*BREAKING CHANGE* remove unnecessary arguments from populate_any_indicators(), accommodate tests

freqtrade/freqai/data_kitchen.py

@@ -892,29 +892,26 @@ class FreqaiDataKitchen:
         else:
             dataframe = base_dataframes[self.config["timeframe"]].copy()
 
-        sgi = True
+        sgi = False
         for tf in tfs:
+            if tf == tfs[-1]:
+                sgi = True  # doing this last allows user to use all tf raw prices in labels
             dataframe = strategy.populate_any_indicators(
-                pair,
                 pair,
                 dataframe.copy(),
                 tf,
                 informative=base_dataframes[tf],
-                coin=pair.split("/")[0] + "-",
+                set_generalized_indicators=sgi
-                set_generalized_indicators=sgi,
             )
-            sgi = False
             if pairs:
                 for i in pairs:
                     if pair in i:
                         continue  # dont repeat anything from whitelist
                     dataframe = strategy.populate_any_indicators(
-                        pair,
                         i,
                         dataframe.copy(),
                         tf,
-                        informative=corr_dataframes[i][tf],
+                        informative=corr_dataframes[i][tf]
-                        coin=i.split("/")[0] + "-",
                     )
 
         return dataframe

freqtrade/strategy/interface.py

@@ -557,8 +557,8 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         return None
 
-    def populate_any_indicators(self, basepair: str, pair: str, df: DataFrame, tf: str,
+    def populate_any_indicators(self, pair: str, df: DataFrame, tf: str,
-                                informative: DataFrame = None, coin: str = "",
+                                informative: DataFrame = None,
                                 set_generalized_indicators: bool = False) -> DataFrame:
         """
         Function designed to automatically generate, name and merge features
@@ -570,7 +570,6 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param df: strategy dataframe which will receive merges from informatives
         :param tf: timeframe of the dataframe which will modify the feature names
         :param informative: the dataframe associated with the informative pair
-        :param coin: the name of the coin which will modify the feature names.
         """
         return df
 

freqtrade/templates/FreqaiExampleStrategy.py

@@ -65,7 +65,7 @@ class FreqaiExampleStrategy(IStrategy):
         return informative_pairs
 
     def populate_any_indicators(
-        self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
     ):
         """
         Function designed to automatically generate, name and merge features
@@ -78,9 +78,10 @@ class FreqaiExampleStrategy(IStrategy):
         :param df: strategy dataframe which will receive merges from informatives
         :param tf: timeframe of the dataframe which will modify the feature names
         :param informative: the dataframe associated with the informative pair
-        :param coin: the name of the coin which will modify the feature names.
         """
 
+        coin = pair.split('/')[0]
+
         with self.freqai.lock:
             if informative is None:
                 informative = self.dp.get_pair_dataframe(pair, tf)
@@ -92,11 +93,8 @@ class FreqaiExampleStrategy(IStrategy):
                 informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
                 informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
                 informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
-                informative[f"{coin}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
+                informative[f"{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
-                informative[f"{coin}21ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
+                informative[f"{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
-                informative[f"%-{coin}close_over_20sma-period_{t}"] = (
-                    informative["close"] / informative[f"{coin}20sma-period_{t}"]
-                )
 
                 informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
 
@@ -148,8 +146,6 @@ class FreqaiExampleStrategy(IStrategy):
                 df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
 
                 # user adds targets here by prepending them with &- (see convention below)
-                # If user wishes to use multiple targets, a multioutput prediction model
-                # needs to be used such as templates/CatboostPredictionMultiModel.py
                 df["&-s_close"] = (
                     df["close"]
                     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
@@ -159,6 +155,23 @@ class FreqaiExampleStrategy(IStrategy):
                     - 1
                 )
 
+                # If user wishes to use multiple targets, they can add more by
+                # appending more columns with '&'. User should keep in mind that multi targets
+                # requires a multioutput prediction model such as
+                # templates/CatboostPredictionMultiModel.py,
+
+                # df["&-s_range"] = (
+                #     df["close"]
+                #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+                #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+                #     .max()
+                #     -
+                #     df["close"]
+                #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+                #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+                #     .min()
+                # )
+
         return df
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

tests/strategy/strats/freqai_test_multimodel_strat.py

@@ -62,7 +62,7 @@ class freqai_test_multimodel_strat(IStrategy):
         return informative_pairs
 
     def populate_any_indicators(
-        self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
     ):
         """
         Function designed to automatically generate, name and merge features
@@ -79,6 +79,8 @@ class freqai_test_multimodel_strat(IStrategy):
         :coin: the name of the coin which will modify the feature names.
         """
 
+        coin = pair.split('/')[0]
+
         with self.freqai.lock:
             if informative is None:
                 informative = self.dp.get_pair_dataframe(pair, tf)

tests/strategy/strats/freqai_test_strat.py

@@ -62,7 +62,7 @@ class freqai_test_strat(IStrategy):
         return informative_pairs
 
     def populate_any_indicators(
-        self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
     ):
         """
         Function designed to automatically generate, name and merge features
@@ -79,6 +79,8 @@ class freqai_test_strat(IStrategy):
         :coin: the name of the coin which will modify the feature names.
         """
 
+        coin = pair.split('/')[0]
+
         with self.freqai.lock:
             if informative is None:
                 informative = self.dp.get_pair_dataframe(pair, tf)