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Merge branch 'develop' into pr/initrv/8426
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Matthias
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docs/assets/freqai_pytorch-diagram.png
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@@ -274,19 +274,20 @@ A backtesting result will look like that:
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| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 0 23 34.3 |
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| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 |
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| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
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========================================================= EXIT REASON STATS ==========================================================
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| Exit Reason | Exits | Wins | Draws | Losses |
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|:-------------------|--------:|------:|-------:|--------:|
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| trailing_stop_loss | 205 | 150 | 0 | 55 |
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| stop_loss | 166 | 0 | 0 | 166 |
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| exit_signal | 56 | 36 | 0 | 20 |
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| force_exit | 2 | 0 | 0 | 2 |
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====================================================== LEFT OPEN TRADES REPORT ======================================================
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| Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
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|:---------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
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| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 |
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| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 |
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| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 |
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==================== EXIT REASON STATS ====================
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| Exit Reason | Exits | Wins | Draws | Losses |
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|:-------------------|--------:|------:|-------:|--------:|
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| trailing_stop_loss | 205 | 150 | 0 | 55 |
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| stop_loss | 166 | 0 | 0 | 166 |
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| exit_signal | 56 | 36 | 0 | 20 |
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| force_exit | 2 | 0 | 0 | 2 |
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================== SUMMARY METRICS ==================
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| Metric | Value |
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|-----------------------------+---------------------|
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@@ -236,3 +236,161 @@ If you want to predict multiple targets you must specify all labels in the same
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df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
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df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down'])
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```
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## PyTorch Module
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### Quick start
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The easiest way to quickly run a pytorch model is with the following command (for regression task):
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```bash
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freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel PyTorchMLPRegressor --strategy-path freqtrade/templates
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```
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!!! note "Installation/docker"
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The PyTorch module requires large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl or PyTorch (~700mb additional space required) [y/N]?".
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Users who prefer docker should ensure they use the docker image appended with `_freqaitorch`.
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### Structure
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#### Model
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You can construct your own Neural Network architecture in PyTorch by simply defining your `nn.Module` class inside your custom [`IFreqaiModel` file](#using-different-prediction-models) and then using that class in your `def train()` function. Here is an example of logistic regression model implementation using PyTorch (should be used with nn.BCELoss criterion) for classification tasks.
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```python
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class LogisticRegression(nn.Module):
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def __init__(self, input_size: int):
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super().__init__()
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# Define your layers
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self.linear = nn.Linear(input_size, 1)
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self.activation = nn.Sigmoid()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# Define the forward pass
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out = self.linear(x)
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out = self.activation(out)
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return out
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class MyCoolPyTorchClassifier(BasePyTorchClassifier):
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"""
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This is a custom IFreqaiModel showing how a user might setup their own
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custom Neural Network architecture for their training.
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"""
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@property
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def data_convertor(self) -> PyTorchDataConvertor:
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return DefaultPyTorchDataConvertor(target_tensor_type=torch.float)
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def __init__(self, **kwargs) -> None:
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super().__init__(**kwargs)
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config = self.freqai_info.get("model_training_parameters", {})
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self.learning_rate: float = config.get("learning_rate", 3e-4)
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self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {})
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self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary holding all data for train, test,
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labels, weights
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:param dk: The datakitchen object for the current coin/model
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"""
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class_names = self.get_class_names()
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self.convert_label_column_to_int(data_dictionary, dk, class_names)
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n_features = data_dictionary["train_features"].shape[-1]
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model = LogisticRegression(
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input_dim=n_features
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)
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model.to(self.device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
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criterion = torch.nn.CrossEntropyLoss()
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init_model = self.get_init_model(dk.pair)
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trainer = PyTorchModelTrainer(
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model=model,
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optimizer=optimizer,
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criterion=criterion,
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model_meta_data={"class_names": class_names},
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device=self.device,
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init_model=init_model,
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data_convertor=self.data_convertor,
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**self.trainer_kwargs,
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)
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trainer.fit(data_dictionary, self.splits)
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return trainer
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```
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#### Trainer
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The `PyTorchModelTrainer` performs the idiomatic PyTorch train loop:
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Define our model, loss function, and optimizer, and then move them to the appropriate device (GPU or CPU). Inside the loop, we iterate through the batches in the dataloader, move the data to the device, compute the prediction and loss, backpropagate, and update the model parameters using the optimizer.
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In addition, the trainer is responsible for the following:
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- saving and loading the model
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- converting the data from `pandas.DataFrame` to `torch.Tensor`.
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#### Integration with Freqai module
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Like all freqai models, PyTorch models inherit `IFreqaiModel`. `IFreqaiModel` declares three abstract methods: `train`, `fit`, and `predict`. we implement these methods in three levels of hierarchy.
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From top to bottom:
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1. `BasePyTorchModel` - Implements the `train` method. all `BasePyTorch*` inherit it. responsible for general data preparation (e.g., data normalization) and calling the `fit` method. Sets `device` attribute used by children classes. Sets `model_type` attribute used by the parent class.
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2. `BasePyTorch*` - Implements the `predict` method. Here, the `*` represents a group of algorithms, such as classifiers or regressors. responsible for data preprocessing, predicting, and postprocessing if needed.
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3. `PyTorch*Classifier` / `PyTorch*Regressor` - implements the `fit` method. responsible for the main train flaw, where we initialize the trainer and model objects.
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#### Full example
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Building a PyTorch regressor using MLP (multilayer perceptron) model, MSELoss criterion, and AdamW optimizer.
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```python
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class PyTorchMLPRegressor(BasePyTorchRegressor):
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def __init__(self, **kwargs) -> None:
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super().__init__(**kwargs)
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config = self.freqai_info.get("model_training_parameters", {})
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self.learning_rate: float = config.get("learning_rate", 3e-4)
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self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {})
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self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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n_features = data_dictionary["train_features"].shape[-1]
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model = PyTorchMLPModel(
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input_dim=n_features,
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output_dim=1,
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**self.model_kwargs
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)
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model.to(self.device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
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criterion = torch.nn.MSELoss()
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init_model = self.get_init_model(dk.pair)
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trainer = PyTorchModelTrainer(
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model=model,
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optimizer=optimizer,
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criterion=criterion,
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device=self.device,
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init_model=init_model,
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target_tensor_type=torch.float,
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**self.trainer_kwargs,
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)
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trainer.fit(data_dictionary)
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return trainer
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```
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Here we create a `PyTorchMLPRegressor` class that implements the `fit` method. The `fit` method specifies the training building blocks: model, optimizer, criterion, and trainer. We inherit both `BasePyTorchRegressor` and `BasePyTorchModel`, where the former implements the `predict` method that is suitable for our regression task, and the latter implements the train method.
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??? Note "Setting Class Names for Classifiers"
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When using classifiers, the user must declare the class names (or targets) by overriding the `IFreqaiModel.class_names` attribute. This is achieved by setting `self.freqai.class_names` in the FreqAI strategy inside the `set_freqai_targets` method.
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For example, if you are using a binary classifier to predict price movements as up or down, you can set the class names as follows:
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```python
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def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
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self.freqai.class_names = ["down", "up"]
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dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
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dataframe["close"], 'up', 'down')
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return dataframe
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```
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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).
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@@ -6,8 +6,8 @@ Low level feature engineering is performed in the user strategy within a set of
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| Function | Description |
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|---------------|-------------|
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| `feature_engineering__expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
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| `feature_engineering__expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `include_periods_candles`.
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| `feature_engineering_expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
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| `feature_engineering_expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `include_periods_candles`.
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| `feature_engineering_standard()` | This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns from the base asset created by the other `feature_engineering_expand` functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is also a good place for any feature that should not be auto-expanded upon (e.g., day of the week).
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| `set_freqai_targets()` | Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals.
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@@ -87,6 +87,27 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
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| `drop_ohlc_from_features` | Do not include the normalized ohlc data in the feature set passed to the agent during training (ohlc will still be used for driving the environment in all cases) <br> **Datatype:** Boolean. <br> **Default:** `False`
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| `progress_bar` | Display a progress bar with the current progress, elapsed time and estimated remaining time. <br> **Datatype:** Boolean. <br> Default: `False`.
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### PyTorch parameters
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#### general
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| Parameter | Description |
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|------------|-------------|
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| | **Model training parameters within the `freqai.model_training_parameters` sub dictionary**
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| `learning_rate` | Learning rate to be passed to the optimizer. <br> **Datatype:** float. <br> Default: `3e-4`.
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| `model_kwargs` | Parameters to be passed to the model class. <br> **Datatype:** dict. <br> Default: `{}`.
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| `trainer_kwargs` | Parameters to be passed to the trainer class. <br> **Datatype:** dict. <br> Default: `{}`.
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#### trainer_kwargs
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| Parameter | Description |
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|------------|-------------|
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| | **Model training parameters within the `freqai.model_training_parameters.model_kwargs` sub dictionary**
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| `max_iters` | The number of training iterations to run. iteration here refers to the number of times we call self.optimizer.step(). used to calculate n_epochs. <br> **Datatype:** int. <br> Default: `100`.
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| `batch_size` | The size of the batches to use during training.. <br> **Datatype:** int. <br> Default: `64`.
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| `max_n_eval_batches` | The maximum number batches to use for evaluation.. <br> **Datatype:** int, optional. <br> Default: `None`.
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### Additional parameters
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| Parameter | Description |
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@@ -180,7 +180,7 @@ As you begin to modify the strategy and the prediction model, you will quickly r
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# you can use feature values from dataframe
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# Assumes the shifted RSI indicator has been generated in the strategy.
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rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{pair}_"
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rsi_now = self.raw_features[f"%-rsi-period_10_shift-1_{pair}_"
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f"{self.config['timeframe']}"].iloc[self._current_tick]
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# reward agent for entering trades
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@@ -1,6 +1,6 @@
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markdown==3.3.7
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mkdocs==1.4.2
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mkdocs-material==9.1.4
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mkdocs-material==9.1.6
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mdx_truly_sane_lists==1.3
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pymdown-extensions==9.10
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pymdown-extensions==9.11
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jinja2==3.1.2
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@@ -9,9 +9,6 @@ This same command can also be used to update freqUI, should there be a new relea
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Once the bot is started in trade / dry-run mode (with `freqtrade trade`) - the UI will be available under the configured port below (usually `http://127.0.0.1:8080`).
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!!! info "Alpha release"
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FreqUI is still considered an alpha release - if you encounter bugs or inconsistencies please open a [FreqUI issue](https://github.com/freqtrade/frequi/issues/new/choose).
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!!! Note "developers"
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Developers should not use this method, but instead use the method described in the [freqUI repository](https://github.com/freqtrade/frequi) to get the source-code of freqUI.
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@@ -23,10 +23,22 @@ These modes can be configured with these values:
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'stoploss_on_exchange_limit_ratio': 0.99
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```
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!!! Note
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Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), Gate (stop-limit), and Kucoin (stop-limit and stop-market) as of now.
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<ins>Do not set too low/tight stoploss value if using stop loss on exchange!</ins>
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If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work.
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Stoploss on exchange is only supported for the following exchanges, and not all exchanges support both stop-limit and stop-market.
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The Order-type will be ignored if only one mode is available.
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| Exchange | stop-loss type |
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|----------|-------------|
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| Binance | limit |
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| Binance Futures | market, limit |
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| Huobi | limit |
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| kraken | market, limit |
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| Gate | limit |
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| Okx | limit |
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| Kucoin | stop-limit, stop-market|
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!!! Note "Tight stoploss"
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<ins>Do not set too low/tight stoploss value when using stop loss on exchange!</ins>
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If set to low/tight you will have greater risk of missing fill on the order and stoploss will not work.
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### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
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@@ -279,6 +279,7 @@ Return a summary of your profit/loss and performance.
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> ∙ `33.095 EUR`
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>
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> **Total Trade Count:** `138`
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> **Bot started:** `2022-07-11 18:40:44`
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> **First Trade opened:** `3 days ago`
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> **Latest Trade opened:** `2 minutes ago`
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> **Avg. Duration:** `2:33:45`
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@@ -292,6 +293,7 @@ The relative profit of `15.2 Σ%` is be based on the starting capital - so in th
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Starting capital is either taken from the `available_capital` setting, or calculated by using current wallet size - profits.
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Profit Factor is calculated as gross profits / gross losses - and should serve as an overall metric for the strategy.
|
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Max drawdown corresponds to the backtesting metric `Absolute Drawdown (Account)` - calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
|
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Bot started date will refer to the date the bot was first started. For older bots, this will default to the first trade's open date.
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### /forceexit <trade_id>
|
||||
|
||||
|
||||
Reference in New Issue
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