Merge remote-tracking branch 'upstream/develop' into feature/fetch-public-trades

.devcontainer/Dockerfile

@@ -1,22 +0,0 @@
-FROM freqtradeorg/freqtrade:develop_freqairl
-
-USER root
-# Install dependencies
-COPY requirements-dev.txt /freqtrade/
-
-RUN apt-get update \
-    && apt-get -y install --no-install-recommends apt-utils dialog \
-    && apt-get -y install --no-install-recommends git sudo vim build-essential \
-    && apt-get clean \
-    && mkdir -p /home/ftuser/.vscode-server /home/ftuser/.vscode-server-insiders /home/ftuser/commandhistory \
-    && echo "export PROMPT_COMMAND='history -a'" >> /home/ftuser/.bashrc \
-    && echo "export HISTFILE=~/commandhistory/.bash_history" >> /home/ftuser/.bashrc \
-    && chown ftuser:ftuser -R /home/ftuser/.local/ \
-    && chown ftuser: -R /home/ftuser/
-
-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 []

.devcontainer/devcontainer.json

@@ -1,42 +1,44 @@
 {
     "name": "freqtrade Develop",
-    "build": {
+    "image": "ghcr.io/freqtrade/freqtrade-devcontainer:latest",
-        "dockerfile": "Dockerfile",
-        "context": ".."
-    },
     // Use 'forwardPorts' to make a list of ports inside the container available locally.
     "forwardPorts": [
         8080
     ],
-    "mounts": [
-        "source=freqtrade-bashhistory,target=/home/ftuser/commandhistory,type=volume"
-    ],
     "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": {
-        "settings": {
+        "vscode": {
-            "terminal.integrated.shell.linux": "/bin/bash",
+            "settings": {
-            "editor.insertSpaces": true,
+                "terminal.integrated.shell.linux": "/bin/bash",
-            "files.trimTrailingWhitespace": true,
+                "editor.insertSpaces": true,
-            "[markdown]": {
+                "files.trimTrailingWhitespace": true,
-                "files.trimTrailingWhitespace": false,
+                "[markdown]": {
+                    "files.trimTrailingWhitespace": false
+                },
+                "python.pythonPath": "/usr/local/bin/python",
+                "[python]": {
+                    "editor.codeActionsOnSave": {
+                        "source.organizeImports": "explicit"
+                    },
+                    "editor.formatOnSave": true,
+                    "editor.defaultFormatter": "charliermarsh.ruff"
+                }
             },
-            "python.pythonPath": "/usr/local/bin/python",
+            // Add the IDs of extensions you want installed when the container is created.
-        },
+            "extensions": [
-
+                "ms-python.python",
-        // Add the IDs of extensions you want installed when the container is created.
+                "ms-python.vscode-pylance",
-        "extensions": [
+                "charliermarsh.ruff",
-            "ms-python.python",
+                "davidanson.vscode-markdownlint",
-            "ms-python.vscode-pylance",
+                "ms-azuretools.vscode-docker",
-            "davidanson.vscode-markdownlint",
+                "vscode-icons-team.vscode-icons",
-            "ms-azuretools.vscode-docker",
+                "github.vscode-github-actions",
-            "vscode-icons-team.vscode-icons",
+            ],
-        ],
+        }
     }
 }

.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 []

.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": {}
+    }
+}

.github/dependabot.yml

@@ -21,6 +21,9 @@ updates:
     pytest:
       patterns:
         - "pytest*"
+    mkdocs:
+      patterns:
+        - "mkdocs*"
 
 - package-ecosystem: "github-actions"
   directory: "/"

.github/workflows/ci.yml

@@ -111,7 +111,11 @@ jobs:
 
     - name: Run Ruff
       run: |
-        ruff check --output-format=github .
+        ruff check --output-format=github
+
+    - name: Run Ruff format check
+      run: |
+        ruff format --check
 
     - name: Mypy
       run: |
@@ -129,7 +133,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ "macos-latest", "macos-13", "macos-14" ]
+        os: [ "macos-12", "macos-13", "macos-14" ]
         python-version: ["3.9", "3.10", "3.11", "3.12"]
         exclude:
           - os: "macos-14"
@@ -230,7 +234,11 @@ jobs:
 
     - name: Run Ruff
       run: |
-        ruff check --output-format=github .
+        ruff check --output-format=github
+
+    - name: Run Ruff format check
+      run: |
+        ruff format --check
 
     - name: Mypy
       run: |
@@ -300,7 +308,11 @@ jobs:
 
     - name: Run Ruff
       run: |
-        ruff check --output-format=github .
+        ruff check --output-format=github
+
+    - name: Run Ruff format check
+      run: |
+        ruff format --check
 
     - name: Mypy
       run: |
@@ -414,7 +426,7 @@ jobs:
         pytest --random-order --longrun --durations 20 -n auto
 
 
-  # Notify only once - when CI completes (and after deploy) in case it's successfull
+  # Notify only once - when CI completes (and after deploy) in case it's successful
   notify-complete:
     needs: [
       build-linux,

.github/workflows/devcontainer-build.yml

@@ -0,0 +1,45 @@
+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
+
+permissions:
+  packages: write
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    steps:
+    -
+      name: Checkout
+      id: checkout
+      uses: actions/checkout@v4
+    -
+        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

.pre-commit-config.yaml

@@ -9,17 +9,17 @@ repos:
         # stages: [push]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v1.9.0"
+    rev: "v1.10.0"
     hooks:
       - id: mypy
         exclude: build_helpers
         additional_dependencies:
           - types-cachetools==5.3.0.7
           - types-filelock==3.2.7
-          - types-requests==2.31.0.20240311
+          - types-requests==2.31.0.20240406
           - types-tabulate==0.9.0.20240106
           - types-python-dateutil==2.9.0.20240316
-          - SQLAlchemy==2.0.29
+          - SQLAlchemy==2.0.30
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort
@@ -31,12 +31,12 @@ repos:
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.3.5'
+    rev: 'v0.4.4'
     hooks:
       - id: ruff
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: end-of-file-fixer
         exclude: |
@@ -54,3 +54,10 @@ repos:
           (?x)^(
                 .*\.md
             )$
+
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.6
+    hooks:
+    - id: codespell
+      additional_dependencies:
+        - tomli

.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",
+    ]
+}

CONTRIBUTING.md

@@ -72,12 +72,12 @@ you can manually run pre-commit with `pre-commit run -a`.
 mypy freqtrade
 ```
 
-### 4. Ensure all imports are correct
+### 4. Ensure formatting is correct
 
-#### Run isort
+#### Run ruff
 
 ``` bash
-isort .
+ruff format .
 ```
 
 ## (Core)-Committer Guide

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.12.2-slim-bookworm as base
+FROM python:3.12.3-slim-bookworm as base
 
 # Setup env
 ENV LANG C.UTF-8

build_helpers/binance_update_lev_tiers.py

@@ -6,21 +6,18 @@ from pathlib import Path
 import ccxt
 
 
-key = os.environ.get('FREQTRADE__EXCHANGE__KEY')
+key = os.environ.get("FREQTRADE__EXCHANGE__KEY")
-secret = os.environ.get('FREQTRADE__EXCHANGE__SECRET')
+secret = os.environ.get("FREQTRADE__EXCHANGE__SECRET")
 
-proxy = os.environ.get('CI_WEB_PROXY')
+proxy = os.environ.get("CI_WEB_PROXY")
 
-exchange = ccxt.binance({
+exchange = ccxt.binance(
-    'apiKey': key,
+    {"apiKey": key, "secret": secret, "httpsProxy": proxy, "options": {"defaultType": "swap"}}
-    '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')
+file = Path("freqtrade/exchange/binance_leverage_tiers.json")
-json.dump(dict(sorted(lev_tiers.items())), file.open('w'), indent=2)
+json.dump(dict(sorted(lev_tiers.items())), file.open("w"), indent=2)

build_helpers/freqtrade_client_version_align.py

@@ -1,18 +1,15 @@
 #!/usr/bin/env python3
-from freqtrade_client import __version__ as client_version
-
 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: \n"
+        print(f"Versions do not match: \nft: {ft_version} \nclient: {client_version}")
-              f"ft: {ft_version} \n"
-              f"client: {client_version}")
         exit(1)
     print(f"Versions match: ft: {ft_version}, client: {client_version}")
     exit(0)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

build_helpers/pre_commit_update.py

@@ -1,4 +1,4 @@
-# File used in CI to ensure pre-commit dependencies are kept uptodate.
+# File used in CI to ensure pre-commit dependencies are kept up-to-date.
 
 import sys
 from pathlib import Path
@@ -6,28 +6,30 @@ from pathlib import Path
 import yaml
 
 
-pre_commit_file = Path('.pre-commit-config.yaml')
+pre_commit_file = Path(".pre-commit-config.yaml")
-require_dev = Path('requirements-dev.txt')
+require_dev = Path("requirements-dev.txt")
-require = Path('requirements.txt')
+require = Path("requirements.txt")
 
-with require_dev.open('r') as rfile:
+with require_dev.open("r") as rfile:
     requirements = rfile.readlines()
 
-with require.open('r') as rfile:
+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(
+type_reqs = [
-    'types-') or r.startswith('SQLAlchemy')]
+    r.strip("\n") for r in requirements if r.startswith("types-") or r.startswith("SQLAlchemy")
+]
 
-with pre_commit_file.open('r') as file:
+with pre_commit_file.open("r") as file:
-    f = yaml.load(file, Loader=yaml.FullLoader)
+    f = yaml.load(file, Loader=yaml.SafeLoader)
 
 
-mypy_repo = [repo for repo in f['repos'] if repo['repo']
+mypy_repo = [
-             == 'https://github.com/pre-commit/mirrors-mypy']
+    repo for repo in f["repos"] if repo["repo"] == "https://github.com/pre-commit/mirrors-mypy"
+]
 
-hooks = mypy_repo[0]['hooks'][0]['additional_dependencies']
+hooks = mypy_repo[0]["hooks"][0]["additional_dependencies"]
 
 errors = []
 for hook in hooks:

docs/advanced-backtesting.md

@@ -36,7 +36,7 @@ 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 fo each group or trade,
+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

docs/advanced-orderflow.md

@@ -22,7 +22,7 @@ This guide walks you through utilizing public trade data for advanced orderflow
 ```json
 "orderflow": {
     "scale": 0.5, 
-    "stacked_imbalance_range": 3, //  needs at least this amount of imblance next to each other
+    "stacked_imbalance_range": 3, //  needs at least this amount of imbalance next to each other
     "imbalance_volume": 1, //  filters out below
     "imbalance_ratio": 300 //  filters out ratio lower than
   },

docs/backtesting.md

@@ -522,8 +522,8 @@ To save time, by default backtest will reuse a cached result from within the las
 
 ### Further backtest-result analysis
 
-To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
+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](data-analysis.md#backtesting) backtesting section.
+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.
 
 ## Assumptions made by backtesting
 
@@ -531,12 +531,13 @@ Since backtesting lacks some detailed information about what happens within a ca
 
 - Exchange [trading limits](#trading-limits-in-backtesting) are respected
 - Entries happen at open-price
-- All orders are filled at the requested price (no slippage, no unfilled orders)
+- 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 don't free their trade slot for a new trade until the next candle
 - 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 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
+  - 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
@@ -587,7 +588,7 @@ These precision values are based on current exchange limits (as described in the
 
 ## Improved backtest accuracy
 
-One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).
+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.

docs/configuration.md

@@ -197,7 +197,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `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. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
+| `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
@@ -252,7 +252,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `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](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
+| `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
@@ -370,7 +370,7 @@ This setting works in combination with `max_open_trades`. The maximum capital en
 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](#available-balance).
+    This setting respects the [available balance configuration](#tradable-balance).
 
 #### Dynamic stake amount
 
@@ -547,7 +547,7 @@ 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 at least time in an unfilled state.
+This means the order must be placed on orderbook for at least time in an unfilled state.
 
 #### time_in_force config
 

docs/developer.md

@@ -83,7 +83,7 @@ Details will obviously vary between setups - but this should work to get you sta
 ``` json
 {
     "name": "freqtrade trade",
-    "type": "python",
+    "type": "debugpy",
     "request": "launch",
     "module": "freqtrade",
     "console": "integratedTerminal",
@@ -261,7 +261,7 @@ For that reason, they must implement the following methods:
 
 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 a pair (or all pairs) should be locked.
+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.
@@ -305,7 +305,7 @@ The `IProtection` parent class provides a helper method for this in `calculate_l
 
 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 `test_ccxt_compat.py` and run these tests with `pytest --longrun tests/exchange/test_ccxt_compat.py`.
+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).

docs/edge.md

@@ -137,7 +137,7 @@ $$ R = \frac{\text{average_profit}}{\text{average_loss}} = \frac{\mu_{win}}{\mu_
 
 ### Expectancy
 
-By combining the Win Rate $W$ and 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:
+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:
 
 $$E = R * W - L$$
 

docs/exchanges.md

@@ -299,7 +299,7 @@ $ pip3 install web3
 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.
+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.
 

docs/faq.md

@@ -2,7 +2,7 @@
 
 ## Supported Markets
 
-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 uptodate list of supported exchanges.
+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?
 
@@ -14,7 +14,7 @@ In spot markets, you can in some cases use leveraged spot tokens, which reflect
 
 ### 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 uptodate list of supported exchanges.
+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
 

docs/freq-ui.md

@@ -0,0 +1,85 @@
+# 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"

docs/freqai-feature-engineering.md

@@ -235,7 +235,7 @@ By default, FreqAI builds a dynamic pipeline based on user congfiguration settin
 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 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. 
+    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
@@ -391,3 +391,18 @@ Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters
 ![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.

docs/freqai-parameter-table.md

@@ -31,12 +31,12 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `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 is used in `feature_engineering_expand_all()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
+| `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](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
+| `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.
@@ -55,7 +55,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 |  |  **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> Defaut: `False`.
+| `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
 

docs/hyperopt.md

@@ -14,8 +14,7 @@ To learn how to get data for the pairs and exchange you're interested in, head o
 
 !!! 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 is still supported, but it is no longer the recommended way of setting up hyperopt. 
+    The legacy method was supported up to 2021.8 and has been removed in 2021.9.
-    The legacy documentation is available at [Legacy Hyperopt](advanced-hyperopt.md#legacy-hyperopt).
 
 ## Install hyperopt dependencies
 
@@ -765,7 +764,7 @@ Override the `roi_space()` method if you need components of the ROI tables to va
 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#pverriding-pre-defined-spaces) to change this to your needs.
+    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
 
@@ -807,7 +806,7 @@ If you have the `stoploss_space()` method in your custom hyperopt file, remove i
 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#pverriding-pre-defined-spaces) to change this to your needs.
+    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
 

docs/includes/cors.md

@@ -0,0 +1,43 @@
+## 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.

docs/installation.md

@@ -51,7 +51,7 @@ These requirements apply to both [Script Installation](#script-installation) and
 ### 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](#common) section below is necessary for all systems.
+OS Specific steps are listed first, the common section below is necessary for all systems.
 
 !!! Note
     Python3.9 or higher and the corresponding pip are assumed to be available.

docs/leverage.md

@@ -17,7 +17,7 @@ If you already have an existing strategy, please read the [strategy migration gu
 
 ## Shorting
 
-Shorting is not possible when trading with [`trading_mode`](#understand-tradingmode) 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)
+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`
 

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.6
-mkdocs==1.5.3
+mkdocs==1.6.0
-mkdocs-material==9.5.16
+mkdocs-material==9.5.22
 mdx_truly_sane_lists==1.3
-pymdown-extensions==10.7.1
+pymdown-extensions==10.8.1
-jinja2==3.1.3
+jinja2==3.1.4

docs/rest-api.md

@@ -1,16 +1,8 @@
-# REST API & FreqUI
+# REST API
 
 ## FreqUI
 
-Freqtrade provides a builtin webserver, which can serve [FreqUI](https://github.com/freqtrade/frequi), the freqtrade UI.
+FreqUI now has it's own dedicated [documentation section](frequi.md) - please refer to that section for all information regarding the FreqUI.
-
-By default, the UI is not included in the installation (except for docker images), and must be installed explicitly with `freqtrade install-ui`.
-This same command can also be used to update freqUI, should there be a new release.
-
-Once the bot is started in trade / dry-run mode (with `freqtrade trade`) - the UI will be available under the configured port below (usually `http://127.0.0.1:8080`).
-
-!!! Note "developers"
-    Developers should not use this method, but instead use the method described in the [freqUI repository](https://github.com/freqtrade/frequi) to get the source-code of freqUI.
 
 ## Configuration
 
@@ -89,7 +81,8 @@ Make sure that the following 2 lines are available in your docker-compose file:
 ```
 
 !!! Danger "Security warning"
-    By using `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.
+    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
 
@@ -454,7 +447,7 @@ To properly configure your reverse proxy (securely), please consult it's documen
 - **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 fo the above reverse proxies.
+    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
@@ -487,42 +480,4 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
 {"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk5NzQsIm5iZiI6MTU4OTExOTk3NCwianRpIjoiMDBjNTlhMWUtMjBmYS00ZTk0LTliZjAtNWQwNTg2MTdiZDIyIiwiZXhwIjoxNTg5MTIwODc0LCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.1seHlII3WprjjclY6DpRhen0rqdF4j6jbvxIhUFaSbs"}
 ```
 
-### CORS
+--8<-- "includes/cors.md"
-
-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"],
-    //...
-}
-```
-
-!!! 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.

docs/stoploss.md

@@ -158,7 +158,7 @@ You could also have a default stop loss when you are in the red with your buy (b
 For example, your default stop loss is -10%, but once you have more than 0% profit (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).
+    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.
 
@@ -240,7 +240,7 @@ When using leverage, the same principle is applied - with stoploss defining the
 
 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 - therfore stoploss will trigger in this case.
+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).
 

docs/strategy-advanced.md

@@ -209,7 +209,7 @@ def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_r
 
 ## Exit tag
 
-Similar to [Buy Tagging](#buy-tag), you can also specify a sell 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:
@@ -326,4 +326,4 @@ for val in self.buy_ema_short.range:
 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-existant.
+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.

docs/strategy-callbacks.md

@@ -167,7 +167,7 @@ During backtesting, `current_rate` (and `current_profit`) are provided against t
 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.
 
-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-exchange-freqtrade)).
+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.
@@ -332,7 +332,7 @@ class AwesomeStrategy(IStrategy):
                         **kwargs) -> Optional[float]:
 
         if current_profit < 0.04:
-            return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
+            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
@@ -450,7 +450,7 @@ Stoploss values returned from `custom_stoploss()` must specify a percentage rela
 
     ```
 
-    Full examples can be found in the [Custom stoploss](strategy-advanced.md#custom-stoploss) section of the Documentation.
+    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.
@@ -809,6 +809,7 @@ Returning a value more than the above (so remaining stake_amount would become ne
 
 ``` python
 from freqtrade.persistence import Trade
+from typing import Optional, Tuple, Union
 
 
 class DigDeeperStrategy(IStrategy):
@@ -948,7 +949,7 @@ If the cancellation of the original order fails, then the order will not be repl
 
 ```python
 from freqtrade.persistence import Trade
-from datetime import timedelta
+from datetime import timedelta, datetime
 
 class AwesomeStrategy(IStrategy):
 

docs/strategy-customization.md

@@ -405,7 +405,7 @@ The metadata-dict (available for `populate_entry_trend`, `populate_exit_trend`,
 Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
 
 The Metadata-dict should not be modified and does not persist information across multiple calls.
-Instead, have a look at the [Storing information](strategy-advanced.md#Storing-information) section.
+Instead, have a look at the [Storing information](strategy-advanced.md#storing-information-persistent) section.
 
 ## Strategy file loading
 
@@ -551,8 +551,8 @@ for more information.
     
         # 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'.
+        # formatting. Available in populate_indicators and other methods as 'rsi_upper_1h'.
-        @informative('1h', 'BTC/{stake}', '{column}')
+        @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
@@ -776,7 +776,7 @@ The orderbook structure is aligned with the order structure from [ccxt](https://
 Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using 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 uptodate values.
+    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)*
 

docs/telegram-usage.md

@@ -53,7 +53,7 @@ You can use bots in telegram groups by just adding them to the group. You can fi
 }
 ```
 
-For the Freqtrade configuration, you can then use the the full value (including `-` if it's there) as string:
+For the Freqtrade configuration, you can then use the full value (including `-` if it's there) as string:
 
 ```json
    "chat_id": "-1001332619709"

docs/trade-object.md

@@ -126,7 +126,7 @@ An `Order` object will always be tied to it's corresponding [`Trade`](#trade-obj
 ### Order - Available attributes
 
 an Order object is typically attached to a trade.
-Most properties here can be None as they are dependant on the exchange response.
+Most properties here can be None as they are dependent on the exchange response.
 
 |  Attribute | DataType | Description |
 |------------|-------------|-------------|
@@ -141,7 +141,7 @@ Most properties here can be None as they are dependant on the exchange response.
 `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 dependant on futures, may contain the cost with or without leverage and may be in contracts.*)
+`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.*
 `order_date` | datetime | Order creation date **use `order_date_utc` instead**
 `order_date_utc` | datetime | Order creation date (in UTC)

freqtrade/__init__.py

@@ -1,21 +1,33 @@
-""" Freqtrade bot """
+"""Freqtrade bot"""
-__version__ = '2024.4-dev'
 
-if 'dev' in __version__:
+__version__ = "2024.5-dev"
+
+if "dev" in __version__:
     from pathlib import Path
+
     try:
         import subprocess
+
         freqtrade_basedir = Path(__file__).parent
 
-        __version__ = __version__ + '-' + subprocess.check_output(
+        __version__ = (
-            ['git', 'log', '--format="%h"', '-n 1'],
+            __version__
-            stderr=subprocess.DEVNULL, cwd=freqtrade_basedir).decode("utf-8").rstrip().strip('"')
+            + "-"
+            + 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')
+            versionfile = Path("./freqtrade_commit")
             if versionfile.is_file():
                 __version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"
         except Exception:

freqtrade/__main__.py

@@ -9,5 +9,5 @@ To launch Freqtrade as a module
 from freqtrade import main
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main.main()

freqtrade/commands/__init__.py

@@ -6,22 +6,39 @@ 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,
+from freqtrade.commands.data_commands import (
-                                              start_download_data, start_list_data)
+    start_convert_data,
+    start_convert_trades,
+    start_download_data,
+    start_list_data,
+)
 from freqtrade.commands.db_commands import start_convert_db
-from freqtrade.commands.deploy_commands import (start_create_userdir, start_install_ui,
+from freqtrade.commands.deploy_commands import (
-                                                start_new_strategy)
+    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,
+from freqtrade.commands.list_commands import (
-                                              start_list_markets, start_list_strategies,
+    start_list_exchanges,
-                                              start_list_timeframes, start_show_trades)
+    start_list_freqAI_models,
-from freqtrade.commands.optimize_commands import (start_backtesting, start_backtesting_show,
+    start_list_markets,
-                                                  start_edge, start_hyperopt,
+    start_list_strategies,
-                                                  start_lookahead_analysis,
+    start_list_timeframes,
-                                                  start_recursive_analysis)
+    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

freqtrade/commands/analyze_commands.py

@@ -20,25 +20,25 @@ def setup_analyze_configuration(args: Dict[str, Any], method: RunMode) -> Dict[s
     config = setup_utils_configuration(args, method)
 
     no_unlimited_runmodes = {
-        RunMode.BACKTEST: 'backtesting',
+        RunMode.BACKTEST: "backtesting",
     }
     if method in no_unlimited_runmodes.keys():
         from freqtrade.data.btanalysis import get_latest_backtest_filename
 
-        if 'exportfilename' in config:
+        if "exportfilename" in config:
-            if config['exportfilename'].is_dir():
+            if config["exportfilename"].is_dir():
-                btfile = Path(get_latest_backtest_filename(config['exportfilename']))
+                btfile = Path(get_latest_backtest_filename(config["exportfilename"]))
                 signals_file = f"{config['exportfilename']}/{btfile.stem}_signals.pkl"
             else:
-                if config['exportfilename'].exists():
+                if config["exportfilename"].exists():
-                    btfile = Path(config['exportfilename'])
+                    btfile = Path(config["exportfilename"])
                     signals_file = f"{btfile.parent}/{btfile.stem}_signals.pkl"
                 else:
                     raise ConfigurationError(f"{config['exportfilename']} does not exist.")
         else:
-            raise ConfigurationError('exportfilename not in config.')
+            raise ConfigurationError("exportfilename not in config.")
 
-        if (not Path(signals_file).exists()):
+        if not Path(signals_file).exists():
             raise OperationalException(
                 f"Cannot find latest backtest signals file: {signals_file}."
                 "Run backtesting with `--export signals`."
@@ -58,6 +58,6 @@ def start_analysis_entries_exits(args: Dict[str, Any]) -> None:
     # Initialize configuration
     config = setup_analyze_configuration(args, RunMode.BACKTEST)
 
-    logger.info('Starting freqtrade in analysis mode')
+    logger.info("Starting freqtrade in analysis mode")
 
     process_entry_exit_reasons(config)

freqtrade/commands/arguments.py

@@ -1,6 +1,7 @@
 """
 This module contains the argument manager class
 """
+
 import argparse
 from functools import partial
 from pathlib import Path
@@ -12,35 +13,72 @@ from freqtrade.constants import DEFAULT_CONFIG
 
 ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
 
-ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search", "freqaimodel",
+ARGS_STRATEGY = [
-                 "freqaimodel_path"]
+    "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",
+ARGS_COMMON_OPTIMIZE = [
-                        "max_open_trades", "stake_amount", "fee", "pairs"]
+    "timeframe",
+    "timerange",
+    "dataformat_ohlcv",
+    "max_open_trades",
+    "stake_amount",
+    "fee",
+    "pairs",
+]
 
-ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
+ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + [
-                                        "enable_protections", "dry_run_wallet", "timeframe_detail",
+    "position_stacking",
-                                        "strategy_list", "export", "exportfilename",
+    "use_max_market_positions",
-                                        "backtest_breakdown", "backtest_cache",
+    "enable_protections",
-                                        "freqai_backtest_live_models"]
+    "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",
+ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + [
-                                        "position_stacking", "use_max_market_positions",
+    "hyperopt",
-                                        "enable_protections", "dry_run_wallet", "timeframe_detail",
+    "hyperopt_path",
-                                        "epochs", "spaces", "print_all",
+    "position_stacking",
-                                        "print_colorized", "print_json", "hyperopt_jobs",
+    "use_max_market_positions",
-                                        "hyperopt_random_state", "hyperopt_min_trades",
+    "enable_protections",
-                                        "hyperopt_loss", "disableparamexport",
+    "dry_run_wallet",
-                                        "hyperopt_ignore_missing_space", "analyze_per_epoch"]
+    "timeframe_detail",
+    "epochs",
+    "spaces",
+    "print_all",
+    "print_colorized",
+    "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", "print_colorized",
+ARGS_LIST_STRATEGIES = [
-                        "recursive_strategy_search"]
+    "strategy_path",
+    "print_one_column",
+    "print_colorized",
+    "recursive_strategy_search",
+]
 
 ARGS_LIST_FREQAIMODELS = ["freqaimodel_path", "print_one_column", "print_colorized"]
 
@@ -52,12 +90,27 @@ 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",
+ARGS_LIST_PAIRS = [
-                   "print_csv", "base_currencies", "quote_currencies", "list_pairs_all",
+    "exchange",
-                   "trading_mode"]
+    "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",
+ARGS_TEST_PAIRLIST = [
-                      "print_one_column", "list_pairs_print_json", "exchange"]
+    "user_data_dir",
+    "verbosity",
+    "config",
+    "quote_currencies",
+    "print_one_column",
+    "list_pairs_print_json",
+    "exchange",
+]
 
 ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
 
@@ -70,22 +123,58 @@ ARGS_CONVERT_DATA_TRADES = ["pairs", "format_from_trades", "format_to", "erase",
 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",
+ARGS_CONVERT_TRADES = [
-                       "trading_mode"]
+    "pairs",
+    "timeframes",
+    "exchange",
+    "dataformat_ohlcv",
+    "dataformat_trades",
+    "trading_mode",
+]
 
 ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs", "trading_mode", "show_timerange"]
 
-ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "include_inactive",
+ARGS_DOWNLOAD_DATA = [
-                      "timerange", "download_trades", "exchange", "timeframes",
+    "pairs",
-                      "erase", "dataformat_ohlcv", "dataformat_trades", "trading_mode",
+    "pairs_file",
-                      "prepend_data"]
+    "days",
+    "new_pairs_days",
+    "include_inactive",
+    "timerange",
+    "download_trades",
+    "exchange",
+    "timeframes",
+    "erase",
+    "dataformat_ohlcv",
+    "dataformat_trades",
+    "trading_mode",
+    "prepend_data",
+]
 
-ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
+ARGS_PLOT_DATAFRAME = [
-                       "db_url", "trade_source", "export", "exportfilename",
+    "pairs",
-                       "timerange", "timeframe", "no_trades"]
+    "indicators1",
+    "indicators2",
+    "plot_limit",
+    "db_url",
+    "trade_source",
+    "export",
+    "exportfilename",
+    "timerange",
+    "timeframe",
+    "no_trades",
+]
 
-ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
+ARGS_PLOT_PROFIT = [
-                    "trade_source", "timeframe", "plot_auto_open", ]
+    "pairs",
+    "timerange",
+    "export",
+    "exportfilename",
+    "db_url",
+    "trade_source",
+    "timeframe",
+    "plot_auto_open",
+]
 
 ARGS_CONVERT_DB = ["db_url", "db_url_from"]
 
@@ -93,36 +182,76 @@ ARGS_INSTALL_UI = ["erase_ui_only", "ui_version"]
 
 ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
 
-ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
+ARGS_HYPEROPT_LIST = [
-                      "hyperopt_list_min_trades", "hyperopt_list_max_trades",
+    "hyperopt_list_best",
-                      "hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
+    "hyperopt_list_profitable",
-                      "hyperopt_list_min_avg_profit", "hyperopt_list_max_avg_profit",
+    "hyperopt_list_min_trades",
-                      "hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
+    "hyperopt_list_max_trades",
-                      "hyperopt_list_min_objective", "hyperopt_list_max_objective",
+    "hyperopt_list_min_avg_time",
-                      "print_colorized", "print_json", "hyperopt_list_no_details",
+    "hyperopt_list_max_avg_time",
-                      "hyperoptexportfilename", "export_csv"]
+    "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_colorized",
+    "print_json",
+    "hyperopt_list_no_details",
+    "hyperoptexportfilename",
+    "export_csv",
+]
 
-ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
+ARGS_HYPEROPT_SHOW = [
-                      "print_json", "hyperoptexportfilename", "hyperopt_show_no_header",
+    "hyperopt_list_best",
-                      "disableparamexport", "backtest_breakdown"]
+    "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",
+ARGS_ANALYZE_ENTRIES_EXITS = [
-                              "exit_reason_list", "indicator_list", "timerange",
+    "exportfilename",
-                              "analysis_rejected", "analysis_to_csv", "analysis_csv_path"]
+    "analysis_groups",
+    "enter_reason_list",
+    "exit_reason_list",
+    "indicator_list",
+    "timerange",
+    "analysis_rejected",
+    "analysis_to_csv",
+    "analysis_csv_path",
+]
 
-NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
+NO_CONF_REQURIED = [
-                    "list-markets", "list-pairs", "list-strategies", "list-freqaimodels",
+    "convert-data",
-                    "list-data", "hyperopt-list", "hyperopt-show", "backtest-filter",
+    "convert-trade-data",
-                    "plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv",
+    "download-data",
-                    "strategy-updater"]
+    "list-timeframes",
+    "list-markets",
+    "list-pairs",
+    "list-strategies",
+    "list-freqaimodels",
+    "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"]
 
 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", "use_max_market_positions", 'cache')
+    a for a in ARGS_BACKTEST if a not in ("position_stacking", "use_max_market_positions", "cache")
-    ] + ["minimum_trade_amount", "targeted_trade_amount", "lookahead_analysis_exportfilename"]
+] + ["minimum_trade_amount", "targeted_trade_amount", "lookahead_analysis_exportfilename"]
 
 ARGS_RECURSIVE_ANALYSIS = ["timeframe", "timerange", "dataformat_ohlcv", "pairs", "startup_candle"]
 
@@ -156,14 +285,14 @@ class Arguments:
         # 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):
+        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)
+            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:
+            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'
+                user_dir = "user_data"
                 # Try loading from "user_data/config.json"
             cfgfile = Path(user_dir) / DEFAULT_CONFIG
             if cfgfile.is_file():
@@ -177,7 +306,6 @@ class Arguments:
         return parsed_arg
 
     def _build_args(self, optionlist, parser):
-
         for val in optionlist:
             opt = AVAILABLE_CLI_OPTIONS[val]
             parser.add_argument(*opt.cli, dest=val, **opt.kwargs)
@@ -197,41 +325,62 @@ class Arguments:
         self._build_args(optionlist=ARGS_STRATEGY, parser=strategy_group)
 
         # Build main command
-        self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
+        self.parser = argparse.ArgumentParser(
-        self._build_args(optionlist=['version'], parser=self.parser)
+            prog="freqtrade", description="Free, open source crypto trading bot"
+        )
+        self._build_args(optionlist=["version"], parser=self.parser)
 
-        from freqtrade.commands import (start_analysis_entries_exits, start_backtesting,
+        from freqtrade.commands import (
-                                        start_backtesting_show, start_convert_data,
+            start_analysis_entries_exits,
-                                        start_convert_db, start_convert_trades,
+            start_backtesting,
-                                        start_create_userdir, start_download_data, start_edge,
+            start_backtesting_show,
-                                        start_hyperopt, start_hyperopt_list, start_hyperopt_show,
+            start_convert_data,
-                                        start_install_ui, start_list_data, start_list_exchanges,
+            start_convert_db,
-                                        start_list_freqAI_models, start_list_markets,
+            start_convert_trades,
-                                        start_list_strategies, start_list_timeframes,
+            start_create_userdir,
-                                        start_lookahead_analysis, start_new_config,
+            start_download_data,
-                                        start_new_strategy, start_plot_dataframe, start_plot_profit,
+            start_edge,
-                                        start_recursive_analysis, start_show_config,
+            start_hyperopt,
-                                        start_show_trades, start_strategy_update,
+            start_hyperopt_list,
-                                        start_test_pairlist, start_trading, start_webserver)
+            start_hyperopt_show,
+            start_install_ui,
+            start_list_data,
+            start_list_exchanges,
+            start_list_freqAI_models,
+            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',
+        subparsers = self.parser.add_subparsers(
-                                                # Use custom message when no subhandler is added
+            dest="command",
-                                                # shown from `main.py`
+            # Use custom message when no subhandler is added
-                                                # required=True
+            # shown from `main.py`
-                                                )
+            # required=True
+        )
 
         # Add trade subcommand
         trade_cmd = subparsers.add_parser(
-            'trade',
+            "trade", help="Trade module.", parents=[_common_parser, _strategy_parser]
-            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',
+            "create-userdir",
             help="Create user-data directory.",
         )
         create_userdir_cmd.set_defaults(func=start_create_userdir)
@@ -239,7 +388,7 @@ class Arguments:
 
         # add new-config subcommand
         build_config_cmd = subparsers.add_parser(
-            'new-config',
+            "new-config",
             help="Create new config",
         )
         build_config_cmd.set_defaults(func=start_new_config)
@@ -247,7 +396,7 @@ class Arguments:
 
         # add show-config subcommand
         show_config_cmd = subparsers.add_parser(
-            'show-config',
+            "show-config",
             help="Show resolved config",
         )
         show_config_cmd.set_defaults(func=start_show_config)
@@ -255,7 +404,7 @@ class Arguments:
 
         # add new-strategy subcommand
         build_strategy_cmd = subparsers.add_parser(
-            'new-strategy',
+            "new-strategy",
             help="Create new strategy",
         )
         build_strategy_cmd.set_defaults(func=start_new_strategy)
@@ -263,8 +412,8 @@ class Arguments:
 
         # Add download-data subcommand
         download_data_cmd = subparsers.add_parser(
-            'download-data',
+            "download-data",
-            help='Download backtesting data.',
+            help="Download backtesting data.",
             parents=[_common_parser],
         )
         download_data_cmd.set_defaults(func=start_download_data)
@@ -272,8 +421,8 @@ class Arguments:
 
         # Add convert-data subcommand
         convert_data_cmd = subparsers.add_parser(
-            'convert-data',
+            "convert-data",
-            help='Convert candle (OHLCV) data from one format to another.',
+            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))
@@ -281,8 +430,8 @@ class Arguments:
 
         # Add convert-trade-data subcommand
         convert_trade_data_cmd = subparsers.add_parser(
-            'convert-trade-data',
+            "convert-trade-data",
-            help='Convert trade data from one format to another.',
+            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))
@@ -290,8 +439,8 @@ class Arguments:
 
         # Add trades-to-ohlcv subcommand
         convert_trade_data_cmd = subparsers.add_parser(
-            'trades-to-ohlcv',
+            "trades-to-ohlcv",
-            help='Convert trade data to OHLCV data.',
+            help="Convert trade data to OHLCV data.",
             parents=[_common_parser],
         )
         convert_trade_data_cmd.set_defaults(func=start_convert_trades)
@@ -299,8 +448,8 @@ class Arguments:
 
         # Add list-data subcommand
         list_data_cmd = subparsers.add_parser(
-            'list-data',
+            "list-data",
-            help='List downloaded data.',
+            help="List downloaded data.",
             parents=[_common_parser],
         )
         list_data_cmd.set_defaults(func=start_list_data)
@@ -308,17 +457,15 @@ class Arguments:
 
         # Add backtesting subcommand
         backtesting_cmd = subparsers.add_parser(
-            'backtesting',
+            "backtesting", help="Backtesting module.", parents=[_common_parser, _strategy_parser]
-            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',
+            "backtesting-show",
-            help='Show past Backtest results',
+            help="Show past Backtest results",
             parents=[_common_parser],
         )
         backtesting_show_cmd.set_defaults(func=start_backtesting_show)
@@ -326,26 +473,22 @@ class Arguments:
 
         # Add backtesting analysis subcommand
         analysis_cmd = subparsers.add_parser(
-            'backtesting-analysis',
+            "backtesting-analysis", help="Backtest Analysis module.", parents=[_common_parser]
-            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',
+            "edge", help="Edge module.", parents=[_common_parser, _strategy_parser]
-            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',
+            "hyperopt",
-            help='Hyperopt module.',
+            help="Hyperopt module.",
             parents=[_common_parser, _strategy_parser],
         )
         hyperopt_cmd.set_defaults(func=start_hyperopt)
@@ -353,8 +496,8 @@ class Arguments:
 
         # Add hyperopt-list subcommand
         hyperopt_list_cmd = subparsers.add_parser(
-            'hyperopt-list',
+            "hyperopt-list",
-            help='List Hyperopt results',
+            help="List Hyperopt results",
             parents=[_common_parser],
         )
         hyperopt_list_cmd.set_defaults(func=start_hyperopt_list)
@@ -362,8 +505,8 @@ class Arguments:
 
         # Add hyperopt-show subcommand
         hyperopt_show_cmd = subparsers.add_parser(
-            'hyperopt-show',
+            "hyperopt-show",
-            help='Show details of Hyperopt results',
+            help="Show details of Hyperopt results",
             parents=[_common_parser],
         )
         hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
@@ -371,8 +514,8 @@ class Arguments:
 
         # Add list-exchanges subcommand
         list_exchanges_cmd = subparsers.add_parser(
-            'list-exchanges',
+            "list-exchanges",
-            help='Print available exchanges.',
+            help="Print available exchanges.",
             parents=[_common_parser],
         )
         list_exchanges_cmd.set_defaults(func=start_list_exchanges)
@@ -380,8 +523,8 @@ class Arguments:
 
         # Add list-markets subcommand
         list_markets_cmd = subparsers.add_parser(
-            'list-markets',
+            "list-markets",
-            help='Print markets on exchange.',
+            help="Print markets on exchange.",
             parents=[_common_parser],
         )
         list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
@@ -389,8 +532,8 @@ class Arguments:
 
         # Add list-pairs subcommand
         list_pairs_cmd = subparsers.add_parser(
-            'list-pairs',
+            "list-pairs",
-            help='Print pairs on exchange.',
+            help="Print pairs on exchange.",
             parents=[_common_parser],
         )
         list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
@@ -398,8 +541,8 @@ class Arguments:
 
         # Add list-strategies subcommand
         list_strategies_cmd = subparsers.add_parser(
-            'list-strategies',
+            "list-strategies",
-            help='Print available strategies.',
+            help="Print available strategies.",
             parents=[_common_parser],
         )
         list_strategies_cmd.set_defaults(func=start_list_strategies)
@@ -407,8 +550,8 @@ class Arguments:
 
         # Add list-freqAI Models subcommand
         list_freqaimodels_cmd = subparsers.add_parser(
-            'list-freqaimodels',
+            "list-freqaimodels",
-            help='Print available freqAI models.',
+            help="Print available freqAI models.",
             parents=[_common_parser],
         )
         list_freqaimodels_cmd.set_defaults(func=start_list_freqAI_models)
@@ -416,8 +559,8 @@ class Arguments:
 
         # Add list-timeframes subcommand
         list_timeframes_cmd = subparsers.add_parser(
-            'list-timeframes',
+            "list-timeframes",
-            help='Print available timeframes for the exchange.',
+            help="Print available timeframes for the exchange.",
             parents=[_common_parser],
         )
         list_timeframes_cmd.set_defaults(func=start_list_timeframes)
@@ -425,8 +568,8 @@ class Arguments:
 
         # Add show-trades subcommand
         show_trades = subparsers.add_parser(
-            'show-trades',
+            "show-trades",
-            help='Show trades.',
+            help="Show trades.",
             parents=[_common_parser],
         )
         show_trades.set_defaults(func=start_show_trades)
@@ -434,8 +577,8 @@ class Arguments:
 
         # Add test-pairlist subcommand
         test_pairlist_cmd = subparsers.add_parser(
-            'test-pairlist',
+            "test-pairlist",
-            help='Test your pairlist configuration.',
+            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)
@@ -450,16 +593,16 @@ class Arguments:
 
         # Add install-ui subcommand
         install_ui_cmd = subparsers.add_parser(
-            'install-ui',
+            "install-ui",
-            help='Install FreqUI',
+            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',
+            "plot-dataframe",
-            help='Plot candles with indicators.',
+            help="Plot candles with indicators.",
             parents=[_common_parser, _strategy_parser],
         )
         plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
@@ -467,8 +610,8 @@ class Arguments:
 
         # Plot profit
         plot_profit_cmd = subparsers.add_parser(
-            'plot-profit',
+            "plot-profit",
-            help='Generate plot showing profits.',
+            help="Generate plot showing profits.",
             parents=[_common_parser, _strategy_parser],
         )
         plot_profit_cmd.set_defaults(func=start_plot_profit)
@@ -476,40 +619,36 @@ class Arguments:
 
         # Add webserver subcommand
         webserver_cmd = subparsers.add_parser(
-            'webserver',
+            "webserver", help="Webserver module.", parents=[_common_parser]
-            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',
+            "strategy-updater",
-            help='updates outdated strategy files to the current version',
+            help="updates outdated strategy files to the current version",
-            parents=[_common_parser]
+            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',
+            "lookahead-analysis",
             help="Check for potential look ahead bias.",
-            parents=[_common_parser, _strategy_parser]
+            parents=[_common_parser, _strategy_parser],
         )
         lookahead_analayis_cmd.set_defaults(func=start_lookahead_analysis)
 
-        self._build_args(optionlist=ARGS_LOOKAHEAD_ANALYSIS,
+        self._build_args(optionlist=ARGS_LOOKAHEAD_ANALYSIS, parser=lookahead_analayis_cmd)
-                         parser=lookahead_analayis_cmd)
 
         # Add recursive_analysis subcommand
         recursive_analayis_cmd = subparsers.add_parser(
-            'recursive-analysis',
+            "recursive-analysis",
             help="Check for potential recursive formula issue.",
-            parents=[_common_parser, _strategy_parser]
+            parents=[_common_parser, _strategy_parser],
         )
         recursive_analayis_cmd.set_defaults(func=start_recursive_analysis)
 
-        self._build_args(optionlist=ARGS_RECURSIVE_ANALYSIS,
+        self._build_args(optionlist=ARGS_RECURSIVE_ANALYSIS, parser=recursive_analayis_cmd)
-                         parser=recursive_analayis_cmd)

freqtrade/commands/build_config_commands.py

@@ -45,7 +45,7 @@ def ask_user_overwrite(config_path: Path) -> bool:
         },
     ]
     answers = prompt(questions)
-    return answers['overwrite']
+    return answers["overwrite"]
 
 
 def ask_user_config() -> Dict[str, Any]:
@@ -65,7 +65,7 @@ def ask_user_config() -> Dict[str, Any]:
             "type": "text",
             "name": "stake_currency",
             "message": "Please insert your stake currency:",
-            "default": 'USDT',
+            "default": "USDT",
         },
         {
             "type": "text",
@@ -73,36 +73,35 @@ def ask_user_config() -> Dict[str, Any]:
             "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 + '"'
+            "filter": lambda val: (
-            if val == UNLIMITED_STAKE_AMOUNT
+                '"' + UNLIMITED_STAKE_AMOUNT + '"' if val == UNLIMITED_STAKE_AMOUNT else val
-            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)
+            "validate": lambda val: validate_is_int(val),
         },
         {
             "type": "select",
             "name": "timeframe_in_config",
             "message": "Time",
-            "choices": ["Have the strategy define timeframe.", "Override in configuration."]
+            "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.'
+            "when": lambda x: x["timeframe_in_config"] == "Override in configuration.",
-
         },
         {
             "type": "text",
             "name": "fiat_display_currency",
             "message": "Please insert your display Currency (for reporting):",
-            "default": 'USD',
+            "default": "USD",
         },
         {
             "type": "select",
@@ -125,33 +124,33 @@ def ask_user_config() -> Dict[str, Any]:
             "name": "trading_mode",
             "message": "Do you want to trade Perpetual Swaps (perpetual futures)?",
             "default": False,
-            "filter": lambda val: 'futures' if val else 'spot',
+            "filter": lambda val: "futures" if val else "spot",
-            "when": lambda x: x["exchange_name"] in ['binance', 'gate', 'okx'],
+            "when": lambda x: x["exchange_name"] in ["binance", "gate", "okx"],
         },
         {
             "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'
+            "when": lambda x: x["exchange_name"] == "other",
         },
         {
             "type": "password",
             "name": "exchange_key",
             "message": "Insert Exchange Key",
-            "when": lambda x: not x['dry_run']
+            "when": lambda x: not x["dry_run"],
         },
         {
             "type": "password",
             "name": "exchange_secret",
             "message": "Insert Exchange Secret",
-            "when": lambda x: not x['dry_run']
+            "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')
+            "when": lambda x: not x["dry_run"] and x["exchange_name"] in ("kucoin", "okx"),
         },
         {
             "type": "confirm",
@@ -163,13 +162,13 @@ def ask_user_config() -> Dict[str, Any]:
             "type": "password",
             "name": "telegram_token",
             "message": "Insert Telegram token",
-            "when": lambda x: x['telegram']
+            "when": lambda x: x["telegram"],
         },
         {
             "type": "password",
             "name": "telegram_chat_id",
             "message": "Insert Telegram chat id",
-            "when": lambda x: x['telegram']
+            "when": lambda x: x["telegram"],
         },
         {
             "type": "confirm",
@@ -180,23 +179,25 @@ def ask_user_config() -> Dict[str, Any]:
         {
             "type": "text",
             "name": "api_server_listen_addr",
-            "message": ("Insert Api server Listen Address (0.0.0.0 for docker, "
+            "message": (
-                        "otherwise best left untouched)"),
+                "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",
-            "when": lambda x: x['api_server']
+            "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']
+            "when": lambda x: x["api_server"],
         },
         {
             "type": "password",
             "name": "api_server_password",
             "message": "Insert api-server password",
-            "when": lambda x: x['api_server']
+            "when": lambda x: x["api_server"],
         },
     ]
     answers = prompt(questions)
@@ -205,15 +206,11 @@ def ask_user_config() -> Dict[str, Any]:
         # 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["trading_mode"] = answers.get("trading_mode", "spot")
-    answers['margin_mode'] = (
+    answers["margin_mode"] = "isolated" if answers.get("trading_mode") == "futures" else ""
-        '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_jwt_key"] = secrets.token_hex()
-    answers['api_server_ws_token'] = secrets.token_urlsafe(25)
+    answers["api_server_ws_token"] = secrets.token_urlsafe(25)
 
     return answers
 
@@ -225,26 +222,26 @@ def deploy_new_config(config_path: Path, selections: Dict[str, Any]) -> None:
     :param selections: Dict containing selections taken by the user.
     """
     from jinja2.exceptions import TemplateNotFound
+
     try:
         exchange_template = MAP_EXCHANGE_CHILDCLASS.get(
-            selections['exchange_name'], selections['exchange_name'])
+            selections["exchange_name"], selections["exchange_name"]
+        )
 
-        selections['exchange'] = render_template(
+        selections["exchange"] = render_template(
-            templatefile=f"subtemplates/exchange_{exchange_template}.j2",
+            templatefile=f"subtemplates/exchange_{exchange_template}.j2", arguments=selections
-            arguments=selections
         )
     except TemplateNotFound:
-        selections['exchange'] = render_template(
+        selections["exchange"] = render_template(
-            templatefile="subtemplates/exchange_generic.j2",
+            templatefile="subtemplates/exchange_generic.j2", arguments=selections
-            arguments=selections
         )
 
-    config_text = render_template(templatefile='base_config.json.j2',
+    config_text = render_template(templatefile="base_config.json.j2", arguments=selections)
-                                  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.")
+        "Please make sure to check the configuration contents and adjust settings to your needs."
+    )
 
     config_path.write_text(config_text)
 
@@ -255,7 +252,7 @@ def start_new_config(args: Dict[str, Any]) -> None:
     Asking the user questions to fill out the template accordingly.
     """
 
-    config_path = Path(args['config'][0])
+    config_path = Path(args["config"][0])
     chown_user_directory(config_path.parent)
     if config_path.exists():
         overwrite = ask_user_overwrite(config_path)
@@ -264,22 +261,22 @@ def start_new_config(args: Dict[str, Any]) -> None:
         else:
             raise OperationalException(
                 f"Configuration file `{config_path}` already exists. "
-                "Please delete it or use a different configuration file name.")
+                "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:
-
     config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE, set_dry=False)
 
     # TODO: Sanitize from sensitive info before printing
 
     print("Your combined configuration is:")
     config_sanitized = sanitize_config(
-        config['original_config'],
+        config["original_config"], show_sensitive=args.get("show_sensitive", False)
-        show_sensitive=args.get('show_sensitive', False)
     )
 
     from rich import print_json
+
     print_json(data=config_sanitized)

freqtrade/commands/data_commands.py

@@ -5,8 +5,11 @@ from typing import Any, Dict
 
 from freqtrade.configuration import TimeRange, setup_utils_configuration
 from freqtrade.constants import DATETIME_PRINT_FORMAT, DL_DATA_TIMEFRAMES, Config
-from freqtrade.data.converter import (convert_ohlcv_format, convert_trades_format,
+from freqtrade.data.converter import (
-                                      convert_trades_to_ohlcv)
+    convert_ohlcv_format,
+    convert_trades_format,
+    convert_trades_to_ohlcv,
+)
 from freqtrade.data.history import download_data_main
 from freqtrade.enums import CandleType, RunMode, TradingMode
 from freqtrade.exceptions import ConfigurationError
@@ -20,14 +23,17 @@ logger = logging.getLogger(__name__)
 
 
 def _check_data_config_download_sanity(config: Config) -> None:
-    if 'days' in config and 'timerange' in config:
+    if "days" in config and "timerange" in config:
-        raise ConfigurationError("--days and --timerange are mutually exclusive. "
+        raise ConfigurationError(
-                                 "You can only specify one or the other.")
+            "--days and --timerange are mutually exclusive. "
+            "You can only specify one or the other."
+        )
 
-    if 'pairs' not in config:
+    if "pairs" not in config:
         raise ConfigurationError(
             "Downloading data requires a list of pairs. "
-            "Please check the documentation on how to configure this.")
+            "Please check the documentation on how to configure this."
+        )
 
 
 def start_download_data(args: Dict[str, Any]) -> None:
@@ -46,38 +52,41 @@ def start_download_data(args: Dict[str, Any]) -> None:
 
 
 def start_convert_trades(args: Dict[str, Any]) -> None:
-
     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'] = ''
+    config["stake_currency"] = ""
 
-    if 'timeframes' not in config:
+    if "timeframes" not in config:
-        config['timeframes'] = DL_DATA_TIMEFRAMES
+        config["timeframes"] = DL_DATA_TIMEFRAMES
 
     # Init exchange
     exchange = ExchangeResolver.load_exchange(config, validate=False)
     # Manual validations of relevant settings
 
-    for timeframe in config['timeframes']:
+    for timeframe in config["timeframes"]:
         exchange.validate_timeframes(timeframe)
     available_pairs = [
-        p for p in exchange.get_markets(
+        p
-            tradable_only=True, active_only=not config.get('include_inactive')
+        for p in exchange.get_markets(
-            ).keys()
+            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'],
+        pairs=expanded_pairs,
-        datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
+        timeframes=config["timeframes"],
-        data_format_ohlcv=config['dataformat_ohlcv'],
+        datadir=config["datadir"],
-        data_format_trades=config['dataformat_trades'],
+        timerange=timerange,
-        candle_type=config.get('candle_type_def', CandleType.SPOT)
+        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),
     )
 
 
@@ -88,14 +97,19 @@ def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
     if ohlcv:
         migrate_data(config)
-        convert_ohlcv_format(config,
+        convert_ohlcv_format(
-                             convert_from=args['format_from'],
+            config,
-                             convert_to=args['format_to'],
+            convert_from=args["format_from"],
-                             erase=args['erase'])
+            convert_to=args["format_to"],
+            erase=args["erase"],
+        )
     else:
-        convert_trades_format(config,
+        convert_trades_format(
-                              convert_from=args['format_from_trades'], convert_to=args['format_to'],
+            config,
-                              erase=args['erase'])
+            convert_from=args["format_from_trades"],
+            convert_to=args["format_to"],
+            erase=args["erase"],
+        )
 
 
 def start_list_data(args: Dict[str, Any]) -> None:
@@ -108,45 +122,59 @@ def start_list_data(args: Dict[str, Any]) -> None:
     from tabulate import tabulate
 
     from freqtrade.data.history import get_datahandler
-    dhc = get_datahandler(config['datadir'], config['dataformat_ohlcv'])
+
+    dhc = get_datahandler(config["datadir"], config["dataformat_ohlcv"])
 
     paircombs = dhc.ohlcv_get_available_data(
-        config['datadir'],
+        config["datadir"], config.get("trading_mode", TradingMode.SPOT)
-        config.get('trading_mode', TradingMode.SPOT)
+    )
-        )
 
-    if args['pairs']:
+    if args["pairs"]:
-        paircombs = [comb for comb in paircombs if comb[0] in args['pairs']]
+        paircombs = [comb for comb in paircombs if comb[0] in args["pairs"]]
 
     print(f"Found {len(paircombs)} pair / timeframe combinations.")
-    if not config.get('show_timerange'):
+    if not config.get("show_timerange"):
         groupedpair = defaultdict(list)
         for pair, timeframe, candle_type in sorted(
-            paircombs,
+            paircombs, key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
-            key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
         ):
             groupedpair[(pair, candle_type)].append(timeframe)
 
         if groupedpair:
-            print(tabulate([
+            print(
-                (pair, ', '.join(timeframes), candle_type)
+                tabulate(
-                for (pair, candle_type), timeframes in groupedpair.items()
+                    [
-            ],
+                        (pair, ", ".join(timeframes), candle_type)
-                headers=("Pair", "Timeframe", "Type"),
+                        for (pair, candle_type), timeframes in groupedpair.items()
-                tablefmt='psql', stralign='right'))
+                    ],
+                    headers=("Pair", "Timeframe", "Type"),
+                    tablefmt="psql",
+                    stralign="right",
+                )
+            )
     else:
-        paircombs1 = [(
+        paircombs1 = [
-            pair, timeframe, candle_type,
+            (pair, timeframe, candle_type, *dhc.ohlcv_data_min_max(pair, timeframe, candle_type))
-            *dhc.ohlcv_data_min_max(pair, timeframe, candle_type)
+            for pair, timeframe, candle_type in paircombs
-        ) for pair, timeframe, candle_type in paircombs]
+        ]
 
-        print(tabulate([
+        print(
-            (pair, timeframe, candle_type,
+            tabulate(
-                start.strftime(DATETIME_PRINT_FORMAT),
+                [
-                end.strftime(DATETIME_PRINT_FORMAT), length)
+                    (
-            for pair, timeframe, candle_type, start, end, length in sorted(
+                        pair,
-                paircombs1,
+                        timeframe,
-                key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2]))
+                        candle_type,
-            ],
+                        start.strftime(DATETIME_PRINT_FORMAT),
-            headers=("Pair", "Timeframe", "Type", 'From', 'To', 'Candles'),
+                        end.strftime(DATETIME_PRINT_FORMAT),
-            tablefmt='psql', stralign='right'))
+                        length,
+                    )
+                    for pair, timeframe, candle_type, start, end, length in sorted(
+                        paircombs1, key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])
+                    )
+                ],
+                headers=("Pair", "Timeframe", "Type", "From", "To", "Candles"),
+                tablefmt="psql",
+                stralign="right",
+            )
+        )

freqtrade/commands/db_commands.py

@@ -19,9 +19,9 @@ def start_convert_db(args: Dict[str, Any]) -> None:
 
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
-    init_db(config['db_url'])
+    init_db(config["db_url"])
     session_target = Trade.session
-    init_db(config['db_url_from'])
+    init_db(config["db_url_from"])
     logger.info("Starting db migration.")
 
     trade_count = 0
@@ -47,9 +47,11 @@ def start_convert_db(args: Dict[str, Any]) -> None:
     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(),
+    set_sequence_ids(
-                     trade_id=max_trade_id,
+        session_target.get_bind(),
-                     order_id=max_order_id,
+        trade_id=max_trade_id,
-                     pairlock_id=max_pairlock_id)
+        order_id=max_order_id,
+        pairlock_id=max_pairlock_id,
+    )
 
     logger.info(f"Migrated {trade_count} Trades, and {pairlock_count} Pairlocks.")

freqtrade/commands/deploy_commands.py

@@ -16,6 +16,10 @@ from freqtrade.util import render_template, render_template_with_fallback
 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, ...)
@@ -34,7 +38,7 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
     """
     Deploy new strategy from template to strategy_path
     """
-    fallback = 'full'
+    fallback = "full"
     attributes = render_template_with_fallback(
         templatefile=f"strategy_subtemplates/strategy_attributes_{subtemplate}.j2",
         templatefallbackfile=f"strategy_subtemplates/strategy_attributes_{fallback}.j2",
@@ -60,33 +64,35 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
         templatefallbackfile="strategy_subtemplates/strategy_methods_empty.j2",
     )
 
-    strategy_text = render_template(templatefile='base_strategy.py.j2',
+    strategy_text = render_template(
-                                    arguments={"strategy": strategy_name,
+        templatefile="base_strategy.py.j2",
-                                               "attributes": attributes,
+        arguments={
-                                               "indicators": indicators,
+            "strategy": strategy_name,
-                                               "buy_trend": buy_trend,
+            "attributes": attributes,
-                                               "sell_trend": sell_trend,
+            "indicators": indicators,
-                                               "plot_config": plot_config,
+            "buy_trend": buy_trend,
-                                               "additional_methods": additional_methods,
+            "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:
-
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
     if "strategy" in args and args["strategy"]:
-
+        new_path = config["user_data_dir"] / USERPATH_STRATEGIES / (args["strategy"] + ".py")
-        new_path = config['user_data_dir'] / USERPATH_STRATEGIES / (args['strategy'] + '.py')
 
         if new_path.exists():
-            raise OperationalException(f"`{new_path}` already exists. "
+            raise OperationalException(
-                                       "Please choose another Strategy Name.")
+                f"`{new_path}` already exists. Please choose another Strategy Name."
+            )
 
-        deploy_new_strategy(args['strategy'], new_path, args['template'])
+        deploy_new_strategy(args["strategy"], new_path, args["template"])
 
     else:
         raise ConfigurationError("`new-strategy` requires --strategy to be set.")
@@ -96,8 +102,8 @@ 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
+        for p in reversed(list(directory.glob("**/*"))):  # iterate contents from leaves to root
-            if p.name in ('.gitkeep', 'fallback_file.html'):
+            if p.name in (".gitkeep", "fallback_file.html"):
                 continue
             if p.is_file():
                 p.unlink()
@@ -106,11 +112,11 @@ def clean_ui_subdir(directory: Path):
 
 
 def read_ui_version(dest_folder: Path) -> Optional[str]:
-    file = dest_folder / '.uiversion'
+    file = dest_folder / ".uiversion"
     if not file.is_file():
         return None
 
-    with file.open('r') as f:
+    with file.open("r") as f:
         return f.read()
 
 
@@ -119,7 +125,7 @@ def download_and_install_ui(dest_folder: Path, dl_url: str, version: str):
     from zipfile import ZipFile
 
     logger.info(f"Downloading {dl_url}")
-    resp = requests.get(dl_url).content
+    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:
@@ -129,55 +135,54 @@ def download_and_install_ui(dest_folder: Path, dl_url: str, version: str):
                     destfile.mkdir(exist_ok=True)
                 else:
                     destfile.write_bytes(x.read())
-    with (dest_folder / '.uiversion').open('w') as f:
+    with (dest_folder / ".uiversion").open("w") as f:
         f.write(version)
 
 
 def get_ui_download_url(version: Optional[str] = None) -> Tuple[str, str]:
-    base_url = 'https://api.github.com/repos/freqtrade/frequi/'
+    base_url = "https://api.github.com/repos/freqtrade/frequi/"
     # Get base UI Repo path
 
-    resp = requests.get(f"{base_url}releases")
+    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]
+        tmp = [x for x in r if x["name"] == version]
         if tmp:
-            latest_version = tmp[0]['name']
+            latest_version = tmp[0]["name"]
-            assets = tmp[0].get('assets', [])
+            assets = tmp[0].get("assets", [])
         else:
             raise ValueError("UI-Version not found.")
     else:
-        latest_version = r[0]['name']
+        latest_version = r[0]["name"]
-        assets = r[0].get('assets', [])
+        assets = r[0].get("assets", [])
-    dl_url = ''
+    dl_url = ""
     if assets and len(assets) > 0:
-        dl_url = assets[0]['browser_download_url']
+        dl_url = assets[0]["browser_download_url"]
 
     # URL not found - try assets url
     if not dl_url:
-        assets = r[0]['assets_url']
+        assets = r[0]["assets_url"]
-        resp = requests.get(assets)
+        resp = requests.get(assets, timeout=req_timeout)
         r = resp.json()
-        dl_url = r[0]['browser_download_url']
+        dl_url = r[0]["browser_download_url"]
 
     return dl_url, latest_version
 
 
 def start_install_ui(args: Dict[str, Any]) -> None:
-
+    dest_folder = Path(__file__).parents[1] / "rpc/api_server/ui/installed/"
-    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'))
+    dl_url, latest_version = get_ui_download_url(args.get("ui_version"))
 
     curr_version = read_ui_version(dest_folder)
-    if curr_version == latest_version and not args.get('erase_ui_only'):
+    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'):
+    if args.get("erase_ui_only"):
         logger.info("Erased UI directory content. Not downloading new version.")
     else:
         # Download a new version

freqtrade/commands/hyperopt_commands.py

@@ -22,15 +22,15 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
 
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
-    print_colorized = config.get('print_colorized', False)
+    print_colorized = config.get("print_colorized", False)
-    print_json = config.get('print_json', False)
+    print_json = config.get("print_json", False)
-    export_csv = config.get('export_csv')
+    export_csv = config.get("export_csv")
-    no_details = config.get('hyperopt_list_no_details', False)
+    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["user_data_dir"] / "hyperopt_results", config.get("hyperoptexportfilename")
-        config.get('hyperoptexportfilename'))
+    )
 
     # Previous evaluations
     epochs, total_epochs = HyperoptTools.load_filtered_results(results_file, config)
@@ -40,21 +40,26 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
 
     if not export_csv:
         try:
-            print(HyperoptTools.get_result_table(config, epochs, total_epochs,
+            print(
-                                                 not config.get('hyperopt_list_best', False),
+                HyperoptTools.get_result_table(
-                                                 print_colorized, 0))
+                    config,
+                    epochs,
+                    total_epochs,
+                    not config.get("hyperopt_list_best", False),
+                    print_colorized,
+                    0,
+                )
+            )
         except KeyboardInterrupt:
-            print('User interrupted..')
+            print("User interrupted..")
 
     if epochs and not no_details:
-        sorted_epochs = sorted(epochs, key=itemgetter('loss'))
+        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(
+        HyperoptTools.export_csv_file(config, epochs, export_csv)
-            config, epochs, export_csv
-        )
 
 
 def start_hyperopt_show(args: Dict[str, Any]) -> None:
@@ -65,13 +70,13 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
-    print_json = config.get('print_json', False)
+    print_json = config.get("print_json", False)
-    no_header = config.get('hyperopt_show_no_header', False)
+    no_header = config.get("hyperopt_show_no_header", False)
     results_file = get_latest_hyperopt_file(
-        config['user_data_dir'] / 'hyperopt_results',
+        config["user_data_dir"] / "hyperopt_results", config.get("hyperoptexportfilename")
-        config.get('hyperoptexportfilename'))
+    )
 
-    n = config.get('hyperopt_show_index', -1)
+    n = config.get("hyperopt_show_index", -1)
 
     # Previous evaluations
     epochs, total_epochs = HyperoptTools.load_filtered_results(results_file, config)
@@ -80,10 +85,12 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 
     if n > filtered_epochs:
         raise OperationalException(
-            f"The index of the epoch to show should be less than {filtered_epochs + 1}.")
+            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}.")
+            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:
@@ -92,13 +99,18 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
     if epochs:
         val = epochs[n]
 
-        metrics = val['results_metrics']
+        metrics = val["results_metrics"]
-        if 'strategy_name' in metrics:
+        if "strategy_name" in metrics:
-            strategy_name = metrics['strategy_name']
+            strategy_name = metrics["strategy_name"]
-            show_backtest_result(strategy_name, metrics,
+            show_backtest_result(
-                                 metrics['stake_currency'], config.get('backtest_breakdown', []))
+                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,
+        HyperoptTools.show_epoch_details(
-                                         header_str="Epoch details")
+            val, total_epochs, print_json, no_header, header_str="Epoch details"
+        )

freqtrade/commands/list_commands.py

@@ -26,42 +26,47 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
     :param args: Cli args from Arguments()
     :return: None
     """
-    exchanges = list_available_exchanges(args['list_exchanges_all'])
+    exchanges = list_available_exchanges(args["list_exchanges_all"])
 
-    if args['print_one_column']:
+    if args["print_one_column"]:
-        print('\n'.join([e['name'] for e in exchanges]))
+        print("\n".join([e["name"] for e in exchanges]))
     else:
         headers = {
-            'name': 'Exchange name',
+            "name": "Exchange name",
-            'supported': 'Supported',
+            "supported": "Supported",
-            'trade_modes': 'Markets',
+            "trade_modes": "Markets",
-            'comment': 'Reason',
+            "comment": "Reason",
-            }
+        }
-        headers.update({'valid': 'Valid'} if args['list_exchanges_all'] else {})
+        headers.update({"valid": "Valid"} if args["list_exchanges_all"] else {})
 
         def build_entry(exchange: ValidExchangesType, valid: bool):
-            valid_entry = {'valid': exchange['valid']} if valid else {}
+            valid_entry = {"valid": exchange["valid"]} if valid else {}
             result: Dict[str, Union[str, bool]] = {
-                'name': exchange['name'],
+                "name": exchange["name"],
                 **valid_entry,
-                'supported': 'Official' if exchange['supported'] else '',
+                "supported": "Official" if exchange["supported"] else "",
-                'trade_modes': ', '.join(
+                "trade_modes": ", ".join(
-                    (f"{a['margin_mode']} " if a['margin_mode'] else '') + a['trading_mode']
+                    (f"{a['margin_mode']} " if a["margin_mode"] else "") + a["trading_mode"]
-                    for a in exchange['trade_modes']
+                    for a in exchange["trade_modes"]
                 ),
-                'comment': exchange['comment'],
+                "comment": exchange["comment"],
             }
 
             return result
 
-        if args['list_exchanges_all']:
+        if args["list_exchanges_all"]:
             print("All exchanges supported by the ccxt library:")
             exchanges = [build_entry(e, True) for e in exchanges]
         else:
             print("Exchanges available for Freqtrade:")
-            exchanges = [build_entry(e, False) for e in exchanges if e['valid'] is not False]
+            exchanges = [build_entry(e, False) for e in exchanges if e["valid"] is not False]
 
-        print(tabulate(exchanges, headers=headers, ))
+        print(
+            tabulate(
+                exchanges,
+                headers=headers,
+            )
+        )
 
 
 def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
@@ -71,26 +76,35 @@ def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
         yellow = Fore.YELLOW
         reset = Style.RESET_ALL
     else:
-        red = ''
+        red = ""
-        yellow = ''
+        yellow = ""
-        reset = ''
+        reset = ""
 
-    names = [s['name'] for s in objs]
+    names = [s["name"] for s in objs]
-    objs_to_print = [{
+    objs_to_print = [
-        'name': s['name'] if s['name'] else "--",
+        {
-        'location': s['location_rel'],
+            "name": s["name"] if s["name"] else "--",
-        'status': (red + "LOAD FAILED" + reset if s['class'] is None
+            "location": s["location_rel"],
-                   else "OK" if names.count(s['name']) == 1
+            "status": (
-                   else yellow + "DUPLICATE NAME" + reset)
+                red + "LOAD FAILED" + reset
-    } for s in objs]
+                if s["class"] is None
+                else "OK"
+                if names.count(s["name"]) == 1
+                else yellow + "DUPLICATE NAME" + reset
+            ),
+        }
+        for s in objs
+    ]
     for idx, s in enumerate(objs):
-        if 'hyperoptable' in s:
+        if "hyperoptable" in s:
-            objs_to_print[idx].update({
+            objs_to_print[idx].update(
-                'hyperoptable': "Yes" if s['hyperoptable']['count'] > 0 else "No",
+                {
-                'buy-Params': len(s['hyperoptable'].get('buy', [])),
+                    "hyperoptable": "Yes" if s["hyperoptable"]["count"] > 0 else "No",
-                'sell-Params': len(s['hyperoptable'].get('sell', [])),
+                    "buy-Params": len(s["hyperoptable"].get("buy", [])),
-            })
+                    "sell-Params": len(s["hyperoptable"].get("sell", [])),
-    print(tabulate(objs_to_print, headers='keys', tablefmt='psql', stralign='right'))
+                }
+            )
+    print(tabulate(objs_to_print, headers="keys", tablefmt="psql", stralign="right"))
 
 
 def start_list_strategies(args: Dict[str, Any]) -> None:
@@ -100,19 +114,20 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
     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))
+        config, not args["print_one_column"], config.get("recursive_strategy_search", False)
+    )
     # Sort alphabetically
-    strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
+    strategy_objs = sorted(strategy_objs, key=lambda x: x["name"])
     for obj in strategy_objs:
-        if obj['class']:
+        if obj["class"]:
-            obj['hyperoptable'] = obj['class'].detect_all_parameters()
+            obj["hyperoptable"] = obj["class"].detect_all_parameters()
         else:
-            obj['hyperoptable'] = {'count': 0}
+            obj["hyperoptable"] = {"count": 0}
 
-    if args['print_one_column']:
+    if args["print_one_column"]:
-        print('\n'.join([s['name'] for s in strategy_objs]))
+        print("\n".join([s["name"] for s in strategy_objs]))
     else:
-        _print_objs_tabular(strategy_objs, config.get('print_colorized', False))
+        _print_objs_tabular(strategy_objs, config.get("print_colorized", False))
 
 
 def start_list_freqAI_models(args: Dict[str, Any]) -> None:
@@ -121,13 +136,14 @@ def start_list_freqAI_models(args: Dict[str, Any]) -> None:
     """
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
     from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
-    model_objs = FreqaiModelResolver.search_all_objects(config, not args['print_one_column'])
+
+    model_objs = FreqaiModelResolver.search_all_objects(config, not args["print_one_column"])
     # Sort alphabetically
-    model_objs = sorted(model_objs, key=lambda x: x['name'])
+    model_objs = sorted(model_objs, key=lambda x: x["name"])
-    if args['print_one_column']:
+    if args["print_one_column"]:
-        print('\n'.join([s['name'] for s in model_objs]))
+        print("\n".join([s["name"] for s in model_objs]))
     else:
-        _print_objs_tabular(model_objs, config.get('print_colorized', False))
+        _print_objs_tabular(model_objs, config.get("print_colorized", False))
 
 
 def start_list_timeframes(args: Dict[str, Any]) -> None:
@@ -136,16 +152,18 @@ def start_list_timeframes(args: Dict[str, Any]) -> None:
     """
     config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
     # Do not use timeframe set in the config
-    config['timeframe'] = None
+    config["timeframe"] = None
 
     # Init exchange
     exchange = ExchangeResolver.load_exchange(config, validate=False)
 
-    if args['print_one_column']:
+    if args["print_one_column"]:
-        print('\n'.join(exchange.timeframes))
+        print("\n".join(exchange.timeframes))
     else:
-        print(f"Timeframes available for the exchange `{exchange.name}`: "
+        print(
-              f"{', '.join(exchange.timeframes)}")
+            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:
@@ -161,51 +179,75 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
     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)
+    active_only = not args.get("list_pairs_all", False)
 
-    base_currencies = args.get('base_currencies', [])
+    base_currencies = args.get("base_currencies", [])
-    quote_currencies = args.get('quote_currencies', [])
+    quote_currencies = args.get("quote_currencies", [])
 
     try:
-        pairs = exchange.get_markets(base_currencies=base_currencies,
+        pairs = exchange.get_markets(
-                                     quote_currencies=quote_currencies,
+            base_currencies=base_currencies,
-                                     tradable_only=pairs_only,
+            quote_currencies=quote_currencies,
-                                     active_only=active_only)
+            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
 
     else:
-        summary_str = ((f"Exchange {exchange.name} has {len(pairs)} ") +
+        summary_str = (
-                       ("active " if active_only else "") +
+            (f"Exchange {exchange.name} has {len(pairs)} ")
-                       (plural(len(pairs), "pair" if pairs_only else "market")) +
+            + ("active " if active_only else "")
-                       (f" with {', '.join(base_currencies)} as base "
+            + (plural(len(pairs), "pair" if pairs_only else "market"))
-                        f"{plural(len(base_currencies), 'currency', 'currencies')}"
+            + (
-                        if base_currencies else "") +
+                f" with {', '.join(base_currencies)} as base "
-                       (" and" if base_currencies and quote_currencies else "") +
+                f"{plural(len(base_currencies), 'currency', 'currencies')}"
-                       (f" with {', '.join(quote_currencies)} as quote "
+                if base_currencies
-                        f"{plural(len(quote_currencies), 'currency', 'currencies')}"
+                else ""
-                        if quote_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",
+        headers = [
-                   "Spot", "Margin", "Future", "Leverage"]
+            "Id",
+            "Symbol",
+            "Base",
+            "Quote",
+            "Active",
+            "Spot",
+            "Margin",
+            "Future",
+            "Leverage",
+        ]
 
-        tabular_data = [{
+        tabular_data = [
-                'Id': v['id'],
+            {
-                'Symbol': v['symbol'],
+                "Id": v["id"],
-                'Base': v['base'],
+                "Symbol": v["symbol"],
-                'Quote': v['quote'],
+                "Base": v["base"],
-                'Active': market_is_active(v),
+                "Quote": v["quote"],
-                'Spot': 'Spot' if exchange.market_is_spot(v) else '',
+                "Active": market_is_active(v),
-                'Margin': 'Margin' if exchange.market_is_margin(v) else '',
+                "Spot": "Spot" if exchange.market_is_spot(v) else "",
-                'Future': 'Future' if exchange.market_is_future(v) else '',
+                "Margin": "Margin" if exchange.market_is_margin(v) else "",
-                'Leverage': exchange.get_max_leverage(v['symbol'], 20)
+                "Future": "Future" if exchange.market_is_future(v) else "",
-            } for _, v in pairs.items()]
+                "Leverage": exchange.get_max_leverage(v["symbol"], 20),
+            }
+            for _, v in pairs.items()
+        ]
 
-        if (args.get('print_one_column', False) or
+        if (
-                args.get('list_pairs_print_json', False) or
+            args.get("print_one_column", False)
-                args.get('print_csv', 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}.")
@@ -215,24 +257,26 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
             print()
 
         if pairs:
-            if args.get('print_list', False):
+            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):
+            elif args.get("print_one_column", False):
-                print('\n'.join(pairs.keys()))
+                print("\n".join(pairs.keys()))
-            elif args.get('list_pairs_print_json', False):
+            elif args.get("list_pairs_print_json", False):
                 print(rapidjson.dumps(list(pairs.keys()), default=str))
-            elif args.get('print_csv', False):
+            elif args.get("print_csv", False):
                 writer = csv.DictWriter(sys.stdout, fieldnames=headers)
                 writer.writeheader()
                 writer.writerows(tabular_data)
             else:
                 # print data as a table, with the human-readable summary
                 print(f"{summary_str}:")
-                print(tabulate(tabular_data, headers='keys', tablefmt='psql', stralign='right'))
+                print(tabulate(tabular_data, headers="keys", tablefmt="psql", stralign="right"))
-        elif not (args.get('print_one_column', False) or
+        elif not (
-                  args.get('list_pairs_print_json', False) or
+            args.get("print_one_column", False)
-                  args.get('print_csv', False)):
+            or args.get("list_pairs_print_json", False)
+            or args.get("print_csv", False)
+        ):
             print(f"{summary_str}.")
 
 
@@ -243,21 +287,22 @@ def start_show_trades(args: Dict[str, Any]) -> None:
     import json
 
     from freqtrade.persistence import Trade, init_db
+
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
-    if 'db_url' not in config:
+    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'])
+    init_db(config["db_url"])
     tfilter = []
 
-    if config.get('trade_ids'):
+    if config.get("trade_ids"):
-        tfilter.append(Trade.id.in_(config['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):
+    if config.get("print_json", False):
         print(json.dumps([trade.to_json() for trade in trades], indent=4))
     else:
         for trade in trades:

freqtrade/commands/optimize_commands.py

@@ -21,20 +21,22 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
     config = setup_utils_configuration(args, method)
 
     no_unlimited_runmodes = {
-        RunMode.BACKTEST: 'backtesting',
+        RunMode.BACKTEST: "backtesting",
-        RunMode.HYPEROPT: 'hyperoptimization',
+        RunMode.HYPEROPT: "hyperoptimization",
     }
     if method in no_unlimited_runmodes.keys():
-        wallet_size = config['dry_run_wallet'] * config['tradable_balance_ratio']
+        wallet_size = config["dry_run_wallet"] * config["tradable_balance_ratio"]
         # tradable_balance_ratio
-        if (config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT
+        if (
-                and config['stake_amount'] > wallet_size):
+            config["stake_amount"] != constants.UNLIMITED_STAKE_AMOUNT
-            wallet = fmt_coin(wallet_size, config['stake_currency'])
+            and config["stake_amount"] > wallet_size
-            stake = fmt_coin(config['stake_amount'], config['stake_currency'])
+        ):
+            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
 
@@ -51,7 +53,7 @@ def start_backtesting(args: Dict[str, Any]) -> None:
     # Initialize configuration
     config = setup_optimize_configuration(args, RunMode.BACKTEST)
 
-    logger.info('Starting freqtrade in Backtesting mode')
+    logger.info("Starting freqtrade in Backtesting mode")
 
     # Initialize backtesting object
     backtesting = Backtesting(config)
@@ -68,7 +70,7 @@ def start_backtesting_show(args: Dict[str, Any]) -> None:
     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'])
+    results = load_backtest_stats(config["exportfilename"])
 
     show_backtest_results(config, results)
     show_sorted_pairlist(config, results)
@@ -87,20 +89,20 @@ def start_hyperopt(args: Dict[str, Any]) -> None:
         from freqtrade.optimize.hyperopt import Hyperopt
     except ImportError as e:
         raise OperationalException(
-            f"{e}. Please ensure that the hyperopt dependencies are installed.") from e
+            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')
+    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("hyperopt.tpe").setLevel(logging.WARNING)
-            logging.getLogger('filelock').setLevel(logging.WARNING)
+            logging.getLogger("filelock").setLevel(logging.WARNING)
 
             # Initialize backtesting object
             hyperopt = Hyperopt(config)
@@ -108,9 +110,11 @@ def start_hyperopt(args: Dict[str, Any]) -> None:
 
     except Timeout:
         logger.info("Another running instance of freqtrade Hyperopt detected.")
-        logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
+        logger.info(
-                    "Hyperopt module is resource hungry. Please run your Hyperopt sequentially "
+            "Simultaneous execution of multiple Hyperopt commands is not supported. "
-                    "or on separate machines.")
+            "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...
@@ -127,7 +131,7 @@ def start_edge(args: Dict[str, Any]) -> None:
 
     # Initialize configuration
     config = setup_optimize_configuration(args, RunMode.EDGE)
-    logger.info('Starting freqtrade in Edge mode')
+    logger.info("Starting freqtrade in Edge mode")
 
     # Initialize Edge object
     edge_cli = EdgeCli(config)

freqtrade/commands/pairlist_commands.py

@@ -17,28 +17,29 @@ def start_test_pairlist(args: Dict[str, Any]) -> None:
     """
     from freqtrade.persistence import FtNoDBContext
     from freqtrade.plugins.pairlistmanager import PairListManager
+
     config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
 
     exchange = ExchangeResolver.load_exchange(config, validate=False)
 
-    quote_currencies = args.get('quote_currencies')
+    quote_currencies = args.get("quote_currencies")
     if not quote_currencies:
-        quote_currencies = [config.get('stake_currency')]
+        quote_currencies = [config.get("stake_currency")]
     results = {}
     with FtNoDBContext():
         for curr in quote_currencies:
-            config['stake_currency'] = curr
+            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):
+        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):
+        if args.get("print_one_column", False):
-            print('\n'.join(pairlist))
+            print("\n".join(pairlist))
-        elif args.get('list_pairs_print_json', False):
+        elif args.get("list_pairs_print_json", False):
             print(rapidjson.dumps(list(pairlist), default=str))
         else:
             print(pairlist)

freqtrade/commands/plot_commands.py

@@ -6,10 +6,11 @@ from freqtrade.exceptions import ConfigurationError
 
 
 def validate_plot_args(args: Dict[str, Any]) -> None:
-    if not args.get('datadir') and not args.get('config'):
+    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.")
+            "for plot-profit and plot-dataframe."
+        )
 
 
 def start_plot_dataframe(args: Dict[str, Any]) -> None:
@@ -18,6 +19,7 @@ def start_plot_dataframe(args: Dict[str, Any]) -> None:
     """
     # Import here to avoid errors if plot-dependencies are not installed.
     from freqtrade.plot.plotting import load_and_plot_trades
+
     validate_plot_args(args)
     config = setup_utils_configuration(args, RunMode.PLOT)
 
@@ -30,6 +32,7 @@ def start_plot_profit(args: Dict[str, Any]) -> None:
     """
     # Import here to avoid errors if plot-dependencies are not installed.
     from freqtrade.plot.plotting import plot_profit
+
     validate_plot_args(args)
     config = setup_utils_configuration(args, RunMode.PLOT)
 

freqtrade/commands/strategy_utils_commands.py

@@ -26,13 +26,15 @@ def start_strategy_update(args: Dict[str, Any]) -> None:
     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))
+        config, enum_failed=False, recursive=config.get("recursive_strategy_search", False)
+    )
 
     filtered_strategy_objs = []
-    if args['strategy_list']:
+    if args["strategy_list"]:
         filtered_strategy_objs = [
-            strategy_obj for strategy_obj in strategy_objs
+            strategy_obj
-            if strategy_obj['name'] in args['strategy_list']
+            for strategy_obj in strategy_objs
+            if strategy_obj["name"] in args["strategy_list"]
         ]
 
     else:
@@ -41,8 +43,8 @@ def start_strategy_update(args: Dict[str, Any]) -> None:
 
     processed_locations = set()
     for strategy_obj in filtered_strategy_objs:
-        if strategy_obj['location'] not in processed_locations:
+        if strategy_obj["location"] not in processed_locations:
-            processed_locations.add(strategy_obj['location'])
+            processed_locations.add(strategy_obj["location"])
             start_conversion(strategy_obj, config)
 
 

freqtrade/configuration/config_secrets.py

@@ -24,13 +24,13 @@ def sanitize_config(config: Config, *, show_sensitive: bool = False) -> Config:
     ]
     config = deepcopy(config)
     for key in keys_to_remove:
-        if '.' in key:
+        if "." in key:
-            nested_keys = key.split('.')
+            nested_keys = key.split(".")
             nested_config = config
             for nested_key in nested_keys[:-1]:
                 nested_config = nested_config.get(nested_key, {})
-            nested_config[nested_keys[-1]] = 'REDACTED'
+            nested_config[nested_keys[-1]] = "REDACTED"
         else:
-            config[key] = 'REDACTED'
+            config[key] = "REDACTED"
 
     return config

freqtrade/configuration/config_setup.py

@@ -11,7 +11,8 @@ logger = logging.getLogger(__name__)
 
 
 def setup_utils_configuration(
-        args: Dict[str, Any], method: RunMode, *, set_dry: bool = True) -> Dict[str, Any]:
+    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()
@@ -23,7 +24,7 @@ def setup_utils_configuration(
 
     # Ensure these modes are using Dry-run
     if set_dry:
-        config['dry_run'] = True
+        config["dry_run"] = True
     validate_config_consistency(config, preliminary=True)
 
     return config

freqtrade/configuration/config_validation.py

@@ -20,18 +20,16 @@ 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']
+    validate_properties = validator_class.VALIDATORS["properties"]
 
     def set_defaults(validator, properties, instance, schema):
         for prop, subschema in properties.items():
-            if 'default' in subschema:
+            if "default" in subschema:
-                instance.setdefault(prop, subschema['default'])
+                instance.setdefault(prop, subschema["default"])
 
         yield from validate_properties(validator, properties, instance, schema)
 
-    return validators.extend(
+    return validators.extend(validator_class, {"properties": set_defaults})
-        validator_class, {'properties': set_defaults}
-    )
 
 
 FreqtradeValidator = _extend_validator(Draft4Validator)
@@ -44,27 +42,23 @@ def validate_config_schema(conf: Dict[str, Any], preliminary: bool = False) -> D
     :return: Returns the config if valid, otherwise throw an exception
     """
     conf_schema = deepcopy(constants.CONF_SCHEMA)
-    if conf.get('runmode', RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
+    if conf.get("runmode", RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
-        conf_schema['required'] = constants.SCHEMA_TRADE_REQUIRED
+        conf_schema["required"] = constants.SCHEMA_TRADE_REQUIRED
-    elif conf.get('runmode', RunMode.OTHER) in (RunMode.BACKTEST, RunMode.HYPEROPT):
+    elif conf.get("runmode", RunMode.OTHER) in (RunMode.BACKTEST, RunMode.HYPEROPT):
         if preliminary:
-            conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
+            conf_schema["required"] = constants.SCHEMA_BACKTEST_REQUIRED
         else:
-            conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED_FINAL
+            conf_schema["required"] = constants.SCHEMA_BACKTEST_REQUIRED_FINAL
-    elif conf.get('runmode', RunMode.OTHER) == RunMode.WEBSERVER:
+    elif conf.get("runmode", RunMode.OTHER) == RunMode.WEBSERVER:
-        conf_schema['required'] = constants.SCHEMA_MINIMAL_WEBSERVER
+        conf_schema["required"] = constants.SCHEMA_MINIMAL_WEBSERVER
     else:
-        conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
+        conf_schema["required"] = constants.SCHEMA_MINIMAL_REQUIRED
     try:
         FreqtradeValidator(conf_schema).validate(conf)
         return conf
     except ValidationError as e:
-        logger.critical(
+        logger.critical(f"Invalid configuration. Reason: {e}")
-            f"Invalid configuration. Reason: {e}"
+        raise ValidationError(best_match(Draft4Validator(conf_schema).iter_errors(conf)).message)
-        )
-        raise ValidationError(
-            best_match(Draft4Validator(conf_schema).iter_errors(conf)).message
-        )
 
 
 def validate_config_consistency(conf: Dict[str, Any], *, preliminary: bool = False) -> None:
@@ -92,7 +86,7 @@ def validate_config_consistency(conf: Dict[str, Any], *, preliminary: bool = Fal
     _validate_orderflow(conf)
 
     # validate configuration before returning
-    logger.info('Validating configuration ...')
+    logger.info("Validating configuration ...")
     validate_config_schema(conf, preliminary=preliminary)
 
 
@@ -101,9 +95,11 @@ 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')
+    if (
-        and conf.get('max_open_trades') == float('inf')
+        not conf.get("edge", {}).get("enabled")
-            and conf.get('stake_amount') == constants.UNLIMITED_STAKE_AMOUNT):
+        and conf.get("max_open_trades") == float("inf")
+        and conf.get("stake_amount") == constants.UNLIMITED_STAKE_AMOUNT
+    ):
         raise ConfigurationError("`max_open_trades` and `stake_amount` cannot both be unlimited.")
 
 
@@ -112,45 +108,47 @@ 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'
+    if conf.get("order_types", {}).get("entry") == "market" and conf.get("entry_pricing", {}).get(
-            and conf.get('entry_pricing', {}).get('price_side') not in ('ask', 'other')):
+        "price_side"
-        raise ConfigurationError(
+    ) not in ("ask", "other"):
-            'Market entry orders require entry_pricing.price_side = "other".')
+        raise ConfigurationError('Market entry orders require entry_pricing.price_side = "other".')
 
-    if (conf.get('order_types', {}).get('exit') == 'market'
+    if conf.get("order_types", {}).get("exit") == "market" and conf.get("exit_pricing", {}).get(
-            and conf.get('exit_pricing', {}).get('price_side') not in ('bid', 'other')):
+        "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:
-    if conf.get('stoploss') == 0.0:
         raise ConfigurationError(
-            'The config stoploss needs to be different from 0 to avoid problems with sell orders.'
+            "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):
+    if not conf.get("trailing_stop", False):
         return
 
-    tsl_positive = float(conf.get('trailing_stop_positive', 0))
+    tsl_positive = float(conf.get("trailing_stop_positive", 0))
-    tsl_offset = float(conf.get('trailing_stop_positive_offset', 0))
+    tsl_offset = float(conf.get("trailing_stop_positive_offset", 0))
-    tsl_only_offset = conf.get('trailing_only_offset_is_reached', False)
+    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 '
+                "The config trailing_only_offset_is_reached needs "
-                'trailing_stop_positive_offset to be more than 0 in your config.')
+                "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 '
+            "The config trailing_stop_positive_offset needs "
-            'to be greater than trailing_stop_positive in your config.')
+            "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:
+    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 '
+            "The config trailing_stop_positive needs to be different from 0 "
-            'to avoid problems with sell orders.'
+            "to avoid problems with sell orders."
         )
 
 
@@ -159,10 +157,10 @@ 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'):
+    if not conf.get("edge", {}).get("enabled"):
         return
 
-    if not conf.get('use_exit_signal', True):
+    if not conf.get("use_exit_signal", True):
         raise ConfigurationError(
             "Edge requires `use_exit_signal` to be True, otherwise no sells will happen."
         )
@@ -172,13 +170,20 @@ 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,
+    if conf.get("runmode", RunMode.OTHER) in [
-                                              RunMode.UTIL_NO_EXCHANGE, RunMode.UTIL_EXCHANGE]:
+        RunMode.OTHER,
+        RunMode.PLOT,
+        RunMode.UTIL_NO_EXCHANGE,
+        RunMode.UTIL_EXCHANGE,
+    ]:
         return
 
-    for pl in conf.get('pairlists', [{'method': 'StaticPairList'}]):
+    for pl in conf.get("pairlists", [{"method": "StaticPairList"}]):
-        if (isinstance(pl, dict) and pl.get('method') == 'StaticPairList'
+        if (
-                and not conf.get('exchange', {}).get('pair_whitelist')):
+            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.")
 
 
@@ -187,14 +192,14 @@ def _validate_protections(conf: Dict[str, Any]) -> None:
     Validate protection configuration validity
     """
 
-    for prot in conf.get('protections', []):
+    for prot in conf.get("protections", []):
-        if ('stop_duration' in prot and 'stop_duration_candles' in prot):
+        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):
+        if "lookback_period" in prot and "lookback_period_candles" in prot:
             raise ConfigurationError(
                 "Protections must specify either `lookback_period` or `lookback_period_candles`.\n"
                 f"Please fix the protection {prot.get('method')}"
@@ -202,10 +207,10 @@ def _validate_protections(conf: Dict[str, Any]) -> None:
 
 
 def _validate_ask_orderbook(conf: Dict[str, Any]) -> None:
-    ask_strategy = conf.get('exit_pricing', {})
+    ask_strategy = conf.get("exit_pricing", {})
-    ob_min = ask_strategy.get('order_book_min')
+    ob_min = ask_strategy.get("order_book_min")
-    ob_max = ask_strategy.get('order_book_max')
+    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 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."
@@ -213,7 +218,7 @@ def _validate_ask_orderbook(conf: Dict[str, Any]) -> None:
             )
         else:
             # Move value to order_book_top
-            ask_strategy['order_book_top'] = ob_min
+            ask_strategy["order_book_top"] = ob_min
             logger.warning(
                 "DEPRECATED: "
                 "Please use `order_book_top` instead of `order_book_min` and `order_book_max` "
@@ -222,7 +227,6 @@ def _validate_ask_orderbook(conf: Dict[str, Any]) -> None:
 
 
 def validate_migrated_strategy_settings(conf: Dict[str, Any]) -> None:
-
     _validate_time_in_force(conf)
     _validate_order_types(conf)
     _validate_unfilledtimeout(conf)
@@ -231,119 +235,129 @@ def validate_migrated_strategy_settings(conf: Dict[str, Any]) -> None:
 
 
 def _validate_time_in_force(conf: Dict[str, Any]) -> None:
-
+    time_in_force = conf.get("order_time_in_force", {})
-    time_in_force = conf.get('order_time_in_force', {})
+    if "buy" in time_in_force or "sell" in time_in_force:
-    if 'buy' in time_in_force or 'sell' in time_in_force:
+        if conf.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT:
-        if conf.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT:
             raise ConfigurationError(
-                "Please migrate your time_in_force settings to use 'entry' and 'exit'.")
+                "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')
+                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')
+                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", {})
-    order_types = conf.get('order_types', {})
+    old_order_types = [
-    old_order_types = ['buy', 'sell', 'emergencysell', 'forcebuy',
+        "buy",
-                       'forcesell', 'emergencyexit', 'forceexit', 'forceentry']
+        "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:
+        if conf.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT:
             raise ConfigurationError(
-                "Please migrate your order_types settings to use the new wording.")
+                "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'),
+                ("buy", "entry"),
-                ('sell', 'exit'),
+                ("sell", "exit"),
-                ('emergencysell', 'emergency_exit'),
+                ("emergencysell", "emergency_exit"),
-                ('forcesell', 'force_exit'),
+                ("forcesell", "force_exit"),
-                ('forcebuy', 'force_entry'),
+                ("forcebuy", "force_entry"),
-                ('emergencyexit', 'emergency_exit'),
+                ("emergencyexit", "emergency_exit"),
-                ('forceexit', 'force_exit'),
+                ("forceexit", "force_exit"),
-                ('forceentry', 'force_entry'),
+                ("forceentry", "force_entry"),
             ]:
-
+                process_deprecated_setting(conf, "order_types", o, "order_types", n)
-                process_deprecated_setting(conf, 'order_types', o, 'order_types', n)
 
 
 def _validate_unfilledtimeout(conf: Dict[str, Any]) -> None:
-    unfilledtimeout = conf.get('unfilledtimeout', {})
+    unfilledtimeout = conf.get("unfilledtimeout", {})
-    if any(x in unfilledtimeout for x in ['buy', 'sell']):
+    if any(x in unfilledtimeout for x in ["buy", "sell"]):
-        if conf.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT:
+        if conf.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT:
             raise ConfigurationError(
-                "Please migrate your unfilledtimeout settings to use the new wording.")
+                "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'),
+                ("buy", "entry"),
-                ('sell', 'exit'),
+                ("sell", "exit"),
             ]:
-
+                process_deprecated_setting(conf, "unfilledtimeout", o, "unfilledtimeout", n)
-                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('ask_strategy') or conf.get('bid_strategy'):
+        if conf.get("trading_mode", TradingMode.SPOT) != TradingMode.SPOT:
-        if conf.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT:
+            raise ConfigurationError("Please migrate your pricing settings to use the new wording.")
-            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'] = {}
+            conf["entry_pricing"] = {}
-            for obj in list(conf.get('bid_strategy', {}).keys()):
+            for obj in list(conf.get("bid_strategy", {}).keys()):
-                if obj == 'ask_last_balance':
+                if obj == "ask_last_balance":
-                    process_deprecated_setting(conf, 'bid_strategy', obj,
+                    process_deprecated_setting(
-                                               'entry_pricing', 'price_last_balance')
+                        conf, "bid_strategy", obj, "entry_pricing", "price_last_balance"
+                    )
                 else:
-                    process_deprecated_setting(conf, 'bid_strategy', obj, 'entry_pricing', obj)
+                    process_deprecated_setting(conf, "bid_strategy", obj, "entry_pricing", obj)
-            del conf['bid_strategy']
+            del conf["bid_strategy"]
 
-            conf['exit_pricing'] = {}
+            conf["exit_pricing"] = {}
-            for obj in list(conf.get('ask_strategy', {}).keys()):
+            for obj in list(conf.get("ask_strategy", {}).keys()):
-                if obj == 'bid_last_balance':
+                if obj == "bid_last_balance":
-                    process_deprecated_setting(conf, 'ask_strategy', obj,
+                    process_deprecated_setting(
-                                               'exit_pricing', 'price_last_balance')
+                        conf, "ask_strategy", obj, "exit_pricing", "price_last_balance"
+                    )
                 else:
-                    process_deprecated_setting(conf, 'ask_strategy', obj, 'exit_pricing', obj)
+                    process_deprecated_setting(conf, "ask_strategy", obj, "exit_pricing", obj)
-            del conf['ask_strategy']
+            del conf["ask_strategy"]
 
 
 def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
-    freqai_enabled = conf.get('freqai', {}).get('enabled', False)
+    freqai_enabled = conf.get("freqai", {}).get("enabled", False)
-    analyze_per_epoch = conf.get('analyze_per_epoch', 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.')
+            "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)
+    freqai_enabled = conf.get("freqai", {}).get("enabled", False)
     if freqai_enabled:
-        main_tf = conf.get('timeframe', '5m')
+        main_tf = conf.get("timeframe", "5m")
-        freqai_include_timeframes = conf.get('freqai', {}).get('feature_parameters', {}
+        freqai_include_timeframes = (
-                                                               ).get('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:
@@ -353,51 +367,59 @@ def _validate_freqai_include_timeframes(conf: Dict[str, Any], preliminary: bool)
         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)}")
+                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', {})
+            feature_parameters = conf.get("freqai", {}).get("feature_parameters", {})
             include_timeframes = [main_tf] + freqai_include_timeframes
-            conf.get('freqai', {}).get('feature_parameters', {}) \
+            conf.get("freqai", {}).get("feature_parameters", {}).update(
-                .update({**feature_parameters, 'include_timeframes': include_timeframes})
+                {**feature_parameters, "include_timeframes": include_timeframes}
+            )
 
 
 def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
-    if conf.get('runmode', RunMode.OTHER) == RunMode.BACKTEST:
+    if conf.get("runmode", RunMode.OTHER) == RunMode.BACKTEST:
-        freqai_enabled = conf.get('freqai', {}).get('enabled', False)
+        freqai_enabled = conf.get("freqai", {}).get("enabled", False)
-        timerange = conf.get('timerange')
+        timerange = conf.get("timerange")
-        freqai_backtest_live_models = conf.get('freqai_backtest_live_models', False)
+        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 '
+                "Using timerange parameter is not supported with "
-                '--freqai-backtest-live-models parameter.')
+                "--freqai-backtest-live-models parameter."
+            )
 
         if freqai_backtest_live_models and not freqai_enabled:
             raise ConfigurationError(
-                'Using --freqai-backtest-live-models parameter is only '
+                "Using --freqai-backtest-live-models parameter is only "
-                'supported with a FreqAI strategy.')
+                "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.')
+                "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', {})
+    emc_conf = conf.get("external_message_consumer", {})
-    if emc_conf.get('enabled', False):
+    if emc_conf.get("enabled", False):
-        if len(emc_conf.get('producers', [])) < 1:
+        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', [])]
+        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)}")
+                f"Producer names must be unique. Duplicate: {', '.join(duplicates)}"
-        if conf.get('process_only_new_candles', True):
+            )
+        if conf.get("process_only_new_candles", True):
             # Warning here or require it?
-            logger.warning("To receive best performance with external data, "
+            logger.warning(
-                           "please set `process_only_new_candles` to False")
+                "To receive best performance with external data, "
+                "please set `process_only_new_candles` to False"
+            )
 
 
 def _validate_orderflow(conf: Dict[str, Any]) -> None:
@@ -409,9 +431,9 @@ def _validate_orderflow(conf: Dict[str, Any]) -> None:
 
 
 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, '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_only', None, 'exit_profit_only')
+    process_deprecated_setting(conf, None, "sell_profit_offset", None, "exit_profit_offset")
-    process_deprecated_setting(conf, None, 'sell_profit_offset', None, 'exit_profit_offset')
+    process_deprecated_setting(
-    process_deprecated_setting(conf, None, 'ignore_roi_if_buy_signal',
+        conf, None, "ignore_roi_if_buy_signal", None, "ignore_roi_if_entry_signal"
-                               None, 'ignore_roi_if_entry_signal')
+    )

freqtrade/configuration/configuration.py

@@ -1,6 +1,7 @@
 """
 This module contains the configuration class
 """
+
 import logging
 import warnings
 from copy import deepcopy
@@ -13,7 +14,7 @@ from freqtrade.configuration.directory_operations import create_datadir, create_
 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, TRADING_MODES, CandleType, RunMode, TradingMode
+from freqtrade.enums import NON_UTIL_MODES, TRADE_MODES, CandleType, 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
@@ -56,7 +57,7 @@ class Configuration:
         :return: configuration dictionary
         """
         # Keep this method as staticmethod, so it can be used from interactive environments
-        c = Configuration({'config': files}, RunMode.OTHER)
+        c = Configuration({"config": files}, RunMode.OTHER)
         return c.get_config()
 
     def load_config(self) -> Dict[str, Any]:
@@ -69,19 +70,20 @@ class Configuration:
 
         # Load environment variables
         from freqtrade.commands.arguments import NO_CONF_ALLOWED
-        if self.args.get('command') not in 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:
+        if "internals" not in config:
-            config['internals'] = {}
+            config["internals"] = {}
 
-        if 'pairlists' not in config:
+        if "pairlists" not in config:
-            config['pairlists'] = []
+            config["pairlists"] = []
 
         # Keep a copy of the original configuration file
-        config['original_config'] = deepcopy(config)
+        config["original_config"] = deepcopy(config)
 
         self._process_logging_options(config)
 
@@ -105,7 +107,7 @@ class Configuration:
         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))
+        check_exchange(config, config.get("experimental", {}).get("block_bad_exchanges", True))
 
         self._resolve_pairs_list(config)
 
@@ -119,52 +121,56 @@ class Configuration:
         the -v/--verbose, --logfile options
         """
         # Log level
-        config.update({'verbosity': self.args.get('verbosity', 0)})
+        config.update({"verbosity": self.args.get("verbosity", 0)})
 
-        if 'logfile' in self.args and self.args['logfile']:
+        if "logfile" in self.args and self.args["logfile"]:
-            config.update({'logfile': self.args['logfile']})
+            config.update({"logfile": self.args["logfile"]})
 
         setup_logging(config)
 
     def _process_trading_options(self, config: Config) -> None:
-        if config['runmode'] not in TRADING_MODES:
+        if config["runmode"] not in TRADE_MODES:
             return
 
-        if config.get('dry_run', False):
+        if config.get("dry_run", False):
-            logger.info('Dry run is enabled')
+            logger.info("Dry run is enabled")
-            if config.get('db_url') in [None, constants.DEFAULT_DB_PROD_URL]:
+            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
+                config["db_url"] = constants.DEFAULT_DB_DRYRUN_URL
         else:
-            if not config.get('db_url'):
+            if not config.get("db_url"):
-                config['db_url'] = constants.DEFAULT_DB_PROD_URL
+                config["db_url"] = constants.DEFAULT_DB_PROD_URL
-            logger.info('Dry run is disabled')
+            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'):
+        if self.args.get("strategy") or not config.get("strategy"):
-            config.update({'strategy': self.args.get('strategy')})
+            config.update({"strategy": self.args.get("strategy")})
 
-        self._args_to_config(config, argname='strategy_path',
+        self._args_to_config(
-                             logstring='Using additional Strategy lookup path: {}')
+            config, argname="strategy_path", logstring="Using additional Strategy lookup path: {}"
+        )
 
-        if ('db_url' in self.args and self.args['db_url'] and
+        if (
-                self.args['db_url'] != constants.DEFAULT_DB_PROD_URL):
+            "db_url" in self.args
-            config.update({'db_url': self.args['db_url']})
+            and self.args["db_url"]
-            logger.info('Parameter --db-url detected ...')
+            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',
+        self._args_to_config(
-                             logstring='Parameter --db-url-from detected ...')
+            config, argname="db_url_from", logstring="Parameter --db-url-from detected ..."
+        )
 
-        if config.get('force_entry_enable', False):
+        if config.get("force_entry_enable", False):
-            logger.warning('`force_entry_enable` RPC message enabled.')
+            logger.warning("`force_entry_enable` RPC message enabled.")
 
         # Support for sd_notify
-        if 'sd_notify' in self.args and self.args['sd_notify']:
+        if "sd_notify" in self.args and self.args["sd_notify"]:
-            config['internals'].update({'sd_notify': True})
+            config["internals"].update({"sd_notify": True})
 
     def _process_datadir_options(self, config: Config) -> None:
         """
@@ -172,245 +178,274 @@ class Configuration:
         --user-data, --datadir
         """
         # Check exchange parameter here - otherwise `datadir` might be wrong.
-        if 'exchange' in self.args and self.args['exchange']:
+        if "exchange" in self.args and self.args["exchange"]:
-            config['exchange']['name'] = self.args['exchange']
+            config["exchange"]["name"] = self.args["exchange"]
             logger.info(f"Using exchange {config['exchange']['name']}")
 
-        if 'pair_whitelist' not in config['exchange']:
+        if "pair_whitelist" not in config["exchange"]:
-            config['exchange']['pair_whitelist'] = []
+            config["exchange"]["pair_whitelist"] = []
 
-        if 'user_data_dir' in self.args and self.args['user_data_dir']:
+        if "user_data_dir" in self.args and self.args["user_data_dir"]:
-            config.update({'user_data_dir': self.args['user_data_dir']})
+            config.update({"user_data_dir": self.args["user_data_dir"]})
-        elif 'user_data_dir' not in config:
+        elif "user_data_dir" not in config:
             # Default to cwd/user_data (legacy option ...)
-            config.update({'user_data_dir': str(Path.cwd() / 'user_data')})
+            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)
+        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'])
+        logger.info("Using user-data directory: %s ...", config["user_data_dir"])
 
-        config.update({'datadir': create_datadir(config, self.args.get('datadir'))})
+        config.update({"datadir": create_datadir(config, self.args.get("datadir"))})
-        logger.info('Using data directory: %s ...', config.get('datadir'))
+        logger.info("Using data directory: %s ...", config.get("datadir"))
 
-        if self.args.get('exportfilename'):
+        if self.args.get("exportfilename"):
-            self._args_to_config(config, argname='exportfilename',
+            self._args_to_config(
-                                 logstring='Storing backtest results to {} ...')
+                config, argname="exportfilename", logstring="Storing backtest results to {} ..."
-            config['exportfilename'] = Path(config['exportfilename'])
+            )
+            config["exportfilename"] = Path(config["exportfilename"])
         else:
-            config['exportfilename'] = (config['user_data_dir']
+            config["exportfilename"] = config["user_data_dir"] / "backtest_results"
-                                        / 'backtest_results')
 
-        if self.args.get('show_sensitive'):
+        if self.args.get("show_sensitive"):
             logger.warning(
-                "Sensitive information will be shown in the upcomming output. "
+                "Sensitive information will be shown in the upcoming output. "
                 "Please make sure to never share this output without redacting "
-                "the information yourself.")
+                "the information yourself."
+            )
 
     def _process_optimize_options(self, config: Config) -> None:
-
         # This will override the strategy configuration
-        self._args_to_config(config, argname='timeframe',
+        self._args_to_config(
-                             logstring='Parameter -i/--timeframe detected ... '
+            config,
-                                       'Using timeframe: {} ...')
+            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',
+            config,
-            logstring='Parameter --enable-protections detected, enabling Protections. ...')
+            argname="position_stacking",
+            logstring="Parameter --enable-position-stacking detected ...",
+        )
 
-        if 'use_max_market_positions' in self.args and not self.args["use_max_market_positions"]:
+        self._args_to_config(
-            config.update({'use_max_market_positions': False})
+            config,
-            logger.info('Parameter --disable-max-market-positions detected ...')
+            argname="enable_protections",
-            logger.info('max_open_trades set to unlimited ...')
+            logstring="Parameter --enable-protections detected, enabling Protections. ...",
-        elif 'max_open_trades' in self.args and self.args['max_open_trades']:
+        )
-            config.update({'max_open_trades': self.args['max_open_trades']})
+
-            logger.info('Parameter --max-open-trades detected, '
+        if "use_max_market_positions" in self.args and not self.args["use_max_market_positions"]:
-                        'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
+            config.update({"use_max_market_positions": False})
-        elif config['runmode'] in NON_UTIL_MODES:
+            logger.info("Parameter --disable-max-market-positions detected ...")
-            logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
+            logger.info("max_open_trades set to unlimited ...")
+        elif "max_open_trades" in self.args and self.args["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:
+        if config.get("max_open_trades") == -1:
-            config['max_open_trades'] = float('inf')
+            config["max_open_trades"] = float("inf")
 
-        if self.args.get('stake_amount'):
+        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'])
+                self.args["stake_amount"] = float(self.args["stake_amount"])
             except ValueError:
                 pass
 
         configurations = [
-            ('timeframe_detail',
+            (
-             'Parameter --timeframe-detail detected, using {} for intra-candle backtesting ...'),
+                "timeframe_detail",
-            ('backtest_show_pair_list', 'Parameter --show-pair-list detected.'),
+                "Parameter --timeframe-detail detected, using {} for intra-candle backtesting ...",
-            ('stake_amount',
+            ),
-             'Parameter --stake-amount detected, overriding stake_amount to: {} ...'),
+            ("backtest_show_pair_list", "Parameter --show-pair-list detected."),
-            ('dry_run_wallet',
+            (
-             'Parameter --dry-run-wallet detected, overriding dry_run_wallet to: {} ...'),
+                "stake_amount",
-            ('fee', 'Parameter --fee detected, setting fee to: {} ...'),
+                "Parameter --stake-amount detected, overriding stake_amount to: {} ...",
-            ('timerange', 'Parameter --timerange detected: {} ...'),
+            ),
-            ]
+            (
+                "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',
+        self._args_to_config(
-                             logstring='Using strategy list of {} strategies', logfun=len)
+            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.'),
+                "recursive_strategy_search",
-            ('timeframe', 'Overriding timeframe with Command line argument'),
+                "Recursively searching for a strategy in the strategies folder.",
-            ('export', 'Parameter --export detected: {} ...'),
+            ),
-            ('backtest_breakdown', 'Parameter --breakdown detected ...'),
+            ("timeframe", "Overriding timeframe with Command line argument"),
-            ('backtest_cache', 'Parameter --cache={} detected ...'),
+            ("export", "Parameter --export detected: {} ..."),
-            ('disableparamexport', 'Parameter --disableparamexport detected: {} ...'),
+            ("backtest_breakdown", "Parameter --breakdown detected ..."),
-            ('freqai_backtest_live_models',
+            ("backtest_cache", "Parameter --cache={} detected ..."),
-             'Parameter --freqai-backtest-live-models 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 'stoploss_range' in self.args and self.args["stoploss_range"]:
+        if "stoploss_range" in self.args and self.args["stoploss_range"]:
             txt_range = eval(self.args["stoploss_range"])
-            config['edge'].update({'stoploss_range_min': txt_range[0]})
+            config["edge"].update({"stoploss_range_min": txt_range[0]})
-            config['edge'].update({'stoploss_range_max': txt_range[1]})
+            config["edge"].update({"stoploss_range_max": txt_range[1]})
-            config['edge'].update({'stoploss_range_step': txt_range[2]})
+            config["edge"].update({"stoploss_range_step": txt_range[2]})
-            logger.info('Parameter --stoplosses detected: %s ...', self.args["stoploss_range"])
+            logger.info("Parameter --stoplosses detected: %s ...", self.args["stoploss_range"])
 
         # Hyperopt section
 
         configurations = [
-            ('hyperopt', 'Using Hyperopt class name: {}'),
+            ("hyperopt", "Using Hyperopt class name: {}"),
-            ('hyperopt_path', 'Using additional Hyperopt lookup path: {}'),
+            ("hyperopt_path", "Using additional Hyperopt lookup path: {}"),
-            ('hyperoptexportfilename', 'Using hyperopt file: {}'),
+            ("hyperoptexportfilename", "Using hyperopt file: {}"),
-            ('lookahead_analysis_exportfilename', 'Saving lookahead analysis results into {} ...'),
+            ("lookahead_analysis_exportfilename", "Saving lookahead analysis results into {} ..."),
-            ('epochs', 'Parameter --epochs detected ... Will run Hyperopt with for {} epochs ...'),
+            ("epochs", "Parameter --epochs detected ... Will run Hyperopt with for {} epochs ..."),
-            ('spaces', 'Parameter -s/--spaces detected: {}'),
+            ("spaces", "Parameter -s/--spaces detected: {}"),
-            ('analyze_per_epoch', 'Parameter --analyze-per-epoch detected.'),
+            ("analyze_per_epoch", "Parameter --analyze-per-epoch detected."),
-            ('print_all', 'Parameter --print-all detected ...'),
+            ("print_all", "Parameter --print-all detected ..."),
         ]
         self._args_to_config_loop(config, configurations)
 
-        if 'print_colorized' in self.args and not self.args["print_colorized"]:
+        if "print_colorized" in self.args and not self.args["print_colorized"]:
-            logger.info('Parameter --no-color detected ...')
+            logger.info("Parameter --no-color detected ...")
-            config.update({'print_colorized': False})
+            config.update({"print_colorized": False})
         else:
-            config.update({'print_colorized': True})
+            config.update({"print_colorized": True})
 
         configurations = [
-            ('print_json', 'Parameter --print-json detected ...'),
+            ("print_json", "Parameter --print-json detected ..."),
-            ('export_csv', 'Parameter --export-csv detected: {}'),
+            ("export_csv", "Parameter --export-csv detected: {}"),
-            ('hyperopt_jobs', 'Parameter -j/--job-workers detected: {}'),
+            ("hyperopt_jobs", "Parameter -j/--job-workers detected: {}"),
-            ('hyperopt_random_state', 'Parameter --random-state detected: {}'),
+            ("hyperopt_random_state", "Parameter --random-state detected: {}"),
-            ('hyperopt_min_trades', 'Parameter --min-trades detected: {}'),
+            ("hyperopt_min_trades", "Parameter --min-trades detected: {}"),
-            ('hyperopt_loss', 'Using Hyperopt loss class name: {}'),
+            ("hyperopt_loss", "Using Hyperopt loss class name: {}"),
-            ('hyperopt_show_index', 'Parameter -n/--index detected: {}'),
+            ("hyperopt_show_index", "Parameter -n/--index detected: {}"),
-            ('hyperopt_list_best', 'Parameter --best detected: {}'),
+            ("hyperopt_list_best", "Parameter --best detected: {}"),
-            ('hyperopt_list_profitable', 'Parameter --profitable detected: {}'),
+            ("hyperopt_list_profitable", "Parameter --profitable detected: {}"),
-            ('hyperopt_list_min_trades', 'Parameter --min-trades detected: {}'),
+            ("hyperopt_list_min_trades", "Parameter --min-trades detected: {}"),
-            ('hyperopt_list_max_trades', 'Parameter --max-trades detected: {}'),
+            ("hyperopt_list_max_trades", "Parameter --max-trades detected: {}"),
-            ('hyperopt_list_min_avg_time', 'Parameter --min-avg-time detected: {}'),
+            ("hyperopt_list_min_avg_time", "Parameter --min-avg-time detected: {}"),
-            ('hyperopt_list_max_avg_time', 'Parameter --max-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_min_avg_profit", "Parameter --min-avg-profit detected: {}"),
-            ('hyperopt_list_max_avg_profit', 'Parameter --max-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_min_total_profit", "Parameter --min-total-profit detected: {}"),
-            ('hyperopt_list_max_total_profit', 'Parameter --max-total-profit detected: {}'),
+            ("hyperopt_list_max_total_profit", "Parameter --max-total-profit detected: {}"),
-            ('hyperopt_list_min_objective', 'Parameter --min-objective detected: {}'),
+            ("hyperopt_list_min_objective", "Parameter --min-objective detected: {}"),
-            ('hyperopt_list_max_objective', 'Parameter --max-objective detected: {}'),
+            ("hyperopt_list_max_objective", "Parameter --max-objective detected: {}"),
-            ('hyperopt_list_no_details', 'Parameter --no-details detected: {}'),
+            ("hyperopt_list_no_details", "Parameter --no-details detected: {}"),
-            ('hyperopt_show_no_header', 'Parameter --no-header detected: {}'),
+            ("hyperopt_show_no_header", "Parameter --no-header detected: {}"),
-            ('hyperopt_ignore_missing_space', 'Paramter --ignore-missing-space 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 {}'),
+            ("pairs", "Using pairs {}"),
-            ('indicators1', 'Using indicators1: {}'),
+            ("indicators1", "Using indicators1: {}"),
-            ('indicators2', 'Using indicators2: {}'),
+            ("indicators2", "Using indicators2: {}"),
-            ('trade_ids', 'Filtering on trade_ids: {}'),
+            ("trade_ids", "Filtering on trade_ids: {}"),
-            ('plot_limit', 'Limiting plot to: {}'),
+            ("plot_limit", "Limiting plot to: {}"),
-            ('plot_auto_open', 'Parameter --auto-open detected.'),
+            ("plot_auto_open", "Parameter --auto-open detected."),
-            ('trade_source', 'Using trades from: {}'),
+            ("trade_source", "Using trades from: {}"),
-            ('prepend_data', 'Prepend detected. Allowing data prepending.'),
+            ("prepend_data", "Prepend detected. Allowing data prepending."),
-            ('erase', 'Erase detected. Deleting existing data.'),
+            ("erase", "Erase detected. Deleting existing data."),
-            ('no_trades', 'Parameter --no-trades detected.'),
+            ("no_trades", "Parameter --no-trades detected."),
-            ('timeframes', 'timeframes --timeframes: {}'),
+            ("timeframes", "timeframes --timeframes: {}"),
-            ('days', 'Detected --days: {}'),
+            ("days", "Detected --days: {}"),
-            ('include_inactive', 'Detected --include-inactive-pairs: {}'),
+            ("include_inactive", "Detected --include-inactive-pairs: {}"),
-            ('download_trades', 'Detected --dl-trades: {}'),
+            ("download_trades", "Detected --dl-trades: {}"),
-            ('dataformat_ohlcv', 'Using "{}" to store OHLCV data.'),
+            ("dataformat_ohlcv", 'Using "{}" to store OHLCV data.'),
-            ('dataformat_trades', 'Using "{}" to store trades data.'),
+            ("dataformat_trades", 'Using "{}" to store trades data.'),
-            ('show_timerange', 'Detected --show-timerange'),
+            ("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',
+        self._args_to_config(
-                             logstring='Detected --new-pairs-days: {}')
+            config, argname="new_pairs_days", logstring="Detected --new-pairs-days: {}"
-        self._args_to_config(config, argname='trading_mode',
+        )
-                             logstring='Detected --trading-mode: {}')
+        self._args_to_config(
-        config['candle_type_def'] = CandleType.get_default(
+            config, argname="trading_mode", logstring="Detected --trading-mode: {}"
-            config.get('trading_mode', 'spot') or 'spot')
+        )
-        config['trading_mode'] = TradingMode(config.get('trading_mode', 'spot') or 'spot')
+        config["candle_type_def"] = CandleType.get_default(
-        self._args_to_config(config, argname='candle_types',
+            config.get("trading_mode", "spot") or "spot"
-                             logstring='Detected --candle-types: {}')
+        )
+        config["trading_mode"] = TradingMode(config.get("trading_mode", "spot") or "spot")
+        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: {}'),
+            ("analysis_groups", "Analysis reason groups: {}"),
-            ('enter_reason_list', 'Analysis enter tag list: {}'),
+            ("enter_reason_list", "Analysis enter tag list: {}"),
-            ('exit_reason_list', 'Analysis exit tag list: {}'),
+            ("exit_reason_list", "Analysis exit tag list: {}"),
-            ('indicator_list', 'Analysis indicator list: {}'),
+            ("indicator_list", "Analysis indicator list: {}"),
-            ('timerange', 'Filter trades by timerange: {}'),
+            ("timerange", "Filter trades by timerange: {}"),
-            ('analysis_rejected', 'Analyse rejected signals: {}'),
+            ("analysis_rejected", "Analyse rejected signals: {}"),
-            ('analysis_to_csv', 'Store analysis tables to CSV: {}'),
+            ("analysis_to_csv", "Store analysis tables to CSV: {}"),
-            ('analysis_csv_path', 'Path to store analysis CSVs: {}'),
+            ("analysis_csv_path", "Path to store analysis CSVs: {}"),
             # Lookahead analysis results
-            ('targeted_trade_amount', 'Targeted Trade amount: {}'),
+            ("targeted_trade_amount", "Targeted Trade amount: {}"),
-            ('minimum_trade_amount', 'Minimum Trade amount: {}'),
+            ("minimum_trade_amount", "Minimum Trade amount: {}"),
-            ('lookahead_analysis_exportfilename', 'Path to store lookahead-analysis-results: {}'),
+            ("lookahead_analysis_exportfilename", "Path to store lookahead-analysis-results: {}"),
-            ('startup_candle', 'Startup candle to be used on recursive analysis: {}'),
+            ("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(
-        self._args_to_config(config, argname='dry_run',
+            config,
-                             logstring='Parameter --dry-run detected, '
+            argname="dry_run",
-                             'overriding dry_run to: {} ...')
+            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
+            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})
+        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',
+        self._args_to_config(
-                             logstring='Using freqaimodel class name: {}')
+            config, argname="freqaimodel_path", logstring="Using freqaimodel path: {}"
-
+        )
-        self._args_to_config(config, argname='freqaimodel_path',
-                             logstring='Using freqaimodel path: {}')
 
         return
 
-    def _args_to_config(self, config: Config, argname: str,
+    def _args_to_config(
-                        logstring: str, logfun: Optional[Callable] = None,
+        self,
-                        deprecated_msg: Optional[str] = None) -> None:
+        config: Config,
+        argname: str,
+        logstring: str,
+        logfun: Optional[Callable] = None,
+        deprecated_msg: Optional[str] = None,
+    ) -> None:
         """
         :param config: Configuration dictionary
         :param argname: Argumentname in self.args - will be copied to config dict.
@@ -420,9 +455,11 @@ class Configuration:
                         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
+        if (
-                and self.args[argname] is not False):
+            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])))
@@ -441,7 +478,7 @@ class Configuration:
         """
 
         if "pairs" in config:
-            config['exchange']['pair_whitelist'] = config['pairs']
+            config["exchange"]["pair_whitelist"] = config["pairs"]
             return
 
         if "pairs_file" in self.args and self.args["pairs_file"]:
@@ -451,19 +488,19 @@ class Configuration:
             # 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)
+            config["pairs"] = load_file(pairs_file)
-            if isinstance(config['pairs'], list):
+            if isinstance(config["pairs"], list):
-                config['pairs'].sort()
+                config["pairs"].sort()
             return
 
-        if 'config' in self.args and self.args['config']:
+        if "config" in self.args and self.args["config"]:
             logger.info("Using pairlist from configuration.")
-            config['pairs'] = config.get('exchange', {}).get('pair_whitelist')
+            config["pairs"] = config.get("exchange", {}).get("pair_whitelist")
         else:
             # Fall back to /dl_path/pairs.json
-            pairs_file = config['datadir'] / '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)
+                config["pairs"] = load_file(pairs_file)
-                if 'pairs' in config and isinstance(config['pairs'], list):
+                if "pairs" in config and isinstance(config["pairs"], list):
-                    config['pairs'].sort()
+                    config["pairs"].sort()

freqtrade/configuration/deprecated_settings.py

@@ -12,9 +12,13 @@ from freqtrade.exceptions import ConfigurationError, OperationalException
 logger = logging.getLogger(__name__)
 
 
-def check_conflicting_settings(config: Config,
+def check_conflicting_settings(
-                               section_old: Optional[str], name_old: str,
+    config: Config,
-                               section_new: Optional[str], name_new: str) -> None:
+    section_old: Optional[str],
+    name_old: str,
+    section_new: Optional[str],
+    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:
@@ -29,9 +33,9 @@ def check_conflicting_settings(config: Config,
         )
 
 
-def process_removed_setting(config: Config,
+def process_removed_setting(
-                            section1: str, name1: str,
+    config: Config, section1: str, name1: str, section2: Optional[str], name2: str
-                            section2: Optional[str], name2: str) -> None:
+) -> None:
     """
     :param section1: Removed section
     :param name1: Removed setting name
@@ -48,10 +52,13 @@ def process_removed_setting(config: Config,
         )
 
 
-def process_deprecated_setting(config: Config,
+def process_deprecated_setting(
-                               section_old: Optional[str], name_old: str,
+    config: Config,
-                               section_new: Optional[str], name_new: str
+    section_old: Optional[str],
-                               ) -> None:
+    name_old: str,
+    section_new: Optional[str],
+    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
 
@@ -71,57 +78,91 @@ def process_deprecated_setting(config: Config,
 
 
 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',
+    process_deprecated_setting(
-                               None, 'ignore_buying_expired_candle_after')
+        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')
+    process_deprecated_setting(config, None, "forcebuy_enable", None, "force_entry_enable")
 
     # New settings
-    if config.get('telegram'):
+    if config.get("telegram"):
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'sell',
+        process_deprecated_setting(
-                                   'notification_settings', 'exit')
+            config["telegram"], "notification_settings", "sell", "notification_settings", "exit"
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'sell_fill',
+        )
-                                   'notification_settings', 'exit_fill')
+        process_deprecated_setting(
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'sell_cancel',
+            config["telegram"],
-                                   'notification_settings', 'exit_cancel')
+            "notification_settings",
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'buy',
+            "sell_fill",
-                                   'notification_settings', 'entry')
+            "notification_settings",
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'buy_fill',
+            "exit_fill",
-                                   'notification_settings', 'entry_fill')
+        )
-        process_deprecated_setting(config['telegram'], 'notification_settings', 'buy_cancel',
+        process_deprecated_setting(
-                                   'notification_settings', 'entry_cancel')
+            config["telegram"],
-    if config.get('webhook'):
+            "notification_settings",
-        process_deprecated_setting(config, 'webhook', 'webhookbuy', 'webhook', 'webhookentry')
+            "sell_cancel",
-        process_deprecated_setting(config, 'webhook', 'webhookbuycancel',
+            "notification_settings",
-                                   'webhook', 'webhookentrycancel')
+            "exit_cancel",
-        process_deprecated_setting(config, 'webhook', 'webhookbuyfill',
+        )
-                                   'webhook', 'webhookentryfill')
+        process_deprecated_setting(
-        process_deprecated_setting(config, 'webhook', 'webhooksell', 'webhook', 'webhookexit')
+            config["telegram"], "notification_settings", "buy", "notification_settings", "entry"
-        process_deprecated_setting(config, 'webhook', 'webhooksellcancel',
+        )
-                                   'webhook', 'webhookexitcancel')
+        process_deprecated_setting(
-        process_deprecated_setting(config, 'webhook', 'webhooksellfill',
+            config["telegram"],
-                                   'webhook', 'webhookexitfill')
+            "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", "use_sell_signal", None, "use_exit_signal")
-    process_removed_setting(config, 'experimental', 'sell_profit_only', None, 'exit_profit_only')
+    process_removed_setting(config, "experimental", "sell_profit_only", None, "exit_profit_only")
-    process_removed_setting(config, 'experimental', 'ignore_roi_if_buy_signal',
+    process_removed_setting(
-                            None, 'ignore_roi_if_entry_signal')
+        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", "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_only", None, "exit_profit_only")
-    process_removed_setting(config, 'ask_strategy', 'sell_profit_offset',
+    process_removed_setting(
-                            None, 'exit_profit_offset')
+        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')
+    process_removed_setting(
-    if (config.get('edge', {}).get('enabled', False)
+        config, "ask_strategy", "ignore_roi_if_buy_signal", None, "ignore_roi_if_entry_signal"
-       and 'capital_available_percentage' in config.get('edge', {})):
+    )
+    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 "
@@ -129,12 +170,11 @@ def process_temporary_deprecated_settings(config: Config) -> None:
             "'tradable_balance_ratio' and remove 'capital_available_percentage' "
             "from the edge configuration."
         )
-    if 'ticker_interval' in config:
+    if "ticker_interval" in config:
-
         raise ConfigurationError(
             "DEPRECATED: 'ticker_interval' detected. "
             "Please use 'timeframe' instead of 'ticker_interval."
         )
 
-    if 'protections' in config:
+    if "protections" in config:
         logger.warning("DEPRECATED: Setting 'protections' in the configuration is deprecated.")

freqtrade/configuration/detect_environment.py

@@ -5,4 +5,4 @@ def running_in_docker() -> bool:
     """
     Check if we are running in a docker container
     """
-    return os.environ.get('FT_APP_ENV') == 'docker'
+    return os.environ.get("FT_APP_ENV") == "docker"

freqtrade/configuration/directory_operations.py

@@ -4,8 +4,14 @@ from pathlib import Path
 from typing import Optional
 
 from freqtrade.configuration.detect_environment import running_in_docker
-from freqtrade.constants import (USER_DATA_FILES, USERPATH_FREQAIMODELS, USERPATH_HYPEROPTS,
+from freqtrade.constants import (
-                                 USERPATH_NOTEBOOKS, USERPATH_STRATEGIES, Config)
+    USER_DATA_FILES,
+    USERPATH_FREQAIMODELS,
+    USERPATH_HYPEROPTS,
+    USERPATH_NOTEBOOKS,
+    USERPATH_STRATEGIES,
+    Config,
+)
 from freqtrade.exceptions import OperationalException
 
 
@@ -13,16 +19,15 @@ logger = logging.getLogger(__name__)
 
 
 def create_datadir(config: Config, datadir: Optional[str] = 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()
+        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}')
+        logger.info(f"Created data directory: {datadir}")
     return folder
 
 
@@ -34,8 +39,8 @@ def chown_user_directory(directory: Path) -> None:
     if running_in_docker():
         try:
             import subprocess
-            subprocess.check_output(
+
-                ['sudo', 'chown', '-R', 'ftuser:', str(directory.resolve())])
+            subprocess.check_output(["sudo", "chown", "-R", "ftuser:", str(directory.resolve())])
         except Exception:
             logger.warning(f"Could not chown {directory}")
 
@@ -50,18 +55,28 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
     :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",
+    sub_dirs = [
-                USERPATH_NOTEBOOKS, "plot", USERPATH_STRATEGIES, USERPATH_FREQAIMODELS]
+        "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}')
+            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")
+                "Please use `freqtrade create-userdir` to create a user directory"
+            )
 
     # Create required subdirectories
     for f in sub_dirs:

freqtrade/configuration/environment_vars.py

@@ -16,9 +16,9 @@ def _get_var_typed(val):
         try:
             return float(val)
         except ValueError:
-            if val.lower() in ('t', 'true'):
+            if val.lower() in ("t", "true"):
                 return True
-            elif val.lower() in ('f', 'false'):
+            elif val.lower() in ("f", "false"):
                 return False
     # keep as string
     return val
@@ -32,16 +32,21 @@ def _flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str
     :param prefix: Prefix to consider (usually FREQTRADE__)
     :return: Nested dict based on available and relevant variables.
     """
-    no_convert = ['CHAT_ID', 'PASSWORD']
+    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, '')
+            key = env_var.replace(prefix, "")
-            for k in reversed(key.split('__')):
+            for k in reversed(key.split("__")):
-                val = {k.lower(): _get_var_typed(val)
+                val = {
-                       if not isinstance(val, dict) and k not in no_convert else 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
 

freqtrade/configuration/load_config.py

@@ -1,6 +1,7 @@
 """
 This module contain functions to load the configuration file
 """
+
 import logging
 import re
 import sys
@@ -25,25 +26,25 @@ def log_config_error_range(path: str, errmsg: str) -> str:
     """
     Parses configuration file and prints range around error
     """
-    if path != '-':
+    if path != "-":
-        offsetlist = re.findall(r'(?<=Parse\serror\sat\soffset\s)\d+', errmsg)
+        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]
+            subtext = text[offset - min(80, offset) : offset + 80]
-            segments = subtext.split('\n')
+            segments = subtext.split("\n")
             if len(segments) > 3:
                 # Remove first and last lines, to avoid odd truncations
-                return '\n'.join(segments[1:-1])
+                return "\n".join(segments[1:-1])
             else:
                 return subtext
-    return ''
+    return ""
 
 
 def load_file(path: Path) -> Dict[str, Any]:
     try:
-        with path.open('r') as file:
+        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
@@ -58,25 +59,27 @@ def load_config_file(path: str) -> Dict[str, Any]:
     """
     try:
         # Read config from stdin if requested in the options
-        with Path(path).open() if path != '-' else sys.stdin as file:
+        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
+            " 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}\n'
+            f"{e}\nPlease verify the following segment of your configuration:\n{err_range}"
-            f'Please verify the following segment of your configuration:\n{err_range}'
+            if err_range
-            if err_range else 'Please verify your configuration file for syntax errors.'
+            else "Please verify your configuration file for syntax errors."
         )
 
     return config
 
 
 def load_from_files(
-        files: List[str], base_path: Optional[Path] = None, level: int = 0) -> Dict[str, Any]:
+    files: List[str], base_path: Optional[Path] = None, level: int = 0
+) -> Dict[str, Any]:
     """
     Recursively load configuration files if specified.
     Sub-files are assumed to be relative to the initial config.
@@ -90,8 +93,8 @@ def load_from_files(
     files_loaded = []
     # We expect here a list of config filenames
     for filename in files:
-        logger.info(f'Using config: {filename} ...')
+        logger.info(f"Using config: {filename} ...")
-        if filename == '-':
+        if filename == "-":
             # Immediately load stdin and return
             return load_config_file(filename)
         file = Path(filename)
@@ -100,10 +103,11 @@ def load_from_files(
             file = base_path / file
 
         config_tmp = load_config_file(str(file))
-        if 'add_config_files' in config_tmp:
+        if "add_config_files" in config_tmp:
             config_sub = load_from_files(
-                config_tmp['add_config_files'], file.resolve().parent, level + 1)
+                config_tmp["add_config_files"], file.resolve().parent, level + 1
-            files_loaded.extend(config_sub.get('config_files', []))
+            )
+            files_loaded.extend(config_sub.get("config_files", []))
             config_tmp = deep_merge_dicts(config_tmp, config_sub)
 
         files_loaded.insert(0, str(file))
@@ -111,6 +115,6 @@ def load_from_files(
         # Merge config options, overwriting prior values
         config = deep_merge_dicts(config_tmp, config)
 
-    config['config_files'] = files_loaded
+    config["config_files"] = files_loaded
 
     return config

freqtrade/configuration/timerange.py

@@ -1,6 +1,7 @@
 """
 This module contains the argument manager class
 """
+
 import logging
 import re
 from datetime import datetime, timezone
@@ -22,9 +23,13 @@ class TimeRange:
     if *type is None, don't use corresponding startvalue.
     """
 
-    def __init__(self, starttype: Optional[str] = None, stoptype: Optional[str] = None,
+    def __init__(
-                 startts: int = 0, stopts: int = 0):
+        self,
-
+        starttype: Optional[str] = None,
+        stoptype: Optional[str] = None,
+        startts: int = 0,
+        stopts: int = 0,
+    ):
         self.starttype: Optional[str] = starttype
         self.stoptype: Optional[str] = stoptype
         self.startts: int = startts
@@ -48,12 +53,12 @@ class TimeRange:
         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 = ''
+        start = ""
-        stop = ''
+        stop = ""
         if startdt := self.startdt:
-            start = startdt.strftime('%Y%m%d')
+            start = startdt.strftime("%Y%m%d")
         if stopdt := self.stopdt:
-            stop = stopdt.strftime('%Y%m%d')
+            stop = stopdt.strftime("%Y%m%d")
         return f"{start}-{stop}"
 
     @property
@@ -61,7 +66,7 @@ class TimeRange:
         """
         Returns a string representation of the start date
         """
-        val = 'unbounded'
+        val = "unbounded"
         if (startdt := self.startdt) is not None:
             val = startdt.strftime(DATETIME_PRINT_FORMAT)
         return val
@@ -71,15 +76,19 @@ class TimeRange:
         """
         Returns a string representation of the stop date
         """
-        val = 'unbounded'
+        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
+        return (
-                and self.startts == other.startts and self.stopts == other.stopts)
+            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:
         """
@@ -90,8 +99,9 @@ class TimeRange:
         if self.startts:
             self.startts = self.startts - seconds
 
-    def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
+    def adjust_start_if_necessary(
-                                  min_date: datetime) -> None:
+        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.
@@ -101,13 +111,13 @@ class TimeRange:
                          has to be moved
         :return: None (Modifies the object in place)
         """
-        if (not self.starttype or (startup_candles
+        if not self.starttype or (startup_candles and min_date.timestamp() >= self.startts):
-                                   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.",
+            logger.warning(
-                           startup_candles)
+                "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'
+            self.starttype = "date"
 
     @classmethod
     def parse_timerange(cls, text: Optional[str]) -> Self:
@@ -118,16 +128,17 @@ class TimeRange:
         """
         if not text:
             return cls(None, None, 0, 0)
-        syntax = [(r'^-(\d{8})$', (None, 'date')),
+        syntax = [
-                  (r'^(\d{8})-$', ('date', None)),
+            (r"^-(\d{8})$", (None, "date")),
-                  (r'^(\d{8})-(\d{8})$', ('date', 'date')),
+            (r"^(\d{8})-$", ("date", None)),
-                  (r'^-(\d{10})$', (None, 'date')),
+            (r"^(\d{8})-(\d{8})$", ("date", "date")),
-                  (r'^(\d{10})-$', ('date', None)),
+            (r"^-(\d{10})$", (None, "date")),
-                  (r'^(\d{10})-(\d{10})$', ('date', 'date')),
+            (r"^(\d{10})-$", ("date", None)),
-                  (r'^-(\d{13})$', (None, 'date')),
+            (r"^(\d{10})-(\d{10})$", ("date", "date")),
-                  (r'^(\d{13})-$', ('date', None)),
+            (r"^-(\d{13})$", (None, "date")),
-                  (r'^(\d{13})-(\d{13})$', ('date', '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)
@@ -138,9 +149,12 @@ class TimeRange:
                 stop: int = 0
                 if stype[0]:
                     starts = rvals[index]
-                    if stype[0] == 'date' and len(starts) == 8:
+                    if stype[0] == "date" and len(starts) == 8:
-                        start = int(datetime.strptime(starts, '%Y%m%d').replace(
+                        start = int(
-                            tzinfo=timezone.utc).timestamp())
+                            datetime.strptime(starts, "%Y%m%d")
+                            .replace(tzinfo=timezone.utc)
+                            .timestamp()
+                        )
                     elif len(starts) == 13:
                         start = int(starts) // 1000
                     else:
@@ -148,15 +162,19 @@ class TimeRange:
                     index += 1
                 if stype[1]:
                     stops = rvals[index]
-                    if stype[1] == 'date' and len(stops) == 8:
+                    if stype[1] == "date" and len(stops) == 8:
-                        stop = int(datetime.strptime(stops, '%Y%m%d').replace(
+                        stop = int(
-                            tzinfo=timezone.utc).timestamp())
+                            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}"')
+                        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}"')

freqtrade/data/__init__.py

@@ -3,6 +3,4 @@ Module to handle data operations for freqtrade
 """
 
 # limit what's imported when using `from freqtrade.data import *`
-__all__ = [
+__all__ = ["converter"]
-    'converter'
-]

freqtrade/data/btanalysis.py

@@ -1,6 +1,7 @@
 """
 Helpers when analyzing backtest data
 """
+
 import logging
 from copy import copy
 from datetime import datetime, timezone
@@ -21,14 +22,35 @@ from freqtrade.types import BacktestHistoryEntryType, BacktestResultType
 logger = logging.getLogger(__name__)
 
 # Newest format
-BT_DATA_COLUMNS = ['pair', 'stake_amount', 'max_stake_amount', 'amount',
+BT_DATA_COLUMNS = [
-                   'open_date', 'close_date', 'open_rate', 'close_rate',
+    "pair",
-                   'fee_open', 'fee_close', 'trade_duration',
+    "stake_amount",
-                   'profit_ratio', 'profit_abs', 'exit_reason',
+    "max_stake_amount",
-                   'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
+    "amount",
-                   'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'enter_tag',
+    "open_date",
-                   'leverage', 'is_short', 'open_timestamp', 'close_timestamp', 'orders'
+    "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: Union[Path, str], variant: str) -> str:
@@ -50,15 +72,16 @@ def get_latest_optimize_filename(directory: Union[Path, str], variant: str) -> s
 
     if not filename.is_file():
         raise ValueError(
-            f"Directory '{directory}' does not seem to contain backtest statistics yet.")
+            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:
+    if f"latest_{variant}" not in data:
         raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
 
-    return data[f'latest_{variant}']
+    return data[f"latest_{variant}"]
 
 
 def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
@@ -71,7 +94,7 @@ def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
         * `directory/.last_result.json` does not exist
         * `directory/.last_result.json` has the wrong content
     """
-    return get_latest_optimize_filename(directory, 'backtest')
+    return get_latest_optimize_filename(directory, "backtest")
 
 
 def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
@@ -85,14 +108,15 @@ def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
         * `directory/.last_result.json` has the wrong content
     """
     try:
-        return get_latest_optimize_filename(directory, 'hyperopt')
+        return get_latest_optimize_filename(directory, "hyperopt")
     except ValueError:
         # Return default (legacy) pickle filename
-        return 'hyperopt_results.pickle'
+        return "hyperopt_results.pickle"
 
 
 def get_latest_hyperopt_file(
-        directory: Union[Path, str], predef_filename: Optional[str] = None) -> Path:
+    directory: Union[Path, str], predef_filename: Optional[str] = None
+) -> Path:
     """
     Get latest hyperopt export based on '.last_result.json'.
     :param directory: Directory to search for last result
@@ -107,7 +131,8 @@ def get_latest_hyperopt_file(
     if predef_filename:
         if Path(predef_filename).is_absolute():
             raise ConfigurationError(
-                "--hyperopt-filename expects only the filename, not an absolute path.")
+                "--hyperopt-filename expects only the filename, not an absolute path."
+            )
         return directory / predef_filename
     return directory / get_latest_hyperopt_filename(directory)
 
@@ -126,7 +151,7 @@ def load_backtest_metadata(filename: Union[Path, str]) -> Dict[str, Any]:
     except FileNotFoundError:
         return {}
     except Exception as e:
-        raise OperationalException('Unexpected error while loading backtest metadata.') from e
+        raise OperationalException("Unexpected error while loading backtest metadata.") from e
 
 
 def load_backtest_stats(filename: Union[Path, str]) -> BacktestResultType:
@@ -147,7 +172,7 @@ def load_backtest_stats(filename: Union[Path, str]) -> BacktestResultType:
 
     # Legacy list format does not contain metadata.
     if isinstance(data, dict):
-        data['metadata'] = load_backtest_metadata(filename)
+        data["metadata"] = load_backtest_metadata(filename)
     return data
 
 
@@ -159,38 +184,39 @@ def load_and_merge_backtest_result(strategy_name: str, filename: Path, results:
     :param results: dict to merge the result to.
     """
     bt_data = load_backtest_stats(filename)
-    k: Literal['metadata', 'strategy']
+    k: Literal["metadata", "strategy"]
-    for k in ('metadata', 'strategy'):  # type: ignore
+    for k in ("metadata", "strategy"):  # type: ignore
         results[k][strategy_name] = bt_data[k][strategy_name]
-    results['metadata'][strategy_name]['filename'] = filename.stem
+    results["metadata"][strategy_name]["filename"] = filename.stem
-    comparison = bt_data['strategy_comparison']
+    comparison = bt_data["strategy_comparison"]
     for i in range(len(comparison)):
-        if comparison[i]['key'] == strategy_name:
+        if comparison[i]["key"] == strategy_name:
-            results['strategy_comparison'].append(comparison[i])
+            results["strategy_comparison"].append(comparison[i])
             break
 
 
 def _get_backtest_files(dirname: Path) -> List[Path]:
     # Weird glob expression here avoids including .meta.json files.
-    return list(reversed(sorted(dirname.glob('backtest-result-*-[0-9][0-9].json'))))
+    return list(reversed(sorted(dirname.glob("backtest-result-*-[0-9][0-9].json"))))
 
 
 def _extract_backtest_result(filename: Path) -> List[BacktestHistoryEntryType]:
     metadata = load_backtest_metadata(filename)
     return [
         {
-            'filename': filename.stem,
+            "filename": filename.stem,
-            'strategy': s,
+            "strategy": s,
-            'run_id': v['run_id'],
+            "run_id": v["run_id"],
-            'notes': v.get('notes', ''),
+            "notes": v.get("notes", ""),
             # Backtest "run" time
-            'backtest_start_time': v['backtest_start_time'],
+            "backtest_start_time": v["backtest_start_time"],
             # Backtest timerange
-            'backtest_start_ts': v.get('backtest_start_ts', None),
+            "backtest_start_ts": v.get("backtest_start_ts", None),
-            'backtest_end_ts': v.get('backtest_end_ts', None),
+            "backtest_end_ts": v.get("backtest_end_ts", None),
-            'timeframe': v.get('timeframe', None),
+            "timeframe": v.get("timeframe", None),
-            'timeframe_detail': v.get('timeframe_detail', None),
+            "timeframe_detail": v.get("timeframe_detail", None),
-        } for s, v in metadata.items()
+        }
+        for s, v in metadata.items()
     ]
 
 
@@ -218,7 +244,7 @@ def delete_backtest_result(file_abs: Path):
     """
     # *.meta.json
     logger.info(f"Deleting backtest result file: {file_abs.name}")
-    file_abs_meta = file_abs.with_suffix('.meta.json')
+    file_abs_meta = file_abs.with_suffix(".meta.json")
     file_abs.unlink()
     file_abs_meta.unlink()
 
@@ -238,8 +264,19 @@ def update_backtest_metadata(filename: Path, strategy: str, content: Dict[str, A
     file_dump_json(get_backtest_metadata_filename(filename), metadata)
 
 
-def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
+def get_backtest_market_change(filename: Path, include_ts: bool = True) -> pd.DataFrame:
-                                 min_backtest_date: Optional[datetime] = None) -> Dict[str, Any]:
+    """
+    Read backtest market change file.
+    """
+    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: Union[Path, str], run_ids: Dict[str, str], min_backtest_date: Optional[datetime] = 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.
@@ -251,9 +288,9 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
     run_ids = copy(run_ids)
     dirname = Path(dirname)
     results: Dict[str, Any] = {
-        'metadata': {},
+        "metadata": {},
-        'strategy': {},
+        "strategy": {},
-        'strategy_comparison': [],
+        "strategy_comparison": [],
     }
 
     for filename in _get_backtest_files(dirname):
@@ -270,14 +307,14 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
                 continue
 
             if min_backtest_date is not None:
-                backtest_date = strategy_metadata['backtest_start_time']
+                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:
+            if strategy_metadata["run_id"] == run_id:
                 del run_ids[strategy_name]
                 load_and_merge_backtest_result(strategy_name, filename, results)
 
@@ -290,20 +327,20 @@ 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["open_date"] = pd.to_datetime(df["open_date"], utc=True)
-    df['close_date'] = pd.to_datetime(df['close_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:
+    if "is_short" not in df.columns:
-        df['is_short'] = False
+        df["is_short"] = False
-    if 'leverage' not in df.columns:
+    if "leverage" not in df.columns:
-        df['leverage'] = 1.0
+        df["leverage"] = 1.0
-    if 'enter_tag' not in df.columns:
+    if "enter_tag" not in df.columns:
-        df['enter_tag'] = df['buy_tag']
+        df["enter_tag"] = df["buy_tag"]
-        df = df.drop(['buy_tag'], axis=1)
+        df = df.drop(["buy_tag"], axis=1)
-    if 'max_stake_amount' not in df.columns:
+    if "max_stake_amount" not in df.columns:
-        df['max_stake_amount'] = df['stake_amount']
+        df["max_stake_amount"] = df["stake_amount"]
-    if 'orders' not in df.columns:
+    if "orders" not in df.columns:
-        df['orders'] = None
+        df["orders"] = None
     return df
 
 
@@ -319,23 +356,25 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
     data = load_backtest_stats(filename)
     if not isinstance(data, list):
         # new, nested format
-        if 'strategy' not in data:
+        if "strategy" not in data:
             raise ValueError("Unknown dataformat.")
 
         if not strategy:
-            if len(data['strategy']) == 1:
+            if len(data["strategy"]) == 1:
-                strategy = list(data['strategy'].keys())[0]
+                strategy = list(data["strategy"].keys())[0]
             else:
-                raise ValueError("Detected backtest result with more than one strategy. "
+                raise ValueError(
-                                 "Please specify a strategy.")
+                    "Detected backtest result with more than one strategy. "
+                    "Please specify a strategy."
+                )
 
-        if strategy not in data['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']
+        data = data["strategy"][strategy]["trades"]
         df = pd.DataFrame(data)
         if not df.empty:
             df = _load_backtest_data_df_compatibility(df)
@@ -343,7 +382,8 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
     else:
         # old format - only with lists.
         raise OperationalException(
-            "Backtest-results with only trades data are no longer supported.")
+            "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
@@ -358,23 +398,26 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
     :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'],
+    dates = [
-                                     freq=timeframe_freq))
+        pd.Series(pd.date_range(row[1]["open_date"], row[1]["close_date"], freq=timeframe_freq))
-             for row in results[['open_date', 'close_date']].iterrows()]
+        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')
+    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')
+    df2 = df2.set_index("date")
-    df_final = df2.resample(timeframe_freq)[['pair']].count()
+    df_final = df2.resample(timeframe_freq)[["pair"]].count()
-    df_final = df_final.rename({'pair': 'open_trades'}, axis=1)
+    df_final = df_final.rename({"pair": "open_trades"}, axis=1)
     return df_final
 
 
-def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
+def evaluate_result_multi(
-                          max_open_trades: IntOrInf) -> pd.DataFrame:
+    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
@@ -384,7 +427,7 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
     :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]
+    return df_final[df_final["open_trades"] > max_open_trades]
 
 
 def trade_list_to_dataframe(trades: Union[List[Trade], List[LocalTrade]]) -> pd.DataFrame:
@@ -395,9 +438,9 @@ def trade_list_to_dataframe(trades: Union[List[Trade], List[LocalTrade]]) -> pd.
     """
     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["close_date"] = pd.to_datetime(df["close_date"], utc=True)
-        df['open_date'] = pd.to_datetime(df['open_date'], utc=True)
+        df["open_date"] = pd.to_datetime(df["open_date"], utc=True)
-        df['close_rate'] = df['close_rate'].astype('float64')
+        df["close_rate"] = df["close_rate"].astype("float64")
     return df
 
 
@@ -419,8 +462,13 @@ def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataF
     return trades
 
 
-def load_trades(source: str, db_url: str, exportfilename: Path,
+def load_trades(
-                no_trades: bool = False, strategy: Optional[str] = None) -> pd.DataFrame:
+    source: str,
+    db_url: str,
+    exportfilename: Path,
+    no_trades: bool = False,
+    strategy: Optional[str] = None,
+) -> pd.DataFrame:
     """
     Based on configuration option 'trade_source':
     * loads data from DB (using `db_url`)
@@ -441,8 +489,9 @@ def load_trades(source: str, db_url: str, exportfilename: Path,
         return load_backtest_data(exportfilename, strategy)
 
 
-def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
+def extract_trades_of_period(
-                             date_index=False) -> pd.DataFrame:
+    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
@@ -451,8 +500,9 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
         trades_start = dataframe.index[0]
         trades_stop = dataframe.index[-1]
     else:
-        trades_start = dataframe.iloc[0]['date']
+        trades_start = dataframe.iloc[0]["date"]
-        trades_stop = dataframe.iloc[-1]['date']
+        trades_stop = dataframe.iloc[-1]["date"]
-    trades = trades.loc[(trades['open_date'] >= trades_start) &
+    trades = trades.loc[
-                        (trades['close_date'] <= trades_stop)]
+        (trades["open_date"] >= trades_start) & (trades["close_date"] <= trades_stop)
+    ]
     return trades

freqtrade/data/converter/__init__.py

@@ -1,13 +1,23 @@
-from freqtrade.data.converter.converter import (clean_ohlcv_dataframe, convert_ohlcv_format,
+from freqtrade.data.converter.converter import (
-                                                ohlcv_fill_up_missing_data, ohlcv_to_dataframe,
+    clean_ohlcv_dataframe,
-                                                order_book_to_dataframe, reduce_dataframe_footprint,
+    convert_ohlcv_format,
-                                                trim_dataframe, trim_dataframes)
+    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,
+from freqtrade.data.converter.trade_converter import (
-                                                      convert_trades_to_ohlcv, trades_convert_types,
+    convert_trades_format,
-                                                      trades_df_remove_duplicates,
+    convert_trades_to_ohlcv,
-                                                      trades_dict_to_list, trades_list_to_df,
+    trades_convert_types,
-                                                      trades_to_ohlcv)
+    trades_df_remove_duplicates,
+    trades_dict_to_list,
+    trades_list_to_df,
+    trades_to_ohlcv,
+)
 
 
 __all__ = [

freqtrade/data/converter/converter.py

@@ -1,6 +1,7 @@
 """
 Functions to convert data from one format to another
 """
+
 import logging
 from typing import Dict
 
@@ -15,8 +16,14 @@ from freqtrade.enums import CandleType, TradingMode
 logger = logging.getLogger(__name__)
 
 
-def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
+def ohlcv_to_dataframe(
-                       fill_missing: bool = True, drop_incomplete: bool = True) -> 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
@@ -32,20 +39,28 @@ def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
     cols = DEFAULT_DATAFRAME_COLUMNS
     df = DataFrame(ohlcv, columns=cols)
 
-    df['date'] = to_datetime(df['date'], unit='ms', utc=True)
+    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',
+    df = df.astype(
-                          'volume': 'float'})
+        dtype={
-    return clean_ohlcv_dataframe(df, timeframe, pair,
+            "open": "float",
-                                 fill_missing=fill_missing,
+            "high": "float",
-                                 drop_incomplete=drop_incomplete)
+            "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, *,
+def clean_ohlcv_dataframe(
-                          fill_missing: bool, drop_incomplete: bool) -> 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)
@@ -60,17 +75,19 @@ def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
     :return: DataFrame
     """
     # group by index and aggregate results to eliminate duplicate ticks
-    data = data.groupby(by='date', as_index=False, sort=True).agg({
+    data = data.groupby(by="date", as_index=False, sort=True).agg(
-        'open': 'first',
+        {
-        'high': 'max',
+            "open": "first",
-        'low': 'min',
+            "high": "max",
-        'close': 'last',
+            "low": "min",
-        'volume': 'max',
+            "close": "last",
-    })
+            "volume": "max",
+        }
+    )
     # eliminate partial candle
     if drop_incomplete:
         data.drop(data.tail(1).index, inplace=True)
-        logger.debug('Dropping last candle')
+        logger.debug("Dropping last candle")
 
     if fill_missing:
         return ohlcv_fill_up_missing_data(data, timeframe, pair)
@@ -81,37 +98,35 @@ def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
 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
+    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 = {
+    ohlcv_dict = {"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"}
-        '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)
+    df = dataframe.resample(resample_interval, on="date").agg(ohlcv_dict)
 
     # Forwardfill close for missing columns
-    df['close'] = df['close'].ffill()
+    df["close"] = df["close"].ffill()
     # Use close for "open, high, low"
-    df.loc[:, ['open', 'high', 'low']] = df[['open', 'high', 'low']].fillna(
+    df.loc[:, ["open", "high", "low"]] = df[["open", "high", "low"]].fillna(
-        value={'open': df['close'],
+        value={
-               'high': df['close'],
+            "open": df["close"],
-               'low': 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}: "
+        message = (
-                   f"before: {len_before} - after: {len_after} - {pct_missing:.2%}")
+            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:
@@ -120,8 +135,9 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
     return df
 
 
-def trim_dataframe(df: DataFrame, timerange, *, df_date_col: str = 'date',
+def trim_dataframe(
-                   startup_candles: int = 0) -> 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
@@ -134,15 +150,16 @@ def trim_dataframe(df: DataFrame, timerange, *, df_date_col: str = 'date',
         # Trim candles instead of timeframe in case of given startup_candle count
         df = df.iloc[startup_candles:, :]
     else:
-        if timerange.starttype == 'date':
+        if timerange.starttype == "date":
             df = df.loc[df[df_date_col] >= timerange.startdt, :]
-    if timerange.stoptype == 'date':
+    if timerange.stoptype == "date":
         df = df.loc[df[df_date_col] <= timerange.stopdt, :]
     return df
 
 
-def trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
+def trim_dataframes(
-                    startup_candles: int) -> Dict[str, DataFrame]:
+    preprocessed: Dict[str, DataFrame], timerange, startup_candles: int
+) -> Dict[str, DataFrame]:
     """
     Trim startup period from analyzed dataframes
     :param preprocessed: Dict of pair: dataframe
@@ -157,8 +174,9 @@ def trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
         if not trimed_df.empty:
             processed[pair] = trimed_df
         else:
-            logger.warning(f'{pair} has no data left after adjusting for startup candles, '
+            logger.warning(
-                           f'skipping.')
+                f"{pair} has no data left after adjusting for startup candles, skipping."
+            )
     return processed
 
 
@@ -170,19 +188,28 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
      b_sum       b_size       bids       asks       a_size       a_sum
     -------------------------------------------------------------------
     """
-    cols = ['bids', 'b_size']
+    cols = ["bids", "b_size"]
 
     bids_frame = DataFrame(bids, columns=cols)
     # add cumulative sum column
-    bids_frame['b_sum'] = bids_frame['b_size'].cumsum()
+    bids_frame["b_sum"] = bids_frame["b_size"].cumsum()
-    cols2 = ['asks', 'a_size']
+    cols2 = ["asks", "a_size"]
     asks_frame = DataFrame(asks, columns=cols2)
     # add cumulative sum column
-    asks_frame['a_sum'] = asks_frame['a_size'].cumsum()
+    asks_frame["a_sum"] = asks_frame["a_size"].cumsum()
 
-    frame = pd.concat([bids_frame['b_sum'], bids_frame['b_size'], bids_frame['bids'],
+    frame = pd.concat(
-                       asks_frame['asks'], asks_frame['a_size'], asks_frame['a_sum']], axis=1,
+        [
-                      keys=['b_sum', 'b_size', 'bids', 'asks', 'a_size', 'a_sum'])
+            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
 
@@ -201,47 +228,51 @@ def convert_ohlcv_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)
+    src = get_datahandler(config["datadir"], convert_from)
-    timeframes = config.get('timeframes', [config.get('timeframe')])
+    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', [
+    candle_types = [
-        c.value for c in CandleType])]
+        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 = src.ohlcv_get_available_data(config["datadir"], TradingMode.SPOT)
-    paircombs.extend(src.ohlcv_get_available_data(config['datadir'], TradingMode.FUTURES))
+    paircombs.extend(src.ohlcv_get_available_data(config["datadir"], TradingMode.FUTURES))
 
-    if 'pairs' in config:
+    if "pairs" in config:
         # Filter pairs
-        paircombs = [comb for comb in paircombs if comb[0] in config['pairs']]
+        paircombs = [comb for comb in paircombs if comb[0] in config["pairs"]]
 
-    if 'timeframes' in config:
+    if "timeframes" in config:
-        paircombs = [comb for comb in paircombs if comb[1] in config['timeframes']]
+        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}"
+    formatted_paircombs = "\n".join(
-                                    for pair, timeframe, candle_type in paircombs])
+        [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"
+    logger.info(
-                f"{formatted_paircombs}")
+        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,
+        data = src.ohlcv_load(
-                              timerange=None,
+            pair=pair,
-                              fill_missing=False,
+            timeframe=timeframe,
-                              drop_incomplete=False,
+            timerange=None,
-                              startup_candles=0,
+            fill_missing=False,
-                              candle_type=candle_type)
+            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(
+            trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data, candle_type=candle_type)
-                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)
@@ -254,12 +285,11 @@ def reduce_dataframe_footprint(df: DataFrame) -> DataFrame:
     :return: Dataframe converted to float/int 32s
     """
 
-    logger.debug(f"Memory usage of dataframe is "
+    logger.debug(f"Memory usage of dataframe is {df.memory_usage().sum() / 1024**2:.2f} MB")
-                 f"{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']:
+        if column in ["open", "high", "low", "close", "volume"]:
             continue
         if dtype == np.float64:
             df_dtypes[column] = np.float32
@@ -267,7 +297,6 @@ def reduce_dataframe_footprint(df: DataFrame) -> DataFrame:
             df_dtypes[column] = np.int32
     df = df.astype(df_dtypes)
 
-    logger.debug(f"Memory usage after optimization is: "
+    logger.debug(f"Memory usage after optimization is: {df.memory_usage().sum() / 1024**2:.2f} MB")
-                 f"{df.memory_usage().sum() / 1024**2:.2f} MB")
 
     return df

freqtrade/data/converter/trade_converter.py

@@ -1,6 +1,7 @@
 """
 Functions to convert data from one format to another
 """
+
 import logging
 from pathlib import Path
 from typing import Dict, List
@@ -9,8 +10,13 @@ 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,
+from freqtrade.constants import (
-                                 Config, TradeList)
+    DEFAULT_DATAFRAME_COLUMNS,
+    DEFAULT_TRADES_COLUMNS,
+    TRADES_DTYPES,
+    Config,
+    TradeList,
+)
 from freqtrade.enums import CandleType, TradingMode
 from freqtrade.exceptions import OperationalException
 
@@ -25,7 +31,7 @@ def trades_df_remove_duplicates(trades: pd.DataFrame) -> pd.DataFrame:
     :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'])
+    return trades.drop_duplicates(subset=["timestamp", "id"])
 
 
 def trades_dict_to_list(trades: List[Dict]) -> TradeList:
@@ -42,7 +48,7 @@ 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)
+    trades["date"] = to_datetime(trades["timestamp"], unit="ms", utc=True)
     return trades
 
 
@@ -71,13 +77,14 @@ def trades_to_ohlcv(trades: DataFrame, timeframe: str) -> DataFrame:
     :raises: ValueError if no trades are provided
     """
     from freqtrade.exchange import timeframe_to_resample_freq
+
     if trades.empty:
-        raise ValueError('Trade-list empty.')
+        raise ValueError("Trade-list empty.")
-    df = trades.set_index('date', drop=True)
+    df = trades.set_index("date", drop=True)
     resample_interval = timeframe_to_resample_freq(timeframe)
-    df_new = df['price'].resample(resample_interval).ohlc()
+    df_new = df["price"].resample(resample_interval).ohlc()
-    df_new['volume'] = df['amount'].resample(resample_interval).sum()
+    df_new["volume"] = df["amount"].resample(resample_interval).sum()
-    df_new['date'] = df_new.index
+    df_new["date"] = df_new.index
     # Drop 0 volume rows
     df_new = df_new.dropna()
     return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
@@ -97,24 +104,27 @@ def convert_trades_to_ohlcv(
     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)}', "
+    logger.info(
-                f"intervals: '{', '.join(timeframes)}' to {datadir}")
+        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}.')
+                    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.')
+                logger.warning(f"Could not convert {pair} to OHLCV.")
 
 
 def convert_trades_format(config: Config, convert_from: str, convert_to: str, erase: bool):
@@ -125,25 +135,27 @@ def convert_trades_format(config: Config, convert_from: str, convert_to: str, er
     :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 convert_from == "kraken_csv":
-        if config['exchange']['name'] != 'kraken':
+        if config["exchange"]["name"] != "kraken":
             raise OperationalException(
-                'Converting from csv is only supported for kraken.'
+                "Converting from csv is only supported for kraken."
-                'Please refer to the documentation for details about this special mode.'
+                "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:
+    src = get_datahandler(config["datadir"], convert_from)
-        config['pairs'] = src.trades_get_pairs(config['datadir'])
+    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)
+    trading_mode: TradingMode = config.get("trading_mode", TradingMode.SPOT)
-    for pair in config['pairs']:
+    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)

freqtrade/data/converter/trade_converter_kraken.py

@@ -4,8 +4,10 @@ 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,
+from freqtrade.data.converter.trade_converter import (
-                                                      trades_df_remove_duplicates)
+    trades_convert_types,
+    trades_df_remove_duplicates,
+)
 from freqtrade.data.history import get_datahandler
 from freqtrade.enums import TradingMode
 from freqtrade.exceptions import OperationalException
@@ -15,32 +17,33 @@ from freqtrade.resolvers import ExchangeResolver
 
 logger = logging.getLogger(__name__)
 
-KRAKEN_CSV_TRADE_COLUMNS = ['timestamp', 'price', 'amount']
+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':
+    if config["exchange"]["name"] != "kraken":
-        raise OperationalException('This function is only for the kraken exchange.')
+        raise OperationalException("This function is only for the kraken exchange.")
 
-    datadir: Path = config['datadir']
+    datadir: Path = config["datadir"]
     data_handler = get_datahandler(datadir, data_format=convert_to)
 
-    tradesdir: Path = config['datadir'] / 'trades_csv'
+    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')}
+    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()
+        (m["symbol"], m["altname"])
-        if m.get('altname') in data_symbols
+        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'):
+    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:
@@ -66,18 +69,20 @@ def import_kraken_trades_from_csv(config: Config, convert_to: str):
         trades = pd.concat(dfs, ignore_index=True)
         del dfs
 
-        trades.loc[:, 'timestamp'] = trades['timestamp'] * 1e3
+        trades.loc[:, "timestamp"] = trades["timestamp"] * 1e3
-        trades.loc[:, 'cost'] = trades['price'] * trades['amount']
+        trades.loc[:, "cost"] = trades["price"] * trades["amount"]
         for col in DEFAULT_TRADES_COLUMNS:
             if col not in trades.columns:
-                trades.loc[:, col] = ''
+                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 "
+        logger.info(
-                    f"{trades_df['date'].min():{DATETIME_PRINT_FORMAT}} to "
+            f"{pair}: {len(trades_df)} trades, from "
-                    f"{trades_df['date'].max():{DATETIME_PRINT_FORMAT}}")
+            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)

freqtrade/data/dataprovider.py

@@ -4,6 +4,7 @@ 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
@@ -12,8 +13,12 @@ from typing import Any, Dict, List, Optional, Tuple
 from pandas import DataFrame, Timedelta, Timestamp, to_timedelta
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import (FULL_DATAFRAME_THRESHOLD, Config, ListPairsWithTimeframes,
+from freqtrade.constants import (
-                                 PairWithTimeframe)
+    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
@@ -27,18 +32,17 @@ from freqtrade.util import PeriodicCache
 
 logger = logging.getLogger(__name__)
 
-NO_EXCHANGE_EXCEPTION = 'Exchange is not available to DataProvider.'
+NO_EXCHANGE_EXCEPTION = "Exchange is not available to DataProvider."
 MAX_DATAFRAME_CANDLES = 1000
 
 
 class DataProvider:
-
     def __init__(
         self,
         config: Config,
         exchange: Optional[Exchange],
         pairlists=None,
-        rpc: Optional[RPCManager] = None
+        rpc: Optional[RPCManager] = None,
     ) -> None:
         self._config = config
         self._exchange = exchange
@@ -49,18 +53,20 @@ class DataProvider:
         self.__slice_date: Optional[datetime] = None
 
         self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {}
-        self.__producer_pairs_df: Dict[str,
+        self.__producer_pairs_df: Dict[
-                                       Dict[PairWithTimeframe, Tuple[DataFrame, datetime]]] = {}
+            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_candle_type = self._config.get("candle_type_def", CandleType.SPOT)
-        self._default_timeframe = self._config.get('timeframe', '1h')
+        self._default_timeframe = self._config.get("timeframe", "1h")
 
         self.__msg_cache = PeriodicCache(
-            maxsize=1000, ttl=timeframe_to_seconds(self._default_timeframe))
+            maxsize=1000, ttl=timeframe_to_seconds(self._default_timeframe)
+        )
 
-        self.producers = self._config.get('external_message_consumer', {}).get('producers', [])
+        self.producers = self._config.get("external_message_consumer", {}).get("producers", [])
         self.external_data_enabled = len(self.producers) > 0
 
     def _set_dataframe_max_index(self, limit_index: int):
@@ -80,11 +86,7 @@ class DataProvider:
         self.__slice_date = limit_date
 
     def _set_cached_df(
-        self,
+        self, pair: str, timeframe: str, dataframe: DataFrame, candle_type: CandleType
-        pair: str,
-        timeframe: str,
-        dataframe: DataFrame,
-        candle_type: CandleType
     ) -> None:
         """
         Store cached Dataframe.
@@ -96,8 +98,7 @@ class DataProvider:
         :param candle_type: Any of the enum CandleType (must match trading mode!)
         """
         pair_key = (pair, timeframe, candle_type)
-        self.__cached_pairs[pair_key] = (
+        self.__cached_pairs[pair_key] = (dataframe, datetime.now(timezone.utc))
-            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"):
@@ -116,12 +117,7 @@ class DataProvider:
         """
         return self.__producer_pairs.get(producer_name, []).copy()
 
-    def _emit_df(
+    def _emit_df(self, pair_key: PairWithTimeframe, dataframe: DataFrame, new_candle: bool) -> None:
-        self,
-        pair_key: PairWithTimeframe,
-        dataframe: DataFrame,
-        new_candle: bool
-    ) -> None:
         """
         Send this dataframe as an ANALYZED_DF message to RPC
 
@@ -131,19 +127,21 @@ class DataProvider:
         """
         if self.__rpc:
             msg: RPCAnalyzedDFMsg = {
-                    'type': RPCMessageType.ANALYZED_DF,
+                "type": RPCMessageType.ANALYZED_DF,
-                    'data': {
+                "data": {
-                        'key': pair_key,
+                    "key": pair_key,
-                        'df': dataframe.tail(1),
+                    "df": dataframe.tail(1),
-                        'la': datetime.now(timezone.utc)
+                    "la": datetime.now(timezone.utc),
-                    }
+                },
-                }
+            }
             self.__rpc.send_msg(msg)
             if new_candle:
-                self.__rpc.send_msg({
+                self.__rpc.send_msg(
-                        'type': RPCMessageType.NEW_CANDLE,
+                    {
-                        'data': pair_key,
+                        "type": RPCMessageType.NEW_CANDLE,
-                    })
+                        "data": pair_key,
+                    }
+                )
 
     def _replace_external_df(
         self,
@@ -152,7 +150,7 @@ class DataProvider:
         last_analyzed: datetime,
         timeframe: str,
         candle_type: CandleType,
-        producer_name: str = "default"
+        producer_name: str = "default",
     ) -> None:
         """
         Add the pair data to this class from an external source.
@@ -178,7 +176,7 @@ class DataProvider:
         last_analyzed: datetime,
         timeframe: str,
         candle_type: CandleType,
-        producer_name: str = "default"
+        producer_name: str = "default",
     ) -> Tuple[bool, int]:
         """
         Append a candle to the existing external dataframe. The incoming dataframe
@@ -204,12 +202,14 @@ class DataProvider:
                 last_analyzed=last_analyzed,
                 timeframe=timeframe,
                 candle_type=candle_type,
-                producer_name=producer_name
+                producer_name=producer_name,
             )
             return (True, 0)
 
-        if (producer_name not in self.__producer_pairs_df
+        if (
-           or pair_key not in self.__producer_pairs_df[producer_name]):
+            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
@@ -220,12 +220,12 @@ class DataProvider:
         # 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
+        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']
+        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]
+        existing_df1 = existing_df[existing_df["date"] < incoming_first]
 
         candle_difference = (incoming_first - local_last) / timeframe_delta
 
@@ -243,13 +243,13 @@ class DataProvider:
 
         # Everything is good, we appended
         self._replace_external_df(
-                    pair,
+            pair,
-                    appended_df,
+            appended_df,
-                    last_analyzed=last_analyzed,
+            last_analyzed=last_analyzed,
-                    timeframe=timeframe,
+            timeframe=timeframe,
-                    candle_type=candle_type,
+            candle_type=candle_type,
-                    producer_name=producer_name
+            producer_name=producer_name,
-                    )
+        )
         return (True, 0)
 
     def get_producer_df(
@@ -257,7 +257,7 @@ class DataProvider:
         pair: str,
         timeframe: Optional[str] = None,
         candle_type: Optional[CandleType] = None,
-        producer_name: str = "default"
+        producer_name: str = "default",
     ) -> Tuple[DataFrame, datetime]:
         """
         Get the pair data from producers.
@@ -292,64 +292,64 @@ class DataProvider:
         """
         self._pairlists = pairlists
 
-    def historic_ohlcv(
+    def historic_ohlcv(self, pair: str, timeframe: str, candle_type: str = "") -> DataFrame:
-        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 = (
-            candle_type) if candle_type != '' else self._config['candle_type_def']
+            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 = TimeRange.parse_timerange(
-                'timerange') is None else str(self._config.get('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} "
+            logger.info(
-                        f"from {timerange.start_fmt} to {timerange.stop_fmt}")
+                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'],
+                datadir=self._config["datadir"],
                 timerange=timerange,
-                data_format=self._config['dataformat_ohlcv'],
+                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', {})
+        freqai_config = self._config.get("freqai", {})
-        if not freqai_config.get('enabled', False):
+        if not freqai_config.get("enabled", False):
-            return self._config.get('startup_candle_count', 0)
+            return self._config.get("startup_candle_count", 0)
         else:
-            startup_candles = self._config.get('startup_candle_count', 0)
+            startup_candles = self._config.get("startup_candle_count", 0)
-            indicator_periods = freqai_config['feature_parameters']['indicator_periods_candles']
+            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))
+            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
+            train_candles = freqai_config["train_period_days"] * 86400 / tf_seconds
-            total_candles = int(self._config['startup_candle_count'] + train_candles)
+            total_candles = int(self._config["startup_candle_count"] + train_candles)
             logger.info(
-                f'Increasing startup_candle_count for freqai on {timeframe} to {total_candles}')
+                f"Increasing startup_candle_count for freqai on {timeframe} to {total_candles}"
+            )
         return total_candles
 
     def get_pair_dataframe(
-        self,
+        self, pair: str, timeframe: Optional[str] = None, candle_type: str = ""
-        pair: str,
-        timeframe: Optional[str] = None,
-        candle_type: str = ''
     ) -> DataFrame:
         """
         Return pair candle (OHLCV) data, either live or cached historical -- depending
@@ -366,13 +366,13 @@ class DataProvider:
             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']
+            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]
+                data = data.loc[data["date"] < cutoff_date]
         if len(data) == 0:
             logger.warning(f"No data found for ({pair}, {timeframe}, {candle_type}).")
         return data
@@ -387,7 +387,7 @@ class DataProvider:
             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))
+        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]
@@ -395,7 +395,7 @@ class DataProvider:
                 df, date = self.__cached_pairs[pair_key]
                 if self.__slice_index is not None:
                     max_index = self.__slice_index
-                    df = df.iloc[max(0, max_index - MAX_DATAFRAME_CANDLES):max_index]
+                    df = df.iloc[max(0, max_index - MAX_DATAFRAME_CANDLES) : max_index]
             return df, date
         else:
             return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
@@ -406,7 +406,7 @@ class DataProvider:
         Get runmode of the bot
         can be "live", "dry-run", "backtest", "edgecli", "hyperopt" or "other".
         """
-        return RunMode(self._config.get('runmode', RunMode.OTHER))
+        return RunMode(self._config.get("runmode", RunMode.OTHER))
 
     def current_whitelist(self) -> List[str]:
         """
@@ -434,9 +434,11 @@ class DataProvider:
 
     # Exchange functions
 
-    def refresh(self,
+    def refresh(
-                pairlist: ListPairsWithTimeframes,
+        self,
-                helping_pairs: Optional[ListPairsWithTimeframes] = None) -> None:
+        pairlist: ListPairsWithTimeframes,
+        helping_pairs: Optional[ListPairsWithTimeframes] = None,
+    ) -> None:
         """
         Refresh data, called with each cycle
         """
@@ -469,11 +471,7 @@ class DataProvider:
         return list(self._exchange._klines.keys())
 
     def ohlcv(
-        self,
+        self, pair: str, timeframe: Optional[str] = None, copy: bool = True, candle_type: str = ""
-        pair: str,
-        timeframe: Optional[str] = None,
-        copy: bool = True,
-        candle_type: str = ''
     ) -> DataFrame:
         """
         Get candle (OHLCV) data for the given pair as DataFrame
@@ -487,11 +485,13 @@ class DataProvider:
         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 = (
-                candle_type) if candle_type != '' else self._config['candle_type_def']
+                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),
+                (pair, timeframe or self._config["timeframe"], _candle_type), copy=copy
-                copy=copy
             )
         else:
             return DataFrame()
@@ -572,7 +572,7 @@ class DataProvider:
         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 surpress
+        :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

freqtrade/data/entryexitanalysis.py

@@ -8,8 +8,11 @@ from tabulate import tabulate
 
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import Config
-from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
+from freqtrade.data.btanalysis import (
-                                       load_backtest_stats)
+    get_latest_backtest_filename,
+    load_backtest_data,
+    load_backtest_stats,
+)
 from freqtrade.exceptions import OperationalException
 
 
@@ -18,9 +21,10 @@ logger = logging.getLogger(__name__)
 
 def _load_backtest_analysis_data(backtest_dir: Path, name: str):
     if backtest_dir.is_dir():
-        scpf = Path(backtest_dir,
+        scpf = Path(
-                    Path(get_latest_backtest_filename(backtest_dir)).stem + "_" + name + ".pkl"
+            backtest_dir,
-                    )
+            Path(get_latest_backtest_filename(backtest_dir)).stem + "_" + name + ".pkl",
+        )
     else:
         scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")
 
@@ -53,7 +57,8 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
         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])
+                    pair, trades, signal_candles[strategy_name][pair]
+                )
     except Exception as e:
         print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
 
@@ -64,28 +69,28 @@ def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles:
     buyf = signal_candles
 
     if len(buyf) > 0:
-        buyf = buyf.set_index('date', drop=False)
+        buyf = buyf.set_index("date", drop=False)
-        trades_red = trades.loc[trades['pair'] == pair].copy()
+        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.open_date.items():
-                allinds = buyf.loc[(buyf['date'] < v)]
+                allinds = buyf.loc[(buyf["date"] < v)]
                 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, "signal_date"] = tmp_inds["date"].values[0]
-                    trades_red.loc[t, 'enter_reason'] = trades_red.loc[t, 'enter_tag']
+                    trades_red.loc[t, "enter_reason"] = trades_red.loc[t, "enter_tag"]
-                    tmp_inds.index.rename('signal_date', inplace=True)
+                    tmp_inds.index.rename("signal_date", inplace=True)
                     trades_inds = pd.concat([trades_inds, tmp_inds])
 
-            if 'signal_date' in trades_red:
+            if "signal_date" in trades_red:
-                trades_red['signal_date'] = pd.to_datetime(trades_red['signal_date'], utc=True)
+                trades_red["signal_date"] = pd.to_datetime(trades_red["signal_date"], utc=True)
-                trades_red.set_index('signal_date', inplace=True)
+                trades_red.set_index("signal_date", inplace=True)
 
                 try:
-                    trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
+                    trades_red = pd.merge(trades_red, trades_inds, on="signal_date", how="outer")
                 except Exception as e:
                     raise e
         return trades_red
@@ -93,138 +98,166 @@ def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles:
         return pd.DataFrame()
 
 
-def _do_group_table_output(bigdf, glist, csv_path: Path, to_csv=False, ):
+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']
+            group_mask = ["enter_reason"]
-            wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
+            wins = (
-                        .groupby(group_mask) \
+                bigdf.loc[bigdf["profit_abs"] >= 0].groupby(group_mask).agg({"profit_abs": ["sum"]})
-                        .agg({'profit_abs': ['sum']})
+            )
 
-            wins.columns = ['profit_abs_wins']
+            wins.columns = ["profit_abs_wins"]
-            loss = bigdf.loc[bigdf['profit_abs'] < 0] \
+            loss = (
-                        .groupby(group_mask) \
+                bigdf.loc[bigdf["profit_abs"] < 0].groupby(group_mask).agg({"profit_abs": ["sum"]})
-                        .agg({'profit_abs': ['sum']})
+            )
-            loss.columns = ['profit_abs_loss']
+            loss.columns = ["profit_abs_loss"]
 
-            new = bigdf.groupby(group_mask).agg({'profit_abs': [
+            new = bigdf.groupby(group_mask).agg(
-                                                    'count',
+                {"profit_abs": ["count", lambda x: sum(x > 0), lambda x: sum(x <= 0)]}
-                                                    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["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["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_win"] = (new["profit_abs_wins"] / new.iloc[:, 1]).fillna(0)
-            new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0)
+            new["avg_loss"] = (new["profit_abs_loss"] / new.iloc[:, 2]).fillna(0)
 
-            new['exp_ratio'] = (
+            new["exp_ratio"] = (
-                (
+                ((1 + (new["avg_win"] / abs(new["avg_loss"]))) * (new["wl_ratio_pct"] / 100)) - 1
-                    (1 + (new['avg_win'] / abs(new['avg_loss']))) * (new['wl_ratio_pct'] / 100)
+            ).fillna(0)
-                ) - 1).fillna(0)
 
-            new.columns = ['total_num_buys', 'wins', 'losses',
+            new.columns = [
-                           'profit_abs_wins', 'profit_abs_loss',
+                "total_num_buys",
-                           'profit_tot', 'wl_ratio_pct',
+                "wins",
-                           'avg_win', 'avg_loss', 'exp_ratio']
+                "losses",
+                "profit_abs_wins",
+                "profit_abs_loss",
+                "profit_tot",
+                "wl_ratio_pct",
+                "avg_win",
+                "avg_loss",
+                "exp_ratio",
+            ]
 
-            sortcols = ['total_num_buys']
+            sortcols = ["total_num_buys"]
 
-            _print_table(new, sortcols, show_index=True, name="Group 0:",
+            _print_table(
-                         to_csv=to_csv, csv_path=csv_path)
+                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'],
+            agg_mask = {
-                        'profit_ratio': ['median', 'mean', 'sum']}
+                "profit_abs": ["count", "sum", "median", "mean"],
-            agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
+                "profit_ratio": ["median", "mean", "sum"],
-                        'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
+            }
-                        'total_profit_pct']
+            agg_cols = [
-            sortcols = ['profit_abs_sum', 'enter_reason']
+                "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']
+                group_mask = ["enter_reason"]
 
             # 2: profit summaries grouped by enter_tag and exit_tag
             if g == "2":
-                group_mask = ['enter_reason', 'exit_reason']
+                group_mask = ["enter_reason", "exit_reason"]
 
             # 3: profit summaries grouped by pair and enter_tag
             if g == "3":
-                group_mask = ['pair', 'enter_reason']
+                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']
+                group_mask = ["pair", "enter_reason", "exit_reason"]
 
             # 5: profit summaries grouped by exit_tag
             if g == "5":
-                group_mask = ['exit_reason']
+                group_mask = ["exit_reason"]
-                sortcols = ['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["median_profit_pct"] = new["median_profit_pct"] * 100
-                new['mean_profit_pct'] = new['mean_profit_pct'] * 100
+                new["mean_profit_pct"] = new["mean_profit_pct"] * 100
-                new['total_profit_pct'] = new['total_profit_pct'] * 100
+                new["total_profit_pct"] = new["total_profit_pct"] * 100
 
-                _print_table(new, sortcols, name=f"Group {g}:",
+                _print_table(new, sortcols, name=f"Group {g}:", to_csv=to_csv, csv_path=csv_path)
-                             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,
+def _do_rejected_signals_output(
-                                to_csv: bool = False, csv_path=None) -> None:
+    rejected_signals_df: pd.DataFrame, to_csv: bool = False, csv_path=None
-    cols = ['pair', 'date', 'enter_tag']
+) -> None:
-    sortcols = ['date', 'pair', 'enter_tag']
+    cols = ["pair", "date", "enter_tag"]
-    _print_table(rejected_signals_df[cols],
+    sortcols = ["date", "pair", "enter_tag"]
-                 sortcols,
+    _print_table(
-                 show_index=False,
+        rejected_signals_df[cols],
-                 name="Rejected Signals:",
+        sortcols,
-                 to_csv=to_csv,
+        show_index=False,
-                 csv_path=csv_path)
+        name="Rejected Signals:",
+        to_csv=to_csv,
+        csv_path=csv_path,
+    )
 
 
-def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'):
+def _select_rows_within_dates(df, timerange=None, df_date_col: str = "date"):
     if timerange:
-        if timerange.starttype == 'date':
+        if timerange.starttype == "date":
             df = df.loc[(df[df_date_col] >= timerange.startdt)]
-        if timerange.stoptype == 'date':
+        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))]
+        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))]
+        df = df.loc[(df["exit_reason"].isin(exit_reason_list))]
     return df
 
 
-def prepare_results(analysed_trades, stratname,
+def prepare_results(
-                    enter_reason_list, exit_reason_list,
+    analysed_trades, stratname, enter_reason_list, exit_reason_list, timerange=None
-                    timerange=None):
+):
     res_df = pd.DataFrame()
     for pair, trades in analysed_trades[stratname].items():
-        if (trades.shape[0] > 0):
+        if trades.shape[0] > 0:
-            trades.dropna(subset=['close_date'], inplace=True)
+            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):
+    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, analysis_groups: List[str], indicator_list: List[str],
+def print_results(
-                  csv_path: Path, rejected_signals=None, to_csv=False):
+    res_df: pd.DataFrame,
+    analysis_groups: List[str],
+    indicator_list: List[str],
+    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)
@@ -237,30 +270,31 @@ def print_results(res_df: pd.DataFrame, analysis_groups: List[str], indicator_li
 
         # NB this can be large for big dataframes!
         if "all" in indicator_list:
-            _print_table(res_df,
+            _print_table(
-                         show_index=False,
+                res_df, show_index=False, name="Indicators:", to_csv=to_csv, csv_path=csv_path
-                         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)
             ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
-            _print_table(res_df[ilist],
+            _print_table(
-                         sortcols=['exit_reason'],
+                res_df[ilist],
-                         show_index=False,
+                sortcols=["exit_reason"],
-                         name="Indicators:",
+                show_index=False,
-                         to_csv=to_csv,
+                name="Indicators:",
-                         csv_path=csv_path)
+                to_csv=to_csv,
+                csv_path=csv_path,
+            )
     else:
         print("\\No trades to show")
 
 
-def _print_table(df: pd.DataFrame, sortcols=None, *, show_index=False, name=None,
+def _print_table(
-                 to_csv=False, csv_path: Path):
+    df: pd.DataFrame, sortcols=None, *, show_index=False, name=None, to_csv=False, csv_path: Path
-    if (sortcols is not None):
+):
+    if sortcols is not None:
         data = df.sort_values(sortcols)
     else:
         data = df
@@ -273,60 +307,64 @@ def _print_table(df: pd.DataFrame, sortcols=None, *, show_index=False, name=None
         if name is not None:
             print(name)
 
-        print(
+        print(tabulate(data, headers="keys", tablefmt="psql", showindex=show_index))
-            tabulate(
-                data,
-                headers='keys',
-                tablefmt='psql',
-                showindex=show_index
-            )
-        )
 
 
 def process_entry_exit_reasons(config: Config):
     try:
-        analysis_groups = config.get('analysis_groups', [])
+        analysis_groups = config.get("analysis_groups", [])
-        enter_reason_list = config.get('enter_reason_list', ["all"])
+        enter_reason_list = config.get("enter_reason_list", ["all"])
-        exit_reason_list = config.get('exit_reason_list', ["all"])
+        exit_reason_list = config.get("exit_reason_list", ["all"])
-        indicator_list = config.get('indicator_list', [])
+        indicator_list = config.get("indicator_list", [])
-        do_rejected = config.get('analysis_rejected', False)
+        do_rejected = config.get("analysis_rejected", False)
-        to_csv = config.get('analysis_to_csv', False)
+        to_csv = config.get("analysis_to_csv", False)
-        csv_path = Path(config.get('analysis_csv_path', config['exportfilename']))
+        csv_path = Path(config.get("analysis_csv_path", config["exportfilename"]))
         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 = TimeRange.parse_timerange(
-            'timerange') is None else str(config.get('timerange')))
+            None if config.get("timerange") is None else str(config.get("timerange"))
+        )
 
-        backtest_stats = load_backtest_stats(config['exportfilename'])
+        backtest_stats = load_backtest_stats(config["exportfilename"])
 
-        for strategy_name, results in backtest_stats['strategy'].items():
+        for strategy_name, results in backtest_stats["strategy"].items():
-            trades = load_backtest_data(config['exportfilename'], strategy_name)
+            trades = load_backtest_data(config["exportfilename"], strategy_name)
 
             if trades is not None and not trades.empty:
-                signal_candles = _load_signal_candles(config['exportfilename'])
+                signal_candles = _load_signal_candles(config["exportfilename"])
 
                 rej_df = None
                 if do_rejected:
-                    rejected_signals_dict = _load_rejected_signals(config['exportfilename'])
+                    rejected_signals_dict = _load_rejected_signals(config["exportfilename"])
-                    rej_df = prepare_results(rejected_signals_dict, strategy_name,
+                    rej_df = prepare_results(
-                                             enter_reason_list, exit_reason_list,
+                        rejected_signals_dict,
-                                             timerange=timerange)
+                        strategy_name,
+                        enter_reason_list,
+                        exit_reason_list,
+                        timerange=timerange,
+                    )
 
                 analysed_trades_dict = _process_candles_and_indicators(
-                                        config['exchange']['pair_whitelist'], strategy_name,
+                    config["exchange"]["pair_whitelist"], strategy_name, trades, signal_candles
-                                        trades, signal_candles)
+                )
 
-                res_df = prepare_results(analysed_trades_dict, strategy_name,
+                res_df = prepare_results(
-                                         enter_reason_list, exit_reason_list,
+                    analysed_trades_dict,
-                                         timerange=timerange)
+                    strategy_name,
+                    enter_reason_list,
+                    exit_reason_list,
+                    timerange=timerange,
+                )
 
-                print_results(res_df,
+                print_results(
-                              analysis_groups,
+                    res_df,
-                              indicator_list,
+                    analysis_groups,
-                              rejected_signals=rej_df,
+                    indicator_list,
-                              to_csv=to_csv,
+                    rejected_signals=rej_df,
-                              csv_path=csv_path)
+                    to_csv=to_csv,
+                    csv_path=csv_path,
+                )
 
     except ValueError as e:
         raise OperationalException(e) from e

freqtrade/data/history/__init__.py

@@ -5,8 +5,17 @@ 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,
+from .history_utils import (
-                            load_pair_history, refresh_backtest_ohlcv_data,
+    convert_trades_to_ohlcv,
-                            refresh_backtest_trades_data, refresh_data, validate_backtest_data)
+    download_data_main,
+    get_timerange,
+    load_data,
+    load_pair_history,
+    refresh_backtest_ohlcv_data,
+    refresh_backtest_trades_data,
+    refresh_data,
+    validate_backtest_data,
+)

freqtrade/data/history/datahandlers/featherdatahandler.py

@@ -14,11 +14,11 @@ 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:
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
         """
         Store data in json format "values".
             format looks as follows:
@@ -33,11 +33,12 @@ class FeatherDataHandler(IDataHandler):
         self.create_dir_if_needed(filename)
 
         data.reset_index(drop=True).loc[:, self._columns].to_feather(
-            filename, compression_level=9, compression='lz4')
+            filename, compression_level=9, compression="lz4"
+        )
 
-    def _ohlcv_load(self, pair: str, timeframe: str,
+    def _ohlcv_load(
-                    timerange: Optional[TimeRange], candle_type: CandleType
+        self, pair: str, timeframe: str, timerange: Optional[TimeRange], candle_type: CandleType
-                    ) -> DataFrame:
+    ) -> DataFrame:
         """
         Internal method used to load data for one pair from disk.
         Implements the loading and conversion to a Pandas dataframe.
@@ -50,28 +51,31 @@ class FeatherDataHandler(IDataHandler):
         :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(
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type)
-            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)
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True
+            )
             if not filename.exists():
                 return DataFrame(columns=self._columns)
 
         pairdata = read_feather(filename)
         pairdata.columns = self._columns
-        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+        pairdata = pairdata.astype(
-                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+            dtype={
-        pairdata['date'] = to_datetime(pairdata['date'], unit='ms', utc=True)
+                "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,
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
-        pair: str,
-        timeframe: str,
-        data: DataFrame,
-        candle_type: CandleType
     ) -> None:
         """
         Append data to existing data structures
@@ -92,7 +96,7 @@ class FeatherDataHandler(IDataHandler):
         """
         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')
+        data.reset_index(drop=True).to_feather(filename, compression_level=9, compression="lz4")
 
     def trades_append(self, pair: str, data: DataFrame):
         """
@@ -104,7 +108,7 @@ class FeatherDataHandler(IDataHandler):
         raise NotImplementedError()
 
     def _trades_load(
-            self, pair: str, trading_mode: TradingMode, timerange: Optional[TimeRange] = None
+        self, pair: str, trading_mode: TradingMode, timerange: Optional[TimeRange] = None
     ) -> DataFrame:
         """
         Load a pair from file, either .json.gz or .json

freqtrade/data/history/datahandlers/hdf5datahandler.py

@@ -15,11 +15,11 @@ logger = logging.getLogger(__name__)
 
 
 class HDF5DataHandler(IDataHandler):
-
     _columns = DEFAULT_DATAFRAME_COLUMNS
 
     def ohlcv_store(
-            self, pair: str, timeframe: str, data: pd.DataFrame, candle_type: CandleType) -> None:
+        self, pair: str, timeframe: str, data: pd.DataFrame, candle_type: CandleType
+    ) -> None:
         """
         Store data in hdf5 file.
         :param pair: Pair - used to generate filename
@@ -35,13 +35,18 @@ class HDF5DataHandler(IDataHandler):
         self.create_dir_if_needed(filename)
 
         _data.loc[:, self._columns].to_hdf(
-            filename, key=key, mode='a', complevel=9, complib='blosc',
+            filename,
-            format='table', data_columns=['date']
+            key=key,
+            mode="a",
+            complevel=9,
+            complib="blosc",
+            format="table",
+            data_columns=["date"],
         )
 
-    def _ohlcv_load(self, pair: str, timeframe: str,
+    def _ohlcv_load(
-                    timerange: Optional[TimeRange], candle_type: CandleType
+        self, pair: str, timeframe: str, timerange: Optional[TimeRange], candle_type: CandleType
-                    ) -> pd.DataFrame:
+    ) -> pd.DataFrame:
         """
         Internal method used to load data for one pair from disk.
         Implements the loading and conversion to a Pandas dataframe.
@@ -55,41 +60,40 @@ class HDF5DataHandler(IDataHandler):
         :return: DataFrame with ohlcv data, or empty DataFrame
         """
         key = self._pair_ohlcv_key(pair, timeframe)
-        filename = self._pair_data_filename(
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type)
-            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)
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True
+            )
             if not filename.exists():
                 return pd.DataFrame(columns=self._columns)
         where = []
         if timerange:
-            if timerange.starttype == 'date':
+            if timerange.starttype == "date":
                 where.append(f"date >= Timestamp({timerange.startts * 1e9})")
-            if timerange.stoptype == 'date':
+            if timerange.stoptype == "date":
                 where.append(f"date <= Timestamp({timerange.stopts * 1e9})")
 
         pairdata = pd.read_hdf(filename, key=key, mode="r", where=where)
 
         if list(pairdata.columns) != self._columns:
             raise ValueError("Wrong dataframe format")
-        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+        pairdata = pairdata.astype(
-                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+            dtype={
+                "open": "float",
+                "high": "float",
+                "low": "float",
+                "close": "float",
+                "volume": "float",
+            }
+        )
         pairdata = pairdata.reset_index(drop=True)
         return pairdata
 
     def ohlcv_append(
-        self,
+        self, pair: str, timeframe: str, data: pd.DataFrame, candle_type: CandleType
-        pair: str,
-        timeframe: str,
-        data: pd.DataFrame,
-        candle_type: CandleType
     ) -> None:
         """
         Append data to existing data structures
@@ -111,9 +115,13 @@ class HDF5DataHandler(IDataHandler):
         key = self._pair_trades_key(pair)
 
         data.to_hdf(
-            self._pair_trades_filename(self._datadir, pair, trading_mode), key=key,
+            self._pair_trades_filename(self._datadir, pair, trading_mode),
-            mode='a', complevel=9, complib='blosc',
+            key=key,
-            format='table', data_columns=['timestamp']
+            mode="a",
+            complevel=9,
+            complib="blosc",
+            format="table",
+            data_columns=["timestamp"],
         )
 
     def trades_append(self, pair: str, data: pd.DataFrame):
@@ -142,13 +150,13 @@ class HDF5DataHandler(IDataHandler):
             return pd.DataFrame(columns=DEFAULT_TRADES_COLUMNS)
         where = []
         if timerange:
-            if timerange.starttype == 'date':
+            if timerange.starttype == "date":
                 where.append(f"timestamp >= {timerange.startts * 1e3}")
-            if timerange.stoptype == 'date':
+            if timerange.stoptype == "date":
                 where.append(f"timestamp < {timerange.stopts * 1e3}")
 
         trades: pd.DataFrame = pd.read_hdf(filename, key=key, mode="r", where=where)
-        trades[['id', 'type']] = trades[['id', 'type']].replace({np.nan: None})
+        trades[["id", "type"]] = trades[["id", "type"]].replace({np.nan: None})
         return trades
 
     @classmethod
@@ -158,7 +166,7 @@ class HDF5DataHandler(IDataHandler):
     @classmethod
     def _pair_ohlcv_key(cls, pair: str, timeframe: str) -> str:
         # Escape futures pairs to avoid warnings
-        pair_esc = pair.replace(':', '_')
+        pair_esc = pair.replace(":", "_")
         return f"{pair_esc}/ohlcv/tf_{timeframe}"
 
     @classmethod

freqtrade/data/history/datahandlers/idatahandler.py

@@ -3,6 +3,7 @@ Abstract datahandler interface.
 It's subclasses handle and storing data from disk.
 
 """
+
 import logging
 import re
 from abc import ABC, abstractmethod
@@ -16,8 +17,12 @@ from pandas import DataFrame
 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,
+from freqtrade.data.converter import (
-                                      trades_df_remove_duplicates, trim_dataframe)
+    clean_ohlcv_dataframe,
+    trades_convert_types,
+    trades_df_remove_duplicates,
+    trim_dataframe,
+)
 from freqtrade.enums import CandleType, TradingMode
 from freqtrade.exchange import timeframe_to_seconds
 
@@ -26,8 +31,7 @@ logger = logging.getLogger(__name__)
 
 
 class IDataHandler(ABC):
-
+    _OHLCV_REGEX = r"^([a-zA-Z_\d-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)"
-    _OHLCV_REGEX = r'^([a-zA-Z_\d-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
 
     def __init__(self, datadir: Path) -> None:
         self._datadir = datadir
@@ -41,7 +45,8 @@ class IDataHandler(ABC):
 
     @classmethod
     def ohlcv_get_available_data(
-            cls, datadir: Path, trading_mode: TradingMode) -> ListPairsWithTimeframes:
+        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
@@ -49,17 +54,20 @@ class IDataHandler(ABC):
         :return: List of Tuples of (pair, timeframe, CandleType)
         """
         if trading_mode == TradingMode.FUTURES:
-            datadir = datadir.joinpath('futures')
+            datadir = datadir.joinpath("futures")
         _tmp = [
-            re.search(
+            re.search(cls._OHLCV_REGEX, p.name)
-                cls._OHLCV_REGEX, p.name
+            for p in datadir.glob(f"*.{cls._get_file_extension()}")
-            ) 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])
+                CandleType.from_string(match[3]),
-            ) for match in _tmp if match and len(match.groups()) > 1]
+            )
+            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]:
@@ -73,17 +81,20 @@ class IDataHandler(ABC):
         """
         candle = ""
         if candle_type != CandleType.SPOT:
-            datadir = datadir.joinpath('futures')
+            datadir = datadir.joinpath("futures")
             candle = f"-{candle_type}"
         ext = cls._get_file_extension()
-        _tmp = [re.search(r'^(\S+)(?=\-' + timeframe + candle + f'.{ext})', p.name)
+        _tmp = [
-                for p in datadir.glob(f"*{timeframe}{candle}.{ext}")]
+            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:
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
         """
         Store ohlcv data.
         :param pair: Pair - used to generate filename
@@ -93,8 +104,9 @@ class IDataHandler(ABC):
         :return: None
         """
 
-    def ohlcv_data_min_max(self, pair: str, timeframe: str,
+    def ohlcv_data_min_max(
-                           candle_type: CandleType) -> Tuple[datetime, datetime, int]:
+        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
@@ -109,12 +121,12 @@ class IDataHandler(ABC):
                 datetime.fromtimestamp(0, tz=timezone.utc),
                 0,
             )
-        return df.iloc[0]['date'].to_pydatetime(), df.iloc[-1]['date'].to_pydatetime(), len(df)
+        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: Optional[TimeRange],
+    def _ohlcv_load(
-                    candle_type: CandleType
+        self, pair: str, timeframe: str, timerange: Optional[TimeRange], candle_type: CandleType
-                    ) -> DataFrame:
+    ) -> DataFrame:
         """
         Internal method used to load data for one pair from disk.
         Implements the loading and conversion to a Pandas dataframe.
@@ -144,11 +156,7 @@ class IDataHandler(ABC):
 
     @abstractmethod
     def ohlcv_append(
-        self,
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
-        pair: str,
-        timeframe: str,
-        data: DataFrame,
-        candle_type: CandleType
     ) -> None:
         """
         Append data to existing data structures
@@ -166,8 +174,10 @@ class IDataHandler(ABC):
         :return: List of Pairs
         """
         _ext = cls._get_file_extension()
-        _tmp = [re.search(r'^(\S+)(?=\-trades.' + _ext + ')', p.name)
+        _tmp = [
-                for p in datadir.glob(f"*trades.{_ext}")]
+            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]
 
@@ -227,7 +237,7 @@ class IDataHandler(ABC):
         return False
 
     def trades_load(
-            self, pair: str, trading_mode: TradingMode, timerange: Optional[TimeRange] = None
+        self, pair: str, trading_mode: TradingMode, timerange: Optional[TimeRange] = None
     ) -> DataFrame:
         """
         Load a pair from file, either .json.gz or .json
@@ -260,7 +270,7 @@ class IDataHandler(ABC):
         pair: str,
         timeframe: str,
         candle_type: CandleType,
-        no_timeframe_modify: bool = False
+        no_timeframe_modify: bool = False,
     ) -> Path:
         pair_s = misc.pair_to_filename(pair)
         candle = ""
@@ -268,10 +278,9 @@ class IDataHandler(ABC):
             timeframe = cls.timeframe_to_file(timeframe)
 
         if candle_type != CandleType.SPOT:
-            datadir = datadir.joinpath('futures')
+            datadir = datadir.joinpath("futures")
             candle = f"-{candle_type}"
-        filename = datadir.joinpath(
+        filename = datadir.joinpath(f"{pair_s}-{timeframe}{candle}.{cls._get_file_extension()}")
-            f'{pair_s}-{timeframe}{candle}.{cls._get_file_extension()}')
         return filename
 
     @classmethod
@@ -279,14 +288,14 @@ class IDataHandler(ABC):
         pair_s = misc.pair_to_filename(pair)
         if trading_mode == TradingMode.FUTURES:
             # Futures pair ...
-            datadir = datadir.joinpath('futures')
+            datadir = datadir.joinpath("futures")
 
-        filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
+        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')
+        return timeframe.replace("M", "Mo")
 
     @staticmethod
     def rebuild_timeframe_from_filename(timeframe: str) -> str:
@@ -294,7 +303,7 @@ class IDataHandler(ABC):
         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)
+        return re.sub("1mo", "1M", timeframe, flags=re.IGNORECASE)
 
     @staticmethod
     def rebuild_pair_from_filename(pair: str) -> str:
@@ -302,18 +311,22 @@ class IDataHandler(ABC):
         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, 1)
+        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, 1)
+        res = re.sub("_", ":", res, count=1)
         return res
 
-    def ohlcv_load(self, pair, timeframe: str,
+    def ohlcv_load(
-                   candle_type: CandleType, *,
+        self,
-                   timerange: Optional[TimeRange] = None,
+        pair,
-                   fill_missing: bool = True,
+        timeframe: str,
-                   drop_incomplete: bool = False,
+        candle_type: CandleType,
-                   startup_candles: int = 0,
+        *,
-                   warn_no_data: bool = True,
+        timerange: Optional[TimeRange] = None,
-                   ) -> DataFrame:
+        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.
 
@@ -333,15 +346,12 @@ class IDataHandler(ABC):
             timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
 
         pairdf = self._ohlcv_load(
-            pair,
+            pair, timeframe, timerange=timerange_startup, candle_type=candle_type
-            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']
+            enddate = pairdf.iloc[-1]["date"]
 
             if timerange_startup:
                 self._validate_pairdata(pair, pairdf, timeframe, candle_type, timerange_startup)
@@ -350,17 +360,25 @@ class IDataHandler(ABC):
                     return pairdf
 
             # incomplete candles should only be dropped if we didn't trim the end beforehand.
-            pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
+            pairdf = clean_ohlcv_dataframe(
-                                           pair=pair,
+                pairdf,
-                                           fill_missing=fill_missing,
+                timeframe,
-                                           drop_incomplete=(drop_incomplete and
+                pair=pair,
-                                                            enddate == pairdf.iloc[-1]['date']))
+                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,
+        self,
-            warn_no_data: bool, warn_price: bool = False) -> bool:
+        pairdf: DataFrame,
+        pair: str,
+        timeframe: str,
+        candle_type: CandleType,
+        warn_no_data: bool,
+        warn_price: bool = False,
+    ) -> bool:
         """
         Warn on empty dataframe
         """
@@ -373,39 +391,55 @@ class IDataHandler(ABC):
             return True
         elif warn_price:
             candle_price_gap = 0
-            if (candle_type in (CandleType.SPOT, CandleType.FUTURES) and
+            if (
-                    not pairdf.empty
+                candle_type in (CandleType.SPOT, CandleType.FUTURES)
-                    and 'close' in pairdf.columns and 'open' in pairdf.columns):
+                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 = (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 "
+                logger.info(
-                            f"of {candle_price_gap:.2%} detected.")
+                    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,
+    def _validate_pairdata(
-                           candle_type: CandleType, timerange: TimeRange):
+        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 timerange.starttype == "date":
-            if pairdata.iloc[0]['date'] > timerange.startdt:
+            if pairdata.iloc[0]["date"] > timerange.startdt:
-                logger.warning(f"{pair}, {candle_type}, {timeframe}, "
+                logger.warning(
-                               f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
+                    f"{pair}, {candle_type}, {timeframe}, "
-        if timerange.stoptype == 'date':
+                    f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}"
-            if pairdata.iloc[-1]['date'] < timerange.stopdt:
+                )
-                logger.warning(f"{pair}, {candle_type}, {timeframe}, "
+        if timerange.stoptype == "date":
-                               f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
+            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):
+        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.
@@ -431,18 +465,19 @@ class IDataHandler(ABC):
 
         if funding_rate_combs:
             logger.warning(
-                f'Migrating {len(funding_rate_combs)} funding fees to correct timeframe.')
+                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.')
+                logger.warning(f"{old_name} does not exist, skipping.")
                 continue
 
             if Path(new_name).exists():
-                logger.warning(f'{new_name} already exists, Removing.')
+                logger.warning(f"{new_name} already exists, Removing.")
                 Path(new_name).unlink()
 
             Path(old_name).rename(new_name)
@@ -457,27 +492,33 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
     :return: Datahandler class
     """
 
-    if datatype == 'json':
+    if datatype == "json":
         from .jsondatahandler import JsonDataHandler
+
         return JsonDataHandler
-    elif datatype == 'jsongz':
+    elif datatype == "jsongz":
         from .jsondatahandler import JsonGzDataHandler
+
         return JsonGzDataHandler
-    elif datatype == 'hdf5':
+    elif datatype == "hdf5":
         from .hdf5datahandler import HDF5DataHandler
+
         return HDF5DataHandler
-    elif datatype == 'feather':
+    elif datatype == "feather":
         from .featherdatahandler import FeatherDataHandler
+
         return FeatherDataHandler
-    elif datatype == 'parquet':
+    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: Optional[str] = None,
+def get_datahandler(
-                    data_handler: Optional[IDataHandler] = None) -> IDataHandler:
+    datadir: Path, data_format: Optional[str] = None, data_handler: Optional[IDataHandler] = None
+) -> IDataHandler:
     """
     :param datadir: Folder to save data
     :param data_format: dataformat to use
@@ -485,6 +526,6 @@ def get_datahandler(datadir: Path, data_format: Optional[str] = None,
     """
 
     if not data_handler:
-        HandlerClass = get_datahandlerclass(data_format or 'feather')
+        HandlerClass = get_datahandlerclass(data_format or "feather")
         data_handler = HandlerClass(datadir)
     return data_handler

freqtrade/data/history/datahandlers/jsondatahandler.py

@@ -17,12 +17,12 @@ 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:
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
         """
         Store data in json format "values".
             format looks as follows:
@@ -37,16 +37,16 @@ class JsonDataHandler(IDataHandler):
         self.create_dir_if_needed(filename)
         _data = data.copy()
         # Convert date to int
-        _data['date'] = _data['date'].astype(np.int64) // 1000 // 1000
+        _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",
+            filename, orient="values", compression="gzip" if self._use_zip else None
-            compression='gzip' if self._use_zip else None)
+        )
 
-    def _ohlcv_load(self, pair: str, timeframe: str,
+    def _ohlcv_load(
-                    timerange: Optional[TimeRange], candle_type: CandleType
+        self, pair: str, timeframe: str, timerange: Optional[TimeRange], candle_type: CandleType
-                    ) -> DataFrame:
+    ) -> DataFrame:
         """
         Internal method used to load data for one pair from disk.
         Implements the loading and conversion to a Pandas dataframe.
@@ -59,31 +59,34 @@ class JsonDataHandler(IDataHandler):
         :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(
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type)
-            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)
+                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 = 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',
+        pairdata = pairdata.astype(
-                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+            dtype={
-        pairdata['date'] = to_datetime(pairdata['date'], unit='ms', utc=True)
+                "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,
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
-        pair: str,
-        timeframe: str,
-        data: DataFrame,
-        candle_type: CandleType
     ) -> None:
         """
         Append data to existing data structures
@@ -145,5 +148,4 @@ class JsonDataHandler(IDataHandler):
 
 
 class JsonGzDataHandler(JsonDataHandler):
-
     _use_zip = True

freqtrade/data/history/datahandlers/parquetdatahandler.py

@@ -14,11 +14,11 @@ 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:
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
+    ) -> None:
         """
         Store data in json format "values".
             format looks as follows:
@@ -34,9 +34,9 @@ class ParquetDataHandler(IDataHandler):
 
         data.reset_index(drop=True).loc[:, self._columns].to_parquet(filename)
 
-    def _ohlcv_load(self, pair: str, timeframe: str,
+    def _ohlcv_load(
-                    timerange: Optional[TimeRange], candle_type: CandleType
+        self, pair: str, timeframe: str, timerange: Optional[TimeRange], candle_type: CandleType
-                    ) -> DataFrame:
+    ) -> DataFrame:
         """
         Internal method used to load data for one pair from disk.
         Implements the loading and conversion to a Pandas dataframe.
@@ -49,28 +49,31 @@ class ParquetDataHandler(IDataHandler):
         :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(
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type)
-            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)
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True
+            )
             if not filename.exists():
                 return DataFrame(columns=self._columns)
 
         pairdata = read_parquet(filename)
         pairdata.columns = self._columns
-        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+        pairdata = pairdata.astype(
-                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+            dtype={
-        pairdata['date'] = to_datetime(pairdata['date'], unit='ms', utc=True)
+                "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,
+        self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType
-        pair: str,
-        timeframe: str,
-        data: DataFrame,
-        candle_type: CandleType
     ) -> None:
         """
         Append data to existing data structures

freqtrade/data/history/history_utils.py

@@ -7,11 +7,20 @@ from typing import Dict, List, Optional, Tuple
 from pandas import DataFrame, concat
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import (DATETIME_PRINT_FORMAT, DEFAULT_DATAFRAME_COLUMNS,
+from freqtrade.constants import (
-                                 DL_DATA_TIMEFRAMES, DOCS_LINK, Config)
+    DATETIME_PRINT_FORMAT,
-from freqtrade.data.converter import (clean_ohlcv_dataframe, convert_trades_to_ohlcv,
+    DEFAULT_DATAFRAME_COLUMNS,
-                                      ohlcv_to_dataframe, trades_df_remove_duplicates,
+    DL_DATA_TIMEFRAMES,
-                                      trades_list_to_df)
+    DOCS_LINK,
+    Config,
+)
+from freqtrade.data.converter import (
+    clean_ohlcv_dataframe,
+    convert_trades_to_ohlcv,
+    ohlcv_to_dataframe,
+    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
@@ -25,17 +34,19 @@ from freqtrade.util.migrations import migrate_data
 logger = logging.getLogger(__name__)
 
 
-def load_pair_history(pair: str,
+def load_pair_history(
-                      timeframe: str,
+    pair: str,
-                      datadir: Path, *,
+    timeframe: str,
-                      timerange: Optional[TimeRange] = None,
+    datadir: Path,
-                      fill_up_missing: bool = True,
+    *,
-                      drop_incomplete: bool = False,
+    timerange: Optional[TimeRange] = None,
-                      startup_candles: int = 0,
+    fill_up_missing: bool = True,
-                      data_format: Optional[str] = None,
+    drop_incomplete: bool = False,
-                      data_handler: Optional[IDataHandler] = None,
+    startup_candles: int = 0,
-                      candle_type: CandleType = CandleType.SPOT
+    data_format: Optional[str] = None,
-                      ) -> DataFrame:
+    data_handler: Optional[IDataHandler] = None,
+    candle_type: CandleType = CandleType.SPOT,
+) -> DataFrame:
     """
     Load cached ohlcv history for the given pair.
 
@@ -54,27 +65,30 @@ def load_pair_history(pair: str,
     """
     data_handler = get_datahandler(datadir, data_format, data_handler)
 
-    return data_handler.ohlcv_load(pair=pair,
+    return data_handler.ohlcv_load(
-                                   timeframe=timeframe,
+        pair=pair,
-                                   timerange=timerange,
+        timeframe=timeframe,
-                                   fill_missing=fill_up_missing,
+        timerange=timerange,
-                                   drop_incomplete=drop_incomplete,
+        fill_missing=fill_up_missing,
-                                   startup_candles=startup_candles,
+        drop_incomplete=drop_incomplete,
-                                   candle_type=candle_type,
+        startup_candles=startup_candles,
-                                   )
+        candle_type=candle_type,
+    )
 
 
-def load_data(datadir: Path,
+def load_data(
-              timeframe: str,
+    datadir: Path,
-              pairs: List[str], *,
+    timeframe: str,
-              timerange: Optional[TimeRange] = None,
+    pairs: List[str],
-              fill_up_missing: bool = True,
+    *,
-              startup_candles: int = 0,
+    timerange: Optional[TimeRange] = None,
-              fail_without_data: bool = False,
+    fill_up_missing: bool = True,
-              data_format: str = 'feather',
+    startup_candles: int = 0,
-              candle_type: CandleType = CandleType.SPOT,
+    fail_without_data: bool = False,
-              user_futures_funding_rate: Optional[int] = None,
+    data_format: str = "feather",
-              ) -> Dict[str, DataFrame]:
+    candle_type: CandleType = CandleType.SPOT,
+    user_futures_funding_rate: Optional[int] = None,
+) -> Dict[str, DataFrame]:
     """
     Load ohlcv history data for a list of pairs.
 
@@ -91,18 +105,21 @@ def load_data(datadir: Path,
     """
     result: Dict[str, DataFrame] = {}
     if startup_candles > 0 and timerange:
-        logger.info(f'Using indicator startup period: {startup_candles} ...')
+        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,
+        hist = load_pair_history(
-                                 datadir=datadir, timerange=timerange,
+            pair=pair,
-                                 fill_up_missing=fill_up_missing,
+            timeframe=timeframe,
-                                 startup_candles=startup_candles,
+            datadir=datadir,
-                                 data_handler=data_handler,
+            timerange=timerange,
-                                 candle_type=candle_type,
+            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:
@@ -116,14 +133,16 @@ def load_data(datadir: Path,
     return result
 
 
-def refresh_data(*, datadir: Path,
+def refresh_data(
-                 timeframe: str,
+    *,
-                 pairs: List[str],
+    datadir: Path,
-                 exchange: Exchange,
+    timeframe: str,
-                 data_format: Optional[str] = None,
+    pairs: List[str],
-                 timerange: Optional[TimeRange] = None,
+    exchange: Exchange,
-                 candle_type: CandleType,
+    data_format: Optional[str] = None,
-                 ) -> None:
+    timerange: Optional[TimeRange] = None,
+    candle_type: CandleType,
+) -> None:
     """
     Refresh ohlcv history data for a list of pairs.
 
@@ -137,11 +156,17 @@ def refresh_data(*, datadir: Path,
     """
     data_handler = get_datahandler(datadir, data_format)
     for idx, pair in enumerate(pairs):
-        process = f'{idx}/{len(pairs)}'
+        process = f"{idx}/{len(pairs)}"
-        _download_pair_history(pair=pair, process=process,
+        _download_pair_history(
-                               timeframe=timeframe, datadir=datadir,
+            pair=pair,
-                               timerange=timerange, exchange=exchange, data_handler=data_handler,
+            process=process,
-                               candle_type=candle_type)
+            timeframe=timeframe,
+            datadir=datadir,
+            timerange=timerange,
+            exchange=exchange,
+            data_handler=data_handler,
+            candle_type=candle_type,
+        )
 
 
 def _load_cached_data_for_updating(
@@ -163,42 +188,49 @@ def _load_cached_data_for_updating(
     start = None
     end = None
     if timerange:
-        if timerange.starttype == 'date':
+        if timerange.starttype == "date":
             start = timerange.startdt
-        if timerange.stoptype == 'date':
+        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,
+    data = data_handler.ohlcv_load(
-                                   timerange=None, fill_missing=False,
+        pair,
-                                   drop_incomplete=True, warn_no_data=False,
+        timeframe=timeframe,
-                                   candle_type=candle_type)
+        timerange=None,
+        fill_missing=False,
+        drop_incomplete=True,
+        warn_no_data=False,
+        candle_type=candle_type,
+    )
     if not data.empty:
-        if not prepend and start and start < data.iloc[0]['date']:
+        if not prepend and start and start < data.iloc[0]["date"]:
             # Earlier data than existing data requested, redownload all
             data = DataFrame(columns=DEFAULT_DATAFRAME_COLUMNS)
         else:
             if prepend:
-                end = data.iloc[0]['date']
+                end = data.iloc[0]["date"]
             else:
-                start = data.iloc[-1]['date']
+                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, *,
+def _download_pair_history(
-                           datadir: Path,
+    pair: str,
-                           exchange: Exchange,
+    *,
-                           timeframe: str = '5m',
+    datadir: Path,
-                           process: str = '',
+    exchange: Exchange,
-                           new_pairs_days: int = 30,
+    timeframe: str = "5m",
-                           data_handler: Optional[IDataHandler] = None,
+    process: str = "",
-                           timerange: Optional[TimeRange] = None,
+    new_pairs_days: int = 30,
-                           candle_type: CandleType,
+    data_handler: Optional[IDataHandler] = None,
-                           erase: bool = False,
+    timerange: Optional[TimeRange] = None,
-                           prepend: bool = False,
+    candle_type: CandleType,
-                           ) -> bool:
+    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
@@ -217,54 +249,71 @@ def _download_pair_history(pair: str, *,
     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}.')
+                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,
+            pair,
+            timeframe,
+            timerange,
             data_handler=data_handler,
             candle_type=candle_type,
-            prepend=prepend)
+            prepend=prepend,
+        )
 
-        logger.info(f'({process}) - Download history data for "{pair}", {timeframe}, '
+        logger.info(
-                    f'{candle_type} and store in {datadir}. '
+            f'({process}) - Download history data for "{pair}", {timeframe}, '
-                    f'From {format_ms_time(since_ms) if since_ms else "start"} to '
+            f"{candle_type} and store in {datadir}. "
-                    f'{format_ms_time(until_ms) if until_ms else "now"}'
+            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",
+        logger.debug(
-                     f"{data.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}"
+            "Current Start: %s",
-                     if not data.empty else 'None')
+            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}}"
+        logger.debug(
-                     if not data.empty else 'None')
+            "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_data = exchange.get_historic_ohlcv(pair=pair,
+        new_data = exchange.get_historic_ohlcv(
-                                               timeframe=timeframe,
+            pair=pair,
-                                               since_ms=since_ms if since_ms else
+            timeframe=timeframe,
-                                               int((datetime.now() - timedelta(days=new_pairs_days)
+            since_ms=(
-                                                    ).timestamp()) * 1000,
+                since_ms
-                                               is_new_pair=data.empty,
+                if since_ms
-                                               candle_type=candle_type,
+                else int((datetime.now() - timedelta(days=new_pairs_days)).timestamp()) * 1000
-                                               until_ms=until_ms if until_ms else None
+            ),
-                                               )
+            is_new_pair=data.empty,
+            candle_type=candle_type,
+            until_ms=until_ms if until_ms else None,
+        )
         # TODO: Maybe move parsing to exchange class (?)
-        new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
+        new_dataframe = ohlcv_to_dataframe(
-                                           fill_missing=False, drop_incomplete=True)
+            new_data, timeframe, pair, fill_missing=False, drop_incomplete=True
+        )
         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,
+            data = clean_ohlcv_dataframe(
-                                         fill_missing=False, drop_incomplete=False)
+                concat([data, new_dataframe], axis=0),
+                timeframe,
+                pair,
+                fill_missing=False,
+                drop_incomplete=False,
+            )
 
-        logger.debug("New Start: %s",
+        logger.debug(
-                     f"{data.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}"
+            "New Start: %s",
-                     if not data.empty else 'None')
+            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}}"
+        logger.debug(
-                     if not data.empty else 'None')
+            "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
@@ -276,13 +325,18 @@ def _download_pair_history(pair: str, *,
         return False
 
 
-def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
+def refresh_backtest_ohlcv_data(
-                                datadir: Path, trading_mode: str,
+    exchange: Exchange,
-                                timerange: Optional[TimeRange] = None,
+    pairs: List[str],
-                                new_pairs_days: int = 30, erase: bool = False,
+    timeframes: List[str],
-                                data_format: Optional[str] = None,
+    datadir: Path,
-                                prepend: bool = False,
+    trading_mode: str,
-                                ) -> List[str]:
+    timerange: Optional[TimeRange] = None,
+    new_pairs_days: int = 30,
+    erase: bool = False,
+    data_format: Optional[str] = None,
+    prepend: bool = False,
+) -> List[str]:
     """
     Refresh stored ohlcv data for backtesting and hyperopt operations.
     Used by freqtrade download-data subcommand.
@@ -291,63 +345,77 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
     pairs_not_available = []
     data_handler = get_datahandler(datadir, data_format)
     candle_type = CandleType.get_default(trading_mode)
-    process = ''
+    process = ""
     for idx, pair in enumerate(pairs, start=1):
         if pair not in exchange.markets:
             pairs_not_available.append(pair)
             logger.info(f"Skipping pair {pair}...")
             continue
         for timeframe in timeframes:
-
+            logger.debug(f"Downloading pair {pair}, {candle_type}, interval {timeframe}.")
-            logger.debug(f'Downloading pair {pair}, {candle_type}, interval {timeframe}.')
+            process = f"{idx}/{len(pairs)}"
-            process = f'{idx}/{len(pairs)}'
+            _download_pair_history(
-            _download_pair_history(pair=pair, process=process,
+                pair=pair,
-                                   datadir=datadir, exchange=exchange,
+                process=process,
-                                   timerange=timerange, data_handler=data_handler,
+                datadir=datadir,
-                                   timeframe=str(timeframe), new_pairs_days=new_pairs_days,
+                exchange=exchange,
-                                   candle_type=candle_type,
+                timerange=timerange,
-                                   erase=erase, prepend=prepend)
+                data_handler=data_handler,
-        if trading_mode == 'futures':
+                timeframe=str(timeframe),
+                new_pairs_days=new_pairs_days,
+                candle_type=candle_type,
+                erase=erase,
+                prepend=prepend,
+            )
+        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_mark = exchange.get_option("mark_ohlcv_timeframe")
-            tf_funding_rate = exchange.get_option('funding_fee_timeframe')
+            tf_funding_rate = exchange.get_option("funding_fee_timeframe")
 
-            fr_candle_type = CandleType.from_string(exchange.get_option('mark_ohlcv_price'))
+            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}.')
+                logger.debug(f"Downloading pair {pair}, {candle_type_f}, interval {tf}.")
-                _download_pair_history(pair=pair, process=process,
+                _download_pair_history(
-                                       datadir=datadir, exchange=exchange,
+                    pair=pair,
-                                       timerange=timerange, data_handler=data_handler,
+                    process=process,
-                                       timeframe=str(tf), new_pairs_days=new_pairs_days,
+                    datadir=datadir,
-                                       candle_type=candle_type_f,
+                    exchange=exchange,
-                                       erase=erase, prepend=prepend)
+                    timerange=timerange,
+                    data_handler=data_handler,
+                    timeframe=str(tf),
+                    new_pairs_days=new_pairs_days,
+                    candle_type=candle_type_f,
+                    erase=erase,
+                    prepend=prepend,
+                )
 
     return pairs_not_available
 
 
-def _download_trades_history(exchange: Exchange,
+def _download_trades_history(
-                             pair: str, *,
+    exchange: Exchange,
-                             new_pairs_days: int = 30,
+    pair: str,
-                             timerange: Optional[TimeRange] = None,
+    *,
-                             data_handler: IDataHandler,
+    new_pairs_days: int = 30,
-                             trading_mode: TradingMode,
+    timerange: Optional[TimeRange] = None,
-                             ) -> bool:
+    data_handler: IDataHandler,
+    trading_mode: TradingMode,
+) -> bool:
     """
     Download trade history from the exchange.
     Appends to previously downloaded trades data.
     """
     try:
-
         until = None
         since = 0
         if timerange:
-            if timerange.starttype == 'date':
+            if timerange.starttype == "date":
                 since = timerange.startts * 1000
-            if timerange.stoptype == 'date':
+            if timerange.stoptype == "date":
                 until = timerange.stopts * 1000
 
         trades = data_handler.trades_load(pair, trading_mode)
@@ -356,45 +424,58 @@ def _download_trades_history(exchange: Exchange,
         # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
         # DEFAULT_TRADES_COLUMNS: 1 -> id
 
-        if not trades.empty and since > 0 and since < trades.iloc[0]['timestamp']:
+        if not trades.empty and since > 0 and since < trades.iloc[0]["timestamp"]:
             # since is before the first trade
-            logger.info(f"Start ({trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}) earlier than "
+            logger.info(
-                        f"available data. Redownloading trades for {pair}...")
+                f"Start ({trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}) earlier than "
+                f"available data. Redownloading trades for {pair}..."
+            )
             trades = trades_list_to_df([])
 
-        from_id = trades.iloc[-1]['id'] if not trades.empty else None
+        from_id = trades.iloc[-1]["id"] if not trades.empty else None
-        if not trades.empty and since < trades.iloc[-1]['timestamp']:
+        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 = trades.iloc[-1]['timestamp'] - (5 * 1000)
+            since = trades.iloc[-1]["timestamp"] - (5 * 1000)
-            logger.info(f"Using last trade date -5s - Downloading trades for {pair} "
+            logger.info(
-                        f"since: {format_ms_time(since)}.")
+                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
+        logger.debug(
-                     f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}")
+            "Current Start: %s",
-        logger.debug("Current End: %s", 'None' if trades.empty else
+            "None" if trades.empty else f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}",
-                     f"{trades.iloc[-1]['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)}")
 
         # Default since_ms to 30 days if nothing is given
-        new_trades = exchange.get_historic_trades(pair=pair,
+        new_trades = exchange.get_historic_trades(
-                                                  since=since,
+            pair=pair,
-                                                  until=until,
+            since=since,
-                                                  from_id=from_id,
+            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
+        logger.debug(
-                     f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}")
+            "New Start: %s",
-        logger.debug("New End: %s", 'None' if trades.empty else
+            "None" if trades.empty else f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}",
-                     f"{trades.iloc[-1]['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
 
@@ -405,11 +486,16 @@ def _download_trades_history(exchange: Exchange,
         return False
 
 
-def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
+def refresh_backtest_trades_data(
-                                 timerange: TimeRange, trading_mode: TradingMode,
+    exchange: Exchange,
-                                 new_pairs_days: int = 30,
+    pairs: List[str],
-                                 erase: bool = False, data_format: str = 'feather',
+    datadir: Path,
-                                 ) -> List[str]:
+    timerange: TimeRange,
+    trading_mode: TradingMode,
+    new_pairs_days: int = 30,
+    erase: bool = False,
+    data_format: str = "feather",
+) -> List[str]:
     """
     Refresh stored trades data for backtesting and hyperopt operations.
     Used by freqtrade download-data subcommand.
@@ -425,15 +511,17 @@ def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir:
 
         if erase:
             if data_handler.trades_purge(pair, trading_mode):
-                logger.info(f'Deleting existing data for pair {pair}.')
+                logger.info(f"Deleting existing data for pair {pair}.")
 
-        logger.info(f'Downloading trades for pair {pair}.')
+        logger.info(f"Downloading trades for pair {pair}.")
-        _download_trades_history(exchange=exchange,
+        _download_trades_history(
-                                 pair=pair,
+            exchange=exchange,
-                                 new_pairs_days=new_pairs_days,
+            pair=pair,
-                                 timerange=timerange,
+            new_pairs_days=new_pairs_days,
-                                 data_handler=data_handler,
+            timerange=timerange,
-                                 trading_mode=trading_mode)
+            data_handler=data_handler,
+            trading_mode=trading_mode,
+        )
     return pairs_not_available
 
 
@@ -445,15 +533,18 @@ def get_timerange(data: Dict[str, DataFrame]) -> Tuple[datetime, datetime]:
     :return: tuple containing min_date, max_date
     """
     timeranges = [
-        (frame['date'].min().to_pydatetime(), frame['date'].max().to_pydatetime())
+        (frame["date"].min().to_pydatetime(), frame["date"].max().to_pydatetime())
         for frame in data.values()
     ]
-    return (min(timeranges, key=operator.itemgetter(0))[0],
+    return (
-            max(timeranges, key=operator.itemgetter(1))[1])
+        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,
+def validate_backtest_data(
-                           max_date: datetime, timeframe_min: int) -> bool:
+    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.
 
@@ -469,89 +560,111 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
     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",
+        logger.warning(
-                       pair, expected_frames, dflen, expected_frames - dflen)
+            "%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:
-
     timerange = TimeRange()
-    if 'days' in config:
+    if "days" in config:
-        time_since = (datetime.now() - timedelta(days=config['days'])).strftime("%Y%m%d")
+        time_since = (datetime.now() - timedelta(days=config["days"])).strftime("%Y%m%d")
-        timerange = TimeRange.parse_timerange(f'{time_since}-')
+        timerange = TimeRange.parse_timerange(f"{time_since}-")
 
-    if 'timerange' in config:
+    if "timerange" in config:
-        timerange = timerange.parse_timerange(config['timerange'])
+        timerange = timerange.parse_timerange(config["timerange"])
 
     # Remove stake-currency to skip checks which are not relevant for datadownload
-    config['stake_currency'] = ''
+    config["stake_currency"] = ""
 
     pairs_not_available: List[str] = []
 
     # Init exchange
     from freqtrade.resolvers.exchange_resolver import ExchangeResolver
+
     exchange = ExchangeResolver.load_exchange(config, validate=False)
     available_pairs = [
-        p for p in exchange.get_markets(
+        p
-            tradable_only=True, active_only=not config.get('include_inactive')
+        for p in exchange.get_markets(
-            ).keys()
+            tradable_only=True, active_only=not config.get("include_inactive")
+        ).keys()
     ]
 
     expanded_pairs = dynamic_expand_pairlist(config, available_pairs)
-    if 'timeframes' not in config:
+    if "timeframes" not in config:
-        config['timeframes'] = DL_DATA_TIMEFRAMES
+        config["timeframes"] = DL_DATA_TIMEFRAMES
 
     # Manual validations of relevant settings
-    if not config['exchange'].get('skip_pair_validation', False):
+    if not config["exchange"].get("skip_pair_validation", False):
         exchange.validate_pairs(expanded_pairs)
-    logger.info(f"About to download pairs: {expanded_pairs}, "
+    logger.info(
-                f"intervals: {config['timeframes']} to {config['datadir']}")
+        f"About to download pairs: {expanded_pairs}, "
+        f"intervals: {config['timeframes']} to {config['datadir']}"
+    )
 
     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")
+            f"More info: {DOCS_LINK}/bot-basics/#pair-naming"
+        )
 
-    for timeframe in config['timeframes']:
+    for timeframe in config["timeframes"]:
         exchange.validate_timeframes(timeframe)
 
     # Start downloading
     try:
-        if config.get('download_trades'):
+        if config.get("download_trades"):
             pairs_not_available = refresh_backtest_trades_data(
-                exchange, pairs=expanded_pairs, datadir=config['datadir'],
+                exchange,
-                timerange=timerange, new_pairs_days=config['new_pairs_days'],
+                pairs=expanded_pairs,
-                erase=bool(config.get('erase')), data_format=config['dataformat_trades'],
+                datadir=config["datadir"],
-                trading_mode=config.get('trading_mode', TradingMode.SPOT),
+                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),
+            )
 
             # Convert downloaded trade data to different timeframes
             convert_trades_to_ohlcv(
-                pairs=expanded_pairs, timeframes=config['timeframes'],
+                pairs=expanded_pairs,
-                datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
+                timeframes=config["timeframes"],
-                data_format_ohlcv=config['dataformat_ohlcv'],
+                datadir=config["datadir"],
-                data_format_trades=config['dataformat_trades'],
+                timerange=timerange,
-                candle_type=config.get('candle_type_def', CandleType.SPOT),
+                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("ohlcv_has_history", True):
                 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'],
+                exchange,
-                datadir=config['datadir'], timerange=timerange,
+                pairs=expanded_pairs,
-                new_pairs_days=config['new_pairs_days'],
+                timeframes=config["timeframes"],
-                erase=bool(config.get('erase')), data_format=config['dataformat_ohlcv'],
+                datadir=config["datadir"],
-                trading_mode=config.get('trading_mode', 'spot'),
+                timerange=timerange,
-                prepend=config.get('prepend_data', False)
+                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),
             )
     finally:
         if pairs_not_available:
-            logger.info(f"Pairs [{','.join(pairs_not_available)}] not available "
+            logger.info(
-                        f"on exchange {exchange.name}.")
+                f"Pairs [{','.join(pairs_not_available)}] not available "
+                f"on exchange {exchange.name}."
+            )

freqtrade/data/metrics.py

@@ -30,8 +30,27 @@ def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close"
     return float(np.mean(tmp_means))
 
 
-def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
+def combine_dataframes_by_column(
-                                 column: str = "close") -> pd.DataFrame:
+    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.
@@ -40,16 +59,36 @@ def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
         named mean, containing the mean of all pairs.
     :raise: ValueError if no data is provided.
     """
-    df_comb = pd.concat([data[pair].set_index('date').rename(
+    df_comb = combine_dataframes_by_column(data, column)
-        {column: pair}, axis=1)[pair] for pair in data], axis=1)
+    # 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"]]
 
-    df_comb['mean'] = df_comb.mean(axis=1)
+
+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,
+def create_cum_profit(
-                      timeframe: str) -> pd.DataFrame:
+    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
@@ -62,11 +101,11 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     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'
+    _trades_sum = trades.resample(timeframe_freq, on="close_date")[["profit_abs"]].sum()
-                                  )[['profit_abs']].sum()
+    df.loc[:, col_name] = _trades_sum["profit_abs"].cumsum()
-    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
@@ -74,29 +113,34 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     return df
 
 
-def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str,
+def _calc_drawdown_series(
-                          starting_balance: float) -> pd.DataFrame:
+    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["cumulative"] = profit_results[value_col].cumsum()
-    max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
+    max_drawdown_df["high_value"] = max_drawdown_df["cumulative"].cummax()
-    max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
+    max_drawdown_df["drawdown"] = max_drawdown_df["cumulative"] - max_drawdown_df["high_value"]
-    max_drawdown_df['date'] = profit_results.loc[:, date_col]
+    max_drawdown_df["date"] = profit_results.loc[:, date_col]
     if starting_balance:
-        cumulative_balance = starting_balance + max_drawdown_df['cumulative']
+        cumulative_balance = starting_balance + max_drawdown_df["cumulative"]
-        max_balance = starting_balance + max_drawdown_df['high_value']
+        max_balance = starting_balance + max_drawdown_df["high_value"]
-        max_drawdown_df['drawdown_relative'] = ((max_balance - cumulative_balance) / max_balance)
+        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["drawdown_relative"] = (
-            (max_drawdown_df['high_value'] - max_drawdown_df['cumulative'])
+            max_drawdown_df["high_value"] - max_drawdown_df["cumulative"]
-            / max_drawdown_df['high_value'])
+        ) / max_drawdown_df["high_value"]
     return max_drawdown_df
 
 
-def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
+def calculate_underwater(
-                         value_col: str = 'profit_ratio', starting_balance: float = 0.0
+    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)
@@ -110,18 +154,20 @@ def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
         raise ValueError("Trade dataframe empty.")
     profit_results = trades.sort_values(date_col).reset_index(drop=True)
     max_drawdown_df = _calc_drawdown_series(
-        profit_results,
+        profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance
-        date_col=date_col,
+    )
-        value_col=value_col,
-        starting_balance=starting_balance)
 
     return max_drawdown_df
 
 
-def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
+def calculate_max_drawdown(
-                           value_col: str = 'profit_abs', starting_balance: float = 0,
+    trades: pd.DataFrame,
-                           relative: bool = False
+    *,
-                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
+    date_col: str = "close_date",
+    value_col: str = "profit_abs",
+    starting_balance: float = 0,
+    relative: bool = False,
+) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
     """
     Calculate max drawdown and the corresponding close dates
     :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
@@ -137,32 +183,31 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
         raise ValueError("Trade dataframe empty.")
     profit_results = trades.sort_values(date_col).reset_index(drop=True)
     max_drawdown_df = _calc_drawdown_series(
-        profit_results,
+        profit_results, date_col=date_col, value_col=value_col, starting_balance=starting_balance
-        date_col=date_col,
-        value_col=value_col,
-        starting_balance=starting_balance
     )
 
     idxmin = (
-        max_drawdown_df['drawdown_relative'].idxmax()
+        max_drawdown_df["drawdown_relative"].idxmax()
-        if relative else max_drawdown_df['drawdown'].idxmin()
+        if relative
+        else max_drawdown_df["drawdown"].idxmin()
     )
     if idxmin == 0:
         raise ValueError("No losing trade, therefore no drawdown.")
-    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
+    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]["high_value"].idxmax(), date_col]
     low_date = profit_results.loc[idxmin, date_col]
-    high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
+    high_val = max_drawdown_df.loc[
-                                   ['high_value'].idxmax(), 'cumulative']
+        max_drawdown_df.iloc[:idxmin]["high_value"].idxmax(), "cumulative"
-    low_val = max_drawdown_df.loc[idxmin, 'cumulative']
+    ]
-    max_drawdown_rel = max_drawdown_df.loc[idxmin, 'drawdown_relative']
+    low_val = max_drawdown_df.loc[idxmin, "cumulative"]
+    max_drawdown_rel = max_drawdown_df.loc[idxmin, "drawdown_relative"]
 
     return (
-        abs(max_drawdown_df.loc[idxmin, 'drawdown']),
+        abs(max_drawdown_df.loc[idxmin, "drawdown"]),
         high_date,
         low_date,
         high_val,
         low_val,
-        max_drawdown_rel
+        max_drawdown_rel,
     )
 
 
@@ -178,9 +223,9 @@ def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[f
         raise ValueError("Trade dataframe empty.")
 
     csum_df = pd.DataFrame()
-    csum_df['sum'] = trades['profit_abs'].cumsum()
+    csum_df["sum"] = trades["profit_abs"].cumsum()
-    csum_min = csum_df['sum'].min() + starting_balance
+    csum_min = csum_df["sum"].min() + starting_balance
-    csum_max = csum_df['sum'].max() + starting_balance
+    csum_max = csum_df["sum"].max() + starting_balance
 
     return csum_min, csum_max
 
@@ -210,28 +255,29 @@ def calculate_expectancy(trades: pd.DataFrame) -> Tuple[float, float]:
     expectancy_ratio = 100
 
     if len(trades) > 0:
-        winning_trades = trades.loc[trades['profit_abs'] > 0]
+        winning_trades = trades.loc[trades["profit_abs"] > 0]
-        losing_trades = trades.loc[trades['profit_abs'] < 0]
+        losing_trades = trades.loc[trades["profit_abs"] < 0]
-        profit_sum = winning_trades['profit_abs'].sum()
+        profit_sum = winning_trades["profit_abs"].sum()
-        loss_sum = abs(losing_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))
+        winrate = nb_win_trades / len(trades)
-        loserate = (nb_loss_trades / len(trades))
+        loserate = nb_loss_trades / len(trades)
 
         expectancy = (winrate * average_win) - (loserate * average_loss)
-        if (average_loss > 0):
+        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,
+def calculate_sortino(
-                      starting_balance: float) -> float:
+    trades: pd.DataFrame, min_date: datetime, max_date: datetime, starting_balance: float
+) -> float:
     """
     Calculate sortino
     :param trades: DataFrame containing trades (requires columns profit_abs)
@@ -240,12 +286,12 @@ def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: dateti
     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
+    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)
+    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)
@@ -257,8 +303,9 @@ def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: dateti
     return sortino_ratio
 
 
-def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+def calculate_sharpe(
-                     starting_balance: float) -> float:
+    trades: pd.DataFrame, min_date: datetime, max_date: datetime, starting_balance: float
+) -> float:
     """
     Calculate sharpe
     :param trades: DataFrame containing trades (requires column profit_abs)
@@ -267,7 +314,7 @@ def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetim
     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
+    total_profit = trades["profit_abs"] / starting_balance
     days_period = max(1, (max_date - min_date).days)
 
     expected_returns_mean = total_profit.sum() / days_period
@@ -283,8 +330,9 @@ def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetim
     return sharp_ratio
 
 
-def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+def calculate_calmar(
-                     starting_balance: float) -> float:
+    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)
@@ -293,7 +341,7 @@ def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetim
     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
+    total_profit = trades["profit_abs"].sum() / starting_balance
     days_period = max(1, (max_date - min_date).days)
 
     # adding slippage of 0.1% per trade

freqtrade/edge/edge_positioning.py

@@ -1,5 +1,6 @@
 # pragma pylint: disable=W0603
-""" Edge positioning package """
+"""Edge positioning package"""
+
 import logging
 from collections import defaultdict
 from copy import deepcopy
@@ -46,48 +47,49 @@ class Edge:
     _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.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'):
+        if self.config["max_open_trades"] != float("inf"):
-            logger.critical('max_open_trades should be -1 in config !')
+            logger.critical("max_open_trades should be -1 in config !")
 
-        if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
+        if self.config["stake_amount"] != UNLIMITED_STAKE_AMOUNT:
-            raise OperationalException('Edge works only with unlimited stake amount')
+            raise OperationalException("Edge works only with unlimited stake amount")
 
-        self._capital_ratio: float = self.config['tradable_balance_ratio']
+        self._capital_ratio: float = self.config["tradable_balance_ratio"]
-        self._allowed_risk: float = self.edge_config.get('allowed_risk')
+        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._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_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_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))
+        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_min, self._stoploss_range_max, self._stoploss_range_step
-            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')}-")
+            f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-"
-        if config.get('fee'):
+        )
-            self.fee = config['fee']
+        if config.get("fee"):
+            self.fee = config["fee"]
         else:
             try:
-                self.fee = self.exchange.get_fee(symbol=expand_pairlist(
+                self.fee = self.exchange.get_fee(
-                    self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
+                    symbol=expand_pairlist(
+                        self.config["exchange"]["pair_whitelist"], list(self.exchange.markets)
+                    )[0]
+                )
             except IndexError:
                 self.fee = None
 
@@ -95,28 +97,30 @@ class Edge:
         if self.fee is None and pairs:
             self.fee = self.exchange.get_fee(pairs[0])
 
-        heartbeat = self.edge_config.get('process_throttle_secs')
+        heartbeat = self.edge_config.get("process_throttle_secs")
 
         if (self._last_updated > 0) and (
-                self._last_updated + heartbeat > int(dt_now().timestamp())):
+            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 stake_currency: %s ...", self.config["stake_currency"])
-        logger.info('Using local backtesting data (using whitelist in given config) ...')
+        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(
+            timerange_startup.subtract_start(
-                self.strategy.timeframe) * self.strategy.startup_candle_count)
+                timeframe_to_seconds(self.strategy.timeframe) * self.strategy.startup_candle_count
+            )
             refresh_data(
-                datadir=self.config['datadir'],
+                datadir=self.config["datadir"],
                 pairs=pairs,
                 exchange=self.exchange,
                 timeframe=self.strategy.timeframe,
                 timerange=timerange_startup,
-                data_format=self.config['dataformat_ohlcv'],
+                data_format=self.config["dataformat_ohlcv"],
-                candle_type=self.config.get('candle_type_def', CandleType.SPOT),
+                candle_type=self.config.get("candle_type_def", CandleType.SPOT),
             )
             # Download informative pairs too
             res = defaultdict(list)
@@ -124,26 +128,27 @@ class Edge:
                 res[timeframe].append(pair)
             for timeframe, inf_pairs in res.items():
                 timerange_startup = deepcopy(self._timerange)
-                timerange_startup.subtract_start(timeframe_to_seconds(
+                timerange_startup.subtract_start(
-                    timeframe) * self.strategy.startup_candle_count)
+                    timeframe_to_seconds(timeframe) * self.strategy.startup_candle_count
+                )
                 refresh_data(
-                    datadir=self.config['datadir'],
+                    datadir=self.config["datadir"],
                     pairs=inf_pairs,
                     exchange=self.exchange,
                     timeframe=timeframe,
                     timerange=timerange_startup,
-                    data_format=self.config['dataformat_ohlcv'],
+                    data_format=self.config["dataformat_ohlcv"],
-                    candle_type=self.config.get('candle_type_def', CandleType.SPOT),
+                    candle_type=self.config.get("candle_type_def", CandleType.SPOT),
                 )
 
         data = load_data(
-            datadir=self.config['datadir'],
+            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'],
+            data_format=self.config["dataformat_ohlcv"],
-            candle_type=self.config.get('candle_type_def', CandleType.SPOT),
+            candle_type=self.config.get("candle_type_def", CandleType.SPOT),
         )
 
         if not data:
@@ -152,27 +157,29 @@ class Edge:
             logger.critical("No data found. Edge is stopped ...")
             return False
         # Fake run-mode to Edge
-        prior_rm = self.config['runmode']
+        prior_rm = self.config["runmode"]
-        self.config['runmode'] = RunMode.EDGE
+        self.config["runmode"] = RunMode.EDGE
         preprocessed = self.strategy.advise_all_indicators(data)
-        self.config['runmode'] = prior_rm
+        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)} '
+        logger.info(
-                    f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
+            f"Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} "
-                    f'({(max_date - min_date).days} days)..')
+            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']
+        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.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()
+            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)
 
@@ -188,8 +195,9 @@ class Edge:
 
         return True
 
-    def stake_amount(self, pair: str, free_capital: float,
+    def stake_amount(
-                     total_capital: float, capital_in_trade: float) -> float:
+        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
@@ -198,14 +206,18 @@ class Edge:
         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,'
+                "winrate: %s, expectancy: %s, position size: %s, pair: %s,"
-                ' capital in trade: %s, free capital: %s, total capital: %s,'
+                " capital in trade: %s, free capital: %s, total capital: %s,"
-                ' stoploss: %s, available capital: %s.',
+                " stoploss: %s, available capital: %s.",
                 self._cached_pairs[pair].winrate,
                 self._cached_pairs[pair].expectancy,
-                position_size, pair,
+                position_size,
-                capital_in_trade, free_capital, total_capital,
+                pair,
-                stoploss, available_capital
+                capital_in_trade,
+                free_capital,
+                total_capital,
+                stoploss,
+                available_capital,
             )
         return round(position_size, 15)
 
@@ -213,8 +225,10 @@ class Edge:
         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}, '
+            logger.warning(
-                           'strategy stoploss is returned instead.')
+                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:
@@ -224,8 +238,8 @@ class Edge:
         final = []
         for pair, info in self._cached_pairs.items():
             if (
-                info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2))
+                info.expectancy > float(self.edge_config.get("minimum_expectancy", 0.2))
-                and info.winrate > float(self.edge_config.get('minimum_winrate', 0.60))
+                and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60))
                 and pair in pairs
             ):
                 final.append(pair)
@@ -234,14 +248,14 @@ class Edge:
             self._final_pairs = final
             if self._final_pairs:
                 logger.info(
-                    'Minimum expectancy and minimum winrate are met only for %s,'
+                    "Minimum expectancy and minimum winrate are met only for %s,"
-                    ' so other pairs are filtered out.',
+                    " so other pairs are filtered out.",
-                    self._final_pairs
+                    self._final_pairs,
                 )
             else:
                 logger.info(
-                    'Edge removed all pairs as no pair with minimum expectancy '
+                    "Edge removed all pairs as no pair with minimum expectancy "
-                    'and minimum winrate was found !'
+                    "and minimum winrate was found !"
                 )
 
         return self._final_pairs
@@ -252,14 +266,17 @@ class Edge:
         """
         final = []
         for pair, info in self._cached_pairs.items():
-            if (info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and
+            if info.expectancy > float(
-                    info.winrate > float(self.edge_config.get('minimum_winrate', 0.60))):
+                self.edge_config.get("minimum_expectancy", 0.2)
-                final.append({
+            ) and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60)):
-                    'Pair': pair,
+                final.append(
-                    'Winrate': info.winrate,
+                    {
-                    'Expectancy': info.expectancy,
+                        "Pair": pair,
-                    'Stoploss': info.stoploss,
+                        "Winrate": info.winrate,
-                })
+                        "Expectancy": info.expectancy,
+                        "Stoploss": info.stoploss,
+                    }
+                )
         return final
 
     def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
@@ -279,28 +296,29 @@ class Edge:
         # 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["close_date"] - result["open_date"]
 
-        result['trade_duration'] = result['trade_duration'].map(
+        result["trade_duration"] = result["trade_duration"].map(
-            lambda x: int(x.total_seconds() / 60))
+            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_vol"] = stake / result["open_rate"]  # How many target are we buying
-        result['buy_fee'] = stake * self.fee
+        result["buy_fee"] = stake * self.fee
-        result['buy_spend'] = stake + result['buy_fee']  # How much we're spending
+        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_sum"] = result["buy_vol"] * result["close_rate"]
-        result['sell_fee'] = result['sell_sum'] * self.fee
+        result["sell_fee"] = result["sell_sum"] * self.fee
-        result['sell_take'] = result['sell_sum'] - result['sell_fee']
+        result["sell_take"] = result["sell_sum"] - result["sell_fee"]
 
         # profit_ratio
-        result['profit_ratio'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
+        result["profit_ratio"] = (result["sell_take"] - result["buy_spend"]) / result["buy_spend"]
 
         # Absolute profit
-        result['profit_abs'] = result['sell_take'] - result['buy_spend']
+        result["profit_abs"] = result["sell_take"] - result["buy_spend"]
 
         return result
 
@@ -310,8 +328,8 @@ class Edge:
         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)
+        min_trades_number = self.edge_config.get("min_trade_number", 10)
-        results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
+        results = results.groupby(["pair", "stoploss"]).filter(lambda x: len(x) > min_trades_number)
         ###################################
 
         # Removing outliers (Only Pumps) from the dataset
@@ -319,13 +337,15 @@ class Edge:
         # Then every value more than (standard deviation + 2*average) is out (pump)
         #
         # Removing Pumps
-        if self.edge_config.get('remove_pumps', False):
+        if self.edge_config.get("remove_pumps", False):
-            results = results[results['profit_abs'] < 2 * results['profit_abs'].std()
+            results = results[
-                              + results['profit_abs'].mean()]
+                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)
+        max_trade_duration = self.edge_config.get("max_trade_duration_minute", 1440)
         results = results[results.trade_duration < max_trade_duration]
         #######################################################################
 
@@ -333,44 +353,54 @@ class Edge:
             return {}
 
         groupby_aggregator = {
-            'profit_abs': [
+            "profit_abs": [
-                ('nb_trades', 'count'),  # number of all trades
+                ("nb_trades", "count"),  # number of all trades
-                ('profit_sum', lambda x: x[x > 0].sum()),  # cumulative profit of all winning 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
+                ("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
+                ("nb_win_trades", lambda x: x[x > 0].count()),  # number of winning trades
             ],
-            'trade_duration': [('avg_trade_duration', 'mean')]
+            "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(
+        df = (
-            groupby_aggregator).reset_index(col_level=1)
+            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["nb_loss_trades"] = df["nb_trades"] - df["nb_win_trades"]
-        df['average_win'] = np.where(df['nb_win_trades'] == 0, 0.0,
+        df["average_win"] = np.where(
-                                     df['profit_sum'] / df['nb_win_trades'])
+            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'])
+        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']
+        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']
+        df["risk_reward_ratio"] = df["average_win"] / df["average_loss"]
 
         # required_risk_reward = (1 / winrate) - 1
-        df['required_risk_reward'] = (1 / df['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'])
+        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(
+        df = (
-            'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
+            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():
@@ -381,17 +411,17 @@ class Edge:
                 x.required_risk_reward,
                 x.expectancy,
                 x.nb_trades,
-                x.avg_trade_duration
+                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
+        buy_column = df["enter_long"].values
-        sell_column = df['exit_long'].values
+        sell_column = df["exit_long"].values
-        date_column = df['date'].values
+        date_column = df["date"].values
-        ohlc_columns = df[['open', 'high', 'low', 'close']].values
+        ohlc_columns = df[["open", "high", "low", "close"]].values
 
         result: list = []
         for stoploss in stoploss_range:
@@ -401,8 +431,9 @@ class Edge:
 
         return result
 
-    def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
+    def _detect_next_stop_or_sell_point(
-                                        ohlc_columns, stoploss, pair: str):
+        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
@@ -429,27 +460,28 @@ class Edge:
                 open_trade_index += 1
 
             open_price = ohlc_columns[open_trade_index, 0]
-            stop_price = (open_price * (stoploss + 1))
+            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)
+                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')
+                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')
+                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'):
+            if stop_index == sell_index == float("inf"):
                 break
 
             if stop_index <= sell_index:
@@ -467,17 +499,18 @@ class Edge:
                 exit_type = ExitType.EXIT_SIGNAL
                 exit_price = ohlc_columns[exit_index, 0]
 
-            trade = {'pair': pair,
+            trade = {
-                     'stoploss': stoploss,
+                "pair": pair,
-                     'profit_ratio': '',
+                "stoploss": stoploss,
-                     'profit_abs': '',
+                "profit_ratio": "",
-                     'open_date': date_column[open_trade_index],
+                "profit_abs": "",
-                     'close_date': date_column[exit_index],
+                "open_date": date_column[open_trade_index],
-                     'trade_duration': '',
+                "close_date": date_column[exit_index],
-                     'open_rate': round(open_price, 15),
+                "trade_duration": "",
-                     'close_rate': round(exit_price, 15),
+                "open_rate": round(open_price, 15),
-                     'exit_type': exit_type
+                "close_rate": round(exit_price, 15),
-                     }
+                "exit_type": exit_type,
+            }
 
             result.append(trade)
 

freqtrade/enums/__init__.py

@@ -9,7 +9,7 @@ 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, TRADING_MODES, RunMode
+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

freqtrade/enums/backteststate.py

@@ -5,6 +5,7 @@ class BacktestState(Enum):
     """
     Bot application states
     """
+
     STARTUP = 1
     DATALOAD = 2
     ANALYZE = 3

freqtrade/enums/candletype.py

@@ -3,6 +3,7 @@ from enum import Enum
 
 class CandleType(str, Enum):
     """Enum to distinguish candle types"""
+
     SPOT = "spot"
     FUTURES = "futures"
     MARK = "mark"
@@ -17,14 +18,14 @@ class CandleType(str, Enum):
         return f"{self.name.lower()}"
 
     @staticmethod
-    def from_string(value: str) -> 'CandleType':
+    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':
+    def get_default(trading_mode: str) -> "CandleType":
-        if trading_mode == 'futures':
+        if trading_mode == "futures":
             return CandleType.FUTURES
         return CandleType.SPOT

freqtrade/enums/exitchecktuple.py

@@ -5,10 +5,11 @@ class ExitCheckTuple:
     """
     NamedTuple for Exit type + reason
     """
-    exit_type: ExitType
-    exit_reason: str = ''
 
-    def __init__(self, exit_type: ExitType, exit_reason: str = ''):
+    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
 

freqtrade/enums/exittype.py

@@ -5,6 +5,7 @@ class ExitType(Enum):
     """
     Enum to distinguish between exit reasons
     """
+
     ROI = "roi"
     STOP_LOSS = "stop_loss"
     STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"

freqtrade/enums/hyperoptstate.py

@@ -2,7 +2,8 @@ from enum import Enum
 
 
 class HyperoptState(Enum):
-    """ Hyperopt states """
+    """Hyperopt states"""
+
     STARTUP = 1
     DATALOAD = 2
     INDICATORS = 3