diff --git a/freqtrade/edge/edge_positioning.py b/freqtrade/edge/edge_positioning.py
index f2df0d3f2..ac0262d18 100644
--- a/freqtrade/edge/edge_positioning.py
+++ b/freqtrade/edge/edge_positioning.py
@@ -172,13 +172,7 @@ class Edge:
             pair_data = pair_data.sort_values(by=['date'])
             pair_data = pair_data.reset_index(drop=True)
 
-            df_analyzed = self.strategy.advise_exit(
-                dataframe=self.strategy.advise_entry(
-                    dataframe=pair_data,
-                    metadata={'pair': pair}
-                ),
-                metadata={'pair': pair}
-            )[headers].copy()
+            df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})[headers].copy()
 
             trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
 
diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py
index 3dca9d480..852f9df8d 100644
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@@ -367,11 +367,7 @@ class Backtesting:
             if not pair_data.empty:
                 # Cleanup from prior runs
                 pair_data.drop(HEADERS[5:] + ['buy', 'sell'], axis=1, errors='ignore')
-
-            df_analyzed = self.strategy.advise_exit(
-                self.strategy.advise_entry(pair_data, {'pair': pair}),
-                {'pair': pair}
-            ).copy()
+            df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})
             # Trim startup period from analyzed dataframe
             df_analyzed = processed[pair] = pair_data = trim_dataframe(
                 df_analyzed, self.timerange, startup_candles=self.required_startup)
diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py
index 1e9ebe1ae..0f848130f 100644
--- a/freqtrade/strategy/interface.py
+++ b/freqtrade/strategy/interface.py
@@ -825,6 +825,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         Parses the given candle (OHLCV) data and returns a populated DataFrame
         add several TA indicators and entry order signal to it
+        Should only be used in live.
         :param dataframe: Dataframe containing data from exchange
         :param metadata: Metadata dictionary with additional data (e.g. 'pair')
         :return: DataFrame of candle (OHLCV) data with indicator data and signals added
@@ -1321,6 +1322,20 @@ class IStrategy(ABC, HyperStrategyMixin):
         return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
                 for pair, pair_data in data.items()}
 
+    def ft_advise_signals(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Call advise_entry and advise_exit and return the resulting dataframe.
+        :param dataframe: Dataframe containing data from exchange, as well as pre-calculated
+                          indicators
+        :param metadata: Metadata dictionary with additional data (e.g. 'pair')
+        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
+
+        """
+
+        dataframe = self.advise_entry(dataframe, metadata)
+        dataframe = self.advise_exit(dataframe, metadata)
+        return dataframe
+
     def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         Populate indicators that will be used in the Buy, Sell, short, exit_short strategy