diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 7ea2daf02..23bba3f1a 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -460,6 +460,18 @@ class FreqaiDataKitchen: return df + def check_pred_labels(self, df_predictions: DataFrame) -> None: + """ + Check that prediction feature labels match training feature labels. + :params: + :df_predictions: incoming predictions + """ + train_labels = self.data_dictionary["train_features"].columns + pred_labels = df_predictions.columns + if len(train_labels.difference(pred_labels)) != 0: + self.data_dictionary["prediction_features"] = df_predictions[train_labels] + return + def principal_component_analysis(self) -> None: """ Performs Principal Component Analysis on the data for dimensionality reduction diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 5ac7bc32c..62c814c72 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -492,6 +492,8 @@ class IFreqaiModel(ABC): # ensure user is feeding the correct indicators to the model self.check_if_feature_list_matches_strategy(dk) + dk.check_pred_labels(dk.data_dictionary['prediction_features']) + if ft_params.get('inlier_metric_window', 0): dk.compute_inlier_metric(set_='predict') diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index 026b45afc..df61b284a 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -107,6 +107,8 @@ def make_unfiltered_dataframe(mocker, freqai_conf): unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators( strategy, corr_dataframes, base_dataframes, freqai.dk.pair ) + for i in range(5): + unfiltered_dataframe[f'constant_{i}'] = i unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe) diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index a61853c47..238c0418c 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -181,6 +181,8 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat): corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") + for i in range(5): + df[f'constant_{i}'] = i metadata = {"pair": "LTC/BTC"} freqai.start_backtesting(df, metadata, freqai.dk) @@ -208,6 +210,8 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf): corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") + for i in range(5): + df[f'constant_{i}'] = i metadata = {"pair": "LTC/BTC"} freqai.start_backtesting(df, metadata, freqai.dk) @@ -233,6 +237,8 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog): corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") + for i in range(5): + df[f'constant_{i}'] = i metadata = {"pair": "ADA/BTC"} freqai.start_backtesting(df, metadata, freqai.dk) @@ -256,6 +262,8 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog): corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") + for i in range(5): + df[f'constant_{i}'] = i freqai.start_backtesting(df, metadata, freqai.dk) assert log_has_re( @@ -312,6 +320,8 @@ def test_follow_mode(mocker, freqai_conf): freqai.dd.load_all_pair_histories(timerange, freqai.dk) df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m') + for i in range(5): + df[f'constant_{i}'] = i freqai.start_live(df, metadata, strategy, freqai.dk) assert len(freqai.dk.return_dataframe.index) == 5702