Merge pull request #8297 from Yinon-Polak/feat/add-pytorch-model-support

Feat/add pytorch model support

freqtrade/freqai/base_models/BasePyTorchClassifier.py

@@ -0,0 +1,147 @@
+import logging
+from typing import Dict, List, Tuple
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+import torch
+from pandas import DataFrame
+from torch.nn import functional as F
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class BasePyTorchClassifier(BasePyTorchModel):
+    """
+    A PyTorch implementation of a classifier.
+    User must implement fit method
+
+    Important!
+
+    - User must declare the target class names in the strategy,
+    under IStrategy.set_freqai_targets method.
+
+    for example, in your strategy:
+    ```
+        def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
+            self.freqai.class_names = ["down", "up"]
+            dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
+                                                  dataframe["close"], 'up', 'down')
+
+            return dataframe
+    """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.class_name_to_index = None
+        self.index_to_class_name = None
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> Tuple[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)
+        :raises ValueError: if 'class_names' doesn't exist in model meta_data.
+        """
+
+        class_names = self.model.model_meta_data.get("class_names", None)
+        if not class_names:
+            raise ValueError(
+                "Missing class names. "
+                "self.model.model_meta_data['class_names'] is None."
+            )
+
+        if not self.class_name_to_index:
+            self.init_class_names_to_index_mapping(class_names)
+
+        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
+        )
+        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)
+        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)
+
+    def encode_class_names(
+            self,
+            data_dictionary: Dict[str, pd.DataFrame],
+            dk: FreqaiDataKitchen,
+            class_names: List[str],
+    ):
+        """
+        encode class name, str -> int
+        assuming first column of *_labels data frame to be the target column
+        containing the class names
+        """
+
+        target_column_name = dk.label_list[0]
+        for split in self.splits:
+            label_df = data_dictionary[f"{split}_labels"]
+            self.assert_valid_class_names(label_df[target_column_name], class_names)
+            label_df[target_column_name] = list(
+                map(lambda x: self.class_name_to_index[x], label_df[target_column_name])
+            )
+
+    @staticmethod
+    def assert_valid_class_names(
+            target_column: pd.Series,
+            class_names: List[str]
+    ):
+        non_defined_labels = set(target_column) - set(class_names)
+        if len(non_defined_labels) != 0:
+            raise OperationalException(
+                f"Found non defined labels: {non_defined_labels}, ",
+                f"expecting labels: {class_names}"
+            )
+
+    def decode_class_names(self, class_ints: torch.Tensor) -> List[str]:
+        """
+        decode class name, int -> str
+        """
+
+        return list(map(lambda x: self.index_to_class_name[x.item()], class_ints))
+
+    def init_class_names_to_index_mapping(self, class_names):
+        self.class_name_to_index = {s: i for i, s in enumerate(class_names)}
+        self.index_to_class_name = {i: s for i, s in enumerate(class_names)}
+        logger.info(f"encoded class name to index: {self.class_name_to_index}")
+
+    def convert_label_column_to_int(
+            self,
+            data_dictionary: Dict[str, pd.DataFrame],
+            dk: FreqaiDataKitchen,
+            class_names: List[str]
+    ):
+        self.init_class_names_to_index_mapping(class_names)
+        self.encode_class_names(data_dictionary, dk, class_names)
+
+    def get_class_names(self) -> List[str]:
+        if not self.class_names:
+            raise ValueError(
+                "self.class_names is empty, "
+                "set self.freqai.class_names = ['class a', 'class b', 'class c'] "
+                "inside IStrategy.set_freqai_targets method."
+            )
+
+        return self.class_names

freqtrade/freqai/base_models/BasePyTorchModel.py

@@ -0,0 +1,83 @@
+import logging
+from abc import ABC, abstractmethod
+from time import time
+from typing import Any
+
+import torch
+from pandas import DataFrame
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
+
+
+logger = logging.getLogger(__name__)
+
+
+class BasePyTorchModel(IFreqaiModel, ABC):
+    """
+    Base class for PyTorch type models.
+    User *must* inherit from this class and set fit() and predict() and
+    data_convertor property.
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(config=kwargs["config"])
+        self.dd.model_type = "pytorch"
+        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"]
+
+    def train(
+        self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
+    ) -> Any:
+        """
+        Filter the training data and train a model to it. Train makes heavy use of the datakitchen
+        for storing, saving, loading, and analyzing the data.
+        :param unfiltered_df: Full dataframe for the current training period
+        :return:
+        :model: Trained model which can be used to inference (self.predict)
+        """
+
+        logger.info(f"-------------------- Starting training {pair} --------------------")
+
+        start_time = time()
+
+        features_filtered, labels_filtered = dk.filter_features(
+            unfiltered_df,
+            dk.training_features_list,
+            dk.label_list,
+            training_filter=True,
+        )
+
+        # split data into train/test data.
+        data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
+            dk.fit_labels()
+        # normalize all data based on train_dataset only
+        data_dictionary = dk.normalize_data(data_dictionary)
+
+        # optional additional data cleaning/analysis
+        self.data_cleaning_train(dk)
+
+        logger.info(
+            f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
+        )
+        logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
+
+        model = self.fit(data_dictionary, dk)
+        end_time = time()
+
+        logger.info(f"-------------------- Done training {pair} "
+                    f"({end_time - start_time:.2f} secs) --------------------")
+
+        return model
+
+    @property
+    @abstractmethod
+    def data_convertor(self) -> PyTorchDataConvertor:
+        """
+        a class responsible for converting `*_features` & `*_labels` pandas dataframes
+        to pytorch tensors.
+        """
+        raise NotImplementedError("Abstract property")

freqtrade/freqai/base_models/BasePyTorchRegressor.py

@@ -0,0 +1,49 @@
+import logging
+from typing import Tuple
+
+import numpy as np
+import numpy.typing as npt
+from pandas import DataFrame
+
+from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class BasePyTorchRegressor(BasePyTorchModel):
+    """
+    A PyTorch implementation of a regressor.
+    User must implement fit method
+    """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> Tuple[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
+        )
+        y = self.model.model(x)
+        pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])
+        return (pred_df, dk.do_predict)

freqtrade/freqai/data_drawer.py

@@ -446,7 +446,7 @@ class FreqaiDataDrawer:
             dump(model, save_path / f"{dk.model_filename}_model.joblib")
         elif self.model_type == 'keras':
             model.save(save_path / f"{dk.model_filename}_model.h5")
-        elif 'stable_baselines' in self.model_type or 'sb3_contrib' == self.model_type:
+        elif self.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]:
             model.save(save_path / f"{dk.model_filename}_model.zip")
 
         if dk.svm_model is not None:
@@ -496,7 +496,7 @@ class FreqaiDataDrawer:
             dk.training_features_list = dk.data["training_features_list"]
             dk.label_list = dk.data["label_list"]
 
-    def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
+    def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:  # noqa: C901
         """
         loads all data required to make a prediction on a sub-train time range
         :returns:
@@ -537,6 +537,11 @@ class FreqaiDataDrawer:
                 self.model_type, self.freqai_info['rl_config']['model_type'])
             MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
             model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
+        elif self.model_type == 'pytorch':
+            import torch
+            zip = torch.load(dk.data_path / f"{dk.model_filename}_model.zip")
+            model = zip["pytrainer"]
+            model = model.load_from_checkpoint(zip)
 
         if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
             dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")

freqtrade/freqai/freqai_interface.py

@@ -83,6 +83,7 @@ class IFreqaiModel(ABC):
         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
+        self.class_names: List[str] = []  # used in classification subclasses
         self.pair_it = 0
         self.pair_it_train = 0
         self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
@@ -571,8 +572,9 @@ class IFreqaiModel(ABC):
             file_type = ".joblib"
         elif self.dd.model_type == 'keras':
             file_type = ".h5"
-        elif 'stable_baselines' in self.dd.model_type or 'sb3_contrib' == self.dd.model_type:
+        elif self.dd.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]:
             file_type = ".zip"
+
         path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model{file_type}")
         file_exists = path_to_modelfile.is_file()
         if file_exists:

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -14,16 +14,20 @@ logger = logging.getLogger(__name__)
 
 class CatboostClassifier(BaseClassifierModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         train_data = Pool(

freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py

@@ -15,16 +15,20 @@ logger = logging.getLogger(__name__)
 
 class CatboostClassifierMultiTarget(BaseClassifierModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         cbc = CatBoostClassifier(

freqtrade/freqai/prediction_models/CatboostRegressor.py

@@ -14,16 +14,20 @@ logger = logging.getLogger(__name__)
 
 class CatboostRegressor(BaseRegressionModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         train_data = Pool(

freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

@@ -15,16 +15,20 @@ logger = logging.getLogger(__name__)
 
 class CatboostRegressorMultiTarget(BaseRegressionModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         cbr = CatBoostRegressor(

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -12,16 +12,20 @@ logger = logging.getLogger(__name__)
 
 class LightGBMClassifier(BaseClassifierModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:

freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py

@@ -13,16 +13,20 @@ logger = logging.getLogger(__name__)
 
 class LightGBMClassifierMultiTarget(BaseClassifierModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         lgb = LGBMClassifier(**self.model_training_parameters)

freqtrade/freqai/prediction_models/LightGBMRegressor.py

@@ -12,18 +12,20 @@ logger = logging.getLogger(__name__)
 
 class LightGBMRegressor(BaseRegressionModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
-        Most regressors use the same function names and arguments e.g. user
-        can drop in LGBMRegressor in place of CatBoostRegressor and all data
-        management will be properly handled by Freqai.
-        :param data_dictionary: the dictionary constructed by DataHandler to hold
-                                all the training and test data/labels.
+        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
         """
 
         if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:

freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py

@@ -13,16 +13,20 @@ logger = logging.getLogger(__name__)
 
 class LightGBMRegressorMultiTarget(BaseRegressionModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         lgb = LGBMRegressor(**self.model_training_parameters)

freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py

@@ -0,0 +1,89 @@
+from typing import Any, Dict
+
+import torch
+
+from freqtrade.freqai.base_models.BasePyTorchClassifier import BasePyTorchClassifier
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.torch.PyTorchDataConvertor import (DefaultPyTorchDataConvertor,
+                                                         PyTorchDataConvertor)
+from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel
+from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer
+
+
+class PyTorchMLPClassifier(BasePyTorchClassifier):
+    """
+    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 PyTorchClassifier
+    predict method that expects the model to predict a tensor of type long.
+
+    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.long,
+            squeeze_target_tensor=True
+        )
+
+    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
+        :raises ValueError: If self.class_names is not defined in the parent class.
+        """
+
+        class_names = self.get_class_names()
+        self.convert_label_column_to_int(data_dictionary, dk, class_names)
+        n_features = data_dictionary["train_features"].shape[-1]
+        model = PyTorchMLPModel(
+            input_dim=n_features,
+            output_dim=len(class_names),
+            **self.model_kwargs
+        )
+        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,
+        )
+        trainer.fit(data_dictionary, self.splits)
+        return trainer

freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py

@@ -0,0 +1,83 @@
+from typing import Any, Dict
+
+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.PyTorchMLPModel import PyTorchMLPModel
+from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer
+
+
+class PyTorchMLPRegressor(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]
+        model = PyTorchMLPModel(
+            input_dim=n_features,
+            output_dim=1,
+            **self.model_kwargs
+        )
+        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,
+        )
+        trainer.fit(data_dictionary, self.splits)
+        return trainer

freqtrade/freqai/prediction_models/XGBoostClassifier.py

@@ -18,16 +18,20 @@ logger = logging.getLogger(__name__)
 
 class XGBoostClassifier(BaseClassifierModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         X = data_dictionary["train_features"].to_numpy()

freqtrade/freqai/prediction_models/XGBoostRFClassifier.py

@@ -18,16 +18,20 @@ logger = logging.getLogger(__name__)
 
 class XGBoostRFClassifier(BaseClassifierModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-            all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         X = data_dictionary["train_features"].to_numpy()

freqtrade/freqai/prediction_models/XGBoostRFRegressor.py

@@ -12,16 +12,20 @@ logger = logging.getLogger(__name__)
 
 class XGBoostRFRegressor(BaseRegressionModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         X = data_dictionary["train_features"]

freqtrade/freqai/prediction_models/XGBoostRegressor.py

@@ -12,16 +12,20 @@ logger = logging.getLogger(__name__)
 
 class XGBoostRegressor(BaseRegressionModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         X = data_dictionary["train_features"]

freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py

@@ -13,16 +13,20 @@ logger = logging.getLogger(__name__)
 
 class XGBoostRegressorMultiTarget(BaseRegressionModel):
     """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
     """
 
     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 constructed by DataHandler to hold
-                                all the training and test data/labels.
+        :param data_dictionary: the dictionary holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
         """
 
         xgb = XGBRegressor(**self.model_training_parameters)

freqtrade/freqai/torch/PyTorchDataConvertor.py

@@ -0,0 +1,67 @@
+from abc import ABC, abstractmethod
+from typing import List, Optional
+
+import pandas as pd
+import torch
+
+
+class PyTorchDataConvertor(ABC):
+    """
+    This class is responsible for converting `*_features` & `*_labels` pandas dataframes
+    to pytorch tensors.
+    """
+
+    @abstractmethod
+    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> List[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]:
+        """
+        :param df: "*_labels" dataframe.
+        :param device: The device to use for training (e.g. 'cpu', 'cuda').
+        """
+
+
+class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
+    """
+    A default conversion that keeps features dataframe shapes.
+    """
+
+    def __init__(
+            self,
+            target_tensor_type: Optional[torch.dtype] = None,
+            squeeze_target_tensor: bool = False
+    ):
+        """
+        :param target_tensor_type: type of target tensor, for classification use
+            torch.long, for regressor use torch.float or torch.double.
+        :param squeeze_target_tensor: controls the target shape, used for loss functions
+            that requires 0D or 1D.
+        """
+        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]:
+        x = torch.from_numpy(df.values).float()
+        if device:
+            x = x.to(device)
+
+        return [x]
+
+    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]:
+        y = torch.from_numpy(df.values)
+
+        if self._target_tensor_type:
+            y = y.to(self._target_tensor_type)
+
+        if self._squeeze_target_tensor:
+            y = y.squeeze()
+
+        if device:
+            y = y.to(device)
+
+        return [y]

freqtrade/freqai/torch/PyTorchMLPModel.py

@@ -0,0 +1,97 @@
+import logging
+from typing import List
+
+import torch
+import torch.nn as nn
+
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchMLPModel(nn.Module):
+    """
+    A multi-layer perceptron (MLP) model implemented using PyTorch.
+
+    This class mainly serves as a simple example for the integration of PyTorch model's
+    to freqai. It is not optimized at all and should not be used for production purposes.
+
+    :param input_dim: The number of input features. This parameter specifies the number
+        of features in the input data that the MLP will use to make predictions.
+    :param output_dim: The number of output classes. This parameter specifies the number
+        of classes that the MLP will predict.
+    :param hidden_dim: The number of hidden units in each layer. This parameter controls
+        the complexity of the MLP and determines how many nonlinear relationships the MLP
+        can represent. Increasing the number of hidden units can increase the capacity of
+        the MLP to model complex patterns, but it also increases the risk of overfitting
+        the training data. Default: 256
+    :param dropout_percent: The dropout rate for regularization. This parameter specifies
+        the probability of dropping out a neuron during training to prevent overfitting.
+        The dropout rate should be tuned carefully to balance between underfitting and
+        overfitting. Default: 0.2
+    :param n_layer: The number of layers in the MLP. This parameter specifies the number
+        of layers in the MLP architecture. Adding more layers to the MLP can increase its
+        capacity to model complex patterns, but it also increases the risk of overfitting
+        the training data. Default: 1
+
+    :returns: The output of the MLP, with shape (batch_size, output_dim)
+    """
+
+    def __init__(self, input_dim: int, output_dim: int, **kwargs):
+        super().__init__()
+        hidden_dim: int = kwargs.get("hidden_dim", 256)
+        dropout_percent: int = kwargs.get("dropout_percent", 0.2)
+        n_layer: int = kwargs.get("n_layer", 1)
+        self.input_layer = nn.Linear(input_dim, hidden_dim)
+        self.blocks = nn.Sequential(*[Block(hidden_dim, dropout_percent) for _ in range(n_layer)])
+        self.output_layer = nn.Linear(hidden_dim, output_dim)
+        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]
+        x = self.relu(self.input_layer(x))
+        x = self.dropout(x)
+        x = self.blocks(x)
+        x = self.output_layer(x)
+        return x
+
+
+class Block(nn.Module):
+    """
+    A building block for a multi-layer perceptron (MLP).
+
+    :param hidden_dim: The number of hidden units in the feedforward network.
+    :param dropout_percent: The dropout rate for regularization.
+
+    :returns: torch.Tensor. with shape (batch_size, hidden_dim)
+    """
+
+    def __init__(self, hidden_dim: int, dropout_percent: int):
+        super().__init__()
+        self.ff = FeedForward(hidden_dim)
+        self.dropout = nn.Dropout(p=dropout_percent)
+        self.ln = nn.LayerNorm(hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.ff(self.ln(x))
+        x = self.dropout(x)
+        return x
+
+
+class FeedForward(nn.Module):
+    """
+    A simple fully-connected feedforward neural network block.
+
+    :param hidden_dim: The number of hidden units in the block.
+    :return: torch.Tensor. with shape (batch_size, hidden_dim)
+    """
+
+    def __init__(self, hidden_dim: int):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU(),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.net(x)

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -0,0 +1,208 @@
+import logging
+import math
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import pandas as pd
+import torch
+from torch import nn
+from torch.optim import Optimizer
+from torch.utils.data import DataLoader, TensorDataset
+
+from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
+from freqtrade.freqai.torch.PyTorchTrainerInterface import PyTorchTrainerInterface
+
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchModelTrainer(PyTorchTrainerInterface):
+    def __init__(
+            self,
+            model: nn.Module,
+            optimizer: Optimizer,
+            criterion: nn.Module,
+            device: str,
+            init_model: Dict,
+            data_convertor: PyTorchDataConvertor,
+            model_meta_data: Dict[str, Any] = {},
+            **kwargs
+    ):
+        """
+        :param model: The PyTorch model to be trained.
+        :param optimizer: The optimizer to use for training.
+        :param criterion: The loss function to use for training.
+        :param device: The device to use for training (e.g. 'cpu', 'cuda').
+        :param init_model: A dictionary containing the initial model/optimizer
+            state_dict and model_meta_data saved by self.save() method.
+        :param model_meta_data: Additional metadata about the model (optional).
+        :param data_convertor: convertor from pd.DataFrame to torch.tensor.
+        :param 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.
+        :param batch_size: The size of the batches to use during training.
+        :param max_n_eval_batches: The maximum number batches to use for evaluation.
+        """
+        self.model = model
+        self.optimizer = optimizer
+        self.criterion = criterion
+        self.model_meta_data = model_meta_data
+        self.device = device
+        self.max_iters: int = kwargs.get("max_iters", 100)
+        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)
+
+    def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]):
+        """
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+        all the training and test data/labels.
+        :param splits: splits to use in training, splits must contain "train",
+        optional "test" could be added by setting freqai.data_split_parameters.test_size > 0
+        in the config file.
+
+         - Calculates the predicted output for the batch using the PyTorch model.
+         - Calculates the loss between the predicted and actual output using a loss function.
+         - Computes the gradients of the loss with respect to the model's parameters using
+           backpropagation.
+         - Updates the model's parameters using an optimizer.
+        """
+        data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
+        epochs = self.calc_n_epochs(
+            n_obs=len(data_dictionary["train_features"]),
+            batch_size=self.batch_size,
+            n_iters=self.max_iters
+        )
+        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]
+                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}"
+
+            # evaluation
+            if "test" in splits:
+                test_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(
+            self,
+            data_loader_dictionary: Dict[str, DataLoader],
+            max_n_eval_batches: Optional[int],
+            split: str,
+    ) -> float:
+        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
+
+            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.model.train()
+        return sum(losses) / len(losses)
+
+    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"])
+            y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"])
+            dataset = TensorDataset(*x, *y)
+            data_loader = DataLoader(
+                dataset,
+                batch_size=self.batch_size,
+                shuffle=True,
+                drop_last=True,
+                num_workers=0,
+            )
+            data_loader_dictionary[split] = data_loader
+
+        return data_loader_dictionary
+
+    @staticmethod
+    def calc_n_epochs(n_obs: int, batch_size: int, n_iters: int) -> int:
+        """
+        Calculates the number of epochs required to reach the maximum number
+        of iterations specified in the model training parameters.
+
+        the motivation here is that `max_iters` is easier to optimize and keep stable,
+        across different n_obs - the number of data points.
+        """
+
+        n_batches = math.ceil(n_obs // batch_size)
+        epochs = math.ceil(n_iters // n_batches)
+        if epochs <= 10:
+            logger.warning("User set `max_iters` in such a way that the trainer will only perform "
+                           f" {epochs} epochs. Please consider increasing this value accordingly")
+            if epochs <= 1:
+                logger.warning("Epochs set to 1. Please review your `max_iters` value")
+                epochs = 1
+        return epochs
+
+    def save(self, path: Path):
+        """
+        - Saving any nn.Module state_dict
+        - Saving model_meta_data, this dict should contain any additional data that the
+          user needs to store. e.g class_names for classification models.
+        """
+
+        torch.save({
+            "model_state_dict": self.model.state_dict(),
+            "optimizer_state_dict": self.optimizer.state_dict(),
+            "model_meta_data": self.model_meta_data,
+            "pytrainer": self
+        }, path)
+
+    def load(self, path: Path):
+        checkpoint = torch.load(path)
+        return self.load_from_checkpoint(checkpoint)
+
+    def load_from_checkpoint(self, checkpoint: Dict):
+        """
+        when using continual_learning, DataDrawer will load the dictionary
+        (containing state dicts and model_meta_data) by calling torch.load(path).
+        you can access this dict from any class that inherits IFreqaiModel by calling
+        get_init_model method.
+        """
+        self.model.load_state_dict(checkpoint["model_state_dict"])
+        self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+        self.model_meta_data = checkpoint["model_meta_data"]
+        return self

freqtrade/freqai/torch/PyTorchTrainerInterface.py

@@ -0,0 +1,53 @@
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Dict, List
+
+import pandas as pd
+import torch
+from torch import nn
+
+
+class PyTorchTrainerInterface(ABC):
+
+    @abstractmethod
+    def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]) -> None:
+        """
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+        all the training and test data/labels.
+        :param splits: splits to use in training, splits must contain "train",
+        optional "test" could be added by setting freqai.data_split_parameters.test_size > 0
+        in the config file.
+
+         - Calculates the predicted output for the batch using the PyTorch model.
+         - Calculates the loss between the predicted and actual output using a loss function.
+         - Computes the gradients of the loss with respect to the model's parameters using
+           backpropagation.
+         - Updates the model's parameters using an optimizer.
+        """
+
+    @abstractmethod
+    def save(self, path: Path) -> None:
+        """
+        - Saving any nn.Module state_dict
+        - Saving model_meta_data, this dict should contain any additional data that the
+          user needs to store. e.g class_names for classification models.
+        """
+
+    def load(self, path: Path) -> nn.Module:
+        """
+        :param path: path to zip file.
+        :returns: pytorch model.
+        """
+        checkpoint = torch.load(path)
+        return self.load_from_checkpoint(checkpoint)
+
+    @abstractmethod
+    def load_from_checkpoint(self, checkpoint: Dict) -> nn.Module:
+        """
+        when using continual_learning, DataDrawer will load the dictionary
+        (containing state dicts and model_meta_data) by calling torch.load(path).
+        you can access this dict from any class that inherits IFreqaiModel by calling
+        get_init_model method.
+        :checkpoint checkpoint: dict containing the model & optimizer state dicts,
+        model_meta_data, etc..
+        """

freqtrade/templates/FreqaiExampleHybridStrategy.py

@@ -223,6 +223,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
         :param metadata: metadata of current pair
         usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
         """
+        self.freqai.class_names = ["down", "up"]
         dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
                                               dataframe["close"], 'up', 'down')