diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 421b30bf5..1a80a1f86 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -658,6 +658,114 @@ class FreqaiDataKitchen: return + def compute_inlier_metric(self, set_='train') -> None: + """ + + Compute inlier metric from backwards distance distributions. + This metric defines how well features from a timepoint fit + into previous timepoints. + """ + + import scipy.stats as ss + + no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"] + weib_pct = self.freqai_config["feature_parameters"]["inlier_metric_weibull_cutoff"] + + if set_ == 'train': + compute_df = copy.deepcopy(self.data_dictionary['train_features']) + elif set_ == 'test': + compute_df = copy.deepcopy(self.data_dictionary['test_features']) + else: + compute_df = copy.deepcopy(self.data_dictionary['prediction_features']) + + compute_df_reindexed = compute_df.reindex( + index=np.flip(compute_df.index) + ) + + pairwise = pd.DataFrame( + np.triu( + pairwise_distances(compute_df_reindexed, n_jobs=self.thread_count) + ), + columns=compute_df_reindexed.index, + index=compute_df_reindexed.index + ) + pairwise = pairwise.round(5) + + column_labels = [ + '{}{}'.format('d', i) for i in range(1, no_prev_pts + 1) + ] + distances = pd.DataFrame( + columns=column_labels, index=compute_df.index + ) + + for index in compute_df.index[no_prev_pts:]: + current_row = pairwise.loc[[index]] + current_row_no_zeros = current_row.loc[ + :, (current_row != 0).any(axis=0) + ] + distances.loc[[index]] = current_row_no_zeros.iloc[ + :, :no_prev_pts + ] + distances = distances.replace([np.inf, -np.inf], np.nan) + drop_index = pd.isnull(distances).any(1) + distances = distances[drop_index == 0] + + inliers = pd.DataFrame(index=distances.index) + for key in distances.keys(): + current_distances = distances[key].dropna() + fit_params = ss.weibull_min.fit(current_distances) + cutoff = ss.weibull_min.ppf(weib_pct, *fit_params) + is_inlier = np.where( + current_distances <= cutoff, 1, 0 + ) + df_inlier = pd.DataFrame( + {key + '_IsInlier': is_inlier}, index=distances.index + ) + inliers = pd.concat( + [inliers, df_inlier], axis=1 + ) + + inlier_metric = pd.DataFrame( + data=inliers.sum(axis=1) / no_prev_pts, + columns=['inlier_metric'], + index=compute_df.index + ) + + inlier_metric = 2 * (inlier_metric - inlier_metric.min()) / \ + (inlier_metric.max() - inlier_metric.min()) - 1 + + if set_ in ('train', 'test'): + inlier_metric = inlier_metric.iloc[no_prev_pts:] + compute_df = compute_df.iloc[no_prev_pts:] + self.remove_beginning_points_from_data_dict(set_, no_prev_pts) + self.data_dictionary[f'{set_}_features'] = pd.concat( + [compute_df, inlier_metric], axis=1) + else: + self.data_dictionary['prediction_features'] = pd.concat( + [compute_df, inlier_metric], axis=1) + self.data_dictionary['prediction_features'].fillna(0, inplace=True) + + return None + + def remove_beginning_points_from_data_dict(self, set_='train', no_prev_pts: int = 10): + features = self.data_dictionary[f'{set_}_features'] + weights = self.data_dictionary[f'{set_}_weights'] + labels = self.data_dictionary[f'{set_}_labels'] + self.data_dictionary[f'{set_}_weights'] = weights[no_prev_pts:] + self.data_dictionary[f'{set_}_features'] = features.iloc[no_prev_pts:] + self.data_dictionary[f'{set_}_labels'] = labels.iloc[no_prev_pts:] + + def add_noise_to_training_features(self) -> None: + """ + Add noise to train features to reduce the risk of overfitting. + """ + mu = 0 # no shift + sigma = self.freqai_config["feature_parameters"]["noise_standard_deviation"] + compute_df = self.data_dictionary['train_features'] + noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]]) + self.data_dictionary['train_features'] += noise + return + def find_features(self, dataframe: DataFrame) -> None: """ Find features in the strategy provided dataframe diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 49e4ce5c3..07303b49f 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -66,7 +66,6 @@ class IFreqaiModel(ABC): "data_split_parameters", {}) self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get( "model_training_parameters", {}) - self.feature_parameters = config.get("freqai", {}).get("feature_parameters") self.retrain = False self.first = True self.set_full_path() @@ -74,11 +73,14 @@ class IFreqaiModel(ABC): self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode) self.identifier: str = self.freqai_info.get("identifier", "no_id_provided") self.scanning = False + self.ft_params = self.freqai_info["feature_parameters"] self.keras: bool = self.freqai_info.get("keras", False) - if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0): - self.freqai_info["feature_parameters"]["DI_threshold"] = 0 + if self.keras and self.ft_params.get("DI_threshold", 0): + self.ft_params["DI_threshold"] = 0 logger.warning("DI threshold is not configured for Keras models yet. Deactivating.") self.CONV_WIDTH = self.freqai_info.get("conv_width", 2) + if self.ft_params.get("inlier_metric_window", 0): + self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2 self.pair_it = 0 self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist")) self.last_trade_database_summary: DataFrame = {} @@ -383,24 +385,25 @@ class IFreqaiModel(ABC): def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None: """ - Base data cleaning method for train - Any function inside this method should drop training data points from the filtered_dataframe - based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an - example of how outlier data points are dropped from the dataframe used for training. + Base data cleaning method for train. + Functions here improve/modify the input data by identifying outliers, + computing additional metrics, adding noise, reducing dimensionality etc. """ - if self.freqai_info["feature_parameters"].get( + ft_params = self.freqai_info["feature_parameters"] + + if ft_params.get( "principal_component_analysis", False ): dk.principal_component_analysis() - if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False): + if ft_params.get("use_SVM_to_remove_outliers", False): dk.use_SVM_to_remove_outliers(predict=False) - if self.freqai_info["feature_parameters"].get("DI_threshold", 0): + if ft_params.get("DI_threshold", 0): dk.data["avg_mean_dist"] = dk.compute_distances() - if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False): + if ft_params.get("use_DBSCAN_to_remove_outliers", False): if dk.pair in self.dd.old_DBSCAN_eps: eps = self.dd.old_DBSCAN_eps[dk.pair] else: @@ -408,29 +411,36 @@ class IFreqaiModel(ABC): dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps) self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps'] + if ft_params.get('inlier_metric_window', 0): + dk.compute_inlier_metric(set_='train') + if self.freqai_info["data_split_parameters"]["test_size"] > 0: + dk.compute_inlier_metric(set_='test') + + if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0): + dk.add_noise_to_training_features() + def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None: """ Base data cleaning method for predict. - These functions each modify dk.do_predict, which is a dataframe with equal length - to the number of candles coming from and returning to the strategy. Inside do_predict, - 1 allows prediction and < 0 signals to the strategy that the model is not confident in - the prediction. - See FreqaiDataKitchen::remove_outliers() for an example - of how the do_predict vector is modified. do_predict is ultimately passed back to strategy - for buy signals. + Functions here are complementary to the functions of data_cleaning_train. """ - if self.freqai_info["feature_parameters"].get( + ft_params = self.freqai_info["feature_parameters"] + + if ft_params.get('inlier_metric_window', 0): + dk.compute_inlier_metric(set_='predict') + + if ft_params.get( "principal_component_analysis", False ): dk.pca_transform(dataframe) - if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False): + if ft_params.get("use_SVM_to_remove_outliers", False): dk.use_SVM_to_remove_outliers(predict=True) - if self.freqai_info["feature_parameters"].get("DI_threshold", 0): + if ft_params.get("DI_threshold", 0): dk.check_if_pred_in_training_spaces() - if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False): + if ft_params.get("use_DBSCAN_to_remove_outliers", False): dk.use_DBSCAN_to_remove_outliers(predict=True) def model_exists(