Rehaul organization of return values

freqtrade/freqai/freqai_interface.py

@@ -59,9 +59,7 @@ class IFreqaiModel(ABC):
         self.update_historic_data = 0
         self.set_full_path()
         self.follow_mode = self.freqai_info.get('follow_mode', False)
-        self.data_drawer = FreqaiDataDrawer(Path(self.full_path),
-                                            self.config,
-                                            self.follow_mode)
+        self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
         self.lock = threading.Lock()
         self.follow_mode = self.freqai_info.get('follow_mode', False)
         self.identifier = self.freqai_info.get('identifier', 'no_id_provided')
@@ -91,12 +89,12 @@ class IFreqaiModel(ABC):
         """
 
         self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
-        self.data_drawer.set_pair_dict_info(metadata)
+        self.dd.set_pair_dict_info(metadata)
 
         if self.live:
-            self.dh = FreqaiDataKitchen(self.config, self.data_drawer,
+            self.dk = FreqaiDataKitchen(self.config, self.dd,
                                         self.live, metadata["pair"])
-            dh = self.start_live(dataframe, metadata, strategy, self.dh)
+            dk = self.start_live(dataframe, metadata, strategy, self.dk)
 
         # For backtesting, each pair enters and then gets trained for each window along the
         # sliding window defined by "train_period" (training window) and "backtest_period"
@@ -104,19 +102,19 @@ class IFreqaiModel(ABC):
         # FreqAI slides the window and sequentially builds the backtesting results before returning
         # the concatenated results for the full backtesting period back to the strategy.
         elif not self.follow_mode:
-            self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"])
-            logger.info(f'Training {len(self.dh.training_timeranges)} timeranges')
-            dh = self.start_backtesting(dataframe, metadata, self.dh)
+            self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"])
+            logger.info(f'Training {len(self.dk.training_timeranges)} timeranges')
+            dk = self.start_backtesting(dataframe, metadata, self.dk)
 
-        dataframe = self.remove_features_from_df(dataframe)
-        return self.return_values(dataframe, dh)
+        dataframe = self.remove_features_from_df(dk.return_dataframe)
+        return self.return_values(dataframe, dk)
 
     @threaded
     def start_scanning(self, strategy: IStrategy) -> None:
         """
         Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
         to improve model youth. This function is agnostic to data preparation/collection/storage,
-        it simply trains on what ever data is available in the self.data_drawer.
+        it simply trains on what ever data is available in the self.dd.
         :params:
         strategy: IStrategy = The user defined strategy class
         """
@@ -124,33 +122,33 @@ class IFreqaiModel(ABC):
             time.sleep(1)
             for pair in self.config.get('exchange', {}).get('pair_whitelist'):
 
-                (_, trained_timestamp, _, _) = self.data_drawer.get_pair_dict_info(pair)
+                (_, trained_timestamp, _, _) = self.dd.get_pair_dict_info(pair)
 
-                if self.data_drawer.pair_dict[pair]['priority'] != 1:
+                if self.dd.pair_dict[pair]['priority'] != 1:
                     continue
-                dh = FreqaiDataKitchen(self.config, self.data_drawer,
+                dk = FreqaiDataKitchen(self.config, self.dd,
                                        self.live, pair)
 
                 # file_exists = False
 
-                dh.set_paths(pair, trained_timestamp)
+                dk.set_paths(pair, trained_timestamp)
                 # file_exists = self.model_exists(pair,
-                #                                 dh,
+                #                                 dk,
                 #                                 trained_timestamp=trained_timestamp,
                 #                                 model_filename=model_filename,
                 #                                 scanning=True)
 
                 (retrain,
                  new_trained_timerange,
-                 data_load_timerange) = dh.check_if_new_training_required(trained_timestamp)
-                dh.set_paths(pair, new_trained_timerange.stopts)
+                 data_load_timerange) = dk.check_if_new_training_required(trained_timestamp)
+                dk.set_paths(pair, new_trained_timerange.stopts)
 
                 if retrain:  # or not file_exists:
                     self.train_model_in_series(new_trained_timerange, pair,
-                                               strategy, dh, data_load_timerange)
+                                               strategy, dk, data_load_timerange)
 
     def start_backtesting(self, dataframe: DataFrame, metadata: dict,
-                          dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
+                          dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
         """
         The main broad execution for backtesting. For backtesting, each pair enters and then gets
         trained for each window along the sliding window defined by "train_period" (training window)
@@ -161,9 +159,9 @@ class IFreqaiModel(ABC):
         :params:
         dataframe: DataFrame = strategy passed dataframe
         metadata: Dict = pair metadata
-        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
+        dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
         :returns:
-        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
+        dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
         """
 
         # Loop enforcing the sliding window training/backtesting paradigm
@@ -172,15 +170,15 @@ class IFreqaiModel(ABC):
         # following tr_train. Both of these windows slide through the
         # entire backtest
         for tr_train, tr_backtest in zip(
-            dh.training_timeranges, dh.backtesting_timeranges
+            dk.training_timeranges, dk.backtesting_timeranges
         ):
-            (_, _, _, _) = self.data_drawer.get_pair_dict_info(metadata['pair'])
+            (_, _, _, _) = self.dd.get_pair_dict_info(metadata['pair'])
             gc.collect()
-            dh.data = {}  # clean the pair specific data between training window sliding
+            dk.data = {}  # clean the pair specific data between training window sliding
             self.training_timerange = tr_train
             # self.training_timerange_timerange = tr_train
-            dataframe_train = dh.slice_dataframe(tr_train, dataframe)
-            dataframe_backtest = dh.slice_dataframe(tr_backtest, dataframe)
+            dataframe_train = dk.slice_dataframe(tr_train, dataframe)
+            dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
 
             trained_timestamp = tr_train  # TimeRange.parse_timerange(tr_train)
             tr_train_startts_str = datetime.datetime.utcfromtimestamp(
@@ -190,33 +188,33 @@ class IFreqaiModel(ABC):
             logger.info("Training %s", metadata["pair"])
             logger.info(f'Training {tr_train_startts_str} to {tr_train_stopts_str}')
 
-            dh.data_path = Path(dh.full_path /
+            dk.data_path = Path(dk.full_path /
                                 str("sub-train" + "-" + metadata['pair'].split("/")[0] +
                                     str(int(trained_timestamp.stopts))))
-            if not self.model_exists(metadata["pair"], dh,
+            if not self.model_exists(metadata["pair"], dk,
                                      trained_timestamp=trained_timestamp.stopts):
-                self.model = self.train(dataframe_train, metadata['pair'], dh)
-                self.data_drawer.pair_dict[metadata['pair']][
+                self.model = self.train(dataframe_train, metadata['pair'], dk)
+                self.dd.pair_dict[metadata['pair']][
                                         'trained_timestamp'] = trained_timestamp.stopts
-                dh.set_new_model_names(metadata['pair'], trained_timestamp)
-                dh.save_data(self.model, metadata['pair'], keras=self.keras)
+                dk.set_new_model_names(metadata['pair'], trained_timestamp)
+                dk.save_data(self.model, metadata['pair'], keras_model=self.keras)
             else:
-                self.model = dh.load_data(metadata['pair'], keras=self.keras)
+                self.model = dk.load_data(metadata['pair'], keras_model=self.keras)
 
-            self.check_if_feature_list_matches_strategy(dataframe_train, dh)
+            self.check_if_feature_list_matches_strategy(dataframe_train, dk)
 
-            preds, do_preds = self.predict(dataframe_backtest, dh)
+            preds, do_preds = self.predict(dataframe_backtest, dk)
 
-            dh.append_predictions(preds, do_preds, len(dataframe_backtest))
-            print('predictions', len(dh.full_predictions),
-                  'do_predict', len(dh.full_do_predict))
+            dk.append_predictions(preds, do_preds, len(dataframe_backtest))
+            print('predictions', len(dk.full_predictions),
+                  'do_predict', len(dk.full_do_predict))
 
-        dh.fill_predictions(len(dataframe))
+        dk.fill_predictions(len(dataframe))
 
-        return dh
+        return dk
 
     def start_live(self, dataframe: DataFrame, metadata: dict,
-                   strategy: IStrategy, dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
+                   strategy: IStrategy, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
         """
         The main broad execution for dry/live. This function will check if a retraining should be
         performed, and if so, retrain and reset the model.
@@ -224,30 +222,30 @@ class IFreqaiModel(ABC):
         dataframe: DataFrame = strategy passed dataframe
         metadata: Dict = pair metadata
         strategy: IStrategy = currently employed strategy
-        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
+        dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
         :returns:
-        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
+        dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
         """
 
         # update follower
         if self.follow_mode:
-            self.data_drawer.update_follower_metadata()
+            self.dd.update_follower_metadata()
 
         # get the model metadata associated with the current pair
         (_,
          trained_timestamp,
          _,
-         return_null_array) = self.data_drawer.get_pair_dict_info(metadata['pair'])
+         return_null_array) = self.dd.get_pair_dict_info(metadata['pair'])
 
         # if the metadata doesnt exist, the follower returns null arrays to strategy
         if self.follow_mode and return_null_array:
             logger.info('Returning null array from follower to strategy')
-            self.data_drawer.return_null_values_to_strategy(dataframe, dh)
-            return dh
+            self.dd.return_null_values_to_strategy(dataframe, dk)
+            return dk
 
         # append the historic data once per round
-        if self.data_drawer.historic_data:
-            dh.update_historic_data(strategy)
+        if self.dd.historic_data:
+            dk.update_historic_data(strategy)
             logger.debug(f'Updating historic data on pair {metadata["pair"]}')
 
         # if trainable, check if model needs training, if so compute new timerange,
@@ -257,95 +255,100 @@ class IFreqaiModel(ABC):
 
             (_,
              new_trained_timerange,
-             data_load_timerange) = dh.check_if_new_training_required(trained_timestamp)
-            dh.set_paths(metadata['pair'], new_trained_timerange.stopts)
+             data_load_timerange) = dk.check_if_new_training_required(trained_timestamp)
+            dk.set_paths(metadata['pair'], new_trained_timerange.stopts)
 
             # download candle history if it is not already in memory
-            if not self.data_drawer.historic_data:
+            if not self.dd.historic_data:
                 logger.info('Downloading all training data for all pairs in whitelist and '
                             'corr_pairlist, this may take a while if you do not have the '
                             'data saved')
-                dh.download_all_data_for_training(data_load_timerange)
-                dh.load_all_pair_histories(data_load_timerange)
+                dk.download_all_data_for_training(data_load_timerange)
+                dk.load_all_pair_histories(data_load_timerange)
 
             if not self.scanning:
                 self.scanning = True
                 self.start_scanning(strategy)
 
         elif self.follow_mode:
-            dh.set_paths(metadata['pair'], trained_timestamp)
+            dk.set_paths(metadata['pair'], trained_timestamp)
             logger.info('FreqAI instance set to follow_mode, finding existing pair'
                         f'using { self.identifier }')
 
         # load the model and associated data into the data kitchen
-        self.model = dh.load_data(coin=metadata['pair'], keras=self.keras)
+        self.model = dk.load_data(coin=metadata['pair'], keras_model=self.keras)
 
         if not self.model:
             logger.warning('No model ready, returning null values to strategy.')
-            self.data_drawer.return_null_values_to_strategy(dataframe, dh)
-            return dh
+            self.dd.return_null_values_to_strategy(dataframe, dk)
+            return dk
 
         # ensure user is feeding the correct indicators to the model
-        self.check_if_feature_list_matches_strategy(dataframe, dh)
+        self.check_if_feature_list_matches_strategy(dataframe, dk)
 
-        self.build_strategy_return_arrays(dataframe, dh, metadata['pair'], trained_timestamp)
+        self.build_strategy_return_arrays(dataframe, dk, metadata['pair'], trained_timestamp)
 
-        return dh
+        return dk
 
     def build_strategy_return_arrays(self, dataframe: DataFrame,
-                                     dh: FreqaiDataKitchen, pair: str,
+                                     dk: FreqaiDataKitchen, pair: str,
                                      trained_timestamp: int) -> None:
 
         # hold the historical predictions in memory so we are sending back
         # correct array to strategy
 
-        if pair not in self.data_drawer.model_return_values:
-            preds, do_preds = self.predict(dataframe, dh)
+        if pair not in self.dd.model_return_values:
+            pred_df, do_preds = self.predict(dataframe, dk)
             # mypy doesnt like the typing in else statement, so we need to explicitly add to
             # dataframe separately
-            dataframe['prediction'], dataframe['do_predict'] = preds, do_preds
-            # dh.append_predictions(preds, do_preds, len(dataframe))
-            # dh.fill_predictions(len(dataframe))
-            self.data_drawer.set_initial_return_values(pair, dh, dataframe)
+
+            # for label in dk.label_list:
+            #     dataframe[label] = pred_df[label]
+
+            # dataframe['do_predict'] = do_preds
+
+            # dk.append_predictions(preds, do_preds, len(dataframe))
+            # dk.fill_predictions(len(dataframe))
+            self.dd.set_initial_return_values(pair, dk, pred_df, do_preds)
+            dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
             return
-        elif self.dh.check_if_model_expired(trained_timestamp):
-            preds, do_preds, dh.DI_values = np.zeros(2), np.ones(2) * 2, np.zeros(2)
+        elif self.dk.check_if_model_expired(trained_timestamp):
+            pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
+            do_preds, dk.DI_values = np.ones(2) * 2, np.zeros(2)
             logger.warning('Model expired, returning null values to strategy. Strategy '
                            'construction should take care to consider this event with '
                            'prediction == 0 and do_predict == 2')
         else:
             # Only feed in the most recent candle for prediction in live scenario
-            preds, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dh, first=False)
+            pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
+
+        self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
+        dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
 
-        self.data_drawer.append_model_predictions(pair, preds, do_preds,
-                                                  dh.data["target_mean"],
-                                                  dh.data["target_std"],
-                                                  dh,
-                                                  len(dataframe))
         return
 
     def check_if_feature_list_matches_strategy(self, dataframe: DataFrame,
-                                               dh: FreqaiDataKitchen) -> None:
+                                               dk: FreqaiDataKitchen) -> None:
         """
         Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
         to a folder holding existing models.
         :params:
         dataframe: DataFrame = strategy provided dataframe
-        dh: FreqaiDataKitchen = non-persistent data container/analyzer for current coin/bot loop
+        dk: FreqaiDataKitchen = non-persistent data container/analyzer for current coin/bot loop
         """
-        strategy_provided_features = dh.find_features(dataframe)
-        if 'training_features_list_raw' in dh.data:
-            feature_list = dh.data['training_features_list_raw']
+        dk.find_features(dataframe)
+        if 'training_features_list_raw' in dk.data:
+            feature_list = dk.data['training_features_list_raw']
         else:
-            feature_list = dh.training_features_list
-        if strategy_provided_features != feature_list:
+            feature_list = dk.training_features_list
+        if dk.training_features_list != feature_list:
             raise OperationalException("Trying to access pretrained model with `identifier` "
                                        "but found different features furnished by current strategy."
                                        "Change `identifer` to train from scratch, or ensure the"
                                        "strategy is furnishing the same features as the pretrained"
                                        "model")
 
-    def data_cleaning_train(self, dh: FreqaiDataKitchen) -> None:
+    def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
         """
         Base data cleaning method for train
         Any function inside this method should drop training data points from the filtered_dataframe
@@ -354,23 +357,23 @@ class IFreqaiModel(ABC):
         """
 
         if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
-            dh.principal_component_analysis()
+            dk.principal_component_analysis()
 
         if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
-            dh.use_SVM_to_remove_outliers(predict=False)
+            dk.use_SVM_to_remove_outliers(predict=False)
 
         if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
-            dh.data["avg_mean_dist"] = dh.compute_distances()
+            dk.data["avg_mean_dist"] = dk.compute_distances()
 
         # if self.feature_parameters["determine_statistical_distributions"]:
-        #     dh.determine_statistical_distributions()
+        #     dk.determine_statistical_distributions()
         # if self.feature_parameters["remove_outliers"]:
-        #     dh.remove_outliers(predict=False)
+        #     dk.remove_outliers(predict=False)
 
-    def data_cleaning_predict(self, dh: FreqaiDataKitchen, dataframe: DataFrame) -> None:
+    def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
         """
         Base data cleaning method for predict.
-        These functions each modify dh.do_predict, which is a dataframe with equal length
+        These functions each modify dk.do_predict, which is a dataframe with equal length
         to the number of candles coming from and returning to the strategy. Inside do_predict,
          1 allows prediction and < 0 signals to the strategy that the model is not confident in
          the prediction.
@@ -379,20 +382,20 @@ class IFreqaiModel(ABC):
         for buy signals.
         """
         if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
-            dh.pca_transform(dataframe)
+            dk.pca_transform(dataframe)
 
         if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
-            dh.use_SVM_to_remove_outliers(predict=True)
+            dk.use_SVM_to_remove_outliers(predict=True)
 
         if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
-            dh.check_if_pred_in_training_spaces()
+            dk.check_if_pred_in_training_spaces()
 
         # if self.feature_parameters["determine_statistical_distributions"]:
-        #     dh.determine_statistical_distributions()
+        #     dk.determine_statistical_distributions()
         # if self.feature_parameters["remove_outliers"]:
-        #     dh.remove_outliers(predict=True)  # creates dropped index
+        #     dk.remove_outliers(predict=True)  # creates dropped index
 
-    def model_exists(self, pair: str, dh: FreqaiDataKitchen, trained_timestamp: int = None,
+    def model_exists(self, pair: str, dk: FreqaiDataKitchen, trained_timestamp: int = None,
                      model_filename: str = '', scanning: bool = False) -> bool:
         """
         Given a pair and path, check if a model already exists
@@ -402,14 +405,14 @@ class IFreqaiModel(ABC):
         coin, _ = pair.split("/")
 
         if not self.live:
-            dh.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
+            dk.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
 
-        path_to_modelfile = Path(dh.data_path / str(model_filename + "_model.joblib"))
+        path_to_modelfile = Path(dk.data_path / str(model_filename + "_model.joblib"))
         file_exists = path_to_modelfile.is_file()
         if file_exists and not scanning:
-            logger.info("Found model at %s", dh.data_path / dh.model_filename)
+            logger.info("Found model at %s", dk.data_path / dk.model_filename)
         elif not scanning:
-            logger.info("Could not find model at %s", dh.data_path / dh.model_filename)
+            logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
         return file_exists
 
     def set_full_path(self) -> None:
@@ -430,7 +433,7 @@ class IFreqaiModel(ABC):
         return dataframe[to_keep]
 
     def train_model_in_series(self, new_trained_timerange: TimeRange, pair: str,
-                              strategy: IStrategy, dh: FreqaiDataKitchen,
+                              strategy: IStrategy, dk: FreqaiDataKitchen,
                               data_load_timerange: TimeRange):
         """
         Retreive data and train model in single threaded mode (only used if model directory is empty
@@ -439,41 +442,43 @@ class IFreqaiModel(ABC):
         new_trained_timerange: TimeRange = the timerange to train the model on
         metadata: dict = strategy provided metadata
         strategy: IStrategy = user defined strategy object
-        dh: FreqaiDataKitchen = non-persistent data container for current coin/loop
+        dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
         data_load_timerange: TimeRange = the amount of data to be loaded for populate_any_indicators
         (larger than new_trained_timerange so that new_trained_timerange does not contain any NaNs)
         """
 
-        corr_dataframes, base_dataframes = dh.get_base_and_corr_dataframes(data_load_timerange,
+        corr_dataframes, base_dataframes = dk.get_base_and_corr_dataframes(data_load_timerange,
                                                                            pair)
 
-        unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
+        unfiltered_dataframe = dk.use_strategy_to_populate_indicators(strategy,
                                                                       corr_dataframes,
                                                                       base_dataframes,
                                                                       pair)
 
-        unfiltered_dataframe = dh.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
+        unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
 
-        model = self.train(unfiltered_dataframe, pair, dh)
+        # find the features indicated by strategy and store in datakitchen
+        dk.find_features(unfiltered_dataframe)
 
-        self.data_drawer.pair_dict[pair][
-                                   'trained_timestamp'] = new_trained_timerange.stopts
-        dh.set_new_model_names(pair, new_trained_timerange)
-        self.data_drawer.pair_dict[pair]['first'] = False
-        if self.data_drawer.pair_dict[pair]['priority'] == 1 and self.scanning:
+        model = self.train(unfiltered_dataframe, pair, dk)
+
+        self.dd.pair_dict[pair]['trained_timestamp'] = new_trained_timerange.stopts
+        dk.set_new_model_names(pair, new_trained_timerange)
+        self.dd.pair_dict[pair]['first'] = False
+        if self.dd.pair_dict[pair]['priority'] == 1 and self.scanning:
             with self.lock:
-                self.data_drawer.pair_to_end_of_training_queue(pair)
-        dh.save_data(model, coin=pair, keras=self.keras)
+                self.dd.pair_to_end_of_training_queue(pair)
+        dk.save_data(model, coin=pair, keras_model=self.keras)
 
         if self.freqai_info.get('purge_old_models', False):
-            self.data_drawer.purge_old_models()
+            self.dd.purge_old_models()
         # self.retrain = False
 
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModlel.py for an example.
 
     @abstractmethod
-    def train(self, unfiltered_dataframe: DataFrame, pair: str, dh: FreqaiDataKitchen) -> Any:
+    def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> 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.
@@ -499,37 +504,36 @@ class IFreqaiModel(ABC):
 
     @abstractmethod
     def predict(self, dataframe: DataFrame,
-                dh: FreqaiDataKitchen, first: bool = True) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
+                dk: FreqaiDataKitchen, first: bool = True) -> Tuple[DataFrame, npt.ArrayLike]:
         """
         Filter the prediction features data and predict with it.
         :param:
         unfiltered_dataframe: Full dataframe for the current backtest period.
-        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
+        dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
         :return:
         :predictions: np.array of predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
         data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
         """
 
-    @abstractmethod
-    def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
+    def make_labels(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
         """
         User defines the labels here (target values).
         :params:
         dataframe: DataFrame = the full dataframe for the present training period
-        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
+        dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
         """
 
         return
 
     @abstractmethod
-    def return_values(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
+    def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
         """
         User defines the dataframe to be returned to strategy here.
         :params:
         dataframe: DataFrame = the full dataframe for the current prediction (live)
         or --timerange (backtesting)
-        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
+        dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
         :returns:
         dataframe: DataFrame = dataframe filled with user defined data
         """