diff --git a/freqtrade/edge/edge_positioning.py b/freqtrade/edge/edge_positioning.py
index a24e29efb..256b67383 100644
--- a/freqtrade/edge/edge_positioning.py
+++ b/freqtrade/edge/edge_positioning.py
@@ -137,10 +137,10 @@ class Edge:
             pair_data = pair_data.sort_values(by=['date'])
             pair_data = pair_data.reset_index(drop=True)
 
-            dataframe = self.strategy.advise_sell(
+            df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
 
-            trades += self._find_trades_for_stoploss_range(dataframe, pair, self._stoploss_range)
+            trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
 
         # If no trade found then exit
         if len(trades) == 0:
@@ -359,11 +359,11 @@ class Edge:
         # Returning a list of pairs in order of "expectancy"
         return final
 
-    def _find_trades_for_stoploss_range(self, dataframe, pair, stoploss_range):
-        buy_column = dataframe['buy'].values
-        sell_column = dataframe['sell'].values
-        date_column = dataframe['date'].values
-        ohlc_columns = dataframe[['open', 'high', 'low', 'close']].values
+    def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
+        buy_column = df['buy'].values
+        sell_column = df['sell'].values
+        date_column = df['date'].values
+        ohlc_columns = df[['open', 'high', 'low', 'close']].values
 
         result: list = []
         for stoploss in stoploss_range:
diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py
index 949c072c5..210fe3c66 100644
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@@ -164,19 +164,19 @@ class Backtesting:
             pair_data.loc[:, 'buy'] = 0  # cleanup from previous run
             pair_data.loc[:, 'sell'] = 0  # cleanup from previous run
 
-            dataframe = self.strategy.advise_sell(
+            df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
 
             # To avoid using data from future, we use buy/sell signals shifted
             # from the previous candle
-            dataframe.loc[:, 'buy'] = dataframe['buy'].shift(1)
-            dataframe.loc[:, 'sell'] = dataframe['sell'].shift(1)
+            df_analyzed.loc[:, 'buy'] = df_analyzed['buy'].shift(1)
+            df_analyzed.loc[:, 'sell'] = df_analyzed['sell'].shift(1)
 
-            dataframe.drop(dataframe.head(1).index, inplace=True)
+            df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
 
             # Convert from Pandas to list for performance reasons
             # (Looping Pandas is slow.)
-            data[pair] = [x for x in dataframe.itertuples()]
+            data[pair] = [x for x in df_analyzed.itertuples()]
         return data
 
     def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,