diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 101df88ec..e4f77a9cf 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -673,12 +673,12 @@ class IFreqaiModel(ABC):
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.
 
     @abstractmethod
-    def train(self, unfiltered_dataframe: DataFrame, pair: str,
+    def train(self, unfiltered_df: DataFrame, pair: str,
               dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         Filter the training data and train a model to it. Train makes heavy use of the datahandler
         for storing, saving, loading, and analyzing the data.
-        :param unfiltered_dataframe: Full dataframe for the current training period
+        :param unfiltered_df: Full dataframe for the current training period
         :param metadata: pair metadata from strategy.
         :return: Trained model which can be used to inference (self.predict)
         """
@@ -697,11 +697,11 @@ class IFreqaiModel(ABC):
 
     @abstractmethod
     def predict(
-        self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True, **kwargs
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
     ) -> Tuple[DataFrame, NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param unfiltered_dataframe: Full dataframe for the current backtest period.
+        :param unfiltered_df: Full dataframe for the current backtest period.
         :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
         :param first: boolean = whether this is the first prediction or not.
         :return:
diff --git a/freqtrade/freqai/prediction_models/BaseClassifierModel.py b/freqtrade/freqai/prediction_models/BaseClassifierModel.py
index 291bacc82..5142ffb0d 100644
--- a/freqtrade/freqai/prediction_models/BaseClassifierModel.py
+++ b/freqtrade/freqai/prediction_models/BaseClassifierModel.py
@@ -21,12 +21,12 @@ class BaseClassifierModel(IFreqaiModel):
     """
 
     def train(
-        self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
+        self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
     ) -> Any:
         """
         Filter the training data and train a model to it. Train makes heavy use of the datakitchen
         for storing, saving, loading, and analyzing the data.
-        :param unfiltered_dataframe: Full dataframe for the current training period
+        :param unfiltered_df: Full dataframe for the current training period
         :param metadata: pair metadata from strategy.
         :return:
         :model: Trained model which can be used to inference (self.predict)
@@ -36,14 +36,14 @@ class BaseClassifierModel(IFreqaiModel):
 
         # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(
-            unfiltered_dataframe,
+            unfiltered_df,
             dk.training_features_list,
             dk.label_list,
             training_filter=True,
         )
 
-        start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
-        end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
+        start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
+        end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
         logger.info(f"-------------------- Training on data from {start_date} to "
                     f"{end_date}--------------------")
         # split data into train/test data.
@@ -68,25 +68,25 @@ class BaseClassifierModel(IFreqaiModel):
         return model
 
     def predict(
-        self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False, **kwargs
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param: unfiltered_dataframe: Full dataframe for the current backtest period.
+        :param: unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
         data (NaNs) or felt uncertain about data (PCA and DI index)
         """
 
-        dk.find_features(dataframe)
-        filtered_dataframe, _ = dk.filter_features(
-            dataframe, dk.training_features_list, training_filter=False
+        dk.find_features(unfiltered_df)
+        filtered_df, _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
         )
-        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
-        dk.data_dictionary["prediction_features"] = filtered_dataframe
+        filtered_df = dk.normalize_data_from_metadata(filtered_df)
+        dk.data_dictionary["prediction_features"] = filtered_df
 
-        self.data_cleaning_predict(dk, filtered_dataframe)
+        self.data_cleaning_predict(dk, filtered_df)
 
         predictions = self.model.predict(dk.data_dictionary["prediction_features"])
         pred_df = DataFrame(predictions, columns=dk.label_list)
diff --git a/freqtrade/freqai/prediction_models/BaseRegressionModel.py b/freqtrade/freqai/prediction_models/BaseRegressionModel.py
index da6fba571..1d87e42c0 100644
--- a/freqtrade/freqai/prediction_models/BaseRegressionModel.py
+++ b/freqtrade/freqai/prediction_models/BaseRegressionModel.py
@@ -20,12 +20,12 @@ class BaseRegressionModel(IFreqaiModel):
     """
 
     def train(
-        self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
+        self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
     ) -> Any:
         """
         Filter the training data and train a model to it. Train makes heavy use of the datakitchen
         for storing, saving, loading, and analyzing the data.
-        :param unfiltered_dataframe: Full dataframe for the current training period
+        :param unfiltered_df: Full dataframe for the current training period
         :param metadata: pair metadata from strategy.
         :return:
         :model: Trained model which can be used to inference (self.predict)
@@ -35,14 +35,14 @@ class BaseRegressionModel(IFreqaiModel):
 
         # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(
-            unfiltered_dataframe,
+            unfiltered_df,
             dk.training_features_list,
             dk.label_list,
             training_filter=True,
         )
 
-        start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
-        end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
+        start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
+        end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
         logger.info(f"-------------------- Training on data from {start_date} to "
                     f"{end_date}--------------------")
         # split data into train/test data.
@@ -67,26 +67,26 @@ class BaseRegressionModel(IFreqaiModel):
         return model
 
     def predict(
-        self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False, **kwargs
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param: unfiltered_dataframe: Full dataframe for the current backtest period.
+        :param: unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
         data (NaNs) or felt uncertain about data (PCA and DI index)
         """
 
-        dk.find_features(dataframe)
-        filtered_dataframe, _ = dk.filter_features(
-            dataframe, dk.training_features_list, training_filter=False
+        dk.find_features(unfiltered_df)
+        filtered_df, _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
         )
-        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
-        dk.data_dictionary["prediction_features"] = filtered_dataframe
+        filtered_df = dk.normalize_data_from_metadata(filtered_df)
+        dk.data_dictionary["prediction_features"] = filtered_df
 
         # optional additional data cleaning/analysis
-        self.data_cleaning_predict(dk, filtered_dataframe)
+        self.data_cleaning_predict(dk, filtered_df)
 
         predictions = self.model.predict(dk.data_dictionary["prediction_features"])
         pred_df = DataFrame(predictions, columns=dk.label_list)
diff --git a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
index 6fb49239b..eea80f3a2 100644
--- a/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
+++ b/freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
@@ -17,12 +17,12 @@ class BaseTensorFlowModel(IFreqaiModel):
     """
 
     def train(
-        self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
+        self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
     ) -> Any:
         """
         Filter the training data and train a model to it. Train makes heavy use of the datakitchen
         for storing, saving, loading, and analyzing the data.
-        :param unfiltered_dataframe: Full dataframe for the current training period
+        :param unfiltered_df: Full dataframe for the current training period
         :param metadata: pair metadata from strategy.
         :return:
         :model: Trained model which can be used to inference (self.predict)
@@ -32,14 +32,14 @@ class BaseTensorFlowModel(IFreqaiModel):
 
         # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(
-            unfiltered_dataframe,
+            unfiltered_df,
             dk.training_features_list,
             dk.label_list,
             training_filter=True,
         )
 
-        start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
-        end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
+        start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
+        end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
         logger.info(f"-------------------- Training on data from {start_date} to "
                     f"{end_date}--------------------")
         # split data into train/test data.