Merge branch 'freqtrade:feat/freqai' into feat/freqai

freqtrade/freqai/prediction_models/BaseRegressionModel.py

@@ -12,9 +12,9 @@ logger = logging.getLogger(__name__)
 
 class BaseRegressionModel(IFreqaiModel):
     """
-    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.
+    Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
+    User *must* inherit from this class and set fit() and predict(). See example scripts
+    such as prediction_models/CatboostPredictionModel.py for guidance.
     """
 
     def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
@@ -71,7 +71,8 @@ class BaseRegressionModel(IFreqaiModel):
                 data_dictionary['train_features'], model, dk, pair)
         elif self.freqai_info.get('fit_live_predictions_candles', 0):
             dk.fit_live_predictions()
-            self.dd.save_historic_predictions_to_disk()
+
+        self.dd.save_historic_predictions_to_disk()
 
         logger.info(f"--------------------done training {pair}--------------------")
 

freqtrade/freqai/prediction_models/BaseTensorFlowModel.py

@@ -0,0 +1,78 @@
+import logging
+from typing import Tuple
+
+from pandas import DataFrame
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseTensorFlowModel(IFreqaiModel):
+    """
+    Base class for TensorFlow type models.
+    User *must* inherit from this class and set fit() and predict().
+    """
+
+    def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
+        """
+        User uses this function to add any additional return values to the dataframe.
+        e.g.
+        dataframe['volatility'] = dk.volatility_values
+        """
+
+        return dataframe
+
+    def train(
+        self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
+    ) -> Tuple[DataFrame, DataFrame]:
+        """
+        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.
+        :params:
+        :unfiltered_dataframe: Full dataframe for the current training period
+        :metadata: pair metadata from strategy.
+        :returns:
+        :model: Trained model which can be used to inference (self.predict)
+        """
+
+        logger.info("--------------------Starting training " f"{pair} --------------------")
+
+        # filter the features requested by user in the configuration file and elegantly handle NaNs
+        features_filtered, labels_filtered = dk.filter_features(
+            unfiltered_dataframe,
+            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):
+            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)
+
+        if pair not in self.dd.historic_predictions:
+            self.set_initial_historic_predictions(
+                data_dictionary['train_features'], model, dk, pair)
+        elif self.freqai_info.get('fit_live_predictions_candles', 0):
+            dk.fit_live_predictions()
+
+        self.dd.save_historic_predictions_to_disk()
+
+        logger.info(f"--------------------done training {pair}--------------------")
+
+        return model