Merge pull request #10163 from freqtrade/new_release

New release 2024.4

.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 +1,37 @@
 {
     "name": "freqtrade Develop",
-    "build": {
-        "dockerfile": "Dockerfile",
-        "context": ".."
-    },
+    "image": "ghcr.io/freqtrade/freqtrade-devcontainer:latest",
     // 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": {
-            "terminal.integrated.shell.linux": "/bin/bash",
-            "editor.insertSpaces": true,
-            "files.trimTrailingWhitespace": true,
-            "[markdown]": {
-                "files.trimTrailingWhitespace": false,
+        "vscode": {
+            "settings": {
+                "terminal.integrated.shell.linux": "/bin/bash",
+                "editor.insertSpaces": true,
+                "files.trimTrailingWhitespace": true,
+                "[markdown]": {
+                    "files.trimTrailingWhitespace": false,
+                },
+                "python.pythonPath": "/usr/local/bin/python",
             },
-            "python.pythonPath": "/usr/local/bin/python",
-        },
-
-        // Add the IDs of extensions you want installed when the container is created.
-        "extensions": [
-            "ms-python.python",
-            "ms-python.vscode-pylance",
-            "davidanson.vscode-markdownlint",
-            "ms-azuretools.vscode-docker",
-            "vscode-icons-team.vscode-icons",
-        ],
+            // Add the IDs of extensions you want installed when the container is created.
+            "extensions": [
+                "ms-python.python",
+                "ms-python.vscode-pylance",
+                "ms-python.isort",
+                "davidanson.vscode-markdownlint",
+                "ms-azuretools.vscode-docker",
+                "vscode-icons-team.vscode-icons",
+                "github.vscode-github-actions",
+            ],
+        }
     }
 }

.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

@@ -129,7 +129,7 @@ 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"
@@ -414,7 +414,7 @@ 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 +1,43 @@
+name: Devcontainer Pre-Build
+
+on:
+  workflow_dispatch:
+  # 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

@@ -9,14 +9,14 @@ 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
@@ -31,12 +31,12 @@ repos:
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.3.4'
+    rev: 'v0.4.2'
     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

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/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
@@ -21,7 +21,7 @@ type_reqs = [r.strip('\n') for r in requirements if r.startswith(
     'types-') or r.startswith('SQLAlchemy')]
 
 with pre_commit_file.open('r') as file:
-    f = yaml.load(file, Loader=yaml.FullLoader)
+    f = yaml.load(file, Loader=yaml.SafeLoader)
 
 
 mypy_repo = [repo for repo in f['repos'] if repo['repo']

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/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).
-You can then load the trades to perform further analysis as shown in the [data analysis](data-analysis.md#backtesting) backtesting section.
+To further analyze your backtest results, freqtrade will export the trades to file by default.
+You can then load the trades to perform further analysis as shown in the [data analysis](strategy_analysis_example.md#load-backtest-results-to-pandas-dataframe) backtesting section.
 
 ## 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 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 compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
+  - Exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
   - ROI entries which came into effect on the triggering candle (e.g. `120: 0.02` for 1h candles, from `60: 0.05`) will use the candle's open as exit rate
   - Force-exits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
 - Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
@@ -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/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

docs/freqai-parameter-table.md

@@ -31,7 +31,7 @@ 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.
@@ -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 documentation is available at [Legacy Hyperopt](advanced-hyperopt.md#legacy-hyperopt).
+    The legacy method was supported up to 2021.8 and has been removed in 2021.9.
 
 ## 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/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-material==9.5.15
+mkdocs==1.6.0
+mkdocs-material==9.5.19
 mdx_truly_sane_lists==1.3
-pymdown-extensions==10.7.1
+pymdown-extensions==10.8.1
 jinja2==3.1.3

docs/rest-api.md

@@ -89,7 +89,8 @@ 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
 
@@ -166,6 +167,7 @@ freqtrade-client --config rest_config.json <command> [optional parameters]
 | `mix_tags [pair]` | Shows profit statistics for each combinations of enter tag + exit reasons for given pair (or all pairs if pair isn't given). Pair is optional.
 | `locks` | Displays currently locked pairs.
 | `delete_lock <lock_id>` | Deletes (disables) the lock by id.
+| `locks add <pair>, <until>, [side], [reason]` | Locks a pair until "until". (Until will be rounded up to the nearest timeframe).
 | `profit` | Display a summary of your profit/loss from close trades and some stats about your performance.
 | `forceexit <trade_id>` | Instantly exits the given trade  (Ignoring `minimum_roi`).
 | `forceexit all` | Instantly exits all open trades (Ignoring `minimum_roi`).
@@ -453,7 +455,7 @@ 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

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'.
-        @informative('1h', 'BTC/{stake}', '{column}')
+        # formatting. Available in populate_indicators and other methods as 'rsi_upper_1h'.
+        @informative('1h', 'BTC/{stake}', '{column}_{timeframe}')
         def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
             dataframe['rsi_upper'] = ta.RSI(dataframe, timeperiod=14)
             return dataframe
@@ -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

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2024.3'
+__version__ = '2024.4'
 
 if 'dev' in __version__:
     from pathlib import Path

freqtrade/commands/arguments.py

@@ -197,7 +197,10 @@ 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(
+            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,

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, ...)
@@ -119,7 +123,7 @@ 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:
@@ -137,7 +141,7 @@ def get_ui_download_url(version: Optional[str] = None) -> Tuple[str, str]:
     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()
 
@@ -158,7 +162,7 @@ def get_ui_download_url(version: Optional[str] = None) -> Tuple[str, str]:
     # URL not found - try assets url
     if not dl_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']
 

freqtrade/configuration/configuration.py

@@ -13,7 +13,7 @@ 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
@@ -127,7 +127,7 @@ class Configuration:
         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):
@@ -202,7 +202,7 @@ class Configuration:
 
         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.")
 

freqtrade/data/btanalysis.py

@@ -238,6 +238,16 @@ def update_backtest_metadata(filename: Path, strategy: str, content: Dict[str, A
     file_dump_json(get_backtest_metadata_filename(filename), metadata)
 
 
+def get_backtest_market_change(filename: Path, include_ts: bool = True) -> pd.DataFrame:
+    """
+    Read backtest market change file.
+    """
+    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]:
     """

freqtrade/data/dataprovider.py

@@ -523,7 +523,7 @@ 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/history/datahandlers/idatahandler.py

@@ -302,8 +302,8 @@ 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('_', ':', res, 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, count=1)
         return res
 
     def ohlcv_load(self, pair, timeframe: str,

freqtrade/data/metrics.py

@@ -30,8 +30,25 @@ 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],
-                                 column: str = "close") -> pd.DataFrame:
+def combine_dataframes_by_column(
+        data: Dict[str, pd.DataFrame], column: str = "close") -> pd.DataFrame:
+    """
+    Combine multiple dataframes "column"
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key.
+    :raise: ValueError if no data is provided.
+    """
+    if not data:
+        raise ValueError("No data provided.")
+    df_comb = pd.concat([data[pair].set_index('date').rename(
+        {column: pair}, axis=1)[pair] for pair in data], axis=1)
+    return df_comb
+
+
+def combined_dataframes_with_rel_mean(
+        data: Dict[str, pd.DataFrame], fromdt: datetime, todt: datetime,
+        column: str = "close") -> pd.DataFrame:
     """
     Combine multiple dataframes "column"
     :param data: Dict of Dataframes, dict key should be pair.
@@ -40,8 +57,26 @@ 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(
-        {column: pair}, axis=1)[pair] for pair in data], axis=1)
+    df_comb = combine_dataframes_by_column(data, column)
+    # Trim dataframes to the given timeframe
+    df_comb = df_comb.iloc[(df_comb.index >= fromdt) & (df_comb.index < todt)]
+    df_comb['count'] = df_comb.count(axis=1)
+    df_comb['mean'] = df_comb.mean(axis=1)
+    df_comb['rel_mean'] = df_comb['mean'].pct_change().fillna(0).cumsum()
+    return df_comb[['mean', 'rel_mean', 'count']]
+
+
+def combine_dataframes_with_mean(
+        data: Dict[str, pd.DataFrame], column: str = "close") -> pd.DataFrame:
+    """
+    Combine multiple dataframes "column"
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key, and a column
+        named mean, containing the mean of all pairs.
+    :raise: ValueError if no data is provided.
+    """
+    df_comb = combine_dataframes_by_column(data, column)
 
     df_comb['mean'] = df_comb.mean(axis=1)
 

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/runmode.py

@@ -18,6 +18,6 @@ class RunMode(Enum):
     OTHER = "other"
 
 
-TRADING_MODES = [RunMode.LIVE, RunMode.DRY_RUN]
+TRADE_MODES = [RunMode.LIVE, RunMode.DRY_RUN]
 OPTIMIZE_MODES = [RunMode.BACKTEST, RunMode.EDGE, RunMode.HYPEROPT]
-NON_UTIL_MODES = TRADING_MODES + OPTIMIZE_MODES
+NON_UTIL_MODES = TRADE_MODES + OPTIMIZE_MODES

freqtrade/exchange/__init__.py

@@ -4,6 +4,7 @@ from freqtrade.exchange.common import remove_exchange_credentials, MAP_EXCHANGE_
 from freqtrade.exchange.exchange import Exchange
 # isort: on
 from freqtrade.exchange.binance import Binance
+from freqtrade.exchange.bingx import Bingx
 from freqtrade.exchange.bitmart import Bitmart
 from freqtrade.exchange.bitpanda import Bitpanda
 from freqtrade.exchange.bitvavo import Bitvavo
@@ -24,6 +25,7 @@ from freqtrade.exchange.exchange_utils_timeframe import (timeframe_to_minutes, t
 from freqtrade.exchange.gate import Gate
 from freqtrade.exchange.hitbtc import Hitbtc
 from freqtrade.exchange.htx import Htx
+from freqtrade.exchange.idex import Idex
 from freqtrade.exchange.kraken import Kraken
 from freqtrade.exchange.kucoin import Kucoin
 from freqtrade.exchange.okx import Okx

freqtrade/exchange/binance.py

@@ -84,7 +84,7 @@ class Binance(Exchange):
                     raise OperationalException(msg)
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
                 ) from e
@@ -210,7 +210,7 @@ class Binance(Exchange):
                     return self._api.fetch_leverage_tiers()
                 except ccxt.DDoSProtection as e:
                     raise DDosProtection(e) from e
-                except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+                except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
                     raise TemporaryError(f'Could not fetch leverage amounts due to'
                                          f'{e.__class__.__name__}. Message: {e}') from e
                 except ccxt.BaseError as e:

freqtrade/exchange/bingx.py

@@ -0,0 +1,19 @@
+""" Bingx exchange subclass """
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bingx(Exchange):
+    """
+    Bingx exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 1000,
+    }

freqtrade/exchange/bybit.py

@@ -99,7 +99,7 @@ class Bybit(Exchange):
                     logger.info("Bybit: Standard account.")
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
                 ) from e
@@ -239,7 +239,7 @@ class Bybit(Exchange):
 
         return orders
 
-    def fetch_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         order = super().fetch_order(order_id, pair, params)
         if (
             order.get('status') == 'canceled'

freqtrade/exchange/check_exchange.py

@@ -56,7 +56,7 @@ def check_exchange(config: Config, check_for_bad: bool = True) -> bool:
         logger.info(f'Exchange "{exchange}" is officially supported '
                     f'by the Freqtrade development team.')
     else:
-        logger.warning(f'Exchange "{exchange}" is known to the the ccxt library, '
+        logger.warning(f'Exchange "{exchange}" is known to the ccxt library, '
                        f'available for the bot, but not officially supported '
                        f'by the Freqtrade development team. '
                        f'It may work flawlessly (please report back) or have serious issues. '

freqtrade/exchange/exchange.py

@@ -44,7 +44,7 @@ from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_
                             safe_value_fallback2)
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.util import dt_from_ts, dt_now
-from freqtrade.util.datetime_helpers import dt_humanize, dt_ts
+from freqtrade.util.datetime_helpers import dt_humanize_delta, dt_ts
 from freqtrade.util.periodic_cache import PeriodicCache
 
 
@@ -239,8 +239,8 @@ class Exchange:
         self.validate_pricing(config['exit_pricing'])
         self.validate_pricing(config['entry_pricing'])
 
-    def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
-                   ccxt_kwargs: Dict = {}) -> ccxt.Exchange:
+    def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt, *,
+                   ccxt_kwargs: Dict) -> ccxt.Exchange:
         """
         Initialize ccxt with given config and return valid
         ccxt instance.
@@ -348,10 +348,13 @@ class Exchange:
         return int(self._ft_has.get('ohlcv_candle_limit_per_timeframe', {}).get(
             timeframe, self._ft_has.get('ohlcv_candle_limit')))
 
-    def get_markets(self, base_currencies: List[str] = [], quote_currencies: List[str] = [],
-                    spot_only: bool = False, margin_only: bool = False, futures_only: bool = False,
-                    tradable_only: bool = True,
-                    active_only: bool = False) -> Dict[str, Any]:
+    def get_markets(
+            self,
+            base_currencies: Optional[List[str]] = None,
+            quote_currencies: Optional[List[str]] = None,
+            spot_only: bool = False, margin_only: bool = False, futures_only: bool = False,
+            tradable_only: bool = True,
+            active_only: bool = False) -> Dict[str, Any]:
         """
         Return exchange ccxt markets, filtered out by base currency and quote currency
         if this was requested in parameters.
@@ -758,7 +761,7 @@ class Exchange:
 
     def price_get_one_pip(self, pair: str, price: float) -> float:
         """
-        Get's the "1 pip" value for this pair.
+        Gets the "1 pip" value for this pair.
         Used in PriceFilter to calculate the 1pip movements.
         """
         precision = self.markets[pair]['precision']['price']
@@ -848,7 +851,7 @@ class Exchange:
     # Dry-run methods
 
     def create_dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
-                             rate: float, leverage: float, params: Dict = {},
+                             rate: float, leverage: float, params: Optional[Dict] = None,
                              stop_loss: bool = False) -> Dict[str, Any]:
         now = dt_now()
         order_id = f'dry_run_{side}_{pair}_{now.timestamp()}'
@@ -1122,7 +1125,7 @@ class Exchange:
                 f'Message: {e}') from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not place {side} order due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1259,7 +1262,7 @@ class Exchange:
                 f'stop-price {stop_price_norm}. Message: {e}') from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f"Could not place stoploss order due to {e.__class__.__name__}. "
                 f"Message: {e}") from e
@@ -1290,16 +1293,18 @@ class Exchange:
                 f'Tried to get an invalid order (pair: {pair} id: {order_id}). Message: {e}') from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
     @retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
-    def fetch_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         if self._config['dry_run']:
             return self.fetch_dry_run_order(order_id)
+        if params is None:
+            params = {}
         try:
             if not self.exchange_has('fetchOrder'):
                 return self.fetch_order_emulated(order_id, pair, params)
@@ -1315,13 +1320,13 @@ class Exchange:
                 f'Tried to get an invalid order (pair: {pair} id: {order_id}). Message: {e}') from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
-    def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_stoploss_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         return self.fetch_order(order_id, pair, params)
 
     def fetch_order_or_stoploss_order(self, order_id: str, pair: str,
@@ -1347,7 +1352,7 @@ class Exchange:
                 and order.get('filled') == 0.0)
 
     @retrier
-    def cancel_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def cancel_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         if self._config['dry_run']:
             try:
                 order = self.fetch_dry_run_order(order_id)
@@ -1357,6 +1362,8 @@ class Exchange:
             except InvalidOrderException:
                 return {}
 
+        if params is None:
+            params = {}
         try:
             order = self._api.cancel_order(order_id, pair, params=params)
             self._log_exchange_response('cancel_order', order)
@@ -1367,13 +1374,14 @@ class Exchange:
                 f'Could not cancel order. Message: {e}') from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
-    def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def cancel_stoploss_order(
+            self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         return self.cancel_order(order_id, pair, params)
 
     def is_cancel_order_result_suitable(self, corder) -> bool:
@@ -1449,7 +1457,7 @@ class Exchange:
             return balances
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1473,7 +1481,7 @@ class Exchange:
             return positions
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get positions due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1517,7 +1525,7 @@ class Exchange:
             return orders
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not fetch positions due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1538,7 +1546,7 @@ class Exchange:
             return trading_fees
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not fetch trading fees due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1569,7 +1577,7 @@ class Exchange:
                 f'Message: {e}') from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not load bids/asks due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1606,7 +1614,7 @@ class Exchange:
             raise TemporaryError from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not load tickers due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1624,7 +1632,7 @@ class Exchange:
             return data
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1665,7 +1673,7 @@ class Exchange:
                 f'Message: {e}') from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get order book due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1844,7 +1852,7 @@ class Exchange:
             return matched_trades
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get trades due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -1878,7 +1886,7 @@ class Exchange:
                                            price=price, takerOrMaker=taker_or_maker)['rate']
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get fee info due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -2000,14 +2008,14 @@ class Exchange:
         logger.debug(
             "one_call: %s msecs (%s)",
             one_call,
-            dt_humanize(dt_now() - timedelta(milliseconds=one_call), only_distance=True)
+            dt_humanize_delta(dt_now() - timedelta(milliseconds=one_call))
         )
         input_coroutines = [self._async_get_candle_history(
             pair, timeframe, candle_type, since) for since in
             range(since_ms, until_ms or dt_ts(), one_call)]
 
         data: List = []
-        # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
+        # Chunk requests into batches of 100 to avoid overwhelming ccxt Throttling
         for input_coro in chunks(input_coroutines, 100):
 
             results = await asyncio.gather(*input_coro, return_exceptions=True)
@@ -2124,7 +2132,7 @@ class Exchange:
         Only used in the dataprovider.refresh() method.
         :param pair_list: List of 2 element tuples containing pair, interval to refresh
         :param since_ms: time since when to download, in milliseconds
-        :param cache: Assign result to _klines. Usefull for one-off downloads like for pairlists
+        :param cache: Assign result to _klines. Useful for one-off downloads like for pairlists
         :param drop_incomplete: Control candle dropping.
             Specifying None defaults to _ohlcv_partial_candle
         :return: Dict of [{(pair, timeframe): Dataframe}]
@@ -2135,7 +2143,7 @@ class Exchange:
         input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
 
         results_df = {}
-        # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
+        # Chunk requests into batches of 100 to avoid overwhelming ccxt Throttling
         for input_coro in chunks(input_coroutines, 100):
             async def gather_stuff():
                 return await asyncio.gather(*input_coro, return_exceptions=True)
@@ -2262,7 +2270,7 @@ class Exchange:
                 f'candle (OHLCV) data. Message: {e}') from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(f'Could not fetch historical candle (OHLCV) data '
                                  f'for pair {pair} due to {e.__class__.__name__}. '
                                  f'Message: {e}') from e
@@ -2295,7 +2303,7 @@ class Exchange:
                                   since: Optional[int] = None,
                                   params: Optional[dict] = None) -> Tuple[List[List], Any]:
         """
-        Asyncronously gets trade history using fetch_trades.
+        Asynchronously gets trade history using fetch_trades.
         Handles exchange errors, does one call to the exchange.
         :param pair: Pair to fetch trade data for
         :param since: Since as integer timestamp in milliseconds
@@ -2322,7 +2330,7 @@ class Exchange:
                 f'Message: {e}') from e
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(f'Could not load trade history due to {e.__class__.__name__}. '
                                  f'Message: {e}') from e
         except ccxt.BaseError as e:
@@ -2352,7 +2360,7 @@ class Exchange:
                                           since: Optional[int] = None,
                                           from_id: Optional[str] = None) -> Tuple[str, List[List]]:
         """
-        Asyncronously gets trade history using fetch_trades
+        Asynchronously gets trade history using fetch_trades
         use this when exchange uses id-based iteration (check `self._trades_pagination`)
         :param pair: Pair to fetch trade data for
         :param since: Since as integer timestamp in milliseconds
@@ -2403,7 +2411,7 @@ class Exchange:
     async def _async_get_trade_history_time(self, pair: str, until: int,
                                             since: Optional[int] = None) -> Tuple[str, List[List]]:
         """
-        Asyncronously gets trade history using fetch_trades,
+        Asynchronously gets trade history using fetch_trades,
         when the exchange uses time-based iteration (check `self._trades_pagination`)
         :param pair: Pair to fetch trade data for
         :param since: Since as integer timestamp in milliseconds
@@ -2521,7 +2529,7 @@ class Exchange:
             return sum(fee['amount'] for fee in funding_history)
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get funding fees due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -2533,7 +2541,7 @@ class Exchange:
             return self._api.fetch_leverage_tiers()
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not load leverage tiers due to {e.__class__.__name__}. Message: {e}'
             ) from e
@@ -2548,7 +2556,7 @@ class Exchange:
             return symbol, tier
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not load leverage tiers for {symbol}'
                 f' due to {e.__class__.__name__}. Message: {e}'
@@ -2762,7 +2770,7 @@ class Exchange:
             if not accept_fail:
                 raise TemporaryError(
                     f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
@@ -2786,7 +2794,7 @@ class Exchange:
 
     @retrier
     def set_margin_mode(self, pair: str, margin_mode: MarginMode, accept_fail: bool = False,
-                        params: dict = {}):
+                        params: Optional[Dict] = None):
         """
         Set's the margin mode on the exchange to cross or isolated for a specific pair
         :param pair: base/quote currency pair (e.g. "ADA/USDT")
@@ -2795,6 +2803,8 @@ class Exchange:
             # Some exchanges only support one margin_mode type
             return
 
+        if params is None:
+            params = {}
         try:
             res = self._api.set_margin_mode(margin_mode.value, pair, params)
             self._log_exchange_response('set_margin_mode', res)
@@ -2804,7 +2814,7 @@ class Exchange:
             if not accept_fail:
                 raise TemporaryError(
                     f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:

freqtrade/exchange/gate.py

@@ -79,7 +79,7 @@ class Gate(Exchange):
             # As such, futures orders on gate will not contain a fee, which causes
             # a repeated "update fee" cycle and wrong calculations.
             # Therefore we patch the response with fees if it's not available.
-            # An alternative also contianing fees would be
+            # An alternative also containing fees would be
             # privateFuturesGetSettleAccountBook({"settle": "usdt"})
             pair_fees = self._trading_fees.get(pair, {})
             if pair_fees:
@@ -98,7 +98,7 @@ class Gate(Exchange):
     def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
         return safe_value_fallback2(order, order, 'id_stop', 'id')
 
-    def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_stoploss_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         order = self.fetch_order(
             order_id=order_id,
             pair=pair,
@@ -119,7 +119,8 @@ class Gate(Exchange):
                 return order1
         return order
 
-    def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def cancel_stoploss_order(
+            self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         return self.cancel_order(
             order_id=order_id,
             pair=pair,

freqtrade/exchange/idex.py

@@ -0,0 +1,19 @@
+""" Idex exchange subclass """
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Idex(Exchange):
+    """
+    Idex exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 1000,
+    }

freqtrade/exchange/kraken.py

@@ -84,7 +84,7 @@ class Kraken(Exchange):
             return balances
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:

freqtrade/exchange/okx.py

@@ -56,7 +56,7 @@ class Okx(Exchange):
         """
         Exchange ohlcv candle limit
         OKX has the following behaviour:
-        * 300 candles for uptodate data
+        * 300 candles for up-to-date data
         * 100 candles for historic data
         * 100 candles for additional candles (not futures or spot).
         :param timeframe: Timeframe to check
@@ -87,7 +87,7 @@ class Okx(Exchange):
                     self.net_only = accounts[0].get('info', {}).get('posMode') == 'net_mode'
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+        except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
                 ) from e
@@ -153,7 +153,7 @@ class Okx(Exchange):
 
             except ccxt.DDoSProtection as e:
                 raise DDosProtection(e) from e
-            except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
                 already_set = self.__fetch_leverage_already_set(pair, leverage, side)
                 if not already_set:
                     raise TemporaryError(
@@ -202,7 +202,7 @@ class Okx(Exchange):
         order['type'] = 'stoploss'
         return order
 
-    def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_stoploss_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         if self._config['dry_run']:
             return self.fetch_dry_run_order(order_id)
 
@@ -232,7 +232,8 @@ class Okx(Exchange):
             return safe_value_fallback2(order, order, 'id_stop', 'id')
         return order['id']
 
-    def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def cancel_stoploss_order(
+            self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         params1 = {'stop': True}
         # 'ordType': 'conditional'
         #

freqtrade/freqai/RL/BaseEnvironment.py

@@ -222,7 +222,7 @@ class BaseEnvironment(gym.Env):
     @abstractmethod
     def step(self, action: int):
         """
-        Step depeneds on action types, this must be inherited.
+        Step depends on action types, this must be inherited.
         """
         return
 
@@ -326,7 +326,7 @@ class BaseEnvironment(gym.Env):
 
     def _update_unrealized_total_profit(self):
         """
-        Update the unrealized total profit incase of episode end.
+        Update the unrealized total profit in case of episode end.
         """
         if self._position in (Positions.Long, Positions.Short):
             pnl = self.get_unrealized_profit()
@@ -357,7 +357,7 @@ class BaseEnvironment(gym.Env):
         """
         return self.actions
 
-    # Keeping around incase we want to start building more complex environment
+    # Keeping around in case we want to start building more complex environment
     # templates in the future.
     # def most_recent_return(self):
     #     """

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -311,7 +311,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             if not prices_train_old.empty:
                 prices_train = prices_train_old
                 rename_dict = rename_dict_old
-            logger.warning('Reinforcement learning module didnt find the correct raw prices '
+            logger.warning('Reinforcement learning module didn\'t find the correct raw prices '
                            'assigned in feature_engineering_standard(). '
                            'Please assign them with:\n'
                            'dataframe["%-raw_close"] = dataframe["close"]\n'
@@ -458,7 +458,7 @@ def make_env(MyRLEnv: Type[BaseEnvironment], env_id: str, rank: int,
 
     :param env_id: (str) the environment ID
     :param num_env: (int) the number of environment you wish to have in subprocesses
-    :param seed: (int) the inital seed for RNG
+    :param seed: (int) the initial seed for RNG
     :param rank: (int) index of the subprocess
     :param env_info: (dict) all required arguments to instantiate the environment.
     :return: (Callable)

freqtrade/freqai/data_drawer.py

@@ -4,6 +4,7 @@ import logging
 import re
 import shutil
 import threading
+import warnings
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Any, Dict, Tuple, TypedDict
@@ -262,10 +263,11 @@ class FreqaiDataDrawer:
             self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
             return
 
-    def set_initial_return_values(self, pair: str,
-                                  pred_df: DataFrame,
-                                  dataframe: DataFrame
-                                  ) -> None:
+    def set_initial_return_values(
+        self, pair: str,
+        pred_df: DataFrame,
+        dataframe: DataFrame
+    ) -> None:
         """
         Set the initial return values to the historical predictions dataframe. This avoids needing
         to repredict on historical candles, and also stores historical predictions despite
@@ -278,10 +280,15 @@ class FreqaiDataDrawer:
 
         new_pred = pred_df.copy()
         # set new_pred values to nans (we want to signal to user that there was nothing
-        # historically made during downtime. The newest pred will get appeneded later in
+        # historically made during downtime. The newest pred will get appended later in
         # append_model_predictions)
-        new_pred.iloc[:, :] = np.nan
+
         new_pred["date_pred"] = dataframe["date"]
+        # set everything to nan except date_pred
+        columns_to_nan = new_pred.columns.difference(['date_pred', 'date'])
+        new_pred[columns_to_nan] = new_pred[columns_to_nan].astype(
+            float).values * np.nan
+
         hist_preds = self.historic_predictions[pair].copy()
 
         # ensure both dataframes have the same date format so they can be merged
@@ -290,7 +297,8 @@ class FreqaiDataDrawer:
 
         # find the closest common date between new_pred and historic predictions
         # and cut off the new_pred dataframe at that date
-        common_dates = pd.merge(new_pred, hist_preds, on="date_pred", how="inner")
+        common_dates = pd.merge(new_pred, hist_preds,
+                                on="date_pred", how="inner")
         if len(common_dates.index) > 0:
             new_pred = new_pred.iloc[len(common_dates):]
         else:
@@ -298,15 +306,23 @@ class FreqaiDataDrawer:
                            "predictions. You likely left your FreqAI instance offline "
                            f"for more than {len(dataframe.index)} candles.")
 
-        # reindex new_pred columns to match the historic predictions dataframe
-        new_pred_reindexed = new_pred.reindex(columns=hist_preds.columns)
-        df_concat = pd.concat([hist_preds, new_pred_reindexed], ignore_index=True)
+        # Pandas warns that its keeping dtypes of non NaN columns...
+        # yea we know and we already want that behavior. Ignoring.
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=FutureWarning)
+            # reindex new_pred columns to match the historic predictions dataframe
+            new_pred_reindexed = new_pred.reindex(columns=hist_preds.columns)
+            df_concat = pd.concat(
+                [hist_preds, new_pred_reindexed],
+                ignore_index=True
+            )
 
         # any missing values will get zeroed out so users can see the exact
         # downtime in FreqUI
         df_concat = df_concat.fillna(0)
         self.historic_predictions[pair] = df_concat
-        self.model_return_values[pair] = df_concat.tail(len(dataframe.index)).reset_index(drop=True)
+        self.model_return_values[pair] = df_concat.tail(
+            len(dataframe.index)).reset_index(drop=True)
 
     def append_model_predictions(self, pair: str, predictions: DataFrame,
                                  do_preds: NDArray[np.int_],
@@ -323,38 +339,56 @@ class FreqaiDataDrawer:
         index = self.historic_predictions[pair].index[-1:]
         columns = self.historic_predictions[pair].columns
 
-        zeros_df = pd.DataFrame(np.zeros((1, len(columns))), index=index, columns=columns)
+        zeros_df = pd.DataFrame(
+            np.zeros((1, len(columns))),
+            index=index,
+            columns=columns
+        )
         self.historic_predictions[pair] = pd.concat(
-            [self.historic_predictions[pair], zeros_df], ignore_index=True, axis=0)
+            [self.historic_predictions[pair], zeros_df],
+            ignore_index=True,
+            axis=0
+        )
         df = self.historic_predictions[pair]
 
         # model outputs and associated statistics
         for label in predictions.columns:
-            df[label].iloc[-1] = predictions[label].iloc[-1]
+            label_loc = df.columns.get_loc(label)
+            pred_label_loc = predictions.columns.get_loc(label)
+            df.iloc[-1, label_loc] = predictions.iloc[-1, pred_label_loc]
             if df[label].dtype == object:
                 continue
-            df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
-            df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
+            label_mean_loc = df.columns.get_loc(f"{label}_mean")
+            label_std_loc = df.columns.get_loc(f"{label}_std")
+            df.iloc[-1, label_mean_loc] = dk.data["labels_mean"][label]
+            df.iloc[-1, label_std_loc] = dk.data["labels_std"][label]
 
         # outlier indicators
-        df["do_predict"].iloc[-1] = do_preds[-1]
+        do_predict_loc = df.columns.get_loc("do_predict")
+        df.iloc[-1, do_predict_loc] = do_preds[-1]
         if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
-            df["DI_values"].iloc[-1] = dk.DI_values[-1]
+            DI_values_loc = df.columns.get_loc("DI_values")
+            df.iloc[-1, DI_values_loc] = dk.DI_values[-1]
 
         # extra values the user added within custom prediction model
         if dk.data['extra_returns_per_train']:
             rets = dk.data['extra_returns_per_train']
             for return_str in rets:
-                df[return_str].iloc[-1] = rets[return_str]
+                return_loc = df.columns.get_loc(return_str)
+                df.iloc[-1, return_loc] = rets[return_str]
 
-        # this logic carries users between version without needing to
-        # change their identifier
-        if 'close_price' not in df.columns:
-            df['close_price'] = np.nan
-            df['date_pred'] = np.nan
-
-        df['close_price'].iloc[-1] = strat_df['close'].iloc[-1]
-        df['date_pred'].iloc[-1] = strat_df['date'].iloc[-1]
+        high_price_loc = df.columns.get_loc("high_price")
+        high_loc = strat_df.columns.get_loc("high")
+        df.iloc[-1, high_price_loc] = strat_df.iloc[-1, high_loc]
+        low_price_loc = df.columns.get_loc("low_price")
+        low_loc = strat_df.columns.get_loc("low")
+        df.iloc[-1, low_price_loc] = strat_df.iloc[-1, low_loc]
+        close_price_loc = df.columns.get_loc("close_price")
+        close_loc = strat_df.columns.get_loc("close")
+        df.iloc[-1, close_price_loc] = strat_df.iloc[-1, close_loc]
+        date_pred_loc = df.columns.get_loc("date_pred")
+        date_loc = strat_df.columns.get_loc("date")
+        df.iloc[-1, date_pred_loc] = strat_df.iloc[-1, date_loc]
 
         self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True)
 

freqtrade/freqai/data_kitchen.py

@@ -24,6 +24,8 @@ from freqtrade.strategy import merge_informative_pair
 from freqtrade.strategy.interface import IStrategy
 
 
+pd.set_option('future.no_silent_downcasting', True)
+
 SECONDS_IN_DAY = 86400
 SECONDS_IN_HOUR = 3600
 
@@ -221,7 +223,7 @@ class FreqaiDataKitchen:
         filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
 
         drop_index = pd.isnull(filtered_df).any(axis=1)  # get the rows that have NaNs,
-        drop_index = drop_index.replace(True, 1).replace(False, 0)  # pep8 requirement.
+        drop_index = drop_index.replace(True, 1).replace(False, 0).infer_objects(copy=False)
         if (training_filter):
 
             # we don't care about total row number (total no. datapoints) in training, we only care
@@ -229,7 +231,9 @@ class FreqaiDataKitchen:
             # if labels has multiple columns (user wants to train multiple modelEs), we detect here
             labels = unfiltered_df.filter(label_list, axis=1)
             drop_index_labels = pd.isnull(labels).any(axis=1)
-            drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
+            drop_index_labels = drop_index_labels.replace(
+                True, 1
+            ).replace(False, 0).infer_objects(copy=False)
             dates = unfiltered_df['date']
             filtered_df = filtered_df[
                 (drop_index == 0) & (drop_index_labels == 0)
@@ -608,7 +612,7 @@ class FreqaiDataKitchen:
         pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
 
         for pair in pairs:
-            pair = pair.replace(':', '')  # lightgbm doesnt like colons
+            pair = pair.replace(':', '')  # lightgbm does not like colons
             pair_cols = [col for col in dataframe.columns if col.startswith("%")
                          and f"{pair}_" in col]
 
@@ -634,7 +638,7 @@ class FreqaiDataKitchen:
         pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
         current_pair = current_pair.replace(':', '')
         for pair in pairs:
-            pair = pair.replace(':', '')  # lightgbm doesnt work with colons
+            pair = pair.replace(':', '')  # lightgbm does not work with colons
             if current_pair != pair:
                 dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')
 
@@ -837,7 +841,7 @@ class FreqaiDataKitchen:
             f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
             self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
 
-        # incase targets are classifications
+        # in case targets are classifications
         for label in self.unique_class_list:
             self.data["labels_mean"][label], self.data["labels_std"][label] = 0, 0
 

freqtrade/freqai/freqai_interface.py

@@ -222,7 +222,7 @@ class IFreqaiModel(ABC):
             time.sleep(1)
             pair = self.train_queue[0]
 
-            # ensure pair is avaialble in dp
+            # ensure pair is available in dp
             if pair not in strategy.dp.current_whitelist():
                 self.train_queue.popleft()
                 logger.warning(f'{pair} not in current whitelist, removing from train queue.')
@@ -675,6 +675,8 @@ class IFreqaiModel(ABC):
         for return_str in dk.data['extra_returns_per_train']:
             hist_preds_df[return_str] = dk.data['extra_returns_per_train'][return_str]
 
+        hist_preds_df['high_price'] = strat_df['high']
+        hist_preds_df['low_price'] = strat_df['low']
         hist_preds_df['close_price'] = strat_df['close']
         hist_preds_df['date_pred'] = strat_df['date']
 
@@ -716,9 +718,6 @@ class IFreqaiModel(ABC):
             if self.pair_it == self.total_pairs:
                 logger.info(
                     f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
-                if self.inference_time > 0.25 * self.base_tf_seconds:
-                    logger.warning("Inference took over 25% of the candle time. Reduce pairlist to"
-                                   " avoid blinding open trades and degrading performance.")
                 self.pair_it = 0
                 self.inference_time = 0
         return

freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py

@@ -74,7 +74,7 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.CrossEntropyLoss()
-        # check if continual_learning is activated, and retreive the model to continue training
+        # check if continual_learning is activated, and retrieve the model to continue training
         trainer = self.get_init_model(dk.pair)
         if trainer is None:
             trainer = PyTorchModelTrainer(

freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py

@@ -69,7 +69,7 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.MSELoss()
-        # check if continual_learning is activated, and retreive the model to continue training
+        # check if continual_learning is activated, and retrieve the model to continue training
         trainer = self.get_init_model(dk.pair)
         if trainer is None:
             trainer = PyTorchModelTrainer(

freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py

@@ -80,7 +80,7 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.MSELoss()
-        # check if continual_learning is activated, and retreive the model to continue training
+        # check if continual_learning is activated, and retrieve the model to continue training
         trainer = self.get_init_model(dk.pair)
         if trainer is None:
             trainer = PyTorchTransformerTrainer(

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -63,6 +63,6 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
                                                                is_masking_supported(self.eval_env)))
 
         # TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
-        # IT WILL RETURN FALSE INFORMATIONS, NEVERTHLESS NOT THREAD SAFE WITH SB3!!!
+        # IT WILL RETURN FALSE INFORMATION, NEVERTHELESS NOT THREAD SAFE WITH SB3!!!
         actions = self.train_env.env_method("get_actions")[0]
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -38,7 +38,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         :param init_model: A dictionary containing the initial model/optimizer
             state_dict and model_meta_data saved by self.save() method.
         :param model_meta_data: Additional metadata about the model (optional).
-        :param data_convertor: convertor from pd.DataFrame to torch.tensor.
+        :param data_convertor: converter from pd.DataFrame to torch.tensor.
         :param n_steps: used to calculate n_epochs. The number of training iterations to run.
             iteration here refers to the number of times optimizer.step() is called.
             ignored if n_epochs is set.

freqtrade/freqai/utils.py

@@ -178,7 +178,7 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
 
 def get_timerange_backtest_live_models(config: Config) -> str:
     """
-    Returns a formated timerange for backtest live/ready models
+    Returns a formatted timerange for backtest live/ready models
     :param config: Configuration dictionary
 
     :return: a string timerange (format example: '20220801-20220822')

freqtrade/freqtradebot.py

@@ -37,6 +37,7 @@ from freqtrade.rpc.rpc_types import (ProfitLossStr, RPCCancelMsg, RPCEntryMsg, R
                                      RPCExitMsg, RPCProtectionMsg)
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util import MeasureTime
 from freqtrade.util.migrations import migrate_binance_futures_names
 from freqtrade.wallets import Wallets
 
@@ -64,7 +65,7 @@ class FreqtradeBot(LoggingMixin):
         # Init objects
         self.config = config
         exchange_config: ExchangeConfig = deepcopy(config['exchange'])
-        # Remove credentials from original exchange config to avoid accidental credentail exposure
+        # Remove credentials from original exchange config to avoid accidental credential exposure
         remove_exchange_credentials(config['exchange'], True)
 
         self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
@@ -117,7 +118,8 @@ class FreqtradeBot(LoggingMixin):
 
         # Protect exit-logic from forcesell and vice versa
         self._exit_lock = Lock()
-        LoggingMixin.__init__(self, logger, timeframe_to_seconds(self.strategy.timeframe))
+        timeframe_secs = timeframe_to_seconds(self.strategy.timeframe)
+        LoggingMixin.__init__(self, logger, timeframe_secs)
 
         self._schedule = Scheduler()
 
@@ -139,6 +141,16 @@ class FreqtradeBot(LoggingMixin):
         # Initialize protections AFTER bot start - otherwise parameters are not loaded.
         self.protections = ProtectionManager(self.config, self.strategy.protections)
 
+        def log_took_too_long(duration: float, time_limit: float):
+            logger.warning(
+                f"Strategy analysis took {duration:.2f}, which is 25% of the timeframe. "
+                "This can lead to delayed orders and missed signals."
+                "Consider either reducing the amount of work your strategy performs "
+                "or reduce the amount of pairs in the Pairlist."
+            )
+
+        self._measure_execution = MeasureTime(log_took_too_long, timeframe_secs * 0.25)
+
     def notify_status(self, msg: str, msg_type=RPCMessageType.STATUS) -> None:
         """
         Public method for users of this class (worker, etc.) to send notifications
@@ -175,7 +187,7 @@ class FreqtradeBot(LoggingMixin):
         try:
             Trade.commit()
         except Exception:
-            # Exeptions here will be happening if the db disappeared.
+            # Exceptions here will be happening if the db disappeared.
             # At which point we can no longer commit anyway.
             pass
 
@@ -223,10 +235,11 @@ class FreqtradeBot(LoggingMixin):
         strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)(
             current_time=datetime.now(timezone.utc))
 
-        self.strategy.analyze(self.active_pair_whitelist)
+        with self._measure_execution:
+            self.strategy.analyze(self.active_pair_whitelist)
 
         with self._exit_lock:
-            # Check for exchange cancelations, timeouts and user requested replace
+            # Check for exchange cancellations, timeouts and user requested replace
             self.manage_open_orders()
 
         # Protect from collisions with force_exit.
@@ -237,12 +250,12 @@ class FreqtradeBot(LoggingMixin):
             # First process current opened trades (positions)
             self.exit_positions(trades)
 
-        # Check if we need to adjust our current positions before attempting to buy new trades.
+        # Check if we need to adjust our current positions before attempting to enter new trades.
         if self.strategy.position_adjustment_enable:
             with self._exit_lock:
                 self.process_open_trade_positions()
 
-        # Then looking for buy opportunities
+        # Then looking for entry opportunities
         if self.get_free_open_trades():
             self.enter_positions()
         if self.trading_mode == TradingMode.FUTURES:
@@ -277,7 +290,7 @@ class FreqtradeBot(LoggingMixin):
             }
             self.rpc.send_msg(msg)
 
-    def _refresh_active_whitelist(self, trades: List[Trade] = []) -> List[str]:
+    def _refresh_active_whitelist(self, trades: Optional[List[Trade]] = None) -> List[str]:
         """
         Refresh active whitelist from pairlist or edge and extend it with
         pairs that have open trades.
@@ -449,6 +462,7 @@ class FreqtradeBot(LoggingMixin):
                 trade.pair, trade.open_date_utc - timedelta(seconds=10))
             prev_exit_reason = trade.exit_reason
             prev_trade_state = trade.is_open
+            prev_trade_amount = trade.amount
             for order in orders:
                 trade_order = [o for o in trade.orders if o.order_id == order['id']]
 
@@ -480,6 +494,26 @@ class FreqtradeBot(LoggingMixin):
                                         send_msg=prev_trade_state != trade.is_open)
             else:
                 trade.exit_reason = prev_exit_reason
+                total = self.wallets.get_total(trade.base_currency) if trade.base_currency else 0
+                if total < trade.amount:
+                    if total > trade.amount * 0.98:
+                        logger.warning(
+                            f"{trade} has a total of {trade.amount} {trade.base_currency}, "
+                            f"but the Wallet shows a total of {total} {trade.base_currency}. "
+                            f"Adjusting trade amount to {total}."
+                            "This may however lead to further issues."
+                        )
+                        trade.amount = total
+                    else:
+                        logger.warning(
+                            f"{trade} has a total of {trade.amount} {trade.base_currency}, "
+                            f"but the Wallet shows a total of {total} {trade.base_currency}. "
+                            "Refusing to adjust as the difference is too large."
+                            "This may however lead to further issues."
+                        )
+                if prev_trade_amount != trade.amount:
+                    # Cancel stoploss on exchange if the amount changed
+                    trade = self.cancel_stoploss_on_exchange(trade)
             Trade.commit()
 
         except ExchangeError:
@@ -488,7 +522,7 @@ class FreqtradeBot(LoggingMixin):
             # catching https://github.com/freqtrade/freqtrade/issues/9025
             logger.warning("Error finding onexchange order", exc_info=True)
 #
-# BUY / enter positions / open trades logic and methods
+# enter positions / open trades logic and methods
 #
 
     def enter_positions(self) -> int:
@@ -538,10 +572,10 @@ class FreqtradeBot(LoggingMixin):
 
     def create_trade(self, pair: str) -> bool:
         """
-        Check the implemented trading strategy for buy signals.
+        Check the implemented trading strategy for entry signals.
 
-        If the pair triggers the buy signal a new trade record gets created
-        and the buy-order opening the trade gets issued towards the exchange.
+        If the pair triggers the enter signal a new trade record gets created
+        and the entry-order opening the trade gets issued towards the exchange.
 
         :return: True if a trade has been created.
         """
@@ -600,7 +634,7 @@ class FreqtradeBot(LoggingMixin):
             return False
 
 #
-# BUY / increase positions / DCA logic and methods
+# Modify positions / DCA logic and methods
 #
     def process_open_trade_positions(self):
         """
@@ -683,7 +717,7 @@ class FreqtradeBot(LoggingMixin):
 
     def _check_depth_of_market(self, pair: str, conf: Dict, side: SignalDirection) -> bool:
         """
-        Checks depth of market before executing a buy
+        Checks depth of market before executing an entry
         """
         conf_bids_to_ask_delta = conf.get('bids_to_ask_delta', 0)
         logger.info(f"Checking depth of market for {pair} ...")
@@ -727,10 +761,10 @@ class FreqtradeBot(LoggingMixin):
         leverage_: Optional[float] = None,
     ) -> bool:
         """
-        Executes a limit buy for the given pair
-        :param pair: pair for which we want to create a LIMIT_BUY
+        Executes an entry for the given pair
+        :param pair: pair for which we want to create a LIMIT order
         :param stake_amount: amount of stake-currency for the pair
-        :return: True if a buy order is created, false if it fails.
+        :return: True if an entry order is created, False if it fails.
         :raise: DependencyException or it's subclasses like ExchangeError.
         """
         time_in_force = self.strategy.order_time_in_force['entry']
@@ -859,7 +893,7 @@ class FreqtradeBot(LoggingMixin):
             trade.adjust_stop_loss(trade.open_rate, stoploss, initial=True)
 
         else:
-            # This is additional buy, we reset fee_open_currency so timeout checking can work
+            # This is additional entry, we reset fee_open_currency so timeout checking can work
             trade.is_open = True
             trade.fee_open_currency = None
             trade.open_rate_requested = enter_limit_requested
@@ -1232,7 +1266,7 @@ class FreqtradeBot(LoggingMixin):
                 return True
 
         if trade.has_open_orders or not trade.is_open:
-            # Trade has an open Buy or Sell order, Stoploss-handling can't happen in this case
+            # Trade has an open order, Stoploss-handling can't happen in this case
             # as the Amount on the exchange is tied up in another trade.
             # The trade can be closed already (sell-order fill confirmation came in this iteration)
             return False
@@ -1290,12 +1324,12 @@ class FreqtradeBot(LoggingMixin):
 
     def manage_trade_stoploss_orders(self, trade: Trade, stoploss_orders: List[Dict]):
         """
-        Perform required actions acording to existing stoploss orders of trade
+        Perform required actions according to existing stoploss orders of trade
         :param trade: Corresponding Trade
         :param stoploss_orders: Current on exchange stoploss orders
         :return: None
         """
-        # If all stoploss orderd are canceled for some reason we add it again
+        # If all stoploss ordered are canceled for some reason we add it again
         canceled_sl_orders = [o for o in stoploss_orders
                               if o['status'] in ('canceled', 'cancelled')]
         if (
@@ -1935,21 +1969,23 @@ class FreqtradeBot(LoggingMixin):
 
         trade.update_trade(order_obj, not send_msg)
 
-        trade = self._update_trade_after_fill(trade, order_obj)
+        trade = self._update_trade_after_fill(trade, order_obj, send_msg)
         Trade.commit()
 
         self.order_close_notify(trade, order_obj, stoploss_order, send_msg)
 
         return False
 
-    def _update_trade_after_fill(self, trade: Trade, order: Order) -> Trade:
+    def _update_trade_after_fill(self, trade: Trade, order: Order, send_msg: bool) -> Trade:
         if order.status in constants.NON_OPEN_EXCHANGE_STATES:
             strategy_safe_wrapper(
                 self.strategy.order_filled, default_retval=None)(
                 pair=trade.pair, trade=trade, order=order, current_time=datetime.now(timezone.utc))
             # If a entry order was closed, force update on stoploss on exchange
             if order.ft_order_side == trade.entry_side:
-                trade = self.cancel_stoploss_on_exchange(trade)
+                if send_msg:
+                    # Don't cancel stoploss in recovery modes immediately
+                    trade = self.cancel_stoploss_on_exchange(trade)
                 if not self.edge:
                     # TODO: should shorting/leverage be supported by Edge,
                     # then this will need to be fixed.
@@ -1999,7 +2035,7 @@ class FreqtradeBot(LoggingMixin):
             self._notify_enter(trade, order, order.order_type, fill=True, sub_trade=sub_trade)
 
     def handle_protections(self, pair: str, side: LongShort) -> None:
-        # Lock pair for one candle to prevent immediate rebuys
+        # Lock pair for one candle to prevent immediate re-entries
         self.strategy.lock_pair(pair, datetime.now(timezone.utc), reason='Auto lock')
         prot_trig = self.protections.stop_per_pair(pair, side=side)
         if prot_trig:
@@ -2035,7 +2071,7 @@ class FreqtradeBot(LoggingMixin):
             amount_ = trade.amount - amount
 
         if trade.nr_of_successful_entries >= 1 and order_obj.ft_order_side == trade.entry_side:
-            # In case of rebuy's, trade.amount doesn't contain the amount of the last entry.
+            # In case of re-entry's, trade.amount doesn't contain the amount of the last entry.
             amount_ = trade.amount + amount
 
         if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount_:

freqtrade/optimize/backtesting.py

@@ -19,6 +19,7 @@ from freqtrade.data import history
 from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
 from freqtrade.data.dataprovider import DataProvider
+from freqtrade.data.metrics import combined_dataframes_with_rel_mean
 from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType, RunMode,
                              TradingMode)
 from freqtrade.exceptions import DependencyException, OperationalException
@@ -296,7 +297,7 @@ class Backtesting:
                 candle_type=CandleType.FUNDING_RATE
             )
 
-            # For simplicity, assign to CandleType.Mark (might contian index candles!)
+            # For simplicity, assign to CandleType.Mark (might contain index candles!)
             mark_rates_dict = history.load_data(
                 datadir=self.config['datadir'],
                 pairs=self.pairlists.whitelist,
@@ -565,7 +566,8 @@ class Backtesting:
 
         if stake_amount is not None and stake_amount < 0.0:
             amount = amount_to_contract_precision(
-                abs(stake_amount * trade.leverage) / current_rate, trade.amount_precision,
+                abs(stake_amount * trade.amount / trade.stake_amount),
+                trade.amount_precision,
                 self.precision_mode, trade.contract_size)
             if amount == 0.0:
                 return trade
@@ -1215,7 +1217,7 @@ class Backtesting:
         :return: DataFrame with trades (results of backtesting)
         """
         self.prepare_backtest(self.enable_protections)
-        # Ensure wallets are uptodate (important for --strategy-list)
+        # Ensure wallets are up-to-date (important for --strategy-list)
         self.wallets.update()
         # Use dict of lists with data for performance
         # (looping lists is a lot faster than pandas DataFrames)
@@ -1392,9 +1394,8 @@ class Backtesting:
     def start(self) -> None:
         """
         Run backtesting end-to-end
-        :return: None
         """
-        data: Dict[str, Any] = {}
+        data: Dict[str, DataFrame] = {}
 
         data, timerange = self.load_bt_data()
         self.load_bt_data_detail()
@@ -1421,7 +1422,9 @@ class Backtesting:
                 self.results = results
             dt_appendix = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
             if self.config.get('export', 'none') in ('trades', 'signals'):
-                store_backtest_stats(self.config['exportfilename'], self.results, dt_appendix)
+                combined_res = combined_dataframes_with_rel_mean(data, min_date, max_date)
+                store_backtest_stats(self.config['exportfilename'], self.results, dt_appendix,
+                                     market_change_data=combined_res)
 
             if (self.config.get('export', 'none') == 'signals' and
                     self.dataprovider.runmode == RunMode.BACKTEST):

freqtrade/optimize/hyperopt_tools.py

@@ -237,8 +237,10 @@ class HyperoptTools:
                 result_dict.update(all_space_params)
 
     @staticmethod
-    def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
-        if space in params or space in non_optimized:
+    def _params_pretty_print(
+            params, space: str, header: str, non_optimized: Optional[Dict] = None) -> None:
+
+        if space in params or (non_optimized and space in non_optimized):
             space_params = HyperoptTools._space_params(params, space, 5)
             no_params = HyperoptTools._space_params(non_optimized, space, 5)
             appendix = ''

freqtrade/optimize/optimize_reports/__init__.py

@@ -6,13 +6,12 @@ from freqtrade.optimize.optimize_reports.bt_output import (generate_edge_table,
                                                            show_sorted_pairlist,
                                                            text_table_add_metrics,
                                                            text_table_bt_results,
-                                                           text_table_exit_reason,
                                                            text_table_periodic_breakdown,
                                                            text_table_strategy, text_table_tags)
 from freqtrade.optimize.optimize_reports.bt_storage import (store_backtest_analysis_results,
                                                             store_backtest_stats)
 from freqtrade.optimize.optimize_reports.optimize_reports import (
     generate_all_periodic_breakdown_stats, generate_backtest_stats, generate_daily_stats,
-    generate_exit_reason_stats, generate_pair_metrics, generate_periodic_breakdown_stats,
-    generate_rejected_signals, generate_strategy_comparison, generate_strategy_stats,
-    generate_tag_metrics, generate_trade_signal_candles, generate_trading_stats)
+    generate_pair_metrics, generate_periodic_breakdown_stats, generate_rejected_signals,
+    generate_strategy_comparison, generate_strategy_stats, generate_tag_metrics,
+    generate_trade_signal_candles, generate_trading_stats)

freqtrade/optimize/optimize_reports/bt_output.py

@@ -60,32 +60,6 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
                     floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
 
 
-def text_table_exit_reason(exit_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
-    """
-    Generate small table outlining Backtest results
-    :param exit_reason_stats: Exit reason metrics
-    :param stake_currency: Stakecurrency used
-    :return: pretty printed table with tabulate as string
-    """
-    headers = [
-        'Exit Reason',
-        'Exits',
-        'Win  Draws  Loss  Win%',
-        'Avg Profit %',
-        f'Tot Profit {stake_currency}',
-        'Tot Profit %',
-    ]
-
-    output = [[
-        t.get('exit_reason', t.get('sell_reason')), t['trades'],
-        generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
-        t['profit_mean_pct'],
-        fmt_coin(t['profit_total_abs'], stake_currency, False),
-        t['profit_total_pct'],
-    ] for t in exit_reason_stats]
-    return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
-
-
 def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str:
     """
     Generates and returns a text table for the given backtest data and the results dataframe
@@ -93,20 +67,23 @@ def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_curr
     :param stake_currency: stake-currency - used to correctly name headers
     :return: pretty printed table with tabulate as string
     """
+    fallback: str = ''
     if (tag_type == "enter_tag"):
         headers = _get_line_header("TAG", stake_currency)
     else:
-        headers = _get_line_header("TAG", stake_currency, 'Exits')
+        headers = _get_line_header("Exit Reason", stake_currency, 'Exits')
+        fallback = 'exit_reason'
+
     floatfmt = _get_line_floatfmt(stake_currency)
     output = [
         [
-            t['key'] if t['key'] is not None and len(
-                t['key']) > 0 else "OTHER",
+            t['key'] if t.get('key') is not None and len(
+                str(t['key'])) > 0 else t.get(fallback, "OTHER"),
             t['trades'],
             t['profit_mean_pct'],
             t['profit_total_abs'],
             t['profit_total_pct'],
-            t['duration_avg'],
+            t.get('duration_avg'),
             generate_wins_draws_losses(
                 t['wins'],
                 t['draws'],
@@ -301,7 +278,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
 
 
 def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str,
-                         backtest_breakdown=[]):
+                         backtest_breakdown: List[str]):
     """
     Print results for one strategy
     """
@@ -317,17 +294,16 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
         print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
     print(table)
 
-    if (results.get('results_per_enter_tag') is not None):
-        table = text_table_tags("enter_tag", results['results_per_enter_tag'], stake_currency)
+    if (enter_tags := results.get('results_per_enter_tag')) is not None:
+        table = text_table_tags("enter_tag", enter_tags, stake_currency)
 
         if isinstance(table, str) and len(table) > 0:
             print(' ENTER TAG STATS '.center(len(table.splitlines()[0]), '='))
         print(table)
 
-    exit_reasons = results.get('exit_reason_summary')
-    if exit_reasons:
-        table = text_table_exit_reason(exit_reason_stats=exit_reasons,
-                                       stake_currency=stake_currency)
+    if (exit_reasons := results.get('exit_reason_summary')) is not None:
+        table = text_table_tags("exit_tag", exit_reasons, stake_currency)
+
         if isinstance(table, str) and len(table) > 0:
             print(' EXIT REASON STATS '.center(len(table.splitlines()[0]), '='))
         print(table)

freqtrade/optimize/optimize_reports/bt_storage.py

@@ -1,6 +1,8 @@
 import logging
 from pathlib import Path
-from typing import Dict
+from typing import Dict, Optional
+
+from pandas import DataFrame
 
 from freqtrade.constants import LAST_BT_RESULT_FN
 from freqtrade.misc import file_dump_joblib, file_dump_json
@@ -11,8 +13,26 @@ from freqtrade.types import BacktestResultType
 logger = logging.getLogger(__name__)
 
 
+def _generate_filename(recordfilename: Path, appendix: str, suffix: str) -> Path:
+    """
+    Generates a filename based on the provided parameters.
+    :param recordfilename: Path object, which can either be a filename or a directory.
+    :param appendix: use for the filename. e.g. backtest-result-<datetime>
+    :param suffix: Suffix to use for the file, e.g. .json, .pkl
+    :return: Generated filename as a Path object
+    """
+    if recordfilename.is_dir():
+        filename = (recordfilename / f'backtest-result-{appendix}').with_suffix(suffix)
+    else:
+        filename = Path.joinpath(
+            recordfilename.parent, f'{recordfilename.stem}-{appendix}'
+        ).with_suffix(suffix)
+    return filename
+
+
 def store_backtest_stats(
-        recordfilename: Path, stats: BacktestResultType, dtappendix: str) -> Path:
+        recordfilename: Path, stats: BacktestResultType, dtappendix: str, *,
+        market_change_data: Optional[DataFrame] = None) -> Path:
     """
     Stores backtest results
     :param recordfilename: Path object, which can either be a filename or a directory.
@@ -21,12 +41,7 @@ def store_backtest_stats(
     :param stats: Dataframe containing the backtesting statistics
     :param dtappendix: Datetime to use for the filename
     """
-    if recordfilename.is_dir():
-        filename = (recordfilename / f'backtest-result-{dtappendix}.json')
-    else:
-        filename = Path.joinpath(
-            recordfilename.parent, f'{recordfilename.stem}-{dtappendix}'
-        ).with_suffix(recordfilename.suffix)
+    filename = _generate_filename(recordfilename, dtappendix, '.json')
 
     # Store metadata separately.
     file_dump_json(get_backtest_metadata_filename(filename), stats['metadata'])
@@ -41,6 +56,11 @@ def store_backtest_stats(
     latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
     file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
 
+    if market_change_data is not None:
+        filename_mc = _generate_filename(recordfilename, f"{dtappendix}_market_change", '.feather')
+        market_change_data.reset_index().to_feather(
+            filename_mc, compression_level=9, compression='lz4')
+
     return filename
 
 
@@ -57,12 +77,7 @@ def _store_backtest_analysis_data(
     :param dtappendix: Datetime to use for the filename
     :param name: Name to use for the file, e.g. signals, rejected
     """
-    if recordfilename.is_dir():
-        filename = (recordfilename / f'backtest-result-{dtappendix}_{name}.pkl')
-    else:
-        filename = Path.joinpath(
-            recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_{name}.pkl'
-        )
+    filename = _generate_filename(recordfilename, f"{dtappendix}_{name}", '.pkl')
 
     file_dump_joblib(filename, data)
 

freqtrade/optimize/optimize_reports/optimize_reports.py

@@ -6,7 +6,7 @@ from typing import Any, Dict, List, Tuple, Union
 import numpy as np
 from pandas import DataFrame, Series, concat, to_datetime
 
-from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, IntOrInf
+from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT
 from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
                                     calculate_expectancy, calculate_market_change,
                                     calculate_max_drawdown, calculate_sharpe, calculate_sortino)
@@ -71,7 +71,8 @@ def _generate_result_line(result: DataFrame, starting_balance: int, first_column
         'key': first_column,
         'trades': len(result),
         'profit_mean': result['profit_ratio'].mean() if len(result) > 0 else 0.0,
-        'profit_mean_pct': result['profit_ratio'].mean() * 100.0 if len(result) > 0 else 0.0,
+        'profit_mean_pct': round(result['profit_ratio'].mean() * 100.0, 2
+                                 ) if len(result) > 0 else 0.0,
         'profit_sum': profit_sum,
         'profit_sum_pct': round(profit_sum * 100.0, 2),
         'profit_total_abs': result['profit_abs'].sum(),
@@ -154,42 +155,6 @@ def generate_tag_metrics(tag_type: str,
         return []
 
 
-def generate_exit_reason_stats(max_open_trades: IntOrInf, results: DataFrame) -> List[Dict]:
-    """
-    Generate small table outlining Backtest results
-    :param max_open_trades: Max_open_trades parameter
-    :param results: Dataframe containing the backtest result for one strategy
-    :return: List of Dicts containing the metrics per Sell reason
-    """
-    tabular_data = []
-
-    for reason, count in results['exit_reason'].value_counts().items():
-        result = results.loc[results['exit_reason'] == reason]
-
-        profit_mean = result['profit_ratio'].mean()
-        profit_sum = result['profit_ratio'].sum()
-        profit_total = profit_sum / max_open_trades
-
-        tabular_data.append(
-            {
-                'exit_reason': reason,
-                'trades': count,
-                'wins': len(result[result['profit_abs'] > 0]),
-                'draws': len(result[result['profit_abs'] == 0]),
-                'losses': len(result[result['profit_abs'] < 0]),
-                'winrate': len(result[result['profit_abs'] > 0]) / count if count else 0.0,
-                'profit_mean': profit_mean,
-                'profit_mean_pct': round(profit_mean * 100, 2),
-                'profit_sum': profit_sum,
-                'profit_sum_pct': round(profit_sum * 100, 2),
-                'profit_total_abs': result['profit_abs'].sum(),
-                'profit_total': profit_total,
-                'profit_total_pct': round(profit_total * 100, 2),
-            }
-        )
-    return tabular_data
-
-
 def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
     """
     Generate summary per strategy
@@ -383,9 +348,8 @@ def generate_strategy_stats(pairlist: List[str],
 
     enter_tag_results = generate_tag_metrics("enter_tag", starting_balance=start_balance,
                                              results=results, skip_nan=False)
-
-    exit_reason_stats = generate_exit_reason_stats(max_open_trades=max_open_trades,
-                                                   results=results)
+    exit_reason_stats = generate_tag_metrics('exit_reason', starting_balance=start_balance,
+                                             results=results, skip_nan=False)
     left_open_results = generate_pair_metrics(
         pairlist, stake_currency=stake_currency, starting_balance=start_balance,
         results=results.loc[results['exit_reason'] == 'force_exit'], skip_nan=True)

freqtrade/persistence/custom_data.py

@@ -18,7 +18,7 @@ class _CustomData(ModelBase):
     """
     CustomData database model
     Keeps records of metadata as key/value store
-    for trades or global persistant values
+    for trades or global persistent values
     One to many relationship with Trades:
       - One trade can have many metadata entries
       - One metadata entry can only be associated with one Trade

freqtrade/persistence/trade_model.py

@@ -847,7 +847,7 @@ class LocalTrade:
                 isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC)
                 or (not recalculating and order.safe_amount_after_fee > amount_tr)
             ):
-                # When recalculating a trade, only comming out to 0 can force a close
+                # When recalculating a trade, only coming out to 0 can force a close
                 self.close(order.safe_price)
             else:
                 self.recalc_trade_from_orders()
@@ -1125,7 +1125,7 @@ class LocalTrade:
                 prof = self.calculate_profit(exit_rate, exit_amount, float(avg_price))
                 close_profit_abs += prof.profit_abs
                 if total_stake > 0:
-                    # This needs to be calculated based on the last occuring exit to be aligned
+                    # This needs to be calculated based on the last occurring exit to be aligned
                     # with realized_profit.
                     close_profit = (close_profit_abs / total_stake) * self.leverage
             else:
@@ -1538,7 +1538,7 @@ class Trade(ModelBase, LocalTrade):
     amount: Mapped[float] = mapped_column(Float())  # type: ignore
     amount_requested: Mapped[Optional[float]] = mapped_column(Float())  # type: ignore
     open_date: Mapped[datetime] = mapped_column(
-        nullable=False, default=datetime.utcnow)  # type: ignore
+        nullable=False, default=datetime.now)  # type: ignore
     close_date: Mapped[Optional[datetime]] = mapped_column()  # type: ignore
     # absolute value of the stop loss
     stop_loss: Mapped[float] = mapped_column(Float(), nullable=True, default=0.0)  # type: ignore

freqtrade/plot/plotting.py

@@ -440,12 +440,12 @@ def create_scatter(
 
 def generate_candlestick_graph(
         pair: str, data: pd.DataFrame, trades: Optional[pd.DataFrame] = None, *,
-        indicators1: List[str] = [], indicators2: List[str] = [],
-        plot_config: Dict[str, Dict] = {},
+        indicators1: Optional[List[str]] = None, indicators2: Optional[List[str]] = None,
+        plot_config: Optional[Dict[str, Dict]] = None,
         ) -> go.Figure:
     """
     Generate the graph from the data generated by Backtesting or from DB
-    Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators
+    Volume will always be plotted in row2, so Row 1 and 3 are to our disposal for custom indicators
     :param pair: Pair to Display on the graph
     :param data: OHLCV DataFrame containing indicators and entry/exit signals
     :param trades: All trades created
@@ -454,7 +454,11 @@ def generate_candlestick_graph(
     :param plot_config: Dict of Dicts containing advanced plot configuration
     :return: Plotly figure
     """
-    plot_config = create_plotconfig(indicators1, indicators2, plot_config)
+    plot_config = create_plotconfig(
+        indicators1 or [],
+        indicators2 or [],
+        plot_config or {},
+    )
     rows = 2 + len(plot_config['subplots'])
     row_widths = [1 for _ in plot_config['subplots']]
     # Define the graph
@@ -673,7 +677,7 @@ def plot_profit(config: Config) -> None:
     """
     Plots the total profit for all pairs.
     Note, the profit calculation isn't realistic.
-    But should be somewhat proportional, and therefor useful
+    But should be somewhat proportional, and therefore useful
     in helping out to find a good algorithm.
     """
     if 'timeframe' not in config:

freqtrade/plugins/pairlist/MarketCapPairList.py

@@ -38,7 +38,7 @@ class MarketCapPairList(IPairList):
         self._refresh_period = self._pairlistconfig.get('refresh_period', 86400)
         self._marketcap_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
         self._def_candletype = self._config['candle_type_def']
-        self._coingekko: CoinGeckoAPI = CoinGeckoAPI()
+        self._coingecko: CoinGeckoAPI = CoinGeckoAPI()
 
         if self._max_rank > 250:
             raise OperationalException(
@@ -127,7 +127,7 @@ class MarketCapPairList(IPairList):
         marketcap_list = self._marketcap_cache.get('marketcap')
 
         if marketcap_list is None:
-            data = self._coingekko.get_coins_markets(vs_currency='usd', order='market_cap_desc',
+            data = self._coingecko.get_coins_markets(vs_currency='usd', order='market_cap_desc',
                                                      per_page='250', page='1', sparkline='false',
                                                      locale='en')
             if data:

freqtrade/plugins/pairlist/PriceFilter.py

@@ -101,7 +101,7 @@ class PriceFilter(IPairList):
 
     def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
         """
-        Check if if one price-step (pip) is > than a certain barrier.
+        Check if one price-step (pip) is > than a certain barrier.
         :param pair: Pair that's currently validated
         :param ticker: ticker dict as returned from ccxt.fetch_ticker
         :return: True if the pair can stay, false if it should be removed

freqtrade/plugins/pairlist/RemotePairList.py

@@ -116,7 +116,7 @@ class RemotePairList(IPairList):
                 "default": "filter",
                 "options": ["filter", "append"],
                 "description": "Processing mode",
-                "help": "Append pairs to incomming pairlist or filter them?",
+                "help": "Append pairs to incoming pairlist or filter them?",
             },
             **IPairList.refresh_period_parameter(),
             "keep_pairlist_on_failure": {

freqtrade/plugins/pairlist/VolumePairList.py

@@ -65,7 +65,7 @@ class VolumePairList(IPairList):
         self._tf_in_min = timeframe_to_minutes(self._lookback_timeframe)
         _tf_in_sec = self._tf_in_min * 60
 
-        # wether to use range lookback or not
+        # whether to use range lookback or not
         self._use_range = (self._tf_in_min > 0) & (self._lookback_period > 0)
 
         if self._use_range & (self._refresh_period < _tf_in_sec):

freqtrade/plugins/protections/iprotection.py

@@ -110,7 +110,7 @@ class IProtection(LoggingMixin, ABC):
         Get lock end time
         """
         max_date: datetime = max([trade.close_date for trade in trades if trade.close_date])
-        # comming from Database, tzinfo is not set.
+        # coming from Database, tzinfo is not set.
         if max_date.tzinfo is None:
             max_date = max_date.replace(tzinfo=timezone.utc)
 

freqtrade/resolvers/iresolver.py

@@ -47,7 +47,7 @@ class IResolver:
 
     @classmethod
     def build_search_paths(cls, config: Config, user_subdir: Optional[str] = None,
-                           extra_dirs: List[str] = []) -> List[Path]:
+                           extra_dirs: Optional[List[str]] = None) -> List[Path]:
 
         abs_paths: List[Path] = []
         if cls.initial_search_path:
@@ -57,8 +57,9 @@ class IResolver:
             abs_paths.insert(0, config['user_data_dir'].joinpath(user_subdir))
 
         # Add extra directory to the top of the search paths
-        for dir in extra_dirs:
-            abs_paths.insert(0, Path(dir).resolve())
+        if extra_dirs:
+            for dir in extra_dirs:
+                abs_paths.insert(0, Path(dir).resolve())
 
         if cls.extra_path and (extra := config.get(cls.extra_path)):
             abs_paths.insert(0, Path(extra).resolve())
@@ -139,7 +140,7 @@ class IResolver:
 
     @classmethod
     def _load_object(cls, paths: List[Path], *, object_name: str, add_source: bool = False,
-                     kwargs: dict = {}) -> Optional[Any]:
+                     kwargs: Dict) -> Optional[Any]:
         """
         Try to load object from path list.
         """
@@ -163,7 +164,7 @@ class IResolver:
     def load_object(cls, object_name: str, config: Config, *, kwargs: dict,
                     extra_dir: Optional[str] = None) -> Any:
         """
-        Search and loads the specified object as configured in hte child class.
+        Search and loads the specified object as configured in the child class.
         :param object_name: name of the module to import
         :param config: configuration dictionary
         :param extra_dir: additional directory to search for the given pairlist

freqtrade/rpc/api_server/api_auth.py

@@ -26,6 +26,7 @@ def verify_auth(api_config, username: str, password: str):
 
 
 httpbasic = HTTPBasic(auto_error=False)
+security = HTTPBasic()
 oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
 
 
@@ -117,7 +118,7 @@ def http_basic_or_jwt_token(form_data: HTTPBasicCredentials = Depends(httpbasic)
 
 
 @router_login.post('/token/login', response_model=AccessAndRefreshToken)
-def token_login(form_data: HTTPBasicCredentials = Depends(HTTPBasic()),
+def token_login(form_data: HTTPBasicCredentials = Depends(security),
                 api_config=Depends(get_api_config)):
 
     if verify_auth(api_config, form_data.username, form_data.password):

freqtrade/rpc/api_server/api_backtest.py

@@ -10,15 +10,16 @@ from fastapi.exceptions import HTTPException
 
 from freqtrade.configuration.config_validation import validate_config_consistency
 from freqtrade.constants import Config
-from freqtrade.data.btanalysis import (delete_backtest_result, get_backtest_result,
-                                       get_backtest_resultlist, load_and_merge_backtest_result,
-                                       update_backtest_metadata)
+from freqtrade.data.btanalysis import (delete_backtest_result, get_backtest_market_change,
+                                       get_backtest_result, get_backtest_resultlist,
+                                       load_and_merge_backtest_result, update_backtest_metadata)
 from freqtrade.enums import BacktestState
 from freqtrade.exceptions import ConfigurationError, DependencyException, OperationalException
 from freqtrade.exchange.common import remove_exchange_credentials
 from freqtrade.misc import deep_merge_dicts, is_file_in_dir
-from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestMetadataUpdate,
-                                                  BacktestRequest, BacktestResponse)
+from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestMarketChange,
+                                                  BacktestMetadataUpdate, BacktestRequest,
+                                                  BacktestResponse)
 from freqtrade.rpc.api_server.deps import get_config
 from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
 from freqtrade.rpc.rpc import RPCException
@@ -32,8 +33,10 @@ router = APIRouter()
 
 
 def __run_backtest_bg(btconfig: Config):
+    from freqtrade.data.metrics import combined_dataframes_with_rel_mean
     from freqtrade.optimize.optimize_reports import generate_backtest_stats, store_backtest_stats
     from freqtrade.resolvers import StrategyResolver
+
     asyncio.set_event_loop(asyncio.new_event_loop())
     try:
         # Reload strategy
@@ -89,11 +92,14 @@ def __run_backtest_bg(btconfig: Config):
                 min_date=min_date, max_date=max_date)
 
         if btconfig.get('export', 'none') == 'trades':
+            combined_res = combined_dataframes_with_rel_mean(ApiBG.bt['data'], min_date, max_date)
             fn = store_backtest_stats(
-                btconfig['exportfilename'], ApiBG.bt['bt'].results,
-                datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+                btconfig['exportfilename'],
+                ApiBG.bt['bt'].results,
+                datetime.now().strftime("%Y-%m-%d_%H-%M-%S"),
+                market_change_data=combined_res
                 )
-            ApiBG.bt['bt'].results['metadata'][strategy_name]['filename'] = str(fn.name)
+            ApiBG.bt['bt'].results['metadata'][strategy_name]['filename'] = str(fn.stem)
             ApiBG.bt['bt'].results['metadata'][strategy_name]['strategy'] = strategy_name
 
         logger.info("Backtest finished.")
@@ -308,3 +314,20 @@ def api_update_backtest_history_entry(file: str, body: BacktestMetadataUpdate,
         raise HTTPException(status_code=400, detail=str(e))
 
     return get_backtest_result(file_abs)
+
+
+@router.get('/backtest/history/{file}/market_change', response_model=BacktestMarketChange,
+            tags=['webserver', 'backtest'])
+def api_get_backtest_market_change(file: str, config=Depends(get_config)):
+    bt_results_base: Path = config['user_data_dir'] / 'backtest_results'
+    file_abs = (bt_results_base / f"{file}_market_change").with_suffix('.feather')
+    # Ensure file is in backtest_results directory
+    if not is_file_in_dir(file_abs, bt_results_base):
+        raise HTTPException(status_code=404, detail="File not found.")
+    df = get_backtest_market_change(file_abs)
+
+    return {
+        'columns': df.columns.tolist(),
+        'data': df.values.tolist(),
+        'length': len(df),
+    }

freqtrade/rpc/api_server/api_schemas.py

@@ -1,7 +1,7 @@
 from datetime import date, datetime
 from typing import Any, Dict, List, Optional, Union
 
-from pydantic import BaseModel, RootModel, SerializeAsAny
+from pydantic import AwareDatetime, BaseModel, RootModel, SerializeAsAny
 
 from freqtrade.constants import IntOrInf
 from freqtrade.enums import MarginMode, OrderTypeValues, SignalDirection, TradingMode
@@ -378,6 +378,13 @@ class Locks(BaseModel):
     locks: List[LockModel]
 
 
+class LocksPayload(BaseModel):
+    pair: str
+    side: str = '*'  # Default to both sides
+    until: AwareDatetime
+    reason: Optional[str] = None
+
+
 class DeleteLockRequest(BaseModel):
     pair: Optional[str] = None
     lockid: Optional[int] = None
@@ -482,12 +489,26 @@ class AvailablePairs(BaseModel):
     pair_interval: List[List[str]]
 
 
+class PairCandlesRequest(BaseModel):
+    pair: str
+    timeframe: str
+    limit: Optional[int] = None
+    columns: Optional[List[str]] = None
+
+
+class PairHistoryRequest(PairCandlesRequest):
+    timerange: str
+    strategy: str
+    freqaimodel: Optional[str] = None
+
+
 class PairHistory(BaseModel):
     strategy: str
     pair: str
     timeframe: str
     timeframe_ms: int
     columns: List[str]
+    all_columns: List[str] = []
     data: SerializeAsAny[List[Any]]
     length: int
     buy_signals: int
@@ -551,6 +572,12 @@ class BacktestMetadataUpdate(BaseModel):
     notes: str = ''
 
 
+class BacktestMarketChange(BaseModel):
+    columns: List[str]
+    length: int
+    data: List[List[Any]]
+
+
 class SysInfo(BaseModel):
     cpu_pct: List[float]
     ram_pct: float

freqtrade/rpc/api_server/api_v1.py

@@ -15,12 +15,13 @@ from freqtrade.rpc.api_server.api_schemas import (AvailablePairs, Balances, Blac
                                                   DeleteLockRequest, DeleteTrade, Entry,
                                                   ExchangeListResponse, Exit, ForceEnterPayload,
                                                   ForceEnterResponse, ForceExitPayload,
-                                                  FreqAIModelListResponse, Health, Locks, Logs,
-                                                  MixTag, OpenTradeSchema, PairHistory,
-                                                  PerformanceEntry, Ping, PlotConfig, Profit,
-                                                  ResultMsg, ShowConfig, Stats, StatusMsg,
-                                                  StrategyListResponse, StrategyResponse, SysInfo,
-                                                  Version, WhitelistResponse)
+                                                  FreqAIModelListResponse, Health, Locks,
+                                                  LocksPayload, Logs, MixTag, OpenTradeSchema,
+                                                  PairCandlesRequest, PairHistory,
+                                                  PairHistoryRequest, PerformanceEntry, Ping,
+                                                  PlotConfig, Profit, ResultMsg, ShowConfig, Stats,
+                                                  StatusMsg, StrategyListResponse, StrategyResponse,
+                                                  SysInfo, Version, WhitelistResponse)
 from freqtrade.rpc.api_server.deps import get_config, get_exchange, get_rpc, get_rpc_optional
 from freqtrade.rpc.rpc import RPCException
 
@@ -53,7 +54,8 @@ logger = logging.getLogger(__name__)
 # 2.32: new /backtest/history/ patch endpoint
 # 2.33: Additional weekly/monthly metrics
 # 2.34: new entries/exits/mix_tags endpoints
-API_VERSION = 2.34
+# 2.35: pair_candles and pair_history endpoints as Post variant
+API_VERSION = 2.35
 
 # Public API, requires no auth.
 router_public = APIRouter()
@@ -255,6 +257,13 @@ def delete_lock_pair(payload: DeleteLockRequest, rpc: RPC = Depends(get_rpc)):
     return rpc._rpc_delete_lock(lockid=payload.lockid, pair=payload.pair)
 
 
+@router.post('/locks', response_model=Locks, tags=['info', 'locks'])
+def add_locks(payload: List[LocksPayload], rpc: RPC = Depends(get_rpc)):
+    for lock in payload:
+        rpc._rpc_add_lock(lock.pair, lock.until, lock.reason, lock.side)
+    return rpc._rpc_locks()
+
+
 @router.get('/logs', response_model=Logs, tags=['info'])
 def logs(limit: Optional[int] = None):
     return RPC._rpc_get_logs(limit)
@@ -284,7 +293,14 @@ def reload_config(rpc: RPC = Depends(get_rpc)):
 @router.get('/pair_candles', response_model=PairHistory, tags=['candle data'])
 def pair_candles(
         pair: str, timeframe: str, limit: Optional[int] = None, rpc: RPC = Depends(get_rpc)):
-    return rpc._rpc_analysed_dataframe(pair, timeframe, limit)
+    return rpc._rpc_analysed_dataframe(pair, timeframe, limit, None)
+
+
+@router.post('/pair_candles', response_model=PairHistory, tags=['candle data'])
+def pair_candles_filtered(payload: PairCandlesRequest, rpc: RPC = Depends(get_rpc)):
+    # Advanced pair_candles endpoint with column filtering
+    return rpc._rpc_analysed_dataframe(
+        payload.pair, payload.timeframe, payload.limit, payload.columns)
 
 
 @router.get('/pair_history', response_model=PairHistory, tags=['candle data'])
@@ -300,7 +316,25 @@ def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
         'freqaimodel': freqaimodel if freqaimodel else config.get('freqaimodel'),
     })
     try:
-        return RPC._rpc_analysed_history_full(config, pair, timeframe, exchange)
+        return RPC._rpc_analysed_history_full(config, pair, timeframe, exchange, None)
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=str(e))
+
+
+@router.post('/pair_history', response_model=PairHistory, tags=['candle data'])
+def pair_history_filtered(payload: PairHistoryRequest,
+                          config=Depends(get_config), exchange=Depends(get_exchange)):
+    # The initial call to this endpoint can be slow, as it may need to initialize
+    # the exchange class.
+    config = deepcopy(config)
+    config.update({
+        'strategy': payload.strategy,
+        'timerange': payload.timerange,
+        'freqaimodel': payload.freqaimodel if payload.freqaimodel else config.get('freqaimodel'),
+    })
+    try:
+        return RPC._rpc_analysed_history_full(
+            config, payload.pair, payload.timeframe, exchange, payload.columns)
     except Exception as e:
         raise HTTPException(status_code=502, detail=str(e))
 

freqtrade/rpc/api_server/ws/channel.py

@@ -152,7 +152,7 @@ class WebSocketChannel:
         """
         return self._closed.is_set()
 
-    def set_subscriptions(self, subscriptions: List[str] = []) -> None:
+    def set_subscriptions(self, subscriptions: List[str]) -> None:
         """
         Set which subscriptions this channel is subscribed to
 

freqtrade/rpc/external_message_consumer.py

@@ -237,7 +237,7 @@ class ExternalMessageConsumer:
                 continue
 
             except Exception as e:
-                # An unforseen error has occurred, log and continue
+                # An unforeseen error has occurred, log and continue
                 logger.error("Unexpected error has occurred:")
                 logger.exception(e)
                 await asyncio.sleep(self.sleep_time)
@@ -387,7 +387,7 @@ class ExternalMessageConsumer:
             )
 
         if not did_append:
-            # We want an overlap in candles incase some data has changed
+            # We want an overlap in candles in case some data has changed
             n_missing += 1
             # Set to None for all candles if we missed a full df's worth of candles
             n_missing = n_missing if n_missing < FULL_DATAFRAME_THRESHOLD else 1500

freqtrade/rpc/fiat_convert.py

@@ -39,7 +39,7 @@ class CryptoToFiatConverter(LoggingMixin):
     This object is also a Singleton
     """
     __instance = None
-    _coingekko: CoinGeckoAPI = None
+    _coingecko: CoinGeckoAPI = None
     _coinlistings: List[Dict] = []
     _backoff: float = 0.0
 
@@ -52,9 +52,9 @@ class CryptoToFiatConverter(LoggingMixin):
             try:
                 # Limit retires to 1 (0 and 1)
                 # otherwise we risk bot impact if coingecko is down.
-                CryptoToFiatConverter._coingekko = CoinGeckoAPI(retries=1)
+                CryptoToFiatConverter._coingecko = CoinGeckoAPI(retries=1)
             except BaseException:
-                CryptoToFiatConverter._coingekko = None
+                CryptoToFiatConverter._coingecko = None
         return CryptoToFiatConverter.__instance
 
     def __init__(self) -> None:
@@ -67,7 +67,7 @@ class CryptoToFiatConverter(LoggingMixin):
     def _load_cryptomap(self) -> None:
         try:
             # Use list-comprehension to ensure we get a list.
-            self._coinlistings = [x for x in self._coingekko.get_coins_list()]
+            self._coinlistings = [x for x in self._coingecko.get_coins_list()]
         except RequestException as request_exception:
             if "429" in str(request_exception):
                 logger.warning(
@@ -84,7 +84,7 @@ class CryptoToFiatConverter(LoggingMixin):
             logger.error(
                 f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
 
-    def _get_gekko_id(self, crypto_symbol):
+    def _get_gecko_id(self, crypto_symbol):
         if not self._coinlistings:
             if self._backoff <= datetime.now().timestamp():
                 self._load_cryptomap()
@@ -180,9 +180,9 @@ class CryptoToFiatConverter(LoggingMixin):
         if crypto_symbol == fiat_symbol:
             return 1.0
 
-        _gekko_id = self._get_gekko_id(crypto_symbol)
+        _gecko_id = self._get_gecko_id(crypto_symbol)
 
-        if not _gekko_id:
+        if not _gecko_id:
             # return 0 for unsupported stake currencies (fiat-convert should not break the bot)
             self.log_once(
                 f"unsupported crypto-symbol {crypto_symbol.upper()} - returning 0.0",
@@ -191,10 +191,10 @@ class CryptoToFiatConverter(LoggingMixin):
 
         try:
             return float(
-                self._coingekko.get_price(
-                    ids=_gekko_id,
+                self._coingecko.get_price(
+                    ids=_gecko_id,
                     vs_currencies=fiat_symbol
-                )[_gekko_id][fiat_symbol]
+                )[_gecko_id][fiat_symbol]
             )
         except Exception as exception:
             logger.error("Error in _find_price: %s", exception)

freqtrade/rpc/rpc.py

@@ -16,7 +16,7 @@ from sqlalchemy import func, select
 
 from freqtrade import __version__
 from freqtrade.configuration.timerange import TimeRange
-from freqtrade.constants import CANCEL_REASON, Config
+from freqtrade.constants import CANCEL_REASON, DEFAULT_DATAFRAME_COLUMNS, Config
 from freqtrade.data.history import load_data
 from freqtrade.data.metrics import calculate_expectancy, calculate_max_drawdown
 from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirection, SignalDirection,
@@ -30,8 +30,8 @@ from freqtrade.persistence.models import PairLock
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
 from freqtrade.rpc.rpc_types import RPCSendMsg
-from freqtrade.util import (decimals_per_coin, dt_humanize, dt_now, dt_ts_def, format_date,
-                            shorten_date)
+from freqtrade.util import decimals_per_coin, dt_now, dt_ts_def, format_date, shorten_date
+from freqtrade.util.datetime_helpers import dt_humanize_delta
 from freqtrade.wallets import PositionWallet, Wallet
 
 
@@ -155,7 +155,7 @@ class RPC:
         }
         return val
 
-    def _rpc_trade_status(self, trade_ids: List[int] = []) -> List[Dict[str, Any]]:
+    def _rpc_trade_status(self, trade_ids: Optional[List[int]] = None) -> List[Dict[str, Any]]:
         """
         Below follows the RPC backend it is prefixed with rpc_ to raise awareness that it is
         a remotely exposed function
@@ -301,13 +301,13 @@ class RPC:
                     for oo in trade.open_orders
                 ]
 
-                # exemple: '*.**.**' trying to enter, exit and exit with 3 different orders
+                # example: '*.**.**' trying to enter, exit and exit with 3 different orders
                 active_attempt_side_symbols_str = '.'.join(active_attempt_side_symbols)
 
                 detail_trade = [
                     f'{trade.id} {direction_str}',
                     trade.pair + active_attempt_side_symbols_str,
-                    shorten_date(dt_humanize(trade.open_date, only_distance=True)),
+                    shorten_date(dt_humanize_delta(trade.open_date_utc)),
                     profit_str
                 ]
 
@@ -460,8 +460,11 @@ class RPC:
 
     def _rpc_trade_statistics(
             self, stake_currency: str, fiat_display_currency: str,
-            start_date: datetime = datetime.fromtimestamp(0)) -> Dict[str, Any]:
+            start_date: Optional[datetime] = None) -> Dict[str, Any]:
         """ Returns cumulative profit statistics """
+
+        start_date = datetime.fromtimestamp(0) if start_date is None else start_date
+
         trade_filter = ((Trade.is_open.is_(False) & (Trade.close_date >= start_date)) |
                         Trade.is_open.is_(True))
         trades: Sequence[Trade] = Trade.session.scalars(Trade.get_trades_query(
@@ -596,10 +599,10 @@ class RPC:
             'trade_count': len(trades),
             'closed_trade_count': closed_trade_count,
             'first_trade_date': format_date(first_date),
-            'first_trade_humanized': dt_humanize(first_date) if first_date else '',
+            'first_trade_humanized': dt_humanize_delta(first_date) if first_date else '',
             'first_trade_timestamp': dt_ts_def(first_date, 0),
             'latest_trade_date': format_date(last_date),
-            'latest_trade_humanized': dt_humanize(last_date) if last_date else '',
+            'latest_trade_humanized': dt_humanize_delta(last_date) if last_date else '',
             'latest_trade_timestamp': dt_ts_def(last_date, 0),
             'avg_duration': str(timedelta(seconds=sum(durations) / num)).split('.')[0],
             'best_pair': best_pair[0] if best_pair else '',
@@ -1104,6 +1107,16 @@ class RPC:
 
         return self._rpc_locks()
 
+    def _rpc_add_lock(
+            self, pair: str, until: datetime, reason: Optional[str], side: str) -> PairLock:
+        lock = PairLocks.lock_pair(
+            pair=pair,
+            until=until,
+            reason=reason,
+            side=side,
+        )
+        return lock
+
     def _rpc_whitelist(self) -> Dict:
         """ Returns the currently active whitelist"""
         res = {'method': self._freqtrade.pairlists.name_list,
@@ -1177,9 +1190,11 @@ class RPC:
         return self._freqtrade.edge.accepted_pairs()
 
     @staticmethod
-    def _convert_dataframe_to_dict(strategy: str, pair: str, timeframe: str, dataframe: DataFrame,
-                                   last_analyzed: datetime) -> Dict[str, Any]:
+    def _convert_dataframe_to_dict(
+            strategy: str, pair: str, timeframe: str, dataframe: DataFrame,
+            last_analyzed: datetime, selected_cols: Optional[List[str]]) -> Dict[str, Any]:
         has_content = len(dataframe) != 0
+        dataframe_columns = list(dataframe.columns)
         signals = {
             'enter_long': 0,
             'exit_long': 0,
@@ -1187,6 +1202,11 @@ class RPC:
             'exit_short': 0,
         }
         if has_content:
+            if selected_cols is not None:
+                # Ensure OHLCV columns are always present
+                cols_set = set(DEFAULT_DATAFRAME_COLUMNS + list(signals.keys()) + selected_cols)
+                df_cols = [col for col in dataframe_columns if col in cols_set]
+                dataframe = dataframe.loc[:, df_cols]
 
             dataframe.loc[:, '__date_ts'] = dataframe.loc[:, 'date'].astype(int64) // 1000 // 1000
             # Move signal close to separate column when signal for easy plotting
@@ -1211,6 +1231,7 @@ class RPC:
             'timeframe': timeframe,
             'timeframe_ms': timeframe_to_msecs(timeframe),
             'strategy': strategy,
+            'all_columns': dataframe_columns,
             'columns': list(dataframe.columns),
             'data': dataframe.values.tolist(),
             'length': len(dataframe),
@@ -1236,13 +1257,16 @@ class RPC:
             })
         return res
 
-    def _rpc_analysed_dataframe(self, pair: str, timeframe: str,
-                                limit: Optional[int]) -> Dict[str, Any]:
+    def _rpc_analysed_dataframe(
+            self, pair: str, timeframe: str, limit: Optional[int],
+            selected_cols: Optional[List[str]]) -> Dict[str, Any]:
         """ Analyzed dataframe in Dict form """
 
         _data, last_analyzed = self.__rpc_analysed_dataframe_raw(pair, timeframe, limit)
-        return RPC._convert_dataframe_to_dict(self._freqtrade.config['strategy'],
-                                              pair, timeframe, _data, last_analyzed)
+        return RPC._convert_dataframe_to_dict(
+            self._freqtrade.config['strategy'], pair, timeframe, _data, last_analyzed,
+            selected_cols
+        )
 
     def __rpc_analysed_dataframe_raw(
         self,
@@ -1309,7 +1333,7 @@ class RPC:
 
     @staticmethod
     def _rpc_analysed_history_full(config: Config, pair: str, timeframe: str,
-                                   exchange) -> Dict[str, Any]:
+                                   exchange, selected_cols: Optional[List[str]]) -> Dict[str, Any]:
         timerange_parsed = TimeRange.parse_timerange(config.get('timerange'))
 
         from freqtrade.data.converter import trim_dataframe
@@ -1339,7 +1363,8 @@ class RPC:
         df_analyzed = trim_dataframe(df_analyzed, timerange_parsed, startup_candles=startup_candles)
 
         return RPC._convert_dataframe_to_dict(strategy.get_strategy_name(), pair, timeframe,
-                                              df_analyzed.copy(), dt_now())
+                                              df_analyzed.copy(), dt_now(),
+                                              selected_cols)
 
     def _rpc_plot_config(self) -> Dict[str, Any]:
         if (self._freqtrade.strategy.plot_config and

freqtrade/rpc/telegram.py

@@ -33,7 +33,7 @@ from freqtrade.misc import chunks, plural
 from freqtrade.persistence import Trade
 from freqtrade.rpc import RPC, RPCException, RPCHandler
 from freqtrade.rpc.rpc_types import RPCEntryMsg, RPCExitMsg, RPCOrderMsg, RPCSendMsg
-from freqtrade.util import dt_humanize, fmt_coin, format_date, round_value
+from freqtrade.util import dt_from_ts, dt_humanize_delta, fmt_coin, format_date, round_value
 
 
 MAX_MESSAGE_LENGTH = MessageLimit.MAX_TEXT_LENGTH
@@ -488,7 +488,7 @@ class Telegram(RPCHandler):
         elif msg['type'] == RPCMessageType.WARNING:
             message = f"\N{WARNING SIGN} *Warning:* `{msg['status']}`"
         elif msg['type'] == RPCMessageType.EXCEPTION:
-            # Errors will contain exceptions, which are wrapped in tripple ticks.
+            # Errors will contain exceptions, which are wrapped in triple ticks.
             message = f"\N{WARNING SIGN} *ERROR:* \n {msg['status']}"
 
         elif msg['type'] == RPCMessageType.STARTUP:
@@ -573,8 +573,7 @@ class Telegram(RPCHandler):
                 # TODO: This calculation ignores fees.
                 price_to_1st_entry = ((cur_entry_average - first_avg) / first_avg)
                 if is_open:
-                    lines.append("({})".format(dt_humanize(order["order_filled_date"],
-                                                           granularity=["day", "hour", "minute"])))
+                    lines.append("({})".format(dt_humanize_delta(order["order_filled_date"])))
                 lines.append(f"*Amount:* {round_value(cur_entry_amount, 8)} "
                              f"({fmt_coin(order['cost'], quote_currency)})")
                 lines.append(f"*Average {wording} Price:* {round_value(cur_entry_average, 8)} "
@@ -657,7 +656,7 @@ class Telegram(RPCHandler):
         position_adjust = self._config.get('position_adjustment_enable', False)
         max_entries = self._config.get('max_entry_position_adjustment', -1)
         for r in results:
-            r['open_date_hum'] = dt_humanize(r['open_date'])
+            r['open_date_hum'] = dt_humanize_delta(r['open_date'])
             r['num_entries'] = len([o for o in r['orders'] if o['ft_is_entry']])
             r['num_exits'] = len([o for o in r['orders'] if not o['ft_is_entry']
                                  and not o['ft_order_side'] == 'stoploss'])
@@ -1174,7 +1173,7 @@ class Telegram(RPCHandler):
                 text='Cancel', callback_data='force_exit__cancel')])
             await self._send_msg(msg="Which trade?", keyboard=buttons_aligned)
 
-    async def _force_exit_action(self, trade_id):
+    async def _force_exit_action(self, trade_id: str):
         if trade_id != 'cancel':
             try:
                 loop = asyncio.get_running_loop()
@@ -1289,7 +1288,7 @@ class Telegram(RPCHandler):
             nrecent
         )
         trades_tab = tabulate(
-            [[dt_humanize(trade['close_date']),
+            [[dt_humanize_delta(dt_from_ts(trade['close_timestamp'])),
                 trade['pair'] + " (#" + str(trade['trade_id']) + ")",
                 f"{(trade['close_profit']):.2%} ({trade['close_profit_abs']})"]
                 for trade in trades['trades']],
@@ -1549,7 +1548,7 @@ class Telegram(RPCHandler):
 
     async def send_blacklist_msg(self, blacklist: Dict):
         errmsgs = []
-        for pair, error in blacklist['errors'].items():
+        for _, error in blacklist['errors'].items():
             errmsgs.append(f"Error: {error['error_msg']}")
         if errmsgs:
             await self._send_msg('\n'.join(errmsgs))

freqtrade/strategy/informative_decorator.py

@@ -64,7 +64,7 @@ def informative(timeframe: str, asset: str = '',
     def decorator(fn: PopulateIndicators):
         informative_pairs = getattr(fn, '_ft_informative', [])
         informative_pairs.append(InformativeData(_asset, _timeframe, _fmt, _ffill, _candle_type))
-        setattr(fn, '_ft_informative', informative_pairs)
+        setattr(fn, '_ft_informative', informative_pairs)  # noqa: B010
         return fn
     return decorator
 

freqtrade/strategy/strategy_helper.py

@@ -78,7 +78,7 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     # all indicators on the informative sample MUST be calculated before this point
     if ffill:
         # https://pandas.pydata.org/docs/user_guide/merging.html#timeseries-friendly-merging
-        # merge_ordered - ffill method is 2.5x faster than seperate ffill()
+        # merge_ordered - ffill method is 2.5x faster than separate ffill()
         dataframe = pd.merge_ordered(dataframe, informative, fill_method="ffill", left_on='date',
                                      right_on=date_merge, how='left')
     else:

freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2

@@ -3,7 +3,7 @@ def bot_loop_start(self, current_time: datetime, **kwargs) -> None:
     """
     Called at the start of the bot iteration (one loop).
     Might be used to perform pair-independent tasks
-    (e.g. gather some remote ressource for comparison)
+    (e.g. gather some remote resource for comparison)
 
     For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
 

freqtrade/util/__init__.py

@@ -1,8 +1,9 @@
-from freqtrade.util.datetime_helpers import (dt_floor_day, dt_from_ts, dt_humanize, dt_now, dt_ts,
-                                             dt_ts_def, dt_ts_none, dt_utc, format_date,
+from freqtrade.util.datetime_helpers import (dt_floor_day, dt_from_ts, dt_humanize_delta, dt_now,
+                                             dt_ts, dt_ts_def, dt_ts_none, dt_utc, format_date,
                                              format_ms_time, shorten_date)
 from freqtrade.util.formatters import decimals_per_coin, fmt_coin, round_value
 from freqtrade.util.ft_precise import FtPrecise
+from freqtrade.util.measure_time import MeasureTime
 from freqtrade.util.periodic_cache import PeriodicCache
 from freqtrade.util.template_renderer import render_template, render_template_with_fallback  # noqa
 
@@ -10,7 +11,7 @@ from freqtrade.util.template_renderer import render_template, render_template_wi
 __all__ = [
     'dt_floor_day',
     'dt_from_ts',
-    'dt_humanize',
+    'dt_humanize_delta',
     'dt_now',
     'dt_ts',
     'dt_ts_def',
@@ -24,4 +25,5 @@ __all__ = [
     'decimals_per_coin',
     'round_value',
     'fmt_coin',
+    'MeasureTime',
 ]

freqtrade/util/datetime_helpers.py

@@ -1,8 +1,9 @@
 import re
 from datetime import datetime, timezone
-from typing import Optional
+from time import time
+from typing import Optional, Union
 
-import arrow
+import humanize
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 
@@ -25,7 +26,7 @@ def dt_ts(dt: Optional[datetime] = None) -> int:
     """
     if dt:
         return int(dt.timestamp() * 1000)
-    return int(dt_now().timestamp() * 1000)
+    return int(time() * 1000)
 
 
 def dt_ts_def(dt: Optional[datetime], default: int = 0) -> int:
@@ -76,13 +77,11 @@ def shorten_date(_date: str) -> str:
     return new_date
 
 
-def dt_humanize(dt: datetime, **kwargs) -> str:
+def dt_humanize_delta(dt: datetime):
     """
-    Return a humanized string for the given datetime.
-    :param dt: datetime to humanize
-    :param kwargs: kwargs to pass to arrow's humanize()
+    Return a humanized string for the given timedelta.
     """
-    return arrow.get(dt).humanize(**kwargs)
+    return humanize.naturaltime(dt)
 
 
 def format_date(date: Optional[datetime]) -> str:
@@ -96,9 +95,9 @@ def format_date(date: Optional[datetime]) -> str:
     return ''
 
 
-def format_ms_time(date: int) -> str:
+def format_ms_time(date: Union[int, float]) -> str:
     """
     convert MS date to readable format.
     : epoch-string in ms
     """
-    return datetime.fromtimestamp(date / 1000.0).strftime('%Y-%m-%dT%H:%M:%S')
+    return dt_from_ts(date).strftime('%Y-%m-%dT%H:%M:%S')

freqtrade/util/measure_time.py

@@ -0,0 +1,43 @@
+import logging
+import time
+from typing import Callable
+
+from cachetools import TTLCache
+
+
+logger = logging.getLogger(__name__)
+
+
+class MeasureTime:
+    """
+    Measure the time of a block of code and call a callback if the time limit is exceeded.
+    """
+    def __init__(
+            self, callback: Callable[[float, float], None], time_limit: float, ttl: int = 3600 * 4):
+        """
+        :param callback: The callback to call if the time limit is exceeded.
+            This callback will be called once every "ttl" seconds,
+            with the parameters "duration" (in seconds) and
+            "time limit" - representing the passed in time limit.
+        :param time_limit: The time limit in seconds.
+        :param ttl: The time to live of the cache in seconds.
+            defaults to 4 hours.
+        """
+        self._callback = callback
+        self._time_limit = time_limit
+        self.__cache: TTLCache = TTLCache(maxsize=1, ttl=ttl)
+
+    def __enter__(self):
+        self._start = time.time()
+
+    def __exit__(self, *args):
+        end = time.time()
+        if self.__cache.get('value'):
+            return
+        duration = end - self._start
+
+        if duration < self._time_limit:
+            return
+        self._callback(duration, self._time_limit)
+
+        self.__cache['value'] = True

freqtrade/util/template_renderer.py

@@ -3,7 +3,10 @@ Jinja2 rendering utils, used to generate new strategy and configurations.
 """
 
 
-def render_template(templatefile: str, arguments: dict = {}) -> str:
+from typing import Dict, Optional
+
+
+def render_template(templatefile: str, arguments: Dict) -> str:
 
     from jinja2 import Environment, PackageLoader, select_autoescape
 
@@ -16,11 +19,13 @@ def render_template(templatefile: str, arguments: dict = {}) -> str:
 
 
 def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
-                                  arguments: dict = {}) -> str:
+                                  arguments: Optional[Dict] = None) -> str:
     """
     Use templatefile if possible, otherwise fall back to templatefallbackfile
     """
     from jinja2.exceptions import TemplateNotFound
+    if arguments is None:
+        arguments = {}
     try:
         return render_template(templatefile, arguments)
     except TemplateNotFound:

freqtrade/wallets.py

@@ -70,7 +70,7 @@ class Wallets:
     def _update_dry(self) -> None:
         """
         Update from database in dry-run mode
-        - Apply apply profits of closed trades on top of stake amount
+        - Apply profits of closed trades on top of stake amount
         - Subtract currently tied up stake_amount in open trades
         - update balances for currencies currently in trades
         """
@@ -306,7 +306,7 @@ class Wallets:
         :raise: DependencyException if the available stake amount is too low
         """
         stake_amount: float
-        # Ensure wallets are uptodate.
+        # Ensure wallets are up-to-date.
         if update:
             self.update()
         val_tied_up = Trade.total_open_trades_stakes()

freqtrade/worker.py

@@ -137,7 +137,7 @@ class Worker:
         Throttles the given callable that it
         takes at least `min_secs` to finish execution.
         :param func: Any callable
-        :param throttle_secs: throttling interation execution time limit in seconds
+        :param throttle_secs: throttling iteration execution time limit in seconds
         :param timeframe: ensure iteration is executed at the beginning of the next candle.
         :param timeframe_offset: offset in seconds to apply to the next candle time.
         :return: Any (result of execution of func)

ft_client/freqtrade_client/__init__.py

@@ -1,7 +1,7 @@
 from freqtrade_client.ft_rest_client import FtRestClient
 
 
-__version__ = '2024.3'
+__version__ = '2024.4'
 
 if 'dev' in __version__:
     from pathlib import Path

ft_client/freqtrade_client/ft_client.py

@@ -20,7 +20,10 @@ logger = logging.getLogger("ft_rest_client")
 
 
 def add_arguments(args: Any = None):
-    parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser(
+            prog="freqtrade-client",
+            description="Client for the freqtrade REST API",
+    )
     parser.add_argument("command",
                         help="Positional argument defining the command to execute.",
                         nargs="?"
@@ -67,7 +70,7 @@ def print_commands():
     # Print dynamic help for the different commands using the commands doc-strings
     client = FtRestClient(None)
     print("Possible commands:\n")
-    for x, y in inspect.getmembers(client):
+    for x, _ in inspect.getmembers(client):
         if not x.startswith('_'):
             doc = re.sub(':return:.*', '', getattr(client, x).__doc__, flags=re.MULTILINE).rstrip()
             print(f"{x}\n\t{doc}\n")