*breaking change* simplify user strat by consolidating feature loops into backend

docs/freqai.md

@@ -368,32 +368,15 @@ The Freqai strategy requires the user to include the following lines of code in
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
         self.freqai_info = self.config["freqai"]
-        self.pair = metadata["pair"]
+
-        sgi = True
-        # the following loops are necessary for building the features
-        # indicated by the user in the configuration file.
         # All indicators must be populated by populate_any_indicators() for live functionality
         # to work correctly.
-        for tf in self.freqai_info["feature_parameters"]["include_timeframes"]:
-            dataframe = self.populate_any_indicators(
-                metadata,
-                self.pair,
-                dataframe.copy(),
-                tf,
-                coin=self.pair.split("/")[0] + "-",
-                set_generalized_indicators=sgi,
-            )
-            sgi = False
-            for pair in self.freqai_info["feature_parameters"]["include_corr_pairlist"]:
-                if metadata["pair"] in pair:
-                    continue  # do not include whitelisted pair twice if it is in corr_pairlist
-                dataframe = self.populate_any_indicators(
-                    metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
-                )
 
-        # the model will return 4 values, its prediction, an indication of whether or not the
+        # the model will return all labels created by user in `populate_any_indicators` 
-        # prediction should be accepted, the target mean/std values from the labels used during
+        # (& appended targets), an indication of whether or not the prediction should be accepted, 
-        # each training period.
+        # the target mean/std values for each of the labels created by user in 
+        # `populate_any_indicators()` for each training period.
+        
         dataframe = self.model.bridge.start(dataframe, metadata, self)
 
         return dataframe

freqtrade/freqai/data_kitchen.py

@@ -1043,7 +1043,12 @@ class FreqaiDataKitchen:
     #     return corr_dataframes, base_dataframes
 
     def use_strategy_to_populate_indicators(
-        self, strategy: IStrategy, corr_dataframes: dict, base_dataframes: dict, pair: str
+        self,
+        strategy: IStrategy,
+        corr_dataframes: dict = {},
+        base_dataframes: dict = {},
+        pair: str = "",
+        prediction_dataframe: DataFrame = pd.DataFrame(),
     ) -> DataFrame:
         """
         Use the user defined strategy for populating indicators during
@@ -1058,16 +1063,31 @@ class FreqaiDataKitchen:
         :returns:
         dataframe: DataFrame = dataframe containing populated indicators
         """
-        dataframe = base_dataframes[self.config["timeframe"]].copy()
+
+        # for prediction dataframe creation, we let dataprovider handle everything in the strategy
+        # so we create empty dictionaries, which allows us to pass None to
+        # `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
+        tfs = self.freqai_config.get("feature_parameters", {}).get("include_timeframes")
         pairs = self.freqai_config.get("feature_parameters", {}).get("include_corr_pairlist", [])
+        if not prediction_dataframe.empty:
+            dataframe = prediction_dataframe.copy()
+            for tf in tfs:
+                base_dataframes[tf] = None
+                for p in pairs:
+                    if p not in corr_dataframes:
+                        corr_dataframes[p] = {}
+                    corr_dataframes[p][tf] = None
+        else:
+            dataframe = base_dataframes[self.config["timeframe"]].copy()
+
         sgi = True
-        for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"):
+        for tf in tfs:
             dataframe = strategy.populate_any_indicators(
                 pair,
                 pair,
                 dataframe.copy(),
                 tf,
-                base_dataframes[tf],
+                informative=base_dataframes[tf],
                 coin=pair.split("/")[0] + "-",
                 set_generalized_indicators=sgi,
             )
@@ -1081,7 +1101,7 @@ class FreqaiDataKitchen:
                         i,
                         dataframe.copy(),
                         tf,
-                        corr_dataframes[i][tf],
+                        informative=corr_dataframes[i][tf],
                         coin=i.split("/")[0] + "-",
                     )
 

freqtrade/freqai/freqai_interface.py

@@ -281,6 +281,10 @@ class IFreqaiModel(ABC):
         # load the model and associated data into the data kitchen
         self.model = dk.load_data(coin=metadata["pair"])
 
+        dataframe = self.dk.use_strategy_to_populate_indicators(
+            strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
+        )
+
         if not self.model:
             logger.warning(
                 f"No model ready for {metadata['pair']}, returning null values to strategy."

freqtrade/templates/FreqaiExampleStrategy.py

@@ -171,32 +171,15 @@ class FreqaiExampleStrategy(IStrategy):
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
         self.freqai_info = self.config["freqai"]
-        self.pair = metadata["pair"]
+
-        sgi = True
-        # the following loops are necessary for building the features
-        # indicated by the user in the configuration file.
         # All indicators must be populated by populate_any_indicators() for live functionality
         # to work correctly.
-        for tf in self.freqai_info["feature_parameters"]["include_timeframes"]:
-            dataframe = self.populate_any_indicators(
-                metadata,
-                self.pair,
-                dataframe.copy(),
-                tf,
-                coin=self.pair.split("/")[0] + "-",
-                set_generalized_indicators=sgi,
-            )
-            sgi = False
-            for pair in self.freqai_info["feature_parameters"]["include_corr_pairlist"]:
-                if metadata["pair"] in pair:
-                    continue  # do not include whitelisted pair twice if it is in corr_pairlist
-                dataframe = self.populate_any_indicators(
-                    metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
-                )
 
-        # the model will return 4 values, its prediction, an indication of whether or not the
+        # the model will return all labels created by user in `populate_any_indicators`
-        # prediction should be accepted, the target mean/std values from the labels used during
+        # (& appended targets), an indication of whether or not the prediction should be accepted,
-        # each training period.
+        # the target mean/std values for each of the labels created by user in
+        # `populate_any_indicators()` for each training period.
+
         dataframe = self.model.bridge.start(dataframe, metadata, self)
 
         dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25

tests/strategy/strats/freqai_test_strat.py

@@ -140,29 +140,9 @@ class freqai_test_strat(IStrategy):
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
         self.freqai_info = self.config["freqai"]
-        self.pair = metadata["pair"]
+
-        sgi = True
-        # the following loops are necessary for building the features
-        # indicated by the user in the configuration file.
         # All indicators must be populated by populate_any_indicators() for live functionality
         # to work correctly.
-        for tf in self.freqai_info["feature_parameters"]["include_timeframes"]:
-            dataframe = self.populate_any_indicators(
-                metadata,
-                self.pair,
-                dataframe.copy(),
-                tf,
-                coin=self.pair.split("/")[0] + "-",
-                set_generalized_indicators=sgi,
-            )
-            sgi = False
-            for pair in self.freqai_info["feature_parameters"]["include_corr_pairlist"]:
-                if metadata["pair"] in pair:
-                    continue  # do not include whitelisted pair twice if it is in corr_pairlist
-                dataframe = self.populate_any_indicators(
-                    metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
-                )
-
         # the model will return 4 values, its prediction, an indication of whether or not the
         # prediction should be accepted, the target mean/std values from the labels used during
         # each training period.