Merge pull request #8697 from freqtrade/new_release

New release 2023.5

.github/workflows/ci.yml

@@ -14,7 +14,7 @@ on:
     - cron:  '0 5 * * 4'
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
+  group: "${{ github.workflow }}-${{ github.ref }}-${{ github.event_name }}"
   cancel-in-progress: true
 permissions:
   repository-projects: read
@@ -57,7 +57,7 @@ jobs:
     - name: Installation - *nix
       if: runner.os == 'Linux'
       run: |
-        python -m pip install --upgrade pip==23.0.1 wheel==0.38.4
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -77,6 +77,17 @@ jobs:
         # Allow failure for coveralls
         coveralls || true
 
+    - name: Check for repository changes
+      run: |
+        if [ -n "$(git status --porcelain)" ]; then
+          echo "Repository is dirty, changes detected:"
+          git status
+          git diff
+          exit 1
+        else
+          echo "Repository is clean, no changes detected."
+        fi
+
     - name: Backtesting (multi)
       run: |
         cp config_examples/config_bittrex.example.json config.json
@@ -163,7 +174,7 @@ jobs:
         rm /usr/local/bin/python3.11-config || true
 
         brew install hdf5 c-blosc
-        python -m pip install --upgrade pip==23.0.1 wheel==0.38.4
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -174,6 +185,17 @@ jobs:
       run: |
         pytest --random-order
 
+    - name: Check for repository changes
+      run: |
+        if [ -n "$(git status --porcelain)" ]; then
+          echo "Repository is dirty, changes detected:"
+          git status
+          git diff
+          exit 1
+        else
+          echo "Repository is clean, no changes detected."
+        fi
+
     - name: Backtesting
       run: |
         cp config_examples/config_bittrex.example.json config.json
@@ -237,6 +259,18 @@ jobs:
       run: |
         pytest --random-order
 
+    - name: Check for repository changes
+      run: |
+        if (git status --porcelain) {
+          Write-Host "Repository is dirty, changes detected:"
+          git status
+          git diff
+          exit 1
+        }
+        else {
+          Write-Host "Repository is clean, no changes detected."
+        }
+
     - name: Backtesting
       run: |
         cp config_examples/config_bittrex.example.json config.json
@@ -302,7 +336,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: "3.10"
+        python-version: "3.11"
 
     - name: Documentation build
       run: |
@@ -352,7 +386,7 @@ jobs:
     - name: Installation - *nix
       if: runner.os == 'Linux'
       run: |
-        python -m pip install --upgrade pip==23.0.1 wheel==0.38.4
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -425,7 +459,7 @@ jobs:
         python setup.py sdist bdist_wheel
 
     - name: Publish to PyPI (Test)
-      uses: pypa/gh-action-pypi-publish@v1.8.5
+      uses: pypa/gh-action-pypi-publish@v1.8.6
       if: (github.event_name == 'release')
       with:
         user: __token__
@@ -433,7 +467,7 @@ jobs:
         repository_url: https://test.pypi.org/legacy/
 
     - name: Publish to PyPI
-      uses: pypa/gh-action-pypi-publish@v1.8.5
+      uses: pypa/gh-action-pypi-publish@v1.8.6
       if: (github.event_name == 'release')
       with:
         user: __token__

.pre-commit-config.yaml

@@ -15,10 +15,10 @@ repos:
         additional_dependencies:
           - types-cachetools==5.3.0.5
           - types-filelock==3.2.7
-          - types-requests==2.28.11.17
+          - types-requests==2.30.0.0
           - types-tabulate==0.9.0.2
-          - types-python-dateutil==2.8.19.12
-          - SQLAlchemy==2.0.10
+          - types-python-dateutil==2.8.19.13
+          - SQLAlchemy==2.0.15
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort
@@ -30,7 +30,7 @@ repos:
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.0.255'
+    rev: 'v0.0.263'
     hooks:
       - id: ruff
 

Dockerfile

@@ -25,7 +25,7 @@ FROM base as python-deps
 RUN  apt-get update \
   && apt-get -y install build-essential libssl-dev git libffi-dev libgfortran5 pkg-config cmake gcc \
   && apt-get clean \
-  && pip install --upgrade pip==23.0.1 wheel==0.38.4
+  && pip install --upgrade pip wheel
 
 # Install TA-lib
 COPY build_helpers/* /tmp/

build_helpers/install_windows.ps1

@@ -1,7 +1,7 @@
 # Downloads don't work automatically, since the URL is regenerated via javascript.
 # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
 
-python -m pip install --upgrade pip==23.0.1 wheel==0.38.4
+python -m pip install --upgrade pip wheel
 
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 

docker-compose.yml

@@ -6,6 +6,15 @@ services:
     # image: freqtradeorg/freqtrade:develop
     # Use plotting image
     # image: freqtradeorg/freqtrade:develop_plot
+    # # Enable GPU Image and GPU Resources (only relevant for freqAI)
+    # # Make sure to uncomment the whole deploy section
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: 1
+    #           capabilities: [gpu]
     # Build step - only needed when additional dependencies are needed
     # build:
     #   context: .
@@ -16,7 +25,7 @@ services:
       - "./user_data:/freqtrade/user_data"
     # Expose api on port 8080 (localhost only)
     # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
-    # before enabling this.
+    # for more information.
     ports:
       - "127.0.0.1:8080:8080"
     # Default command used when running `docker compose up`

docker/docker-compose-freqai.yml

@@ -0,0 +1,36 @@
+---
+version: '3'
+services:
+  freqtrade:
+    image: freqtradeorg/freqtrade:stable_freqaitorch
+    # # Enable GPU Image and GPU Resources
+    # # Make sure to uncomment the whole deploy section
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: 1
+    #           capabilities: [gpu]
+
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: unless-stopped
+    container_name: freqtrade
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
+    # for more information.
+    ports:
+      - "127.0.0.1:8080:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
+      --config /freqtrade/user_data/config.json
+      --freqai-model XGBoostClassifier
+      --strategy SampleStrategy

docs/advanced-backtesting.md

@@ -29,7 +29,7 @@ If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl`
 `user_data/backtest_results` folder.
 
 To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-analysis` command
-with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
+with `--analysis-groups` option provided with space-separated arguments:
 
 ``` bash
 freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
@@ -39,6 +39,7 @@ This command will read from the last backtesting results. The `--analysis-groups
 used to specify the various tabular outputs showing the profit fo each group or trade,
 ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
 
+* 0: overall winrate and profit summary by enter_tag
 * 1: profit summaries grouped by enter_tag
 * 2: profit summaries grouped by enter_tag and exit_tag
 * 3: profit summaries grouped by pair and enter_tag
@@ -115,3 +116,38 @@ For example, if your backtest timerange was `20220101-20221231` but you only wan
 ```bash
 freqtrade backtesting-analysis -c <config.json> --timerange 20220101-20220201
 ```
+
+### Printing out rejected signals
+
+Use the `--rejected-signals` option to print out rejected signals.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --rejected-signals
+```
+
+### Writing tables to CSV
+
+Some of the tabular outputs can become large, so printing them out to the terminal is not preferable.
+Use the `--analysis-to-csv` option to disable printing out of tables to standard out and write them to CSV files.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-to-csv
+```
+
+By default this will write one file per output table you specified in the `backtesting-analysis` command, e.g.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --rejected-signals --analysis-groups 0 1
+```
+
+This will write to `user_data/backtest_results`:
+
+* rejected_signals.csv
+* group_0.csv
+* group_1.csv
+
+To override where the files will be written, also specify the `--analysis-csv-path` option.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --analysis-csv-path another/data/path/
+```

docs/developer.md

@@ -327,18 +327,18 @@ To check how the new exchange behaves, you can use the following snippet:
 
 ``` python
 import ccxt
-from datetime import datetime
+from datetime import datetime, timezone
 from freqtrade.data.converter import ohlcv_to_dataframe
-ct = ccxt.binance()
+ct = ccxt.binance()  # Use the exchange you're testing
 timeframe = "1d"
-pair = "XLM/BTC"  # Make sure to use a pair that exists on that exchange!
+pair = "BTC/USDT"  # Make sure to use a pair that exists on that exchange!
 raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
 
 # convert to dataframe
 df1 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
 
 print(df1.tail(1))
-print(datetime.utcnow())
+print(datetime.now(timezone.utc))
 ```
 
 ``` output

docs/freqai-configuration.md

@@ -248,9 +248,11 @@ The easiest way to quickly run a pytorch model is with the following command (fo
 freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel PyTorchMLPRegressor --strategy-path freqtrade/templates 
 ```
 
-!!! note "Installation/docker"
+!!! Note "Installation/docker"
     The PyTorch module requires large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl or PyTorch (~700mb additional space required) [y/N]?".
     Users who prefer docker should ensure they use the docker image appended with `_freqaitorch`.
+    We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file.
+    This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
 
 ### Structure
 
@@ -395,3 +397,21 @@ Here we create a `PyTorchMLPRegressor` class that implements the `fit` method. T
         return dataframe
     ```
     To see a full example, you can refer to the [classifier test strategy class](https://github.com/freqtrade/freqtrade/blob/develop/tests/strategy/strats/freqai_test_classifier.py).
+
+
+#### Improving performance with `torch.compile()`
+
+Torch provides a `torch.compile()` method that can be used to improve performance for specific GPU hardware. More details can be found [here](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html). In brief, you simply wrap your `model` in `torch.compile()`:
+
+
+```python
+        model = PyTorchMLPModel(
+            input_dim=n_features,
+            output_dim=1,
+            **self.model_kwargs
+        )
+        model.to(self.device)
+        model = torch.compile(model)
+```
+
+Then proceed to use the model as normal. Keep in mind that doing this will remove eager execution, which means errors and tracebacks will not be informative.

docs/freqai-parameter-table.md

@@ -18,9 +18,10 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `purge_old_models` | Number of models to keep on disk (not relevant to backtesting). Default is 2, which means that dry/live runs will keep the latest 2 models on disk. Setting to 0 keeps all models. This parameter also accepts a boolean to maintain backwards compatibility. <br> **Datatype:** Integer. <br> Default: `2`.
 | `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
 | `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
-| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
+| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). Beware that this is currently a naive approach to incremental learning, and it has a high probability of overfitting/getting stuck in local minima while the market moves away from your model. We have the connections here primarily for experimental purposes and so that it is ready for more mature approaches to continual learning in chaotic systems like the crypto market. <br> **Datatype:** Boolean. <br> Default: `False`.
 | `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
 | `data_kitchen_thread_count` | <br> Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.). This has no impact on the number of threads used for training. If user does not set it (default), FreqAI will use max number of threads - 2 (leaving 1 physical core available for Freqtrade bot and FreqUI) <br> **Datatype:** Positive integer.
+| `activate_tensorboard` | <br> Indicate whether or not to activate tensorboard for the tensorboard enabled modules (currently Reinforcment Learning, XGBoost, Catboost, and PyTorch). Tensorboard needs Torch installed, which means you will need the torch/RL docker image or you need to answer "yes" to the install question about whether or not you wish to install Torch. <br> **Datatype:** Boolean. <br> Default: `True`.
 
 ### Feature parameters
 
@@ -114,5 +115,5 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 |------------|-------------|
 |  |  **Extraneous parameters**
 | `freqai.keras` | If the selected model makes use of Keras (typical for TensorFlow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
-| `freqai.conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
+| `freqai.conv_width` | The width of a neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
 | `freqai.reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.

docs/freqai-reinforcement-learning.md

@@ -135,92 +135,104 @@ Parameter details can be found [here](freqai-parameter-table.md), but in general
 
 ## Creating a custom reward function
 
-As you begin to modify the strategy and the prediction model, you will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, you set the `calculate_reward()` function inside the `MyRLEnv` class (see below). A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to demonstrate the necessary building blocks for creating rewards, but users are encouraged to create their own custom reinforcement learning model class (see below) and save it to `user_data/freqaimodels`. It is inside the `calculate_reward()` where creative theories about the market can be expressed. For example, you can reward your agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, you wish to reward the agent for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
+!!! danger "Not for production"
+    Warning!
+    The reward function provided with the Freqtrade source code is a showcase of functionality designed to show/test as many possible environment control features as possible. It is also designed to run quickly on small computers. This is a benchmark, it is *not* for live production. Please beware that you will need to create your own custom_reward() function or use a template built by other users outside of the Freqtrade source code.
+
+As you begin to modify the strategy and the prediction model, you will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, you set the `calculate_reward()` function inside the `MyRLEnv` class (see below). A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to demonstrate the necessary building blocks for creating rewards, but this is *not* designed for production. Users *must* create their own custom reinforcement learning model class or use a pre-built one from outside the Freqtrade source code and save it to `user_data/freqaimodels`. It is inside the `calculate_reward()` where creative theories about the market can be expressed. For example, you can reward your agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, you wish to reward the agent for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
+
+!!! note "Hint"
+    The best reward functions are ones that are continuously differentiable, and well scaled. In other words, adding a single large negative penalty to a rare event is not a good idea, and the neural net will not be able to learn that function. Instead, it is better to add a small negative penalty to a common event. This will help the agent learn faster. Not only this, but you can help improve the continuity of your rewards/penalties by having them scale with severity according to some linear/exponential functions. In other words, you'd slowly scale the penalty as the duration of the trade increases. This is better than a single large penalty occuring at a single point in time.
 
 ```python
-    from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
-    from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
 
 
-    class MyCoolRLModel(ReinforcementLearner):
+class MyCoolRLModel(ReinforcementLearner):
+    """
+    User created RL prediction model.
+
+    Save this file to `freqtrade/user_data/freqaimodels`
+
+    then use it with:
+
+    freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
+
+    Here the users can override any of the functions
+    available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
+    is where the user overrides `MyRLEnv` (see below), to define custom
+    `calculate_reward()` function, or to override any other parts of the environment.
+
+    This class also allows users to override any other part of the IFreqaiModel tree.
+    For example, the user can override `def fit()` or `def train()` or `def predict()`
+    to take fine-tuned control over these processes.
+
+    Another common override may be `def data_cleaning_predict()` where the user can
+    take fine-tuned control over the data handling pipeline.
+    """
+    class MyRLEnv(Base5ActionRLEnv):
         """
-        User created RL prediction model.
+        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        Users can override any functions from those parent classes. Here is an example
+        of a user customized `calculate_reward()` function.
 
-        Save this file to `freqtrade/user_data/freqaimodels`
-
-        then use it with:
-
-        freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
-
-        Here the users can override any of the functions
-        available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
-        is where the user overrides `MyRLEnv` (see below), to define custom
-        `calculate_reward()` function, or to override any other parts of the environment.
-
-        This class also allows users to override any other part of the IFreqaiModel tree.
-        For example, the user can override `def fit()` or `def train()` or `def predict()`
-        to take fine-tuned control over these processes.
-
-        Another common override may be `def data_cleaning_predict()` where the user can
-        take fine-tuned control over the data handling pipeline.
+        Warning!
+        This is function is a showcase of functionality designed to show as many possible
+        environment control features as possible. It is also designed to run quickly
+        on small computers. This is a benchmark, it is *not* for live production.
         """
-        class MyRLEnv(Base5ActionRLEnv):
-            """
-            User made custom environment. This class inherits from BaseEnvironment and gym.env.
-            Users can override any functions from those parent classes. Here is an example
-            of a user customized `calculate_reward()` function.
-            """
-            def calculate_reward(self, action: int) -> float:
-                # first, penalize if the action is not valid
-                if not self._is_valid(action):
-                    return -2
-                pnl = self.get_unrealized_profit()
+        def calculate_reward(self, action: int) -> float:
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+            pnl = self.get_unrealized_profit()
 
-                factor = 100
+            factor = 100
 
-                pair = self.pair.replace(':', '')
+            pair = self.pair.replace(':', '')
 
-                # you can use feature values from dataframe
-                # Assumes the shifted RSI indicator has been generated in the strategy.
-                rsi_now = self.raw_features[f"%-rsi-period_10_shift-1_{pair}_"
-                                f"{self.config['timeframe']}"].iloc[self._current_tick]
+            # you can use feature values from dataframe
+            # Assumes the shifted RSI indicator has been generated in the strategy.
+            rsi_now = self.raw_features[f"%-rsi-period_10_shift-1_{pair}_"
+                            f"{self.config['timeframe']}"].iloc[self._current_tick]
 
-                # reward agent for entering trades
-                if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
-                        and self._position == Positions.Neutral):
-                    if rsi_now < 40:
-                        factor = 40 / rsi_now
-                    else:
-                        factor = 1
-                    return 25 * factor
+            # reward agent for entering trades
+            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                    and self._position == Positions.Neutral):
+                if rsi_now < 40:
+                    factor = 40 / rsi_now
+                else:
+                    factor = 1
+                return 25 * factor
 
-                # discourage agent from not entering trades
-                if action == Actions.Neutral.value and self._position == Positions.Neutral:
-                    return -1
-                max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
-                trade_duration = self._current_tick - self._last_trade_tick
-                if trade_duration <= max_trade_duration:
-                    factor *= 1.5
-                elif trade_duration > max_trade_duration:
-                    factor *= 0.5
-                # discourage sitting in position
-                if self._position in (Positions.Short, Positions.Long) and \
-                action == Actions.Neutral.value:
-                    return -1 * trade_duration / max_trade_duration
-                # close long
-                if action == Actions.Long_exit.value and self._position == Positions.Long:
-                    if pnl > self.profit_aim * self.rr:
-                        factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
-                    return float(pnl * factor)
-                # close short
-                if action == Actions.Short_exit.value and self._position == Positions.Short:
-                    if pnl > self.profit_aim * self.rr:
-                        factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
-                    return float(pnl * factor)
-                return 0.
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
+            trade_duration = self._current_tick - self._last_trade_tick
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+            # discourage sitting in position
+            if self._position in (Positions.Short, Positions.Long) and \
+            action == Actions.Neutral.value:
+                return -1 * trade_duration / max_trade_duration
+            # close long
+            if action == Actions.Long_exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+            # close short
+            if action == Actions.Short_exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+            return 0.
 ```
 
-### Using Tensorboard
+## Using Tensorboard
 
 Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
 
@@ -233,32 +245,30 @@ where `unique-id` is the `identifier` set in the `freqai` configuration file. Th
 
 ![tensorboard](assets/tensorboard.jpg)
 
-
-### Custom logging
+## Custom logging
 
 FreqAI also provides a built in episodic summary logger called `self.tensorboard_log` for adding custom information to the Tensorboard log. By default, this function is already called once per step inside the environment to record the agent actions. All values accumulated for all steps in a single episode are reported at the conclusion of each episode, followed by a full reset of all metrics to 0 in preparation for the subsequent episode.
 
-
 `self.tensorboard_log` can also be used anywhere inside the environment, for example, it can be added to the `calculate_reward` function to collect more detailed information about how often various parts of the reward were called:
 
-```py
-        class MyRLEnv(Base5ActionRLEnv):
-            """
-            User made custom environment. This class inherits from BaseEnvironment and gym.env.
-            Users can override any functions from those parent classes. Here is an example
-            of a user customized `calculate_reward()` function.
-            """
-            def calculate_reward(self, action: int) -> float:
-                if not self._is_valid(action):
-                    self.tensorboard_log("invalid")
-                    return -2
+```python
+    class MyRLEnv(Base5ActionRLEnv):
+        """
+        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        Users can override any functions from those parent classes. Here is an example
+        of a user customized `calculate_reward()` function.
+        """
+        def calculate_reward(self, action: int) -> float:
+            if not self._is_valid(action):
+                self.tensorboard_log("invalid")
+                return -2
 
 ```
 
 !!! Note
     The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)`. In this case the metric values are not incremented.
 
-### Choosing a base environment
+## Choosing a base environment
 
 FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:
 

docs/freqai-running.md

@@ -131,6 +131,9 @@ You can choose to adopt a continual learning scheme by setting `"continual_learn
 ???+ danger "Continual learning enforces a constant parameter space"
     Since `continual_learning` means that the model parameter space *cannot* change between trainings, `principal_component_analysis` is automatically disabled when `continual_learning` is enabled. Hint: PCA changes the parameter space and the number of features, learn more about PCA [here](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis).
 
+???+ danger "Experimental functionality"
+    Beware that this is currently a naive approach to incremental learning, and it has a high probability of overfitting/getting stuck in local minima while the market moves away from your model. We have the mechanics available in FreqAI primarily for experimental purposes and so that it is ready for more mature approaches to continual learning in chaotic systems like the crypto market.
+
 ## Hyperopt
 
 You can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md):
@@ -158,7 +161,14 @@ This specific hyperopt would help you understand the appropriate `DI_values` for
 
 ## Using Tensorboard
 
-CatBoost models benefit from tracking training metrics via Tensorboard. You can take advantage of the FreqAI integration to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
+!!! note "Availability"
+    FreqAI includes tensorboard for a variety of models, including XGBoost, all PyTorch models, Reinforcement Learning, and Catboost. If you would like to see Tensorboard integrated into another model type, please open an issue on the [Freqtrade GitHub](https://github.com/freqtrade/freqtrade/issues)
+
+!!! danger "Requirements"
+    Tensorboard logging requires the FreqAI torch installation/docker image.
+
+
+The easiest way to use tensorboard is to ensure `freqai.activate_tensorboard` is set to `True` (default setting) in your configuration file, run FreqAI, then open a separate shell and run:
 
 ```bash
 cd freqtrade
@@ -168,3 +178,7 @@ tensorboard --logdir user_data/models/unique-id
 where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell if you wish to view the output in your browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
 
 ![tensorboard](assets/tensorboard.jpg)
+
+
+!!! note "Deactivate for improved performance"
+    Tensorboard logging can slow down training and should be deactivated for production use.

docs/freqai.md

@@ -32,7 +32,10 @@ The easiest way to quickly test FreqAI is to run it in dry mode with the followi
 freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
 ```
 
-You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading.
+You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading. 
+
+!!! danger "Not for production"
+    The example strategy provided with the Freqtrade source code is designed for showcasing/testing a wide variety of FreqAI features. It is also designed to run on small computers so that it can be used as a benchmark between developers and users. It is *not* designed to be run in production.
 
 An example strategy, prediction model, and config to use as a starting points can be found in
 `freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and
@@ -69,16 +72,15 @@ pip install -r requirements-freqai.txt
 ```
 
 !!! Note
-    Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since it does not provide wheels for this platform.
-
-!!! Note "python 3.11"
-    Some dependencies (Catboost, Torch) currently don't support python 3.11. Freqtrade therefore only supports python 3.10 for these models/dependencies.
-    Tests involving these dependencies are skipped on 3.11.
+    Catboost will not be installed on low-powered arm devices (raspberry), since it does not provide wheels for this platform.
 
 ### Usage with docker
 
 If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
 
+!!! note "docker-compose-freqai.yml"
+   We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file. This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
+
 ### FreqAI position in open-source machine learning landscape
 
 Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citzen scientists" to use their basic Python skills for data exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. 

docs/installation.md

@@ -30,12 +30,6 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
 !!! Warning "Up-to-date clock"
     The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
 
-!!! Error "Running setup.py install for gym did not run successfully."
-    If you get an error related with gym we suggest you to downgrade setuptools it to version 65.5.0 you can do it with the following command:
-    ```bash
-    pip install setuptools==65.5.0
-    ```
-
 ------
 
 ## Requirements
@@ -242,6 +236,7 @@ source .env/bin/activate
 
 ```bash
 python3 -m pip install --upgrade pip
+python3 -m pip install -r requirements.txt
 python3 -m pip install -e .
 ```
 

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
-mkdocs==1.4.2
-mkdocs-material==9.1.7
+mkdocs==1.4.3
+mkdocs-material==9.1.14
 mdx_truly_sane_lists==1.3
-pymdown-extensions==9.11
+pymdown-extensions==10.0.1
 jinja2==3.1.2

docs/rest-api.md

@@ -134,7 +134,9 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `reload_config` | Reloads the configuration file.
 | `trades` | List last trades. Limited to 500 trades per call.
 | `trade/<tradeid>` | Get specific trade.
-| `delete_trade <trade_id>` | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
+| `trade/<tradeid>` | DELETE - Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
+| `trade/<tradeid>/open-order` | DELETE - Cancel open order for this trade.
+| `trade/<tradeid>/reload` | GET - Reload a trade from the Exchange. Only works in live, and can potentially help recover a trade that was manually sold on the exchange.
 | `show_config` | Shows part of the current configuration with relevant settings to operation.
 | `logs` | Shows last log messages.
 | `status` | Lists all open trades.

docs/strategy-advanced.md

@@ -227,8 +227,8 @@ for val in self.buy_ema_short.range:
         f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
     }))
 
-# Append columns to existing dataframe
-merged_frame = pd.concat(frames, axis=1)
+# Combine all dataframes, and reassign the original dataframe column
+dataframe = pd.concat(frames, axis=1)
 ```
 
 Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.

docs/telegram-usage.md

@@ -187,11 +187,13 @@ official commands. You can ask at any moment for help with `/help`.
 | `/forcelong <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`force_entry_enable` must be set to True)
 | `/forceshort <pair> [rate]` | Instantly shorts the given pair. Rate is optional and only applies to limit orders. This will only work on non-spot markets. (`force_entry_enable` must be set to True)
 | `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
+| `/reload_trade <trade_id>` | Reload a trade from the Exchange. Only works in live, and can potentially help recover a trade that was manually sold on the exchange.
 | `/cancel_open_order <trade_id> | /coo <trade_id>` | Cancel an open order for a trade.
 | **Metrics** |
 | `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
 | `/performance` | Show performance of each finished trade grouped by pair
-| `/balance` | Show account balance per currency
+| `/balance` | Show bot managed balance per currency
+| `/balance full` | Show account balance per currency
 | `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
 | `/weekly <n>` | Shows profit or loss per week, over the last n weeks (n defaults to 8)
 | `/monthly <n>` | Shows profit or loss per month, over the last n months (n defaults to 6)
@@ -202,7 +204,6 @@ official commands. You can ask at any moment for help with `/help`.
 | `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
 | `/edge` | Show validated pairs by Edge if it is enabled.
 
-
 ## Telegram commands in action
 
 Below, example of Telegram message you will receive for each command.

docs/utils.md

@@ -723,6 +723,9 @@ usage: freqtrade backtesting-analysis [-h] [-v] [--logfile FILE] [-V]
                                       [--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
                                       [--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
                                       [--timerange YYYYMMDD-[YYYYMMDD]]
+                                      [--rejected]
+                                      [--analysis-to-csv]
+                                      [--analysis-csv-path PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -736,19 +739,27 @@ optional arguments:
                         pair and enter_tag, 4: by pair, enter_ and exit_tag
                         (this can get quite large)
   --enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]
-                        Comma separated list of entry signals to analyse.
-                        Default: all. e.g. 'entry_tag_a,entry_tag_b'
+                        Space separated list of entry signals to analyse.
+                        Default: all. e.g. 'entry_tag_a entry_tag_b'
   --exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]
-                        Comma separated list of exit signals to analyse.
+                        Space separated list of exit signals to analyse.
                         Default: all. e.g.
-                        'exit_tag_a,roi,stop_loss,trailing_stop_loss'
+                        'exit_tag_a roi stop_loss trailing_stop_loss'
   --indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
-                        Comma separated list of indicators to analyse. e.g.
-                        'close,rsi,bb_lowerband,profit_abs'
+                        Space separated list of indicators to analyse. e.g.
+                        'close rsi bb_lowerband profit_abs'
   --timerange YYYYMMDD-[YYYYMMDD]
                         Timerange to filter trades for analysis, 
                         start inclusive, end exclusive. e.g.
                         20220101-20220201
+  --rejected
+                        Print out rejected trades table
+  --analysis-to-csv
+                        Write out tables to individual CSVs, by default to 
+                        'user_data/backtest_results' unless '--analysis-csv-path' is given.
+  --analysis-csv-path [PATH]
+                        Optional path where individual CSVs will be written. If not used,
+                        CSVs will be written to 'user_data/backtest_results'.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2023.4'
+__version__ = '2023.5'
 
 if 'dev' in __version__:
     from pathlib import Path

freqtrade/commands/arguments.py

@@ -106,7 +106,8 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
                       "disableparamexport", "backtest_breakdown"]
 
 ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
-                              "exit_reason_list", "indicator_list", "timerange"]
+                              "exit_reason_list", "indicator_list", "timerange",
+                              "analysis_rejected", "analysis_to_csv", "analysis_csv_path"]
 
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies", "list-freqaimodels",

freqtrade/commands/cli_options.py

@@ -636,30 +636,45 @@ AVAILABLE_CLI_OPTIONS = {
               "4: by pair, enter_ and exit_tag (this can get quite large), "
               "5: by exit_tag"),
         nargs='+',
-        default=['0', '1', '2'],
+        default=[],
         choices=['0', '1', '2', '3', '4', '5'],
     ),
     "enter_reason_list": Arg(
         "--enter-reason-list",
-        help=("Comma separated list of entry signals to analyse. Default: all. "
-              "e.g. 'entry_tag_a,entry_tag_b'"),
+        help=("Space separated list of entry signals to analyse. Default: all. "
+              "e.g. 'entry_tag_a entry_tag_b'"),
         nargs='+',
         default=['all'],
     ),
     "exit_reason_list": Arg(
         "--exit-reason-list",
-        help=("Comma separated list of exit signals to analyse. Default: all. "
-              "e.g. 'exit_tag_a,roi,stop_loss,trailing_stop_loss'"),
+        help=("Space separated list of exit signals to analyse. Default: all. "
+              "e.g. 'exit_tag_a roi stop_loss trailing_stop_loss'"),
         nargs='+',
         default=['all'],
     ),
     "indicator_list": Arg(
         "--indicator-list",
-        help=("Comma separated list of indicators to analyse. "
-              "e.g. 'close,rsi,bb_lowerband,profit_abs'"),
+        help=("Space separated list of indicators to analyse. "
+              "e.g. 'close rsi bb_lowerband profit_abs'"),
         nargs='+',
         default=[],
     ),
+    "analysis_rejected": Arg(
+        '--rejected-signals',
+        help='Analyse rejected signals',
+        action='store_true',
+    ),
+    "analysis_to_csv": Arg(
+        '--analysis-to-csv',
+        help='Save selected analysis tables to individual CSVs',
+        action='store_true',
+    ),
+    "analysis_csv_path": Arg(
+        '--analysis-csv-path',
+        help=("Specify a path to save the analysis CSVs "
+              "if --analysis-to-csv is enabled. Default: user_data/basktesting_results/"),
+    ),
     "freqaimodel": Arg(
         '--freqaimodel',
         help='Specify a custom freqaimodels.',

freqtrade/commands/data_commands.py

@@ -52,7 +52,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
     pairs_not_available: List[str] = []
 
     # Init exchange
-    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
     markets = [p for p, m in exchange.markets.items() if market_is_active(m)
                or config.get('include_inactive')]
 
@@ -125,7 +125,7 @@ def start_convert_trades(args: Dict[str, Any]) -> None:
             "Please check the documentation on how to configure this.")
 
     # Init exchange
-    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
     # Manual validations of relevant settings
     if not config['exchange'].get('skip_pair_validation', False):
         exchange.validate_pairs(config['pairs'])

freqtrade/commands/list_commands.py

@@ -114,7 +114,7 @@ def start_list_timeframes(args: Dict[str, Any]) -> None:
     config['timeframe'] = None
 
     # Init exchange
-    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
 
     if args['print_one_column']:
         print('\n'.join(exchange.timeframes))
@@ -133,7 +133,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
     config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
 
     # Init exchange
-    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
 
     # By default only active pairs/markets are to be shown
     active_only = not args.get('list_pairs_all', False)

freqtrade/commands/pairlist_commands.py

@@ -18,7 +18,7 @@ def start_test_pairlist(args: Dict[str, Any]) -> None:
     from freqtrade.plugins.pairlistmanager import PairListManager
     config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
 
-    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config, validate=False)
 
     quote_currencies = args.get('quote_currencies')
     if not quote_currencies:

freqtrade/configuration/config_validation.py

@@ -174,7 +174,7 @@ def _validate_whitelist(conf: Dict[str, Any]) -> None:
         return
 
     for pl in conf.get('pairlists', [{'method': 'StaticPairList'}]):
-        if (pl.get('method') == 'StaticPairList'
+        if (isinstance(pl, dict) and pl.get('method') == 'StaticPairList'
                 and not conf.get('exchange', {}).get('pair_whitelist')):
             raise OperationalException("StaticPairList requires pair_whitelist to be set.")
 

freqtrade/configuration/configuration.py

@@ -465,6 +465,15 @@ class Configuration:
         self._args_to_config(config, argname='timerange',
                              logstring='Filter trades by timerange: {}')
 
+        self._args_to_config(config, argname='analysis_rejected',
+                             logstring='Analyse rejected signals: {}')
+
+        self._args_to_config(config, argname='analysis_to_csv',
+                             logstring='Store analysis tables to CSV: {}')
+
+        self._args_to_config(config, argname='analysis_csv_path',
+                             logstring='Path to store analysis CSVs: {}')
+
     def _process_runmode(self, config: Config) -> None:
 
         self._args_to_config(config, argname='dry_run',

freqtrade/configuration/timerange.py

@@ -6,8 +6,6 @@ import re
 from datetime import datetime, timezone
 from typing import Optional
 
-import arrow
-
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.exceptions import OperationalException
 
@@ -139,7 +137,8 @@ class TimeRange:
                 if stype[0]:
                     starts = rvals[index]
                     if stype[0] == 'date' and len(starts) == 8:
-                        start = arrow.get(starts, 'YYYYMMDD').int_timestamp
+                        start = int(datetime.strptime(starts, '%Y%m%d').replace(
+                            tzinfo=timezone.utc).timestamp())
                     elif len(starts) == 13:
                         start = int(starts) // 1000
                     else:
@@ -148,7 +147,8 @@ class TimeRange:
                 if stype[1]:
                     stops = rvals[index]
                     if stype[1] == 'date' and len(stops) == 8:
-                        stop = arrow.get(stops, 'YYYYMMDD').int_timestamp
+                        stop = int(datetime.strptime(stops, '%Y%m%d').replace(
+                            tzinfo=timezone.utc).timestamp())
                     elif len(stops) == 13:
                         stop = int(stops) // 1000
                     else:

freqtrade/constants.py

@@ -690,4 +690,6 @@ BidAsk = Literal['bid', 'ask']
 OBLiteral = Literal['asks', 'bids']
 
 Config = Dict[str, Any]
+# Exchange part of the configuration.
+ExchangeConfig = Dict[str, Any]
 IntOrInf = float

freqtrade/data/entryexitanalysis.py

@@ -1,5 +1,6 @@
 import logging
 from pathlib import Path
+from typing import List
 
 import joblib
 import pandas as pd
@@ -15,22 +16,31 @@ from freqtrade.exceptions import OperationalException
 logger = logging.getLogger(__name__)
 
 
-def _load_signal_candles(backtest_dir: Path):
+def _load_backtest_analysis_data(backtest_dir: Path, name: str):
     if backtest_dir.is_dir():
         scpf = Path(backtest_dir,
-                    Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
+                    Path(get_latest_backtest_filename(backtest_dir)).stem + "_" + name + ".pkl"
                     )
     else:
-        scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
+        scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")
 
     try:
         with scpf.open("rb") as scp:
-            signal_candles = joblib.load(scp)
-            logger.info(f"Loaded signal candles: {str(scpf)}")
+            loaded_data = joblib.load(scp)
+            logger.info(f"Loaded {name} candles: {str(scpf)}")
     except Exception as e:
-        logger.error("Cannot load signal candles from pickled results: ", e)
+        logger.error(f"Cannot load {name} data from pickled results: ", e)
+        return None
 
-    return signal_candles
+    return loaded_data
+
+
+def _load_rejected_signals(backtest_dir: Path):
+    return _load_backtest_analysis_data(backtest_dir, "rejected")
+
+
+def _load_signal_candles(backtest_dir: Path):
+    return _load_backtest_analysis_data(backtest_dir, "signals")
 
 
 def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
@@ -43,9 +53,7 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
         for pair in pairlist:
             if pair in signal_candles[strategy_name]:
                 analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
-                                                              pair,
-                                                              trades,
-                                                              signal_candles[strategy_name][pair])
+                    pair, trades, signal_candles[strategy_name][pair])
     except Exception as e:
         print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
 
@@ -85,7 +93,7 @@ def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles:
         return pd.DataFrame()
 
 
-def _do_group_table_output(bigdf, glist):
+def _do_group_table_output(bigdf, glist, csv_path: Path, to_csv=False, ):
     for g in glist:
         # 0: summary wins/losses grouped by enter tag
         if g == "0":
@@ -116,7 +124,8 @@ def _do_group_table_output(bigdf, glist):
 
             sortcols = ['total_num_buys']
 
-            _print_table(new, sortcols, show_index=True)
+            _print_table(new, sortcols, show_index=True, name="Group 0:",
+                         to_csv=to_csv, csv_path=csv_path)
 
         else:
             agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
@@ -154,11 +163,24 @@ def _do_group_table_output(bigdf, glist):
                 new['mean_profit_pct'] = new['mean_profit_pct'] * 100
                 new['total_profit_pct'] = new['total_profit_pct'] * 100
 
-                _print_table(new, sortcols)
+                _print_table(new, sortcols, name=f"Group {g}:",
+                             to_csv=to_csv, csv_path=csv_path)
             else:
                 logger.warning("Invalid group mask specified.")
 
 
+def _do_rejected_signals_output(rejected_signals_df: pd.DataFrame,
+                                to_csv: bool = False, csv_path=None) -> None:
+    cols = ['pair', 'date', 'enter_tag']
+    sortcols = ['date', 'pair', 'enter_tag']
+    _print_table(rejected_signals_df[cols],
+                 sortcols,
+                 show_index=False,
+                 name="Rejected Signals:",
+                 to_csv=to_csv,
+                 csv_path=csv_path)
+
+
 def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'):
     if timerange:
         if timerange.starttype == 'date':
@@ -192,38 +214,64 @@ def prepare_results(analysed_trades, stratname,
     return res_df
 
 
-def print_results(res_df, analysis_groups, indicator_list):
+def print_results(res_df: pd.DataFrame, analysis_groups: List[str], indicator_list: List[str],
+                  csv_path: Path, rejected_signals=None, to_csv=False):
     if res_df.shape[0] > 0:
         if analysis_groups:
-            _do_group_table_output(res_df, analysis_groups)
+            _do_group_table_output(res_df, analysis_groups, to_csv=to_csv, csv_path=csv_path)
 
+        if rejected_signals is not None:
+            if rejected_signals.empty:
+                print("There were no rejected signals.")
+            else:
+                _do_rejected_signals_output(rejected_signals, to_csv=to_csv, csv_path=csv_path)
+
+        # NB this can be large for big dataframes!
         if "all" in indicator_list:
-            print(res_df)
-        elif indicator_list is not None:
+            _print_table(res_df,
+                         show_index=False,
+                         name="Indicators:",
+                         to_csv=to_csv,
+                         csv_path=csv_path)
+        elif indicator_list is not None and indicator_list:
             available_inds = []
             for ind in indicator_list:
                 if ind in res_df:
                     available_inds.append(ind)
             ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
-            _print_table(res_df[ilist], sortcols=['exit_reason'], show_index=False)
+            _print_table(res_df[ilist],
+                         sortcols=['exit_reason'],
+                         show_index=False,
+                         name="Indicators:",
+                         to_csv=to_csv,
+                         csv_path=csv_path)
     else:
         print("\\No trades to show")
 
 
-def _print_table(df, sortcols=None, show_index=False):
+def _print_table(df: pd.DataFrame, sortcols=None, *, show_index=False, name=None,
+                 to_csv=False, csv_path: Path):
     if (sortcols is not None):
         data = df.sort_values(sortcols)
     else:
         data = df
 
-    print(
-        tabulate(
-            data,
-            headers='keys',
-            tablefmt='psql',
-            showindex=show_index
+    if to_csv:
+        safe_name = Path(csv_path, name.lower().replace(" ", "_").replace(":", "") + ".csv")
+        data.to_csv(safe_name)
+        print(f"Saved {name} to {safe_name}")
+    else:
+        if name is not None:
+            print(name)
+
+        print(
+            tabulate(
+                data,
+                headers='keys',
+                tablefmt='psql',
+                showindex=show_index
+            )
         )
-    )
 
 
 def process_entry_exit_reasons(config: Config):
@@ -232,6 +280,11 @@ def process_entry_exit_reasons(config: Config):
         enter_reason_list = config.get('enter_reason_list', ["all"])
         exit_reason_list = config.get('exit_reason_list', ["all"])
         indicator_list = config.get('indicator_list', [])
+        do_rejected = config.get('analysis_rejected', False)
+        to_csv = config.get('analysis_to_csv', False)
+        csv_path = Path(config.get('analysis_csv_path', config['exportfilename']))
+        if to_csv and not csv_path.is_dir():
+            raise OperationalException(f"Specified directory {csv_path} does not exist.")
 
         timerange = TimeRange.parse_timerange(None if config.get(
             'timerange') is None else str(config.get('timerange')))
@@ -241,8 +294,16 @@ def process_entry_exit_reasons(config: Config):
         for strategy_name, results in backtest_stats['strategy'].items():
             trades = load_backtest_data(config['exportfilename'], strategy_name)
 
-            if not trades.empty:
+            if trades is not None and not trades.empty:
                 signal_candles = _load_signal_candles(config['exportfilename'])
+
+                rej_df = None
+                if do_rejected:
+                    rejected_signals_dict = _load_rejected_signals(config['exportfilename'])
+                    rej_df = prepare_results(rejected_signals_dict, strategy_name,
+                                             enter_reason_list, exit_reason_list,
+                                             timerange=timerange)
+
                 analysed_trades_dict = _process_candles_and_indicators(
                                         config['exchange']['pair_whitelist'], strategy_name,
                                         trades, signal_candles)
@@ -253,7 +314,10 @@ def process_entry_exit_reasons(config: Config):
 
                 print_results(res_df,
                               analysis_groups,
-                              indicator_list)
+                              indicator_list,
+                              rejected_signals=rej_df,
+                              to_csv=to_csv,
+                              csv_path=csv_path)
 
     except ValueError as e:
         raise OperationalException(e) from e

freqtrade/data/history/history_utils.py

@@ -1,10 +1,9 @@
 import logging
 import operator
-from datetime import datetime
+from datetime import datetime, timedelta
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
-import arrow
 from pandas import DataFrame, concat
 
 from freqtrade.configuration import TimeRange
@@ -236,8 +235,8 @@ def _download_pair_history(pair: str, *,
         new_data = exchange.get_historic_ohlcv(pair=pair,
                                                timeframe=timeframe,
                                                since_ms=since_ms if since_ms else
-                                               arrow.utcnow().shift(
-                                                   days=-new_pairs_days).int_timestamp * 1000,
+                                               int((datetime.now() - timedelta(days=new_pairs_days)
+                                                    ).timestamp()) * 1000,
                                                is_new_pair=data.empty,
                                                candle_type=candle_type,
                                                until_ms=until_ms if until_ms else None
@@ -349,7 +348,7 @@ def _download_trades_history(exchange: Exchange,
             trades = []
 
         if not since:
-            since = arrow.utcnow().shift(days=-new_pairs_days).int_timestamp * 1000
+            since = int((datetime.now() - timedelta(days=-new_pairs_days)).timestamp()) * 1000
 
         from_id = trades[-1][1] if trades else None
         if trades and since < trades[-1][0]:

freqtrade/edge/edge_positioning.py

@@ -3,9 +3,9 @@
 import logging
 from collections import defaultdict
 from copy import deepcopy
+from datetime import timedelta
 from typing import Any, Dict, List, NamedTuple
 
-import arrow
 import numpy as np
 import utils_find_1st as utf1st
 from pandas import DataFrame
@@ -18,6 +18,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.strategy.interface import IStrategy
+from freqtrade.util import dt_now
 
 
 logger = logging.getLogger(__name__)
@@ -79,8 +80,8 @@ class Edge:
             self._stoploss_range_step
         )
 
-        self._timerange: TimeRange = TimeRange.parse_timerange("%s-" % arrow.now().shift(
-            days=-1 * self._since_number_of_days).format('YYYYMMDD'))
+        self._timerange: TimeRange = TimeRange.parse_timerange(
+            f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-")
         if config.get('fee'):
             self.fee = config['fee']
         else:
@@ -97,7 +98,7 @@ class Edge:
         heartbeat = self.edge_config.get('process_throttle_secs')
 
         if (self._last_updated > 0) and (
-                self._last_updated + heartbeat > arrow.utcnow().int_timestamp):
+                self._last_updated + heartbeat > int(dt_now().timestamp())):
             return False
 
         data: Dict[str, Any] = {}
@@ -189,7 +190,7 @@ class Edge:
         # Fill missing, calculable columns, profit, duration , abs etc.
         trades_df = self._fill_calculable_fields(DataFrame(trades))
         self._cached_pairs = self._process_expectancy(trades_df)
-        self._last_updated = arrow.utcnow().int_timestamp
+        self._last_updated = int(dt_now().timestamp())
 
         return True
 

freqtrade/enums/exittype.py

@@ -15,6 +15,7 @@ class ExitType(Enum):
     EMERGENCY_EXIT = "emergency_exit"
     CUSTOM_EXIT = "custom_exit"
     PARTIAL_EXIT = "partial_exit"
+    SOLD_ON_EXCHANGE = "sold_on_exchange"
     NONE = ""
 
     def __str__(self):

freqtrade/exchange/__init__.py

@@ -1,6 +1,6 @@
 # flake8: noqa: F401
 # isort: off
-from freqtrade.exchange.common import remove_credentials, MAP_EXCHANGE_CHILDCLASS
+from freqtrade.exchange.common import remove_exchange_credentials, MAP_EXCHANGE_CHILDCLASS
 from freqtrade.exchange.exchange import Exchange
 # isort: on
 from freqtrade.exchange.binance import Binance

freqtrade/exchange/binance.py

@@ -1,10 +1,9 @@
 """ Binance exchange subclass """
 import logging
-from datetime import datetime
+from datetime import datetime, timezone
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
-import arrow
 import ccxt
 
 from freqtrade.enums import CandleType, MarginMode, PriceType, TradingMode
@@ -66,7 +65,7 @@ class Binance(Exchange):
         """
         try:
             if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
-                position_side = self._api.fapiPrivateGetPositionsideDual()
+                position_side = self._api.fapiPrivateGetPositionSideDual()
                 self._log_exchange_response('position_side_setting', position_side)
                 assets_margin = self._api.fapiPrivateGetMultiAssetsMargin()
                 self._log_exchange_response('multi_asset_margin', assets_margin)
@@ -105,8 +104,9 @@ class Binance(Exchange):
             if x and x[3] and x[3][0] and x[3][0][0] > since_ms:
                 # Set starting date to first available candle.
                 since_ms = x[3][0][0]
-                logger.info(f"Candle-data for {pair} available starting with "
-                            f"{arrow.get(since_ms // 1000).isoformat()}.")
+                logger.info(
+                    f"Candle-data for {pair} available starting with "
+                    f"{datetime.fromtimestamp(since_ms // 1000, tz=timezone.utc).isoformat()}.")
 
         return await super()._async_get_historic_ohlcv(
             pair=pair,

freqtrade/exchange/common.py

@@ -4,6 +4,7 @@ import time
 from functools import wraps
 from typing import Any, Callable, Optional, TypeVar, cast, overload
 
+from freqtrade.constants import ExchangeConfig
 from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
 from freqtrade.mixins import LoggingMixin
 
@@ -84,20 +85,22 @@ EXCHANGE_HAS_OPTIONAL = [
     # 'fetchPositions',  # Futures trading
     # 'fetchLeverageTiers',  # Futures initialization
     # 'fetchMarketLeverageTiers',  # Futures initialization
+    # 'fetchOpenOrders', 'fetchClosedOrders',  # 'fetchOrders',  # Refinding balance...
 ]
 
 
-def remove_credentials(config) -> None:
+def remove_exchange_credentials(exchange_config: ExchangeConfig, dry_run: bool) -> None:
     """
     Removes exchange keys from the configuration and specifies dry-run
     Used for backtesting / hyperopt / edge and utils.
     Modifies the input dict!
     """
-    if config.get('dry_run', False):
-        config['exchange']['key'] = ''
-        config['exchange']['secret'] = ''
-        config['exchange']['password'] = ''
-        config['exchange']['uid'] = ''
+    if dry_run:
+        exchange_config['key'] = ''
+        exchange_config['apiKey'] = ''
+        exchange_config['secret'] = ''
+        exchange_config['password'] = ''
+        exchange_config['uid'] = ''
 
 
 def calculate_backoff(retrycount, max_retries):

freqtrade/exchange/exchange.py

@@ -11,7 +11,6 @@ from math import floor
 from threading import Lock
 from typing import Any, Coroutine, Dict, List, Literal, Optional, Tuple, Union
 
-import arrow
 import ccxt
 import ccxt.async_support as ccxt_async
 from cachetools import TTLCache
@@ -20,16 +19,16 @@ from dateutil import parser
 from pandas import DataFrame, concat
 
 from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BidAsk,
-                                 BuySell, Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
-                                 OBLiteral, PairWithTimeframe)
+                                 BuySell, Config, EntryExit, ExchangeConfig,
+                                 ListPairsWithTimeframes, MakerTaker, OBLiteral, PairWithTimeframe)
 from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
 from freqtrade.enums.pricetype import PriceType
 from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, OperationalException, PricingError,
                                   RetryableOrderError, TemporaryError)
-from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, remove_credentials, retrier,
-                                       retrier_async)
+from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, remove_exchange_credentials,
+                                       retrier, retrier_async)
 from freqtrade.exchange.exchange_utils import (ROUND, ROUND_DOWN, ROUND_UP, CcxtModuleType,
                                                amount_to_contract_precision, amount_to_contracts,
                                                amount_to_precision, contracts_to_amount,
@@ -42,6 +41,8 @@ from freqtrade.exchange.types import OHLCVResponse, OrderBook, Ticker, Tickers
 from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
                             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
 
 
 logger = logging.getLogger(__name__)
@@ -92,8 +93,8 @@ class Exchange:
         # TradingMode.SPOT always supported and not required in this list
     ]
 
-    def __init__(self, config: Config, validate: bool = True,
-                 load_leverage_tiers: bool = False) -> None:
+    def __init__(self, config: Config, *, exchange_config: Optional[ExchangeConfig] = None,
+                 validate: bool = True, load_leverage_tiers: bool = False) -> None:
         """
         Initializes this module with the given config,
         it does basic validation whether the specified exchange and pairs are valid.
@@ -107,8 +108,7 @@ class Exchange:
         # Lock event loop. This is necessary to avoid race-conditions when using force* commands
         # Due to funding fee fetching.
         self._loop_lock = Lock()
-        self.loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(self.loop)
+        self.loop = self._init_async_loop()
         self._config: Config = {}
 
         self._config.update(config)
@@ -132,13 +132,13 @@ class Exchange:
 
         # Holds all open sell orders for dry_run
         self._dry_run_open_orders: Dict[str, Any] = {}
-        remove_credentials(config)
 
         if config['dry_run']:
             logger.info('Instance is running with dry_run enabled')
         logger.info(f"Using CCXT {ccxt.__version__}")
-        exchange_config = config['exchange']
-        self.log_responses = exchange_config.get('log_responses', False)
+        exchange_conf: Dict[str, Any] = exchange_config if exchange_config else config['exchange']
+        remove_exchange_credentials(exchange_conf, config.get('dry_run', False))
+        self.log_responses = exchange_conf.get('log_responses', False)
 
         # Leverage properties
         self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
@@ -153,8 +153,8 @@ class Exchange:
         self._ft_has = deep_merge_dicts(self._ft_has, deepcopy(self._ft_has_default))
         if self.trading_mode == TradingMode.FUTURES:
             self._ft_has = deep_merge_dicts(self._ft_has_futures, self._ft_has)
-        if exchange_config.get('_ft_has_params'):
-            self._ft_has = deep_merge_dicts(exchange_config.get('_ft_has_params'),
+        if exchange_conf.get('_ft_has_params'):
+            self._ft_has = deep_merge_dicts(exchange_conf.get('_ft_has_params'),
                                             self._ft_has)
             logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
 
@@ -166,18 +166,18 @@ class Exchange:
 
         # Initialize ccxt objects
         ccxt_config = self._ccxt_config
-        ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}), ccxt_config)
-        ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_sync_config', {}), ccxt_config)
+        ccxt_config = deep_merge_dicts(exchange_conf.get('ccxt_config', {}), ccxt_config)
+        ccxt_config = deep_merge_dicts(exchange_conf.get('ccxt_sync_config', {}), ccxt_config)
 
-        self._api = self._init_ccxt(exchange_config, ccxt_kwargs=ccxt_config)
+        self._api = self._init_ccxt(exchange_conf, ccxt_kwargs=ccxt_config)
 
         ccxt_async_config = self._ccxt_config
-        ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
+        ccxt_async_config = deep_merge_dicts(exchange_conf.get('ccxt_config', {}),
                                              ccxt_async_config)
-        ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_async_config', {}),
+        ccxt_async_config = deep_merge_dicts(exchange_conf.get('ccxt_async_config', {}),
                                              ccxt_async_config)
         self._api_async = self._init_ccxt(
-            exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
+            exchange_conf, ccxt_async, ccxt_kwargs=ccxt_async_config)
 
         logger.info(f'Using Exchange "{self.name}"')
         self.required_candle_call_count = 1
@@ -190,7 +190,7 @@ class Exchange:
                 self._startup_candle_count, config.get('timeframe', ''))
 
         # Converts the interval provided in minutes in config to seconds
-        self.markets_refresh_interval: int = exchange_config.get(
+        self.markets_refresh_interval: int = exchange_conf.get(
             "markets_refresh_interval", 60) * 60
 
         if self.trading_mode != TradingMode.SPOT and load_leverage_tiers:
@@ -212,6 +212,11 @@ class Exchange:
         if self.loop and not self.loop.is_closed():
             self.loop.close()
 
+    def _init_async_loop(self) -> asyncio.AbstractEventLoop:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        return loop
+
     def validate_config(self, config):
         # Check if timeframe is available
         self.validate_timeframes(config.get('timeframe'))
@@ -486,7 +491,7 @@ class Exchange:
         try:
             self._markets = self._api.load_markets(params={})
             self._load_async_markets()
-            self._last_markets_refresh = arrow.utcnow().int_timestamp
+            self._last_markets_refresh = dt_ts()
             if self._ft_has['needs_trading_fees']:
                 self._trading_fees = self.fetch_trading_fees()
 
@@ -497,15 +502,14 @@ class Exchange:
         """Reload markets both sync and async if refresh interval has passed """
         # Check whether markets have to be reloaded
         if (self._last_markets_refresh > 0) and (
-                self._last_markets_refresh + self.markets_refresh_interval
-                > arrow.utcnow().int_timestamp):
+                self._last_markets_refresh + self.markets_refresh_interval > dt_ts()):
             return None
         logger.debug("Performing scheduled market reload..")
         try:
             self._markets = self._api.load_markets(reload=True, params={})
             # Also reload async markets to avoid issues with newly listed pairs
             self._load_async_markets(reload=True)
-            self._last_markets_refresh = arrow.utcnow().int_timestamp
+            self._last_markets_refresh = dt_ts()
             self.fill_leverage_tiers()
         except ccxt.BaseError:
             logger.exception("Could not reload markets.")
@@ -839,7 +843,8 @@ class Exchange:
     def create_dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
                              rate: float, leverage: float, params: Dict = {},
                              stop_loss: bool = False) -> Dict[str, Any]:
-        order_id = f'dry_run_{side}_{datetime.now().timestamp()}'
+        now = dt_now()
+        order_id = f'dry_run_{side}_{now.timestamp()}'
         # Rounding here must respect to contract sizes
         _amount = self._contracts_to_amount(
             pair, self.amount_to_precision(pair, self._amount_to_contracts(pair, amount)))
@@ -854,8 +859,8 @@ class Exchange:
             'side': side,
             'filled': 0,
             'remaining': _amount,
-            'datetime': arrow.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
-            'timestamp': arrow.utcnow().int_timestamp * 1000,
+            'datetime': now.strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
+            'timestamp': dt_ts(now),
             'status': "open",
             'fee': None,
             'info': {},
@@ -863,7 +868,7 @@ class Exchange:
         }
         if stop_loss:
             dry_order["info"] = {"stopPrice": dry_order["price"]}
-            dry_order["stopPrice"] = dry_order["price"]
+            dry_order[self._ft_has['stop_price_param']] = dry_order["price"]
             # Workaround to avoid filling stoploss orders immediately
             dry_order["ft_order_type"] = "stoploss"
         orderbook: Optional[OrderBook] = None
@@ -1015,7 +1020,7 @@ class Exchange:
             from freqtrade.persistence import Order
             order = Order.order_by_id(order_id)
             if order:
-                ccxt_order = order.to_ccxt_object()
+                ccxt_order = order.to_ccxt_object(self._ft_has['stop_price_param'])
                 self._dry_run_open_orders[order_id] = ccxt_order
                 return ccxt_order
             # Gracefully handle errors with dry-run orders.
@@ -1428,6 +1433,47 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
+    @retrier(retries=0)
+    def fetch_orders(self, pair: str, since: datetime) -> List[Dict]:
+        """
+        Fetch all orders for a pair "since"
+        :param pair: Pair for the query
+        :param since: Starting time for the query
+        """
+        if self._config['dry_run']:
+            return []
+
+        def fetch_orders_emulate() -> List[Dict]:
+            orders = []
+            if self.exchange_has('fetchClosedOrders'):
+                orders = self._api.fetch_closed_orders(pair, since=since_ms)
+                if self.exchange_has('fetchOpenOrders'):
+                    orders_open = self._api.fetch_open_orders(pair, since=since_ms)
+                    orders.extend(orders_open)
+            return orders
+
+        try:
+            since_ms = int((since.timestamp() - 10) * 1000)
+            if self.exchange_has('fetchOrders'):
+                try:
+                    orders: List[Dict] = self._api.fetch_orders(pair, since=since_ms)
+                except ccxt.NotSupported:
+                    # Some exchanges don't support fetchOrders
+                    # attempt to fetch open and closed orders separately
+                    orders = fetch_orders_emulate()
+            else:
+                orders = fetch_orders_emulate()
+            self._log_exchange_response('fetch_orders', orders)
+            orders = [self._order_contracts_to_amount(o) for o in orders]
+            return orders
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not fetch positions due to {e.__class__.__name__}. Message: {e}') from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
     @retrier
     def fetch_trading_fees(self) -> Dict[str, Any]:
         """
@@ -1885,11 +1931,11 @@ class Exchange:
         logger.debug(
             "one_call: %s msecs (%s)",
             one_call,
-            arrow.utcnow().shift(seconds=one_call // 1000).humanize(only_distance=True)
+            dt_humanize(dt_now() - timedelta(milliseconds=one_call), only_distance=True)
         )
         input_coroutines = [self._async_get_candle_history(
             pair, timeframe, candle_type, since) for since in
-            range(since_ms, until_ms or (arrow.utcnow().int_timestamp * 1000), one_call)]
+            range(since_ms, until_ms or dt_ts(), one_call)]
 
         data: List = []
         # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
@@ -2072,7 +2118,7 @@ class Exchange:
         """
         try:
             # Fetch OHLCV asynchronously
-            s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
+            s = '(' + dt_from_ts(since_ms).isoformat() + ') ' if since_ms is not None else ''
             logger.debug(
                 "Fetching pair %s, %s, interval %s, since %s %s...",
                 pair, candle_type, timeframe, since_ms, s
@@ -2162,7 +2208,7 @@ class Exchange:
                 logger.debug(
                     "Fetching trades for pair %s, since %s %s...",
                     pair, since,
-                    '(' + arrow.get(since // 1000).isoformat() + ') ' if since is not None else ''
+                    '(' + dt_from_ts(since).isoformat() + ') ' if since is not None else ''
                 )
                 trades = await self._api_async.fetch_trades(pair, since=since, limit=1000)
             trades = self._trades_contracts_to_amount(trades)
@@ -2896,8 +2942,8 @@ class Exchange:
                 if nominal_value >= tier['minNotional']:
                     return (tier['maintenanceMarginRate'], tier['maintAmt'])
 
-            raise OperationalException("nominal value can not be lower than 0")
+            raise ExchangeError("nominal value can not be lower than 0")
             # The lowest notional_floor for any pair in fetch_leverage_tiers is always 0 because it
             # describes the min amt for a tier, and the lowest tier will always go down to 0
         else:
-            raise OperationalException(f"Cannot get maintenance ratio using {self.name}")
+            raise ExchangeError(f"Cannot get maintenance ratio using {self.name}")

freqtrade/exchange/exchange_utils.py

@@ -11,6 +11,7 @@ from ccxt import (DECIMAL_PLACES, ROUND, ROUND_DOWN, ROUND_UP, SIGNIFICANT_DIGIT
 
 from freqtrade.exchange.common import BAD_EXCHANGES, EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED
 from freqtrade.util import FtPrecise
+from freqtrade.util.datetime_helpers import dt_from_ts, dt_ts
 
 
 CcxtModuleType = Any
@@ -99,9 +100,8 @@ def timeframe_to_prev_date(timeframe: str, date: Optional[datetime] = None) -> d
     if not date:
         date = datetime.now(timezone.utc)
 
-    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
-                                                  ROUND_DOWN) // 1000
-    return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
+    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, dt_ts(date), ROUND_DOWN) // 1000
+    return dt_from_ts(new_timestamp)
 
 
 def timeframe_to_next_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
@@ -113,9 +113,8 @@ def timeframe_to_next_date(timeframe: str, date: Optional[datetime] = None) -> d
     """
     if not date:
         date = datetime.now(timezone.utc)
-    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
-                                                  ROUND_UP) // 1000
-    return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
+    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, dt_ts(date), ROUND_UP) // 1000
+    return dt_from_ts(new_timestamp)
 
 
 def date_minus_candles(

freqtrade/exchange/okx.py

@@ -169,6 +169,22 @@ class Okx(Exchange):
             params['posSide'] = self._get_posSide(side, True)
         return params
 
+    def _convert_stop_order(self, pair: str, order_id: str, order: Dict) -> Dict:
+        if (
+            order['status'] == 'closed'
+            and (real_order_id := order.get('info', {}).get('ordId')) is not None
+        ):
+            # Once a order triggered, we fetch the regular followup order.
+            order_reg = self.fetch_order(real_order_id, pair)
+            self._log_exchange_response('fetch_stoploss_order1', order_reg)
+            order_reg['id_stop'] = order_reg['id']
+            order_reg['id'] = order_id
+            order_reg['type'] = 'stoploss'
+            order_reg['status_stop'] = 'triggered'
+            return order_reg
+        order['type'] = 'stoploss'
+        return order
+
     def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
         if self._config['dry_run']:
             return self.fetch_dry_run_order(order_id)
@@ -177,7 +193,7 @@ class Okx(Exchange):
             params1 = {'stop': True}
             order_reg = self._api.fetch_order(order_id, pair, params=params1)
             self._log_exchange_response('fetch_stoploss_order', order_reg)
-            return order_reg
+            return self._convert_stop_order(pair, order_id, order_reg)
         except ccxt.OrderNotFound:
             pass
         params2 = {'stop': True, 'ordType': 'conditional'}
@@ -188,18 +204,7 @@ class Okx(Exchange):
                 orders_f = [order for order in orders if order['id'] == order_id]
                 if orders_f:
                     order = orders_f[0]
-                    if (order['status'] == 'closed'
-                            and (real_order_id := order.get('info', {}).get('ordId')) is not None):
-                        # Once a order triggered, we fetch the regular followup order.
-                        order_reg = self.fetch_order(real_order_id, pair)
-                        self._log_exchange_response('fetch_stoploss_order1', order_reg)
-                        order_reg['id_stop'] = order_reg['id']
-                        order_reg['id'] = order_id
-                        order_reg['type'] = 'stoploss'
-                        order_reg['status_stop'] = 'triggered'
-                        return order_reg
-                    order['type'] = 'stoploss'
-                    return order
+                    return self._convert_stop_order(pair, order_id, order)
             except ccxt.BaseError:
                 pass
         raise RetryableOrderError(

freqtrade/freqai/RL/Base3ActionRLEnv.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gym import spaces
+from gymnasium import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -94,9 +94,12 @@ class Base3ActionRLEnv(BaseEnvironment):
 
         observation = self._get_observation()
 
+        # user can play with time if they want
+        truncated = False
+
         self._update_history(info)
 
-        return observation, step_reward, self._done, info
+        return observation, step_reward, self._done, truncated, info
 
     def is_tradesignal(self, action: int) -> bool:
         """

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gym import spaces
+from gymnasium import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -96,9 +96,12 @@ class Base4ActionRLEnv(BaseEnvironment):
 
         observation = self._get_observation()
 
+        # user can play with time if they want
+        truncated = False
+
         self._update_history(info)
 
-        return observation, step_reward, self._done, info
+        return observation, step_reward, self._done, truncated, info
 
     def is_tradesignal(self, action: int) -> bool:
         """

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gym import spaces
+from gymnasium import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -101,10 +101,12 @@ class Base5ActionRLEnv(BaseEnvironment):
         )
 
         observation = self._get_observation()
+        # user can play with time if they want
+        truncated = False
 
         self._update_history(info)
 
-        return observation, step_reward, self._done, info
+        return observation, step_reward, self._done, truncated, info
 
     def is_tradesignal(self, action: int) -> bool:
         """

freqtrade/freqai/RL/BaseEnvironment.py

@@ -4,11 +4,11 @@ from abc import abstractmethod
 from enum import Enum
 from typing import Optional, Type, Union
 
-import gym
+import gymnasium as gym
 import numpy as np
 import pandas as pd
-from gym import spaces
-from gym.utils import seeding
+from gymnasium import spaces
+from gymnasium.utils import seeding
 from pandas import DataFrame
 
 
@@ -127,6 +127,14 @@ class BaseEnvironment(gym.Env):
         self.history: dict = {}
         self.trade_history: list = []
 
+    def get_attr(self, attr: str):
+        """
+        Returns the attribute of the environment
+        :param attr: attribute to return
+        :return: attribute
+        """
+        return getattr(self, attr)
+
     @abstractmethod
     def set_action_space(self):
         """
@@ -172,7 +180,7 @@ class BaseEnvironment(gym.Env):
     def reset_tensorboard_log(self):
         self.tensorboard_metrics = {}
 
-    def reset(self):
+    def reset(self, seed=None):
         """
         Reset is called at the beginning of every episode
         """
@@ -203,7 +211,7 @@ class BaseEnvironment(gym.Env):
         self.close_trade_profit = []
         self._total_unrealized_profit = 1
 
-        return self._get_observation()
+        return self._get_observation(), self.history
 
     @abstractmethod
     def step(self, action: int):
@@ -298,6 +306,12 @@ class BaseEnvironment(gym.Env):
         """
         An example reward function. This is the one function that users will likely
         wish to inject their own creativity into.
+
+        Warning!
+        This is function is a showcase of functionality designed to show as many possible
+        environment control features as possible. It is also designed to run quickly
+        on small computers. This is a benchmark, it is *not* for live production.
+
         :param action: int = The action made by the agent for the current candle.
         :return:
         float = the reward to give to the agent for current step (used for optimization

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -6,7 +6,7 @@ from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
 
-import gym
+import gymnasium as gym
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
@@ -16,14 +16,14 @@ from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.monitor import Monitor
 from stable_baselines3.common.utils import set_random_seed
-from stable_baselines3.common.vec_env import SubprocVecEnv
+from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
-from freqtrade.freqai.RL.BaseEnvironment import BaseActions, Positions
-from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment, Positions
+from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
 from freqtrade.persistence import Trade
 
 
@@ -46,8 +46,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             'cpu_count', 1), max(int(self.max_system_threads / 2), 1))
         th.set_num_threads(self.max_threads)
         self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
-        self.train_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
-        self.eval_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
+        self.train_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
+        self.eval_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
         self.eval_callback: Optional[EvalCallback] = None
         self.model_type = self.freqai_info['rl_config']['model_type']
         self.rl_config = self.freqai_info['rl_config']
@@ -371,6 +371,12 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             """
             An example reward function. This is the one function that users will likely
             wish to inject their own creativity into.
+
+            Warning!
+            This is function is a showcase of functionality designed to show as many possible
+            environment control features as possible. It is also designed to run quickly
+            on small computers. This is a benchmark, it is *not* for live production.
+
             :param action: int = The action made by the agent for the current candle.
             :return:
             float = the reward to give to the agent for current step (used for optimization
@@ -431,9 +437,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             return 0.
 
 
-def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
+def make_env(MyRLEnv: Type[BaseEnvironment], env_id: str, rank: int,
              seed: int, train_df: DataFrame, price: DataFrame,
-             monitor: bool = False,
              env_info: Dict[str, Any] = {}) -> Callable:
     """
     Utility function for multiprocessed env.
@@ -450,8 +455,7 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
 
         env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
                       **env_info)
-        if monitor:
-            env = Monitor(env)
+
         return env
     set_random_seed(seed)
     return _init

freqtrade/freqai/base_models/BasePyTorchClassifier.py

@@ -45,6 +45,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
+        :param dk: dk: The datakitchen object
         :param unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
@@ -74,11 +75,14 @@ class BasePyTorchClassifier(BasePyTorchModel):
             dk.data_dictionary["prediction_features"],
             device=self.device
         )
+        self.model.model.eval()
         logits = self.model.model(x)
         probs = F.softmax(logits, dim=-1)
         predicted_classes = torch.argmax(probs, dim=-1)
         predicted_classes_str = self.decode_class_names(predicted_classes)
-        pred_df_prob = DataFrame(probs.detach().numpy(), columns=class_names)
+        # used .tolist to convert probs into an iterable, in this way Tensors
+        # are automatically moved to the CPU first if necessary.
+        pred_df_prob = DataFrame(probs.detach().tolist(), columns=class_names)
         pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
         pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
         return (pred_df, dk.do_predict)

freqtrade/freqai/base_models/BasePyTorchModel.py

@@ -27,6 +27,7 @@ class BasePyTorchModel(IFreqaiModel, ABC):
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         test_size = self.freqai_info.get('data_split_parameters', {}).get('test_size')
         self.splits = ["train", "test"] if test_size != 0 else ["train"]
+        self.window_size = self.freqai_info.get("conv_width", 1)
 
     def train(
         self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs

freqtrade/freqai/base_models/BasePyTorchRegressor.py

@@ -44,7 +44,8 @@ class BasePyTorchRegressor(BasePyTorchModel):
             dk.data_dictionary["prediction_features"],
             device=self.device
         )
+        self.model.model.eval()
         y = self.model.model(x)
-        y = y.cpu()
-        pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])
+        pred_df = DataFrame(y.detach().tolist(), columns=[dk.label_list[0]])
+        pred_df = dk.denormalize_labels_from_metadata(pred_df)
         return (pred_df, dk.do_predict)

freqtrade/freqai/freqai_interface.py

@@ -21,7 +21,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.freqai.data_drawer import FreqaiDataDrawer
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.utils import plot_feature_importance, record_params
+from freqtrade.freqai.utils import get_tb_logger, plot_feature_importance, record_params
 from freqtrade.strategy.interface import IStrategy
 
 
@@ -80,6 +80,7 @@ class IFreqaiModel(ABC):
         if self.keras and self.ft_params.get("DI_threshold", 0):
             self.ft_params["DI_threshold"] = 0
             logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
+
         self.CONV_WIDTH = self.freqai_info.get('conv_width', 1)
         if self.ft_params.get("inlier_metric_window", 0):
             self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
@@ -109,6 +110,7 @@ class IFreqaiModel(ABC):
         if self.ft_params.get('principal_component_analysis', False) and self.continual_learning:
             self.ft_params.update({'principal_component_analysis': False})
             logger.warning('User tried to use PCA with continual learning. Deactivating PCA.')
+        self.activate_tensorboard: bool = self.freqai_info.get('activate_tensorboard', True)
 
         record_params(config, self.full_path)
 
@@ -242,8 +244,8 @@ class IFreqaiModel(ABC):
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
                     )
                 except Exception as msg:
-                    logger.warning(f"Training {pair} raised exception {msg.__class__.__name__}. "
-                                   f"Message: {msg}, skipping.")
+                    logger.exception(f"Training {pair} raised exception {msg.__class__.__name__}. "
+                                     f"Message: {msg}, skipping.")
 
                 self.train_timer('stop', pair)
 
@@ -306,10 +308,11 @@ class IFreqaiModel(ABC):
             if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
                 if check_features:
                     self.dd.load_metadata(dk)
-                    dataframe_dummy_features = self.dk.use_strategy_to_populate_indicators(
+                    df_fts = self.dk.use_strategy_to_populate_indicators(
                         strategy, prediction_dataframe=dataframe.tail(1), pair=pair
                     )
-                    dk.find_features(dataframe_dummy_features)
+                    df_fts = dk.remove_special_chars_from_feature_names(df_fts)
+                    dk.find_features(df_fts)
                     self.check_if_feature_list_matches_strategy(dk)
                     check_features = False
                 append_df = dk.get_backtesting_prediction()
@@ -342,7 +345,10 @@ class IFreqaiModel(ABC):
                     dk.find_labels(dataframe_train)
 
                     try:
+                        self.tb_logger = get_tb_logger(self.dd.model_type, dk.data_path,
+                                                       self.activate_tensorboard)
                         self.model = self.train(dataframe_train, pair, dk)
+                        self.tb_logger.close()
                     except Exception as msg:
                         logger.warning(
                             f"Training {pair} raised exception {msg.__class__.__name__}. "
@@ -489,9 +495,9 @@ class IFreqaiModel(ABC):
         if dk.training_features_list != feature_list:
             raise OperationalException(
                 "Trying to access pretrained model with `identifier` "
-                "but found different features furnished by current strategy."
-                "Change `identifier` to train from scratch, or ensure the"
-                "strategy is furnishing the same features as the pretrained"
+                "but found different features furnished by current strategy. "
+                "Change `identifier` to train from scratch, or ensure the "
+                "strategy is furnishing the same features as the pretrained "
                 "model. In case of --strategy-list, please be aware that FreqAI "
                 "requires all strategies to maintain identical "
                 "feature_engineering_* functions"
@@ -620,18 +626,23 @@ class IFreqaiModel(ABC):
             strategy, corr_dataframes, base_dataframes, pair
         )
 
-        new_trained_timerange = dk.buffer_timerange(new_trained_timerange)
+        trained_timestamp = new_trained_timerange.stopts
 
-        unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
+        buffered_timerange = dk.buffer_timerange(new_trained_timerange)
+
+        unfiltered_dataframe = dk.slice_dataframe(buffered_timerange, unfiltered_dataframe)
 
         # find the features indicated by strategy and store in datakitchen
         dk.find_features(unfiltered_dataframe)
         dk.find_labels(unfiltered_dataframe)
 
+        self.tb_logger = get_tb_logger(self.dd.model_type, dk.data_path,
+                                       self.activate_tensorboard)
         model = self.train(unfiltered_dataframe, pair, dk)
+        self.tb_logger.close()
 
-        self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
-        dk.set_new_model_names(pair, new_trained_timerange.stopts)
+        self.dd.pair_dict[pair]["trained_timestamp"] = trained_timestamp
+        dk.set_new_model_names(pair, trained_timestamp)
         self.dd.save_data(model, pair, dk)
 
         if self.plot_features:

freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py

@@ -74,16 +74,18 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.CrossEntropyLoss()
-        init_model = self.get_init_model(dk.pair)
-        trainer = PyTorchModelTrainer(
-            model=model,
-            optimizer=optimizer,
-            criterion=criterion,
-            model_meta_data={"class_names": class_names},
-            device=self.device,
-            init_model=init_model,
-            data_convertor=self.data_convertor,
-            **self.trainer_kwargs,
-        )
+        # check if continual_learning is activated, and retreive the model to continue training
+        trainer = self.get_init_model(dk.pair)
+        if trainer is None:
+            trainer = PyTorchModelTrainer(
+                model=model,
+                optimizer=optimizer,
+                criterion=criterion,
+                model_meta_data={"class_names": class_names},
+                device=self.device,
+                data_convertor=self.data_convertor,
+                tb_logger=self.tb_logger,
+                **self.trainer_kwargs,
+            )
         trainer.fit(data_dictionary, self.splits)
         return trainer

freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py

@@ -69,15 +69,17 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.MSELoss()
-        init_model = self.get_init_model(dk.pair)
-        trainer = PyTorchModelTrainer(
-            model=model,
-            optimizer=optimizer,
-            criterion=criterion,
-            device=self.device,
-            init_model=init_model,
-            data_convertor=self.data_convertor,
-            **self.trainer_kwargs,
-        )
+        # check if continual_learning is activated, and retreive the model to continue training
+        trainer = self.get_init_model(dk.pair)
+        if trainer is None:
+            trainer = PyTorchModelTrainer(
+                model=model,
+                optimizer=optimizer,
+                criterion=criterion,
+                device=self.device,
+                data_convertor=self.data_convertor,
+                tb_logger=self.tb_logger,
+                **self.trainer_kwargs,
+            )
         trainer.fit(data_dictionary, self.splits)
         return trainer

freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py

@@ -0,0 +1,140 @@
+from typing import Any, Dict, Tuple
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+import torch
+
+from freqtrade.freqai.base_models.BasePyTorchRegressor import BasePyTorchRegressor
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.torch.PyTorchDataConvertor import (DefaultPyTorchDataConvertor,
+                                                         PyTorchDataConvertor)
+from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchTransformerTrainer
+from freqtrade.freqai.torch.PyTorchTransformerModel import PyTorchTransformerModel
+
+
+class PyTorchTransformerRegressor(BasePyTorchRegressor):
+    """
+    This class implements the fit method of IFreqaiModel.
+    in the fit method we initialize the model and trainer objects.
+    the only requirement from the model is to be aligned to PyTorchRegressor
+    predict method that expects the model to predict tensor of type float.
+    the trainer defines the training loop.
+
+    parameters are passed via `model_training_parameters` under the freqai
+    section in the config file. e.g:
+    {
+        ...
+        "freqai": {
+            ...
+            "model_training_parameters" : {
+                "learning_rate": 3e-4,
+                "trainer_kwargs": {
+                    "max_iters": 5000,
+                    "batch_size": 64,
+                    "max_n_eval_batches": null
+                },
+                "model_kwargs": {
+                    "hidden_dim": 512,
+                    "dropout_percent": 0.2,
+                    "n_layer": 1,
+                },
+            }
+        }
+    }
+    """
+
+    @property
+    def data_convertor(self) -> PyTorchDataConvertor:
+        return DefaultPyTorchDataConvertor(target_tensor_type=torch.float)
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        config = self.freqai_info.get("model_training_parameters", {})
+        self.learning_rate: float = config.get("learning_rate",  3e-4)
+        self.model_kwargs: Dict[str, Any] = config.get("model_kwargs",  {})
+        self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs",  {})
+
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        n_features = data_dictionary["train_features"].shape[-1]
+        n_labels = data_dictionary["train_labels"].shape[-1]
+        model = PyTorchTransformerModel(
+            input_dim=n_features,
+            output_dim=n_labels,
+            time_window=self.window_size,
+            **self.model_kwargs
+        )
+        model.to(self.device)
+        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
+        criterion = torch.nn.MSELoss()
+        # check if continual_learning is activated, and retreive the model to continue training
+        trainer = self.get_init_model(dk.pair)
+        if trainer is None:
+            trainer = PyTorchTransformerTrainer(
+                model=model,
+                optimizer=optimizer,
+                criterion=criterion,
+                device=self.device,
+                data_convertor=self.data_convertor,
+                window_size=self.window_size,
+                tb_logger=self.tb_logger,
+                **self.trainer_kwargs,
+            )
+        trainer.fit(data_dictionary, self.splits)
+        return trainer
+
+    def predict(
+        self, unfiltered_df: pd.DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> Tuple[pd.DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_df)
+        filtered_df, _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
+        )
+        filtered_df = dk.normalize_data_from_metadata(filtered_df)
+        dk.data_dictionary["prediction_features"] = filtered_df
+
+        self.data_cleaning_predict(dk)
+        x = self.data_convertor.convert_x(
+            dk.data_dictionary["prediction_features"],
+            device=self.device
+        )
+        # if user is asking for multiple predictions, slide the window
+        # along the tensor
+        x = x.unsqueeze(0)
+        # create empty torch tensor
+        self.model.model.eval()
+        yb = torch.empty(0).to(self.device)
+        if x.shape[1] > 1:
+            ws = self.window_size
+            for i in range(0, x.shape[1] - ws):
+                xb = x[:, i:i + ws, :].to(self.device)
+                y = self.model.model(xb)
+                yb = torch.cat((yb, y), dim=0)
+        else:
+            yb = self.model.model(x)
+
+        yb = yb.cpu().squeeze()
+        pred_df = pd.DataFrame(yb.detach().numpy(), columns=dk.label_list)
+        pred_df = dk.denormalize_labels_from_metadata(pred_df)
+
+        if x.shape[1] > 1:
+            zeros_df = pd.DataFrame(np.zeros((x.shape[1] - len(pred_df), len(pred_df.columns))),
+                                    columns=pred_df.columns)
+            pred_df = pd.concat([zeros_df, pred_df], axis=0, ignore_index=True)
+        return (pred_df, dk.do_predict)

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -1,11 +1,12 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any, Dict, Type
 
 import torch as th
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
 
 
@@ -57,10 +58,14 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
         policy_kwargs = dict(activation_fn=th.nn.ReLU,
                              net_arch=self.net_arch)
 
+        if self.activate_tensorboard:
+            tb_path = Path(dk.full_path / "tensorboard" / dk.pair.split('/')[0])
+        else:
+            tb_path = None
+
         if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
             model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
-                                    tensorboard_log=Path(
-                                        dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
+                                    tensorboard_log=tb_path,
                                     **self.freqai_info.get('model_training_parameters', {})
                                     )
         else:
@@ -84,7 +89,9 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         return model
 
-    class MyRLEnv(Base5ActionRLEnv):
+    MyRLEnv: Type[BaseEnvironment]
+
+    class MyRLEnv(Base5ActionRLEnv):  # type: ignore[no-redef]
         """
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.
@@ -94,6 +101,12 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             """
             An example reward function. This is the one function that users will likely
             wish to inject their own creativity into.
+
+                        Warning!
+            This is function is a showcase of functionality designed to show as many possible
+            environment control features as possible. It is also designed to run quickly
+            on small computers. This is a benchmark, it is *not* for live production.
+
             :param action: int = The action made by the agent for the current candle.
             :return:
             float = the reward to give to the agent for current step (used for optimization

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -3,12 +3,12 @@ from typing import Any, Dict
 
 from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
-from stable_baselines3.common.vec_env import SubprocVecEnv
+from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
-from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
+from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
 
 
 logger = logging.getLogger(__name__)
@@ -41,22 +41,25 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
 
         env_info = self.pack_env_dict(dk.pair)
 
+        eval_freq = len(train_df) // self.max_threads
+
         env_id = "train_env"
-        self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
-                                        train_df, prices_train,
-                                        monitor=True,
-                                        env_info=env_info) for i
-                                        in range(self.max_threads)])
+        self.train_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
+                                                            train_df, prices_train,
+                                                            env_info=env_info) for i
+                                                   in range(self.max_threads)]))
 
         eval_env_id = 'eval_env'
-        self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
-                                                test_df, prices_test,
-                                                monitor=True,
-                                                env_info=env_info) for i
-                                       in range(self.max_threads)])
+        self.eval_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
+                                                           test_df, prices_test,
+                                                           env_info=env_info) for i
+                                                  in range(self.max_threads)]))
+
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
-                                          render=False, eval_freq=len(train_df),
+                                          render=False, eval_freq=eval_freq,
                                           best_model_save_path=str(dk.data_path))
 
+        # TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
+        # IT WILL RETURN FALSE INFORMATIONS, NEVERTHLESS NOT THREAD SAFE WITH SB3!!!
         actions = self.train_env.env_method("get_actions")[0]
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

freqtrade/freqai/prediction_models/XGBoostRegressor.py

@@ -5,6 +5,7 @@ from xgboost import XGBRegressor
 
 from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.tensorboard import TBCallback
 
 
 logger = logging.getLogger(__name__)
@@ -44,7 +45,10 @@ class XGBoostRegressor(BaseRegressionModel):
 
         model = XGBRegressor(**self.model_training_parameters)
 
+        model.set_params(callbacks=[TBCallback(dk.data_path)], activate=self.activate_tensorboard)
         model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
                   sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
+        # set the callbacks to empty so that we can serialize to disk later
+        model.set_params(callbacks=[])
 
         return model

freqtrade/freqai/tensorboard/TensorboardCallback.py

@@ -3,8 +3,9 @@ from typing import Any, Dict, Type, Union
 
 from stable_baselines3.common.callbacks import BaseCallback
 from stable_baselines3.common.logger import HParam
+from stable_baselines3.common.vec_env import VecEnv
 
-from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions
 
 
 class TensorboardCallback(BaseCallback):
@@ -12,11 +13,13 @@ class TensorboardCallback(BaseCallback):
     Custom callback for plotting additional values in tensorboard and
     episodic summary reports.
     """
+    # Override training_env type to fix type errors
+    training_env: Union[VecEnv, None] = None
+
     def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
         super().__init__(verbose)
         self.model: Any = None
-        self.logger = None  # type: Any
-        self.training_env: BaseEnvironment = None  # type: ignore
+        self.logger: Any = None
         self.actions: Type[Enum] = actions
 
     def _on_training_start(self) -> None:
@@ -44,6 +47,8 @@ class TensorboardCallback(BaseCallback):
     def _on_step(self) -> bool:
 
         local_info = self.locals["infos"][0]
+        if self.training_env is None:
+            return True
         tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
 
         for metric in local_info:

freqtrade/freqai/tensorboard/__init__.py

@@ -0,0 +1,15 @@
+# ensure users can still use a non-torch freqai version
+try:
+    from freqtrade.freqai.tensorboard.tensorboard import TensorBoardCallback, TensorboardLogger
+    TBLogger = TensorboardLogger
+    TBCallback = TensorBoardCallback
+except ModuleNotFoundError:
+    from freqtrade.freqai.tensorboard.base_tensorboard import (BaseTensorBoardCallback,
+                                                               BaseTensorboardLogger)
+    TBLogger = BaseTensorboardLogger  # type: ignore
+    TBCallback = BaseTensorBoardCallback  # type: ignore
+
+__all__ = (
+    "TBLogger",
+    "TBCallback"
+)

freqtrade/freqai/tensorboard/base_tensorboard.py

@@ -0,0 +1,33 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from xgboost.callback import TrainingCallback
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseTensorboardLogger:
+    def __init__(self, logdir: Path, activate: bool = True):
+        pass
+
+    def log_scalar(self, tag: str, scalar_value: Any, step: int):
+        return
+
+    def close(self):
+        return
+
+
+class BaseTensorBoardCallback(TrainingCallback):
+
+    def __init__(self, logdir: Path, activate: bool = True):
+        pass
+
+    def after_iteration(
+        self, model, epoch: int, evals_log: TrainingCallback.EvalsLog
+    ) -> bool:
+        return False
+
+    def after_training(self, model):
+        return model

freqtrade/freqai/tensorboard/tensorboard.py

@@ -0,0 +1,62 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from torch.utils.tensorboard import SummaryWriter
+from xgboost import callback
+
+from freqtrade.freqai.tensorboard.base_tensorboard import (BaseTensorBoardCallback,
+                                                           BaseTensorboardLogger)
+
+
+logger = logging.getLogger(__name__)
+
+
+class TensorboardLogger(BaseTensorboardLogger):
+    def __init__(self, logdir: Path, activate: bool = True):
+        self.activate = activate
+        if self.activate:
+            self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")
+
+    def log_scalar(self, tag: str, scalar_value: Any, step: int):
+        if self.activate:
+            self.writer.add_scalar(tag, scalar_value, step)
+
+    def close(self):
+        if self.activate:
+            self.writer.flush()
+            self.writer.close()
+
+
+class TensorBoardCallback(BaseTensorBoardCallback):
+
+    def __init__(self, logdir: Path, activate: bool = True):
+        self.activate = activate
+        if self.activate:
+            self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")
+
+    def after_iteration(
+        self, model, epoch: int, evals_log: callback.TrainingCallback.EvalsLog
+    ) -> bool:
+        if not self.activate:
+            return False
+        if not evals_log:
+            return False
+
+        for data, metric in evals_log.items():
+            for metric_name, log in metric.items():
+                score = log[-1][0] if isinstance(log[-1], tuple) else log[-1]
+                if data == "train":
+                    self.writer.add_scalar("train_loss", score, epoch)
+                else:
+                    self.writer.add_scalar("valid_loss", score, epoch)
+
+        return False
+
+    def after_training(self, model):
+        if not self.activate:
+            return model
+        self.writer.flush()
+        self.writer.close()
+
+        return model

freqtrade/freqai/torch/PyTorchDataConvertor.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import List, Optional
+from typing import Optional
 
 import pandas as pd
 import torch
@@ -12,14 +12,14 @@ class PyTorchDataConvertor(ABC):
     """
 
     @abstractmethod
-    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]:
+    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
         """
         :param df: "*_features" dataframe.
         :param device: The device to use for training (e.g. 'cpu', 'cuda').
         """
 
     @abstractmethod
-    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]:
+    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
         """
         :param df: "*_labels" dataframe.
         :param device: The device to use for training (e.g. 'cpu', 'cuda').
@@ -45,14 +45,14 @@ class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
         self._target_tensor_type = target_tensor_type
         self._squeeze_target_tensor = squeeze_target_tensor
 
-    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]:
+    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
         x = torch.from_numpy(df.values).float()
         if device:
             x = x.to(device)
 
-        return [x]
+        return x
 
-    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]:
+    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
         y = torch.from_numpy(df.values)
 
         if self._target_tensor_type:
@@ -64,4 +64,4 @@ class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
         if device:
             y = y.to(device)
 
-        return [y]
+        return y

freqtrade/freqai/torch/PyTorchMLPModel.py

@@ -1,5 +1,4 @@
 import logging
-from typing import List
 
 import torch
 from torch import nn
@@ -47,8 +46,8 @@ class PyTorchMLPModel(nn.Module):
         self.relu = nn.ReLU()
         self.dropout = nn.Dropout(p=dropout_percent)
 
-    def forward(self, tensors: List[torch.Tensor]) -> torch.Tensor:
-        x: torch.Tensor = tensors[0]
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x: torch.Tensor = tensors[0]
         x = self.relu(self.input_layer(x))
         x = self.dropout(x)
         x = self.blocks(x)

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -12,6 +12,8 @@ from torch.utils.data import DataLoader, TensorDataset
 from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
 from freqtrade.freqai.torch.PyTorchTrainerInterface import PyTorchTrainerInterface
 
+from .datasets import WindowDataset
+
 
 logger = logging.getLogger(__name__)
 
@@ -23,9 +25,10 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
             optimizer: Optimizer,
             criterion: nn.Module,
             device: str,
-            init_model: Dict,
             data_convertor: PyTorchDataConvertor,
             model_meta_data: Dict[str, Any] = {},
+            window_size: int = 1,
+            tb_logger: Any = None,
             **kwargs
     ):
         """
@@ -52,8 +55,8 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         self.batch_size: int = kwargs.get("batch_size", 64)
         self.max_n_eval_batches: Optional[int] = kwargs.get("max_n_eval_batches", None)
         self.data_convertor = data_convertor
-        if init_model:
-            self.load_from_checkpoint(init_model)
+        self.window_size: int = window_size
+        self.tb_logger = tb_logger
 
     def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]):
         """
@@ -75,36 +78,28 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
             batch_size=self.batch_size,
             n_iters=self.max_iters
         )
+        self.model.train()
         for epoch in range(1, epochs + 1):
-            # training
-            losses = []
             for i, batch_data in enumerate(data_loaders_dictionary["train"]):
 
-                for tensor in batch_data:
-                    tensor.to(self.device)
-
-                xb = batch_data[:-1]
-                yb = batch_data[-1]
+                xb, yb = batch_data
+                xb.to(self.device)
+                yb.to(self.device)
                 yb_pred = self.model(xb)
                 loss = self.criterion(yb_pred, yb)
 
                 self.optimizer.zero_grad(set_to_none=True)
                 loss.backward()
                 self.optimizer.step()
-                losses.append(loss.item())
-            train_loss = sum(losses) / len(losses)
-            log_message = f"epoch {epoch}/{epochs}: train loss {train_loss:.4f}"
+                self.tb_logger.log_scalar("train_loss", loss.item(), i)
 
             # evaluation
             if "test" in splits:
-                test_loss = self.estimate_loss(
+                self.estimate_loss(
                     data_loaders_dictionary,
                     self.max_n_eval_batches,
                     "test"
                 )
-                log_message += f" ; test loss {test_loss:.4f}"
-
-            logger.info(log_message)
 
     @torch.no_grad()
     def estimate_loss(
@@ -112,26 +107,22 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
             data_loader_dictionary: Dict[str, DataLoader],
             max_n_eval_batches: Optional[int],
             split: str,
-    ) -> float:
+    ) -> None:
         self.model.eval()
         n_batches = 0
-        losses = []
         for i, batch_data in enumerate(data_loader_dictionary[split]):
             if max_n_eval_batches and i > max_n_eval_batches:
                 n_batches += 1
                 break
+            xb, yb = batch_data
+            xb.to(self.device)
+            yb.to(self.device)
 
-            for tensor in batch_data:
-                tensor.to(self.device)
-
-            xb = batch_data[:-1]
-            yb = batch_data[-1]
             yb_pred = self.model(xb)
             loss = self.criterion(yb_pred, yb)
-            losses.append(loss.item())
+            self.tb_logger.log_scalar(f"{split}_loss", loss.item(), i)
 
         self.model.train()
-        return sum(losses) / len(losses)
 
     def create_data_loaders_dictionary(
             self,
@@ -145,7 +136,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         for split in splits:
             x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"], self.device)
             y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device)
-            dataset = TensorDataset(*x, *y)
+            dataset = TensorDataset(x, y)
             data_loader = DataLoader(
                 dataset,
                 batch_size=self.batch_size,
@@ -206,3 +197,33 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
         self.model_meta_data = checkpoint["model_meta_data"]
         return self
+
+
+class PyTorchTransformerTrainer(PyTorchModelTrainer):
+    """
+    Creating a trainer for the Transformer model.
+    """
+
+    def create_data_loaders_dictionary(
+            self,
+            data_dictionary: Dict[str, pd.DataFrame],
+            splits: List[str]
+    ) -> Dict[str, DataLoader]:
+        """
+        Converts the input data to PyTorch tensors using a data loader.
+        """
+        data_loader_dictionary = {}
+        for split in splits:
+            x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"], self.device)
+            y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device)
+            dataset = WindowDataset(x, y, self.window_size)
+            data_loader = DataLoader(
+                dataset,
+                batch_size=self.batch_size,
+                shuffle=False,
+                drop_last=True,
+                num_workers=0,
+            )
+            data_loader_dictionary[split] = data_loader
+
+        return data_loader_dictionary

freqtrade/freqai/torch/PyTorchTransformerModel.py

@@ -0,0 +1,93 @@
+import math
+
+import torch
+from torch import nn
+
+
+"""
+The architecture is based on the paper “Attention Is All You Need”.
+Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
+Lukasz Kaiser, and Illia Polosukhin. 2017.
+"""
+
+
+class PyTorchTransformerModel(nn.Module):
+    """
+    A transformer approach to time series modeling using positional encoding.
+    The architecture is based on the paper “Attention Is All You Need”.
+    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
+    Lukasz Kaiser, and Illia Polosukhin. 2017.
+    """
+
+    def __init__(self, input_dim: int = 7, output_dim: int = 7, hidden_dim=1024,
+                 n_layer=2, dropout_percent=0.1, time_window=10, nhead=8):
+        super().__init__()
+        self.time_window = time_window
+        # ensure the input dimension to the transformer is divisible by nhead
+        self.dim_val = input_dim - (input_dim % nhead)
+        self.input_net = nn.Sequential(
+            nn.Dropout(dropout_percent), nn.Linear(input_dim, self.dim_val)
+        )
+
+        # Encode the timeseries with Positional encoding
+        self.positional_encoding = PositionalEncoding(d_model=self.dim_val, max_len=self.dim_val)
+
+        # Define the encoder block of the Transformer
+        self.encoder_layer = nn.TransformerEncoderLayer(
+            d_model=self.dim_val, nhead=nhead, dropout=dropout_percent, batch_first=True)
+        self.transformer = nn.TransformerEncoder(self.encoder_layer, num_layers=n_layer)
+
+        # the pseudo decoding FC
+        self.output_net = nn.Sequential(
+            nn.Linear(self.dim_val * time_window, int(hidden_dim)),
+            nn.ReLU(),
+            nn.Dropout(dropout_percent),
+            nn.Linear(int(hidden_dim), int(hidden_dim / 2)),
+            nn.ReLU(),
+            nn.Dropout(dropout_percent),
+            nn.Linear(int(hidden_dim / 2), int(hidden_dim / 4)),
+            nn.ReLU(),
+            nn.Dropout(dropout_percent),
+            nn.Linear(int(hidden_dim / 4), output_dim)
+        )
+
+    def forward(self, x, mask=None, add_positional_encoding=True):
+        """
+        Args:
+            x: Input features of shape [Batch, SeqLen, input_dim]
+            mask: Mask to apply on the attention outputs (optional)
+            add_positional_encoding: If True, we add the positional encoding to the input.
+                                      Might not be desired for some tasks.
+        """
+        x = self.input_net(x)
+        if add_positional_encoding:
+            x = self.positional_encoding(x)
+        x = self.transformer(x, mask=mask)
+        x = x.reshape(-1, 1, self.time_window * x.shape[-1])
+        x = self.output_net(x)
+        return x
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        """
+        Args
+            d_model: Hidden dimensionality of the input.
+            max_len: Maximum length of a sequence to expect.
+        """
+        super().__init__()
+
+        # Create matrix of [SeqLen, HiddenDim] representing the positional encoding
+        # for max_len inputs
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+
+        self.register_buffer("pe", pe, persistent=False)
+
+    def forward(self, x):
+        x = x + self.pe[:, : x.size(1)]
+        return x

freqtrade/freqai/torch/datasets.py

@@ -0,0 +1,19 @@
+import torch
+
+
+class WindowDataset(torch.utils.data.Dataset):
+    def __init__(self, xs, ys, window_size):
+        self.xs = xs
+        self.ys = ys
+        self.window_size = window_size
+
+    def __len__(self):
+        return len(self.xs) - self.window_size
+
+    def __getitem__(self, index):
+        idx_rev = len(self.xs) - self.window_size - index - 1
+        window_x = self.xs[idx_rev:idx_rev + self.window_size, :]
+        # Beware of indexing, these two window_x and window_y are aimed at the same row!
+        # this is what happens when you use :
+        window_y = self.ys[idx_rev + self.window_size - 1, :].unsqueeze(0)
+        return window_x, window_y

freqtrade/freqai/utils.py

@@ -92,55 +92,6 @@ def get_required_data_timerange(config: Config) -> TimeRange:
     return data_load_timerange
 
 
-# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
-# def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
-#     """
-#     Called only once upon start of bot to download the necessary data for
-#     populating indicators and training a FreqAI model.
-#     :param timerange: TimeRange = The full data timerange for populating the indicators
-#                                     and training the model.
-#     :param dp: DataProvider instance attached to the strategy
-#     """
-
-#     if dp._exchange is not None:
-#         markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
-#                    or config.get('include_inactive')]
-#     else:
-#         # This should not occur:
-#         raise OperationalException('No exchange object found.')
-
-#     all_pairs = dynamic_expand_pairlist(config, markets)
-
-#     if not dp._exchange:
-#         # Not realistic - this is only called in live mode.
-#         raise OperationalException("Dataprovider did not have an exchange attached.")
-
-#     time = datetime.now(tz=timezone.utc).timestamp()
-
-#     for tf in config["freqai"]["feature_parameters"].get("include_timeframes"):
-#         timerange = TimeRange()
-#         timerange.startts = int(time)
-#         timerange.stopts = int(time)
-#         startup_candles = dp.get_required_startup(str(tf))
-#         tf_seconds = timeframe_to_seconds(str(tf))
-#         timerange.subtract_start(tf_seconds * startup_candles)
-#         new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
-#         # FIXME: now that we are looping on `refresh_backtest_ohlcv_data`, the function
-#         # redownloads the funding rate for each pair.
-#         refresh_backtest_ohlcv_data(
-#             dp._exchange,
-#             pairs=all_pairs,
-#             timeframes=[tf],
-#             datadir=config["datadir"],
-#             timerange=timerange,
-#             new_pairs_days=new_pairs_days,
-#             erase=False,
-#             data_format=config.get("dataformat_ohlcv", "json"),
-#             trading_mode=config.get("trading_mode", "spot"),
-#             prepend=config.get("prepend_data", False),
-#         )
-
-
 def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
                             count_max: int = 25) -> None:
     """
@@ -233,3 +184,13 @@ def get_timerange_backtest_live_models(config: Config) -> str:
     dd = FreqaiDataDrawer(models_path, config)
     timerange = dd.get_timerange_from_live_historic_predictions()
     return timerange.timerange_str
+
+
+def get_tb_logger(model_type: str, path: Path, activate: bool) -> Any:
+
+    if model_type == "pytorch" and activate:
+        from freqtrade.freqai.tensorboard import TBLogger
+        return TBLogger(path, activate)
+    else:
+        from freqtrade.freqai.tensorboard.base_tensorboard import BaseTensorboardLogger
+        return BaseTensorboardLogger(path, activate)

freqtrade/freqtradebot.py

@@ -1,9 +1,9 @@
 """
 Freqtrade is the main module of this bot. It contains the class Freqtrade()
 """
-import copy
 import logging
 import traceback
+from copy import deepcopy
 from datetime import datetime, time, timedelta, timezone
 from math import isclose
 from threading import Lock
@@ -13,7 +13,7 @@ from schedule import Scheduler
 
 from freqtrade import constants
 from freqtrade.configuration import validate_config_consistency
-from freqtrade.constants import BuySell, Config, LongShort
+from freqtrade.constants import BuySell, Config, ExchangeConfig, LongShort
 from freqtrade.data.converter import order_book_to_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
@@ -23,6 +23,7 @@ from freqtrade.exceptions import (DependencyException, ExchangeError, Insufficie
                                   InvalidOrderException, PricingError)
 from freqtrade.exchange import (ROUND_DOWN, ROUND_UP, timeframe_to_minutes, timeframe_to_next_date,
                                 timeframe_to_seconds)
+from freqtrade.exchange.common import remove_exchange_credentials
 from freqtrade.misc import safe_value_fallback, safe_value_fallback2
 from freqtrade.mixins import LoggingMixin
 from freqtrade.persistence import Order, PairLocks, Trade, init_db
@@ -63,6 +64,9 @@ 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_exchange_credentials(config['exchange'], True)
 
         self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
 
@@ -70,7 +74,7 @@ class FreqtradeBot(LoggingMixin):
         validate_config_consistency(config)
 
         self.exchange = ExchangeResolver.load_exchange(
-            self.config['exchange']['name'], self.config, load_leverage_tiers=True)
+            self.config, exchange_config=exchange_config, load_leverage_tiers=True)
 
         init_db(self.config['db_url'])
 
@@ -420,7 +424,7 @@ class FreqtradeBot(LoggingMixin):
         """
         Try refinding a lost trade.
         Only used when InsufficientFunds appears on exit orders (stoploss or long sell/short buy).
-        Tries to walk the stored orders and sell them off eventually.
+        Tries to walk the stored orders and updates the trade state if necessary.
         """
         logger.info(f"Trying to refind lost order for {trade}")
         for order in trade.orders:
@@ -451,6 +455,42 @@ class FreqtradeBot(LoggingMixin):
             except ExchangeError:
                 logger.warning(f"Error updating {order.order_id}.")
 
+    def handle_onexchange_order(self, trade: Trade):
+        """
+        Try refinding a order that is not in the database.
+        Only used balance disappeared, which would make exiting impossible.
+        """
+        try:
+            orders = self.exchange.fetch_orders(trade.pair, trade.open_date_utc)
+            for order in orders:
+                trade_order = [o for o in trade.orders if o.order_id == order['id']]
+                if trade_order:
+                    continue
+                logger.info(f"Found previously unknown order {order['id']} for {trade.pair}.")
+
+                order_obj = Order.parse_from_ccxt_object(order, trade.pair, order['side'])
+                order_obj.order_filled_date = datetime.fromtimestamp(
+                    safe_value_fallback(order, 'lastTradeTimestamp', 'timestamp') // 1000,
+                    tz=timezone.utc)
+                trade.orders.append(order_obj)
+                # TODO: how do we handle open_order_id ...
+                Trade.commit()
+                prev_exit_reason = trade.exit_reason
+                trade.exit_reason = ExitType.SOLD_ON_EXCHANGE.value
+                self.update_trade_state(trade, order['id'], order)
+
+                logger.info(f"handled order {order['id']}")
+                if not trade.is_open:
+                    # Trade was just closed
+                    trade.close_date = order_obj.order_filled_date
+                    Trade.commit()
+                    break
+                else:
+                    trade.exit_reason = prev_exit_reason
+                    Trade.commit()
+
+        except ExchangeError:
+            logger.warning("Error finding onexchange order")
 #
 # BUY / enter positions / open trades logic and methods
 #
@@ -461,7 +501,7 @@ class FreqtradeBot(LoggingMixin):
         """
         trades_created = 0
 
-        whitelist = copy.deepcopy(self.active_pair_whitelist)
+        whitelist = deepcopy(self.active_pair_whitelist)
         if not whitelist:
             self.log_once("Active pair whitelist is empty.", logger.info)
             return trades_created
@@ -490,7 +530,8 @@ class FreqtradeBot(LoggingMixin):
         # Create entity and execute trade for each pair from whitelist
         for pair in whitelist:
             try:
-                trades_created += self.create_trade(pair)
+                with self._exit_lock:
+                    trades_created += self.create_trade(pair)
             except DependencyException as exception:
                 logger.warning('Unable to create trade for %s: %s', pair, exception)
 
@@ -981,7 +1022,7 @@ class FreqtradeBot(LoggingMixin):
             'base_currency': self.exchange.get_pair_base_currency(trade.pair),
             'fiat_currency': self.config.get('fiat_display_currency', None),
             'amount': order.safe_amount_after_fee if fill else (order.amount or trade.amount),
-            'open_date': trade.open_date or datetime.utcnow(),
+            'open_date': trade.open_date_utc or datetime.now(timezone.utc),
             'current_rate': current_rate,
             'sub_trade': sub_trade,
         }
@@ -1033,6 +1074,13 @@ class FreqtradeBot(LoggingMixin):
         """
         trades_closed = 0
         for trade in trades:
+
+            if trade.open_order_id is None and not self.wallets.check_exit_amount(trade):
+                logger.warning(
+                    f'Not enough {trade.safe_base_currency} in wallet to exit {trade}. '
+                    'Trying to recover.')
+                self.handle_onexchange_order(trade)
+
             try:
                 try:
                     if (self.strategy.order_types.get('stoploss_on_exchange') and
@@ -1535,13 +1583,13 @@ class FreqtradeBot(LoggingMixin):
         # Update wallets to ensure amounts tied up in a stoploss is now free!
         self.wallets.update()
         if self.trading_mode == TradingMode.FUTURES:
+            # A safe exit amount isn't needed for futures, you can just exit/close the position
             return amount
 
         trade_base_currency = self.exchange.get_pair_base_currency(pair)
         wallet_amount = self.wallets.get_free(trade_base_currency)
         logger.debug(f"{pair} - Wallet: {wallet_amount} - Trade-amount: {amount}")
         if wallet_amount >= amount:
-            # A safe exit amount isn't needed for futures, you can just exit/close the position
             return amount
         elif wallet_amount > amount * 0.98:
             logger.info(f"{pair} - Falling back to wallet-amount {wallet_amount} -> {amount}.")
@@ -1697,8 +1745,8 @@ class FreqtradeBot(LoggingMixin):
             'enter_tag': trade.enter_tag,
             'sell_reason': trade.exit_reason,  # Deprecated
             'exit_reason': trade.exit_reason,
-            'open_date': trade.open_date,
-            'close_date': trade.close_date or datetime.utcnow(),
+            'open_date': trade.open_date_utc,
+            'close_date': trade.close_date_utc or datetime.now(timezone.utc),
             'stake_amount': trade.stake_amount,
             'stake_currency': self.config['stake_currency'],
             'base_currency': self.exchange.get_pair_base_currency(trade.pair),
@@ -1720,10 +1768,8 @@ class FreqtradeBot(LoggingMixin):
         else:
             trade.exit_order_status = reason
 
-        order = trade.select_order_by_order_id(order_id)
-        if not order:
-            raise DependencyException(
-                f"Order_obj not found for {order_id}. This should not have happened.")
+        order_or_none = trade.select_order_by_order_id(order_id)
+        order = self.order_obj_or_raise(order_id, order_or_none)
 
         profit_rate: float = trade.safe_close_rate
         profit_trade = trade.calc_profit(rate=profit_rate)
@@ -1764,6 +1810,12 @@ class FreqtradeBot(LoggingMixin):
         # Send the message
         self.rpc.send_msg(msg)
 
+    def order_obj_or_raise(self, order_id: str, order_obj: Optional[Order]) -> Order:
+        if not order_obj:
+            raise DependencyException(
+                f"Order_obj not found for {order_id}. This should not have happened.")
+        return order_obj
+
 #
 # Common update trade state methods
 #
@@ -1802,10 +1854,8 @@ class FreqtradeBot(LoggingMixin):
             # Handling of this will happen in check_handle_timedout.
             return True
 
-        order_obj = trade.select_order_by_order_id(order_id)
-        if not order_obj:
-            raise DependencyException(
-                f"Order_obj not found for {order_id}. This should not have happened.")
+        order_obj_or_none = trade.select_order_by_order_id(order_id)
+        order_obj = self.order_obj_or_raise(order_id, order_obj_or_none)
 
         self.handle_order_fee(trade, order_obj, order)
 
@@ -1823,16 +1873,18 @@ class FreqtradeBot(LoggingMixin):
                 # Must also run for partial exits
                 # TODO: Margin will need to use interest_rate as well.
                 # interest_rate = self.exchange.get_interest_rate()
-                trade.set_liquidation_price(self.exchange.get_liquidation_price(
-                    pair=trade.pair,
-                    open_rate=trade.open_rate,
-                    is_short=trade.is_short,
-                    amount=trade.amount,
-                    stake_amount=trade.stake_amount,
-                    leverage=trade.leverage,
-                    wallet_balance=trade.stake_amount,
-                ))
-
+                try:
+                    trade.set_liquidation_price(self.exchange.get_liquidation_price(
+                        pair=trade.pair,
+                        open_rate=trade.open_rate,
+                        is_short=trade.is_short,
+                        amount=trade.amount,
+                        stake_amount=trade.stake_amount,
+                        leverage=trade.leverage,
+                        wallet_balance=trade.stake_amount,
+                    ))
+                except DependencyException:
+                    logger.warning('Unable to calculate liquidation price')
             # Updating wallets when order is closed
             self.wallets.update()
         Trade.commit()

freqtrade/loggers/__init__.py

@@ -32,6 +32,7 @@ def _set_loggers(verbosity: int = 0, api_verbosity: str = 'info') -> None:
         logging.INFO if verbosity <= 2 else logging.DEBUG
     )
     logging.getLogger('telegram').setLevel(logging.INFO)
+    logging.getLogger('httpx').setLevel(logging.INFO)
 
     logging.getLogger('werkzeug').setLevel(
         logging.ERROR if api_verbosity == 'error' else logging.INFO

freqtrade/misc.py

@@ -3,13 +3,11 @@ Various tool function for Freqtrade and scripts
 """
 import gzip
 import logging
-import re
 from datetime import datetime
 from pathlib import Path
 from typing import Any, Dict, Iterator, List, Mapping, Optional, TextIO, Union
 from urllib.parse import urlparse
 
-import orjson
 import pandas as pd
 import rapidjson
 
@@ -48,18 +46,6 @@ def round_coin_value(
     return val
 
 
-def shorten_date(_date: str) -> str:
-    """
-    Trim the date so it fits on small screens
-    """
-    new_date = re.sub('seconds?', 'sec', _date)
-    new_date = re.sub('minutes?', 'min', new_date)
-    new_date = re.sub('hours?', 'h', new_date)
-    new_date = re.sub('days?', 'd', new_date)
-    new_date = re.sub('^an?', '1', new_date)
-    return new_date
-
-
 def file_dump_json(filename: Path, data: Any, is_zip: bool = False, log: bool = True) -> None:
     """
     Dump JSON data into a file
@@ -262,17 +248,7 @@ def dataframe_to_json(dataframe: pd.DataFrame) -> str:
     :param dataframe: A pandas DataFrame
     :returns: A JSON string of the pandas DataFrame
     """
-    # https://github.com/pandas-dev/pandas/issues/24889
-    # https://github.com/pandas-dev/pandas/issues/40443
-    # We need to convert to a dict to avoid mem leak
-    def default(z):
-        if isinstance(z, pd.Timestamp):
-            return z.timestamp() * 1e3
-        if z is pd.NaT:
-            return 'NaT'
-        raise TypeError
-
-    return str(orjson.dumps(dataframe.to_dict(orient='split'), default=default), 'utf-8')
+    return dataframe.to_json(orient='split')
 
 
 def json_to_dataframe(data: str) -> pd.DataFrame:

freqtrade/optimize/backtesting.py

@@ -9,7 +9,6 @@ from copy import deepcopy
 from datetime import datetime, timedelta, timezone
 from typing import Any, Dict, List, Optional, Tuple
 
-import pandas as pd
 from numpy import nan
 from pandas import DataFrame
 
@@ -28,8 +27,10 @@ from freqtrade.exchange import (amount_to_contract_precision, price_to_precision
 from freqtrade.mixins import LoggingMixin
 from freqtrade.optimize.backtest_caching import get_strategy_run_id
 from freqtrade.optimize.bt_progress import BTProgress
-from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
-                                                 store_backtest_signal_candles,
+from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_rejected_signals,
+                                                 generate_trade_signal_candles,
+                                                 show_backtest_results,
+                                                 store_backtest_analysis_results,
                                                  store_backtest_stats)
 from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
 from freqtrade.plugins.pairlistmanager import PairListManager
@@ -84,10 +85,11 @@ class Backtesting:
         self.strategylist: List[IStrategy] = []
         self.all_results: Dict[str, Dict] = {}
         self.processed_dfs: Dict[str, Dict] = {}
+        self.rejected_dict: Dict[str, List] = {}
+        self.rejected_df: Dict[str, Dict] = {}
 
         self._exchange_name = self.config['exchange']['name']
-        self.exchange = ExchangeResolver.load_exchange(
-            self._exchange_name, self.config, load_leverage_tiers=True)
+        self.exchange = ExchangeResolver.load_exchange(self.config, load_leverage_tiers=True)
         self.dataprovider = DataProvider(self.config, self.exchange)
 
         if self.config.get('strategy_list'):
@@ -1056,6 +1058,18 @@ class Backtesting:
             return None
         return row
 
+    def _collate_rejected(self, pair, row):
+        """
+        Temporarily store rejected signal information for downstream use in backtesting_analysis
+        """
+        # It could be fun to enable hyperopt mode to write
+        # a loss function to reduce rejected signals
+        if (self.config.get('export', 'none') == 'signals' and
+                self.dataprovider.runmode == RunMode.BACKTEST):
+            if pair not in self.rejected_dict:
+                self.rejected_dict[pair] = []
+            self.rejected_dict[pair].append([row[DATE_IDX], row[ENTER_TAG_IDX]])
+
     def backtest_loop(
             self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
             open_trade_count_start: int, trade_dir: Optional[LongShort],
@@ -1081,20 +1095,22 @@ class Backtesting:
         if (
             (self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
             and is_first
-            and self.trade_slot_available(open_trade_count_start)
             and current_time != end_date
             and trade_dir is not None
             and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
         ):
-            trade = self._enter_trade(pair, row, trade_dir)
-            if trade:
-                # TODO: hacky workaround to avoid opening > max_open_trades
-                # This emulates previous behavior - not sure if this is correct
-                # Prevents entering if the trade-slot was freed in this candle
-                open_trade_count_start += 1
-                # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
-                LocalTrade.add_bt_trade(trade)
-                self.wallets.update()
+            if (self.trade_slot_available(open_trade_count_start)):
+                trade = self._enter_trade(pair, row, trade_dir)
+                if trade:
+                    # TODO: hacky workaround to avoid opening > max_open_trades
+                    # This emulates previous behavior - not sure if this is correct
+                    # Prevents entering if the trade-slot was freed in this candle
+                    open_trade_count_start += 1
+                    # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
+                    LocalTrade.add_bt_trade(trade)
+                    self.wallets.update()
+            else:
+                self._collate_rejected(pair, row)
 
         for trade in list(LocalTrade.bt_trades_open_pp[pair]):
             # 3. Process entry orders.
@@ -1236,8 +1252,8 @@ class Backtesting:
     def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
                               timerange: TimeRange):
         self.progress.init_step(BacktestState.ANALYZE, 0)
-
-        logger.info(f"Running backtesting for Strategy {strat.get_strategy_name()}")
+        strategy_name = strat.get_strategy_name()
+        logger.info(f"Running backtesting for Strategy {strategy_name}")
         backtest_start_time = datetime.now(timezone.utc)
         self._set_strategy(strat)
 
@@ -1272,37 +1288,21 @@ class Backtesting:
         )
         backtest_end_time = datetime.now(timezone.utc)
         results.update({
-            'run_id': self.run_ids.get(strat.get_strategy_name(), ''),
+            'run_id': self.run_ids.get(strategy_name, ''),
             'backtest_start_time': int(backtest_start_time.timestamp()),
             'backtest_end_time': int(backtest_end_time.timestamp()),
         })
-        self.all_results[self.strategy.get_strategy_name()] = results
+        self.all_results[strategy_name] = results
 
         if (self.config.get('export', 'none') == 'signals' and
                 self.dataprovider.runmode == RunMode.BACKTEST):
-            self._generate_trade_signal_candles(preprocessed_tmp, results)
+            self.processed_dfs[strategy_name] = generate_trade_signal_candles(
+                preprocessed_tmp, results)
+            self.rejected_df[strategy_name] = generate_rejected_signals(
+                preprocessed_tmp, self.rejected_dict)
 
         return min_date, max_date
 
-    def _generate_trade_signal_candles(self, preprocessed_df, bt_results):
-        signal_candles_only = {}
-        for pair in preprocessed_df.keys():
-            signal_candles_only_df = DataFrame()
-
-            pairdf = preprocessed_df[pair]
-            resdf = bt_results['results']
-            pairresults = resdf.loc[(resdf["pair"] == pair)]
-
-            if pairdf.shape[0] > 0:
-                for t, v in pairresults.open_date.items():
-                    allinds = pairdf.loc[(pairdf['date'] < v)]
-                    signal_inds = allinds.iloc[[-1]]
-                    signal_candles_only_df = pd.concat([signal_candles_only_df, signal_inds])
-
-                signal_candles_only[pair] = signal_candles_only_df
-
-        self.processed_dfs[self.strategy.get_strategy_name()] = signal_candles_only
-
     def _get_min_cached_backtest_date(self):
         min_backtest_date = None
         backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
@@ -1365,8 +1365,9 @@ class Backtesting:
 
             if (self.config.get('export', 'none') == 'signals' and
                     self.dataprovider.runmode == RunMode.BACKTEST):
-                store_backtest_signal_candles(
-                    self.config['exportfilename'], self.processed_dfs, dt_appendix)
+                store_backtest_analysis_results(
+                    self.config['exportfilename'], self.processed_dfs, self.rejected_df,
+                    dt_appendix)
 
         # Results may be mixed up now. Sort them so they follow --strategy-list order.
         if 'strategy_list' in self.config and len(self.results) > 0:

freqtrade/optimize/edge_cli.py

@@ -32,7 +32,7 @@ class EdgeCli:
         # Ensure using dry-run
         self.config['dry_run'] = True
         self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
-        self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
+        self.exchange = ExchangeResolver.load_exchange(self.config)
         self.strategy = StrategyResolver.load_strategy(self.config)
         self.strategy.dp = DataProvider(config, self.exchange)
 

freqtrade/optimize/optimize_reports.py

@@ -4,7 +4,7 @@ from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Any, Dict, List, Union
 
-from pandas import DataFrame, to_datetime
+from pandas import DataFrame, concat, to_datetime
 from tabulate import tabulate
 
 from freqtrade.constants import (BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN,
@@ -46,29 +46,80 @@ def store_backtest_stats(
     file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
 
 
-def store_backtest_signal_candles(
-        recordfilename: Path, candles: Dict[str, Dict], dtappendix: str) -> Path:
+def _store_backtest_analysis_data(
+        recordfilename: Path, data: Dict[str, Dict],
+        dtappendix: str, name: str) -> Path:
     """
-    Stores backtest trade signal candles
+    Stores backtest trade candles for analysis
     :param recordfilename: Path object, which can either be a filename or a directory.
         Filenames will be appended with a timestamp right before the suffix
-        while for directories, <directory>/backtest-result-<datetime>_signals.pkl will be used
+        while for directories, <directory>/backtest-result-<datetime>_<name>.pkl will be used
         as filename
-    :param stats: Dict containing the backtesting signal candles
+    :param candles: Dict containing the backtesting data for analysis
     :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}_signals.pkl')
+        filename = (recordfilename / f'backtest-result-{dtappendix}_{name}.pkl')
     else:
         filename = Path.joinpath(
-            recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_signals.pkl'
+            recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_{name}.pkl'
         )
 
-    file_dump_joblib(filename, candles)
+    file_dump_joblib(filename, data)
 
     return filename
 
 
+def store_backtest_analysis_results(
+        recordfilename: Path, candles: Dict[str, Dict], trades: Dict[str, Dict],
+        dtappendix: str) -> None:
+    _store_backtest_analysis_data(recordfilename, candles, dtappendix, "signals")
+    _store_backtest_analysis_data(recordfilename, trades, dtappendix, "rejected")
+
+
+def generate_trade_signal_candles(preprocessed_df: Dict[str, DataFrame],
+                                  bt_results: Dict[str, Any]) -> DataFrame:
+    signal_candles_only = {}
+    for pair in preprocessed_df.keys():
+        signal_candles_only_df = DataFrame()
+
+        pairdf = preprocessed_df[pair]
+        resdf = bt_results['results']
+        pairresults = resdf.loc[(resdf["pair"] == pair)]
+
+        if pairdf.shape[0] > 0:
+            for t, v in pairresults.open_date.items():
+                allinds = pairdf.loc[(pairdf['date'] < v)]
+                signal_inds = allinds.iloc[[-1]]
+                signal_candles_only_df = concat([
+                    signal_candles_only_df.infer_objects(),
+                    signal_inds.infer_objects()])
+
+            signal_candles_only[pair] = signal_candles_only_df
+    return signal_candles_only
+
+
+def generate_rejected_signals(preprocessed_df: Dict[str, DataFrame],
+                              rejected_dict: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
+    rejected_candles_only = {}
+    for pair, signals in rejected_dict.items():
+        rejected_signals_only_df = DataFrame()
+        pairdf = preprocessed_df[pair]
+
+        for t in signals:
+            data_df_row = pairdf.loc[(pairdf['date'] == t[0])].copy()
+            data_df_row['pair'] = pair
+            data_df_row['enter_tag'] = t[1]
+
+            rejected_signals_only_df = concat([
+                rejected_signals_only_df.infer_objects(),
+                data_df_row.infer_objects()])
+
+        rejected_candles_only[pair] = rejected_signals_only_df
+    return rejected_candles_only
+
+
 def _get_line_floatfmt(stake_currency: str) -> List[str]:
     """
     Generate floatformat (goes in line with _generate_result_line())

freqtrade/persistence/key_value_store.py

@@ -36,7 +36,7 @@ class _KeyValueStoreModel(ModelBase):
 
     value_type: Mapped[ValueTypesEnum] = mapped_column(String(20), nullable=False)
 
-    string_value: Mapped[Optional[str]]
+    string_value: Mapped[Optional[str]] = mapped_column(String(255), nullable=True)
     datetime_value: Mapped[Optional[datetime]]
     float_value: Mapped[Optional[float]]
     int_value: Mapped[Optional[int]]

freqtrade/persistence/trade_model.py

@@ -19,7 +19,7 @@ from freqtrade.exchange import (ROUND_DOWN, ROUND_UP, amount_to_contract_precisi
                                 price_to_precision)
 from freqtrade.leverage import interest
 from freqtrade.persistence.base import ModelBase, SessionType
-from freqtrade.util import FtPrecise
+from freqtrade.util import FtPrecise, dt_now
 
 
 logger = logging.getLogger(__name__)
@@ -68,7 +68,7 @@ class Order(ModelBase):
     remaining: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
     cost: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
     stop_price: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
-    order_date: Mapped[datetime] = mapped_column(nullable=True, default=datetime.utcnow)
+    order_date: Mapped[datetime] = mapped_column(nullable=True, default=dt_now)
     order_filled_date: Mapped[Optional[datetime]] = mapped_column(nullable=True)
     order_update_date: Mapped[Optional[datetime]] = mapped_column(nullable=True)
     funding_fee: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
@@ -158,7 +158,7 @@ class Order(ModelBase):
                 self.order_filled_date = datetime.now(timezone.utc)
         self.order_update_date = datetime.now(timezone.utc)
 
-    def to_ccxt_object(self) -> Dict[str, Any]:
+    def to_ccxt_object(self, stopPriceName: str = 'stopPrice') -> Dict[str, Any]:
         order: Dict[str, Any] = {
             'id': self.order_id,
             'symbol': self.ft_pair,
@@ -170,7 +170,6 @@ class Order(ModelBase):
             'side': self.ft_order_side,
             'filled': self.filled,
             'remaining': self.remaining,
-            'stopPrice': self.stop_price,
             'datetime': self.order_date_utc.strftime('%Y-%m-%dT%H:%M:%S.%f'),
             'timestamp': int(self.order_date_utc.timestamp() * 1000),
             'status': self.status,
@@ -178,7 +177,11 @@ class Order(ModelBase):
             'info': {},
         }
         if self.ft_order_side == 'stoploss':
-            order['ft_order_type'] = 'stoploss'
+            order.update({
+                stopPriceName: self.stop_price,
+                'ft_order_type': 'stoploss',
+            })
+
         return order
 
     def to_json(self, entry_side: str, minified: bool = False) -> Dict[str, Any]:
@@ -422,7 +425,7 @@ class LocalTrade():
 
     @property
     def close_date_utc(self):
-        return self.close_date.replace(tzinfo=timezone.utc)
+        return self.close_date.replace(tzinfo=timezone.utc) if self.close_date else None
 
     @property
     def entry_side(self) -> str:
@@ -708,7 +711,10 @@ class LocalTrade():
         if order.ft_order_side != self.entry_side:
             amount_tr = amount_to_contract_precision(self.amount, self.amount_precision,
                                                      self.precision_mode, self.contract_size)
-            if isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC):
+            if (
+                isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC)
+                or order.safe_amount_after_fee > amount_tr
+            ):
                 self.close(order.safe_price)
             else:
                 self.recalc_trade_from_orders()

freqtrade/plot/plotting.py

@@ -633,7 +633,7 @@ def load_and_plot_trades(config: Config):
     """
     strategy = StrategyResolver.load_strategy(config)
 
-    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
+    exchange = ExchangeResolver.load_exchange(config)
     IStrategy.dp = DataProvider(config, exchange)
     strategy.ft_bot_start()
     strategy.bot_loop_start(datetime.now(timezone.utc))
@@ -678,7 +678,7 @@ def plot_profit(config: Config) -> None:
     if 'timeframe' not in config:
         raise OperationalException('Timeframe must be set in either config or via --timeframe.')
 
-    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
+    exchange = ExchangeResolver.load_exchange(config)
     plot_elements = init_plotscript(config, list(exchange.markets))
     trades = plot_elements['trades']
     # Filter trades to relevant pairs

freqtrade/plugins/pairlist/AgeFilter.py

@@ -3,9 +3,9 @@ Minimum age (days listed) pair list filter
 """
 import logging
 from copy import deepcopy
+from datetime import timedelta
 from typing import Any, Dict, List, Optional
 
-import arrow
 from pandas import DataFrame
 
 from freqtrade.constants import Config, ListPairsWithTimeframes
@@ -13,7 +13,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange.types import Tickers
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
-from freqtrade.util import PeriodicCache
+from freqtrade.util import PeriodicCache, dt_floor_day, dt_now, dt_ts
 
 
 logger = logging.getLogger(__name__)
@@ -84,10 +84,7 @@ class AgeFilter(IPairList):
         since_days = -(
             self._max_days_listed if self._max_days_listed else self._min_days_listed
         ) - 1
-        since_ms = int(arrow.utcnow()
-                       .floor('day')
-                       .shift(days=since_days)
-                       .float_timestamp) * 1000
+        since_ms = dt_ts(dt_floor_day(dt_now()) + timedelta(days=since_days))
         candles = self._exchange.refresh_latest_ohlcv(needed_pairs, since_ms=since_ms, cache=False)
         if self._enabled:
             for p in deepcopy(pairlist):
@@ -116,7 +113,7 @@ class AgeFilter(IPairList):
             ):
                 # We have fetched at least the minimum required number of daily candles
                 # Add to cache, store the time we last checked this symbol
-                self._symbolsChecked[pair] = arrow.utcnow().int_timestamp * 1000
+                self._symbolsChecked[pair] = dt_ts()
                 return True
             else:
                 self.log_once((
@@ -127,6 +124,6 @@ class AgeFilter(IPairList):
                     " or more than "
                     f"{self._max_days_listed} {plural(self._max_days_listed, 'day')}"
                 ) if self._max_days_listed else ''), logger.info)
-                self._symbolsCheckFailed[pair] = arrow.utcnow().int_timestamp * 1000
+                self._symbolsCheckFailed[pair] = dt_ts()
                 return False
         return False

freqtrade/plugins/pairlist/VolatilityFilter.py

@@ -4,9 +4,9 @@ Volatility pairlist filter
 import logging
 import sys
 from copy import deepcopy
+from datetime import timedelta
 from typing import Any, Dict, List, Optional
 
-import arrow
 import numpy as np
 from cachetools import TTLCache
 from pandas import DataFrame
@@ -16,6 +16,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange.types import Tickers
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
+from freqtrade.util import dt_floor_day, dt_now, dt_ts
 
 
 logger = logging.getLogger(__name__)
@@ -73,10 +74,7 @@ class VolatilityFilter(IPairList):
         needed_pairs: ListPairsWithTimeframes = [
             (p, '1d', self._def_candletype) for p in pairlist if p not in self._pair_cache]
 
-        since_ms = (arrow.utcnow()
-                         .floor('day')
-                         .shift(days=-self._days - 1)
-                         .int_timestamp) * 1000
+        since_ms = dt_ts(dt_floor_day(dt_now()) - timedelta(days=self._days))
         # Get all candles
         candles = {}
         if needed_pairs:
@@ -105,7 +103,7 @@ class VolatilityFilter(IPairList):
 
         result = False
         if daily_candles is not None and not daily_candles.empty:
-            returns = (np.log(daily_candles.close / daily_candles.close.shift(-1)))
+            returns = (np.log(daily_candles["close"].shift(1) / daily_candles["close"]))
             returns.fillna(0, inplace=True)
 
             volatility_series = returns.rolling(window=self._days).std() * np.sqrt(self._days)

freqtrade/plugins/pairlist/VolumePairList.py

@@ -4,7 +4,7 @@ Volume PairList provider
 Provides dynamic pair list based on trade volumes
 """
 import logging
-from datetime import datetime, timedelta, timezone
+from datetime import timedelta
 from typing import Any, Dict, List, Literal
 
 from cachetools import TTLCache
@@ -15,6 +15,7 @@ from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
 from freqtrade.exchange.types import Tickers
 from freqtrade.misc import format_ms_time
 from freqtrade.plugins.pairlist.IPairList import IPairList
+from freqtrade.util import dt_now
 
 
 logger = logging.getLogger(__name__)
@@ -161,13 +162,13 @@ class VolumePairList(IPairList):
             # get lookback period in ms, for exchange ohlcv fetch
             since_ms = int(timeframe_to_prev_date(
                 self._lookback_timeframe,
-                datetime.now(timezone.utc) + timedelta(
+                dt_now() + timedelta(
                     minutes=-(self._lookback_period * self._tf_in_min) - self._tf_in_min)
                     ).timestamp()) * 1000
 
             to_ms = int(timeframe_to_prev_date(
                             self._lookback_timeframe,
-                            datetime.now(timezone.utc) - timedelta(minutes=self._tf_in_min)
+                            dt_now() - timedelta(minutes=self._tf_in_min)
                             ).timestamp()) * 1000
 
             # todo: utc date output for starting date

freqtrade/plugins/pairlist/rangestabilityfilter.py

@@ -3,9 +3,9 @@ Rate of change pairlist filter
 """
 import logging
 from copy import deepcopy
+from datetime import timedelta
 from typing import Any, Dict, List, Optional
 
-import arrow
 from cachetools import TTLCache
 from pandas import DataFrame
 
@@ -14,6 +14,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange.types import Tickers
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
+from freqtrade.util import dt_floor_day, dt_now, dt_ts
 
 
 logger = logging.getLogger(__name__)
@@ -71,10 +72,7 @@ class RangeStabilityFilter(IPairList):
         needed_pairs: ListPairsWithTimeframes = [
             (p, '1d', self._def_candletype) for p in pairlist if p not in self._pair_cache]
 
-        since_ms = (arrow.utcnow()
-                         .floor('day')
-                         .shift(days=-self._days - 1)
-                         .int_timestamp) * 1000
+        since_ms = dt_ts(dt_floor_day(dt_now()) - timedelta(days=self._days - 1))
         # Get all candles
         candles = {}
         if needed_pairs:

freqtrade/resolvers/exchange_resolver.py

@@ -2,9 +2,10 @@
 This module loads custom exchanges
 """
 import logging
+from typing import Optional
 
 import freqtrade.exchange as exchanges
-from freqtrade.constants import Config
+from freqtrade.constants import Config, ExchangeConfig
 from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, Exchange
 from freqtrade.resolvers import IResolver
 
@@ -19,13 +20,14 @@ class ExchangeResolver(IResolver):
     object_type = Exchange
 
     @staticmethod
-    def load_exchange(exchange_name: str, config: Config, validate: bool = True,
-                      load_leverage_tiers: bool = False) -> Exchange:
+    def load_exchange(config: Config, *, exchange_config: Optional[ExchangeConfig] = None,
+                      validate: bool = True, load_leverage_tiers: bool = False) -> Exchange:
         """
         Load the custom class from config parameter
         :param exchange_name: name of the Exchange to load
         :param config: configuration dictionary
         """
+        exchange_name: str = config['exchange']['name']
         # Map exchange name to avoid duplicate classes for identical exchanges
         exchange_name = MAP_EXCHANGE_CHILDCLASS.get(exchange_name, exchange_name)
         exchange_name = exchange_name.title()
@@ -36,13 +38,14 @@ class ExchangeResolver(IResolver):
                 kwargs={
                     'config': config,
                     'validate': validate,
+                    'exchange_config': exchange_config,
                     'load_leverage_tiers': load_leverage_tiers}
             )
         except ImportError:
             logger.info(
                 f"No {exchange_name} specific subclass found. Using the generic class instead.")
         if not exchange:
-            exchange = Exchange(config, validate=validate)
+            exchange = Exchange(config, validate=validate, exchange_config=exchange_config,)
         return exchange
 
     @staticmethod

freqtrade/rpc/api_server/api_backtest.py

@@ -8,14 +8,16 @@ from fastapi import APIRouter, BackgroundTasks, Depends
 from fastapi.exceptions import HTTPException
 
 from freqtrade.configuration.config_validation import validate_config_consistency
+from freqtrade.constants import Config
 from freqtrade.data.btanalysis import get_backtest_resultlist, load_and_merge_backtest_result
 from freqtrade.enums import BacktestState
 from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.exchange.common import remove_exchange_credentials
 from freqtrade.misc import deep_merge_dicts
 from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestRequest,
                                                   BacktestResponse)
 from freqtrade.rpc.api_server.deps import get_config, is_webserver_mode
-from freqtrade.rpc.api_server.webserver import ApiServer
+from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
 from freqtrade.rpc.rpc import RPCException
 
 
@@ -25,19 +27,92 @@ logger = logging.getLogger(__name__)
 router = APIRouter()
 
 
+def __run_backtest_bg(btconfig: Config):
+    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
+        lastconfig = ApiBG.bt['last_config']
+        strat = StrategyResolver.load_strategy(btconfig)
+        validate_config_consistency(btconfig)
+
+        if (
+            not ApiBG.bt['bt']
+            or lastconfig.get('timeframe') != strat.timeframe
+            or lastconfig.get('timeframe_detail') != btconfig.get('timeframe_detail')
+            or lastconfig.get('timerange') != btconfig['timerange']
+        ):
+            from freqtrade.optimize.backtesting import Backtesting
+            ApiBG.bt['bt'] = Backtesting(btconfig)
+            ApiBG.bt['bt'].load_bt_data_detail()
+        else:
+            ApiBG.bt['bt'].config = btconfig
+            ApiBG.bt['bt'].init_backtest()
+        # Only reload data if timeframe changed.
+        if (
+            not ApiBG.bt['data']
+            or not ApiBG.bt['timerange']
+            or lastconfig.get('timeframe') != strat.timeframe
+            or lastconfig.get('timerange') != btconfig['timerange']
+        ):
+            ApiBG.bt['data'], ApiBG.bt['timerange'] = ApiBG.bt[
+                'bt'].load_bt_data()
+
+        lastconfig['timerange'] = btconfig['timerange']
+        lastconfig['timeframe'] = strat.timeframe
+        lastconfig['protections'] = btconfig.get('protections', [])
+        lastconfig['enable_protections'] = btconfig.get('enable_protections')
+        lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
+
+        ApiBG.bt['bt'].enable_protections = btconfig.get('enable_protections', False)
+        ApiBG.bt['bt'].strategylist = [strat]
+        ApiBG.bt['bt'].results = {}
+        ApiBG.bt['bt'].load_prior_backtest()
+
+        ApiBG.bt['bt'].abort = False
+        if (ApiBG.bt['bt'].results and
+                strat.get_strategy_name() in ApiBG.bt['bt'].results['strategy']):
+            # When previous result hash matches - reuse that result and skip backtesting.
+            logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
+        else:
+            min_date, max_date = ApiBG.bt['bt'].backtest_one_strategy(
+                strat, ApiBG.bt['data'], ApiBG.bt['timerange'])
+
+            ApiBG.bt['bt'].results = generate_backtest_stats(
+                ApiBG.bt['data'], ApiBG.bt['bt'].all_results,
+                min_date=min_date, max_date=max_date)
+
+        if btconfig.get('export', 'none') == 'trades':
+            store_backtest_stats(
+                btconfig['exportfilename'], ApiBG.bt['bt'].results,
+                datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+                )
+
+        logger.info("Backtest finished.")
+
+    except (Exception, OperationalException, DependencyException) as e:
+        logger.exception(f"Backtesting caused an error: {e}")
+        ApiBG.bt['bt_error'] = str(e)
+        pass
+    finally:
+        ApiBG.bgtask_running = False
+
+
 @router.post('/backtest', response_model=BacktestResponse, tags=['webserver', 'backtest'])
-async def api_start_backtest(  # noqa: C901
+async def api_start_backtest(
         bt_settings: BacktestRequest, background_tasks: BackgroundTasks,
         config=Depends(get_config), ws_mode=Depends(is_webserver_mode)):
-    ApiServer._bt['bt_error'] = None
+    ApiBG.bt['bt_error'] = None
     """Start backtesting if not done so already"""
-    if ApiServer._bgtask_running:
+    if ApiBG.bgtask_running:
         raise RPCException('Bot Background task already running')
 
     if ':' in bt_settings.strategy:
         raise HTTPException(status_code=500, detail="base64 encoded strategies are not allowed.")
 
     btconfig = deepcopy(config)
+    remove_exchange_credentials(btconfig['exchange'], True)
     settings = dict(bt_settings)
     if settings.get('freqai', None) is not None:
         settings['freqai'] = dict(settings['freqai'])
@@ -54,80 +129,9 @@ async def api_start_backtest(  # noqa: C901
 
     # Start backtesting
     # Initialize backtesting object
-    def run_backtest():
-        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
-            lastconfig = ApiServer._bt['last_config']
-            strat = StrategyResolver.load_strategy(btconfig)
-            validate_config_consistency(btconfig)
 
-            if (
-                not ApiServer._bt['bt']
-                or lastconfig.get('timeframe') != strat.timeframe
-                or lastconfig.get('timeframe_detail') != btconfig.get('timeframe_detail')
-                or lastconfig.get('timerange') != btconfig['timerange']
-            ):
-                from freqtrade.optimize.backtesting import Backtesting
-                ApiServer._bt['bt'] = Backtesting(btconfig)
-                ApiServer._bt['bt'].load_bt_data_detail()
-            else:
-                ApiServer._bt['bt'].config = btconfig
-                ApiServer._bt['bt'].init_backtest()
-            # Only reload data if timeframe changed.
-            if (
-                not ApiServer._bt['data']
-                or not ApiServer._bt['timerange']
-                or lastconfig.get('timeframe') != strat.timeframe
-                or lastconfig.get('timerange') != btconfig['timerange']
-            ):
-                ApiServer._bt['data'], ApiServer._bt['timerange'] = ApiServer._bt[
-                    'bt'].load_bt_data()
-
-            lastconfig['timerange'] = btconfig['timerange']
-            lastconfig['timeframe'] = strat.timeframe
-            lastconfig['protections'] = btconfig.get('protections', [])
-            lastconfig['enable_protections'] = btconfig.get('enable_protections')
-            lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
-
-            ApiServer._bt['bt'].enable_protections = btconfig.get('enable_protections', False)
-            ApiServer._bt['bt'].strategylist = [strat]
-            ApiServer._bt['bt'].results = {}
-            ApiServer._bt['bt'].load_prior_backtest()
-
-            ApiServer._bt['bt'].abort = False
-            if (ApiServer._bt['bt'].results and
-                    strat.get_strategy_name() in ApiServer._bt['bt'].results['strategy']):
-                # When previous result hash matches - reuse that result and skip backtesting.
-                logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
-            else:
-                min_date, max_date = ApiServer._bt['bt'].backtest_one_strategy(
-                    strat, ApiServer._bt['data'], ApiServer._bt['timerange'])
-
-                ApiServer._bt['bt'].results = generate_backtest_stats(
-                    ApiServer._bt['data'], ApiServer._bt['bt'].all_results,
-                    min_date=min_date, max_date=max_date)
-
-            if btconfig.get('export', 'none') == 'trades':
-                store_backtest_stats(
-                    btconfig['exportfilename'], ApiServer._bt['bt'].results,
-                    datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-                    )
-
-            logger.info("Backtest finished.")
-
-        except (Exception, OperationalException, DependencyException) as e:
-            logger.exception(f"Backtesting caused an error: {e}")
-            ApiServer._bt['bt_error'] = str(e)
-            pass
-        finally:
-            ApiServer._bgtask_running = False
-
-    background_tasks.add_task(run_backtest)
-    ApiServer._bgtask_running = True
+    background_tasks.add_task(__run_backtest_bg, btconfig=btconfig)
+    ApiBG.bgtask_running = True
 
     return {
         "status": "running",
@@ -145,18 +149,18 @@ def api_get_backtest(ws_mode=Depends(is_webserver_mode)):
     Returns Result after backtesting has been ran.
     """
     from freqtrade.persistence import LocalTrade
-    if ApiServer._bgtask_running:
+    if ApiBG.bgtask_running:
         return {
             "status": "running",
             "running": True,
-            "step": (ApiServer._bt['bt'].progress.action if ApiServer._bt['bt']
+            "step": (ApiBG.bt['bt'].progress.action if ApiBG.bt['bt']
                      else str(BacktestState.STARTUP)),
-            "progress": ApiServer._bt['bt'].progress.progress if ApiServer._bt['bt'] else 0,
+            "progress": ApiBG.bt['bt'].progress.progress if ApiBG.bt['bt'] else 0,
             "trade_count": len(LocalTrade.trades),
             "status_msg": "Backtest running",
         }
 
-    if not ApiServer._bt['bt']:
+    if not ApiBG.bt['bt']:
         return {
             "status": "not_started",
             "running": False,
@@ -164,13 +168,13 @@ def api_get_backtest(ws_mode=Depends(is_webserver_mode)):
             "progress": 0,
             "status_msg": "Backtest not yet executed"
         }
-    if ApiServer._bt['bt_error']:
+    if ApiBG.bt['bt_error']:
         return {
             "status": "error",
             "running": False,
             "step": "",
             "progress": 0,
-            "status_msg": f"Backtest failed with {ApiServer._bt['bt_error']}"
+            "status_msg": f"Backtest failed with {ApiBG.bt['bt_error']}"
         }
 
     return {
@@ -179,14 +183,14 @@ def api_get_backtest(ws_mode=Depends(is_webserver_mode)):
         "status_msg": "Backtest ended",
         "step": "finished",
         "progress": 1,
-        "backtest_result": ApiServer._bt['bt'].results,
+        "backtest_result": ApiBG.bt['bt'].results,
     }
 
 
 @router.delete('/backtest', response_model=BacktestResponse, tags=['webserver', 'backtest'])
 def api_delete_backtest(ws_mode=Depends(is_webserver_mode)):
     """Reset backtesting"""
-    if ApiServer._bgtask_running:
+    if ApiBG.bgtask_running:
         return {
             "status": "running",
             "running": True,
@@ -194,12 +198,12 @@ def api_delete_backtest(ws_mode=Depends(is_webserver_mode)):
             "progress": 0,
             "status_msg": "Backtest running",
         }
-    if ApiServer._bt['bt']:
-        ApiServer._bt['bt'].cleanup()
-        del ApiServer._bt['bt']
-        ApiServer._bt['bt'] = None
-        del ApiServer._bt['data']
-        ApiServer._bt['data'] = None
+    if ApiBG.bt['bt']:
+        ApiBG.bt['bt'].cleanup()
+        del ApiBG.bt['bt']
+        ApiBG.bt['bt'] = None
+        del ApiBG.bt['data']
+        ApiBG.bt['data'] = None
         logger.info("Backtesting reset")
     return {
         "status": "reset",
@@ -212,7 +216,7 @@ def api_delete_backtest(ws_mode=Depends(is_webserver_mode)):
 
 @router.get('/backtest/abort', response_model=BacktestResponse, tags=['webserver', 'backtest'])
 def api_backtest_abort(ws_mode=Depends(is_webserver_mode)):
-    if not ApiServer._bgtask_running:
+    if not ApiBG.bgtask_running:
         return {
             "status": "not_running",
             "running": False,
@@ -220,7 +224,7 @@ def api_backtest_abort(ws_mode=Depends(is_webserver_mode)):
             "progress": 0,
             "status_msg": "Backtest ended",
         }
-    ApiServer._bt['bt'].abort = True
+    ApiBG.bt['bt'].abort = True
     return {
         "status": "stopping",
         "running": False,

freqtrade/rpc/api_server/api_schemas.py

@@ -36,20 +36,25 @@ class Balance(BaseModel):
     free: float
     balance: float
     used: float
+    bot_owned: Optional[float]
     est_stake: float
+    est_stake_bot: Optional[float]
     stake: str
     # Starting with 2.x
     side: str
     leverage: float
     is_position: bool
     position: float
+    is_bot_managed: bool
 
 
 class Balances(BaseModel):
     currencies: List[Balance]
     total: float
+    total_bot: float
     symbol: str
     value: float
+    value_bot: float
     stake: str
     note: str
     starting_capital: float
@@ -95,8 +100,10 @@ class Profit(BaseModel):
     trade_count: int
     closed_trade_count: int
     first_trade_date: str
+    first_trade_humanized: str
     first_trade_timestamp: int
     latest_trade_date: str
+    latest_trade_humanized: str
     latest_trade_timestamp: int
     avg_duration: str
     best_pair: str

freqtrade/rpc/api_server/api_v1.py

@@ -43,7 +43,10 @@ logger = logging.getLogger(__name__)
 # 2.23: Allow plot config request in webserver mode
 # 2.24: Add cancel_open_order endpoint
 # 2.25: Add several profit values to /status endpoint
-API_VERSION = 2.25
+# 2.26: increase /balance output
+# 2.27: Add /trades/<id>/reload endpoint
+# 2.28: Switch reload endpoint to Post
+API_VERSION = 2.28
 
 # Public API, requires no auth.
 router_public = APIRouter()
@@ -126,11 +129,17 @@ def trades_delete(tradeid: int, rpc: RPC = Depends(get_rpc)):
 
 
 @router.delete('/trades/{tradeid}/open-order', response_model=OpenTradeSchema,  tags=['trading'])
-def cancel_open_order(tradeid: int, rpc: RPC = Depends(get_rpc)):
+def trade_cancel_open_order(tradeid: int, rpc: RPC = Depends(get_rpc)):
     rpc._rpc_cancel_open_order(tradeid)
     return rpc._rpc_trade_status([tradeid])[0]
 
 
+@router.post('/trades/{tradeid}/reload', response_model=OpenTradeSchema,  tags=['trading'])
+def trade_reload(tradeid: int, rpc: RPC = Depends(get_rpc)):
+    rpc._rpc_reload_trade_from_exchange(tradeid)
+    return rpc._rpc_trade_status([tradeid])[0]
+
+
 # TODO: Missing response model
 @router.get('/edge', tags=['info'])
 def edge(rpc: RPC = Depends(get_rpc)):
@@ -246,14 +255,17 @@ def pair_candles(
 
 @router.get('/pair_history', response_model=PairHistory, tags=['candle data'])
 def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
+                 freqaimodel: Optional[str] = None,
                  config=Depends(get_config), exchange=Depends(get_exchange)):
     # The initial call to this endpoint can be slow, as it may need to initialize
     # the exchange class.
     config = deepcopy(config)
     config.update({
         'strategy': strategy,
+        'timerange': timerange,
+        'freqaimodel': freqaimodel if freqaimodel else config.get('freqaimodel'),
     })
-    return RPC._rpc_analysed_history_full(config, pair, timeframe, timerange, exchange)
+    return RPC._rpc_analysed_history_full(config, pair, timeframe, exchange)
 
 
 @router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])

freqtrade/rpc/api_server/deps.py

@@ -6,6 +6,7 @@ from fastapi import Depends
 from freqtrade.enums import RunMode
 from freqtrade.persistence import Trade
 from freqtrade.persistence.models import _request_id_ctx_var
+from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
 from freqtrade.rpc.rpc import RPC, RPCException
 
 from .webserver import ApiServer
@@ -43,11 +44,11 @@ def get_api_config() -> Dict[str, Any]:
 
 
 def get_exchange(config=Depends(get_config)):
-    if not ApiServer._exchange:
+    if not ApiBG.exchange:
         from freqtrade.resolvers import ExchangeResolver
-        ApiServer._exchange = ExchangeResolver.load_exchange(
-            config['exchange']['name'], config, load_leverage_tiers=False)
-    return ApiServer._exchange
+        ApiBG.exchange = ExchangeResolver.load_exchange(
+            config, load_leverage_tiers=False)
+    return ApiBG.exchange
 
 
 def get_message_stream():

freqtrade/rpc/api_server/webserver.py

@@ -1,6 +1,6 @@
 import logging
 from ipaddress import IPv4Address
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 import orjson
 import uvicorn
@@ -36,19 +36,8 @@ class ApiServer(RPCHandler):
     __initialized = False
 
     _rpc: RPC
-    # Backtesting type: Backtesting
-    _bt: Dict[str, Any] = {
-        'bt': None,
-        'data': None,
-        'timerange': None,
-        'last_config': {},
-        'bt_error': None,
-    }
     _has_rpc: bool = False
-    _bgtask_running: bool = False
     _config: Config = {}
-    # Exchange - only available in webserver mode.
-    _exchange = None
     # websocket message stuff
     _message_stream: Optional[MessageStream] = None
 
@@ -85,7 +74,7 @@ class ApiServer(RPCHandler):
         """
         Attach rpc handler
         """
-        if not self._has_rpc:
+        if not ApiServer._has_rpc:
             ApiServer._rpc = rpc
             ApiServer._has_rpc = True
         else:

freqtrade/rpc/api_server/webserver_bgwork.py

@@ -0,0 +1,16 @@
+
+from typing import Any, Dict
+
+
+class ApiBG():
+    # Backtesting type: Backtesting
+    bt: Dict[str, Any] = {
+        'bt': None,
+        'data': None,
+        'timerange': None,
+        'last_config': {},
+        'bt_error': None,
+    }
+    bgtask_running: bool = False
+    # Exchange - only available in webserver mode.
+    exchange = None

freqtrade/rpc/rpc.py

@@ -7,7 +7,6 @@ from datetime import date, datetime, timedelta, timezone
 from math import isnan
 from typing import Any, Dict, Generator, List, Optional, Sequence, Tuple, Union
 
-import arrow
 import psutil
 from dateutil.relativedelta import relativedelta
 from dateutil.tz import tzlocal
@@ -26,12 +25,13 @@ from freqtrade.exceptions import ExchangeError, PricingError
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_msecs
 from freqtrade.exchange.types import Tickers
 from freqtrade.loggers import bufferHandler
-from freqtrade.misc import decimals_per_coin, shorten_date
+from freqtrade.misc import decimals_per_coin
 from freqtrade.persistence import KeyStoreKeys, KeyValueStore, Order, PairLocks, Trade
 from freqtrade.persistence.models import PairLock
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
 from freqtrade.rpc.rpc_types import RPCSendMsg
+from freqtrade.util import dt_humanize, dt_now, shorten_date
 from freqtrade.wallets import PositionWallet, Wallet
 
 
@@ -292,7 +292,7 @@ class RPC:
                                   and open_order.ft_order_side == trade.entry_side) else '')
                     + ('**' if (open_order and
                                 open_order.ft_order_side == trade.exit_side is not None) else ''),
-                    shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
+                    shorten_date(dt_humanize(trade.open_date, only_distance=True)),
                     profit_str
                 ]
                 if self._config.get('position_adjustment_enable', False):
@@ -420,16 +420,15 @@ class RPC:
             else:
                 return 'draws'
         trades = Trade.get_trades([Trade.is_open.is_(False)], include_orders=False)
-        # Sell reason
+        # Duration
+        dur: Dict[str, List[float]] = {'wins': [], 'draws': [], 'losses': []}
+        # Exit reason
         exit_reasons = {}
         for trade in trades:
             if trade.exit_reason not in exit_reasons:
                 exit_reasons[trade.exit_reason] = {'wins': 0, 'losses': 0, 'draws': 0}
             exit_reasons[trade.exit_reason][trade_win_loss(trade)] += 1
 
-        # Duration
-        dur: Dict[str, List[float]] = {'wins': [], 'draws': [], 'losses': []}
-        for trade in trades:
             if trade.close_date is not None and trade.open_date is not None:
                 trade_dur = (trade.close_date - trade.open_date).total_seconds()
                 dur[trade_win_loss(trade)].append(trade_dur)
@@ -541,8 +540,8 @@ class RPC:
             fiat_display_currency
         ) if self._fiat_converter else 0
 
-        first_date = trades[0].open_date if trades else None
-        last_date = trades[-1].open_date if trades else None
+        first_date = trades[0].open_date_utc if trades else None
+        last_date = trades[-1].open_date_utc if trades else None
         num = float(len(durations) or 1)
         bot_start = KeyValueStore.get_datetime_value(KeyStoreKeys.BOT_START_TIME)
         return {
@@ -564,9 +563,11 @@ class RPC:
             'profit_all_fiat': profit_all_fiat,
             'trade_count': len(trades),
             'closed_trade_count': len([t for t in trades if not t.is_open]),
-            'first_trade_date': arrow.get(first_date).humanize() if first_date else '',
+            'first_trade_date': first_date.strftime(DATETIME_PRINT_FORMAT) if first_date else '',
+            'first_trade_humanized': dt_humanize(first_date) if first_date else '',
             'first_trade_timestamp': int(first_date.timestamp() * 1000) if first_date else 0,
-            'latest_trade_date': arrow.get(last_date).humanize() if last_date else '',
+            'latest_trade_date': last_date.strftime(DATETIME_PRINT_FORMAT) if last_date else '',
+            'latest_trade_humanized': dt_humanize(last_date) if last_date else '',
             'latest_trade_timestamp': int(last_date.timestamp() * 1000) if last_date else 0,
             'avg_duration': str(timedelta(seconds=sum(durations) / num)).split('.')[0],
             'best_pair': best_pair[0] if best_pair else '',
@@ -583,13 +584,16 @@ class RPC:
         }
 
     def __balance_get_est_stake(
-            self, coin: str, stake_currency: str, balance: Wallet, tickers) -> float:
+            self, coin: str, stake_currency: str, amount: float,
+            balance: Wallet, tickers) -> Tuple[float, float]:
         est_stake = 0.0
+        est_bot_stake = 0.0
         if coin == stake_currency:
             est_stake = balance.total
             if self._config.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT:
                 # in Futures, "total" includes the locked stake, and therefore all positions
                 est_stake = balance.free
+            est_bot_stake = amount
         else:
             try:
                 pair = self._freqtrade.exchange.get_valid_pair_combination(coin, stake_currency)
@@ -598,11 +602,12 @@ class RPC:
                     if pair.startswith(stake_currency) and not pair.endswith(stake_currency):
                         rate = 1.0 / rate
                     est_stake = rate * balance.total
+                    est_bot_stake = rate * amount
             except (ExchangeError):
                 logger.warning(f"Could not get rate for pair {coin}.")
                 raise ValueError()
 
-        return est_stake
+        return est_stake, est_bot_stake
 
     def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
         """ Returns current account balance per crypto """
@@ -615,7 +620,7 @@ class RPC:
             raise RPCException('Error getting current tickers.')
 
         open_trades: List[Trade] = Trade.get_open_trades()
-        open_assets = [t.base_currency for t in open_trades]
+        open_assets: Dict[str, Trade] = {t.safe_base_currency: t for t in open_trades}
         self._freqtrade.wallets.update(require_update=False)
         starting_capital = self._freqtrade.wallets.get_starting_balance()
         starting_cap_fiat = self._fiat_converter.convert_amount(
@@ -625,30 +630,43 @@ class RPC:
         for coin, balance in self._freqtrade.wallets.get_all_balances().items():
             if not balance.total:
                 continue
+
+            trade = open_assets.get(coin, None)
+            is_bot_managed = coin == stake_currency or trade is not None
+            trade_amount = trade.amount if trade else 0
+            if coin == stake_currency:
+                trade_amount = self._freqtrade.wallets.get_available_stake_amount()
+
             try:
-                est_stake = self.__balance_get_est_stake(coin, stake_currency, balance, tickers)
+                est_stake, est_stake_bot = self.__balance_get_est_stake(
+                    coin, stake_currency, trade_amount, balance, tickers)
             except ValueError:
                 continue
 
             total += est_stake
-            if coin == stake_currency or coin in open_assets:
-                total_bot += est_stake
+
+            if is_bot_managed:
+                total_bot += est_stake_bot
             currencies.append({
                 'currency': coin,
                 'free': balance.free,
                 'balance': balance.total,
                 'used': balance.used,
+                'bot_owned': trade_amount,
                 'est_stake': est_stake or 0,
+                'est_stake_bot': est_stake_bot if is_bot_managed else 0,
                 'stake': stake_currency,
                 'side': 'long',
                 'leverage': 1,
                 'position': 0,
+                'is_bot_managed': is_bot_managed,
                 'is_position': False,
             })
         symbol: str
         position: PositionWallet
         for symbol, position in self._freqtrade.wallets.get_all_positions().items():
             total += position.collateral
+            total_bot += position.collateral
 
             currencies.append({
                 'currency': symbol,
@@ -657,9 +675,11 @@ class RPC:
                 'used': 0,
                 'position': position.position,
                 'est_stake': position.collateral,
+                'est_stake_bot': position.collateral,
                 'stake': stake_currency,
                 'leverage': position.leverage,
                 'side': position.side,
+                'is_bot_managed': True,
                 'is_position': True
             })
 
@@ -675,8 +695,10 @@ class RPC:
         return {
             'currencies': currencies,
             'total': total,
+            'total_bot': total_bot,
             'symbol': fiat_display_currency,
             'value': value,
+            'value_bot': value_bot,
             'stake': stake_currency,
             'starting_capital': starting_capital,
             'starting_capital_ratio': starting_capital_ratio,
@@ -720,6 +742,18 @@ class RPC:
 
         return {'status': 'No more entries will occur from now. Run /reload_config to reset.'}
 
+    def _rpc_reload_trade_from_exchange(self, trade_id: int) -> Dict[str, str]:
+        """
+        Handler for reload_trade_from_exchange.
+        Reloads a trade from it's orders, should manual interaction have happened.
+        """
+        trade = Trade.get_trades(trade_filter=[Trade.id == trade_id]).first()
+        if not trade:
+            raise RPCException(f"Could not find trade with id {trade_id}.")
+
+        self._freqtrade.handle_onexchange_order(trade)
+        return {'status': 'Reloaded from orders from exchange'}
+
     def __exec_force_exit(self, trade: Trade, ordertype: Optional[str],
                           amount: Optional[float] = None) -> None:
         # Check if there is there is an open order
@@ -1195,8 +1229,8 @@ class RPC:
 
     @staticmethod
     def _rpc_analysed_history_full(config: Config, pair: str, timeframe: str,
-                                   timerange: str, exchange) -> Dict[str, Any]:
-        timerange_parsed = TimeRange.parse_timerange(timerange)
+                                   exchange) -> Dict[str, Any]:
+        timerange_parsed = TimeRange.parse_timerange(config.get('timerange'))
 
         _data = load_data(
             datadir=config["datadir"],
@@ -1207,7 +1241,8 @@ class RPC:
             candle_type=config.get('candle_type_def', CandleType.SPOT)
         )
         if pair not in _data:
-            raise RPCException(f"No data for {pair}, {timeframe} in {timerange} found.")
+            raise RPCException(
+                f"No data for {pair}, {timeframe} in {config.get('timerange')} found.")
         from freqtrade.data.dataprovider import DataProvider
         from freqtrade.resolvers.strategy_resolver import StrategyResolver
         strategy = StrategyResolver.load_strategy(config)
@@ -1217,7 +1252,7 @@ class RPC:
         df_analyzed = strategy.analyze_ticker(_data[pair], {'pair': pair})
 
         return RPC._convert_dataframe_to_dict(strategy.get_strategy_name(), pair, timeframe,
-                                              df_analyzed, arrow.Arrow.utcnow().datetime)
+                                              df_analyzed, dt_now())
 
     def _rpc_plot_config(self) -> Dict[str, Any]:
         if (self._freqtrade.strategy.plot_config and

freqtrade/rpc/webhook.py

@@ -44,8 +44,11 @@ class Webhook(RPCHandler):
 
     def _get_value_dict(self, msg: RPCSendMsg) -> Optional[Dict[str, Any]]:
         whconfig = self._config['webhook']
+        if msg['type'].value in whconfig:
+            # Explicit types should have priority
+            valuedict = whconfig.get(msg['type'].value)
         # Deprecated 2022.10 - only keep generic method.
-        if msg['type'] in [RPCMessageType.ENTRY]:
+        elif msg['type'] in [RPCMessageType.ENTRY]:
             valuedict = whconfig.get('webhookentry')
         elif msg['type'] in [RPCMessageType.ENTRY_CANCEL]:
             valuedict = whconfig.get('webhookentrycancel')
@@ -62,9 +65,6 @@ class Webhook(RPCHandler):
                              RPCMessageType.EXCEPTION,
                              RPCMessageType.WARNING):
             valuedict = whconfig.get('webhookstatus')
-        elif msg['type'].value in whconfig:
-            # Allow all types ...
-            valuedict = whconfig.get(msg['type'].value)
         elif msg['type'] in (
                 RPCMessageType.PROTECTION_TRIGGER,
                 RPCMessageType.PROTECTION_TRIGGER_GLOBAL,

freqtrade/strategy/interface.py

@@ -7,7 +7,6 @@ from abc import ABC, abstractmethod
 from datetime import datetime, timedelta, timezone
 from typing import Dict, List, Optional, Tuple, Union
 
-import arrow
 from pandas import DataFrame
 
 from freqtrade.constants import CUSTOM_TAG_MAX_LENGTH, Config, IntOrInf, ListPairsWithTimeframes
@@ -23,6 +22,7 @@ from freqtrade.strategy.informative_decorator import (InformativeData, PopulateI
                                                       _create_and_merge_informative_pair,
                                                       _format_pair_name)
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util import dt_now
 from freqtrade.wallets import Wallets
 
 
@@ -938,7 +938,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         pair: str,
         timeframe: str,
         dataframe: DataFrame,
-    ) -> Tuple[Optional[DataFrame], Optional[arrow.Arrow]]:
+    ) -> Tuple[Optional[DataFrame], Optional[datetime]]:
         """
         Calculates current signal based based on the entry order or exit order
         columns of the dataframe.
@@ -954,16 +954,16 @@ class IStrategy(ABC, HyperStrategyMixin):
 
         latest_date = dataframe['date'].max()
         latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
-        # Explicitly convert to arrow object to ensure the below comparison does not fail
-        latest_date = arrow.get(latest_date)
+        # Explicitly convert to datetime object to ensure the below comparison does not fail
+        latest_date = latest_date.to_pydatetime()
 
         # Check if dataframe is out of date
         timeframe_minutes = timeframe_to_minutes(timeframe)
         offset = self.config.get('exchange', {}).get('outdated_offset', 5)
-        if latest_date < (arrow.utcnow().shift(minutes=-(timeframe_minutes * 2 + offset))):
+        if latest_date < (dt_now() - timedelta(minutes=timeframe_minutes * 2 + offset)):
             logger.warning(
                 'Outdated history for pair %s. Last tick is %s minutes old',
-                pair, int((arrow.utcnow() - latest_date).total_seconds() // 60)
+                pair, int((dt_now() - latest_date).total_seconds() // 60)
             )
             return None, None
         return latest, latest_date
@@ -1046,8 +1046,8 @@ class IStrategy(ABC, HyperStrategyMixin):
         timeframe_seconds = timeframe_to_seconds(timeframe)
 
         if self.ignore_expired_candle(
-            latest_date=latest_date.datetime,
-            current_time=datetime.now(timezone.utc),
+            latest_date=latest_date,
+            current_time=dt_now(),
             timeframe_seconds=timeframe_seconds,
             enter=bool(enter_signal)
         ):

freqtrade/templates/FreqaiExampleStrategy.py

@@ -15,12 +15,15 @@ logger = logging.getLogger(__name__)
 class FreqaiExampleStrategy(IStrategy):
     """
     Example strategy showing how the user connects their own
-    IFreqaiModel to the strategy. Namely, the user uses:
-    self.freqai.start(dataframe, metadata)
+    IFreqaiModel to the strategy.
 
-    to make predictions on their data. feature_engineering_*() automatically
-    generate the variety of features indicated by the user in the
-    canonical freqtrade configuration file under config['freqai'].
+    Warning! This is a showcase of functionality,
+    which means that it is designed to show various functions of FreqAI
+    and it runs on all computers. We use this showcase to help users
+    understand how to build a strategy, and we use it as a benchmark
+    to help debug possible problems.
+
+    This means this is *not* meant to be run live in production.
     """
 
     minimal_roi = {"0": 0.1, "240": -1}

freqtrade/util/__init__.py

@@ -1,2 +1,17 @@
-from freqtrade.util.ft_precise import FtPrecise  # noqa: F401
-from freqtrade.util.periodic_cache import PeriodicCache  # noqa: F401
+from freqtrade.util.datetime_helpers import (dt_floor_day, dt_from_ts, dt_humanize, dt_now, dt_ts,
+                                             dt_utc, shorten_date)
+from freqtrade.util.ft_precise import FtPrecise
+from freqtrade.util.periodic_cache import PeriodicCache
+
+
+__all__ = [
+    'dt_floor_day',
+    'dt_from_ts',
+    'dt_now',
+    'dt_ts',
+    'dt_utc',
+    'dt_humanize',
+    'shorten_date',
+    'FtPrecise',
+    'PeriodicCache',
+]

freqtrade/util/datetime_helpers.py

@@ -0,0 +1,63 @@
+import re
+from datetime import datetime, timezone
+from typing import Optional
+
+import arrow
+
+
+def dt_now() -> datetime:
+    """Return the current datetime in UTC."""
+    return datetime.now(timezone.utc)
+
+
+def dt_utc(year: int, month: int, day: int, hour: int = 0, minute: int = 0, second: int = 0,
+           microsecond: int = 0) -> datetime:
+    """Return a datetime in UTC."""
+    return datetime(year, month, day, hour, minute, second, microsecond, tzinfo=timezone.utc)
+
+
+def dt_ts(dt: Optional[datetime] = None) -> int:
+    """
+    Return dt in ms as a timestamp in UTC.
+    If dt is None, return the current datetime in UTC.
+    """
+    if dt:
+        return int(dt.timestamp() * 1000)
+    return int(dt_now().timestamp() * 1000)
+
+
+def dt_floor_day(dt: datetime) -> datetime:
+    """Return the floor of the day for the given datetime."""
+    return dt.replace(hour=0, minute=0, second=0, microsecond=0)
+
+
+def dt_from_ts(timestamp: float) -> datetime:
+    """
+    Return a datetime from a timestamp.
+    :param timestamp: timestamp in seconds or milliseconds
+    """
+    if timestamp > 1e10:
+        # Timezone in ms - convert to seconds
+        timestamp /= 1000
+    return datetime.fromtimestamp(timestamp, tz=timezone.utc)
+
+
+def shorten_date(_date: str) -> str:
+    """
+    Trim the date so it fits on small screens
+    """
+    new_date = re.sub('seconds?', 'sec', _date)
+    new_date = re.sub('minutes?', 'min', new_date)
+    new_date = re.sub('hours?', 'h', new_date)
+    new_date = re.sub('days?', 'd', new_date)
+    new_date = re.sub('^an?', '1', new_date)
+    return new_date
+
+
+def dt_humanize(dt: datetime, **kwargs) -> str:
+    """
+    Return a humanized string for the given datetime.
+    :param dt: datetime to humanize
+    :param kwargs: kwargs to pass to arrow's humanize()
+    """
+    return arrow.get(dt).humanize(**kwargs)

freqtrade/wallets.py

@@ -3,16 +3,16 @@
 
 import logging
 from copy import deepcopy
+from datetime import datetime, timedelta
 from typing import Dict, NamedTuple, Optional
 
-import arrow
-
 from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config
 from freqtrade.enums import RunMode, TradingMode
 from freqtrade.exceptions import DependencyException
 from freqtrade.exchange import Exchange
 from freqtrade.misc import safe_value_fallback
 from freqtrade.persistence import LocalTrade, Trade
+from freqtrade.util.datetime_helpers import dt_now
 
 
 logger = logging.getLogger(__name__)
@@ -43,7 +43,7 @@ class Wallets:
         self._wallets: Dict[str, Wallet] = {}
         self._positions: Dict[str, PositionWallet] = {}
         self.start_cap = config['dry_run_wallet']
-        self._last_wallet_refresh = 0
+        self._last_wallet_refresh: Optional[datetime] = None
         self.update()
 
     def get_free(self, currency: str) -> float:
@@ -166,14 +166,19 @@ class Wallets:
         for trading operations, the latest balance is needed.
         :param require_update: Allow skipping an update if balances were recently refreshed
         """
-        if (require_update or (self._last_wallet_refresh + 3600 < arrow.utcnow().int_timestamp)):
+        now = dt_now()
+        if (
+            require_update
+            or self._last_wallet_refresh is None
+            or (self._last_wallet_refresh + timedelta(seconds=3600) < now)
+        ):
             if (not self._config['dry_run'] or self._config.get('runmode') == RunMode.LIVE):
                 self._update_live()
             else:
                 self._update_dry()
             if self._log:
                 logger.info('Wallets synced.')
-            self._last_wallet_refresh = arrow.utcnow().int_timestamp
+            self._last_wallet_refresh = dt_now()
 
     def get_all_balances(self) -> Dict[str, Wallet]:
         return self._wallets
@@ -181,6 +186,35 @@ class Wallets:
     def get_all_positions(self) -> Dict[str, PositionWallet]:
         return self._positions
 
+    def _check_exit_amount(self, trade: Trade) -> bool:
+        if trade.trading_mode != TradingMode.FUTURES:
+            # Slightly higher offset than in safe_exit_amount.
+            wallet_amount: float = self.get_total(trade.safe_base_currency) * (2 - 0.981)
+        else:
+            # wallet_amount: float = self.wallets.get_free(trade.safe_base_currency)
+            position = self._positions.get(trade.pair)
+            if position is None:
+                # We don't own anything :O
+                return False
+            wallet_amount = position.position
+
+        if wallet_amount >= trade.amount:
+            return True
+        return False
+
+    def check_exit_amount(self, trade: Trade) -> bool:
+        """
+        Checks if the exit amount is available in the wallet.
+        :param trade: Trade to check
+        :return: True if the exit amount is available, False otherwise
+        """
+        if not self._check_exit_amount(trade):
+            # Update wallets just to make sure
+            self.update()
+            return self._check_exit_amount(trade)
+
+        return True
+
     def get_starting_balance(self) -> float:
         """
         Retrieves starting balance - based on either available capital,

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools >= 46.4.0", "wheel"]
+requires = ["setuptools >= 64.0.0", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [tool.black]

requirements-dev.txt

@@ -7,9 +7,9 @@
 -r docs/requirements-docs.txt
 
 coveralls==3.3.1
-ruff==0.0.262
-mypy==1.2.0
-pre-commit==3.2.2
+ruff==0.0.269
+mypy==1.3.0
+pre-commit==3.3.2
 pytest==7.3.1
 pytest-asyncio==0.21.0
 pytest-cov==4.0.0
@@ -18,15 +18,13 @@ pytest-random-order==1.1.0
 isort==5.12.0
 # For datetime mocking
 time-machine==2.9.0
-# fastapi testing
-httpx==0.24.0
 
 # Convert jupyter notebooks to markdown documents
-nbconvert==7.3.1
+nbconvert==7.4.0
 
 # mypy types
 types-cachetools==5.3.0.5
 types-filelock==3.2.7
-types-requests==2.28.11.17
+types-requests==2.30.0.0
 types-tabulate==0.9.0.2
-types-python-dateutil==2.8.19.12
+types-python-dateutil==2.8.19.13

requirements-freqai-rl.txt

@@ -2,11 +2,10 @@
 -r requirements-freqai.txt
 
 # Required for freqai-rl
-torch==1.13.1; python_version < '3.11'
-stable-baselines3==1.7.0; python_version < '3.11'
-sb3-contrib==1.7.0; python_version < '3.11'
-# Gym is forced to this version by stable-baselines3.
-setuptools==65.5.1 # Should be removed when gym is fixed.
-gym==0.21; python_version < '3.11'
+torch==2.0.1
+#until these branches will be released we can use this
+gymnasium==0.28.1
+stable_baselines3==2.0.0a10
+sb3_contrib>=2.0.0a9
 # Progress bar for stable-baselines3 and sb3-contrib
-tqdm==4.65.0; python_version < '3.11'
+tqdm==4.65.0

requirements-freqai.txt

@@ -5,7 +5,8 @@
 # Required for freqai
 scikit-learn==1.1.3
 joblib==1.2.0
-catboost==1.1.1; platform_machine != 'aarch64' and 'arm' not in platform_machine and python_version < '3.11'
+catboost==1.1.1; sys_platform == 'darwin' and python_version < '3.9'
+catboost==1.2; 'arm' not in platform_machine and (sys_platform != 'darwin' or python_version >= '3.9')
 lightgbm==3.3.5
 xgboost==1.7.5
-tensorboard==2.12.2
+tensorboard==2.13.0

requirements.txt

@@ -1,16 +1,19 @@
 numpy==1.24.3
-pandas==1.5.3
+pandas==2.0.1
 pandas-ta==0.3.14b
 
-ccxt==3.0.75
-cryptography==40.0.2
+ccxt==3.1.5
+cryptography==40.0.2; platform_machine != 'armv7l'
+cryptography==40.0.1; platform_machine == 'armv7l'
 aiohttp==3.8.4
-SQLAlchemy==2.0.10
-python-telegram-bot==13.15
+SQLAlchemy==2.0.15
+python-telegram-bot==20.3
+# can't be hard-pinned due to telegram-bot pinning httpx with ~
+httpx>=0.23.3
 arrow==1.2.3
-cachetools==4.2.2
-requests==2.28.2
-urllib3==1.26.15
+cachetools==5.3.0
+requests==2.31.0
+urllib3==2.0.2
 jsonschema==4.17.3
 TA-Lib==0.4.26
 technical==1.4.0
@@ -20,8 +23,8 @@ jinja2==3.1.2
 tables==3.8.0
 blosc==1.11.1
 joblib==1.2.0
-rich==13.3.4
-pyarrow==11.0.0; platform_machine != 'armv7l'
+rich==13.3.5
+pyarrow==12.0.0; platform_machine != 'armv7l'
 
 # find first, C search in arrays
 py_find_1st==1.1.5
@@ -29,16 +32,16 @@ py_find_1st==1.1.5
 # Load ticker files 30% faster
 python-rapidjson==1.10
 # Properly format api responses
-orjson==3.8.10
+orjson==3.8.12
 
 # Notify systemd
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.95.1
+fastapi==0.95.2
 pydantic==1.10.7
-uvicorn==0.21.1
-pyjwt==2.6.0
+uvicorn==0.22.0
+pyjwt==2.7.0
 aiofiles==23.1.0
 psutil==5.9.5
 
@@ -54,7 +57,8 @@ python-dateutil==2.8.2
 schedule==1.2.0
 
 #WS Messages
-websockets==11.0.2
+websockets==11.0.3
 janus==1.0.0
 
 ast-comments==1.0.1
+packaging==23.1

scripts/rest_client.py

@@ -279,8 +279,9 @@ class FtRestClient():
         """
         data = {"pair": pair,
                 "side": side,
-                "price": price,
                 }
+        if price:
+            data['price'] = price
         return self._post("forceenter", data=data)
 
     def forceexit(self, tradeid, ordertype=None, amount=None):
@@ -348,12 +349,13 @@ class FtRestClient():
             params['limit'] = limit
         return self._get("pair_candles", params=params)
 
-    def pair_history(self, pair, timeframe, strategy, timerange=None):
+    def pair_history(self, pair, timeframe, strategy, timerange=None, freqaimodel=None):
         """Return historic, analyzed dataframe
 
         :param pair: Pair to get data for
         :param timeframe: Only pairs with this timeframe available.
         :param strategy: Strategy to analyze and get values for
+        :param freqaimodel: FreqAI model to use for analysis
         :param timerange: Timerange to get data for (same format than --timerange endpoints)
         :return: json object
         """
@@ -361,6 +363,7 @@ class FtRestClient():
             "pair": pair,
             "timeframe": timeframe,
             "strategy": strategy,
+            "freqaimodel": freqaimodel,
             "timerange": timerange if timerange else '',
         })