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fix inlier metric in backtesting

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This commit is contained in:
robcaulk 2022-10-01 14:18:46 +02:00
parent f2b875483f
commit cd514cf15d
5 changed files with 9 additions and 7 deletions
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2
freqtrade/freqai/base_models/BaseClassifierModel.py
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@ -92,7 +92,7 @@ class BaseClassifierModel(IFreqaiModel):
filtered_df = dk.normalize_data_from_metadata(filtered_df) filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df dk.data_dictionary["prediction_features"] = filtered_df
self.data_cleaning_predict(dk, filtered_df) self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"]) predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list) pred_df = DataFrame(predictions, columns=dk.label_list)

2
freqtrade/freqai/base_models/BaseRegressionModel.py
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@ -92,7 +92,7 @@ class BaseRegressionModel(IFreqaiModel):
dk.data_dictionary["prediction_features"] = filtered_df dk.data_dictionary["prediction_features"] = filtered_df
# optional additional data cleaning/analysis # optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_df) self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"]) predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list) pred_df = DataFrame(predictions, columns=dk.label_list)

2
freqtrade/freqai/data_drawer.py
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@ -423,7 +423,7 @@ class FreqaiDataDrawer:
dk.data["data_path"] = str(dk.data_path) dk.data["data_path"] = str(dk.data_path)
dk.data["model_filename"] = str(dk.model_filename) dk.data["model_filename"] = str(dk.model_filename)
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns) dk.data["training_features_list"] = dk.training_features_list
dk.data["label_list"] = dk.label_list dk.data["label_list"] = dk.label_list
# store the metadata # store the metadata
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp: with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:

2
freqtrade/freqai/data_kitchen.py
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@ -844,10 +844,12 @@ class FreqaiDataKitchen:
self.remove_beginning_points_from_data_dict(set_, no_prev_pts) self.remove_beginning_points_from_data_dict(set_, no_prev_pts)
self.data_dictionary[f'{set_}_features'] = pd.concat( self.data_dictionary[f'{set_}_features'] = pd.concat(
[compute_df, inlier_metric], axis=1) [compute_df, inlier_metric], axis=1)
# self.find_features(self.data_dictionary[f'{set_}_features'])
else: else:
self.data_dictionary['prediction_features'] = pd.concat( self.data_dictionary['prediction_features'] = pd.concat(
[compute_df, inlier_metric], axis=1) [compute_df, inlier_metric], axis=1)
self.data_dictionary['prediction_features'].fillna(0, inplace=True) self.data_dictionary['prediction_features'].fillna(0, inplace=True)
# self.find_features(self.data_dictionary['prediction_features'])
logger.info('Inlier metric computed and added to features.') logger.info('Inlier metric computed and added to features.')

8
freqtrade/freqai/freqai_interface.py
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@ -482,13 +482,16 @@ class IFreqaiModel(ABC):
if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0): if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
dk.add_noise_to_training_features() dk.add_noise_to_training_features()
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None: def data_cleaning_predict(self, dk: FreqaiDataKitchen) -> None:
""" """
Base data cleaning method for predict. Base data cleaning method for predict.
Functions here are complementary to the functions of data_cleaning_train. Functions here are complementary to the functions of data_cleaning_train.
""" """
ft_params = self.freqai_info["feature_parameters"] ft_params = self.freqai_info["feature_parameters"]
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dk)
if ft_params.get('inlier_metric_window', 0): if ft_params.get('inlier_metric_window', 0):
dk.compute_inlier_metric(set_='predict') dk.compute_inlier_metric(set_='predict')
@ -506,9 +509,6 @@ class IFreqaiModel(ABC):
if ft_params.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) dk.use_DBSCAN_to_remove_outliers(predict=True)
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dk)
def model_exists(self, dk: FreqaiDataKitchen) -> bool: def model_exists(self, dk: FreqaiDataKitchen) -> bool:
""" """
Given a pair and path, check if a model already exists Given a pair and path, check if a model already exists