resolve conflict, ensure gpu works with transformer

.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==2.0.13
         # 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}')"
 

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/freqai-configuration.md

@@ -397,3 +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
@@ -69,11 +72,7 @@ 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
 

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==9.1.12
 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

@@ -187,6 +187,7 @@ 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)

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/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/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/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/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/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

@@ -20,16 +20,16 @@ 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,
@@ -92,8 +92,8 @@ 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.
@@ -131,13 +131,13 @@ 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)
@@ -152,8 +152,8 @@ 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)
 
@@ -165,18 +165,18 @@ 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
@@ -189,7 +189,7 @@ 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:
@@ -1432,6 +1432,47 @@ 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]:
         """
@@ -2900,8 +2941,8 @@ 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/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):
         """
@@ -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

@@ -44,8 +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__}. "
@@ -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 +1,35 @@
+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):
+        logger.warning("Tensorboard is not installed, no logs will be written."
+                       "Ensure torch is installed, or use the torch/RL docker images")
+
+    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):
+        logger.warning("Tensorboard is not installed, no logs will be written."
+                       "Ensure torch is installed, or use the torch/RL docker images")
+
+    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
+import torch.nn as 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(torch.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'])
 
@@ -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
@@ -982,7 +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,
         }
@@ -1034,6 +1074,13 @@ class FreqtradeBot(LoggingMixin):
         """
         trades_closed = 0
         for trade in trades:
+
+            if 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
@@ -1536,13 +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}.")
@@ -1698,8 +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),
@@ -1721,10 +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)
@@ -1765,6 +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
 #
@@ -1803,10 +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)
 
@@ -1824,16 +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/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/trade_model.py

@@ -425,7 +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:

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/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

@@ -11,6 +11,7 @@ from freqtrade.configuration.config_validation import validate_config_consistenc
 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)
@@ -38,6 +39,7 @@ async def api_start_backtest(  # noqa: C901
         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'])

freqtrade/rpc/api_server/api_schemas.py

@@ -100,8 +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

@@ -44,7 +44,9 @@ logger = logging.getLogger(__name__)
 # 2.24: Add cancel_open_order endpoint
 # 2.25: Add several profit values to /status endpoint
 # 2.26: increase /balance output
-API_VERSION = 2.26
+# 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()
@@ -127,11 +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)):
@@ -247,14 +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

@@ -46,7 +46,7 @@ def get_exchange(config=Depends(get_config)):
     if not ApiServer._exchange:
         from freqtrade.resolvers import ExchangeResolver
         ApiServer._exchange = ExchangeResolver.load_exchange(
-            config['exchange']['name'], config, load_leverage_tiers=False)
+            config, load_leverage_tiers=False)
     return ApiServer._exchange
 
 

freqtrade/rpc/rpc.py

@@ -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': arrow.get(first_date).humanize() 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': arrow.get(last_date).humanize() 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 '',
@@ -741,6 +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
@@ -1216,8 +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"],
@@ -1228,7 +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)

freqtrade/rpc/telegram.py

@@ -196,6 +196,7 @@ class Telegram(RPCHandler):
                 self._force_enter, order_side=SignalDirection.LONG)),
             CommandHandler('forceshort', partial(
                 self._force_enter, order_side=SignalDirection.SHORT)),
+            CommandHandler('reload_trade', self._reload_trade_from_exchange),
             CommandHandler('trades', self._trades),
             CommandHandler('delete', self._delete_trade),
             CommandHandler(['coo', 'cancel_open_order'], self._cancel_open_order),
@@ -852,8 +853,8 @@ class Telegram(RPCHandler):
         profit_all_percent = stats['profit_all_percent']
         profit_all_fiat = stats['profit_all_fiat']
         trade_count = stats['trade_count']
-        first_trade_date = stats['first_trade_date']
-        latest_trade_date = stats['latest_trade_date']
+        first_trade_date = f"{stats['first_trade_humanized']} ({stats['first_trade_date']})"
+        latest_trade_date = f"{stats['latest_trade_humanized']} ({stats['latest_trade_date']})"
         avg_duration = stats['avg_duration']
         best_pair = stats['best_pair']
         best_pair_profit_ratio = stats['best_pair_profit_ratio']
@@ -1074,6 +1075,17 @@ class Telegram(RPCHandler):
         msg = self._rpc._rpc_stopentry()
         await self._send_msg(f"Status: `{msg['status']}`")
 
+    @authorized_only
+    async def _reload_trade_from_exchange(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /reload_trade <tradeid>.
+        """
+        if not context.args or len(context.args) == 0:
+            raise RPCException("Trade-id not set.")
+        trade_id = int(context.args[0])
+        msg = self._rpc._rpc_reload_trade_from_exchange(trade_id)
+        await self._send_msg(f"Status: `{msg['status']}`")
+
     @authorized_only
     async def _force_exit(self, update: Update, context: CallbackContext) -> None:
         """
@@ -1561,6 +1573,7 @@ class Telegram(RPCHandler):
             "*/fx <trade_id>|all:* `Alias to /forceexit`\n"
             f"{force_enter_text if self._config.get('force_entry_enable', False) else ''}"
             "*/delete <trade_id>:* `Instantly delete the given trade in the database`\n"
+            "*/reload_trade <trade_id>:* `Relade trade from exchange Orders`\n"
             "*/cancel_open_order <trade_id>:* `Cancels open orders for trade. "
             "Only valid when the trade has open orders.`\n"
             "*/coo <trade_id>|all:* `Alias to /cancel_open_order`\n"

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/wallets.py

@@ -181,6 +181,35 @@ 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==0.0.267
+mypy==1.3.0
+pre-commit==3.3.1
 pytest==7.3.1
 pytest-asyncio==0.21.0
 pytest-cov==4.0.0
@@ -20,11 +20,11 @@ isort==5.12.0
 time-machine==2.9.0
 
 # 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==2.0.0a5
+sb3_contrib>=2.0.0a4
 # 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

@@ -2,17 +2,18 @@ numpy==1.24.3
 pandas==2.0.1
 pandas-ta==0.3.14b
 
-ccxt==3.0.75
-cryptography==40.0.2
+ccxt==3.0.103
+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==20.2
+SQLAlchemy==2.0.13
+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
+requests==2.30.0
+urllib3==2.0.2
 jsonschema==4.17.3
 TA-Lib==0.4.26
 technical==1.4.0
@@ -22,8 +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
@@ -31,7 +32,7 @@ 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
@@ -39,8 +40,8 @@ sdnotify==0.3.2
 # API Server
 fastapi==0.95.1
 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
 
@@ -56,7 +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

@@ -348,12 +348,13 @@ 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 +362,7 @@ class FtRestClient():
             "pair": pair,
             "timeframe": timeframe,
             "strategy": strategy,
+            "freqaimodel": freqaimodel,
             "timerange": timerange if timerange else '',
         })
 

setup.py

@@ -12,16 +12,19 @@ hyperopt = [
 
 freqai = [
     'scikit-learn',
+    'joblib',
     'catboost; platform_machine != "aarch64"',
     'lightgbm',
-    'xgboost'
+    'xgboost',
+    'tensorboard'
 ]
 
 freqai_rl = [
     'torch',
+    'gymnasium',
     'stable-baselines3',
-    'gym==0.21',
-    'sb3-contrib'
+    'sb3-contrib',
+    'tqdm'
 ]
 
 hdf5 = [
@@ -32,11 +35,20 @@ hdf5 = [
 develop = [
     'coveralls',
     'mypy',
+    'ruff',
+    'pre-commit',
     'pytest',
     'pytest-asyncio',
     'pytest-cov',
     'pytest-mock',
     'pytest-random-order',
+    'isort',
+    'time-machine',
+    'types-cachetools',
+    'types-filelock',
+    'types-requests',
+    'types-tabulate',
+    'types-python-dateutil'
 ]
 
 jupyter = [
@@ -57,9 +69,9 @@ setup(
     ],
     install_requires=[
         # from requirements.txt
-        'ccxt>=2.6.26',
+        'ccxt>=3.0.0',
         'SQLAlchemy>=2.0.6',
-        'python-telegram-bot>=13.4',
+        'python-telegram-bot>=20.1',
         'arrow>=0.17.0',
         'cachetools',
         'requests',
@@ -91,7 +103,13 @@ setup(
         'aiofiles',
         'schedule',
         'websockets',
-        'janus'
+        'janus',
+        'ast-comments',
+        'aiohttp',
+        'cryptography',
+        'httpx',
+        'python-dateutil',
+        'packaging',
     ],
     extras_require={
         'dev': all_extra,

setup.sh

@@ -25,7 +25,7 @@ function check_installed_python() {
         exit 2
     fi
 
-    for v in 10 9 8
+    for v in 11 10 9 8
     do
         PYTHON="python3.${v}"
         which $PYTHON
@@ -49,8 +49,7 @@ function updateenv() {
     source .env/bin/activate
     SYS_ARCH=$(uname -m)
     echo "pip install in-progress. Please wait..."
-    # Setuptools 65.5.0 is the last version that can install gym==0.21.0
-    ${PYTHON} -m pip install --upgrade pip==23.0.1 wheel==0.38.4 setuptools==65.5.1
+    ${PYTHON} -m pip install --upgrade pip wheel setuptools
     REQUIREMENTS_HYPEROPT=""
     REQUIREMENTS_PLOT=""
     REQUIREMENTS_FREQAI=""
@@ -259,7 +258,7 @@ function install() {
         install_redhat
     else
         echo "This script does not support your OS."
-        echo "If you have Python version 3.8 - 3.10, pip, virtualenv, ta-lib you can continue."
+        echo "If you have Python version 3.8 - 3.11, pip, virtualenv, ta-lib you can continue."
         echo "Wait 10 seconds to continue the next install steps or use ctrl+c to interrupt this shell."
         sleep 10
     fi

tests/conftest.py

@@ -181,7 +181,7 @@ def get_patched_exchange(mocker, config, api_mock=None, id='binance',
     patch_exchange(mocker, api_mock, id, mock_markets, mock_supported_modes)
     config['exchange']['name'] = id
     try:
-        exchange = ExchangeResolver.load_exchange(id, config, load_leverage_tiers=True)
+        exchange = ExchangeResolver.load_exchange(config, load_leverage_tiers=True)
     except ImportError:
         exchange = Exchange(config)
     return exchange
@@ -411,6 +411,14 @@ def patch_gc(mocker) -> None:
     mocker.patch("freqtrade.main.gc_set_threshold")
 
 
+@pytest.fixture(autouse=True)
+def user_dir(mocker, tmpdir) -> Path:
+    user_dir = Path(tmpdir) / "user_data"
+    mocker.patch('freqtrade.configuration.configuration.create_userdata_dir',
+                 return_value=user_dir)
+    return user_dir
+
+
 @pytest.fixture(autouse=True)
 def patch_coingekko(mocker) -> None:
     """
@@ -485,7 +493,6 @@ def get_default_conf(testdatadir):
         },
         "exchange": {
             "name": "binance",
-            "enabled": True,
             "key": "key",
             "secret": "secret",
             "pair_whitelist": [

tests/data/test_entryexitanalysis.py

@@ -18,8 +18,9 @@ def entryexitanalysis_cleanup() -> None:
     Backtesting.cleanup()
 
 
-def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmpdir, capsys):
+def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, user_dir, capsys):
     caplog.set_level(logging.INFO)
+    (user_dir / 'backtest_results').mkdir(parents=True, exist_ok=True)
 
     default_conf.update({
         "use_exit_signal": True,
@@ -80,7 +81,7 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
         'backtesting',
         '--config', 'config.json',
         '--datadir', str(testdatadir),
-        '--user-data-dir', str(tmpdir),
+        '--user-data-dir', str(user_dir),
         '--timeframe', '5m',
         '--timerange', '1515560100-1517287800',
         '--export', 'signals',
@@ -98,7 +99,7 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
         'backtesting-analysis',
         '--config', 'config.json',
         '--datadir', str(testdatadir),
-        '--user-data-dir', str(tmpdir),
+        '--user-data-dir', str(user_dir),
     ]
 
     # test group 0 and indicator list
@@ -200,8 +201,17 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
     assert 'trailing_stop_loss' in captured.out
 
     # test date filtering
-    args = get_args(base_args + ['--timerange', "20180129-20180130"])
+    args = get_args(base_args +
+                    ['--analysis-groups', "0", "1", "2",
+                     '--timerange', "20180129-20180130"]
+                    )
     start_analysis_entries_exits(args)
     captured = capsys.readouterr()
     assert 'enter_tag_long_a' in captured.out
     assert 'enter_tag_long_b' not in captured.out
+
+    # Due to the backtest mock, there's no rejected signals generated.
+    args = get_args(base_args + ['--rejected-signals'])
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert 'no rejected signals' in captured.out

tests/exchange/test_ccxt_compat.py

@@ -302,7 +302,7 @@ def exchange(request, exchange_conf):
         exchange_conf, EXCHANGES[request.param].get('use_ci_proxy', False))
     exchange_conf['exchange']['name'] = request.param
     exchange_conf['stake_currency'] = EXCHANGES[request.param]['stake_currency']
-    exchange = ExchangeResolver.load_exchange(request.param, exchange_conf, validate=True)
+    exchange = ExchangeResolver.load_exchange(exchange_conf, validate=True)
 
     yield exchange, request.param
 
@@ -330,7 +330,7 @@ def exchange_futures(request, exchange_conf, class_mocker):
         class_mocker.patch(f'{EXMS}.cache_leverage_tiers')
 
         exchange = ExchangeResolver.load_exchange(
-            request.param, exchange_conf, validate=True, load_leverage_tiers=True)
+            exchange_conf, validate=True, load_leverage_tiers=True)
 
         yield exchange, request.param
 

tests/exchange/test_exchange.py

@@ -20,7 +20,7 @@ from freqtrade.exchange import (Binance, Bittrex, Exchange, Kraken, amount_to_pr
                                 timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date,
                                 timeframe_to_prev_date, timeframe_to_seconds)
 from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, API_RETRY_COUNT,
-                                       calculate_backoff, remove_credentials)
+                                       calculate_backoff, remove_exchange_credentials)
 from freqtrade.exchange.exchange import amount_to_contract_precision
 from freqtrade.resolvers.exchange_resolver import ExchangeResolver
 from tests.conftest import (EXMS, generate_test_data_raw, get_mock_coro, get_patched_exchange,
@@ -137,16 +137,14 @@ def test_init(default_conf, mocker, caplog):
     assert log_has('Instance is running with dry_run enabled', caplog)
 
 
-def test_remove_credentials(default_conf, caplog) -> None:
+def test_remove_exchange_credentials(default_conf) -> None:
     conf = deepcopy(default_conf)
-    conf['dry_run'] = False
-    remove_credentials(conf)
+    remove_exchange_credentials(conf['exchange'], False)
 
     assert conf['exchange']['key'] != ''
     assert conf['exchange']['secret'] != ''
 
-    conf['dry_run'] = True
-    remove_credentials(conf)
+    remove_exchange_credentials(conf['exchange'], True)
     assert conf['exchange']['key'] == ''
     assert conf['exchange']['secret'] == ''
     assert conf['exchange']['password'] == ''
@@ -228,27 +226,30 @@ def test_exchange_resolver(default_conf, mocker, caplog):
     mocker.patch(f'{EXMS}.validate_timeframes')
     mocker.patch(f'{EXMS}.validate_stakecurrency')
     mocker.patch(f'{EXMS}.validate_pricing')
-
-    exchange = ExchangeResolver.load_exchange('zaif', default_conf)
+    default_conf['exchange']['name'] = 'zaif'
+    exchange = ExchangeResolver.load_exchange(default_conf)
     assert isinstance(exchange, Exchange)
     assert log_has_re(r"No .* specific subclass found. Using the generic class instead.", caplog)
     caplog.clear()
 
-    exchange = ExchangeResolver.load_exchange('Bittrex', default_conf)
+    default_conf['exchange']['name'] = 'Bittrex'
+    exchange = ExchangeResolver.load_exchange(default_conf)
     assert isinstance(exchange, Exchange)
     assert isinstance(exchange, Bittrex)
     assert not log_has_re(r"No .* specific subclass found. Using the generic class instead.",
                           caplog)
     caplog.clear()
 
-    exchange = ExchangeResolver.load_exchange('kraken', default_conf)
+    default_conf['exchange']['name'] = 'kraken'
+    exchange = ExchangeResolver.load_exchange(default_conf)
     assert isinstance(exchange, Exchange)
     assert isinstance(exchange, Kraken)
     assert not isinstance(exchange, Binance)
     assert not log_has_re(r"No .* specific subclass found. Using the generic class instead.",
                           caplog)
 
-    exchange = ExchangeResolver.load_exchange('binance', default_conf)
+    default_conf['exchange']['name'] = 'binance'
+    exchange = ExchangeResolver.load_exchange(default_conf)
     assert isinstance(exchange, Exchange)
     assert isinstance(exchange, Binance)
     assert not isinstance(exchange, Kraken)
@@ -257,7 +258,8 @@ def test_exchange_resolver(default_conf, mocker, caplog):
                           caplog)
 
     # Test mapping
-    exchange = ExchangeResolver.load_exchange('binanceus', default_conf)
+    default_conf['exchange']['name'] = 'binanceus'
+    exchange = ExchangeResolver.load_exchange(default_conf)
     assert isinstance(exchange, Exchange)
     assert isinstance(exchange, Binance)
     assert not isinstance(exchange, Kraken)
@@ -990,19 +992,20 @@ def test_validate_pricing(default_conf, mocker):
     mocker.patch(f'{EXMS}.validate_timeframes')
     mocker.patch(f'{EXMS}.validate_stakecurrency')
     mocker.patch(f'{EXMS}.name', 'Binance')
-    ExchangeResolver.load_exchange('binance', default_conf)
+    default_conf['exchange']['name'] = 'binance'
+    ExchangeResolver.load_exchange(default_conf)
     has.update({'fetchTicker': False})
     with pytest.raises(OperationalException, match="Ticker pricing not available for .*"):
-        ExchangeResolver.load_exchange('binance', default_conf)
+        ExchangeResolver.load_exchange(default_conf)
 
     has.update({'fetchTicker': True})
 
     default_conf['exit_pricing']['use_order_book'] = True
-    ExchangeResolver.load_exchange('binance', default_conf)
+    ExchangeResolver.load_exchange(default_conf)
     has.update({'fetchL2OrderBook': False})
 
     with pytest.raises(OperationalException, match="Orderbook not available for .*"):
-        ExchangeResolver.load_exchange('binance', default_conf)
+        ExchangeResolver.load_exchange(default_conf)
 
     has.update({'fetchL2OrderBook': True})
 
@@ -1011,7 +1014,7 @@ def test_validate_pricing(default_conf, mocker):
     default_conf['margin_mode'] = MarginMode.ISOLATED
 
     with pytest.raises(OperationalException, match="Ticker pricing not available for .*"):
-        ExchangeResolver.load_exchange('binance', default_conf)
+        ExchangeResolver.load_exchange(default_conf)
 
 
 def test_validate_ordertypes(default_conf, mocker):
@@ -1091,12 +1094,13 @@ def test_validate_ordertypes_stop_advanced(default_conf, mocker, exchange_name,
         'stoploss_on_exchange': True,
         'stoploss_price_type': stopadv,
     }
+    default_conf['exchange']['name'] = exchange_name
     if expected:
-        ExchangeResolver.load_exchange(exchange_name, default_conf)
+        ExchangeResolver.load_exchange(default_conf)
     else:
         with pytest.raises(OperationalException,
                            match=r'On exchange stoploss price type is not supported for .*'):
-            ExchangeResolver.load_exchange(exchange_name, default_conf)
+            ExchangeResolver.load_exchange(default_conf)
 
 
 def test_validate_order_types_not_in_config(default_conf, mocker):
@@ -1773,6 +1777,71 @@ def test_fetch_positions(default_conf, mocker, exchange_name):
                            "fetch_positions", "fetch_positions")
 
 
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_fetch_orders(default_conf, mocker, exchange_name, limit_order):
+
+    api_mock = MagicMock()
+    api_mock.fetch_orders = MagicMock(return_value=[
+        limit_order['buy'],
+        limit_order['sell'],
+    ])
+    api_mock.fetch_open_orders = MagicMock(return_value=[limit_order['buy']])
+    api_mock.fetch_closed_orders = MagicMock(return_value=[limit_order['buy']])
+
+    mocker.patch(f'{EXMS}.exchange_has', return_value=True)
+    start_time = datetime.now(timezone.utc) - timedelta(days=5)
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
+    # Not available in dry-run
+    assert exchange.fetch_orders('mocked', start_time) == []
+    assert api_mock.fetch_orders.call_count == 0
+    default_conf['dry_run'] = False
+
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
+    res = exchange.fetch_orders('mocked', start_time)
+    assert api_mock.fetch_orders.call_count == 1
+    assert api_mock.fetch_open_orders.call_count == 0
+    assert api_mock.fetch_closed_orders.call_count == 0
+    assert len(res) == 2
+
+    res = exchange.fetch_orders('mocked', start_time)
+
+    api_mock.fetch_orders.reset_mock()
+
+    def has_resp(_, endpoint):
+        if endpoint == 'fetchOrders':
+            return False
+        if endpoint == 'fetchClosedOrders':
+            return True
+        if endpoint == 'fetchOpenOrders':
+            return True
+
+    mocker.patch(f'{EXMS}.exchange_has', has_resp)
+
+    # happy path without fetchOrders
+    res = exchange.fetch_orders('mocked', start_time)
+    assert api_mock.fetch_orders.call_count == 0
+    assert api_mock.fetch_open_orders.call_count == 1
+    assert api_mock.fetch_closed_orders.call_count == 1
+
+    mocker.patch(f'{EXMS}.exchange_has', return_value=True)
+
+    ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
+                           "fetch_orders", "fetch_orders", retries=1,
+                           pair='mocked', since=start_time)
+
+    # Unhappy path - first fetch-orders call fails.
+    api_mock.fetch_orders = MagicMock(side_effect=ccxt.NotSupported())
+    api_mock.fetch_open_orders.reset_mock()
+    api_mock.fetch_closed_orders.reset_mock()
+
+    res = exchange.fetch_orders('mocked', start_time)
+
+    assert api_mock.fetch_orders.call_count == 1
+    assert api_mock.fetch_open_orders.call_count == 1
+    assert api_mock.fetch_closed_orders.call_count == 1
+
+
 def test_fetch_trading_fees(default_conf, mocker):
     api_mock = MagicMock()
     tick = {
@@ -4932,7 +5001,7 @@ def test_get_maintenance_ratio_and_amt_exceptions(mocker, default_conf, leverage
 
     exchange._leverage_tiers = leverage_tiers
     with pytest.raises(
-        OperationalException,
+        DependencyException,
         match='nominal value can not be lower than 0',
     ):
         exchange.get_maintenance_ratio_and_amt('1000SHIB/USDT:USDT', -1)

tests/freqai/conftest.py

@@ -1,3 +1,4 @@
+import platform
 from copy import deepcopy
 from pathlib import Path
 from typing import Any, Dict
@@ -14,6 +15,11 @@ from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
 from tests.conftest import get_patched_exchange
 
 
+def is_mac() -> bool:
+    machine = platform.system()
+    return "Darwin" in machine
+
+
 @pytest.fixture(scope="function")
 def freqai_conf(default_conf, tmpdir):
     freqaiconf = deepcopy(default_conf)
@@ -36,6 +42,7 @@ def freqai_conf(default_conf, tmpdir):
                 "identifier": "uniqe-id100",
                 "live_trained_timestamp": 0,
                 "data_kitchen_thread_count": 2,
+                "activate_tensorboard": False,
                 "feature_parameters": {
                     "include_timeframes": ["5m"],
                     "include_corr_pairlist": ["ADA/BTC"],

tests/freqai/test_freqai_datakitchen.py

@@ -12,6 +12,7 @@ from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from tests.conftest import get_patched_exchange, log_has_re
 from tests.freqai.conftest import (get_patched_data_kitchen, get_patched_freqai_strategy,
                                    make_data_dictionary, make_unfiltered_dataframe)
+from tests.freqai.test_freqai_interface import is_mac
 
 
 @pytest.mark.parametrize(
@@ -173,6 +174,9 @@ def test_get_full_model_path(mocker, freqai_conf, model):
     freqai_conf.update({"timerange": "20180110-20180130"})
     freqai_conf.update({"strategy": "freqai_test_strat"})
 
+    if is_mac():
+        pytest.skip("Mac is confused during this test for unknown reasons")
+
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
     strategy.dp = DataProvider(freqai_conf, exchange)
@@ -188,7 +192,7 @@ def test_get_full_model_path(mocker, freqai_conf, model):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
-
+    freqai.dk.set_paths('ADA/BTC', None)
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
 

tests/freqai/test_freqai_interface.py

@@ -15,7 +15,7 @@ from freqtrade.optimize.backtesting import Backtesting
 from freqtrade.persistence import Trade
 from freqtrade.plugins.pairlistmanager import PairListManager
 from tests.conftest import EXMS, create_mock_trades, get_patched_exchange, log_has_re
-from tests.freqai.conftest import (get_patched_freqai_strategy, make_rl_config,
+from tests.freqai.conftest import (get_patched_freqai_strategy, is_mac, make_rl_config,
                                    mock_pytorch_mlp_model_training_parameters)
 
 
@@ -28,29 +28,22 @@ def is_arm() -> bool:
     return "arm" in machine or "aarch64" in machine
 
 
-def is_mac() -> bool:
-    machine = platform.system()
-    return "Darwin" in machine
-
-
 def can_run_model(model: str) -> None:
-    if (is_arm() or is_py11()) and "Catboost" in model:
+    if is_arm() and "Catboost" in model:
         pytest.skip("CatBoost is not supported on ARM.")
 
     is_pytorch_model = 'Reinforcement' in model or 'PyTorch' in model
     if is_pytorch_model and is_mac() and not is_arm():
         pytest.skip("Reinforcement learning / PyTorch module not available on intel based Mac OS.")
 
-    if is_pytorch_model and is_py11():
-        pytest.skip("Reinforcement learning / PyTorch currently not available on python 3.11.")
-
 
 @pytest.mark.parametrize('model, pca, dbscan, float32, can_short, shuffle, buffer', [
     ('LightGBMRegressor', True, False, True, True, False, 0),
     ('XGBoostRegressor', False, True, False, True, False, 10),
     ('XGBoostRFRegressor', False, False, False, True, False, 0),
     ('CatboostRegressor', False, False, False, True, True, 0),
-    ('PyTorchMLPRegressor', False, False, False, True, False, 0),
+    ('PyTorchMLPRegressor', False, False, False, False, False, 0),
+    ('PyTorchTransformerRegressor', False, False, False, False, False, 0),
     ('ReinforcementLearner', False, True, False, True, False, 0),
     ('ReinforcementLearner_multiproc', False, False, False, True, False, 0),
     ('ReinforcementLearner_test_3ac', False, False, False, False, False, 0),
@@ -61,6 +54,11 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
                                                dbscan, float32, can_short, shuffle, buffer):
 
     can_run_model(model)
+
+    test_tb = True
+    if is_mac():
+        test_tb = False
+
     model_save_ext = 'joblib'
     freqai_conf.update({"freqaimodel": model})
     freqai_conf.update({"timerange": "20180110-20180130"})
@@ -82,10 +80,13 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
         freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
         freqai_conf["freqai"]["rl_config"]["drop_ohlc_from_features"] = True
 
-    if 'PyTorchMLPRegressor' in model:
+    if 'PyTorch' in model:
         model_save_ext = 'zip'
         pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
         freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp)
+        if 'Transformer' in model:
+            # transformer model takes a window, unlike the MLP regressor
+            freqai_conf.update({"conv_width": 10})
 
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
@@ -93,6 +94,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
     strategy.freqai_info = freqai_conf.get("freqai", {})
     freqai = strategy.freqai
     freqai.live = True
+    freqai.activate_tensorboard = test_tb
     freqai.can_short = can_short
     freqai.dk = FreqaiDataKitchen(freqai_conf)
     freqai.dk.live = True
@@ -228,6 +230,7 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
         ("XGBoostRegressor", 2, "freqai_test_strat"),
         ("CatboostRegressor", 2, "freqai_test_strat"),
         ("PyTorchMLPRegressor", 2, "freqai_test_strat"),
+        ("PyTorchTransformerRegressor", 2, "freqai_test_strat"),
         ("ReinforcementLearner", 3, "freqai_rl_test_strat"),
         ("XGBoostClassifier", 2, "freqai_test_classifier"),
         ("LightGBMClassifier", 2, "freqai_test_classifier"),
@@ -237,6 +240,9 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
     )
 def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
     can_run_model(model)
+    test_tb = True
+    if is_mac():
+        test_tb = False
 
     freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
     freqai_conf['runmode'] = RunMode.BACKTEST
@@ -253,9 +259,12 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
     if 'test_4ac' in model:
         freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
 
-    if 'PyTorchMLP' in model:
+    if 'PyTorch' in model:
         pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
         freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp)
+        if 'Transformer' in model:
+            # transformer model takes a window, unlike the MLP regressor
+            freqai_conf.update({"conv_width": 10})
 
     freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
         {"indicator_periods_candles": [2]})
@@ -266,6 +275,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
     strategy.freqai_info = freqai_conf.get("freqai", {})
     freqai = strategy.freqai
     freqai.live = False
+    freqai.activate_tensorboard = test_tb
     freqai.dk = FreqaiDataKitchen(freqai_conf)
     timerange = TimeRange.parse_timerange("20180110-20180130")
     freqai.dd.load_all_pair_histories(timerange, freqai.dk)
@@ -277,6 +287,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
         df[f'%-constant_{i}'] = i
 
     metadata = {"pair": "LTC/BTC"}
+    freqai.dk.set_paths('LTC/BTC', None)
     freqai.start_backtesting(df, metadata, freqai.dk, strategy)
     model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
 
@@ -434,6 +445,7 @@ def test_principal_component_analysis(mocker, freqai_conf):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
@@ -467,6 +479,7 @@ def test_plot_feature_importance(mocker, freqai_conf):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)

tests/freqai/test_models/ReinforcementLearner_test_3ac.py

@@ -18,6 +18,11 @@ class ReinforcementLearner_test_3ac(ReinforcementLearner):
         """
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.
+
+        Warning!
+        This is function is a showcase of functionality designed to show as many possible
+        environment control features as possible. It is also designed to run quickly
+        on small computers. This is a benchmark, it is *not* for live production.
         """
 
         def calculate_reward(self, action: int) -> float:

tests/freqai/test_models/ReinforcementLearner_test_4ac.py

@@ -18,6 +18,11 @@ class ReinforcementLearner_test_4ac(ReinforcementLearner):
         """
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.
+
+        Warning!
+        This is function is a showcase of functionality designed to show as many possible
+        environment control features as possible. It is also designed to run quickly
+        on small computers. This is a benchmark, it is *not* for live production.
         """
 
         def calculate_reward(self, action: int) -> float:

tests/optimize/test_backtesting.py

@@ -354,7 +354,7 @@ def test_backtesting_start(default_conf, mocker, caplog) -> None:
     mocker.patch('freqtrade.optimize.backtesting.generate_backtest_stats')
     mocker.patch('freqtrade.optimize.backtesting.show_backtest_results')
     sbs = mocker.patch('freqtrade.optimize.backtesting.store_backtest_stats')
-    sbc = mocker.patch('freqtrade.optimize.backtesting.store_backtest_signal_candles')
+    sbc = mocker.patch('freqtrade.optimize.backtesting.store_backtest_analysis_results')
     mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                  PropertyMock(return_value=['UNITTEST/BTC']))
 

tests/optimize/test_optimize_reports.py

@@ -21,7 +21,7 @@ from freqtrade.optimize.optimize_reports import (_get_resample_from_period, gene
                                                  generate_periodic_breakdown_stats,
                                                  generate_strategy_comparison,
                                                  generate_trading_stats, show_sorted_pairlist,
-                                                 store_backtest_signal_candles,
+                                                 store_backtest_analysis_results,
                                                  store_backtest_stats, text_table_bt_results,
                                                  text_table_exit_reason, text_table_strategy)
 from freqtrade.resolvers.strategy_resolver import StrategyResolver
@@ -232,17 +232,17 @@ def test_store_backtest_candles(testdatadir, mocker):
     candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}
 
     # mock directory exporting
-    store_backtest_signal_candles(testdatadir, candle_dict, '2022_01_01_15_05_13')
+    store_backtest_analysis_results(testdatadir, candle_dict, {}, '2022_01_01_15_05_13')
 
-    assert dump_mock.call_count == 1
+    assert dump_mock.call_count == 2
     assert isinstance(dump_mock.call_args_list[0][0][0], Path)
     assert str(dump_mock.call_args_list[0][0][0]).endswith('_signals.pkl')
 
     dump_mock.reset_mock()
     # mock file exporting
     filename = Path(testdatadir / 'testresult')
-    store_backtest_signal_candles(filename, candle_dict, '2022_01_01_15_05_13')
-    assert dump_mock.call_count == 1
+    store_backtest_analysis_results(filename, candle_dict, {}, '2022_01_01_15_05_13')
+    assert dump_mock.call_count == 2
     assert isinstance(dump_mock.call_args_list[0][0][0], Path)
     # result will be testdatadir / testresult-<timestamp>_signals.pkl
     assert str(dump_mock.call_args_list[0][0][0]).endswith('_signals.pkl')
@@ -254,10 +254,11 @@ def test_write_read_backtest_candles(tmpdir):
     candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}
 
     # test directory exporting
-    stored_file = store_backtest_signal_candles(Path(tmpdir), candle_dict, '2022_01_01_15_05_13')
-    scp = stored_file.open("rb")
-    pickled_signal_candles = joblib.load(scp)
-    scp.close()
+    sample_date = '2022_01_01_15_05_13'
+    store_backtest_analysis_results(Path(tmpdir), candle_dict, {}, sample_date)
+    stored_file = Path(tmpdir / f'backtest-result-{sample_date}_signals.pkl')
+    with stored_file.open("rb") as scp:
+        pickled_signal_candles = joblib.load(scp)
 
     assert pickled_signal_candles.keys() == candle_dict.keys()
     assert pickled_signal_candles['DefStrat'].keys() == pickled_signal_candles['DefStrat'].keys()
@@ -268,10 +269,10 @@ def test_write_read_backtest_candles(tmpdir):
 
     # test file exporting
     filename = Path(tmpdir / 'testresult')
-    stored_file = store_backtest_signal_candles(filename, candle_dict, '2022_01_01_15_05_13')
-    scp = stored_file.open("rb")
-    pickled_signal_candles = joblib.load(scp)
-    scp.close()
+    store_backtest_analysis_results(filename, candle_dict, {}, sample_date)
+    stored_file = Path(tmpdir / f'testresult-{sample_date}_signals.pkl')
+    with stored_file.open("rb") as scp:
+        pickled_signal_candles = joblib.load(scp)
 
     assert pickled_signal_candles.keys() == candle_dict.keys()
     assert pickled_signal_candles['DefStrat'].keys() == pickled_signal_candles['DefStrat'].keys()

tests/persistence/test_persistence.py

@@ -239,7 +239,7 @@ def test_interest(fee, exchange, is_short, lev, minutes, rate, interest,
         stake_amount=20.0,
         amount=30.0,
         open_rate=2.0,
-        open_date=datetime.utcnow() - timedelta(minutes=minutes),
+        open_date=datetime.now(timezone.utc) - timedelta(minutes=minutes),
         fee_open=fee.return_value,
         fee_close=fee.return_value,
         exchange=exchange,
@@ -2063,7 +2063,7 @@ def test_trade_truncates_string_fields():
         stake_amount=20.0,
         amount=30.0,
         open_rate=2.0,
-        open_date=datetime.utcnow() - timedelta(minutes=20),
+        open_date=datetime.now(timezone.utc) - timedelta(minutes=20),
         fee_open=0.001,
         fee_close=0.001,
         exchange='binance',

tests/plugins/test_protections.py

@@ -1,5 +1,5 @@
 import random
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone
 
 import pytest
 
@@ -24,8 +24,8 @@ def generate_mock_trade(pair: str, fee: float, is_open: bool,
         stake_amount=0.01,
         fee_open=fee,
         fee_close=fee,
-        open_date=datetime.utcnow() - timedelta(minutes=min_ago_open or 200),
-        close_date=datetime.utcnow() - timedelta(minutes=min_ago_close or 30),
+        open_date=datetime.now(timezone.utc) - timedelta(minutes=min_ago_open or 200),
+        close_date=datetime.now(timezone.utc) - timedelta(minutes=min_ago_close or 30),
         open_rate=open_rate,
         is_open=is_open,
         amount=0.01 / open_rate,
@@ -87,9 +87,9 @@ def test_protectionmanager(mocker, default_conf):
     for handler in freqtrade.protections._protection_handlers:
         assert handler.name in constants.AVAILABLE_PROTECTIONS
         if not handler.has_global_stop:
-            assert handler.global_stop(datetime.utcnow(), '*') is None
+            assert handler.global_stop(datetime.now(timezone.utc), '*') is None
         if not handler.has_local_stop:
-            assert handler.stop_per_pair('XRP/BTC', datetime.utcnow(), '*') is None
+            assert handler.stop_per_pair('XRP/BTC', datetime.now(timezone.utc), '*') is None
 
 
 @pytest.mark.parametrize('timeframe,expected,protconf', [

tests/rpc/test_rpc.py

@@ -261,8 +261,7 @@ def test_rpc_status_table(default_conf, ticker, fee, mocker) -> None:
     assert isnan(fiat_profit_sum)
 
 
-def test__rpc_timeunit_profit(default_conf_usdt, ticker, fee,
-                              limit_buy_order, limit_sell_order, markets, mocker) -> None:
+def test__rpc_timeunit_profit(default_conf_usdt, ticker, fee, markets, mocker) -> None:
     mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
     mocker.patch.multiple(
         EXMS,
@@ -295,7 +294,7 @@ def test__rpc_timeunit_profit(default_conf_usdt, ticker, fee,
         assert day['starting_balance'] in (pytest.approx(1062.37), pytest.approx(1066.46))
         assert day['fiat_value'] in (0.0, )
     # ensure first day is current date
-    assert str(days['data'][0]['date']) == str(datetime.utcnow().date())
+    assert str(days['data'][0]['date']) == str(datetime.now(timezone.utc).date())
 
     # Try invalid data
     with pytest.raises(RPCException, match=r'.*must be an integer greater than 0*'):
@@ -415,8 +414,8 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
     assert pytest.approx(stats['profit_all_percent_mean']) == -57.86
     assert pytest.approx(stats['profit_all_fiat']) == -85.205614098
     assert stats['trade_count'] == 7
-    assert stats['first_trade_date'] == '2 days ago'
-    assert stats['latest_trade_date'] == '17 minutes ago'
+    assert stats['first_trade_humanized'] == '2 days ago'
+    assert stats['latest_trade_humanized'] == '17 minutes ago'
     assert stats['avg_duration'] in ('0:17:40')
     assert stats['best_pair'] == 'XRP/USDT'
     assert stats['best_rate'] == 10.0
@@ -426,8 +425,8 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
                  MagicMock(side_effect=ExchangeError("Pair 'XRP/USDT' not available")))
     stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
     assert stats['trade_count'] == 7
-    assert stats['first_trade_date'] == '2 days ago'
-    assert stats['latest_trade_date'] == '17 minutes ago'
+    assert stats['first_trade_humanized'] == '2 days ago'
+    assert stats['latest_trade_humanized'] == '17 minutes ago'
     assert stats['avg_duration'] in ('0:17:40')
     assert stats['best_pair'] == 'XRP/USDT'
     assert stats['best_rate'] == 10.0

tests/rpc/test_rpc_apiserver.py

@@ -601,7 +601,7 @@ def test_api_daily(botclient, mocker, ticker, fee, markets):
     assert len(rc.json()['data']) == 7
     assert rc.json()['stake_currency'] == 'BTC'
     assert rc.json()['fiat_display_currency'] == 'USD'
-    assert rc.json()['data'][0]['date'] == str(datetime.utcnow().date())
+    assert rc.json()['data'][0]['date'] == str(datetime.now(timezone.utc).date())
 
 
 @pytest.mark.parametrize('is_short', [True, False])
@@ -740,6 +740,33 @@ def test_api_delete_open_order(botclient, mocker, fee, markets, ticker, is_short
     assert cancel_mock.call_count == 1
 
 
+@pytest.mark.parametrize('is_short', [True, False])
+def test_api_trade_reload_trade(botclient, mocker, fee, markets, ticker, is_short):
+    ftbot, client = botclient
+    patch_get_signal(ftbot, enter_long=not is_short, enter_short=is_short)
+    stoploss_mock = MagicMock()
+    cancel_mock = MagicMock()
+    ftbot.handle_onexchange_order = MagicMock()
+    mocker.patch.multiple(
+        EXMS,
+        markets=PropertyMock(return_value=markets),
+        fetch_ticker=ticker,
+        cancel_order=cancel_mock,
+        cancel_stoploss_order=stoploss_mock,
+    )
+
+    rc = client_post(client, f"{BASE_URI}/trades/10/reload")
+    assert_response(rc, 502)
+    assert 'Could not find trade with id 10.' in rc.json()['error']
+    assert ftbot.handle_onexchange_order.call_count == 0
+
+    create_mock_trades(fee, is_short=is_short)
+    Trade.commit()
+
+    rc = client_post(client, f"{BASE_URI}/trades/5/reload")
+    assert ftbot.handle_onexchange_order.call_count == 1
+
+
 def test_api_logs(botclient):
     ftbot, client = botclient
     rc = client_get(client, f"{BASE_URI}/logs")
@@ -861,8 +888,10 @@ def test_api_profit(botclient, mocker, ticker, fee, markets, is_short, expected)
         'best_pair_profit_ratio': expected['best_pair_profit_ratio'],
         'best_rate': expected['best_rate'],
         'first_trade_date': ANY,
+        'first_trade_humanized': ANY,
         'first_trade_timestamp': ANY,
-        'latest_trade_date': '5 minutes ago',
+        'latest_trade_date': ANY,
+        'latest_trade_humanized': '5 minutes ago',
         'latest_trade_timestamp': ANY,
         'profit_all_coin': pytest.approx(expected['profit_all_coin']),
         'profit_all_fiat': pytest.approx(expected['profit_all_fiat']),
@@ -1197,7 +1226,7 @@ def test_api_force_entry(botclient, mocker, fee, endpoint):
         stake_amount=1,
         open_rate=0.245441,
         open_order_id="123456",
-        open_date=datetime.utcnow(),
+        open_date=datetime.now(timezone.utc),
         is_open=False,
         is_short=False,
         fee_close=fee.return_value,

tests/rpc/test_rpc_telegram.py

@@ -52,7 +52,7 @@ def default_conf(default_conf) -> dict:
 
 @pytest.fixture
 def update():
-    message = Message(0, datetime.utcnow(), Chat(0, 0))
+    message = Message(0, datetime.now(timezone.utc), Chat(0, 0))
     _update = Update(0, message=message)
 
     return _update
@@ -143,8 +143,8 @@ def test_telegram_init(default_conf, mocker, caplog) -> None:
     message_str = ("rpc.telegram is listening for following commands: [['status'], ['profit'], "
                    "['balance'], ['start'], ['stop'], "
                    "['forceexit', 'forcesell', 'fx'], ['forcebuy', 'forcelong'], ['forceshort'], "
-                   "['trades'], ['delete'], ['cancel_open_order', 'coo'], ['performance'], "
-                   "['buys', 'entries'], ['exits', 'sells'], ['mix_tags'], "
+                   "['reload_trade'], ['trades'], ['delete'], ['cancel_open_order', 'coo'], "
+                   "['performance'], ['buys', 'entries'], ['exits', 'sells'], ['mix_tags'], "
                    "['stats'], ['daily'], ['weekly'], ['monthly'], "
                    "['count'], ['locks'], ['delete_locks', 'unlock'], "
                    "['reload_conf', 'reload_config'], ['show_conf', 'show_config'], "
@@ -213,7 +213,7 @@ async def test_authorized_only_unauthorized(default_conf, mocker, caplog) -> Non
     patch_exchange(mocker)
     caplog.set_level(logging.DEBUG)
     chat = Chat(0xdeadbeef, 0)
-    message = Message(randint(1, 100), datetime.utcnow(), chat)
+    message = Message(randint(1, 100), datetime.now(timezone.utc), chat)
     update = Update(randint(1, 100), message=message)
 
     default_conf['telegram']['enabled'] = False
@@ -520,7 +520,7 @@ async def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time
     assert msg_mock.call_count == 1
     assert "Daily Profit over the last 2 days</b>:" in msg_mock.call_args_list[0][0][0]
     assert 'Day ' in msg_mock.call_args_list[0][0][0]
-    assert str(datetime.utcnow().date()) in msg_mock.call_args_list[0][0][0]
+    assert str(datetime.now(timezone.utc).date()) in msg_mock.call_args_list[0][0][0]
     assert '  6.83 USDT' in msg_mock.call_args_list[0][0][0]
     assert '  7.51 USD' in msg_mock.call_args_list[0][0][0]
     assert '(2)' in msg_mock.call_args_list[0][0][0]
@@ -533,8 +533,9 @@ async def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time
     await telegram._daily(update=update, context=context)
     assert msg_mock.call_count == 1
     assert "Daily Profit over the last 7 days</b>:" in msg_mock.call_args_list[0][0][0]
-    assert str(datetime.utcnow().date()) in msg_mock.call_args_list[0][0][0]
-    assert str((datetime.utcnow() - timedelta(days=5)).date()) in msg_mock.call_args_list[0][0][0]
+    assert str(datetime.now(timezone.utc).date()) in msg_mock.call_args_list[0][0][0]
+    assert str((datetime.now(timezone.utc) - timedelta(days=5)).date()
+               ) in msg_mock.call_args_list[0][0][0]
     assert '  6.83 USDT' in msg_mock.call_args_list[0][0][0]
     assert '  7.51 USD' in msg_mock.call_args_list[0][0][0]
     assert '(2)' in msg_mock.call_args_list[0][0][0]
@@ -608,7 +609,7 @@ async def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, tim
     assert "Weekly Profit over the last 2 weeks (starting from Monday)</b>:" \
            in msg_mock.call_args_list[0][0][0]
     assert 'Monday ' in msg_mock.call_args_list[0][0][0]
-    today = datetime.utcnow().date()
+    today = datetime.now(timezone.utc).date()
     first_iso_day_of_current_week = today - timedelta(days=today.weekday())
     assert str(first_iso_day_of_current_week) in msg_mock.call_args_list[0][0][0]
     assert '  2.74 USDT' in msg_mock.call_args_list[0][0][0]
@@ -677,7 +678,7 @@ async def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, ti
     assert msg_mock.call_count == 1
     assert 'Monthly Profit over the last 2 months</b>:' in msg_mock.call_args_list[0][0][0]
     assert 'Month ' in msg_mock.call_args_list[0][0][0]
-    today = datetime.utcnow().date()
+    today = datetime.now(timezone.utc).date()
     current_month = f"{today.year}-{today.month:02} "
     assert current_month in msg_mock.call_args_list[0][0][0]
     assert '  2.74 USDT' in msg_mock.call_args_list[0][0][0]
@@ -825,6 +826,9 @@ async def test_telegram_stats(default_conf, update, ticker, fee, mocker, is_shor
     assert 'Exit Reason' in msg_mock.call_args_list[-1][0][0]
     assert 'ROI' in msg_mock.call_args_list[-1][0][0]
     assert 'Avg. Duration' in msg_mock.call_args_list[-1][0][0]
+    # Duration is not only N/A
+    assert '0:19:00' in msg_mock.call_args_list[-1][0][0]
+    assert 'N/A' in msg_mock.call_args_list[-1][0][0]
     msg_mock.reset_mock()
 
 
@@ -1760,6 +1764,25 @@ async def test_telegram_delete_trade(mocker, update, default_conf, fee, is_short
     assert "Please make sure to take care of this asset" in msg_mock.call_args_list[0][0][0]
 
 
+@pytest.mark.parametrize('is_short', [True, False])
+async def test_telegram_reload_trade_from_exchange(mocker, update, default_conf, fee, is_short):
+
+    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    context = MagicMock()
+    context.args = []
+
+    await telegram._reload_trade_from_exchange(update=update, context=context)
+    assert "Trade-id not set." in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
+    create_mock_trades(fee, is_short=is_short)
+
+    context.args = [5]
+
+    await telegram._reload_trade_from_exchange(update=update, context=context)
+    assert "Status: `Reloaded from orders from exchange`" in msg_mock.call_args_list[0][0][0]
+
+
 @pytest.mark.parametrize('is_short', [True, False])
 async def test_telegram_delete_open_order(mocker, update, default_conf, fee, is_short, ticker):
 

tests/strategy/test_default_strategy.py

@@ -1,4 +1,4 @@
-from datetime import datetime
+from datetime import datetime, timezone
 
 import pytest
 from pandas import DataFrame
@@ -43,12 +43,12 @@ def test_strategy_test_v3(dataframe_1m, fee, is_short, side):
 
     assert strategy.confirm_trade_entry(pair='ETH/BTC', order_type='limit', amount=0.1,
                                         rate=20000, time_in_force='gtc',
-                                        current_time=datetime.utcnow(),
+                                        current_time=datetime.now(timezone.utc),
                                         side=side, entry_tag=None) is True
     assert strategy.confirm_trade_exit(pair='ETH/BTC', trade=trade, order_type='limit', amount=0.1,
                                        rate=20000, time_in_force='gtc', exit_reason='roi',
                                        sell_reason='roi',
-                                       current_time=datetime.utcnow(),
+                                       current_time=datetime.now(timezone.utc),
                                        side=side) is True
 
     assert strategy.custom_stoploss(pair='ETH/BTC', trade=trade, current_time=datetime.now(),

tests/test_configuration.py

@@ -1271,7 +1271,7 @@ def test_pairlist_resolving_with_config_pl_not_exists(mocker, default_conf):
         configuration.get_config()
 
 
-def test_pairlist_resolving_fallback(mocker):
+def test_pairlist_resolving_fallback(mocker, tmpdir):
     mocker.patch.object(Path, "exists", MagicMock(return_value=True))
     mocker.patch.object(Path, "open", MagicMock(return_value=MagicMock()))
     mocker.patch("freqtrade.configuration.configuration.load_file",
@@ -1290,7 +1290,7 @@ def test_pairlist_resolving_fallback(mocker):
 
     assert config['pairs'] == ['ETH/BTC', 'XRP/BTC']
     assert config['exchange']['name'] == 'binance'
-    assert config['datadir'] == Path.cwd() / "user_data/data/binance"
+    assert config['datadir'] == Path(tmpdir) / "user_data/data/binance"
 
 
 @pytest.mark.parametrize("setting", [

tests/test_freqtradebot.py

@@ -121,7 +121,7 @@ def test_order_dict(default_conf_usdt, mocker, runmode, caplog) -> None:
 
     freqtrade = FreqtradeBot(conf)
     if runmode == RunMode.LIVE:
-        assert not log_has_re(".*stoploss_on_exchange .* dry-run", caplog)
+        assert not log_has_re(r".*stoploss_on_exchange .* dry-run", caplog)
     assert freqtrade.strategy.order_types['stoploss_on_exchange']
 
     caplog.clear()
@@ -136,7 +136,7 @@ def test_order_dict(default_conf_usdt, mocker, runmode, caplog) -> None:
     }
     freqtrade = FreqtradeBot(conf)
     assert not freqtrade.strategy.order_types['stoploss_on_exchange']
-    assert not log_has_re(".*stoploss_on_exchange .* dry-run", caplog)
+    assert not log_has_re(r".*stoploss_on_exchange .* dry-run", caplog)
 
 
 def test_get_trade_stake_amount(default_conf_usdt, mocker) -> None:
@@ -149,6 +149,34 @@ def test_get_trade_stake_amount(default_conf_usdt, mocker) -> None:
     assert result == default_conf_usdt['stake_amount']
 
 
+@pytest.mark.parametrize('runmode', [
+    RunMode.DRY_RUN,
+    RunMode.LIVE
+])
+def test_load_strategy_no_keys(default_conf_usdt, mocker, runmode, caplog) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    conf = deepcopy(default_conf_usdt)
+    conf['runmode'] = runmode
+    erm = mocker.patch('freqtrade.freqtradebot.ExchangeResolver.load_exchange')
+
+    freqtrade = FreqtradeBot(conf)
+    strategy_config = freqtrade.strategy.config
+    assert id(strategy_config['exchange']) == id(conf['exchange'])
+    # Keys have been removed and are not passed to the exchange
+    assert strategy_config['exchange']['key'] == ''
+    assert strategy_config['exchange']['secret'] == ''
+
+    assert erm.call_count == 1
+    ex_conf = erm.call_args_list[0][1]['exchange_config']
+    assert id(ex_conf) != id(conf['exchange'])
+    # Keys are still present
+    assert ex_conf['key'] != ''
+    assert ex_conf['key'] == default_conf_usdt['exchange']['key']
+    assert ex_conf['secret'] != ''
+    assert ex_conf['secret'] == default_conf_usdt['exchange']['secret']
+
+
 @pytest.mark.parametrize("amend_last,wallet,max_open,lsamr,expected", [
                         (False, 120, 2, 0.5, [60, None]),
                         (True, 120, 2, 0.5, [60, 58.8]),
@@ -5552,6 +5580,51 @@ def test_handle_insufficient_funds(mocker, default_conf_usdt, fee, is_short, cap
     assert log_has(f"Error updating {order['id']}.", caplog)
 
 
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_onexchange_order(mocker, default_conf_usdt, limit_order, is_short, caplog):
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mock_uts = mocker.spy(freqtrade, 'update_trade_state')
+
+    entry_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    mock_fo = mocker.patch(f'{EXMS}.fetch_orders', return_value=[
+        entry_order,
+        exit_order,
+    ])
+
+    order_id = entry_order['id']
+
+    trade = Trade(
+            open_order_id=order_id,
+            pair='ETH/USDT',
+            fee_open=0.001,
+            fee_close=0.001,
+            open_rate=entry_order['price'],
+            open_date=arrow.utcnow().datetime,
+            stake_amount=entry_order['cost'],
+            amount=entry_order['amount'],
+            exchange="binance",
+            is_short=is_short,
+            leverage=1,
+            )
+
+    trade.orders.append(Order.parse_from_ccxt_object(
+        entry_order, 'ADA/USDT', entry_side(is_short))
+    )
+    Trade.session.add(trade)
+    freqtrade.handle_onexchange_order(trade)
+    assert log_has_re(r"Found previously unknown order .*", caplog)
+    assert mock_uts.call_count == 1
+    assert mock_fo.call_count == 1
+
+    trade = Trade.session.scalars(select(Trade)).first()
+
+    assert len(trade.orders) == 2
+    assert trade.is_open is False
+    assert trade.exit_reason == ExitType.SOLD_ON_EXCHANGE.value
+
+
 def test_get_valid_price(mocker, default_conf_usdt) -> None:
     patch_RPCManager(mocker)
     patch_exchange(mocker)

tests/test_integration.py

@@ -75,8 +75,9 @@ def test_may_execute_exit_stoploss_on_exchange_multi(default_conf, ticker, fee,
         _notify_exit=MagicMock(),
     )
     mocker.patch("freqtrade.strategy.interface.IStrategy.should_exit", should_sell_mock)
-    wallets_mock = mocker.patch("freqtrade.wallets.Wallets.update", MagicMock())
-    mocker.patch("freqtrade.wallets.Wallets.get_free", MagicMock(return_value=1000))
+    wallets_mock = mocker.patch("freqtrade.wallets.Wallets.update")
+    mocker.patch("freqtrade.wallets.Wallets.get_free", return_value=1000)
+    mocker.patch("freqtrade.wallets.Wallets.check_exit_amount", return_value=True)
 
     freqtrade = get_patched_freqtradebot(mocker, default_conf)
     freqtrade.strategy.order_types['stoploss_on_exchange'] = True

tests/test_plotting.py

@@ -1,5 +1,4 @@
 from copy import deepcopy
-from pathlib import Path
 from unittest.mock import MagicMock
 
 import pandas as pd
@@ -282,13 +281,13 @@ def test_generate_Plot_filename():
     assert fn == "freqtrade-plot-UNITTEST_BTC-5m.html"
 
 
-def test_generate_plot_file(mocker, caplog):
+def test_generate_plot_file(mocker, caplog, user_dir):
     fig = generate_empty_figure()
     plot_mock = mocker.patch("freqtrade.plot.plotting.plot", MagicMock())
     store_plot_file(fig, filename="freqtrade-plot-UNITTEST_BTC-5m.html",
-                    directory=Path("user_data/plot"))
+                    directory=user_dir / "plot")
 
-    expected_fn = str(Path("user_data/plot/freqtrade-plot-UNITTEST_BTC-5m.html"))
+    expected_fn = str(user_dir / "plot/freqtrade-plot-UNITTEST_BTC-5m.html")
     assert plot_mock.call_count == 1
     assert plot_mock.call_args[0][0] == fig
     assert (plot_mock.call_args_list[0][1]['filename']