import logging from typing import Any, Dict, Tuple from catboost import CatBoostRegressor, Pool from pandas import DataFrame from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.freqai_interface import IFreqaiModel logger = logging.getLogger(__name__) class CatboostPredictionModel(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 make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame: """ User defines the labels here (target values). :params: :dataframe: the full dataframe for the present training period """ dataframe["s"] = ( dataframe["close"] .shift(-self.feature_parameters["period"]) .rolling(self.feature_parameters["period"]) .mean() / dataframe["close"] - 1 ) return dataframe["s"] def train(self, unfiltered_dataframe: DataFrame, metadata: dict, dh: 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'{metadata["pair"]} --------------------') # create the full feature list based on user config info dh.training_features_list = dh.find_features(unfiltered_dataframe) unfiltered_labels = self.make_labels(unfiltered_dataframe, dh) # filter the features requested by user in the configuration file and elegantly handle NaNs features_filtered, labels_filtered = dh.filter_features( unfiltered_dataframe, dh.training_features_list, unfiltered_labels, training_filter=True, ) # split data into train/test data. data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered) dh.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 = dh.normalize_data(data_dictionary) # optional additional data cleaning/analysis self.data_cleaning_train(dh) logger.info(f'Training model on {len(dh.training_features_list)} features') logger.info(f'Training model on {len(data_dictionary["train_features"])} data points') model = self.fit(data_dictionary) logger.info(f'--------------------done training {metadata["pair"]}--------------------') return model def fit(self, data_dictionary: Dict) -> Any: """ User sets up the training and test data to fit their desired model here :params: :data_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels. """ train_data = Pool( data=data_dictionary["train_features"], label=data_dictionary["train_labels"], weight=data_dictionary["train_weights"], ) test_data = Pool( data=data_dictionary["test_features"], label=data_dictionary["test_labels"], weight=data_dictionary["test_weights"], ) model = CatBoostRegressor( allow_writing_files=False, verbose=100, early_stopping_rounds=400, **self.model_training_parameters ) model.fit(X=train_data, eval_set=test_data) return model def predict(self, unfiltered_dataframe: DataFrame, dh: FreqaiDataKitchen) -> 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--------------------") original_feature_list = dh.find_features(unfiltered_dataframe) filtered_dataframe, _ = dh.filter_features( unfiltered_dataframe, original_feature_list, training_filter=False ) filtered_dataframe = dh.normalize_data_from_metadata(filtered_dataframe) dh.data_dictionary["prediction_features"] = filtered_dataframe # optional additional data cleaning/analysis self.data_cleaning_predict(dh) predictions = self.model.predict(dh.data_dictionary["prediction_features"]) # compute the non-normalized predictions dh.predictions = (predictions + 1) * (dh.data["labels_max"] - dh.data["labels_min"]) / 2 + dh.data["labels_min"] # logger.info("--------------------Finished prediction--------------------") return (dh.predictions, dh.do_predict)