import logging from time import time from typing import Any, Tuple import numpy as np import numpy.typing as npt from pandas import DataFrame from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.freqai_interface import IFreqaiModel logger = logging.getLogger(__name__) class BaseRegressionModel(IFreqaiModel): """ 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 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 :param metadata: pair metadata from strategy. :return: :model: Trained model which can be used to inference (self.predict) """ logger.info(f"-------------------- Starting training {pair} --------------------") start_time = time() # filter the features requested by user in the configuration file and elegantly handle NaNs features_filtered, labels_filtered = dk.filter_features( unfiltered_df, dk.training_features_list, dk.label_list, training_filter=True, ) start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d") end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d") logger.info(f"-------------------- Training on data from {start_date} to " f"{end_date} --------------------") # 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 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 # optional additional data cleaning/analysis self.data_cleaning_predict(dk) predictions = self.model.predict(dk.data_dictionary["prediction_features"]) pred_df = DataFrame(predictions, columns=dk.label_list) pred_df = dk.denormalize_labels_from_metadata(pred_df) return (pred_df, dk.do_predict)