diff --git a/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py b/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py
index b0868b5..313a571 100644
--- a/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py
+++ b/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py
@@ -1,17 +1,17 @@
 # --- Do not remove these libs ---
+import freqtrade.vendor.qtpylib.indicators as qtpylib
 import numpy as np
 # --------------------------------
 import talib.abstract as ta
+from freqtrade.strategy.interface import IStrategy
 from pandas import DataFrame
 
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-from freqtrade.strategy.interface import IStrategy
 
 def bollinger_bands(stock_price, window_size, num_of_std):
     rolling_mean = stock_price.rolling(window=window_size).mean()
     rolling_std = stock_price.rolling(window=window_size).std()
     lower_band = rolling_mean - (rolling_std * num_of_std)
-    return rolling_mean, lower_band
+    return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)
 
 
 class CombinedBinHAndCluc(IStrategy):
@@ -21,58 +21,55 @@ class CombinedBinHAndCluc(IStrategy):
     # - if the market is constantly green(like in JAN 2018) the best performance is reached with
     #   "max_open_trades" = 2 and minimal_roi = 0.01
     minimal_roi = {
-        "0": 0.02
+        "0": 0.05
     }
-    stoploss = -0.15
+    stoploss = -0.05
     ticker_interval = '5m'
 
+    use_sell_signal = True
+    sell_profit_only = True
+    ignore_roi_if_buy_signal = False
+
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # strategy BinHV45
         mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
-        dataframe['mid'] = np.nan_to_num(mid)
-        dataframe['lower'] = np.nan_to_num(lower)
-        dataframe['bbdelta'] = (dataframe['mid'] - dataframe['lower']).abs()
-        dataframe['pricedelta'] = (dataframe['open'] - dataframe['close']).abs()
+        dataframe['lower'] = lower
+        dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
         dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
         dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
-        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=5)
-        rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
-        dataframe['emarsi'] = ta.EMA(rsiframe, timeperiod=5)
-        macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['adx'] = ta.ADX(dataframe)
+        # strategy ClucMay72018
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
         dataframe['bb_lowerband'] = bollinger['lower']
         dataframe['bb_middleband'] = bollinger['mid']
-        dataframe['bb_upperband'] = bollinger['upper']
-        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
+
         return dataframe
 
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         dataframe.loc[
-            (
+            (  # strategy BinHV45
                     dataframe['lower'].shift().gt(0) &
                     dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
                     dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
                     dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
                     dataframe['close'].lt(dataframe['lower'].shift()) &
                     dataframe['close'].le(dataframe['close'].shift())
-            )
-            |
-            (
-                    (dataframe['close'] < dataframe['ema100']) &
+            ) |
+            (  # strategy ClucMay72018
+                    (dataframe['close'] < dataframe['ema_slow']) &
                     (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
-                    (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20))
-            )
-            ,
-            'buy'] = 1
+                    (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20))
+            ),
+            'buy'
+        ] = 1
         return dataframe
 
     def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         """
         dataframe.loc[
-            (
-                (dataframe['close'] > dataframe['bb_middleband'])
-            ),
-            'sell'] = 1
+            (dataframe['close'] > dataframe['bb_middleband']),
+            'sell'
+        ] = 1
         return dataframe