diff --git a/user_data/strategies/berlinguyinca/CCIStrategy.py b/user_data/strategies/berlinguyinca/CCIStrategy.py
index 839b812..0ea39da 100644
--- a/user_data/strategies/berlinguyinca/CCIStrategy.py
+++ b/user_data/strategies/berlinguyinca/CCIStrategy.py
@@ -24,8 +24,7 @@ class CCIStrategy(IStrategy):
     ticker_interval = '1m'
 
     def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-        macd = ta.MACD(dataframe)
-        dataframe = CCIStrategy.resample(dataframe, self.ticker_interval, 5)
+        dataframe = self.resample(dataframe, self.ticker_interval, 5)
 
         dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170)
         dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34)
@@ -95,8 +94,7 @@ class CCIStrategy(IStrategy):
 
         return Series(cmf, name='cmf')
 
-    @staticmethod
-    def resample(dataframe, interval, factor):
+    def resample(self, dataframe, interval, factor):
         # defines the reinforcement logic
         # resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
         df = dataframe.copy()
@@ -107,7 +105,7 @@ class CCIStrategy(IStrategy):
             'low': 'min',
             'close': 'last'
         }
-        df = df.resample(str(int(interval[:-1]) * factor) + 'min').agg(ohlc_dict)
+        df = df.resample(str(int(interval[:-1]) * factor) + 'min', label="right").agg(ohlc_dict)
         df['resample_sma'] = ta.SMA(df, timeperiod=100, price='close')
         df['resample_medium'] = ta.SMA(df, timeperiod=50, price='close')
         df['resample_short'] = ta.SMA(df, timeperiod=25, price='close')
diff --git a/user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py b/user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py
index 042e7bc..cc36ff5 100644
--- a/user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py
+++ b/user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py
@@ -32,7 +32,6 @@ class ReinforcedAverageStrategy(IStrategy):
     ticker_interval = '4h'
 
     def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-        macd = ta.MACD(dataframe)
 
         dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
         dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21)
@@ -50,7 +49,7 @@ class ReinforcedAverageStrategy(IStrategy):
         :param dataframe: DataFrame
         :return: DataFrame with buy column
         """
-        dataframe = ReinforcedAverageStrategy.resample(dataframe, self.ticker_interval, 12)
+        dataframe = self.resample(dataframe, self.ticker_interval, 12)
 
         dataframe.loc[
             (
@@ -74,9 +73,7 @@ class ReinforcedAverageStrategy(IStrategy):
             'sell'] = 1
         return dataframe
 
-    @staticmethod
-    def resample( dataframe, interval, factor):
-
+    def resample(self, dataframe, interval, factor):
 
         # defines the reinforcement logic
         # resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
@@ -88,7 +85,8 @@ class ReinforcedAverageStrategy(IStrategy):
             'low': 'min',
             'close': 'last'
         }
-        df = df.resample(str(int(interval[:-1]) * factor) + 'min').agg(ohlc_dict)
+        df = df.resample(str(int(interval[:-1]) * factor) + 'min',
+                         label="right").agg(ohlc_dict).dropna(how='any')
         df['resample_sma'] = ta.SMA(df, timeperiod=50, price='close')
         df = df.drop(columns=['open', 'high', 'low', 'close'])
         df = df.resample(interval[:-1] + 'min')
diff --git a/user_data/strategies/berlinguyinca/ReinforcedQuickie.py b/user_data/strategies/berlinguyinca/ReinforcedQuickie.py
index 29026e7..f622acf 100644
--- a/user_data/strategies/berlinguyinca/ReinforcedQuickie.py
+++ b/user_data/strategies/berlinguyinca/ReinforcedQuickie.py
@@ -48,7 +48,7 @@ class ReinforcedQuickie(IStrategy):
     EMA_LONG_TERM = 21
 
     def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-        dataframe = ReinforcedQuickie.resample(dataframe, self.ticker_interval, self.resample_factor)
+        dataframe = self.resample(dataframe, self.ticker_interval, self.resample_factor)
 
         ##################################################################################
         # buy and sell indicators
@@ -171,8 +171,7 @@ class ReinforcedQuickie(IStrategy):
         ] = 1
         return dataframe
 
-    @staticmethod
-    def resample( dataframe, interval, factor):
+    def resample(self, dataframe, interval, factor):
         # defines the reinforcement logic
         # resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
         df = dataframe.copy()
@@ -183,8 +182,8 @@ class ReinforcedQuickie(IStrategy):
             'low': 'min',
             'close': 'last'
         }
-        df = df.resample(str(int(interval[:-1]) * factor) + 'min', how=ohlc_dict).dropna(
-            how='any')
+        df = df.resample(str(int(interval[:-1]) * factor) + 'min',
+                         label="right").agg(ohlc_dict).dropna(how='any')
         df['resample_sma'] = ta.SMA(df, timeperiod=25, price='close')
         df = df.drop(columns=['open', 'high', 'low', 'close'])
         df = df.resample(interval[:-1] + 'min')
diff --git a/user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py b/user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py
index 881fead..b540edb 100644
--- a/user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py
+++ b/user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py
@@ -40,7 +40,7 @@ class ReinforcedSmoothScalp(IStrategy):
     resample_factor = 5
 
     def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-        dataframe = ReinforcedSmoothScalp.resample(dataframe, self.ticker_interval, self.resample_factor)
+        dataframe = self.resample(dataframe, self.ticker_interval, self.resample_factor)
 
         dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
         dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
@@ -103,8 +103,7 @@ class ReinforcedSmoothScalp(IStrategy):
             'sell'] = 1
         return dataframe
 
-    @staticmethod
-    def resample(dataframe, interval, factor):
+    def resample(self, dataframe, interval, factor):
         # defines the reinforcement logic
         # resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
         df = dataframe.copy()
@@ -115,8 +114,8 @@ class ReinforcedSmoothScalp(IStrategy):
             'low': 'min',
             'close': 'last'
         }
-        df = df.resample(str(int(interval[:-1]) * factor) + 'min', how=ohlc_dict).dropna(
-            how='any')
+        df = df.resample(str(int(interval[:-1]) * factor) + 'min',
+                         label="right").agg(ohlc_dict).dropna(how='any')
         df['resample_sma'] = ta.SMA(df, timeperiod=50, price='close')
         df = df.drop(columns=['open', 'high', 'low', 'close'])
         df = df.resample(interval[:-1] + 'min')