diff --git a/user_data/strategies/berlinguyinca/Low_BB.py b/user_data/strategies/berlinguyinca/Low_BB.py
index 1252981..0a3c540 100644
--- a/user_data/strategies/berlinguyinca/Low_BB.py
+++ b/user_data/strategies/berlinguyinca/Low_BB.py
@@ -66,8 +66,8 @@ class Low_BB(IStrategy):
         # dataframe['mfi'] = ta.MFI(dataframe)
         # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
 
-        # dataframe['canbuy'] = np.NaN
-        # dataframe['canbuy2'] = np.NaN
+        # dataframe['canbuy'] = np.nan
+        # dataframe['canbuy2'] = np.nan
         # dataframe.loc[dataframe.close.rolling(49).min() <= 1.1 * dataframe.close, 'canbuy'] == 1
         # dataframe.loc[dataframe.close.rolling(600).max() < 1.2 * dataframe.close, 'canbuy'] = 1
         # dataframe.loc[dataframe.close.rolling(600).max() * 0.8 >  dataframe.close, 'canbuy2'] = 1
diff --git a/user_data/strategies/berlinguyinca/TDSequentialStrategy.py b/user_data/strategies/berlinguyinca/TDSequentialStrategy.py
index 66013fb..1893052 100644
--- a/user_data/strategies/berlinguyinca/TDSequentialStrategy.py
+++ b/user_data/strategies/berlinguyinca/TDSequentialStrategy.py
@@ -142,7 +142,7 @@ class TDSequentialStrategy(IStrategy):
         Based on TA indicators, populates the sell signal for the given dataframe
         :param dataframe: DataFrame
         :param metadata: Additional information, like the currently traded pair
-        :return: DataFrame with buy columnNA / NaN values
+        :return: DataFrame with buy columnNA / nan values
         """
         dataframe["exit_long"] = 0
         dataframe.loc[((dataframe['exceed_high']) |
diff --git a/user_data/strategies/futures/FSupertrendStrategy.py b/user_data/strategies/futures/FSupertrendStrategy.py
index 438639f..810f4ad 100644
--- a/user_data/strategies/futures/FSupertrendStrategy.py
+++ b/user_data/strategies/futures/FSupertrendStrategy.py
@@ -238,7 +238,7 @@ class FSupertrendStrategy(IStrategy):
             )
         # Mark the trend direction up/down
         df[stx] = np.where(
-            (df[st] > 0.00), np.where((df["close"] < df[st]), "down", "up"), np.NaN
+            (df[st] > 0.00), np.where((df["close"] < df[st]), "down", "up"), None
         )
 
         # Remove basic and final bands from the columns