Remove constant labels from prediction

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

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')
 

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)
 

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