ReinforcementLearningModel

freqtrade/freqai/prediction_models/ReinforcementLearning.py

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+import logging
+from typing import Any, Tuple, Dict
+from freqtrade.freqai.prediction_models.RL.RLPrediction_env import GymAnytrading
+from freqtrade.freqai.prediction_models.RL.RLPrediction_agent import RLPrediction_agent
+from pandas import DataFrame
+import pandas as pd
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+import numpy as np
+import numpy.typing as npt
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearningModel(IFreqaiModel):
+    """
+    User created Reinforcement Learning Model prediction model.
+    """
+
+    def train(
+        self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
+    ) -> 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_dataframe: Full dataframe for the current training period
+        :param 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,
+        )
+
+        data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
+            features_filtered, labels_filtered)
+        dk.fit_labels()  # useless for now, but just satiating append methods
+
+        # 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, pair)
+
+        if pair not in self.dd.historic_predictions:
+            self.set_initial_historic_predictions(
+                data_dictionary['train_features'], model, dk, pair)
+
+        self.dd.save_historic_predictions_to_disk()
+
+        logger.info(f"--------------------done training {pair}--------------------")
+
+        return model
+
+    def fit(self, data_dictionary: Dict[str, Any], pair: str = ''):
+
+        train_df = data_dictionary["train_features"]
+
+        sep = '/'
+        coin = pair.split(sep, 1)[0]
+        price = train_df[f"%-{coin}raw_price_{self.config['timeframe']}"]
+        price.reset_index(inplace=True, drop=True)
+
+        model_name = 'ppo'
+
+        env_instance = GymAnytrading(train_df, price, self.CONV_WIDTH)
+
+        agent_params = self.freqai_info['model_training_parameters']
+        total_timesteps = agent_params.get('total_timesteps', 1000)
+
+        agent = RLPrediction_agent(env_instance)
+
+        model = agent.get_model(model_name, model_kwargs=agent_params)
+        trained_model = agent.train_model(model=model,
+                                          tb_log_name=model_name,
+                                          total_timesteps=total_timesteps)
+        print('Training finished!')
+
+        return trained_model
+
+    def predict(
+        self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
+    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param: unfiltered_dataframe: 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_dataframe)
+        filtered_dataframe, _ = dk.filter_features(
+            unfiltered_dataframe, dk.training_features_list, training_filter=False
+        )
+        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
+        dk.data_dictionary["prediction_features"] = filtered_dataframe
+
+        # optional additional data cleaning/analysis
+        self.data_cleaning_predict(dk, filtered_dataframe)
+
+        pred_df = self.rl_model_predict(dk.data_dictionary["prediction_features"], dk, self.model)
+        pred_df.fillna(0, inplace=True)
+
+        return (pred_df, dk.do_predict)
+
+    def rl_model_predict(self, dataframe: DataFrame,
+                         dk: FreqaiDataKitchen, model: Any) -> DataFrame:
+
+        output = pd.DataFrame(np.full((len(dataframe), 1), 2), columns=dk.label_list)
+
+        def _predict(window):
+            observations = dataframe.iloc[window.index]
+            res, _ = model.predict(observations, deterministic=True)
+            return res
+
+        output = output.rolling(window=self.CONV_WIDTH).apply(_predict)
+
+        return output
+
+    def set_initial_historic_predictions(
+        self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
+    ) -> None:
+
+        pred_df = self.rl_model_predict(df, dk, model)
+        pred_df.fillna(0, inplace=True)
+        self.dd.historic_predictions[pair] = pred_df
+        hist_preds_df = self.dd.historic_predictions[pair]
+
+        for label in hist_preds_df.columns:
+            if hist_preds_df[label].dtype == object:
+                continue
+            hist_preds_df[f'{label}_mean'] = 0
+            hist_preds_df[f'{label}_std'] = 0
+
+        hist_preds_df['do_predict'] = 0
+
+        if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
+            hist_preds_df['DI_values'] = 0
+
+        for return_str in dk.data['extra_returns_per_train']:
+            hist_preds_df[return_str] = 0