Remove constant labels from prediction
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@ -460,6 +460,18 @@ class FreqaiDataKitchen:
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return df
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def check_pred_labels(self, df_predictions: DataFrame) -> None:
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"""
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Check that prediction feature labels match training feature labels.
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:params:
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:df_predictions: incoming predictions
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"""
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train_labels = self.data_dictionary["train_features"].columns
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pred_labels = df_predictions.columns
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if len(train_labels.difference(pred_labels)) != 0:
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self.data_dictionary["prediction_features"] = df_predictions[train_labels]
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return
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def principal_component_analysis(self) -> None:
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"""
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Performs Principal Component Analysis on the data for dimensionality reduction
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@ -492,6 +492,8 @@ class IFreqaiModel(ABC):
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# ensure user is feeding the correct indicators to the model
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self.check_if_feature_list_matches_strategy(dk)
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dk.check_pred_labels(dk.data_dictionary['prediction_features'])
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if ft_params.get('inlier_metric_window', 0):
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dk.compute_inlier_metric(set_='predict')
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@ -107,6 +107,8 @@ def make_unfiltered_dataframe(mocker, freqai_conf):
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unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
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strategy, corr_dataframes, base_dataframes, freqai.dk.pair
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)
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for i in range(5):
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unfiltered_dataframe[f'constant_{i}'] = i
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unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
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@ -181,6 +181,8 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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for i in range(5):
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df[f'constant_{i}'] = i
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metadata = {"pair": "LTC/BTC"}
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freqai.start_backtesting(df, metadata, freqai.dk)
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@ -208,6 +210,8 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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for i in range(5):
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df[f'constant_{i}'] = i
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metadata = {"pair": "LTC/BTC"}
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freqai.start_backtesting(df, metadata, freqai.dk)
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@ -233,6 +237,8 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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for i in range(5):
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df[f'constant_{i}'] = i
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metadata = {"pair": "ADA/BTC"}
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freqai.start_backtesting(df, metadata, freqai.dk)
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@ -256,6 +262,8 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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for i in range(5):
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df[f'constant_{i}'] = i
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freqai.start_backtesting(df, metadata, freqai.dk)
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assert log_has_re(
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@ -312,6 +320,8 @@ def test_follow_mode(mocker, freqai_conf):
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
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for i in range(5):
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df[f'constant_{i}'] = i
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freqai.start_live(df, metadata, strategy, freqai.dk)
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assert len(freqai.dk.return_dataframe.index) == 5702
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