import logging from typing import Any, Dict, Tuple from lightgbm import LGBMRegressor from pandas import DataFrame from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.freqai_interface import IFreqaiModel logger = logging.getLogger(__name__) class LightGBMPredictionModel(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. """ 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 datahkitchen 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} --------------------") # unfiltered_labels = self.make_labels(unfiltered_dataframe, dk) # 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) dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy # 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) logger.info(f"--------------------done training {pair}--------------------") return model def fit(self, data_dictionary: Dict) -> 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. :params: :data_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels. """ eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"]) X = data_dictionary["train_features"] y = data_dictionary["train_labels"] model = LGBMRegressor(**self.model_training_parameters) model.fit(X=X, y=y, eval_set=eval_set) return model def predict( self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False ) -> Tuple[DataFrame, DataFrame]: """ Filter the prediction features data and predict with it. :param: unfiltered_dataframe: Full dataframe for the current backtest period. :return: :predictions: np.array of 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) """ # logger.info("--------------------Starting prediction--------------------") 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) predictions = self.model.predict(dk.data_dictionary["prediction_features"]) pred_df = DataFrame(predictions, columns=dk.label_list) for label in dk.label_list: pred_df[label] = ( (pred_df[label] + 1) * (dk.data["labels_max"][label] - dk.data["labels_min"][label]) / 2 ) + dk.data["labels_min"][label] return (pred_df, dk.do_predict)