diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index f0fad0651..8839317f8 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -134,6 +134,7 @@ class FreqaiDataDrawer: model_filename = self.pair_dict[pair]['model_filename'] = '' coin_first = self.pair_dict[pair]['first'] = True trained_timestamp = self.pair_dict[pair]['trained_timestamp'] = 0 + self.pair_dict[pair]['priority'] = len(self.pair_dict) if not data_path_set and self.follow_mode: logger.warning(f'Follower could not find current pair {pair} in ' diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index fb6e49342..99b5024ed 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -317,11 +317,12 @@ class FreqaiDataKitchen: # that was based on a single NaN is ultimately protected from buys with do_predict drop_index = ~drop_index self.do_predict = np.array(drop_index.replace(True, 1).replace(False, 0)) - logger.info( - "dropped %s of %s prediction data points due to NaNs.", - len(self.do_predict) - self.do_predict.sum(), - len(filtered_dataframe), - ) + if (len(self.do_predict) - self.do_predict.sum()) > 0: + logger.info( + "dropped %s of %s prediction data points due to NaNs.", + len(self.do_predict) - self.do_predict.sum(), + len(filtered_dataframe), + ) return filtered_dataframe, labels @@ -562,9 +563,10 @@ class FreqaiDataKitchen: y_pred = self.svm_model.predict(self.data_dictionary["prediction_features"]) do_predict = np.where(y_pred == -1, 0, y_pred) - logger.info( - f'svm_remove_outliers() tossed {len(do_predict) - do_predict.sum()} predictions' - ) + if (len(do_predict) - do_predict.sum()) > 0: + logger.info( + f'svm_remove_outliers() tossed {len(do_predict) - do_predict.sum()} predictions' + ) self.do_predict += do_predict self.do_predict -= 1 @@ -642,10 +644,11 @@ class FreqaiDataKitchen: 0, ) - logger.info( - f'DI tossed {len(do_predict) - do_predict.sum():.2f} predictions for ' - 'being too far from training data' - ) + if (len(do_predict) - do_predict.sum()) > 0: + logger.info( + f'DI tossed {len(do_predict) - do_predict.sum():.2f} predictions for ' + 'being too far from training data' + ) self.do_predict += do_predict self.do_predict -= 1 @@ -908,7 +911,7 @@ class FreqaiDataKitchen: ignore_index=True, axis=0 ) - logger.info(f'Length of history data {len(history_data[pair][tf])}') + # logger.info(f'Length of history data {len(history_data[pair][tf])}') def set_all_pairs(self) -> None: diff --git a/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py b/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py new file mode 100644 index 000000000..04bba2a90 --- /dev/null +++ b/freqtrade/freqai/prediction_models/LightGBMPredictionModel.py @@ -0,0 +1,145 @@ +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, dh: FreqaiDataKitchen) -> DataFrame: + + dataframe["prediction"] = dh.full_predictions + dataframe["do_predict"] = dh.full_do_predict + dataframe["target_mean"] = dh.full_target_mean + dataframe["target_std"] = dh.full_target_std + if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0: + dataframe["DI"] = dh.full_DI_values + + return dataframe + + 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, + pair: str, 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'{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.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(seed=42, n_estimators=2000, verbosity=1, force_col_wise=True) + model.fit(X=X, y=y, eval_set=eval_set) + + 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, filtered_dataframe) + + 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) diff --git a/requirements-freqai.txt b/requirements-freqai.txt index 4ebf62b59..a06a41b96 100644 --- a/requirements-freqai.txt +++ b/requirements-freqai.txt @@ -6,4 +6,4 @@ scikit-learn==1.0.2 scikit-optimize==0.9.0 joblib==1.1.0 catboost==1.0.4 -#lightgbm==3.3.2 +lightgbm==3.3.2