
+
+    assert res == []
+    strategy.min_roi_reached = MagicMock(return_value=True)
+
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=False, low=None, high=None)
+    assert len(res) == 1
+    assert res == [ExitCheckTuple(exit_type=ExitType.ROI)]
+
+    strategy.min_roi_reached = MagicMock(return_value=True)
+    strategy.ft_stoploss_reached = MagicMock(
+        return_value=ExitCheckTuple(exit_type=ExitType.STOP_LOSS)
+    )
+
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=False, low=None, high=None)
+    assert len(res) == 2
+    assert res == [
+        ExitCheckTuple(exit_type=ExitType.STOP_LOSS),
+        ExitCheckTuple(exit_type=ExitType.ROI),
+    ]
+
+    strategy.custom_exit = MagicMock(return_value="hello world")
+    # custom-exit and exit-signal is first
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=False, low=None, high=None)
+    assert len(res) == 3
+    assert res == [
+        ExitCheckTuple(exit_type=ExitType.CUSTOM_EXIT, exit_reason="hello world"),
+        ExitCheckTuple(exit_type=ExitType.STOP_LOSS),
+        ExitCheckTuple(exit_type=ExitType.ROI),
+    ]
+
+    strategy.ft_stoploss_reached = MagicMock(
+        return_value=ExitCheckTuple(exit_type=ExitType.TRAILING_STOP_LOSS)
+    )
+    # Regular exit signal
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=True, low=None, high=None)
+    assert len(res) == 3
+    assert res == [
+        ExitCheckTuple(exit_type=ExitType.EXIT_SIGNAL),
+        ExitCheckTuple(exit_type=ExitType.ROI),
+        ExitCheckTuple(exit_type=ExitType.TRAILING_STOP_LOSS),
+    ]
+
+    # Regular exit signal, no ROI
+    strategy.min_roi_reached = MagicMock(return_value=False)
+    res = strategy.should_exit(trade, 1, now, enter=False, exit_=True, low=None, high=None)
+    assert len(res) == 2
+    assert res == [
+        ExitCheckTuple(exit_type=ExitType.EXIT_SIGNAL),
+        ExitCheckTuple(exit_type=ExitType.TRAILING_STOP_LOSS),
+    ]
+
+
+@pytest.mark.parametrize("side", TRADE_SIDES)
+def test_leverage_callback(default_conf, side) -> None:
+    default_conf["strategy"] = "StrategyTestV2"
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert (
+        strategy.leverage(
+            pair="XRP/USDT",
+            current_time=datetime.now(timezone.utc),
+            current_rate=2.2,
+            proposed_leverage=1.0,
+            max_leverage=5.0,
+            side=side,
+            entry_tag=None,
+        )
+        == 1
+    )
+
+    default_conf["strategy"] = CURRENT_TEST_STRATEGY
+    strategy = StrategyResolver.load_strategy(default_conf)
+    assert (
+        strategy.leverage(
+            pair="XRP/USDT",
+            current_time=datetime.now(timezone.utc),
+            current_rate=2.2,
+            proposed_leverage=1.0,
+            max_leverage=5.0,
+            side=side,
+            entry_tag="entry_tag_test",
+        )
+        == 3
+    )
+
+
+def test_analyze_ticker_default(ohlcv_history, mocker, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    ind_mock = MagicMock(side_effect=lambda x, meta: x)
+    entry_mock = MagicMock(side_effect=lambda x, meta: x)
+    exit_mock = MagicMock(side_effect=lambda x, meta: x)
+    mocker.patch.multiple(
+        "freqtrade.strategy.interface.IStrategy",
+        advise_indicators=ind_mock,
+        advise_entry=entry_mock,
+        advise_exit=exit_mock,
+    )
+    strategy = StrategyTestV3({})
+    strategy.analyze_ticker(ohlcv_history, {"pair": "ETH/BTC"})
+    assert ind_mock.call_count == 1
+    assert entry_mock.call_count == 1
+    assert entry_mock.call_count == 1
+
+    assert log_has("TA Analysis Launched", caplog)
+    assert not log_has("Skipping TA Analysis for already analyzed candle", caplog)
+    caplog.clear()
+
+    strategy.analyze_ticker(ohlcv_history, {"pair": "ETH/BTC"})
+    # No analysis happens as process_only_new_candles is true
+    assert ind_mock.call_count == 2
+    assert entry_mock.call_count == 2
+    assert entry_mock.call_count == 2
+    assert log_has("TA Analysis Launched", caplog)
+    assert not log_has("Skipping TA Analysis for already analyzed candle", caplog)
+
+
+def test__analyze_ticker_internal_skip_analyze(ohlcv_history, mocker, caplog) -> None:
+    caplog.set_level(logging.DEBUG)
+    ind_mock = MagicMock(side_effect=lambda x, meta: x)
+    entry_mock = MagicMock(side_effect=lambda x, meta: x)
+    exit_mock = MagicMock(side_effect=lambda x, meta: x)
+    mocker.patch.multiple(
+        "freqtrade.strategy.interface.IStrategy",
+        advise_indicators=ind_mock,
+        advise_entry=entry_mock,
+        advise_exit=exit_mock,
+    )
+    strategy = StrategyTestV3({})
+    strategy.dp = DataProvider({}, None, None)
+    strategy.process_only_new_candles = True
+
+    ret = strategy._analyze_ticker_internal(ohlcv_history, {"pair": "ETH/BTC"})
+    assert "high" in ret.columns
+    assert "low" in ret.columns
+    assert "close" in ret.columns
+    assert isinstance(ret, DataFrame)
+    assert ind_mock.call_count == 1
+    assert entry_mock.call_count == 1
+    assert entry_mock.call_count == 1
+    assert log_has("TA Analysis Launched", caplog)
+    assert not log_has("Skipping TA Analysis for already analyzed candle", caplog)
+    caplog.clear()
+
+    ret = strategy._analyze_ticker_internal(ohlcv_history, {"pair": "ETH/BTC"})
+    # No analysis happens as process_only_new_candles is true
+    assert ind_mock.call_count == 1
+    assert entry_mock.call_count == 1
+    assert entry_mock.call_count == 1
+    # only skipped analyze adds buy and sell columns, otherwise it's all mocked
+    assert "enter_long" in ret.columns
+    assert "exit_long" in ret.columns
+    assert ret["enter_long"].sum() == 0
+    assert ret["exit_long"].sum() == 0
+    assert not log_has("TA Analysis Launched", caplog)
+    assert log_has("Skipping TA Analysis for already analyzed candle", caplog)
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_is_pair_locked(default_conf):
+    PairLocks.timeframe = default_conf["timeframe"]
+    PairLocks.use_db = True
+    strategy = StrategyResolver.load_strategy(default_conf)
+    # No lock should be present
+    assert len(PairLocks.get_pair_locks(None)) == 0
+
+    pair = "ETH/BTC"
+    assert not strategy.is_pair_locked(pair)
+    strategy.lock_pair(pair, dt_now() + timedelta(minutes=4))
+    # ETH/BTC locked for 4 minutes
+    assert strategy.is_pair_locked(pair)
+
+    # XRP/BTC should not be locked now
+    pair = "XRP/BTC"
+    assert not strategy.is_pair_locked(pair)
+
+    # Unlocking a pair that's not locked should not raise an error
+    strategy.unlock_pair(pair)
+
+    # Unlock original pair
+    pair = "ETH/BTC"
+    strategy.unlock_pair(pair)
+    assert not strategy.is_pair_locked(pair)
+
+    # Lock with reason
+    reason = "TestLockR"
+    strategy.lock_pair(pair, dt_now() + timedelta(minutes=4), reason)
+    assert strategy.is_pair_locked(pair)
+    strategy.unlock_reason(reason)
+    assert not strategy.is_pair_locked(pair)
+
+    pair = "BTC/USDT"
+    # Lock until 14:30
+    lock_time = datetime(2020, 5, 1, 14, 30, 0, tzinfo=timezone.utc)
+    # Subtract 2 seconds, as locking rounds up to the next candle.
+    strategy.lock_pair(pair, lock_time - timedelta(seconds=2))
+
+    assert not strategy.is_pair_locked(pair)
+    # latest candle is from 14:20, lock goes to 14:30
+    assert strategy.is_pair_locked(pair, candle_date=lock_time + timedelta(minutes=-10))
+    assert strategy.is_pair_locked(pair, candle_date=lock_time + timedelta(minutes=-50))
+
+    # latest candle is from 14:25 (lock should be lifted)
+    # Since this is the "new candle" available at 14:30
+    assert not strategy.is_pair_locked(pair, candle_date=lock_time + timedelta(minutes=-4))
+
+    # Should not be locked after time expired
+    assert not strategy.is_pair_locked(pair, candle_date=lock_time + timedelta(minutes=10))
+
+    # Change timeframe to 15m
+    strategy.timeframe = "15m"
+    # Candle from 14:14 - lock goes until 14:30
+    assert strategy.is_pair_locked(pair, candle_date=lock_time + timedelta(minutes=-16))
+    assert strategy.is_pair_locked(pair, candle_date=lock_time + timedelta(minutes=-15, seconds=-2))
+    # Candle from 14:15 - lock goes until 14:30
+    assert not strategy.is_pair_locked(pair, candle_date=lock_time + timedelta(minutes=-15))
+
+
+def test_is_informative_pairs_callback(default_conf):
+    default_conf.update({"strategy": "StrategyTestV2"})
+    strategy = StrategyResolver.load_strategy(default_conf)
+    # Should return empty
+    # Uses fallback to base implementation
+    assert [] == strategy.gather_informative_pairs()
+
+
+def test_hyperopt_parameters():
+    HyperoptStateContainer.set_state(HyperoptState.INDICATORS)
+    from skopt.space import Categorical, Integer, Real
+
+    with pytest.raises(OperationalException, match=r"Name is determined.*"):
+        IntParameter(low=0, high=5, default=1, name="hello")
+
+    with pytest.raises(OperationalException, match=r"IntParameter space must be.*"):
+        IntParameter(low=0, default=5, space="buy")
+
+    with pytest.raises(OperationalException, match=r"RealParameter space must be.*"):
+        RealParameter(low=0, default=5, space="buy")
+
+    with pytest.raises(OperationalException, match=r"DecimalParameter space must be.*"):
+        DecimalParameter(low=0, default=5, space="buy")
+
+    with pytest.raises(OperationalException, match=r"IntParameter space invalid\."):
+        IntParameter([0, 10], high=7, default=5, space="buy")
+
+    with pytest.raises(OperationalException, match=r"RealParameter space invalid\."):
+        RealParameter([0, 10], high=7, default=5, space="buy")
+
+    with pytest.raises(OperationalException, match=r"DecimalParameter space invalid\."):
+        DecimalParameter([0, 10], high=7, default=5, space="buy")
+
+    with pytest.raises(OperationalException, match=r"CategoricalParameter space must.*"):
+        CategoricalParameter(["aa"], default="aa", space="buy")
+
+    with pytest.raises(TypeError):
+        BaseParameter(opt_range=[0, 1], default=1, space="buy")
+
+    intpar = IntParameter(low=0, high=5, default=1, space="buy")
+    assert intpar.value == 1
+    assert isinstance(intpar.get_space(""), Integer)
+    assert isinstance(intpar.range, range)
+    assert len(list(intpar.range)) == 1
+    # Range contains ONLY the default / value.
+    assert list(intpar.range) == [intpar.value]
+    intpar.in_space = True
+
+    assert len(list(intpar.range)) == 6
+    assert list(intpar.range) == [0, 1, 2, 3, 4, 5]
+
+    fltpar = RealParameter(low=0.0, high=5.5, default=1.0, space="buy")
+    assert fltpar.value == 1
+    assert isinstance(fltpar.get_space(""), Real)
+
+    fltpar = DecimalParameter(low=0.0, high=0.5, default=0.14, decimals=1, space="buy")
+    assert fltpar.value == 0.1
+    assert isinstance(fltpar.get_space(""), SKDecimal)
+    assert isinstance(fltpar.range, list)
+    assert len(list(fltpar.range)) == 1
+    # Range contains ONLY the default / value.
+    assert list(fltpar.range) == [fltpar.value]
+    fltpar.in_space = True
+    assert len(list(fltpar.range)) == 6
+    assert list(fltpar.range) == [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
+
+    catpar = CategoricalParameter(
+        ["buy_rsi", "buy_macd", "buy_none"], default="buy_macd", space="buy"
+    )
+    assert catpar.value == "buy_macd"
+    assert isinstance(catpar.get_space(""), Categorical)
+    assert isinstance(catpar.range, list)
+    assert len(list(catpar.range)) == 1
+    # Range contains ONLY the default / value.
+    assert list(catpar.range) == [catpar.value]
+    catpar.in_space = True
+    assert len(list(catpar.range)) == 3
+    assert list(catpar.range) == ["buy_rsi", "buy_macd", "buy_none"]
+
+    boolpar = BooleanParameter(default=True, space="buy")
+    assert boolpar.value is True
+    assert isinstance(boolpar.get_space(""), Categorical)
+    assert isinstance(boolpar.range, list)
+    assert len(list(boolpar.range)) == 1
+
+    boolpar.in_space = True
+    assert len(list(boolpar.range)) == 2
+
+    assert list(boolpar.range) == [True, False]
+
+    HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE)
+    assert len(list(intpar.range)) == 1
+    assert len(list(fltpar.range)) == 1
+    assert len(list(catpar.range)) == 1
+    assert len(list(boolpar.range)) == 1
+
+
+def test_auto_hyperopt_interface(default_conf):
+    default_conf.update({"strategy": "HyperoptableStrategyV2"})
+    PairLocks.timeframe = default_conf["timeframe"]
+    strategy = StrategyResolver.load_strategy(default_conf)
+    strategy.ft_bot_start()
+    with pytest.raises(OperationalException):
+        next(strategy.enumerate_parameters("deadBeef"))
+
+    assert strategy.buy_rsi.value == strategy.buy_params["buy_rsi"]
+    # PlusDI is NOT in the buy-params, so default should be used
+    assert strategy.buy_plusdi.value == 0.5
+    assert strategy.sell_rsi.value == strategy.sell_params["sell_rsi"]
+
+    assert repr(strategy.sell_rsi) == "IntParameter(74)"
+
+    # Parameter is disabled - so value from sell_param dict will NOT be used.
+    assert strategy.sell_minusdi.value == 0.5
+    all_params = strategy.detect_all_parameters()
+    assert isinstance(all_params, dict)
+    # Only one buy param at class level
+    assert len(all_params["buy"]) == 1
+    # Running detect params at instance level reveals both parameters.
+    assert len(list(detect_parameters(strategy, "buy"))) == 2
+    assert len(all_params["sell"]) == 2
+    # Number of Hyperoptable parameters
+    assert all_params["count"] == 5
+
+    strategy.__class__.sell_rsi = IntParameter([0, 10], default=5, space="buy")
+
+    with pytest.raises(OperationalException, match=r"Inconclusive parameter.*"):
+        [x for x in detect_parameters(strategy, "sell")]
+
+
+def test_auto_hyperopt_interface_loadparams(default_conf, mocker, caplog):
+    default_conf.update({"strategy": "HyperoptableStrategy"})
+    del default_conf["stoploss"]
+    del default_conf["minimal_roi"]
+    mocker.patch.object(Path, "is_file", MagicMock(return_value=True))
+    mocker.patch.object(Path, "open")
+    expected_result = {
+        "strategy_name": "HyperoptableStrategy",
+        "params": {
+            "stoploss": {
+                "stoploss": -0.05,
+            },
+            "roi": {"0": 0.2, "1200": 0.01},
+        },
+    }
+    mocker.patch("freqtrade.strategy.hyper.HyperoptTools.load_params", return_value=expected_result)
+    PairLocks.timeframe = default_conf["timeframe"]
+    strategy = StrategyResolver.load_strategy(default_conf)
+    assert strategy.stoploss == -0.05
+    assert strategy.minimal_roi == {0: 0.2, 1200: 0.01}
+
+    expected_result = {
+        "strategy_name": "HyperoptableStrategy_No",
+        "params": {
+            "stoploss": {
+                "stoploss": -0.05,
+            },
+            "roi": {"0": 0.2, "1200": 0.01},
+        },
+    }
+
+    mocker.patch("freqtrade.strategy.hyper.HyperoptTools.load_params", return_value=expected_result)
+    with pytest.raises(OperationalException, match="Invalid parameter file provided."):
+        StrategyResolver.load_strategy(default_conf)
+
+    mocker.patch(
+        "freqtrade.strategy.hyper.HyperoptTools.load_params", MagicMock(side_effect=ValueError())
+    )
+
+    StrategyResolver.load_strategy(default_conf)
+    assert log_has("Invalid parameter file format.", caplog)
+
+
+@pytest.mark.parametrize(
+    "function,raises",
+    [
+        ("populate_entry_trend", False),
+        ("advise_entry", False),
+        ("populate_exit_trend", False),
+        ("advise_exit", False),
+    ],
+)
+def test_pandas_warning_direct(ohlcv_history, function, raises, recwarn):
+    df = _STRATEGY.populate_indicators(ohlcv_history, {"pair": "ETH/BTC"})
+    if raises:
+        assert len(recwarn) == 1
+        # https://github.com/pandas-dev/pandas/issues/56503
+        # Fixed in 2.2.x
+        getattr(_STRATEGY, function)(df, {"pair": "ETH/BTC"})
+    else:
+        assert len(recwarn) == 0
+
+        getattr(_STRATEGY, function)(df, {"pair": "ETH/BTC"})
+
+
+def test_pandas_warning_through_analyze_pair(ohlcv_history, mocker, recwarn):
+    mocker.patch.object(_STRATEGY.dp, "ohlcv", return_value=ohlcv_history)
+    _STRATEGY.analyze_pair("ETH/BTC")
+    assert len(recwarn) == 0
diff --git a/tests/strategy/test_strategy_helpers.py b/tests/strategy/test_strategy_helpers.py
new file mode 100644
index 000000000..aeb46a4e4
--- /dev/null
+++ b/tests/strategy/test_strategy_helpers.py
@@ -0,0 +1,406 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import CandleType
+from freqtrade.resolvers.strategy_resolver import StrategyResolver
+from freqtrade.strategy import merge_informative_pair, stoploss_from_absolute, stoploss_from_open
+from tests.conftest import generate_test_data, get_patched_exchange
+
+
+def test_merge_informative_pair():
+    data = generate_test_data("15m", 40)
+    informative = generate_test_data("1h", 40)
+    cols_inf = list(informative.columns)
+
+    result = merge_informative_pair(data, informative, "15m", "1h", ffill=True)
+    assert isinstance(result, pd.DataFrame)
+    assert list(informative.columns) == cols_inf
+    assert len(result) == len(data)
+    assert "date" in result.columns
+    assert result["date"].equals(data["date"])
+    assert "date_1h" in result.columns
+
+    assert "open" in result.columns
+    assert "open_1h" in result.columns
+    assert result["open"].equals(data["open"])
+
+    assert "close" in result.columns
+    assert "close_1h" in result.columns
+    assert result["close"].equals(data["close"])
+
+    assert "volume" in result.columns
+    assert "volume_1h" in result.columns
+    assert result["volume"].equals(data["volume"])
+
+    # First 3 rows are empty
+    assert result.iloc[0]["date_1h"] is pd.NaT
+    assert result.iloc[1]["date_1h"] is pd.NaT
+    assert result.iloc[2]["date_1h"] is pd.NaT
+    # Next 4 rows contain the starting date (0:00)
+    assert result.iloc[3]["date_1h"] == result.iloc[0]["date"]
+    assert result.iloc[4]["date_1h"] == result.iloc[0]["date"]
+    assert result.iloc[5]["date_1h"] == result.iloc[0]["date"]
+    assert result.iloc[6]["date_1h"] == result.iloc[0]["date"]
+    # Next 4 rows contain the next Hourly date original date row 4
+    assert result.iloc[7]["date_1h"] == result.iloc[4]["date"]
+    assert result.iloc[8]["date_1h"] == result.iloc[4]["date"]
+
+    informative = generate_test_data("1h", 40)
+    result = merge_informative_pair(data, informative, "15m", "1h", ffill=False)
+    # First 3 rows are empty
+    assert result.iloc[0]["date_1h"] is pd.NaT
+    assert result.iloc[1]["date_1h"] is pd.NaT
+    assert result.iloc[2]["date_1h"] is pd.NaT
+    # Next 4 rows contain the starting date (0:00)
+    assert result.iloc[3]["date_1h"] == result.iloc[0]["date"]
+    assert result.iloc[4]["date_1h"] is pd.NaT
+    assert result.iloc[5]["date_1h"] is pd.NaT
+    assert result.iloc[6]["date_1h"] is pd.NaT
+    # Next 4 rows contain the next Hourly date original date row 4
+    assert result.iloc[7]["date_1h"] == result.iloc[4]["date"]
+    assert result.iloc[8]["date_1h"] is pd.NaT
+
+
+def test_merge_informative_pair_weekly():
+    # Covers roughly 2 months - until 2023-01-10
+    data = generate_test_data("1h", 1040, "2022-11-28")
+    informative = generate_test_data("1w", 40, "2022-11-01")
+    informative["day"] = informative["date"].dt.day_name()
+
+    result = merge_informative_pair(data, informative, "1h", "1w", ffill=True)
+    assert isinstance(result, pd.DataFrame)
+    # 2022-12-24 is a Saturday
+    candle1 = result.loc[(result["date"] == "2022-12-24T22:00:00.000Z")]
+    assert candle1.iloc[0]["date"] == pd.Timestamp("2022-12-24T22:00:00.000Z")
+    assert candle1.iloc[0]["date_1w"] == pd.Timestamp("2022-12-12T00:00:00.000Z")
+
+    candle2 = result.loc[(result["date"] == "2022-12-24T23:00:00.000Z")]
+    assert candle2.iloc[0]["date"] == pd.Timestamp("2022-12-24T23:00:00.000Z")
+    assert candle2.iloc[0]["date_1w"] == pd.Timestamp("2022-12-12T00:00:00.000Z")
+
+    # 2022-12-25 is a Sunday
+    candle3 = result.loc[(result["date"] == "2022-12-25T22:00:00.000Z")]
+    assert candle3.iloc[0]["date"] == pd.Timestamp("2022-12-25T22:00:00.000Z")
+    # Still old candle
+    assert candle3.iloc[0]["date_1w"] == pd.Timestamp("2022-12-12T00:00:00.000Z")
+
+    candle4 = result.loc[(result["date"] == "2022-12-25T23:00:00.000Z")]
+    assert candle4.iloc[0]["date"] == pd.Timestamp("2022-12-25T23:00:00.000Z")
+    assert candle4.iloc[0]["date_1w"] == pd.Timestamp("2022-12-19T00:00:00.000Z")
+
+
+def test_merge_informative_pair_monthly():
+    # Covers roughly 2 months - until 2023-01-10
+    data = generate_test_data("1h", 1040, "2022-11-28")
+    informative = generate_test_data("1M", 40, "2022-01-01")
+
+    result = merge_informative_pair(data, informative, "1h", "1M", ffill=True)
+    assert isinstance(result, pd.DataFrame)
+    candle1 = result.loc[(result["date"] == "2022-12-31T22:00:00.000Z")]
+    assert candle1.iloc[0]["date"] == pd.Timestamp("2022-12-31T22:00:00.000Z")
+    assert candle1.iloc[0]["date_1M"] == pd.Timestamp("2022-11-01T00:00:00.000Z")
+
+    candle2 = result.loc[(result["date"] == "2022-12-31T23:00:00.000Z")]
+    assert candle2.iloc[0]["date"] == pd.Timestamp("2022-12-31T23:00:00.000Z")
+    assert candle2.iloc[0]["date_1M"] == pd.Timestamp("2022-12-01T00:00:00.000Z")
+
+    # Candle is empty, as the start-date did fail.
+    candle3 = result.loc[(result["date"] == "2022-11-30T22:00:00.000Z")]
+    assert candle3.iloc[0]["date"] == pd.Timestamp("2022-11-30T22:00:00.000Z")
+    assert candle3.iloc[0]["date_1M"] is pd.NaT
+
+    # First candle with 1M data merged.
+    candle4 = result.loc[(result["date"] == "2022-11-30T23:00:00.000Z")]
+    assert candle4.iloc[0]["date"] == pd.Timestamp("2022-11-30T23:00:00.000Z")
+    assert candle4.iloc[0]["date_1M"] == pd.Timestamp("2022-11-01T00:00:00.000Z")
+
+
+def test_merge_informative_pair_same():
+    data = generate_test_data("15m", 40)
+    informative = generate_test_data("15m", 40)
+
+    result = merge_informative_pair(data, informative, "15m", "15m", ffill=True)
+    assert isinstance(result, pd.DataFrame)
+    assert len(result) == len(data)
+    assert "date" in result.columns
+    assert result["date"].equals(data["date"])
+    assert "date_15m" in result.columns
+
+    assert "open" in result.columns
+    assert "open_15m" in result.columns
+    assert result["open"].equals(data["open"])
+
+    assert "close" in result.columns
+    assert "close_15m" in result.columns
+    assert result["close"].equals(data["close"])
+
+    assert "volume" in result.columns
+    assert "volume_15m" in result.columns
+    assert result["volume"].equals(data["volume"])
+
+    # Dates match 1:1
+    assert result["date_15m"].equals(result["date"])
+
+
+def test_merge_informative_pair_lower():
+    data = generate_test_data("1h", 40)
+    informative = generate_test_data("15m", 40)
+
+    with pytest.raises(ValueError, match=r"Tried to merge a faster timeframe .*"):
+        merge_informative_pair(data, informative, "1h", "15m", ffill=True)
+
+
+def test_merge_informative_pair_empty():
+    data = generate_test_data("1h", 40)
+    informative = pd.DataFrame(columns=data.columns)
+
+    result = merge_informative_pair(data, informative, "1h", "2h", ffill=True)
+    assert result["date"].equals(data["date"])
+
+    assert list(result.columns) == [
+        "date",
+        "open",
+        "high",
+        "low",
+        "close",
+        "volume",
+        "date_2h",
+        "open_2h",
+        "high_2h",
+        "low_2h",
+        "close_2h",
+        "volume_2h",
+    ]
+    # We merge an empty dataframe, so all values should be NaN
+    for col in ["date_2h", "open_2h", "high_2h", "low_2h", "close_2h", "volume_2h"]:
+        assert result[col].isnull().all()
+
+
+def test_merge_informative_pair_suffix():
+    data = generate_test_data("15m", 20)
+    informative = generate_test_data("1h", 20)
+
+    result = merge_informative_pair(
+        data, informative, "15m", "1h", append_timeframe=False, suffix="suf"
+    )
+
+    assert "date" in result.columns
+    assert result["date"].equals(data["date"])
+    assert "date_suf" in result.columns
+
+    assert "open_suf" in result.columns
+    assert "open_1h" not in result.columns
+
+    assert list(result.columns) == [
+        "date",
+        "open",
+        "high",
+        "low",
+        "close",
+        "volume",
+        "date_suf",
+        "open_suf",
+        "high_suf",
+        "low_suf",
+        "close_suf",
+        "volume_suf",
+    ]
+
+
+def test_merge_informative_pair_suffix_append_timeframe():
+    data = generate_test_data("15m", 20)
+    informative = generate_test_data("1h", 20)
+
+    with pytest.raises(ValueError, match=r"You can not specify `append_timeframe` .*"):
+        merge_informative_pair(data, informative, "15m", "1h", suffix="suf")
+
+
+@pytest.mark.parametrize(
+    "side,profitrange",
+    [
+        # profit range for long is [-1, inf] while for shorts is [-inf, 1]
+        ("long", [-0.99, 2, 30]),
+        ("short", [-2.0, 0.99, 30]),
+    ],
+)
+def test_stoploss_from_open(side, profitrange):
+    open_price_ranges = [
+        [0.01, 1.00, 30],
+        [1, 100, 30],
+        [100, 10000, 30],
+    ]
+
+    for open_range in open_price_ranges:
+        for open_price in np.linspace(*open_range):
+            for desired_stop in np.linspace(-0.50, 0.50, 30):
+                if side == "long":
+                    # -1 is not a valid current_profit, should return 1
+                    assert stoploss_from_open(desired_stop, -1) == 1
+                else:
+                    # 1 is not a valid current_profit for shorts, should return 1
+                    assert stoploss_from_open(desired_stop, 1, True) == 1
+
+                for current_profit in np.linspace(*profitrange):
+                    if side == "long":
+                        current_price = open_price * (1 + current_profit)
+                        expected_stop_price = open_price * (1 + desired_stop)
+                        stoploss = stoploss_from_open(desired_stop, current_profit)
+                        stop_price = current_price * (1 - stoploss)
+                    else:
+                        current_price = open_price * (1 - current_profit)
+                        expected_stop_price = open_price * (1 - desired_stop)
+                        stoploss = stoploss_from_open(desired_stop, current_profit, True)
+                        stop_price = current_price * (1 + stoploss)
+
+                    assert stoploss >= 0
+                    # Technically the formula can yield values greater than 1 for shorts
+                    # even though it doesn't make sense because the position would be liquidated
+                    if side == "long":
+                        assert stoploss <= 1
+
+                    # there is no correct answer if the expected stop price is above
+                    # the current price
+                    if (side == "long" and expected_stop_price > current_price) or (
+                        side == "short" and expected_stop_price < current_price
+                    ):
+                        assert stoploss == 0
+                    else:
+                        assert pytest.approx(stop_price) == expected_stop_price
+
+
+@pytest.mark.parametrize(
+    "side,rel_stop,curr_profit,leverage,expected",
+    [
+        # profit range for long is [-1, inf] while for shorts is [-inf, 1]
+        ("long", 0, -1, 1, 1),
+        ("long", 0, 0.1, 1, 0.09090909),
+        ("long", -0.1, 0.1, 1, 0.18181818),
+        ("long", 0.1, 0.2, 1, 0.08333333),
+        ("long", 0.1, 0.5, 1, 0.266666666),
+        ("long", 0.1, 5, 1, 0.816666666),  # 500% profit, set stoploss to 10% above open price
+        ("long", 0, 5, 10, 3.3333333),  # 500% profit, set stoploss break even
+        ("long", 0.1, 5, 10, 3.26666666),  # 500% profit, set stoploss to 10% above open price
+        ("long", -0.1, 5, 10, 3.3999999),  # 500% profit, set stoploss to 10% belowopen price
+        ("short", 0, 0.1, 1, 0.1111111),
+        ("short", -0.1, 0.1, 1, 0.2222222),
+        ("short", 0.1, 0.2, 1, 0.125),
+        ("short", 0.1, 1, 1, 1),
+        ("short", -0.01, 5, 10, 10.01999999),  # 500% profit at 10x
+    ],
+)
+def test_stoploss_from_open_leverage(side, rel_stop, curr_profit, leverage, expected):
+    stoploss = stoploss_from_open(rel_stop, curr_profit, side == "short", leverage)
+    assert pytest.approx(stoploss) == expected
+    open_rate = 100
+    if stoploss != 1:
+        if side == "long":
+            current_rate = open_rate * (1 + curr_profit / leverage)
+            stop = current_rate * (1 - stoploss / leverage)
+            assert pytest.approx(stop) == open_rate * (1 + rel_stop / leverage)
+        else:
+            current_rate = open_rate * (1 - curr_profit / leverage)
+            stop = current_rate * (1 + stoploss / leverage)
+            assert pytest.approx(stop) == open_rate * (1 - rel_stop / leverage)
+
+
+def test_stoploss_from_absolute():
+    assert pytest.approx(stoploss_from_absolute(90, 100)) == 1 - (90 / 100)
+    assert pytest.approx(stoploss_from_absolute(90, 100)) == 0.1
+    assert pytest.approx(stoploss_from_absolute(95, 100)) == 0.05
+    assert pytest.approx(stoploss_from_absolute(100, 100)) == 0
+    assert pytest.approx(stoploss_from_absolute(110, 100)) == 0
+    assert pytest.approx(stoploss_from_absolute(100, 0)) == 1
+    assert pytest.approx(stoploss_from_absolute(0, 100)) == 1
+    assert pytest.approx(stoploss_from_absolute(0, 100, False, leverage=5)) == 5
+
+    assert pytest.approx(stoploss_from_absolute(90, 100, True)) == 0
+    assert pytest.approx(stoploss_from_absolute(100, 100, True)) == 0
+    assert pytest.approx(stoploss_from_absolute(110, 100, True)) == -(1 - (110 / 100))
+    assert pytest.approx(stoploss_from_absolute(110, 100, True)) == 0.1
+    assert pytest.approx(stoploss_from_absolute(105, 100, True)) == 0.05
+    assert pytest.approx(stoploss_from_absolute(105, 100, True, 5)) == 0.05 * 5
+    assert pytest.approx(stoploss_from_absolute(100, 0, True)) == 1
+    assert pytest.approx(stoploss_from_absolute(0, 100, True)) == 0
+    assert pytest.approx(stoploss_from_absolute(100, 1, is_short=True)) == 1
+    assert pytest.approx(stoploss_from_absolute(100, 1, is_short=True, leverage=5)) == 5
+
+
+@pytest.mark.parametrize("trading_mode", ["futures", "spot"])
+def test_informative_decorator(mocker, default_conf_usdt, trading_mode):
+    candle_def = CandleType.get_default(trading_mode)
+    default_conf_usdt["candle_type_def"] = candle_def
+    test_data_5m = generate_test_data("5m", 40)
+    test_data_30m = generate_test_data("30m", 40)
+    test_data_1h = generate_test_data("1h", 40)
+    data = {
+        ("XRP/USDT", "5m", candle_def): test_data_5m,
+        ("XRP/USDT", "30m", candle_def): test_data_30m,
+        ("XRP/USDT", "1h", candle_def): test_data_1h,
+        ("XRP/BTC", "1h", candle_def): test_data_1h,  # from {base}/BTC
+        ("LTC/USDT", "5m", candle_def): test_data_5m,
+        ("LTC/USDT", "30m", candle_def): test_data_30m,
+        ("LTC/USDT", "1h", candle_def): test_data_1h,
+        ("LTC/BTC", "1h", candle_def): test_data_1h,  # from {base}/BTC
+        ("NEO/USDT", "30m", candle_def): test_data_30m,
+        ("NEO/USDT", "5m", CandleType.SPOT): test_data_5m,  # Explicit request with '' as candletype
+        ("NEO/USDT", "15m", candle_def): test_data_5m,  # Explicit request with '' as candletype
+        ("NEO/USDT", "1h", candle_def): test_data_1h,
+        ("ETH/USDT", "1h", candle_def): test_data_1h,
+        ("ETH/USDT", "30m", candle_def): test_data_30m,
+        ("ETH/BTC", "1h", CandleType.SPOT): test_data_1h,  # Explicitly selected as spot
+    }
+    default_conf_usdt["strategy"] = "InformativeDecoratorTest"
+    strategy = StrategyResolver.load_strategy(default_conf_usdt)
+    exchange = get_patched_exchange(mocker, default_conf_usdt)
+    strategy.dp = DataProvider({}, exchange, None)
+    mocker.patch.object(
+        strategy.dp, "current_whitelist", return_value=["XRP/USDT", "LTC/USDT", "NEO/USDT"]
+    )
+
+    assert len(strategy._ft_informative) == 7  # Equal to number of decorators used
+    informative_pairs = [
+        ("XRP/USDT", "1h", candle_def),
+        ("XRP/BTC", "1h", candle_def),
+        ("LTC/USDT", "1h", candle_def),
+        ("LTC/BTC", "1h", candle_def),
+        ("XRP/USDT", "30m", candle_def),
+        ("LTC/USDT", "30m", candle_def),
+        ("NEO/USDT", "1h", candle_def),
+        ("NEO/USDT", "30m", candle_def),
+        ("NEO/USDT", "5m", candle_def),
+        ("NEO/USDT", "15m", candle_def),
+        ("NEO/USDT", "2h", CandleType.FUTURES),
+        ("ETH/BTC", "1h", CandleType.SPOT),  # One candle remains as spot
+        ("ETH/USDT", "30m", candle_def),
+    ]
+    for inf_pair in informative_pairs:
+        assert inf_pair in strategy.gather_informative_pairs()
+
+    def test_historic_ohlcv(pair, timeframe, candle_type):
+        return data[
+            (pair, timeframe or strategy.timeframe, CandleType.from_string(candle_type))
+        ].copy()
+
+    mocker.patch(
+        "freqtrade.data.dataprovider.DataProvider.historic_ohlcv", side_effect=test_historic_ohlcv
+    )
+
+    analyzed = strategy.advise_all_indicators(
+        {p: data[(p, strategy.timeframe, candle_def)] for p in ("XRP/USDT", "LTC/USDT")}
+    )
+    expected_columns = [
+        "rsi_1h",
+        "rsi_30m",  # Stacked informative decorators
+        "neo_usdt_rsi_1h",  # NEO 1h informative
+        "rsi_NEO_USDT_neo_usdt_NEO/USDT_30m",  # Column formatting
+        "rsi_from_callable",  # Custom column formatter
+        "eth_btc_rsi_1h",  # Quote currency not matching stake currency
+        "rsi",
+        "rsi_less",  # Non-informative columns
+        "rsi_5m",  # Manual informative dataframe
+    ]
+    for _, dataframe in analyzed.items():
+        for col in expected_columns:
+            assert col in dataframe.columns
diff --git a/tests/strategy/test_strategy_loading.py b/tests/strategy/test_strategy_loading.py
new file mode 100644
index 000000000..927f33461
--- /dev/null
+++ b/tests/strategy/test_strategy_loading.py
@@ -0,0 +1,510 @@
+# pragma pylint: disable=missing-docstring, protected-access, C0103
+import logging
+from base64 import urlsafe_b64encode
+from pathlib import Path
+
+import pytest
+from pandas import DataFrame
+
+from freqtrade.configuration import Configuration
+from freqtrade.exceptions import OperationalException
+from freqtrade.resolvers import StrategyResolver
+from freqtrade.strategy.interface import IStrategy
+from tests.conftest import CURRENT_TEST_STRATEGY, log_has, log_has_re
+
+
+def test_search_strategy():
+    default_location = Path(__file__).parent / "strats"
+
+    s, _ = StrategyResolver._search_object(
+        directory=default_location,
+        object_name=CURRENT_TEST_STRATEGY,
+        add_source=True,
+    )
+    assert issubclass(s, IStrategy)
+
+    s, _ = StrategyResolver._search_object(
+        directory=default_location,
+        object_name="NotFoundStrategy",
+        add_source=True,
+    )
+    assert s is None
+
+
+def test_search_all_strategies_no_failed():
+    directory = Path(__file__).parent / "strats"
+    strategies = StrategyResolver._search_all_objects(directory, enum_failed=False)
+    assert isinstance(strategies, list)
+    assert len(strategies) == 13
+    assert isinstance(strategies[0], dict)
+
+
+def test_search_all_strategies_with_failed():
+    directory = Path(__file__).parent / "strats"
+    strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
+    assert isinstance(strategies, list)
+    assert len(strategies) == 14
+    # with enum_failed=True search_all_objects() shall find 2 good strategies
+    # and 1 which fails to load
+    assert len([x for x in strategies if x["class"] is not None]) == 13
+
+    assert len([x for x in strategies if x["class"] is None]) == 1
+
+    directory = Path(__file__).parent / "strats_nonexistingdir"
+    strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
+    assert len(strategies) == 0
+
+
+def test_load_strategy(default_conf, dataframe_1m):
+    default_conf.update(
+        {
+            "strategy": "SampleStrategy",
+            "strategy_path": str(Path(__file__).parents[2] / "freqtrade/templates"),
+        }
+    )
+    strategy = StrategyResolver.load_strategy(default_conf)
+    assert isinstance(strategy.__source__, str)
+    assert "class SampleStrategy" in strategy.__source__
+    assert isinstance(strategy.__file__, str)
+    assert "rsi" in strategy.advise_indicators(dataframe_1m, {"pair": "ETH/BTC"})
+
+
+def test_load_strategy_base64(dataframe_1m, caplog, default_conf):
+    filepath = Path(__file__).parents[2] / "freqtrade/templates/sample_strategy.py"
+    encoded_string = urlsafe_b64encode(filepath.read_bytes()).decode("utf-8")
+    default_conf.update({"strategy": f"SampleStrategy:{encoded_string}"})
+
+    strategy = StrategyResolver.load_strategy(default_conf)
+    assert "rsi" in strategy.advise_indicators(dataframe_1m, {"pair": "ETH/BTC"})
+    # Make sure strategy was loaded from base64 (using temp directory)!!
+    assert log_has_re(
+        r"Using resolved strategy SampleStrategy from '"
+        r".*(/|\\).*(/|\\)SampleStrategy\.py'\.\.\.",
+        caplog,
+    )
+
+
+def test_load_strategy_invalid_directory(caplog, default_conf, tmp_path):
+    default_conf["user_data_dir"] = tmp_path
+
+    extra_dir = Path.cwd() / "some/path"
+    with pytest.raises(OperationalException, match=r"Impossible to load Strategy.*"):
+        StrategyResolver._load_strategy(
+            "StrategyTestV333", config=default_conf, extra_dir=extra_dir
+        )
+
+    assert log_has_re(r"Path .*" + r"some.*path.*" + r".* does not exist", caplog)
+
+
+def test_load_not_found_strategy(default_conf, tmp_path):
+    default_conf["user_data_dir"] = tmp_path
+    default_conf["strategy"] = "NotFoundStrategy"
+    with pytest.raises(
+        OperationalException,
+        match=r"Impossible to load Strategy 'NotFoundStrategy'. "
+        r"This class does not exist or contains Python code errors.",
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+
+def test_load_strategy_noname(default_conf):
+    default_conf["strategy"] = ""
+    with pytest.raises(
+        OperationalException,
+        match="No strategy set. Please use `--strategy` to specify the strategy class to use.",
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+
+@pytest.mark.filterwarnings("ignore:deprecated")
+@pytest.mark.parametrize("strategy_name", ["StrategyTestV2"])
+def test_strategy_pre_v3(dataframe_1m, default_conf, strategy_name):
+    default_conf.update({"strategy": strategy_name})
+
+    strategy = StrategyResolver.load_strategy(default_conf)
+    metadata = {"pair": "ETH/BTC"}
+    assert strategy.minimal_roi[0] == 0.04
+    assert default_conf["minimal_roi"]["0"] == 0.04
+
+    assert strategy.stoploss == -0.10
+    assert default_conf["stoploss"] == -0.10
+
+    assert strategy.timeframe == "5m"
+    assert default_conf["timeframe"] == "5m"
+
+    df_indicators = strategy.advise_indicators(dataframe_1m, metadata=metadata)
+    assert "adx" in df_indicators
+
+    dataframe = strategy.advise_entry(df_indicators, metadata=metadata)
+    assert "buy" not in dataframe.columns
+    assert "enter_long" in dataframe.columns
+
+    dataframe = strategy.advise_exit(df_indicators, metadata=metadata)
+    assert "sell" not in dataframe.columns
+    assert "exit_long" in dataframe.columns
+
+
+def test_strategy_can_short(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+    default_conf.update(
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+        }
+    )
+    strat = StrategyResolver.load_strategy(default_conf)
+    assert isinstance(strat, IStrategy)
+    default_conf["strategy"] = "StrategyTestV3Futures"
+    with pytest.raises(ImportError, match=""):
+        StrategyResolver.load_strategy(default_conf)
+
+    default_conf["trading_mode"] = "futures"
+    strat = StrategyResolver.load_strategy(default_conf)
+    assert isinstance(strat, IStrategy)
+
+
+def test_strategy_override_minimal_roi(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY, "minimal_roi": {"20": 0.1, "0": 0.5}})
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert strategy.minimal_roi[0] == 0.5
+    assert log_has(
+        "Override strategy 'minimal_roi' with value in config file: {'20': 0.1, '0': 0.5}.", caplog
+    )
+
+
+def test_strategy_override_stoploss(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY, "stoploss": -0.5})
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert strategy.stoploss == -0.5
+    assert log_has("Override strategy 'stoploss' with value in config file: -0.5.", caplog)
+
+
+def test_strategy_override_max_open_trades(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY, "max_open_trades": 7})
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert strategy.max_open_trades == 7
+    assert log_has("Override strategy 'max_open_trades' with value in config file: 7.", caplog)
+
+
+def test_strategy_override_trailing_stop(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY, "trailing_stop": True})
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert strategy.trailing_stop
+    assert isinstance(strategy.trailing_stop, bool)
+    assert log_has("Override strategy 'trailing_stop' with value in config file: True.", caplog)
+
+
+def test_strategy_override_trailing_stop_positive(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+    default_conf.update(
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+            "trailing_stop_positive": -0.1,
+            "trailing_stop_positive_offset": -0.2,
+        }
+    )
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert strategy.trailing_stop_positive == -0.1
+    assert log_has(
+        "Override strategy 'trailing_stop_positive' with value in config file: -0.1.", caplog
+    )
+
+    assert strategy.trailing_stop_positive_offset == -0.2
+    assert log_has(
+        "Override strategy 'trailing_stop_positive' with value in config file: -0.1.", caplog
+    )
+
+
+def test_strategy_override_timeframe(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+
+    default_conf.update(
+        {"strategy": CURRENT_TEST_STRATEGY, "timeframe": 60, "stake_currency": "ETH"}
+    )
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert strategy.timeframe == 60
+    assert strategy.stake_currency == "ETH"
+    assert log_has("Override strategy 'timeframe' with value in config file: 60.", caplog)
+
+
+def test_strategy_override_process_only_new_candles(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY, "process_only_new_candles": False})
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert not strategy.process_only_new_candles
+    assert log_has(
+        "Override strategy 'process_only_new_candles' with value in config file: False.", caplog
+    )
+
+
+def test_strategy_override_order_types(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+
+    order_types = {
+        "entry": "market",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": True,
+    }
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY, "order_types": order_types})
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert strategy.order_types
+    for method in ["entry", "exit", "stoploss", "stoploss_on_exchange"]:
+        assert strategy.order_types[method] == order_types[method]
+
+    assert log_has(
+        "Override strategy 'order_types' with value in config file:"
+        " {'entry': 'market', 'exit': 'limit', 'stoploss': 'limit',"
+        " 'stoploss_on_exchange': True}.",
+        caplog,
+    )
+
+    default_conf.update({"strategy": CURRENT_TEST_STRATEGY, "order_types": {"exit": "market"}})
+    # Raise error for invalid configuration
+    with pytest.raises(
+        ImportError,
+        match=r"Impossible to load Strategy '" + CURRENT_TEST_STRATEGY + "'. "
+        r"Order-types mapping is incomplete.",
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+
+def test_strategy_override_order_tif(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+
+    order_time_in_force = {
+        "entry": "FOK",
+        "exit": "GTC",
+    }
+
+    default_conf.update(
+        {"strategy": CURRENT_TEST_STRATEGY, "order_time_in_force": order_time_in_force}
+    )
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert strategy.order_time_in_force
+    for method in ["entry", "exit"]:
+        assert strategy.order_time_in_force[method] == order_time_in_force[method]
+
+    assert log_has(
+        "Override strategy 'order_time_in_force' with value in config file:"
+        " {'entry': 'FOK', 'exit': 'GTC'}.",
+        caplog,
+    )
+
+    default_conf.update(
+        {"strategy": CURRENT_TEST_STRATEGY, "order_time_in_force": {"entry": "FOK"}}
+    )
+    # Raise error for invalid configuration
+    with pytest.raises(
+        ImportError,
+        match=f"Impossible to load Strategy '{CURRENT_TEST_STRATEGY}'. "
+        "Order-time-in-force mapping is incomplete.",
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+
+def test_strategy_override_use_exit_signal(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+    default_conf.update(
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+        }
+    )
+    strategy = StrategyResolver.load_strategy(default_conf)
+    assert strategy.use_exit_signal
+    assert isinstance(strategy.use_exit_signal, bool)
+    # must be inserted to configuration
+    assert "use_exit_signal" in default_conf
+    assert default_conf["use_exit_signal"]
+
+    default_conf.update(
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+            "use_exit_signal": False,
+        }
+    )
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert not strategy.use_exit_signal
+    assert isinstance(strategy.use_exit_signal, bool)
+    assert log_has("Override strategy 'use_exit_signal' with value in config file: False.", caplog)
+
+
+def test_strategy_override_use_exit_profit_only(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+    default_conf.update(
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+        }
+    )
+    strategy = StrategyResolver.load_strategy(default_conf)
+    assert not strategy.exit_profit_only
+    assert isinstance(strategy.exit_profit_only, bool)
+    # must be inserted to configuration
+    assert "exit_profit_only" in default_conf
+    assert not default_conf["exit_profit_only"]
+
+    default_conf.update(
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+            "exit_profit_only": True,
+        }
+    )
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    assert strategy.exit_profit_only
+    assert isinstance(strategy.exit_profit_only, bool)
+    assert log_has("Override strategy 'exit_profit_only' with value in config file: True.", caplog)
+
+
+def test_strategy_max_open_trades_infinity_from_strategy(caplog, default_conf):
+    caplog.set_level(logging.INFO)
+    default_conf.update(
+        {
+            "strategy": CURRENT_TEST_STRATEGY,
+        }
+    )
+    del default_conf["max_open_trades"]
+
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    # this test assumes -1 set to 'max_open_trades' in CURRENT_TEST_STRATEGY
+    assert strategy.max_open_trades == float("inf")
+    assert default_conf["max_open_trades"] == float("inf")
+
+
+def test_strategy_max_open_trades_infinity_from_config(caplog, default_conf, mocker):
+    caplog.set_level(logging.INFO)
+    default_conf.update(
+        {"strategy": CURRENT_TEST_STRATEGY, "max_open_trades": -1, "exchange": "binance"}
+    )
+
+    configuration = Configuration(args=default_conf)
+    parsed_config = configuration.get_config()
+
+    assert parsed_config["max_open_trades"] == float("inf")
+
+    strategy = StrategyResolver.load_strategy(parsed_config)
+
+    assert strategy.max_open_trades == float("inf")
+
+
+@pytest.mark.filterwarnings("ignore:deprecated")
+def test_missing_implements(default_conf, caplog):
+    default_location = Path(__file__).parent / "strats"
+    default_conf.update({"strategy": "StrategyTestV2", "strategy_path": default_location})
+    StrategyResolver.load_strategy(default_conf)
+
+    log_has_re(r"DEPRECATED: .*use_sell_signal.*use_exit_signal.", caplog)
+
+    default_conf["trading_mode"] = "futures"
+    with pytest.raises(
+        OperationalException, match=r"DEPRECATED: .*use_sell_signal.*use_exit_signal."
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+    default_conf["trading_mode"] = "spot"
+
+    default_location = Path(__file__).parent / "strats/broken_strats"
+    default_conf.update({"strategy": "TestStrategyNoImplements", "strategy_path": default_location})
+    with pytest.raises(
+        OperationalException, match=r"`populate_entry_trend` or `populate_buy_trend`.*"
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+    default_conf["strategy"] = "TestStrategyNoImplementSell"
+
+    with pytest.raises(
+        OperationalException, match=r"`populate_exit_trend` or `populate_sell_trend`.*"
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+    # Futures mode is more strict ...
+    default_conf["trading_mode"] = "futures"
+
+    with pytest.raises(OperationalException, match=r"`populate_exit_trend` must be implemented.*"):
+        StrategyResolver.load_strategy(default_conf)
+
+    default_conf["strategy"] = "TestStrategyNoImplements"
+    with pytest.raises(OperationalException, match=r"`populate_entry_trend` must be implemented.*"):
+        StrategyResolver.load_strategy(default_conf)
+
+    default_conf["strategy"] = "TestStrategyImplementCustomSell"
+    with pytest.raises(
+        OperationalException, match=r"Please migrate your implementation of `custom_sell`.*"
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+    default_conf["strategy"] = "TestStrategyImplementBuyTimeout"
+    with pytest.raises(
+        OperationalException, match=r"Please migrate your implementation of `check_buy_timeout`.*"
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+    default_conf["strategy"] = "TestStrategyImplementSellTimeout"
+    with pytest.raises(
+        OperationalException, match=r"Please migrate your implementation of `check_sell_timeout`.*"
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+    default_conf["strategy"] = "TestStrategyAdjustOrderPrice"
+    with pytest.raises(OperationalException, match=r"If you implement `adjust_order_price`.*"):
+        StrategyResolver.load_strategy(default_conf)
+
+
+def test_call_deprecated_function(default_conf):
+    default_location = Path(__file__).parent / "strats/broken_strats/"
+    del default_conf["timeframe"]
+    default_conf.update({"strategy": "TestStrategyLegacyV1", "strategy_path": default_location})
+    with pytest.raises(
+        OperationalException, match=r"Strategy Interface v1 is no longer supported.*"
+    ):
+        StrategyResolver.load_strategy(default_conf)
+
+
+def test_strategy_interface_versioning(dataframe_1m, default_conf):
+    default_conf.update({"strategy": "StrategyTestV2"})
+    strategy = StrategyResolver.load_strategy(default_conf)
+    metadata = {"pair": "ETH/BTC"}
+
+    assert strategy.INTERFACE_VERSION == 2
+
+    indicator_df = strategy.advise_indicators(dataframe_1m, metadata=metadata)
+    assert isinstance(indicator_df, DataFrame)
+    assert "adx" in indicator_df.columns
+
+    enterdf = strategy.advise_entry(dataframe_1m, metadata=metadata)
+    assert isinstance(enterdf, DataFrame)
+
+    assert "buy" not in enterdf.columns
+    assert "enter_long" in enterdf.columns
+
+    exitdf = strategy.advise_exit(dataframe_1m, metadata=metadata)
+    assert isinstance(exitdf, DataFrame)
+    assert "sell" not in exitdf
+    assert "exit_long" in exitdf
+
+
+def test_strategy_ft_load_params_from_file(mocker, default_conf):
+    default_conf.update({"strategy": "StrategyTestV2"})
+    del default_conf["max_open_trades"]
+    mocker.patch(
+        "freqtrade.strategy.hyper.HyperStrategyMixin.load_params_from_file",
+        return_value={"params": {"max_open_trades": {"max_open_trades": -1}}},
+    )
+    strategy = StrategyResolver.load_strategy(default_conf)
+    assert strategy.max_open_trades == float("inf")
+    assert strategy.config["max_open_trades"] == float("inf")
diff --git a/tests/strategy/test_strategy_safe_wrapper.py b/tests/strategy/test_strategy_safe_wrapper.py
new file mode 100644
index 000000000..007b3c1a6
--- /dev/null
+++ b/tests/strategy/test_strategy_safe_wrapper.py
@@ -0,0 +1,93 @@
+import pytest
+
+from freqtrade.exceptions import StrategyError
+from freqtrade.persistence import Trade
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util.datetime_helpers import dt_now
+from tests.conftest import create_mock_trades, log_has_re
+
+from .strats.strategy_test_v3 import StrategyTestV3
+
+
+@pytest.mark.parametrize(
+    "error",
+    [
+        ValueError,
+        KeyError,
+        Exception,
+    ],
+)
+def test_strategy_safe_wrapper_error(caplog, error):
+    def failing_method():
+        raise error("This is an error.")
+
+    with pytest.raises(StrategyError, match=r"This is an error."):
+        strategy_safe_wrapper(failing_method, message="DeadBeef")()
+
+    assert log_has_re(r"DeadBeef.*", caplog)
+    ret = strategy_safe_wrapper(failing_method, message="DeadBeef", default_retval=True)()
+
+    assert isinstance(ret, bool)
+    assert ret
+
+    caplog.clear()
+    # Test suppressing error
+    ret = strategy_safe_wrapper(failing_method, message="DeadBeef", supress_error=True)()
+    assert log_has_re(r"DeadBeef.*", caplog)
+
+
+@pytest.mark.parametrize(
+    "value", [1, 22, 55, True, False, {"a": 1, "b": "112"}, [1, 2, 3, 4], (4, 2, 3, 6)]
+)
+def test_strategy_safe_wrapper(value):
+    def working_method(argumentpassedin):
+        return argumentpassedin
+
+    ret = strategy_safe_wrapper(working_method, message="DeadBeef")(value)
+
+    assert isinstance(ret, type(value))
+    assert ret == value
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_strategy_safe_wrapper_trade_copy(fee, mocker):
+    create_mock_trades(fee)
+    import freqtrade.strategy.strategy_wrapper as swm
+
+    deepcopy_mock = mocker.spy(swm, "deepcopy")
+
+    trade_ = Trade.get_open_trades()[0]
+    strat = StrategyTestV3(config={})
+
+    def working_method(trade):
+        assert len(trade.orders) > 0
+        assert trade.orders
+        trade.orders = []
+        assert len(trade.orders) == 0
+        assert id(trade_) != id(trade)
+        return trade
+
+    strat.working_method = working_method
+
+    # Don't assert anything before strategy_wrapper.
+    # This ensures that relationship loading works correctly.
+    ret = strategy_safe_wrapper(strat.working_method, message="DeadBeef")(trade=trade_)
+    assert isinstance(ret, Trade)
+    assert id(trade_) != id(ret)
+    # Did not modify the original order
+    assert len(trade_.orders) > 0
+    assert len(ret.orders) == 0
+    assert deepcopy_mock.call_count == 1
+    deepcopy_mock.reset_mock()
+
+    # Call with non-overridden method - shouldn't deep-copy the trade
+    ret = strategy_safe_wrapper(strat.custom_entry_price, message="DeadBeef")(
+        pair="ETH/USDT",
+        trade=trade_,
+        current_time=dt_now(),
+        proposed_rate=0.5,
+        entry_tag="",
+        side="long",
+    )
+
+    assert deepcopy_mock.call_count == 0
diff --git a/tests/test_arguments.py b/tests/test_arguments.py
new file mode 100644
index 000000000..913f4759d
--- /dev/null
+++ b/tests/test_arguments.py
@@ -0,0 +1,340 @@
+# pragma pylint: disable=missing-docstring, C0103
+import argparse
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.commands import Arguments, arguments
+from freqtrade.commands.cli_options import (
+    AVAILABLE_CLI_OPTIONS,
+    check_int_nonzero,
+    check_int_positive,
+)
+from tests.conftest import CURRENT_TEST_STRATEGY
+
+
+def test_available_cli_options():
+    """
+    AVAILABLE_CLI_OPTIONS has keys that are the union of the values in all ARGS_* - required by CLI
+    each of the ARGS_* lists has a list of members which is assumed to also
+    be in AVAILABLE_CLI_OPTIONS
+    """
+    args_union = {
+        arg
+        for variable, value in vars(arguments).items()
+        if variable.startswith("ARGS_")
+        for arg in value
+    }
+    expected_options = set(AVAILABLE_CLI_OPTIONS)
+    only_in_command_args = expected_options.difference(args_union)
+    only_in_all_args = args_union.difference(expected_options)
+    if only_in_all_args or only_in_command_args:
+        pytest.fail(
+            "variables around command line arguments not kept in sync:\n"
+            f" * args only in some ARGS_* list but not AVAILABLE_CLI_OPTIONS: {only_in_all_args}\n"
+            " * args only in AVAILABLE_CLI_OPTIONS but not some ARGS_* list: "
+            f"{only_in_command_args}"
+        )
+
+
+def test_arguments_match_available_cli_options(monkeypatch):
+    """All entries in AVAILABLE_CLI_OPTIONS are used in argument parsing."""
+    parsed_options = set()
+    actual_build_args = Arguments._build_args
+
+    def build_args_monitor(self, optionlist, parser):
+        parsed_options.update(optionlist)
+        return actual_build_args(self, optionlist=optionlist, parser=parser)
+
+    monkeypatch.setattr(Arguments, "_build_args", build_args_monitor)
+    # this will result in a parser being built so we can check the arguments used
+    Arguments([]).get_parsed_arg()
+    assert parsed_options == set(AVAILABLE_CLI_OPTIONS)
+
+
+# Parse common command-line-arguments. Used for all tools
+def test_parse_args_none() -> None:
+    arguments = Arguments(["trade"])
+    assert isinstance(arguments, Arguments)
+    x = arguments.get_parsed_arg()
+    assert isinstance(x, dict)
+    assert isinstance(arguments.parser, argparse.ArgumentParser)
+
+
+def test_parse_args_defaults(mocker) -> None:
+    mocker.patch.object(Path, "is_file", MagicMock(side_effect=[False, True]))
+    args = Arguments(["trade"]).get_parsed_arg()
+    assert args["config"] == ["config.json"]
+    assert args["strategy_path"] is None
+    assert args["datadir"] is None
+    assert args["verbosity"] is None
+
+
+def test_parse_args_default_userdatadir(mocker) -> None:
+    mocker.patch.object(Path, "is_file", MagicMock(return_value=True))
+    args = Arguments(["trade"]).get_parsed_arg()
+    # configuration defaults to user_data if that is available.
+    assert args["config"] == [str(Path("user_data/config.json"))]
+    assert args["strategy_path"] is None
+    assert args["datadir"] is None
+    assert args["verbosity"] is None
+
+
+def test_parse_args_userdatadir(mocker) -> None:
+    mocker.patch.object(Path, "is_file", MagicMock(return_value=True))
+    args = Arguments(["trade", "--user-data-dir", "user_data", "-v"]).get_parsed_arg()
+    # configuration defaults to user_data if that is available.
+    assert args["config"] == [str(Path("user_data/config.json"))]
+    assert args["strategy_path"] is None
+    assert args["datadir"] is None
+    assert args["verbosity"] == 1
+
+
+def test_parse_args_config() -> None:
+    args = Arguments(["trade", "-c", "/dev/null"]).get_parsed_arg()
+    assert args["config"] == ["/dev/null"]
+
+    args = Arguments(["trade", "--config", "/dev/null"]).get_parsed_arg()
+    assert args["config"] == ["/dev/null"]
+
+    args = Arguments(
+        ["trade", "--config", "/dev/null", "--config", "/dev/zero"],
+    ).get_parsed_arg()
+    assert args["config"] == ["/dev/null", "/dev/zero"]
+
+
+def test_parse_args_db_url() -> None:
+    args = Arguments(["trade", "--db-url", "sqlite:///test.sqlite"]).get_parsed_arg()
+    assert args["db_url"] == "sqlite:///test.sqlite"
+
+
+def test_parse_args_verbose() -> None:
+    args = Arguments(["trade", "-v"]).get_parsed_arg()
+    assert args["verbosity"] == 1
+
+    args = Arguments(["trade", "--verbose"]).get_parsed_arg()
+    assert args["verbosity"] == 1
+
+
+def test_common_scripts_options() -> None:
+    args = Arguments(["download-data", "-p", "ETH/BTC", "XRP/BTC"]).get_parsed_arg()
+
+    assert args["pairs"] == ["ETH/BTC", "XRP/BTC"]
+    assert "func" in args
+
+
+def test_parse_args_version() -> None:
+    args = Arguments(["--version"]).get_parsed_arg()
+    assert args["version_main"] is True
+
+
+def test_parse_args_invalid() -> None:
+    with pytest.raises(SystemExit, match=r"2"):
+        Arguments(["-c"]).get_parsed_arg()
+
+
+def test_parse_args_strategy() -> None:
+    args = Arguments(["trade", "--strategy", "SomeStrategy"]).get_parsed_arg()
+    assert args["strategy"] == "SomeStrategy"
+
+
+def test_parse_args_strategy_invalid() -> None:
+    with pytest.raises(SystemExit, match=r"2"):
+        Arguments(["--strategy"]).get_parsed_arg()
+
+
+def test_parse_args_strategy_path() -> None:
+    args = Arguments(["trade", "--strategy-path", "/some/path"]).get_parsed_arg()
+    assert args["strategy_path"] == "/some/path"
+
+
+def test_parse_args_strategy_path_invalid() -> None:
+    with pytest.raises(SystemExit, match=r"2"):
+        Arguments(["--strategy-path"]).get_parsed_arg()
+
+
+def test_parse_args_backtesting_invalid() -> None:
+    with pytest.raises(SystemExit, match=r"2"):
+        Arguments(["backtesting --timeframe"]).get_parsed_arg()
+
+    with pytest.raises(SystemExit, match=r"2"):
+        Arguments(["backtesting --timeframe", "abc"]).get_parsed_arg()
+
+
+def test_parse_args_backtesting_custom() -> None:
+    args = [
+        "backtesting",
+        "-c",
+        "test_conf.json",
+        "--timeframe",
+        "1m",
+        "--strategy-list",
+        CURRENT_TEST_STRATEGY,
+        "SampleStrategy",
+    ]
+    call_args = Arguments(args).get_parsed_arg()
+    assert call_args["config"] == ["test_conf.json"]
+    assert call_args["verbosity"] is None
+    assert call_args["command"] == "backtesting"
+    assert call_args["func"] is not None
+    assert call_args["timeframe"] == "1m"
+    assert isinstance(call_args["strategy_list"], list)
+    assert len(call_args["strategy_list"]) == 2
+
+
+def test_parse_args_hyperopt_custom() -> None:
+    args = ["hyperopt", "-c", "test_conf.json", "--epochs", "20", "--spaces", "buy"]
+    call_args = Arguments(args).get_parsed_arg()
+    assert call_args["config"] == ["test_conf.json"]
+    assert call_args["epochs"] == 20
+    assert call_args["verbosity"] is None
+    assert call_args["command"] == "hyperopt"
+    assert call_args["spaces"] == ["buy"]
+    assert call_args["func"] is not None
+    assert callable(call_args["func"])
+
+
+def test_download_data_options() -> None:
+    args = [
+        "download-data",
+        "--datadir",
+        "datadir/directory",
+        "--pairs-file",
+        "file_with_pairs",
+        "--days",
+        "30",
+        "--exchange",
+        "binance",
+    ]
+    pargs = Arguments(args).get_parsed_arg()
+
+    assert pargs["pairs_file"] == "file_with_pairs"
+    assert pargs["datadir"] == "datadir/directory"
+    assert pargs["days"] == 30
+    assert pargs["exchange"] == "binance"
+
+
+def test_plot_dataframe_options() -> None:
+    args = [
+        "plot-dataframe",
+        "-c",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--indicators1",
+        "sma10",
+        "sma100",
+        "--indicators2",
+        "macd",
+        "fastd",
+        "fastk",
+        "--plot-limit",
+        "30",
+        "-p",
+        "UNITTEST/BTC",
+    ]
+    pargs = Arguments(args).get_parsed_arg()
+
+    assert pargs["indicators1"] == ["sma10", "sma100"]
+    assert pargs["indicators2"] == ["macd", "fastd", "fastk"]
+    assert pargs["plot_limit"] == 30
+    assert pargs["pairs"] == ["UNITTEST/BTC"]
+
+
+@pytest.mark.parametrize("auto_open_arg", [True, False])
+def test_plot_profit_options(auto_open_arg: bool) -> None:
+    args = [
+        "plot-profit",
+        "-p",
+        "UNITTEST/BTC",
+        "--trade-source",
+        "DB",
+        "--db-url",
+        "sqlite:///whatever.sqlite",
+    ]
+    if auto_open_arg:
+        args.append("--auto-open")
+    pargs = Arguments(args).get_parsed_arg()
+
+    assert pargs["trade_source"] == "DB"
+    assert pargs["pairs"] == ["UNITTEST/BTC"]
+    assert pargs["db_url"] == "sqlite:///whatever.sqlite"
+    assert pargs["plot_auto_open"] == auto_open_arg
+
+
+def test_config_notallowed(mocker) -> None:
+    mocker.patch.object(Path, "is_file", MagicMock(return_value=False))
+    args = [
+        "create-userdir",
+    ]
+    pargs = Arguments(args).get_parsed_arg()
+
+    assert "config" not in pargs
+
+    # When file exists:
+    mocker.patch.object(Path, "is_file", MagicMock(return_value=True))
+    args = [
+        "create-userdir",
+    ]
+    pargs = Arguments(args).get_parsed_arg()
+    # config is not added even if it exists, since create-userdir is in the notallowed list
+    assert "config" not in pargs
+
+
+def test_config_notrequired(mocker) -> None:
+    mocker.patch.object(Path, "is_file", MagicMock(return_value=False))
+    args = [
+        "download-data",
+    ]
+    pargs = Arguments(args).get_parsed_arg()
+
+    assert pargs["config"] is None
+
+    # When file exists:
+    mocker.patch.object(Path, "is_file", MagicMock(side_effect=[False, True]))
+    args = [
+        "download-data",
+    ]
+    pargs = Arguments(args).get_parsed_arg()
+    # config is added if it exists
+    assert pargs["config"] == ["config.json"]
+
+
+def test_check_int_positive() -> None:
+    assert check_int_positive("3") == 3
+    assert check_int_positive("1") == 1
+    assert check_int_positive("100") == 100
+
+    with pytest.raises(argparse.ArgumentTypeError):
+        check_int_positive("-2")
+
+    with pytest.raises(argparse.ArgumentTypeError):
+        check_int_positive("0")
+
+    with pytest.raises(argparse.ArgumentTypeError):
+        check_int_positive(0)
+
+    with pytest.raises(argparse.ArgumentTypeError):
+        check_int_positive("3.5")
+
+    with pytest.raises(argparse.ArgumentTypeError):
+        check_int_positive("DeadBeef")
+
+
+def test_check_int_nonzero() -> None:
+    assert check_int_nonzero("3") == 3
+    assert check_int_nonzero("1") == 1
+    assert check_int_nonzero("100") == 100
+
+    assert check_int_nonzero("-2") == -2
+
+    with pytest.raises(argparse.ArgumentTypeError):
+        check_int_nonzero("0")
+
+    with pytest.raises(argparse.ArgumentTypeError):
+        check_int_nonzero(0)
+
+    with pytest.raises(argparse.ArgumentTypeError):
+        check_int_nonzero("3.5")
+
+    with pytest.raises(argparse.ArgumentTypeError):
+        check_int_nonzero("DeadBeef")
diff --git a/tests/test_configuration.py b/tests/test_configuration.py
new file mode 100644
index 000000000..6c54ad350
--- /dev/null
+++ b/tests/test_configuration.py
@@ -0,0 +1,1611 @@
+# pragma pylint: disable=missing-docstring, protected-access, invalid-name
+import json
+import warnings
+from copy import deepcopy
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+from jsonschema import ValidationError
+
+from freqtrade.commands import Arguments
+from freqtrade.configuration import Configuration, validate_config_consistency
+from freqtrade.configuration.config_secrets import sanitize_config
+from freqtrade.configuration.config_validation import validate_config_schema
+from freqtrade.configuration.deprecated_settings import (
+    check_conflicting_settings,
+    process_deprecated_setting,
+    process_removed_setting,
+    process_temporary_deprecated_settings,
+)
+from freqtrade.configuration.environment_vars import _flat_vars_to_nested_dict
+from freqtrade.configuration.load_config import (
+    load_config_file,
+    load_file,
+    load_from_files,
+    log_config_error_range,
+)
+from freqtrade.constants import DEFAULT_DB_DRYRUN_URL, DEFAULT_DB_PROD_URL, ENV_VAR_PREFIX
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import ConfigurationError, OperationalException
+from tests.conftest import (
+    CURRENT_TEST_STRATEGY,
+    log_has,
+    log_has_re,
+    patched_configuration_load_config_file,
+)
+
+
+@pytest.fixture(scope="function")
+def all_conf():
+    config_file = Path(__file__).parents[1] / "config_examples/config_full.example.json"
+    conf = load_config_file(str(config_file))
+    return conf
+
+
+def test_load_config_missing_attributes(default_conf) -> None:
+    conf = deepcopy(default_conf)
+    conf.pop("exchange")
+
+    with pytest.raises(ValidationError, match=r".*'exchange' is a required property.*"):
+        validate_config_schema(conf)
+
+    conf = deepcopy(default_conf)
+    conf.pop("stake_currency")
+    conf["runmode"] = RunMode.DRY_RUN
+    with pytest.raises(ValidationError, match=r".*'stake_currency' is a required property.*"):
+        validate_config_schema(conf)
+
+
+def test_load_config_incorrect_stake_amount(default_conf) -> None:
+    default_conf["stake_amount"] = "fake"
+
+    with pytest.raises(ValidationError, match=r".*'fake' does not match 'unlimited'.*"):
+        validate_config_schema(default_conf)
+
+
+def test_load_config_file(default_conf, mocker, caplog) -> None:
+    del default_conf["user_data_dir"]
+    default_conf["datadir"] = str(default_conf["datadir"])
+    file_mock = mocker.patch(
+        "freqtrade.configuration.load_config.Path.open",
+        mocker.mock_open(read_data=json.dumps(default_conf)),
+    )
+
+    validated_conf = load_config_file("somefile")
+    assert file_mock.call_count == 1
+    assert validated_conf.items() >= default_conf.items()
+
+
+def test_load_config_file_error(default_conf, mocker, caplog) -> None:
+    del default_conf["user_data_dir"]
+    default_conf["datadir"] = str(default_conf["datadir"])
+    filedata = json.dumps(default_conf).replace('"stake_amount": 0.001,', '"stake_amount": .001,')
+    mocker.patch(
+        "freqtrade.configuration.load_config.Path.open", mocker.mock_open(read_data=filedata)
+    )
+    mocker.patch.object(Path, "read_text", MagicMock(return_value=filedata))
+
+    with pytest.raises(OperationalException, match=r".*Please verify the following segment.*"):
+        load_config_file("somefile")
+
+
+def test_load_config_file_error_range(default_conf, mocker, caplog) -> None:
+    del default_conf["user_data_dir"]
+    default_conf["datadir"] = str(default_conf["datadir"])
+    filedata = json.dumps(default_conf).replace('"stake_amount": 0.001,', '"stake_amount": .001,')
+    mocker.patch.object(Path, "read_text", MagicMock(return_value=filedata))
+
+    x = log_config_error_range("somefile", "Parse error at offset 64: Invalid value.")
+    assert isinstance(x, str)
+    assert (
+        x == '{"max_open_trades": 1, "stake_currency": "BTC", '
+        '"stake_amount": .001, "fiat_display_currency": "USD", '
+        '"timeframe": "5m", "dry_run": true, "cance'
+    )
+
+    filedata = json.dumps(default_conf, indent=2).replace(
+        '"stake_amount": 0.001,', '"stake_amount": .001,'
+    )
+    mocker.patch.object(Path, "read_text", MagicMock(return_value=filedata))
+
+    x = log_config_error_range("somefile", "Parse error at offset 4: Invalid value.")
+    assert isinstance(x, str)
+    assert x == '  "max_open_trades": 1,\n  "stake_currency": "BTC",\n  "stake_amount": .001,'
+
+    x = log_config_error_range("-", "")
+    assert x == ""
+
+
+def test_load_file_error(tmp_path):
+    testpath = tmp_path / "config.json"
+    with pytest.raises(OperationalException, match=r"File .* not found!"):
+        load_file(testpath)
+
+
+def test__args_to_config(caplog):
+    arg_list = ["trade", "--strategy-path", "TestTest"]
+    args = Arguments(arg_list).get_parsed_arg()
+    configuration = Configuration(args)
+    config = {}
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        # No warnings ...
+        configuration._args_to_config(config, argname="strategy_path", logstring="DeadBeef")
+        assert len(w) == 0
+        assert log_has("DeadBeef", caplog)
+        assert config["strategy_path"] == "TestTest"
+
+    configuration = Configuration(args)
+    config = {}
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        # Deprecation warnings!
+        configuration._args_to_config(
+            config, argname="strategy_path", logstring="DeadBeef", deprecated_msg="Going away soon!"
+        )
+        assert len(w) == 1
+        assert issubclass(w[-1].category, DeprecationWarning)
+        assert "DEPRECATED: Going away soon!" in str(w[-1].message)
+        assert log_has("DeadBeef", caplog)
+        assert config["strategy_path"] == "TestTest"
+
+
+def test_load_config_max_open_trades_zero(default_conf, mocker, caplog) -> None:
+    default_conf["max_open_trades"] = 0
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = Arguments(["trade"]).get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf["max_open_trades"] == 0
+    assert "internals" in validated_conf
+
+
+def test_load_config_combine_dicts(default_conf, mocker, caplog) -> None:
+    conf1 = deepcopy(default_conf)
+    conf2 = deepcopy(default_conf)
+    del conf1["exchange"]["key"]
+    del conf1["exchange"]["secret"]
+    del conf2["exchange"]["name"]
+    conf2["exchange"]["pair_whitelist"] += ["NANO/BTC"]
+
+    config_files = [conf1, conf2]
+
+    configsmock = MagicMock(side_effect=config_files)
+    mocker.patch("freqtrade.configuration.load_config.load_config_file", configsmock)
+
+    arg_list = [
+        "trade",
+        "-c",
+        "test_conf.json",
+        "--config",
+        "test2_conf.json",
+    ]
+    args = Arguments(arg_list).get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    exchange_conf = default_conf["exchange"]
+    assert validated_conf["exchange"]["name"] == exchange_conf["name"]
+    assert validated_conf["exchange"]["key"] == exchange_conf["key"]
+    assert validated_conf["exchange"]["secret"] == exchange_conf["secret"]
+    assert validated_conf["exchange"]["pair_whitelist"] != conf1["exchange"]["pair_whitelist"]
+    assert validated_conf["exchange"]["pair_whitelist"] == conf2["exchange"]["pair_whitelist"]
+
+    assert "internals" in validated_conf
+
+
+def test_from_config(default_conf, mocker, caplog) -> None:
+    conf1 = deepcopy(default_conf)
+    conf2 = deepcopy(default_conf)
+    del conf1["exchange"]["key"]
+    del conf1["exchange"]["secret"]
+    del conf2["exchange"]["name"]
+    conf2["exchange"]["pair_whitelist"] += ["NANO/BTC"]
+    conf2["fiat_display_currency"] = "EUR"
+    config_files = [conf1, conf2]
+    mocker.patch("freqtrade.configuration.configuration.create_datadir", lambda c, x: x)
+
+    configsmock = MagicMock(side_effect=config_files)
+    mocker.patch("freqtrade.configuration.load_config.load_config_file", configsmock)
+
+    validated_conf = Configuration.from_files(["test_conf.json", "test2_conf.json"])
+
+    exchange_conf = default_conf["exchange"]
+    assert validated_conf["exchange"]["name"] == exchange_conf["name"]
+    assert validated_conf["exchange"]["key"] == exchange_conf["key"]
+    assert validated_conf["exchange"]["secret"] == exchange_conf["secret"]
+    assert validated_conf["exchange"]["pair_whitelist"] != conf1["exchange"]["pair_whitelist"]
+    assert validated_conf["exchange"]["pair_whitelist"] == conf2["exchange"]["pair_whitelist"]
+    assert validated_conf["fiat_display_currency"] == "EUR"
+    assert "internals" in validated_conf
+    assert isinstance(validated_conf["user_data_dir"], Path)
+
+
+def test_from_recursive_files(testdatadir) -> None:
+    files = testdatadir / "testconfigs/testconfig.json"
+
+    conf = Configuration.from_files([files])
+
+    assert conf
+    # Exchange comes from "the first config"
+    assert conf["exchange"]
+    # Pricing comes from the 2nd config
+    assert conf["entry_pricing"]
+    assert conf["entry_pricing"]["price_side"] == "same"
+    assert conf["exit_pricing"]
+    # The other key comes from pricing2, which is imported by pricing.json.
+    # pricing.json is a level higher, therefore wins.
+    assert conf["exit_pricing"]["price_side"] == "same"
+
+    assert len(conf["config_files"]) == 4
+    assert "testconfig.json" in conf["config_files"][0]
+    assert "test_pricing_conf.json" in conf["config_files"][1]
+    assert "test_base_config.json" in conf["config_files"][2]
+    assert "test_pricing2_conf.json" in conf["config_files"][3]
+
+    files = testdatadir / "testconfigs/recursive.json"
+    with pytest.raises(OperationalException, match="Config loop detected."):
+        load_from_files([files])
+
+
+def test_print_config(default_conf, mocker, caplog) -> None:
+    conf1 = deepcopy(default_conf)
+    # Delete non-json elements from default_conf
+    del conf1["user_data_dir"]
+    conf1["datadir"] = str(conf1["datadir"])
+    config_files = [conf1]
+
+    configsmock = MagicMock(side_effect=config_files)
+    mocker.patch("freqtrade.configuration.configuration.create_datadir", lambda c, x: x)
+    mocker.patch("freqtrade.configuration.configuration.load_from_files", configsmock)
+
+    validated_conf = Configuration.from_files(["test_conf.json"])
+
+    assert isinstance(validated_conf["user_data_dir"], Path)
+    assert "user_data_dir" in validated_conf
+    assert "original_config" in validated_conf
+    assert isinstance(json.dumps(validated_conf["original_config"]), str)
+
+
+def test_load_config_max_open_trades_minus_one(default_conf, mocker, caplog) -> None:
+    default_conf["max_open_trades"] = -1
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = Arguments(["trade"]).get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf["max_open_trades"] > 999999999
+    assert validated_conf["max_open_trades"] == float("inf")
+    assert "runmode" in validated_conf
+    assert validated_conf["runmode"] == RunMode.DRY_RUN
+
+
+def test_load_config_file_exception(mocker) -> None:
+    mocker.patch(
+        "freqtrade.configuration.configuration.Path.open",
+        MagicMock(side_effect=FileNotFoundError("File not found")),
+    )
+
+    with pytest.raises(OperationalException, match=r'.*Config file "somefile" not found!*'):
+        load_config_file("somefile")
+
+
+def test_load_config(default_conf, mocker) -> None:
+    del default_conf["strategy_path"]
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = Arguments(["trade"]).get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf.get("strategy_path") is None
+    assert "edge" not in validated_conf
+
+
+def test_load_config_with_params(default_conf, mocker) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    arglist = [
+        "trade",
+        "--strategy",
+        "TestStrategy",
+        "--strategy-path",
+        "/some/path",
+        "--db-url",
+        "sqlite:///someurl",
+    ]
+    args = Arguments(arglist).get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf.get("strategy") == "TestStrategy"
+    assert validated_conf.get("strategy_path") == "/some/path"
+    assert validated_conf.get("db_url") == "sqlite:///someurl"
+
+    # Test conf provided db_url prod
+    conf = default_conf.copy()
+    conf["dry_run"] = False
+    conf["db_url"] = "sqlite:///path/to/db.sqlite"
+    patched_configuration_load_config_file(mocker, conf)
+
+    arglist = ["trade", "--strategy", "TestStrategy", "--strategy-path", "/some/path"]
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+    assert validated_conf.get("db_url") == "sqlite:///path/to/db.sqlite"
+
+    # Test conf provided db_url dry_run
+    conf = default_conf.copy()
+    conf["dry_run"] = True
+    conf["db_url"] = "sqlite:///path/to/db.sqlite"
+    patched_configuration_load_config_file(mocker, conf)
+
+    arglist = ["trade", "--strategy", "TestStrategy", "--strategy-path", "/some/path"]
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+    assert validated_conf.get("db_url") == "sqlite:///path/to/db.sqlite"
+
+    # Test args provided db_url prod
+    conf = default_conf.copy()
+    conf["dry_run"] = False
+    del conf["db_url"]
+    patched_configuration_load_config_file(mocker, conf)
+
+    arglist = ["trade", "--strategy", "TestStrategy", "--strategy-path", "/some/path"]
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+    assert validated_conf.get("db_url") == DEFAULT_DB_PROD_URL
+    assert "runmode" in validated_conf
+    assert validated_conf["runmode"] == RunMode.LIVE
+
+    # Test args provided db_url dry_run
+    conf = default_conf.copy()
+    conf["dry_run"] = True
+    conf["db_url"] = DEFAULT_DB_PROD_URL
+    patched_configuration_load_config_file(mocker, conf)
+
+    arglist = ["trade", "--strategy", "TestStrategy", "--strategy-path", "/some/path"]
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+    assert validated_conf.get("db_url") == DEFAULT_DB_DRYRUN_URL
+
+
+@pytest.mark.parametrize(
+    "config_value,expected,arglist",
+    [
+        (True, True, ["trade", "--dry-run"]),  # Leave config untouched
+        (False, True, ["trade", "--dry-run"]),  # Override config untouched
+        (False, False, ["trade"]),  # Leave config untouched
+        (True, True, ["trade"]),  # Leave config untouched
+    ],
+)
+def test_load_dry_run(default_conf, mocker, config_value, expected, arglist) -> None:
+    default_conf["dry_run"] = config_value
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    configuration = Configuration(Arguments(arglist).get_parsed_arg())
+    validated_conf = configuration.load_config()
+
+    assert validated_conf["dry_run"] is expected
+    assert validated_conf["runmode"] == (RunMode.DRY_RUN if expected else RunMode.LIVE)
+
+
+def test_load_custom_strategy(default_conf, mocker, tmp_path) -> None:
+    default_conf.update(
+        {
+            "strategy": "CustomStrategy",
+            "strategy_path": f"{tmp_path}/strategies",
+        }
+    )
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = Arguments(["trade"]).get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf.get("strategy") == "CustomStrategy"
+    assert validated_conf.get("strategy_path") == f"{tmp_path}/strategies"
+
+
+def test_show_info(default_conf, mocker, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    arglist = [
+        "trade",
+        "--strategy",
+        "TestStrategy",
+        "--db-url",
+        "sqlite:///tmp/testdb",
+    ]
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    configuration.get_config()
+
+    assert log_has('Using DB: "sqlite:///tmp/testdb"', caplog)
+    assert log_has("Dry run is enabled", caplog)
+
+
+def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    arglist = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+    ]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+    assert "max_open_trades" in config
+    assert "stake_currency" in config
+    assert "stake_amount" in config
+    assert "exchange" in config
+    assert "pair_whitelist" in config["exchange"]
+    assert "datadir" in config
+    assert "user_data_dir" in config
+    assert log_has("Using data directory: {} ...".format(config["datadir"]), caplog)
+    assert "timeframe" in config
+    assert not log_has("Parameter -i/--timeframe detected ...", caplog)
+
+    assert "position_stacking" not in config
+    assert not log_has("Parameter --enable-position-stacking detected ...", caplog)
+
+    assert "timerange" not in config
+
+
+def test_setup_configuration_with_arguments(mocker, default_conf, caplog, tmp_path) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.configuration.configuration.create_datadir", lambda c, x: x)
+    mocker.patch(
+        "freqtrade.configuration.configuration.create_userdata_dir",
+        lambda x, *args, **kwargs: Path(x),
+    )
+    arglist = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--datadir",
+        "/foo/bar",
+        "--userdir",
+        f"{tmp_path}/freqtrade",
+        "--timeframe",
+        "1m",
+        "--enable-position-stacking",
+        "--timerange",
+        ":100",
+        "--export",
+        "trades",
+        "--stake-amount",
+        "unlimited",
+    ]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+    assert "max_open_trades" in config
+    assert "stake_currency" in config
+    assert "stake_amount" in config
+    assert "exchange" in config
+    assert "pair_whitelist" in config["exchange"]
+    assert "datadir" in config
+    assert log_has("Using data directory: {} ...".format("/foo/bar"), caplog)
+    assert log_has(f"Using user-data directory: {tmp_path / 'freqtrade'} ...", caplog)
+    assert "user_data_dir" in config
+
+    assert "timeframe" in config
+    assert log_has("Parameter -i/--timeframe detected ... Using timeframe: 1m ...", caplog)
+
+    assert "position_stacking" in config
+    assert log_has("Parameter --enable-position-stacking detected ...", caplog)
+
+    assert "timerange" in config
+    assert log_has("Parameter --timerange detected: {} ...".format(config["timerange"]), caplog)
+
+    assert "export" in config
+    assert log_has("Parameter --export detected: {} ...".format(config["export"]), caplog)
+    assert "stake_amount" in config
+    assert config["stake_amount"] == "unlimited"
+
+
+def test_setup_configuration_with_stratlist(mocker, default_conf, caplog) -> None:
+    """
+    Test setup_configuration() function
+    """
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    arglist = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--timeframe",
+        "1m",
+        "--export",
+        "trades",
+        "--strategy-list",
+        CURRENT_TEST_STRATEGY,
+        "TestStrategy",
+    ]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args, RunMode.BACKTEST)
+    config = configuration.get_config()
+    assert config["runmode"] == RunMode.BACKTEST
+    assert "max_open_trades" in config
+    assert "stake_currency" in config
+    assert "stake_amount" in config
+    assert "exchange" in config
+    assert "pair_whitelist" in config["exchange"]
+    assert "datadir" in config
+    assert log_has("Using data directory: {} ...".format(config["datadir"]), caplog)
+    assert "timeframe" in config
+    assert log_has("Parameter -i/--timeframe detected ... Using timeframe: 1m ...", caplog)
+
+    assert "strategy_list" in config
+    assert log_has("Using strategy list of 2 strategies", caplog)
+
+    assert "position_stacking" not in config
+
+    assert "timerange" not in config
+
+    assert "export" in config
+    assert log_has("Parameter --export detected: {} ...".format(config["export"]), caplog)
+
+
+def test_hyperopt_with_arguments(mocker, default_conf, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    arglist = [
+        "hyperopt",
+        "--epochs",
+        "10",
+        "--spaces",
+        "all",
+    ]
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args, RunMode.HYPEROPT)
+    config = configuration.get_config()
+
+    assert "epochs" in config
+    assert int(config["epochs"]) == 10
+    assert log_has(
+        "Parameter --epochs detected ... Will run Hyperopt with for 10 epochs ...", caplog
+    )
+
+    assert "spaces" in config
+    assert config["spaces"] == ["all"]
+    assert log_has("Parameter -s/--spaces detected: ['all']", caplog)
+    assert "runmode" in config
+    assert config["runmode"] == RunMode.HYPEROPT
+
+
+def test_cli_verbose_with_params(default_conf, mocker, caplog) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    # Prevent setting loggers
+    mocker.patch("freqtrade.loggers.logging.config.dictConfig", MagicMock)
+    arglist = ["trade", "-vvv"]
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf.get("verbosity") == 3
+    assert log_has("Verbosity set to 3", caplog)
+
+
+@pytest.mark.usefixtures("keep_log_config_loggers")
+def test_set_logfile(default_conf, mocker, tmp_path):
+    default_conf["ft_tests_force_logging"] = True
+    patched_configuration_load_config_file(mocker, default_conf)
+    f = tmp_path / "test_file.log"
+    assert not f.is_file()
+    arglist = [
+        "trade",
+        "--logfile",
+        str(f),
+    ]
+    args = Arguments(arglist).get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf["logfile"] == str(f)
+    assert f.is_file()
+    try:
+        f.unlink()
+    except Exception:
+        pass
+
+
+def test_load_config_warn_forcebuy(default_conf, mocker, caplog) -> None:
+    default_conf["force_entry_enable"] = True
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = Arguments(["trade"]).get_parsed_arg()
+    configuration = Configuration(args)
+    validated_conf = configuration.load_config()
+
+    assert validated_conf.get("force_entry_enable")
+    assert log_has("`force_entry_enable` RPC message enabled.", caplog)
+
+
+def test_validate_default_conf(default_conf) -> None:
+    # Validate via our validator - we allow setting defaults!
+    validate_config_schema(default_conf)
+
+
+@pytest.mark.parametrize("fiat", ["EUR", "USD", "", None])
+def test_validate_fiat_currency_options(default_conf, fiat) -> None:
+    # Validate via our validator - we allow setting defaults!
+    if fiat is not None:
+        default_conf["fiat_display_currency"] = fiat
+    else:
+        del default_conf["fiat_display_currency"]
+    validate_config_schema(default_conf)
+
+
+def test_validate_max_open_trades(default_conf):
+    default_conf["max_open_trades"] = float("inf")
+    default_conf["stake_amount"] = "unlimited"
+    with pytest.raises(
+        OperationalException,
+        match="`max_open_trades` and `stake_amount` cannot both be unlimited.",
+    ):
+        validate_config_consistency(default_conf)
+
+
+def test_validate_price_side(default_conf):
+    default_conf["order_types"] = {
+        "entry": "limit",
+        "exit": "limit",
+        "stoploss": "limit",
+        "stoploss_on_exchange": False,
+    }
+    # Default should pass
+    validate_config_consistency(default_conf)
+
+    conf = deepcopy(default_conf)
+    conf["order_types"]["entry"] = "market"
+    with pytest.raises(
+        OperationalException,
+        match='Market entry orders require entry_pricing.price_side = "other".',
+    ):
+        validate_config_consistency(conf)
+
+    conf = deepcopy(default_conf)
+    conf["order_types"]["exit"] = "market"
+    with pytest.raises(
+        OperationalException, match='Market exit orders require exit_pricing.price_side = "other".'
+    ):
+        validate_config_consistency(conf)
+
+    # Validate inversed case
+    conf = deepcopy(default_conf)
+    conf["order_types"]["exit"] = "market"
+    conf["order_types"]["entry"] = "market"
+    conf["exit_pricing"]["price_side"] = "bid"
+    conf["entry_pricing"]["price_side"] = "ask"
+
+    validate_config_consistency(conf)
+
+
+def test_validate_tsl(default_conf):
+    default_conf["stoploss"] = 0.0
+    with pytest.raises(
+        OperationalException,
+        match="The config stoploss needs to be different "
+        "from 0 to avoid problems with sell orders.",
+    ):
+        validate_config_consistency(default_conf)
+    default_conf["stoploss"] = -0.10
+
+    default_conf["trailing_stop"] = True
+    default_conf["trailing_stop_positive"] = 0
+    default_conf["trailing_stop_positive_offset"] = 0
+
+    default_conf["trailing_only_offset_is_reached"] = True
+    with pytest.raises(
+        OperationalException,
+        match=r"The config trailing_only_offset_is_reached needs "
+        "trailing_stop_positive_offset to be more than 0 in your config.",
+    ):
+        validate_config_consistency(default_conf)
+
+    default_conf["trailing_stop_positive_offset"] = 0.01
+    default_conf["trailing_stop_positive"] = 0.015
+    with pytest.raises(
+        OperationalException,
+        match=r"The config trailing_stop_positive_offset needs "
+        "to be greater than trailing_stop_positive in your config.",
+    ):
+        validate_config_consistency(default_conf)
+
+    default_conf["trailing_stop_positive"] = 0.01
+    default_conf["trailing_stop_positive_offset"] = 0.015
+    validate_config_consistency(default_conf)
+
+    # 0 trailing stop positive - results in "Order would trigger immediately"
+    default_conf["trailing_stop_positive"] = 0
+    default_conf["trailing_stop_positive_offset"] = 0.02
+    default_conf["trailing_only_offset_is_reached"] = False
+    with pytest.raises(
+        OperationalException,
+        match="The config trailing_stop_positive needs to be different from 0 "
+        "to avoid problems with sell orders",
+    ):
+        validate_config_consistency(default_conf)
+
+
+def test_validate_edge2(edge_conf):
+    edge_conf.update(
+        {
+            "use_exit_signal": True,
+        }
+    )
+    # Passes test
+    validate_config_consistency(edge_conf)
+
+    edge_conf.update(
+        {
+            "use_exit_signal": False,
+        }
+    )
+    with pytest.raises(
+        OperationalException,
+        match="Edge requires `use_exit_signal` to be True, otherwise no sells will happen.",
+    ):
+        validate_config_consistency(edge_conf)
+
+
+def test_validate_whitelist(default_conf):
+    default_conf["runmode"] = RunMode.DRY_RUN
+    # Test regular case - has whitelist and uses StaticPairlist
+    validate_config_consistency(default_conf)
+    conf = deepcopy(default_conf)
+    del conf["exchange"]["pair_whitelist"]
+    # Test error case
+    with pytest.raises(
+        OperationalException, match="StaticPairList requires pair_whitelist to be set."
+    ):
+        validate_config_consistency(conf)
+
+    conf = deepcopy(default_conf)
+
+    conf.update(
+        {
+            "pairlists": [
+                {
+                    "method": "VolumePairList",
+                }
+            ]
+        }
+    )
+    # Dynamic whitelist should not care about pair_whitelist
+    validate_config_consistency(conf)
+    del conf["exchange"]["pair_whitelist"]
+
+    validate_config_consistency(conf)
+
+
+def test_validate_ask_orderbook(default_conf, caplog) -> None:
+    conf = deepcopy(default_conf)
+    conf["exit_pricing"]["use_order_book"] = True
+    conf["exit_pricing"]["order_book_min"] = 2
+    conf["exit_pricing"]["order_book_max"] = 2
+
+    validate_config_consistency(conf)
+    assert log_has_re(r"DEPRECATED: Please use `order_book_top` instead of.*", caplog)
+    assert conf["exit_pricing"]["order_book_top"] == 2
+
+    conf["exit_pricing"]["order_book_max"] = 5
+
+    with pytest.raises(
+        OperationalException, match=r"Using order_book_max != order_book_min in exit_pricing.*"
+    ):
+        validate_config_consistency(conf)
+
+
+def test_validate_time_in_force(default_conf, caplog) -> None:
+    conf = deepcopy(default_conf)
+    conf["order_time_in_force"] = {
+        "buy": "gtc",
+        "sell": "GTC",
+    }
+    validate_config_consistency(conf)
+    assert log_has_re(r"DEPRECATED: Using 'buy' and 'sell' for time_in_force is.*", caplog)
+    assert conf["order_time_in_force"]["entry"] == "gtc"
+    assert conf["order_time_in_force"]["exit"] == "GTC"
+
+    conf = deepcopy(default_conf)
+    conf["order_time_in_force"] = {
+        "buy": "GTC",
+        "sell": "GTC",
+    }
+    conf["trading_mode"] = "futures"
+    with pytest.raises(
+        OperationalException,
+        match=r"Please migrate your time_in_force settings .* 'entry' and 'exit'\.",
+    ):
+        validate_config_consistency(conf)
+
+
+def test__validate_order_types(default_conf, caplog) -> None:
+    conf = deepcopy(default_conf)
+    conf["order_types"] = {
+        "buy": "limit",
+        "sell": "market",
+        "forcesell": "market",
+        "forcebuy": "limit",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+    validate_config_consistency(conf)
+    assert log_has_re(r"DEPRECATED: Using 'buy' and 'sell' for order_types is.*", caplog)
+    assert conf["order_types"]["entry"] == "limit"
+    assert conf["order_types"]["exit"] == "market"
+    assert conf["order_types"]["force_entry"] == "limit"
+    assert "buy" not in conf["order_types"]
+    assert "sell" not in conf["order_types"]
+    assert "forcebuy" not in conf["order_types"]
+    assert "forcesell" not in conf["order_types"]
+
+    conf = deepcopy(default_conf)
+    conf["order_types"] = {
+        "buy": "limit",
+        "sell": "market",
+        "forcesell": "market",
+        "forcebuy": "limit",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+    conf["trading_mode"] = "futures"
+    with pytest.raises(
+        OperationalException,
+        match=r"Please migrate your order_types settings to use the new wording\.",
+    ):
+        validate_config_consistency(conf)
+
+
+def test__validate_unfilledtimeout(default_conf, caplog) -> None:
+    conf = deepcopy(default_conf)
+    conf["unfilledtimeout"] = {
+        "buy": 30,
+        "sell": 35,
+    }
+    validate_config_consistency(conf)
+    assert log_has_re(r"DEPRECATED: Using 'buy' and 'sell' for unfilledtimeout is.*", caplog)
+    assert conf["unfilledtimeout"]["entry"] == 30
+    assert conf["unfilledtimeout"]["exit"] == 35
+    assert "buy" not in conf["unfilledtimeout"]
+    assert "sell" not in conf["unfilledtimeout"]
+
+    conf = deepcopy(default_conf)
+    conf["unfilledtimeout"] = {
+        "buy": 30,
+        "sell": 35,
+    }
+    conf["trading_mode"] = "futures"
+    with pytest.raises(
+        OperationalException,
+        match=r"Please migrate your unfilledtimeout settings to use the new wording\.",
+    ):
+        validate_config_consistency(conf)
+
+
+def test__validate_pricing_rules(default_conf, caplog) -> None:
+    def_conf = deepcopy(default_conf)
+    del def_conf["entry_pricing"]
+    del def_conf["exit_pricing"]
+
+    def_conf["ask_strategy"] = {
+        "price_side": "ask",
+        "use_order_book": True,
+        "bid_last_balance": 0.5,
+    }
+    def_conf["bid_strategy"] = {
+        "price_side": "bid",
+        "use_order_book": False,
+        "ask_last_balance": 0.7,
+    }
+    conf = deepcopy(def_conf)
+
+    validate_config_consistency(conf)
+    assert log_has_re(r"DEPRECATED: Using 'ask_strategy' and 'bid_strategy' is.*", caplog)
+    assert conf["exit_pricing"]["price_side"] == "ask"
+    assert conf["exit_pricing"]["use_order_book"] is True
+    assert conf["exit_pricing"]["price_last_balance"] == 0.5
+    assert conf["entry_pricing"]["price_side"] == "bid"
+    assert conf["entry_pricing"]["use_order_book"] is False
+    assert conf["entry_pricing"]["price_last_balance"] == 0.7
+    assert "ask_strategy" not in conf
+    assert "bid_strategy" not in conf
+
+    conf = deepcopy(def_conf)
+
+    conf["trading_mode"] = "futures"
+    with pytest.raises(
+        OperationalException, match=r"Please migrate your pricing settings to use the new wording\."
+    ):
+        validate_config_consistency(conf)
+
+
+def test__validate_freqai_include_timeframes(default_conf, caplog) -> None:
+    conf = deepcopy(default_conf)
+    conf.update(
+        {
+            "freqai": {
+                "enabled": True,
+                "feature_parameters": {
+                    "include_timeframes": ["1m", "5m"],
+                    "include_corr_pairlist": [],
+                },
+                "data_split_parameters": {},
+                "model_training_parameters": {},
+            }
+        }
+    )
+    with pytest.raises(OperationalException, match=r"Main timeframe of .*"):
+        validate_config_consistency(conf)
+    # Validation pass
+    conf.update({"timeframe": "1m"})
+    validate_config_consistency(conf)
+
+    # Ensure base timeframe is in include_timeframes
+    conf["freqai"]["feature_parameters"]["include_timeframes"] = ["5m", "15m"]
+    validate_config_consistency(conf)
+    assert conf["freqai"]["feature_parameters"]["include_timeframes"] == ["1m", "5m", "15m"]
+
+    conf.update({"analyze_per_epoch": True})
+    with pytest.raises(
+        OperationalException,
+        match=r"Using analyze-per-epoch .* not supported with a FreqAI strategy.",
+    ):
+        validate_config_consistency(conf)
+
+
+def test__validate_consumers(default_conf, caplog) -> None:
+    conf = deepcopy(default_conf)
+    conf.update({"external_message_consumer": {"enabled": True, "producers": []}})
+    with pytest.raises(
+        OperationalException, match="You must specify at least 1 Producer to connect to."
+    ):
+        validate_config_consistency(conf)
+
+    conf = deepcopy(default_conf)
+    conf.update(
+        {
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {
+                        "name": "default",
+                        "host": "127.0.0.1",
+                        "port": 8081,
+                        "ws_token": "secret_ws_t0ken.",
+                    },
+                    {
+                        "name": "default",
+                        "host": "127.0.0.1",
+                        "port": 8080,
+                        "ws_token": "secret_ws_t0ken.",
+                    },
+                ],
+            }
+        }
+    )
+    with pytest.raises(
+        OperationalException, match="Producer names must be unique. Duplicate: default"
+    ):
+        validate_config_consistency(conf)
+
+    conf = deepcopy(default_conf)
+    conf.update(
+        {
+            "process_only_new_candles": True,
+            "external_message_consumer": {
+                "enabled": True,
+                "producers": [
+                    {
+                        "name": "default",
+                        "host": "127.0.0.1",
+                        "port": 8081,
+                        "ws_token": "secret_ws_t0ken.",
+                    }
+                ],
+            },
+        }
+    )
+    validate_config_consistency(conf)
+    assert log_has_re("To receive best performance with external data.*", caplog)
+
+
+def test__validate_orderflow(default_conf) -> None:
+    conf = deepcopy(default_conf)
+    conf["exchange"]["use_public_trades"] = True
+    with pytest.raises(
+        ConfigurationError,
+        match="Orderflow is a required configuration key when using public trades.",
+    ):
+        validate_config_consistency(conf)
+
+    conf.update(
+        {
+            "orderflow": {
+                "scale": 0.5,
+                "stacked_imbalance_range": 3,
+                "imbalance_volume": 100,
+                "imbalance_ratio": 3,
+            }
+        }
+    )
+    # Should pass.
+    validate_config_consistency(conf)
+
+
+def test_load_config_test_comments() -> None:
+    """
+    Load config with comments
+    """
+    config_file = Path(__file__).parents[0] / "config_test_comments.json"
+    conf = load_config_file(str(config_file))
+
+    assert conf
+
+
+def test_load_config_default_exchange(all_conf) -> None:
+    """
+    config['exchange'] subtree has required options in it
+    so it cannot be omitted in the config
+    """
+    del all_conf["exchange"]
+
+    assert "exchange" not in all_conf
+
+    with pytest.raises(ValidationError, match=r"'exchange' is a required property"):
+        validate_config_schema(all_conf)
+
+
+def test_load_config_default_exchange_name(all_conf) -> None:
+    """
+    config['exchange']['name'] option is required
+    so it cannot be omitted in the config
+    """
+    del all_conf["exchange"]["name"]
+
+    assert "name" not in all_conf["exchange"]
+
+    with pytest.raises(ValidationError, match=r"'name' is a required property"):
+        validate_config_schema(all_conf)
+
+
+def test_load_config_stoploss_exchange_limit_ratio(all_conf) -> None:
+    all_conf["order_types"]["stoploss_on_exchange_limit_ratio"] = 1.15
+
+    with pytest.raises(ValidationError, match=r"1.15 is greater than the maximum"):
+        validate_config_schema(all_conf)
+
+
+@pytest.mark.parametrize(
+    "keys",
+    [
+        ("exchange", "key", ""),
+        ("exchange", "secret", ""),
+        ("exchange", "password", ""),
+    ],
+)
+def test_load_config_default_subkeys(all_conf, keys) -> None:
+    """
+    Test for parameters with default values in sub-paths
+    so they can be omitted in the config and the default value
+    should is added to the config.
+    """
+    # Get first level key
+    key = keys[0]
+    # get second level key
+    subkey = keys[1]
+
+    del all_conf[key][subkey]
+
+    assert subkey not in all_conf[key]
+
+    validate_config_schema(all_conf)
+    assert subkey in all_conf[key]
+    assert all_conf[key][subkey] == keys[2]
+
+
+def test_pairlist_resolving():
+    arglist = ["download-data", "--pairs", "ETH/BTC", "XRP/BTC", "--exchange", "binance"]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args, RunMode.OTHER)
+    config = configuration.get_config()
+
+    assert config["pairs"] == ["ETH/BTC", "XRP/BTC"]
+    assert config["exchange"]["pair_whitelist"] == ["ETH/BTC", "XRP/BTC"]
+    assert config["exchange"]["name"] == "binance"
+
+
+def test_pairlist_resolving_with_config(mocker, default_conf):
+    patched_configuration_load_config_file(mocker, default_conf)
+    arglist = [
+        "download-data",
+        "--config",
+        "config.json",
+    ]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+
+    assert config["pairs"] == default_conf["exchange"]["pair_whitelist"]
+    assert config["exchange"]["name"] == default_conf["exchange"]["name"]
+
+    # Override pairs
+    arglist = [
+        "download-data",
+        "--config",
+        "config.json",
+        "--pairs",
+        "ETH/BTC",
+        "XRP/BTC",
+    ]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+
+    assert config["pairs"] == ["ETH/BTC", "XRP/BTC"]
+    assert config["exchange"]["name"] == default_conf["exchange"]["name"]
+
+
+def test_pairlist_resolving_with_config_pl(mocker, default_conf):
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    arglist = [
+        "download-data",
+        "--config",
+        "config.json",
+        "--pairs-file",
+        "tests/testdata/pairs.json",
+    ]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+    assert len(config["pairs"]) == 23
+    assert "ETH/BTC" in config["pairs"]
+    assert "XRP/BTC" in config["pairs"]
+    assert config["exchange"]["name"] == default_conf["exchange"]["name"]
+
+
+def test_pairlist_resolving_with_config_pl_not_exists(mocker, default_conf):
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    arglist = [
+        "download-data",
+        "--config",
+        "config.json",
+        "--pairs-file",
+        "tests/testdata/pairs_doesnotexist.json",
+    ]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    with pytest.raises(OperationalException, match=r"No pairs file found with path.*"):
+        configuration = Configuration(args)
+        configuration.get_config()
+
+
+def test_pairlist_resolving_fallback(mocker, tmp_path):
+    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
+    mocker.patch.object(Path, "open", MagicMock(return_value=MagicMock()))
+    mocker.patch(
+        "freqtrade.configuration.configuration.load_file",
+        MagicMock(return_value=["XRP/BTC", "ETH/BTC"]),
+    )
+    arglist = ["download-data", "--exchange", "binance"]
+
+    args = Arguments(arglist).get_parsed_arg()
+    # Fix flaky tests if config.json exists
+    args["config"] = None
+
+    configuration = Configuration(args, RunMode.OTHER)
+    config = configuration.get_config()
+
+    assert config["pairs"] == ["ETH/BTC", "XRP/BTC"]
+    assert config["exchange"]["name"] == "binance"
+    assert config["datadir"] == tmp_path / "user_data/data/binance"
+
+
+@pytest.mark.parametrize(
+    "setting",
+    [
+        ("webhook", "webhookbuy", "testWEbhook", "webhook", "webhookentry", "testWEbhook"),
+        (
+            "ask_strategy",
+            "ignore_buying_expired_candle_after",
+            5,
+            None,
+            "ignore_buying_expired_candle_after",
+            6,
+        ),
+    ],
+)
+def test_process_temporary_deprecated_settings(mocker, default_conf, setting, caplog):
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    # Create sections for new and deprecated settings
+    # (they may not exist in the config)
+    default_conf[setting[0]] = {}
+    default_conf[setting[3]] = {}
+
+    # Assign deprecated setting
+    default_conf[setting[0]][setting[1]] = setting[2]
+    # Assign new setting
+    if setting[3]:
+        default_conf[setting[3]][setting[4]] = setting[5]
+    else:
+        default_conf[setting[4]] = setting[5]
+
+    # New and deprecated settings are conflicting ones
+    with pytest.raises(OperationalException, match=r"DEPRECATED"):
+        process_temporary_deprecated_settings(default_conf)
+
+    caplog.clear()
+
+    # Delete new setting
+    if setting[3]:
+        del default_conf[setting[3]][setting[4]]
+    else:
+        del default_conf[setting[4]]
+
+    process_temporary_deprecated_settings(default_conf)
+    assert log_has_re("DEPRECATED", caplog)
+    # The value of the new setting shall have been set to the
+    # value of the deprecated one
+    if setting[3]:
+        assert default_conf[setting[3]][setting[4]] == setting[2]
+    else:
+        assert default_conf[setting[4]] == setting[2]
+
+
+@pytest.mark.parametrize(
+    "setting",
+    [
+        ("experimental", "use_sell_signal", False),
+        ("experimental", "sell_profit_only", True),
+        ("experimental", "ignore_roi_if_buy_signal", True),
+    ],
+)
+def test_process_removed_settings(mocker, default_conf, setting):
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    # Create sections for new and deprecated settings
+    # (they may not exist in the config)
+    default_conf[setting[0]] = {}
+    # Assign removed setting
+    default_conf[setting[0]][setting[1]] = setting[2]
+
+    # New and deprecated settings are conflicting ones
+    with pytest.raises(OperationalException, match=r"Setting .* has been moved"):
+        process_temporary_deprecated_settings(default_conf)
+
+
+def test_process_deprecated_setting_edge(mocker, edge_conf):
+    patched_configuration_load_config_file(mocker, edge_conf)
+    edge_conf.update(
+        {
+            "edge": {
+                "enabled": True,
+                "capital_available_percentage": 0.5,
+            }
+        }
+    )
+
+    with pytest.raises(
+        OperationalException, match=r"DEPRECATED.*Using 'edge.capital_available_percentage'*"
+    ):
+        process_temporary_deprecated_settings(edge_conf)
+
+
+def test_check_conflicting_settings(mocker, default_conf, caplog):
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    # Create sections for new and deprecated settings
+    # (they may not exist in the config)
+    default_conf["sectionA"] = {}
+    default_conf["sectionB"] = {}
+    # Assign new setting
+    default_conf["sectionA"]["new_setting"] = "valA"
+    # Assign deprecated setting
+    default_conf["sectionB"]["deprecated_setting"] = "valB"
+
+    # New and deprecated settings are conflicting ones
+    with pytest.raises(OperationalException, match=r"DEPRECATED"):
+        check_conflicting_settings(
+            default_conf, "sectionB", "deprecated_setting", "sectionA", "new_setting"
+        )
+
+    caplog.clear()
+
+    # Delete new setting (deprecated exists)
+    del default_conf["sectionA"]["new_setting"]
+    check_conflicting_settings(
+        default_conf, "sectionB", "deprecated_setting", "sectionA", "new_setting"
+    )
+    assert not log_has_re("DEPRECATED", caplog)
+    assert "new_setting" not in default_conf["sectionA"]
+
+    caplog.clear()
+
+    # Assign new setting
+    default_conf["sectionA"]["new_setting"] = "valA"
+    # Delete deprecated setting
+    del default_conf["sectionB"]["deprecated_setting"]
+    check_conflicting_settings(
+        default_conf, "sectionB", "deprecated_setting", "sectionA", "new_setting"
+    )
+    assert not log_has_re("DEPRECATED", caplog)
+    assert default_conf["sectionA"]["new_setting"] == "valA"
+
+
+def test_process_deprecated_setting(mocker, default_conf, caplog):
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    # Create sections for new and deprecated settings
+    # (they may not exist in the config)
+    default_conf["sectionA"] = {}
+    default_conf["sectionB"] = {}
+    # Assign deprecated setting
+    default_conf["sectionB"]["deprecated_setting"] = "valB"
+
+    # Both new and deprecated settings exists
+    process_deprecated_setting(
+        default_conf, "sectionB", "deprecated_setting", "sectionA", "new_setting"
+    )
+    assert log_has_re("DEPRECATED", caplog)
+    # The value of the new setting shall have been set to the
+    # value of the deprecated one
+    assert default_conf["sectionA"]["new_setting"] == "valB"
+    # Old setting is removed
+    assert "deprecated_setting" not in default_conf["sectionB"]
+
+    caplog.clear()
+
+    # Delete new setting (deprecated exists)
+    del default_conf["sectionA"]["new_setting"]
+    default_conf["sectionB"]["deprecated_setting"] = "valB"
+    process_deprecated_setting(
+        default_conf, "sectionB", "deprecated_setting", "sectionA", "new_setting"
+    )
+    assert log_has_re("DEPRECATED", caplog)
+    # The value of the new setting shall have been set to the
+    # value of the deprecated one
+    assert default_conf["sectionA"]["new_setting"] == "valB"
+
+    caplog.clear()
+
+    # Assign new setting
+    default_conf["sectionA"]["new_setting"] = "valA"
+    # Delete deprecated setting
+    default_conf["sectionB"].pop("deprecated_setting", None)
+    process_deprecated_setting(
+        default_conf, "sectionB", "deprecated_setting", "sectionA", "new_setting"
+    )
+    assert not log_has_re("DEPRECATED", caplog)
+    assert default_conf["sectionA"]["new_setting"] == "valA"
+
+    caplog.clear()
+    # Test moving to root
+    default_conf["sectionB"]["deprecated_setting2"] = "DeadBeef"
+    process_deprecated_setting(default_conf, "sectionB", "deprecated_setting2", None, "new_setting")
+
+    assert log_has_re("DEPRECATED", caplog)
+    assert default_conf["new_setting"]
+
+
+def test_process_removed_setting(mocker, default_conf, caplog):
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    # Create sections for new and deprecated settings
+    # (they may not exist in the config)
+    default_conf["sectionA"] = {}
+    default_conf["sectionB"] = {}
+    # Assign new setting
+    default_conf["sectionB"]["somesetting"] = "valA"
+
+    # Only new setting exists (nothing should happen)
+    process_removed_setting(default_conf, "sectionA", "somesetting", "sectionB", "somesetting")
+    # Assign removed setting
+    default_conf["sectionA"]["somesetting"] = "valB"
+
+    with pytest.raises(OperationalException, match=r"Setting .* has been moved"):
+        process_removed_setting(default_conf, "sectionA", "somesetting", "sectionB", "somesetting")
+
+
+def test_process_deprecated_ticker_interval(default_conf, caplog):
+    message = "DEPRECATED: Please use 'timeframe' instead of 'ticker_interval."
+    config = deepcopy(default_conf)
+
+    process_temporary_deprecated_settings(config)
+    assert not log_has(message, caplog)
+
+    del config["timeframe"]
+    config["ticker_interval"] = "15m"
+    with pytest.raises(
+        OperationalException, match=r"DEPRECATED: 'ticker_interval' detected. Please use.*"
+    ):
+        process_temporary_deprecated_settings(config)
+
+
+def test_process_deprecated_protections(default_conf, caplog):
+    message = "DEPRECATED: Setting 'protections' in the configuration is deprecated."
+    config = deepcopy(default_conf)
+    process_temporary_deprecated_settings(config)
+    assert not log_has(message, caplog)
+
+    config["protections"] = []
+    with pytest.raises(ConfigurationError, match=message):
+        process_temporary_deprecated_settings(config)
+
+
+def test_flat_vars_to_nested_dict(caplog):
+    test_args = {
+        "FREQTRADE__EXCHANGE__SOME_SETTING": "true",
+        "FREQTRADE__EXCHANGE__SOME_FALSE_SETTING": "false",
+        "FREQTRADE__EXCHANGE__CONFIG__whatever": "sometime",
+        "FREQTRADE__EXIT_PRICING__PRICE_SIDE": "bid",
+        "FREQTRADE__EXIT_PRICING__cccc": "500",
+        "FREQTRADE__STAKE_AMOUNT": "200.05",
+        "FREQTRADE__TELEGRAM__CHAT_ID": "2151",
+        "NOT_RELEVANT": "200.0",  # Will be ignored
+        "FREQTRADE__ARRAY": '[{"name":"default","host":"xxx"}]',
+        "FREQTRADE__EXCHANGE__PAIR_WHITELIST": '["BTC/USDT", "ETH/USDT"]',
+        # Fails due to trailing comma
+        "FREQTRADE__ARRAY_TRAIL_COMMA": '[{"name":"default","host":"xxx",}]',
+        # Object fails
+        "FREQTRADE__OBJECT": '{"name":"default","host":"xxx"}',
+    }
+    expected = {
+        "stake_amount": 200.05,
+        "exit_pricing": {
+            "price_side": "bid",
+            "cccc": 500,
+        },
+        "exchange": {
+            "config": {
+                "whatever": "sometime",
+            },
+            "some_setting": True,
+            "some_false_setting": False,
+            "pair_whitelist": ["BTC/USDT", "ETH/USDT"],
+        },
+        "telegram": {"chat_id": "2151"},
+        "array": [{"name": "default", "host": "xxx"}],
+        "object": '{"name":"default","host":"xxx"}',
+        "array_trail_comma": '[{"name":"default","host":"xxx",}]',
+    }
+    res = _flat_vars_to_nested_dict(test_args, ENV_VAR_PREFIX)
+    assert res == expected
+
+    assert log_has("Loading variable 'FREQTRADE__EXCHANGE__SOME_SETTING'", caplog)
+    assert not log_has("Loading variable 'NOT_RELEVANT'", caplog)
+
+
+def test_setup_hyperopt_freqai(mocker, default_conf) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.configuration.configuration.create_datadir", lambda c, x: x)
+    mocker.patch(
+        "freqtrade.configuration.configuration.create_userdata_dir",
+        lambda x, *args, **kwargs: Path(x),
+    )
+    arglist = [
+        "hyperopt",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--timerange",
+        "20220801-20220805",
+        "--freqaimodel",
+        "LightGBMRegressorMultiTarget",
+        "--analyze-per-epoch",
+    ]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+    config["freqai"] = {"enabled": True}
+    with pytest.raises(
+        OperationalException, match=r".*analyze-per-epoch parameter is not supported.*"
+    ):
+        validate_config_consistency(config)
+
+
+def test_setup_freqai_backtesting(mocker, default_conf) -> None:
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.configuration.configuration.create_datadir", lambda c, x: x)
+    mocker.patch(
+        "freqtrade.configuration.configuration.create_userdata_dir",
+        lambda x, *args, **kwargs: Path(x),
+    )
+    arglist = [
+        "backtesting",
+        "--config",
+        "config.json",
+        "--strategy",
+        CURRENT_TEST_STRATEGY,
+        "--timerange",
+        "20220801-20220805",
+        "--freqaimodel",
+        "LightGBMRegressorMultiTarget",
+        "--freqai-backtest-live-models",
+    ]
+
+    args = Arguments(arglist).get_parsed_arg()
+
+    configuration = Configuration(args)
+    config = configuration.get_config()
+    config["runmode"] = RunMode.BACKTEST
+
+    with pytest.raises(
+        OperationalException, match=r".*--freqai-backtest-live-models parameter is only.*"
+    ):
+        validate_config_consistency(config)
+
+    conf = deepcopy(config)
+    conf["freqai"] = {"enabled": True}
+    with pytest.raises(
+        OperationalException, match=r".* timerange parameter is not supported with .*"
+    ):
+        validate_config_consistency(conf)
+
+    conf["timerange"] = None
+    conf["freqai_backtest_live_models"] = False
+
+    with pytest.raises(
+        OperationalException, match=r".* pass --timerange if you intend to use FreqAI .*"
+    ):
+        validate_config_consistency(conf)
+
+
+def test_sanitize_config(default_conf_usdt):
+    assert default_conf_usdt["exchange"]["key"] != "REDACTED"
+    res = sanitize_config(default_conf_usdt)
+    # Didn't modify original dict
+    assert default_conf_usdt["exchange"]["key"] != "REDACTED"
+    assert "accountId" not in default_conf_usdt["exchange"]
+
+    assert res["exchange"]["key"] == "REDACTED"
+    assert res["exchange"]["secret"] == "REDACTED"
+    # Didn't add a non-existing key
+    assert "accountId" not in res["exchange"]
+
+    res = sanitize_config(default_conf_usdt, show_sensitive=True)
+    assert res["exchange"]["key"] == default_conf_usdt["exchange"]["key"]
+    assert res["exchange"]["secret"] == default_conf_usdt["exchange"]["secret"]
diff --git a/tests/test_directory_operations.py b/tests/test_directory_operations.py
new file mode 100644
index 000000000..933d38b38
--- /dev/null
+++ b/tests/test_directory_operations.py
@@ -0,0 +1,110 @@
+# pragma pylint: disable=missing-docstring, protected-access, invalid-name
+import os
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+
+from freqtrade.configuration.directory_operations import (
+    chown_user_directory,
+    copy_sample_files,
+    create_datadir,
+    create_userdata_dir,
+)
+from freqtrade.exceptions import OperationalException
+from tests.conftest import log_has, log_has_re
+
+
+def test_create_datadir(mocker, default_conf, caplog) -> None:
+    mocker.patch.object(Path, "is_dir", MagicMock(return_value=False))
+    md = mocker.patch.object(Path, "mkdir", MagicMock())
+
+    create_datadir(default_conf, "/foo/bar")
+    assert md.call_args[1]["parents"] is True
+    assert log_has("Created data directory: /foo/bar", caplog)
+
+
+def test_create_userdata_dir(mocker, tmp_path, caplog) -> None:
+    mocker.patch.object(Path, "is_dir", MagicMock(return_value=False))
+    md = mocker.patch.object(Path, "mkdir", MagicMock())
+
+    x = create_userdata_dir(tmp_path / "bar", create_dir=True)
+    assert md.call_count == 10
+    assert md.call_args[1]["parents"] is False
+    assert log_has(f"Created user-data directory: {tmp_path / 'bar'}", caplog)
+    assert isinstance(x, Path)
+    assert str(x) == str(tmp_path / "bar")
+
+
+def test_create_userdata_dir_and_chown(mocker, tmp_path, caplog) -> None:
+    sp_mock = mocker.patch("subprocess.check_output")
+    path = tmp_path / "bar"
+    assert not path.is_dir()
+
+    x = create_userdata_dir(str(path), create_dir=True)
+    assert sp_mock.call_count == 0
+    assert log_has(f"Created user-data directory: {path}", caplog)
+    assert isinstance(x, Path)
+    assert path.is_dir()
+    assert (path / "data").is_dir()
+
+    os.environ["FT_APP_ENV"] = "docker"
+    chown_user_directory(path / "data")
+    assert sp_mock.call_count == 1
+    del os.environ["FT_APP_ENV"]
+
+
+def test_create_userdata_dir_exists(mocker, tmp_path) -> None:
+    mocker.patch.object(Path, "is_dir", MagicMock(return_value=True))
+    md = mocker.patch.object(Path, "mkdir", MagicMock())
+
+    create_userdata_dir(f"{tmp_path}/bar")
+    assert md.call_count == 0
+
+
+def test_create_userdata_dir_exists_exception(mocker, tmp_path) -> None:
+    mocker.patch.object(Path, "is_dir", MagicMock(return_value=False))
+    md = mocker.patch.object(Path, "mkdir", MagicMock())
+
+    with pytest.raises(OperationalException, match=r"Directory `.*.{1,2}bar` does not exist.*"):
+        create_userdata_dir(f"{tmp_path}/bar", create_dir=False)
+    assert md.call_count == 0
+
+
+def test_copy_sample_files(mocker, tmp_path) -> None:
+    mocker.patch.object(Path, "is_dir", MagicMock(return_value=True))
+    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
+    copymock = mocker.patch("shutil.copy", MagicMock())
+
+    copy_sample_files(Path(f"{tmp_path}/bar"))
+    assert copymock.call_count == 3
+    assert copymock.call_args_list[0][0][1] == str(tmp_path / "bar/strategies/sample_strategy.py")
+    assert copymock.call_args_list[1][0][1] == str(
+        tmp_path / "bar/hyperopts/sample_hyperopt_loss.py"
+    )
+    assert copymock.call_args_list[2][0][1] == str(
+        tmp_path / "bar/notebooks/strategy_analysis_example.ipynb"
+    )
+
+
+def test_copy_sample_files_errors(mocker, tmp_path, caplog) -> None:
+    mocker.patch.object(Path, "is_dir", MagicMock(return_value=False))
+    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
+    mocker.patch("shutil.copy", MagicMock())
+    with pytest.raises(OperationalException, match=r"Directory `.*.{1,2}bar` does not exist\."):
+        copy_sample_files(Path(f"{tmp_path}/bar"))
+
+    mocker.patch.object(Path, "is_dir", MagicMock(side_effect=[True, False]))
+
+    with pytest.raises(
+        OperationalException,
+        match=r"Directory `.*.{1,2}bar.{1,2}strategies` does not exist\.",
+    ):
+        copy_sample_files(Path(f"{tmp_path}/bar"))
+    mocker.patch.object(Path, "is_dir", MagicMock(return_value=True))
+    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
+    copy_sample_files(Path(f"{tmp_path}/bar"))
+    assert log_has_re(r"File `.*` exists already, not deploying sample file\.", caplog)
+    caplog.clear()
+    copy_sample_files(Path(f"{tmp_path}/bar"), overwrite=True)
+    assert log_has_re(r"File `.*` exists already, overwriting\.", caplog)
diff --git a/tests/test_docs.sh b/tests/test_docs.sh
new file mode 100755
index 000000000..09e142b99
--- /dev/null
+++ b/tests/test_docs.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+# Test Documentation boxes -
+# !!! <TYPE>: is not allowed!
+# !!! <TYPE> "title" - Title needs to be quoted!
+grep -Er '^!{3}\s\S+:|^!{3}\s\S+\s[^"]' docs/*
+
+if  [ $? -ne 0 ]; then
+    echo "Docs test success."
+    exit 0
+fi
+echo "Docs test failed."
+exit 1
diff --git a/tests/test_indicators.py b/tests/test_indicators.py
new file mode 100644
index 000000000..8d02330a1
--- /dev/null
+++ b/tests/test_indicators.py
@@ -0,0 +1,19 @@
+import numpy as np
+import pandas as pd
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+def test_crossed_numpy_types():
+    """
+    This test is only present since this method currently diverges from the qtpylib implementation.
+    And we must ensure to not break this again once we update from the original source.
+    """
+    series = pd.Series([56, 97, 19, 76, 65, 25, 87, 91, 79, 79])
+    expected_result = pd.Series([False, True, False, True, False, False, True, False, False, False])
+
+    assert qtpylib.crossed_above(series, 60).equals(expected_result)
+    assert qtpylib.crossed_above(series, 60.0).equals(expected_result)
+    assert qtpylib.crossed_above(series, np.int32(60)).equals(expected_result)
+    assert qtpylib.crossed_above(series, np.int64(60)).equals(expected_result)
+    assert qtpylib.crossed_above(series, np.float64(60.0)).equals(expected_result)
diff --git a/tests/test_log_setup.py b/tests/test_log_setup.py
new file mode 100644
index 000000000..169a65005
--- /dev/null
+++ b/tests/test_log_setup.py
@@ -0,0 +1,248 @@
+import logging
+import re
+import sys
+
+import pytest
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.loggers import (
+    FTBufferingHandler,
+    FtRichHandler,
+    setup_logging,
+    setup_logging_pre,
+)
+from freqtrade.loggers.set_log_levels import (
+    reduce_verbosity_for_bias_tester,
+    restore_verbosity_for_bias_tester,
+)
+
+
+@pytest.mark.usefixtures("keep_log_config_loggers")
+def test_set_loggers() -> None:
+    # Reset Logging to Debug, otherwise this fails randomly as it's set globally
+    logging.getLogger("requests").setLevel(logging.DEBUG)
+    logging.getLogger("urllib3").setLevel(logging.DEBUG)
+    logging.getLogger("ccxt.base.exchange").setLevel(logging.DEBUG)
+    logging.getLogger("telegram").setLevel(logging.DEBUG)
+
+    previous_value1 = logging.getLogger("requests").level
+    previous_value2 = logging.getLogger("ccxt.base.exchange").level
+    previous_value3 = logging.getLogger("telegram").level
+    config = {
+        "verbosity": 1,
+        "ft_tests_force_logging": True,
+    }
+    setup_logging(config)
+
+    value1 = logging.getLogger("requests").level
+    assert previous_value1 is not value1
+    assert value1 is logging.INFO
+
+    value2 = logging.getLogger("ccxt.base.exchange").level
+    assert previous_value2 is not value2
+    assert value2 is logging.INFO
+
+    value3 = logging.getLogger("telegram").level
+    assert previous_value3 is not value3
+    assert value3 is logging.INFO
+    config["verbosity"] = 2
+    setup_logging(config)
+
+    assert logging.getLogger("requests").level is logging.DEBUG
+    assert logging.getLogger("ccxt.base.exchange").level is logging.INFO
+    assert logging.getLogger("telegram").level is logging.INFO
+    assert logging.getLogger("werkzeug").level is logging.INFO
+
+    config["verbosity"] = 3
+    config["api_server"] = {"verbosity": "error"}
+    setup_logging(config)
+
+    assert logging.getLogger("requests").level is logging.DEBUG
+    assert logging.getLogger("ccxt.base.exchange").level is logging.DEBUG
+    assert logging.getLogger("telegram").level is logging.INFO
+    assert logging.getLogger("werkzeug").level is logging.ERROR
+
+
+@pytest.mark.skipif(sys.platform == "win32", reason="does not run on windows")
+@pytest.mark.usefixtures("keep_log_config_loggers")
+def test_set_loggers_syslog():
+    logger = logging.getLogger()
+    orig_handlers = logger.handlers
+    logger.handlers = []
+
+    config = {
+        "ft_tests_force_logging": True,
+        "verbosity": 2,
+        "logfile": "syslog:/dev/log",
+    }
+
+    setup_logging_pre()
+    setup_logging(config)
+    assert len(logger.handlers) == 3
+    assert [x for x in logger.handlers if isinstance(x, logging.handlers.SysLogHandler)]
+    assert [x for x in logger.handlers if isinstance(x, FtRichHandler)]
+    assert [x for x in logger.handlers if isinstance(x, FTBufferingHandler)]
+    # setting up logging again should NOT cause the loggers to be added a second time.
+    setup_logging(config)
+    assert len(logger.handlers) == 3
+    # reset handlers to not break pytest
+    logger.handlers = orig_handlers
+
+
+@pytest.mark.skipif(sys.platform == "win32", reason="does not run on windows")
+@pytest.mark.usefixtures("keep_log_config_loggers")
+def test_set_loggers_Filehandler(tmp_path):
+    logger = logging.getLogger()
+    orig_handlers = logger.handlers
+    logger.handlers = []
+    logfile = tmp_path / "logs/ft_logfile.log"
+    config = {
+        "ft_tests_force_logging": True,
+        "verbosity": 2,
+        "logfile": str(logfile),
+    }
+
+    setup_logging_pre()
+    setup_logging(config)
+    assert len(logger.handlers) == 3
+    assert [x for x in logger.handlers if isinstance(x, logging.handlers.RotatingFileHandler)]
+    assert [x for x in logger.handlers if isinstance(x, FtRichHandler)]
+    assert [x for x in logger.handlers if isinstance(x, FTBufferingHandler)]
+    # setting up logging again should NOT cause the loggers to be added a second time.
+    setup_logging(config)
+    assert len(logger.handlers) == 3
+    # reset handlers to not break pytest
+    if logfile.exists:
+        logfile.unlink()
+    logger.handlers = orig_handlers
+
+
+@pytest.mark.skipif(sys.platform == "win32", reason="does not run on windows")
+@pytest.mark.usefixtures("keep_log_config_loggers")
+def test_set_loggers_Filehandler_without_permission(tmp_path):
+    logger = logging.getLogger()
+    orig_handlers = logger.handlers
+    logger.handlers = []
+
+    try:
+        tmp_path.chmod(0o400)
+        logfile = tmp_path / "logs/ft_logfile.log"
+        config = {
+            "ft_tests_force_logging": True,
+            "verbosity": 2,
+            "logfile": str(logfile),
+        }
+
+        setup_logging_pre()
+        with pytest.raises(OperationalException):
+            setup_logging(config)
+
+        logger.handlers = orig_handlers
+    finally:
+        tmp_path.chmod(0o700)
+
+
+@pytest.mark.skip(reason="systemd is not installed on every system, so we're not testing this.")
+@pytest.mark.usefixtures("keep_log_config_loggers")
+def test_set_loggers_journald():
+    logger = logging.getLogger()
+    orig_handlers = logger.handlers
+    logger.handlers = []
+
+    config = {
+        "ft_tests_force_logging": True,
+        "verbosity": 2,
+        "logfile": "journald",
+    }
+
+    setup_logging_pre()
+    setup_logging(config)
+    assert len(logger.handlers) == 3
+    assert [x for x in logger.handlers if type(x).__name__ == "JournaldLogHandler"]
+    assert [x for x in logger.handlers if isinstance(x, FtRichHandler)]
+    # reset handlers to not break pytest
+    logger.handlers = orig_handlers
+
+
+@pytest.mark.usefixtures("keep_log_config_loggers")
+def test_set_loggers_journald_importerror(import_fails):
+    logger = logging.getLogger()
+    orig_handlers = logger.handlers
+    logger.handlers = []
+
+    config = {
+        "ft_tests_force_logging": True,
+        "verbosity": 2,
+        "logfile": "journald",
+    }
+    with pytest.raises(OperationalException, match=r"You need the cysystemd python package.*"):
+        setup_logging(config)
+    logger.handlers = orig_handlers
+
+
+@pytest.mark.usefixtures("keep_log_config_loggers")
+def test_set_loggers_json_format(capsys):
+    logger = logging.getLogger()
+    orig_handlers = logger.handlers
+    logger.handlers = []
+
+    config = {
+        "ft_tests_force_logging": True,
+        "verbosity": 2,
+        "log_config": {
+            "version": 1,
+            "formatters": {
+                "json": {
+                    "()": "freqtrade.loggers.json_formatter.JsonFormatter",
+                    "fmt_dict": {
+                        "timestamp": "asctime",
+                        "level": "levelname",
+                        "logger": "name",
+                        "message": "message",
+                    },
+                }
+            },
+            "handlers": {
+                "json": {
+                    "class": "logging.StreamHandler",
+                    "formatter": "json",
+                }
+            },
+            "root": {
+                "handlers": ["json"],
+                "level": "DEBUG",
+            },
+        },
+    }
+
+    setup_logging_pre()
+    setup_logging(config)
+    assert len(logger.handlers) == 2
+    assert [x for x in logger.handlers if type(x).__name__ == "StreamHandler"]
+    assert [x for x in logger.handlers if isinstance(x, FTBufferingHandler)]
+
+    logger.info("Test message")
+
+    captured = capsys.readouterr()
+    assert re.search(r'{"timestamp": ".*"Test message".*', captured.err)
+
+    # reset handlers to not break pytest
+    logger.handlers = orig_handlers
+
+
+def test_reduce_verbosity():
+    setup_logging_pre()
+    reduce_verbosity_for_bias_tester()
+    prior_level = logging.getLogger("freqtrade").getEffectiveLevel()
+
+    assert logging.getLogger("freqtrade.resolvers").getEffectiveLevel() == logging.WARNING
+    assert logging.getLogger("freqtrade.strategy.hyper").getEffectiveLevel() == logging.WARNING
+    # base level wasn't changed
+    assert logging.getLogger("freqtrade").getEffectiveLevel() == prior_level
+
+    restore_verbosity_for_bias_tester()
+
+    assert logging.getLogger("freqtrade.resolvers").getEffectiveLevel() == prior_level
+    assert logging.getLogger("freqtrade.strategy.hyper").getEffectiveLevel() == prior_level
+    assert logging.getLogger("freqtrade").getEffectiveLevel() == prior_level
+    # base level wasn't changed
diff --git a/tests/test_main.py b/tests/test_main.py
new file mode 100644
index 000000000..0551def2d
--- /dev/null
+++ b/tests/test_main.py
@@ -0,0 +1,234 @@
+# pragma pylint: disable=missing-docstring
+
+import re
+from copy import deepcopy
+from pathlib import Path
+from unittest.mock import MagicMock, PropertyMock
+
+import pytest
+
+from freqtrade.commands import Arguments
+from freqtrade.enums import State
+from freqtrade.exceptions import ConfigurationError, FreqtradeException, OperationalException
+from freqtrade.freqtradebot import FreqtradeBot
+from freqtrade.main import main
+from freqtrade.worker import Worker
+from tests.conftest import (
+    log_has,
+    log_has_re,
+    patch_exchange,
+    patched_configuration_load_config_file,
+)
+
+
+def test_parse_args_None(caplog) -> None:
+    with pytest.raises(SystemExit):
+        main([])
+    assert log_has_re(r"Usage of Freqtrade requires a subcommand.*", caplog)
+
+
+def test_parse_args_version(capsys) -> None:
+    with pytest.raises(SystemExit):
+        main(["-V"])
+    captured = capsys.readouterr()
+    assert re.search(r"CCXT Version:\s.*", captured.out, re.MULTILINE)
+    assert re.search(r"Freqtrade Version:\s+freqtrade\s.*", captured.out, re.MULTILINE)
+
+
+def test_parse_args_backtesting(mocker) -> None:
+    """
+    Test that main() can start backtesting and also ensure we can pass some specific arguments
+    further argument parsing is done in test_arguments.py
+    """
+    mocker.patch.object(Path, "is_file", MagicMock(side_effect=[False, True]))
+    backtesting_mock = mocker.patch("freqtrade.commands.start_backtesting")
+    backtesting_mock.__name__ = PropertyMock("start_backtesting")
+    # it's sys.exit(0) at the end of backtesting
+    with pytest.raises(SystemExit):
+        main(["backtesting"])
+    assert backtesting_mock.call_count == 1
+    call_args = backtesting_mock.call_args[0][0]
+    assert call_args["config"] == ["config.json"]
+    assert call_args["verbosity"] is None
+    assert call_args["command"] == "backtesting"
+    assert call_args["func"] is not None
+    assert callable(call_args["func"])
+    assert call_args["timeframe"] is None
+
+
+def test_main_start_hyperopt(mocker) -> None:
+    mocker.patch.object(Path, "is_file", MagicMock(side_effect=[False, True]))
+    hyperopt_mock = mocker.patch("freqtrade.commands.start_hyperopt", MagicMock())
+    hyperopt_mock.__name__ = PropertyMock("start_hyperopt")
+    # it's sys.exit(0) at the end of hyperopt
+    with pytest.raises(SystemExit):
+        main(["hyperopt"])
+    assert hyperopt_mock.call_count == 1
+    call_args = hyperopt_mock.call_args[0][0]
+    assert call_args["config"] == ["config.json"]
+    assert call_args["verbosity"] is None
+    assert call_args["command"] == "hyperopt"
+    assert call_args["func"] is not None
+    assert callable(call_args["func"])
+
+
+def test_main_fatal_exception(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.cleanup", MagicMock())
+    mocker.patch("freqtrade.worker.Worker._worker", MagicMock(side_effect=Exception))
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.freqtradebot.RPCManager", MagicMock())
+    mocker.patch("freqtrade.freqtradebot.init_db", MagicMock())
+
+    args = ["trade", "-c", "tests/testdata/testconfigs/main_test_config.json"]
+
+    # Test Main + the KeyboardInterrupt exception
+    with pytest.raises(SystemExit):
+        main(args)
+    assert log_has("Using config: tests/testdata/testconfigs/main_test_config.json ...", caplog)
+    assert log_has("Fatal exception!", caplog)
+
+
+def test_main_keyboard_interrupt(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.cleanup", MagicMock())
+    mocker.patch("freqtrade.worker.Worker._worker", MagicMock(side_effect=KeyboardInterrupt))
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.freqtradebot.RPCManager", MagicMock())
+    mocker.patch("freqtrade.wallets.Wallets.update", MagicMock())
+    mocker.patch("freqtrade.freqtradebot.init_db", MagicMock())
+
+    args = ["trade", "-c", "tests/testdata/testconfigs/main_test_config.json"]
+
+    # Test Main + the KeyboardInterrupt exception
+    with pytest.raises(SystemExit):
+        main(args)
+    assert log_has("Using config: tests/testdata/testconfigs/main_test_config.json ...", caplog)
+    assert log_has("SIGINT received, aborting ...", caplog)
+
+
+def test_main_operational_exception(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.cleanup", MagicMock())
+    mocker.patch(
+        "freqtrade.worker.Worker._worker", MagicMock(side_effect=FreqtradeException("Oh snap!"))
+    )
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.wallets.Wallets.update", MagicMock())
+    mocker.patch("freqtrade.freqtradebot.RPCManager", MagicMock())
+    mocker.patch("freqtrade.freqtradebot.init_db", MagicMock())
+
+    args = ["trade", "-c", "tests/testdata/testconfigs/main_test_config.json"]
+
+    # Test Main + the KeyboardInterrupt exception
+    with pytest.raises(SystemExit):
+        main(args)
+    assert log_has("Using config: tests/testdata/testconfigs/main_test_config.json ...", caplog)
+    assert log_has("Oh snap!", caplog)
+
+
+def test_main_operational_exception1(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch(
+        "freqtrade.exchange.list_available_exchanges",
+        MagicMock(side_effect=ValueError("Oh snap!")),
+    )
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = ["list-exchanges"]
+
+    # Test Main + the KeyboardInterrupt exception
+    with pytest.raises(SystemExit):
+        main(args)
+
+    assert log_has("Fatal exception!", caplog)
+    assert not log_has_re(r"SIGINT.*", caplog)
+    mocker.patch(
+        "freqtrade.exchange.list_available_exchanges",
+        MagicMock(side_effect=KeyboardInterrupt),
+    )
+    with pytest.raises(SystemExit):
+        main(args)
+
+    assert log_has_re(r"SIGINT.*", caplog)
+
+
+def test_main_ConfigurationError(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch(
+        "freqtrade.exchange.list_available_exchanges",
+        MagicMock(side_effect=ConfigurationError("Oh snap!")),
+    )
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = ["list-exchanges"]
+
+    # Test Main + the KeyboardInterrupt exception
+    with pytest.raises(SystemExit):
+        main(args)
+    assert log_has_re("Configuration error: Oh snap!", caplog)
+
+
+def test_main_reload_config(mocker, default_conf, caplog) -> None:
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.cleanup", MagicMock())
+    # Simulate Running, reload, running workflow
+    worker_mock = MagicMock(
+        side_effect=[
+            State.RUNNING,
+            State.RELOAD_CONFIG,
+            State.RUNNING,
+            OperationalException("Oh snap!"),
+        ]
+    )
+    mocker.patch("freqtrade.worker.Worker._worker", worker_mock)
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.wallets.Wallets.update", MagicMock())
+    reconfigure_mock = mocker.patch("freqtrade.worker.Worker._reconfigure", MagicMock())
+
+    mocker.patch("freqtrade.freqtradebot.RPCManager", MagicMock())
+    mocker.patch("freqtrade.freqtradebot.init_db", MagicMock())
+
+    args = Arguments(
+        ["trade", "-c", "tests/testdata/testconfigs/main_test_config.json"]
+    ).get_parsed_arg()
+    worker = Worker(args=args, config=default_conf)
+    with pytest.raises(SystemExit):
+        main(["trade", "-c", "tests/testdata/testconfigs/main_test_config.json"])
+
+    assert log_has("Using config: tests/testdata/testconfigs/main_test_config.json ...", caplog)
+    assert worker_mock.call_count == 4
+    assert reconfigure_mock.call_count == 1
+    assert isinstance(worker.freqtrade, FreqtradeBot)
+
+
+def test_reconfigure(mocker, default_conf) -> None:
+    patch_exchange(mocker)
+    mocker.patch("freqtrade.freqtradebot.FreqtradeBot.cleanup", MagicMock())
+    mocker.patch(
+        "freqtrade.worker.Worker._worker", MagicMock(side_effect=OperationalException("Oh snap!"))
+    )
+    mocker.patch("freqtrade.wallets.Wallets.update", MagicMock())
+    patched_configuration_load_config_file(mocker, default_conf)
+    mocker.patch("freqtrade.freqtradebot.RPCManager", MagicMock())
+    mocker.patch("freqtrade.freqtradebot.init_db", MagicMock())
+
+    args = Arguments(
+        ["trade", "-c", "tests/testdata/testconfigs/main_test_config.json"]
+    ).get_parsed_arg()
+    worker = Worker(args=args, config=default_conf)
+    freqtrade = worker.freqtrade
+
+    # Renew mock to return modified data
+    conf = deepcopy(default_conf)
+    conf["stake_amount"] += 1
+    patched_configuration_load_config_file(mocker, conf)
+
+    worker._config = conf
+    # reconfigure should return a new instance
+    worker._reconfigure()
+    freqtrade2 = worker.freqtrade
+
+    # Verify we have a new instance with the new config
+    assert freqtrade is not freqtrade2
+    assert freqtrade.config["stake_amount"] + 1 == freqtrade2.config["stake_amount"]
diff --git a/tests/test_misc.py b/tests/test_misc.py
new file mode 100644
index 000000000..f213d6759
--- /dev/null
+++ b/tests/test_misc.py
@@ -0,0 +1,222 @@
+# pragma pylint: disable=missing-docstring,C0103
+
+from copy import deepcopy
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pandas as pd
+import pytest
+
+from freqtrade.misc import (
+    dataframe_to_json,
+    deep_merge_dicts,
+    file_dump_json,
+    file_load_json,
+    is_file_in_dir,
+    json_to_dataframe,
+    pair_to_filename,
+    parse_db_uri_for_logging,
+    plural,
+    safe_value_fallback,
+    safe_value_fallback2,
+)
+
+
+def test_file_dump_json(mocker) -> None:
+    file_open = mocker.patch("freqtrade.misc.Path.open", MagicMock())
+    json_dump = mocker.patch("rapidjson.dump", MagicMock())
+    file_dump_json(Path("somefile"), [1, 2, 3])
+    assert file_open.call_count == 1
+    assert json_dump.call_count == 1
+    file_open = mocker.patch("freqtrade.misc.gzip.open", MagicMock())
+    json_dump = mocker.patch("rapidjson.dump", MagicMock())
+    file_dump_json(Path("somefile"), [1, 2, 3], True)
+    assert file_open.call_count == 1
+    assert json_dump.call_count == 1
+
+
+def test_file_load_json(mocker, testdatadir) -> None:
+    # 7m .json does not exist
+    ret = file_load_json(testdatadir / "UNITTEST_BTC-7m.json")
+    assert not ret
+    # 1m json exists (but no .gz exists)
+    ret = file_load_json(testdatadir / "UNITTEST_BTC-1m.json")
+    assert ret
+    # 8 .json is empty and will fail if it's loaded. .json.gz is a copy of 1.json
+    ret = file_load_json(testdatadir / "UNITTEST_BTC-8m.json")
+    assert ret
+
+
+def test_is_file_in_dir(tmp_path):
+    # Create a temporary directory and file
+    dir_path = tmp_path / "subdir"
+    dir_path.mkdir()
+    file_path = dir_path / "test.txt"
+    file_path.touch()
+
+    # Test that the function returns True when the file is in the directory
+    assert is_file_in_dir(file_path, dir_path) is True
+
+    # Test that the function returns False when the file is not in the directory
+    assert is_file_in_dir(file_path, tmp_path) is False
+
+    file_path2 = tmp_path / "../../test2.txt"
+    assert is_file_in_dir(file_path2, tmp_path) is False
+
+
+@pytest.mark.parametrize(
+    "pair,expected_result",
+    [
+        ("ETH/BTC", "ETH_BTC"),
+        ("ETH/USDT", "ETH_USDT"),
+        ("ETH/USDT:USDT", "ETH_USDT_USDT"),  # swap with USDT as settlement currency
+        ("ETH/USD:USD", "ETH_USD_USD"),  # swap with USD as settlement currency
+        ("AAVE/USD:USD", "AAVE_USD_USD"),  # swap with USDT as settlement currency
+        ("ETH/USDT:USDT-210625", "ETH_USDT_USDT-210625"),  # expiring futures
+        ("Fabric Token/ETH", "Fabric_Token_ETH"),
+        ("ETHH20", "ETHH20"),
+        (".XBTBON2H", "_XBTBON2H"),
+        ("ETHUSD.d", "ETHUSD_d"),
+        ("ADA-0327", "ADA-0327"),
+        ("BTC-USD-200110", "BTC-USD-200110"),
+        ("BTC-PERP:USDT", "BTC-PERP_USDT"),
+        ("F-AKRO/USDT", "F-AKRO_USDT"),
+        ("LC+/ETH", "LC__ETH"),
+        ("CMT@18/ETH", "CMT_18_ETH"),
+        ("LBTC:1022/SAI", "LBTC_1022_SAI"),
+        ("$PAC/BTC", "_PAC_BTC"),
+        ("ACC_OLD/BTC", "ACC_OLD_BTC"),
+    ],
+)
+def test_pair_to_filename(pair, expected_result):
+    pair_s = pair_to_filename(pair)
+    assert pair_s == expected_result
+
+
+def test_safe_value_fallback():
+    dict1 = {"keya": None, "keyb": 2, "keyc": 5, "keyd": None}
+    assert safe_value_fallback(dict1, "keya", "keyb") == 2
+    assert safe_value_fallback(dict1, "keyb", "keya") == 2
+
+    assert safe_value_fallback(dict1, "keyb", "keyc") == 2
+    assert safe_value_fallback(dict1, "keya", "keyc") == 5
+
+    assert safe_value_fallback(dict1, "keyc", "keyb") == 5
+
+    assert safe_value_fallback(dict1, "keya", "keyd") is None
+
+    assert safe_value_fallback(dict1, "keyNo", "keyNo") is None
+    assert safe_value_fallback(dict1, "keyNo", "keyNo", 55) == 55
+    assert safe_value_fallback(dict1, "keyNo", default_value=55) == 55
+    assert safe_value_fallback(dict1, "keyNo", None, default_value=55) == 55
+
+
+def test_safe_value_fallback2():
+    dict1 = {"keya": None, "keyb": 2, "keyc": 5, "keyd": None}
+    dict2 = {"keya": 20, "keyb": None, "keyc": 6, "keyd": None}
+    assert safe_value_fallback2(dict1, dict2, "keya", "keya") == 20
+    assert safe_value_fallback2(dict2, dict1, "keya", "keya") == 20
+
+    assert safe_value_fallback2(dict1, dict2, "keyb", "keyb") == 2
+    assert safe_value_fallback2(dict2, dict1, "keyb", "keyb") == 2
+
+    assert safe_value_fallback2(dict1, dict2, "keyc", "keyc") == 5
+    assert safe_value_fallback2(dict2, dict1, "keyc", "keyc") == 6
+
+    assert safe_value_fallback2(dict1, dict2, "keyd", "keyd") is None
+    assert safe_value_fallback2(dict2, dict1, "keyd", "keyd") is None
+    assert safe_value_fallback2(dict2, dict1, "keyd", "keyd", 1234) == 1234
+
+    assert safe_value_fallback2(dict1, dict2, "keyNo", "keyNo") is None
+    assert safe_value_fallback2(dict2, dict1, "keyNo", "keyNo") is None
+    assert safe_value_fallback2(dict2, dict1, "keyNo", "keyNo", 1234) == 1234
+
+
+def test_plural() -> None:
+    assert plural(0, "page") == "pages"
+    assert plural(0.0, "page") == "pages"
+    assert plural(1, "page") == "page"
+    assert plural(1.0, "page") == "page"
+    assert plural(2, "page") == "pages"
+    assert plural(2.0, "page") == "pages"
+    assert plural(-1, "page") == "page"
+    assert plural(-1.0, "page") == "page"
+    assert plural(-2, "page") == "pages"
+    assert plural(-2.0, "page") == "pages"
+    assert plural(0.5, "page") == "pages"
+    assert plural(1.5, "page") == "pages"
+    assert plural(-0.5, "page") == "pages"
+    assert plural(-1.5, "page") == "pages"
+
+    assert plural(0, "ox", "oxen") == "oxen"
+    assert plural(0.0, "ox", "oxen") == "oxen"
+    assert plural(1, "ox", "oxen") == "ox"
+    assert plural(1.0, "ox", "oxen") == "ox"
+    assert plural(2, "ox", "oxen") == "oxen"
+    assert plural(2.0, "ox", "oxen") == "oxen"
+    assert plural(-1, "ox", "oxen") == "ox"
+    assert plural(-1.0, "ox", "oxen") == "ox"
+    assert plural(-2, "ox", "oxen") == "oxen"
+    assert plural(-2.0, "ox", "oxen") == "oxen"
+    assert plural(0.5, "ox", "oxen") == "oxen"
+    assert plural(1.5, "ox", "oxen") == "oxen"
+    assert plural(-0.5, "ox", "oxen") == "oxen"
+    assert plural(-1.5, "ox", "oxen") == "oxen"
+
+
+@pytest.mark.parametrize(
+    "conn_url,expected",
+    [
+        (
+            "postgresql+psycopg2://scott123:scott123@host:1245/dbname",
+            "postgresql+psycopg2://scott123:*****@host:1245/dbname",
+        ),
+        (
+            "postgresql+psycopg2://scott123:scott123@host.name.com/dbname",
+            "postgresql+psycopg2://scott123:*****@host.name.com/dbname",
+        ),
+        (
+            "mariadb+mariadbconnector://app_user:Password123!@127.0.0.1:3306/company",
+            "mariadb+mariadbconnector://app_user:*****@127.0.0.1:3306/company",
+        ),
+        (
+            "mysql+pymysql://user:pass@some_mariadb/dbname?charset=utf8mb4",
+            "mysql+pymysql://user:*****@some_mariadb/dbname?charset=utf8mb4",
+        ),
+        (
+            "sqlite:////freqtrade/user_data/tradesv3.sqlite",
+            "sqlite:////freqtrade/user_data/tradesv3.sqlite",
+        ),
+    ],
+)
+def test_parse_db_uri_for_logging(conn_url, expected) -> None:
+    assert parse_db_uri_for_logging(conn_url) == expected
+
+
+def test_deep_merge_dicts():
+    a = {"first": {"rows": {"pass": "dog", "number": "1", "test": None}}}
+    b = {"first": {"rows": {"fail": "cat", "number": "5", "test": "asdf"}}}
+    res = {"first": {"rows": {"pass": "dog", "fail": "cat", "number": "5", "test": "asdf"}}}
+    res2 = {"first": {"rows": {"pass": "dog", "fail": "cat", "number": "1", "test": None}}}
+    assert deep_merge_dicts(b, deepcopy(a)) == res
+
+    assert deep_merge_dicts(a, deepcopy(b)) == res2
+
+    res2["first"]["rows"]["test"] = "asdf"
+    assert deep_merge_dicts(a, deepcopy(b), allow_null_overrides=False) == res2
+
+
+def test_dataframe_json(ohlcv_history):
+    from pandas.testing import assert_frame_equal
+
+    json = dataframe_to_json(ohlcv_history)
+    dataframe = json_to_dataframe(json)
+
+    assert list(ohlcv_history.columns) == list(dataframe.columns)
+    assert len(ohlcv_history) == len(dataframe)
+
+    assert_frame_equal(ohlcv_history, dataframe)
+    ohlcv_history.at[1, "date"] = pd.NaT
+    json = dataframe_to_json(ohlcv_history)
+
+    dataframe = json_to_dataframe(json)
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
new file mode 100644
index 000000000..4ce976b59
--- /dev/null
+++ b/tests/test_plotting.py
@@ -0,0 +1,568 @@
+from copy import deepcopy
+from unittest.mock import MagicMock
+
+import pandas as pd
+import plotly.graph_objects as go
+import pytest
+from plotly.subplots import make_subplots
+
+from freqtrade.commands import start_plot_dataframe, start_plot_profit
+from freqtrade.configuration import TimeRange
+from freqtrade.data import history
+from freqtrade.data.btanalysis import load_backtest_data
+from freqtrade.data.metrics import create_cum_profit
+from freqtrade.exceptions import OperationalException
+from freqtrade.plot.plotting import (
+    add_areas,
+    add_indicators,
+    add_profit,
+    create_plotconfig,
+    generate_candlestick_graph,
+    generate_plot_filename,
+    generate_profit_graph,
+    init_plotscript,
+    load_and_plot_trades,
+    plot_profit,
+    plot_trades,
+    store_plot_file,
+)
+from freqtrade.resolvers import StrategyResolver
+from tests.conftest import get_args, log_has, log_has_re, patch_exchange
+
+
+def fig_generating_mock(fig, *args, **kwargs):
+    """Return Fig - used to mock add_indicators and plot_trades"""
+    return fig
+
+
+def find_trace_in_fig_data(data, search_string: str):
+    matches = (d for d in data if d.name == search_string)
+    return next(matches)
+
+
+def generate_empty_figure():
+    return make_subplots(
+        rows=3,
+        cols=1,
+        shared_xaxes=True,
+        row_width=[1, 1, 4],
+        vertical_spacing=0.0001,
+    )
+
+
+def test_init_plotscript(default_conf, mocker, testdatadir):
+    default_conf["timerange"] = "20180110-20180112"
+    default_conf["trade_source"] = "file"
+    default_conf["timeframe"] = "5m"
+    default_conf["exportfilename"] = testdatadir / "backtest-result.json"
+    supported_markets = ["TRX/BTC", "ADA/BTC"]
+    ret = init_plotscript(default_conf, supported_markets)
+    assert "ohlcv" in ret
+    assert "trades" in ret
+    assert "pairs" in ret
+    assert "timerange" in ret
+
+    default_conf["pairs"] = ["TRX/BTC", "ADA/BTC"]
+    ret = init_plotscript(default_conf, supported_markets, 20)
+    assert "ohlcv" in ret
+    assert "TRX/BTC" in ret["ohlcv"]
+    assert "ADA/BTC" in ret["ohlcv"]
+
+
+def test_add_indicators(default_conf, testdatadir, caplog):
+    pair = "UNITTEST/BTC"
+    timerange = TimeRange()
+
+    data = history.load_pair_history(
+        pair=pair, timeframe="1m", datadir=testdatadir, timerange=timerange
+    )
+    indicators1 = {"ema10": {}}
+    indicators2 = {"macd": {"color": "red"}}
+
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    # Generate entry/exit signals and indicators
+    data = strategy.analyze_ticker(data, {"pair": pair})
+    fig = generate_empty_figure()
+
+    # Row 1
+    fig1 = add_indicators(fig=deepcopy(fig), row=1, indicators=indicators1, data=data)
+    figure = fig1.layout.figure
+    ema10 = find_trace_in_fig_data(figure.data, "ema10")
+    assert isinstance(ema10, go.Scatter)
+    assert ema10.yaxis == "y"
+
+    fig2 = add_indicators(fig=deepcopy(fig), row=3, indicators=indicators2, data=data)
+    figure = fig2.layout.figure
+    macd = find_trace_in_fig_data(figure.data, "macd")
+    assert isinstance(macd, go.Scatter)
+    assert macd.yaxis == "y3"
+    assert macd.line.color == "red"
+
+    # No indicator found
+    fig3 = add_indicators(fig=deepcopy(fig), row=3, indicators={"no_indicator": {}}, data=data)
+    assert fig == fig3
+    assert log_has_re(r'Indicator "no_indicator" ignored\..*', caplog)
+
+
+def test_add_areas(default_conf, testdatadir, caplog):
+    pair = "UNITTEST/BTC"
+    timerange = TimeRange(None, "line", 0, -1000)
+
+    data = history.load_pair_history(
+        pair=pair, timeframe="1m", datadir=testdatadir, timerange=timerange
+    )
+    indicators = {
+        "macd": {
+            "color": "red",
+            "fill_color": "black",
+            "fill_to": "macdhist",
+            "fill_label": "MACD Fill",
+        }
+    }
+
+    ind_no_label = {"macd": {"fill_color": "red", "fill_to": "macdhist"}}
+
+    ind_plain = {"macd": {"fill_to": "macdhist"}}
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    # Generate entry/exit signals and indicators
+    data = strategy.analyze_ticker(data, {"pair": pair})
+    fig = generate_empty_figure()
+
+    # indicator mentioned in fill_to does not exist
+    fig1 = add_areas(fig, 1, data, {"ema10": {"fill_to": "no_fill_indicator"}})
+    assert fig == fig1
+    assert log_has_re(r'fill_to: "no_fill_indicator" ignored\..*', caplog)
+
+    # indicator does not exist
+    fig2 = add_areas(fig, 1, data, {"no_indicator": {"fill_to": "ema10"}})
+    assert fig == fig2
+    assert log_has_re(r'Indicator "no_indicator" ignored\..*', caplog)
+
+    # everything given in plot config, row 3
+    fig3 = add_areas(fig, 3, data, indicators)
+    figure = fig3.layout.figure
+    fill_macd = find_trace_in_fig_data(figure.data, "MACD Fill")
+    assert isinstance(fill_macd, go.Scatter)
+    assert fill_macd.yaxis == "y3"
+    assert fill_macd.fillcolor == "black"
+
+    # label missing, row 1
+    fig4 = add_areas(fig, 1, data, ind_no_label)
+    figure = fig4.layout.figure
+    fill_macd = find_trace_in_fig_data(figure.data, "macd<>macdhist")
+    assert isinstance(fill_macd, go.Scatter)
+    assert fill_macd.yaxis == "y"
+    assert fill_macd.fillcolor == "red"
+
+    # fit_to only
+    fig5 = add_areas(fig, 1, data, ind_plain)
+    figure = fig5.layout.figure
+    fill_macd = find_trace_in_fig_data(figure.data, "macd<>macdhist")
+    assert isinstance(fill_macd, go.Scatter)
+    assert fill_macd.yaxis == "y"
+
+
+def test_plot_trades(testdatadir, caplog):
+    fig1 = generate_empty_figure()
+    # nothing happens when no trades are available
+    fig = plot_trades(fig1, None)
+    assert fig == fig1
+    assert log_has("No trades found.", caplog)
+    pair = "ADA/BTC"
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    trades = load_backtest_data(filename)
+    trades = trades.loc[trades["pair"] == pair]
+
+    fig = plot_trades(fig, trades)
+    figure = fig1.layout.figure
+
+    # Check entry - color, should be in first graph, ...
+    trade_entries = find_trace_in_fig_data(figure.data, "Trade entry")
+    assert isinstance(trade_entries, go.Scatter)
+    assert trade_entries.yaxis == "y"
+    assert len(trades) == len(trade_entries.x)
+    assert trade_entries.marker.color == "cyan"
+    assert trade_entries.marker.symbol == "circle-open"
+    assert trade_entries.text[0] == "3.99%, buy_tag, roi, 15 min"
+
+    trade_exit = find_trace_in_fig_data(figure.data, "Exit - Profit")
+    assert isinstance(trade_exit, go.Scatter)
+    assert trade_exit.yaxis == "y"
+    assert len(trades.loc[trades["profit_ratio"] > 0]) == len(trade_exit.x)
+    assert trade_exit.marker.color == "green"
+    assert trade_exit.marker.symbol == "square-open"
+    assert trade_exit.text[0] == "3.99%, buy_tag, roi, 15 min"
+
+    trade_sell_loss = find_trace_in_fig_data(figure.data, "Exit - Loss")
+    assert isinstance(trade_sell_loss, go.Scatter)
+    assert trade_sell_loss.yaxis == "y"
+    assert len(trades.loc[trades["profit_ratio"] <= 0]) == len(trade_sell_loss.x)
+    assert trade_sell_loss.marker.color == "red"
+    assert trade_sell_loss.marker.symbol == "square-open"
+    assert trade_sell_loss.text[5] == "-10.45%, stop_loss, 720 min"
+
+
+def test_generate_candlestick_graph_no_signals_no_trades(default_conf, mocker, testdatadir, caplog):
+    row_mock = mocker.patch(
+        "freqtrade.plot.plotting.add_indicators", MagicMock(side_effect=fig_generating_mock)
+    )
+    trades_mock = mocker.patch(
+        "freqtrade.plot.plotting.plot_trades", MagicMock(side_effect=fig_generating_mock)
+    )
+
+    pair = "UNITTEST/BTC"
+    timerange = TimeRange(None, "line", 0, -1000)
+    data = history.load_pair_history(
+        pair=pair, timeframe="1m", datadir=testdatadir, timerange=timerange
+    )
+    data["enter_long"] = 0
+    data["exit_long"] = 0
+    data["enter_short"] = 0
+    data["exit_short"] = 0
+
+    indicators1 = []
+    indicators2 = []
+    fig = generate_candlestick_graph(
+        pair=pair, data=data, trades=None, indicators1=indicators1, indicators2=indicators2
+    )
+    assert isinstance(fig, go.Figure)
+    assert fig.layout.title.text == pair
+    figure = fig.layout.figure
+
+    assert len(figure.data) == 2
+    # Candlesticks are plotted first
+    candles = find_trace_in_fig_data(figure.data, "Price")
+    assert isinstance(candles, go.Candlestick)
+
+    volume = find_trace_in_fig_data(figure.data, "Volume")
+    assert isinstance(volume, go.Bar)
+
+    assert row_mock.call_count == 2
+    assert trades_mock.call_count == 1
+
+    assert log_has("No enter_long-signals found.", caplog)
+    assert log_has("No exit_long-signals found.", caplog)
+    assert log_has("No enter_short-signals found.", caplog)
+    assert log_has("No exit_short-signals found.", caplog)
+
+
+def test_generate_candlestick_graph_no_trades(default_conf, mocker, testdatadir):
+    row_mock = mocker.patch(
+        "freqtrade.plot.plotting.add_indicators", MagicMock(side_effect=fig_generating_mock)
+    )
+    trades_mock = mocker.patch(
+        "freqtrade.plot.plotting.plot_trades", MagicMock(side_effect=fig_generating_mock)
+    )
+    pair = "UNITTEST/BTC"
+    timerange = TimeRange(None, "line", 0, -1000)
+    data = history.load_pair_history(
+        pair=pair, timeframe="1m", datadir=testdatadir, timerange=timerange
+    )
+
+    strategy = StrategyResolver.load_strategy(default_conf)
+
+    # Generate buy/sell signals and indicators
+    data = strategy.analyze_ticker(data, {"pair": pair})
+
+    indicators1 = []
+    indicators2 = []
+    fig = generate_candlestick_graph(
+        pair=pair, data=data, trades=None, indicators1=indicators1, indicators2=indicators2
+    )
+    assert isinstance(fig, go.Figure)
+    assert fig.layout.title.text == pair
+    figure = fig.layout.figure
+
+    assert len(figure.data) == 8
+    # Candlesticks are plotted first
+    candles = find_trace_in_fig_data(figure.data, "Price")
+    assert isinstance(candles, go.Candlestick)
+
+    volume = find_trace_in_fig_data(figure.data, "Volume")
+    assert isinstance(volume, go.Bar)
+
+    enter_long = find_trace_in_fig_data(figure.data, "enter_long")
+    assert isinstance(enter_long, go.Scatter)
+    # All buy-signals should be plotted
+    assert int(data["enter_long"].sum()) == len(enter_long.x)
+
+    exit_long = find_trace_in_fig_data(figure.data, "exit_long")
+    assert isinstance(exit_long, go.Scatter)
+    # All buy-signals should be plotted
+    assert int(data["exit_long"].sum()) == len(exit_long.x)
+
+    assert find_trace_in_fig_data(figure.data, "Bollinger Band")
+
+    assert row_mock.call_count == 2
+    assert trades_mock.call_count == 1
+
+
+def test_generate_Plot_filename():
+    fn = generate_plot_filename("UNITTEST/BTC", "5m")
+    assert fn == "freqtrade-plot-UNITTEST_BTC-5m.html"
+
+
+def test_generate_plot_file(mocker, caplog, user_dir):
+    fig = generate_empty_figure()
+    plot_mock = mocker.patch("freqtrade.plot.plotting.plot", MagicMock())
+    store_plot_file(
+        fig, filename="freqtrade-plot-UNITTEST_BTC-5m.html", directory=user_dir / "plot"
+    )
+
+    expected_fn = str(user_dir / "plot/freqtrade-plot-UNITTEST_BTC-5m.html")
+    assert plot_mock.call_count == 1
+    assert plot_mock.call_args[0][0] == fig
+    assert plot_mock.call_args_list[0][1]["filename"] == expected_fn
+    assert log_has(f"Stored plot as {expected_fn}", caplog)
+
+
+def test_add_profit(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    timerange = TimeRange.parse_timerange("20180110-20180112")
+
+    df = history.load_pair_history(
+        pair="TRX/BTC", timeframe="5m", datadir=testdatadir, timerange=timerange
+    )
+    fig = generate_empty_figure()
+
+    cum_profits = create_cum_profit(
+        df.set_index("date"), bt_data[bt_data["pair"] == "TRX/BTC"], "cum_profits", timeframe="5m"
+    )
+
+    fig1 = add_profit(fig, row=2, data=cum_profits, column="cum_profits", name="Profits")
+    figure = fig1.layout.figure
+    profits = find_trace_in_fig_data(figure.data, "Profits")
+    assert isinstance(profits, go.Scatter)
+    assert profits.yaxis == "y2"
+
+
+def test_generate_profit_graph(testdatadir):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    trades = load_backtest_data(filename)
+    timerange = TimeRange.parse_timerange("20180110-20180112")
+    pairs = ["TRX/BTC", "XLM/BTC"]
+    trades = trades[trades["close_date"] < pd.Timestamp("2018-01-12", tz="UTC")]
+
+    data = history.load_data(datadir=testdatadir, pairs=pairs, timeframe="5m", timerange=timerange)
+
+    trades = trades[trades["pair"].isin(pairs)]
+
+    fig = generate_profit_graph(
+        pairs, data, trades, timeframe="5m", stake_currency="BTC", starting_balance=0
+    )
+    assert isinstance(fig, go.Figure)
+
+    assert fig.layout.title.text == "Freqtrade Profit plot"
+    assert fig.layout.yaxis.title.text == "Price"
+    assert fig.layout.yaxis2.title.text == "Profit BTC"
+    assert fig.layout.yaxis3.title.text == "Profit BTC"
+
+    figure = fig.layout.figure
+    assert len(figure.data) == 8
+
+    avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
+    assert isinstance(avgclose, go.Scatter)
+
+    profit = find_trace_in_fig_data(figure.data, "Profit")
+    assert isinstance(profit, go.Scatter)
+    drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 73.89%")
+    assert isinstance(drawdown, go.Scatter)
+    parallel = find_trace_in_fig_data(figure.data, "Parallel trades")
+    assert isinstance(parallel, go.Scatter)
+
+    underwater = find_trace_in_fig_data(figure.data, "Underwater Plot")
+    assert isinstance(underwater, go.Scatter)
+
+    underwater_relative = find_trace_in_fig_data(figure.data, "Underwater Plot (%)")
+    assert isinstance(underwater_relative, go.Scatter)
+
+    for pair in pairs:
+        profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")
+        assert isinstance(profit_pair, go.Scatter)
+
+    with pytest.raises(OperationalException, match=r"No trades found.*"):
+        # Pair cannot be empty - so it's an empty dataframe.
+        generate_profit_graph(
+            pairs,
+            data,
+            trades.loc[trades["pair"].isnull()],
+            timeframe="5m",
+            stake_currency="BTC",
+            starting_balance=0,
+        )
+
+
+def test_start_plot_dataframe(mocker):
+    aup = mocker.patch("freqtrade.plot.plotting.load_and_plot_trades", MagicMock())
+    args = [
+        "plot-dataframe",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--pairs",
+        "ETH/BTC",
+    ]
+    start_plot_dataframe(get_args(args))
+
+    assert aup.call_count == 1
+    called_config = aup.call_args_list[0][0][0]
+    assert "pairs" in called_config
+    assert called_config["pairs"] == ["ETH/BTC"]
+
+
+def test_load_and_plot_trades(default_conf, mocker, caplog, testdatadir):
+    patch_exchange(mocker)
+
+    default_conf["trade_source"] = "file"
+    default_conf["exportfilename"] = testdatadir / "backtest-result.json"
+    default_conf["indicators1"] = ["sma5", "ema10"]
+    default_conf["indicators2"] = ["macd"]
+    default_conf["pairs"] = ["ETH/BTC", "LTC/BTC"]
+
+    candle_mock = MagicMock()
+    store_mock = MagicMock()
+    mocker.patch.multiple(
+        "freqtrade.plot.plotting",
+        generate_candlestick_graph=candle_mock,
+        store_plot_file=store_mock,
+    )
+    load_and_plot_trades(default_conf)
+
+    # Both mocks should be called once per pair
+    assert candle_mock.call_count == 2
+    assert store_mock.call_count == 2
+
+    assert candle_mock.call_args_list[0][1]["indicators1"] == ["sma5", "ema10"]
+    assert candle_mock.call_args_list[0][1]["indicators2"] == ["macd"]
+
+    assert log_has("End of plotting process. 2 plots generated", caplog)
+
+
+def test_start_plot_profit(mocker):
+    aup = mocker.patch("freqtrade.plot.plotting.plot_profit", MagicMock())
+    args = [
+        "plot-profit",
+        "--config",
+        "tests/testdata/testconfigs/main_test_config.json",
+        "--pairs",
+        "ETH/BTC",
+    ]
+    start_plot_profit(get_args(args))
+
+    assert aup.call_count == 1
+    called_config = aup.call_args_list[0][0][0]
+    assert "pairs" in called_config
+    assert called_config["pairs"] == ["ETH/BTC"]
+
+
+def test_start_plot_profit_error(mocker):
+    args = ["plot-profit", "--pairs", "ETH/BTC"]
+    argsp = get_args(args)
+    # Make sure we use no config. Details: #2241
+    # not resetting config causes random failures if config.json exists
+    argsp["config"] = []
+    with pytest.raises(OperationalException):
+        start_plot_profit(argsp)
+
+
+def test_plot_profit(default_conf, mocker, testdatadir):
+    patch_exchange(mocker)
+    default_conf["trade_source"] = "file"
+    default_conf["exportfilename"] = testdatadir / "backtest-result_test_nofile.json"
+    default_conf["pairs"] = ["ETH/BTC", "LTC/BTC"]
+
+    profit_mock = MagicMock()
+    store_mock = MagicMock()
+    mocker.patch.multiple(
+        "freqtrade.plot.plotting", generate_profit_graph=profit_mock, store_plot_file=store_mock
+    )
+    with pytest.raises(
+        OperationalException, match=r"No trades found, cannot generate Profit-plot.*"
+    ):
+        plot_profit(default_conf)
+
+    default_conf["exportfilename"] = testdatadir / "backtest_results/backtest-result.json"
+
+    plot_profit(default_conf)
+
+    # Plot-profit generates one combined plot
+    assert profit_mock.call_count == 1
+    assert store_mock.call_count == 1
+
+    assert profit_mock.call_args_list[0][0][0] == default_conf["pairs"]
+    assert store_mock.call_args_list[0][1]["auto_open"] is False
+
+    del default_conf["timeframe"]
+    with pytest.raises(OperationalException, match=r"Timeframe must be set.*--timeframe.*"):
+        plot_profit(default_conf)
+
+
+@pytest.mark.parametrize(
+    "ind1,ind2,plot_conf,exp",
+    [
+        # No indicators, use plot_conf
+        (
+            [],
+            [],
+            {},
+            {
+                "main_plot": {"sma": {}, "ema3": {}, "ema5": {}},
+                "subplots": {"Other": {"macd": {}, "macdsignal": {}}},
+            },
+        ),
+        # use indicators
+        (
+            ["sma", "ema3"],
+            ["macd"],
+            {},
+            {"main_plot": {"sma": {}, "ema3": {}}, "subplots": {"Other": {"macd": {}}}},
+        ),
+        # only main_plot - adds empty subplots
+        ([], [], {"main_plot": {"sma": {}}}, {"main_plot": {"sma": {}}, "subplots": {}}),
+        # Main and subplots
+        (
+            [],
+            [],
+            {"main_plot": {"sma": {}}, "subplots": {"RSI": {"rsi": {"color": "red"}}}},
+            {"main_plot": {"sma": {}}, "subplots": {"RSI": {"rsi": {"color": "red"}}}},
+        ),
+        # no main_plot, adds empty main_plot
+        (
+            [],
+            [],
+            {"subplots": {"RSI": {"rsi": {"color": "red"}}}},
+            {"main_plot": {}, "subplots": {"RSI": {"rsi": {"color": "red"}}}},
+        ),
+        # indicator 1 / 2 should have prevalence
+        (
+            ["sma", "ema3"],
+            ["macd"],
+            {"main_plot": {"sma": {}}, "subplots": {"RSI": {"rsi": {"color": "red"}}}},
+            {"main_plot": {"sma": {}, "ema3": {}}, "subplots": {"Other": {"macd": {}}}},
+        ),
+        # indicator 1 - overrides plot_config main_plot
+        (
+            ["sma", "ema3"],
+            [],
+            {"main_plot": {"sma": {}}, "subplots": {"RSI": {"rsi": {"color": "red"}}}},
+            {"main_plot": {"sma": {}, "ema3": {}}, "subplots": {"RSI": {"rsi": {"color": "red"}}}},
+        ),
+        # indicator 2 - overrides plot_config subplots
+        (
+            [],
+            ["macd", "macd_signal"],
+            {"main_plot": {"sma": {}}, "subplots": {"RSI": {"rsi": {"color": "red"}}}},
+            {"main_plot": {"sma": {}}, "subplots": {"Other": {"macd": {}, "macd_signal": {}}}},
+        ),
+    ],
+)
+def test_create_plotconfig(ind1, ind2, plot_conf, exp):
+    res = create_plotconfig(ind1, ind2, plot_conf)
+    assert "main_plot" in res
+    assert "subplots" in res
+    assert isinstance(res["main_plot"], dict)
+    assert isinstance(res["subplots"], dict)
+
+    assert res == exp
diff --git a/tests/test_strategy_updater.py b/tests/test_strategy_updater.py
new file mode 100644
index 000000000..96daee973
--- /dev/null
+++ b/tests/test_strategy_updater.py
@@ -0,0 +1,218 @@
+# pragma pylint: disable=missing-docstring, protected-access, invalid-name
+
+import re
+import shutil
+from pathlib import Path
+
+from freqtrade.commands.strategy_utils_commands import start_strategy_update
+from freqtrade.strategy.strategyupdater import StrategyUpdater
+from tests.conftest import get_args
+
+
+def test_strategy_updater_start(user_dir, capsys) -> None:
+    # Effective test without mocks.
+    teststrats = Path(__file__).parent / "strategy/strats"
+    tmpdirp = Path(user_dir) / "strategies"
+    tmpdirp.mkdir(parents=True, exist_ok=True)
+    shutil.copy(teststrats / "strategy_test_v2.py", tmpdirp)
+    old_code = (teststrats / "strategy_test_v2.py").read_text()
+
+    args = ["strategy-updater", "--userdir", str(user_dir), "--strategy-list", "StrategyTestV2"]
+    pargs = get_args(args)
+    pargs["config"] = None
+
+    start_strategy_update(pargs)
+
+    assert Path(user_dir / "strategies_orig_updater").exists()
+    # Backup file exists
+    assert Path(user_dir / "strategies_orig_updater" / "strategy_test_v2.py").exists()
+    # updated file exists
+    new_file = tmpdirp / "strategy_test_v2.py"
+    assert new_file.exists()
+    new_code = new_file.read_text()
+    assert "INTERFACE_VERSION = 3" in new_code
+    assert "INTERFACE_VERSION = 2" in old_code
+    captured = capsys.readouterr()
+
+    assert "Conversion of strategy_test_v2.py started." in captured.out
+    assert re.search(r"Conversion of strategy_test_v2\.py took .* seconds", captured.out)
+
+
+def test_strategy_updater_methods(default_conf, caplog) -> None:
+    instance_strategy_updater = StrategyUpdater()
+    modified_code1 = instance_strategy_updater.update_code(
+        """
+class testClass(IStrategy):
+    def populate_buy_trend():
+        pass
+    def populate_sell_trend():
+        pass
+    def check_buy_timeout():
+        pass
+    def check_sell_timeout():
+        pass
+    def custom_sell():
+        pass
+"""
+    )
+
+    assert "populate_entry_trend" in modified_code1
+    assert "populate_exit_trend" in modified_code1
+    assert "check_entry_timeout" in modified_code1
+    assert "check_exit_timeout" in modified_code1
+    assert "custom_exit" in modified_code1
+    assert "INTERFACE_VERSION = 3" in modified_code1
+
+
+def test_strategy_updater_params(default_conf, caplog) -> None:
+    instance_strategy_updater = StrategyUpdater()
+
+    modified_code2 = instance_strategy_updater.update_code(
+        """
+ticker_interval = '15m'
+buy_some_parameter = IntParameter(space='buy')
+sell_some_parameter = IntParameter(space='sell')
+"""
+    )
+
+    assert "timeframe" in modified_code2
+    # check for not editing hyperopt spaces
+    assert "space='buy'" in modified_code2
+    assert "space='sell'" in modified_code2
+
+
+def test_strategy_updater_constants(default_conf, caplog) -> None:
+    instance_strategy_updater = StrategyUpdater()
+    modified_code3 = instance_strategy_updater.update_code(
+        """
+use_sell_signal = True
+sell_profit_only = True
+sell_profit_offset = True
+ignore_roi_if_buy_signal = True
+forcebuy_enable = True
+"""
+    )
+
+    assert "use_exit_signal" in modified_code3
+    assert "exit_profit_only" in modified_code3
+    assert "exit_profit_offset" in modified_code3
+    assert "ignore_roi_if_entry_signal" in modified_code3
+    assert "force_entry_enable" in modified_code3
+
+
+def test_strategy_updater_df_columns(default_conf, caplog) -> None:
+    instance_strategy_updater = StrategyUpdater()
+    modified_code = instance_strategy_updater.update_code(
+        """
+dataframe.loc[reduce(lambda x, y: x & y, conditions), ["buy", "buy_tag"]] = (1, "buy_signal_1")
+dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
+"""
+    )
+
+    assert "enter_long" in modified_code
+    assert "exit_long" in modified_code
+    assert "enter_tag" in modified_code
+
+
+def test_strategy_updater_method_params(default_conf, caplog) -> None:
+    instance_strategy_updater = StrategyUpdater()
+    modified_code = instance_strategy_updater.update_code(
+        """
+def confirm_trade_exit(sell_reason: str):
+    nr_orders = trade.nr_of_successful_buys
+    pass
+    """
+    )
+    assert "exit_reason" in modified_code
+    assert "nr_orders = trade.nr_of_successful_entries" in modified_code
+
+
+def test_strategy_updater_dicts(default_conf, caplog) -> None:
+    instance_strategy_updater = StrategyUpdater()
+    modified_code = instance_strategy_updater.update_code(
+        """
+order_time_in_force = {
+    'buy': 'gtc',
+    'sell': 'ioc'
+}
+order_types = {
+    'buy': 'limit',
+    'sell': 'market',
+    'stoploss': 'market',
+    'stoploss_on_exchange': False
+}
+unfilledtimeout = {
+    'buy': 1,
+    'sell': 2
+}
+"""
+    )
+
+    assert "'entry': 'gtc'" in modified_code
+    assert "'exit': 'ioc'" in modified_code
+    assert "'entry': 'limit'" in modified_code
+    assert "'exit': 'market'" in modified_code
+    assert "'entry': 1" in modified_code
+    assert "'exit': 2" in modified_code
+
+
+def test_strategy_updater_comparisons(default_conf, caplog) -> None:
+    instance_strategy_updater = StrategyUpdater()
+    modified_code = instance_strategy_updater.update_code(
+        """
+def confirm_trade_exit(sell_reason):
+    if (sell_reason == 'stop_loss'):
+        pass
+"""
+    )
+    assert "exit_reason" in modified_code
+    assert "exit_reason == 'stop_loss'" in modified_code
+
+
+def test_strategy_updater_strings(default_conf, caplog) -> None:
+    instance_strategy_updater = StrategyUpdater()
+
+    modified_code = instance_strategy_updater.update_code(
+        """
+sell_reason == 'sell_signal'
+sell_reason == 'force_sell'
+sell_reason == 'emergency_sell'
+"""
+    )
+
+    # those tests currently don't work, next in line.
+    assert "exit_signal" in modified_code
+    assert "exit_reason" in modified_code
+    assert "force_exit" in modified_code
+    assert "emergency_exit" in modified_code
+
+
+def test_strategy_updater_comments(default_conf, caplog) -> None:
+    instance_strategy_updater = StrategyUpdater()
+    modified_code = instance_strategy_updater.update_code(
+        """
+# This is the 1st comment
+import talib.abstract as ta
+# This is the 2nd comment
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class someStrategy(IStrategy):
+    INTERFACE_VERSION = 2
+    # This is the 3rd comment
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "0": 0.50
+    }
+
+    # This is the 4th comment
+    stoploss = -0.1
+"""
+    )
+
+    assert "This is the 1st comment" in modified_code
+    assert "This is the 2nd comment" in modified_code
+    assert "This is the 3rd comment" in modified_code
+    assert "INTERFACE_VERSION = 3" in modified_code
+    # currently still missing:
+    # Webhook terminology, Telegram notification settings, Strategy/Config settings
diff --git a/tests/test_talib.py b/tests/test_talib.py
new file mode 100644
index 000000000..97551bec9
--- /dev/null
+++ b/tests/test_talib.py
@@ -0,0 +1,16 @@
+import pandas as pd
+import talib.abstract as ta
+
+
+def test_talib_bollingerbands_near_zero_values():
+    inputs = pd.DataFrame(
+        [
+            {"close": 0.00000010},
+            {"close": 0.00000011},
+            {"close": 0.00000012},
+            {"close": 0.00000013},
+            {"close": 0.00000014},
+        ]
+    )
+    bollinger = ta.BBANDS(inputs, matype=0, timeperiod=2)
+    assert bollinger["upperband"][3] != bollinger["middleband"][3]
diff --git a/tests/test_timerange.py b/tests/test_timerange.py
new file mode 100644
index 000000000..c7471e263
--- /dev/null
+++ b/tests/test_timerange.py
@@ -0,0 +1,86 @@
+# pragma pylint: disable=missing-docstring, C0103
+from datetime import datetime, timezone
+
+import pytest
+
+from freqtrade.configuration import TimeRange
+from freqtrade.exceptions import OperationalException
+
+
+def test_parse_timerange_incorrect():
+    timerange = TimeRange.parse_timerange("")
+    assert timerange == TimeRange(None, None, 0, 0)
+    timerange = TimeRange.parse_timerange("20100522-")
+    assert TimeRange("date", None, 1274486400, 0) == timerange
+    assert timerange.timerange_str == "20100522-"
+    timerange = TimeRange.parse_timerange("-20100522")
+    assert TimeRange(None, "date", 0, 1274486400) == timerange
+    assert timerange.timerange_str == "-20100522"
+    timerange = TimeRange.parse_timerange("20100522-20150730")
+    assert timerange == TimeRange("date", "date", 1274486400, 1438214400)
+    assert timerange.timerange_str == "20100522-20150730"
+    assert timerange.start_fmt == "2010-05-22 00:00:00"
+    assert timerange.stop_fmt == "2015-07-30 00:00:00"
+
+    # Added test for unix timestamp - BTC genesis date
+    assert TimeRange("date", None, 1231006505, 0) == TimeRange.parse_timerange("1231006505-")
+    assert TimeRange(None, "date", 0, 1233360000) == TimeRange.parse_timerange("-1233360000")
+    timerange = TimeRange.parse_timerange("1231006505-1233360000")
+    assert TimeRange("date", "date", 1231006505, 1233360000) == timerange
+    assert isinstance(timerange.startdt, datetime)
+    assert isinstance(timerange.stopdt, datetime)
+    assert timerange.startdt == datetime.fromtimestamp(1231006505, tz=timezone.utc)
+    assert timerange.stopdt == datetime.fromtimestamp(1233360000, tz=timezone.utc)
+    assert timerange.timerange_str == "20090103-20090131"
+
+    timerange = TimeRange.parse_timerange("1231006505000-1233360000000")
+    assert TimeRange("date", "date", 1231006505, 1233360000) == timerange
+
+    timerange = TimeRange.parse_timerange("1231006505000-")
+    assert TimeRange("date", None, 1231006505, 0) == timerange
+
+    timerange = TimeRange.parse_timerange("-1231006505000")
+    assert TimeRange(None, "date", 0, 1231006505) == timerange
+
+    with pytest.raises(OperationalException, match=r"Incorrect syntax.*"):
+        TimeRange.parse_timerange("-")
+
+    with pytest.raises(
+        OperationalException, match=r"Start date is after stop date for timerange.*"
+    ):
+        TimeRange.parse_timerange("20100523-20100522")
+
+
+def test_subtract_start():
+    x = TimeRange("date", "date", 1274486400, 1438214400)
+    x.subtract_start(300)
+    assert x.startts == 1274486400 - 300
+
+    # Do nothing if no startdate exists
+    x = TimeRange(None, "date", 0, 1438214400)
+    x.subtract_start(300)
+    assert not x.startts
+    assert not x.startdt
+
+    x = TimeRange("date", None, 1274486400, 0)
+    x.subtract_start(300)
+    assert x.startts == 1274486400 - 300
+
+
+def test_adjust_start_if_necessary():
+    min_date = datetime(2017, 11, 14, 21, 15, 00, tzinfo=timezone.utc)
+
+    x = TimeRange("date", "date", 1510694100, 1510780500)
+    # Adjust by 20 candles - min_date == startts
+    x.adjust_start_if_necessary(300, 20, min_date)
+    assert x.startts == 1510694100 + (20 * 300)
+
+    x = TimeRange("date", "date", 1510700100, 1510780500)
+    # Do nothing, startup is set and different min_date
+    x.adjust_start_if_necessary(300, 20, min_date)
+    assert x.startts == 1510694100 + (20 * 300)
+
+    x = TimeRange(None, "date", 0, 1510780500)
+    # Adjust by 20 candles = 20 * 5m
+    x.adjust_start_if_necessary(300, 20, min_date)
+    assert x.startts == 1510694100 + (20 * 300)
diff --git a/tests/test_wallets.py b/tests/test_wallets.py
new file mode 100644
index 000000000..002e27c18
--- /dev/null
+++ b/tests/test_wallets.py
@@ -0,0 +1,607 @@
+# pragma pylint: disable=missing-docstring
+from copy import deepcopy
+from unittest.mock import MagicMock
+
+import pytest
+from sqlalchemy import select
+
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
+from freqtrade.exceptions import DependencyException
+from freqtrade.persistence import Trade
+from tests.conftest import (
+    EXMS,
+    create_mock_trades,
+    create_mock_trades_usdt,
+    get_patched_freqtradebot,
+    patch_wallet,
+)
+
+
+def test_sync_wallet_at_boot(mocker, default_conf):
+    default_conf["dry_run"] = False
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(
+            return_value={
+                "BNT": {"free": 1.0, "used": 2.0, "total": 3.0},
+                "GAS": {"free": 0.260739, "used": 0.0, "total": 0.260739},
+                "USDT": {"free": 20, "used": 20, "total": 40},
+            }
+        ),
+    )
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    assert len(freqtrade.wallets._wallets) == 3
+    assert freqtrade.wallets._wallets["BNT"].free == 1.0
+    assert freqtrade.wallets._wallets["BNT"].used == 2.0
+    assert freqtrade.wallets._wallets["BNT"].total == 3.0
+    assert freqtrade.wallets._wallets["GAS"].free == 0.260739
+    assert freqtrade.wallets._wallets["GAS"].used == 0.0
+    assert freqtrade.wallets._wallets["GAS"].total == 0.260739
+    assert freqtrade.wallets.get_free("BNT") == 1.0
+    assert "USDT" in freqtrade.wallets._wallets
+    assert freqtrade.wallets._last_wallet_refresh is not None
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(
+            return_value={
+                "BNT": {"free": 1.2, "used": 1.9, "total": 3.5},
+                "GAS": {"free": 0.270739, "used": 0.1, "total": 0.260439},
+            }
+        ),
+    )
+
+    freqtrade.wallets.update()
+
+    # USDT is missing from the 2nd result - so should not be in this either.
+    assert len(freqtrade.wallets._wallets) == 2
+    assert freqtrade.wallets._wallets["BNT"].free == 1.2
+    assert freqtrade.wallets._wallets["BNT"].used == 1.9
+    assert freqtrade.wallets._wallets["BNT"].total == 3.5
+    assert freqtrade.wallets._wallets["GAS"].free == 0.270739
+    assert freqtrade.wallets._wallets["GAS"].used == 0.1
+    assert freqtrade.wallets._wallets["GAS"].total == 0.260439
+    assert freqtrade.wallets.get_free("GAS") == 0.270739
+    assert freqtrade.wallets.get_used("GAS") == 0.1
+    assert freqtrade.wallets.get_total("GAS") == 0.260439
+    assert freqtrade.wallets.get_owned("GAS/USDT", "GAS") == 0.260439
+    update_mock = mocker.patch("freqtrade.wallets.Wallets._update_live")
+    freqtrade.wallets.update(False)
+    assert update_mock.call_count == 0
+    freqtrade.wallets.update()
+    assert update_mock.call_count == 1
+
+    assert freqtrade.wallets.get_free("NOCURRENCY") == 0
+    assert freqtrade.wallets.get_used("NOCURRENCY") == 0
+    assert freqtrade.wallets.get_total("NOCURRENCY") == 0
+    assert freqtrade.wallets.get_owned("NOCURRENCY/USDT", "NOCURRENCY") == 0
+
+
+def test_sync_wallet_missing_data(mocker, default_conf):
+    default_conf["dry_run"] = False
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(
+            return_value={
+                "BNT": {"free": 1.0, "used": 2.0, "total": 3.0},
+                "GAS": {"free": 0.260739, "total": 0.260739},
+            }
+        ),
+    )
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    assert len(freqtrade.wallets._wallets) == 2
+    assert freqtrade.wallets._wallets["BNT"].free == 1.0
+    assert freqtrade.wallets._wallets["BNT"].used == 2.0
+    assert freqtrade.wallets._wallets["BNT"].total == 3.0
+    assert freqtrade.wallets._wallets["GAS"].free == 0.260739
+    assert freqtrade.wallets._wallets["GAS"].used == 0.0
+    assert freqtrade.wallets._wallets["GAS"].total == 0.260739
+    assert freqtrade.wallets.get_free("GAS") == 0.260739
+
+
+def test_get_trade_stake_amount_no_stake_amount(default_conf, mocker) -> None:
+    patch_wallet(mocker, free=default_conf["stake_amount"] * 0.5)
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    with pytest.raises(DependencyException, match=r".*stake amount.*"):
+        freqtrade.wallets.get_trade_stake_amount("ETH/BTC", 1)
+
+
+@pytest.mark.parametrize(
+    "balance_ratio,capital,result1,result2",
+    [
+        (1, None, 50, 66.66666),
+        (0.99, None, 49.5, 66.0),
+        (0.50, None, 25, 33.3333),
+        # Tests with capital ignore balance_ratio
+        (1, 100, 50, 0.0),
+        (0.99, 200, 50, 66.66666),
+        (0.99, 150, 50, 50),
+        (0.50, 50, 25, 0.0),
+        (0.50, 10, 5, 0.0),
+    ],
+)
+def test_get_trade_stake_amount_unlimited_amount(
+    default_conf,
+    ticker,
+    balance_ratio,
+    capital,
+    result1,
+    result2,
+    limit_buy_order_open,
+    fee,
+    mocker,
+) -> None:
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=ticker,
+        create_order=MagicMock(return_value=limit_buy_order_open),
+        get_fee=fee,
+    )
+
+    conf = deepcopy(default_conf)
+    conf["stake_amount"] = UNLIMITED_STAKE_AMOUNT
+    conf["dry_run_wallet"] = 100
+    conf["tradable_balance_ratio"] = balance_ratio
+    if capital is not None:
+        conf["available_capital"] = capital
+
+    freqtrade = get_patched_freqtradebot(mocker, conf)
+
+    # no open trades, order amount should be 'balance / max_open_trades'
+    result = freqtrade.wallets.get_trade_stake_amount("ETH/USDT", 2)
+    assert result == result1
+
+    # create one trade, order amount should be 'balance / (max_open_trades - num_open_trades)'
+    freqtrade.execute_entry("ETH/USDT", result)
+
+    result = freqtrade.wallets.get_trade_stake_amount("LTC/USDT", 2)
+    assert result == result1
+
+    # create 2 trades, order amount should be None
+    freqtrade.execute_entry("LTC/BTC", result)
+
+    result = freqtrade.wallets.get_trade_stake_amount("XRP/USDT", 2)
+    assert result == 0
+
+    freqtrade.config["dry_run_wallet"] = 200
+    freqtrade.wallets._start_cap["BTC"] = 200
+    result = freqtrade.wallets.get_trade_stake_amount("XRP/USDT", 3)
+    assert round(result, 4) == round(result2, 4)
+
+    # set max_open_trades = None, so do not trade
+    result = freqtrade.wallets.get_trade_stake_amount("NEO/USDT", 0)
+    assert result == 0
+
+
+@pytest.mark.parametrize(
+    "stake_amount,min_stake,stake_available,max_stake,trade_amount,expected",
+    [
+        (22, 11, 50, 10000, None, 22),
+        (100, 11, 500, 10000, None, 100),
+        (1000, 11, 500, 10000, None, 500),  # Above stake_available
+        (700, 11, 1000, 400, None, 400),  # Above max_stake, below stake available
+        (20, 15, 10, 10000, None, 0),  # Minimum stake > stake_available
+        (9, 11, 100, 10000, None, 11),  # Below min stake
+        (1, 15, 10, 10000, None, 0),  # Below min stake and min_stake > stake_available
+        (20, 50, 100, 10000, None, 0),  # Below min stake and stake * 1.3 > min_stake
+        (1000, None, 1000, 10000, None, 1000),  # No min-stake-amount could be determined
+        # Rebuy - resulting in too high stake amount. Adjusting.
+        (2000, 15, 2000, 3000, 1500, 1500),
+    ],
+)
+def test_validate_stake_amount(
+    mocker,
+    default_conf,
+    stake_amount,
+    min_stake,
+    stake_available,
+    max_stake,
+    trade_amount,
+    expected,
+):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    mocker.patch(
+        "freqtrade.wallets.Wallets.get_available_stake_amount", return_value=stake_available
+    )
+    res = freqtrade.wallets.validate_stake_amount(
+        "XRP/USDT", stake_amount, min_stake, max_stake, trade_amount
+    )
+    assert res == expected
+
+
+@pytest.mark.parametrize(
+    "available_capital,closed_profit,open_stakes,free,expected",
+    [
+        (None, 10, 100, 910, 1000),
+        (None, 0, 0, 2500, 2500),
+        (None, 500, 0, 2500, 2000),
+        (None, 500, 0, 2500, 2000),
+        (None, -70, 0, 1930, 2000),
+        # Only available balance matters when it's set.
+        (100, 0, 0, 0, 100),
+        (1000, 0, 2, 5, 1000),
+        (1235, 2250, 2, 5, 1235),
+        (1235, -2250, 2, 5, 1235),
+    ],
+)
+def test_get_starting_balance(
+    mocker, default_conf, available_capital, closed_profit, open_stakes, free, expected
+):
+    if available_capital:
+        default_conf["available_capital"] = available_capital
+    mocker.patch(
+        "freqtrade.persistence.models.Trade.get_total_closed_profit", return_value=closed_profit
+    )
+    mocker.patch(
+        "freqtrade.persistence.models.Trade.total_open_trades_stakes", return_value=open_stakes
+    )
+    mocker.patch("freqtrade.wallets.Wallets.get_free", return_value=free)
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    assert freqtrade.wallets.get_starting_balance() == expected * (1 if available_capital else 0.99)
+
+
+def test_sync_wallet_futures_live(mocker, default_conf):
+    default_conf["dry_run"] = False
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    mock_result = [
+        {
+            "symbol": "ETH/USDT:USDT",
+            "timestamp": None,
+            "datetime": None,
+            "initialMargin": 0.0,
+            "initialMarginPercentage": None,
+            "maintenanceMargin": 0.0,
+            "maintenanceMarginPercentage": 0.005,
+            "entryPrice": 0.0,
+            "notional": 100.0,
+            "leverage": 5.0,
+            "unrealizedPnl": 0.0,
+            "contracts": 100.0,
+            "contractSize": 1,
+            "marginRatio": None,
+            "liquidationPrice": 0.0,
+            "markPrice": 2896.41,
+            "collateral": 20,
+            "marginType": "isolated",
+            "side": "short",
+            "percentage": None,
+        },
+        {
+            "symbol": "ADA/USDT:USDT",
+            "timestamp": None,
+            "datetime": None,
+            "initialMargin": 0.0,
+            "initialMarginPercentage": None,
+            "maintenanceMargin": 0.0,
+            "maintenanceMarginPercentage": 0.005,
+            "entryPrice": 0.0,
+            "notional": 100.0,
+            "leverage": 5.0,
+            "unrealizedPnl": 0.0,
+            "contracts": 100.0,
+            "contractSize": 1,
+            "marginRatio": None,
+            "liquidationPrice": 0.0,
+            "markPrice": 0.91,
+            "collateral": 20,
+            "marginType": "isolated",
+            "side": "short",
+            "percentage": None,
+        },
+        {
+            # Closed position
+            "symbol": "SOL/BUSD:BUSD",
+            "timestamp": None,
+            "datetime": None,
+            "initialMargin": 0.0,
+            "initialMarginPercentage": None,
+            "maintenanceMargin": 0.0,
+            "maintenanceMarginPercentage": 0.005,
+            "entryPrice": 0.0,
+            "notional": 0.0,
+            "leverage": 5.0,
+            "unrealizedPnl": 0.0,
+            "contracts": 0.0,
+            "contractSize": 1,
+            "marginRatio": None,
+            "liquidationPrice": 0.0,
+            "markPrice": 15.41,
+            "collateral": 0.0,
+            "marginType": "isolated",
+            "side": "short",
+            "percentage": None,
+        },
+    ]
+    mocker.patch.multiple(
+        EXMS,
+        get_balances=MagicMock(
+            return_value={
+                "USDT": {"free": 900, "used": 100, "total": 1000},
+            }
+        ),
+        fetch_positions=MagicMock(return_value=mock_result),
+    )
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+
+    assert len(freqtrade.wallets._wallets) == 1
+    assert len(freqtrade.wallets._positions) == 2
+
+    assert "USDT" in freqtrade.wallets._wallets
+    assert "ETH/USDT:USDT" in freqtrade.wallets._positions
+    assert freqtrade.wallets._last_wallet_refresh is not None
+    assert freqtrade.wallets.get_owned("ETH/USDT:USDT", "ETH") == 1000
+    assert freqtrade.wallets.get_owned("SOL/USDT:USDT", "SOL") == 0
+
+    # Remove ETH/USDT:USDT position
+    del mock_result[0]
+    freqtrade.wallets.update()
+    assert len(freqtrade.wallets._positions) == 1
+    assert "ETH/USDT:USDT" not in freqtrade.wallets._positions
+
+
+def test_sync_wallet_dry(mocker, default_conf_usdt, fee):
+    default_conf_usdt["dry_run"] = True
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    assert len(freqtrade.wallets._wallets) == 1
+    assert len(freqtrade.wallets._positions) == 0
+    assert freqtrade.wallets.get_total("USDT") == 1000
+
+    create_mock_trades_usdt(fee, is_short=None)
+
+    freqtrade.wallets.update()
+
+    assert len(freqtrade.wallets._wallets) == 5
+    assert len(freqtrade.wallets._positions) == 0
+    bal = freqtrade.wallets.get_all_balances()
+    # NEO trade is not filled yet.
+    assert bal["NEO"].total == 0
+    assert bal["XRP"].total == 10
+    assert bal["LTC"].total == 2
+    usdt_bal = bal["USDT"]
+    assert usdt_bal.free == 922.74
+    assert usdt_bal.total == 942.74
+    assert usdt_bal.used == 20.0
+    # sum of used and free should be total.
+    assert usdt_bal.total == usdt_bal.free + usdt_bal.used
+
+    assert (
+        freqtrade.wallets.get_starting_balance()
+        == default_conf_usdt["dry_run_wallet"] * default_conf_usdt["tradable_balance_ratio"]
+    )
+    total = freqtrade.wallets.get_total("LTC")
+    free = freqtrade.wallets.get_free("LTC")
+    used = freqtrade.wallets.get_used("LTC")
+    assert used != 0
+    assert free + used == total
+
+
+def test_sync_wallet_futures_dry(mocker, default_conf, fee):
+    default_conf["dry_run"] = True
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    assert len(freqtrade.wallets._wallets) == 1
+    assert len(freqtrade.wallets._positions) == 0
+
+    create_mock_trades(fee, is_short=None)
+
+    freqtrade.wallets.update()
+
+    assert len(freqtrade.wallets._wallets) == 1
+    assert len(freqtrade.wallets._positions) == 4
+    positions = freqtrade.wallets.get_all_positions()
+    assert positions["ETH/BTC"].side == "short"
+    assert positions["ETC/BTC"].side == "long"
+    assert positions["XRP/BTC"].side == "long"
+    assert positions["LTC/BTC"].side == "short"
+
+    assert (
+        freqtrade.wallets.get_starting_balance()
+        == default_conf["dry_run_wallet"] * default_conf["tradable_balance_ratio"]
+    )
+    total = freqtrade.wallets.get_total("BTC")
+    free = freqtrade.wallets.get_free("BTC")
+    used = freqtrade.wallets.get_used("BTC")
+    assert free + used == total
+
+
+def test_check_exit_amount(mocker, default_conf, fee):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    update_mock = mocker.patch("freqtrade.wallets.Wallets.update")
+    total_mock = mocker.patch("freqtrade.wallets.Wallets.get_total", return_value=50.0)
+
+    create_mock_trades(fee, is_short=None)
+    trade = Trade.session.scalars(select(Trade)).first()
+    assert trade.amount == 50.0
+
+    assert freqtrade.wallets.check_exit_amount(trade) is True
+    assert update_mock.call_count == 0
+    assert total_mock.call_count == 1
+
+    update_mock.reset_mock()
+    # Reduce returned amount to below the trade amount - which should
+    # trigger a wallet update and return False, triggering "order refinding"
+    total_mock = mocker.patch("freqtrade.wallets.Wallets.get_total", return_value=40)
+    assert freqtrade.wallets.check_exit_amount(trade) is False
+    assert update_mock.call_count == 1
+    assert total_mock.call_count == 2
+
+
+def test_check_exit_amount_futures(mocker, default_conf, fee):
+    default_conf["trading_mode"] = "futures"
+    default_conf["margin_mode"] = "isolated"
+    freqtrade = get_patched_freqtradebot(mocker, default_conf)
+    total_mock = mocker.patch("freqtrade.wallets.Wallets.get_total", return_value=50)
+
+    create_mock_trades(fee, is_short=None)
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.trading_mode = "futures"
+    assert trade.amount == 50
+
+    assert freqtrade.wallets.check_exit_amount(trade) is True
+    assert total_mock.call_count == 0
+
+    update_mock = mocker.patch("freqtrade.wallets.Wallets.update")
+    trade.amount = 150
+    # Reduce returned amount to below the trade amount - which should
+    # trigger a wallet update and return False, triggering "order refinding"
+    assert freqtrade.wallets.check_exit_amount(trade) is False
+    assert total_mock.call_count == 0
+    assert update_mock.call_count == 1
+
+
+@pytest.mark.parametrize(
+    "config,wallets",
+    [
+        (
+            {"stake_currency": "USDT", "dry_run_wallet": 1000.0},
+            {"USDT": {"currency": "USDT", "free": 1000.0, "used": 0.0, "total": 1000.0}},
+        ),
+        (
+            {"stake_currency": "USDT", "dry_run_wallet": {"USDT": 1000.0, "BTC": 0.1, "ETH": 2.0}},
+            {
+                "USDT": {"currency": "USDT", "free": 1000.0, "used": 0.0, "total": 1000.0},
+                "BTC": {"currency": "BTC", "free": 0.1, "used": 0.0, "total": 0.1},
+                "ETH": {"currency": "ETH", "free": 2.0, "used": 0.0, "total": 2.0},
+            },
+        ),
+        (
+            {
+                "stake_currency": "USDT",
+                "margin_mode": "cross",
+                "dry_run_wallet": {"USDC": 1000.0, "BTC": 0.1, "ETH": 2.0},
+            },
+            {
+                # USDT wallet should be created with 0 balance, but Free balance, since
+                # it's converted from the other currencies
+                "USDT": {"currency": "USDT", "free": 4200.0, "used": 0.0, "total": 0.0},
+                "USDC": {"currency": "USDC", "free": 1000.0, "used": 0.0, "total": 1000.0},
+                "BTC": {"currency": "BTC", "free": 0.1, "used": 0.0, "total": 0.1},
+                "ETH": {"currency": "ETH", "free": 2.0, "used": 0.0, "total": 2.0},
+            },
+        ),
+        (
+            {
+                "stake_currency": "USDT",
+                "margin_mode": "cross",
+                "dry_run_wallet": {"USDT": 500, "USDC": 1000.0, "BTC": 0.1, "ETH": 2.0},
+            },
+            {
+                # USDT wallet should be created with 500 balance, but Free balance, since
+                # it's converted from the other currencies
+                "USDT": {"currency": "USDT", "free": 4700.0, "used": 0.0, "total": 500.0},
+                "USDC": {"currency": "USDC", "free": 1000.0, "used": 0.0, "total": 1000.0},
+                "BTC": {"currency": "BTC", "free": 0.1, "used": 0.0, "total": 0.1},
+                "ETH": {"currency": "ETH", "free": 2.0, "used": 0.0, "total": 2.0},
+            },
+        ),
+        (
+            # Same as above, but without cross
+            {
+                "stake_currency": "USDT",
+                "dry_run_wallet": {"USDT": 500, "USDC": 1000.0, "BTC": 0.1, "ETH": 2.0},
+            },
+            {
+                # No "free" transfer for USDT wallet
+                "USDT": {"currency": "USDT", "free": 500.0, "used": 0.0, "total": 500.0},
+                "USDC": {"currency": "USDC", "free": 1000.0, "used": 0.0, "total": 1000.0},
+                "BTC": {"currency": "BTC", "free": 0.1, "used": 0.0, "total": 0.1},
+                "ETH": {"currency": "ETH", "free": 2.0, "used": 0.0, "total": 2.0},
+            },
+        ),
+        (
+            # Same as above, but with futures and cross
+            {
+                "stake_currency": "USDT",
+                "margin_mode": "cross",
+                "trading_mode": "futures",
+                "dry_run_wallet": {"USDT": 500, "USDC": 1000.0, "BTC": 0.1, "ETH": 2.0},
+            },
+            {
+                # USDT wallet should be created with 500 balance, but Free balance, since
+                # it's converted from the other currencies
+                "USDT": {"currency": "USDT", "free": 4700.0, "used": 0.0, "total": 500.0},
+                "USDC": {"currency": "USDC", "free": 1000.0, "used": 0.0, "total": 1000.0},
+                "BTC": {"currency": "BTC", "free": 0.1, "used": 0.0, "total": 0.1},
+                "ETH": {"currency": "ETH", "free": 2.0, "used": 0.0, "total": 2.0},
+            },
+        ),
+    ],
+)
+def test_dry_run_wallet_initialization(mocker, default_conf_usdt, config, wallets):
+    default_conf_usdt.update(config)
+    mocker.patch(
+        f"{EXMS}.get_tickers",
+        return_value={
+            "USDC/USDT": {"last": 1.0},
+            "BTC/USDT": {"last": 20_000.0},
+            "ETH/USDT": {"last": 1100.0},
+        },
+    )
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    stake_currency = config["stake_currency"]
+    # Verify each wallet matches the expected values
+    for currency, expected_wallet in wallets.items():
+        wallet = freqtrade.wallets._wallets[currency]
+        assert wallet.currency == expected_wallet["currency"]
+        assert wallet.free == expected_wallet["free"]
+        assert wallet.used == expected_wallet["used"]
+        assert wallet.total == expected_wallet["total"]
+
+    # Verify no extra wallets were created
+    assert len(freqtrade.wallets._wallets) == len(wallets)
+
+    # Create a trade and verify the new currency is added to the wallets
+    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.0)
+    mocker.patch(f"{EXMS}.get_rate", return_value=2.22)
+    mocker.patch(
+        f"{EXMS}.fetch_ticker",
+        return_value={
+            "bid": 0.20,
+            "ask": 0.22,
+            "last": 0.22,
+        },
+    )
+    # Without position, collateral will be the same as free
+    assert freqtrade.wallets.get_collateral() == freqtrade.wallets.get_free(stake_currency)
+    freqtrade.execute_entry("NEO/USDT", 100.0)
+
+    # Update wallets and verify NEO is now included
+    freqtrade.wallets.update()
+    if default_conf_usdt["trading_mode"] != "futures":
+        assert "NEO" in freqtrade.wallets._wallets
+
+        assert freqtrade.wallets._wallets["NEO"].total == 45.04504504  # 100 USDT / 0.22
+        assert freqtrade.wallets._wallets["NEO"].used == 0.0
+        assert freqtrade.wallets._wallets["NEO"].free == 45.04504504
+        assert freqtrade.wallets.get_collateral() == freqtrade.wallets.get_free(stake_currency)
+        # Verify USDT wallet was reduced by trade amount
+        assert (
+            pytest.approx(freqtrade.wallets._wallets[stake_currency].total)
+            == wallets[stake_currency]["total"] - 100.0
+        )
+        assert len(freqtrade.wallets._wallets) == len(wallets) + 1  # Original wallets + NEO
+    else:
+        # Futures mode
+        assert "NEO" not in freqtrade.wallets._wallets
+        assert freqtrade.wallets._positions["NEO/USDT"].position == 45.04504504
+        assert pytest.approx(freqtrade.wallets._positions["NEO/USDT"].collateral) == 100
+
+        # Verify USDT wallet's free was reduced by trade amount
+        assert (
+            pytest.approx(freqtrade.wallets.get_collateral())
+            == freqtrade.wallets.get_free(stake_currency) + 100
+        )
+        assert (
+            pytest.approx(freqtrade.wallets._wallets[stake_currency].free)
+            == wallets[stake_currency]["free"] - 100.0
+        )
diff --git a/tests/testdata/ADA_BTC-5m.feather b/tests/testdata/ADA_BTC-5m.feather
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diff --git a/tests/testdata/NOPAIR_XXX-4m.json b/tests/testdata/NOPAIR_XXX-4m.json
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+++ b/tests/testdata/NOPAIR_XXX-4m.json
@@ -0,0 +1 @@
+[]
diff --git a/tests/testdata/NXT_BTC-5m.feather b/tests/testdata/NXT_BTC-5m.feather
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new file mode 100644
index 000000000..2ef3dfcc9
--- /dev/null
+++ b/tests/testdata/UNITTEST_BTC-1m.json
@@ -0,0 +1 @@
+[[1509836520000, 0.00162008, 0.00162008, 0.00162008, 0.00162008, 108.14853839], [1509836580000, 0.00161, 0.00161, 0.00161, 0.00161, 82.390199], [1509836640000, 0.00161, 0.00161, 0.00161, 0.00161, 236.33244948], [1509836700000, 0.00161, 0.00162, 0.00161, 0.00162, 64.53000899], [1509836760000, 0.00162, 0.00162, 0.00162, 0.00162, 13.7469126], [1509836820000, 0.00162, 0.00162, 0.00160345, 0.00160345, 19.60000001], [1509837240000, 0.00161902, 0.00161902, 0.00161902, 0.00161902, 6.34875668], [1509837300000, 0.0016034, 0.0016034, 0.0016034, 0.0016034, 3.0], [1509837360000, 0.0016034, 0.0016034, 0.0016034, 0.0016034, 16.23657741], [1509837420000, 0.0016034, 0.0016034, 0.0016009, 0.0016009, 85.1959082], [1509837480000, 0.00160356, 0.00160356, 0.0016034, 0.0016034, 4.06965488], [1509837600000, 0.00160372, 0.00160372, 0.00160356, 0.00160356, 117.25068418], [1509837660000, 0.00160356, 0.00160356, 0.00160086, 0.00160086, 62.45416445], [1509837720000, 0.00160086, 0.00160086, 0.0016, 0.0016, 80.23600309], [1509837780000, 0.0016, 0.0016, 0.0016, 0.0016, 103.06684176], [1509837840000, 0.0016, 0.0016, 0.0016, 0.0016, 35.00973373], [1509837900000, 0.0016, 0.0016, 0.00159984, 0.00159984, 31.85485231], [1509837960000, 0.00160005, 0.00160005, 0.00159795, 0.00159795, 26.61171032], [1509838020000, 0.00159795, 0.00159795, 0.00159601, 0.00159601, 3.59315959], [1509838080000, 0.00159601, 0.00159601, 0.0015946, 0.0015946, 2.9234879], [1509838140000, 0.0015946, 0.0015946, 0.00158752, 0.00158785, 42.44967389], [1509838200000, 0.00158817, 0.00158817, 0.00158817, 0.00158817, 15.48527342], [1509838320000, 0.00159795, 0.00159999, 0.00159795, 0.00159999, 13.33735834], [1509838680000, 0.00158608, 0.00159965, 0.00158608, 0.00159965, 87.8529794], [1509838740000, 0.00159965, 0.00159965, 0.00158632, 0.00158632, 62.20356042], [1509838800000, 0.001586, 0.001586, 0.001586, 0.001586, 3.99298255], [1509838860000, 0.001586, 0.001586, 0.00158246, 0.00158246, 10.0], [1509838920000, 0.00158286, 0.00158286, 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index 000000000..96440fdf5
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diff --git a/tests/testdata/backtest_results/backtest-result.meta.json b/tests/testdata/backtest_results/backtest-result.meta.json
new file mode 100644
index 000000000..57ecdb19d
--- /dev/null
+++ b/tests/testdata/backtest_results/backtest-result.meta.json
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+{
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diff --git a/tests/testdata/backtest_results/backtest-result_multistrat.json b/tests/testdata/backtest_results/backtest-result_multistrat.json
new file mode 100644
index 000000000..786795d67
--- /dev/null
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diff --git a/tests/testdata/backtest_results/backtest-result_multistrat.meta.json b/tests/testdata/backtest_results/backtest-result_multistrat.meta.json
new file mode 100644
index 000000000..906edcece
--- /dev/null
+++ b/tests/testdata/backtest_results/backtest-result_multistrat.meta.json
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+{
+    "StrategyTestV2": {
+        "run_id": "430d0271075ef327edbb23088f4db4ebe51a3dbf",
+        "backtest_start_time": 1648904006
+    },
+    "TestStrategy": {
+        "run_id": "110d0271075ef327edbb23085102b4ebe51a3d55",
+        "backtest_start_time": 1648904006
+    }
+}
diff --git a/tests/testdata/binance/binance_public_data/futures-APEUSDT-aggTrades-2024-10-18.zip b/tests/testdata/binance/binance_public_data/futures-APEUSDT-aggTrades-2024-10-18.zip
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diff --git a/tests/testdata/config.tests.json b/tests/testdata/config.tests.json
new file mode 100644
index 000000000..fdfa97350
--- /dev/null
+++ b/tests/testdata/config.tests.json
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+{
+    "max_open_trades": 3,
+    "stake_currency": "BTC",
+    "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "timeframe": "5m",
+    "dry_run": true,
+    "cancel_open_orders_on_exit": false,
+    "unfilledtimeout": {
+        "entry": 5,
+        "exit": 5,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "gate",
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "ETH/BTC",
+            "LTC/BTC",
+            "ETC/BTC",
+            "XLM/BTC",
+            "XRP/BTC",
+            "ADA/BTC",
+            "DOT/BTC"
+        ],
+        "pair_blacklist": [
+            "DOGE/BTC"
+        ]
+    },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id"
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}
diff --git a/tests/testdata/futures/UNITTEST_USDT_USDT-1h-mark.feather b/tests/testdata/futures/UNITTEST_USDT_USDT-1h-mark.feather
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diff --git a/tests/testdata/kraken/trades_csv/BCHEUR.csv b/tests/testdata/kraken/trades_csv/BCHEUR.csv
new file mode 100644
index 000000000..7a9ca8eb3
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+1672531471,90.450000,1.00000000
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diff --git a/tests/testdata/kraken/trades_csv/incremental_q2/BCHEUR.csv b/tests/testdata/kraken/trades_csv/incremental_q2/BCHEUR.csv
new file mode 100644
index 000000000..68fd844ea
--- /dev/null
+++ b/tests/testdata/kraken/trades_csv/incremental_q2/BCHEUR.csv
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diff --git a/tests/testdata/orderflow/public_trades_list_simple_example.csv b/tests/testdata/orderflow/public_trades_list_simple_example.csv
new file mode 100644
index 000000000..4ffbdc024
--- /dev/null
+++ b/tests/testdata/orderflow/public_trades_list_simple_example.csv
@@ -0,0 +1,8 @@
+,timestamp,id,type,side,price,amount,cost,date
+0,1675311000092, 1588563957,    ,buy, 23438.0,  0.013, 0, 2023-02-02 04:10:00.092000+00:00
+1,1675311000211, 1588563958,    ,sell, 23437.5,  0.001, 0, 2023-02-02 04:10:00.211000+00:00
+2,1675311000335, 1588563959,    ,sell , 23437.5,  0.196, 0, 2023-02-02 04:10:00.335000+00:00
+3,1675311000769, 1588563960,    ,  sell, 23437.5,  0.046, 0, 2023-02-02 04:10:00.769000+00:00
+4,1675311000773, 1588563961,    ,buy , 23438.0,  0.127, 0, 2023-02-02 04:10:00.773000+00:00
+5,1675311000774, 1588563959,    ,sell, 23437.5,  0.001, 0, 2023-02-02 04:10:00.774000+00:00
+6,1675311000775, 1588563960,    ,sell, 23437.5,  0.001, 0, 2023-02-02 04:10:00.775000+00:00
diff --git a/tests/testdata/pairs.json b/tests/testdata/pairs.json
new file mode 100644
index 000000000..15aae2643
--- /dev/null
+++ b/tests/testdata/pairs.json
@@ -0,0 +1,26 @@
+[
+    "ADA/BTC",
+    "BAT/BTC",
+    "DASH/BTC",
+    "ETC/BTC",
+    "ETH/BTC",
+    "GBYTE/BTC",
+    "LSK/BTC",
+    "LTC/BTC",
+    "NEO/BTC",
+    "NXT/BTC",
+    "TRX/BTC",
+    "STORJ/BTC",
+    "QTUM/BTC",
+    "WAVES/BTC",
+    "VTC/BTC",
+    "XLM/BTC",
+    "XMR/BTC",
+    "XVG/BTC",
+    "XRP/BTC",
+    "ZEC/BTC",
+    "BTC/USDT",
+    "LTC/USDT",
+    "ETH/USDT"
+]
+
diff --git a/tests/testdata/strategy_SampleStrategy.fthypt b/tests/testdata/strategy_SampleStrategy.fthypt
new file mode 100644
index 000000000..e71102043
--- /dev/null
+++ b/tests/testdata/strategy_SampleStrategy.fthypt
@@ -0,0 +1,5 @@
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Avg profit   0.02%. Median profit   0.00%. Total profit  0.00011576 BTC (   0.00\u03A3%). Avg duration 3:03:00 min.","total_profit":1.1576000000000034e-07,"current_epoch":5,"is_initial_point":true,"is_best":false}
diff --git a/tests/testdata/testconfigs/main_test_config.json b/tests/testdata/testconfigs/main_test_config.json
new file mode 100644
index 000000000..0af5816dc
--- /dev/null
+++ b/tests/testdata/testconfigs/main_test_config.json
@@ -0,0 +1,77 @@
+{
+    "max_open_trades": 3,
+    "stake_currency": "BTC",
+    "stake_amount": 0.05,
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "timeframe": "5m",
+    "dry_run": true,
+    "cancel_open_orders_on_exit": false,
+    "unfilledtimeout": {
+        "entry": 10,
+        "exit": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "ETH/BTC",
+            "LTC/BTC",
+            "ETC/BTC",
+            "RVN/BTC",
+            "CRO/BTC",
+            "XLM/BTC",
+            "XRP/BTC",
+            "TRX/BTC",
+            "ADA/BTC",
+            "DOT/BTC"
+        ],
+        "pair_blacklist": [
+            "DOGE/BTC"
+        ]
+    },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id"
+    },
+    "api_server": {
+        "enabled": false,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "jwt_secret_key": "somethingrandom",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "SuperSecurePassword"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}
diff --git a/tests/testdata/testconfigs/recursive.json b/tests/testdata/testconfigs/recursive.json
new file mode 100644
index 000000000..33ab12008
--- /dev/null
+++ b/tests/testdata/testconfigs/recursive.json
@@ -0,0 +1,6 @@
+{
+    // This file fails as it's loading itself over and over
+    "add_config_files": [
+        "./recursive.json"
+    ]
+}
diff --git a/tests/testdata/testconfigs/test_base_config.json b/tests/testdata/testconfigs/test_base_config.json
new file mode 100644
index 000000000..40a114150
--- /dev/null
+++ b/tests/testdata/testconfigs/test_base_config.json
@@ -0,0 +1,12 @@
+{
+    "stake_currency": "",
+    "dry_run": false,
+    "exchange": {
+        "name": "",
+        "key": "",
+        "secret": "",
+        "pair_whitelist": [],
+        "ccxt_async_config": {
+        }
+    }
+}
diff --git a/tests/testdata/testconfigs/test_pricing2_conf.json b/tests/testdata/testconfigs/test_pricing2_conf.json
new file mode 100644
index 000000000..094783a60
--- /dev/null
+++ b/tests/testdata/testconfigs/test_pricing2_conf.json
@@ -0,0 +1,18 @@
+{
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "other",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0
+    }
+}
diff --git a/tests/testdata/testconfigs/test_pricing_conf.json b/tests/testdata/testconfigs/test_pricing_conf.json
new file mode 100644
index 000000000..59516d65e
--- /dev/null
+++ b/tests/testdata/testconfigs/test_pricing_conf.json
@@ -0,0 +1,21 @@
+{
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0
+    },
+    "add_config_files": [
+        "./test_pricing2_conf.json"
+    ]
+}
diff --git a/tests/testdata/testconfigs/testconfig.json b/tests/testdata/testconfigs/testconfig.json
new file mode 100644
index 000000000..87ed6daef
--- /dev/null
+++ b/tests/testdata/testconfigs/testconfig.json
@@ -0,0 +1,6 @@
+{
+    "add_config_files": [
+        "test_base_config.json",
+        "test_pricing_conf.json"
+    ]
+}
diff --git a/tests/util/test_binance_mig.py b/tests/util/test_binance_mig.py
new file mode 100644
index 000000000..f700ff73a
--- /dev/null
+++ b/tests/util/test_binance_mig.py
@@ -0,0 +1,65 @@
+import shutil
+
+import pytest
+
+from freqtrade.persistence import Trade
+from freqtrade.util.migrations import migrate_binance_futures_data, migrate_data
+from freqtrade.util.migrations.binance_mig import migrate_binance_futures_names
+from tests.conftest import create_mock_trades_usdt, log_has
+
+
+def test_binance_mig_data_conversion(default_conf_usdt, tmp_path, testdatadir):
+    # call doing nothing (spot mode)
+    migrate_binance_futures_data(default_conf_usdt)
+    default_conf_usdt["trading_mode"] = "futures"
+    pair_old = "XRP_USDT"
+    pair_unified = "XRP_USDT_USDT"
+    futures_src = testdatadir / "futures"
+    futures_dst = tmp_path / "futures"
+    futures_dst.mkdir()
+    files = [
+        "-1h-mark.feather",
+        "-1h-futures.feather",
+        "-8h-funding_rate.feather",
+        "-8h-mark.feather",
+    ]
+
+    # Copy files to tmpdir and rename to old naming
+    for file in files:
+        fn_after = futures_dst / f"{pair_old}{file}"
+        shutil.copy(futures_src / f"{pair_unified}{file}", fn_after)
+
+    default_conf_usdt["datadir"] = tmp_path
+    # Migrate files to unified namings
+    migrate_binance_futures_data(default_conf_usdt)
+
+    for file in files:
+        fn_after = futures_dst / f"{pair_unified}{file}"
+        assert fn_after.exists()
+
+
+@pytest.mark.usefixtures("init_persistence")
+def test_binance_mig_db_conversion(default_conf_usdt, fee, caplog):
+    # Does nothing in spot mode
+    migrate_binance_futures_names(default_conf_usdt)
+
+    create_mock_trades_usdt(fee, None)
+
+    for t in Trade.get_trades():
+        t.trading_mode = "FUTURES"
+        t.exchange = "binance"
+    Trade.commit()
+
+    default_conf_usdt["trading_mode"] = "futures"
+    migrate_binance_futures_names(default_conf_usdt)
+    assert log_has("Migrating binance futures pairs in database.", caplog)
+
+
+def test_migration_wrapper(default_conf_usdt, mocker):
+    default_conf_usdt["trading_mode"] = "futures"
+    binmock = mocker.patch("freqtrade.util.migrations.migrate_binance_futures_data")
+    funding_mock = mocker.patch("freqtrade.util.migrations.migrate_funding_fee_timeframe")
+    migrate_data(default_conf_usdt)
+
+    assert binmock.call_count == 1
+    assert funding_mock.call_count == 1
diff --git a/tests/util/test_ccxt_precise.py b/tests/util/test_ccxt_precise.py
new file mode 100644
index 000000000..536a66182
--- /dev/null
+++ b/tests/util/test_ccxt_precise.py
@@ -0,0 +1,82 @@
+from freqtrade.util import FtPrecise
+
+
+ws = FtPrecise("-1.123e-6")
+ws = FtPrecise("-1.123e-6")
+xs = FtPrecise("0.00000002")
+ys = FtPrecise("69696900000")
+zs = FtPrecise("0")
+
+
+def test_FtPrecise():
+    assert ys * xs == "1393.938"
+    assert xs * ys == "1393.938"
+
+    assert ys + xs == "69696900000.00000002"
+    assert xs + ys == "69696900000.00000002"
+    assert xs - ys == "-69696899999.99999998"
+    assert ys - xs == "69696899999.99999998"
+    assert xs / ys == "0"
+    assert ys / xs == "3484845000000000000"
+
+    assert ws * xs == "-0.00000000000002246"
+    assert xs * ws == "-0.00000000000002246"
+
+    assert ws + xs == "-0.000001103"
+    assert xs + ws == "-0.000001103"
+
+    assert xs - ws == "0.000001143"
+    assert ws - xs == "-0.000001143"
+
+    assert xs / ws == "-0.017809439002671415"
+    assert ws / xs == "-56.15"
+
+    assert zs * ws == "0"
+    assert zs * xs == "0"
+    assert zs * ys == "0"
+    assert ws * zs == "0"
+    assert xs * zs == "0"
+    assert ys * zs == "0"
+
+    assert zs + ws == "-0.000001123"
+    assert zs + xs == "0.00000002"
+    assert zs + ys == "69696900000"
+    assert ws + zs == "-0.000001123"
+    assert xs + zs == "0.00000002"
+    assert ys + zs == "69696900000"
+
+    assert abs(FtPrecise("-500.1")) == "500.1"
+    assert abs(FtPrecise("213")) == "213"
+
+    assert abs(FtPrecise("-500.1")) == "500.1"
+    assert -FtPrecise("213") == "-213"
+
+    assert FtPrecise("10.1") % FtPrecise("0.5") == "0.1"
+    assert FtPrecise("5550") % FtPrecise("120") == "30"
+
+    assert FtPrecise("-0.0") == FtPrecise("0")
+    assert FtPrecise("5.534000") == FtPrecise("5.5340")
+
+    assert min(FtPrecise("-3.1415"), FtPrecise("-2")) == "-3.1415"
+
+    assert max(FtPrecise("3.1415"), FtPrecise("-2")) == "3.1415"
+
+    assert FtPrecise("2") > FtPrecise("1.2345")
+    assert not FtPrecise("-3.1415") > FtPrecise("-2")
+    assert not FtPrecise("3.1415") > FtPrecise("3.1415")
+    assert FtPrecise.string_gt("3.14150000000000000000001", "3.1415")
+
+    assert FtPrecise("3.1415") >= FtPrecise("3.1415")
+    assert FtPrecise("3.14150000000000000000001") >= FtPrecise("3.1415")
+
+    assert not FtPrecise("3.1415") < FtPrecise("3.1415")
+
+    assert FtPrecise("3.1415") <= FtPrecise("3.1415")
+    assert FtPrecise("3.1415") <= FtPrecise("3.14150000000000000000001")
+
+    assert FtPrecise(213) == "213"
+    assert FtPrecise(-213) == "-213"
+    assert str(FtPrecise(-213)) == "-213"
+    assert FtPrecise(213.2) == "213.2"
+    assert float(FtPrecise(213.2)) == 213.2
+    assert float(FtPrecise(-213.2)) == -213.2
diff --git a/tests/util/test_datetime_helpers.py b/tests/util/test_datetime_helpers.py
new file mode 100644
index 000000000..258c5a0b9
--- /dev/null
+++ b/tests/util/test_datetime_helpers.py
@@ -0,0 +1,128 @@
+from datetime import datetime, timedelta, timezone
+
+import pytest
+import time_machine
+
+from freqtrade.util import (
+    dt_floor_day,
+    dt_from_ts,
+    dt_now,
+    dt_ts,
+    dt_ts_def,
+    dt_ts_none,
+    dt_utc,
+    format_date,
+    format_ms_time,
+    format_ms_time_det,
+    shorten_date,
+)
+from freqtrade.util.datetime_helpers import dt_humanize_delta
+
+
+def test_dt_now():
+    with time_machine.travel("2021-09-01 05:01:00 +00:00", tick=False) as t:
+        now = datetime.now(timezone.utc)
+        assert dt_now() == now
+        assert dt_ts() == int(now.timestamp() * 1000)
+        assert dt_ts(now) == int(now.timestamp() * 1000)
+
+        t.shift(timedelta(hours=5))
+        assert dt_now() >= now
+        assert dt_now() == datetime.now(timezone.utc)
+        assert dt_ts() == int(dt_now().timestamp() * 1000)
+        # Test with different time than now
+        assert dt_ts(now) == int(now.timestamp() * 1000)
+
+
+def test_dt_ts_def():
+    assert dt_ts_def(None) == 0
+    assert dt_ts_def(None, 123) == 123
+    assert dt_ts_def(datetime(2023, 5, 5, tzinfo=timezone.utc)) == 1683244800000
+    assert dt_ts_def(datetime(2023, 5, 5, tzinfo=timezone.utc), 123) == 1683244800000
+
+
+def test_dt_ts_none():
+    assert dt_ts_none(None) is None
+    assert dt_ts_none(None) is None
+    assert dt_ts_none(datetime(2023, 5, 5, tzinfo=timezone.utc)) == 1683244800000
+    assert dt_ts_none(datetime(2023, 5, 5, tzinfo=timezone.utc)) == 1683244800000
+
+
+def test_dt_utc():
+    assert dt_utc(2023, 5, 5) == datetime(2023, 5, 5, tzinfo=timezone.utc)
+    assert dt_utc(2023, 5, 5, 0, 0, 0, 555500) == datetime(
+        2023, 5, 5, 0, 0, 0, 555500, tzinfo=timezone.utc
+    )
+
+
+@pytest.mark.parametrize("as_ms", [True, False])
+def test_dt_from_ts(as_ms):
+    multi = 1000 if as_ms else 1
+    assert dt_from_ts(1683244800.0 * multi) == datetime(2023, 5, 5, tzinfo=timezone.utc)
+    assert dt_from_ts(1683244800.5555 * multi) == datetime(
+        2023, 5, 5, 0, 0, 0, 555500, tzinfo=timezone.utc
+    )
+    # As int
+    assert dt_from_ts(1683244800 * multi) == datetime(2023, 5, 5, tzinfo=timezone.utc)
+    # As milliseconds
+    assert dt_from_ts(1683244800 * multi) == datetime(2023, 5, 5, tzinfo=timezone.utc)
+    assert dt_from_ts(1683242400 * multi) == datetime(2023, 5, 4, 23, 20, tzinfo=timezone.utc)
+
+
+def test_dt_floor_day():
+    now = datetime(2023, 9, 1, 5, 2, 3, 455555, tzinfo=timezone.utc)
+
+    assert dt_floor_day(now) == datetime(2023, 9, 1, tzinfo=timezone.utc)
+
+
+def test_shorten_date() -> None:
+    str_data = "1 day, 2 hours, 3 minutes, 4 seconds ago"
+    str_shorten_data = "1 d, 2 h, 3 min, 4 sec ago"
+    assert shorten_date(str_data) == str_shorten_data
+
+
+def test_dt_humanize() -> None:
+    assert dt_humanize_delta(dt_now()) == "now"
+    assert dt_humanize_delta(dt_now() - timedelta(minutes=50)) == "50 minutes ago"
+    assert dt_humanize_delta(dt_now() - timedelta(hours=16)) == "16 hours ago"
+    assert dt_humanize_delta(dt_now() - timedelta(hours=16, minutes=30)) == "16 hours ago"
+    assert dt_humanize_delta(dt_now() - timedelta(days=16, hours=10, minutes=25)) == "16 days ago"
+    assert dt_humanize_delta(dt_now() - timedelta(minutes=50)) == "50 minutes ago"
+
+
+def test_format_ms_time() -> None:
+    # Date 2018-04-10 18:02:01
+    date_in_epoch_ms = 1523383321132
+    date = format_ms_time(date_in_epoch_ms)
+    assert isinstance(date, str)
+    res = datetime(2018, 4, 10, 18, 2, 1, tzinfo=timezone.utc)
+    assert date == res.strftime("%Y-%m-%dT%H:%M:%S")
+    assert date == "2018-04-10T18:02:01"
+    res = datetime(2017, 12, 13, 8, 2, 1, tzinfo=timezone.utc)
+    # Date 2017-12-13 08:02:01
+    date_in_epoch_ms = 1513152121000
+    assert format_ms_time(date_in_epoch_ms) == res.strftime("%Y-%m-%dT%H:%M:%S")
+
+
+def test_format_date() -> None:
+    date = datetime(2023, 9, 1, 5, 2, 3, 455555, tzinfo=timezone.utc)
+    assert format_date(date) == "2023-09-01 05:02:03"
+    assert format_date(None) == ""
+
+    date = datetime(2021, 9, 30, 22, 59, 3, 455555, tzinfo=timezone.utc)
+    assert format_date(date) == "2021-09-30 22:59:03"
+    assert format_date(None) == ""
+
+
+def test_format_ms_time_detailed() -> None:
+    # Date 2018-04-10 18:02:01
+    date_in_epoch_ms = 1523383321132
+    date = format_ms_time_det(date_in_epoch_ms)
+    assert isinstance(date, str)
+    res = datetime(2018, 4, 10, 18, 2, 1, 132145, tzinfo=timezone.utc)
+    assert date == res.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3]
+    assert date == "2018-04-10T18:02:01.132"
+    res = datetime(2017, 12, 13, 8, 2, 1, 512321, tzinfo=timezone.utc)
+    # Date 2017-12-13 08:02:01
+    date_in_epoch_ms = 1513152121512
+    assert format_ms_time_det(date_in_epoch_ms) == res.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3]
diff --git a/tests/util/test_formatters.py b/tests/util/test_formatters.py
new file mode 100644
index 000000000..ba77eb9a6
--- /dev/null
+++ b/tests/util/test_formatters.py
@@ -0,0 +1,47 @@
+from freqtrade.util import decimals_per_coin, fmt_coin, round_value
+from freqtrade.util.formatters import fmt_coin2
+
+
+def test_decimals_per_coin():
+    assert decimals_per_coin("USDT") == 3
+    assert decimals_per_coin("EUR") == 3
+    assert decimals_per_coin("BTC") == 8
+    assert decimals_per_coin("ETH") == 5
+
+
+def test_fmt_coin():
+    assert fmt_coin(222.222222, "USDT") == "222.222 USDT"
+    assert fmt_coin(222.2, "USDT", keep_trailing_zeros=True) == "222.200 USDT"
+    assert fmt_coin(222.2, "USDT") == "222.2 USDT"
+    assert fmt_coin(222.12745, "EUR") == "222.127 EUR"
+    assert fmt_coin(0.1274512123, "BTC") == "0.12745121 BTC"
+    assert fmt_coin(0.1274512123, "ETH") == "0.12745 ETH"
+
+    assert fmt_coin(222.222222, "USDT", False) == "222.222"
+    assert fmt_coin(222.2, "USDT", False) == "222.2"
+    assert fmt_coin(222.00, "USDT", False) == "222"
+    assert fmt_coin(222.12745, "EUR", False) == "222.127"
+    assert fmt_coin(0.1274512123, "BTC", False) == "0.12745121"
+    assert fmt_coin(0.1274512123, "ETH", False) == "0.12745"
+    assert fmt_coin(222.2, "USDT", False, True) == "222.200"
+
+
+def test_fmt_coin2():
+    assert fmt_coin2(222.222222, "USDT") == "222.222222 USDT"
+    assert fmt_coin2(222.2, "XRP", 3, keep_trailing_zeros=True) == "222.200 XRP"
+    assert fmt_coin2(222.2, "USDT") == "222.2 USDT"
+    assert fmt_coin2(222.12745, "EUR") == "222.12745 EUR"
+    assert fmt_coin2(0.1274512123, "BTC") == "0.12745121 BTC"
+    assert fmt_coin2(0.1274512123, "ETH") == "0.12745121 ETH"
+    assert fmt_coin2(0.00001245, "PEPE") == "0.00001245 PEPE"
+
+
+def test_round_value():
+    assert round_value(222.222222, 3) == "222.222"
+    assert round_value(222.2, 3) == "222.2"
+    assert round_value(222.00, 3) == "222"
+    assert round_value(222.12745, 3) == "222.127"
+    assert round_value(0.1274512123, 8) == "0.12745121"
+    assert round_value(0.1274512123, 5) == "0.12745"
+    assert round_value(222.2, 3, True) == "222.200"
+    assert round_value(222.2, 0, True) == "222"
diff --git a/tests/util/test_funding_rate_migration.py b/tests/util/test_funding_rate_migration.py
new file mode 100644
index 000000000..094ee1562
--- /dev/null
+++ b/tests/util/test_funding_rate_migration.py
@@ -0,0 +1,28 @@
+from shutil import copytree
+
+from freqtrade.util.migrations import migrate_funding_fee_timeframe
+
+
+def test_migrate_funding_rate_timeframe(default_conf_usdt, tmp_path, testdatadir):
+    copytree(testdatadir / "futures", tmp_path / "futures")
+    file_4h = tmp_path / "futures" / "XRP_USDT_USDT-4h-funding_rate.feather"
+    file_8h = tmp_path / "futures" / "XRP_USDT_USDT-8h-funding_rate.feather"
+    file_1h = tmp_path / "futures" / "XRP_USDT_USDT-1h-futures.feather"
+    file_8h.rename(file_4h)
+    assert file_1h.exists()
+    assert file_4h.exists()
+    assert not file_8h.exists()
+
+    default_conf_usdt["datadir"] = tmp_path
+
+    # Inactive on spot trading ...
+    migrate_funding_fee_timeframe(default_conf_usdt, None)
+
+    default_conf_usdt["trading_mode"] = "futures"
+
+    migrate_funding_fee_timeframe(default_conf_usdt, None)
+
+    assert not file_4h.exists()
+    assert file_8h.exists()
+    # futures files is untouched.
+    assert file_1h.exists()
diff --git a/tests/util/test_measure_time.py b/tests/util/test_measure_time.py
new file mode 100644
index 000000000..2d44a3254
--- /dev/null
+++ b/tests/util/test_measure_time.py
@@ -0,0 +1,32 @@
+from unittest.mock import MagicMock
+
+import time_machine
+
+from freqtrade.util import MeasureTime
+
+
+def test_measure_time():
+    callback = MagicMock()
+    with time_machine.travel("2021-09-01 05:00:00 +00:00", tick=False) as t:
+        measure = MeasureTime(callback, 5, ttl=60)
+        with measure:
+            pass
+
+        assert callback.call_count == 0
+
+        with measure:
+            t.shift(10)
+
+        assert callback.call_count == 1
+        callback.reset_mock()
+        with measure:
+            t.shift(10)
+        assert callback.call_count == 0
+
+        callback.reset_mock()
+        # Shift past the ttl
+        t.shift(45)
+
+        with measure:
+            t.shift(10)
+        assert callback.call_count == 1
diff --git a/tests/util/test_periodiccache.py b/tests/util/test_periodiccache.py
new file mode 100644
index 000000000..684e7755d
--- /dev/null
+++ b/tests/util/test_periodiccache.py
@@ -0,0 +1,32 @@
+import time_machine
+
+from freqtrade.util import PeriodicCache
+
+
+def test_ttl_cache():
+    with time_machine.travel("2021-09-01 05:00:00 +00:00", tick=False) as t:
+        cache = PeriodicCache(5, ttl=60)
+        cache1h = PeriodicCache(5, ttl=3600)
+
+        assert cache.timer() == 1630472400.0
+        cache["a"] = 1235
+        cache1h["a"] = 555123
+        assert "a" in cache
+        assert "a" in cache1h
+
+        t.move_to("2021-09-01 05:00:59 +00:00")
+        assert "a" in cache
+        assert "a" in cache1h
+
+        # Cache expired
+        t.move_to("2021-09-01 05:01:00 +00:00")
+        assert "a" not in cache
+        assert "a" in cache1h
+
+        t.move_to("2021-09-01 05:59:59 +00:00")
+        assert "a" not in cache
+        assert "a" in cache1h
+
+        t.move_to("2021-09-01 06:00:00 +00:00")
+        assert "a" not in cache
+        assert "a" not in cache1h
diff --git a/tests/util/test_rendering_utils.py b/tests/util/test_rendering_utils.py
new file mode 100644
index 000000000..f42c77dea
--- /dev/null
+++ b/tests/util/test_rendering_utils.py
@@ -0,0 +1,20 @@
+import pytest
+
+from freqtrade.util import render_template, render_template_with_fallback
+
+
+def test_render_template_fallback():
+    from jinja2.exceptions import TemplateNotFound
+
+    with pytest.raises(TemplateNotFound):
+        val = render_template(
+            templatefile="subtemplates/indicators_does-not-exist.j2",
+            arguments={},
+        )
+
+    val = render_template_with_fallback(
+        templatefile="strategy_subtemplates/indicators_does-not-exist.j2",
+        templatefallbackfile="strategy_subtemplates/indicators_minimal.j2",
+    )
+    assert isinstance(val, str)
+    assert "if self.dp" in val
diff --git a/user_data/backtest_results/.gitkeep b/user_data/backtest_results/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/user_data/freqaimodels/.gitkeep b/user_data/freqaimodels/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/user_data/hyperopt_conf.py b/user_data/hyperopt_conf.py
deleted file mode 100644
index 8e044e549..000000000
--- a/user_data/hyperopt_conf.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""
-File that contains the configuration for Hyperopt
-"""
-
-
-def hyperopt_optimize_conf() -> dict:
-    """
-    This function is used to define which parameters Hyperopt must used.
-    The "pair_whitelist" is only used is your are using Hyperopt with MongoDB,
-    without MongoDB, Hyperopt will use the pair your have set in your config file.
-    :return:
-    """
-    return {
-        'max_open_trades': 3,
-        'stake_currency': 'BTC',
-        'stake_amount': 0.01,
-        "minimal_roi": {
-            '40': 0.0,
-            '30': 0.01,
-            '20': 0.02,
-            '0': 0.04,
-        },
-        'stoploss': -0.10,
-        "bid_strategy": {
-            "ask_last_balance": 0.0
-        },
-        "exchange": {
-            "pair_whitelist": [
-                "BTC_ETH",
-                "BTC_LTC",
-                "BTC_ETC",
-                "BTC_DASH",
-                "BTC_ZEC",
-                "BTC_XLM",
-                "BTC_NXT",
-                "BTC_POWR",
-                "BTC_ADA",
-                "BTC_XMR"
-            ]
-        }
-    }
diff --git a/user_data/hyperopts/.gitkeep b/user_data/hyperopts/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/user_data/logs/.gitkeep b/user_data/logs/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/user_data/notebooks/.gitkeep b/user_data/notebooks/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/user_data/strategies/test_strategy.py b/user_data/strategies/test_strategy.py
deleted file mode 100644
index a164812c4..000000000
--- a/user_data/strategies/test_strategy.py
+++ /dev/null
@@ -1,246 +0,0 @@
-
-# --- Do not remove these libs ---
-from freqtrade.strategy.interface import IStrategy
-from pandas import DataFrame
-# --------------------------------
-
-# Add your lib to import here
-import talib.abstract as ta
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-import numpy # noqa
-
-
-# Update this variable if you change the class name
-class_name = 'TestStrategy'
-
-
-# This class is a sample. Feel free to customize it.
-class TestStrategy(IStrategy):
-    """
-    This is a test strategy to inspire you.
-    More information in https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md
-
-    You can:
-    - Rename the class name (Do not forget to update class_name)
-    - Add any methods you want to build your strategy
-    - Add any lib you need to build your strategy
-
-    You must keep:
-    - the lib in the section "Do not remove these libs"
-    - the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
-    populate_sell_trend, hyperopt_space, buy_strategy_generator
-    """
-
-    # Minimal ROI designed for the strategy.
-    # This attribute will be overridden if the config file contains "minimal_roi"
-    minimal_roi = {
-        "40": 0.0,
-        "30": 0.01,
-        "20": 0.02,
-        "0": 0.04
-    }
-
-    # Optimal stoploss designed for the strategy
-    # This attribute will be overridden if the config file contains "stoploss"
-    stoploss = -0.10
-
-    # Optimal ticker interval for the strategy
-    ticker_interval = 5
-
-    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Adds several different TA indicators to the given DataFrame
-
-        Performance Note: For the best performance be frugal on the number of indicators
-        you are using. Let uncomment only the indicator you are using in your strategies
-        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
-        """
-
-        # Momentum Indicator
-        # ------------------------------------
-
-        # ADX
-        dataframe['adx'] = ta.ADX(dataframe)
-
-        """
-        # Awesome oscillator
-        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
-
-        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
-        dataframe['cci'] = ta.CCI(dataframe)
-
-        # MACD
-        macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['macdsignal'] = macd['macdsignal']
-        dataframe['macdhist'] = macd['macdhist']
-
-        # MFI
-        dataframe['mfi'] = ta.MFI(dataframe)
-
-        # Minus Directional Indicator / Movement
-        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
-        # Plus Directional Indicator / Movement
-        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
-        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-
-        # ROC
-        dataframe['roc'] = ta.ROC(dataframe)
-
-        # RSI
-        dataframe['rsi'] = ta.RSI(dataframe)
-
-        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
-        rsi = 0.1 * (dataframe['rsi'] - 50)
-        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
-
-        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
-        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
-
-        # Stoch
-        stoch = ta.STOCH(dataframe)
-        dataframe['slowd'] = stoch['slowd']
-        dataframe['slowk'] = stoch['slowk']
-
-        # Stoch fast
-        stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        dataframe['fastk'] = stoch_fast['fastk']
-
-        # Stoch RSI
-        stoch_rsi = ta.STOCHRSI(dataframe)
-        dataframe['fastd_rsi'] = stoch_rsi['fastd']
-        dataframe['fastk_rsi'] = stoch_rsi['fastk']
-        """
-
-        # Overlap Studies
-        # ------------------------------------
-
-        # Bollinger bands
-        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-        dataframe['bb_lowerband'] = bollinger['lower']
-        dataframe['bb_middleband'] = bollinger['mid']
-        dataframe['bb_upperband'] = bollinger['upper']
-
-        """
-        # EMA - Exponential Moving Average
-        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
-        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
-        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
-        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
-        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
-
-        # SAR Parabol
-        dataframe['sar'] = ta.SAR(dataframe)
-
-        # SMA - Simple Moving Average
-        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-        """
-
-        # TEMA - Triple Exponential Moving Average
-        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
-
-        # Cycle Indicator
-        # ------------------------------------
-        # Hilbert Transform Indicator - SineWave
-        hilbert = ta.HT_SINE(dataframe)
-        dataframe['htsine'] = hilbert['sine']
-        dataframe['htleadsine'] = hilbert['leadsine']
-
-        # Pattern Recognition - Bullish candlestick patterns
-        # ------------------------------------
-        """
-        # Hammer: values [0, 100]
-        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
-        # Inverted Hammer: values [0, 100]
-        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
-        # Dragonfly Doji: values [0, 100]
-        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
-        # Piercing Line: values [0, 100]
-        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
-        # Morningstar: values [0, 100]
-        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
-        # Three White Soldiers: values [0, 100]
-        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
-        """
-
-        # Pattern Recognition - Bearish candlestick patterns
-        # ------------------------------------
-        """
-        # Hanging Man: values [0, 100]
-        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
-        # Shooting Star: values [0, 100]
-        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
-        # Gravestone Doji: values [0, 100]
-        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
-        # Dark Cloud Cover: values [0, 100]
-        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
-        # Evening Doji Star: values [0, 100]
-        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
-        # Evening Star: values [0, 100]
-        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
-        """
-
-        # Pattern Recognition - Bullish/Bearish candlestick patterns
-        # ------------------------------------
-        """
-        # Three Line Strike: values [0, -100, 100]
-        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
-        # Spinning Top: values [0, -100, 100]
-        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
-        # Engulfing: values [0, -100, 100]
-        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
-        # Harami: values [0, -100, 100]
-        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
-        # Three Outside Up/Down: values [0, -100, 100]
-        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
-        # Three Inside Up/Down: values [0, -100, 100]
-        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
-        """
-
-        # Chart type
-        # ------------------------------------
-        """
-        # Heikinashi stategy
-        heikinashi = qtpylib.heikinashi(dataframe)
-        dataframe['ha_open'] = heikinashi['open']
-        dataframe['ha_close'] = heikinashi['close']
-        dataframe['ha_high'] = heikinashi['high']
-        dataframe['ha_low'] = heikinashi['low']
-        """
-
-        return dataframe
-
-    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Based on TA indicators, populates the buy signal for the given dataframe
-        :param dataframe: DataFrame
-        :return: DataFrame with buy column
-        """
-        dataframe.loc[
-            (
-                (dataframe['adx'] > 30) &
-                (dataframe['tema'] <= dataframe['bb_middleband']) &
-                (dataframe['tema'] > dataframe['tema'].shift(1))
-            ),
-            'buy'] = 1
-
-        return dataframe
-
-    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Based on TA indicators, populates the sell signal for the given dataframe
-        :param dataframe: DataFrame
-        :return: DataFrame with buy column
-        """
-        dataframe.loc[
-            (
-                (dataframe['adx'] > 70) &
-                (dataframe['tema'] > dataframe['bb_middleband']) &
-                (dataframe['tema'] < dataframe['tema'].shift(1))
-            ),
-            'sell'] = 1
-        return dataframe