Rehaul organization of return values
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robcaulk
@@ -18,37 +18,17 @@ class LightGBMPredictionModel(IFreqaiModel):
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has its own DataHandler where data is held, saved, loaded, and managed.
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"""
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def return_values(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
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dataframe["prediction"] = dh.full_predictions
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dataframe["do_predict"] = dh.full_do_predict
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dataframe["target_mean"] = dh.full_target_mean
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dataframe["target_std"] = dh.full_target_std
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
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dataframe["DI"] = dh.full_DI_values
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def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
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"""
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User uses this function to add any additional return values to the dataframe.
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e.g.
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dataframe['volatility'] = dk.volatility_values
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"""
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return dataframe
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def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
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"""
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User defines the labels here (target values).
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:params:
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:dataframe: the full dataframe for the present training period
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"""
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dataframe["s"] = (
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dataframe["close"]
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.shift(-self.feature_parameters["period"])
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.rolling(self.feature_parameters["period"])
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.mean()
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/ dataframe["close"]
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- 1
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)
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return dataframe["s"]
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def train(self, unfiltered_dataframe: DataFrame,
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pair: str, dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
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for storing, saving, loading, and analyzing the data.
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@@ -62,27 +42,25 @@ class LightGBMPredictionModel(IFreqaiModel):
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logger.info('--------------------Starting training '
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f'{pair} --------------------')
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# create the full feature list based on user config info
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dh.training_features_list = dh.find_features(unfiltered_dataframe)
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unfiltered_labels = self.make_labels(unfiltered_dataframe, dh)
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# unfiltered_labels = self.make_labels(unfiltered_dataframe, dk)
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dh.filter_features(
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_dataframe,
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dh.training_features_list,
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unfiltered_labels,
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dk.training_features_list,
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dk.label_list,
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training_filter=True,
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)
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# split data into train/test data.
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data_dictionary = dh.make_train_test_datasets(features_filtered, labels_filtered)
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dh.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
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data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
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dk.fit_labels() # fit labels to a cauchy distribution so we know what to expect in strategy
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# normalize all data based on train_dataset only
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data_dictionary = dh.normalize_data(data_dictionary)
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data_dictionary = dk.normalize_data(data_dictionary)
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# optional additional data cleaning/analysis
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self.data_cleaning_train(dh)
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self.data_cleaning_train(dk)
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logger.info(f'Training model on {len(dh.data_dictionary["train_features"].columns)}'
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logger.info(f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
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' features')
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logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
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@@ -112,7 +90,7 @@ class LightGBMPredictionModel(IFreqaiModel):
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return model
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def predict(self, unfiltered_dataframe: DataFrame,
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dh: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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dk: FreqaiDataKitchen) -> Tuple[DataFrame, DataFrame]:
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"""
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Filter the prediction features data and predict with it.
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:param: unfiltered_dataframe: Full dataframe for the current backtest period.
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@@ -124,22 +102,22 @@ class LightGBMPredictionModel(IFreqaiModel):
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# logger.info("--------------------Starting prediction--------------------")
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original_feature_list = dh.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dh.filter_features(
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original_feature_list = dk.find_features(unfiltered_dataframe)
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filtered_dataframe, _ = dk.filter_features(
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unfiltered_dataframe, original_feature_list, training_filter=False
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)
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filtered_dataframe = dh.normalize_data_from_metadata(filtered_dataframe)
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dh.data_dictionary["prediction_features"] = filtered_dataframe
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filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
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dk.data_dictionary["prediction_features"] = filtered_dataframe
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# optional additional data cleaning/analysis
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self.data_cleaning_predict(dh, filtered_dataframe)
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self.data_cleaning_predict(dk, filtered_dataframe)
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predictions = self.model.predict(dh.data_dictionary["prediction_features"])
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predictions = self.model.predict(dk.data_dictionary["prediction_features"])
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pred_df = DataFrame(predictions, columns=dk.label_list)
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# compute the non-normalized predictions
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dh.predictions = (predictions + 1) * (dh.data["labels_max"] -
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dh.data["labels_min"]) / 2 + dh.data["labels_min"]
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for label in dk.label_list:
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pred_df[label] = ((pred_df[label] + 1) *
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(dk.data["labels_max"][label] -
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dk.data["labels_min"][label]) / 2) + dk.data["labels_min"][label]
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# logger.info("--------------------Finished prediction--------------------")
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return (dh.predictions, dh.do_predict)
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return (pred_df, dk.do_predict)
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